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with:
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python-version: '3.10'
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- name: Install wheel
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run: pip install wheel==0.44.0 && pip install -r requirements.txt
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run: pip install wheel && pip install -r requirements.txt
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- name: Build DiffSynth
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run: python setup.py sdist bdist_wheel
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- name: Publish package to PyPI
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README.md
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README.md
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# DiffSynth-Studio
|
||||
|
||||
<a href="https://github.com/modelscope/DiffSynth-Studio"><img src=".github/workflows/logo.gif" title="Logo" style="max-width:100%;" width="55" /></a> <a href="https://trendshift.io/repositories/10946" target="_blank"><img src="https://trendshift.io/api/badge/repositories/10946" alt="modelscope%2FDiffSynth-Studio | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a></p>
|
||||
|
||||
# DiffSynth Studio
|
||||
[](https://pypi.org/project/DiffSynth/)
|
||||
[](https://github.com/modelscope/DiffSynth-Studio/blob/master/LICENSE)
|
||||
[](https://github.com/modelscope/DiffSynth-Studio/issues)
|
||||
[](https://GitHub.com/modelscope/DiffSynth-Studio/pull/)
|
||||
[](https://GitHub.com/modelscope/DiffSynth-Studio/commit/)
|
||||
[](https://GitHub.com/modelscope/DiffSynth-Studio/commit/)
|
||||
|
||||
[切换到中文](./README_zh.md)
|
||||
<p align="center">
|
||||
<a href="https://trendshift.io/repositories/10946" target="_blank"><img src="https://trendshift.io/api/badge/repositories/10946" alt="modelscope%2FDiffSynth-Studio | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
|
||||
</p>
|
||||
|
||||
## Introduction
|
||||
|
||||
Welcome to the magic world of Diffusion models! DiffSynth-Studio is an open-source Diffusion model engine developed and maintained by [ModelScope](https://www.modelscope.cn/) team. We aim to foster technical innovation through framework development, bring together the power of the open-source community, and explore the limits of generative models!
|
||||
|
||||
DiffSynth currently includes two open-source projects:
|
||||
* [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio): Focused on aggressive technical exploration, for academia, providing support for more cutting-edge model capabilities.
|
||||
* [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine): Focused on stable model deployment, for industry, offering higher computing performance and more stable features.
|
||||
|
||||
[DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) and [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine) are the core projects behind ModelScope [AIGC zone](https://modelscope.cn/aigc/home), offering powerful AI content generation abilities. Come and try our carefully designed features and start your AI creation journey!
|
||||
|
||||
## Installation
|
||||
|
||||
Install from source (recommended):
|
||||
|
||||
```
|
||||
git clone https://github.com/modelscope/DiffSynth-Studio.git
|
||||
cd DiffSynth-Studio
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
<details>
|
||||
<summary>Other installation methods</summary>
|
||||
|
||||
Install from PyPI (version updates may be delayed; for latest features, install from source)
|
||||
|
||||
```
|
||||
pip install diffsynth
|
||||
```
|
||||
|
||||
If you meet problems during installation, they might be caused by upstream dependencies. Please check the docs of these packages:
|
||||
|
||||
* [torch](https://pytorch.org/get-started/locally/)
|
||||
* [sentencepiece](https://github.com/google/sentencepiece)
|
||||
* [cmake](https://cmake.org)
|
||||
* [cupy](https://docs.cupy.dev/en/stable/install.html)
|
||||
|
||||
</details>
|
||||
|
||||
## Basic Framework
|
||||
|
||||
DiffSynth-Studio redesigns the inference and training pipelines for mainstream Diffusion models (including FLUX, Wan, etc.), enabling efficient memory management and flexible model training.
|
||||
|
||||
### Qwen-Image Series (🔥New Model)
|
||||
|
||||
Details: [./examples/qwen_image/](./examples/qwen_image/)
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Quick Start</summary>
|
||||
|
||||
```python
|
||||
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
|
||||
import torch
|
||||
|
||||
pipe = QwenImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
|
||||
)
|
||||
prompt = "A detailed portrait of a girl underwater, wearing a blue flowing dress, hair gently floating, clear light and shadow, surrounded by bubbles, calm expression, fine details, dreamy and beautiful."
|
||||
image = pipe(prompt, seed=0, num_inference_steps=40)
|
||||
image.save("image.jpg")
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Model Overview</summary>
|
||||
|
||||
|Model ID|Inference|Full Training|Validation after Full Training|LoRA Training|Validation after LoRA Training|
|
||||
|-|-|-|-|-|-|
|
||||
|[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](./examples/qwen_image/model_inference/Qwen-Image.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image.py)|
|
||||
|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py)|
|
||||
|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](./examples/qwen_image/model_inference/Qwen-Image-EliGen.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
|
||||
</details>
|
||||
|
||||
### FLUX Series
|
||||
|
||||
Detail page: [./examples/flux/](./examples/flux/)
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Quick Start</summary>
|
||||
|
||||
```python
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
],
|
||||
)
|
||||
|
||||
image = pipe(prompt="a cat", seed=0)
|
||||
image.save("image.jpg")
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Model Overview</summary>
|
||||
|
||||
| Model ID | Extra Parameters | Inference | Low VRAM Inference | Full Training | Validate After Full Training | LoRA Training | Validate After LoRA Training |
|
||||
|-|-|-|-|-|-|-|-|
|
||||
|[FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev)||[code](./examples/flux/model_inference/FLUX.1-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev.py)|
|
||||
|[FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev)||[code](./examples/flux/model_inference/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-Krea-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-Krea-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py)|
|
||||
|[FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev)|`kontext_images`|[code](./examples/flux/model_inference/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-Kontext-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py)|
|
||||
|[FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|
|
||||
|[FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py)|
|
||||
|[FLUX.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py)|
|
||||
|[FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter)|`ipadapter_images`, `ipadapter_scale`|[code](./examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py)|
|
||||
|[FLUX.1-dev-InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou)|`infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py)|
|
||||
|[FLUX.1-dev-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen)|`eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint`|[code](./examples/flux/model_inference/FLUX.1-dev-EliGen.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py)|-|-|[code](./examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py)|
|
||||
|[FLUX.1-dev-LoRA-Encoder](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev)|`lora_encoder_inputs`, `lora_encoder_scale`|[code](./examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py)|-|-|
|
||||
|[FLUX.1-dev-LoRA-Fusion-Preview](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev)||[code](./examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py)|-|-|-|-|-|
|
||||
|[Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit)|`step1x_reference_image`|[code](./examples/flux/model_inference/Step1X-Edit.py)|[code](./examples/flux/model_inference_low_vram/Step1X-Edit.py)|[code](./examples/flux/model_training/full/Step1X-Edit.sh)|[code](./examples/flux/model_training/validate_full/Step1X-Edit.py)|[code](./examples/flux/model_training/lora/Step1X-Edit.sh)|[code](./examples/flux/model_training/validate_lora/Step1X-Edit.py)|
|
||||
|[FLEX.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview)|`flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop`|[code](./examples/flux/model_inference/FLEX.2-preview.py)|[code](./examples/flux/model_inference_low_vram/FLEX.2-preview.py)|[code](./examples/flux/model_training/full/FLEX.2-preview.sh)|[code](./examples/flux/model_training/validate_full/FLEX.2-preview.py)|[code](./examples/flux/model_training/lora/FLEX.2-preview.sh)|[code](./examples/flux/model_training/validate_lora/FLEX.2-preview.py)|
|
||||
|[Nexus-Gen](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2)|`nexus_gen_reference_image`|[code](./examples/flux/model_inference/Nexus-Gen-Editing.py)|[code](./examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py)|[code](./examples/flux/model_training/full/Nexus-Gen.sh)|[code](./examples/flux/model_training/validate_full/Nexus-Gen.py)|[code](./examples/flux/model_training/lora/Nexus-Gen.sh)|[code](./examples/flux/model_training/validate_lora/Nexus-Gen.py)|
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
### Wan Series
|
||||
|
||||
Detail page: [./examples/wanvideo/](./examples/wanvideo/)
|
||||
|
||||
https://github.com/user-attachments/assets/1d66ae74-3b02-40a9-acc3-ea95fc039314
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Quick Start</summary>
|
||||
|
||||
```python
|
||||
import torch
|
||||
from diffsynth import save_video
|
||||
from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
|
||||
|
||||
pipe = WanVideoPipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
|
||||
],
|
||||
)
|
||||
pipe.enable_vram_management()
|
||||
|
||||
video = pipe(
|
||||
prompt="A documentary photography style scene: a lively puppy rapidly running on green grass. The puppy has brown-yellow fur, upright ears, and looks focused and joyful. Sunlight shines on its body, making the fur appear soft and shiny. The background is an open field with occasional wildflowers, and faint blue sky and clouds in the distance. Strong sense of perspective captures the motion of the puppy and the vitality of the surrounding grass. Mid-shot side-moving view.",
|
||||
negative_prompt="Bright colors, overexposed, static, blurry details, subtitles, style, artwork, image, still, overall gray, worst quality, low quality, JPEG compression artifacts, ugly, deformed, extra fingers, poorly drawn hands, poorly drawn face, malformed limbs, fused fingers, still frame, messy background, three legs, crowded background people, walking backwards",
|
||||
seed=0, tiled=True,
|
||||
)
|
||||
save_video(video, "video1.mp4", fps=15, quality=5)
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Model Overview</summary>
|
||||
|
||||
| Model ID | Extra Parameters | Inference | Full Training | Validate After Full Training | LoRA Training | Validate After LoRA Training |
|
||||
|-|-|-|-|-|-|-|
|
||||
|[Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py)|
|
||||
|[Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)||[code](./examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py)|
|
||||
|[Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py)|
|
||||
|[Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py)|
|
||||
|[Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py)|
|
||||
|[Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py)|
|
||||
|[Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py)|
|
||||
|[Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py)|
|
||||
|[PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py)|
|
||||
|[PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control)|`control_video`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py)|
|
||||
|[PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py)|
|
||||
|[PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control)|`control_video`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py)|
|
||||
|[PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py)|
|
||||
|[PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./examples/wanvideo/examples/wanmodel_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py)|
|
||||
|[PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py)|
|
||||
|[PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py)|
|
||||
|[PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|
|
||||
|[PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|
|
||||
|[iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py)|
|
||||
|[Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py)|
|
||||
|[Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py)|
|
||||
|[DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1)|`motion_bucket_id`|[code](./examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py)|
|
||||
|
||||
</details>
|
||||
|
||||
### More Models
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
<summary>Image Generation Models</summary>
|
||||
|
||||
Detail page: [./examples/image_synthesis/](./examples/image_synthesis/)
|
||||
|
||||
|FLUX|Stable Diffusion 3|
|
||||
|-|-|
|
||||
|||
|
||||
|
||||
|Kolors|Hunyuan-DiT|
|
||||
|-|-|
|
||||
|||
|
||||
|
||||
|Stable Diffusion|Stable Diffusion XL|
|
||||
|-|-|
|
||||
|||
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
<summary>Video Generation Models</summary>
|
||||
|
||||
- HunyuanVideo: [./examples/HunyuanVideo/](./examples/HunyuanVideo/)
|
||||
|
||||
https://github.com/user-attachments/assets/48dd24bb-0cc6-40d2-88c3-10feed3267e9
|
||||
|
||||
- StepVideo: [./examples/stepvideo/](./examples/stepvideo/)
|
||||
|
||||
https://github.com/user-attachments/assets/5954fdaa-a3cf-45a3-bd35-886e3cc4581b
|
||||
|
||||
- CogVideoX: [./examples/CogVideoX/](./examples/CogVideoX/)
|
||||
|
||||
https://github.com/user-attachments/assets/26b044c1-4a60-44a4-842f-627ff289d006
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
<summary>Image Quality Assessment Models</summary>
|
||||
|
||||
We have integrated a series of image quality assessment models. These models can be used for evaluating image generation models, alignment training, and similar tasks.
|
||||
|
||||
Detail page: [./examples/image_quality_metric/](./examples/image_quality_metric/)
|
||||
|
||||
* [ImageReward](https://github.com/THUDM/ImageReward)
|
||||
* [Aesthetic](https://github.com/christophschuhmann/improved-aesthetic-predictor)
|
||||
* [PickScore](https://github.com/yuvalkirstain/pickscore)
|
||||
* [CLIP](https://github.com/openai/CLIP)
|
||||
* [HPSv2](https://github.com/tgxs002/HPSv2)
|
||||
* [HPSv2.1](https://github.com/tgxs002/HPSv2)
|
||||
* [MPS](https://github.com/Kwai-Kolors/MPS)
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
## Innovative Achievements
|
||||
|
||||
DiffSynth-Studio is not just an engineering model framework, but also a platform for incubating innovative results.
|
||||
|
||||
<details>
|
||||
<summary>Nexus-Gen: Unified Architecture for Image Understanding, Generation, and Editing</summary>
|
||||
|
||||
- Detail page: https://github.com/modelscope/Nexus-Gen
|
||||
- Paper: [Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space](https://arxiv.org/pdf/2504.21356)
|
||||
- Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2), [HuggingFace](https://huggingface.co/modelscope/Nexus-GenV2)
|
||||
- Dataset: [ModelScope Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Nexus-Gen-Training-Dataset)
|
||||
- Online Demo: [ModelScope Nexus-Gen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/Nexus-Gen)
|
||||
|
||||
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>ArtAug: Aesthetic Enhancement for Image Generation Models</summary>
|
||||
|
||||
- Detail page: [./examples/ArtAug/](./examples/ArtAug/)
|
||||
- Paper: [ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction](https://arxiv.org/abs/2412.12888)
|
||||
- Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1)
|
||||
- Online Demo: [ModelScope AIGC Tab](https://www.modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0)
|
||||
|
||||
|FLUX.1-dev|FLUX.1-dev + ArtAug LoRA|
|
||||
|-|-|
|
||||
|||
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>EliGen: Precise Image Region Control</summary>
|
||||
|
||||
- Detail page: [./examples/EntityControl/](./examples/EntityControl/)
|
||||
- Paper: [EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097)
|
||||
- Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen)
|
||||
- Online Demo: [ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen)
|
||||
- Dataset: [EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)
|
||||
|
||||
|Entity Control Mask|Generated Image|
|
||||
|-|-|
|
||||
|||
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>ExVideo: Extended Training for Video Generation Models</summary>
|
||||
|
||||
- Project Page: [Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/)
|
||||
- Paper: [ExVideo: Extending Video Diffusion Models via Parameter-Efficient Post-Tuning](https://arxiv.org/abs/2406.14130)
|
||||
- Code Example: [./examples/ExVideo/](./examples/ExVideo/)
|
||||
- Model: [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1)
|
||||
|
||||
https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Diffutoon: High-Resolution Anime-Style Video Rendering</summary>
|
||||
|
||||
- Project Page: [Project Page](https://ecnu-cilab.github.io/DiffutoonProjectPage/)
|
||||
- Paper: [Diffutoon: High-Resolution Editable Toon Shading via Diffusion Models](https://arxiv.org/abs/2401.16224)
|
||||
- Code Example: [./examples/Diffutoon/](./examples/Diffutoon/)
|
||||
|
||||
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-4709-be5e-b39af82404dd
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>DiffSynth: The Initial Version of This Project</summary>
|
||||
|
||||
- Project Page: [Project Page](https://ecnu-cilab.github.io/DiffSynth.github.io/)
|
||||
- Paper: [DiffSynth: Latent In-Iteration Deflickering for Realistic Video Synthesis](https://arxiv.org/abs/2308.03463)
|
||||
- Code Example: [./examples/diffsynth/](./examples/diffsynth/)
|
||||
|
||||
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
## Update History
|
||||
- **August 7, 2025** We open-sourced the entity control LoRA of Qwen-Image, [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen). Qwen-Image-EliGen is able to achieve entity-level controlled text-to-image generation. See the [paper](https://arxiv.org/abs/2501.01097) for technical details. Training dataset: [EliGenTrainSet](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet).
|
||||
|
||||
- **August 5, 2025** We open-sourced the distilled acceleration model of Qwen-Image, [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full), achieving approximately 5x speedup.
|
||||
|
||||
- **August 4, 2025** 🔥 Qwen-Image is now open source. Welcome the new member to the image generation model family!
|
||||
|
||||
- **August 1, 2025** [FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) with a focus on aesthetic photography is comprehensively supported, including low-GPU-memory layer-by-layer offload, LoRA training and full training. See [./examples/flux/](./examples/flux/).
|
||||
|
||||
- **July 28, 2025** With the open-sourcing of Wan 2.2, we immediately provided comprehensive support, including low-GPU-memory layer-by-layer offload, FP8 quantization, sequence parallelism, LoRA training, full training. See [./examples/wanvideo/](./examples/wanvideo/).
|
||||
|
||||
- **July 11, 2025** We propose Nexus-Gen, a unified model that synergizes the language reasoning capabilities of LLMs with the image synthesis power of diffusion models. This framework enables seamless image understanding, generation, and editing tasks.
|
||||
- Paper: [Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space](https://arxiv.org/pdf/2504.21356)
|
||||
- Github Repo: https://github.com/modelscope/Nexus-Gen
|
||||
- Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2), [HuggingFace](https://huggingface.co/modelscope/Nexus-GenV2)
|
||||
- Training Dataset: [ModelScope Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Nexus-Gen-Training-Dataset)
|
||||
- Online Demo: [ModelScope Nexus-Gen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/Nexus-Gen)
|
||||
|
||||
<details>
|
||||
<summary>More</summary>
|
||||
|
||||
- **June 15, 2025** ModelScope's official evaluation framework, [EvalScope](https://github.com/modelscope/evalscope), now supports text-to-image generation evaluation. Try it with the [Best Practices](https://evalscope.readthedocs.io/zh-cn/latest/best_practice/t2i_eval.html) guide.
|
||||
|
||||
- **March 25, 2025** Our new open-source project, [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine), is now open-sourced! Focused on stable model deployment. Geared towards industry. Offers better engineering support, higher computational performance, and more stable functionality.
|
||||
|
||||
- **March 31, 2025** We support InfiniteYou, an identity preserving method for FLUX. Please refer to [./examples/InfiniteYou/](./examples/InfiniteYou/) for more details.
|
||||
|
||||
- **March 13, 2025** We support HunyuanVideo-I2V, the image-to-video generation version of HunyuanVideo open-sourced by Tencent. Please refer to [./examples/HunyuanVideo/](./examples/HunyuanVideo/) for more details.
|
||||
|
||||
- **February 25, 2025** We support Wan-Video, a collection of SOTA video synthesis models open-sourced by Alibaba. See [./examples/wanvideo/](./examples/wanvideo/).
|
||||
|
||||
- **February 17, 2025** We support [StepVideo](https://modelscope.cn/models/stepfun-ai/stepvideo-t2v/summary)! State-of-the-art video synthesis model! See [./examples/stepvideo](./examples/stepvideo/).
|
||||
|
||||
- **December 31, 2024** We propose EliGen, a novel framework for precise entity-level controlled text-to-image generation, complemented by an inpainting fusion pipeline to extend its capabilities to image inpainting tasks. EliGen seamlessly integrates with existing community models, such as IP-Adapter and In-Context LoRA, enhancing its versatility. For more details, see [./examples/EntityControl](./examples/EntityControl/).
|
||||
- Paper: [EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097)
|
||||
- Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen)
|
||||
- Online Demo: [ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen)
|
||||
- Training Dataset: [EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)
|
||||
|
||||
- **December 19, 2024** We implement advanced VRAM management for HunyuanVideo, making it possible to generate videos at a resolution of 129x720x1280 using 24GB of VRAM, or at 129x512x384 resolution with just 6GB of VRAM. Please refer to [./examples/HunyuanVideo/](./examples/HunyuanVideo/) for more details.
|
||||
|
||||
- **December 18, 2024** We propose ArtAug, an approach designed to improve text-to-image synthesis models through synthesis-understanding interactions. We have trained an ArtAug enhancement module for FLUX.1-dev in the format of LoRA. This model integrates the aesthetic understanding of Qwen2-VL-72B into FLUX.1-dev, leading to an improvement in the quality of generated images.
|
||||
- Paper: https://arxiv.org/abs/2412.12888
|
||||
- Examples: https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/ArtAug
|
||||
- Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1)
|
||||
- Demo: [ModelScope](https://modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0), HuggingFace (Coming soon)
|
||||
|
||||
- **October 25, 2024** We provide extensive FLUX ControlNet support. This project supports many different ControlNet models that can be freely combined, even if their structures differ. Additionally, ControlNet models are compatible with high-resolution refinement and partition control techniques, enabling very powerful controllable image generation. See [`./examples/ControlNet/`](./examples/ControlNet/).
|
||||
|
||||
- **October 8, 2024.** We release the extended LoRA based on CogVideoX-5B and ExVideo. You can download this model from [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1) or [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1).
|
||||
DiffSynth Studio is a Diffusion engine. We have restructured architectures including Text Encoder, UNet, VAE, among others, maintaining compatibility with models from the open-source community while enhancing computational performance. We provide many interesting features. Enjoy the magic of Diffusion models!
|
||||
|
||||
Until now, DiffSynth Studio has supported the following models:
|
||||
|
||||
* [CogVideo](https://huggingface.co/THUDM/CogVideoX-5b)
|
||||
* [FLUX](https://huggingface.co/black-forest-labs/FLUX.1-dev)
|
||||
* [ExVideo](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1)
|
||||
* [Kolors](https://huggingface.co/Kwai-Kolors/Kolors)
|
||||
* [Stable Diffusion 3](https://huggingface.co/stabilityai/stable-diffusion-3-medium)
|
||||
* [Stable Video Diffusion](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt)
|
||||
* [Hunyuan-DiT](https://github.com/Tencent/HunyuanDiT)
|
||||
* [RIFE](https://github.com/hzwer/ECCV2022-RIFE)
|
||||
* [ESRGAN](https://github.com/xinntao/ESRGAN)
|
||||
* [Ip-Adapter](https://github.com/tencent-ailab/IP-Adapter)
|
||||
* [AnimateDiff](https://github.com/guoyww/animatediff/)
|
||||
* [ControlNet](https://github.com/lllyasviel/ControlNet)
|
||||
* [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
|
||||
* [Stable Diffusion](https://huggingface.co/runwayml/stable-diffusion-v1-5)
|
||||
|
||||
## News
|
||||
|
||||
- **August 22, 2024.** CogVideoX-5B is supported in this project. See [here](/examples/video_synthesis/). We provide several interesting features for this text-to-video model, including
|
||||
- Text to video
|
||||
@@ -426,7 +45,7 @@ https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-44
|
||||
- Enable CFG and highres-fix to improve visual quality. See [here](/examples/image_synthesis/README.md)
|
||||
- LoRA, ControlNet, and additional models will be available soon.
|
||||
|
||||
- **June 21, 2024.** We propose ExVideo, a post-tuning technique aimed at enhancing the capability of video generation models. We have extended Stable Video Diffusion to achieve the generation of long videos up to 128 frames.
|
||||
- **June 21, 2024.** 🔥🔥🔥 We propose ExVideo, a post-tuning technique aimed at enhancing the capability of video generation models. We have extended Stable Video Diffusion to achieve the generation of long videos up to 128 frames.
|
||||
- [Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/)
|
||||
- Source code is released in this repo. See [`examples/ExVideo`](./examples/ExVideo/).
|
||||
- Models are released on [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) and [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1).
|
||||
@@ -464,4 +83,127 @@ https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-44
|
||||
- The source codes are released in [EasyNLP](https://github.com/alibaba/EasyNLP/tree/master/diffusion/DiffSynth).
|
||||
- The technical report (ECML PKDD 2024) is released on [arXiv](https://arxiv.org/abs/2308.03463).
|
||||
|
||||
</details>
|
||||
|
||||
## Installation
|
||||
|
||||
Install from source code (recommended):
|
||||
|
||||
```
|
||||
git clone https://github.com/modelscope/DiffSynth-Studio.git
|
||||
cd DiffSynth-Studio
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
Or install from pypi:
|
||||
|
||||
```
|
||||
pip install diffsynth
|
||||
```
|
||||
|
||||
## Usage (in Python code)
|
||||
|
||||
The Python examples are in [`examples`](./examples/). We provide an overview here.
|
||||
|
||||
### Download Models
|
||||
|
||||
Download the pre-set models. Model IDs can be found in [config file](/diffsynth/configs/model_config.py).
|
||||
|
||||
```python
|
||||
from diffsynth import download_models
|
||||
|
||||
download_models(["FLUX.1-dev", "Kolors"])
|
||||
```
|
||||
|
||||
Download your own models.
|
||||
|
||||
```python
|
||||
from diffsynth.models.downloader import download_from_huggingface, download_from_modelscope
|
||||
|
||||
# From Modelscope (recommended)
|
||||
download_from_modelscope("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.fp16.bin", "models/kolors/Kolors/vae")
|
||||
# From Huggingface
|
||||
download_from_huggingface("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.fp16.safetensors", "models/kolors/Kolors/vae")
|
||||
```
|
||||
|
||||
### Video Synthesis
|
||||
|
||||
#### Text-to-video using CogVideoX-5B
|
||||
|
||||
CogVideoX-5B is released by ZhiPu. We provide an improved pipeline, supporting text-to-video, video editing, self-upscaling and video interpolation. [`examples/video_synthesis`](./examples/video_synthesis/)
|
||||
|
||||
The video on the left is generated using the original text-to-video pipeline, while the video on the right is the result after editing and frame interpolation.
|
||||
|
||||
https://github.com/user-attachments/assets/26b044c1-4a60-44a4-842f-627ff289d006
|
||||
|
||||
#### Long Video Synthesis
|
||||
|
||||
We trained an extended video synthesis model, which can generate 128 frames. [`examples/ExVideo`](./examples/ExVideo/)
|
||||
|
||||
https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc
|
||||
|
||||
|
||||
#### Toon Shading
|
||||
|
||||
Render realistic videos in a flatten style and enable video editing features. [`examples/Diffutoon`](./examples/Diffutoon/)
|
||||
|
||||
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-4709-be5e-b39af82404dd
|
||||
|
||||
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/20528af5-5100-474a-8cdc-440b9efdd86c
|
||||
|
||||
#### Video Stylization
|
||||
|
||||
Video stylization without video models. [`examples/diffsynth`](./examples/diffsynth/)
|
||||
|
||||
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea
|
||||
|
||||
### Image Synthesis
|
||||
|
||||
Generate high-resolution images, by breaking the limitation of diffusion models! [`examples/image_synthesis`](./examples/image_synthesis/).
|
||||
|
||||
LoRA fine-tuning is supported in [`examples/train`](./examples/train/).
|
||||
|
||||
|FLUX|Stable Diffusion 3|
|
||||
|-|-|
|
||||
|||
|
||||
|
||||
|Kolors|Hunyuan-DiT|
|
||||
|-|-|
|
||||
|||
|
||||
|
||||
|Stable Diffusion|Stable Diffusion XL|
|
||||
|-|-|
|
||||
|||
|
||||
|
||||
## Usage (in WebUI)
|
||||
|
||||
Create stunning images using the painter, with assistance from AI!
|
||||
|
||||
https://github.com/user-attachments/assets/95265d21-cdd6-4125-a7cb-9fbcf6ceb7b0
|
||||
|
||||
**This video is not rendered in real-time.**
|
||||
|
||||
Before launching the WebUI, please download models to the folder `./models`. See [here](#download-models).
|
||||
|
||||
* `Gradio` version
|
||||
|
||||
```
|
||||
pip install gradio
|
||||
```
|
||||
|
||||
```
|
||||
python apps/gradio/DiffSynth_Studio.py
|
||||
```
|
||||
|
||||
|
||||
|
||||
* `Streamlit` version
|
||||
|
||||
```
|
||||
pip install streamlit streamlit-drawable-canvas
|
||||
```
|
||||
|
||||
```
|
||||
python -m streamlit run apps/streamlit/DiffSynth_Studio.py
|
||||
```
|
||||
|
||||
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/93085557-73f3-4eee-a205-9829591ef954
|
||||
|
||||
484
README_zh.md
484
README_zh.md
@@ -1,484 +0,0 @@
|
||||
# DiffSynth-Studio
|
||||
|
||||
<a href="https://github.com/modelscope/DiffSynth-Studio"><img src=".github/workflows/logo.gif" title="Logo" style="max-width:100%;" width="55" /></a> <a href="https://trendshift.io/repositories/10946" target="_blank"><img src="https://trendshift.io/api/badge/repositories/10946" alt="modelscope%2FDiffSynth-Studio | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a></p>
|
||||
|
||||
[](https://pypi.org/project/DiffSynth/)
|
||||
[](https://github.com/modelscope/DiffSynth-Studio/blob/master/LICENSE)
|
||||
[](https://github.com/modelscope/DiffSynth-Studio/issues)
|
||||
[](https://GitHub.com/modelscope/DiffSynth-Studio/pull/)
|
||||
[](https://GitHub.com/modelscope/DiffSynth-Studio/commit/)
|
||||
|
||||
[Switch to English](./README.md)
|
||||
|
||||
## 简介
|
||||
|
||||
欢迎来到 Diffusion 模型的魔法世界！DiffSynth-Studio 是由[魔搭社区](https://www.modelscope.cn/)团队开发和维护的开源 Diffusion 模型引擎。我们期望以框架建设孵化技术创新，凝聚开源社区的力量，探索生成式模型技术的边界！
|
||||
|
||||
DiffSynth 目前包括两个开源项目：
|
||||
* [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio): 聚焦于激进的技术探索，面向学术界，提供更前沿的模型能力支持。
|
||||
* [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine): 聚焦于稳定的模型部署，面向工业界，提供更高的计算性能与更稳定的功能。
|
||||
|
||||
[DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) 与 [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine) 作为魔搭社区 [AIGC 专区](https://modelscope.cn/aigc/home) 的核心技术支撑，提供了强大的AI生成内容能力。欢迎体验我们精心打造的产品化功能，开启您的AI创作之旅！
|
||||
|
||||
## 安装
|
||||
|
||||
从源码安装（推荐）：
|
||||
|
||||
```
|
||||
git clone https://github.com/modelscope/DiffSynth-Studio.git
|
||||
cd DiffSynth-Studio
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
<details>
|
||||
<summary>其他安装方式</summary>
|
||||
|
||||
从 pypi 安装（存在版本更新延迟，如需使用最新功能，请从源码安装）
|
||||
|
||||
```
|
||||
pip install diffsynth
|
||||
```
|
||||
|
||||
如果在安装过程中遇到问题，可能是由上游依赖包导致的，请参考这些包的文档：
|
||||
|
||||
* [torch](https://pytorch.org/get-started/locally/)
|
||||
* [sentencepiece](https://github.com/google/sentencepiece)
|
||||
* [cmake](https://cmake.org)
|
||||
* [cupy](https://docs.cupy.dev/en/stable/install.html)
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
## 基础框架
|
||||
|
||||
DiffSynth-Studio 为主流 Diffusion 模型（包括 FLUX、Wan 等）重新设计了推理和训练流水线，能够实现高效的显存管理、灵活的模型训练。
|
||||
|
||||
### Qwen-Image 系列 (🔥新模型)
|
||||
|
||||
详细页面：[./examples/qwen_image/](./examples/qwen_image/)
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
|
||||
<summary>快速开始</summary>
|
||||
|
||||
```python
|
||||
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
|
||||
import torch
|
||||
|
||||
pipe = QwenImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
|
||||
)
|
||||
prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
|
||||
image = pipe(prompt, seed=0, num_inference_steps=40)
|
||||
image.save("image.jpg")
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
|
||||
<summary>模型总览</summary>
|
||||
|
||||
|模型 ID|推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
|
||||
|-|-|-|-|-|-|
|
||||
|[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](./examples/qwen_image/model_inference/Qwen-Image.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image.py)|
|
||||
|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py)|
|
||||
|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](./examples/qwen_image/model_inference/Qwen-Image-EliGen.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
|
||||
|
||||
</details>
|
||||
|
||||
### FLUX 系列
|
||||
|
||||
详细页面：[./examples/flux/](./examples/flux/)
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
|
||||
<summary>快速开始</summary>
|
||||
|
||||
```python
|
||||
import torch
|
||||
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
|
||||
|
||||
pipe = FluxImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
|
||||
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
|
||||
],
|
||||
)
|
||||
|
||||
image = pipe(prompt="a cat", seed=0)
|
||||
image.save("image.jpg")
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
|
||||
<summary>模型总览</summary>
|
||||
|
||||
|模型 ID|额外参数|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
|
||||
|-|-|-|-|-|-|-|-|
|
||||
|[FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev)||[code](./examples/flux/model_inference/FLUX.1-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev.py)|
|
||||
|[FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev)||[code](./examples/flux/model_inference/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-Krea-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-Krea-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py)|
|
||||
|[FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev)|`kontext_images`|[code](./examples/flux/model_inference/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-Kontext-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py)|
|
||||
|[FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|
|
||||
|[FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py)|
|
||||
|[FLUX.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py)|
|
||||
|[FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter)|`ipadapter_images`, `ipadapter_scale`|[code](./examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py)|
|
||||
|[FLUX.1-dev-InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou)|`infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py)|
|
||||
|[FLUX.1-dev-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen)|`eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint`|[code](./examples/flux/model_inference/FLUX.1-dev-EliGen.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py)|-|-|[code](./examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py)|
|
||||
|[FLUX.1-dev-LoRA-Encoder](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev)|`lora_encoder_inputs`, `lora_encoder_scale`|[code](./examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py)|-|-|
|
||||
|[FLUX.1-dev-LoRA-Fusion-Preview](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev)||[code](./examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py)|-|-|-|-|-|
|
||||
|[Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit)|`step1x_reference_image`|[code](./examples/flux/model_inference/Step1X-Edit.py)|[code](./examples/flux/model_inference_low_vram/Step1X-Edit.py)|[code](./examples/flux/model_training/full/Step1X-Edit.sh)|[code](./examples/flux/model_training/validate_full/Step1X-Edit.py)|[code](./examples/flux/model_training/lora/Step1X-Edit.sh)|[code](./examples/flux/model_training/validate_lora/Step1X-Edit.py)|
|
||||
|[FLEX.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview)|`flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop`|[code](./examples/flux/model_inference/FLEX.2-preview.py)|[code](./examples/flux/model_inference_low_vram/FLEX.2-preview.py)|[code](./examples/flux/model_training/full/FLEX.2-preview.sh)|[code](./examples/flux/model_training/validate_full/FLEX.2-preview.py)|[code](./examples/flux/model_training/lora/FLEX.2-preview.sh)|[code](./examples/flux/model_training/validate_lora/FLEX.2-preview.py)|
|
||||
|[Nexus-Gen](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2)|`nexus_gen_reference_image`|[code](./examples/flux/model_inference/Nexus-Gen-Editing.py)|[code](./examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py)|[code](./examples/flux/model_training/full/Nexus-Gen.sh)|[code](./examples/flux/model_training/validate_full/Nexus-Gen.py)|[code](./examples/flux/model_training/lora/Nexus-Gen.sh)|[code](./examples/flux/model_training/validate_lora/Nexus-Gen.py)|
|
||||
|
||||
</details>
|
||||
|
||||
### Wan 系列
|
||||
|
||||
详细页面：[./examples/wanvideo/](./examples/wanvideo/)
|
||||
|
||||
https://github.com/user-attachments/assets/1d66ae74-3b02-40a9-acc3-ea95fc039314
|
||||
|
||||
<details>
|
||||
|
||||
<summary>快速开始</summary>
|
||||
|
||||
```python
|
||||
import torch
|
||||
from diffsynth import save_video
|
||||
from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
|
||||
|
||||
pipe = WanVideoPipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
|
||||
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
|
||||
],
|
||||
)
|
||||
pipe.enable_vram_management()
|
||||
|
||||
video = pipe(
|
||||
prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。",
|
||||
negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
|
||||
seed=0, tiled=True,
|
||||
)
|
||||
save_video(video, "video1.mp4", fps=15, quality=5)
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
|
||||
<summary>模型总览</summary>
|
||||
|
||||
|模型 ID|额外参数|推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
|
||||
|-|-|-|-|-|-|-|
|
||||
|[Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py)|
|
||||
|[Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)||[code](./examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py)|
|
||||
|[Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py)|
|
||||
|[Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py)|
|
||||
|[Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py)|
|
||||
|[Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py)|
|
||||
|[Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py)|
|
||||
|[Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py)|
|
||||
|[PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py)|
|
||||
|[PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control)|`control_video`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py)|
|
||||
|[PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py)|
|
||||
|[PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control)|`control_video`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py)|
|
||||
|[PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py)|
|
||||
|[PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./examples/wanvideo/examples/wanmodel_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py)|
|
||||
|[PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py)|
|
||||
|[PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py)|
|
||||
|[PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|
|
||||
|[PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|
|
||||
|[iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py)|
|
||||
|[Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py)|
|
||||
|[Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py)|
|
||||
|[DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1)|`motion_bucket_id`|[code](./examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py)|
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
### 更多模型
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
<summary>图像生成模型</summary>
|
||||
|
||||
详细页面：[./examples/image_synthesis/](./examples/image_synthesis/)
|
||||
|
||||
|FLUX|Stable Diffusion 3|
|
||||
|-|-|
|
||||
|||
|
||||
|
||||
|Kolors|Hunyuan-DiT|
|
||||
|-|-|
|
||||
|||
|
||||
|
||||
|Stable Diffusion|Stable Diffusion XL|
|
||||
|-|-|
|
||||
|||
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
<summary>视频生成模型</summary>
|
||||
|
||||
- HunyuanVideo：[./examples/HunyuanVideo/](./examples/HunyuanVideo/)
|
||||
|
||||
https://github.com/user-attachments/assets/48dd24bb-0cc6-40d2-88c3-10feed3267e9
|
||||
|
||||
- StepVideo：[./examples/stepvideo/](./examples/stepvideo/)
|
||||
|
||||
https://github.com/user-attachments/assets/5954fdaa-a3cf-45a3-bd35-886e3cc4581b
|
||||
|
||||
- CogVideoX：[./examples/CogVideoX/](./examples/CogVideoX/)
|
||||
|
||||
https://github.com/user-attachments/assets/26b044c1-4a60-44a4-842f-627ff289d006
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
<summary>图像质量评估模型</summary>
|
||||
|
||||
我们集成了一系列图像质量评估模型，这些模型可以用于图像生成模型的评测、对齐训练等场景中。
|
||||
|
||||
详细页面：[./examples/image_quality_metric/](./examples/image_quality_metric/)
|
||||
|
||||
* [ImageReward](https://github.com/THUDM/ImageReward)
|
||||
* [Aesthetic](https://github.com/christophschuhmann/improved-aesthetic-predictor)
|
||||
* [PickScore](https://github.com/yuvalkirstain/pickscore)
|
||||
* [CLIP](https://github.com/openai/CLIP)
|
||||
* [HPSv2](https://github.com/tgxs002/HPSv2)
|
||||
* [HPSv2.1](https://github.com/tgxs002/HPSv2)
|
||||
* [MPS](https://github.com/Kwai-Kolors/MPS)
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
## 创新成果
|
||||
|
||||
DiffSynth-Studio 不仅仅是一个工程化的模型框架，更是创新成果的孵化器。
|
||||
|
||||
<details>
|
||||
<summary>Nexus-Gen: 统一架构的图像理解、生成、编辑</summary>
|
||||
|
||||
- 详细页面：https://github.com/modelscope/Nexus-Gen
|
||||
- 论文：[Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space](https://arxiv.org/pdf/2504.21356)
|
||||
- 模型：[ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2), [HuggingFace](https://huggingface.co/modelscope/Nexus-GenV2)
|
||||
- 数据集：[ModelScope Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Nexus-Gen-Training-Dataset)
|
||||
- 在线体验：[ModelScope Nexus-Gen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/Nexus-Gen)
|
||||
|
||||
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
<summary>ArtAug: 图像生成模型的美学提升</summary>
|
||||
|
||||
- 详细页面：[./examples/ArtAug/](./examples/ArtAug/)
|
||||
- 论文：[ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction](https://arxiv.org/abs/2412.12888)
|
||||
- 模型：[ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1)
|
||||
- 在线体验：[ModelScope AIGC Tab](https://www.modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0)
|
||||
|
||||
|FLUX.1-dev|FLUX.1-dev + ArtAug LoRA|
|
||||
|-|-|
|
||||
|||
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
|
||||
<summary>EliGen: 精准的图像分区控制</summary>
|
||||
|
||||
- 详细页面：[./examples/EntityControl/](./examples/EntityControl/)
|
||||
- 论文：[EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097)
|
||||
- 模型：[ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen)
|
||||
- 在线体验：[ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen)
|
||||
- 数据集：[EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)
|
||||
|
||||
|实体控制区域|生成图像|
|
||||
|-|-|
|
||||
|||
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
|
||||
<summary>ExVideo: 视频生成模型的扩展训练</summary>
|
||||
|
||||
- 项目页面：[Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/)
|
||||
- 论文：[ExVideo: Extending Video Diffusion Models via Parameter-Efficient Post-Tuning](https://arxiv.org/abs/2406.14130)
|
||||
- 代码样例：[./examples/ExVideo/](./examples/ExVideo/)
|
||||
- 模型：[ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1)
|
||||
|
||||
https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Diffutoon: 高分辨率动漫风格视频渲染</summary>
|
||||
|
||||
- 项目页面：[Project Page](https://ecnu-cilab.github.io/DiffutoonProjectPage/)
|
||||
- 论文：[Diffutoon: High-Resolution Editable Toon Shading via Diffusion Models](https://arxiv.org/abs/2401.16224)
|
||||
- 代码样例：[./examples/Diffutoon/](./examples/Diffutoon/)
|
||||
|
||||
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-4709-be5e-b39af82404dd
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
<details>
|
||||
|
||||
<summary>DiffSynth: 本项目的初代版本</summary>
|
||||
|
||||
- 项目页面：[Project Page](https://ecnu-cilab.github.io/DiffSynth.github.io/)
|
||||
- 论文：[DiffSynth: Latent In-Iteration Deflickering for Realistic Video Synthesis](https://arxiv.org/abs/2308.03463)
|
||||
- 代码样例：[./examples/diffsynth/](./examples/diffsynth/)
|
||||
|
||||
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
|
||||
## 更新历史
|
||||
- **2025年8月7日** 我们开源了 Qwen-Image 的实体控制 LoRA 模型 [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)。Qwen-Image-EliGen 能够实现实体级可控的文生图。技术细节请参见[论文](https://arxiv.org/abs/2501.01097)。训练数据集：[EliGenTrainSet](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)。
|
||||
|
||||
- **2025年8月5日** 我们开源了 Qwen-Image 的蒸馏加速模型 [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)，实现了约 5 倍加速。
|
||||
|
||||
- **2025年8月4日** 🔥 Qwen-Image 开源，欢迎图像生成模型家族新成员！
|
||||
|
||||
- **2025年8月1日** [FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) 开源，这是一个专注于美学摄影的文生图模型。我们第一时间提供了全方位支持，包括低显存逐层 offload、LoRA 训练、全量训练。详细信息请参考 [./examples/flux/](./examples/flux/)。
|
||||
|
||||
- **2025年7月28日** Wan 2.2 开源，我们第一时间提供了全方位支持，包括低显存逐层 offload、FP8 量化、序列并行、LoRA 训练、全量训练。详细信息请参考 [./examples/wanvideo/](./examples/wanvideo/)。
|
||||
|
||||
- **2025年7月11日** 我们提出 Nexus-Gen，一个将大语言模型（LLM）的语言推理能力与扩散模型的图像生成能力相结合的统一框架。该框架支持无缝的图像理解、生成和编辑任务。
|
||||
- 论文: [Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space](https://arxiv.org/pdf/2504.21356)
|
||||
- Github 仓库: https://github.com/modelscope/Nexus-Gen
|
||||
- 模型: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2), [HuggingFace](https://huggingface.co/modelscope/Nexus-GenV2)
|
||||
- 训练数据集: [ModelScope Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Nexus-Gen-Training-Dataset)
|
||||
- 在线体验: [ModelScope Nexus-Gen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/Nexus-Gen)
|
||||
|
||||
<details>
|
||||
<summary>更多</summary>
|
||||
|
||||
- **2025年6月15日** ModelScope 官方评测框架 [EvalScope](https://github.com/modelscope/evalscope) 现已支持文生图生成评测。请参考[最佳实践](https://evalscope.readthedocs.io/zh-cn/latest/best_practice/t2i_eval.html)指南进行尝试。
|
||||
|
||||
- **2025年3月25日** 我们的新开源项目 [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine) 现已开源！专注于稳定的模型部署，面向工业界，提供更好的工程支持、更高的计算性能和更稳定的功能。
|
||||
|
||||
- **2025年3月31日** 我们支持 InfiniteYou，一种用于 FLUX 的人脸特征保留方法。更多细节请参考 [./examples/InfiniteYou/](./examples/InfiniteYou/)。
|
||||
|
||||
- **2025年3月13日** 我们支持 HunyuanVideo-I2V，即腾讯开源的 HunyuanVideo 的图像到视频生成版本。更多细节请参考 [./examples/HunyuanVideo/](./examples/HunyuanVideo/)。
|
||||
|
||||
- **2025年2月25日** 我们支持 Wan-Video，这是阿里巴巴开源的一系列最先进的视频合成模型。详见 [./examples/wanvideo/](./examples/wanvideo/)。
|
||||
|
||||
- **2025年2月17日** 我们支持 [StepVideo](https://modelscope.cn/models/stepfun-ai/stepvideo-t2v/summary)！先进的视频合成模型！详见 [./examples/stepvideo](./examples/stepvideo/)。
|
||||
|
||||
- **2024年12月31日** 我们提出 EliGen，一种用于精确实体级别控制的文本到图像生成的新框架，并辅以修复融合管道，将其能力扩展到图像修复任务。EliGen 可以无缝集成现有的社区模型，如 IP-Adapter 和 In-Context LoRA，提升其通用性。更多详情，请见 [./examples/EntityControl](./examples/EntityControl/)。
|
||||
- 论文: [EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097)
|
||||
- 模型: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen)
|
||||
- 在线体验: [ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen)
|
||||
- 训练数据集: [EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)
|
||||
|
||||
- **2024年12月19日** 我们为 HunyuanVideo 实现了高级显存管理，使得在 24GB 显存下可以生成分辨率为 129x720x1280 的视频，或在仅 6GB 显存下生成分辨率为 129x512x384 的视频。更多细节请参考 [./examples/HunyuanVideo/](./examples/HunyuanVideo/)。
|
||||
|
||||
- **2024年12月18日** 我们提出 ArtAug，一种通过合成-理解交互来改进文生图模型的方法。我们以 LoRA 格式为 FLUX.1-dev 训练了一个 ArtAug 增强模块。该模型将 Qwen2-VL-72B 的美学理解融入 FLUX.1-dev，从而提升了生成图像的质量。
|
||||
- 论文: https://arxiv.org/abs/2412.12888
|
||||
- 示例: https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/ArtAug
|
||||
- 模型: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1)
|
||||
- 演示: [ModelScope](https://modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0), HuggingFace (即将上线)
|
||||
|
||||
- **2024年10月25日** 我们提供了广泛的 FLUX ControlNet 支持。该项目支持许多不同的 ControlNet 模型，并且可以自由组合，即使它们的结构不同。此外，ControlNet 模型兼容高分辨率优化和分区控制技术，能够实现非常强大的可控图像生成。详见 [`./examples/ControlNet/`](./examples/ControlNet/)。
|
||||
|
||||
- **2024年10月8日** 我们发布了基于 CogVideoX-5B 和 ExVideo 的扩展 LoRA。您可以从 [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1) 或 [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1) 下载此模型。
|
||||
|
||||
- **2024年8月22日** 本项目现已支持 CogVideoX-5B。详见 [此处](/examples/video_synthesis/)。我们为这个文生视频模型提供了几个有趣的功能，包括：
|
||||
- 文本到视频
|
||||
- 视频编辑
|
||||
- 自我超分
|
||||
- 视频插帧
|
||||
|
||||
- **2024年8月22日** 我们实现了一个有趣的画笔功能，支持所有文生图模型。现在，您可以在 AI 的辅助下使用画笔创作惊艳的图像了！
|
||||
- 在我们的 [WebUI](#usage-in-webui) 中使用它。
|
||||
|
||||
- **2024年8月21日** DiffSynth-Studio 现已支持 FLUX。
|
||||
- 启用 CFG 和高分辨率修复以提升视觉质量。详见 [此处](/examples/image_synthesis/README.md)
|
||||
- LoRA、ControlNet 和其他附加模型将很快推出。
|
||||
|
||||
- **2024年6月21日** 我们提出 ExVideo，一种旨在增强视频生成模型能力的后训练微调技术。我们将 Stable Video Diffusion 进行了扩展，实现了长达 128 帧的长视频生成。
|
||||
- [项目页面](https://ecnu-cilab.github.io/ExVideoProjectPage/)
|
||||
- 源代码已在此仓库中发布。详见 [`examples/ExVideo`](./examples/ExVideo/)。
|
||||
- 模型已发布于 [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) 和 [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1)。
|
||||
- 技术报告已发布于 [arXiv](https://arxiv.org/abs/2406.14130)。
|
||||
- 您可以在此 [演示](https://huggingface.co/spaces/modelscope/ExVideo-SVD-128f-v1) 中试用 ExVideo！
|
||||
|
||||
- **2024年6月13日** DiffSynth Studio 已迁移至 ModelScope。开发团队也从“我”转变为“我们”。当然，我仍会参与后续的开发和维护工作。
|
||||
|
||||
- **2024年1月29日** 我们提出 Diffutoon，这是一个出色的卡通着色解决方案。
|
||||
- [项目页面](https://ecnu-cilab.github.io/DiffutoonProjectPage/)
|
||||
- 源代码已在此项目中发布。
|
||||
- 技术报告（IJCAI 2024）已发布于 [arXiv](https://arxiv.org/abs/2401.16224)。
|
||||
|
||||
- **2023年12月8日** 我们决定启动一个新项目，旨在释放扩散模型的潜力，尤其是在视频合成方面。该项目的开发工作正式开始。
|
||||
|
||||
- **2023年11月15日** 我们提出 FastBlend，一种强大的视频去闪烁算法。
|
||||
- sd-webui 扩展已发布于 [GitHub](https://github.com/Artiprocher/sd-webui-fastblend)。
|
||||
- 演示视频已在 Bilibili 上展示，包含三个任务：
|
||||
- [视频去闪烁](https://www.bilibili.com/video/BV1d94y1W7PE)
|
||||
- [视频插帧](https://www.bilibili.com/video/BV1Lw411m71p)
|
||||
- [图像驱动的视频渲染](https://www.bilibili.com/video/BV1RB4y1Z7LF)
|
||||
- 技术报告已发布于 [arXiv](https://arxiv.org/abs/2311.09265)。
|
||||
- 其他用户开发的非官方 ComfyUI 扩展已发布于 [GitHub](https://github.com/AInseven/ComfyUI-fastblend)。
|
||||
|
||||
- **2023年10月1日** 我们发布了该项目的早期版本，名为 FastSDXL。这是构建一个扩散引擎的初步尝试。
|
||||
- 源代码已发布于 [GitHub](https://github.com/Artiprocher/FastSDXL)。
|
||||
- FastSDXL 包含一个可训练的 OLSS 调度器，以提高效率。
|
||||
- OLSS 的原始仓库位于 [此处](https://github.com/alibaba/EasyNLP/tree/master/diffusion/olss_scheduler)。
|
||||
- 技术报告（CIKM 2023）已发布于 [arXiv](https://arxiv.org/abs/2305.14677)。
|
||||
- 演示视频已发布于 [Bilibili](https://www.bilibili.com/video/BV1w8411y7uj)。
|
||||
- 由于 OLSS 需要额外训练，我们未在本项目中实现它。
|
||||
|
||||
- **2023年8月29日** 我们提出 DiffSynth，一个视频合成框架。
|
||||
- [项目页面](https://ecnu-cilab.github.io/DiffSynth.github.io/)。
|
||||
- 源代码已发布在 [EasyNLP](https://github.com/alibaba/EasyNLP/tree/master/diffusion/DiffSynth)。
|
||||
- 技术报告（ECML PKDD 2024）已发布于 [arXiv](https://arxiv.org/abs/2308.03463)。
|
||||
|
||||
</details>
|
||||
@@ -1,390 +0,0 @@
|
||||
import os
|
||||
import torch
|
||||
import numpy as np
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
import random
|
||||
import json
|
||||
import gradio as gr
|
||||
from diffsynth import ModelManager, FluxImagePipeline, download_customized_models
|
||||
from modelscope import dataset_snapshot_download
|
||||
|
||||
|
||||
dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/entity_control/*")
|
||||
example_json = 'data/examples/eligen/entity_control/ui_examples.json'
|
||||
with open(example_json, 'r') as f:
|
||||
examples = json.load(f)['examples']
|
||||
|
||||
for idx in range(len(examples)):
|
||||
example_id = examples[idx]['example_id']
|
||||
entity_prompts = examples[idx]['local_prompt_list']
|
||||
examples[idx]['mask_lists'] = [Image.open(f"data/examples/eligen/entity_control/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))]
|
||||
|
||||
def create_canvas_data(background, masks):
|
||||
if background.shape[-1] == 3:
|
||||
background = np.dstack([background, np.full(background.shape[:2], 255, dtype=np.uint8)])
|
||||
layers = []
|
||||
for mask in masks:
|
||||
if mask is not None:
|
||||
mask_single_channel = mask if mask.ndim == 2 else mask[..., 0]
|
||||
layer = np.zeros((mask_single_channel.shape[0], mask_single_channel.shape[1], 4), dtype=np.uint8)
|
||||
layer[..., -1] = mask_single_channel
|
||||
layers.append(layer)
|
||||
else:
|
||||
layers.append(np.zeros_like(background))
|
||||
|
||||
composite = background.copy()
|
||||
for layer in layers:
|
||||
if layer.size > 0:
|
||||
composite = np.where(layer[..., -1:] > 0, layer, composite)
|
||||
return {
|
||||
"background": background,
|
||||
"layers": layers,
|
||||
"composite": composite,
|
||||
}
|
||||
|
||||
def load_example(load_example_button):
|
||||
example_idx = int(load_example_button.split()[-1]) - 1
|
||||
example = examples[example_idx]
|
||||
result = [
|
||||
50,
|
||||
example["global_prompt"],
|
||||
example["negative_prompt"],
|
||||
example["seed"],
|
||||
*example["local_prompt_list"],
|
||||
]
|
||||
num_entities = len(example["local_prompt_list"])
|
||||
result += [""] * (config["max_num_painter_layers"] - num_entities)
|
||||
masks = []
|
||||
for mask in example["mask_lists"]:
|
||||
mask_single_channel = np.array(mask.convert("L"))
|
||||
masks.append(mask_single_channel)
|
||||
for _ in range(config["max_num_painter_layers"] - len(masks)):
|
||||
blank_mask = np.zeros_like(masks[0]) if masks else np.zeros((512, 512), dtype=np.uint8)
|
||||
masks.append(blank_mask)
|
||||
background = np.ones((masks[0].shape[0], masks[0].shape[1], 4), dtype=np.uint8) * 255
|
||||
canvas_data_list = []
|
||||
for mask in masks:
|
||||
canvas_data = create_canvas_data(background, [mask])
|
||||
canvas_data_list.append(canvas_data)
|
||||
result.extend(canvas_data_list)
|
||||
return result
|
||||
|
||||
def save_mask_prompts(masks, mask_prompts, global_prompt, seed=0, random_dir='0000000'):
|
||||
save_dir = os.path.join('workdirs/tmp_mask', random_dir)
|
||||
print(f'save to {save_dir}')
|
||||
os.makedirs(save_dir, exist_ok=True)
|
||||
for i, mask in enumerate(masks):
|
||||
save_path = os.path.join(save_dir, f'{i}.png')
|
||||
mask.save(save_path)
|
||||
sample = {
|
||||
"global_prompt": global_prompt,
|
||||
"mask_prompts": mask_prompts,
|
||||
"seed": seed,
|
||||
}
|
||||
with open(os.path.join(save_dir, f"prompts.json"), 'w') as f:
|
||||
json.dump(sample, f, indent=4)
|
||||
|
||||
def visualize_masks(image, masks, mask_prompts, font_size=35, use_random_colors=False):
|
||||
# Create a blank image for overlays
|
||||
overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
|
||||
colors = [
|
||||
(165, 238, 173, 80),
|
||||
(76, 102, 221, 80),
|
||||
(221, 160, 77, 80),
|
||||
(204, 93, 71, 80),
|
||||
(145, 187, 149, 80),
|
||||
(134, 141, 172, 80),
|
||||
(157, 137, 109, 80),
|
||||
(153, 104, 95, 80),
|
||||
(165, 238, 173, 80),
|
||||
(76, 102, 221, 80),
|
||||
(221, 160, 77, 80),
|
||||
(204, 93, 71, 80),
|
||||
(145, 187, 149, 80),
|
||||
(134, 141, 172, 80),
|
||||
(157, 137, 109, 80),
|
||||
(153, 104, 95, 80),
|
||||
]
|
||||
# Generate random colors for each mask
|
||||
if use_random_colors:
|
||||
colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))]
|
||||
# Font settings
|
||||
try:
|
||||
font = ImageFont.truetype("arial", font_size) # Adjust as needed
|
||||
except IOError:
|
||||
font = ImageFont.load_default(font_size)
|
||||
# Overlay each mask onto the overlay image
|
||||
for mask, mask_prompt, color in zip(masks, mask_prompts, colors):
|
||||
if mask is None:
|
||||
continue
|
||||
# Convert mask to RGBA mode
|
||||
mask_rgba = mask.convert('RGBA')
|
||||
mask_data = mask_rgba.getdata()
|
||||
new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data]
|
||||
mask_rgba.putdata(new_data)
|
||||
# Draw the mask prompt text on the mask
|
||||
draw = ImageDraw.Draw(mask_rgba)
|
||||
mask_bbox = mask.getbbox() # Get the bounding box of the mask
|
||||
if mask_bbox is None:
|
||||
continue
|
||||
text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position
|
||||
draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font)
|
||||
# Alpha composite the overlay with this mask
|
||||
overlay = Image.alpha_composite(overlay, mask_rgba)
|
||||
# Composite the overlay onto the original image
|
||||
result = Image.alpha_composite(image.convert('RGBA'), overlay)
|
||||
return result
|
||||
|
||||
config = {
|
||||
"model_config": {
|
||||
"FLUX": {
|
||||
"model_folder": "models/FLUX",
|
||||
"pipeline_class": FluxImagePipeline,
|
||||
"default_parameters": {
|
||||
"cfg_scale": 3.0,
|
||||
"embedded_guidance": 3.5,
|
||||
"num_inference_steps": 30,
|
||||
}
|
||||
},
|
||||
},
|
||||
"max_num_painter_layers": 8,
|
||||
"max_num_model_cache": 1,
|
||||
}
|
||||
|
||||
model_dict = {}
|
||||
|
||||
def load_model(model_type='FLUX', model_path='FLUX.1-dev'):
|
||||
global model_dict
|
||||
model_key = f"{model_type}:{model_path}"
|
||||
if model_key in model_dict:
|
||||
return model_dict[model_key]
|
||||
model_path = os.path.join(config["model_config"][model_type]["model_folder"], model_path)
|
||||
model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"])
|
||||
model_manager.load_lora(
|
||||
download_customized_models(
|
||||
model_id="DiffSynth-Studio/Eligen",
|
||||
origin_file_path="model_bf16.safetensors",
|
||||
local_dir="models/lora/entity_control",
|
||||
),
|
||||
lora_alpha=1,
|
||||
)
|
||||
pipe = config["model_config"][model_type]["pipeline_class"].from_model_manager(model_manager)
|
||||
model_dict[model_key] = model_manager, pipe
|
||||
return model_manager, pipe
|
||||
|
||||
|
||||
with gr.Blocks() as app:
|
||||
gr.Markdown(
|
||||
"""## EliGen: Entity-Level Controllable Text-to-Image Model
|
||||
1. On the left, input the **global prompt** for the overall image, such as "a person stands by the river."
|
||||
2. On the right, input the **local prompt** for each entity, such as "person," and draw the corresponding mask in the **Entity Mask Painter**. Generally, solid rectangular masks yield better results.
|
||||
3. Click the **Generate** button to create the image. By selecting different **random seeds**, you can generate diverse images.
|
||||
4. **You can directly click the "Load Example" button on any sample at the bottom to load example inputs.**
|
||||
"""
|
||||
)
|
||||
|
||||
loading_status = gr.Textbox(label="Loading Model...", value="Loading model... Please wait...", visible=True)
|
||||
main_interface = gr.Column(visible=False)
|
||||
|
||||
def initialize_model():
|
||||
try:
|
||||
load_model()
|
||||
return {
|
||||
loading_status: gr.update(value="Model loaded successfully!", visible=False),
|
||||
main_interface: gr.update(visible=True),
|
||||
}
|
||||
except Exception as e:
|
||||
print(f'Failed to load model with error: {e}')
|
||||
return {
|
||||
loading_status: gr.update(value=f"Failed to load model: {str(e)}", visible=True),
|
||||
main_interface: gr.update(visible=True),
|
||||
}
|
||||
|
||||
app.load(initialize_model, inputs=None, outputs=[loading_status, main_interface])
|
||||
|
||||
with main_interface:
|
||||
with gr.Row():
|
||||
local_prompt_list = []
|
||||
canvas_list = []
|
||||
random_mask_dir = gr.State(f'{random.randint(0, 1000000):08d}')
|
||||
with gr.Column(scale=382, min_width=100):
|
||||
model_type = gr.State('FLUX')
|
||||
model_path = gr.State('FLUX.1-dev')
|
||||
with gr.Accordion(label="Global prompt"):
|
||||
prompt = gr.Textbox(label="Global Prompt", lines=3)
|
||||
negative_prompt = gr.Textbox(label="Negative prompt", value="worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw, blur,", lines=3)
|
||||
with gr.Accordion(label="Inference Options", open=True):
|
||||
seed = gr.Number(minimum=0, maximum=10**9, value=42, interactive=True, label="Random seed", show_label=True)
|
||||
num_inference_steps = gr.Slider(minimum=1, maximum=100, value=30, step=1, interactive=True, label="Inference steps")
|
||||
cfg_scale = gr.Slider(minimum=2.0, maximum=10.0, value=3.0, step=0.1, interactive=True, label="Classifier-free guidance scale")
|
||||
embedded_guidance = gr.Slider(minimum=0.0, maximum=10.0, value=3.5, step=0.1, interactive=True, label="Embedded guidance scale")
|
||||
height = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Height")
|
||||
width = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Width")
|
||||
with gr.Accordion(label="Inpaint Input Image", open=False):
|
||||
input_image = gr.Image(sources=None, show_label=False, interactive=True, type="pil")
|
||||
background_weight = gr.Slider(minimum=0.0, maximum=1000., value=0., step=1, interactive=False, label="background_weight", visible=False)
|
||||
|
||||
with gr.Column():
|
||||
reset_input_button = gr.Button(value="Reset Inpaint Input")
|
||||
send_input_to_painter = gr.Button(value="Set as painter's background")
|
||||
@gr.on(inputs=[input_image], outputs=[input_image], triggers=reset_input_button.click)
|
||||
def reset_input_image(input_image):
|
||||
return None
|
||||
|
||||
with gr.Column(scale=618, min_width=100):
|
||||
with gr.Accordion(label="Entity Painter"):
|
||||
for painter_layer_id in range(config["max_num_painter_layers"]):
|
||||
with gr.Tab(label=f"Entity {painter_layer_id}"):
|
||||
local_prompt = gr.Textbox(label="Local prompt", key=f"local_prompt_{painter_layer_id}")
|
||||
canvas = gr.ImageEditor(
|
||||
canvas_size=(512, 512),
|
||||
sources=None,
|
||||
layers=False,
|
||||
interactive=True,
|
||||
image_mode="RGBA",
|
||||
brush=gr.Brush(
|
||||
default_size=50,
|
||||
default_color="#000000",
|
||||
colors=["#000000"],
|
||||
),
|
||||
label="Entity Mask Painter",
|
||||
key=f"canvas_{painter_layer_id}",
|
||||
width=width,
|
||||
height=height,
|
||||
)
|
||||
@gr.on(inputs=[height, width, canvas], outputs=canvas, triggers=[height.change, width.change, canvas.clear], show_progress="hidden")
|
||||
def resize_canvas(height, width, canvas):
|
||||
h, w = canvas["background"].shape[:2]
|
||||
if h != height or width != w:
|
||||
return np.ones((height, width, 3), dtype=np.uint8) * 255
|
||||
else:
|
||||
return canvas
|
||||
local_prompt_list.append(local_prompt)
|
||||
canvas_list.append(canvas)
|
||||
with gr.Accordion(label="Results"):
|
||||
run_button = gr.Button(value="Generate", variant="primary")
|
||||
output_image = gr.Image(sources=None, show_label=False, interactive=False, type="pil")
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
output_to_painter_button = gr.Button(value="Set as painter's background")
|
||||
with gr.Column():
|
||||
return_with_mask = gr.Checkbox(value=False, interactive=True, label="show result with mask painting")
|
||||
output_to_input_button = gr.Button(value="Set as input image", visible=False, interactive=False)
|
||||
real_output = gr.State(None)
|
||||
mask_out = gr.State(None)
|
||||
|
||||
@gr.on(
|
||||
inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir] + local_prompt_list + canvas_list,
|
||||
outputs=[output_image, real_output, mask_out],
|
||||
triggers=run_button.click
|
||||
)
|
||||
def generate_image(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir, *args, progress=gr.Progress()):
|
||||
_, pipe = load_model(model_type, model_path)
|
||||
input_params = {
|
||||
"prompt": prompt,
|
||||
"negative_prompt": negative_prompt,
|
||||
"cfg_scale": cfg_scale,
|
||||
"num_inference_steps": num_inference_steps,
|
||||
"height": height,
|
||||
"width": width,
|
||||
"progress_bar_cmd": progress.tqdm,
|
||||
}
|
||||
if isinstance(pipe, FluxImagePipeline):
|
||||
input_params["embedded_guidance"] = embedded_guidance
|
||||
if input_image is not None:
|
||||
input_params["input_image"] = input_image.resize((width, height)).convert("RGB")
|
||||
input_params["enable_eligen_inpaint"] = True
|
||||
|
||||
local_prompt_list, canvas_list = (
|
||||
args[0 * config["max_num_painter_layers"]: 1 * config["max_num_painter_layers"]],
|
||||
args[1 * config["max_num_painter_layers"]: 2 * config["max_num_painter_layers"]],
|
||||
)
|
||||
local_prompts, masks = [], []
|
||||
for local_prompt, canvas in zip(local_prompt_list, canvas_list):
|
||||
if isinstance(local_prompt, str) and len(local_prompt) > 0:
|
||||
local_prompts.append(local_prompt)
|
||||
masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB"))
|
||||
entity_masks = None if len(masks) == 0 else masks
|
||||
entity_prompts = None if len(local_prompts) == 0 else local_prompts
|
||||
input_params.update({
|
||||
"eligen_entity_prompts": entity_prompts,
|
||||
"eligen_entity_masks": entity_masks,
|
||||
})
|
||||
torch.manual_seed(seed)
|
||||
# save_mask_prompts(masks, local_prompts, prompt, seed, random_mask_dir)
|
||||
image = pipe(**input_params)
|
||||
masks = [mask.resize(image.size) for mask in masks]
|
||||
image_with_mask = visualize_masks(image, masks, local_prompts)
|
||||
|
||||
real_output = gr.State(image)
|
||||
mask_out = gr.State(image_with_mask)
|
||||
|
||||
if return_with_mask:
|
||||
return image_with_mask, real_output, mask_out
|
||||
return image, real_output, mask_out
|
||||
|
||||
@gr.on(inputs=[input_image] + canvas_list, outputs=canvas_list, triggers=send_input_to_painter.click)
|
||||
def send_input_to_painter_background(input_image, *canvas_list):
|
||||
if input_image is None:
|
||||
return tuple(canvas_list)
|
||||
for canvas in canvas_list:
|
||||
h, w = canvas["background"].shape[:2]
|
||||
canvas["background"] = input_image.resize((w, h))
|
||||
return tuple(canvas_list)
|
||||
@gr.on(inputs=[real_output] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click)
|
||||
def send_output_to_painter_background(real_output, *canvas_list):
|
||||
if real_output is None:
|
||||
return tuple(canvas_list)
|
||||
for canvas in canvas_list:
|
||||
h, w = canvas["background"].shape[:2]
|
||||
canvas["background"] = real_output.value.resize((w, h))
|
||||
return tuple(canvas_list)
|
||||
@gr.on(inputs=[return_with_mask, real_output, mask_out], outputs=[output_image], triggers=[return_with_mask.change], show_progress="hidden")
|
||||
def show_output(return_with_mask, real_output, mask_out):
|
||||
if return_with_mask:
|
||||
return mask_out.value
|
||||
else:
|
||||
return real_output.value
|
||||
@gr.on(inputs=[real_output], outputs=[input_image], triggers=output_to_input_button.click)
|
||||
def send_output_to_pipe_input(real_output):
|
||||
return real_output.value
|
||||
|
||||
with gr.Column():
|
||||
gr.Markdown("## Examples")
|
||||
for i in range(0, len(examples), 2):
|
||||
with gr.Row():
|
||||
if i < len(examples):
|
||||
example = examples[i]
|
||||
with gr.Column():
|
||||
example_image = gr.Image(
|
||||
value=f"data/examples/eligen/entity_control/example_{example['example_id']}/example_image.png",
|
||||
label=example["description"],
|
||||
interactive=False,
|
||||
width=1024,
|
||||
height=512
|
||||
)
|
||||
load_example_button = gr.Button(value=f"Load Example {example['example_id']}")
|
||||
load_example_button.click(
|
||||
load_example,
|
||||
inputs=[load_example_button],
|
||||
outputs=[num_inference_steps, prompt, negative_prompt, seed] + local_prompt_list + canvas_list
|
||||
)
|
||||
|
||||
if i + 1 < len(examples):
|
||||
example = examples[i + 1]
|
||||
with gr.Column():
|
||||
example_image = gr.Image(
|
||||
value=f"data/examples/eligen/entity_control/example_{example['example_id']}/example_image.png",
|
||||
label=example["description"],
|
||||
interactive=False,
|
||||
width=1024,
|
||||
height=512
|
||||
)
|
||||
load_example_button = gr.Button(value=f"Load Example {example['example_id']}")
|
||||
load_example_button.click(
|
||||
load_example,
|
||||
inputs=[load_example_button],
|
||||
outputs=[num_inference_steps, prompt, negative_prompt, seed] + local_prompt_list + canvas_list
|
||||
)
|
||||
app.config["show_progress"] = "hidden"
|
||||
app.launch()
|
||||
@@ -1,382 +0,0 @@
|
||||
import os
|
||||
import torch
|
||||
import numpy as np
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
import random
|
||||
import json
|
||||
import gradio as gr
|
||||
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
|
||||
from modelscope import dataset_snapshot_download, snapshot_download
|
||||
|
||||
# pip install pydantic==2.10.6
|
||||
# pip install gradio==5.4.0
|
||||
|
||||
snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen", allow_file_pattern="model.safetensors")
|
||||
|
||||
dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/*")
|
||||
example_json = 'data/examples/eligen/qwen-image/ui_examples.json'
|
||||
with open(example_json, 'r') as f:
|
||||
examples = json.load(f)['examples']
|
||||
|
||||
for idx in range(len(examples)):
|
||||
example_id = examples[idx]['example_id']
|
||||
entity_prompts = examples[idx]['local_prompt_list']
|
||||
examples[idx]['mask_lists'] = [Image.open(f"data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))]
|
||||
|
||||
def create_canvas_data(background, masks):
|
||||
if background.shape[-1] == 3:
|
||||
background = np.dstack([background, np.full(background.shape[:2], 255, dtype=np.uint8)])
|
||||
layers = []
|
||||
for mask in masks:
|
||||
if mask is not None:
|
||||
mask_single_channel = mask if mask.ndim == 2 else mask[..., 0]
|
||||
layer = np.zeros((mask_single_channel.shape[0], mask_single_channel.shape[1], 4), dtype=np.uint8)
|
||||
layer[..., -1] = mask_single_channel
|
||||
layers.append(layer)
|
||||
else:
|
||||
layers.append(np.zeros_like(background))
|
||||
|
||||
composite = background.copy()
|
||||
for layer in layers:
|
||||
if layer.size > 0:
|
||||
composite = np.where(layer[..., -1:] > 0, layer, composite)
|
||||
return {
|
||||
"background": background,
|
||||
"layers": layers,
|
||||
"composite": composite,
|
||||
}
|
||||
|
||||
def load_example(load_example_button):
|
||||
example_idx = int(load_example_button.split()[-1]) - 1
|
||||
example = examples[example_idx]
|
||||
result = [
|
||||
50,
|
||||
example["global_prompt"],
|
||||
example["negative_prompt"],
|
||||
example["seed"],
|
||||
*example["local_prompt_list"],
|
||||
]
|
||||
num_entities = len(example["local_prompt_list"])
|
||||
result += [""] * (config["max_num_painter_layers"] - num_entities)
|
||||
masks = []
|
||||
for mask in example["mask_lists"]:
|
||||
mask_single_channel = np.array(mask.convert("L"))
|
||||
masks.append(mask_single_channel)
|
||||
for _ in range(config["max_num_painter_layers"] - len(masks)):
|
||||
blank_mask = np.zeros_like(masks[0]) if masks else np.zeros((512, 512), dtype=np.uint8)
|
||||
masks.append(blank_mask)
|
||||
background = np.ones((masks[0].shape[0], masks[0].shape[1], 4), dtype=np.uint8) * 255
|
||||
canvas_data_list = []
|
||||
for mask in masks:
|
||||
canvas_data = create_canvas_data(background, [mask])
|
||||
canvas_data_list.append(canvas_data)
|
||||
result.extend(canvas_data_list)
|
||||
return result
|
||||
|
||||
def save_mask_prompts(masks, mask_prompts, global_prompt, seed=0, random_dir='0000000'):
|
||||
save_dir = os.path.join('workdirs/tmp_mask', random_dir)
|
||||
print(f'save to {save_dir}')
|
||||
os.makedirs(save_dir, exist_ok=True)
|
||||
for i, mask in enumerate(masks):
|
||||
save_path = os.path.join(save_dir, f'{i}.png')
|
||||
mask.save(save_path)
|
||||
sample = {
|
||||
"global_prompt": global_prompt,
|
||||
"mask_prompts": mask_prompts,
|
||||
"seed": seed,
|
||||
}
|
||||
with open(os.path.join(save_dir, f"prompts.json"), 'w', encoding='utf-8') as f:
|
||||
json.dump(sample, f, ensure_ascii=False, indent=4)
|
||||
|
||||
def visualize_masks(image, masks, mask_prompts, font_size=35, use_random_colors=False):
|
||||
# Create a blank image for overlays
|
||||
overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
|
||||
colors = [
|
||||
(165, 238, 173, 80),
|
||||
(76, 102, 221, 80),
|
||||
(221, 160, 77, 80),
|
||||
(204, 93, 71, 80),
|
||||
(145, 187, 149, 80),
|
||||
(134, 141, 172, 80),
|
||||
(157, 137, 109, 80),
|
||||
(153, 104, 95, 80),
|
||||
(165, 238, 173, 80),
|
||||
(76, 102, 221, 80),
|
||||
(221, 160, 77, 80),
|
||||
(204, 93, 71, 80),
|
||||
(145, 187, 149, 80),
|
||||
(134, 141, 172, 80),
|
||||
(157, 137, 109, 80),
|
||||
(153, 104, 95, 80),
|
||||
]
|
||||
# Generate random colors for each mask
|
||||
if use_random_colors:
|
||||
colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))]
|
||||
# Font settings
|
||||
try:
|
||||
font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed
|
||||
except IOError:
|
||||
font = ImageFont.load_default(font_size)
|
||||
# Overlay each mask onto the overlay image
|
||||
for mask, mask_prompt, color in zip(masks, mask_prompts, colors):
|
||||
if mask is None:
|
||||
continue
|
||||
# Convert mask to RGBA mode
|
||||
mask_rgba = mask.convert('RGBA')
|
||||
mask_data = mask_rgba.getdata()
|
||||
new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data]
|
||||
mask_rgba.putdata(new_data)
|
||||
# Draw the mask prompt text on the mask
|
||||
draw = ImageDraw.Draw(mask_rgba)
|
||||
mask_bbox = mask.getbbox() # Get the bounding box of the mask
|
||||
if mask_bbox is None:
|
||||
continue
|
||||
text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position
|
||||
draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font)
|
||||
# Alpha composite the overlay with this mask
|
||||
overlay = Image.alpha_composite(overlay, mask_rgba)
|
||||
# Composite the overlay onto the original image
|
||||
result = Image.alpha_composite(image.convert('RGBA'), overlay)
|
||||
return result
|
||||
|
||||
config = {
|
||||
"max_num_painter_layers": 8,
|
||||
"max_num_model_cache": 1,
|
||||
}
|
||||
|
||||
model_dict = {}
|
||||
|
||||
def load_model(model_type='qwen-image'):
|
||||
global model_dict
|
||||
model_key = f"{model_type}"
|
||||
if model_key in model_dict:
|
||||
return model_dict[model_key]
|
||||
pipe = QwenImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
|
||||
)
|
||||
pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen/model.safetensors")
|
||||
model_dict[model_key] = pipe
|
||||
return pipe
|
||||
|
||||
load_model('qwen-image')
|
||||
|
||||
with gr.Blocks() as app:
|
||||
gr.Markdown(
|
||||
"""## EliGen: Entity-Level Controllable Text-to-Image Model
|
||||
1. On the left, input the **global prompt** for the overall image, such as "a person stands by the river."
|
||||
2. On the right, input the **local prompt** for each entity, such as "person," and draw the corresponding mask in the **Entity Mask Painter**. Generally, solid rectangular masks yield better results.
|
||||
3. Click the **Generate** button to create the image. By selecting different **random seeds**, you can generate diverse images.
|
||||
4. **You can directly click the "Load Example" button on any sample at the bottom to load example inputs.**
|
||||
"""
|
||||
)
|
||||
|
||||
loading_status = gr.Textbox(label="Loading Model...", value="Loading model... Please wait...", visible=True)
|
||||
main_interface = gr.Column(visible=False)
|
||||
|
||||
def initialize_model():
|
||||
try:
|
||||
load_model('qwen-image')
|
||||
return {
|
||||
loading_status: gr.update(value="Model loaded successfully!", visible=False),
|
||||
main_interface: gr.update(visible=True),
|
||||
}
|
||||
except Exception as e:
|
||||
print(f'Failed to load model with error: {e}')
|
||||
return {
|
||||
loading_status: gr.update(value=f"Failed to load model: {str(e)}", visible=True),
|
||||
main_interface: gr.update(visible=True),
|
||||
}
|
||||
|
||||
app.load(initialize_model, inputs=None, outputs=[loading_status, main_interface])
|
||||
|
||||
with main_interface:
|
||||
with gr.Row():
|
||||
local_prompt_list = []
|
||||
canvas_list = []
|
||||
random_mask_dir = gr.State(f'{random.randint(0, 1000000):08d}')
|
||||
with gr.Column(scale=382, min_width=100):
|
||||
model_type = gr.State('qwen-image')
|
||||
with gr.Accordion(label="Global prompt"):
|
||||
prompt = gr.Textbox(label="Global Prompt", lines=3)
|
||||
negative_prompt = gr.Textbox(label="Negative prompt", value="", lines=3)
|
||||
with gr.Accordion(label="Inference Options", open=True):
|
||||
seed = gr.Number(minimum=0, maximum=10**9, value=42, interactive=True, label="Random seed", show_label=True)
|
||||
num_inference_steps = gr.Slider(minimum=1, maximum=100, value=30, step=1, interactive=True, label="Inference steps")
|
||||
cfg_scale = gr.Slider(minimum=2.0, maximum=10.0, value=4.0, step=0.1, interactive=True, label="Classifier-free guidance scale")
|
||||
height = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Height")
|
||||
width = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Width")
|
||||
with gr.Accordion(label="Inpaint Input Image", open=False, visible=False):
|
||||
input_image = gr.Image(sources=None, show_label=False, interactive=True, type="pil")
|
||||
background_weight = gr.Slider(minimum=0.0, maximum=1000., value=0., step=1, interactive=False, label="background_weight", visible=False)
|
||||
|
||||
with gr.Column():
|
||||
reset_input_button = gr.Button(value="Reset Inpaint Input")
|
||||
send_input_to_painter = gr.Button(value="Set as painter's background")
|
||||
@gr.on(inputs=[input_image], outputs=[input_image], triggers=reset_input_button.click)
|
||||
def reset_input_image(input_image):
|
||||
return None
|
||||
|
||||
with gr.Column(scale=618, min_width=100):
|
||||
with gr.Accordion(label="Entity Painter"):
|
||||
for painter_layer_id in range(config["max_num_painter_layers"]):
|
||||
with gr.Tab(label=f"Entity {painter_layer_id}"):
|
||||
local_prompt = gr.Textbox(label="Local prompt", key=f"local_prompt_{painter_layer_id}")
|
||||
canvas = gr.ImageEditor(
|
||||
canvas_size=(1024, 1024),
|
||||
sources=None,
|
||||
layers=False,
|
||||
interactive=True,
|
||||
image_mode="RGBA",
|
||||
brush=gr.Brush(
|
||||
default_size=50,
|
||||
default_color="#000000",
|
||||
colors=["#000000"],
|
||||
),
|
||||
label="Entity Mask Painter",
|
||||
key=f"canvas_{painter_layer_id}",
|
||||
width=width,
|
||||
height=height,
|
||||
)
|
||||
@gr.on(inputs=[height, width, canvas], outputs=canvas, triggers=[height.change, width.change, canvas.clear], show_progress="hidden")
|
||||
def resize_canvas(height, width, canvas):
|
||||
if canvas is None or canvas["background"] is None:
|
||||
return np.ones((height, width, 3), dtype=np.uint8) * 255
|
||||
h, w = canvas["background"].shape[:2]
|
||||
if h != height or width != w:
|
||||
return np.ones((height, width, 3), dtype=np.uint8) * 255
|
||||
else:
|
||||
return canvas
|
||||
local_prompt_list.append(local_prompt)
|
||||
canvas_list.append(canvas)
|
||||
with gr.Accordion(label="Results"):
|
||||
run_button = gr.Button(value="Generate", variant="primary")
|
||||
output_image = gr.Image(sources=None, show_label=False, interactive=False, type="pil")
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
output_to_painter_button = gr.Button(value="Set as painter's background")
|
||||
with gr.Column():
|
||||
return_with_mask = gr.Checkbox(value=False, interactive=True, label="show result with mask painting")
|
||||
output_to_input_button = gr.Button(value="Set as input image", visible=False, interactive=False)
|
||||
real_output = gr.State(None)
|
||||
mask_out = gr.State(None)
|
||||
|
||||
@gr.on(
|
||||
inputs=[model_type, prompt, negative_prompt, cfg_scale, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir] + local_prompt_list + canvas_list,
|
||||
outputs=[output_image, real_output, mask_out],
|
||||
triggers=run_button.click
|
||||
)
|
||||
def generate_image(model_type, prompt, negative_prompt, cfg_scale, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir, *args, progress=gr.Progress()):
|
||||
pipe = load_model(model_type)
|
||||
input_params = {
|
||||
"prompt": prompt,
|
||||
"negative_prompt": negative_prompt,
|
||||
"cfg_scale": cfg_scale,
|
||||
"num_inference_steps": num_inference_steps,
|
||||
"height": height,
|
||||
"width": width,
|
||||
"progress_bar_cmd": progress.tqdm,
|
||||
}
|
||||
# if input_image is not None:
|
||||
# input_params["input_image"] = input_image.resize((width, height)).convert("RGB")
|
||||
# input_params["enable_eligen_inpaint"] = True
|
||||
|
||||
local_prompt_list, canvas_list = (
|
||||
args[0 * config["max_num_painter_layers"]: 1 * config["max_num_painter_layers"]],
|
||||
args[1 * config["max_num_painter_layers"]: 2 * config["max_num_painter_layers"]],
|
||||
)
|
||||
local_prompts, masks = [], []
|
||||
for local_prompt, canvas in zip(local_prompt_list, canvas_list):
|
||||
if isinstance(local_prompt, str) and len(local_prompt) > 0:
|
||||
local_prompts.append(local_prompt)
|
||||
masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB"))
|
||||
entity_prompts = None if len(local_prompts) == 0 else local_prompts
|
||||
entity_masks = None if len(masks) == 0 or entity_prompts is None else masks
|
||||
input_params.update({
|
||||
"eligen_entity_prompts": entity_prompts,
|
||||
"eligen_entity_masks": entity_masks,
|
||||
})
|
||||
torch.manual_seed(seed)
|
||||
save_mask_prompts(masks, local_prompts, prompt, seed, random_mask_dir)
|
||||
image = pipe(**input_params)
|
||||
masks = [mask.resize(image.size) for mask in masks]
|
||||
image_with_mask = visualize_masks(image, masks, local_prompts)
|
||||
|
||||
real_output = gr.State(image)
|
||||
mask_out = gr.State(image_with_mask)
|
||||
|
||||
if return_with_mask:
|
||||
return image_with_mask, real_output, mask_out
|
||||
return image, real_output, mask_out
|
||||
|
||||
@gr.on(inputs=[input_image] + canvas_list, outputs=canvas_list, triggers=send_input_to_painter.click)
|
||||
def send_input_to_painter_background(input_image, *canvas_list):
|
||||
if input_image is None:
|
||||
return tuple(canvas_list)
|
||||
for canvas in canvas_list:
|
||||
h, w = canvas["background"].shape[:2]
|
||||
canvas["background"] = input_image.resize((w, h))
|
||||
return tuple(canvas_list)
|
||||
@gr.on(inputs=[real_output] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click)
|
||||
def send_output_to_painter_background(real_output, *canvas_list):
|
||||
if real_output is None:
|
||||
return tuple(canvas_list)
|
||||
for canvas in canvas_list:
|
||||
h, w = canvas["background"].shape[:2]
|
||||
canvas["background"] = real_output.value.resize((w, h))
|
||||
return tuple(canvas_list)
|
||||
@gr.on(inputs=[return_with_mask, real_output, mask_out], outputs=[output_image], triggers=[return_with_mask.change], show_progress="hidden")
|
||||
def show_output(return_with_mask, real_output, mask_out):
|
||||
if return_with_mask:
|
||||
return mask_out.value
|
||||
else:
|
||||
return real_output.value
|
||||
@gr.on(inputs=[real_output], outputs=[input_image], triggers=output_to_input_button.click)
|
||||
def send_output_to_pipe_input(real_output):
|
||||
return real_output.value
|
||||
|
||||
with gr.Column():
|
||||
gr.Markdown("## Examples")
|
||||
for i in range(0, len(examples), 2):
|
||||
with gr.Row():
|
||||
if i < len(examples):
|
||||
example = examples[i]
|
||||
with gr.Column():
|
||||
example_image = gr.Image(
|
||||
value=f"data/examples/eligen/qwen-image/example_{example['example_id']}/example_image.png",
|
||||
label=example["description"],
|
||||
interactive=False,
|
||||
width=1024,
|
||||
height=512
|
||||
)
|
||||
load_example_button = gr.Button(value=f"Load Example {example['example_id']}")
|
||||
load_example_button.click(
|
||||
load_example,
|
||||
inputs=[load_example_button],
|
||||
outputs=[num_inference_steps, prompt, negative_prompt, seed] + local_prompt_list + canvas_list
|
||||
)
|
||||
|
||||
if i + 1 < len(examples):
|
||||
example = examples[i + 1]
|
||||
with gr.Column():
|
||||
example_image = gr.Image(
|
||||
value=f"data/examples/eligen/qwen-image/example_{example['example_id']}/example_image.png",
|
||||
label=example["description"],
|
||||
interactive=False,
|
||||
width=1024,
|
||||
height=512
|
||||
)
|
||||
load_example_button = gr.Button(value=f"Load Example {example['example_id']}")
|
||||
load_example_button.click(
|
||||
load_example,
|
||||
inputs=[load_example_button],
|
||||
outputs=[num_inference_steps, prompt, negative_prompt, seed] + local_prompt_list + canvas_list
|
||||
)
|
||||
app.config["show_progress"] = "hidden"
|
||||
app.launch(share=False)
|
||||
@@ -1,7 +1,7 @@
|
||||
# Set web page format
|
||||
import streamlit as st
|
||||
st.set_page_config(layout="wide")
|
||||
# Disable virtual VRAM on windows system
|
||||
# Diasble virtual VRAM on windows system
|
||||
import torch
|
||||
torch.cuda.set_per_process_memory_fraction(0.999, 0)
|
||||
|
||||
|
||||
@@ -33,49 +33,16 @@ from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, Hunyuan
|
||||
from ..models.hunyuan_dit import HunyuanDiT
|
||||
|
||||
from ..models.flux_dit import FluxDiT
|
||||
from ..models.flux_text_encoder import FluxTextEncoder2
|
||||
from ..models.flux_text_encoder import FluxTextEncoder1, FluxTextEncoder2
|
||||
from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder
|
||||
from ..models.flux_controlnet import FluxControlNet
|
||||
from ..models.flux_ipadapter import FluxIpAdapter
|
||||
from ..models.flux_infiniteyou import InfiniteYouImageProjector
|
||||
|
||||
from ..models.cog_vae import CogVAEEncoder, CogVAEDecoder
|
||||
from ..models.cog_dit import CogDiT
|
||||
|
||||
from ..models.omnigen import OmniGenTransformer
|
||||
|
||||
from ..models.hunyuan_video_vae_decoder import HunyuanVideoVAEDecoder
|
||||
from ..models.hunyuan_video_vae_encoder import HunyuanVideoVAEEncoder
|
||||
|
||||
from ..extensions.RIFE import IFNet
|
||||
from ..extensions.ESRGAN import RRDBNet
|
||||
|
||||
from ..models.hunyuan_video_dit import HunyuanVideoDiT
|
||||
|
||||
from ..models.stepvideo_vae import StepVideoVAE
|
||||
from ..models.stepvideo_dit import StepVideoModel
|
||||
|
||||
from ..models.wan_video_dit import WanModel
|
||||
from ..models.wan_video_text_encoder import WanTextEncoder
|
||||
from ..models.wan_video_image_encoder import WanImageEncoder
|
||||
from ..models.wan_video_vae import WanVideoVAE, WanVideoVAE38
|
||||
from ..models.wan_video_motion_controller import WanMotionControllerModel
|
||||
from ..models.wan_video_vace import VaceWanModel
|
||||
|
||||
from ..models.step1x_connector import Qwen2Connector
|
||||
|
||||
from ..models.flux_value_control import SingleValueEncoder
|
||||
|
||||
from ..lora.flux_lora import FluxLoraPatcher
|
||||
from ..models.flux_lora_encoder import FluxLoRAEncoder
|
||||
|
||||
from ..models.nexus_gen_projector import NexusGenAdapter, NexusGenImageEmbeddingMerger
|
||||
from ..models.nexus_gen import NexusGenAutoregressiveModel
|
||||
|
||||
from ..models.qwen_image_accelerate_adapter import QwenImageAccelerateAdapter
|
||||
from ..models.qwen_image_dit import QwenImageDiT
|
||||
from ..models.qwen_image_text_encoder import QwenImageTextEncoder
|
||||
from ..models.qwen_image_vae import QwenImageVAE
|
||||
|
||||
model_loader_configs = [
|
||||
# These configs are provided for detecting model type automatically.
|
||||
@@ -105,70 +72,13 @@ model_loader_configs = [
|
||||
(None, "c96a285a6888465f87de22a984d049fb", ["sd_motion_modules"], [SDMotionModel], "civitai"),
|
||||
(None, "72907b92caed19bdb2adb89aa4063fe2", ["sdxl_motion_modules"], [SDXLMotionModel], "civitai"),
|
||||
(None, "31d2d9614fba60511fc9bf2604aa01f7", ["sdxl_controlnet"], [SDXLControlNetUnion], "diffusers"),
|
||||
(None, "94eefa3dac9cec93cb1ebaf1747d7b78", ["sd3_text_encoder_1"], [SD3TextEncoder1], "diffusers"),
|
||||
(None, "94eefa3dac9cec93cb1ebaf1747d7b78", ["flux_text_encoder_1"], [FluxTextEncoder1], "diffusers"),
|
||||
(None, "1aafa3cc91716fb6b300cc1cd51b85a3", ["flux_vae_encoder", "flux_vae_decoder"], [FluxVAEEncoder, FluxVAEDecoder], "diffusers"),
|
||||
(None, "21ea55f476dfc4fd135587abb59dfe5d", ["flux_vae_encoder", "flux_vae_decoder"], [FluxVAEEncoder, FluxVAEDecoder], "civitai"),
|
||||
(None, "a29710fea6dddb0314663ee823598e50", ["flux_dit"], [FluxDiT], "civitai"),
|
||||
(None, "57b02550baab820169365b3ee3afa2c9", ["flux_dit"], [FluxDiT], "civitai"),
|
||||
(None, "3394f306c4cbf04334b712bf5aaed95f", ["flux_dit"], [FluxDiT], "civitai"),
|
||||
(None, "023f054d918a84ccf503481fd1e3379e", ["flux_dit"], [FluxDiT], "civitai"),
|
||||
(None, "d02f41c13549fa5093d3521f62a5570a", ["flux_dit"], [FluxDiT], "civitai"),
|
||||
(None, "605c56eab23e9e2af863ad8f0813a25d", ["flux_dit"], [FluxDiT], "diffusers"),
|
||||
(None, "0629116fce1472503a66992f96f3eb1a", ["flux_value_controller"], [SingleValueEncoder], "civitai"),
|
||||
(None, "280189ee084bca10f70907bf6ce1649d", ["cog_vae_encoder", "cog_vae_decoder"], [CogVAEEncoder, CogVAEDecoder], "diffusers"),
|
||||
(None, "9b9313d104ac4df27991352fec013fd4", ["rife"], [IFNet], "civitai"),
|
||||
(None, "6b7116078c4170bfbeaedc8fe71f6649", ["esrgan"], [RRDBNet], "civitai"),
|
||||
(None, "61cbcbc7ac11f169c5949223efa960d1", ["omnigen_transformer"], [OmniGenTransformer], "diffusers"),
|
||||
(None, "78d18b9101345ff695f312e7e62538c0", ["flux_controlnet"], [FluxControlNet], "diffusers"),
|
||||
(None, "b001c89139b5f053c715fe772362dd2a", ["flux_controlnet"], [FluxControlNet], "diffusers"),
|
||||
(None, "52357cb26250681367488a8954c271e8", ["flux_controlnet"], [FluxControlNet], "diffusers"),
|
||||
(None, "0cfd1740758423a2a854d67c136d1e8c", ["flux_controlnet"], [FluxControlNet], "diffusers"),
|
||||
(None, "7f9583eb8ba86642abb9a21a4b2c9e16", ["flux_controlnet"], [FluxControlNet], "diffusers"),
|
||||
(None, "43ad5aaa27dd4ee01b832ed16773fa52", ["flux_controlnet"], [FluxControlNet], "diffusers"),
|
||||
(None, "c07c0f04f5ff55e86b4e937c7a40d481", ["infiniteyou_image_projector"], [InfiniteYouImageProjector], "diffusers"),
|
||||
(None, "4daaa66cc656a8fe369908693dad0a35", ["flux_ipadapter"], [FluxIpAdapter], "diffusers"),
|
||||
(None, "51aed3d27d482fceb5e0739b03060e8f", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
|
||||
(None, "98cc34ccc5b54ae0e56bdea8688dcd5a", ["sd3_text_encoder_2"], [SD3TextEncoder2], "civitai"),
|
||||
(None, "77ff18050dbc23f50382e45d51a779fe", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
|
||||
(None, "5da81baee73198a7c19e6d2fe8b5148e", ["sd3_text_encoder_1"], [SD3TextEncoder1], "diffusers"),
|
||||
(None, "aeb82dce778a03dcb4d726cb03f3c43f", ["hunyuan_video_vae_decoder", "hunyuan_video_vae_encoder"], [HunyuanVideoVAEDecoder, HunyuanVideoVAEEncoder], "diffusers"),
|
||||
(None, "b9588f02e78f5ccafc9d7c0294e46308", ["hunyuan_video_dit"], [HunyuanVideoDiT], "civitai"),
|
||||
(None, "84ef4bd4757f60e906b54aa6a7815dc6", ["hunyuan_video_dit"], [HunyuanVideoDiT], "civitai"),
|
||||
(None, "68beaf8429b7c11aa8ca05b1bd0058bd", ["stepvideo_vae"], [StepVideoVAE], "civitai"),
|
||||
(None, "5c0216a2132b082c10cb7a0e0377e681", ["stepvideo_dit"], [StepVideoModel], "civitai"),
|
||||
(None, "9269f8db9040a9d860eaca435be61814", ["wan_video_dit"], [WanModel], "civitai"),
|
||||
(None, "aafcfd9672c3a2456dc46e1cb6e52c70", ["wan_video_dit"], [WanModel], "civitai"),
|
||||
(None, "6bfcfb3b342cb286ce886889d519a77e", ["wan_video_dit"], [WanModel], "civitai"),
|
||||
(None, "6d6ccde6845b95ad9114ab993d917893", ["wan_video_dit"], [WanModel], "civitai"),
|
||||
(None, "6bfcfb3b342cb286ce886889d519a77e", ["wan_video_dit"], [WanModel], "civitai"),
|
||||
(None, "349723183fc063b2bfc10bb2835cf677", ["wan_video_dit"], [WanModel], "civitai"),
|
||||
(None, "efa44cddf936c70abd0ea28b6cbe946c", ["wan_video_dit"], [WanModel], "civitai"),
|
||||
(None, "3ef3b1f8e1dab83d5b71fd7b617f859f", ["wan_video_dit"], [WanModel], "civitai"),
|
||||
(None, "70ddad9d3a133785da5ea371aae09504", ["wan_video_dit"], [WanModel], "civitai"),
|
||||
(None, "26bde73488a92e64cc20b0a7485b9e5b", ["wan_video_dit"], [WanModel], "civitai"),
|
||||
(None, "ac6a5aa74f4a0aab6f64eb9a72f19901", ["wan_video_dit"], [WanModel], "civitai"),
|
||||
(None, "b61c605c2adbd23124d152ed28e049ae", ["wan_video_dit"], [WanModel], "civitai"),
|
||||
(None, "1f5ab7703c6fc803fdded85ff040c316", ["wan_video_dit"], [WanModel], "civitai"),
|
||||
(None, "5b013604280dd715f8457c6ed6d6a626", ["wan_video_dit"], [WanModel], "civitai"),
|
||||
(None, "a61453409b67cd3246cf0c3bebad47ba", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"),
|
||||
(None, "7a513e1f257a861512b1afd387a8ecd9", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"),
|
||||
(None, "cb104773c6c2cb6df4f9529ad5c60d0b", ["wan_video_dit"], [WanModel], "diffusers"),
|
||||
(None, "9c8818c2cbea55eca56c7b447df170da", ["wan_video_text_encoder"], [WanTextEncoder], "civitai"),
|
||||
(None, "5941c53e207d62f20f9025686193c40b", ["wan_video_image_encoder"], [WanImageEncoder], "civitai"),
|
||||
(None, "1378ea763357eea97acdef78e65d6d96", ["wan_video_vae"], [WanVideoVAE], "civitai"),
|
||||
(None, "ccc42284ea13e1ad04693284c7a09be6", ["wan_video_vae"], [WanVideoVAE], "civitai"),
|
||||
(None, "e1de6c02cdac79f8b739f4d3698cd216", ["wan_video_vae"], [WanVideoVAE38], "civitai"),
|
||||
(None, "dbd5ec76bbf977983f972c151d545389", ["wan_video_motion_controller"], [WanMotionControllerModel], "civitai"),
|
||||
(None, "d30fb9e02b1dbf4e509142f05cf7dd50", ["flux_dit", "step1x_connector"], [FluxDiT, Qwen2Connector], "civitai"),
|
||||
(None, "30143afb2dea73d1ac580e0787628f8c", ["flux_lora_patcher"], [FluxLoraPatcher], "civitai"),
|
||||
(None, "77c2e4dd2440269eb33bfaa0d004f6ab", ["flux_lora_encoder"], [FluxLoRAEncoder], "civitai"),
|
||||
(None, "3e6c61b0f9471135fc9c6d6a98e98b6d", ["flux_dit", "nexus_gen_generation_adapter"], [FluxDiT, NexusGenAdapter], "civitai"),
|
||||
(None, "63c969fd37cce769a90aa781fbff5f81", ["flux_dit", "nexus_gen_editing_adapter"], [FluxDiT, NexusGenImageEmbeddingMerger], "civitai"),
|
||||
(None, "2bd19e845116e4f875a0a048e27fc219", ["nexus_gen_llm"], [NexusGenAutoregressiveModel], "civitai"),
|
||||
(None, "0319a1cb19835fb510907dd3367c95ff", ["qwen_image_dit"], [QwenImageDiT], "civitai"),
|
||||
(None, "ae9d13bfc578702baf6445d2cf3d1d46", ["qwen_image_accelerate_adapter"], [QwenImageAccelerateAdapter], "civitai"),
|
||||
(None, "8004730443f55db63092006dd9f7110e", ["qwen_image_text_encoder"], [QwenImageTextEncoder], "diffusers"),
|
||||
(None, "ed4ea5824d55ec3107b09815e318123a", ["qwen_image_vae"], [QwenImageVAE], "diffusers"),
|
||||
]
|
||||
huggingface_model_loader_configs = [
|
||||
# These configs are provided for detecting model type automatically.
|
||||
@@ -177,14 +87,9 @@ huggingface_model_loader_configs = [
|
||||
("MarianMTModel", "transformers.models.marian.modeling_marian", "translator", None),
|
||||
("BloomForCausalLM", "transformers.models.bloom.modeling_bloom", "beautiful_prompt", None),
|
||||
("Qwen2ForCausalLM", "transformers.models.qwen2.modeling_qwen2", "qwen_prompt", None),
|
||||
# ("LlamaForCausalLM", "transformers.models.llama.modeling_llama", "omost_prompt", None),
|
||||
("LlamaForCausalLM", "transformers.models.llama.modeling_llama", "omost_prompt", None),
|
||||
("T5EncoderModel", "diffsynth.models.flux_text_encoder", "flux_text_encoder_2", "FluxTextEncoder2"),
|
||||
("CogVideoXTransformer3DModel", "diffsynth.models.cog_dit", "cog_dit", "CogDiT"),
|
||||
("SiglipModel", "transformers.models.siglip.modeling_siglip", "siglip_vision_model", "SiglipVisionModel"),
|
||||
("LlamaForCausalLM", "diffsynth.models.hunyuan_video_text_encoder", "hunyuan_video_text_encoder_2", "HunyuanVideoLLMEncoder"),
|
||||
("LlavaForConditionalGeneration", "diffsynth.models.hunyuan_video_text_encoder", "hunyuan_video_text_encoder_2", "HunyuanVideoMLLMEncoder"),
|
||||
("Step1Model", "diffsynth.models.stepvideo_text_encoder", "stepvideo_text_encoder_2", "STEP1TextEncoder"),
|
||||
("Qwen2_5_VLForConditionalGeneration", "diffsynth.models.qwenvl", "qwenvl", "Qwen25VL_7b_Embedder"),
|
||||
]
|
||||
patch_model_loader_configs = [
|
||||
# These configs are provided for detecting model type automatically.
|
||||
@@ -205,117 +110,6 @@ preset_models_on_huggingface = {
|
||||
"ExVideo-SVD-128f-v1": [
|
||||
("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"),
|
||||
],
|
||||
# Stable Diffusion
|
||||
"StableDiffusion_v15": [
|
||||
("benjamin-paine/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"),
|
||||
],
|
||||
"DreamShaper_8": [
|
||||
("Yntec/Dreamshaper8", "dreamshaper_8.safetensors", "models/stable_diffusion"),
|
||||
],
|
||||
# Textual Inversion
|
||||
"TextualInversion_VeryBadImageNegative_v1.3": [
|
||||
("gemasai/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"),
|
||||
],
|
||||
# Stable Diffusion XL
|
||||
"StableDiffusionXL_v1": [
|
||||
("stabilityai/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"),
|
||||
],
|
||||
"BluePencilXL_v200": [
|
||||
("frankjoshua/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"),
|
||||
],
|
||||
"StableDiffusionXL_Turbo": [
|
||||
("stabilityai/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"),
|
||||
],
|
||||
# Stable Diffusion 3
|
||||
"StableDiffusion3": [
|
||||
("stabilityai/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"),
|
||||
],
|
||||
"StableDiffusion3_without_T5": [
|
||||
("stabilityai/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"),
|
||||
],
|
||||
# ControlNet
|
||||
"ControlNet_v11f1p_sd15_depth": [
|
||||
("lllyasviel/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"),
|
||||
("lllyasviel/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
|
||||
],
|
||||
"ControlNet_v11p_sd15_softedge": [
|
||||
("lllyasviel/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"),
|
||||
("lllyasviel/Annotators", "ControlNetHED.pth", "models/Annotators")
|
||||
],
|
||||
"ControlNet_v11f1e_sd15_tile": [
|
||||
("lllyasviel/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet")
|
||||
],
|
||||
"ControlNet_v11p_sd15_lineart": [
|
||||
("lllyasviel/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"),
|
||||
("lllyasviel/Annotators", "sk_model.pth", "models/Annotators"),
|
||||
("lllyasviel/Annotators", "sk_model2.pth", "models/Annotators")
|
||||
],
|
||||
"ControlNet_union_sdxl_promax": [
|
||||
("xinsir/controlnet-union-sdxl-1.0", "diffusion_pytorch_model_promax.safetensors", "models/ControlNet/controlnet_union"),
|
||||
("lllyasviel/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
|
||||
],
|
||||
# AnimateDiff
|
||||
"AnimateDiff_v2": [
|
||||
("guoyww/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"),
|
||||
],
|
||||
"AnimateDiff_xl_beta": [
|
||||
("guoyww/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"),
|
||||
],
|
||||
|
||||
# Qwen Prompt
|
||||
"QwenPrompt": [
|
||||
("Qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("Qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("Qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("Qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("Qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("Qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("Qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("Qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
],
|
||||
# Beautiful Prompt
|
||||
"BeautifulPrompt": [
|
||||
("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
],
|
||||
# Omost prompt
|
||||
"OmostPrompt":[
|
||||
("lllyasviel/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("lllyasviel/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("lllyasviel/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("lllyasviel/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("lllyasviel/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("lllyasviel/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("lllyasviel/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("lllyasviel/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
],
|
||||
# Translator
|
||||
"opus-mt-zh-en": [
|
||||
("Helsinki-NLP/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"),
|
||||
("Helsinki-NLP/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"),
|
||||
("Helsinki-NLP/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"),
|
||||
("Helsinki-NLP/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"),
|
||||
("Helsinki-NLP/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"),
|
||||
("Helsinki-NLP/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"),
|
||||
("Helsinki-NLP/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"),
|
||||
("Helsinki-NLP/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"),
|
||||
],
|
||||
# IP-Adapter
|
||||
"IP-Adapter-SD": [
|
||||
("h94/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"),
|
||||
("h94/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"),
|
||||
],
|
||||
"IP-Adapter-SDXL": [
|
||||
("h94/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"),
|
||||
("h94/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"),
|
||||
],
|
||||
"SDXL-vae-fp16-fix": [
|
||||
("madebyollin/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix")
|
||||
],
|
||||
# Kolors
|
||||
"Kolors": [
|
||||
("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"),
|
||||
@@ -340,40 +134,6 @@ preset_models_on_huggingface = {
|
||||
("black-forest-labs/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"),
|
||||
("black-forest-labs/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"),
|
||||
],
|
||||
"InstantX/FLUX.1-dev-IP-Adapter": {
|
||||
"file_list": [
|
||||
("InstantX/FLUX.1-dev-IP-Adapter", "ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter"),
|
||||
("google/siglip-so400m-patch14-384", "model.safetensors", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
|
||||
("google/siglip-so400m-patch14-384", "config.json", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
|
||||
],
|
||||
"load_path": [
|
||||
"models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin",
|
||||
"models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder",
|
||||
],
|
||||
},
|
||||
# RIFE
|
||||
"RIFE": [
|
||||
("AlexWortega/RIFE", "flownet.pkl", "models/RIFE"),
|
||||
],
|
||||
# CogVideo
|
||||
"CogVideoX-5B": [
|
||||
("THUDM/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
||||
("THUDM/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
||||
("THUDM/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
||||
("THUDM/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
||||
("THUDM/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"),
|
||||
("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"),
|
||||
("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
|
||||
("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
|
||||
("THUDM/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"),
|
||||
],
|
||||
# Stable Diffusion 3.5
|
||||
"StableDiffusion3.5-large": [
|
||||
("stabilityai/stable-diffusion-3.5-large", "sd3.5_large.safetensors", "models/stable_diffusion_3"),
|
||||
("stabilityai/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
|
||||
("stabilityai/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
|
||||
("stabilityai/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
|
||||
],
|
||||
}
|
||||
preset_models_on_modelscope = {
|
||||
# Hunyuan DiT
|
||||
@@ -391,9 +151,6 @@ preset_models_on_modelscope = {
|
||||
"ExVideo-SVD-128f-v1": [
|
||||
("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"),
|
||||
],
|
||||
"ExVideo-CogVideoX-LoRA-129f-v1": [
|
||||
("ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1", "ExVideo-CogVideoX-LoRA-129f-v1.safetensors", "models/lora"),
|
||||
],
|
||||
# Stable Diffusion
|
||||
"StableDiffusion_v15": [
|
||||
("AI-ModelScope/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"),
|
||||
@@ -452,24 +209,6 @@ preset_models_on_modelscope = {
|
||||
("AI-ModelScope/controlnet-union-sdxl-1.0", "diffusion_pytorch_model_promax.safetensors", "models/ControlNet/controlnet_union"),
|
||||
("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
|
||||
],
|
||||
"Annotators:Depth": [
|
||||
("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators"),
|
||||
],
|
||||
"Annotators:Softedge": [
|
||||
("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators"),
|
||||
],
|
||||
"Annotators:Lineart": [
|
||||
("sd_lora/Annotators", "sk_model.pth", "models/Annotators"),
|
||||
("sd_lora/Annotators", "sk_model2.pth", "models/Annotators"),
|
||||
],
|
||||
"Annotators:Normal": [
|
||||
("sd_lora/Annotators", "scannet.pt", "models/Annotators"),
|
||||
],
|
||||
"Annotators:Openpose": [
|
||||
("sd_lora/Annotators", "body_pose_model.pth", "models/Annotators"),
|
||||
("sd_lora/Annotators", "facenet.pth", "models/Annotators"),
|
||||
("sd_lora/Annotators", "hand_pose_model.pth", "models/Annotators"),
|
||||
],
|
||||
# AnimateDiff
|
||||
"AnimateDiff_v2": [
|
||||
("Shanghai_AI_Laboratory/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"),
|
||||
@@ -482,67 +221,48 @@ preset_models_on_modelscope = {
|
||||
("Damo_XR_Lab/cv_rife_video-frame-interpolation", "flownet.pkl", "models/RIFE"),
|
||||
],
|
||||
# Qwen Prompt
|
||||
"QwenPrompt": {
|
||||
"file_list": [
|
||||
("qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
],
|
||||
"load_path": [
|
||||
"models/QwenPrompt/qwen2-1.5b-instruct",
|
||||
],
|
||||
},
|
||||
"QwenPrompt": [
|
||||
("qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
("qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"),
|
||||
],
|
||||
# Beautiful Prompt
|
||||
"BeautifulPrompt": {
|
||||
"file_list": [
|
||||
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
],
|
||||
"load_path": [
|
||||
"models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd",
|
||||
],
|
||||
},
|
||||
"BeautifulPrompt": [
|
||||
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"),
|
||||
],
|
||||
# Omost prompt
|
||||
"OmostPrompt": {
|
||||
"file_list": [
|
||||
("Omost/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("Omost/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("Omost/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("Omost/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("Omost/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("Omost/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("Omost/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("Omost/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
],
|
||||
"load_path": [
|
||||
"models/OmostPrompt/omost-llama-3-8b-4bits",
|
||||
],
|
||||
},
|
||||
"OmostPrompt":[
|
||||
("Omost/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("Omost/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("Omost/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("Omost/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("Omost/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("Omost/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("Omost/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
("Omost/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"),
|
||||
],
|
||||
|
||||
# Translator
|
||||
"opus-mt-zh-en": {
|
||||
"file_list": [
|
||||
("moxying/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"),
|
||||
("moxying/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"),
|
||||
("moxying/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"),
|
||||
("moxying/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"),
|
||||
("moxying/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"),
|
||||
("moxying/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"),
|
||||
("moxying/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"),
|
||||
("moxying/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"),
|
||||
],
|
||||
"load_path": [
|
||||
"models/translator/opus-mt-zh-en",
|
||||
],
|
||||
},
|
||||
"opus-mt-zh-en": [
|
||||
("moxying/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"),
|
||||
("moxying/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"),
|
||||
("moxying/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"),
|
||||
("moxying/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"),
|
||||
("moxying/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"),
|
||||
("moxying/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"),
|
||||
("moxying/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"),
|
||||
("moxying/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"),
|
||||
],
|
||||
# IP-Adapter
|
||||
"IP-Adapter-SD": [
|
||||
("AI-ModelScope/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"),
|
||||
@@ -553,118 +273,32 @@ preset_models_on_modelscope = {
|
||||
("AI-ModelScope/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"),
|
||||
],
|
||||
# Kolors
|
||||
"Kolors": {
|
||||
"file_list": [
|
||||
("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00002-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00003-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00004-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00005-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00006-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00007-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"),
|
||||
("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"),
|
||||
],
|
||||
"load_path": [
|
||||
"models/kolors/Kolors/text_encoder",
|
||||
"models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors",
|
||||
"models/kolors/Kolors/vae/diffusion_pytorch_model.safetensors",
|
||||
],
|
||||
},
|
||||
"Kolors": [
|
||||
("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00002-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00003-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00004-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00005-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00006-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00007-of-00007.bin", "models/kolors/Kolors/text_encoder"),
|
||||
("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"),
|
||||
("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"),
|
||||
],
|
||||
"SDXL-vae-fp16-fix": [
|
||||
("AI-ModelScope/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix")
|
||||
],
|
||||
# FLUX
|
||||
"FLUX.1-dev": {
|
||||
"file_list": [
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"),
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
||||
("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"),
|
||||
("AI-ModelScope/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"),
|
||||
],
|
||||
"load_path": [
|
||||
"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
|
||||
"models/FLUX/FLUX.1-dev/text_encoder_2",
|
||||
"models/FLUX/FLUX.1-dev/ae.safetensors",
|
||||
"models/FLUX/FLUX.1-dev/flux1-dev.safetensors"
|
||||
],
|
||||
},
|
||||
"FLUX.1-schnell": {
|
||||
"file_list": [
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"),
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
||||
("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"),
|
||||
("AI-ModelScope/FLUX.1-schnell", "flux1-schnell.safetensors", "models/FLUX/FLUX.1-schnell"),
|
||||
],
|
||||
"load_path": [
|
||||
"models/FLUX/FLUX.1-dev/text_encoder/model.safetensors",
|
||||
"models/FLUX/FLUX.1-dev/text_encoder_2",
|
||||
"models/FLUX/FLUX.1-dev/ae.safetensors",
|
||||
"models/FLUX/FLUX.1-schnell/flux1-schnell.safetensors"
|
||||
],
|
||||
},
|
||||
"InstantX/FLUX.1-dev-Controlnet-Union-alpha": [
|
||||
("InstantX/FLUX.1-dev-Controlnet-Union-alpha", "diffusion_pytorch_model.safetensors", "models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha"),
|
||||
"FLUX.1-dev": [
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"),
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
||||
("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
|
||||
("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"),
|
||||
("AI-ModelScope/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"),
|
||||
],
|
||||
"jasperai/Flux.1-dev-Controlnet-Depth": [
|
||||
("jasperai/Flux.1-dev-Controlnet-Depth", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Depth"),
|
||||
],
|
||||
"jasperai/Flux.1-dev-Controlnet-Surface-Normals": [
|
||||
("jasperai/Flux.1-dev-Controlnet-Surface-Normals", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Surface-Normals"),
|
||||
],
|
||||
"jasperai/Flux.1-dev-Controlnet-Upscaler": [
|
||||
("jasperai/Flux.1-dev-Controlnet-Upscaler", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler"),
|
||||
],
|
||||
"alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha": [
|
||||
("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha", "diffusion_pytorch_model.safetensors", "models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha"),
|
||||
],
|
||||
"alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta": [
|
||||
("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", "diffusion_pytorch_model.safetensors", "models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta"),
|
||||
],
|
||||
"Shakker-Labs/FLUX.1-dev-ControlNet-Depth": [
|
||||
("Shakker-Labs/FLUX.1-dev-ControlNet-Depth", "diffusion_pytorch_model.safetensors", "models/ControlNet/Shakker-Labs/FLUX.1-dev-ControlNet-Depth"),
|
||||
],
|
||||
"Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro": [
|
||||
("Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "diffusion_pytorch_model.safetensors", "models/ControlNet/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro"),
|
||||
],
|
||||
"InstantX/FLUX.1-dev-IP-Adapter": {
|
||||
"file_list": [
|
||||
("InstantX/FLUX.1-dev-IP-Adapter", "ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter"),
|
||||
("AI-ModelScope/siglip-so400m-patch14-384", "model.safetensors", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
|
||||
("AI-ModelScope/siglip-so400m-patch14-384", "config.json", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"),
|
||||
],
|
||||
"load_path": [
|
||||
"models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin",
|
||||
"models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder",
|
||||
],
|
||||
},
|
||||
"InfiniteYou":{
|
||||
"file_list":[
|
||||
("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors", "models/InfiniteYou/InfuseNetModel"),
|
||||
("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors", "models/InfiniteYou/InfuseNetModel"),
|
||||
("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/image_proj_model.bin", "models/InfiniteYou"),
|
||||
("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/1k3d68.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
|
||||
("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/2d106det.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
|
||||
("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/genderage.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
|
||||
("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/glintr100.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
|
||||
("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/scrfd_10g_bnkps.onnx", "models/InfiniteYou/insightface/models/antelopev2"),
|
||||
],
|
||||
"load_path":[
|
||||
[
|
||||
"models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors",
|
||||
"models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors"
|
||||
],
|
||||
"models/InfiniteYou/image_proj_model.bin",
|
||||
],
|
||||
},
|
||||
# ESRGAN
|
||||
"ESRGAN_x4": [
|
||||
("AI-ModelScope/Real-ESRGAN", "RealESRGAN_x4.pth", "models/ESRGAN"),
|
||||
@@ -673,122 +307,23 @@ preset_models_on_modelscope = {
|
||||
"RIFE": [
|
||||
("AI-ModelScope/RIFE", "flownet.pkl", "models/RIFE"),
|
||||
],
|
||||
# Omnigen
|
||||
"OmniGen-v1": {
|
||||
"file_list": [
|
||||
("BAAI/OmniGen-v1", "vae/diffusion_pytorch_model.safetensors", "models/OmniGen/OmniGen-v1/vae"),
|
||||
("BAAI/OmniGen-v1", "model.safetensors", "models/OmniGen/OmniGen-v1"),
|
||||
("BAAI/OmniGen-v1", "config.json", "models/OmniGen/OmniGen-v1"),
|
||||
("BAAI/OmniGen-v1", "special_tokens_map.json", "models/OmniGen/OmniGen-v1"),
|
||||
("BAAI/OmniGen-v1", "tokenizer_config.json", "models/OmniGen/OmniGen-v1"),
|
||||
("BAAI/OmniGen-v1", "tokenizer.json", "models/OmniGen/OmniGen-v1"),
|
||||
],
|
||||
"load_path": [
|
||||
"models/OmniGen/OmniGen-v1/vae/diffusion_pytorch_model.safetensors",
|
||||
"models/OmniGen/OmniGen-v1/model.safetensors",
|
||||
]
|
||||
},
|
||||
# CogVideo
|
||||
"CogVideoX-5B": {
|
||||
"file_list": [
|
||||
("ZhipuAI/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
||||
("ZhipuAI/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
||||
("ZhipuAI/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
||||
("ZhipuAI/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
||||
("ZhipuAI/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"),
|
||||
("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"),
|
||||
("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
|
||||
("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
|
||||
("ZhipuAI/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"),
|
||||
],
|
||||
"load_path": [
|
||||
"models/CogVideo/CogVideoX-5b/text_encoder",
|
||||
"models/CogVideo/CogVideoX-5b/transformer",
|
||||
"models/CogVideo/CogVideoX-5b/vae/diffusion_pytorch_model.safetensors",
|
||||
],
|
||||
},
|
||||
# Stable Diffusion 3.5
|
||||
"StableDiffusion3.5-large": [
|
||||
("AI-ModelScope/stable-diffusion-3.5-large", "sd3.5_large.safetensors", "models/stable_diffusion_3"),
|
||||
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
|
||||
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
|
||||
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
|
||||
"CogVideoX-5B": [
|
||||
("ZhipuAI/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
||||
("ZhipuAI/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
||||
("ZhipuAI/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
||||
("ZhipuAI/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"),
|
||||
("ZhipuAI/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"),
|
||||
("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"),
|
||||
("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
|
||||
("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"),
|
||||
("ZhipuAI/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"),
|
||||
],
|
||||
"StableDiffusion3.5-medium": [
|
||||
("AI-ModelScope/stable-diffusion-3.5-medium", "sd3.5_medium.safetensors", "models/stable_diffusion_3"),
|
||||
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
|
||||
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
|
||||
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
|
||||
],
|
||||
"StableDiffusion3.5-large-turbo": [
|
||||
("AI-ModelScope/stable-diffusion-3.5-large-turbo", "sd3.5_large_turbo.safetensors", "models/stable_diffusion_3"),
|
||||
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"),
|
||||
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"),
|
||||
("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"),
|
||||
],
|
||||
"HunyuanVideo":{
|
||||
"file_list": [
|
||||
("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideo/text_encoder"),
|
||||
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00001-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
||||
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00002-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
||||
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00003-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
||||
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00004-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
||||
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "config.json", "models/HunyuanVideo/text_encoder_2"),
|
||||
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model.safetensors.index.json", "models/HunyuanVideo/text_encoder_2"),
|
||||
("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/vae/pytorch_model.pt", "models/HunyuanVideo/vae"),
|
||||
("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", "models/HunyuanVideo/transformers")
|
||||
],
|
||||
"load_path": [
|
||||
"models/HunyuanVideo/text_encoder/model.safetensors",
|
||||
"models/HunyuanVideo/text_encoder_2",
|
||||
"models/HunyuanVideo/vae/pytorch_model.pt",
|
||||
"models/HunyuanVideo/transformers/mp_rank_00_model_states.pt"
|
||||
],
|
||||
},
|
||||
"HunyuanVideoI2V":{
|
||||
"file_list": [
|
||||
("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideoI2V/text_encoder"),
|
||||
("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00001-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
|
||||
("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00002-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
|
||||
("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00003-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
|
||||
("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00004-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"),
|
||||
("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "config.json", "models/HunyuanVideoI2V/text_encoder_2"),
|
||||
("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model.safetensors.index.json", "models/HunyuanVideoI2V/text_encoder_2"),
|
||||
("AI-ModelScope/HunyuanVideo-I2V", "hunyuan-video-i2v-720p/vae/pytorch_model.pt", "models/HunyuanVideoI2V/vae"),
|
||||
("AI-ModelScope/HunyuanVideo-I2V", "hunyuan-video-i2v-720p/transformers/mp_rank_00_model_states.pt", "models/HunyuanVideoI2V/transformers")
|
||||
],
|
||||
"load_path": [
|
||||
"models/HunyuanVideoI2V/text_encoder/model.safetensors",
|
||||
"models/HunyuanVideoI2V/text_encoder_2",
|
||||
"models/HunyuanVideoI2V/vae/pytorch_model.pt",
|
||||
"models/HunyuanVideoI2V/transformers/mp_rank_00_model_states.pt"
|
||||
],
|
||||
},
|
||||
"HunyuanVideo-fp8":{
|
||||
"file_list": [
|
||||
("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideo/text_encoder"),
|
||||
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00001-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
||||
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00002-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
||||
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00003-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
||||
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00004-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"),
|
||||
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "config.json", "models/HunyuanVideo/text_encoder_2"),
|
||||
("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model.safetensors.index.json", "models/HunyuanVideo/text_encoder_2"),
|
||||
("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/vae/pytorch_model.pt", "models/HunyuanVideo/vae"),
|
||||
("DiffSynth-Studio/HunyuanVideo-safetensors", "model.fp8.safetensors", "models/HunyuanVideo/transformers")
|
||||
],
|
||||
"load_path": [
|
||||
"models/HunyuanVideo/text_encoder/model.safetensors",
|
||||
"models/HunyuanVideo/text_encoder_2",
|
||||
"models/HunyuanVideo/vae/pytorch_model.pt",
|
||||
"models/HunyuanVideo/transformers/model.fp8.safetensors"
|
||||
],
|
||||
},
|
||||
}
|
||||
Preset_model_id: TypeAlias = Literal[
|
||||
"HunyuanDiT",
|
||||
"stable-video-diffusion-img2vid-xt",
|
||||
"ExVideo-SVD-128f-v1",
|
||||
"ExVideo-CogVideoX-LoRA-129f-v1",
|
||||
"StableDiffusion_v15",
|
||||
"DreamShaper_8",
|
||||
"AingDiffusion_v12",
|
||||
@@ -814,32 +349,10 @@ Preset_model_id: TypeAlias = Literal[
|
||||
"SDXL-vae-fp16-fix",
|
||||
"ControlNet_union_sdxl_promax",
|
||||
"FLUX.1-dev",
|
||||
"FLUX.1-schnell",
|
||||
"InstantX/FLUX.1-dev-Controlnet-Union-alpha",
|
||||
"jasperai/Flux.1-dev-Controlnet-Depth",
|
||||
"jasperai/Flux.1-dev-Controlnet-Surface-Normals",
|
||||
"jasperai/Flux.1-dev-Controlnet-Upscaler",
|
||||
"alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha",
|
||||
"alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta",
|
||||
"Shakker-Labs/FLUX.1-dev-ControlNet-Depth",
|
||||
"Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro",
|
||||
"InstantX/FLUX.1-dev-IP-Adapter",
|
||||
"InfiniteYou",
|
||||
"SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0",
|
||||
"QwenPrompt",
|
||||
"OmostPrompt",
|
||||
"ESRGAN_x4",
|
||||
"RIFE",
|
||||
"OmniGen-v1",
|
||||
"CogVideoX-5B",
|
||||
"Annotators:Depth",
|
||||
"Annotators:Softedge",
|
||||
"Annotators:Lineart",
|
||||
"Annotators:Normal",
|
||||
"Annotators:Openpose",
|
||||
"StableDiffusion3.5-large",
|
||||
"StableDiffusion3.5-medium",
|
||||
"HunyuanVideo",
|
||||
"HunyuanVideo-fp8",
|
||||
"HunyuanVideoI2V",
|
||||
]
|
||||
]
|
||||
@@ -1,2 +1,2 @@
|
||||
from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager, FluxMultiControlNetManager
|
||||
from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager
|
||||
from .processors import Annotator
|
||||
|
||||
@@ -4,11 +4,10 @@ from .processors import Processor_id
|
||||
|
||||
|
||||
class ControlNetConfigUnit:
|
||||
def __init__(self, processor_id: Processor_id, model_path, scale=1.0, skip_processor=False):
|
||||
def __init__(self, processor_id: Processor_id, model_path, scale=1.0):
|
||||
self.processor_id = processor_id
|
||||
self.model_path = model_path
|
||||
self.scale = scale
|
||||
self.skip_processor = skip_processor
|
||||
|
||||
|
||||
class ControlNetUnit:
|
||||
@@ -31,8 +30,6 @@ class MultiControlNetManager:
|
||||
def to(self, device):
|
||||
for model in self.models:
|
||||
model.to(device)
|
||||
for processor in self.processors:
|
||||
processor.to(device)
|
||||
|
||||
def process_image(self, image, processor_id=None):
|
||||
if processor_id is None:
|
||||
@@ -63,29 +60,3 @@ class MultiControlNetManager:
|
||||
else:
|
||||
res_stack = [i + j for i, j in zip(res_stack, res_stack_)]
|
||||
return res_stack
|
||||
|
||||
|
||||
class FluxMultiControlNetManager(MultiControlNetManager):
|
||||
def __init__(self, controlnet_units=[]):
|
||||
super().__init__(controlnet_units=controlnet_units)
|
||||
|
||||
def process_image(self, image, processor_id=None):
|
||||
if processor_id is None:
|
||||
processed_image = [processor(image) for processor in self.processors]
|
||||
else:
|
||||
processed_image = [self.processors[processor_id](image)]
|
||||
return processed_image
|
||||
|
||||
def __call__(self, conditionings, **kwargs):
|
||||
res_stack, single_res_stack = None, None
|
||||
for processor, conditioning, model, scale in zip(self.processors, conditionings, self.models, self.scales):
|
||||
res_stack_, single_res_stack_ = model(controlnet_conditioning=conditioning, processor_id=processor.processor_id, **kwargs)
|
||||
res_stack_ = [res * scale for res in res_stack_]
|
||||
single_res_stack_ = [res * scale for res in single_res_stack_]
|
||||
if res_stack is None:
|
||||
res_stack = res_stack_
|
||||
single_res_stack = single_res_stack_
|
||||
else:
|
||||
res_stack = [i + j for i, j in zip(res_stack, res_stack_)]
|
||||
single_res_stack = [i + j for i, j in zip(single_res_stack, single_res_stack_)]
|
||||
return res_stack, single_res_stack
|
||||
|
||||
@@ -1,50 +1,39 @@
|
||||
from typing_extensions import Literal, TypeAlias
|
||||
import warnings
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore")
|
||||
from controlnet_aux.processor import (
|
||||
CannyDetector, MidasDetector, HEDdetector, LineartDetector, LineartAnimeDetector, OpenposeDetector
|
||||
)
|
||||
|
||||
|
||||
Processor_id: TypeAlias = Literal[
|
||||
"canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "normal", "tile", "none", "inpaint"
|
||||
"canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "tile"
|
||||
]
|
||||
|
||||
class Annotator:
|
||||
def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None, device='cuda', skip_processor=False):
|
||||
if not skip_processor:
|
||||
if processor_id == "canny":
|
||||
from controlnet_aux.processor import CannyDetector
|
||||
self.processor = CannyDetector()
|
||||
elif processor_id == "depth":
|
||||
from controlnet_aux.processor import MidasDetector
|
||||
self.processor = MidasDetector.from_pretrained(model_path).to(device)
|
||||
elif processor_id == "softedge":
|
||||
from controlnet_aux.processor import HEDdetector
|
||||
self.processor = HEDdetector.from_pretrained(model_path).to(device)
|
||||
elif processor_id == "lineart":
|
||||
from controlnet_aux.processor import LineartDetector
|
||||
self.processor = LineartDetector.from_pretrained(model_path).to(device)
|
||||
elif processor_id == "lineart_anime":
|
||||
from controlnet_aux.processor import LineartAnimeDetector
|
||||
self.processor = LineartAnimeDetector.from_pretrained(model_path).to(device)
|
||||
elif processor_id == "openpose":
|
||||
from controlnet_aux.processor import OpenposeDetector
|
||||
self.processor = OpenposeDetector.from_pretrained(model_path).to(device)
|
||||
elif processor_id == "normal":
|
||||
from controlnet_aux.processor import NormalBaeDetector
|
||||
self.processor = NormalBaeDetector.from_pretrained(model_path).to(device)
|
||||
elif processor_id == "tile" or processor_id == "none" or processor_id == "inpaint":
|
||||
self.processor = None
|
||||
else:
|
||||
raise ValueError(f"Unsupported processor_id: {processor_id}")
|
||||
else:
|
||||
def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None, device='cuda'):
|
||||
if processor_id == "canny":
|
||||
self.processor = CannyDetector()
|
||||
elif processor_id == "depth":
|
||||
self.processor = MidasDetector.from_pretrained(model_path).to(device)
|
||||
elif processor_id == "softedge":
|
||||
self.processor = HEDdetector.from_pretrained(model_path).to(device)
|
||||
elif processor_id == "lineart":
|
||||
self.processor = LineartDetector.from_pretrained(model_path).to(device)
|
||||
elif processor_id == "lineart_anime":
|
||||
self.processor = LineartAnimeDetector.from_pretrained(model_path).to(device)
|
||||
elif processor_id == "openpose":
|
||||
self.processor = OpenposeDetector.from_pretrained(model_path).to(device)
|
||||
elif processor_id == "tile":
|
||||
self.processor = None
|
||||
else:
|
||||
raise ValueError(f"Unsupported processor_id: {processor_id}")
|
||||
|
||||
self.processor_id = processor_id
|
||||
self.detect_resolution = detect_resolution
|
||||
|
||||
def to(self,device):
|
||||
if hasattr(self.processor,"model") and hasattr(self.processor.model,"to"):
|
||||
|
||||
self.processor.model.to(device)
|
||||
|
||||
def __call__(self, image, mask=None):
|
||||
def __call__(self, image):
|
||||
width, height = image.size
|
||||
if self.processor_id == "openpose":
|
||||
kwargs = {
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
import torch, os, torchvision
|
||||
import torch, os
|
||||
from torchvision import transforms
|
||||
import pandas as pd
|
||||
from PIL import Image
|
||||
@@ -11,10 +11,9 @@ class TextImageDataset(torch.utils.data.Dataset):
|
||||
metadata = pd.read_csv(os.path.join(dataset_path, "train/metadata.csv"))
|
||||
self.path = [os.path.join(dataset_path, "train", file_name) for file_name in metadata["file_name"]]
|
||||
self.text = metadata["text"].to_list()
|
||||
self.height = height
|
||||
self.width = width
|
||||
self.image_processor = transforms.Compose(
|
||||
[
|
||||
transforms.Resize(max(height, width), interpolation=transforms.InterpolationMode.BILINEAR),
|
||||
transforms.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)),
|
||||
transforms.RandomHorizontalFlip() if random_flip else transforms.Lambda(lambda x: x),
|
||||
transforms.ToTensor(),
|
||||
@@ -28,11 +27,6 @@ class TextImageDataset(torch.utils.data.Dataset):
|
||||
data_id = (data_id + index) % len(self.path) # For fixed seed.
|
||||
text = self.text[data_id]
|
||||
image = Image.open(self.path[data_id]).convert("RGB")
|
||||
target_height, target_width = self.height, self.width
|
||||
width, height = image.size
|
||||
scale = max(target_width / width, target_height / height)
|
||||
shape = [round(height*scale),round(width*scale)]
|
||||
image = torchvision.transforms.functional.resize(image,shape,interpolation=transforms.InterpolationMode.BILINEAR)
|
||||
image = self.image_processor(image)
|
||||
return {"text": text, "image": image}
|
||||
|
||||
|
||||
@@ -135,8 +135,8 @@ class VideoData:
|
||||
frame.save(os.path.join(folder, f"{i}.png"))
|
||||
|
||||
|
||||
def save_video(frames, save_path, fps, quality=9, ffmpeg_params=None):
|
||||
writer = imageio.get_writer(save_path, fps=fps, quality=quality, ffmpeg_params=ffmpeg_params)
|
||||
def save_video(frames, save_path, fps, quality=9):
|
||||
writer = imageio.get_writer(save_path, fps=fps, quality=quality)
|
||||
for frame in tqdm(frames, desc="Saving video"):
|
||||
frame = np.array(frame)
|
||||
writer.append_data(frame)
|
||||
|
||||
@@ -1,131 +0,0 @@
|
||||
import torch
|
||||
from typing import Optional
|
||||
from einops import rearrange
|
||||
from xfuser.core.distributed import (get_sequence_parallel_rank,
|
||||
get_sequence_parallel_world_size,
|
||||
get_sp_group)
|
||||
from xfuser.core.long_ctx_attention import xFuserLongContextAttention
|
||||
|
||||
def sinusoidal_embedding_1d(dim, position):
|
||||
sinusoid = torch.outer(position.type(torch.float64), torch.pow(
|
||||
10000, -torch.arange(dim//2, dtype=torch.float64, device=position.device).div(dim//2)))
|
||||
x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
|
||||
return x.to(position.dtype)
|
||||
|
||||
def pad_freqs(original_tensor, target_len):
|
||||
seq_len, s1, s2 = original_tensor.shape
|
||||
pad_size = target_len - seq_len
|
||||
padding_tensor = torch.ones(
|
||||
pad_size,
|
||||
s1,
|
||||
s2,
|
||||
dtype=original_tensor.dtype,
|
||||
device=original_tensor.device)
|
||||
padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0)
|
||||
return padded_tensor
|
||||
|
||||
def rope_apply(x, freqs, num_heads):
|
||||
x = rearrange(x, "b s (n d) -> b s n d", n=num_heads)
|
||||
s_per_rank = x.shape[1]
|
||||
|
||||
x_out = torch.view_as_complex(x.to(torch.float64).reshape(
|
||||
x.shape[0], x.shape[1], x.shape[2], -1, 2))
|
||||
|
||||
sp_size = get_sequence_parallel_world_size()
|
||||
sp_rank = get_sequence_parallel_rank()
|
||||
freqs = pad_freqs(freqs, s_per_rank * sp_size)
|
||||
freqs_rank = freqs[(sp_rank * s_per_rank):((sp_rank + 1) * s_per_rank), :, :]
|
||||
|
||||
x_out = torch.view_as_real(x_out * freqs_rank).flatten(2)
|
||||
return x_out.to(x.dtype)
|
||||
|
||||
def usp_dit_forward(self,
|
||||
x: torch.Tensor,
|
||||
timestep: torch.Tensor,
|
||||
context: torch.Tensor,
|
||||
clip_feature: Optional[torch.Tensor] = None,
|
||||
y: Optional[torch.Tensor] = None,
|
||||
use_gradient_checkpointing: bool = False,
|
||||
use_gradient_checkpointing_offload: bool = False,
|
||||
**kwargs,
|
||||
):
|
||||
t = self.time_embedding(
|
||||
sinusoidal_embedding_1d(self.freq_dim, timestep))
|
||||
t_mod = self.time_projection(t).unflatten(1, (6, self.dim))
|
||||
context = self.text_embedding(context)
|
||||
|
||||
if self.has_image_input:
|
||||
x = torch.cat([x, y], dim=1) # (b, c_x + c_y, f, h, w)
|
||||
clip_embdding = self.img_emb(clip_feature)
|
||||
context = torch.cat([clip_embdding, context], dim=1)
|
||||
|
||||
x, (f, h, w) = self.patchify(x)
|
||||
|
||||
freqs = torch.cat([
|
||||
self.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
|
||||
self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
|
||||
self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
|
||||
], dim=-1).reshape(f * h * w, 1, -1).to(x.device)
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
return module(*inputs)
|
||||
return custom_forward
|
||||
|
||||
# Context Parallel
|
||||
chunks = torch.chunk(x, get_sequence_parallel_world_size(), dim=1)
|
||||
pad_shape = chunks[0].shape[1] - chunks[-1].shape[1]
|
||||
chunks = [torch.nn.functional.pad(chunk, (0, 0, 0, chunks[0].shape[1]-chunk.shape[1]), value=0) for chunk in chunks]
|
||||
x = chunks[get_sequence_parallel_rank()]
|
||||
|
||||
for block in self.blocks:
|
||||
if self.training and use_gradient_checkpointing:
|
||||
if use_gradient_checkpointing_offload:
|
||||
with torch.autograd.graph.save_on_cpu():
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
x, context, t_mod, freqs,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
x, context, t_mod, freqs,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
x = block(x, context, t_mod, freqs)
|
||||
|
||||
x = self.head(x, t)
|
||||
|
||||
# Context Parallel
|
||||
x = get_sp_group().all_gather(x, dim=1)
|
||||
x = x[:, :-pad_shape] if pad_shape > 0 else x
|
||||
|
||||
# unpatchify
|
||||
x = self.unpatchify(x, (f, h, w))
|
||||
return x
|
||||
|
||||
|
||||
def usp_attn_forward(self, x, freqs):
|
||||
q = self.norm_q(self.q(x))
|
||||
k = self.norm_k(self.k(x))
|
||||
v = self.v(x)
|
||||
|
||||
q = rope_apply(q, freqs, self.num_heads)
|
||||
k = rope_apply(k, freqs, self.num_heads)
|
||||
q = rearrange(q, "b s (n d) -> b s n d", n=self.num_heads)
|
||||
k = rearrange(k, "b s (n d) -> b s n d", n=self.num_heads)
|
||||
v = rearrange(v, "b s (n d) -> b s n d", n=self.num_heads)
|
||||
|
||||
x = xFuserLongContextAttention()(
|
||||
None,
|
||||
query=q,
|
||||
key=k,
|
||||
value=v,
|
||||
)
|
||||
x = x.flatten(2)
|
||||
|
||||
del q, k, v
|
||||
torch.cuda.empty_cache()
|
||||
return self.o(x)
|
||||
@@ -107,12 +107,6 @@ class ESRGAN(torch.nn.Module):
|
||||
|
||||
@torch.no_grad()
|
||||
def upscale(self, images, batch_size=4, progress_bar=lambda x:x):
|
||||
if not isinstance(images, list):
|
||||
images = [images]
|
||||
is_single_image = True
|
||||
else:
|
||||
is_single_image = False
|
||||
|
||||
# Preprocess
|
||||
input_tensor = self.process_images(images)
|
||||
|
||||
@@ -132,6 +126,4 @@ class ESRGAN(torch.nn.Module):
|
||||
|
||||
# To images
|
||||
output_images = self.decode_images(output_tensor)
|
||||
if is_single_image:
|
||||
output_images = output_images[0]
|
||||
return output_images
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
from .blip_pretrain import *
|
||||
@@ -1,77 +0,0 @@
|
||||
'''
|
||||
* Adapted from BLIP (https://github.com/salesforce/BLIP)
|
||||
'''
|
||||
|
||||
import warnings
|
||||
warnings.filterwarnings("ignore")
|
||||
|
||||
import torch
|
||||
import os
|
||||
from urllib.parse import urlparse
|
||||
from timm.models.hub import download_cached_file
|
||||
from transformers import BertTokenizer
|
||||
from .vit import VisionTransformer, interpolate_pos_embed
|
||||
|
||||
|
||||
def default_bert():
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
project_root = os.path.abspath(os.path.join(current_dir, '../../../../'))
|
||||
model_path = os.path.join(project_root, 'models', 'QualityMetric')
|
||||
return os.path.join(model_path, "bert-base-uncased")
|
||||
|
||||
|
||||
def init_tokenizer(bert_model_path):
|
||||
tokenizer = BertTokenizer.from_pretrained(bert_model_path)
|
||||
tokenizer.add_special_tokens({'bos_token':'[DEC]'})
|
||||
tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']})
|
||||
tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]
|
||||
return tokenizer
|
||||
|
||||
|
||||
def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):
|
||||
|
||||
assert vit in ['base', 'large'], "vit parameter must be base or large"
|
||||
if vit=='base':
|
||||
vision_width = 768
|
||||
visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12,
|
||||
num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
|
||||
drop_path_rate=0 or drop_path_rate
|
||||
)
|
||||
elif vit=='large':
|
||||
vision_width = 1024
|
||||
visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24,
|
||||
num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer,
|
||||
drop_path_rate=0.1 or drop_path_rate
|
||||
)
|
||||
return visual_encoder, vision_width
|
||||
|
||||
|
||||
def is_url(url_or_filename):
|
||||
parsed = urlparse(url_or_filename)
|
||||
return parsed.scheme in ("http", "https")
|
||||
|
||||
def load_checkpoint(model,url_or_filename):
|
||||
if is_url(url_or_filename):
|
||||
cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
|
||||
checkpoint = torch.load(cached_file, map_location='cpu')
|
||||
elif os.path.isfile(url_or_filename):
|
||||
checkpoint = torch.load(url_or_filename, map_location='cpu')
|
||||
else:
|
||||
raise RuntimeError('checkpoint url or path is invalid')
|
||||
|
||||
state_dict = checkpoint['model']
|
||||
|
||||
state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
|
||||
if 'visual_encoder_m.pos_embed' in model.state_dict().keys():
|
||||
state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],
|
||||
model.visual_encoder_m)
|
||||
for key in model.state_dict().keys():
|
||||
if key in state_dict.keys():
|
||||
if state_dict[key].shape!=model.state_dict()[key].shape:
|
||||
print(key, ": ", state_dict[key].shape, ', ', model.state_dict()[key].shape)
|
||||
del state_dict[key]
|
||||
|
||||
msg = model.load_state_dict(state_dict,strict=False)
|
||||
print('load checkpoint from %s'%url_or_filename)
|
||||
return model,msg
|
||||
|
||||
@@ -1,44 +0,0 @@
|
||||
'''
|
||||
* Adapted from BLIP (https://github.com/salesforce/BLIP)
|
||||
'''
|
||||
|
||||
import transformers
|
||||
transformers.logging.set_verbosity_error()
|
||||
|
||||
from torch import nn
|
||||
import os
|
||||
from .med import BertConfig, BertModel
|
||||
from .blip import create_vit, init_tokenizer
|
||||
|
||||
class BLIP_Pretrain(nn.Module):
|
||||
def __init__(self,
|
||||
med_config = "med_config.json",
|
||||
image_size = 224,
|
||||
vit = 'base',
|
||||
vit_grad_ckpt = False,
|
||||
vit_ckpt_layer = 0,
|
||||
embed_dim = 256,
|
||||
queue_size = 57600,
|
||||
momentum = 0.995,
|
||||
bert_model_path = ""
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
med_config (str): path for the mixture of encoder-decoder model's configuration file
|
||||
image_size (int): input image size
|
||||
vit (str): model size of vision transformer
|
||||
"""
|
||||
super().__init__()
|
||||
|
||||
self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, 0)
|
||||
|
||||
self.tokenizer = init_tokenizer(bert_model_path)
|
||||
encoder_config = BertConfig.from_json_file(med_config)
|
||||
encoder_config.encoder_width = vision_width
|
||||
self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False)
|
||||
|
||||
text_width = self.text_encoder.config.hidden_size
|
||||
|
||||
self.vision_proj = nn.Linear(vision_width, embed_dim)
|
||||
self.text_proj = nn.Linear(text_width, embed_dim)
|
||||
|
||||
@@ -1,947 +0,0 @@
|
||||
'''
|
||||
* Adapted from BLIP (https://github.com/salesforce/BLIP)
|
||||
* Based on huggingface code base
|
||||
* https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
|
||||
'''
|
||||
|
||||
import math
|
||||
from typing import Tuple
|
||||
|
||||
import torch
|
||||
from torch import Tensor, device, nn
|
||||
import torch.utils.checkpoint
|
||||
from torch import nn
|
||||
from torch.nn import CrossEntropyLoss
|
||||
|
||||
from transformers.activations import ACT2FN
|
||||
from transformers.file_utils import (
|
||||
ModelOutput,
|
||||
)
|
||||
from transformers.modeling_outputs import (
|
||||
BaseModelOutputWithPastAndCrossAttentions,
|
||||
BaseModelOutputWithPoolingAndCrossAttentions,
|
||||
CausalLMOutputWithCrossAttentions,
|
||||
MaskedLMOutput,
|
||||
MultipleChoiceModelOutput,
|
||||
NextSentencePredictorOutput,
|
||||
QuestionAnsweringModelOutput,
|
||||
SequenceClassifierOutput,
|
||||
TokenClassifierOutput,
|
||||
)
|
||||
from transformers.modeling_utils import (
|
||||
PreTrainedModel,
|
||||
apply_chunking_to_forward,
|
||||
find_pruneable_heads_and_indices,
|
||||
prune_linear_layer,
|
||||
)
|
||||
from transformers.utils import logging
|
||||
from transformers.models.bert.configuration_bert import BertConfig
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class BertEmbeddings(nn.Module):
|
||||
"""Construct the embeddings from word and position embeddings."""
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
|
||||
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
|
||||
|
||||
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
||||
# any TensorFlow checkpoint file
|
||||
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
||||
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||||
|
||||
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
|
||||
self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
|
||||
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
|
||||
|
||||
self.config = config
|
||||
|
||||
def forward(
|
||||
self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
|
||||
):
|
||||
if input_ids is not None:
|
||||
input_shape = input_ids.size()
|
||||
else:
|
||||
input_shape = inputs_embeds.size()[:-1]
|
||||
|
||||
seq_length = input_shape[1]
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.word_embeddings(input_ids)
|
||||
|
||||
embeddings = inputs_embeds
|
||||
|
||||
if self.position_embedding_type == "absolute":
|
||||
position_embeddings = self.position_embeddings(position_ids)
|
||||
embeddings += position_embeddings
|
||||
embeddings = self.LayerNorm(embeddings)
|
||||
embeddings = self.dropout(embeddings)
|
||||
return embeddings
|
||||
|
||||
|
||||
class BertSelfAttention(nn.Module):
|
||||
def __init__(self, config, is_cross_attention):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
|
||||
raise ValueError(
|
||||
"The hidden size (%d) is not a multiple of the number of attention "
|
||||
"heads (%d)" % (config.hidden_size, config.num_attention_heads)
|
||||
)
|
||||
|
||||
self.num_attention_heads = config.num_attention_heads
|
||||
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
||||
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
||||
|
||||
self.query = nn.Linear(config.hidden_size, self.all_head_size)
|
||||
if is_cross_attention:
|
||||
self.key = nn.Linear(config.encoder_width, self.all_head_size)
|
||||
self.value = nn.Linear(config.encoder_width, self.all_head_size)
|
||||
else:
|
||||
self.key = nn.Linear(config.hidden_size, self.all_head_size)
|
||||
self.value = nn.Linear(config.hidden_size, self.all_head_size)
|
||||
|
||||
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
||||
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
|
||||
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
|
||||
self.max_position_embeddings = config.max_position_embeddings
|
||||
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
|
||||
self.save_attention = False
|
||||
|
||||
def save_attn_gradients(self, attn_gradients):
|
||||
self.attn_gradients = attn_gradients
|
||||
|
||||
def get_attn_gradients(self):
|
||||
return self.attn_gradients
|
||||
|
||||
def save_attention_map(self, attention_map):
|
||||
self.attention_map = attention_map
|
||||
|
||||
def get_attention_map(self):
|
||||
return self.attention_map
|
||||
|
||||
def transpose_for_scores(self, x):
|
||||
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
|
||||
x = x.view(*new_x_shape)
|
||||
return x.permute(0, 2, 1, 3)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
attention_mask=None,
|
||||
head_mask=None,
|
||||
encoder_hidden_states=None,
|
||||
encoder_attention_mask=None,
|
||||
past_key_value=None,
|
||||
output_attentions=False,
|
||||
):
|
||||
mixed_query_layer = self.query(hidden_states)
|
||||
|
||||
# If this is instantiated as a cross-attention module, the keys
|
||||
# and values come from an encoder; the attention mask needs to be
|
||||
# such that the encoder's padding tokens are not attended to.
|
||||
is_cross_attention = encoder_hidden_states is not None
|
||||
|
||||
if is_cross_attention:
|
||||
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
|
||||
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
|
||||
attention_mask = encoder_attention_mask
|
||||
elif past_key_value is not None:
|
||||
key_layer = self.transpose_for_scores(self.key(hidden_states))
|
||||
value_layer = self.transpose_for_scores(self.value(hidden_states))
|
||||
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
|
||||
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
|
||||
else:
|
||||
key_layer = self.transpose_for_scores(self.key(hidden_states))
|
||||
value_layer = self.transpose_for_scores(self.value(hidden_states))
|
||||
|
||||
query_layer = self.transpose_for_scores(mixed_query_layer)
|
||||
|
||||
past_key_value = (key_layer, value_layer)
|
||||
|
||||
# Take the dot product between "query" and "key" to get the raw attention scores.
|
||||
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
||||
|
||||
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
|
||||
seq_length = hidden_states.size()[1]
|
||||
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
|
||||
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
|
||||
distance = position_ids_l - position_ids_r
|
||||
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
|
||||
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
|
||||
|
||||
if self.position_embedding_type == "relative_key":
|
||||
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
|
||||
attention_scores = attention_scores + relative_position_scores
|
||||
elif self.position_embedding_type == "relative_key_query":
|
||||
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
|
||||
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
|
||||
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
|
||||
|
||||
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
|
||||
if attention_mask is not None:
|
||||
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
|
||||
attention_scores = attention_scores + attention_mask
|
||||
|
||||
# Normalize the attention scores to probabilities.
|
||||
attention_probs = nn.Softmax(dim=-1)(attention_scores)
|
||||
|
||||
if is_cross_attention and self.save_attention:
|
||||
self.save_attention_map(attention_probs)
|
||||
attention_probs.register_hook(self.save_attn_gradients)
|
||||
|
||||
# This is actually dropping out entire tokens to attend to, which might
|
||||
# seem a bit unusual, but is taken from the original Transformer paper.
|
||||
attention_probs_dropped = self.dropout(attention_probs)
|
||||
|
||||
# Mask heads if we want to
|
||||
if head_mask is not None:
|
||||
attention_probs_dropped = attention_probs_dropped * head_mask
|
||||
|
||||
context_layer = torch.matmul(attention_probs_dropped, value_layer)
|
||||
|
||||
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
||||
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
||||
context_layer = context_layer.view(*new_context_layer_shape)
|
||||
|
||||
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
|
||||
|
||||
outputs = outputs + (past_key_value,)
|
||||
return outputs
|
||||
|
||||
|
||||
class BertSelfOutput(nn.Module):
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
||||
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
||||
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||||
|
||||
def forward(self, hidden_states, input_tensor):
|
||||
hidden_states = self.dense(hidden_states)
|
||||
hidden_states = self.dropout(hidden_states)
|
||||
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class BertAttention(nn.Module):
|
||||
def __init__(self, config, is_cross_attention=False):
|
||||
super().__init__()
|
||||
self.self = BertSelfAttention(config, is_cross_attention)
|
||||
self.output = BertSelfOutput(config)
|
||||
self.pruned_heads = set()
|
||||
|
||||
def prune_heads(self, heads):
|
||||
if len(heads) == 0:
|
||||
return
|
||||
heads, index = find_pruneable_heads_and_indices(
|
||||
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
|
||||
)
|
||||
|
||||
# Prune linear layers
|
||||
self.self.query = prune_linear_layer(self.self.query, index)
|
||||
self.self.key = prune_linear_layer(self.self.key, index)
|
||||
self.self.value = prune_linear_layer(self.self.value, index)
|
||||
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
||||
|
||||
# Update hyper params and store pruned heads
|
||||
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
|
||||
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
|
||||
self.pruned_heads = self.pruned_heads.union(heads)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
attention_mask=None,
|
||||
head_mask=None,
|
||||
encoder_hidden_states=None,
|
||||
encoder_attention_mask=None,
|
||||
past_key_value=None,
|
||||
output_attentions=False,
|
||||
):
|
||||
self_outputs = self.self(
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
head_mask,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
past_key_value,
|
||||
output_attentions,
|
||||
)
|
||||
attention_output = self.output(self_outputs[0], hidden_states)
|
||||
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
||||
return outputs
|
||||
|
||||
|
||||
class BertIntermediate(nn.Module):
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
|
||||
if isinstance(config.hidden_act, str):
|
||||
self.intermediate_act_fn = ACT2FN[config.hidden_act]
|
||||
else:
|
||||
self.intermediate_act_fn = config.hidden_act
|
||||
|
||||
def forward(self, hidden_states):
|
||||
hidden_states = self.dense(hidden_states)
|
||||
hidden_states = self.intermediate_act_fn(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class BertOutput(nn.Module):
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
|
||||
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
||||
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||||
|
||||
def forward(self, hidden_states, input_tensor):
|
||||
hidden_states = self.dense(hidden_states)
|
||||
hidden_states = self.dropout(hidden_states)
|
||||
hidden_states = self.LayerNorm(hidden_states + input_tensor)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class BertLayer(nn.Module):
|
||||
def __init__(self, config, layer_num):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.chunk_size_feed_forward = config.chunk_size_feed_forward
|
||||
self.seq_len_dim = 1
|
||||
self.attention = BertAttention(config)
|
||||
self.layer_num = layer_num
|
||||
if self.config.add_cross_attention:
|
||||
self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention)
|
||||
self.intermediate = BertIntermediate(config)
|
||||
self.output = BertOutput(config)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
attention_mask=None,
|
||||
head_mask=None,
|
||||
encoder_hidden_states=None,
|
||||
encoder_attention_mask=None,
|
||||
past_key_value=None,
|
||||
output_attentions=False,
|
||||
mode=None,
|
||||
):
|
||||
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
|
||||
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
|
||||
self_attention_outputs = self.attention(
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
head_mask,
|
||||
output_attentions=output_attentions,
|
||||
past_key_value=self_attn_past_key_value,
|
||||
)
|
||||
attention_output = self_attention_outputs[0]
|
||||
|
||||
outputs = self_attention_outputs[1:-1]
|
||||
present_key_value = self_attention_outputs[-1]
|
||||
|
||||
if mode=='multimodal':
|
||||
assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
|
||||
|
||||
cross_attention_outputs = self.crossattention(
|
||||
attention_output,
|
||||
attention_mask,
|
||||
head_mask,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
output_attentions=output_attentions,
|
||||
)
|
||||
attention_output = cross_attention_outputs[0]
|
||||
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
|
||||
layer_output = apply_chunking_to_forward(
|
||||
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
|
||||
)
|
||||
outputs = (layer_output,) + outputs
|
||||
|
||||
outputs = outputs + (present_key_value,)
|
||||
|
||||
return outputs
|
||||
|
||||
def feed_forward_chunk(self, attention_output):
|
||||
intermediate_output = self.intermediate(attention_output)
|
||||
layer_output = self.output(intermediate_output, attention_output)
|
||||
return layer_output
|
||||
|
||||
|
||||
class BertEncoder(nn.Module):
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)])
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
attention_mask=None,
|
||||
head_mask=None,
|
||||
encoder_hidden_states=None,
|
||||
encoder_attention_mask=None,
|
||||
past_key_values=None,
|
||||
use_cache=None,
|
||||
output_attentions=False,
|
||||
output_hidden_states=False,
|
||||
return_dict=True,
|
||||
mode='multimodal',
|
||||
):
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attentions = () if output_attentions else None
|
||||
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
|
||||
|
||||
next_decoder_cache = () if use_cache else None
|
||||
|
||||
for i in range(self.config.num_hidden_layers):
|
||||
layer_module = self.layer[i]
|
||||
if output_hidden_states:
|
||||
all_hidden_states = all_hidden_states + (hidden_states,)
|
||||
|
||||
layer_head_mask = head_mask[i] if head_mask is not None else None
|
||||
past_key_value = past_key_values[i] if past_key_values is not None else None
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
|
||||
if use_cache:
|
||||
logger.warning(
|
||||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
||||
)
|
||||
use_cache = False
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
return module(*inputs, past_key_value, output_attentions)
|
||||
|
||||
return custom_forward
|
||||
|
||||
layer_outputs = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(layer_module),
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
layer_head_mask,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
mode=mode,
|
||||
)
|
||||
else:
|
||||
layer_outputs = layer_module(
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
layer_head_mask,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
past_key_value,
|
||||
output_attentions,
|
||||
mode=mode,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
if use_cache:
|
||||
next_decoder_cache += (layer_outputs[-1],)
|
||||
if output_attentions:
|
||||
all_self_attentions = all_self_attentions + (layer_outputs[1],)
|
||||
|
||||
if output_hidden_states:
|
||||
all_hidden_states = all_hidden_states + (hidden_states,)
|
||||
|
||||
if not return_dict:
|
||||
return tuple(
|
||||
v
|
||||
for v in [
|
||||
hidden_states,
|
||||
next_decoder_cache,
|
||||
all_hidden_states,
|
||||
all_self_attentions,
|
||||
all_cross_attentions,
|
||||
]
|
||||
if v is not None
|
||||
)
|
||||
return BaseModelOutputWithPastAndCrossAttentions(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=next_decoder_cache,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attentions,
|
||||
cross_attentions=all_cross_attentions,
|
||||
)
|
||||
|
||||
|
||||
class BertPooler(nn.Module):
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
||||
self.activation = nn.Tanh()
|
||||
|
||||
def forward(self, hidden_states):
|
||||
# We "pool" the model by simply taking the hidden state corresponding
|
||||
# to the first token.
|
||||
first_token_tensor = hidden_states[:, 0]
|
||||
pooled_output = self.dense(first_token_tensor)
|
||||
pooled_output = self.activation(pooled_output)
|
||||
return pooled_output
|
||||
|
||||
|
||||
class BertPredictionHeadTransform(nn.Module):
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
||||
if isinstance(config.hidden_act, str):
|
||||
self.transform_act_fn = ACT2FN[config.hidden_act]
|
||||
else:
|
||||
self.transform_act_fn = config.hidden_act
|
||||
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
||||
|
||||
def forward(self, hidden_states):
|
||||
hidden_states = self.dense(hidden_states)
|
||||
hidden_states = self.transform_act_fn(hidden_states)
|
||||
hidden_states = self.LayerNorm(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class BertLMPredictionHead(nn.Module):
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
self.transform = BertPredictionHeadTransform(config)
|
||||
|
||||
# The output weights are the same as the input embeddings, but there is
|
||||
# an output-only bias for each token.
|
||||
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||||
|
||||
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
|
||||
|
||||
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
|
||||
self.decoder.bias = self.bias
|
||||
|
||||
def forward(self, hidden_states):
|
||||
hidden_states = self.transform(hidden_states)
|
||||
hidden_states = self.decoder(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class BertOnlyMLMHead(nn.Module):
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
self.predictions = BertLMPredictionHead(config)
|
||||
|
||||
def forward(self, sequence_output):
|
||||
prediction_scores = self.predictions(sequence_output)
|
||||
return prediction_scores
|
||||
|
||||
|
||||
class BertPreTrainedModel(PreTrainedModel):
|
||||
"""
|
||||
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
||||
models.
|
||||
"""
|
||||
|
||||
config_class = BertConfig
|
||||
base_model_prefix = "bert"
|
||||
_keys_to_ignore_on_load_missing = [r"position_ids"]
|
||||
|
||||
def _init_weights(self, module):
|
||||
""" Initialize the weights """
|
||||
if isinstance(module, (nn.Linear, nn.Embedding)):
|
||||
# Slightly different from the TF version which uses truncated_normal for initialization
|
||||
# cf https://github.com/pytorch/pytorch/pull/5617
|
||||
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
||||
elif isinstance(module, nn.LayerNorm):
|
||||
module.bias.data.zero_()
|
||||
module.weight.data.fill_(1.0)
|
||||
if isinstance(module, nn.Linear) and module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
|
||||
|
||||
class BertModel(BertPreTrainedModel):
|
||||
"""
|
||||
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
|
||||
cross-attention is added between the self-attention layers, following the architecture described in `Attention is
|
||||
all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
|
||||
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
|
||||
argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
|
||||
input to the forward pass.
|
||||
"""
|
||||
|
||||
def __init__(self, config, add_pooling_layer=True):
|
||||
super().__init__(config)
|
||||
self.config = config
|
||||
|
||||
self.embeddings = BertEmbeddings(config)
|
||||
|
||||
self.encoder = BertEncoder(config)
|
||||
|
||||
self.pooler = BertPooler(config) if add_pooling_layer else None
|
||||
|
||||
self.init_weights()
|
||||
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embeddings.word_embeddings
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embeddings.word_embeddings = value
|
||||
|
||||
def _prune_heads(self, heads_to_prune):
|
||||
"""
|
||||
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
||||
class PreTrainedModel
|
||||
"""
|
||||
for layer, heads in heads_to_prune.items():
|
||||
self.encoder.layer[layer].attention.prune_heads(heads)
|
||||
|
||||
|
||||
def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor:
|
||||
"""
|
||||
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
|
||||
|
||||
Arguments:
|
||||
attention_mask (:obj:`torch.Tensor`):
|
||||
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
|
||||
input_shape (:obj:`Tuple[int]`):
|
||||
The shape of the input to the model.
|
||||
device: (:obj:`torch.device`):
|
||||
The device of the input to the model.
|
||||
|
||||
Returns:
|
||||
:obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
|
||||
"""
|
||||
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
||||
# ourselves in which case we just need to make it broadcastable to all heads.
|
||||
if attention_mask.dim() == 3:
|
||||
extended_attention_mask = attention_mask[:, None, :, :]
|
||||
elif attention_mask.dim() == 2:
|
||||
# Provided a padding mask of dimensions [batch_size, seq_length]
|
||||
# - if the model is a decoder, apply a causal mask in addition to the padding mask
|
||||
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
||||
if is_decoder:
|
||||
batch_size, seq_length = input_shape
|
||||
|
||||
seq_ids = torch.arange(seq_length, device=device)
|
||||
causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
|
||||
# in case past_key_values are used we need to add a prefix ones mask to the causal mask
|
||||
# causal and attention masks must have same type with pytorch version < 1.3
|
||||
causal_mask = causal_mask.to(attention_mask.dtype)
|
||||
|
||||
if causal_mask.shape[1] < attention_mask.shape[1]:
|
||||
prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
|
||||
causal_mask = torch.cat(
|
||||
[
|
||||
torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype),
|
||||
causal_mask,
|
||||
],
|
||||
axis=-1,
|
||||
)
|
||||
|
||||
extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
|
||||
else:
|
||||
extended_attention_mask = attention_mask[:, None, None, :]
|
||||
else:
|
||||
raise ValueError(
|
||||
"Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
|
||||
input_shape, attention_mask.shape
|
||||
)
|
||||
)
|
||||
|
||||
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
|
||||
# masked positions, this operation will create a tensor which is 0.0 for
|
||||
# positions we want to attend and -10000.0 for masked positions.
|
||||
# Since we are adding it to the raw scores before the softmax, this is
|
||||
# effectively the same as removing these entirely.
|
||||
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
|
||||
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
|
||||
return extended_attention_mask
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids=None,
|
||||
attention_mask=None,
|
||||
position_ids=None,
|
||||
head_mask=None,
|
||||
inputs_embeds=None,
|
||||
encoder_embeds=None,
|
||||
encoder_hidden_states=None,
|
||||
encoder_attention_mask=None,
|
||||
past_key_values=None,
|
||||
use_cache=None,
|
||||
output_attentions=None,
|
||||
output_hidden_states=None,
|
||||
return_dict=None,
|
||||
is_decoder=False,
|
||||
mode='multimodal',
|
||||
):
|
||||
r"""
|
||||
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
|
||||
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
|
||||
the model is configured as a decoder.
|
||||
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
||||
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
|
||||
the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
|
||||
- 1 for tokens that are **not masked**,
|
||||
- 0 for tokens that are **masked**.
|
||||
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
||||
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
|
||||
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
|
||||
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
|
||||
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
|
||||
use_cache (:obj:`bool`, `optional`):
|
||||
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
|
||||
decoding (see :obj:`past_key_values`).
|
||||
"""
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
if is_decoder:
|
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||||
else:
|
||||
use_cache = False
|
||||
|
||||
if input_ids is not None and inputs_embeds is not None:
|
||||
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
||||
elif input_ids is not None:
|
||||
input_shape = input_ids.size()
|
||||
batch_size, seq_length = input_shape
|
||||
device = input_ids.device
|
||||
elif inputs_embeds is not None:
|
||||
input_shape = inputs_embeds.size()[:-1]
|
||||
batch_size, seq_length = input_shape
|
||||
device = inputs_embeds.device
|
||||
elif encoder_embeds is not None:
|
||||
input_shape = encoder_embeds.size()[:-1]
|
||||
batch_size, seq_length = input_shape
|
||||
device = encoder_embeds.device
|
||||
else:
|
||||
raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds")
|
||||
|
||||
# past_key_values_length
|
||||
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
|
||||
|
||||
if attention_mask is None:
|
||||
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
|
||||
|
||||
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
|
||||
# ourselves in which case we just need to make it broadcastable to all heads.
|
||||
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape,
|
||||
device, is_decoder)
|
||||
|
||||
# If a 2D or 3D attention mask is provided for the cross-attention
|
||||
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
|
||||
if encoder_hidden_states is not None:
|
||||
if type(encoder_hidden_states) == list:
|
||||
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
|
||||
else:
|
||||
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
|
||||
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
|
||||
|
||||
if type(encoder_attention_mask) == list:
|
||||
encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
|
||||
elif encoder_attention_mask is None:
|
||||
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
|
||||
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
||||
else:
|
||||
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
||||
else:
|
||||
encoder_extended_attention_mask = None
|
||||
|
||||
# Prepare head mask if needed
|
||||
# 1.0 in head_mask indicate we keep the head
|
||||
# attention_probs has shape bsz x n_heads x N x N
|
||||
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
||||
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
||||
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
||||
|
||||
if encoder_embeds is None:
|
||||
embedding_output = self.embeddings(
|
||||
input_ids=input_ids,
|
||||
position_ids=position_ids,
|
||||
inputs_embeds=inputs_embeds,
|
||||
past_key_values_length=past_key_values_length,
|
||||
)
|
||||
else:
|
||||
embedding_output = encoder_embeds
|
||||
|
||||
encoder_outputs = self.encoder(
|
||||
embedding_output,
|
||||
attention_mask=extended_attention_mask,
|
||||
head_mask=head_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_extended_attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
mode=mode,
|
||||
)
|
||||
sequence_output = encoder_outputs[0]
|
||||
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
||||
|
||||
if not return_dict:
|
||||
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
||||
|
||||
return BaseModelOutputWithPoolingAndCrossAttentions(
|
||||
last_hidden_state=sequence_output,
|
||||
pooler_output=pooled_output,
|
||||
past_key_values=encoder_outputs.past_key_values,
|
||||
hidden_states=encoder_outputs.hidden_states,
|
||||
attentions=encoder_outputs.attentions,
|
||||
cross_attentions=encoder_outputs.cross_attentions,
|
||||
)
|
||||
|
||||
|
||||
|
||||
class BertLMHeadModel(BertPreTrainedModel):
|
||||
|
||||
_keys_to_ignore_on_load_unexpected = [r"pooler"]
|
||||
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
|
||||
self.bert = BertModel(config, add_pooling_layer=False)
|
||||
self.cls = BertOnlyMLMHead(config)
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def get_output_embeddings(self):
|
||||
return self.cls.predictions.decoder
|
||||
|
||||
def set_output_embeddings(self, new_embeddings):
|
||||
self.cls.predictions.decoder = new_embeddings
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids=None,
|
||||
attention_mask=None,
|
||||
position_ids=None,
|
||||
head_mask=None,
|
||||
inputs_embeds=None,
|
||||
encoder_hidden_states=None,
|
||||
encoder_attention_mask=None,
|
||||
labels=None,
|
||||
past_key_values=None,
|
||||
use_cache=None,
|
||||
output_attentions=None,
|
||||
output_hidden_states=None,
|
||||
return_dict=None,
|
||||
return_logits=False,
|
||||
is_decoder=True,
|
||||
reduction='mean',
|
||||
mode='multimodal',
|
||||
):
|
||||
r"""
|
||||
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
|
||||
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
|
||||
the model is configured as a decoder.
|
||||
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
||||
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
|
||||
the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
|
||||
- 1 for tokens that are **not masked**,
|
||||
- 0 for tokens that are **masked**.
|
||||
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
||||
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
|
||||
``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
|
||||
ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
|
||||
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
||||
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
|
||||
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
|
||||
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
|
||||
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
|
||||
use_cache (:obj:`bool`, `optional`):
|
||||
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
|
||||
decoding (see :obj:`past_key_values`).
|
||||
Returns:
|
||||
Example::
|
||||
>>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
|
||||
>>> import torch
|
||||
>>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
|
||||
>>> config = BertConfig.from_pretrained("bert-base-cased")
|
||||
>>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
|
||||
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
|
||||
>>> outputs = model(**inputs)
|
||||
>>> prediction_logits = outputs.logits
|
||||
"""
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
if labels is not None:
|
||||
use_cache = False
|
||||
|
||||
outputs = self.bert(
|
||||
input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
is_decoder=is_decoder,
|
||||
mode=mode,
|
||||
)
|
||||
|
||||
sequence_output = outputs[0]
|
||||
prediction_scores = self.cls(sequence_output)
|
||||
|
||||
if return_logits:
|
||||
return prediction_scores[:, :-1, :].contiguous()
|
||||
|
||||
lm_loss = None
|
||||
if labels is not None:
|
||||
# we are doing next-token prediction; shift prediction scores and input ids by one
|
||||
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
|
||||
labels = labels[:, 1:].contiguous()
|
||||
loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1)
|
||||
lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
|
||||
if reduction=='none':
|
||||
lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1)
|
||||
|
||||
if not return_dict:
|
||||
output = (prediction_scores,) + outputs[2:]
|
||||
return ((lm_loss,) + output) if lm_loss is not None else output
|
||||
|
||||
return CausalLMOutputWithCrossAttentions(
|
||||
loss=lm_loss,
|
||||
logits=prediction_scores,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
cross_attentions=outputs.cross_attentions,
|
||||
)
|
||||
|
||||
def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
|
||||
input_shape = input_ids.shape
|
||||
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
|
||||
if attention_mask is None:
|
||||
attention_mask = input_ids.new_ones(input_shape)
|
||||
|
||||
# cut decoder_input_ids if past is used
|
||||
if past is not None:
|
||||
input_ids = input_ids[:, -1:]
|
||||
|
||||
return {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": attention_mask,
|
||||
"past_key_values": past,
|
||||
"encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
|
||||
"encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
|
||||
"is_decoder": True,
|
||||
}
|
||||
|
||||
def _reorder_cache(self, past, beam_idx):
|
||||
reordered_past = ()
|
||||
for layer_past in past:
|
||||
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
|
||||
return reordered_past
|
||||
@@ -1,301 +0,0 @@
|
||||
'''
|
||||
* Adapted from BLIP (https://github.com/salesforce/BLIP)
|
||||
* Based on timm code base
|
||||
* https://github.com/rwightman/pytorch-image-models/tree/master/timm
|
||||
'''
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from functools import partial
|
||||
|
||||
from timm.models.vision_transformer import _cfg, PatchEmbed
|
||||
from timm.models.registry import register_model
|
||||
from timm.models.layers import trunc_normal_, DropPath
|
||||
from timm.models.helpers import named_apply, adapt_input_conv
|
||||
|
||||
# from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
|
||||
|
||||
class Mlp(nn.Module):
|
||||
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
|
||||
"""
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
self.attn_gradients = None
|
||||
self.attention_map = None
|
||||
|
||||
def save_attn_gradients(self, attn_gradients):
|
||||
self.attn_gradients = attn_gradients
|
||||
|
||||
def get_attn_gradients(self):
|
||||
return self.attn_gradients
|
||||
|
||||
def save_attention_map(self, attention_map):
|
||||
self.attention_map = attention_map
|
||||
|
||||
def get_attention_map(self):
|
||||
return self.attention_map
|
||||
|
||||
def forward(self, x, register_hook=False):
|
||||
B, N, C = x.shape
|
||||
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
||||
|
||||
attn = (q @ k.transpose(-2, -1)) * self.scale
|
||||
attn = attn.softmax(dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
if register_hook:
|
||||
self.save_attention_map(attn)
|
||||
attn.register_hook(self.save_attn_gradients)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
|
||||
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
||||
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False):
|
||||
super().__init__()
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = Attention(
|
||||
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
|
||||
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||
|
||||
# if use_grad_checkpointing:
|
||||
# self.attn = checkpoint_wrapper(self.attn)
|
||||
# self.mlp = checkpoint_wrapper(self.mlp)
|
||||
|
||||
def forward(self, x, register_hook=False):
|
||||
x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
||||
return x
|
||||
|
||||
|
||||
class VisionTransformer(nn.Module):
|
||||
""" Vision Transformer
|
||||
A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` -
|
||||
https://arxiv.org/abs/2010.11929
|
||||
"""
|
||||
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
|
||||
num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
|
||||
drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None,
|
||||
use_grad_checkpointing=False, ckpt_layer=0):
|
||||
"""
|
||||
Args:
|
||||
img_size (int, tuple): input image size
|
||||
patch_size (int, tuple): patch size
|
||||
in_chans (int): number of input channels
|
||||
num_classes (int): number of classes for classification head
|
||||
embed_dim (int): embedding dimension
|
||||
depth (int): depth of transformer
|
||||
num_heads (int): number of attention heads
|
||||
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
|
||||
qkv_bias (bool): enable bias for qkv if True
|
||||
qk_scale (float): override default qk scale of head_dim ** -0.5 if set
|
||||
representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
|
||||
drop_rate (float): dropout rate
|
||||
attn_drop_rate (float): attention dropout rate
|
||||
drop_path_rate (float): stochastic depth rate
|
||||
norm_layer: (nn.Module): normalization layer
|
||||
"""
|
||||
super().__init__()
|
||||
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
|
||||
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
||||
|
||||
self.patch_embed = PatchEmbed(
|
||||
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
|
||||
|
||||
num_patches = self.patch_embed.num_patches
|
||||
|
||||
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
|
||||
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
|
||||
self.blocks = nn.ModuleList([
|
||||
Block(
|
||||
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
|
||||
use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer)
|
||||
)
|
||||
for i in range(depth)])
|
||||
self.norm = norm_layer(embed_dim)
|
||||
|
||||
trunc_normal_(self.pos_embed, std=.02)
|
||||
trunc_normal_(self.cls_token, std=.02)
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
return {'pos_embed', 'cls_token'}
|
||||
|
||||
def forward(self, x, register_blk=-1):
|
||||
B = x.shape[0]
|
||||
x = self.patch_embed(x)
|
||||
|
||||
cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
|
||||
x = x + self.pos_embed[:,:x.size(1),:]
|
||||
x = self.pos_drop(x)
|
||||
|
||||
for i,blk in enumerate(self.blocks):
|
||||
x = blk(x, register_blk==i)
|
||||
x = self.norm(x)
|
||||
|
||||
return x
|
||||
|
||||
@torch.jit.ignore()
|
||||
def load_pretrained(self, checkpoint_path, prefix=''):
|
||||
_load_weights(self, checkpoint_path, prefix)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):
|
||||
""" Load weights from .npz checkpoints for official Google Brain Flax implementation
|
||||
"""
|
||||
import numpy as np
|
||||
|
||||
def _n2p(w, t=True):
|
||||
if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
|
||||
w = w.flatten()
|
||||
if t:
|
||||
if w.ndim == 4:
|
||||
w = w.transpose([3, 2, 0, 1])
|
||||
elif w.ndim == 3:
|
||||
w = w.transpose([2, 0, 1])
|
||||
elif w.ndim == 2:
|
||||
w = w.transpose([1, 0])
|
||||
return torch.from_numpy(w)
|
||||
|
||||
w = np.load(checkpoint_path)
|
||||
if not prefix and 'opt/target/embedding/kernel' in w:
|
||||
prefix = 'opt/target/'
|
||||
|
||||
if hasattr(model.patch_embed, 'backbone'):
|
||||
# hybrid
|
||||
backbone = model.patch_embed.backbone
|
||||
stem_only = not hasattr(backbone, 'stem')
|
||||
stem = backbone if stem_only else backbone.stem
|
||||
stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))
|
||||
stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))
|
||||
stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))
|
||||
if not stem_only:
|
||||
for i, stage in enumerate(backbone.stages):
|
||||
for j, block in enumerate(stage.blocks):
|
||||
bp = f'{prefix}block{i + 1}/unit{j + 1}/'
|
||||
for r in range(3):
|
||||
getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))
|
||||
getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))
|
||||
getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))
|
||||
if block.downsample is not None:
|
||||
block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))
|
||||
block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))
|
||||
block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))
|
||||
embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
|
||||
else:
|
||||
embed_conv_w = adapt_input_conv(
|
||||
model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))
|
||||
model.patch_embed.proj.weight.copy_(embed_conv_w)
|
||||
model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
|
||||
model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
|
||||
pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
|
||||
if pos_embed_w.shape != model.pos_embed.shape:
|
||||
pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights
|
||||
pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
|
||||
model.pos_embed.copy_(pos_embed_w)
|
||||
model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
|
||||
model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
|
||||
# if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
|
||||
# model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
|
||||
# model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
|
||||
# if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
|
||||
# model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
|
||||
# model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
|
||||
for i, block in enumerate(model.blocks.children()):
|
||||
block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
|
||||
mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
|
||||
block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
|
||||
block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
|
||||
block.attn.qkv.weight.copy_(torch.cat([
|
||||
_n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
|
||||
block.attn.qkv.bias.copy_(torch.cat([
|
||||
_n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
|
||||
block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
|
||||
block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
|
||||
for r in range(2):
|
||||
getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel']))
|
||||
getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias']))
|
||||
block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale']))
|
||||
block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))
|
||||
|
||||
|
||||
def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):
|
||||
# interpolate position embedding
|
||||
embedding_size = pos_embed_checkpoint.shape[-1]
|
||||
num_patches = visual_encoder.patch_embed.num_patches
|
||||
num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches
|
||||
# height (== width) for the checkpoint position embedding
|
||||
orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
|
||||
# height (== width) for the new position embedding
|
||||
new_size = int(num_patches ** 0.5)
|
||||
|
||||
if orig_size!=new_size:
|
||||
# class_token and dist_token are kept unchanged
|
||||
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
|
||||
# only the position tokens are interpolated
|
||||
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
|
||||
pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
|
||||
pos_tokens = torch.nn.functional.interpolate(
|
||||
pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
|
||||
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
|
||||
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
|
||||
print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2))
|
||||
|
||||
return new_pos_embed
|
||||
else:
|
||||
return pos_embed_checkpoint
|
||||
@@ -1,148 +0,0 @@
|
||||
from modelscope import snapshot_download
|
||||
from typing_extensions import Literal, TypeAlias
|
||||
import os
|
||||
from diffsynth.extensions.ImageQualityMetric.aesthetic import AestheticScore
|
||||
from diffsynth.extensions.ImageQualityMetric.imagereward import ImageRewardScore
|
||||
from diffsynth.extensions.ImageQualityMetric.pickscore import PickScore
|
||||
from diffsynth.extensions.ImageQualityMetric.clip import CLIPScore
|
||||
from diffsynth.extensions.ImageQualityMetric.hps import HPScore_v2
|
||||
from diffsynth.extensions.ImageQualityMetric.mps import MPScore
|
||||
|
||||
|
||||
preference_model_id: TypeAlias = Literal[
|
||||
"ImageReward",
|
||||
"Aesthetic",
|
||||
"PickScore",
|
||||
"CLIP",
|
||||
"HPSv2",
|
||||
"HPSv2.1",
|
||||
"MPS",
|
||||
]
|
||||
model_dict = {
|
||||
"ImageReward": {
|
||||
"model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained",
|
||||
"allow_file_pattern": [
|
||||
"ImageReward/ImageReward.safetensors",
|
||||
"ImageReward/med_config.json",
|
||||
"bert-base-uncased/config.json",
|
||||
"bert-base-uncased/model.safetensors",
|
||||
"bert-base-uncased/tokenizer.json",
|
||||
"bert-base-uncased/tokenizer_config.json",
|
||||
"bert-base-uncased/vocab.txt",
|
||||
],
|
||||
"load_path": {
|
||||
"imagereward": "ImageReward/ImageReward.safetensors",
|
||||
"med_config": "ImageReward/med_config.json",
|
||||
"bert_model_path": "bert-base-uncased",
|
||||
},
|
||||
"model_class": ImageRewardScore
|
||||
},
|
||||
"Aesthetic": {
|
||||
"model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained",
|
||||
"allow_file_pattern": [
|
||||
"aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors",
|
||||
"clip-vit-large-patch14/config.json",
|
||||
"clip-vit-large-patch14/merges.txt",
|
||||
"clip-vit-large-patch14/model.safetensors",
|
||||
"clip-vit-large-patch14/preprocessor_config.json",
|
||||
"clip-vit-large-patch14/special_tokens_map.json",
|
||||
"clip-vit-large-patch14/tokenizer.json",
|
||||
"clip-vit-large-patch14/tokenizer_config.json",
|
||||
"clip-vit-large-patch14/vocab.json",
|
||||
],
|
||||
"load_path": {
|
||||
"aesthetic_predictor": "aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors",
|
||||
"clip-large": "clip-vit-large-patch14",
|
||||
},
|
||||
"model_class": AestheticScore
|
||||
},
|
||||
"PickScore": {
|
||||
"model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained",
|
||||
"allow_file_pattern": [
|
||||
"PickScore_v1/*",
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/config.json",
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/merges.txt",
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/preprocessor_config.json",
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/special_tokens_map.json",
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer.json",
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer_config.json",
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/vocab.json",
|
||||
],
|
||||
"load_path": {
|
||||
"pickscore": "PickScore_v1",
|
||||
"clip": "CLIP-ViT-H-14-laion2B-s32B-b79K",
|
||||
},
|
||||
"model_class": PickScore
|
||||
},
|
||||
"CLIP": {
|
||||
"model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained",
|
||||
"allow_file_pattern": [
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin",
|
||||
"bpe_simple_vocab_16e6.txt.gz",
|
||||
],
|
||||
"load_path": {
|
||||
"open_clip": "CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin",
|
||||
"open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz",
|
||||
},
|
||||
"model_class": CLIPScore
|
||||
},
|
||||
"HPSv2": {
|
||||
"model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained",
|
||||
"allow_file_pattern": [
|
||||
"HPS_v2/HPS_v2_compressed.safetensors",
|
||||
"bpe_simple_vocab_16e6.txt.gz",
|
||||
],
|
||||
"load_path": {
|
||||
"hpsv2": "HPS_v2/HPS_v2_compressed.safetensors",
|
||||
"open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz",
|
||||
},
|
||||
"model_class": HPScore_v2,
|
||||
"extra_kwargs": {"model_version": "v2"}
|
||||
},
|
||||
"HPSv2.1": {
|
||||
"model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained",
|
||||
"allow_file_pattern": [
|
||||
"HPS_v2/HPS_v2.1_compressed.safetensors",
|
||||
"bpe_simple_vocab_16e6.txt.gz",
|
||||
],
|
||||
"load_path": {
|
||||
"hpsv2.1": "HPS_v2/HPS_v2.1_compressed.safetensors",
|
||||
"open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz",
|
||||
},
|
||||
"model_class": HPScore_v2,
|
||||
"extra_kwargs": {"model_version": "v21"}
|
||||
},
|
||||
"MPS": {
|
||||
"model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained",
|
||||
"allow_file_pattern": [
|
||||
"MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors",
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/config.json",
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/merges.txt",
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/preprocessor_config.json",
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/special_tokens_map.json",
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer.json",
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer_config.json",
|
||||
"CLIP-ViT-H-14-laion2B-s32B-b79K/vocab.json",
|
||||
],
|
||||
"load_path": {
|
||||
"mps": "MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors",
|
||||
"clip": "CLIP-ViT-H-14-laion2B-s32B-b79K",
|
||||
},
|
||||
"model_class": MPScore
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def download_preference_model(model_name: preference_model_id, cache_dir="models"):
|
||||
metadata = model_dict[model_name]
|
||||
snapshot_download(model_id=metadata["model_id"], allow_file_pattern=metadata["allow_file_pattern"], cache_dir=cache_dir)
|
||||
load_path = metadata["load_path"]
|
||||
load_path = {key: os.path.join(cache_dir, metadata["model_id"], path) for key, path in load_path.items()}
|
||||
return load_path
|
||||
|
||||
|
||||
def load_preference_model(model_name: preference_model_id, device = "cuda", path = None):
|
||||
model_class = model_dict[model_name]["model_class"]
|
||||
extra_kwargs = model_dict[model_name].get("extra_kwargs", {})
|
||||
preference_model = model_class(device=device, path=path, **extra_kwargs)
|
||||
return preference_model
|
||||
@@ -1,148 +0,0 @@
|
||||
from typing import List, Optional
|
||||
from PIL import Image
|
||||
import torch
|
||||
from transformers import AutoProcessor, AutoModel
|
||||
from safetensors.torch import load_file
|
||||
import os
|
||||
from typing import Union, List
|
||||
from .config import MODEL_PATHS
|
||||
|
||||
class MLP(torch.nn.Module):
|
||||
def __init__(self, input_size: int, xcol: str = "emb", ycol: str = "avg_rating"):
|
||||
super().__init__()
|
||||
self.input_size = input_size
|
||||
self.xcol = xcol
|
||||
self.ycol = ycol
|
||||
self.layers = torch.nn.Sequential(
|
||||
torch.nn.Linear(self.input_size, 1024),
|
||||
#torch.nn.ReLU(),
|
||||
torch.nn.Dropout(0.2),
|
||||
torch.nn.Linear(1024, 128),
|
||||
#torch.nn.ReLU(),
|
||||
torch.nn.Dropout(0.2),
|
||||
torch.nn.Linear(128, 64),
|
||||
#torch.nn.ReLU(),
|
||||
torch.nn.Dropout(0.1),
|
||||
torch.nn.Linear(64, 16),
|
||||
#torch.nn.ReLU(),
|
||||
torch.nn.Linear(16, 1),
|
||||
)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
return self.layers(x)
|
||||
|
||||
def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor:
|
||||
x = batch[self.xcol]
|
||||
y = batch[self.ycol].reshape(-1, 1)
|
||||
x_hat = self.layers(x)
|
||||
loss = torch.nn.functional.mse_loss(x_hat, y)
|
||||
return loss
|
||||
|
||||
def validation_step(self, batch: dict, batch_idx: int) -> torch.Tensor:
|
||||
x = batch[self.xcol]
|
||||
y = batch[self.ycol].reshape(-1, 1)
|
||||
x_hat = self.layers(x)
|
||||
loss = torch.nn.functional.mse_loss(x_hat, y)
|
||||
return loss
|
||||
|
||||
def configure_optimizers(self) -> torch.optim.Optimizer:
|
||||
return torch.optim.Adam(self.parameters(), lr=1e-3)
|
||||
|
||||
|
||||
class AestheticScore(torch.nn.Module):
|
||||
def __init__(self, device: torch.device, path: str = MODEL_PATHS):
|
||||
super().__init__()
|
||||
self.device = device
|
||||
self.aes_model_path = path.get("aesthetic_predictor")
|
||||
# Load the MLP model
|
||||
self.model = MLP(768)
|
||||
try:
|
||||
if self.aes_model_path.endswith(".safetensors"):
|
||||
state_dict = load_file(self.aes_model_path)
|
||||
else:
|
||||
state_dict = torch.load(self.aes_model_path)
|
||||
self.model.load_state_dict(state_dict)
|
||||
except Exception as e:
|
||||
raise ValueError(f"Error loading model weights from {self.aes_model_path}: {e}")
|
||||
|
||||
self.model.to(device)
|
||||
self.model.eval()
|
||||
|
||||
# Load the CLIP model and processor
|
||||
clip_model_name = path.get('clip-large')
|
||||
self.model2 = AutoModel.from_pretrained(clip_model_name).eval().to(device)
|
||||
self.processor = AutoProcessor.from_pretrained(clip_model_name)
|
||||
|
||||
def _calculate_score(self, image: torch.Tensor) -> float:
|
||||
"""Calculate the aesthetic score for a single image.
|
||||
|
||||
Args:
|
||||
image (torch.Tensor): The processed image tensor.
|
||||
|
||||
Returns:
|
||||
float: The aesthetic score.
|
||||
"""
|
||||
with torch.no_grad():
|
||||
# Get image embeddings
|
||||
image_embs = self.model2.get_image_features(image)
|
||||
image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True)
|
||||
|
||||
# Compute score
|
||||
score = self.model(image_embs).cpu().flatten().item()
|
||||
|
||||
return score
|
||||
|
||||
@torch.no_grad()
|
||||
def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str = "") -> List[float]:
|
||||
"""Score the images based on their aesthetic quality.
|
||||
|
||||
Args:
|
||||
images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
|
||||
|
||||
Returns:
|
||||
List[float]: List of scores for the images.
|
||||
"""
|
||||
try:
|
||||
if isinstance(images, (str, Image.Image)):
|
||||
# Single image
|
||||
if isinstance(images, str):
|
||||
pil_image = Image.open(images)
|
||||
else:
|
||||
pil_image = images
|
||||
|
||||
# Prepare image inputs
|
||||
image_inputs = self.processor(
|
||||
images=pil_image,
|
||||
padding=True,
|
||||
truncation=True,
|
||||
max_length=77,
|
||||
return_tensors="pt",
|
||||
).to(self.device)
|
||||
|
||||
return [self._calculate_score(image_inputs["pixel_values"])]
|
||||
elif isinstance(images, list):
|
||||
# Multiple images
|
||||
scores = []
|
||||
for one_image in images:
|
||||
if isinstance(one_image, str):
|
||||
pil_image = Image.open(one_image)
|
||||
elif isinstance(one_image, Image.Image):
|
||||
pil_image = one_image
|
||||
else:
|
||||
raise TypeError("The type of parameter images is illegal.")
|
||||
|
||||
# Prepare image inputs
|
||||
image_inputs = self.processor(
|
||||
images=pil_image,
|
||||
padding=True,
|
||||
truncation=True,
|
||||
max_length=77,
|
||||
return_tensors="pt",
|
||||
).to(self.device)
|
||||
|
||||
scores.append(self._calculate_score(image_inputs["pixel_values"]))
|
||||
return scores
|
||||
else:
|
||||
raise TypeError("The type of parameter images is illegal.")
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Error in scoring images: {e}")
|
||||
@@ -1,97 +0,0 @@
|
||||
from typing import List, Union
|
||||
from PIL import Image
|
||||
import torch
|
||||
from .open_clip import create_model_and_transforms, get_tokenizer
|
||||
from .config import MODEL_PATHS
|
||||
|
||||
class CLIPScore(torch.nn.Module):
|
||||
def __init__(self, device: torch.device, path: str = MODEL_PATHS):
|
||||
super().__init__()
|
||||
"""Initialize the CLIPScore with a model and tokenizer.
|
||||
|
||||
Args:
|
||||
device (torch.device): The device to load the model on.
|
||||
"""
|
||||
self.device = device
|
||||
|
||||
# Create model and transforms
|
||||
self.model, _, self.preprocess_val = create_model_and_transforms(
|
||||
"ViT-H-14",
|
||||
# "laion2B-s32B-b79K",
|
||||
pretrained=path.get("open_clip"),
|
||||
precision="amp",
|
||||
device=device,
|
||||
jit=False,
|
||||
force_quick_gelu=False,
|
||||
force_custom_text=False,
|
||||
force_patch_dropout=False,
|
||||
force_image_size=None,
|
||||
pretrained_image=False,
|
||||
image_mean=None,
|
||||
image_std=None,
|
||||
light_augmentation=True,
|
||||
aug_cfg={},
|
||||
output_dict=True,
|
||||
with_score_predictor=False,
|
||||
with_region_predictor=False,
|
||||
)
|
||||
|
||||
# Initialize tokenizer
|
||||
self.tokenizer = get_tokenizer("ViT-H-14", path["open_clip_bpe"])
|
||||
self.model = self.model.to(device)
|
||||
self.model.eval()
|
||||
|
||||
def _calculate_score(self, image: torch.Tensor, prompt: str) -> float:
|
||||
"""Calculate the CLIP score for a single image and prompt.
|
||||
|
||||
Args:
|
||||
image (torch.Tensor): The processed image tensor.
|
||||
prompt (str): The prompt text.
|
||||
|
||||
Returns:
|
||||
float: The CLIP score.
|
||||
"""
|
||||
with torch.no_grad():
|
||||
# Process the prompt
|
||||
text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True)
|
||||
|
||||
# Calculate the CLIP score
|
||||
outputs = self.model(image, text)
|
||||
image_features, text_features = outputs["image_features"], outputs["text_features"]
|
||||
logits_per_image = image_features @ text_features.T
|
||||
clip_score = torch.diagonal(logits_per_image).cpu().numpy()
|
||||
|
||||
return clip_score[0].item()
|
||||
|
||||
@torch.no_grad()
|
||||
def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]:
|
||||
"""Score the images based on the prompt.
|
||||
|
||||
Args:
|
||||
images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
|
||||
prompt (str): The prompt text.
|
||||
|
||||
Returns:
|
||||
List[float]: List of CLIP scores for the images.
|
||||
"""
|
||||
if isinstance(images, (str, Image.Image)):
|
||||
# Single image
|
||||
if isinstance(images, str):
|
||||
image = self.preprocess_val(Image.open(images)).unsqueeze(0).to(device=self.device, non_blocking=True)
|
||||
else:
|
||||
image = self.preprocess_val(images).unsqueeze(0).to(device=self.device, non_blocking=True)
|
||||
return [self._calculate_score(image, prompt)]
|
||||
elif isinstance(images, list):
|
||||
# Multiple images
|
||||
scores = []
|
||||
for one_images in images:
|
||||
if isinstance(one_images, str):
|
||||
image = self.preprocess_val(Image.open(one_images)).unsqueeze(0).to(device=self.device, non_blocking=True)
|
||||
elif isinstance(one_images, Image.Image):
|
||||
image = self.preprocess_val(one_images).unsqueeze(0).to(device=self.device, non_blocking=True)
|
||||
else:
|
||||
raise TypeError("The type of parameter images is illegal.")
|
||||
scores.append(self._calculate_score(image, prompt))
|
||||
return scores
|
||||
else:
|
||||
raise TypeError("The type of parameter images is illegal.")
|
||||
@@ -1,23 +0,0 @@
|
||||
import os
|
||||
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
project_root = os.path.abspath(os.path.join(current_dir, '../../../'))
|
||||
model_path = os.path.join(project_root, 'models', 'QualityMetric')
|
||||
|
||||
|
||||
def get_model_path(model_name):
|
||||
return os.path.join(model_path, model_name)
|
||||
|
||||
|
||||
MODEL_PATHS = {
|
||||
"aesthetic_predictor": get_model_path("aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors"),
|
||||
"open_clip": get_model_path("CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin"),
|
||||
"hpsv2": get_model_path("HPS_v2/HPS_v2_compressed.safetensors"),
|
||||
"hpsv2.1": get_model_path("HPS_v2/HPS_v2.1_compressed.safetensors"),
|
||||
"imagereward": get_model_path("ImageReward/ImageReward.safetensors"),
|
||||
"med_config": get_model_path("ImageReward/med_config.json"),
|
||||
"clip": get_model_path("CLIP-ViT-H-14-laion2B-s32B-b79K"),
|
||||
"clip-large": get_model_path("clip-vit-large-patch14"),
|
||||
"mps": get_model_path("MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors"),
|
||||
"pickscore": get_model_path("PickScore_v1")
|
||||
}
|
||||
@@ -1,118 +0,0 @@
|
||||
from typing import List, Union
|
||||
from PIL import Image
|
||||
import torch
|
||||
from .open_clip import create_model_and_transforms, get_tokenizer
|
||||
from safetensors.torch import load_file
|
||||
import os
|
||||
from .config import MODEL_PATHS
|
||||
|
||||
class HPScore_v2(torch.nn.Module):
|
||||
def __init__(self, device: torch.device, path: str = MODEL_PATHS, model_version: str = "v2"):
|
||||
super().__init__()
|
||||
"""Initialize the Selector with a model and tokenizer.
|
||||
|
||||
Args:
|
||||
device (torch.device): The device to load the model on.
|
||||
model_version (str): The version of the model to load. Supports "v2" or "v21". Default is "v2".
|
||||
"""
|
||||
self.device = device
|
||||
|
||||
if model_version == "v2":
|
||||
safetensors_path = path.get("hpsv2")
|
||||
elif model_version == "v21":
|
||||
safetensors_path = path.get("hpsv2.1")
|
||||
else:
|
||||
raise ValueError(f"Unsupported model version: {model_version}. Choose 'v2' or 'v21'.")
|
||||
|
||||
# Create model and transforms
|
||||
model, _, self.preprocess_val = create_model_and_transforms(
|
||||
"ViT-H-14",
|
||||
# "laion2B-s32B-b79K",
|
||||
pretrained=path.get("open_clip"),
|
||||
precision="amp",
|
||||
device=device,
|
||||
jit=False,
|
||||
force_quick_gelu=False,
|
||||
force_custom_text=False,
|
||||
force_patch_dropout=False,
|
||||
force_image_size=None,
|
||||
pretrained_image=False,
|
||||
image_mean=None,
|
||||
image_std=None,
|
||||
light_augmentation=True,
|
||||
aug_cfg={},
|
||||
output_dict=True,
|
||||
with_score_predictor=False,
|
||||
with_region_predictor=False,
|
||||
)
|
||||
|
||||
# Load model weights
|
||||
try:
|
||||
state_dict = load_file(safetensors_path)
|
||||
model.load_state_dict(state_dict)
|
||||
except Exception as e:
|
||||
raise ValueError(f"Error loading model weights from {safetensors_path}: {e}")
|
||||
|
||||
# Initialize tokenizer and model
|
||||
self.tokenizer = get_tokenizer("ViT-H-14", path["open_clip_bpe"])
|
||||
model = model.to(device)
|
||||
model.eval()
|
||||
self.model = model
|
||||
|
||||
def _calculate_score(self, image: torch.Tensor, prompt: str) -> float:
|
||||
"""Calculate the HPS score for a single image and prompt.
|
||||
|
||||
Args:
|
||||
image (torch.Tensor): The processed image tensor.
|
||||
prompt (str): The prompt text.
|
||||
|
||||
Returns:
|
||||
float: The HPS score.
|
||||
"""
|
||||
with torch.no_grad():
|
||||
# Process the prompt
|
||||
text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True)
|
||||
|
||||
# Calculate the HPS score
|
||||
outputs = self.model(image, text)
|
||||
image_features, text_features = outputs["image_features"], outputs["text_features"]
|
||||
logits_per_image = image_features @ text_features.T
|
||||
hps_score = torch.diagonal(logits_per_image).cpu().numpy()
|
||||
|
||||
return hps_score[0].item()
|
||||
|
||||
@torch.no_grad()
|
||||
def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]:
|
||||
"""Score the images based on the prompt.
|
||||
|
||||
Args:
|
||||
images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
|
||||
prompt (str): The prompt text.
|
||||
|
||||
Returns:
|
||||
List[float]: List of HPS scores for the images.
|
||||
"""
|
||||
try:
|
||||
if isinstance(images, (str, Image.Image)):
|
||||
# Single image
|
||||
if isinstance(images, str):
|
||||
image = self.preprocess_val(Image.open(images)).unsqueeze(0).to(device=self.device, non_blocking=True)
|
||||
else:
|
||||
image = self.preprocess_val(images).unsqueeze(0).to(device=self.device, non_blocking=True)
|
||||
return [self._calculate_score(image, prompt)]
|
||||
elif isinstance(images, list):
|
||||
# Multiple images
|
||||
scores = []
|
||||
for one_images in images:
|
||||
if isinstance(one_images, str):
|
||||
image = self.preprocess_val(Image.open(one_images)).unsqueeze(0).to(device=self.device, non_blocking=True)
|
||||
elif isinstance(one_images, Image.Image):
|
||||
image = self.preprocess_val(one_images).unsqueeze(0).to(device=self.device, non_blocking=True)
|
||||
else:
|
||||
raise TypeError("The type of parameter images is illegal.")
|
||||
scores.append(self._calculate_score(image, prompt))
|
||||
return scores
|
||||
else:
|
||||
raise TypeError("The type of parameter images is illegal.")
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Error in scoring images: {e}")
|
||||
@@ -1,212 +0,0 @@
|
||||
import os
|
||||
import torch
|
||||
from PIL import Image
|
||||
from typing import List, Union
|
||||
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
|
||||
from .BLIP.blip_pretrain import BLIP_Pretrain
|
||||
from torchvision.transforms import InterpolationMode
|
||||
from safetensors.torch import load_file
|
||||
from .config import MODEL_PATHS
|
||||
BICUBIC = InterpolationMode.BICUBIC
|
||||
|
||||
def _convert_image_to_rgb(image):
|
||||
return image.convert("RGB")
|
||||
|
||||
def _transform(n_px):
|
||||
return Compose([
|
||||
Resize(n_px, interpolation=BICUBIC),
|
||||
CenterCrop(n_px),
|
||||
_convert_image_to_rgb,
|
||||
ToTensor(),
|
||||
Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
|
||||
])
|
||||
|
||||
class MLP(torch.nn.Module):
|
||||
def __init__(self, input_size):
|
||||
super().__init__()
|
||||
self.input_size = input_size
|
||||
|
||||
self.layers = torch.nn.Sequential(
|
||||
torch.nn.Linear(self.input_size, 1024),
|
||||
#nn.ReLU(),
|
||||
torch.nn.Dropout(0.2),
|
||||
torch.nn.Linear(1024, 128),
|
||||
#nn.ReLU(),
|
||||
torch.nn.Dropout(0.2),
|
||||
torch.nn.Linear(128, 64),
|
||||
#nn.ReLU(),
|
||||
torch.nn.Dropout(0.1),
|
||||
torch.nn.Linear(64, 16),
|
||||
#nn.ReLU(),
|
||||
torch.nn.Linear(16, 1)
|
||||
)
|
||||
|
||||
# initial MLP param
|
||||
for name, param in self.layers.named_parameters():
|
||||
if 'weight' in name:
|
||||
torch.nn.init.normal_(param, mean=0.0, std=1.0/(self.input_size+1))
|
||||
if 'bias' in name:
|
||||
torch.nn.init.constant_(param, val=0)
|
||||
|
||||
def forward(self, input):
|
||||
return self.layers(input)
|
||||
|
||||
class ImageReward(torch.nn.Module):
|
||||
def __init__(self, med_config, device='cpu', bert_model_path=""):
|
||||
super().__init__()
|
||||
self.device = device
|
||||
|
||||
self.blip = BLIP_Pretrain(image_size=224, vit='large', med_config=med_config, bert_model_path=bert_model_path)
|
||||
self.preprocess = _transform(224)
|
||||
self.mlp = MLP(768)
|
||||
|
||||
self.mean = 0.16717362830052426
|
||||
self.std = 1.0333394966054072
|
||||
|
||||
def score_grad(self, prompt_ids, prompt_attention_mask, image):
|
||||
"""Calculate the score with gradient for a single image and prompt.
|
||||
|
||||
Args:
|
||||
prompt_ids (torch.Tensor): Tokenized prompt IDs.
|
||||
prompt_attention_mask (torch.Tensor): Attention mask for the prompt.
|
||||
image (torch.Tensor): The processed image tensor.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: The reward score.
|
||||
"""
|
||||
image_embeds = self.blip.visual_encoder(image)
|
||||
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device)
|
||||
text_output = self.blip.text_encoder(
|
||||
prompt_ids,
|
||||
attention_mask=prompt_attention_mask,
|
||||
encoder_hidden_states=image_embeds,
|
||||
encoder_attention_mask=image_atts,
|
||||
return_dict=True,
|
||||
)
|
||||
txt_features = text_output.last_hidden_state[:, 0, :]
|
||||
rewards = self.mlp(txt_features)
|
||||
rewards = (rewards - self.mean) / self.std
|
||||
return rewards
|
||||
|
||||
def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str = "") -> List[float]:
|
||||
"""Score the images based on the prompt.
|
||||
|
||||
Args:
|
||||
prompt (str): The prompt text.
|
||||
images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
|
||||
|
||||
Returns:
|
||||
List[float]: List of scores for the images.
|
||||
"""
|
||||
if isinstance(images, (str, Image.Image)):
|
||||
# Single image
|
||||
if isinstance(images, str):
|
||||
pil_image = Image.open(images)
|
||||
else:
|
||||
pil_image = images
|
||||
image = self.preprocess(pil_image).unsqueeze(0).to(self.device)
|
||||
return [self._calculate_score(prompt, image).item()]
|
||||
elif isinstance(images, list):
|
||||
# Multiple images
|
||||
scores = []
|
||||
for one_image in images:
|
||||
if isinstance(one_image, str):
|
||||
pil_image = Image.open(one_image)
|
||||
elif isinstance(one_image, Image.Image):
|
||||
pil_image = one_image
|
||||
else:
|
||||
raise TypeError("The type of parameter images is illegal.")
|
||||
image = self.preprocess(pil_image).unsqueeze(0).to(self.device)
|
||||
scores.append(self._calculate_score(prompt, image).item())
|
||||
return scores
|
||||
else:
|
||||
raise TypeError("The type of parameter images is illegal.")
|
||||
|
||||
def _calculate_score(self, prompt: str, image: torch.Tensor) -> torch.Tensor:
|
||||
"""Calculate the score for a single image and prompt.
|
||||
|
||||
Args:
|
||||
prompt (str): The prompt text.
|
||||
image (torch.Tensor): The processed image tensor.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: The reward score.
|
||||
"""
|
||||
text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device)
|
||||
image_embeds = self.blip.visual_encoder(image)
|
||||
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device)
|
||||
text_output = self.blip.text_encoder(
|
||||
text_input.input_ids,
|
||||
attention_mask=text_input.attention_mask,
|
||||
encoder_hidden_states=image_embeds,
|
||||
encoder_attention_mask=image_atts,
|
||||
return_dict=True,
|
||||
)
|
||||
txt_features = text_output.last_hidden_state[:, 0, :].float()
|
||||
rewards = self.mlp(txt_features)
|
||||
rewards = (rewards - self.mean) / self.std
|
||||
return rewards
|
||||
|
||||
def inference_rank(self, prompt: str, generations_list: List[Union[str, Image.Image]]) -> tuple:
|
||||
"""Rank the images based on the prompt.
|
||||
|
||||
Args:
|
||||
prompt (str): The prompt text.
|
||||
generations_list (List[Union[str, Image.Image]]): List of image paths or PIL images.
|
||||
|
||||
Returns:
|
||||
tuple: (indices, rewards) where indices are the ranks and rewards are the scores.
|
||||
"""
|
||||
text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device)
|
||||
txt_set = []
|
||||
for generation in generations_list:
|
||||
if isinstance(generation, str):
|
||||
pil_image = Image.open(generation)
|
||||
elif isinstance(generation, Image.Image):
|
||||
pil_image = generation
|
||||
else:
|
||||
raise TypeError("The type of parameter generations_list is illegal.")
|
||||
image = self.preprocess(pil_image).unsqueeze(0).to(self.device)
|
||||
image_embeds = self.blip.visual_encoder(image)
|
||||
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device)
|
||||
text_output = self.blip.text_encoder(
|
||||
text_input.input_ids,
|
||||
attention_mask=text_input.attention_mask,
|
||||
encoder_hidden_states=image_embeds,
|
||||
encoder_attention_mask=image_atts,
|
||||
return_dict=True,
|
||||
)
|
||||
txt_set.append(text_output.last_hidden_state[:, 0, :])
|
||||
txt_features = torch.cat(txt_set, 0).float()
|
||||
rewards = self.mlp(txt_features)
|
||||
rewards = (rewards - self.mean) / self.std
|
||||
rewards = torch.squeeze(rewards)
|
||||
_, rank = torch.sort(rewards, dim=0, descending=True)
|
||||
_, indices = torch.sort(rank, dim=0)
|
||||
indices = indices + 1
|
||||
return indices.detach().cpu().numpy().tolist(), rewards.detach().cpu().numpy().tolist()
|
||||
|
||||
|
||||
class ImageRewardScore(torch.nn.Module):
|
||||
def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS):
|
||||
super().__init__()
|
||||
self.device = device if isinstance(device, torch.device) else torch.device(device)
|
||||
model_path = path.get("imagereward")
|
||||
med_config = path.get("med_config")
|
||||
state_dict = load_file(model_path)
|
||||
self.model = ImageReward(device=self.device, med_config=med_config, bert_model_path=path.get("bert_model_path")).to(self.device)
|
||||
self.model.load_state_dict(state_dict, strict=False)
|
||||
self.model.eval()
|
||||
|
||||
@torch.no_grad()
|
||||
def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]:
|
||||
"""Score the images based on the prompt.
|
||||
|
||||
Args:
|
||||
images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
|
||||
prompt (str): The prompt text.
|
||||
|
||||
Returns:
|
||||
List[float]: List of scores for the images.
|
||||
"""
|
||||
return self.model.score(images, prompt)
|
||||
@@ -1,129 +0,0 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image
|
||||
from io import BytesIO
|
||||
from tqdm.auto import tqdm
|
||||
from transformers import CLIPFeatureExtractor, CLIPImageProcessor
|
||||
from transformers import CLIPConfig
|
||||
from dataclasses import dataclass
|
||||
from transformers import CLIPModel as HFCLIPModel
|
||||
from safetensors.torch import load_file
|
||||
from torch import nn, einsum
|
||||
|
||||
from .trainer.models.base_model import BaseModelConfig
|
||||
|
||||
from transformers import CLIPConfig
|
||||
from transformers import AutoProcessor, AutoModel, AutoTokenizer
|
||||
from typing import Any, Optional, Tuple, Union, List
|
||||
import torch
|
||||
|
||||
from .trainer.models.cross_modeling import Cross_model
|
||||
from .trainer.models import clip_model
|
||||
import torch.nn.functional as F
|
||||
import gc
|
||||
import json
|
||||
from .config import MODEL_PATHS
|
||||
|
||||
class MPScore(torch.nn.Module):
|
||||
def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS, condition: str = 'overall'):
|
||||
super().__init__()
|
||||
"""Initialize the MPSModel with a processor, tokenizer, and model.
|
||||
|
||||
Args:
|
||||
device (Union[str, torch.device]): The device to load the model on.
|
||||
"""
|
||||
self.device = device
|
||||
processor_name_or_path = path.get("clip")
|
||||
self.image_processor = CLIPImageProcessor.from_pretrained(processor_name_or_path)
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(processor_name_or_path, trust_remote_code=True)
|
||||
self.model = clip_model.CLIPModel(processor_name_or_path, config_file=True)
|
||||
state_dict = load_file(path.get("mps"))
|
||||
self.model.load_state_dict(state_dict, strict=False)
|
||||
self.model.to(device)
|
||||
self.condition = condition
|
||||
|
||||
def _calculate_score(self, image: torch.Tensor, prompt: str) -> float:
|
||||
"""Calculate the reward score for a single image and prompt.
|
||||
|
||||
Args:
|
||||
image (torch.Tensor): The processed image tensor.
|
||||
prompt (str): The prompt text.
|
||||
|
||||
Returns:
|
||||
float: The reward score.
|
||||
"""
|
||||
def _tokenize(caption):
|
||||
input_ids = self.tokenizer(
|
||||
caption,
|
||||
max_length=self.tokenizer.model_max_length,
|
||||
padding="max_length",
|
||||
truncation=True,
|
||||
return_tensors="pt"
|
||||
).input_ids
|
||||
return input_ids
|
||||
|
||||
text_input = _tokenize(prompt).to(self.device)
|
||||
if self.condition == 'overall':
|
||||
condition_prompt = 'light, color, clarity, tone, style, ambiance, artistry, shape, face, hair, hands, limbs, structure, instance, texture, quantity, attributes, position, number, location, word, things'
|
||||
elif self.condition == 'aesthetics':
|
||||
condition_prompt = 'light, color, clarity, tone, style, ambiance, artistry'
|
||||
elif self.condition == 'quality':
|
||||
condition_prompt = 'shape, face, hair, hands, limbs, structure, instance, texture'
|
||||
elif self.condition == 'semantic':
|
||||
condition_prompt = 'quantity, attributes, position, number, location'
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported condition: {self.condition}. Choose 'overall', 'aesthetics', 'quality', or 'semantic'.")
|
||||
condition_batch = _tokenize(condition_prompt).repeat(text_input.shape[0], 1).to(self.device)
|
||||
|
||||
with torch.no_grad():
|
||||
text_f, text_features = self.model.model.get_text_features(text_input)
|
||||
|
||||
image_f = self.model.model.get_image_features(image.half())
|
||||
condition_f, _ = self.model.model.get_text_features(condition_batch)
|
||||
|
||||
sim_text_condition = einsum('b i d, b j d -> b j i', text_f, condition_f)
|
||||
sim_text_condition = torch.max(sim_text_condition, dim=1, keepdim=True)[0]
|
||||
sim_text_condition = sim_text_condition / sim_text_condition.max()
|
||||
mask = torch.where(sim_text_condition > 0.3, 0, float('-inf'))
|
||||
mask = mask.repeat(1, image_f.shape[1], 1)
|
||||
image_features = self.model.cross_model(image_f, text_f, mask.half())[:, 0, :]
|
||||
|
||||
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
|
||||
text_features = text_features / text_features.norm(dim=-1, keepdim=True)
|
||||
image_score = self.model.logit_scale.exp() * text_features @ image_features.T
|
||||
|
||||
return image_score[0].cpu().numpy().item()
|
||||
|
||||
@torch.no_grad()
|
||||
def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]:
|
||||
"""Score the images based on the prompt.
|
||||
|
||||
Args:
|
||||
images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
|
||||
prompt (str): The prompt text.
|
||||
|
||||
Returns:
|
||||
List[float]: List of reward scores for the images.
|
||||
"""
|
||||
if isinstance(images, (str, Image.Image)):
|
||||
# Single image
|
||||
if isinstance(images, str):
|
||||
image = self.image_processor(Image.open(images), return_tensors="pt")["pixel_values"].to(self.device)
|
||||
else:
|
||||
image = self.image_processor(images, return_tensors="pt")["pixel_values"].to(self.device)
|
||||
return [self._calculate_score(image, prompt)]
|
||||
elif isinstance(images, list):
|
||||
# Multiple images
|
||||
scores = []
|
||||
for one_images in images:
|
||||
if isinstance(one_images, str):
|
||||
image = self.image_processor(Image.open(one_images), return_tensors="pt")["pixel_values"].to(self.device)
|
||||
elif isinstance(one_images, Image.Image):
|
||||
image = self.image_processor(one_images, return_tensors="pt")["pixel_values"].to(self.device)
|
||||
else:
|
||||
raise TypeError("The type of parameter images is illegal.")
|
||||
scores.append(self._calculate_score(image, prompt))
|
||||
return scores
|
||||
else:
|
||||
raise TypeError("The type of parameter images is illegal.")
|
||||
@@ -1,14 +0,0 @@
|
||||
from .coca_model import CoCa
|
||||
from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
|
||||
from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer, create_loss
|
||||
from .factory import list_models, add_model_config, get_model_config, load_checkpoint
|
||||
from .loss import ClipLoss, DistillClipLoss, CoCaLoss
|
||||
from .model import CLIP, CustomTextCLIP, CLIPTextCfg, CLIPVisionCfg, \
|
||||
convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype
|
||||
from .openai import load_openai_model, list_openai_models
|
||||
from .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model, \
|
||||
get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained
|
||||
from .push_to_hf_hub import push_pretrained_to_hf_hub, push_to_hf_hub
|
||||
from .tokenizer import SimpleTokenizer
|
||||
from .transform import image_transform, AugmentationCfg
|
||||
from .utils import freeze_batch_norm_2d
|
||||
@@ -1,458 +0,0 @@
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
import numpy as np
|
||||
from dataclasses import dataclass
|
||||
|
||||
from .transformer import (
|
||||
LayerNormFp32,
|
||||
LayerNorm,
|
||||
QuickGELU,
|
||||
MultimodalTransformer,
|
||||
)
|
||||
from .model import CLIPTextCfg, CLIPVisionCfg, _build_vision_tower, _build_text_tower
|
||||
|
||||
try:
|
||||
from transformers import (
|
||||
BeamSearchScorer,
|
||||
LogitsProcessorList,
|
||||
TopPLogitsWarper,
|
||||
TopKLogitsWarper,
|
||||
RepetitionPenaltyLogitsProcessor,
|
||||
MinLengthLogitsProcessor,
|
||||
MaxLengthCriteria,
|
||||
StoppingCriteriaList
|
||||
)
|
||||
|
||||
GENERATION_TYPES = {
|
||||
"top_k": TopKLogitsWarper,
|
||||
"top_p": TopPLogitsWarper,
|
||||
"beam_search": "beam_search"
|
||||
}
|
||||
_has_transformers = True
|
||||
except ImportError as e:
|
||||
GENERATION_TYPES = {
|
||||
"top_k": None,
|
||||
"top_p": None,
|
||||
"beam_search": "beam_search"
|
||||
}
|
||||
_has_transformers = False
|
||||
|
||||
|
||||
@dataclass
|
||||
class MultimodalCfg(CLIPTextCfg):
|
||||
mlp_ratio: int = 4
|
||||
dim_head: int = 64
|
||||
heads: int = 8
|
||||
n_queries: int = 256
|
||||
attn_pooler_heads: int = 8
|
||||
|
||||
|
||||
def _build_text_decoder_tower(
|
||||
embed_dim,
|
||||
multimodal_cfg,
|
||||
quick_gelu: bool = False,
|
||||
cast_dtype: Optional[torch.dtype] = None,
|
||||
):
|
||||
multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg
|
||||
act_layer = QuickGELU if quick_gelu else nn.GELU
|
||||
norm_layer = (
|
||||
LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm
|
||||
)
|
||||
|
||||
decoder = MultimodalTransformer(
|
||||
context_length=multimodal_cfg.context_length,
|
||||
width=multimodal_cfg.width,
|
||||
heads=multimodal_cfg.heads,
|
||||
layers=multimodal_cfg.layers,
|
||||
ls_init_value=multimodal_cfg.ls_init_value,
|
||||
output_dim=embed_dim,
|
||||
act_layer=act_layer,
|
||||
norm_layer=norm_layer,
|
||||
)
|
||||
|
||||
return decoder
|
||||
|
||||
|
||||
class CoCa(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
embed_dim,
|
||||
multimodal_cfg: MultimodalCfg,
|
||||
text_cfg: CLIPTextCfg,
|
||||
vision_cfg: CLIPVisionCfg,
|
||||
quick_gelu: bool = False,
|
||||
cast_dtype: Optional[torch.dtype] = None,
|
||||
pad_id: int = 0,
|
||||
):
|
||||
super().__init__()
|
||||
multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg
|
||||
text_cfg = CLIPTextCfg(**text_cfg) if isinstance(text_cfg, dict) else text_cfg
|
||||
vision_cfg = CLIPVisionCfg(**vision_cfg) if isinstance(vision_cfg, dict) else vision_cfg
|
||||
|
||||
self.text = _build_text_tower(
|
||||
embed_dim=embed_dim,
|
||||
text_cfg=text_cfg,
|
||||
quick_gelu=quick_gelu,
|
||||
cast_dtype=cast_dtype,
|
||||
)
|
||||
|
||||
vocab_size = (
|
||||
text_cfg.vocab_size # for hf models
|
||||
if hasattr(text_cfg, "hf_model_name") and text_cfg.hf_model_name is not None
|
||||
else text_cfg.vocab_size
|
||||
)
|
||||
|
||||
self.visual = _build_vision_tower(
|
||||
embed_dim=embed_dim,
|
||||
vision_cfg=vision_cfg,
|
||||
quick_gelu=quick_gelu,
|
||||
cast_dtype=cast_dtype,
|
||||
)
|
||||
|
||||
self.text_decoder = _build_text_decoder_tower(
|
||||
vocab_size,
|
||||
multimodal_cfg=multimodal_cfg,
|
||||
quick_gelu=quick_gelu,
|
||||
cast_dtype=cast_dtype,
|
||||
)
|
||||
|
||||
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
|
||||
self.pad_id = pad_id
|
||||
|
||||
@torch.jit.ignore
|
||||
def set_grad_checkpointing(self, enable=True):
|
||||
self.visual.set_grad_checkpointing(enable)
|
||||
self.text.set_grad_checkpointing(enable)
|
||||
self.text_decoder.set_grad_checkpointing(enable)
|
||||
|
||||
def _encode_image(self, images, normalize=True):
|
||||
image_latent, tokens_embs = self.visual(images)
|
||||
image_latent = F.normalize(image_latent, dim=-1) if normalize else image_latent
|
||||
return image_latent, tokens_embs
|
||||
|
||||
def _encode_text(self, text, normalize=True, embed_cls=True):
|
||||
text = text[:, :-1] if embed_cls else text # make space for CLS token
|
||||
text_latent, token_emb = self.text(text)
|
||||
text_latent = F.normalize(text_latent, dim=-1) if normalize else text_latent
|
||||
return text_latent, token_emb
|
||||
|
||||
def encode_image(self, images, normalize=True):
|
||||
image_latent, _ = self._encode_image(images, normalize=normalize)
|
||||
return image_latent
|
||||
|
||||
def encode_text(self, text, normalize=True, embed_cls=True):
|
||||
text_latent, _ = self._encode_text(text, normalize=normalize, embed_cls=embed_cls)
|
||||
return text_latent
|
||||
|
||||
def forward(self, image, text, embed_cls=True, image_latent=None, image_embs=None):
|
||||
text_latent, token_embs = self._encode_text(text, embed_cls=embed_cls)
|
||||
if image_latent is None or image_embs is None:
|
||||
image_latent, image_embs = self._encode_image(image)
|
||||
|
||||
# TODO: add assertion to avoid bugs?
|
||||
labels = text[:, -token_embs.shape[1]:]
|
||||
|
||||
logits = self.text_decoder(image_embs, token_embs)
|
||||
return {
|
||||
"image_features": image_latent,
|
||||
"text_features": text_latent,
|
||||
"logits": logits,
|
||||
"labels": labels,
|
||||
"logit_scale": self.logit_scale.exp()
|
||||
}
|
||||
|
||||
def generate(
|
||||
self,
|
||||
image,
|
||||
text=None,
|
||||
seq_len=30,
|
||||
max_seq_len=77,
|
||||
temperature=1.,
|
||||
generation_type="beam_search",
|
||||
top_p=0.1, # keep tokens in the 1 - top_p quantile
|
||||
top_k=1, # keeps the top_k most probable tokens
|
||||
pad_token_id=None,
|
||||
eos_token_id=None,
|
||||
sot_token_id=None,
|
||||
num_beams=6,
|
||||
num_beam_groups=3,
|
||||
min_seq_len=5,
|
||||
stopping_criteria=None,
|
||||
repetition_penalty=1.0,
|
||||
fixed_output_length=False # if True output.shape == (batch_size, seq_len)
|
||||
):
|
||||
# taking many ideas and components from HuggingFace GenerationMixin
|
||||
# https://huggingface.co/docs/transformers/main/en/main_classes/text_generation
|
||||
assert _has_transformers, "Please install transformers for generate functionality. `pip install transformers`."
|
||||
assert seq_len > min_seq_len, "seq_len must be larger than min_seq_len"
|
||||
|
||||
with torch.no_grad():
|
||||
sot_token_id = 49406 if sot_token_id is None else sot_token_id
|
||||
eos_token_id = 49407 if eos_token_id is None else eos_token_id
|
||||
pad_token_id = self.pad_id if pad_token_id is None else pad_token_id
|
||||
logit_processor = LogitsProcessorList(
|
||||
[
|
||||
MinLengthLogitsProcessor(min_seq_len, eos_token_id),
|
||||
RepetitionPenaltyLogitsProcessor(repetition_penalty),
|
||||
]
|
||||
)
|
||||
|
||||
if stopping_criteria is None:
|
||||
stopping_criteria = [MaxLengthCriteria(max_length=seq_len)]
|
||||
|
||||
stopping_criteria = StoppingCriteriaList(
|
||||
stopping_criteria
|
||||
)
|
||||
|
||||
device = image.device
|
||||
|
||||
if generation_type == "beam_search":
|
||||
output = self._generate_beamsearch(
|
||||
image_inputs = image,
|
||||
pad_token_id=pad_token_id,
|
||||
eos_token_id=eos_token_id,
|
||||
sot_token_id=sot_token_id,
|
||||
num_beams=num_beams,
|
||||
num_beam_groups=num_beam_groups,
|
||||
min_seq_len=min_seq_len,
|
||||
stopping_criteria=stopping_criteria,
|
||||
logit_processor=logit_processor,
|
||||
)
|
||||
if fixed_output_length and output.shape[1] < seq_len:
|
||||
return torch.cat(
|
||||
(output, torch.ones(output.shape[0], seq_len-output.shape[1], device=device, dtype=output.dtype) * self.pad_id),
|
||||
dim=1
|
||||
)
|
||||
return output
|
||||
|
||||
elif generation_type == "top_p":
|
||||
logit_warper = GENERATION_TYPES[generation_type](top_p)
|
||||
elif generation_type == "top_k":
|
||||
logit_warper = GENERATION_TYPES[generation_type](top_k)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"generation_type has to be one of "
|
||||
f"{'| ' + ' | '.join(list(GENERATION_TYPES.keys())) + ' |'}."
|
||||
)
|
||||
|
||||
image_latent, image_embs = self._encode_image(image)
|
||||
|
||||
if text is None:
|
||||
text = torch.ones((image.shape[0], 1), device=device, dtype=torch.long) * sot_token_id
|
||||
|
||||
was_training = self.training
|
||||
num_dims = len(text.shape)
|
||||
|
||||
if num_dims == 1:
|
||||
text = text[None, :]
|
||||
|
||||
cur_len = text.shape[1]
|
||||
self.eval()
|
||||
out = text
|
||||
|
||||
while True:
|
||||
x = out[:, -max_seq_len:]
|
||||
cur_len = x.shape[1]
|
||||
logits = self(image, x, image_latent=image_latent, image_embs=image_embs, embed_cls=False)["logits"][:, -1]
|
||||
mask = (out[:, -1] == eos_token_id) | (out[:, -1] == pad_token_id)
|
||||
sample = torch.ones((out.shape[0], 1), device=device, dtype=torch.long) * pad_token_id
|
||||
|
||||
if mask.all():
|
||||
if not fixed_output_length:
|
||||
break
|
||||
else:
|
||||
logits = logits[~mask, :]
|
||||
filtered_logits = logit_processor(x[~mask, :], logits)
|
||||
filtered_logits = logit_warper(x[~mask, :], filtered_logits)
|
||||
probs = F.softmax(filtered_logits / temperature, dim=-1)
|
||||
|
||||
if (cur_len + 1 == seq_len):
|
||||
sample[~mask, :] = torch.ones((sum(~mask), 1), device=device, dtype=torch.long) * eos_token_id
|
||||
else:
|
||||
sample[~mask, :] = torch.multinomial(probs, 1)
|
||||
|
||||
out = torch.cat((out, sample), dim=-1)
|
||||
|
||||
cur_len += 1
|
||||
|
||||
if stopping_criteria(out, None):
|
||||
break
|
||||
|
||||
if num_dims == 1:
|
||||
out = out.squeeze(0)
|
||||
|
||||
self.train(was_training)
|
||||
return out
|
||||
|
||||
def _generate_beamsearch(
|
||||
self,
|
||||
image_inputs,
|
||||
pad_token_id=None,
|
||||
eos_token_id=None,
|
||||
sot_token_id=None,
|
||||
num_beams=6,
|
||||
num_beam_groups=3,
|
||||
min_seq_len=5,
|
||||
stopping_criteria=None,
|
||||
logit_processor=None,
|
||||
logit_warper=None,
|
||||
):
|
||||
device = image_inputs.device
|
||||
batch_size = image_inputs.shape[0]
|
||||
image_inputs = torch.repeat_interleave(image_inputs, num_beams, dim=0)
|
||||
image_latent, image_embs = self._encode_image(image_inputs)
|
||||
|
||||
input_ids = torch.ones((batch_size * num_beams, 1), device=device, dtype=torch.long)
|
||||
input_ids = input_ids * sot_token_id
|
||||
beam_scorer = BeamSearchScorer(
|
||||
batch_size=batch_size,
|
||||
num_beams=num_beams,
|
||||
device=device,
|
||||
num_beam_groups=num_beam_groups,
|
||||
)
|
||||
# instantiate logits processors
|
||||
logits_processor = (
|
||||
LogitsProcessorList([MinLengthLogitsProcessor(min_seq_len, eos_token_id=eos_token_id)])
|
||||
if logit_processor is None
|
||||
else logit_processor
|
||||
)
|
||||
|
||||
batch_size = len(beam_scorer._beam_hyps)
|
||||
num_beams = beam_scorer.num_beams
|
||||
num_beam_groups = beam_scorer.num_beam_groups
|
||||
num_sub_beams = num_beams // num_beam_groups
|
||||
batch_beam_size, cur_len = input_ids.shape
|
||||
beam_indices = None
|
||||
|
||||
if num_beams * batch_size != batch_beam_size:
|
||||
raise ValueError(
|
||||
f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
|
||||
)
|
||||
|
||||
beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device)
|
||||
# initialise score of first beam of each group with 0 and the rest with 1e-9. This ensures that the beams in
|
||||
# the same group don't produce same tokens everytime.
|
||||
beam_scores[:, ::num_sub_beams] = 0
|
||||
beam_scores = beam_scores.view((batch_size * num_beams,))
|
||||
|
||||
while True:
|
||||
|
||||
# predicted tokens in cur_len step
|
||||
current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device)
|
||||
|
||||
# indices which will form the beams in the next time step
|
||||
reordering_indices = torch.zeros(batch_size * num_beams, dtype=torch.long, device=device)
|
||||
|
||||
# do one decoder step on all beams of all sentences in batch
|
||||
model_inputs = prepare_inputs_for_generation(input_ids=input_ids, image_inputs=image_inputs)
|
||||
outputs = self(
|
||||
model_inputs['images'],
|
||||
model_inputs['text'],
|
||||
embed_cls=False,
|
||||
image_latent=image_latent,
|
||||
image_embs=image_embs
|
||||
)
|
||||
|
||||
for beam_group_idx in range(num_beam_groups):
|
||||
group_start_idx = beam_group_idx * num_sub_beams
|
||||
group_end_idx = min(group_start_idx + num_sub_beams, num_beams)
|
||||
group_size = group_end_idx - group_start_idx
|
||||
|
||||
# indices of beams of current group among all sentences in batch
|
||||
batch_group_indices = []
|
||||
|
||||
for batch_idx in range(batch_size):
|
||||
batch_group_indices.extend(
|
||||
[batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)]
|
||||
)
|
||||
group_input_ids = input_ids[batch_group_indices]
|
||||
|
||||
# select outputs of beams of currentg group only
|
||||
next_token_logits = outputs['logits'][batch_group_indices, -1, :]
|
||||
vocab_size = next_token_logits.shape[-1]
|
||||
|
||||
next_token_scores_processed = logits_processor(
|
||||
group_input_ids, next_token_logits, current_tokens=current_tokens, beam_group_idx=beam_group_idx
|
||||
)
|
||||
next_token_scores = next_token_scores_processed + beam_scores[batch_group_indices].unsqueeze(-1)
|
||||
next_token_scores = next_token_scores.expand_as(next_token_scores_processed)
|
||||
|
||||
# reshape for beam search
|
||||
next_token_scores = next_token_scores.view(batch_size, group_size * vocab_size)
|
||||
|
||||
next_token_scores, next_tokens = torch.topk(
|
||||
next_token_scores, 2 * group_size, dim=1, largest=True, sorted=True
|
||||
)
|
||||
|
||||
next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
|
||||
next_tokens = next_tokens % vocab_size
|
||||
|
||||
# stateless
|
||||
process_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
|
||||
beam_outputs = beam_scorer.process(
|
||||
group_input_ids,
|
||||
next_token_scores,
|
||||
next_tokens,
|
||||
next_indices,
|
||||
pad_token_id=pad_token_id,
|
||||
eos_token_id=eos_token_id,
|
||||
beam_indices=process_beam_indices,
|
||||
)
|
||||
beam_scores[batch_group_indices] = beam_outputs["next_beam_scores"]
|
||||
beam_next_tokens = beam_outputs["next_beam_tokens"]
|
||||
beam_idx = beam_outputs["next_beam_indices"]
|
||||
|
||||
input_ids[batch_group_indices] = group_input_ids[beam_idx]
|
||||
group_input_ids = torch.cat([group_input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
|
||||
current_tokens[batch_group_indices] = group_input_ids[:, -1]
|
||||
|
||||
# (beam_idx // group_size) -> batch_idx
|
||||
# (beam_idx % group_size) -> offset of idx inside the group
|
||||
reordering_indices[batch_group_indices] = (
|
||||
num_beams * torch.div(beam_idx, group_size, rounding_mode="floor") + group_start_idx + (beam_idx % group_size)
|
||||
)
|
||||
|
||||
input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-1)
|
||||
|
||||
# increase cur_len
|
||||
cur_len = cur_len + 1
|
||||
if beam_scorer.is_done or stopping_criteria(input_ids, None):
|
||||
break
|
||||
|
||||
final_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
|
||||
sequence_outputs = beam_scorer.finalize(
|
||||
input_ids,
|
||||
beam_scores,
|
||||
next_tokens,
|
||||
next_indices,
|
||||
pad_token_id=pad_token_id,
|
||||
eos_token_id=eos_token_id,
|
||||
max_length=stopping_criteria.max_length,
|
||||
beam_indices=final_beam_indices,
|
||||
)
|
||||
return sequence_outputs['sequences']
|
||||
|
||||
|
||||
def prepare_inputs_for_generation(input_ids, image_inputs, past=None, **kwargs):
|
||||
if past:
|
||||
input_ids = input_ids[:, -1].unsqueeze(-1)
|
||||
|
||||
attention_mask = kwargs.get("attention_mask", None)
|
||||
position_ids = kwargs.get("position_ids", None)
|
||||
|
||||
if attention_mask is not None and position_ids is None:
|
||||
# create position_ids on the fly for batch generation
|
||||
position_ids = attention_mask.long().cumsum(-1) - 1
|
||||
position_ids.masked_fill_(attention_mask == 0, 1)
|
||||
else:
|
||||
position_ids = None
|
||||
return {
|
||||
"text": input_ids,
|
||||
"images": image_inputs,
|
||||
"past_key_values": past,
|
||||
"position_ids": position_ids,
|
||||
"attention_mask": attention_mask,
|
||||
}
|
||||
@@ -1,2 +0,0 @@
|
||||
OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
|
||||
OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
|
||||
@@ -1,433 +0,0 @@
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import pathlib
|
||||
import re
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
# from turtle import forward
|
||||
from typing import Any, Dict, Optional, Tuple, Union
|
||||
|
||||
import torch
|
||||
|
||||
from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
|
||||
from .model import CLIP, CustomTextCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\
|
||||
resize_pos_embed, get_cast_dtype
|
||||
from .coca_model import CoCa
|
||||
from .loss import ClipLoss, DistillClipLoss, CoCaLoss
|
||||
from .openai import load_openai_model
|
||||
from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained, list_pretrained_tags_by_model, download_pretrained_from_hf
|
||||
from .transform import image_transform, AugmentationCfg
|
||||
from .tokenizer import HFTokenizer, SimpleTokenizer
|
||||
|
||||
|
||||
HF_HUB_PREFIX = 'hf-hub:'
|
||||
_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
|
||||
_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs
|
||||
|
||||
|
||||
def _natural_key(string_):
|
||||
return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
|
||||
|
||||
|
||||
def _rescan_model_configs():
|
||||
global _MODEL_CONFIGS
|
||||
|
||||
config_ext = ('.json',)
|
||||
config_files = []
|
||||
for config_path in _MODEL_CONFIG_PATHS:
|
||||
if config_path.is_file() and config_path.suffix in config_ext:
|
||||
config_files.append(config_path)
|
||||
elif config_path.is_dir():
|
||||
for ext in config_ext:
|
||||
config_files.extend(config_path.glob(f'*{ext}'))
|
||||
|
||||
for cf in config_files:
|
||||
with open(cf, 'r') as f:
|
||||
model_cfg = json.load(f)
|
||||
if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')):
|
||||
_MODEL_CONFIGS[cf.stem] = model_cfg
|
||||
|
||||
_MODEL_CONFIGS = {k: v for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))}
|
||||
|
||||
|
||||
_rescan_model_configs() # initial populate of model config registry
|
||||
|
||||
|
||||
def list_models():
|
||||
""" enumerate available model architectures based on config files """
|
||||
return list(_MODEL_CONFIGS.keys())
|
||||
|
||||
|
||||
def add_model_config(path):
|
||||
""" add model config path or file and update registry """
|
||||
if not isinstance(path, Path):
|
||||
path = Path(path)
|
||||
_MODEL_CONFIG_PATHS.append(path)
|
||||
_rescan_model_configs()
|
||||
|
||||
|
||||
def get_model_config(model_name):
|
||||
if model_name in _MODEL_CONFIGS:
|
||||
return deepcopy(_MODEL_CONFIGS[model_name])
|
||||
else:
|
||||
return None
|
||||
|
||||
|
||||
def get_tokenizer(model_name, open_clip_bpe_path=None):
|
||||
if model_name.startswith(HF_HUB_PREFIX):
|
||||
tokenizer = HFTokenizer(model_name[len(HF_HUB_PREFIX):])
|
||||
else:
|
||||
config = get_model_config(model_name)
|
||||
tokenizer = HFTokenizer(
|
||||
config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else SimpleTokenizer(open_clip_bpe_path)
|
||||
return tokenizer
|
||||
|
||||
|
||||
def load_state_dict(checkpoint_path: str, map_location='cpu'):
|
||||
checkpoint = torch.load(checkpoint_path, map_location=map_location)
|
||||
if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
|
||||
state_dict = checkpoint['state_dict']
|
||||
else:
|
||||
state_dict = checkpoint
|
||||
if next(iter(state_dict.items()))[0].startswith('module'):
|
||||
state_dict = {k[7:]: v for k, v in state_dict.items()}
|
||||
return state_dict
|
||||
|
||||
|
||||
def load_checkpoint(model, checkpoint_path, strict=True):
|
||||
state_dict = load_state_dict(checkpoint_path)
|
||||
# detect old format and make compatible with new format
|
||||
if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'):
|
||||
state_dict = convert_to_custom_text_state_dict(state_dict)
|
||||
resize_pos_embed(state_dict, model)
|
||||
incompatible_keys = model.load_state_dict(state_dict, strict=strict)
|
||||
return incompatible_keys
|
||||
|
||||
|
||||
def create_model(
|
||||
model_name: str,
|
||||
pretrained: Optional[str] = None,
|
||||
precision: str = 'fp32',
|
||||
device: Union[str, torch.device] = 'cpu',
|
||||
jit: bool = False,
|
||||
force_quick_gelu: bool = False,
|
||||
force_custom_text: bool = False,
|
||||
force_patch_dropout: Optional[float] = None,
|
||||
force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
|
||||
pretrained_image: bool = False,
|
||||
pretrained_hf: bool = True,
|
||||
cache_dir: Optional[str] = None,
|
||||
output_dict: Optional[bool] = None,
|
||||
require_pretrained: bool = False,
|
||||
):
|
||||
has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX)
|
||||
if has_hf_hub_prefix:
|
||||
model_id = model_name[len(HF_HUB_PREFIX):]
|
||||
checkpoint_path = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
|
||||
config_path = download_pretrained_from_hf(model_id, filename='open_clip_config.json', cache_dir=cache_dir)
|
||||
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
config = json.load(f)
|
||||
pretrained_cfg = config['preprocess_cfg']
|
||||
model_cfg = config['model_cfg']
|
||||
else:
|
||||
model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names
|
||||
checkpoint_path = None
|
||||
pretrained_cfg = {}
|
||||
model_cfg = None
|
||||
|
||||
if isinstance(device, str):
|
||||
device = torch.device(device)
|
||||
|
||||
if pretrained and pretrained.lower() == 'openai':
|
||||
logging.info(f'Loading pretrained {model_name} from OpenAI.')
|
||||
model = load_openai_model(
|
||||
model_name,
|
||||
precision=precision,
|
||||
device=device,
|
||||
jit=jit,
|
||||
cache_dir=cache_dir,
|
||||
)
|
||||
|
||||
# to always output dict even if it is clip
|
||||
if output_dict and hasattr(model, "output_dict"):
|
||||
model.output_dict = True
|
||||
else:
|
||||
model_cfg = model_cfg or get_model_config(model_name)
|
||||
if model_cfg is not None:
|
||||
logging.info(f'Loaded {model_name} model config.')
|
||||
else:
|
||||
logging.error(f'Model config for {model_name} not found; available models {list_models()}.')
|
||||
raise RuntimeError(f'Model config for {model_name} not found.')
|
||||
|
||||
if force_quick_gelu:
|
||||
# override for use of QuickGELU on non-OpenAI transformer models
|
||||
model_cfg["quick_gelu"] = True
|
||||
|
||||
if force_patch_dropout is not None:
|
||||
# override the default patch dropout value
|
||||
model_cfg["vision_cfg"]["patch_dropout"] = force_patch_dropout
|
||||
|
||||
if force_image_size is not None:
|
||||
# override model config's image size
|
||||
model_cfg["vision_cfg"]["image_size"] = force_image_size
|
||||
|
||||
if pretrained_image:
|
||||
if 'timm_model_name' in model_cfg.get('vision_cfg', {}):
|
||||
# pretrained weight loading for timm models set via vision_cfg
|
||||
model_cfg['vision_cfg']['timm_model_pretrained'] = True
|
||||
else:
|
||||
assert False, 'pretrained image towers currently only supported for timm models'
|
||||
|
||||
cast_dtype = get_cast_dtype(precision)
|
||||
is_hf_model = 'hf_model_name' in model_cfg.get('text_cfg', {})
|
||||
custom_text = model_cfg.pop('custom_text', False) or force_custom_text or is_hf_model
|
||||
|
||||
if custom_text:
|
||||
if is_hf_model:
|
||||
model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf
|
||||
if "coca" in model_name:
|
||||
model = CoCa(**model_cfg, cast_dtype=cast_dtype)
|
||||
else:
|
||||
model = CustomTextCLIP(**model_cfg, cast_dtype=cast_dtype)
|
||||
else:
|
||||
model = CLIP(**model_cfg, cast_dtype=cast_dtype)
|
||||
|
||||
pretrained_loaded = False
|
||||
if pretrained:
|
||||
checkpoint_path = ''
|
||||
pretrained_cfg = get_pretrained_cfg(model_name, pretrained)
|
||||
if pretrained_cfg:
|
||||
checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir)
|
||||
elif os.path.exists(pretrained):
|
||||
checkpoint_path = pretrained
|
||||
|
||||
if checkpoint_path:
|
||||
logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')
|
||||
load_checkpoint(model, checkpoint_path)
|
||||
else:
|
||||
error_str = (
|
||||
f'Pretrained weights ({pretrained}) not found for model {model_name}.'
|
||||
f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.')
|
||||
logging.warning(error_str)
|
||||
raise RuntimeError(error_str)
|
||||
pretrained_loaded = True
|
||||
elif has_hf_hub_prefix:
|
||||
logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')
|
||||
load_checkpoint(model, checkpoint_path)
|
||||
pretrained_loaded = True
|
||||
|
||||
if require_pretrained and not pretrained_loaded:
|
||||
# callers of create_model_from_pretrained always expect pretrained weights
|
||||
raise RuntimeError(
|
||||
f'Pretrained weights were required for (model: {model_name}, pretrained: {pretrained}) but not loaded.')
|
||||
|
||||
model.to(device=device)
|
||||
if precision in ("fp16", "bf16"):
|
||||
convert_weights_to_lp(model, dtype=torch.bfloat16 if precision == 'bf16' else torch.float16)
|
||||
|
||||
# set image / mean metadata from pretrained_cfg if available, or use default
|
||||
model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN
|
||||
model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD
|
||||
|
||||
# to always output dict even if it is clip
|
||||
if output_dict and hasattr(model, "output_dict"):
|
||||
model.output_dict = True
|
||||
|
||||
if jit:
|
||||
model = torch.jit.script(model)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
def create_loss(args):
|
||||
if args.distill:
|
||||
return DistillClipLoss(
|
||||
local_loss=args.local_loss,
|
||||
gather_with_grad=args.gather_with_grad,
|
||||
cache_labels=True,
|
||||
rank=args.rank,
|
||||
world_size=args.world_size,
|
||||
use_horovod=args.horovod,
|
||||
)
|
||||
elif "coca" in args.model.lower():
|
||||
return CoCaLoss(
|
||||
caption_loss_weight=args.coca_caption_loss_weight,
|
||||
clip_loss_weight=args.coca_contrastive_loss_weight,
|
||||
local_loss=args.local_loss,
|
||||
gather_with_grad=args.gather_with_grad,
|
||||
cache_labels=True,
|
||||
rank=args.rank,
|
||||
world_size=args.world_size,
|
||||
use_horovod=args.horovod,
|
||||
)
|
||||
return ClipLoss(
|
||||
local_loss=args.local_loss,
|
||||
gather_with_grad=args.gather_with_grad,
|
||||
cache_labels=True,
|
||||
rank=args.rank,
|
||||
world_size=args.world_size,
|
||||
use_horovod=args.horovod,
|
||||
)
|
||||
|
||||
class MLP(torch.nn.Module):
|
||||
def __init__(self, input_size):
|
||||
super().__init__()
|
||||
self.input_size = input_size
|
||||
self.layers = torch.nn.Sequential(
|
||||
torch.nn.Linear(self.input_size, 1024),
|
||||
torch.nn.Dropout(0.2),
|
||||
torch.nn.Linear(1024, 128),
|
||||
torch.nn.Dropout(0.2),
|
||||
torch.nn.Linear(128, 64),
|
||||
torch.nn.Dropout(0.1),
|
||||
torch.nn.Linear(64, 16),
|
||||
torch.nn.Linear(16, 1)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.layers(x)
|
||||
|
||||
# class semantic_head(torch.nn.Module):
|
||||
# def __init__(self, input_size):
|
||||
# super().__init__()
|
||||
# self.input_size = input_size # for ViT-L-14 is 1024
|
||||
# self.seg_head = torch.nn.Sequential(
|
||||
# torch.nn.Linear(input_size, 128),
|
||||
# torch.nn.Dropout(0.2),
|
||||
# torch.nn.Linear(128, 64),
|
||||
# torch.nn.Dropout(0.1),
|
||||
# torch.nn.Linear(64, 16),
|
||||
# torch.nn.Linear(16, 1),
|
||||
# )
|
||||
# self.sigmoid = torch.nn.Sigmoid()
|
||||
|
||||
# def forward(self, x):
|
||||
# return self.sigmoid(self.seg_head(x))
|
||||
|
||||
def create_model_and_transforms(
|
||||
model_name: str,
|
||||
pretrained: Optional[str] = None,
|
||||
precision: str = 'fp32',
|
||||
device: Union[str, torch.device] = 'cpu',
|
||||
jit: bool = False,
|
||||
force_quick_gelu: bool = False,
|
||||
force_custom_text: bool = False,
|
||||
force_patch_dropout: Optional[float] = None,
|
||||
force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
|
||||
pretrained_image: bool = False,
|
||||
pretrained_hf: bool = True,
|
||||
image_mean: Optional[Tuple[float, ...]] = None,
|
||||
image_std: Optional[Tuple[float, ...]] = None,
|
||||
aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,
|
||||
cache_dir: Optional[str] = None,
|
||||
light_augmentation = False,
|
||||
output_dict: Optional[bool] = None,
|
||||
with_score_predictor: bool = False,
|
||||
with_region_predictor: bool = False
|
||||
):
|
||||
model = create_model(
|
||||
model_name,
|
||||
pretrained,
|
||||
precision=precision,
|
||||
device=device,
|
||||
jit=jit,
|
||||
force_quick_gelu=force_quick_gelu,
|
||||
force_custom_text=force_custom_text,
|
||||
force_patch_dropout=force_patch_dropout,
|
||||
force_image_size=force_image_size,
|
||||
pretrained_image=pretrained_image,
|
||||
pretrained_hf=pretrained_hf,
|
||||
cache_dir=cache_dir,
|
||||
output_dict=output_dict,
|
||||
)
|
||||
|
||||
image_mean = image_mean or getattr(model.visual, 'image_mean', None)
|
||||
image_std = image_std or getattr(model.visual, 'image_std', None)
|
||||
|
||||
if with_score_predictor:
|
||||
model.score_predictor = MLP(model.visual.proj.size(1)).to(device=device, dtype=model.visual.proj.dtype)
|
||||
|
||||
if with_region_predictor:
|
||||
# model.region_predictor = semantic_head(model.visual.proj.size(1)).to(device=device, dtype=model.visual.proj.dtype)
|
||||
model.region_predictor = torch.nn.Linear(model.visual.proj.size(0), 1).to(device=device, dtype=model.visual.proj.dtype)
|
||||
# preprocess_train = image_transform_region(
|
||||
# model.visual.image_size,
|
||||
# is_train=True,
|
||||
# mean=image_mean,
|
||||
# std=image_std
|
||||
# )
|
||||
# preprocess_val = image_transform_region(
|
||||
# model.visual.image_size,
|
||||
# is_train=False,
|
||||
# mean=image_mean,
|
||||
# std=image_std
|
||||
# )
|
||||
|
||||
if light_augmentation:
|
||||
preprocess_val = image_transform(
|
||||
model.visual.image_size,
|
||||
is_train=False,
|
||||
mean=image_mean,
|
||||
std=image_std,
|
||||
resize_longest_max=True,
|
||||
)
|
||||
preprocess_train = preprocess_val
|
||||
else:
|
||||
preprocess_train = image_transform(
|
||||
model.visual.image_size,
|
||||
is_train=True,
|
||||
mean=image_mean,
|
||||
std=image_std
|
||||
)
|
||||
preprocess_val = image_transform(
|
||||
model.visual.image_size,
|
||||
is_train=False,
|
||||
mean=image_mean,
|
||||
std=image_std
|
||||
)
|
||||
|
||||
return model, preprocess_train, preprocess_val
|
||||
|
||||
|
||||
def create_model_from_pretrained(
|
||||
model_name: str,
|
||||
pretrained: Optional[str] = None,
|
||||
precision: str = 'fp32',
|
||||
device: Union[str, torch.device] = 'cpu',
|
||||
jit: bool = False,
|
||||
force_quick_gelu: bool = False,
|
||||
force_custom_text: bool = False,
|
||||
force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
|
||||
return_transform: bool = True,
|
||||
image_mean: Optional[Tuple[float, ...]] = None,
|
||||
image_std: Optional[Tuple[float, ...]] = None,
|
||||
cache_dir: Optional[str] = None,
|
||||
):
|
||||
model = create_model(
|
||||
model_name,
|
||||
pretrained,
|
||||
precision=precision,
|
||||
device=device,
|
||||
jit=jit,
|
||||
force_quick_gelu=force_quick_gelu,
|
||||
force_custom_text=force_custom_text,
|
||||
force_image_size=force_image_size,
|
||||
cache_dir=cache_dir,
|
||||
require_pretrained=True,
|
||||
)
|
||||
|
||||
if not return_transform:
|
||||
return model
|
||||
|
||||
image_mean = image_mean or getattr(model.visual, 'image_mean', None)
|
||||
image_std = image_std or getattr(model.visual, 'image_std', None)
|
||||
preprocess = image_transform(
|
||||
model.visual.image_size,
|
||||
is_train=False,
|
||||
mean=image_mean,
|
||||
std=image_std,
|
||||
)
|
||||
|
||||
return model, preprocess
|
||||
@@ -1,45 +0,0 @@
|
||||
# HF architecture dict:
|
||||
arch_dict = {
|
||||
# https://huggingface.co/docs/transformers/model_doc/roberta#roberta
|
||||
"roberta": {
|
||||
"config_names": {
|
||||
"context_length": "max_position_embeddings",
|
||||
"vocab_size": "vocab_size",
|
||||
"width": "hidden_size",
|
||||
"heads": "num_attention_heads",
|
||||
"layers": "num_hidden_layers",
|
||||
"layer_attr": "layer",
|
||||
"token_embeddings_attr": "embeddings"
|
||||
},
|
||||
"pooler": "mean_pooler",
|
||||
},
|
||||
# https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig
|
||||
"xlm-roberta": {
|
||||
"config_names": {
|
||||
"context_length": "max_position_embeddings",
|
||||
"vocab_size": "vocab_size",
|
||||
"width": "hidden_size",
|
||||
"heads": "num_attention_heads",
|
||||
"layers": "num_hidden_layers",
|
||||
"layer_attr": "layer",
|
||||
"token_embeddings_attr": "embeddings"
|
||||
},
|
||||
"pooler": "mean_pooler",
|
||||
},
|
||||
# https://huggingface.co/docs/transformers/model_doc/mt5#mt5
|
||||
"mt5": {
|
||||
"config_names": {
|
||||
# unlimited seqlen
|
||||
# https://github.com/google-research/text-to-text-transfer-transformer/issues/273
|
||||
# https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374
|
||||
"context_length": "",
|
||||
"vocab_size": "vocab_size",
|
||||
"width": "d_model",
|
||||
"heads": "num_heads",
|
||||
"layers": "num_layers",
|
||||
"layer_attr": "block",
|
||||
"token_embeddings_attr": "embed_tokens"
|
||||
},
|
||||
"pooler": "mean_pooler",
|
||||
},
|
||||
}
|
||||
@@ -1,176 +0,0 @@
|
||||
""" huggingface model adapter
|
||||
|
||||
Wraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model.
|
||||
"""
|
||||
|
||||
import re
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch import TensorType
|
||||
|
||||
try:
|
||||
import transformers
|
||||
from transformers import AutoModel, AutoTokenizer, AutoConfig, PretrainedConfig
|
||||
from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, \
|
||||
BaseModelOutputWithPoolingAndCrossAttentions
|
||||
except ImportError as e:
|
||||
transformers = None
|
||||
|
||||
|
||||
class BaseModelOutput:
|
||||
pass
|
||||
|
||||
|
||||
class PretrainedConfig:
|
||||
pass
|
||||
|
||||
from .hf_configs import arch_dict
|
||||
|
||||
|
||||
# utils
|
||||
def _camel2snake(s):
|
||||
return re.sub(r'(?<!^)(?=[A-Z])', '_', s).lower()
|
||||
|
||||
|
||||
# TODO: ?last - for gpt-like models
|
||||
_POOLERS = {}
|
||||
|
||||
|
||||
def register_pooler(cls):
|
||||
"""Decorator registering pooler class"""
|
||||
_POOLERS[_camel2snake(cls.__name__)] = cls
|
||||
return cls
|
||||
|
||||
|
||||
@register_pooler
|
||||
class MeanPooler(nn.Module):
|
||||
"""Mean pooling"""
|
||||
|
||||
def forward(self, x: BaseModelOutput, attention_mask: TensorType):
|
||||
masked_output = x.last_hidden_state * attention_mask.unsqueeze(-1)
|
||||
return masked_output.sum(dim=1) / attention_mask.sum(-1, keepdim=True)
|
||||
|
||||
|
||||
@register_pooler
|
||||
class MaxPooler(nn.Module):
|
||||
"""Max pooling"""
|
||||
|
||||
def forward(self, x: BaseModelOutput, attention_mask: TensorType):
|
||||
masked_output = x.last_hidden_state.masked_fill(attention_mask.unsqueeze(-1), -torch.inf)
|
||||
return masked_output.max(1).values
|
||||
|
||||
|
||||
@register_pooler
|
||||
class ClsPooler(nn.Module):
|
||||
"""CLS token pooling"""
|
||||
|
||||
def __init__(self, use_pooler_output=True):
|
||||
super().__init__()
|
||||
self.cls_token_position = 0
|
||||
self.use_pooler_output = use_pooler_output
|
||||
|
||||
def forward(self, x: BaseModelOutput, attention_mask: TensorType):
|
||||
if (self.use_pooler_output and
|
||||
isinstance(x, (BaseModelOutputWithPooling, BaseModelOutputWithPoolingAndCrossAttentions)) and
|
||||
(x.pooler_output is not None)
|
||||
):
|
||||
return x.pooler_output
|
||||
|
||||
return x.last_hidden_state[:, self.cls_token_position, :]
|
||||
|
||||
|
||||
class HFTextEncoder(nn.Module):
|
||||
"""HuggingFace model adapter"""
|
||||
output_tokens: torch.jit.Final[bool]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_name_or_path: str,
|
||||
output_dim: int,
|
||||
config: PretrainedConfig = None,
|
||||
pooler_type: str = None,
|
||||
proj: str = None,
|
||||
pretrained: bool = True,
|
||||
output_tokens: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
self.output_tokens = output_tokens
|
||||
self.output_dim = output_dim
|
||||
|
||||
# TODO: find better way to get this information
|
||||
uses_transformer_pooler = (pooler_type == "cls_pooler")
|
||||
|
||||
if transformers is None:
|
||||
raise RuntimeError("Please `pip install transformers` to use pre-trained HuggingFace models")
|
||||
if config is None:
|
||||
self.config = AutoConfig.from_pretrained(model_name_or_path)
|
||||
create_func, model_args = (AutoModel.from_pretrained, model_name_or_path) if pretrained else (
|
||||
AutoModel.from_config, self.config)
|
||||
# TODO: do all model configs have this attribute? PretrainedConfig does so yes??
|
||||
if hasattr(self.config, "is_encoder_decoder") and self.config.is_encoder_decoder:
|
||||
self.transformer = create_func(model_args)
|
||||
self.transformer = self.transformer.encoder
|
||||
else:
|
||||
self.transformer = create_func(model_args, add_pooling_layer=uses_transformer_pooler)
|
||||
else:
|
||||
self.config = config
|
||||
self.transformer = AutoModel.from_config(config)
|
||||
if pooler_type is None: # get default arch pooler
|
||||
pooler_type = (arch_dict[self.config.model_type]["pooler"])
|
||||
|
||||
self.pooler = _POOLERS[pooler_type]()
|
||||
|
||||
d_model = getattr(self.config, arch_dict[self.config.model_type]["config_names"]["width"])
|
||||
if (d_model == output_dim) and (proj is None): # do we always need a proj?
|
||||
self.proj = nn.Identity()
|
||||
elif proj == 'linear':
|
||||
self.proj = nn.Linear(d_model, output_dim, bias=False)
|
||||
elif proj == 'mlp':
|
||||
hidden_size = (d_model + output_dim) // 2
|
||||
self.proj = nn.Sequential(
|
||||
nn.Linear(d_model, hidden_size, bias=False),
|
||||
nn.GELU(),
|
||||
nn.Linear(hidden_size, output_dim, bias=False),
|
||||
)
|
||||
|
||||
def forward(self, x: TensorType):
|
||||
attn_mask = (x != self.config.pad_token_id).long()
|
||||
out = self.transformer(input_ids=x, attention_mask=attn_mask)
|
||||
pooled_out = self.pooler(out, attn_mask)
|
||||
projected = self.proj(pooled_out)
|
||||
|
||||
seq_len = out.last_hidden_state.shape[1]
|
||||
tokens = (
|
||||
out.last_hidden_state[:, torch.arange(seq_len) != self.pooler.cls_token_position, :]
|
||||
if type(self.pooler) == ClsPooler
|
||||
else out.last_hidden_state
|
||||
)
|
||||
|
||||
if self.output_tokens:
|
||||
return projected, tokens
|
||||
return projected
|
||||
|
||||
def lock(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):
|
||||
if not unlocked_layers: # full freezing
|
||||
for n, p in self.transformer.named_parameters():
|
||||
p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False
|
||||
return
|
||||
|
||||
encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer
|
||||
layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"])
|
||||
print(f"Unlocking {unlocked_layers}/{len(layer_list) + 1} layers of hf model")
|
||||
embeddings = getattr(
|
||||
self.transformer, arch_dict[self.config.model_type]["config_names"]["token_embeddings_attr"])
|
||||
modules = [embeddings, *layer_list][:-unlocked_layers]
|
||||
# freeze layers
|
||||
for module in modules:
|
||||
for n, p in module.named_parameters():
|
||||
p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False
|
||||
|
||||
@torch.jit.ignore
|
||||
def set_grad_checkpointing(self, enable=True):
|
||||
self.transformer.gradient_checkpointing_enable()
|
||||
|
||||
def init_parameters(self):
|
||||
pass
|
||||
@@ -1,270 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.nn import functional as F
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
|
||||
try:
|
||||
import torch.distributed.nn
|
||||
from torch import distributed as dist
|
||||
|
||||
has_distributed = True
|
||||
except ImportError:
|
||||
has_distributed = False
|
||||
|
||||
try:
|
||||
import horovod.torch as hvd
|
||||
except ImportError:
|
||||
hvd = None
|
||||
|
||||
|
||||
def gather_features(
|
||||
image_features,
|
||||
text_features,
|
||||
local_loss=False,
|
||||
gather_with_grad=False,
|
||||
rank=0,
|
||||
world_size=1,
|
||||
use_horovod=False
|
||||
):
|
||||
assert has_distributed, 'torch.distributed did not import correctly, please use a PyTorch version with support.'
|
||||
if use_horovod:
|
||||
assert hvd is not None, 'Please install horovod'
|
||||
if gather_with_grad:
|
||||
all_image_features = hvd.allgather(image_features)
|
||||
all_text_features = hvd.allgather(text_features)
|
||||
else:
|
||||
with torch.no_grad():
|
||||
all_image_features = hvd.allgather(image_features)
|
||||
all_text_features = hvd.allgather(text_features)
|
||||
if not local_loss:
|
||||
# ensure grads for local rank when all_* features don't have a gradient
|
||||
gathered_image_features = list(all_image_features.chunk(world_size, dim=0))
|
||||
gathered_text_features = list(all_text_features.chunk(world_size, dim=0))
|
||||
gathered_image_features[rank] = image_features
|
||||
gathered_text_features[rank] = text_features
|
||||
all_image_features = torch.cat(gathered_image_features, dim=0)
|
||||
all_text_features = torch.cat(gathered_text_features, dim=0)
|
||||
else:
|
||||
# We gather tensors from all gpus
|
||||
if gather_with_grad:
|
||||
all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0)
|
||||
all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0)
|
||||
else:
|
||||
gathered_image_features = [torch.zeros_like(image_features) for _ in range(world_size)]
|
||||
gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)]
|
||||
dist.all_gather(gathered_image_features, image_features)
|
||||
dist.all_gather(gathered_text_features, text_features)
|
||||
if not local_loss:
|
||||
# ensure grads for local rank when all_* features don't have a gradient
|
||||
gathered_image_features[rank] = image_features
|
||||
gathered_text_features[rank] = text_features
|
||||
all_image_features = torch.cat(gathered_image_features, dim=0)
|
||||
all_text_features = torch.cat(gathered_text_features, dim=0)
|
||||
|
||||
return all_image_features, all_text_features
|
||||
|
||||
|
||||
class ClipLoss(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
local_loss=False,
|
||||
gather_with_grad=False,
|
||||
cache_labels=False,
|
||||
rank=0,
|
||||
world_size=1,
|
||||
use_horovod=False,
|
||||
):
|
||||
super().__init__()
|
||||
self.local_loss = local_loss
|
||||
self.gather_with_grad = gather_with_grad
|
||||
self.cache_labels = cache_labels
|
||||
self.rank = rank
|
||||
self.world_size = world_size
|
||||
self.use_horovod = use_horovod
|
||||
|
||||
# cache state
|
||||
self.prev_num_logits = 0
|
||||
self.labels = {}
|
||||
|
||||
def get_ground_truth(self, device, num_logits) -> torch.Tensor:
|
||||
# calculated ground-truth and cache if enabled
|
||||
if self.prev_num_logits != num_logits or device not in self.labels:
|
||||
labels = torch.arange(num_logits, device=device, dtype=torch.long)
|
||||
if self.world_size > 1 and self.local_loss:
|
||||
labels = labels + num_logits * self.rank
|
||||
if self.cache_labels:
|
||||
self.labels[device] = labels
|
||||
self.prev_num_logits = num_logits
|
||||
else:
|
||||
labels = self.labels[device]
|
||||
return labels
|
||||
|
||||
def get_logits(self, image_features, text_features, logit_scale):
|
||||
if self.world_size > 1:
|
||||
all_image_features, all_text_features = gather_features(
|
||||
image_features, text_features,
|
||||
self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod)
|
||||
|
||||
if self.local_loss:
|
||||
logits_per_image = logit_scale * image_features @ all_text_features.T
|
||||
logits_per_text = logit_scale * text_features @ all_image_features.T
|
||||
else:
|
||||
logits_per_image = logit_scale * all_image_features @ all_text_features.T
|
||||
logits_per_text = logits_per_image.T
|
||||
else:
|
||||
logits_per_image = logit_scale * image_features @ text_features.T
|
||||
logits_per_text = logit_scale * text_features @ image_features.T
|
||||
|
||||
return logits_per_image, logits_per_text
|
||||
|
||||
def forward(self, image_features, text_features, logit_scale, output_dict=False):
|
||||
device = image_features.device
|
||||
logits_per_image, logits_per_text = self.get_logits(image_features, text_features, logit_scale)
|
||||
|
||||
labels = self.get_ground_truth(device, logits_per_image.shape[0])
|
||||
|
||||
total_loss = (
|
||||
F.cross_entropy(logits_per_image, labels) +
|
||||
F.cross_entropy(logits_per_text, labels)
|
||||
) / 2
|
||||
return total_loss
|
||||
|
||||
class PreferenceLoss(nn.Module):
|
||||
|
||||
def forward(self, logits_per_image, num_images, labels):
|
||||
|
||||
paired_logits_list = [logit[:,i] for i, logit in enumerate(logits_per_image.split(num_images.tolist()))]
|
||||
paired_logits = pad_sequence(paired_logits_list, batch_first=True, padding_value=-999)
|
||||
|
||||
ce_loss = F.cross_entropy(paired_logits, labels)
|
||||
return ce_loss
|
||||
|
||||
class HPSLoss(nn.Module):
|
||||
|
||||
def forward(self, text_logits, labels):
|
||||
|
||||
device = text_logits.device
|
||||
text_0_logits, text_1_logits = text_logits.chunk(2, dim=-1)
|
||||
label_0, label_1 = labels.chunk(2, dim=-1)
|
||||
|
||||
index = torch.arange(text_0_logits.shape[0], device=device, dtype=torch.long)
|
||||
text_0_logits = text_0_logits[index, index]
|
||||
text_1_logits = text_1_logits[index, index]
|
||||
text_logits = torch.stack([text_0_logits, text_1_logits], dim=-1)
|
||||
text_0_labels = torch.zeros(text_logits.shape[0], device=device, dtype=torch.long)
|
||||
text_1_labels = text_0_labels + 1
|
||||
|
||||
text_0_loss = torch.nn.functional.cross_entropy(text_logits, text_0_labels, reduction="none")
|
||||
text_1_loss = torch.nn.functional.cross_entropy(text_logits, text_1_labels, reduction="none")
|
||||
|
||||
text_loss = label_0 * text_0_loss + label_1 * text_1_loss
|
||||
|
||||
# absolute_example_weight = 1 / num_per_prompt
|
||||
# denominator = absolute_example_weight.sum()
|
||||
# weight_per_example = absolute_example_weight / denominator
|
||||
# text_loss *= weight_per_example
|
||||
|
||||
text_loss = text_loss.sum()
|
||||
return text_loss
|
||||
|
||||
class RankingLoss(nn.Module):
|
||||
|
||||
def forward(self, logits_per_image, num_images, labels, margin = 1.0):
|
||||
paired_logits_list = [logit[:,i] for i, logit in enumerate(logits_per_image.split(num_images.tolist()))]
|
||||
label_list = [label for label in labels.split(num_images.tolist())]
|
||||
# ranked_logits = [torch.index_select(paired_logits_list[i], 0, rank) for i, rank in enumerate(label_list)]
|
||||
|
||||
paired_logits = pad_sequence(paired_logits_list, batch_first=True, padding_value=-1)
|
||||
padded_labels = pad_sequence(label_list, batch_first=True, padding_value=10)
|
||||
|
||||
# regulized_logits = torch.log(torch.sigmoid(paired_logits))
|
||||
|
||||
diff = paired_logits.unsqueeze(1) - paired_logits.unsqueeze(2)
|
||||
# diff = paired_logits.unsqueeze(1) - paired_logits.unsqueeze(2)
|
||||
# diff_label = torch.clamp(padded_labels.unsqueeze(1) - padded_labels.unsqueeze(2), min=-1, max=1)
|
||||
diff_label = - (padded_labels.unsqueeze(1) - padded_labels.unsqueeze(2))
|
||||
mask = torch.triu(torch.ones(diff.shape[1], diff.shape[1]), diagonal=1).bool().detach()
|
||||
|
||||
loss = torch.clamp(margin - torch.mul(diff[:, ~mask],diff_label[:,~mask]), min=0).mean()
|
||||
return loss
|
||||
|
||||
class CoCaLoss(ClipLoss):
|
||||
def __init__(
|
||||
self,
|
||||
caption_loss_weight,
|
||||
clip_loss_weight,
|
||||
pad_id=0, # pad_token for open_clip custom tokenizer
|
||||
local_loss=False,
|
||||
gather_with_grad=False,
|
||||
cache_labels=False,
|
||||
rank=0,
|
||||
world_size=1,
|
||||
use_horovod=False,
|
||||
):
|
||||
super().__init__(
|
||||
local_loss=local_loss,
|
||||
gather_with_grad=gather_with_grad,
|
||||
cache_labels=cache_labels,
|
||||
rank=rank,
|
||||
world_size=world_size,
|
||||
use_horovod=use_horovod
|
||||
)
|
||||
|
||||
self.clip_loss_weight = clip_loss_weight
|
||||
self.caption_loss_weight = caption_loss_weight
|
||||
self.caption_loss = nn.CrossEntropyLoss(ignore_index=pad_id)
|
||||
|
||||
def forward(self, image_features, text_features, logits, labels, logit_scale, output_dict=False):
|
||||
clip_loss = super().forward(image_features, text_features, logit_scale)
|
||||
clip_loss = self.clip_loss_weight * clip_loss
|
||||
|
||||
caption_loss = self.caption_loss(
|
||||
logits.permute(0, 2, 1),
|
||||
labels,
|
||||
)
|
||||
caption_loss = caption_loss * self.caption_loss_weight
|
||||
|
||||
if output_dict:
|
||||
return {"contrastive_loss": clip_loss, "caption_loss": caption_loss}
|
||||
|
||||
return clip_loss, caption_loss
|
||||
|
||||
|
||||
class DistillClipLoss(ClipLoss):
|
||||
|
||||
def dist_loss(self, teacher_logits, student_logits):
|
||||
return -(teacher_logits.softmax(dim=1) * student_logits.log_softmax(dim=1)).sum(dim=1).mean(dim=0)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
image_features,
|
||||
text_features,
|
||||
logit_scale,
|
||||
dist_image_features,
|
||||
dist_text_features,
|
||||
dist_logit_scale,
|
||||
output_dict=False,
|
||||
):
|
||||
logits_per_image, logits_per_text = \
|
||||
self.get_logits(image_features, text_features, logit_scale)
|
||||
|
||||
dist_logits_per_image, dist_logits_per_text = \
|
||||
self.get_logits(dist_image_features, dist_text_features, dist_logit_scale)
|
||||
|
||||
labels = self.get_ground_truth(image_features.device, logits_per_image.shape[0])
|
||||
|
||||
contrastive_loss = (
|
||||
F.cross_entropy(logits_per_image, labels) +
|
||||
F.cross_entropy(logits_per_text, labels)
|
||||
) / 2
|
||||
|
||||
distill_loss = (
|
||||
self.dist_loss(dist_logits_per_image, logits_per_image) +
|
||||
self.dist_loss(dist_logits_per_text, logits_per_text)
|
||||
) / 2
|
||||
|
||||
if output_dict:
|
||||
return {"contrastive_loss": contrastive_loss, "distill_loss": distill_loss}
|
||||
|
||||
return contrastive_loss, distill_loss
|
||||
@@ -1,461 +0,0 @@
|
||||
""" CLIP Model
|
||||
|
||||
Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
|
||||
"""
|
||||
from dataclasses import dataclass
|
||||
import logging
|
||||
import math
|
||||
from typing import Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import nn
|
||||
from torch.utils.checkpoint import checkpoint
|
||||
|
||||
from .hf_model import HFTextEncoder
|
||||
from .modified_resnet import ModifiedResNet
|
||||
from .timm_model import TimmModel
|
||||
from .transformer import LayerNormFp32, LayerNorm, QuickGELU, Attention, VisionTransformer, TextTransformer
|
||||
from .utils import to_2tuple
|
||||
|
||||
|
||||
@dataclass
|
||||
class CLIPVisionCfg:
|
||||
layers: Union[Tuple[int, int, int, int], int] = 12
|
||||
width: int = 768
|
||||
head_width: int = 64
|
||||
mlp_ratio: float = 4.0
|
||||
patch_size: int = 16
|
||||
image_size: Union[Tuple[int, int], int] = 224
|
||||
ls_init_value: Optional[float] = None # layer scale initial value
|
||||
patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results
|
||||
input_patchnorm: bool = False # whether to use dual patchnorm - would only apply the input layernorm on each patch, as post-layernorm already exist in original clip vit design
|
||||
global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580)
|
||||
attentional_pool: bool = False # whether to use attentional pooler in the last embedding layer
|
||||
n_queries: int = 256 # n_queries for attentional pooler
|
||||
attn_pooler_heads: int = 8 # n heads for attentional_pooling
|
||||
timm_model_name: str = None # a valid model name overrides layers, width, patch_size
|
||||
timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model
|
||||
timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
|
||||
timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '')
|
||||
timm_proj_bias: bool = False # enable bias final projection
|
||||
timm_drop: float = 0. # head dropout
|
||||
timm_drop_path: Optional[float] = None # backbone stochastic depth
|
||||
output_tokens: bool = False
|
||||
|
||||
|
||||
@dataclass
|
||||
class CLIPTextCfg:
|
||||
context_length: int = 77
|
||||
vocab_size: int = 49408
|
||||
width: int = 512
|
||||
heads: int = 8
|
||||
layers: int = 12
|
||||
ls_init_value: Optional[float] = None # layer scale initial value
|
||||
hf_model_name: str = None
|
||||
hf_tokenizer_name: str = None
|
||||
hf_model_pretrained: bool = True
|
||||
proj: str = 'mlp'
|
||||
pooler_type: str = 'mean_pooler'
|
||||
embed_cls: bool = False
|
||||
pad_id: int = 0
|
||||
output_tokens: bool = False
|
||||
|
||||
|
||||
def get_cast_dtype(precision: str):
|
||||
cast_dtype = None
|
||||
if precision == 'bf16':
|
||||
cast_dtype = torch.bfloat16
|
||||
elif precision == 'fp16':
|
||||
cast_dtype = torch.float16
|
||||
return cast_dtype
|
||||
|
||||
|
||||
def _build_vision_tower(
|
||||
embed_dim: int,
|
||||
vision_cfg: CLIPVisionCfg,
|
||||
quick_gelu: bool = False,
|
||||
cast_dtype: Optional[torch.dtype] = None
|
||||
):
|
||||
if isinstance(vision_cfg, dict):
|
||||
vision_cfg = CLIPVisionCfg(**vision_cfg)
|
||||
|
||||
# OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more
|
||||
# memory efficient in recent PyTorch releases (>= 1.10).
|
||||
# NOTE: timm models always use native GELU regardless of quick_gelu flag.
|
||||
act_layer = QuickGELU if quick_gelu else nn.GELU
|
||||
|
||||
if vision_cfg.timm_model_name:
|
||||
visual = TimmModel(
|
||||
vision_cfg.timm_model_name,
|
||||
pretrained=vision_cfg.timm_model_pretrained,
|
||||
pool=vision_cfg.timm_pool,
|
||||
proj=vision_cfg.timm_proj,
|
||||
proj_bias=vision_cfg.timm_proj_bias,
|
||||
drop=vision_cfg.timm_drop,
|
||||
drop_path=vision_cfg.timm_drop_path,
|
||||
embed_dim=embed_dim,
|
||||
image_size=vision_cfg.image_size,
|
||||
)
|
||||
act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models
|
||||
elif isinstance(vision_cfg.layers, (tuple, list)):
|
||||
vision_heads = vision_cfg.width * 32 // vision_cfg.head_width
|
||||
visual = ModifiedResNet(
|
||||
layers=vision_cfg.layers,
|
||||
output_dim=embed_dim,
|
||||
heads=vision_heads,
|
||||
image_size=vision_cfg.image_size,
|
||||
width=vision_cfg.width,
|
||||
)
|
||||
else:
|
||||
vision_heads = vision_cfg.width // vision_cfg.head_width
|
||||
norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm
|
||||
visual = VisionTransformer(
|
||||
image_size=vision_cfg.image_size,
|
||||
patch_size=vision_cfg.patch_size,
|
||||
width=vision_cfg.width,
|
||||
layers=vision_cfg.layers,
|
||||
heads=vision_heads,
|
||||
mlp_ratio=vision_cfg.mlp_ratio,
|
||||
ls_init_value=vision_cfg.ls_init_value,
|
||||
patch_dropout=vision_cfg.patch_dropout,
|
||||
input_patchnorm=vision_cfg.input_patchnorm,
|
||||
global_average_pool=vision_cfg.global_average_pool,
|
||||
attentional_pool=vision_cfg.attentional_pool,
|
||||
n_queries=vision_cfg.n_queries,
|
||||
attn_pooler_heads=vision_cfg.attn_pooler_heads,
|
||||
output_tokens=vision_cfg.output_tokens,
|
||||
output_dim=embed_dim,
|
||||
act_layer=act_layer,
|
||||
norm_layer=norm_layer,
|
||||
)
|
||||
|
||||
return visual
|
||||
|
||||
|
||||
def _build_text_tower(
|
||||
embed_dim: int,
|
||||
text_cfg: CLIPTextCfg,
|
||||
quick_gelu: bool = False,
|
||||
cast_dtype: Optional[torch.dtype] = None,
|
||||
):
|
||||
if isinstance(text_cfg, dict):
|
||||
text_cfg = CLIPTextCfg(**text_cfg)
|
||||
|
||||
if text_cfg.hf_model_name:
|
||||
text = HFTextEncoder(
|
||||
text_cfg.hf_model_name,
|
||||
output_dim=embed_dim,
|
||||
proj=text_cfg.proj,
|
||||
pooler_type=text_cfg.pooler_type,
|
||||
pretrained=text_cfg.hf_model_pretrained,
|
||||
output_tokens=text_cfg.output_tokens,
|
||||
)
|
||||
else:
|
||||
act_layer = QuickGELU if quick_gelu else nn.GELU
|
||||
norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm
|
||||
|
||||
text = TextTransformer(
|
||||
context_length=text_cfg.context_length,
|
||||
vocab_size=text_cfg.vocab_size,
|
||||
width=text_cfg.width,
|
||||
heads=text_cfg.heads,
|
||||
layers=text_cfg.layers,
|
||||
ls_init_value=text_cfg.ls_init_value,
|
||||
output_dim=embed_dim,
|
||||
embed_cls=text_cfg.embed_cls,
|
||||
output_tokens=text_cfg.output_tokens,
|
||||
pad_id=text_cfg.pad_id,
|
||||
act_layer=act_layer,
|
||||
norm_layer=norm_layer,
|
||||
)
|
||||
return text
|
||||
|
||||
|
||||
class CLIP(nn.Module):
|
||||
output_dict: torch.jit.Final[bool]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
embed_dim: int,
|
||||
vision_cfg: CLIPVisionCfg,
|
||||
text_cfg: CLIPTextCfg,
|
||||
quick_gelu: bool = False,
|
||||
cast_dtype: Optional[torch.dtype] = None,
|
||||
output_dict: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
self.output_dict = output_dict
|
||||
self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype)
|
||||
|
||||
text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype)
|
||||
self.transformer = text.transformer
|
||||
self.vocab_size = text.vocab_size
|
||||
self.token_embedding = text.token_embedding
|
||||
self.positional_embedding = text.positional_embedding
|
||||
self.ln_final = text.ln_final
|
||||
self.text_projection = text.text_projection
|
||||
self.register_buffer('attn_mask', text.attn_mask, persistent=False)
|
||||
|
||||
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
|
||||
|
||||
def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
|
||||
# lock image tower as per LiT - https://arxiv.org/abs/2111.07991
|
||||
self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats)
|
||||
|
||||
def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):
|
||||
locked_layers = []
|
||||
locked_layers.append(self.token_embedding)
|
||||
self.positional_embedding.requires_grad = False
|
||||
if unlocked_layers > 0:
|
||||
locked_layers.append(self.transformer.resblocks[:-unlocked_layers])
|
||||
else:
|
||||
locked_layers.append(self.transformer)
|
||||
locked_layers.append(self.ln_final)
|
||||
self.text_projection.requires_grad = False
|
||||
|
||||
# freeze layers
|
||||
for module in locked_layers:
|
||||
for n, p in module.named_parameters():
|
||||
p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False
|
||||
|
||||
@torch.jit.ignore
|
||||
def set_grad_checkpointing(self, enable=True):
|
||||
self.visual.set_grad_checkpointing(enable)
|
||||
self.transformer.grad_checkpointing = enable
|
||||
|
||||
def encode_image(self, image, normalize: bool = False):
|
||||
features = self.visual(image)
|
||||
return F.normalize(features, dim=-1) if normalize else features
|
||||
|
||||
def encode_text(self, text, normalize: bool = False):
|
||||
cast_dtype = self.transformer.get_cast_dtype()
|
||||
|
||||
x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model]
|
||||
|
||||
x = x + self.positional_embedding.to(cast_dtype)
|
||||
x = x.permute(1, 0, 2) # NLD -> LND
|
||||
x = self.transformer(x, attn_mask=self.attn_mask)
|
||||
x = x.permute(1, 0, 2) # LND -> NLD
|
||||
x = self.ln_final(x) # [batch_size, n_ctx, transformer.width]
|
||||
# take features from the eot embedding (eot_token is the highest number in each sequence)
|
||||
x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
|
||||
return F.normalize(x, dim=-1) if normalize else x
|
||||
|
||||
def forward(self, image, text):
|
||||
image_features = self.encode_image(image, normalize=True)
|
||||
text_features = self.encode_text(text, normalize=True)
|
||||
if self.output_dict:
|
||||
return {
|
||||
"image_features": image_features,
|
||||
"text_features": text_features,
|
||||
"logit_scale": self.logit_scale.exp()
|
||||
}
|
||||
return image_features, text_features, self.logit_scale.exp()
|
||||
|
||||
|
||||
class CustomTextCLIP(nn.Module):
|
||||
output_dict: torch.jit.Final[bool]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
embed_dim: int,
|
||||
vision_cfg: CLIPVisionCfg,
|
||||
text_cfg: CLIPTextCfg,
|
||||
quick_gelu: bool = False,
|
||||
cast_dtype: Optional[torch.dtype] = None,
|
||||
output_dict: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
self.output_dict = output_dict
|
||||
self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype)
|
||||
self.text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype)
|
||||
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
|
||||
|
||||
def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False):
|
||||
# lock image tower as per LiT - https://arxiv.org/abs/2111.07991
|
||||
self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats)
|
||||
|
||||
def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):
|
||||
self.text.lock(unlocked_layers, freeze_layer_norm)
|
||||
|
||||
@torch.jit.ignore
|
||||
def set_grad_checkpointing(self, enable=True):
|
||||
self.visual.set_grad_checkpointing(enable)
|
||||
self.text.set_grad_checkpointing(enable)
|
||||
|
||||
def encode_image(self, image, normalize: bool = False):
|
||||
features = self.visual(image)
|
||||
return F.normalize(features, dim=-1) if normalize else features
|
||||
|
||||
def encode_text(self, text, normalize: bool = False):
|
||||
features = self.text(text)
|
||||
return F.normalize(features, dim=-1) if normalize else features
|
||||
|
||||
def forward(self, image, text):
|
||||
image_features = self.encode_image(image, normalize=True)
|
||||
text_features = self.encode_text(text, normalize=True)
|
||||
if self.output_dict:
|
||||
return {
|
||||
"image_features": image_features,
|
||||
"text_features": text_features,
|
||||
"logit_scale": self.logit_scale.exp()
|
||||
}
|
||||
return image_features, text_features, self.logit_scale.exp()
|
||||
|
||||
|
||||
def convert_weights_to_lp(model: nn.Module, dtype=torch.float16):
|
||||
"""Convert applicable model parameters to low-precision (bf16 or fp16)"""
|
||||
|
||||
def _convert_weights(l):
|
||||
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
|
||||
l.weight.data = l.weight.data.to(dtype)
|
||||
if l.bias is not None:
|
||||
l.bias.data = l.bias.data.to(dtype)
|
||||
|
||||
if isinstance(l, (nn.MultiheadAttention, Attention)):
|
||||
for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
|
||||
tensor = getattr(l, attr)
|
||||
if tensor is not None:
|
||||
tensor.data = tensor.data.to(dtype)
|
||||
|
||||
for name in ["text_projection", "proj"]:
|
||||
if hasattr(l, name):
|
||||
attr = getattr(l, name)
|
||||
if attr is not None:
|
||||
attr.data = attr.data.to(dtype)
|
||||
|
||||
model.apply(_convert_weights)
|
||||
|
||||
|
||||
convert_weights_to_fp16 = convert_weights_to_lp # backwards compat
|
||||
|
||||
|
||||
# used to maintain checkpoint compatibility
|
||||
def convert_to_custom_text_state_dict(state_dict: dict):
|
||||
if 'text_projection' in state_dict:
|
||||
# old format state_dict, move text tower -> .text
|
||||
new_state_dict = {}
|
||||
for k, v in state_dict.items():
|
||||
if any(k.startswith(p) for p in (
|
||||
'text_projection',
|
||||
'positional_embedding',
|
||||
'token_embedding',
|
||||
'transformer',
|
||||
'ln_final',
|
||||
)):
|
||||
k = 'text.' + k
|
||||
new_state_dict[k] = v
|
||||
return new_state_dict
|
||||
return state_dict
|
||||
|
||||
|
||||
def build_model_from_openai_state_dict(
|
||||
state_dict: dict,
|
||||
quick_gelu=True,
|
||||
cast_dtype=torch.float16,
|
||||
):
|
||||
vit = "visual.proj" in state_dict
|
||||
|
||||
if vit:
|
||||
vision_width = state_dict["visual.conv1.weight"].shape[0]
|
||||
vision_layers = len(
|
||||
[k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
|
||||
vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
|
||||
grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
|
||||
image_size = vision_patch_size * grid_size
|
||||
else:
|
||||
counts: list = [
|
||||
len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
|
||||
vision_layers = tuple(counts)
|
||||
vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
|
||||
output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
|
||||
vision_patch_size = None
|
||||
assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
|
||||
image_size = output_width * 32
|
||||
|
||||
embed_dim = state_dict["text_projection"].shape[1]
|
||||
context_length = state_dict["positional_embedding"].shape[0]
|
||||
vocab_size = state_dict["token_embedding.weight"].shape[0]
|
||||
transformer_width = state_dict["ln_final.weight"].shape[0]
|
||||
transformer_heads = transformer_width // 64
|
||||
transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
|
||||
|
||||
vision_cfg = CLIPVisionCfg(
|
||||
layers=vision_layers,
|
||||
width=vision_width,
|
||||
patch_size=vision_patch_size,
|
||||
image_size=image_size,
|
||||
)
|
||||
text_cfg = CLIPTextCfg(
|
||||
context_length=context_length,
|
||||
vocab_size=vocab_size,
|
||||
width=transformer_width,
|
||||
heads=transformer_heads,
|
||||
layers=transformer_layers,
|
||||
)
|
||||
model = CLIP(
|
||||
embed_dim,
|
||||
vision_cfg=vision_cfg,
|
||||
text_cfg=text_cfg,
|
||||
quick_gelu=quick_gelu, # OpenAI models were trained with QuickGELU
|
||||
cast_dtype=cast_dtype,
|
||||
)
|
||||
|
||||
for key in ["input_resolution", "context_length", "vocab_size"]:
|
||||
state_dict.pop(key, None)
|
||||
|
||||
convert_weights_to_fp16(model) # OpenAI state dicts are partially converted to float16
|
||||
model.load_state_dict(state_dict)
|
||||
return model.eval()
|
||||
|
||||
|
||||
def trace_model(model, batch_size=256, device=torch.device('cpu')):
|
||||
model.eval()
|
||||
image_size = model.visual.image_size
|
||||
example_images = torch.ones((batch_size, 3, image_size, image_size), device=device)
|
||||
example_text = torch.zeros((batch_size, model.context_length), dtype=torch.int, device=device)
|
||||
model = torch.jit.trace_module(
|
||||
model,
|
||||
inputs=dict(
|
||||
forward=(example_images, example_text),
|
||||
encode_text=(example_text,),
|
||||
encode_image=(example_images,)
|
||||
))
|
||||
model.visual.image_size = image_size
|
||||
return model
|
||||
|
||||
|
||||
def resize_pos_embed(state_dict, model, interpolation: str = 'bicubic', antialias: bool = True):
|
||||
# Rescale the grid of position embeddings when loading from state_dict
|
||||
old_pos_embed = state_dict.get('visual.positional_embedding', None)
|
||||
if old_pos_embed is None or not hasattr(model.visual, 'grid_size'):
|
||||
return
|
||||
grid_size = to_2tuple(model.visual.grid_size)
|
||||
extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more)
|
||||
new_seq_len = grid_size[0] * grid_size[1] + extra_tokens
|
||||
if new_seq_len == old_pos_embed.shape[0]:
|
||||
return
|
||||
|
||||
if extra_tokens:
|
||||
pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:]
|
||||
else:
|
||||
pos_emb_tok, pos_emb_img = None, old_pos_embed
|
||||
old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img))))
|
||||
|
||||
logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size)
|
||||
pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2)
|
||||
pos_emb_img = F.interpolate(
|
||||
pos_emb_img,
|
||||
size=grid_size,
|
||||
mode=interpolation,
|
||||
antialias=antialias,
|
||||
align_corners=False,
|
||||
)
|
||||
pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0]
|
||||
if pos_emb_tok is not None:
|
||||
new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0)
|
||||
else:
|
||||
new_pos_embed = pos_emb_img
|
||||
state_dict['visual.positional_embedding'] = new_pos_embed
|
||||
@@ -1,17 +0,0 @@
|
||||
{
|
||||
"embed_dim": 1024,
|
||||
"vision_cfg": {
|
||||
"image_size": 224,
|
||||
"layers": 32,
|
||||
"width": 1280,
|
||||
"head_width": 80,
|
||||
"patch_size": 14
|
||||
},
|
||||
"text_cfg": {
|
||||
"context_length": 77,
|
||||
"vocab_size": 49408,
|
||||
"width": 1024,
|
||||
"heads": 16,
|
||||
"layers": 24
|
||||
}
|
||||
}
|
||||
@@ -1,181 +0,0 @@
|
||||
from collections import OrderedDict
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from .utils import freeze_batch_norm_2d
|
||||
|
||||
|
||||
class Bottleneck(nn.Module):
|
||||
expansion = 4
|
||||
|
||||
def __init__(self, inplanes, planes, stride=1):
|
||||
super().__init__()
|
||||
|
||||
# all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
|
||||
self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(planes)
|
||||
self.act1 = nn.ReLU(inplace=True)
|
||||
|
||||
self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
|
||||
self.bn2 = nn.BatchNorm2d(planes)
|
||||
self.act2 = nn.ReLU(inplace=True)
|
||||
|
||||
self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
|
||||
|
||||
self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
|
||||
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
|
||||
self.act3 = nn.ReLU(inplace=True)
|
||||
|
||||
self.downsample = None
|
||||
self.stride = stride
|
||||
|
||||
if stride > 1 or inplanes != planes * Bottleneck.expansion:
|
||||
# downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
|
||||
self.downsample = nn.Sequential(OrderedDict([
|
||||
("-1", nn.AvgPool2d(stride)),
|
||||
("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
|
||||
("1", nn.BatchNorm2d(planes * self.expansion))
|
||||
]))
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
identity = x
|
||||
|
||||
out = self.act1(self.bn1(self.conv1(x)))
|
||||
out = self.act2(self.bn2(self.conv2(out)))
|
||||
out = self.avgpool(out)
|
||||
out = self.bn3(self.conv3(out))
|
||||
|
||||
if self.downsample is not None:
|
||||
identity = self.downsample(x)
|
||||
|
||||
out += identity
|
||||
out = self.act3(out)
|
||||
return out
|
||||
|
||||
|
||||
class AttentionPool2d(nn.Module):
|
||||
def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
|
||||
super().__init__()
|
||||
self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
|
||||
self.k_proj = nn.Linear(embed_dim, embed_dim)
|
||||
self.q_proj = nn.Linear(embed_dim, embed_dim)
|
||||
self.v_proj = nn.Linear(embed_dim, embed_dim)
|
||||
self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
|
||||
self.num_heads = num_heads
|
||||
|
||||
def forward(self, x):
|
||||
x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC
|
||||
x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC
|
||||
x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC
|
||||
x, _ = F.multi_head_attention_forward(
|
||||
query=x, key=x, value=x,
|
||||
embed_dim_to_check=x.shape[-1],
|
||||
num_heads=self.num_heads,
|
||||
q_proj_weight=self.q_proj.weight,
|
||||
k_proj_weight=self.k_proj.weight,
|
||||
v_proj_weight=self.v_proj.weight,
|
||||
in_proj_weight=None,
|
||||
in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
|
||||
bias_k=None,
|
||||
bias_v=None,
|
||||
add_zero_attn=False,
|
||||
dropout_p=0.,
|
||||
out_proj_weight=self.c_proj.weight,
|
||||
out_proj_bias=self.c_proj.bias,
|
||||
use_separate_proj_weight=True,
|
||||
training=self.training,
|
||||
need_weights=False
|
||||
)
|
||||
|
||||
return x[0]
|
||||
|
||||
|
||||
class ModifiedResNet(nn.Module):
|
||||
"""
|
||||
A ResNet class that is similar to torchvision's but contains the following changes:
|
||||
- There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
|
||||
- Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
|
||||
- The final pooling layer is a QKV attention instead of an average pool
|
||||
"""
|
||||
|
||||
def __init__(self, layers, output_dim, heads, image_size=224, width=64):
|
||||
super().__init__()
|
||||
self.output_dim = output_dim
|
||||
self.image_size = image_size
|
||||
|
||||
# the 3-layer stem
|
||||
self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(width // 2)
|
||||
self.act1 = nn.ReLU(inplace=True)
|
||||
self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
|
||||
self.bn2 = nn.BatchNorm2d(width // 2)
|
||||
self.act2 = nn.ReLU(inplace=True)
|
||||
self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
|
||||
self.bn3 = nn.BatchNorm2d(width)
|
||||
self.act3 = nn.ReLU(inplace=True)
|
||||
self.avgpool = nn.AvgPool2d(2)
|
||||
|
||||
# residual layers
|
||||
self._inplanes = width # this is a *mutable* variable used during construction
|
||||
self.layer1 = self._make_layer(width, layers[0])
|
||||
self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
|
||||
self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
|
||||
self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
|
||||
|
||||
embed_dim = width * 32 # the ResNet feature dimension
|
||||
self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim)
|
||||
|
||||
self.init_parameters()
|
||||
|
||||
def _make_layer(self, planes, blocks, stride=1):
|
||||
layers = [Bottleneck(self._inplanes, planes, stride)]
|
||||
|
||||
self._inplanes = planes * Bottleneck.expansion
|
||||
for _ in range(1, blocks):
|
||||
layers.append(Bottleneck(self._inplanes, planes))
|
||||
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
def init_parameters(self):
|
||||
if self.attnpool is not None:
|
||||
std = self.attnpool.c_proj.in_features ** -0.5
|
||||
nn.init.normal_(self.attnpool.q_proj.weight, std=std)
|
||||
nn.init.normal_(self.attnpool.k_proj.weight, std=std)
|
||||
nn.init.normal_(self.attnpool.v_proj.weight, std=std)
|
||||
nn.init.normal_(self.attnpool.c_proj.weight, std=std)
|
||||
|
||||
for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:
|
||||
for name, param in resnet_block.named_parameters():
|
||||
if name.endswith("bn3.weight"):
|
||||
nn.init.zeros_(param)
|
||||
|
||||
def lock(self, unlocked_groups=0, freeze_bn_stats=False):
|
||||
assert unlocked_groups == 0, 'partial locking not currently supported for this model'
|
||||
for param in self.parameters():
|
||||
param.requires_grad = False
|
||||
if freeze_bn_stats:
|
||||
freeze_batch_norm_2d(self)
|
||||
|
||||
@torch.jit.ignore
|
||||
def set_grad_checkpointing(self, enable=True):
|
||||
# FIXME support for non-transformer
|
||||
pass
|
||||
|
||||
def stem(self, x):
|
||||
x = self.act1(self.bn1(self.conv1(x)))
|
||||
x = self.act2(self.bn2(self.conv2(x)))
|
||||
x = self.act3(self.bn3(self.conv3(x)))
|
||||
x = self.avgpool(x)
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
x = self.stem(x)
|
||||
x = self.layer1(x)
|
||||
x = self.layer2(x)
|
||||
x = self.layer3(x)
|
||||
x = self.layer4(x)
|
||||
x = self.attnpool(x)
|
||||
|
||||
return x
|
||||
@@ -1,144 +0,0 @@
|
||||
""" OpenAI pretrained model functions
|
||||
|
||||
Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
|
||||
"""
|
||||
|
||||
import os
|
||||
import warnings
|
||||
from typing import List, Optional, Union
|
||||
|
||||
import torch
|
||||
|
||||
from .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype
|
||||
from .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url
|
||||
|
||||
__all__ = ["list_openai_models", "load_openai_model"]
|
||||
|
||||
|
||||
def list_openai_models() -> List[str]:
|
||||
"""Returns the names of available CLIP models"""
|
||||
return list_pretrained_models_by_tag('openai')
|
||||
|
||||
|
||||
def load_openai_model(
|
||||
name: str,
|
||||
precision: Optional[str] = None,
|
||||
device: Optional[Union[str, torch.device]] = None,
|
||||
jit: bool = True,
|
||||
cache_dir: Optional[str] = None,
|
||||
):
|
||||
"""Load a CLIP model
|
||||
|
||||
Parameters
|
||||
----------
|
||||
name : str
|
||||
A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
|
||||
precision: str
|
||||
Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'.
|
||||
device : Union[str, torch.device]
|
||||
The device to put the loaded model
|
||||
jit : bool
|
||||
Whether to load the optimized JIT model (default) or more hackable non-JIT model.
|
||||
cache_dir : Optional[str]
|
||||
The directory to cache the downloaded model weights
|
||||
|
||||
Returns
|
||||
-------
|
||||
model : torch.nn.Module
|
||||
The CLIP model
|
||||
preprocess : Callable[[PIL.Image], torch.Tensor]
|
||||
A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
|
||||
"""
|
||||
if device is None:
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
if precision is None:
|
||||
precision = 'fp32' if device == 'cpu' else 'fp16'
|
||||
|
||||
if get_pretrained_url(name, 'openai'):
|
||||
model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir)
|
||||
elif os.path.isfile(name):
|
||||
model_path = name
|
||||
else:
|
||||
raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}")
|
||||
|
||||
try:
|
||||
# loading JIT archive
|
||||
model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
|
||||
state_dict = None
|
||||
except RuntimeError:
|
||||
# loading saved state dict
|
||||
if jit:
|
||||
warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
|
||||
jit = False
|
||||
state_dict = torch.load(model_path, map_location="cpu")
|
||||
|
||||
if not jit:
|
||||
# Build a non-jit model from the OpenAI jitted model state dict
|
||||
cast_dtype = get_cast_dtype(precision)
|
||||
try:
|
||||
model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype)
|
||||
except KeyError:
|
||||
sd = {k[7:]: v for k, v in state_dict["state_dict"].items()}
|
||||
model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype)
|
||||
|
||||
# model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use
|
||||
model = model.to(device)
|
||||
if precision.startswith('amp') or precision == 'fp32':
|
||||
model.float()
|
||||
elif precision == 'bf16':
|
||||
convert_weights_to_lp(model, dtype=torch.bfloat16)
|
||||
|
||||
return model
|
||||
|
||||
# patch the device names
|
||||
device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
|
||||
device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]
|
||||
|
||||
def patch_device(module):
|
||||
try:
|
||||
graphs = [module.graph] if hasattr(module, "graph") else []
|
||||
except RuntimeError:
|
||||
graphs = []
|
||||
|
||||
if hasattr(module, "forward1"):
|
||||
graphs.append(module.forward1.graph)
|
||||
|
||||
for graph in graphs:
|
||||
for node in graph.findAllNodes("prim::Constant"):
|
||||
if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"):
|
||||
node.copyAttributes(device_node)
|
||||
|
||||
model.apply(patch_device)
|
||||
patch_device(model.encode_image)
|
||||
patch_device(model.encode_text)
|
||||
|
||||
# patch dtype to float32 (typically for CPU)
|
||||
if precision == 'fp32':
|
||||
float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
|
||||
float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
|
||||
float_node = float_input.node()
|
||||
|
||||
def patch_float(module):
|
||||
try:
|
||||
graphs = [module.graph] if hasattr(module, "graph") else []
|
||||
except RuntimeError:
|
||||
graphs = []
|
||||
|
||||
if hasattr(module, "forward1"):
|
||||
graphs.append(module.forward1.graph)
|
||||
|
||||
for graph in graphs:
|
||||
for node in graph.findAllNodes("aten::to"):
|
||||
inputs = list(node.inputs())
|
||||
for i in [1, 2]: # dtype can be the second or third argument to aten::to()
|
||||
if inputs[i].node()["value"] == 5:
|
||||
inputs[i].node().copyAttributes(float_node)
|
||||
|
||||
model.apply(patch_float)
|
||||
patch_float(model.encode_image)
|
||||
patch_float(model.encode_text)
|
||||
model.float()
|
||||
|
||||
# ensure image_size attr available at consistent location for both jit and non-jit
|
||||
model.visual.image_size = model.input_resolution.item()
|
||||
return model
|
||||
@@ -1,376 +0,0 @@
|
||||
import hashlib
|
||||
import os
|
||||
import urllib
|
||||
import warnings
|
||||
from functools import partial
|
||||
from typing import Dict, Union
|
||||
|
||||
from tqdm import tqdm
|
||||
|
||||
from .version import __version__
|
||||
|
||||
try:
|
||||
from huggingface_hub import hf_hub_download
|
||||
hf_hub_download = partial(hf_hub_download, library_name="open_clip", library_version=__version__)
|
||||
_has_hf_hub = True
|
||||
except ImportError:
|
||||
hf_hub_download = None
|
||||
_has_hf_hub = False
|
||||
|
||||
|
||||
def _pcfg(url='', hf_hub='', mean=None, std=None):
|
||||
return dict(
|
||||
url=url,
|
||||
hf_hub=hf_hub,
|
||||
mean=mean,
|
||||
std=std,
|
||||
)
|
||||
|
||||
|
||||
_RN50 = dict(
|
||||
openai=_pcfg(
|
||||
"https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"),
|
||||
yfcc15m=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"),
|
||||
cc12m=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"),
|
||||
)
|
||||
|
||||
_RN50_quickgelu = dict(
|
||||
openai=_pcfg(
|
||||
"https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"),
|
||||
yfcc15m=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"),
|
||||
cc12m=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"),
|
||||
)
|
||||
|
||||
_RN101 = dict(
|
||||
openai=_pcfg(
|
||||
"https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"),
|
||||
yfcc15m=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"),
|
||||
)
|
||||
|
||||
_RN101_quickgelu = dict(
|
||||
openai=_pcfg(
|
||||
"https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"),
|
||||
yfcc15m=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"),
|
||||
)
|
||||
|
||||
_RN50x4 = dict(
|
||||
openai=_pcfg(
|
||||
"https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt"),
|
||||
)
|
||||
|
||||
_RN50x16 = dict(
|
||||
openai=_pcfg(
|
||||
"https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt"),
|
||||
)
|
||||
|
||||
_RN50x64 = dict(
|
||||
openai=_pcfg(
|
||||
"https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt"),
|
||||
)
|
||||
|
||||
_VITB32 = dict(
|
||||
openai=_pcfg(
|
||||
"https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"),
|
||||
laion400m_e31=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"),
|
||||
laion400m_e32=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"),
|
||||
laion2b_e16=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"),
|
||||
laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/')
|
||||
)
|
||||
|
||||
_VITB32_quickgelu = dict(
|
||||
openai=_pcfg(
|
||||
"https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"),
|
||||
laion400m_e31=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"),
|
||||
laion400m_e32=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"),
|
||||
)
|
||||
|
||||
_VITB16 = dict(
|
||||
openai=_pcfg(
|
||||
"https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"),
|
||||
laion400m_e31=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"),
|
||||
laion400m_e32=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"),
|
||||
# laion400m_32k=_pcfg(
|
||||
# url="",
|
||||
# mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
|
||||
# laion400m_64k=_pcfg(
|
||||
# url="",
|
||||
# mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
|
||||
laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'),
|
||||
)
|
||||
|
||||
_VITB16_PLUS_240 = dict(
|
||||
laion400m_e31=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"),
|
||||
laion400m_e32=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"),
|
||||
)
|
||||
|
||||
_VITL14 = dict(
|
||||
openai=_pcfg(
|
||||
"https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"),
|
||||
laion400m_e31=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"),
|
||||
laion400m_e32=_pcfg(
|
||||
"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"),
|
||||
laion2b_s32b_b82k=_pcfg(
|
||||
hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/',
|
||||
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
|
||||
)
|
||||
|
||||
_VITL14_336 = dict(
|
||||
openai=_pcfg(
|
||||
"https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"),
|
||||
)
|
||||
|
||||
_VITH14 = dict(
|
||||
laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'),
|
||||
)
|
||||
|
||||
_VITg14 = dict(
|
||||
laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'),
|
||||
laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s34B-b88K/'),
|
||||
)
|
||||
|
||||
_VITbigG14 = dict(
|
||||
laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'),
|
||||
)
|
||||
|
||||
_robertaViTB32 = dict(
|
||||
laion2b_s12b_b32k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k/'),
|
||||
)
|
||||
|
||||
_xlmRobertaBaseViTB32 = dict(
|
||||
laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k/'),
|
||||
)
|
||||
|
||||
_xlmRobertaLargeFrozenViTH14 = dict(
|
||||
frozen_laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k/'),
|
||||
)
|
||||
|
||||
_convnext_base = dict(
|
||||
laion400m_s13b_b51k=_pcfg(hf_hub='laion/CLIP-convnext_base-laion400M-s13B-b51K/'),
|
||||
)
|
||||
|
||||
_convnext_base_w = dict(
|
||||
laion2b_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion2B-s13B-b82K/'),
|
||||
laion2b_s13b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg/'),
|
||||
laion_aesthetic_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K/'),
|
||||
)
|
||||
|
||||
_convnext_base_w_320 = dict(
|
||||
laion_aesthetic_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K/'),
|
||||
laion_aesthetic_s13b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K-augreg/'),
|
||||
)
|
||||
|
||||
_convnext_large_d = dict(
|
||||
laion2b_s26b_b102k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg/'),
|
||||
)
|
||||
|
||||
_convnext_large_d_320 = dict(
|
||||
laion2b_s29b_b131k_ft=_pcfg(hf_hub='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft/'),
|
||||
laion2b_s29b_b131k_ft_soup=_pcfg(hf_hub='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup/'),
|
||||
)
|
||||
|
||||
_convnext_xxlarge = dict(
|
||||
laion2b_s34b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg/'),
|
||||
laion2b_s34b_b82k_augreg_rewind=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-rewind/'),
|
||||
laion2b_s34b_b82k_augreg_soup=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup/'),
|
||||
)
|
||||
|
||||
_coca_VITB32 = dict(
|
||||
laion2b_s13b_b90k=_pcfg(hf_hub='laion/CoCa-ViT-B-32-laion2B-s13B-b90k/'),
|
||||
mscoco_finetuned_laion2b_s13b_b90k=_pcfg(hf_hub='laion/mscoco_finetuned_CoCa-ViT-B-32-laion2B-s13B-b90k/')
|
||||
)
|
||||
|
||||
_coca_VITL14 = dict(
|
||||
laion2b_s13b_b90k=_pcfg(hf_hub='laion/CoCa-ViT-L-14-laion2B-s13B-b90k/'),
|
||||
mscoco_finetuned_laion2b_s13b_b90k=_pcfg(hf_hub='laion/mscoco_finetuned_CoCa-ViT-L-14-laion2B-s13B-b90k/')
|
||||
)
|
||||
|
||||
|
||||
_PRETRAINED = {
|
||||
"RN50": _RN50,
|
||||
"RN50-quickgelu": _RN50_quickgelu,
|
||||
"RN101": _RN101,
|
||||
"RN101-quickgelu": _RN101_quickgelu,
|
||||
"RN50x4": _RN50x4,
|
||||
"RN50x16": _RN50x16,
|
||||
"RN50x64": _RN50x64,
|
||||
"ViT-B-32": _VITB32,
|
||||
"ViT-B-32-quickgelu": _VITB32_quickgelu,
|
||||
"ViT-B-16": _VITB16,
|
||||
"ViT-B-16-plus-240": _VITB16_PLUS_240,
|
||||
"ViT-L-14": _VITL14,
|
||||
"ViT-L-14-336": _VITL14_336,
|
||||
"ViT-H-14": _VITH14,
|
||||
"ViT-g-14": _VITg14,
|
||||
"ViT-bigG-14": _VITbigG14,
|
||||
"roberta-ViT-B-32": _robertaViTB32,
|
||||
"xlm-roberta-base-ViT-B-32": _xlmRobertaBaseViTB32,
|
||||
"xlm-roberta-large-ViT-H-14": _xlmRobertaLargeFrozenViTH14,
|
||||
"convnext_base": _convnext_base,
|
||||
"convnext_base_w": _convnext_base_w,
|
||||
"convnext_base_w_320": _convnext_base_w_320,
|
||||
"convnext_large_d": _convnext_large_d,
|
||||
"convnext_large_d_320": _convnext_large_d_320,
|
||||
"convnext_xxlarge": _convnext_xxlarge,
|
||||
"coca_ViT-B-32": _coca_VITB32,
|
||||
"coca_ViT-L-14": _coca_VITL14,
|
||||
}
|
||||
|
||||
|
||||
def _clean_tag(tag: str):
|
||||
# normalize pretrained tags
|
||||
return tag.lower().replace('-', '_')
|
||||
|
||||
|
||||
def list_pretrained(as_str: bool = False):
|
||||
""" returns list of pretrained models
|
||||
Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True
|
||||
"""
|
||||
return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()]
|
||||
|
||||
|
||||
def list_pretrained_models_by_tag(tag: str):
|
||||
""" return all models having the specified pretrain tag """
|
||||
models = []
|
||||
tag = _clean_tag(tag)
|
||||
for k in _PRETRAINED.keys():
|
||||
if tag in _PRETRAINED[k]:
|
||||
models.append(k)
|
||||
return models
|
||||
|
||||
|
||||
def list_pretrained_tags_by_model(model: str):
|
||||
""" return all pretrain tags for the specified model architecture """
|
||||
tags = []
|
||||
if model in _PRETRAINED:
|
||||
tags.extend(_PRETRAINED[model].keys())
|
||||
return tags
|
||||
|
||||
|
||||
def is_pretrained_cfg(model: str, tag: str):
|
||||
if model not in _PRETRAINED:
|
||||
return False
|
||||
return _clean_tag(tag) in _PRETRAINED[model]
|
||||
|
||||
|
||||
def get_pretrained_cfg(model: str, tag: str):
|
||||
if model not in _PRETRAINED:
|
||||
return {}
|
||||
model_pretrained = _PRETRAINED[model]
|
||||
return model_pretrained.get(_clean_tag(tag), {})
|
||||
|
||||
|
||||
def get_pretrained_url(model: str, tag: str):
|
||||
cfg = get_pretrained_cfg(model, _clean_tag(tag))
|
||||
return cfg.get('url', '')
|
||||
|
||||
|
||||
def download_pretrained_from_url(
|
||||
url: str,
|
||||
cache_dir: Union[str, None] = None,
|
||||
):
|
||||
if not cache_dir:
|
||||
cache_dir = os.path.expanduser("~/.cache/clip")
|
||||
os.makedirs(cache_dir, exist_ok=True)
|
||||
filename = os.path.basename(url)
|
||||
|
||||
if 'openaipublic' in url:
|
||||
expected_sha256 = url.split("/")[-2]
|
||||
elif 'mlfoundations' in url:
|
||||
expected_sha256 = os.path.splitext(filename)[0].split("-")[-1]
|
||||
else:
|
||||
expected_sha256 = ''
|
||||
|
||||
download_target = os.path.join(cache_dir, filename)
|
||||
|
||||
if os.path.exists(download_target) and not os.path.isfile(download_target):
|
||||
raise RuntimeError(f"{download_target} exists and is not a regular file")
|
||||
|
||||
if os.path.isfile(download_target):
|
||||
if expected_sha256:
|
||||
if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256):
|
||||
return download_target
|
||||
else:
|
||||
warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
|
||||
else:
|
||||
return download_target
|
||||
|
||||
with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
|
||||
with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop:
|
||||
while True:
|
||||
buffer = source.read(8192)
|
||||
if not buffer:
|
||||
break
|
||||
|
||||
output.write(buffer)
|
||||
loop.update(len(buffer))
|
||||
|
||||
if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256):
|
||||
raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match")
|
||||
|
||||
return download_target
|
||||
|
||||
|
||||
def has_hf_hub(necessary=False):
|
||||
if not _has_hf_hub and necessary:
|
||||
# if no HF Hub module installed, and it is necessary to continue, raise error
|
||||
raise RuntimeError(
|
||||
'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.')
|
||||
return _has_hf_hub
|
||||
|
||||
|
||||
def download_pretrained_from_hf(
|
||||
model_id: str,
|
||||
filename: str = 'open_clip_pytorch_model.bin',
|
||||
revision=None,
|
||||
cache_dir: Union[str, None] = None,
|
||||
):
|
||||
has_hf_hub(True)
|
||||
cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir)
|
||||
return cached_file
|
||||
|
||||
|
||||
def download_pretrained(
|
||||
cfg: Dict,
|
||||
force_hf_hub: bool = False,
|
||||
cache_dir: Union[str, None] = None,
|
||||
):
|
||||
target = ''
|
||||
if not cfg:
|
||||
return target
|
||||
|
||||
download_url = cfg.get('url', '')
|
||||
download_hf_hub = cfg.get('hf_hub', '')
|
||||
if download_hf_hub and force_hf_hub:
|
||||
# use HF hub even if url exists
|
||||
download_url = ''
|
||||
|
||||
if download_url:
|
||||
target = download_pretrained_from_url(download_url, cache_dir=cache_dir)
|
||||
elif download_hf_hub:
|
||||
has_hf_hub(True)
|
||||
# we assume the hf_hub entries in pretrained config combine model_id + filename in
|
||||
# 'org/model_name/filename.pt' form. To specify just the model id w/o filename and
|
||||
# use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'.
|
||||
model_id, filename = os.path.split(download_hf_hub)
|
||||
if filename:
|
||||
target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir)
|
||||
else:
|
||||
target = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
|
||||
|
||||
return target
|
||||
@@ -1,243 +0,0 @@
|
||||
import argparse
|
||||
import json
|
||||
from pathlib import Path
|
||||
from tempfile import TemporaryDirectory
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import torch
|
||||
|
||||
try:
|
||||
from huggingface_hub import (
|
||||
create_repo,
|
||||
get_hf_file_metadata,
|
||||
hf_hub_download,
|
||||
hf_hub_url,
|
||||
repo_type_and_id_from_hf_id,
|
||||
upload_folder,
|
||||
)
|
||||
from huggingface_hub.utils import EntryNotFoundError
|
||||
_has_hf_hub = True
|
||||
except ImportError:
|
||||
_has_hf_hub = False
|
||||
|
||||
from .factory import create_model_from_pretrained, get_model_config, get_tokenizer
|
||||
from .tokenizer import HFTokenizer
|
||||
|
||||
|
||||
def save_config_for_hf(
|
||||
model,
|
||||
config_path: str,
|
||||
model_config: Optional[dict]
|
||||
):
|
||||
preprocess_cfg = {
|
||||
'mean': model.visual.image_mean,
|
||||
'std': model.visual.image_std,
|
||||
}
|
||||
hf_config = {
|
||||
'model_cfg': model_config,
|
||||
'preprocess_cfg': preprocess_cfg,
|
||||
}
|
||||
|
||||
with config_path.open('w') as f:
|
||||
json.dump(hf_config, f, indent=2)
|
||||
|
||||
|
||||
def save_for_hf(
|
||||
model,
|
||||
tokenizer: HFTokenizer,
|
||||
model_config: dict,
|
||||
save_directory: str,
|
||||
weights_filename='open_clip_pytorch_model.bin',
|
||||
config_filename='open_clip_config.json',
|
||||
):
|
||||
save_directory = Path(save_directory)
|
||||
save_directory.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
weights_path = save_directory / weights_filename
|
||||
torch.save(model.state_dict(), weights_path)
|
||||
|
||||
tokenizer.save_pretrained(save_directory)
|
||||
|
||||
config_path = save_directory / config_filename
|
||||
save_config_for_hf(model, config_path, model_config=model_config)
|
||||
|
||||
|
||||
def push_to_hf_hub(
|
||||
model,
|
||||
tokenizer,
|
||||
model_config: Optional[dict],
|
||||
repo_id: str,
|
||||
commit_message: str = 'Add model',
|
||||
token: Optional[str] = None,
|
||||
revision: Optional[str] = None,
|
||||
private: bool = False,
|
||||
create_pr: bool = False,
|
||||
model_card: Optional[dict] = None,
|
||||
):
|
||||
if not isinstance(tokenizer, HFTokenizer):
|
||||
# default CLIP tokenizers use https://huggingface.co/openai/clip-vit-large-patch14
|
||||
tokenizer = HFTokenizer('openai/clip-vit-large-patch14')
|
||||
|
||||
# Create repo if it doesn't exist yet
|
||||
repo_url = create_repo(repo_id, token=token, private=private, exist_ok=True)
|
||||
|
||||
# Infer complete repo_id from repo_url
|
||||
# Can be different from the input `repo_id` if repo_owner was implicit
|
||||
_, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url)
|
||||
repo_id = f"{repo_owner}/{repo_name}"
|
||||
|
||||
# Check if README file already exist in repo
|
||||
try:
|
||||
get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename="README.md", revision=revision))
|
||||
has_readme = True
|
||||
except EntryNotFoundError:
|
||||
has_readme = False
|
||||
|
||||
# Dump model and push to Hub
|
||||
with TemporaryDirectory() as tmpdir:
|
||||
# Save model weights and config.
|
||||
save_for_hf(
|
||||
model,
|
||||
tokenizer=tokenizer,
|
||||
model_config=model_config,
|
||||
save_directory=tmpdir,
|
||||
)
|
||||
|
||||
# Add readme if it does not exist
|
||||
if not has_readme:
|
||||
model_card = model_card or {}
|
||||
model_name = repo_id.split('/')[-1]
|
||||
readme_path = Path(tmpdir) / "README.md"
|
||||
readme_text = generate_readme(model_card, model_name)
|
||||
readme_path.write_text(readme_text)
|
||||
|
||||
# Upload model and return
|
||||
return upload_folder(
|
||||
repo_id=repo_id,
|
||||
folder_path=tmpdir,
|
||||
revision=revision,
|
||||
create_pr=create_pr,
|
||||
commit_message=commit_message,
|
||||
)
|
||||
|
||||
|
||||
def push_pretrained_to_hf_hub(
|
||||
model_name,
|
||||
pretrained: str,
|
||||
repo_id: str,
|
||||
image_mean: Optional[Tuple[float, ...]] = None,
|
||||
image_std: Optional[Tuple[float, ...]] = None,
|
||||
commit_message: str = 'Add model',
|
||||
token: Optional[str] = None,
|
||||
revision: Optional[str] = None,
|
||||
private: bool = False,
|
||||
create_pr: bool = False,
|
||||
model_card: Optional[dict] = None,
|
||||
):
|
||||
model, preprocess_eval = create_model_from_pretrained(
|
||||
model_name,
|
||||
pretrained=pretrained,
|
||||
image_mean=image_mean,
|
||||
image_std=image_std,
|
||||
)
|
||||
|
||||
model_config = get_model_config(model_name)
|
||||
assert model_config
|
||||
|
||||
tokenizer = get_tokenizer(model_name)
|
||||
|
||||
push_to_hf_hub(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
model_config=model_config,
|
||||
repo_id=repo_id,
|
||||
commit_message=commit_message,
|
||||
token=token,
|
||||
revision=revision,
|
||||
private=private,
|
||||
create_pr=create_pr,
|
||||
model_card=model_card,
|
||||
)
|
||||
|
||||
|
||||
def generate_readme(model_card: dict, model_name: str):
|
||||
readme_text = "---\n"
|
||||
readme_text += "tags:\n- zero-shot-image-classification\n- clip\n"
|
||||
readme_text += "library_tag: open_clip\n"
|
||||
readme_text += f"license: {model_card.get('license', 'mit')}\n"
|
||||
if 'details' in model_card and 'Dataset' in model_card['details']:
|
||||
readme_text += 'datasets:\n'
|
||||
readme_text += f"- {model_card['details']['Dataset'].lower()}\n"
|
||||
readme_text += "---\n"
|
||||
readme_text += f"# Model card for {model_name}\n"
|
||||
if 'description' in model_card:
|
||||
readme_text += f"\n{model_card['description']}\n"
|
||||
if 'details' in model_card:
|
||||
readme_text += f"\n## Model Details\n"
|
||||
for k, v in model_card['details'].items():
|
||||
if isinstance(v, (list, tuple)):
|
||||
readme_text += f"- **{k}:**\n"
|
||||
for vi in v:
|
||||
readme_text += f" - {vi}\n"
|
||||
elif isinstance(v, dict):
|
||||
readme_text += f"- **{k}:**\n"
|
||||
for ki, vi in v.items():
|
||||
readme_text += f" - {ki}: {vi}\n"
|
||||
else:
|
||||
readme_text += f"- **{k}:** {v}\n"
|
||||
if 'usage' in model_card:
|
||||
readme_text += f"\n## Model Usage\n"
|
||||
readme_text += model_card['usage']
|
||||
readme_text += '\n'
|
||||
|
||||
if 'comparison' in model_card:
|
||||
readme_text += f"\n## Model Comparison\n"
|
||||
readme_text += model_card['comparison']
|
||||
readme_text += '\n'
|
||||
|
||||
if 'citation' in model_card:
|
||||
readme_text += f"\n## Citation\n"
|
||||
if not isinstance(model_card['citation'], (list, tuple)):
|
||||
citations = [model_card['citation']]
|
||||
else:
|
||||
citations = model_card['citation']
|
||||
for c in citations:
|
||||
readme_text += f"```bibtex\n{c}\n```\n"
|
||||
|
||||
return readme_text
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Push to Hugging Face Hub")
|
||||
parser.add_argument(
|
||||
"--model", type=str, help="Name of the model to use.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--pretrained", type=str,
|
||||
help="Use a pretrained CLIP model weights with the specified tag or file path.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--repo-id", type=str,
|
||||
help="Destination HF Hub repo-id ie 'organization/model_id'.",
|
||||
)
|
||||
parser.add_argument(
|
||||
'--image-mean', type=float, nargs='+', default=None, metavar='MEAN',
|
||||
help='Override default image mean value of dataset')
|
||||
parser.add_argument(
|
||||
'--image-std', type=float, nargs='+', default=None, metavar='STD',
|
||||
help='Override default image std deviation of of dataset')
|
||||
args = parser.parse_args()
|
||||
|
||||
print(f'Saving model {args.model} with pretrained weights {args.pretrained} to Hugging Face Hub at {args.repo_id}')
|
||||
|
||||
# FIXME add support to pass model_card json / template from file via cmd line
|
||||
|
||||
push_pretrained_to_hf_hub(
|
||||
args.model,
|
||||
args.pretrained,
|
||||
args.repo_id,
|
||||
image_mean=args.image_mean, # override image mean/std if trained w/ non defaults
|
||||
image_std=args.image_std,
|
||||
)
|
||||
|
||||
print(f'{args.model} saved.')
|
||||
@@ -1,127 +0,0 @@
|
||||
""" timm model adapter
|
||||
|
||||
Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model.
|
||||
"""
|
||||
import logging
|
||||
from collections import OrderedDict
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
try:
|
||||
import timm
|
||||
from timm.models.layers import Mlp, to_2tuple
|
||||
try:
|
||||
# old timm imports < 0.8.1
|
||||
from timm.models.layers.attention_pool2d import RotAttentionPool2d
|
||||
from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d
|
||||
except ImportError:
|
||||
# new timm imports >= 0.8.1
|
||||
from timm.layers import RotAttentionPool2d
|
||||
from timm.layers import AttentionPool2d as AbsAttentionPool2d
|
||||
except ImportError:
|
||||
timm = None
|
||||
|
||||
from .utils import freeze_batch_norm_2d
|
||||
|
||||
|
||||
class TimmModel(nn.Module):
|
||||
""" timm model adapter
|
||||
# FIXME this adapter is a work in progress, may change in ways that break weight compat
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_name,
|
||||
embed_dim,
|
||||
image_size=224,
|
||||
pool='avg',
|
||||
proj='linear',
|
||||
proj_bias=False,
|
||||
drop=0.,
|
||||
drop_path=None,
|
||||
pretrained=False,
|
||||
):
|
||||
super().__init__()
|
||||
if timm is None:
|
||||
raise RuntimeError("Please `pip install timm` to use timm models.")
|
||||
|
||||
self.image_size = to_2tuple(image_size)
|
||||
timm_kwargs = {}
|
||||
if drop_path is not None:
|
||||
timm_kwargs['drop_path_rate'] = drop_path
|
||||
self.trunk = timm.create_model(model_name, pretrained=pretrained, **timm_kwargs)
|
||||
feat_size = self.trunk.default_cfg.get('pool_size', None)
|
||||
feature_ndim = 1 if not feat_size else 2
|
||||
if pool in ('abs_attn', 'rot_attn'):
|
||||
assert feature_ndim == 2
|
||||
# if attn pooling used, remove both classifier and default pool
|
||||
self.trunk.reset_classifier(0, global_pool='')
|
||||
else:
|
||||
# reset global pool if pool config set, otherwise leave as network default
|
||||
reset_kwargs = dict(global_pool=pool) if pool else {}
|
||||
self.trunk.reset_classifier(0, **reset_kwargs)
|
||||
prev_chs = self.trunk.num_features
|
||||
|
||||
head_layers = OrderedDict()
|
||||
if pool == 'abs_attn':
|
||||
head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim)
|
||||
prev_chs = embed_dim
|
||||
elif pool == 'rot_attn':
|
||||
head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim)
|
||||
prev_chs = embed_dim
|
||||
else:
|
||||
assert proj, 'projection layer needed if non-attention pooling is used.'
|
||||
|
||||
# NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used
|
||||
if proj == 'linear':
|
||||
head_layers['drop'] = nn.Dropout(drop)
|
||||
head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias)
|
||||
elif proj == 'mlp':
|
||||
head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=(drop, 0), bias=(True, proj_bias))
|
||||
|
||||
self.head = nn.Sequential(head_layers)
|
||||
|
||||
def lock(self, unlocked_groups=0, freeze_bn_stats=False):
|
||||
""" lock modules
|
||||
Args:
|
||||
unlocked_groups (int): leave last n layer groups unlocked (default: 0)
|
||||
"""
|
||||
if not unlocked_groups:
|
||||
# lock full model
|
||||
for param in self.trunk.parameters():
|
||||
param.requires_grad = False
|
||||
if freeze_bn_stats:
|
||||
freeze_batch_norm_2d(self.trunk)
|
||||
else:
|
||||
# NOTE: partial freeze requires latest timm (master) branch and is subject to change
|
||||
try:
|
||||
# FIXME import here until API stable and in an official release
|
||||
from timm.models.helpers import group_parameters, group_modules
|
||||
except ImportError:
|
||||
raise RuntimeError(
|
||||
'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`')
|
||||
matcher = self.trunk.group_matcher()
|
||||
gparams = group_parameters(self.trunk, matcher)
|
||||
max_layer_id = max(gparams.keys())
|
||||
max_layer_id = max_layer_id - unlocked_groups
|
||||
for group_idx in range(max_layer_id + 1):
|
||||
group = gparams[group_idx]
|
||||
for param in group:
|
||||
self.trunk.get_parameter(param).requires_grad = False
|
||||
if freeze_bn_stats:
|
||||
gmodules = group_modules(self.trunk, matcher, reverse=True)
|
||||
gmodules = {k for k, v in gmodules.items() if v <= max_layer_id}
|
||||
freeze_batch_norm_2d(self.trunk, gmodules)
|
||||
|
||||
@torch.jit.ignore
|
||||
def set_grad_checkpointing(self, enable=True):
|
||||
try:
|
||||
self.trunk.set_grad_checkpointing(enable)
|
||||
except Exception as e:
|
||||
logging.warning('grad checkpointing not supported for this timm image tower, continuing without...')
|
||||
|
||||
def forward(self, x):
|
||||
x = self.trunk(x)
|
||||
x = self.head(x)
|
||||
return x
|
||||
@@ -1,211 +0,0 @@
|
||||
""" CLIP tokenizer
|
||||
|
||||
Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
|
||||
"""
|
||||
import gzip
|
||||
import html
|
||||
import os
|
||||
from functools import lru_cache
|
||||
from typing import Union, List
|
||||
|
||||
import ftfy
|
||||
import regex as re
|
||||
import torch
|
||||
|
||||
# https://stackoverflow.com/q/62691279
|
||||
import os
|
||||
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def default_bpe():
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
project_root = os.path.abspath(os.path.join(current_dir, '../../../../'))
|
||||
quality_metric_path = os.path.join(project_root, 'models', 'QualityMetric')
|
||||
return os.path.join(quality_metric_path, "bpe_simple_vocab_16e6.txt.gz")
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def bytes_to_unicode():
|
||||
"""
|
||||
Returns list of utf-8 byte and a corresponding list of unicode strings.
|
||||
The reversible bpe codes work on unicode strings.
|
||||
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
|
||||
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
|
||||
This is a significant percentage of your normal, say, 32K bpe vocab.
|
||||
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
|
||||
And avoids mapping to whitespace/control characters the bpe code barfs on.
|
||||
"""
|
||||
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
|
||||
cs = bs[:]
|
||||
n = 0
|
||||
for b in range(2**8):
|
||||
if b not in bs:
|
||||
bs.append(b)
|
||||
cs.append(2**8+n)
|
||||
n += 1
|
||||
cs = [chr(n) for n in cs]
|
||||
return dict(zip(bs, cs))
|
||||
|
||||
|
||||
def get_pairs(word):
|
||||
"""Return set of symbol pairs in a word.
|
||||
Word is represented as tuple of symbols (symbols being variable-length strings).
|
||||
"""
|
||||
pairs = set()
|
||||
prev_char = word[0]
|
||||
for char in word[1:]:
|
||||
pairs.add((prev_char, char))
|
||||
prev_char = char
|
||||
return pairs
|
||||
|
||||
|
||||
def basic_clean(text):
|
||||
text = ftfy.fix_text(text)
|
||||
text = html.unescape(html.unescape(text))
|
||||
return text.strip()
|
||||
|
||||
|
||||
def whitespace_clean(text):
|
||||
text = re.sub(r'\s+', ' ', text)
|
||||
text = text.strip()
|
||||
return text
|
||||
|
||||
|
||||
class SimpleTokenizer(object):
|
||||
def __init__(self, bpe_path: str = default_bpe(), special_tokens=None):
|
||||
self.byte_encoder = bytes_to_unicode()
|
||||
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
|
||||
merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
|
||||
merges = merges[1:49152-256-2+1]
|
||||
merges = [tuple(merge.split()) for merge in merges]
|
||||
vocab = list(bytes_to_unicode().values())
|
||||
vocab = vocab + [v+'</w>' for v in vocab]
|
||||
for merge in merges:
|
||||
vocab.append(''.join(merge))
|
||||
if not special_tokens:
|
||||
special_tokens = ['<start_of_text>', '<end_of_text>']
|
||||
else:
|
||||
special_tokens = ['<start_of_text>', '<end_of_text>'] + special_tokens
|
||||
vocab.extend(special_tokens)
|
||||
self.encoder = dict(zip(vocab, range(len(vocab))))
|
||||
self.decoder = {v: k for k, v in self.encoder.items()}
|
||||
self.bpe_ranks = dict(zip(merges, range(len(merges))))
|
||||
self.cache = {t:t for t in special_tokens}
|
||||
special = "|".join(special_tokens)
|
||||
self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)
|
||||
|
||||
self.vocab_size = len(self.encoder)
|
||||
self.all_special_ids = [self.encoder[t] for t in special_tokens]
|
||||
|
||||
def bpe(self, token):
|
||||
if token in self.cache:
|
||||
return self.cache[token]
|
||||
word = tuple(token[:-1]) + ( token[-1] + '</w>',)
|
||||
pairs = get_pairs(word)
|
||||
|
||||
if not pairs:
|
||||
return token+'</w>'
|
||||
|
||||
while True:
|
||||
bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
|
||||
if bigram not in self.bpe_ranks:
|
||||
break
|
||||
first, second = bigram
|
||||
new_word = []
|
||||
i = 0
|
||||
while i < len(word):
|
||||
try:
|
||||
j = word.index(first, i)
|
||||
new_word.extend(word[i:j])
|
||||
i = j
|
||||
except:
|
||||
new_word.extend(word[i:])
|
||||
break
|
||||
|
||||
if word[i] == first and i < len(word)-1 and word[i+1] == second:
|
||||
new_word.append(first+second)
|
||||
i += 2
|
||||
else:
|
||||
new_word.append(word[i])
|
||||
i += 1
|
||||
new_word = tuple(new_word)
|
||||
word = new_word
|
||||
if len(word) == 1:
|
||||
break
|
||||
else:
|
||||
pairs = get_pairs(word)
|
||||
word = ' '.join(word)
|
||||
self.cache[token] = word
|
||||
return word
|
||||
|
||||
def encode(self, text):
|
||||
bpe_tokens = []
|
||||
text = whitespace_clean(basic_clean(text)).lower()
|
||||
for token in re.findall(self.pat, text):
|
||||
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
|
||||
bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
|
||||
return bpe_tokens
|
||||
|
||||
def decode(self, tokens):
|
||||
text = ''.join([self.decoder[token] for token in tokens])
|
||||
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
|
||||
return text
|
||||
|
||||
def __call__(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor:
|
||||
"""
|
||||
Returns the tokenized representation of given input string(s)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
texts : Union[str, List[str]]
|
||||
An input string or a list of input strings to tokenize
|
||||
context_length : int
|
||||
The context length to use; all CLIP models use 77 as the context length
|
||||
|
||||
Returns
|
||||
-------
|
||||
A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
|
||||
"""
|
||||
if isinstance(texts, str):
|
||||
texts = [texts]
|
||||
|
||||
sot_token = self.encoder["<start_of_text>"]
|
||||
eot_token = self.encoder["<end_of_text>"]
|
||||
all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts]
|
||||
result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
|
||||
|
||||
for i, tokens in enumerate(all_tokens):
|
||||
if len(tokens) > context_length:
|
||||
tokens = tokens[:context_length] # Truncate
|
||||
tokens[-1] = eot_token
|
||||
result[i, :len(tokens)] = torch.tensor(tokens)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
|
||||
class HFTokenizer:
|
||||
"""HuggingFace tokenizer wrapper"""
|
||||
|
||||
def __init__(self, tokenizer_name: str):
|
||||
from transformers import AutoTokenizer
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
|
||||
|
||||
def save_pretrained(self, dest):
|
||||
self.tokenizer.save_pretrained(dest)
|
||||
|
||||
def __call__(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.Tensor:
|
||||
# same cleaning as for default tokenizer, except lowercasing
|
||||
# adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance
|
||||
if isinstance(texts, str):
|
||||
texts = [texts]
|
||||
texts = [whitespace_clean(basic_clean(text)) for text in texts]
|
||||
input_ids = self.tokenizer(
|
||||
texts,
|
||||
return_tensors='pt',
|
||||
max_length=context_length,
|
||||
padding='max_length',
|
||||
truncation=True,
|
||||
).input_ids
|
||||
return input_ids
|
||||
@@ -1,216 +0,0 @@
|
||||
import warnings
|
||||
from dataclasses import dataclass, asdict
|
||||
from typing import Any, Dict, Optional, Sequence, Tuple, Union
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torchvision.transforms.functional as F
|
||||
from functools import partial
|
||||
from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \
|
||||
CenterCrop
|
||||
|
||||
from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
|
||||
|
||||
|
||||
@dataclass
|
||||
class AugmentationCfg:
|
||||
scale: Tuple[float, float] = (0.9, 1.0)
|
||||
ratio: Optional[Tuple[float, float]] = None
|
||||
color_jitter: Optional[Union[float, Tuple[float, float, float]]] = None
|
||||
interpolation: Optional[str] = None
|
||||
re_prob: Optional[float] = None
|
||||
re_count: Optional[int] = None
|
||||
use_timm: bool = False
|
||||
|
||||
|
||||
class ResizeMaxSize(nn.Module):
|
||||
|
||||
def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0):
|
||||
super().__init__()
|
||||
if not isinstance(max_size, int):
|
||||
raise TypeError(f"Size should be int. Got {type(max_size)}")
|
||||
self.max_size = max_size
|
||||
self.interpolation = interpolation
|
||||
self.fn = min if fn == 'min' else min
|
||||
self.fill = fill
|
||||
|
||||
def forward(self, img):
|
||||
if isinstance(img, torch.Tensor):
|
||||
height, width = img.shape[1:]
|
||||
else:
|
||||
width, height = img.size
|
||||
scale = self.max_size / float(max(height, width))
|
||||
if scale != 1.0:
|
||||
new_size = tuple(round(dim * scale) for dim in (height, width))
|
||||
img = F.resize(img, new_size, self.interpolation)
|
||||
pad_h = self.max_size - new_size[0]
|
||||
pad_w = self.max_size - new_size[1]
|
||||
img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill)
|
||||
return img
|
||||
|
||||
|
||||
def _convert_to_rgb_or_rgba(image):
|
||||
if image.mode == 'RGBA':
|
||||
return image
|
||||
else:
|
||||
return image.convert('RGB')
|
||||
|
||||
# def transform_and_split(merged, transform_fn, normalize_fn):
|
||||
# transformed = transform_fn(merged)
|
||||
# crop_img, crop_label = torch.split(transformed, [3,1], dim=0)
|
||||
|
||||
# # crop_img = _convert_to_rgb(crop_img)
|
||||
# crop_img = normalize_fn(ToTensor()(crop_img))
|
||||
# return crop_img, crop_label
|
||||
|
||||
class MaskAwareNormalize(nn.Module):
|
||||
def __init__(self, mean, std):
|
||||
super().__init__()
|
||||
self.normalize = Normalize(mean=mean, std=std)
|
||||
|
||||
def forward(self, tensor):
|
||||
if tensor.shape[0] == 4:
|
||||
return torch.cat([self.normalize(tensor[:3]), tensor[3:]], dim=0)
|
||||
else:
|
||||
return self.normalize(tensor)
|
||||
|
||||
def image_transform(
|
||||
image_size: int,
|
||||
is_train: bool,
|
||||
mean: Optional[Tuple[float, ...]] = None,
|
||||
std: Optional[Tuple[float, ...]] = None,
|
||||
resize_longest_max: bool = False,
|
||||
fill_color: int = 0,
|
||||
aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,
|
||||
):
|
||||
mean = mean or OPENAI_DATASET_MEAN
|
||||
if not isinstance(mean, (list, tuple)):
|
||||
mean = (mean,) * 3
|
||||
|
||||
std = std or OPENAI_DATASET_STD
|
||||
if not isinstance(std, (list, tuple)):
|
||||
std = (std,) * 3
|
||||
|
||||
if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]:
|
||||
# for square size, pass size as int so that Resize() uses aspect preserving shortest edge
|
||||
image_size = image_size[0]
|
||||
|
||||
if isinstance(aug_cfg, dict):
|
||||
aug_cfg = AugmentationCfg(**aug_cfg)
|
||||
else:
|
||||
aug_cfg = aug_cfg or AugmentationCfg()
|
||||
normalize = MaskAwareNormalize(mean=mean, std=std)
|
||||
if is_train:
|
||||
aug_cfg_dict = {k: v for k, v in asdict(aug_cfg).items() if v is not None}
|
||||
use_timm = aug_cfg_dict.pop('use_timm', False)
|
||||
if use_timm:
|
||||
assert False, "not tested for augmentation with mask"
|
||||
from timm.data import create_transform # timm can still be optional
|
||||
if isinstance(image_size, (tuple, list)):
|
||||
assert len(image_size) >= 2
|
||||
input_size = (3,) + image_size[-2:]
|
||||
else:
|
||||
input_size = (3, image_size, image_size)
|
||||
# by default, timm aug randomly alternates bicubic & bilinear for better robustness at inference time
|
||||
aug_cfg_dict.setdefault('interpolation', 'random')
|
||||
aug_cfg_dict.setdefault('color_jitter', None) # disable by default
|
||||
train_transform = create_transform(
|
||||
input_size=input_size,
|
||||
is_training=True,
|
||||
hflip=0.,
|
||||
mean=mean,
|
||||
std=std,
|
||||
re_mode='pixel',
|
||||
**aug_cfg_dict,
|
||||
)
|
||||
else:
|
||||
train_transform = Compose([
|
||||
_convert_to_rgb_or_rgba,
|
||||
ToTensor(),
|
||||
RandomResizedCrop(
|
||||
image_size,
|
||||
scale=aug_cfg_dict.pop('scale'),
|
||||
interpolation=InterpolationMode.BICUBIC,
|
||||
),
|
||||
normalize,
|
||||
])
|
||||
if aug_cfg_dict:
|
||||
warnings.warn(f'Unused augmentation cfg items, specify `use_timm` to use ({list(aug_cfg_dict.keys())}).')
|
||||
return train_transform
|
||||
else:
|
||||
transforms = [
|
||||
_convert_to_rgb_or_rgba,
|
||||
ToTensor(),
|
||||
]
|
||||
if resize_longest_max:
|
||||
transforms.extend([
|
||||
ResizeMaxSize(image_size, fill=fill_color)
|
||||
])
|
||||
else:
|
||||
transforms.extend([
|
||||
Resize(image_size, interpolation=InterpolationMode.BICUBIC),
|
||||
CenterCrop(image_size),
|
||||
])
|
||||
transforms.extend([
|
||||
normalize,
|
||||
])
|
||||
return Compose(transforms)
|
||||
|
||||
|
||||
# def image_transform_region(
|
||||
# image_size: int,
|
||||
# is_train: bool,
|
||||
# mean: Optional[Tuple[float, ...]] = None,
|
||||
# std: Optional[Tuple[float, ...]] = None,
|
||||
# resize_longest_max: bool = False,
|
||||
# fill_color: int = 0,
|
||||
# aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,
|
||||
# ):
|
||||
# mean = mean or OPENAI_DATASET_MEAN
|
||||
# if not isinstance(mean, (list, tuple)):
|
||||
# mean = (mean,) * 3
|
||||
|
||||
# std = std or OPENAI_DATASET_STD
|
||||
# if not isinstance(std, (list, tuple)):
|
||||
# std = (std,) * 3
|
||||
|
||||
# if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]:
|
||||
# # for square size, pass size as int so that Resize() uses aspect preserving shortest edge
|
||||
# image_size = image_size[0]
|
||||
|
||||
# if isinstance(aug_cfg, dict):
|
||||
# aug_cfg = AugmentationCfg(**aug_cfg)
|
||||
# else:
|
||||
# aug_cfg = aug_cfg or AugmentationCfg()
|
||||
# normalize = Normalize(mean=mean, std=std)
|
||||
# if is_train:
|
||||
# aug_cfg_dict = {k: v for k, v in asdict(aug_cfg).items() if v is not None}
|
||||
|
||||
# transform = Compose([
|
||||
# RandomResizedCrop(
|
||||
# image_size,
|
||||
# scale=aug_cfg_dict.pop('scale'),
|
||||
# interpolation=InterpolationMode.BICUBIC,
|
||||
# ),
|
||||
# ])
|
||||
# train_transform = Compose([
|
||||
# partial(transform_and_split, transform_fn=transform,normalize_fn=normalize)
|
||||
# ])
|
||||
# return train_transform
|
||||
# else:
|
||||
# if resize_longest_max:
|
||||
# transform = [
|
||||
# ResizeMaxSize(image_size, fill=fill_color)
|
||||
# ]
|
||||
# val_transform = Compose([
|
||||
# partial(transform_and_split, transform_fn=transform,normalize_fn=normalize),
|
||||
# ])
|
||||
# else:
|
||||
# transform = [
|
||||
# Resize(image_size, interpolation=InterpolationMode.BICUBIC),
|
||||
# CenterCrop(image_size),
|
||||
# ]
|
||||
# val_transform = Compose([
|
||||
# partial(transform_and_split, transform_fn=transform,normalize_fn=normalize),
|
||||
# ])
|
||||
# return val_transform
|
||||
@@ -1,727 +0,0 @@
|
||||
from collections import OrderedDict
|
||||
import math
|
||||
from typing import Callable, Optional, Sequence, Tuple
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
from torch.utils.checkpoint import checkpoint
|
||||
|
||||
from .utils import to_2tuple
|
||||
|
||||
|
||||
class LayerNormFp32(nn.LayerNorm):
|
||||
"""Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back)."""
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
orig_type = x.dtype
|
||||
x = F.layer_norm(x.to(torch.float32), self.normalized_shape, self.weight, self.bias, self.eps)
|
||||
return x.to(orig_type)
|
||||
|
||||
|
||||
class LayerNorm(nn.LayerNorm):
|
||||
"""Subclass torch's LayerNorm (with cast back to input dtype)."""
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
orig_type = x.dtype
|
||||
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
|
||||
return x.to(orig_type)
|
||||
|
||||
|
||||
class QuickGELU(nn.Module):
|
||||
# NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory
|
||||
def forward(self, x: torch.Tensor):
|
||||
return x * torch.sigmoid(1.702 * x)
|
||||
|
||||
|
||||
class LayerScale(nn.Module):
|
||||
def __init__(self, dim, init_values=1e-5, inplace=False):
|
||||
super().__init__()
|
||||
self.inplace = inplace
|
||||
self.gamma = nn.Parameter(init_values * torch.ones(dim))
|
||||
|
||||
def forward(self, x):
|
||||
return x.mul_(self.gamma) if self.inplace else x * self.gamma
|
||||
|
||||
|
||||
class PatchDropout(nn.Module):
|
||||
"""
|
||||
https://arxiv.org/abs/2212.00794
|
||||
"""
|
||||
|
||||
def __init__(self, prob, exclude_first_token=True):
|
||||
super().__init__()
|
||||
assert 0 <= prob < 1.
|
||||
self.prob = prob
|
||||
self.exclude_first_token = exclude_first_token # exclude CLS token
|
||||
|
||||
def forward(self, x):
|
||||
if not self.training or self.prob == 0.:
|
||||
return x
|
||||
|
||||
if self.exclude_first_token:
|
||||
cls_tokens, x = x[:, :1], x[:, 1:]
|
||||
else:
|
||||
cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1])
|
||||
|
||||
batch = x.size()[0]
|
||||
num_tokens = x.size()[1]
|
||||
|
||||
batch_indices = torch.arange(batch)
|
||||
batch_indices = batch_indices[..., None]
|
||||
|
||||
keep_prob = 1 - self.prob
|
||||
num_patches_keep = max(1, int(num_tokens * keep_prob))
|
||||
|
||||
rand = torch.randn(batch, num_tokens)
|
||||
patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices
|
||||
|
||||
x = x[batch_indices, patch_indices_keep]
|
||||
|
||||
if self.exclude_first_token:
|
||||
x = torch.cat((cls_tokens, x), dim=1)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
num_heads=8,
|
||||
qkv_bias=True,
|
||||
scaled_cosine=False,
|
||||
scale_heads=False,
|
||||
logit_scale_max=math.log(1. / 0.01),
|
||||
attn_drop=0.,
|
||||
proj_drop=0.
|
||||
):
|
||||
super().__init__()
|
||||
self.scaled_cosine = scaled_cosine
|
||||
self.scale_heads = scale_heads
|
||||
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim // num_heads
|
||||
self.scale = self.head_dim ** -0.5
|
||||
self.logit_scale_max = logit_scale_max
|
||||
|
||||
# keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original
|
||||
self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale)
|
||||
if qkv_bias:
|
||||
self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3))
|
||||
else:
|
||||
self.in_proj_bias = None
|
||||
|
||||
if self.scaled_cosine:
|
||||
self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))))
|
||||
else:
|
||||
self.logit_scale = None
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
if self.scale_heads:
|
||||
self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1)))
|
||||
else:
|
||||
self.head_scale = None
|
||||
self.out_proj = nn.Linear(dim, dim)
|
||||
self.out_drop = nn.Dropout(proj_drop)
|
||||
|
||||
def forward(self, x, attn_mask: Optional[torch.Tensor] = None):
|
||||
L, N, C = x.shape
|
||||
q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1)
|
||||
q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
|
||||
k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
|
||||
v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
|
||||
|
||||
if self.logit_scale is not None:
|
||||
attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2))
|
||||
logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp()
|
||||
attn = attn.view(N, self.num_heads, L, L) * logit_scale
|
||||
attn = attn.view(-1, L, L)
|
||||
else:
|
||||
q = q * self.scale
|
||||
attn = torch.bmm(q, k.transpose(-1, -2))
|
||||
|
||||
if attn_mask is not None:
|
||||
if attn_mask.dtype == torch.bool:
|
||||
new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)
|
||||
new_attn_mask.masked_fill_(attn_mask, float("-inf"))
|
||||
attn_mask = new_attn_mask
|
||||
attn += attn_mask
|
||||
|
||||
attn = attn.softmax(dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = torch.bmm(attn, v)
|
||||
if self.head_scale is not None:
|
||||
x = x.view(N, self.num_heads, L, C) * self.head_scale
|
||||
x = x.view(-1, L, C)
|
||||
x = x.transpose(0, 1).reshape(L, N, C)
|
||||
x = self.out_proj(x)
|
||||
x = self.out_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class AttentionalPooler(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
d_model: int,
|
||||
context_dim: int,
|
||||
n_head: int = 8,
|
||||
n_queries: int = 256,
|
||||
norm_layer: Callable = LayerNorm
|
||||
):
|
||||
super().__init__()
|
||||
self.query = nn.Parameter(torch.randn(n_queries, d_model))
|
||||
self.attn = nn.MultiheadAttention(d_model, n_head, kdim=context_dim, vdim=context_dim)
|
||||
self.ln_q = norm_layer(d_model)
|
||||
self.ln_k = norm_layer(context_dim)
|
||||
|
||||
def forward(self, x: torch.Tensor):
|
||||
x = self.ln_k(x).permute(1, 0, 2) # NLD -> LND
|
||||
N = x.shape[1]
|
||||
q = self.ln_q(self.query)
|
||||
out = self.attn(self._repeat(q, N), x, x, need_weights=False)[0]
|
||||
return out.permute(1, 0, 2) # LND -> NLD
|
||||
|
||||
def _repeat(self, query, N: int):
|
||||
return query.unsqueeze(1).repeat(1, N, 1)
|
||||
|
||||
|
||||
class ResidualAttentionBlock(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
d_model: int,
|
||||
n_head: int,
|
||||
mlp_ratio: float = 4.0,
|
||||
ls_init_value: float = None,
|
||||
act_layer: Callable = nn.GELU,
|
||||
norm_layer: Callable = LayerNorm,
|
||||
is_cross_attention: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.ln_1 = norm_layer(d_model)
|
||||
self.attn = nn.MultiheadAttention(d_model, n_head)
|
||||
self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
|
||||
if is_cross_attention:
|
||||
self.ln_1_kv = norm_layer(d_model)
|
||||
|
||||
self.ln_2 = norm_layer(d_model)
|
||||
mlp_width = int(d_model * mlp_ratio)
|
||||
self.mlp = nn.Sequential(OrderedDict([
|
||||
("c_fc", nn.Linear(d_model, mlp_width)),
|
||||
("gelu", act_layer()),
|
||||
("c_proj", nn.Linear(mlp_width, d_model))
|
||||
]))
|
||||
self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
|
||||
|
||||
def attention(
|
||||
self,
|
||||
q_x: torch.Tensor,
|
||||
k_x: Optional[torch.Tensor] = None,
|
||||
v_x: Optional[torch.Tensor] = None,
|
||||
attn_mask: Optional[torch.Tensor] = None,
|
||||
):
|
||||
k_x = k_x if k_x is not None else q_x
|
||||
v_x = v_x if v_x is not None else q_x
|
||||
|
||||
attn_mask = attn_mask.to(q_x.dtype) if attn_mask is not None else None
|
||||
return self.attn(
|
||||
q_x, k_x, v_x, need_weights=False, attn_mask=attn_mask
|
||||
)[0]
|
||||
|
||||
def forward(
|
||||
self,
|
||||
q_x: torch.Tensor,
|
||||
k_x: Optional[torch.Tensor] = None,
|
||||
v_x: Optional[torch.Tensor] = None,
|
||||
attn_mask: Optional[torch.Tensor] = None,
|
||||
):
|
||||
k_x = self.ln_1_kv(k_x) if hasattr(self, "ln_1_kv") and k_x is not None else None
|
||||
v_x = self.ln_1_kv(v_x) if hasattr(self, "ln_1_kv") and v_x is not None else None
|
||||
|
||||
x = q_x + self.ls_1(self.attention(q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask))
|
||||
x = x + self.ls_2(self.mlp(self.ln_2(x)))
|
||||
return x
|
||||
|
||||
|
||||
class CustomResidualAttentionBlock(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
d_model: int,
|
||||
n_head: int,
|
||||
mlp_ratio: float = 4.0,
|
||||
ls_init_value: float = None,
|
||||
act_layer: Callable = nn.GELU,
|
||||
norm_layer: Callable = LayerNorm,
|
||||
scale_cosine_attn: bool = False,
|
||||
scale_heads: bool = False,
|
||||
scale_attn: bool = False,
|
||||
scale_fc: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.ln_1 = norm_layer(d_model)
|
||||
self.attn = Attention(
|
||||
d_model, n_head,
|
||||
scaled_cosine=scale_cosine_attn,
|
||||
scale_heads=scale_heads,
|
||||
)
|
||||
self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity()
|
||||
self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
|
||||
|
||||
self.ln_2 = norm_layer(d_model)
|
||||
mlp_width = int(d_model * mlp_ratio)
|
||||
self.mlp = nn.Sequential(OrderedDict([
|
||||
("c_fc", nn.Linear(d_model, mlp_width)),
|
||||
('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()),
|
||||
("gelu", act_layer()),
|
||||
("c_proj", nn.Linear(mlp_width, d_model))
|
||||
]))
|
||||
self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity()
|
||||
|
||||
def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
|
||||
x = x + self.ls_1(self.ln_attn(self.attn(self.ln_1(x), attn_mask=attn_mask)))
|
||||
x = x + self.ls_2(self.mlp(self.ln_2(x)))
|
||||
return x
|
||||
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
width: int,
|
||||
layers: int,
|
||||
heads: int,
|
||||
mlp_ratio: float = 4.0,
|
||||
ls_init_value: float = None,
|
||||
act_layer: Callable = nn.GELU,
|
||||
norm_layer: Callable = LayerNorm,
|
||||
):
|
||||
super().__init__()
|
||||
self.width = width
|
||||
self.layers = layers
|
||||
self.grad_checkpointing = False
|
||||
|
||||
self.resblocks = nn.ModuleList([
|
||||
ResidualAttentionBlock(
|
||||
width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer)
|
||||
for _ in range(layers)
|
||||
])
|
||||
|
||||
def get_cast_dtype(self) -> torch.dtype:
|
||||
return self.resblocks[0].mlp.c_fc.weight.dtype
|
||||
|
||||
def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
|
||||
for r in self.resblocks:
|
||||
if self.grad_checkpointing and not torch.jit.is_scripting():
|
||||
# TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372
|
||||
x = checkpoint(r, x, None, None, attn_mask)
|
||||
else:
|
||||
x = r(x, attn_mask=attn_mask)
|
||||
return x
|
||||
|
||||
|
||||
class VisionTransformer(nn.Module):
|
||||
output_tokens: torch.jit.Final[bool]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
image_size: int,
|
||||
patch_size: int,
|
||||
width: int,
|
||||
layers: int,
|
||||
heads: int,
|
||||
mlp_ratio: float,
|
||||
ls_init_value: float = None,
|
||||
global_average_pool: bool = False,
|
||||
attentional_pool: bool = False,
|
||||
n_queries: int = 256,
|
||||
attn_pooler_heads: int = 8,
|
||||
output_dim: int = 512,
|
||||
patch_dropout: float = 0.,
|
||||
input_patchnorm: bool = False,
|
||||
act_layer: Callable = nn.GELU,
|
||||
norm_layer: Callable = LayerNorm,
|
||||
output_tokens: bool = False
|
||||
):
|
||||
super().__init__()
|
||||
self.output_tokens = output_tokens
|
||||
image_height, image_width = self.image_size = to_2tuple(image_size)
|
||||
patch_height, patch_width = self.patch_size = to_2tuple(patch_size)
|
||||
self.grid_size = (image_height // patch_height, image_width // patch_width)
|
||||
self.output_dim = output_dim
|
||||
|
||||
# whether to layernorm each patch, as done in dual patchnorm paper - https://arxiv.org/abs/2302.01327v1
|
||||
self.input_patchnorm = input_patchnorm
|
||||
|
||||
if input_patchnorm:
|
||||
patch_input_dim = patch_height * patch_width * 3
|
||||
self.patchnorm_pre_ln = LayerNorm(patch_input_dim)
|
||||
self.conv1 = nn.Linear(patch_input_dim, width)
|
||||
else:
|
||||
self.patchnorm_pre_ln = nn.Identity()
|
||||
self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
|
||||
|
||||
# class embeddings and positional embeddings
|
||||
scale = width ** -0.5
|
||||
self.class_embedding = nn.Parameter(scale * torch.randn(width))
|
||||
self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width))
|
||||
|
||||
# setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn
|
||||
self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity()
|
||||
|
||||
self.ln_pre = norm_layer(width)
|
||||
self.transformer = Transformer(
|
||||
width,
|
||||
layers,
|
||||
heads,
|
||||
mlp_ratio,
|
||||
ls_init_value=ls_init_value,
|
||||
act_layer=act_layer,
|
||||
norm_layer=norm_layer,
|
||||
)
|
||||
|
||||
self.global_average_pool = global_average_pool
|
||||
if attentional_pool:
|
||||
self.attn_pool = AttentionalPooler(output_dim, width, n_head=attn_pooler_heads, n_queries=n_queries)
|
||||
self.ln_post = norm_layer(output_dim)
|
||||
self.proj = nn.Parameter(scale * torch.randn(output_dim, output_dim))
|
||||
else:
|
||||
self.attn_pool = None
|
||||
self.ln_post = norm_layer(width)
|
||||
self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
|
||||
|
||||
self.init_parameters()
|
||||
|
||||
def lock(self, unlocked_groups=0, freeze_bn_stats=False):
|
||||
for param in self.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
if unlocked_groups != 0:
|
||||
groups = [
|
||||
[
|
||||
self.conv1,
|
||||
self.class_embedding,
|
||||
self.positional_embedding,
|
||||
self.ln_pre,
|
||||
],
|
||||
*self.transformer.resblocks[:-1],
|
||||
[
|
||||
self.transformer.resblocks[-1],
|
||||
self.ln_post,
|
||||
],
|
||||
self.proj,
|
||||
]
|
||||
|
||||
def _unlock(x):
|
||||
if isinstance(x, Sequence):
|
||||
for g in x:
|
||||
_unlock(g)
|
||||
else:
|
||||
if isinstance(x, torch.nn.Parameter):
|
||||
x.requires_grad = True
|
||||
else:
|
||||
for p in x.parameters():
|
||||
p.requires_grad = True
|
||||
|
||||
_unlock(groups[-unlocked_groups:])
|
||||
|
||||
def init_parameters(self):
|
||||
# FIXME OpenAI CLIP did not define an init for the VisualTransformer
|
||||
# TODO experiment if default PyTorch init, below, or alternate init is best.
|
||||
|
||||
# nn.init.normal_(self.class_embedding, std=self.scale)
|
||||
# nn.init.normal_(self.positional_embedding, std=self.scale)
|
||||
#
|
||||
# proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
|
||||
# attn_std = self.transformer.width ** -0.5
|
||||
# fc_std = (2 * self.transformer.width) ** -0.5
|
||||
# for block in self.transformer.resblocks:
|
||||
# nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
|
||||
# nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
|
||||
# nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
|
||||
# nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
|
||||
#
|
||||
# if self.text_projection is not None:
|
||||
# nn.init.normal_(self.text_projection, std=self.scale)
|
||||
pass
|
||||
|
||||
@torch.jit.ignore
|
||||
def set_grad_checkpointing(self, enable=True):
|
||||
self.transformer.grad_checkpointing = enable
|
||||
|
||||
def _global_pool(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
if self.global_average_pool:
|
||||
return x.mean(dim=1), x
|
||||
else:
|
||||
return x[:, 0], x[:, 1:]
|
||||
|
||||
def forward(self, x: torch.Tensor, skip_pool: bool = False):
|
||||
|
||||
# to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1
|
||||
if self.input_patchnorm:
|
||||
# einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)')
|
||||
x = x.reshape(x.shape[0], x.shape[1], self.grid_size[0], self.patch_size[0], self.grid_size[1], self.patch_size[1])
|
||||
x = x.permute(0, 2, 4, 1, 3, 5)
|
||||
x = x.reshape(x.shape[0], self.grid_size[0] * self.grid_size[1], -1)
|
||||
x = self.patchnorm_pre_ln(x)
|
||||
x = self.conv1(x)
|
||||
else:
|
||||
x = self.conv1(x) # shape = [*, width, grid, grid]
|
||||
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
|
||||
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
|
||||
|
||||
# class embeddings and positional embeddings
|
||||
x = torch.cat(
|
||||
[self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device),
|
||||
x], dim=1) # shape = [*, grid ** 2 + 1, width]
|
||||
x = x + self.positional_embedding.to(x.dtype)
|
||||
|
||||
# a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
|
||||
x = self.patch_dropout(x)
|
||||
x = self.ln_pre(x)
|
||||
|
||||
x = x.permute(1, 0, 2) # NLD -> LND
|
||||
x = self.transformer(x)
|
||||
x = x.permute(1, 0, 2) # LND -> NLD
|
||||
|
||||
if skip_pool:
|
||||
return x
|
||||
|
||||
if self.attn_pool is not None:
|
||||
x = self.attn_pool(x)
|
||||
x = self.ln_post(x)
|
||||
pooled, tokens = self._global_pool(x)
|
||||
else:
|
||||
pooled, tokens = self._global_pool(x)
|
||||
pooled = self.ln_post(pooled)
|
||||
|
||||
if self.proj is not None:
|
||||
pooled = pooled @ self.proj
|
||||
|
||||
if self.output_tokens:
|
||||
return pooled, tokens
|
||||
|
||||
return pooled
|
||||
|
||||
|
||||
class TextTransformer(nn.Module):
|
||||
output_tokens: torch.jit.Final[bool]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
context_length: int = 77,
|
||||
vocab_size: int = 49408,
|
||||
width: int = 512,
|
||||
heads: int = 8,
|
||||
layers: int = 12,
|
||||
ls_init_value: float = None,
|
||||
output_dim: int = 512,
|
||||
act_layer: Callable = nn.GELU,
|
||||
norm_layer: Callable = LayerNorm,
|
||||
embed_cls: bool = False,
|
||||
pad_id: int = 0,
|
||||
output_tokens: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
self.output_tokens = output_tokens
|
||||
self.num_pos = self.context_length = context_length
|
||||
self.vocab_size = vocab_size
|
||||
self.width = width
|
||||
self.output_dim = output_dim
|
||||
self.heads = heads
|
||||
self.pad_id = pad_id
|
||||
|
||||
self.text_projection = nn.Parameter(torch.empty(width, output_dim))
|
||||
|
||||
if embed_cls:
|
||||
self.cls_emb = nn.Parameter(torch.empty(width))
|
||||
self.num_pos += 1
|
||||
else:
|
||||
self.cls_emb = None
|
||||
|
||||
self.token_embedding = nn.Embedding(vocab_size, width)
|
||||
self.positional_embedding = nn.Parameter(torch.empty(self.num_pos, width))
|
||||
self.transformer = Transformer(
|
||||
width=width,
|
||||
layers=layers,
|
||||
heads=heads,
|
||||
ls_init_value=ls_init_value,
|
||||
act_layer=act_layer,
|
||||
norm_layer=norm_layer,
|
||||
)
|
||||
self.ln_final = norm_layer(width)
|
||||
|
||||
self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False)
|
||||
|
||||
self.init_parameters()
|
||||
|
||||
def init_parameters(self):
|
||||
nn.init.normal_(self.token_embedding.weight, std=0.02)
|
||||
nn.init.normal_(self.positional_embedding, std=0.01)
|
||||
if self.cls_emb is not None:
|
||||
nn.init.normal_(self.cls_emb, std=0.01)
|
||||
|
||||
proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
|
||||
attn_std = self.transformer.width ** -0.5
|
||||
fc_std = (2 * self.transformer.width) ** -0.5
|
||||
for block in self.transformer.resblocks:
|
||||
nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
|
||||
nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
|
||||
nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
|
||||
nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
|
||||
|
||||
if self.text_projection is not None:
|
||||
nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
|
||||
|
||||
@torch.jit.ignore
|
||||
def set_grad_checkpointing(self, enable=True):
|
||||
self.transformer.grad_checkpointing = enable
|
||||
|
||||
def build_attention_mask(self):
|
||||
# lazily create causal attention mask, with full attention between the tokens
|
||||
# pytorch uses additive attention mask; fill with -inf
|
||||
mask = torch.empty(self.num_pos, self.num_pos)
|
||||
mask.fill_(float("-inf"))
|
||||
mask.triu_(1) # zero out the lower diagonal
|
||||
return mask
|
||||
|
||||
def build_cls_mask(self, text, cast_dtype: torch.dtype):
|
||||
cls_mask = (text != self.pad_id).unsqueeze(1)
|
||||
cls_mask = F.pad(cls_mask, (1, 0, cls_mask.shape[2], 0), value=1.0)
|
||||
additive_mask = torch.empty(cls_mask.shape, dtype=cast_dtype, device=cls_mask.device)
|
||||
additive_mask.fill_(0)
|
||||
additive_mask.masked_fill_(~cls_mask, float("-inf"))
|
||||
additive_mask = torch.repeat_interleave(additive_mask, self.heads, 0)
|
||||
return additive_mask
|
||||
|
||||
def _repeat(self, t, N: int):
|
||||
return t.reshape(1, 1, -1).repeat(N, 1, 1)
|
||||
|
||||
def forward(self, text):
|
||||
cast_dtype = self.transformer.get_cast_dtype()
|
||||
seq_len = text.shape[1]
|
||||
|
||||
x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model]
|
||||
attn_mask = self.attn_mask
|
||||
if self.cls_emb is not None:
|
||||
seq_len += 1
|
||||
x = torch.cat([x, self._repeat(self.cls_emb, x.shape[0])], dim=1)
|
||||
cls_mask = self.build_cls_mask(text, cast_dtype)
|
||||
attn_mask = attn_mask[None, :seq_len, :seq_len] + cls_mask[:, :seq_len, :seq_len]
|
||||
|
||||
x = x + self.positional_embedding[:seq_len].to(cast_dtype)
|
||||
x = x.permute(1, 0, 2) # NLD -> LND
|
||||
x = self.transformer(x, attn_mask=attn_mask)
|
||||
x = x.permute(1, 0, 2) # LND -> NLD
|
||||
|
||||
# x.shape = [batch_size, n_ctx, transformer.width]
|
||||
# take features from the eot embedding (eot_token is the highest number in each sequence)
|
||||
if self.cls_emb is not None:
|
||||
pooled, tokens = x[:, -1], x[:, :-1]
|
||||
pooled = self.ln_final(pooled)
|
||||
else:
|
||||
x = self.ln_final(x)
|
||||
pooled, tokens = x[torch.arange(x.shape[0]), text.argmax(dim=-1)], x
|
||||
|
||||
if self.text_projection is not None:
|
||||
pooled = pooled @ self.text_projection
|
||||
|
||||
if self.output_tokens:
|
||||
return pooled, tokens
|
||||
|
||||
return pooled
|
||||
|
||||
|
||||
class MultimodalTransformer(Transformer):
|
||||
def __init__(
|
||||
self,
|
||||
width: int,
|
||||
layers: int,
|
||||
heads: int,
|
||||
context_length: int = 77,
|
||||
mlp_ratio: float = 4.0,
|
||||
ls_init_value: float = None,
|
||||
act_layer: Callable = nn.GELU,
|
||||
norm_layer: Callable = LayerNorm,
|
||||
output_dim: int = 512,
|
||||
):
|
||||
|
||||
super().__init__(
|
||||
width=width,
|
||||
layers=layers,
|
||||
heads=heads,
|
||||
mlp_ratio=mlp_ratio,
|
||||
ls_init_value=ls_init_value,
|
||||
act_layer=act_layer,
|
||||
norm_layer=norm_layer,
|
||||
)
|
||||
self.context_length = context_length
|
||||
self.cross_attn = nn.ModuleList([
|
||||
ResidualAttentionBlock(
|
||||
width,
|
||||
heads,
|
||||
mlp_ratio,
|
||||
ls_init_value=ls_init_value,
|
||||
act_layer=act_layer,
|
||||
norm_layer=norm_layer,
|
||||
is_cross_attention=True,
|
||||
)
|
||||
for _ in range(layers)
|
||||
])
|
||||
|
||||
self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False)
|
||||
|
||||
self.ln_final = norm_layer(width)
|
||||
self.text_projection = nn.Parameter(torch.empty(width, output_dim))
|
||||
|
||||
def init_parameters(self):
|
||||
proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
|
||||
attn_std = self.transformer.width ** -0.5
|
||||
fc_std = (2 * self.transformer.width) ** -0.5
|
||||
for block in self.transformer.resblocks:
|
||||
nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
|
||||
nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
|
||||
nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
|
||||
nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
|
||||
for block in self.transformer.cross_attn:
|
||||
nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
|
||||
nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
|
||||
nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
|
||||
nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
|
||||
|
||||
if self.text_projection is not None:
|
||||
nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
|
||||
|
||||
def build_attention_mask(self):
|
||||
# lazily create causal attention mask, with full attention between the tokens
|
||||
# pytorch uses additive attention mask; fill with -inf
|
||||
mask = torch.empty(self.context_length, self.context_length)
|
||||
mask.fill_(float("-inf"))
|
||||
mask.triu_(1) # zero out the lower diagonal
|
||||
return mask
|
||||
|
||||
def forward(self, image_embs, text_embs):
|
||||
text_embs = text_embs.permute(1, 0, 2) # NLD -> LNDsq
|
||||
image_embs = image_embs.permute(1, 0, 2) # NLD -> LND
|
||||
seq_len = text_embs.shape[0]
|
||||
|
||||
for resblock, cross_attn in zip(self.resblocks, self.cross_attn):
|
||||
if self.grad_checkpointing and not torch.jit.is_scripting():
|
||||
# TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372
|
||||
text_embs = checkpoint(resblock, text_embs, None, None, self.attn_mask[:seq_len, :seq_len])
|
||||
text_embs = checkpoint(cross_attn, text_embs, image_embs, image_embs, None)
|
||||
else:
|
||||
text_embs = resblock(text_embs, attn_mask=self.attn_mask[:seq_len, :seq_len])
|
||||
text_embs = cross_attn(text_embs, k_x=image_embs, v_x=image_embs)
|
||||
|
||||
x = text_embs.permute(1, 0, 2) # LND -> NLD
|
||||
x = self.ln_final(x)
|
||||
|
||||
if self.text_projection is not None:
|
||||
x = x @ self.text_projection
|
||||
|
||||
return x
|
||||
|
||||
@torch.jit.ignore
|
||||
def set_grad_checkpointing(self, enable=True):
|
||||
self.grad_checkpointing = enable
|
||||
@@ -1,60 +0,0 @@
|
||||
from itertools import repeat
|
||||
import collections.abc
|
||||
|
||||
from torch import nn as nn
|
||||
from torchvision.ops.misc import FrozenBatchNorm2d
|
||||
|
||||
|
||||
def freeze_batch_norm_2d(module, module_match={}, name=''):
|
||||
"""
|
||||
Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is
|
||||
itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and
|
||||
returned. Otherwise, the module is walked recursively and submodules are converted in place.
|
||||
|
||||
Args:
|
||||
module (torch.nn.Module): Any PyTorch module.
|
||||
module_match (dict): Dictionary of full module names to freeze (all if empty)
|
||||
name (str): Full module name (prefix)
|
||||
|
||||
Returns:
|
||||
torch.nn.Module: Resulting module
|
||||
|
||||
Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762
|
||||
"""
|
||||
res = module
|
||||
is_match = True
|
||||
if module_match:
|
||||
is_match = name in module_match
|
||||
if is_match and isinstance(module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)):
|
||||
res = FrozenBatchNorm2d(module.num_features)
|
||||
res.num_features = module.num_features
|
||||
res.affine = module.affine
|
||||
if module.affine:
|
||||
res.weight.data = module.weight.data.clone().detach()
|
||||
res.bias.data = module.bias.data.clone().detach()
|
||||
res.running_mean.data = module.running_mean.data
|
||||
res.running_var.data = module.running_var.data
|
||||
res.eps = module.eps
|
||||
else:
|
||||
for child_name, child in module.named_children():
|
||||
full_child_name = '.'.join([name, child_name]) if name else child_name
|
||||
new_child = freeze_batch_norm_2d(child, module_match, full_child_name)
|
||||
if new_child is not child:
|
||||
res.add_module(child_name, new_child)
|
||||
return res
|
||||
|
||||
|
||||
# From PyTorch internals
|
||||
def _ntuple(n):
|
||||
def parse(x):
|
||||
if isinstance(x, collections.abc.Iterable):
|
||||
return x
|
||||
return tuple(repeat(x, n))
|
||||
return parse
|
||||
|
||||
|
||||
to_1tuple = _ntuple(1)
|
||||
to_2tuple = _ntuple(2)
|
||||
to_3tuple = _ntuple(3)
|
||||
to_4tuple = _ntuple(4)
|
||||
to_ntuple = lambda n, x: _ntuple(n)(x)
|
||||
@@ -1 +0,0 @@
|
||||
__version__ = '2.16.0'
|
||||
@@ -1,112 +0,0 @@
|
||||
import torch
|
||||
from PIL import Image
|
||||
from transformers import AutoProcessor, AutoModel
|
||||
from typing import List, Union
|
||||
import os
|
||||
from .config import MODEL_PATHS
|
||||
|
||||
class PickScore(torch.nn.Module):
|
||||
def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS):
|
||||
super().__init__()
|
||||
"""Initialize the Selector with a processor and model.
|
||||
|
||||
Args:
|
||||
device (Union[str, torch.device]): The device to load the model on.
|
||||
"""
|
||||
self.device = device if isinstance(device, torch.device) else torch.device(device)
|
||||
processor_name_or_path = path.get("clip")
|
||||
model_pretrained_name_or_path = path.get("pickscore")
|
||||
self.processor = AutoProcessor.from_pretrained(processor_name_or_path)
|
||||
self.model = AutoModel.from_pretrained(model_pretrained_name_or_path).eval().to(self.device)
|
||||
|
||||
def _calculate_score(self, image: torch.Tensor, prompt: str, softmax: bool = False) -> float:
|
||||
"""Calculate the score for a single image and prompt.
|
||||
|
||||
Args:
|
||||
image (torch.Tensor): The processed image tensor.
|
||||
prompt (str): The prompt text.
|
||||
softmax (bool): Whether to apply softmax to the scores.
|
||||
|
||||
Returns:
|
||||
float: The score for the image.
|
||||
"""
|
||||
with torch.no_grad():
|
||||
# Prepare text inputs
|
||||
text_inputs = self.processor(
|
||||
text=prompt,
|
||||
padding=True,
|
||||
truncation=True,
|
||||
max_length=77,
|
||||
return_tensors="pt",
|
||||
).to(self.device)
|
||||
|
||||
# Embed images and text
|
||||
image_embs = self.model.get_image_features(pixel_values=image)
|
||||
image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True)
|
||||
text_embs = self.model.get_text_features(**text_inputs)
|
||||
text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True)
|
||||
|
||||
# Compute score
|
||||
score = (text_embs @ image_embs.T)[0]
|
||||
if softmax:
|
||||
# Apply logit scale and softmax
|
||||
score = torch.softmax(self.model.logit_scale.exp() * score, dim=-1)
|
||||
|
||||
return score.cpu().item()
|
||||
|
||||
@torch.no_grad()
|
||||
def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str, softmax: bool = False) -> List[float]:
|
||||
"""Score the images based on the prompt.
|
||||
|
||||
Args:
|
||||
images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s).
|
||||
prompt (str): The prompt text.
|
||||
softmax (bool): Whether to apply softmax to the scores.
|
||||
|
||||
Returns:
|
||||
List[float]: List of scores for the images.
|
||||
"""
|
||||
try:
|
||||
if isinstance(images, (str, Image.Image)):
|
||||
# Single image
|
||||
if isinstance(images, str):
|
||||
pil_image = Image.open(images)
|
||||
else:
|
||||
pil_image = images
|
||||
|
||||
# Prepare image inputs
|
||||
image_inputs = self.processor(
|
||||
images=pil_image,
|
||||
padding=True,
|
||||
truncation=True,
|
||||
max_length=77,
|
||||
return_tensors="pt",
|
||||
).to(self.device)
|
||||
|
||||
return [self._calculate_score(image_inputs["pixel_values"], prompt, softmax)]
|
||||
elif isinstance(images, list):
|
||||
# Multiple images
|
||||
scores = []
|
||||
for one_image in images:
|
||||
if isinstance(one_image, str):
|
||||
pil_image = Image.open(one_image)
|
||||
elif isinstance(one_image, Image.Image):
|
||||
pil_image = one_image
|
||||
else:
|
||||
raise TypeError("The type of parameter images is illegal.")
|
||||
|
||||
# Prepare image inputs
|
||||
image_inputs = self.processor(
|
||||
images=pil_image,
|
||||
padding=True,
|
||||
truncation=True,
|
||||
max_length=77,
|
||||
return_tensors="pt",
|
||||
).to(self.device)
|
||||
|
||||
scores.append(self._calculate_score(image_inputs["pixel_values"], prompt, softmax))
|
||||
return scores
|
||||
else:
|
||||
raise TypeError("The type of parameter images is illegal.")
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Error in scoring images: {e}")
|
||||
@@ -1 +0,0 @@
|
||||
from .models import *
|
||||
@@ -1,3 +0,0 @@
|
||||
from .base_model import *
|
||||
from .clip_model import *
|
||||
from .cross_modeling import *
|
||||
@@ -1,7 +0,0 @@
|
||||
from dataclasses import dataclass
|
||||
|
||||
|
||||
|
||||
@dataclass
|
||||
class BaseModelConfig:
|
||||
pass
|
||||
@@ -1,146 +0,0 @@
|
||||
from dataclasses import dataclass
|
||||
from transformers import CLIPModel as HFCLIPModel
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
from torch import nn, einsum
|
||||
|
||||
from .base_model import BaseModelConfig
|
||||
|
||||
from transformers import CLIPConfig
|
||||
from typing import Any, Optional, Tuple, Union
|
||||
import torch
|
||||
|
||||
from .cross_modeling import Cross_model
|
||||
|
||||
import json, os
|
||||
|
||||
class XCLIPModel(HFCLIPModel):
|
||||
def __init__(self, config: CLIPConfig):
|
||||
super().__init__(config)
|
||||
|
||||
def get_text_features(
|
||||
self,
|
||||
input_ids: Optional[torch.Tensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.Tensor] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
) -> torch.FloatTensor:
|
||||
|
||||
# Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
text_outputs = self.text_model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
# pooled_output = text_outputs[1]
|
||||
# text_features = self.text_projection(pooled_output)
|
||||
last_hidden_state = text_outputs[0]
|
||||
text_features = self.text_projection(last_hidden_state)
|
||||
|
||||
pooled_output = text_outputs[1]
|
||||
text_features_EOS = self.text_projection(pooled_output)
|
||||
|
||||
|
||||
# del last_hidden_state, text_outputs
|
||||
# gc.collect()
|
||||
|
||||
return text_features, text_features_EOS
|
||||
|
||||
def get_image_features(
|
||||
self,
|
||||
pixel_values: Optional[torch.FloatTensor] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
) -> torch.FloatTensor:
|
||||
|
||||
# Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
vision_outputs = self.vision_model(
|
||||
pixel_values=pixel_values,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
# pooled_output = vision_outputs[1] # pooled_output
|
||||
# image_features = self.visual_projection(pooled_output)
|
||||
last_hidden_state = vision_outputs[0]
|
||||
image_features = self.visual_projection(last_hidden_state)
|
||||
|
||||
return image_features
|
||||
|
||||
|
||||
|
||||
@dataclass
|
||||
class ClipModelConfig(BaseModelConfig):
|
||||
_target_: str = "diffsynth.extensions.QualityMetric.trainer.models.clip_model.CLIPModel"
|
||||
pretrained_model_name_or_path: str ="checkpoints/clip-vit-base-patch32"
|
||||
|
||||
|
||||
class CLIPModel(nn.Module):
|
||||
def __init__(self, ckpt, config_file=False):
|
||||
super().__init__()
|
||||
if config_file is None:
|
||||
self.model = XCLIPModel.from_pretrained(ckpt)
|
||||
else:
|
||||
with open(os.path.join(ckpt, "config.json"), "r", encoding="utf-8") as f:
|
||||
config = json.load(f)
|
||||
config = CLIPConfig(**config)
|
||||
self.model = XCLIPModel._from_config(config)
|
||||
self.cross_model = Cross_model(dim=1024, layer_num=4, heads=16)
|
||||
|
||||
def get_text_features(self, *args, **kwargs):
|
||||
return self.model.get_text_features(*args, **kwargs)
|
||||
|
||||
def get_image_features(self, *args, **kwargs):
|
||||
return self.model.get_image_features(*args, **kwargs)
|
||||
|
||||
def forward(self, text_inputs=None, image_inputs=None, condition_inputs=None):
|
||||
outputs = ()
|
||||
|
||||
text_f, text_EOS = self.model.get_text_features(text_inputs) # B*77*1024
|
||||
outputs += text_EOS,
|
||||
|
||||
image_f = self.model.get_image_features(image_inputs.half()) # 2B*257*1024
|
||||
condition_f, _ = self.model.get_text_features(condition_inputs) # B*5*1024
|
||||
|
||||
sim_text_condition = einsum('b i d, b j d -> b j i', text_f, condition_f)
|
||||
sim_text_condition = torch.max(sim_text_condition, dim=1, keepdim=True)[0]
|
||||
sim_text_condition = sim_text_condition / sim_text_condition.max()
|
||||
mask = torch.where(sim_text_condition > 0.01, 0, float('-inf')) # B*1*77
|
||||
|
||||
mask = mask.repeat(1,image_f.shape[1],1) # B*257*77
|
||||
bc = int(image_f.shape[0]/2)
|
||||
|
||||
sim0 = self.cross_model(image_f[:bc,:,:], text_f,mask.half())
|
||||
sim1 = self.cross_model(image_f[bc:,:,:], text_f,mask.half())
|
||||
outputs += sim0[:,0,:],
|
||||
outputs += sim1[:,0,:],
|
||||
|
||||
return outputs
|
||||
|
||||
@property
|
||||
def logit_scale(self):
|
||||
return self.model.logit_scale
|
||||
|
||||
def save(self, path):
|
||||
self.model.save_pretrained(path)
|
||||
|
||||
@@ -1,292 +0,0 @@
|
||||
import torch
|
||||
from torch import einsum, nn
|
||||
import torch.nn.functional as F
|
||||
from einops import rearrange, repeat
|
||||
|
||||
# helper functions
|
||||
|
||||
def exists(val):
|
||||
return val is not None
|
||||
|
||||
def default(val, d):
|
||||
return val if exists(val) else d
|
||||
|
||||
# normalization
|
||||
# they use layernorm without bias, something that pytorch does not offer
|
||||
|
||||
|
||||
class LayerNorm(nn.Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
self.weight = nn.Parameter(torch.ones(dim))
|
||||
self.register_buffer("bias", torch.zeros(dim))
|
||||
|
||||
def forward(self, x):
|
||||
return F.layer_norm(x, x.shape[-1:], self.weight, self.bias)
|
||||
|
||||
# residual
|
||||
|
||||
|
||||
class Residual(nn.Module):
|
||||
def __init__(self, fn):
|
||||
super().__init__()
|
||||
self.fn = fn
|
||||
|
||||
def forward(self, x, *args, **kwargs):
|
||||
return self.fn(x, *args, **kwargs) + x
|
||||
|
||||
|
||||
# rotary positional embedding
|
||||
# https://arxiv.org/abs/2104.09864
|
||||
|
||||
|
||||
class RotaryEmbedding(nn.Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
|
||||
self.register_buffer("inv_freq", inv_freq)
|
||||
|
||||
def forward(self, max_seq_len, *, device):
|
||||
seq = torch.arange(max_seq_len, device=device, dtype=self.inv_freq.dtype)
|
||||
freqs = einsum("i , j -> i j", seq, self.inv_freq)
|
||||
return torch.cat((freqs, freqs), dim=-1)
|
||||
|
||||
|
||||
def rotate_half(x):
|
||||
x = rearrange(x, "... (j d) -> ... j d", j=2)
|
||||
x1, x2 = x.unbind(dim=-2)
|
||||
return torch.cat((-x2, x1), dim=-1)
|
||||
|
||||
|
||||
def apply_rotary_pos_emb(pos, t):
|
||||
return (t * pos.cos()) + (rotate_half(t) * pos.sin())
|
||||
|
||||
|
||||
# classic Noam Shazeer paper, except here they use SwiGLU instead of the more popular GEGLU for gating the feedforward
|
||||
# https://arxiv.org/abs/2002.05202
|
||||
|
||||
|
||||
class SwiGLU(nn.Module):
|
||||
def forward(self, x):
|
||||
x, gate = x.chunk(2, dim=-1)
|
||||
return F.silu(gate) * x
|
||||
|
||||
|
||||
# parallel attention and feedforward with residual
|
||||
# discovered by Wang et al + EleutherAI from GPT-J fame
|
||||
|
||||
class ParallelTransformerBlock(nn.Module):
|
||||
def __init__(self, dim, dim_head=64, heads=8, ff_mult=4):
|
||||
super().__init__()
|
||||
self.norm = LayerNorm(dim)
|
||||
|
||||
attn_inner_dim = dim_head * heads
|
||||
ff_inner_dim = dim * ff_mult
|
||||
self.fused_dims = (attn_inner_dim, dim_head, dim_head, (ff_inner_dim * 2))
|
||||
|
||||
self.heads = heads
|
||||
self.scale = dim_head**-0.5
|
||||
self.rotary_emb = RotaryEmbedding(dim_head)
|
||||
|
||||
self.fused_attn_ff_proj = nn.Linear(dim, sum(self.fused_dims), bias=False)
|
||||
self.attn_out = nn.Linear(attn_inner_dim, dim, bias=False)
|
||||
|
||||
self.ff_out = nn.Sequential(
|
||||
SwiGLU(),
|
||||
nn.Linear(ff_inner_dim, dim, bias=False)
|
||||
)
|
||||
|
||||
self.register_buffer("pos_emb", None, persistent=False)
|
||||
|
||||
|
||||
def get_rotary_embedding(self, n, device):
|
||||
if self.pos_emb is not None and self.pos_emb.shape[-2] >= n:
|
||||
return self.pos_emb[:n]
|
||||
|
||||
pos_emb = self.rotary_emb(n, device=device)
|
||||
self.register_buffer("pos_emb", pos_emb, persistent=False)
|
||||
return pos_emb
|
||||
|
||||
def forward(self, x, attn_mask=None):
|
||||
"""
|
||||
einstein notation
|
||||
b - batch
|
||||
h - heads
|
||||
n, i, j - sequence length (base sequence length, source, target)
|
||||
d - feature dimension
|
||||
"""
|
||||
|
||||
n, device, h = x.shape[1], x.device, self.heads
|
||||
|
||||
# pre layernorm
|
||||
|
||||
x = self.norm(x)
|
||||
|
||||
# attention queries, keys, values, and feedforward inner
|
||||
|
||||
q, k, v, ff = self.fused_attn_ff_proj(x).split(self.fused_dims, dim=-1)
|
||||
|
||||
# split heads
|
||||
# they use multi-query single-key-value attention, yet another Noam Shazeer paper
|
||||
# they found no performance loss past a certain scale, and more efficient decoding obviously
|
||||
# https://arxiv.org/abs/1911.02150
|
||||
|
||||
q = rearrange(q, "b n (h d) -> b h n d", h=h)
|
||||
|
||||
# rotary embeddings
|
||||
|
||||
positions = self.get_rotary_embedding(n, device)
|
||||
q, k = map(lambda t: apply_rotary_pos_emb(positions, t), (q, k))
|
||||
|
||||
# scale
|
||||
|
||||
q = q * self.scale
|
||||
|
||||
# similarity
|
||||
|
||||
sim = einsum("b h i d, b j d -> b h i j", q, k)
|
||||
|
||||
|
||||
# extra attention mask - for masking out attention from text CLS token to padding
|
||||
|
||||
if exists(attn_mask):
|
||||
attn_mask = rearrange(attn_mask, 'b i j -> b 1 i j')
|
||||
sim = sim.masked_fill(~attn_mask, -torch.finfo(sim.dtype).max)
|
||||
|
||||
# attention
|
||||
|
||||
sim = sim - sim.amax(dim=-1, keepdim=True).detach()
|
||||
attn = sim.softmax(dim=-1)
|
||||
|
||||
# aggregate values
|
||||
|
||||
out = einsum("b h i j, b j d -> b h i d", attn, v)
|
||||
|
||||
# merge heads
|
||||
|
||||
out = rearrange(out, "b h n d -> b n (h d)")
|
||||
return self.attn_out(out) + self.ff_out(ff)
|
||||
|
||||
# cross attention - using multi-query + one-headed key / values as in PaLM w/ optional parallel feedforward
|
||||
|
||||
class CrossAttention(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
*,
|
||||
context_dim=None,
|
||||
dim_head=64,
|
||||
heads=12,
|
||||
parallel_ff=False,
|
||||
ff_mult=4,
|
||||
norm_context=False
|
||||
):
|
||||
super().__init__()
|
||||
self.heads = heads
|
||||
self.scale = dim_head ** -0.5
|
||||
inner_dim = heads * dim_head
|
||||
context_dim = default(context_dim, dim)
|
||||
|
||||
self.norm = LayerNorm(dim)
|
||||
self.context_norm = LayerNorm(context_dim) if norm_context else nn.Identity()
|
||||
|
||||
self.to_q = nn.Linear(dim, inner_dim, bias=False)
|
||||
self.to_kv = nn.Linear(context_dim, dim_head * 2, bias=False)
|
||||
self.to_out = nn.Linear(inner_dim, dim, bias=False)
|
||||
|
||||
# whether to have parallel feedforward
|
||||
|
||||
ff_inner_dim = ff_mult * dim
|
||||
|
||||
self.ff = nn.Sequential(
|
||||
nn.Linear(dim, ff_inner_dim * 2, bias=False),
|
||||
SwiGLU(),
|
||||
nn.Linear(ff_inner_dim, dim, bias=False)
|
||||
) if parallel_ff else None
|
||||
|
||||
def forward(self, x, context, mask):
|
||||
"""
|
||||
einstein notation
|
||||
b - batch
|
||||
h - heads
|
||||
n, i, j - sequence length (base sequence length, source, target)
|
||||
d - feature dimension
|
||||
"""
|
||||
|
||||
# pre-layernorm, for queries and context
|
||||
|
||||
x = self.norm(x)
|
||||
context = self.context_norm(context)
|
||||
|
||||
# get queries
|
||||
|
||||
q = self.to_q(x)
|
||||
q = rearrange(q, 'b n (h d) -> b h n d', h = self.heads)
|
||||
|
||||
# scale
|
||||
|
||||
q = q * self.scale
|
||||
|
||||
# get key / values
|
||||
|
||||
k, v = self.to_kv(context).chunk(2, dim=-1)
|
||||
|
||||
# query / key similarity
|
||||
|
||||
sim = einsum('b h i d, b j d -> b h i j', q, k)
|
||||
|
||||
# attention
|
||||
mask = mask.unsqueeze(1).repeat(1,self.heads,1,1)
|
||||
sim = sim + mask # context mask
|
||||
sim = sim - sim.amax(dim=-1, keepdim=True)
|
||||
attn = sim.softmax(dim=-1)
|
||||
|
||||
# aggregate
|
||||
|
||||
out = einsum('b h i j, b j d -> b h i d', attn, v)
|
||||
|
||||
# merge and combine heads
|
||||
|
||||
out = rearrange(out, 'b h n d -> b n (h d)')
|
||||
out = self.to_out(out)
|
||||
|
||||
# add parallel feedforward (for multimodal layers)
|
||||
|
||||
if exists(self.ff):
|
||||
out = out + self.ff(x)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class Cross_model(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim=512,
|
||||
layer_num=4,
|
||||
dim_head=64,
|
||||
heads=8,
|
||||
ff_mult=4
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.layers = nn.ModuleList([])
|
||||
|
||||
|
||||
for ind in range(layer_num):
|
||||
self.layers.append(nn.ModuleList([
|
||||
Residual(CrossAttention(dim=dim, dim_head=dim_head, heads=heads, parallel_ff=True, ff_mult=ff_mult)),
|
||||
Residual(ParallelTransformerBlock(dim=dim, dim_head=dim_head, heads=heads, ff_mult=ff_mult))
|
||||
]))
|
||||
|
||||
def forward(
|
||||
self,
|
||||
query_tokens,
|
||||
context_tokens,
|
||||
mask
|
||||
):
|
||||
|
||||
for cross_attn, self_attn_ff in self.layers:
|
||||
query_tokens = cross_attn(query_tokens, context_tokens,mask)
|
||||
query_tokens = self_attn_ff(query_tokens)
|
||||
|
||||
return query_tokens
|
||||
@@ -1,45 +0,0 @@
|
||||
import torch
|
||||
|
||||
|
||||
|
||||
class GeneralLoRALoader:
|
||||
def __init__(self, device="cpu", torch_dtype=torch.float32):
|
||||
self.device = device
|
||||
self.torch_dtype = torch_dtype
|
||||
|
||||
|
||||
def get_name_dict(self, lora_state_dict):
|
||||
lora_name_dict = {}
|
||||
for key in lora_state_dict:
|
||||
if ".lora_B." not in key:
|
||||
continue
|
||||
keys = key.split(".")
|
||||
if len(keys) > keys.index("lora_B") + 2:
|
||||
keys.pop(keys.index("lora_B") + 1)
|
||||
keys.pop(keys.index("lora_B"))
|
||||
if keys[0] == "diffusion_model":
|
||||
keys.pop(0)
|
||||
keys.pop(-1)
|
||||
target_name = ".".join(keys)
|
||||
lora_name_dict[target_name] = (key, key.replace(".lora_B.", ".lora_A."))
|
||||
return lora_name_dict
|
||||
|
||||
|
||||
def load(self, model: torch.nn.Module, state_dict_lora, alpha=1.0):
|
||||
updated_num = 0
|
||||
lora_name_dict = self.get_name_dict(state_dict_lora)
|
||||
for name, module in model.named_modules():
|
||||
if name in lora_name_dict:
|
||||
weight_up = state_dict_lora[lora_name_dict[name][0]].to(device=self.device, dtype=self.torch_dtype)
|
||||
weight_down = state_dict_lora[lora_name_dict[name][1]].to(device=self.device, dtype=self.torch_dtype)
|
||||
if len(weight_up.shape) == 4:
|
||||
weight_up = weight_up.squeeze(3).squeeze(2)
|
||||
weight_down = weight_down.squeeze(3).squeeze(2)
|
||||
weight_lora = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
|
||||
else:
|
||||
weight_lora = alpha * torch.mm(weight_up, weight_down)
|
||||
state_dict = module.state_dict()
|
||||
state_dict["weight"] = state_dict["weight"].to(device=self.device, dtype=self.torch_dtype) + weight_lora
|
||||
module.load_state_dict(state_dict)
|
||||
updated_num += 1
|
||||
print(f"{updated_num} tensors are updated by LoRA.")
|
||||
@@ -1,324 +0,0 @@
|
||||
import torch, math
|
||||
from . import GeneralLoRALoader
|
||||
from ..utils import ModelConfig
|
||||
from ..models.utils import load_state_dict
|
||||
from typing import Union
|
||||
|
||||
|
||||
class FluxLoRALoader(GeneralLoRALoader):
|
||||
def __init__(self, device="cpu", torch_dtype=torch.float32):
|
||||
super().__init__(device=device, torch_dtype=torch_dtype)
|
||||
|
||||
self.diffusers_rename_dict = {
|
||||
"transformer.single_transformer_blocks.blockid.attn.to_k.lora_A.weight":"single_blocks.blockid.a_to_k.lora_A.default.weight",
|
||||
"transformer.single_transformer_blocks.blockid.attn.to_k.lora_B.weight":"single_blocks.blockid.a_to_k.lora_B.default.weight",
|
||||
"transformer.single_transformer_blocks.blockid.attn.to_q.lora_A.weight":"single_blocks.blockid.a_to_q.lora_A.default.weight",
|
||||
"transformer.single_transformer_blocks.blockid.attn.to_q.lora_B.weight":"single_blocks.blockid.a_to_q.lora_B.default.weight",
|
||||
"transformer.single_transformer_blocks.blockid.attn.to_v.lora_A.weight":"single_blocks.blockid.a_to_v.lora_A.default.weight",
|
||||
"transformer.single_transformer_blocks.blockid.attn.to_v.lora_B.weight":"single_blocks.blockid.a_to_v.lora_B.default.weight",
|
||||
"transformer.single_transformer_blocks.blockid.norm.linear.lora_A.weight":"single_blocks.blockid.norm.linear.lora_A.default.weight",
|
||||
"transformer.single_transformer_blocks.blockid.norm.linear.lora_B.weight":"single_blocks.blockid.norm.linear.lora_B.default.weight",
|
||||
"transformer.single_transformer_blocks.blockid.proj_mlp.lora_A.weight":"single_blocks.blockid.proj_in_besides_attn.lora_A.default.weight",
|
||||
"transformer.single_transformer_blocks.blockid.proj_mlp.lora_B.weight":"single_blocks.blockid.proj_in_besides_attn.lora_B.default.weight",
|
||||
"transformer.single_transformer_blocks.blockid.proj_out.lora_A.weight":"single_blocks.blockid.proj_out.lora_A.default.weight",
|
||||
"transformer.single_transformer_blocks.blockid.proj_out.lora_B.weight":"single_blocks.blockid.proj_out.lora_B.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.add_k_proj.lora_A.weight":"blocks.blockid.attn.b_to_k.lora_A.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.add_k_proj.lora_B.weight":"blocks.blockid.attn.b_to_k.lora_B.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.add_q_proj.lora_A.weight":"blocks.blockid.attn.b_to_q.lora_A.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.add_q_proj.lora_B.weight":"blocks.blockid.attn.b_to_q.lora_B.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.add_v_proj.lora_A.weight":"blocks.blockid.attn.b_to_v.lora_A.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.add_v_proj.lora_B.weight":"blocks.blockid.attn.b_to_v.lora_B.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.to_add_out.lora_A.weight":"blocks.blockid.attn.b_to_out.lora_A.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.to_add_out.lora_B.weight":"blocks.blockid.attn.b_to_out.lora_B.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.to_k.lora_A.weight":"blocks.blockid.attn.a_to_k.lora_A.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.to_k.lora_B.weight":"blocks.blockid.attn.a_to_k.lora_B.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.to_out.0.lora_A.weight":"blocks.blockid.attn.a_to_out.lora_A.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.to_out.0.lora_B.weight":"blocks.blockid.attn.a_to_out.lora_B.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.to_q.lora_A.weight":"blocks.blockid.attn.a_to_q.lora_A.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.to_q.lora_B.weight":"blocks.blockid.attn.a_to_q.lora_B.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.to_v.lora_A.weight":"blocks.blockid.attn.a_to_v.lora_A.default.weight",
|
||||
"transformer.transformer_blocks.blockid.attn.to_v.lora_B.weight":"blocks.blockid.attn.a_to_v.lora_B.default.weight",
|
||||
"transformer.transformer_blocks.blockid.ff.net.0.proj.lora_A.weight":"blocks.blockid.ff_a.0.lora_A.default.weight",
|
||||
"transformer.transformer_blocks.blockid.ff.net.0.proj.lora_B.weight":"blocks.blockid.ff_a.0.lora_B.default.weight",
|
||||
"transformer.transformer_blocks.blockid.ff.net.2.lora_A.weight":"blocks.blockid.ff_a.2.lora_A.default.weight",
|
||||
"transformer.transformer_blocks.blockid.ff.net.2.lora_B.weight":"blocks.blockid.ff_a.2.lora_B.default.weight",
|
||||
"transformer.transformer_blocks.blockid.ff_context.net.0.proj.lora_A.weight":"blocks.blockid.ff_b.0.lora_A.default.weight",
|
||||
"transformer.transformer_blocks.blockid.ff_context.net.0.proj.lora_B.weight":"blocks.blockid.ff_b.0.lora_B.default.weight",
|
||||
"transformer.transformer_blocks.blockid.ff_context.net.2.lora_A.weight":"blocks.blockid.ff_b.2.lora_A.default.weight",
|
||||
"transformer.transformer_blocks.blockid.ff_context.net.2.lora_B.weight":"blocks.blockid.ff_b.2.lora_B.default.weight",
|
||||
"transformer.transformer_blocks.blockid.norm1.linear.lora_A.weight":"blocks.blockid.norm1_a.linear.lora_A.default.weight",
|
||||
"transformer.transformer_blocks.blockid.norm1.linear.lora_B.weight":"blocks.blockid.norm1_a.linear.lora_B.default.weight",
|
||||
"transformer.transformer_blocks.blockid.norm1_context.linear.lora_A.weight":"blocks.blockid.norm1_b.linear.lora_A.default.weight",
|
||||
"transformer.transformer_blocks.blockid.norm1_context.linear.lora_B.weight":"blocks.blockid.norm1_b.linear.lora_B.default.weight",
|
||||
}
|
||||
|
||||
self.civitai_rename_dict = {
|
||||
"lora_unet_double_blocks_blockid_img_mod_lin.lora_down.weight": "blocks.blockid.norm1_a.linear.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_mod_lin.lora_up.weight": "blocks.blockid.norm1_a.linear.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_mod_lin.lora_down.weight": "blocks.blockid.norm1_b.linear.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_mod_lin.lora_up.weight": "blocks.blockid.norm1_b.linear.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_attn_qkv.lora_down.weight": "blocks.blockid.attn.a_to_qkv.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_attn_qkv.lora_up.weight": "blocks.blockid.attn.a_to_qkv.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_attn_qkv.lora_down.weight": "blocks.blockid.attn.b_to_qkv.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_attn_qkv.lora_up.weight": "blocks.blockid.attn.b_to_qkv.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_attn_proj.lora_down.weight": "blocks.blockid.attn.a_to_out.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_attn_proj.lora_up.weight": "blocks.blockid.attn.a_to_out.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_attn_proj.lora_down.weight": "blocks.blockid.attn.b_to_out.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_attn_proj.lora_up.weight": "blocks.blockid.attn.b_to_out.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_mlp_0.lora_down.weight": "blocks.blockid.ff_a.0.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_mlp_0.lora_up.weight": "blocks.blockid.ff_a.0.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_mlp_2.lora_down.weight": "blocks.blockid.ff_a.2.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_mlp_2.lora_up.weight": "blocks.blockid.ff_a.2.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_mlp_0.lora_down.weight": "blocks.blockid.ff_b.0.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_mlp_0.lora_up.weight": "blocks.blockid.ff_b.0.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_mlp_2.lora_down.weight": "blocks.blockid.ff_b.2.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_mlp_2.lora_up.weight": "blocks.blockid.ff_b.2.lora_B.default.weight",
|
||||
"lora_unet_single_blocks_blockid_modulation_lin.lora_down.weight": "single_blocks.blockid.norm.linear.lora_A.default.weight",
|
||||
"lora_unet_single_blocks_blockid_modulation_lin.lora_up.weight": "single_blocks.blockid.norm.linear.lora_B.default.weight",
|
||||
"lora_unet_single_blocks_blockid_linear1.lora_down.weight": "single_blocks.blockid.to_qkv_mlp.lora_A.default.weight",
|
||||
"lora_unet_single_blocks_blockid_linear1.lora_up.weight": "single_blocks.blockid.to_qkv_mlp.lora_B.default.weight",
|
||||
"lora_unet_single_blocks_blockid_linear2.lora_down.weight": "single_blocks.blockid.proj_out.lora_A.default.weight",
|
||||
"lora_unet_single_blocks_blockid_linear2.lora_up.weight": "single_blocks.blockid.proj_out.lora_B.default.weight",
|
||||
}
|
||||
|
||||
def load(self, model: torch.nn.Module, state_dict_lora, alpha=1.0):
|
||||
super().load(model, state_dict_lora, alpha)
|
||||
|
||||
|
||||
def convert_state_dict(self,state_dict):
|
||||
|
||||
def guess_block_id(name,model_resource):
|
||||
if model_resource == 'civitai':
|
||||
names = name.split("_")
|
||||
for i in names:
|
||||
if i.isdigit():
|
||||
return i, name.replace(f"_{i}_", "_blockid_")
|
||||
if model_resource == 'diffusers':
|
||||
names = name.split(".")
|
||||
for i in names:
|
||||
if i.isdigit():
|
||||
return i, name.replace(f"transformer_blocks.{i}.", "transformer_blocks.blockid.")
|
||||
return None, None
|
||||
|
||||
def guess_resource(state_dict):
|
||||
for k in state_dict:
|
||||
if "lora_unet_" in k:
|
||||
return 'civitai'
|
||||
elif k.startswith("transformer."):
|
||||
return 'diffusers'
|
||||
else:
|
||||
None
|
||||
|
||||
model_resource = guess_resource(state_dict)
|
||||
if model_resource is None:
|
||||
return state_dict
|
||||
|
||||
rename_dict = self.diffusers_rename_dict if model_resource == 'diffusers' else self.civitai_rename_dict
|
||||
def guess_alpha(state_dict):
|
||||
for name, param in state_dict.items():
|
||||
if ".alpha" in name:
|
||||
for suffix in [".lora_down.weight", ".lora_A.weight"]:
|
||||
name_ = name.replace(".alpha", suffix)
|
||||
if name_ in state_dict:
|
||||
lora_alpha = param.item() / state_dict[name_].shape[0]
|
||||
lora_alpha = math.sqrt(lora_alpha)
|
||||
return lora_alpha
|
||||
|
||||
return 1
|
||||
|
||||
alpha = guess_alpha(state_dict)
|
||||
|
||||
state_dict_ = {}
|
||||
for name, param in state_dict.items():
|
||||
block_id, source_name = guess_block_id(name,model_resource)
|
||||
if alpha != 1:
|
||||
param *= alpha
|
||||
if source_name in rename_dict:
|
||||
target_name = rename_dict[source_name]
|
||||
target_name = target_name.replace(".blockid.", f".{block_id}.")
|
||||
state_dict_[target_name] = param
|
||||
else:
|
||||
state_dict_[name] = param
|
||||
|
||||
if model_resource == 'diffusers':
|
||||
for name in list(state_dict_.keys()):
|
||||
if "single_blocks." in name and ".a_to_q." in name:
|
||||
mlp = state_dict_.get(name.replace(".a_to_q.", ".proj_in_besides_attn."), None)
|
||||
if mlp is None:
|
||||
dim = 4
|
||||
if 'lora_A' in name:
|
||||
dim = 1
|
||||
mlp = torch.zeros(dim * state_dict_[name].shape[0],
|
||||
*state_dict_[name].shape[1:],
|
||||
dtype=state_dict_[name].dtype)
|
||||
else:
|
||||
state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn."))
|
||||
if 'lora_A' in name:
|
||||
param = torch.concat([
|
||||
state_dict_.pop(name),
|
||||
state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")),
|
||||
state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")),
|
||||
mlp,
|
||||
], dim=0)
|
||||
elif 'lora_B' in name:
|
||||
d, r = state_dict_[name].shape
|
||||
param = torch.zeros((3*d+mlp.shape[0], 3*r+mlp.shape[1]), dtype=state_dict_[name].dtype, device=state_dict_[name].device)
|
||||
param[:d, :r] = state_dict_.pop(name)
|
||||
param[d:2*d, r:2*r] = state_dict_.pop(name.replace(".a_to_q.", ".a_to_k."))
|
||||
param[2*d:3*d, 2*r:3*r] = state_dict_.pop(name.replace(".a_to_q.", ".a_to_v."))
|
||||
param[3*d:, 3*r:] = mlp
|
||||
else:
|
||||
param = torch.concat([
|
||||
state_dict_.pop(name),
|
||||
state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")),
|
||||
state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")),
|
||||
mlp,
|
||||
], dim=0)
|
||||
name_ = name.replace(".a_to_q.", ".to_qkv_mlp.")
|
||||
state_dict_[name_] = param
|
||||
for name in list(state_dict_.keys()):
|
||||
for component in ["a", "b"]:
|
||||
if f".{component}_to_q." in name:
|
||||
name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.")
|
||||
concat_dim = 0
|
||||
if 'lora_A' in name:
|
||||
param = torch.concat([
|
||||
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
|
||||
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
|
||||
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
|
||||
], dim=0)
|
||||
elif 'lora_B' in name:
|
||||
origin = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")]
|
||||
d, r = origin.shape
|
||||
# print(d, r)
|
||||
param = torch.zeros((3*d, 3*r), dtype=origin.dtype, device=origin.device)
|
||||
param[:d, :r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")]
|
||||
param[d:2*d, r:2*r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")]
|
||||
param[2*d:3*d, 2*r:3*r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")]
|
||||
else:
|
||||
param = torch.concat([
|
||||
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
|
||||
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
|
||||
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
|
||||
], dim=0)
|
||||
state_dict_[name_] = param
|
||||
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q."))
|
||||
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k."))
|
||||
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v."))
|
||||
return state_dict_
|
||||
|
||||
|
||||
class LoraMerger(torch.nn.Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
self.weight_base = torch.nn.Parameter(torch.randn((dim,)))
|
||||
self.weight_lora = torch.nn.Parameter(torch.randn((dim,)))
|
||||
self.weight_cross = torch.nn.Parameter(torch.randn((dim,)))
|
||||
self.weight_out = torch.nn.Parameter(torch.ones((dim,)))
|
||||
self.bias = torch.nn.Parameter(torch.randn((dim,)))
|
||||
self.activation = torch.nn.Sigmoid()
|
||||
self.norm_base = torch.nn.LayerNorm(dim, eps=1e-5)
|
||||
self.norm_lora = torch.nn.LayerNorm(dim, eps=1e-5)
|
||||
|
||||
def forward(self, base_output, lora_outputs):
|
||||
norm_base_output = self.norm_base(base_output)
|
||||
norm_lora_outputs = self.norm_lora(lora_outputs)
|
||||
gate = self.activation(
|
||||
norm_base_output * self.weight_base \
|
||||
+ norm_lora_outputs * self.weight_lora \
|
||||
+ norm_base_output * norm_lora_outputs * self.weight_cross + self.bias
|
||||
)
|
||||
output = base_output + (self.weight_out * gate * lora_outputs).sum(dim=0)
|
||||
return output
|
||||
|
||||
|
||||
class FluxLoraPatcher(torch.nn.Module):
|
||||
def __init__(self, lora_patterns=None):
|
||||
super().__init__()
|
||||
if lora_patterns is None:
|
||||
lora_patterns = self.default_lora_patterns()
|
||||
model_dict = {}
|
||||
for lora_pattern in lora_patterns:
|
||||
name, dim = lora_pattern["name"], lora_pattern["dim"]
|
||||
model_dict[name.replace(".", "___")] = LoraMerger(dim)
|
||||
self.model_dict = torch.nn.ModuleDict(model_dict)
|
||||
|
||||
def default_lora_patterns(self):
|
||||
lora_patterns = []
|
||||
lora_dict = {
|
||||
"attn.a_to_qkv": 9216, "attn.a_to_out": 3072, "ff_a.0": 12288, "ff_a.2": 3072, "norm1_a.linear": 18432,
|
||||
"attn.b_to_qkv": 9216, "attn.b_to_out": 3072, "ff_b.0": 12288, "ff_b.2": 3072, "norm1_b.linear": 18432,
|
||||
}
|
||||
for i in range(19):
|
||||
for suffix in lora_dict:
|
||||
lora_patterns.append({
|
||||
"name": f"blocks.{i}.{suffix}",
|
||||
"dim": lora_dict[suffix]
|
||||
})
|
||||
lora_dict = {"to_qkv_mlp": 21504, "proj_out": 3072, "norm.linear": 9216}
|
||||
for i in range(38):
|
||||
for suffix in lora_dict:
|
||||
lora_patterns.append({
|
||||
"name": f"single_blocks.{i}.{suffix}",
|
||||
"dim": lora_dict[suffix]
|
||||
})
|
||||
return lora_patterns
|
||||
|
||||
def forward(self, base_output, lora_outputs, name):
|
||||
return self.model_dict[name.replace(".", "___")](base_output, lora_outputs)
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return FluxLoraPatcherStateDictConverter()
|
||||
|
||||
|
||||
class FluxLoraPatcherStateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
return state_dict
|
||||
|
||||
|
||||
class FluxLoRAFuser:
|
||||
def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
|
||||
self.device = device
|
||||
self.torch_dtype = torch_dtype
|
||||
|
||||
def Matrix_Decomposition_lowrank(self, A, k):
|
||||
U, S, V = torch.svd_lowrank(A.float(), q=k)
|
||||
S_k = torch.diag(S[:k])
|
||||
U_hat = U @ S_k
|
||||
return U_hat, V.t()
|
||||
|
||||
def LoRA_State_Dicts_Decomposition(self, lora_state_dicts=[], q=4):
|
||||
lora_1 = lora_state_dicts[0]
|
||||
state_dict_ = {}
|
||||
for k,v in lora_1.items():
|
||||
if 'lora_A.' in k:
|
||||
lora_B_name = k.replace('lora_A.', 'lora_B.')
|
||||
lora_B = lora_1[lora_B_name]
|
||||
weight = torch.mm(lora_B, v)
|
||||
for lora_dict in lora_state_dicts[1:]:
|
||||
lora_A_ = lora_dict[k]
|
||||
lora_B_ = lora_dict[lora_B_name]
|
||||
weight_ = torch.mm(lora_B_, lora_A_)
|
||||
weight += weight_
|
||||
new_B, new_A = self.Matrix_Decomposition_lowrank(weight, q)
|
||||
state_dict_[lora_B_name] = new_B.to(dtype=torch.bfloat16)
|
||||
state_dict_[k] = new_A.to(dtype=torch.bfloat16)
|
||||
return state_dict_
|
||||
|
||||
def __call__(self, lora_configs: list[Union[ModelConfig, str]]):
|
||||
loras = []
|
||||
loader = FluxLoRALoader(torch_dtype=self.torch_dtype, device=self.device)
|
||||
for lora_config in lora_configs:
|
||||
if isinstance(lora_config, str):
|
||||
lora = load_state_dict(lora_config, torch_dtype=self.torch_dtype, device=self.device)
|
||||
else:
|
||||
lora_config.download_if_necessary()
|
||||
lora = load_state_dict(lora_config.path, torch_dtype=self.torch_dtype, device=self.device)
|
||||
lora = loader.convert_state_dict(lora)
|
||||
loras.append(lora)
|
||||
lora = self.LoRA_State_Dicts_Decomposition(loras)
|
||||
return lora
|
||||
@@ -283,7 +283,7 @@ class CogDiT(torch.nn.Module):
|
||||
return value
|
||||
|
||||
|
||||
def forward(self, hidden_states, timestep, prompt_emb, image_rotary_emb=None, tiled=False, tile_size=90, tile_stride=30, use_gradient_checkpointing=False):
|
||||
def forward(self, hidden_states, timestep, prompt_emb, image_rotary_emb=None, tiled=False, tile_size=90, tile_stride=30):
|
||||
if tiled:
|
||||
return TileWorker2Dto3D().tiled_forward(
|
||||
forward_fn=lambda x: self.forward(x, timestep, prompt_emb),
|
||||
@@ -298,21 +298,8 @@ class CogDiT(torch.nn.Module):
|
||||
hidden_states = self.patchify(hidden_states)
|
||||
time_emb = self.time_embedder(timestep, dtype=hidden_states.dtype)
|
||||
prompt_emb = self.context_embedder(prompt_emb)
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
return module(*inputs)
|
||||
return custom_forward
|
||||
|
||||
for block in self.blocks:
|
||||
if self.training and use_gradient_checkpointing:
|
||||
hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
hidden_states, prompt_emb, time_emb, image_rotary_emb,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
hidden_states, prompt_emb = block(hidden_states, prompt_emb, time_emb, image_rotary_emb)
|
||||
hidden_states, prompt_emb = block(hidden_states, prompt_emb, time_emb, image_rotary_emb)
|
||||
|
||||
hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
|
||||
hidden_states = self.norm_final(hidden_states)
|
||||
|
||||
@@ -8,32 +8,27 @@ from ..configs.model_config import preset_models_on_huggingface, preset_models_o
|
||||
|
||||
def download_from_modelscope(model_id, origin_file_path, local_dir):
|
||||
os.makedirs(local_dir, exist_ok=True)
|
||||
file_name = os.path.basename(origin_file_path)
|
||||
if file_name in os.listdir(local_dir):
|
||||
print(f" {file_name} has been already in {local_dir}.")
|
||||
if os.path.basename(origin_file_path) in os.listdir(local_dir):
|
||||
print(f" {os.path.basename(origin_file_path)} has been already in {local_dir}.")
|
||||
return
|
||||
else:
|
||||
print(f" Start downloading {os.path.join(local_dir, file_name)}")
|
||||
snapshot_download(model_id, allow_file_pattern=origin_file_path, local_dir=local_dir)
|
||||
downloaded_file_path = os.path.join(local_dir, origin_file_path)
|
||||
target_file_path = os.path.join(local_dir, os.path.split(origin_file_path)[-1])
|
||||
if downloaded_file_path != target_file_path:
|
||||
shutil.move(downloaded_file_path, target_file_path)
|
||||
shutil.rmtree(os.path.join(local_dir, origin_file_path.split("/")[0]))
|
||||
print(f" Start downloading {os.path.join(local_dir, os.path.basename(origin_file_path))}")
|
||||
snapshot_download(model_id, allow_file_pattern=origin_file_path, local_dir=local_dir)
|
||||
downloaded_file_path = os.path.join(local_dir, origin_file_path)
|
||||
target_file_path = os.path.join(local_dir, os.path.split(origin_file_path)[-1])
|
||||
if downloaded_file_path != target_file_path:
|
||||
shutil.move(downloaded_file_path, target_file_path)
|
||||
shutil.rmtree(os.path.join(local_dir, origin_file_path.split("/")[0]))
|
||||
|
||||
|
||||
def download_from_huggingface(model_id, origin_file_path, local_dir):
|
||||
os.makedirs(local_dir, exist_ok=True)
|
||||
file_name = os.path.basename(origin_file_path)
|
||||
if file_name in os.listdir(local_dir):
|
||||
print(f" {file_name} has been already in {local_dir}.")
|
||||
if os.path.basename(origin_file_path) in os.listdir(local_dir):
|
||||
print(f" {os.path.basename(origin_file_path)} has been already in {local_dir}.")
|
||||
return
|
||||
else:
|
||||
print(f" Start downloading {os.path.join(local_dir, file_name)}")
|
||||
hf_hub_download(model_id, origin_file_path, local_dir=local_dir)
|
||||
downloaded_file_path = os.path.join(local_dir, origin_file_path)
|
||||
target_file_path = os.path.join(local_dir, file_name)
|
||||
if downloaded_file_path != target_file_path:
|
||||
shutil.move(downloaded_file_path, target_file_path)
|
||||
shutil.rmtree(os.path.join(local_dir, origin_file_path.split("/")[0]))
|
||||
print(f" Start downloading {os.path.join(local_dir, os.path.basename(origin_file_path))}")
|
||||
hf_hub_download(model_id, origin_file_path, local_dir=local_dir)
|
||||
|
||||
|
||||
Preset_model_website: TypeAlias = Literal[
|
||||
@@ -50,47 +45,16 @@ website_to_download_fn = {
|
||||
}
|
||||
|
||||
|
||||
def download_customized_models(
|
||||
model_id,
|
||||
origin_file_path,
|
||||
local_dir,
|
||||
downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"],
|
||||
):
|
||||
downloaded_files = []
|
||||
for website in downloading_priority:
|
||||
# Check if the file is downloaded.
|
||||
file_to_download = os.path.join(local_dir, os.path.basename(origin_file_path))
|
||||
if file_to_download in downloaded_files:
|
||||
continue
|
||||
# Download
|
||||
website_to_download_fn[website](model_id, origin_file_path, local_dir)
|
||||
if os.path.basename(origin_file_path) in os.listdir(local_dir):
|
||||
downloaded_files.append(file_to_download)
|
||||
return downloaded_files
|
||||
|
||||
|
||||
def download_models(
|
||||
model_id_list: List[Preset_model_id] = [],
|
||||
downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"],
|
||||
):
|
||||
print(f"Downloading models: {model_id_list}")
|
||||
downloaded_files = []
|
||||
load_files = []
|
||||
|
||||
for model_id in model_id_list:
|
||||
for website in downloading_priority:
|
||||
if model_id in website_to_preset_models[website]:
|
||||
|
||||
# Parse model metadata
|
||||
model_metadata = website_to_preset_models[website][model_id]
|
||||
if isinstance(model_metadata, list):
|
||||
file_data = model_metadata
|
||||
else:
|
||||
file_data = model_metadata.get("file_list", [])
|
||||
|
||||
# Try downloading the model from this website.
|
||||
model_files = []
|
||||
for model_id, origin_file_path, local_dir in file_data:
|
||||
for model_id, origin_file_path, local_dir in website_to_preset_models[website][model_id]:
|
||||
# Check if the file is downloaded.
|
||||
file_to_download = os.path.join(local_dir, os.path.basename(origin_file_path))
|
||||
if file_to_download in downloaded_files:
|
||||
@@ -99,13 +63,4 @@ def download_models(
|
||||
website_to_download_fn[website](model_id, origin_file_path, local_dir)
|
||||
if os.path.basename(origin_file_path) in os.listdir(local_dir):
|
||||
downloaded_files.append(file_to_download)
|
||||
model_files.append(file_to_download)
|
||||
|
||||
# If the model is successfully downloaded, break.
|
||||
if len(model_files) > 0:
|
||||
if isinstance(model_metadata, dict) and "load_path" in model_metadata:
|
||||
model_files = model_metadata["load_path"]
|
||||
load_files.extend(model_files)
|
||||
break
|
||||
|
||||
return load_files
|
||||
return downloaded_files
|
||||
|
||||
@@ -1,331 +0,0 @@
|
||||
import torch
|
||||
from einops import rearrange, repeat
|
||||
from .flux_dit import RoPEEmbedding, TimestepEmbeddings, FluxJointTransformerBlock, FluxSingleTransformerBlock, RMSNorm
|
||||
from .utils import hash_state_dict_keys, init_weights_on_device
|
||||
|
||||
|
||||
|
||||
class FluxControlNet(torch.nn.Module):
|
||||
def __init__(self, disable_guidance_embedder=False, num_joint_blocks=5, num_single_blocks=10, num_mode=0, mode_dict={}, additional_input_dim=0):
|
||||
super().__init__()
|
||||
self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56])
|
||||
self.time_embedder = TimestepEmbeddings(256, 3072)
|
||||
self.guidance_embedder = None if disable_guidance_embedder else TimestepEmbeddings(256, 3072)
|
||||
self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072))
|
||||
self.context_embedder = torch.nn.Linear(4096, 3072)
|
||||
self.x_embedder = torch.nn.Linear(64, 3072)
|
||||
|
||||
self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(num_joint_blocks)])
|
||||
self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(num_single_blocks)])
|
||||
|
||||
self.controlnet_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_joint_blocks)])
|
||||
self.controlnet_single_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_single_blocks)])
|
||||
|
||||
self.mode_dict = mode_dict
|
||||
self.controlnet_mode_embedder = torch.nn.Embedding(num_mode, 3072) if len(mode_dict) > 0 else None
|
||||
self.controlnet_x_embedder = torch.nn.Linear(64 + additional_input_dim, 3072)
|
||||
|
||||
|
||||
def prepare_image_ids(self, latents):
|
||||
batch_size, _, height, width = latents.shape
|
||||
latent_image_ids = torch.zeros(height // 2, width // 2, 3)
|
||||
latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
|
||||
latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
|
||||
|
||||
latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
|
||||
|
||||
latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1)
|
||||
latent_image_ids = latent_image_ids.reshape(
|
||||
batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
|
||||
)
|
||||
latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype)
|
||||
|
||||
return latent_image_ids
|
||||
|
||||
|
||||
def patchify(self, hidden_states):
|
||||
hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
|
||||
return hidden_states
|
||||
|
||||
|
||||
def align_res_stack_to_original_blocks(self, res_stack, num_blocks, hidden_states):
|
||||
if len(res_stack) == 0:
|
||||
return [torch.zeros_like(hidden_states)] * num_blocks
|
||||
interval = (num_blocks + len(res_stack) - 1) // len(res_stack)
|
||||
aligned_res_stack = [res_stack[block_id // interval] for block_id in range(num_blocks)]
|
||||
return aligned_res_stack
|
||||
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
controlnet_conditioning,
|
||||
timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None,
|
||||
processor_id=None,
|
||||
tiled=False, tile_size=128, tile_stride=64,
|
||||
**kwargs
|
||||
):
|
||||
if image_ids is None:
|
||||
image_ids = self.prepare_image_ids(hidden_states)
|
||||
|
||||
conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb)
|
||||
if self.guidance_embedder is not None:
|
||||
guidance = guidance * 1000
|
||||
conditioning = conditioning + self.guidance_embedder(guidance, hidden_states.dtype)
|
||||
prompt_emb = self.context_embedder(prompt_emb)
|
||||
if self.controlnet_mode_embedder is not None: # Different from FluxDiT
|
||||
processor_id = torch.tensor([self.mode_dict[processor_id]], dtype=torch.int)
|
||||
processor_id = repeat(processor_id, "D -> B D", B=1).to(text_ids.device)
|
||||
prompt_emb = torch.concat([self.controlnet_mode_embedder(processor_id), prompt_emb], dim=1)
|
||||
text_ids = torch.cat([text_ids[:, :1], text_ids], dim=1)
|
||||
image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
|
||||
|
||||
hidden_states = self.patchify(hidden_states)
|
||||
hidden_states = self.x_embedder(hidden_states)
|
||||
controlnet_conditioning = self.patchify(controlnet_conditioning) # Different from FluxDiT
|
||||
hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_conditioning) # Different from FluxDiT
|
||||
|
||||
controlnet_res_stack = []
|
||||
for block, controlnet_block in zip(self.blocks, self.controlnet_blocks):
|
||||
hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb)
|
||||
controlnet_res_stack.append(controlnet_block(hidden_states))
|
||||
|
||||
controlnet_single_res_stack = []
|
||||
hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
|
||||
for block, controlnet_block in zip(self.single_blocks, self.controlnet_single_blocks):
|
||||
hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb)
|
||||
controlnet_single_res_stack.append(controlnet_block(hidden_states[:, prompt_emb.shape[1]:]))
|
||||
|
||||
controlnet_res_stack = self.align_res_stack_to_original_blocks(controlnet_res_stack, 19, hidden_states[:, prompt_emb.shape[1]:])
|
||||
controlnet_single_res_stack = self.align_res_stack_to_original_blocks(controlnet_single_res_stack, 38, hidden_states[:, prompt_emb.shape[1]:])
|
||||
|
||||
return controlnet_res_stack, controlnet_single_res_stack
|
||||
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return FluxControlNetStateDictConverter()
|
||||
|
||||
def quantize(self):
|
||||
def cast_to(weight, dtype=None, device=None, copy=False):
|
||||
if device is None or weight.device == device:
|
||||
if not copy:
|
||||
if dtype is None or weight.dtype == dtype:
|
||||
return weight
|
||||
return weight.to(dtype=dtype, copy=copy)
|
||||
|
||||
r = torch.empty_like(weight, dtype=dtype, device=device)
|
||||
r.copy_(weight)
|
||||
return r
|
||||
|
||||
def cast_weight(s, input=None, dtype=None, device=None):
|
||||
if input is not None:
|
||||
if dtype is None:
|
||||
dtype = input.dtype
|
||||
if device is None:
|
||||
device = input.device
|
||||
weight = cast_to(s.weight, dtype, device)
|
||||
return weight
|
||||
|
||||
def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
|
||||
if input is not None:
|
||||
if dtype is None:
|
||||
dtype = input.dtype
|
||||
if bias_dtype is None:
|
||||
bias_dtype = dtype
|
||||
if device is None:
|
||||
device = input.device
|
||||
bias = None
|
||||
weight = cast_to(s.weight, dtype, device)
|
||||
bias = cast_to(s.bias, bias_dtype, device)
|
||||
return weight, bias
|
||||
|
||||
class quantized_layer:
|
||||
class QLinear(torch.nn.Linear):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
def forward(self,input,**kwargs):
|
||||
weight,bias= cast_bias_weight(self,input)
|
||||
return torch.nn.functional.linear(input,weight,bias)
|
||||
|
||||
class QRMSNorm(torch.nn.Module):
|
||||
def __init__(self, module):
|
||||
super().__init__()
|
||||
self.module = module
|
||||
|
||||
def forward(self,hidden_states,**kwargs):
|
||||
weight= cast_weight(self.module,hidden_states)
|
||||
input_dtype = hidden_states.dtype
|
||||
variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
|
||||
hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps)
|
||||
hidden_states = hidden_states.to(input_dtype) * weight
|
||||
return hidden_states
|
||||
|
||||
class QEmbedding(torch.nn.Embedding):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
def forward(self,input,**kwargs):
|
||||
weight= cast_weight(self,input)
|
||||
return torch.nn.functional.embedding(
|
||||
input, weight, self.padding_idx, self.max_norm,
|
||||
self.norm_type, self.scale_grad_by_freq, self.sparse)
|
||||
|
||||
def replace_layer(model):
|
||||
for name, module in model.named_children():
|
||||
if isinstance(module,quantized_layer.QRMSNorm):
|
||||
continue
|
||||
if isinstance(module, torch.nn.Linear):
|
||||
with init_weights_on_device():
|
||||
new_layer = quantized_layer.QLinear(module.in_features,module.out_features)
|
||||
new_layer.weight = module.weight
|
||||
if module.bias is not None:
|
||||
new_layer.bias = module.bias
|
||||
setattr(model, name, new_layer)
|
||||
elif isinstance(module, RMSNorm):
|
||||
if hasattr(module,"quantized"):
|
||||
continue
|
||||
module.quantized= True
|
||||
new_layer = quantized_layer.QRMSNorm(module)
|
||||
setattr(model, name, new_layer)
|
||||
elif isinstance(module,torch.nn.Embedding):
|
||||
rows, cols = module.weight.shape
|
||||
new_layer = quantized_layer.QEmbedding(
|
||||
num_embeddings=rows,
|
||||
embedding_dim=cols,
|
||||
_weight=module.weight,
|
||||
# _freeze=module.freeze,
|
||||
padding_idx=module.padding_idx,
|
||||
max_norm=module.max_norm,
|
||||
norm_type=module.norm_type,
|
||||
scale_grad_by_freq=module.scale_grad_by_freq,
|
||||
sparse=module.sparse)
|
||||
setattr(model, name, new_layer)
|
||||
else:
|
||||
replace_layer(module)
|
||||
|
||||
replace_layer(self)
|
||||
|
||||
|
||||
|
||||
class FluxControlNetStateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
hash_value = hash_state_dict_keys(state_dict)
|
||||
global_rename_dict = {
|
||||
"context_embedder": "context_embedder",
|
||||
"x_embedder": "x_embedder",
|
||||
"time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0",
|
||||
"time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2",
|
||||
"time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0",
|
||||
"time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2",
|
||||
"time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0",
|
||||
"time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
|
||||
"norm_out.linear": "final_norm_out.linear",
|
||||
"proj_out": "final_proj_out",
|
||||
}
|
||||
rename_dict = {
|
||||
"proj_out": "proj_out",
|
||||
"norm1.linear": "norm1_a.linear",
|
||||
"norm1_context.linear": "norm1_b.linear",
|
||||
"attn.to_q": "attn.a_to_q",
|
||||
"attn.to_k": "attn.a_to_k",
|
||||
"attn.to_v": "attn.a_to_v",
|
||||
"attn.to_out.0": "attn.a_to_out",
|
||||
"attn.add_q_proj": "attn.b_to_q",
|
||||
"attn.add_k_proj": "attn.b_to_k",
|
||||
"attn.add_v_proj": "attn.b_to_v",
|
||||
"attn.to_add_out": "attn.b_to_out",
|
||||
"ff.net.0.proj": "ff_a.0",
|
||||
"ff.net.2": "ff_a.2",
|
||||
"ff_context.net.0.proj": "ff_b.0",
|
||||
"ff_context.net.2": "ff_b.2",
|
||||
"attn.norm_q": "attn.norm_q_a",
|
||||
"attn.norm_k": "attn.norm_k_a",
|
||||
"attn.norm_added_q": "attn.norm_q_b",
|
||||
"attn.norm_added_k": "attn.norm_k_b",
|
||||
}
|
||||
rename_dict_single = {
|
||||
"attn.to_q": "a_to_q",
|
||||
"attn.to_k": "a_to_k",
|
||||
"attn.to_v": "a_to_v",
|
||||
"attn.norm_q": "norm_q_a",
|
||||
"attn.norm_k": "norm_k_a",
|
||||
"norm.linear": "norm.linear",
|
||||
"proj_mlp": "proj_in_besides_attn",
|
||||
"proj_out": "proj_out",
|
||||
}
|
||||
state_dict_ = {}
|
||||
for name, param in state_dict.items():
|
||||
if name.endswith(".weight") or name.endswith(".bias"):
|
||||
suffix = ".weight" if name.endswith(".weight") else ".bias"
|
||||
prefix = name[:-len(suffix)]
|
||||
if prefix in global_rename_dict:
|
||||
state_dict_[global_rename_dict[prefix] + suffix] = param
|
||||
elif prefix.startswith("transformer_blocks."):
|
||||
names = prefix.split(".")
|
||||
names[0] = "blocks"
|
||||
middle = ".".join(names[2:])
|
||||
if middle in rename_dict:
|
||||
name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
|
||||
state_dict_[name_] = param
|
||||
elif prefix.startswith("single_transformer_blocks."):
|
||||
names = prefix.split(".")
|
||||
names[0] = "single_blocks"
|
||||
middle = ".".join(names[2:])
|
||||
if middle in rename_dict_single:
|
||||
name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]])
|
||||
state_dict_[name_] = param
|
||||
else:
|
||||
state_dict_[name] = param
|
||||
else:
|
||||
state_dict_[name] = param
|
||||
for name in list(state_dict_.keys()):
|
||||
if ".proj_in_besides_attn." in name:
|
||||
name_ = name.replace(".proj_in_besides_attn.", ".to_qkv_mlp.")
|
||||
param = torch.concat([
|
||||
state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_q.")],
|
||||
state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_k.")],
|
||||
state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_v.")],
|
||||
state_dict_[name],
|
||||
], dim=0)
|
||||
state_dict_[name_] = param
|
||||
state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_q."))
|
||||
state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_k."))
|
||||
state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_v."))
|
||||
state_dict_.pop(name)
|
||||
for name in list(state_dict_.keys()):
|
||||
for component in ["a", "b"]:
|
||||
if f".{component}_to_q." in name:
|
||||
name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.")
|
||||
param = torch.concat([
|
||||
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
|
||||
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
|
||||
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
|
||||
], dim=0)
|
||||
state_dict_[name_] = param
|
||||
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q."))
|
||||
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k."))
|
||||
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v."))
|
||||
if hash_value == "78d18b9101345ff695f312e7e62538c0":
|
||||
extra_kwargs = {"num_mode": 10, "mode_dict": {"canny": 0, "tile": 1, "depth": 2, "blur": 3, "pose": 4, "gray": 5, "lq": 6}}
|
||||
elif hash_value == "b001c89139b5f053c715fe772362dd2a":
|
||||
extra_kwargs = {"num_single_blocks": 0}
|
||||
elif hash_value == "52357cb26250681367488a8954c271e8":
|
||||
extra_kwargs = {"num_joint_blocks": 6, "num_single_blocks": 0, "additional_input_dim": 4}
|
||||
elif hash_value == "0cfd1740758423a2a854d67c136d1e8c":
|
||||
extra_kwargs = {"num_joint_blocks": 4, "num_single_blocks": 1}
|
||||
elif hash_value == "7f9583eb8ba86642abb9a21a4b2c9e16":
|
||||
extra_kwargs = {"num_joint_blocks": 4, "num_single_blocks": 10}
|
||||
elif hash_value == "43ad5aaa27dd4ee01b832ed16773fa52":
|
||||
extra_kwargs = {"num_joint_blocks": 6, "num_single_blocks": 0}
|
||||
else:
|
||||
extra_kwargs = {}
|
||||
return state_dict_, extra_kwargs
|
||||
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
return self.from_diffusers(state_dict)
|
||||
@@ -1,15 +1,8 @@
|
||||
import torch
|
||||
from .sd3_dit import TimestepEmbeddings, AdaLayerNorm, RMSNorm
|
||||
from .sd3_dit import TimestepEmbeddings, AdaLayerNorm
|
||||
from einops import rearrange
|
||||
from .tiler import TileWorker
|
||||
from .utils import init_weights_on_device, hash_state_dict_keys
|
||||
|
||||
def interact_with_ipadapter(hidden_states, q, ip_k, ip_v, scale=1.0):
|
||||
batch_size, num_tokens = hidden_states.shape[0:2]
|
||||
ip_hidden_states = torch.nn.functional.scaled_dot_product_attention(q, ip_k, ip_v)
|
||||
ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, num_tokens, -1)
|
||||
hidden_states = hidden_states + scale * ip_hidden_states
|
||||
return hidden_states
|
||||
|
||||
|
||||
class RoPEEmbedding(torch.nn.Module):
|
||||
@@ -40,8 +33,23 @@ class RoPEEmbedding(torch.nn.Module):
|
||||
n_axes = ids.shape[-1]
|
||||
emb = torch.cat([self.rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], dim=-3)
|
||||
return emb.unsqueeze(1)
|
||||
|
||||
|
||||
|
||||
class RMSNorm(torch.nn.Module):
|
||||
def __init__(self, dim, eps):
|
||||
super().__init__()
|
||||
self.weight = torch.nn.Parameter(torch.ones((dim,)))
|
||||
self.eps = eps
|
||||
|
||||
def forward(self, hidden_states):
|
||||
input_dtype = hidden_states.dtype
|
||||
variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
|
||||
hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
|
||||
hidden_states = hidden_states.to(input_dtype) * self.weight
|
||||
return hidden_states
|
||||
|
||||
|
||||
|
||||
class FluxJointAttention(torch.nn.Module):
|
||||
def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False):
|
||||
@@ -70,7 +78,8 @@ class FluxJointAttention(torch.nn.Module):
|
||||
xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
|
||||
return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
|
||||
|
||||
def forward(self, hidden_states_a, hidden_states_b, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
|
||||
|
||||
def forward(self, hidden_states_a, hidden_states_b, image_rotary_emb):
|
||||
batch_size = hidden_states_a.shape[0]
|
||||
|
||||
# Part A
|
||||
@@ -91,19 +100,17 @@ class FluxJointAttention(torch.nn.Module):
|
||||
|
||||
q, k = self.apply_rope(q, k, image_rotary_emb)
|
||||
|
||||
hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
|
||||
hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v)
|
||||
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
|
||||
hidden_states = hidden_states.to(q.dtype)
|
||||
hidden_states_b, hidden_states_a = hidden_states[:, :hidden_states_b.shape[1]], hidden_states[:, hidden_states_b.shape[1]:]
|
||||
if ipadapter_kwargs_list is not None:
|
||||
hidden_states_a = interact_with_ipadapter(hidden_states_a, q_a, **ipadapter_kwargs_list)
|
||||
hidden_states_a = self.a_to_out(hidden_states_a)
|
||||
if self.only_out_a:
|
||||
return hidden_states_a
|
||||
else:
|
||||
hidden_states_b = self.b_to_out(hidden_states_b)
|
||||
return hidden_states_a, hidden_states_b
|
||||
|
||||
|
||||
|
||||
|
||||
class FluxJointTransformerBlock(torch.nn.Module):
|
||||
@@ -129,12 +136,12 @@ class FluxJointTransformerBlock(torch.nn.Module):
|
||||
)
|
||||
|
||||
|
||||
def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
|
||||
def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb):
|
||||
norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb)
|
||||
norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb)
|
||||
|
||||
# Attention
|
||||
attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b, image_rotary_emb, attn_mask, ipadapter_kwargs_list)
|
||||
attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b, image_rotary_emb)
|
||||
|
||||
# Part A
|
||||
hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
|
||||
@@ -147,7 +154,7 @@ class FluxJointTransformerBlock(torch.nn.Module):
|
||||
hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b)
|
||||
|
||||
return hidden_states_a, hidden_states_b
|
||||
|
||||
|
||||
|
||||
|
||||
class FluxSingleAttention(torch.nn.Module):
|
||||
@@ -184,7 +191,7 @@ class FluxSingleAttention(torch.nn.Module):
|
||||
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
|
||||
hidden_states = hidden_states.to(q.dtype)
|
||||
return hidden_states
|
||||
|
||||
|
||||
|
||||
|
||||
class AdaLayerNormSingle(torch.nn.Module):
|
||||
@@ -200,7 +207,7 @@ class AdaLayerNormSingle(torch.nn.Module):
|
||||
shift_msa, scale_msa, gate_msa = emb.chunk(3, dim=1)
|
||||
x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
|
||||
return x, gate_msa
|
||||
|
||||
|
||||
|
||||
|
||||
class FluxSingleTransformerBlock(torch.nn.Module):
|
||||
@@ -225,8 +232,8 @@ class FluxSingleTransformerBlock(torch.nn.Module):
|
||||
xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
|
||||
return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
|
||||
|
||||
|
||||
def process_attention(self, hidden_states, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
|
||||
|
||||
def process_attention(self, hidden_states, image_rotary_emb):
|
||||
batch_size = hidden_states.shape[0]
|
||||
|
||||
qkv = hidden_states.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
|
||||
@@ -235,29 +242,27 @@ class FluxSingleTransformerBlock(torch.nn.Module):
|
||||
|
||||
q, k = self.apply_rope(q, k, image_rotary_emb)
|
||||
|
||||
hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
|
||||
hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v)
|
||||
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
|
||||
hidden_states = hidden_states.to(q.dtype)
|
||||
if ipadapter_kwargs_list is not None:
|
||||
hidden_states = interact_with_ipadapter(hidden_states, q, **ipadapter_kwargs_list)
|
||||
return hidden_states
|
||||
|
||||
|
||||
def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
|
||||
def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb):
|
||||
residual = hidden_states_a
|
||||
norm_hidden_states, gate = self.norm(hidden_states_a, emb=temb)
|
||||
hidden_states_a = self.to_qkv_mlp(norm_hidden_states)
|
||||
attn_output, mlp_hidden_states = hidden_states_a[:, :, :self.dim * 3], hidden_states_a[:, :, self.dim * 3:]
|
||||
|
||||
attn_output = self.process_attention(attn_output, image_rotary_emb, attn_mask, ipadapter_kwargs_list)
|
||||
attn_output = self.process_attention(attn_output, image_rotary_emb)
|
||||
mlp_hidden_states = torch.nn.functional.gelu(mlp_hidden_states, approximate="tanh")
|
||||
|
||||
hidden_states_a = torch.cat([attn_output, mlp_hidden_states], dim=2)
|
||||
hidden_states_a = gate.unsqueeze(1) * self.proj_out(hidden_states_a)
|
||||
hidden_states_a = residual + hidden_states_a
|
||||
|
||||
|
||||
return hidden_states_a, hidden_states_b
|
||||
|
||||
|
||||
|
||||
|
||||
class AdaLayerNormContinuous(torch.nn.Module):
|
||||
@@ -276,22 +281,20 @@ class AdaLayerNormContinuous(torch.nn.Module):
|
||||
|
||||
|
||||
class FluxDiT(torch.nn.Module):
|
||||
def __init__(self, disable_guidance_embedder=False, input_dim=64, num_blocks=19):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56])
|
||||
self.time_embedder = TimestepEmbeddings(256, 3072)
|
||||
self.guidance_embedder = None if disable_guidance_embedder else TimestepEmbeddings(256, 3072)
|
||||
self.guidance_embedder = TimestepEmbeddings(256, 3072)
|
||||
self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072))
|
||||
self.context_embedder = torch.nn.Linear(4096, 3072)
|
||||
self.x_embedder = torch.nn.Linear(input_dim, 3072)
|
||||
self.x_embedder = torch.nn.Linear(64, 3072)
|
||||
|
||||
self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(num_blocks)])
|
||||
self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(19)])
|
||||
self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(38)])
|
||||
|
||||
self.final_norm_out = AdaLayerNormContinuous(3072)
|
||||
self.final_proj_out = torch.nn.Linear(3072, 64)
|
||||
|
||||
self.input_dim = input_dim
|
||||
|
||||
|
||||
def patchify(self, hidden_states):
|
||||
@@ -302,7 +305,7 @@ class FluxDiT(torch.nn.Module):
|
||||
def unpatchify(self, hidden_states, height, width):
|
||||
hidden_states = rearrange(hidden_states, "B (H W) (C P Q) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2)
|
||||
return hidden_states
|
||||
|
||||
|
||||
|
||||
def prepare_image_ids(self, latents):
|
||||
batch_size, _, height, width = latents.shape
|
||||
@@ -319,7 +322,7 @@ class FluxDiT(torch.nn.Module):
|
||||
latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype)
|
||||
|
||||
return latent_image_ids
|
||||
|
||||
|
||||
|
||||
def tiled_forward(
|
||||
self,
|
||||
@@ -340,74 +343,11 @@ class FluxDiT(torch.nn.Module):
|
||||
return hidden_states
|
||||
|
||||
|
||||
def construct_mask(self, entity_masks, prompt_seq_len, image_seq_len):
|
||||
N = len(entity_masks)
|
||||
batch_size = entity_masks[0].shape[0]
|
||||
total_seq_len = N * prompt_seq_len + image_seq_len
|
||||
patched_masks = [self.patchify(entity_masks[i]) for i in range(N)]
|
||||
attention_mask = torch.ones((batch_size, total_seq_len, total_seq_len), dtype=torch.bool).to(device=entity_masks[0].device)
|
||||
|
||||
image_start = N * prompt_seq_len
|
||||
image_end = N * prompt_seq_len + image_seq_len
|
||||
# prompt-image mask
|
||||
for i in range(N):
|
||||
prompt_start = i * prompt_seq_len
|
||||
prompt_end = (i + 1) * prompt_seq_len
|
||||
image_mask = torch.sum(patched_masks[i], dim=-1) > 0
|
||||
image_mask = image_mask.unsqueeze(1).repeat(1, prompt_seq_len, 1)
|
||||
# prompt update with image
|
||||
attention_mask[:, prompt_start:prompt_end, image_start:image_end] = image_mask
|
||||
# image update with prompt
|
||||
attention_mask[:, image_start:image_end, prompt_start:prompt_end] = image_mask.transpose(1, 2)
|
||||
# prompt-prompt mask
|
||||
for i in range(N):
|
||||
for j in range(N):
|
||||
if i != j:
|
||||
prompt_start_i = i * prompt_seq_len
|
||||
prompt_end_i = (i + 1) * prompt_seq_len
|
||||
prompt_start_j = j * prompt_seq_len
|
||||
prompt_end_j = (j + 1) * prompt_seq_len
|
||||
attention_mask[:, prompt_start_i:prompt_end_i, prompt_start_j:prompt_end_j] = False
|
||||
|
||||
attention_mask = attention_mask.float()
|
||||
attention_mask[attention_mask == 0] = float('-inf')
|
||||
attention_mask[attention_mask == 1] = 0
|
||||
return attention_mask
|
||||
|
||||
|
||||
def process_entity_masks(self, hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids, repeat_dim):
|
||||
max_masks = 0
|
||||
attention_mask = None
|
||||
prompt_embs = [prompt_emb]
|
||||
if entity_masks is not None:
|
||||
# entity_masks
|
||||
batch_size, max_masks = entity_masks.shape[0], entity_masks.shape[1]
|
||||
entity_masks = entity_masks.repeat(1, 1, repeat_dim, 1, 1)
|
||||
entity_masks = [entity_masks[:, i, None].squeeze(1) for i in range(max_masks)]
|
||||
# global mask
|
||||
global_mask = torch.ones_like(entity_masks[0]).to(device=hidden_states.device, dtype=hidden_states.dtype)
|
||||
entity_masks = entity_masks + [global_mask] # append global to last
|
||||
# attention mask
|
||||
attention_mask = self.construct_mask(entity_masks, prompt_emb.shape[1], hidden_states.shape[1])
|
||||
attention_mask = attention_mask.to(device=hidden_states.device, dtype=hidden_states.dtype)
|
||||
attention_mask = attention_mask.unsqueeze(1)
|
||||
# embds: n_masks * b * seq * d
|
||||
local_embs = [entity_prompt_emb[:, i, None].squeeze(1) for i in range(max_masks)]
|
||||
prompt_embs = local_embs + prompt_embs # append global to last
|
||||
prompt_embs = [self.context_embedder(prompt_emb) for prompt_emb in prompt_embs]
|
||||
prompt_emb = torch.cat(prompt_embs, dim=1)
|
||||
|
||||
# positional embedding
|
||||
text_ids = torch.cat([text_ids] * (max_masks + 1), dim=1)
|
||||
image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
|
||||
return prompt_emb, image_rotary_emb, attention_mask
|
||||
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None,
|
||||
tiled=False, tile_size=128, tile_stride=64, entity_prompt_emb=None, entity_masks=None,
|
||||
tiled=False, tile_size=128, tile_stride=64,
|
||||
use_gradient_checkpointing=False,
|
||||
**kwargs
|
||||
):
|
||||
@@ -418,51 +358,45 @@ class FluxDiT(torch.nn.Module):
|
||||
tile_size=tile_size, tile_stride=tile_stride,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
|
||||
if image_ids is None:
|
||||
image_ids = self.prepare_image_ids(hidden_states)
|
||||
|
||||
conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb)
|
||||
if self.guidance_embedder is not None:
|
||||
guidance = guidance * 1000
|
||||
conditioning = conditioning + self.guidance_embedder(guidance, hidden_states.dtype)
|
||||
|
||||
conditioning = self.time_embedder(timestep, hidden_states.dtype)\
|
||||
+ self.guidance_embedder(guidance, hidden_states.dtype)\
|
||||
+ self.pooled_text_embedder(pooled_prompt_emb)
|
||||
prompt_emb = self.context_embedder(prompt_emb)
|
||||
image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
|
||||
|
||||
height, width = hidden_states.shape[-2:]
|
||||
hidden_states = self.patchify(hidden_states)
|
||||
hidden_states = self.x_embedder(hidden_states)
|
||||
|
||||
if entity_prompt_emb is not None and entity_masks is not None:
|
||||
prompt_emb, image_rotary_emb, attention_mask = self.process_entity_masks(hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids)
|
||||
else:
|
||||
prompt_emb = self.context_embedder(prompt_emb)
|
||||
image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
|
||||
attention_mask = None
|
||||
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
return module(*inputs)
|
||||
return custom_forward
|
||||
|
||||
|
||||
for block in self.blocks:
|
||||
if self.training and use_gradient_checkpointing:
|
||||
hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask,
|
||||
hidden_states, prompt_emb, conditioning, image_rotary_emb,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask)
|
||||
hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb)
|
||||
|
||||
hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
|
||||
for block in self.single_blocks:
|
||||
if self.training and use_gradient_checkpointing:
|
||||
hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask,
|
||||
hidden_states, prompt_emb, conditioning, image_rotary_emb,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask)
|
||||
hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb)
|
||||
hidden_states = hidden_states[:, prompt_emb.shape[1]:]
|
||||
|
||||
hidden_states = self.final_norm_out(hidden_states, conditioning)
|
||||
@@ -472,87 +406,10 @@ class FluxDiT(torch.nn.Module):
|
||||
return hidden_states
|
||||
|
||||
|
||||
def quantize(self):
|
||||
def cast_to(weight, dtype=None, device=None, copy=False):
|
||||
if device is None or weight.device == device:
|
||||
if not copy:
|
||||
if dtype is None or weight.dtype == dtype:
|
||||
return weight
|
||||
return weight.to(dtype=dtype, copy=copy)
|
||||
|
||||
r = torch.empty_like(weight, dtype=dtype, device=device)
|
||||
r.copy_(weight)
|
||||
return r
|
||||
|
||||
def cast_weight(s, input=None, dtype=None, device=None):
|
||||
if input is not None:
|
||||
if dtype is None:
|
||||
dtype = input.dtype
|
||||
if device is None:
|
||||
device = input.device
|
||||
weight = cast_to(s.weight, dtype, device)
|
||||
return weight
|
||||
|
||||
def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
|
||||
if input is not None:
|
||||
if dtype is None:
|
||||
dtype = input.dtype
|
||||
if bias_dtype is None:
|
||||
bias_dtype = dtype
|
||||
if device is None:
|
||||
device = input.device
|
||||
bias = None
|
||||
weight = cast_to(s.weight, dtype, device)
|
||||
bias = cast_to(s.bias, bias_dtype, device)
|
||||
return weight, bias
|
||||
|
||||
class quantized_layer:
|
||||
class Linear(torch.nn.Linear):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
def forward(self,input,**kwargs):
|
||||
weight,bias= cast_bias_weight(self,input)
|
||||
return torch.nn.functional.linear(input,weight,bias)
|
||||
|
||||
class RMSNorm(torch.nn.Module):
|
||||
def __init__(self, module):
|
||||
super().__init__()
|
||||
self.module = module
|
||||
|
||||
def forward(self,hidden_states,**kwargs):
|
||||
weight= cast_weight(self.module,hidden_states)
|
||||
input_dtype = hidden_states.dtype
|
||||
variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
|
||||
hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps)
|
||||
hidden_states = hidden_states.to(input_dtype) * weight
|
||||
return hidden_states
|
||||
|
||||
def replace_layer(model):
|
||||
for name, module in model.named_children():
|
||||
if isinstance(module, torch.nn.Linear):
|
||||
with init_weights_on_device():
|
||||
new_layer = quantized_layer.Linear(module.in_features,module.out_features)
|
||||
new_layer.weight = module.weight
|
||||
if module.bias is not None:
|
||||
new_layer.bias = module.bias
|
||||
# del module
|
||||
setattr(model, name, new_layer)
|
||||
elif isinstance(module, RMSNorm):
|
||||
if hasattr(module,"quantized"):
|
||||
continue
|
||||
module.quantized= True
|
||||
new_layer = quantized_layer.RMSNorm(module)
|
||||
setattr(model, name, new_layer)
|
||||
else:
|
||||
replace_layer(module)
|
||||
|
||||
replace_layer(self)
|
||||
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return FluxDiTStateDictConverter()
|
||||
|
||||
|
||||
|
||||
class FluxDiTStateDictConverter:
|
||||
@@ -629,22 +486,19 @@ class FluxDiTStateDictConverter:
|
||||
else:
|
||||
pass
|
||||
for name in list(state_dict_.keys()):
|
||||
if "single_blocks." in name and ".a_to_q." in name:
|
||||
mlp = state_dict_.get(name.replace(".a_to_q.", ".proj_in_besides_attn."), None)
|
||||
if mlp is None:
|
||||
mlp = torch.zeros(4 * state_dict_[name].shape[0],
|
||||
*state_dict_[name].shape[1:],
|
||||
dtype=state_dict_[name].dtype)
|
||||
else:
|
||||
state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn."))
|
||||
if ".proj_in_besides_attn." in name:
|
||||
name_ = name.replace(".proj_in_besides_attn.", ".to_qkv_mlp.")
|
||||
param = torch.concat([
|
||||
state_dict_.pop(name),
|
||||
state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")),
|
||||
state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")),
|
||||
mlp,
|
||||
state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_q.")],
|
||||
state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_k.")],
|
||||
state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_v.")],
|
||||
state_dict_[name],
|
||||
], dim=0)
|
||||
name_ = name.replace(".a_to_q.", ".to_qkv_mlp.")
|
||||
state_dict_[name_] = param
|
||||
state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_q."))
|
||||
state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_k."))
|
||||
state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_v."))
|
||||
state_dict_.pop(name)
|
||||
for name in list(state_dict_.keys()):
|
||||
for component in ["a", "b"]:
|
||||
if f".{component}_to_q." in name:
|
||||
@@ -659,11 +513,8 @@ class FluxDiTStateDictConverter:
|
||||
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k."))
|
||||
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v."))
|
||||
return state_dict_
|
||||
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
if hash_state_dict_keys(state_dict, with_shape=True) in ["3e6c61b0f9471135fc9c6d6a98e98b6d", "63c969fd37cce769a90aa781fbff5f81"]:
|
||||
dit_state_dict = {key.replace("pipe.dit.", ""): value for key, value in state_dict.items() if key.startswith('pipe.dit.')}
|
||||
return dit_state_dict
|
||||
rename_dict = {
|
||||
"time_in.in_layer.bias": "time_embedder.timestep_embedder.0.bias",
|
||||
"time_in.in_layer.weight": "time_embedder.timestep_embedder.0.weight",
|
||||
@@ -723,8 +574,6 @@ class FluxDiTStateDictConverter:
|
||||
}
|
||||
state_dict_ = {}
|
||||
for name, param in state_dict.items():
|
||||
if name.startswith("model.diffusion_model."):
|
||||
name = name[len("model.diffusion_model."):]
|
||||
names = name.split(".")
|
||||
if name in rename_dict:
|
||||
rename = rename_dict[name]
|
||||
@@ -740,9 +589,5 @@ class FluxDiTStateDictConverter:
|
||||
state_dict_[rename] = param
|
||||
else:
|
||||
pass
|
||||
if "guidance_embedder.timestep_embedder.0.weight" not in state_dict_:
|
||||
return state_dict_, {"disable_guidance_embedder": True}
|
||||
elif "blocks.8.attn.norm_k_a.weight" not in state_dict_:
|
||||
return state_dict_, {"input_dim": 196, "num_blocks": 8}
|
||||
else:
|
||||
return state_dict_
|
||||
return state_dict_
|
||||
|
||||
@@ -1,129 +0,0 @@
|
||||
import math
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
|
||||
# FFN
|
||||
def FeedForward(dim, mult=4):
|
||||
inner_dim = int(dim * mult)
|
||||
return nn.Sequential(
|
||||
nn.LayerNorm(dim),
|
||||
nn.Linear(dim, inner_dim, bias=False),
|
||||
nn.GELU(),
|
||||
nn.Linear(inner_dim, dim, bias=False),
|
||||
)
|
||||
|
||||
|
||||
def reshape_tensor(x, heads):
|
||||
bs, length, width = x.shape
|
||||
#(bs, length, width) --> (bs, length, n_heads, dim_per_head)
|
||||
x = x.view(bs, length, heads, -1)
|
||||
# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
|
||||
x = x.transpose(1, 2)
|
||||
# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
|
||||
x = x.reshape(bs, heads, length, -1)
|
||||
return x
|
||||
|
||||
|
||||
class PerceiverAttention(nn.Module):
|
||||
|
||||
def __init__(self, *, dim, dim_head=64, heads=8):
|
||||
super().__init__()
|
||||
self.scale = dim_head**-0.5
|
||||
self.dim_head = dim_head
|
||||
self.heads = heads
|
||||
inner_dim = dim_head * heads
|
||||
|
||||
self.norm1 = nn.LayerNorm(dim)
|
||||
self.norm2 = nn.LayerNorm(dim)
|
||||
|
||||
self.to_q = nn.Linear(dim, inner_dim, bias=False)
|
||||
self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
|
||||
self.to_out = nn.Linear(inner_dim, dim, bias=False)
|
||||
|
||||
def forward(self, x, latents):
|
||||
"""
|
||||
Args:
|
||||
x (torch.Tensor): image features
|
||||
shape (b, n1, D)
|
||||
latent (torch.Tensor): latent features
|
||||
shape (b, n2, D)
|
||||
"""
|
||||
x = self.norm1(x)
|
||||
latents = self.norm2(latents)
|
||||
|
||||
b, l, _ = latents.shape
|
||||
|
||||
q = self.to_q(latents)
|
||||
kv_input = torch.cat((x, latents), dim=-2)
|
||||
k, v = self.to_kv(kv_input).chunk(2, dim=-1)
|
||||
|
||||
q = reshape_tensor(q, self.heads)
|
||||
k = reshape_tensor(k, self.heads)
|
||||
v = reshape_tensor(v, self.heads)
|
||||
|
||||
# attention
|
||||
scale = 1 / math.sqrt(math.sqrt(self.dim_head))
|
||||
weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
|
||||
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
|
||||
out = weight @ v
|
||||
|
||||
out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
|
||||
|
||||
return self.to_out(out)
|
||||
|
||||
|
||||
class InfiniteYouImageProjector(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim=1280,
|
||||
depth=4,
|
||||
dim_head=64,
|
||||
heads=20,
|
||||
num_queries=8,
|
||||
embedding_dim=512,
|
||||
output_dim=4096,
|
||||
ff_mult=4,
|
||||
):
|
||||
super().__init__()
|
||||
self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
|
||||
self.proj_in = nn.Linear(embedding_dim, dim)
|
||||
|
||||
self.proj_out = nn.Linear(dim, output_dim)
|
||||
self.norm_out = nn.LayerNorm(output_dim)
|
||||
|
||||
self.layers = nn.ModuleList([])
|
||||
for _ in range(depth):
|
||||
self.layers.append(
|
||||
nn.ModuleList([
|
||||
PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
|
||||
FeedForward(dim=dim, mult=ff_mult),
|
||||
]))
|
||||
|
||||
def forward(self, x):
|
||||
|
||||
latents = self.latents.repeat(x.size(0), 1, 1)
|
||||
latents = latents.to(dtype=x.dtype, device=x.device)
|
||||
|
||||
x = self.proj_in(x)
|
||||
|
||||
for attn, ff in self.layers:
|
||||
latents = attn(x, latents) + latents
|
||||
latents = ff(latents) + latents
|
||||
|
||||
latents = self.proj_out(latents)
|
||||
return self.norm_out(latents)
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return FluxInfiniteYouImageProjectorStateDictConverter()
|
||||
|
||||
|
||||
class FluxInfiniteYouImageProjectorStateDictConverter:
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
return state_dict['image_proj']
|
||||
@@ -1,94 +0,0 @@
|
||||
from .svd_image_encoder import SVDImageEncoder
|
||||
from .sd3_dit import RMSNorm
|
||||
from transformers import CLIPImageProcessor
|
||||
import torch
|
||||
|
||||
|
||||
class MLPProjModel(torch.nn.Module):
|
||||
def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
|
||||
super().__init__()
|
||||
|
||||
self.cross_attention_dim = cross_attention_dim
|
||||
self.num_tokens = num_tokens
|
||||
|
||||
self.proj = torch.nn.Sequential(
|
||||
torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
|
||||
torch.nn.GELU(),
|
||||
torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
|
||||
)
|
||||
self.norm = torch.nn.LayerNorm(cross_attention_dim)
|
||||
|
||||
def forward(self, id_embeds):
|
||||
x = self.proj(id_embeds)
|
||||
x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
|
||||
x = self.norm(x)
|
||||
return x
|
||||
|
||||
class IpAdapterModule(torch.nn.Module):
|
||||
def __init__(self, num_attention_heads, attention_head_dim, input_dim):
|
||||
super().__init__()
|
||||
self.num_heads = num_attention_heads
|
||||
self.head_dim = attention_head_dim
|
||||
output_dim = num_attention_heads * attention_head_dim
|
||||
self.to_k_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
|
||||
self.to_v_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
|
||||
self.norm_added_k = RMSNorm(attention_head_dim, eps=1e-5, elementwise_affine=False)
|
||||
|
||||
|
||||
def forward(self, hidden_states):
|
||||
batch_size = hidden_states.shape[0]
|
||||
# ip_k
|
||||
ip_k = self.to_k_ip(hidden_states)
|
||||
ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
ip_k = self.norm_added_k(ip_k)
|
||||
# ip_v
|
||||
ip_v = self.to_v_ip(hidden_states)
|
||||
ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
return ip_k, ip_v
|
||||
|
||||
|
||||
class FluxIpAdapter(torch.nn.Module):
|
||||
def __init__(self, num_attention_heads=24, attention_head_dim=128, cross_attention_dim=4096, num_tokens=128, num_blocks=57):
|
||||
super().__init__()
|
||||
self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(num_attention_heads, attention_head_dim, cross_attention_dim) for _ in range(num_blocks)])
|
||||
self.image_proj = MLPProjModel(cross_attention_dim=cross_attention_dim, id_embeddings_dim=1152, num_tokens=num_tokens)
|
||||
self.set_adapter()
|
||||
|
||||
def set_adapter(self):
|
||||
self.call_block_id = {i:i for i in range(len(self.ipadapter_modules))}
|
||||
|
||||
def forward(self, hidden_states, scale=1.0):
|
||||
hidden_states = self.image_proj(hidden_states)
|
||||
hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1])
|
||||
ip_kv_dict = {}
|
||||
for block_id in self.call_block_id:
|
||||
ipadapter_id = self.call_block_id[block_id]
|
||||
ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states)
|
||||
ip_kv_dict[block_id] = {
|
||||
"ip_k": ip_k,
|
||||
"ip_v": ip_v,
|
||||
"scale": scale
|
||||
}
|
||||
return ip_kv_dict
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return FluxIpAdapterStateDictConverter()
|
||||
|
||||
|
||||
class FluxIpAdapterStateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
state_dict_ = {}
|
||||
for name in state_dict["ip_adapter"]:
|
||||
name_ = 'ipadapter_modules.' + name
|
||||
state_dict_[name_] = state_dict["ip_adapter"][name]
|
||||
for name in state_dict["image_proj"]:
|
||||
name_ = "image_proj." + name
|
||||
state_dict_[name_] = state_dict["image_proj"][name]
|
||||
return state_dict_
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
return self.from_diffusers(state_dict)
|
||||
@@ -1,111 +0,0 @@
|
||||
import torch
|
||||
from .sd_text_encoder import CLIPEncoderLayer
|
||||
|
||||
|
||||
class LoRALayerBlock(torch.nn.Module):
|
||||
def __init__(self, L, dim_in, dim_out):
|
||||
super().__init__()
|
||||
self.x = torch.nn.Parameter(torch.randn(1, L, dim_in))
|
||||
self.layer_norm = torch.nn.LayerNorm(dim_out)
|
||||
|
||||
def forward(self, lora_A, lora_B):
|
||||
x = self.x @ lora_A.T @ lora_B.T
|
||||
x = self.layer_norm(x)
|
||||
return x
|
||||
|
||||
|
||||
class LoRAEmbedder(torch.nn.Module):
|
||||
def __init__(self, lora_patterns=None, L=1, out_dim=2048):
|
||||
super().__init__()
|
||||
if lora_patterns is None:
|
||||
lora_patterns = self.default_lora_patterns()
|
||||
|
||||
model_dict = {}
|
||||
for lora_pattern in lora_patterns:
|
||||
name, dim = lora_pattern["name"], lora_pattern["dim"]
|
||||
model_dict[name.replace(".", "___")] = LoRALayerBlock(L, dim[0], dim[1])
|
||||
self.model_dict = torch.nn.ModuleDict(model_dict)
|
||||
|
||||
proj_dict = {}
|
||||
for lora_pattern in lora_patterns:
|
||||
layer_type, dim = lora_pattern["type"], lora_pattern["dim"]
|
||||
if layer_type not in proj_dict:
|
||||
proj_dict[layer_type.replace(".", "___")] = torch.nn.Linear(dim[1], out_dim)
|
||||
self.proj_dict = torch.nn.ModuleDict(proj_dict)
|
||||
|
||||
self.lora_patterns = lora_patterns
|
||||
|
||||
|
||||
def default_lora_patterns(self):
|
||||
lora_patterns = []
|
||||
lora_dict = {
|
||||
"attn.a_to_qkv": (3072, 9216), "attn.a_to_out": (3072, 3072), "ff_a.0": (3072, 12288), "ff_a.2": (12288, 3072), "norm1_a.linear": (3072, 18432),
|
||||
"attn.b_to_qkv": (3072, 9216), "attn.b_to_out": (3072, 3072), "ff_b.0": (3072, 12288), "ff_b.2": (12288, 3072), "norm1_b.linear": (3072, 18432),
|
||||
}
|
||||
for i in range(19):
|
||||
for suffix in lora_dict:
|
||||
lora_patterns.append({
|
||||
"name": f"blocks.{i}.{suffix}",
|
||||
"dim": lora_dict[suffix],
|
||||
"type": suffix,
|
||||
})
|
||||
lora_dict = {"to_qkv_mlp": (3072, 21504), "proj_out": (15360, 3072), "norm.linear": (3072, 9216)}
|
||||
for i in range(38):
|
||||
for suffix in lora_dict:
|
||||
lora_patterns.append({
|
||||
"name": f"single_blocks.{i}.{suffix}",
|
||||
"dim": lora_dict[suffix],
|
||||
"type": suffix,
|
||||
})
|
||||
return lora_patterns
|
||||
|
||||
def forward(self, lora):
|
||||
lora_emb = []
|
||||
for lora_pattern in self.lora_patterns:
|
||||
name, layer_type = lora_pattern["name"], lora_pattern["type"]
|
||||
lora_A = lora[name + ".lora_A.default.weight"]
|
||||
lora_B = lora[name + ".lora_B.default.weight"]
|
||||
lora_out = self.model_dict[name.replace(".", "___")](lora_A, lora_B)
|
||||
lora_out = self.proj_dict[layer_type.replace(".", "___")](lora_out)
|
||||
lora_emb.append(lora_out)
|
||||
lora_emb = torch.concat(lora_emb, dim=1)
|
||||
return lora_emb
|
||||
|
||||
|
||||
class FluxLoRAEncoder(torch.nn.Module):
|
||||
def __init__(self, embed_dim=4096, encoder_intermediate_size=8192, num_encoder_layers=1, num_embeds_per_lora=16, num_special_embeds=1):
|
||||
super().__init__()
|
||||
self.num_embeds_per_lora = num_embeds_per_lora
|
||||
# embedder
|
||||
self.embedder = LoRAEmbedder(L=num_embeds_per_lora, out_dim=embed_dim)
|
||||
|
||||
# encoders
|
||||
self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=32, head_dim=128) for _ in range(num_encoder_layers)])
|
||||
|
||||
# special embedding
|
||||
self.special_embeds = torch.nn.Parameter(torch.randn(1, num_special_embeds, embed_dim))
|
||||
self.num_special_embeds = num_special_embeds
|
||||
|
||||
# final layer
|
||||
self.final_layer_norm = torch.nn.LayerNorm(embed_dim)
|
||||
self.final_linear = torch.nn.Linear(embed_dim, embed_dim)
|
||||
|
||||
def forward(self, lora):
|
||||
lora_embeds = self.embedder(lora)
|
||||
special_embeds = self.special_embeds.to(dtype=lora_embeds.dtype, device=lora_embeds.device)
|
||||
embeds = torch.concat([special_embeds, lora_embeds], dim=1)
|
||||
for encoder_id, encoder in enumerate(self.encoders):
|
||||
embeds = encoder(embeds)
|
||||
embeds = embeds[:, :self.num_special_embeds]
|
||||
embeds = self.final_layer_norm(embeds)
|
||||
embeds = self.final_linear(embeds)
|
||||
return embeds
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return FluxLoRAEncoderStateDictConverter()
|
||||
|
||||
|
||||
class FluxLoRAEncoderStateDictConverter:
|
||||
def from_civitai(self, state_dict):
|
||||
return state_dict
|
||||
@@ -3,6 +3,26 @@ from transformers import T5EncoderModel, T5Config
|
||||
from .sd_text_encoder import SDTextEncoder
|
||||
|
||||
|
||||
class FluxTextEncoder1(SDTextEncoder):
|
||||
def __init__(self, vocab_size=49408):
|
||||
super().__init__(vocab_size=vocab_size)
|
||||
|
||||
def forward(self, input_ids, clip_skip=2):
|
||||
embeds = self.token_embedding(input_ids) + self.position_embeds
|
||||
attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
|
||||
for encoder_id, encoder in enumerate(self.encoders):
|
||||
embeds = encoder(embeds, attn_mask=attn_mask)
|
||||
if encoder_id + clip_skip == len(self.encoders):
|
||||
hidden_states = embeds
|
||||
embeds = self.final_layer_norm(embeds)
|
||||
pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)]
|
||||
return embeds, pooled_embeds
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return FluxTextEncoder1StateDictConverter()
|
||||
|
||||
|
||||
|
||||
class FluxTextEncoder2(T5EncoderModel):
|
||||
def __init__(self, config):
|
||||
@@ -20,6 +40,47 @@ class FluxTextEncoder2(T5EncoderModel):
|
||||
|
||||
|
||||
|
||||
class FluxTextEncoder1StateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
rename_dict = {
|
||||
"text_model.embeddings.token_embedding.weight": "token_embedding.weight",
|
||||
"text_model.embeddings.position_embedding.weight": "position_embeds",
|
||||
"text_model.final_layer_norm.weight": "final_layer_norm.weight",
|
||||
"text_model.final_layer_norm.bias": "final_layer_norm.bias"
|
||||
}
|
||||
attn_rename_dict = {
|
||||
"self_attn.q_proj": "attn.to_q",
|
||||
"self_attn.k_proj": "attn.to_k",
|
||||
"self_attn.v_proj": "attn.to_v",
|
||||
"self_attn.out_proj": "attn.to_out",
|
||||
"layer_norm1": "layer_norm1",
|
||||
"layer_norm2": "layer_norm2",
|
||||
"mlp.fc1": "fc1",
|
||||
"mlp.fc2": "fc2",
|
||||
}
|
||||
state_dict_ = {}
|
||||
for name in state_dict:
|
||||
if name in rename_dict:
|
||||
param = state_dict[name]
|
||||
if name == "text_model.embeddings.position_embedding.weight":
|
||||
param = param.reshape((1, param.shape[0], param.shape[1]))
|
||||
state_dict_[rename_dict[name]] = param
|
||||
elif name.startswith("text_model.encoder.layers."):
|
||||
param = state_dict[name]
|
||||
names = name.split(".")
|
||||
layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1]
|
||||
name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail])
|
||||
state_dict_[name_] = param
|
||||
return state_dict_
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
return self.from_diffusers(state_dict)
|
||||
|
||||
|
||||
|
||||
class FluxTextEncoder2StateDictConverter():
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@@ -1,60 +0,0 @@
|
||||
import torch
|
||||
from diffsynth.models.svd_unet import TemporalTimesteps
|
||||
|
||||
|
||||
class MultiValueEncoder(torch.nn.Module):
|
||||
def __init__(self, encoders=()):
|
||||
super().__init__()
|
||||
self.encoders = torch.nn.ModuleList(encoders)
|
||||
|
||||
def __call__(self, values, dtype):
|
||||
emb = []
|
||||
for encoder, value in zip(self.encoders, values):
|
||||
if value is not None:
|
||||
value = value.unsqueeze(0)
|
||||
emb.append(encoder(value, dtype))
|
||||
emb = torch.concat(emb, dim=0)
|
||||
return emb
|
||||
|
||||
|
||||
class SingleValueEncoder(torch.nn.Module):
|
||||
def __init__(self, dim_in=256, dim_out=4096, prefer_len=32, computation_device=None):
|
||||
super().__init__()
|
||||
self.prefer_len = prefer_len
|
||||
self.prefer_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device)
|
||||
self.prefer_value_embedder = torch.nn.Sequential(
|
||||
torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out)
|
||||
)
|
||||
self.positional_embedding = torch.nn.Parameter(
|
||||
torch.randn(self.prefer_len, dim_out)
|
||||
)
|
||||
self._initialize_weights()
|
||||
|
||||
def _initialize_weights(self):
|
||||
last_linear = self.prefer_value_embedder[-1]
|
||||
torch.nn.init.zeros_(last_linear.weight)
|
||||
torch.nn.init.zeros_(last_linear.bias)
|
||||
|
||||
def forward(self, value, dtype):
|
||||
value = value * 1000
|
||||
emb = self.prefer_proj(value).to(dtype)
|
||||
emb = self.prefer_value_embedder(emb).squeeze(0)
|
||||
base_embeddings = emb.expand(self.prefer_len, -1)
|
||||
positional_embedding = self.positional_embedding.to(dtype=base_embeddings.dtype, device=base_embeddings.device)
|
||||
learned_embeddings = base_embeddings + positional_embedding
|
||||
return learned_embeddings
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return SingleValueEncoderStateDictConverter()
|
||||
|
||||
|
||||
class SingleValueEncoderStateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
return state_dict
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
return state_dict
|
||||
@@ -1,920 +0,0 @@
|
||||
import torch
|
||||
from .sd3_dit import TimestepEmbeddings, RMSNorm
|
||||
from .utils import init_weights_on_device
|
||||
from einops import rearrange, repeat
|
||||
from tqdm import tqdm
|
||||
from typing import Union, Tuple, List
|
||||
from .utils import hash_state_dict_keys
|
||||
|
||||
|
||||
def HunyuanVideoRope(latents):
|
||||
def _to_tuple(x, dim=2):
|
||||
if isinstance(x, int):
|
||||
return (x,) * dim
|
||||
elif len(x) == dim:
|
||||
return x
|
||||
else:
|
||||
raise ValueError(f"Expected length {dim} or int, but got {x}")
|
||||
|
||||
|
||||
def get_meshgrid_nd(start, *args, dim=2):
|
||||
"""
|
||||
Get n-D meshgrid with start, stop and num.
|
||||
|
||||
Args:
|
||||
start (int or tuple): If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop,
|
||||
step is 1; If len(args) == 2, start is start, args[0] is stop, args[1] is num. For n-dim, start/stop/num
|
||||
should be int or n-tuple. If n-tuple is provided, the meshgrid will be stacked following the dim order in
|
||||
n-tuples.
|
||||
*args: See above.
|
||||
dim (int): Dimension of the meshgrid. Defaults to 2.
|
||||
|
||||
Returns:
|
||||
grid (np.ndarray): [dim, ...]
|
||||
"""
|
||||
if len(args) == 0:
|
||||
# start is grid_size
|
||||
num = _to_tuple(start, dim=dim)
|
||||
start = (0,) * dim
|
||||
stop = num
|
||||
elif len(args) == 1:
|
||||
# start is start, args[0] is stop, step is 1
|
||||
start = _to_tuple(start, dim=dim)
|
||||
stop = _to_tuple(args[0], dim=dim)
|
||||
num = [stop[i] - start[i] for i in range(dim)]
|
||||
elif len(args) == 2:
|
||||
# start is start, args[0] is stop, args[1] is num
|
||||
start = _to_tuple(start, dim=dim) # Left-Top eg: 12,0
|
||||
stop = _to_tuple(args[0], dim=dim) # Right-Bottom eg: 20,32
|
||||
num = _to_tuple(args[1], dim=dim) # Target Size eg: 32,124
|
||||
else:
|
||||
raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}")
|
||||
|
||||
# PyTorch implement of np.linspace(start[i], stop[i], num[i], endpoint=False)
|
||||
axis_grid = []
|
||||
for i in range(dim):
|
||||
a, b, n = start[i], stop[i], num[i]
|
||||
g = torch.linspace(a, b, n + 1, dtype=torch.float32)[:n]
|
||||
axis_grid.append(g)
|
||||
grid = torch.meshgrid(*axis_grid, indexing="ij") # dim x [W, H, D]
|
||||
grid = torch.stack(grid, dim=0) # [dim, W, H, D]
|
||||
|
||||
return grid
|
||||
|
||||
|
||||
def get_1d_rotary_pos_embed(
|
||||
dim: int,
|
||||
pos: Union[torch.FloatTensor, int],
|
||||
theta: float = 10000.0,
|
||||
use_real: bool = False,
|
||||
theta_rescale_factor: float = 1.0,
|
||||
interpolation_factor: float = 1.0,
|
||||
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
|
||||
"""
|
||||
Precompute the frequency tensor for complex exponential (cis) with given dimensions.
|
||||
(Note: `cis` means `cos + i * sin`, where i is the imaginary unit.)
|
||||
|
||||
This function calculates a frequency tensor with complex exponential using the given dimension 'dim'
|
||||
and the end index 'end'. The 'theta' parameter scales the frequencies.
|
||||
The returned tensor contains complex values in complex64 data type.
|
||||
|
||||
Args:
|
||||
dim (int): Dimension of the frequency tensor.
|
||||
pos (int or torch.FloatTensor): Position indices for the frequency tensor. [S] or scalar
|
||||
theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.
|
||||
use_real (bool, optional): If True, return real part and imaginary part separately.
|
||||
Otherwise, return complex numbers.
|
||||
theta_rescale_factor (float, optional): Rescale factor for theta. Defaults to 1.0.
|
||||
|
||||
Returns:
|
||||
freqs_cis: Precomputed frequency tensor with complex exponential. [S, D/2]
|
||||
freqs_cos, freqs_sin: Precomputed frequency tensor with real and imaginary parts separately. [S, D]
|
||||
"""
|
||||
if isinstance(pos, int):
|
||||
pos = torch.arange(pos).float()
|
||||
|
||||
# proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
|
||||
# has some connection to NTK literature
|
||||
if theta_rescale_factor != 1.0:
|
||||
theta *= theta_rescale_factor ** (dim / (dim - 2))
|
||||
|
||||
freqs = 1.0 / (
|
||||
theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)
|
||||
) # [D/2]
|
||||
# assert interpolation_factor == 1.0, f"interpolation_factor: {interpolation_factor}"
|
||||
freqs = torch.outer(pos * interpolation_factor, freqs) # [S, D/2]
|
||||
if use_real:
|
||||
freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D]
|
||||
freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D]
|
||||
return freqs_cos, freqs_sin
|
||||
else:
|
||||
freqs_cis = torch.polar(
|
||||
torch.ones_like(freqs), freqs
|
||||
) # complex64 # [S, D/2]
|
||||
return freqs_cis
|
||||
|
||||
|
||||
def get_nd_rotary_pos_embed(
|
||||
rope_dim_list,
|
||||
start,
|
||||
*args,
|
||||
theta=10000.0,
|
||||
use_real=False,
|
||||
theta_rescale_factor: Union[float, List[float]] = 1.0,
|
||||
interpolation_factor: Union[float, List[float]] = 1.0,
|
||||
):
|
||||
"""
|
||||
This is a n-d version of precompute_freqs_cis, which is a RoPE for tokens with n-d structure.
|
||||
|
||||
Args:
|
||||
rope_dim_list (list of int): Dimension of each rope. len(rope_dim_list) should equal to n.
|
||||
sum(rope_dim_list) should equal to head_dim of attention layer.
|
||||
start (int | tuple of int | list of int): If len(args) == 0, start is num; If len(args) == 1, start is start,
|
||||
args[0] is stop, step is 1; If len(args) == 2, start is start, args[0] is stop, args[1] is num.
|
||||
*args: See above.
|
||||
theta (float): Scaling factor for frequency computation. Defaults to 10000.0.
|
||||
use_real (bool): If True, return real part and imaginary part separately. Otherwise, return complex numbers.
|
||||
Some libraries such as TensorRT does not support complex64 data type. So it is useful to provide a real
|
||||
part and an imaginary part separately.
|
||||
theta_rescale_factor (float): Rescale factor for theta. Defaults to 1.0.
|
||||
|
||||
Returns:
|
||||
pos_embed (torch.Tensor): [HW, D/2]
|
||||
"""
|
||||
|
||||
grid = get_meshgrid_nd(
|
||||
start, *args, dim=len(rope_dim_list)
|
||||
) # [3, W, H, D] / [2, W, H]
|
||||
|
||||
if isinstance(theta_rescale_factor, int) or isinstance(theta_rescale_factor, float):
|
||||
theta_rescale_factor = [theta_rescale_factor] * len(rope_dim_list)
|
||||
elif isinstance(theta_rescale_factor, list) and len(theta_rescale_factor) == 1:
|
||||
theta_rescale_factor = [theta_rescale_factor[0]] * len(rope_dim_list)
|
||||
assert len(theta_rescale_factor) == len(
|
||||
rope_dim_list
|
||||
), "len(theta_rescale_factor) should equal to len(rope_dim_list)"
|
||||
|
||||
if isinstance(interpolation_factor, int) or isinstance(interpolation_factor, float):
|
||||
interpolation_factor = [interpolation_factor] * len(rope_dim_list)
|
||||
elif isinstance(interpolation_factor, list) and len(interpolation_factor) == 1:
|
||||
interpolation_factor = [interpolation_factor[0]] * len(rope_dim_list)
|
||||
assert len(interpolation_factor) == len(
|
||||
rope_dim_list
|
||||
), "len(interpolation_factor) should equal to len(rope_dim_list)"
|
||||
|
||||
# use 1/ndim of dimensions to encode grid_axis
|
||||
embs = []
|
||||
for i in range(len(rope_dim_list)):
|
||||
emb = get_1d_rotary_pos_embed(
|
||||
rope_dim_list[i],
|
||||
grid[i].reshape(-1),
|
||||
theta,
|
||||
use_real=use_real,
|
||||
theta_rescale_factor=theta_rescale_factor[i],
|
||||
interpolation_factor=interpolation_factor[i],
|
||||
) # 2 x [WHD, rope_dim_list[i]]
|
||||
embs.append(emb)
|
||||
|
||||
if use_real:
|
||||
cos = torch.cat([emb[0] for emb in embs], dim=1) # (WHD, D/2)
|
||||
sin = torch.cat([emb[1] for emb in embs], dim=1) # (WHD, D/2)
|
||||
return cos, sin
|
||||
else:
|
||||
emb = torch.cat(embs, dim=1) # (WHD, D/2)
|
||||
return emb
|
||||
|
||||
freqs_cos, freqs_sin = get_nd_rotary_pos_embed(
|
||||
[16, 56, 56],
|
||||
[latents.shape[2], latents.shape[3] // 2, latents.shape[4] // 2],
|
||||
theta=256,
|
||||
use_real=True,
|
||||
theta_rescale_factor=1,
|
||||
)
|
||||
return freqs_cos, freqs_sin
|
||||
|
||||
|
||||
class PatchEmbed(torch.nn.Module):
|
||||
def __init__(self, patch_size=(1, 2, 2), in_channels=16, embed_dim=3072):
|
||||
super().__init__()
|
||||
self.proj = torch.nn.Conv3d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.proj(x)
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
return x
|
||||
|
||||
|
||||
class IndividualTokenRefinerBlock(torch.nn.Module):
|
||||
def __init__(self, hidden_size=3072, num_heads=24):
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
self.norm1 = torch.nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6)
|
||||
self.self_attn_qkv = torch.nn.Linear(hidden_size, hidden_size * 3)
|
||||
self.self_attn_proj = torch.nn.Linear(hidden_size, hidden_size)
|
||||
|
||||
self.norm2 = torch.nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6)
|
||||
self.mlp = torch.nn.Sequential(
|
||||
torch.nn.Linear(hidden_size, hidden_size * 4),
|
||||
torch.nn.SiLU(),
|
||||
torch.nn.Linear(hidden_size * 4, hidden_size)
|
||||
)
|
||||
self.adaLN_modulation = torch.nn.Sequential(
|
||||
torch.nn.SiLU(),
|
||||
torch.nn.Linear(hidden_size, hidden_size * 2, device="cuda", dtype=torch.bfloat16),
|
||||
)
|
||||
|
||||
def forward(self, x, c, attn_mask=None):
|
||||
gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1)
|
||||
|
||||
norm_x = self.norm1(x)
|
||||
qkv = self.self_attn_qkv(norm_x)
|
||||
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
|
||||
|
||||
attn = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
|
||||
attn = rearrange(attn, "B H L D -> B L (H D)")
|
||||
|
||||
x = x + self.self_attn_proj(attn) * gate_msa.unsqueeze(1)
|
||||
x = x + self.mlp(self.norm2(x)) * gate_mlp.unsqueeze(1)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SingleTokenRefiner(torch.nn.Module):
|
||||
def __init__(self, in_channels=4096, hidden_size=3072, depth=2):
|
||||
super().__init__()
|
||||
self.input_embedder = torch.nn.Linear(in_channels, hidden_size, bias=True)
|
||||
self.t_embedder = TimestepEmbeddings(256, hidden_size, computation_device="cpu")
|
||||
self.c_embedder = torch.nn.Sequential(
|
||||
torch.nn.Linear(in_channels, hidden_size),
|
||||
torch.nn.SiLU(),
|
||||
torch.nn.Linear(hidden_size, hidden_size)
|
||||
)
|
||||
self.blocks = torch.nn.ModuleList([IndividualTokenRefinerBlock(hidden_size=hidden_size) for _ in range(depth)])
|
||||
|
||||
def forward(self, x, t, mask=None):
|
||||
timestep_aware_representations = self.t_embedder(t, dtype=torch.float32)
|
||||
|
||||
mask_float = mask.float().unsqueeze(-1)
|
||||
context_aware_representations = (x * mask_float).sum(dim=1) / mask_float.sum(dim=1)
|
||||
context_aware_representations = self.c_embedder(context_aware_representations)
|
||||
c = timestep_aware_representations + context_aware_representations
|
||||
|
||||
x = self.input_embedder(x)
|
||||
|
||||
mask = mask.to(device=x.device, dtype=torch.bool)
|
||||
mask = repeat(mask, "B L -> B 1 D L", D=mask.shape[-1])
|
||||
mask = mask & mask.transpose(2, 3)
|
||||
mask[:, :, :, 0] = True
|
||||
|
||||
for block in self.blocks:
|
||||
x = block(x, c, mask)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class ModulateDiT(torch.nn.Module):
|
||||
def __init__(self, hidden_size, factor=6):
|
||||
super().__init__()
|
||||
self.act = torch.nn.SiLU()
|
||||
self.linear = torch.nn.Linear(hidden_size, factor * hidden_size)
|
||||
|
||||
def forward(self, x):
|
||||
return self.linear(self.act(x))
|
||||
|
||||
|
||||
def modulate(x, shift=None, scale=None, tr_shift=None, tr_scale=None, tr_token=None):
|
||||
if tr_shift is not None:
|
||||
x_zero = x[:, :tr_token] * (1 + tr_scale.unsqueeze(1)) + tr_shift.unsqueeze(1)
|
||||
x_orig = x[:, tr_token:] * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
|
||||
x = torch.concat((x_zero, x_orig), dim=1)
|
||||
return x
|
||||
if scale is None and shift is None:
|
||||
return x
|
||||
elif shift is None:
|
||||
return x * (1 + scale.unsqueeze(1))
|
||||
elif scale is None:
|
||||
return x + shift.unsqueeze(1)
|
||||
else:
|
||||
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
|
||||
|
||||
|
||||
def reshape_for_broadcast(
|
||||
freqs_cis,
|
||||
x: torch.Tensor,
|
||||
head_first=False,
|
||||
):
|
||||
ndim = x.ndim
|
||||
assert 0 <= 1 < ndim
|
||||
|
||||
if isinstance(freqs_cis, tuple):
|
||||
# freqs_cis: (cos, sin) in real space
|
||||
if head_first:
|
||||
assert freqs_cis[0].shape == (
|
||||
x.shape[-2],
|
||||
x.shape[-1],
|
||||
), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}"
|
||||
shape = [
|
||||
d if i == ndim - 2 or i == ndim - 1 else 1
|
||||
for i, d in enumerate(x.shape)
|
||||
]
|
||||
else:
|
||||
assert freqs_cis[0].shape == (
|
||||
x.shape[1],
|
||||
x.shape[-1],
|
||||
), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}"
|
||||
shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
|
||||
return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape)
|
||||
else:
|
||||
# freqs_cis: values in complex space
|
||||
if head_first:
|
||||
assert freqs_cis.shape == (
|
||||
x.shape[-2],
|
||||
x.shape[-1],
|
||||
), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}"
|
||||
shape = [
|
||||
d if i == ndim - 2 or i == ndim - 1 else 1
|
||||
for i, d in enumerate(x.shape)
|
||||
]
|
||||
else:
|
||||
assert freqs_cis.shape == (
|
||||
x.shape[1],
|
||||
x.shape[-1],
|
||||
), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}"
|
||||
shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
|
||||
return freqs_cis.view(*shape)
|
||||
|
||||
|
||||
def rotate_half(x):
|
||||
x_real, x_imag = (
|
||||
x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1)
|
||||
) # [B, S, H, D//2]
|
||||
return torch.stack([-x_imag, x_real], dim=-1).flatten(3)
|
||||
|
||||
|
||||
def apply_rotary_emb(
|
||||
xq: torch.Tensor,
|
||||
xk: torch.Tensor,
|
||||
freqs_cis,
|
||||
head_first: bool = False,
|
||||
):
|
||||
xk_out = None
|
||||
if isinstance(freqs_cis, tuple):
|
||||
cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) # [S, D]
|
||||
cos, sin = cos.to(xq.device), sin.to(xq.device)
|
||||
# real * cos - imag * sin
|
||||
# imag * cos + real * sin
|
||||
xq_out = (xq.float() * cos + rotate_half(xq.float()) * sin).type_as(xq)
|
||||
xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk)
|
||||
else:
|
||||
# view_as_complex will pack [..., D/2, 2](real) to [..., D/2](complex)
|
||||
xq_ = torch.view_as_complex(
|
||||
xq.float().reshape(*xq.shape[:-1], -1, 2)
|
||||
) # [B, S, H, D//2]
|
||||
freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(
|
||||
xq.device
|
||||
) # [S, D//2] --> [1, S, 1, D//2]
|
||||
# (real, imag) * (cos, sin) = (real * cos - imag * sin, imag * cos + real * sin)
|
||||
# view_as_real will expand [..., D/2](complex) to [..., D/2, 2](real)
|
||||
xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq)
|
||||
xk_ = torch.view_as_complex(
|
||||
xk.float().reshape(*xk.shape[:-1], -1, 2)
|
||||
) # [B, S, H, D//2]
|
||||
xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk)
|
||||
|
||||
return xq_out, xk_out
|
||||
|
||||
|
||||
def attention(q, k, v):
|
||||
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
|
||||
x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
|
||||
x = x.transpose(1, 2).flatten(2, 3)
|
||||
return x
|
||||
|
||||
|
||||
def apply_gate(x, gate, tr_gate=None, tr_token=None):
|
||||
if tr_gate is not None:
|
||||
x_zero = x[:, :tr_token] * tr_gate.unsqueeze(1)
|
||||
x_orig = x[:, tr_token:] * gate.unsqueeze(1)
|
||||
return torch.concat((x_zero, x_orig), dim=1)
|
||||
else:
|
||||
return x * gate.unsqueeze(1)
|
||||
|
||||
|
||||
class MMDoubleStreamBlockComponent(torch.nn.Module):
|
||||
def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
|
||||
super().__init__()
|
||||
self.heads_num = heads_num
|
||||
|
||||
self.mod = ModulateDiT(hidden_size)
|
||||
self.norm1 = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
||||
|
||||
self.to_qkv = torch.nn.Linear(hidden_size, hidden_size * 3)
|
||||
self.norm_q = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
|
||||
self.norm_k = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
|
||||
self.to_out = torch.nn.Linear(hidden_size, hidden_size)
|
||||
|
||||
self.norm2 = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
||||
self.ff = torch.nn.Sequential(
|
||||
torch.nn.Linear(hidden_size, hidden_size * mlp_width_ratio),
|
||||
torch.nn.GELU(approximate="tanh"),
|
||||
torch.nn.Linear(hidden_size * mlp_width_ratio, hidden_size)
|
||||
)
|
||||
|
||||
def forward(self, hidden_states, conditioning, freqs_cis=None, token_replace_vec=None, tr_token=None):
|
||||
mod1_shift, mod1_scale, mod1_gate, mod2_shift, mod2_scale, mod2_gate = self.mod(conditioning).chunk(6, dim=-1)
|
||||
if token_replace_vec is not None:
|
||||
assert tr_token is not None
|
||||
tr_mod1_shift, tr_mod1_scale, tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = self.mod(token_replace_vec).chunk(6, dim=-1)
|
||||
else:
|
||||
tr_mod1_shift, tr_mod1_scale, tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = None, None, None, None, None, None
|
||||
|
||||
norm_hidden_states = self.norm1(hidden_states)
|
||||
norm_hidden_states = modulate(norm_hidden_states, shift=mod1_shift, scale=mod1_scale,
|
||||
tr_shift=tr_mod1_shift, tr_scale=tr_mod1_scale, tr_token=tr_token)
|
||||
qkv = self.to_qkv(norm_hidden_states)
|
||||
q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
|
||||
|
||||
q = self.norm_q(q)
|
||||
k = self.norm_k(k)
|
||||
|
||||
if freqs_cis is not None:
|
||||
q, k = apply_rotary_emb(q, k, freqs_cis, head_first=False)
|
||||
return (q, k, v), (mod1_gate, mod2_shift, mod2_scale, mod2_gate), (tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate)
|
||||
|
||||
def process_ff(self, hidden_states, attn_output, mod, mod_tr=None, tr_token=None):
|
||||
mod1_gate, mod2_shift, mod2_scale, mod2_gate = mod
|
||||
if mod_tr is not None:
|
||||
tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = mod_tr
|
||||
else:
|
||||
tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = None, None, None, None
|
||||
hidden_states = hidden_states + apply_gate(self.to_out(attn_output), mod1_gate, tr_mod1_gate, tr_token)
|
||||
x = self.ff(modulate(self.norm2(hidden_states), shift=mod2_shift, scale=mod2_scale, tr_shift=tr_mod2_shift, tr_scale=tr_mod2_scale, tr_token=tr_token))
|
||||
hidden_states = hidden_states + apply_gate(x, mod2_gate, tr_mod2_gate, tr_token)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class MMDoubleStreamBlock(torch.nn.Module):
|
||||
def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
|
||||
super().__init__()
|
||||
self.component_a = MMDoubleStreamBlockComponent(hidden_size, heads_num, mlp_width_ratio)
|
||||
self.component_b = MMDoubleStreamBlockComponent(hidden_size, heads_num, mlp_width_ratio)
|
||||
|
||||
def forward(self, hidden_states_a, hidden_states_b, conditioning, freqs_cis, token_replace_vec=None, tr_token=None, split_token=71):
|
||||
(q_a, k_a, v_a), mod_a, mod_tr = self.component_a(hidden_states_a, conditioning, freqs_cis, token_replace_vec, tr_token)
|
||||
(q_b, k_b, v_b), mod_b, _ = self.component_b(hidden_states_b, conditioning, freqs_cis=None)
|
||||
|
||||
q_a, q_b = torch.concat([q_a, q_b[:, :split_token]], dim=1), q_b[:, split_token:].contiguous()
|
||||
k_a, k_b = torch.concat([k_a, k_b[:, :split_token]], dim=1), k_b[:, split_token:].contiguous()
|
||||
v_a, v_b = torch.concat([v_a, v_b[:, :split_token]], dim=1), v_b[:, split_token:].contiguous()
|
||||
attn_output_a = attention(q_a, k_a, v_a)
|
||||
attn_output_b = attention(q_b, k_b, v_b)
|
||||
attn_output_a, attn_output_b = attn_output_a[:, :-split_token].contiguous(), torch.concat([attn_output_a[:, -split_token:], attn_output_b], dim=1)
|
||||
|
||||
hidden_states_a = self.component_a.process_ff(hidden_states_a, attn_output_a, mod_a, mod_tr, tr_token)
|
||||
hidden_states_b = self.component_b.process_ff(hidden_states_b, attn_output_b, mod_b)
|
||||
return hidden_states_a, hidden_states_b
|
||||
|
||||
|
||||
class MMSingleStreamBlockOriginal(torch.nn.Module):
|
||||
def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
|
||||
super().__init__()
|
||||
self.hidden_size = hidden_size
|
||||
self.heads_num = heads_num
|
||||
self.mlp_hidden_dim = hidden_size * mlp_width_ratio
|
||||
|
||||
self.linear1 = torch.nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
|
||||
self.linear2 = torch.nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
|
||||
|
||||
self.q_norm = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
|
||||
self.k_norm = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
|
||||
|
||||
self.pre_norm = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
||||
|
||||
self.mlp_act = torch.nn.GELU(approximate="tanh")
|
||||
self.modulation = ModulateDiT(hidden_size, factor=3)
|
||||
|
||||
def forward(self, x, vec, freqs_cis=None, txt_len=256):
|
||||
mod_shift, mod_scale, mod_gate = self.modulation(vec).chunk(3, dim=-1)
|
||||
x_mod = modulate(self.pre_norm(x), shift=mod_shift, scale=mod_scale)
|
||||
qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
|
||||
q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
|
||||
q = self.q_norm(q)
|
||||
k = self.k_norm(k)
|
||||
|
||||
q_a, q_b = q[:, :-txt_len, :, :], q[:, -txt_len:, :, :]
|
||||
k_a, k_b = k[:, :-txt_len, :, :], k[:, -txt_len:, :, :]
|
||||
q_a, k_a = apply_rotary_emb(q_a, k_a, freqs_cis, head_first=False)
|
||||
q = torch.cat((q_a, q_b), dim=1)
|
||||
k = torch.cat((k_a, k_b), dim=1)
|
||||
|
||||
attn_output_a = attention(q[:, :-185].contiguous(), k[:, :-185].contiguous(), v[:, :-185].contiguous())
|
||||
attn_output_b = attention(q[:, -185:].contiguous(), k[:, -185:].contiguous(), v[:, -185:].contiguous())
|
||||
attn_output = torch.concat([attn_output_a, attn_output_b], dim=1)
|
||||
|
||||
output = self.linear2(torch.cat((attn_output, self.mlp_act(mlp)), 2))
|
||||
return x + output * mod_gate.unsqueeze(1)
|
||||
|
||||
|
||||
class MMSingleStreamBlock(torch.nn.Module):
|
||||
def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
|
||||
super().__init__()
|
||||
self.heads_num = heads_num
|
||||
|
||||
self.mod = ModulateDiT(hidden_size, factor=3)
|
||||
self.norm = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
||||
|
||||
self.to_qkv = torch.nn.Linear(hidden_size, hidden_size * 3)
|
||||
self.norm_q = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
|
||||
self.norm_k = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
|
||||
self.to_out = torch.nn.Linear(hidden_size, hidden_size)
|
||||
|
||||
self.ff = torch.nn.Sequential(
|
||||
torch.nn.Linear(hidden_size, hidden_size * mlp_width_ratio),
|
||||
torch.nn.GELU(approximate="tanh"),
|
||||
torch.nn.Linear(hidden_size * mlp_width_ratio, hidden_size, bias=False)
|
||||
)
|
||||
|
||||
def forward(self, hidden_states, conditioning, freqs_cis=None, txt_len=256, token_replace_vec=None, tr_token=None, split_token=71):
|
||||
mod_shift, mod_scale, mod_gate = self.mod(conditioning).chunk(3, dim=-1)
|
||||
if token_replace_vec is not None:
|
||||
assert tr_token is not None
|
||||
tr_mod_shift, tr_mod_scale, tr_mod_gate = self.mod(token_replace_vec).chunk(3, dim=-1)
|
||||
else:
|
||||
tr_mod_shift, tr_mod_scale, tr_mod_gate = None, None, None
|
||||
|
||||
norm_hidden_states = self.norm(hidden_states)
|
||||
norm_hidden_states = modulate(norm_hidden_states, shift=mod_shift, scale=mod_scale,
|
||||
tr_shift=tr_mod_shift, tr_scale=tr_mod_scale, tr_token=tr_token)
|
||||
qkv = self.to_qkv(norm_hidden_states)
|
||||
|
||||
q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
|
||||
|
||||
q = self.norm_q(q)
|
||||
k = self.norm_k(k)
|
||||
|
||||
q_a, q_b = q[:, :-txt_len, :, :], q[:, -txt_len:, :, :]
|
||||
k_a, k_b = k[:, :-txt_len, :, :], k[:, -txt_len:, :, :]
|
||||
q_a, k_a = apply_rotary_emb(q_a, k_a, freqs_cis, head_first=False)
|
||||
|
||||
v_len = txt_len - split_token
|
||||
q_a, q_b = torch.concat([q_a, q_b[:, :split_token]], dim=1), q_b[:, split_token:].contiguous()
|
||||
k_a, k_b = torch.concat([k_a, k_b[:, :split_token]], dim=1), k_b[:, split_token:].contiguous()
|
||||
v_a, v_b = v[:, :-v_len].contiguous(), v[:, -v_len:].contiguous()
|
||||
|
||||
attn_output_a = attention(q_a, k_a, v_a)
|
||||
attn_output_b = attention(q_b, k_b, v_b)
|
||||
attn_output = torch.concat([attn_output_a, attn_output_b], dim=1)
|
||||
|
||||
hidden_states = hidden_states + apply_gate(self.to_out(attn_output), mod_gate, tr_mod_gate, tr_token)
|
||||
hidden_states = hidden_states + apply_gate(self.ff(norm_hidden_states), mod_gate, tr_mod_gate, tr_token)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class FinalLayer(torch.nn.Module):
|
||||
def __init__(self, hidden_size=3072, patch_size=(1, 2, 2), out_channels=16):
|
||||
super().__init__()
|
||||
|
||||
self.norm_final = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
||||
self.linear = torch.nn.Linear(hidden_size, patch_size[0] * patch_size[1] * patch_size[2] * out_channels)
|
||||
|
||||
self.adaLN_modulation = torch.nn.Sequential(torch.nn.SiLU(), torch.nn.Linear(hidden_size, 2 * hidden_size))
|
||||
|
||||
def forward(self, x, c):
|
||||
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
|
||||
x = modulate(self.norm_final(x), shift=shift, scale=scale)
|
||||
x = self.linear(x)
|
||||
return x
|
||||
|
||||
|
||||
class HunyuanVideoDiT(torch.nn.Module):
|
||||
def __init__(self, in_channels=16, hidden_size=3072, text_dim=4096, num_double_blocks=20, num_single_blocks=40, guidance_embed=True):
|
||||
super().__init__()
|
||||
self.img_in = PatchEmbed(in_channels=in_channels, embed_dim=hidden_size)
|
||||
self.txt_in = SingleTokenRefiner(in_channels=text_dim, hidden_size=hidden_size)
|
||||
self.time_in = TimestepEmbeddings(256, hidden_size, computation_device="cpu")
|
||||
self.vector_in = torch.nn.Sequential(
|
||||
torch.nn.Linear(768, hidden_size),
|
||||
torch.nn.SiLU(),
|
||||
torch.nn.Linear(hidden_size, hidden_size)
|
||||
)
|
||||
self.guidance_in = TimestepEmbeddings(256, hidden_size, computation_device="cpu") if guidance_embed else None
|
||||
self.double_blocks = torch.nn.ModuleList([MMDoubleStreamBlock(hidden_size) for _ in range(num_double_blocks)])
|
||||
self.single_blocks = torch.nn.ModuleList([MMSingleStreamBlock(hidden_size) for _ in range(num_single_blocks)])
|
||||
self.final_layer = FinalLayer(hidden_size)
|
||||
|
||||
# TODO: remove these parameters
|
||||
self.dtype = torch.bfloat16
|
||||
self.patch_size = [1, 2, 2]
|
||||
self.hidden_size = 3072
|
||||
self.heads_num = 24
|
||||
self.rope_dim_list = [16, 56, 56]
|
||||
|
||||
def unpatchify(self, x, T, H, W):
|
||||
x = rearrange(x, "B (T H W) (C pT pH pW) -> B C (T pT) (H pH) (W pW)", H=H, W=W, pT=1, pH=2, pW=2)
|
||||
return x
|
||||
|
||||
def enable_block_wise_offload(self, warm_device="cuda", cold_device="cpu"):
|
||||
self.warm_device = warm_device
|
||||
self.cold_device = cold_device
|
||||
self.to(self.cold_device)
|
||||
|
||||
def load_models_to_device(self, loadmodel_names=[], device="cpu"):
|
||||
for model_name in loadmodel_names:
|
||||
model = getattr(self, model_name)
|
||||
if model is not None:
|
||||
model.to(device)
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def prepare_freqs(self, latents):
|
||||
return HunyuanVideoRope(latents)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
t: torch.Tensor,
|
||||
prompt_emb: torch.Tensor = None,
|
||||
text_mask: torch.Tensor = None,
|
||||
pooled_prompt_emb: torch.Tensor = None,
|
||||
freqs_cos: torch.Tensor = None,
|
||||
freqs_sin: torch.Tensor = None,
|
||||
guidance: torch.Tensor = None,
|
||||
**kwargs
|
||||
):
|
||||
B, C, T, H, W = x.shape
|
||||
|
||||
vec = self.time_in(t, dtype=torch.float32) + self.vector_in(pooled_prompt_emb)
|
||||
if self.guidance_in is not None:
|
||||
vec += self.guidance_in(guidance * 1000, dtype=torch.float32)
|
||||
img = self.img_in(x)
|
||||
txt = self.txt_in(prompt_emb, t, text_mask)
|
||||
|
||||
for block in tqdm(self.double_blocks, desc="Double stream blocks"):
|
||||
img, txt = block(img, txt, vec, (freqs_cos, freqs_sin))
|
||||
|
||||
x = torch.concat([img, txt], dim=1)
|
||||
for block in tqdm(self.single_blocks, desc="Single stream blocks"):
|
||||
x = block(x, vec, (freqs_cos, freqs_sin))
|
||||
|
||||
img = x[:, :-256]
|
||||
img = self.final_layer(img, vec)
|
||||
img = self.unpatchify(img, T=T//1, H=H//2, W=W//2)
|
||||
return img
|
||||
|
||||
|
||||
def enable_auto_offload(self, dtype=torch.bfloat16, device="cuda"):
|
||||
def cast_to(weight, dtype=None, device=None, copy=False):
|
||||
if device is None or weight.device == device:
|
||||
if not copy:
|
||||
if dtype is None or weight.dtype == dtype:
|
||||
return weight
|
||||
return weight.to(dtype=dtype, copy=copy)
|
||||
|
||||
r = torch.empty_like(weight, dtype=dtype, device=device)
|
||||
r.copy_(weight)
|
||||
return r
|
||||
|
||||
def cast_weight(s, input=None, dtype=None, device=None):
|
||||
if input is not None:
|
||||
if dtype is None:
|
||||
dtype = input.dtype
|
||||
if device is None:
|
||||
device = input.device
|
||||
weight = cast_to(s.weight, dtype, device)
|
||||
return weight
|
||||
|
||||
def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
|
||||
if input is not None:
|
||||
if dtype is None:
|
||||
dtype = input.dtype
|
||||
if bias_dtype is None:
|
||||
bias_dtype = dtype
|
||||
if device is None:
|
||||
device = input.device
|
||||
weight = cast_to(s.weight, dtype, device)
|
||||
bias = cast_to(s.bias, bias_dtype, device) if s.bias is not None else None
|
||||
return weight, bias
|
||||
|
||||
class quantized_layer:
|
||||
class Linear(torch.nn.Linear):
|
||||
def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.dtype = dtype
|
||||
self.device = device
|
||||
|
||||
def block_forward_(self, x, i, j, dtype, device):
|
||||
weight_ = cast_to(
|
||||
self.weight[j * self.block_size: (j + 1) * self.block_size, i * self.block_size: (i + 1) * self.block_size],
|
||||
dtype=dtype, device=device
|
||||
)
|
||||
if self.bias is None or i > 0:
|
||||
bias_ = None
|
||||
else:
|
||||
bias_ = cast_to(self.bias[j * self.block_size: (j + 1) * self.block_size], dtype=dtype, device=device)
|
||||
x_ = x[..., i * self.block_size: (i + 1) * self.block_size]
|
||||
y_ = torch.nn.functional.linear(x_, weight_, bias_)
|
||||
del x_, weight_, bias_
|
||||
torch.cuda.empty_cache()
|
||||
return y_
|
||||
|
||||
def block_forward(self, x, **kwargs):
|
||||
# This feature can only reduce 2GB VRAM, so we disable it.
|
||||
y = torch.zeros(x.shape[:-1] + (self.out_features,), dtype=x.dtype, device=x.device)
|
||||
for i in range((self.in_features + self.block_size - 1) // self.block_size):
|
||||
for j in range((self.out_features + self.block_size - 1) // self.block_size):
|
||||
y[..., j * self.block_size: (j + 1) * self.block_size] += self.block_forward_(x, i, j, dtype=x.dtype, device=x.device)
|
||||
return y
|
||||
|
||||
def forward(self, x, **kwargs):
|
||||
weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device)
|
||||
return torch.nn.functional.linear(x, weight, bias)
|
||||
|
||||
|
||||
class RMSNorm(torch.nn.Module):
|
||||
def __init__(self, module, dtype=torch.bfloat16, device="cuda"):
|
||||
super().__init__()
|
||||
self.module = module
|
||||
self.dtype = dtype
|
||||
self.device = device
|
||||
|
||||
def forward(self, hidden_states, **kwargs):
|
||||
input_dtype = hidden_states.dtype
|
||||
variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
|
||||
hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps)
|
||||
hidden_states = hidden_states.to(input_dtype)
|
||||
if self.module.weight is not None:
|
||||
weight = cast_weight(self.module, hidden_states, dtype=torch.bfloat16, device="cuda")
|
||||
hidden_states = hidden_states * weight
|
||||
return hidden_states
|
||||
|
||||
class Conv3d(torch.nn.Conv3d):
|
||||
def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.dtype = dtype
|
||||
self.device = device
|
||||
|
||||
def forward(self, x):
|
||||
weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device)
|
||||
return torch.nn.functional.conv3d(x, weight, bias, self.stride, self.padding, self.dilation, self.groups)
|
||||
|
||||
class LayerNorm(torch.nn.LayerNorm):
|
||||
def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.dtype = dtype
|
||||
self.device = device
|
||||
|
||||
def forward(self, x):
|
||||
if self.weight is not None and self.bias is not None:
|
||||
weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device)
|
||||
return torch.nn.functional.layer_norm(x, self.normalized_shape, weight, bias, self.eps)
|
||||
else:
|
||||
return torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
|
||||
|
||||
def replace_layer(model, dtype=torch.bfloat16, device="cuda"):
|
||||
for name, module in model.named_children():
|
||||
if isinstance(module, torch.nn.Linear):
|
||||
with init_weights_on_device():
|
||||
new_layer = quantized_layer.Linear(
|
||||
module.in_features, module.out_features, bias=module.bias is not None,
|
||||
dtype=dtype, device=device
|
||||
)
|
||||
new_layer.load_state_dict(module.state_dict(), assign=True)
|
||||
setattr(model, name, new_layer)
|
||||
elif isinstance(module, torch.nn.Conv3d):
|
||||
with init_weights_on_device():
|
||||
new_layer = quantized_layer.Conv3d(
|
||||
module.in_channels, module.out_channels, kernel_size=module.kernel_size, stride=module.stride,
|
||||
dtype=dtype, device=device
|
||||
)
|
||||
new_layer.load_state_dict(module.state_dict(), assign=True)
|
||||
setattr(model, name, new_layer)
|
||||
elif isinstance(module, RMSNorm):
|
||||
new_layer = quantized_layer.RMSNorm(
|
||||
module,
|
||||
dtype=dtype, device=device
|
||||
)
|
||||
setattr(model, name, new_layer)
|
||||
elif isinstance(module, torch.nn.LayerNorm):
|
||||
with init_weights_on_device():
|
||||
new_layer = quantized_layer.LayerNorm(
|
||||
module.normalized_shape, elementwise_affine=module.elementwise_affine, eps=module.eps,
|
||||
dtype=dtype, device=device
|
||||
)
|
||||
new_layer.load_state_dict(module.state_dict(), assign=True)
|
||||
setattr(model, name, new_layer)
|
||||
else:
|
||||
replace_layer(module, dtype=dtype, device=device)
|
||||
|
||||
replace_layer(self, dtype=dtype, device=device)
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return HunyuanVideoDiTStateDictConverter()
|
||||
|
||||
|
||||
class HunyuanVideoDiTStateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
origin_hash_key = hash_state_dict_keys(state_dict, with_shape=True)
|
||||
if "module" in state_dict:
|
||||
state_dict = state_dict["module"]
|
||||
direct_dict = {
|
||||
"img_in.proj": "img_in.proj",
|
||||
"time_in.mlp.0": "time_in.timestep_embedder.0",
|
||||
"time_in.mlp.2": "time_in.timestep_embedder.2",
|
||||
"vector_in.in_layer": "vector_in.0",
|
||||
"vector_in.out_layer": "vector_in.2",
|
||||
"guidance_in.mlp.0": "guidance_in.timestep_embedder.0",
|
||||
"guidance_in.mlp.2": "guidance_in.timestep_embedder.2",
|
||||
"txt_in.input_embedder": "txt_in.input_embedder",
|
||||
"txt_in.t_embedder.mlp.0": "txt_in.t_embedder.timestep_embedder.0",
|
||||
"txt_in.t_embedder.mlp.2": "txt_in.t_embedder.timestep_embedder.2",
|
||||
"txt_in.c_embedder.linear_1": "txt_in.c_embedder.0",
|
||||
"txt_in.c_embedder.linear_2": "txt_in.c_embedder.2",
|
||||
"final_layer.linear": "final_layer.linear",
|
||||
"final_layer.adaLN_modulation.1": "final_layer.adaLN_modulation.1",
|
||||
}
|
||||
txt_suffix_dict = {
|
||||
"norm1": "norm1",
|
||||
"self_attn_qkv": "self_attn_qkv",
|
||||
"self_attn_proj": "self_attn_proj",
|
||||
"norm2": "norm2",
|
||||
"mlp.fc1": "mlp.0",
|
||||
"mlp.fc2": "mlp.2",
|
||||
"adaLN_modulation.1": "adaLN_modulation.1",
|
||||
}
|
||||
double_suffix_dict = {
|
||||
"img_mod.linear": "component_a.mod.linear",
|
||||
"img_attn_qkv": "component_a.to_qkv",
|
||||
"img_attn_q_norm": "component_a.norm_q",
|
||||
"img_attn_k_norm": "component_a.norm_k",
|
||||
"img_attn_proj": "component_a.to_out",
|
||||
"img_mlp.fc1": "component_a.ff.0",
|
||||
"img_mlp.fc2": "component_a.ff.2",
|
||||
"txt_mod.linear": "component_b.mod.linear",
|
||||
"txt_attn_qkv": "component_b.to_qkv",
|
||||
"txt_attn_q_norm": "component_b.norm_q",
|
||||
"txt_attn_k_norm": "component_b.norm_k",
|
||||
"txt_attn_proj": "component_b.to_out",
|
||||
"txt_mlp.fc1": "component_b.ff.0",
|
||||
"txt_mlp.fc2": "component_b.ff.2",
|
||||
}
|
||||
single_suffix_dict = {
|
||||
"linear1": ["to_qkv", "ff.0"],
|
||||
"linear2": ["to_out", "ff.2"],
|
||||
"q_norm": "norm_q",
|
||||
"k_norm": "norm_k",
|
||||
"modulation.linear": "mod.linear",
|
||||
}
|
||||
# single_suffix_dict = {
|
||||
# "linear1": "linear1",
|
||||
# "linear2": "linear2",
|
||||
# "q_norm": "q_norm",
|
||||
# "k_norm": "k_norm",
|
||||
# "modulation.linear": "modulation.linear",
|
||||
# }
|
||||
state_dict_ = {}
|
||||
for name, param in state_dict.items():
|
||||
names = name.split(".")
|
||||
direct_name = ".".join(names[:-1])
|
||||
if direct_name in direct_dict:
|
||||
name_ = direct_dict[direct_name] + "." + names[-1]
|
||||
state_dict_[name_] = param
|
||||
elif names[0] == "double_blocks":
|
||||
prefix = ".".join(names[:2])
|
||||
suffix = ".".join(names[2:-1])
|
||||
name_ = prefix + "." + double_suffix_dict[suffix] + "." + names[-1]
|
||||
state_dict_[name_] = param
|
||||
elif names[0] == "single_blocks":
|
||||
prefix = ".".join(names[:2])
|
||||
suffix = ".".join(names[2:-1])
|
||||
if isinstance(single_suffix_dict[suffix], list):
|
||||
if suffix == "linear1":
|
||||
name_a, name_b = single_suffix_dict[suffix]
|
||||
param_a, param_b = torch.split(param, (3072*3, 3072*4), dim=0)
|
||||
state_dict_[prefix + "." + name_a + "." + names[-1]] = param_a
|
||||
state_dict_[prefix + "." + name_b + "." + names[-1]] = param_b
|
||||
elif suffix == "linear2":
|
||||
if names[-1] == "weight":
|
||||
name_a, name_b = single_suffix_dict[suffix]
|
||||
param_a, param_b = torch.split(param, (3072*1, 3072*4), dim=-1)
|
||||
state_dict_[prefix + "." + name_a + "." + names[-1]] = param_a
|
||||
state_dict_[prefix + "." + name_b + "." + names[-1]] = param_b
|
||||
else:
|
||||
name_a, name_b = single_suffix_dict[suffix]
|
||||
state_dict_[prefix + "." + name_a + "." + names[-1]] = param
|
||||
else:
|
||||
pass
|
||||
else:
|
||||
name_ = prefix + "." + single_suffix_dict[suffix] + "." + names[-1]
|
||||
state_dict_[name_] = param
|
||||
elif names[0] == "txt_in":
|
||||
prefix = ".".join(names[:4]).replace(".individual_token_refiner.", ".")
|
||||
suffix = ".".join(names[4:-1])
|
||||
name_ = prefix + "." + txt_suffix_dict[suffix] + "." + names[-1]
|
||||
state_dict_[name_] = param
|
||||
else:
|
||||
pass
|
||||
|
||||
return state_dict_
|
||||
@@ -1,68 +0,0 @@
|
||||
from transformers import LlamaModel, LlamaConfig, DynamicCache, LlavaForConditionalGeneration
|
||||
from copy import deepcopy
|
||||
import torch
|
||||
|
||||
|
||||
class HunyuanVideoLLMEncoder(LlamaModel):
|
||||
|
||||
def __init__(self, config: LlamaConfig):
|
||||
super().__init__(config)
|
||||
self.auto_offload = False
|
||||
|
||||
def enable_auto_offload(self, **kwargs):
|
||||
self.auto_offload = True
|
||||
|
||||
def forward(self, input_ids, attention_mask, hidden_state_skip_layer=2):
|
||||
embed_tokens = deepcopy(self.embed_tokens).to(input_ids.device) if self.auto_offload else self.embed_tokens
|
||||
inputs_embeds = embed_tokens(input_ids)
|
||||
|
||||
past_key_values = DynamicCache()
|
||||
|
||||
cache_position = torch.arange(0, inputs_embeds.shape[1], device=inputs_embeds.device)
|
||||
position_ids = cache_position.unsqueeze(0)
|
||||
|
||||
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, None, False)
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
# create position embeddings to be shared across the decoder layers
|
||||
rotary_emb = deepcopy(self.rotary_emb).to(input_ids.device) if self.auto_offload else self.rotary_emb
|
||||
position_embeddings = rotary_emb(hidden_states, position_ids)
|
||||
|
||||
# decoder layers
|
||||
for layer_id, decoder_layer in enumerate(self.layers):
|
||||
if self.auto_offload:
|
||||
decoder_layer = deepcopy(decoder_layer).to(hidden_states.device)
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=causal_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_values,
|
||||
output_attentions=False,
|
||||
use_cache=True,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=position_embeddings,
|
||||
)
|
||||
hidden_states = layer_outputs[0]
|
||||
if layer_id + hidden_state_skip_layer + 1 >= len(self.layers):
|
||||
break
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class HunyuanVideoMLLMEncoder(LlavaForConditionalGeneration):
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.auto_offload = False
|
||||
|
||||
def enable_auto_offload(self, **kwargs):
|
||||
self.auto_offload = True
|
||||
|
||||
# TODO: implement the low VRAM inference for MLLM.
|
||||
def forward(self, input_ids, pixel_values, attention_mask, hidden_state_skip_layer=2):
|
||||
outputs = super().forward(input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
output_hidden_states=True,
|
||||
pixel_values=pixel_values)
|
||||
hidden_state = outputs.hidden_states[-(hidden_state_skip_layer + 1)]
|
||||
return hidden_state
|
||||
@@ -1,507 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from einops import rearrange
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
from einops import repeat
|
||||
|
||||
|
||||
class CausalConv3d(nn.Module):
|
||||
|
||||
def __init__(self, in_channel, out_channel, kernel_size, stride=1, dilation=1, pad_mode='replicate', **kwargs):
|
||||
super().__init__()
|
||||
self.pad_mode = pad_mode
|
||||
self.time_causal_padding = (kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size - 1, 0
|
||||
) # W, H, T
|
||||
self.conv = nn.Conv3d(in_channel, out_channel, kernel_size, stride=stride, dilation=dilation, **kwargs)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
|
||||
return self.conv(x)
|
||||
|
||||
|
||||
class UpsampleCausal3D(nn.Module):
|
||||
|
||||
def __init__(self, channels, use_conv=False, out_channels=None, kernel_size=None, bias=True, upsample_factor=(2, 2, 2)):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels or channels
|
||||
self.upsample_factor = upsample_factor
|
||||
self.conv = None
|
||||
if use_conv:
|
||||
kernel_size = 3 if kernel_size is None else kernel_size
|
||||
self.conv = CausalConv3d(self.channels, self.out_channels, kernel_size=kernel_size, bias=bias)
|
||||
|
||||
def forward(self, hidden_states):
|
||||
# Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
|
||||
dtype = hidden_states.dtype
|
||||
if dtype == torch.bfloat16:
|
||||
hidden_states = hidden_states.to(torch.float32)
|
||||
|
||||
# upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
|
||||
if hidden_states.shape[0] >= 64:
|
||||
hidden_states = hidden_states.contiguous()
|
||||
|
||||
# interpolate
|
||||
B, C, T, H, W = hidden_states.shape
|
||||
first_h, other_h = hidden_states.split((1, T - 1), dim=2)
|
||||
if T > 1:
|
||||
other_h = F.interpolate(other_h, scale_factor=self.upsample_factor, mode="nearest")
|
||||
first_h = F.interpolate(first_h.squeeze(2), scale_factor=self.upsample_factor[1:], mode="nearest").unsqueeze(2)
|
||||
hidden_states = torch.cat((first_h, other_h), dim=2) if T > 1 else first_h
|
||||
|
||||
# If the input is bfloat16, we cast back to bfloat16
|
||||
if dtype == torch.bfloat16:
|
||||
hidden_states = hidden_states.to(dtype)
|
||||
|
||||
if self.conv:
|
||||
hidden_states = self.conv(hidden_states)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class ResnetBlockCausal3D(nn.Module):
|
||||
|
||||
def __init__(self, in_channels, out_channels=None, dropout=0.0, groups=32, eps=1e-6, conv_shortcut_bias=True):
|
||||
super().__init__()
|
||||
self.pre_norm = True
|
||||
self.in_channels = in_channels
|
||||
out_channels = in_channels if out_channels is None else out_channels
|
||||
self.out_channels = out_channels
|
||||
|
||||
self.norm1 = nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
|
||||
self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, stride=1)
|
||||
|
||||
self.norm2 = nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
|
||||
self.conv2 = CausalConv3d(out_channels, out_channels, kernel_size=3, stride=1)
|
||||
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.nonlinearity = nn.SiLU()
|
||||
|
||||
self.conv_shortcut = None
|
||||
if in_channels != out_channels:
|
||||
self.conv_shortcut = CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, bias=conv_shortcut_bias)
|
||||
|
||||
def forward(self, input_tensor):
|
||||
hidden_states = input_tensor
|
||||
# conv1
|
||||
hidden_states = self.norm1(hidden_states)
|
||||
hidden_states = self.nonlinearity(hidden_states)
|
||||
hidden_states = self.conv1(hidden_states)
|
||||
|
||||
# conv2
|
||||
hidden_states = self.norm2(hidden_states)
|
||||
hidden_states = self.nonlinearity(hidden_states)
|
||||
hidden_states = self.dropout(hidden_states)
|
||||
hidden_states = self.conv2(hidden_states)
|
||||
# shortcut
|
||||
if self.conv_shortcut is not None:
|
||||
input_tensor = (self.conv_shortcut(input_tensor))
|
||||
# shortcut and scale
|
||||
output_tensor = input_tensor + hidden_states
|
||||
|
||||
return output_tensor
|
||||
|
||||
|
||||
def prepare_causal_attention_mask(n_frame, n_hw, dtype, device, batch_size=None):
|
||||
seq_len = n_frame * n_hw
|
||||
mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device)
|
||||
for i in range(seq_len):
|
||||
i_frame = i // n_hw
|
||||
mask[i, :(i_frame + 1) * n_hw] = 0
|
||||
if batch_size is not None:
|
||||
mask = mask.unsqueeze(0).expand(batch_size, -1, -1)
|
||||
return mask
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
num_heads,
|
||||
head_dim,
|
||||
num_groups=32,
|
||||
dropout=0.0,
|
||||
eps=1e-6,
|
||||
bias=True,
|
||||
residual_connection=True):
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = head_dim
|
||||
self.residual_connection = residual_connection
|
||||
dim_inner = head_dim * num_heads
|
||||
self.group_norm = nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=eps, affine=True)
|
||||
self.to_q = nn.Linear(in_channels, dim_inner, bias=bias)
|
||||
self.to_k = nn.Linear(in_channels, dim_inner, bias=bias)
|
||||
self.to_v = nn.Linear(in_channels, dim_inner, bias=bias)
|
||||
self.to_out = nn.Sequential(nn.Linear(dim_inner, in_channels, bias=bias), nn.Dropout(dropout))
|
||||
|
||||
def forward(self, input_tensor, attn_mask=None):
|
||||
hidden_states = self.group_norm(input_tensor.transpose(1, 2)).transpose(1, 2)
|
||||
batch_size = hidden_states.shape[0]
|
||||
|
||||
q = self.to_q(hidden_states)
|
||||
k = self.to_k(hidden_states)
|
||||
v = self.to_v(hidden_states)
|
||||
|
||||
q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
|
||||
if attn_mask is not None:
|
||||
attn_mask = attn_mask.view(batch_size, self.num_heads, -1, attn_mask.shape[-1])
|
||||
hidden_states = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
|
||||
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
|
||||
hidden_states = self.to_out(hidden_states)
|
||||
if self.residual_connection:
|
||||
output_tensor = input_tensor + hidden_states
|
||||
return output_tensor
|
||||
|
||||
|
||||
class UNetMidBlockCausal3D(nn.Module):
|
||||
|
||||
def __init__(self, in_channels, dropout=0.0, num_layers=1, eps=1e-6, num_groups=32, attention_head_dim=None):
|
||||
super().__init__()
|
||||
resnets = [
|
||||
ResnetBlockCausal3D(
|
||||
in_channels=in_channels,
|
||||
out_channels=in_channels,
|
||||
dropout=dropout,
|
||||
groups=num_groups,
|
||||
eps=eps,
|
||||
)
|
||||
]
|
||||
attentions = []
|
||||
attention_head_dim = attention_head_dim or in_channels
|
||||
|
||||
for _ in range(num_layers):
|
||||
attentions.append(
|
||||
Attention(
|
||||
in_channels,
|
||||
num_heads=in_channels // attention_head_dim,
|
||||
head_dim=attention_head_dim,
|
||||
num_groups=num_groups,
|
||||
dropout=dropout,
|
||||
eps=eps,
|
||||
bias=True,
|
||||
residual_connection=True,
|
||||
))
|
||||
|
||||
resnets.append(
|
||||
ResnetBlockCausal3D(
|
||||
in_channels=in_channels,
|
||||
out_channels=in_channels,
|
||||
dropout=dropout,
|
||||
groups=num_groups,
|
||||
eps=eps,
|
||||
))
|
||||
|
||||
self.attentions = nn.ModuleList(attentions)
|
||||
self.resnets = nn.ModuleList(resnets)
|
||||
|
||||
def forward(self, hidden_states):
|
||||
hidden_states = self.resnets[0](hidden_states)
|
||||
for attn, resnet in zip(self.attentions, self.resnets[1:]):
|
||||
B, C, T, H, W = hidden_states.shape
|
||||
hidden_states = rearrange(hidden_states, "b c f h w -> b (f h w) c")
|
||||
attn_mask = prepare_causal_attention_mask(T, H * W, hidden_states.dtype, hidden_states.device, batch_size=B)
|
||||
hidden_states = attn(hidden_states, attn_mask=attn_mask)
|
||||
hidden_states = rearrange(hidden_states, "b (f h w) c -> b c f h w", f=T, h=H, w=W)
|
||||
hidden_states = resnet(hidden_states)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class UpDecoderBlockCausal3D(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
dropout=0.0,
|
||||
num_layers=1,
|
||||
eps=1e-6,
|
||||
num_groups=32,
|
||||
add_upsample=True,
|
||||
upsample_scale_factor=(2, 2, 2),
|
||||
):
|
||||
super().__init__()
|
||||
resnets = []
|
||||
for i in range(num_layers):
|
||||
cur_in_channel = in_channels if i == 0 else out_channels
|
||||
resnets.append(
|
||||
ResnetBlockCausal3D(
|
||||
in_channels=cur_in_channel,
|
||||
out_channels=out_channels,
|
||||
groups=num_groups,
|
||||
dropout=dropout,
|
||||
eps=eps,
|
||||
))
|
||||
self.resnets = nn.ModuleList(resnets)
|
||||
|
||||
self.upsamplers = None
|
||||
if add_upsample:
|
||||
self.upsamplers = nn.ModuleList([
|
||||
UpsampleCausal3D(
|
||||
out_channels,
|
||||
use_conv=True,
|
||||
out_channels=out_channels,
|
||||
upsample_factor=upsample_scale_factor,
|
||||
)
|
||||
])
|
||||
|
||||
def forward(self, hidden_states):
|
||||
for resnet in self.resnets:
|
||||
hidden_states = resnet(hidden_states)
|
||||
if self.upsamplers is not None:
|
||||
for upsampler in self.upsamplers:
|
||||
hidden_states = upsampler(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class DecoderCausal3D(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_channels=16,
|
||||
out_channels=3,
|
||||
eps=1e-6,
|
||||
dropout=0.0,
|
||||
block_out_channels=[128, 256, 512, 512],
|
||||
layers_per_block=2,
|
||||
num_groups=32,
|
||||
time_compression_ratio=4,
|
||||
spatial_compression_ratio=8,
|
||||
gradient_checkpointing=False,
|
||||
):
|
||||
super().__init__()
|
||||
self.layers_per_block = layers_per_block
|
||||
|
||||
self.conv_in = CausalConv3d(in_channels, block_out_channels[-1], kernel_size=3, stride=1)
|
||||
self.up_blocks = nn.ModuleList([])
|
||||
|
||||
# mid
|
||||
self.mid_block = UNetMidBlockCausal3D(
|
||||
in_channels=block_out_channels[-1],
|
||||
dropout=dropout,
|
||||
eps=eps,
|
||||
num_groups=num_groups,
|
||||
attention_head_dim=block_out_channels[-1],
|
||||
)
|
||||
|
||||
# up
|
||||
reversed_block_out_channels = list(reversed(block_out_channels))
|
||||
output_channel = reversed_block_out_channels[0]
|
||||
for i in range(len(block_out_channels)):
|
||||
prev_output_channel = output_channel
|
||||
output_channel = reversed_block_out_channels[i]
|
||||
is_final_block = i == len(block_out_channels) - 1
|
||||
num_spatial_upsample_layers = int(np.log2(spatial_compression_ratio))
|
||||
num_time_upsample_layers = int(np.log2(time_compression_ratio))
|
||||
|
||||
add_spatial_upsample = bool(i < num_spatial_upsample_layers)
|
||||
add_time_upsample = bool(i >= len(block_out_channels) - 1 - num_time_upsample_layers and not is_final_block)
|
||||
|
||||
upsample_scale_factor_HW = (2, 2) if add_spatial_upsample else (1, 1)
|
||||
upsample_scale_factor_T = (2,) if add_time_upsample else (1,)
|
||||
upsample_scale_factor = tuple(upsample_scale_factor_T + upsample_scale_factor_HW)
|
||||
|
||||
up_block = UpDecoderBlockCausal3D(
|
||||
in_channels=prev_output_channel,
|
||||
out_channels=output_channel,
|
||||
dropout=dropout,
|
||||
num_layers=layers_per_block + 1,
|
||||
eps=eps,
|
||||
num_groups=num_groups,
|
||||
add_upsample=bool(add_spatial_upsample or add_time_upsample),
|
||||
upsample_scale_factor=upsample_scale_factor,
|
||||
)
|
||||
|
||||
self.up_blocks.append(up_block)
|
||||
prev_output_channel = output_channel
|
||||
|
||||
# out
|
||||
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups, eps=eps)
|
||||
self.conv_act = nn.SiLU()
|
||||
self.conv_out = CausalConv3d(block_out_channels[0], out_channels, kernel_size=3)
|
||||
|
||||
self.gradient_checkpointing = gradient_checkpointing
|
||||
|
||||
def forward(self, hidden_states):
|
||||
hidden_states = self.conv_in(hidden_states)
|
||||
if self.training and self.gradient_checkpointing:
|
||||
|
||||
def create_custom_forward(module):
|
||||
|
||||
def custom_forward(*inputs):
|
||||
return module(*inputs)
|
||||
|
||||
return custom_forward
|
||||
|
||||
# middle
|
||||
hidden_states = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(self.mid_block),
|
||||
hidden_states,
|
||||
use_reentrant=False,
|
||||
)
|
||||
# up
|
||||
for up_block in self.up_blocks:
|
||||
hidden_states = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(up_block),
|
||||
hidden_states,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
# middle
|
||||
hidden_states = self.mid_block(hidden_states)
|
||||
# up
|
||||
for up_block in self.up_blocks:
|
||||
hidden_states = up_block(hidden_states)
|
||||
# post-process
|
||||
hidden_states = self.conv_norm_out(hidden_states)
|
||||
hidden_states = self.conv_act(hidden_states)
|
||||
hidden_states = self.conv_out(hidden_states)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class HunyuanVideoVAEDecoder(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_channels=16,
|
||||
out_channels=3,
|
||||
eps=1e-6,
|
||||
dropout=0.0,
|
||||
block_out_channels=[128, 256, 512, 512],
|
||||
layers_per_block=2,
|
||||
num_groups=32,
|
||||
time_compression_ratio=4,
|
||||
spatial_compression_ratio=8,
|
||||
gradient_checkpointing=False,
|
||||
):
|
||||
super().__init__()
|
||||
self.decoder = DecoderCausal3D(
|
||||
in_channels=in_channels,
|
||||
out_channels=out_channels,
|
||||
eps=eps,
|
||||
dropout=dropout,
|
||||
block_out_channels=block_out_channels,
|
||||
layers_per_block=layers_per_block,
|
||||
num_groups=num_groups,
|
||||
time_compression_ratio=time_compression_ratio,
|
||||
spatial_compression_ratio=spatial_compression_ratio,
|
||||
gradient_checkpointing=gradient_checkpointing,
|
||||
)
|
||||
self.post_quant_conv = nn.Conv3d(in_channels, in_channels, kernel_size=1)
|
||||
self.scaling_factor = 0.476986
|
||||
|
||||
|
||||
def forward(self, latents):
|
||||
latents = latents / self.scaling_factor
|
||||
latents = self.post_quant_conv(latents)
|
||||
dec = self.decoder(latents)
|
||||
return dec
|
||||
|
||||
|
||||
def build_1d_mask(self, length, left_bound, right_bound, border_width):
|
||||
x = torch.ones((length,))
|
||||
if not left_bound:
|
||||
x[:border_width] = (torch.arange(border_width) + 1) / border_width
|
||||
if not right_bound:
|
||||
x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,))
|
||||
return x
|
||||
|
||||
|
||||
def build_mask(self, data, is_bound, border_width):
|
||||
_, _, T, H, W = data.shape
|
||||
t = self.build_1d_mask(T, is_bound[0], is_bound[1], border_width[0])
|
||||
h = self.build_1d_mask(H, is_bound[2], is_bound[3], border_width[1])
|
||||
w = self.build_1d_mask(W, is_bound[4], is_bound[5], border_width[2])
|
||||
|
||||
t = repeat(t, "T -> T H W", T=T, H=H, W=W)
|
||||
h = repeat(h, "H -> T H W", T=T, H=H, W=W)
|
||||
w = repeat(w, "W -> T H W", T=T, H=H, W=W)
|
||||
|
||||
mask = torch.stack([t, h, w]).min(dim=0).values
|
||||
mask = rearrange(mask, "T H W -> 1 1 T H W")
|
||||
return mask
|
||||
|
||||
|
||||
def tile_forward(self, hidden_states, tile_size, tile_stride):
|
||||
B, C, T, H, W = hidden_states.shape
|
||||
size_t, size_h, size_w = tile_size
|
||||
stride_t, stride_h, stride_w = tile_stride
|
||||
|
||||
# Split tasks
|
||||
tasks = []
|
||||
for t in range(0, T, stride_t):
|
||||
if (t-stride_t >= 0 and t-stride_t+size_t >= T): continue
|
||||
for h in range(0, H, stride_h):
|
||||
if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue
|
||||
for w in range(0, W, stride_w):
|
||||
if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue
|
||||
t_, h_, w_ = t + size_t, h + size_h, w + size_w
|
||||
tasks.append((t, t_, h, h_, w, w_))
|
||||
|
||||
# Run
|
||||
torch_dtype = self.post_quant_conv.weight.dtype
|
||||
data_device = hidden_states.device
|
||||
computation_device = self.post_quant_conv.weight.device
|
||||
|
||||
weight = torch.zeros((1, 1, (T - 1) * 4 + 1, H * 8, W * 8), dtype=torch_dtype, device=data_device)
|
||||
values = torch.zeros((B, 3, (T - 1) * 4 + 1, H * 8, W * 8), dtype=torch_dtype, device=data_device)
|
||||
|
||||
for t, t_, h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"):
|
||||
hidden_states_batch = hidden_states[:, :, t:t_, h:h_, w:w_].to(computation_device)
|
||||
hidden_states_batch = self.forward(hidden_states_batch).to(data_device)
|
||||
if t > 0:
|
||||
hidden_states_batch = hidden_states_batch[:, :, 1:]
|
||||
|
||||
mask = self.build_mask(
|
||||
hidden_states_batch,
|
||||
is_bound=(t==0, t_>=T, h==0, h_>=H, w==0, w_>=W),
|
||||
border_width=((size_t - stride_t) * 4, (size_h - stride_h) * 8, (size_w - stride_w) * 8)
|
||||
).to(dtype=torch_dtype, device=data_device)
|
||||
|
||||
target_t = 0 if t==0 else t * 4 + 1
|
||||
target_h = h * 8
|
||||
target_w = w * 8
|
||||
values[
|
||||
:,
|
||||
:,
|
||||
target_t: target_t + hidden_states_batch.shape[2],
|
||||
target_h: target_h + hidden_states_batch.shape[3],
|
||||
target_w: target_w + hidden_states_batch.shape[4],
|
||||
] += hidden_states_batch * mask
|
||||
weight[
|
||||
:,
|
||||
:,
|
||||
target_t: target_t + hidden_states_batch.shape[2],
|
||||
target_h: target_h + hidden_states_batch.shape[3],
|
||||
target_w: target_w + hidden_states_batch.shape[4],
|
||||
] += mask
|
||||
return values / weight
|
||||
|
||||
|
||||
def decode_video(self, latents, tile_size=(17, 32, 32), tile_stride=(12, 24, 24)):
|
||||
latents = latents.to(self.post_quant_conv.weight.dtype)
|
||||
return self.tile_forward(latents, tile_size=tile_size, tile_stride=tile_stride)
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return HunyuanVideoVAEDecoderStateDictConverter()
|
||||
|
||||
|
||||
class HunyuanVideoVAEDecoderStateDictConverter:
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
state_dict_ = {}
|
||||
for name in state_dict:
|
||||
if name.startswith('decoder.') or name.startswith('post_quant_conv.'):
|
||||
state_dict_[name] = state_dict[name]
|
||||
return state_dict_
|
||||
@@ -1,307 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from einops import rearrange, repeat
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
from .hunyuan_video_vae_decoder import CausalConv3d, ResnetBlockCausal3D, UNetMidBlockCausal3D
|
||||
|
||||
|
||||
class DownsampleCausal3D(nn.Module):
|
||||
|
||||
def __init__(self, channels, out_channels, kernel_size=3, bias=True, stride=2):
|
||||
super().__init__()
|
||||
self.conv = CausalConv3d(channels, out_channels, kernel_size, stride=stride, bias=bias)
|
||||
|
||||
def forward(self, hidden_states):
|
||||
hidden_states = self.conv(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class DownEncoderBlockCausal3D(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
dropout=0.0,
|
||||
num_layers=1,
|
||||
eps=1e-6,
|
||||
num_groups=32,
|
||||
add_downsample=True,
|
||||
downsample_stride=2,
|
||||
):
|
||||
|
||||
super().__init__()
|
||||
resnets = []
|
||||
for i in range(num_layers):
|
||||
cur_in_channel = in_channels if i == 0 else out_channels
|
||||
resnets.append(
|
||||
ResnetBlockCausal3D(
|
||||
in_channels=cur_in_channel,
|
||||
out_channels=out_channels,
|
||||
groups=num_groups,
|
||||
dropout=dropout,
|
||||
eps=eps,
|
||||
))
|
||||
self.resnets = nn.ModuleList(resnets)
|
||||
|
||||
self.downsamplers = None
|
||||
if add_downsample:
|
||||
self.downsamplers = nn.ModuleList([DownsampleCausal3D(
|
||||
out_channels,
|
||||
out_channels,
|
||||
stride=downsample_stride,
|
||||
)])
|
||||
|
||||
def forward(self, hidden_states):
|
||||
for resnet in self.resnets:
|
||||
hidden_states = resnet(hidden_states)
|
||||
|
||||
if self.downsamplers is not None:
|
||||
for downsampler in self.downsamplers:
|
||||
hidden_states = downsampler(hidden_states)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class EncoderCausal3D(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_channels: int = 3,
|
||||
out_channels: int = 16,
|
||||
eps=1e-6,
|
||||
dropout=0.0,
|
||||
block_out_channels=[128, 256, 512, 512],
|
||||
layers_per_block=2,
|
||||
num_groups=32,
|
||||
time_compression_ratio: int = 4,
|
||||
spatial_compression_ratio: int = 8,
|
||||
gradient_checkpointing=False,
|
||||
):
|
||||
super().__init__()
|
||||
self.conv_in = CausalConv3d(in_channels, block_out_channels[0], kernel_size=3, stride=1)
|
||||
self.down_blocks = nn.ModuleList([])
|
||||
|
||||
# down
|
||||
output_channel = block_out_channels[0]
|
||||
for i in range(len(block_out_channels)):
|
||||
input_channel = output_channel
|
||||
output_channel = block_out_channels[i]
|
||||
is_final_block = i == len(block_out_channels) - 1
|
||||
num_spatial_downsample_layers = int(np.log2(spatial_compression_ratio))
|
||||
num_time_downsample_layers = int(np.log2(time_compression_ratio))
|
||||
|
||||
add_spatial_downsample = bool(i < num_spatial_downsample_layers)
|
||||
add_time_downsample = bool(i >= (len(block_out_channels) - 1 - num_time_downsample_layers) and not is_final_block)
|
||||
|
||||
downsample_stride_HW = (2, 2) if add_spatial_downsample else (1, 1)
|
||||
downsample_stride_T = (2,) if add_time_downsample else (1,)
|
||||
downsample_stride = tuple(downsample_stride_T + downsample_stride_HW)
|
||||
down_block = DownEncoderBlockCausal3D(
|
||||
in_channels=input_channel,
|
||||
out_channels=output_channel,
|
||||
dropout=dropout,
|
||||
num_layers=layers_per_block,
|
||||
eps=eps,
|
||||
num_groups=num_groups,
|
||||
add_downsample=bool(add_spatial_downsample or add_time_downsample),
|
||||
downsample_stride=downsample_stride,
|
||||
)
|
||||
self.down_blocks.append(down_block)
|
||||
|
||||
# mid
|
||||
self.mid_block = UNetMidBlockCausal3D(
|
||||
in_channels=block_out_channels[-1],
|
||||
dropout=dropout,
|
||||
eps=eps,
|
||||
num_groups=num_groups,
|
||||
attention_head_dim=block_out_channels[-1],
|
||||
)
|
||||
# out
|
||||
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=num_groups, eps=eps)
|
||||
self.conv_act = nn.SiLU()
|
||||
self.conv_out = CausalConv3d(block_out_channels[-1], 2 * out_channels, kernel_size=3)
|
||||
|
||||
self.gradient_checkpointing = gradient_checkpointing
|
||||
|
||||
def forward(self, hidden_states):
|
||||
hidden_states = self.conv_in(hidden_states)
|
||||
if self.training and self.gradient_checkpointing:
|
||||
|
||||
def create_custom_forward(module):
|
||||
|
||||
def custom_forward(*inputs):
|
||||
return module(*inputs)
|
||||
|
||||
return custom_forward
|
||||
|
||||
# down
|
||||
for down_block in self.down_blocks:
|
||||
torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(down_block),
|
||||
hidden_states,
|
||||
use_reentrant=False,
|
||||
)
|
||||
# middle
|
||||
hidden_states = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(self.mid_block),
|
||||
hidden_states,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
# down
|
||||
for down_block in self.down_blocks:
|
||||
hidden_states = down_block(hidden_states)
|
||||
# middle
|
||||
hidden_states = self.mid_block(hidden_states)
|
||||
# post-process
|
||||
hidden_states = self.conv_norm_out(hidden_states)
|
||||
hidden_states = self.conv_act(hidden_states)
|
||||
hidden_states = self.conv_out(hidden_states)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class HunyuanVideoVAEEncoder(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_channels=3,
|
||||
out_channels=16,
|
||||
eps=1e-6,
|
||||
dropout=0.0,
|
||||
block_out_channels=[128, 256, 512, 512],
|
||||
layers_per_block=2,
|
||||
num_groups=32,
|
||||
time_compression_ratio=4,
|
||||
spatial_compression_ratio=8,
|
||||
gradient_checkpointing=False,
|
||||
):
|
||||
super().__init__()
|
||||
self.encoder = EncoderCausal3D(
|
||||
in_channels=in_channels,
|
||||
out_channels=out_channels,
|
||||
eps=eps,
|
||||
dropout=dropout,
|
||||
block_out_channels=block_out_channels,
|
||||
layers_per_block=layers_per_block,
|
||||
num_groups=num_groups,
|
||||
time_compression_ratio=time_compression_ratio,
|
||||
spatial_compression_ratio=spatial_compression_ratio,
|
||||
gradient_checkpointing=gradient_checkpointing,
|
||||
)
|
||||
self.quant_conv = nn.Conv3d(2 * out_channels, 2 * out_channels, kernel_size=1)
|
||||
self.scaling_factor = 0.476986
|
||||
|
||||
|
||||
def forward(self, images):
|
||||
latents = self.encoder(images)
|
||||
latents = self.quant_conv(latents)
|
||||
latents = latents[:, :16]
|
||||
latents = latents * self.scaling_factor
|
||||
return latents
|
||||
|
||||
|
||||
def build_1d_mask(self, length, left_bound, right_bound, border_width):
|
||||
x = torch.ones((length,))
|
||||
if not left_bound:
|
||||
x[:border_width] = (torch.arange(border_width) + 1) / border_width
|
||||
if not right_bound:
|
||||
x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,))
|
||||
return x
|
||||
|
||||
|
||||
def build_mask(self, data, is_bound, border_width):
|
||||
_, _, T, H, W = data.shape
|
||||
t = self.build_1d_mask(T, is_bound[0], is_bound[1], border_width[0])
|
||||
h = self.build_1d_mask(H, is_bound[2], is_bound[3], border_width[1])
|
||||
w = self.build_1d_mask(W, is_bound[4], is_bound[5], border_width[2])
|
||||
|
||||
t = repeat(t, "T -> T H W", T=T, H=H, W=W)
|
||||
h = repeat(h, "H -> T H W", T=T, H=H, W=W)
|
||||
w = repeat(w, "W -> T H W", T=T, H=H, W=W)
|
||||
|
||||
mask = torch.stack([t, h, w]).min(dim=0).values
|
||||
mask = rearrange(mask, "T H W -> 1 1 T H W")
|
||||
return mask
|
||||
|
||||
|
||||
def tile_forward(self, hidden_states, tile_size, tile_stride):
|
||||
B, C, T, H, W = hidden_states.shape
|
||||
size_t, size_h, size_w = tile_size
|
||||
stride_t, stride_h, stride_w = tile_stride
|
||||
|
||||
# Split tasks
|
||||
tasks = []
|
||||
for t in range(0, T, stride_t):
|
||||
if (t-stride_t >= 0 and t-stride_t+size_t >= T): continue
|
||||
for h in range(0, H, stride_h):
|
||||
if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue
|
||||
for w in range(0, W, stride_w):
|
||||
if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue
|
||||
t_, h_, w_ = t + size_t, h + size_h, w + size_w
|
||||
tasks.append((t, t_, h, h_, w, w_))
|
||||
|
||||
# Run
|
||||
torch_dtype = self.quant_conv.weight.dtype
|
||||
data_device = hidden_states.device
|
||||
computation_device = self.quant_conv.weight.device
|
||||
|
||||
weight = torch.zeros((1, 1, (T - 1) // 4 + 1, H // 8, W // 8), dtype=torch_dtype, device=data_device)
|
||||
values = torch.zeros((B, 16, (T - 1) // 4 + 1, H // 8, W // 8), dtype=torch_dtype, device=data_device)
|
||||
|
||||
for t, t_, h, h_, w, w_ in tqdm(tasks, desc="VAE encoding"):
|
||||
hidden_states_batch = hidden_states[:, :, t:t_, h:h_, w:w_].to(computation_device)
|
||||
hidden_states_batch = self.forward(hidden_states_batch).to(data_device)
|
||||
if t > 0:
|
||||
hidden_states_batch = hidden_states_batch[:, :, 1:]
|
||||
|
||||
mask = self.build_mask(
|
||||
hidden_states_batch,
|
||||
is_bound=(t==0, t_>=T, h==0, h_>=H, w==0, w_>=W),
|
||||
border_width=((size_t - stride_t) // 4, (size_h - stride_h) // 8, (size_w - stride_w) // 8)
|
||||
).to(dtype=torch_dtype, device=data_device)
|
||||
|
||||
target_t = 0 if t==0 else t // 4 + 1
|
||||
target_h = h // 8
|
||||
target_w = w // 8
|
||||
values[
|
||||
:,
|
||||
:,
|
||||
target_t: target_t + hidden_states_batch.shape[2],
|
||||
target_h: target_h + hidden_states_batch.shape[3],
|
||||
target_w: target_w + hidden_states_batch.shape[4],
|
||||
] += hidden_states_batch * mask
|
||||
weight[
|
||||
:,
|
||||
:,
|
||||
target_t: target_t + hidden_states_batch.shape[2],
|
||||
target_h: target_h + hidden_states_batch.shape[3],
|
||||
target_w: target_w + hidden_states_batch.shape[4],
|
||||
] += mask
|
||||
return values / weight
|
||||
|
||||
|
||||
def encode_video(self, latents, tile_size=(65, 256, 256), tile_stride=(48, 192, 192)):
|
||||
latents = latents.to(self.quant_conv.weight.dtype)
|
||||
return self.tile_forward(latents, tile_size=tile_size, tile_stride=tile_stride)
|
||||
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return HunyuanVideoVAEEncoderStateDictConverter()
|
||||
|
||||
|
||||
class HunyuanVideoVAEEncoderStateDictConverter:
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
state_dict_ = {}
|
||||
for name in state_dict:
|
||||
if name.startswith('encoder.') or name.startswith('quant_conv.'):
|
||||
state_dict_[name] = state_dict[name]
|
||||
return state_dict_
|
||||
@@ -73,6 +73,7 @@ try:
|
||||
)
|
||||
except Exception as exception:
|
||||
kernels = None
|
||||
logger.warning("Failed to load cpm_kernels:" + str(exception))
|
||||
|
||||
|
||||
class W8A16Linear(torch.autograd.Function):
|
||||
@@ -980,7 +981,7 @@ class Embedding(torch.nn.Module):
|
||||
# Embeddings.
|
||||
words_embeddings = self.word_embeddings(input_ids)
|
||||
embeddings = words_embeddings
|
||||
# Data format change to avoid explicit transposes : [b s h] --> [s b h].
|
||||
# Data format change to avoid explicit tranposes : [b s h] --> [s b h].
|
||||
embeddings = embeddings.transpose(0, 1).contiguous()
|
||||
# If the input flag for fp32 residual connection is set, convert for float.
|
||||
if self.fp32_residual_connection:
|
||||
@@ -1373,7 +1374,7 @@ class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
|
||||
elif generation_config.max_new_tokens is not None:
|
||||
generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
|
||||
if not has_default_max_length:
|
||||
logger.warning(
|
||||
logger.warn(
|
||||
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
|
||||
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
|
||||
"Please refer to the documentation for more information. "
|
||||
|
||||
@@ -6,9 +6,6 @@ from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2
|
||||
from .sd3_dit import SD3DiT
|
||||
from .flux_dit import FluxDiT
|
||||
from .hunyuan_dit import HunyuanDiT
|
||||
from .cog_dit import CogDiT
|
||||
from .hunyuan_video_dit import HunyuanVideoDiT
|
||||
from .wan_video_dit import WanModel
|
||||
|
||||
|
||||
|
||||
@@ -80,19 +77,11 @@ class LoRAFromCivitai:
|
||||
state_dict_lora = model.__class__.state_dict_converter().from_diffusers(state_dict_lora)
|
||||
elif model_resource == "civitai":
|
||||
state_dict_lora = model.__class__.state_dict_converter().from_civitai(state_dict_lora)
|
||||
if isinstance(state_dict_lora, tuple):
|
||||
state_dict_lora = state_dict_lora[0]
|
||||
if len(state_dict_lora) > 0:
|
||||
print(f" {len(state_dict_lora)} tensors are updated.")
|
||||
for name in state_dict_lora:
|
||||
fp8=False
|
||||
if state_dict_model[name].dtype == torch.float8_e4m3fn:
|
||||
state_dict_model[name]= state_dict_model[name].to(state_dict_lora[name].dtype)
|
||||
fp8=True
|
||||
state_dict_model[name] += state_dict_lora[name].to(
|
||||
dtype=state_dict_model[name].dtype, device=state_dict_model[name].device)
|
||||
if fp8:
|
||||
state_dict_model[name] = state_dict_model[name].to(torch.float8_e4m3fn)
|
||||
model.load_state_dict(state_dict_model)
|
||||
|
||||
|
||||
@@ -107,8 +96,6 @@ class LoRAFromCivitai:
|
||||
converter_fn = model.__class__.state_dict_converter().from_diffusers if model_resource == "diffusers" \
|
||||
else model.__class__.state_dict_converter().from_civitai
|
||||
state_dict_lora_ = converter_fn(state_dict_lora_)
|
||||
if isinstance(state_dict_lora_, tuple):
|
||||
state_dict_lora_ = state_dict_lora_[0]
|
||||
if len(state_dict_lora_) == 0:
|
||||
continue
|
||||
for name in state_dict_lora_:
|
||||
@@ -195,208 +182,71 @@ class FluxLoRAFromCivitai(LoRAFromCivitai):
|
||||
"txt.mod": "txt_mod",
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
class GeneralLoRAFromPeft:
|
||||
def __init__(self):
|
||||
self.supported_model_classes = [SDUNet, SDXLUNet, SD3DiT, HunyuanDiT, FluxDiT, CogDiT, WanModel]
|
||||
|
||||
|
||||
def get_name_dict(self, lora_state_dict):
|
||||
lora_name_dict = {}
|
||||
for key in lora_state_dict:
|
||||
self.supported_model_classes = [SDUNet, SDXLUNet, SD3DiT, HunyuanDiT, FluxDiT]
|
||||
|
||||
|
||||
def fetch_device_dtype_from_state_dict(self, state_dict):
|
||||
device, torch_dtype = None, None
|
||||
for name, param in state_dict.items():
|
||||
device, torch_dtype = param.device, param.dtype
|
||||
break
|
||||
return device, torch_dtype
|
||||
|
||||
|
||||
def convert_state_dict(self, state_dict, alpha=1.0, target_state_dict={}):
|
||||
device, torch_dtype = self.fetch_device_dtype_from_state_dict(target_state_dict)
|
||||
state_dict_ = {}
|
||||
for key in state_dict:
|
||||
if ".lora_B." not in key:
|
||||
continue
|
||||
weight_up = state_dict[key].to(device=device, dtype=torch_dtype)
|
||||
weight_down = state_dict[key.replace(".lora_B.", ".lora_A.")].to(device=device, dtype=torch_dtype)
|
||||
if len(weight_up.shape) == 4:
|
||||
weight_up = weight_up.squeeze(3).squeeze(2)
|
||||
weight_down = weight_down.squeeze(3).squeeze(2)
|
||||
lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
|
||||
else:
|
||||
lora_weight = alpha * torch.mm(weight_up, weight_down)
|
||||
keys = key.split(".")
|
||||
if len(keys) > keys.index("lora_B") + 2:
|
||||
keys.pop(keys.index("lora_B") + 1)
|
||||
keys.pop(keys.index("lora_B"))
|
||||
if keys[0] == "diffusion_model":
|
||||
keys.pop(0)
|
||||
target_name = ".".join(keys)
|
||||
lora_name_dict[target_name] = (key, key.replace(".lora_B.", ".lora_A."))
|
||||
return lora_name_dict
|
||||
if target_name not in target_state_dict:
|
||||
return {}
|
||||
state_dict_[target_name] = lora_weight.cpu()
|
||||
return state_dict_
|
||||
|
||||
|
||||
def match(self, model: torch.nn.Module, state_dict_lora):
|
||||
lora_name_dict = self.get_name_dict(state_dict_lora)
|
||||
model_name_dict = {name: None for name, _ in model.named_parameters()}
|
||||
matched_num = sum([i in model_name_dict for i in lora_name_dict])
|
||||
if matched_num == len(lora_name_dict):
|
||||
return "", ""
|
||||
else:
|
||||
return None
|
||||
|
||||
|
||||
def fetch_device_and_dtype(self, state_dict):
|
||||
device, dtype = None, None
|
||||
for name, param in state_dict.items():
|
||||
device, dtype = param.device, param.dtype
|
||||
break
|
||||
computation_device = device
|
||||
computation_dtype = dtype
|
||||
if computation_device == torch.device("cpu"):
|
||||
if torch.cuda.is_available():
|
||||
computation_device = torch.device("cuda")
|
||||
if computation_dtype == torch.float8_e4m3fn:
|
||||
computation_dtype = torch.float32
|
||||
return device, dtype, computation_device, computation_dtype
|
||||
|
||||
|
||||
def load(self, model, state_dict_lora, lora_prefix="", alpha=1.0, model_resource=""):
|
||||
state_dict_model = model.state_dict()
|
||||
device, dtype, computation_device, computation_dtype = self.fetch_device_and_dtype(state_dict_model)
|
||||
lora_name_dict = self.get_name_dict(state_dict_lora)
|
||||
for name in lora_name_dict:
|
||||
weight_up = state_dict_lora[lora_name_dict[name][0]].to(device=computation_device, dtype=computation_dtype)
|
||||
weight_down = state_dict_lora[lora_name_dict[name][1]].to(device=computation_device, dtype=computation_dtype)
|
||||
if len(weight_up.shape) == 4:
|
||||
weight_up = weight_up.squeeze(3).squeeze(2)
|
||||
weight_down = weight_down.squeeze(3).squeeze(2)
|
||||
weight_lora = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
|
||||
else:
|
||||
weight_lora = alpha * torch.mm(weight_up, weight_down)
|
||||
weight_model = state_dict_model[name].to(device=computation_device, dtype=computation_dtype)
|
||||
weight_patched = weight_model + weight_lora
|
||||
state_dict_model[name] = weight_patched.to(device=device, dtype=dtype)
|
||||
print(f" {len(lora_name_dict)} tensors are updated.")
|
||||
model.load_state_dict(state_dict_model)
|
||||
|
||||
state_dict_lora = self.convert_state_dict(state_dict_lora, alpha=alpha, target_state_dict=state_dict_model)
|
||||
if len(state_dict_lora) > 0:
|
||||
print(f" {len(state_dict_lora)} tensors are updated.")
|
||||
for name in state_dict_lora:
|
||||
state_dict_model[name] += state_dict_lora[name].to(
|
||||
dtype=state_dict_model[name].dtype,
|
||||
device=state_dict_model[name].device
|
||||
)
|
||||
model.load_state_dict(state_dict_model)
|
||||
|
||||
|
||||
class HunyuanVideoLoRAFromCivitai(LoRAFromCivitai):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.supported_model_classes = [HunyuanVideoDiT, HunyuanVideoDiT]
|
||||
self.lora_prefix = ["diffusion_model.", "transformer."]
|
||||
self.special_keys = {}
|
||||
|
||||
|
||||
class FluxLoRAConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def align_to_opensource_format(state_dict, alpha=None):
|
||||
prefix_rename_dict = {
|
||||
"single_blocks": "lora_unet_single_blocks",
|
||||
"blocks": "lora_unet_double_blocks",
|
||||
}
|
||||
middle_rename_dict = {
|
||||
"norm.linear": "modulation_lin",
|
||||
"to_qkv_mlp": "linear1",
|
||||
"proj_out": "linear2",
|
||||
|
||||
"norm1_a.linear": "img_mod_lin",
|
||||
"norm1_b.linear": "txt_mod_lin",
|
||||
"attn.a_to_qkv": "img_attn_qkv",
|
||||
"attn.b_to_qkv": "txt_attn_qkv",
|
||||
"attn.a_to_out": "img_attn_proj",
|
||||
"attn.b_to_out": "txt_attn_proj",
|
||||
"ff_a.0": "img_mlp_0",
|
||||
"ff_a.2": "img_mlp_2",
|
||||
"ff_b.0": "txt_mlp_0",
|
||||
"ff_b.2": "txt_mlp_2",
|
||||
}
|
||||
suffix_rename_dict = {
|
||||
"lora_B.weight": "lora_up.weight",
|
||||
"lora_A.weight": "lora_down.weight",
|
||||
}
|
||||
state_dict_ = {}
|
||||
for name, param in state_dict.items():
|
||||
names = name.split(".")
|
||||
if names[-2] != "lora_A" and names[-2] != "lora_B":
|
||||
names.pop(-2)
|
||||
prefix = names[0]
|
||||
middle = ".".join(names[2:-2])
|
||||
suffix = ".".join(names[-2:])
|
||||
block_id = names[1]
|
||||
if middle not in middle_rename_dict:
|
||||
def match(self, model, state_dict_lora):
|
||||
for model_class in self.supported_model_classes:
|
||||
if not isinstance(model, model_class):
|
||||
continue
|
||||
rename = prefix_rename_dict[prefix] + "_" + block_id + "_" + middle_rename_dict[middle] + "." + suffix_rename_dict[suffix]
|
||||
state_dict_[rename] = param
|
||||
if rename.endswith("lora_up.weight"):
|
||||
lora_alpha = alpha if alpha is not None else param.shape[-1]
|
||||
state_dict_[rename.replace("lora_up.weight", "alpha")] = torch.tensor((lora_alpha,))[0]
|
||||
return state_dict_
|
||||
state_dict_model = model.state_dict()
|
||||
try:
|
||||
state_dict_lora_ = self.convert_state_dict(state_dict_lora, alpha=1.0, target_state_dict=state_dict_model)
|
||||
if len(state_dict_lora_) > 0:
|
||||
return "", ""
|
||||
except:
|
||||
pass
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def align_to_diffsynth_format(state_dict):
|
||||
rename_dict = {
|
||||
"lora_unet_double_blocks_blockid_img_mod_lin.lora_down.weight": "blocks.blockid.norm1_a.linear.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_mod_lin.lora_up.weight": "blocks.blockid.norm1_a.linear.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_mod_lin.lora_down.weight": "blocks.blockid.norm1_b.linear.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_mod_lin.lora_up.weight": "blocks.blockid.norm1_b.linear.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_attn_qkv.lora_down.weight": "blocks.blockid.attn.a_to_qkv.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_attn_qkv.lora_up.weight": "blocks.blockid.attn.a_to_qkv.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_attn_qkv.lora_down.weight": "blocks.blockid.attn.b_to_qkv.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_attn_qkv.lora_up.weight": "blocks.blockid.attn.b_to_qkv.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_attn_proj.lora_down.weight": "blocks.blockid.attn.a_to_out.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_attn_proj.lora_up.weight": "blocks.blockid.attn.a_to_out.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_attn_proj.lora_down.weight": "blocks.blockid.attn.b_to_out.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_attn_proj.lora_up.weight": "blocks.blockid.attn.b_to_out.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_mlp_0.lora_down.weight": "blocks.blockid.ff_a.0.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_mlp_0.lora_up.weight": "blocks.blockid.ff_a.0.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_mlp_2.lora_down.weight": "blocks.blockid.ff_a.2.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_img_mlp_2.lora_up.weight": "blocks.blockid.ff_a.2.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_mlp_0.lora_down.weight": "blocks.blockid.ff_b.0.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_mlp_0.lora_up.weight": "blocks.blockid.ff_b.0.lora_B.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_mlp_2.lora_down.weight": "blocks.blockid.ff_b.2.lora_A.default.weight",
|
||||
"lora_unet_double_blocks_blockid_txt_mlp_2.lora_up.weight": "blocks.blockid.ff_b.2.lora_B.default.weight",
|
||||
"lora_unet_single_blocks_blockid_modulation_lin.lora_down.weight": "single_blocks.blockid.norm.linear.lora_A.default.weight",
|
||||
"lora_unet_single_blocks_blockid_modulation_lin.lora_up.weight": "single_blocks.blockid.norm.linear.lora_B.default.weight",
|
||||
"lora_unet_single_blocks_blockid_linear1.lora_down.weight": "single_blocks.blockid.to_qkv_mlp.lora_A.default.weight",
|
||||
"lora_unet_single_blocks_blockid_linear1.lora_up.weight": "single_blocks.blockid.to_qkv_mlp.lora_B.default.weight",
|
||||
"lora_unet_single_blocks_blockid_linear2.lora_down.weight": "single_blocks.blockid.proj_out.lora_A.default.weight",
|
||||
"lora_unet_single_blocks_blockid_linear2.lora_up.weight": "single_blocks.blockid.proj_out.lora_B.default.weight",
|
||||
}
|
||||
def guess_block_id(name):
|
||||
names = name.split("_")
|
||||
for i in names:
|
||||
if i.isdigit():
|
||||
return i, name.replace(f"_{i}_", "_blockid_")
|
||||
return None, None
|
||||
state_dict_ = {}
|
||||
for name, param in state_dict.items():
|
||||
block_id, source_name = guess_block_id(name)
|
||||
if source_name in rename_dict:
|
||||
target_name = rename_dict[source_name]
|
||||
target_name = target_name.replace(".blockid.", f".{block_id}.")
|
||||
state_dict_[target_name] = param
|
||||
else:
|
||||
state_dict_[name] = param
|
||||
return state_dict_
|
||||
|
||||
|
||||
class WanLoRAConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def align_to_opensource_format(state_dict, **kwargs):
|
||||
state_dict = {"diffusion_model." + name.replace(".default.", "."): param for name, param in state_dict.items()}
|
||||
return state_dict
|
||||
|
||||
@staticmethod
|
||||
def align_to_diffsynth_format(state_dict, **kwargs):
|
||||
state_dict = {name.replace("diffusion_model.", "").replace(".lora_A.weight", ".lora_A.default.weight").replace(".lora_B.weight", ".lora_B.default.weight"): param for name, param in state_dict.items()}
|
||||
return state_dict
|
||||
|
||||
|
||||
class QwenImageLoRAConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def align_to_opensource_format(state_dict, **kwargs):
|
||||
state_dict = {name.replace(".default.", "."): param for name, param in state_dict.items()}
|
||||
return state_dict
|
||||
|
||||
@staticmethod
|
||||
def align_to_diffsynth_format(state_dict, **kwargs):
|
||||
state_dict = {name.replace(".lora_A.weight", ".lora_A.default.weight").replace(".lora_B.weight", ".lora_B.default.weight"): param for name, param in state_dict.items()}
|
||||
return state_dict
|
||||
|
||||
|
||||
def get_lora_loaders():
|
||||
return [SDLoRAFromCivitai(), SDXLLoRAFromCivitai(), FluxLoRAFromCivitai(), HunyuanVideoLoRAFromCivitai(), GeneralLoRAFromPeft()]
|
||||
return [SDLoRAFromCivitai(), SDXLLoRAFromCivitai(), GeneralLoRAFromPeft(), FluxLoRAFromCivitai()]
|
||||
|
||||
@@ -1,7 +1,10 @@
|
||||
import os, torch, json, importlib
|
||||
import os, torch, hashlib, json, importlib
|
||||
from safetensors import safe_open
|
||||
from torch import Tensor
|
||||
from typing_extensions import Literal, TypeAlias
|
||||
from typing import List
|
||||
|
||||
from .downloader import download_models, download_customized_models, Preset_model_id, Preset_model_website
|
||||
from .downloader import download_models, Preset_model_id, Preset_model_website
|
||||
|
||||
from .sd_text_encoder import SDTextEncoder
|
||||
from .sd_unet import SDUNet
|
||||
@@ -35,13 +38,10 @@ from .sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder
|
||||
|
||||
from .hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder
|
||||
from .hunyuan_dit import HunyuanDiT
|
||||
from .hunyuan_video_vae_decoder import HunyuanVideoVAEDecoder
|
||||
from .hunyuan_video_vae_encoder import HunyuanVideoVAEEncoder
|
||||
|
||||
from .flux_dit import FluxDiT
|
||||
from .flux_text_encoder import FluxTextEncoder2
|
||||
from .flux_text_encoder import FluxTextEncoder1, FluxTextEncoder2
|
||||
from .flux_vae import FluxVAEEncoder, FluxVAEDecoder
|
||||
from .flux_ipadapter import FluxIpAdapter
|
||||
|
||||
from .cog_vae import CogVAEEncoder, CogVAEDecoder
|
||||
from .cog_dit import CogDiT
|
||||
@@ -50,7 +50,45 @@ from ..extensions.RIFE import IFNet
|
||||
from ..extensions.ESRGAN import RRDBNet
|
||||
|
||||
from ..configs.model_config import model_loader_configs, huggingface_model_loader_configs, patch_model_loader_configs
|
||||
from .utils import load_state_dict, init_weights_on_device, hash_state_dict_keys, split_state_dict_with_prefix
|
||||
from .utils import load_state_dict
|
||||
|
||||
|
||||
|
||||
def convert_state_dict_keys_to_single_str(state_dict, with_shape=True):
|
||||
keys = []
|
||||
for key, value in state_dict.items():
|
||||
if isinstance(key, str):
|
||||
if isinstance(value, Tensor):
|
||||
if with_shape:
|
||||
shape = "_".join(map(str, list(value.shape)))
|
||||
keys.append(key + ":" + shape)
|
||||
keys.append(key)
|
||||
elif isinstance(value, dict):
|
||||
keys.append(key + "|" + convert_state_dict_keys_to_single_str(value, with_shape=with_shape))
|
||||
keys.sort()
|
||||
keys_str = ",".join(keys)
|
||||
return keys_str
|
||||
|
||||
|
||||
def split_state_dict_with_prefix(state_dict):
|
||||
keys = sorted([key for key in state_dict if isinstance(key, str)])
|
||||
prefix_dict = {}
|
||||
for key in keys:
|
||||
prefix = key if "." not in key else key.split(".")[0]
|
||||
if prefix not in prefix_dict:
|
||||
prefix_dict[prefix] = []
|
||||
prefix_dict[prefix].append(key)
|
||||
state_dicts = []
|
||||
for prefix, keys in prefix_dict.items():
|
||||
sub_state_dict = {key: state_dict[key] for key in keys}
|
||||
state_dicts.append(sub_state_dict)
|
||||
return state_dicts
|
||||
|
||||
|
||||
def hash_state_dict_keys(state_dict, with_shape=True):
|
||||
keys_str = convert_state_dict_keys_to_single_str(state_dict, with_shape=with_shape)
|
||||
keys_str = keys_str.encode(encoding="UTF-8")
|
||||
return hashlib.md5(keys_str).hexdigest()
|
||||
|
||||
|
||||
def load_model_from_single_file(state_dict, model_names, model_classes, model_resource, torch_dtype, device):
|
||||
@@ -68,12 +106,8 @@ def load_model_from_single_file(state_dict, model_names, model_classes, model_re
|
||||
else:
|
||||
model_state_dict, extra_kwargs = state_dict_results, {}
|
||||
torch_dtype = torch.float32 if extra_kwargs.get("upcast_to_float32", False) else torch_dtype
|
||||
with init_weights_on_device():
|
||||
model = model_class(**extra_kwargs)
|
||||
if hasattr(model, "eval"):
|
||||
model = model.eval()
|
||||
model.load_state_dict(model_state_dict, assign=True)
|
||||
model = model.to(dtype=torch_dtype, device=device)
|
||||
model = model_class(**extra_kwargs).to(dtype=torch_dtype, device=device)
|
||||
model.load_state_dict(model_state_dict)
|
||||
loaded_model_names.append(model_name)
|
||||
loaded_models.append(model)
|
||||
return loaded_model_names, loaded_models
|
||||
@@ -82,10 +116,7 @@ def load_model_from_single_file(state_dict, model_names, model_classes, model_re
|
||||
def load_model_from_huggingface_folder(file_path, model_names, model_classes, torch_dtype, device):
|
||||
loaded_model_names, loaded_models = [], []
|
||||
for model_name, model_class in zip(model_names, model_classes):
|
||||
if torch_dtype in [torch.float32, torch.float16, torch.bfloat16]:
|
||||
model = model_class.from_pretrained(file_path, torch_dtype=torch_dtype).eval()
|
||||
else:
|
||||
model = model_class.from_pretrained(file_path).eval().to(dtype=torch_dtype)
|
||||
model = model_class.from_pretrained(file_path, torch_dtype=torch_dtype).eval()
|
||||
if torch_dtype == torch.float16 and hasattr(model, "half"):
|
||||
model = model.half()
|
||||
try:
|
||||
@@ -160,7 +191,7 @@ class ModelDetectorFromSingleFile:
|
||||
|
||||
|
||||
def match(self, file_path="", state_dict={}):
|
||||
if isinstance(file_path, str) and os.path.isdir(file_path):
|
||||
if os.path.isdir(file_path):
|
||||
return False
|
||||
if len(state_dict) == 0:
|
||||
state_dict = load_state_dict(file_path)
|
||||
@@ -202,7 +233,7 @@ class ModelDetectorFromSplitedSingleFile(ModelDetectorFromSingleFile):
|
||||
|
||||
|
||||
def match(self, file_path="", state_dict={}):
|
||||
if isinstance(file_path, str) and os.path.isdir(file_path):
|
||||
if os.path.isdir(file_path):
|
||||
return False
|
||||
if len(state_dict) == 0:
|
||||
state_dict = load_state_dict(file_path)
|
||||
@@ -245,7 +276,7 @@ class ModelDetectorFromHuggingfaceFolder:
|
||||
|
||||
|
||||
def match(self, file_path="", state_dict={}):
|
||||
if not isinstance(file_path, str) or os.path.isfile(file_path):
|
||||
if os.path.isfile(file_path):
|
||||
return False
|
||||
file_list = os.listdir(file_path)
|
||||
if "config.json" not in file_list:
|
||||
@@ -286,7 +317,7 @@ class ModelDetectorFromPatchedSingleFile:
|
||||
|
||||
|
||||
def match(self, file_path="", state_dict={}):
|
||||
if not isinstance(file_path, str) or os.path.isdir(file_path):
|
||||
if os.path.isdir(file_path):
|
||||
return False
|
||||
if len(state_dict) == 0:
|
||||
state_dict = load_state_dict(file_path)
|
||||
@@ -371,36 +402,22 @@ class ModelManager:
|
||||
|
||||
|
||||
def load_lora(self, file_path="", state_dict={}, lora_alpha=1.0):
|
||||
if isinstance(file_path, list):
|
||||
for file_path_ in file_path:
|
||||
self.load_lora(file_path_, state_dict=state_dict, lora_alpha=lora_alpha)
|
||||
else:
|
||||
print(f"Loading LoRA models from file: {file_path}")
|
||||
is_loaded = False
|
||||
if len(state_dict) == 0:
|
||||
state_dict = load_state_dict(file_path)
|
||||
for model_name, model, model_path in zip(self.model_name, self.model, self.model_path):
|
||||
for lora in get_lora_loaders():
|
||||
match_results = lora.match(model, state_dict)
|
||||
if match_results is not None:
|
||||
print(f" Adding LoRA to {model_name} ({model_path}).")
|
||||
lora_prefix, model_resource = match_results
|
||||
lora.load(model, state_dict, lora_prefix, alpha=lora_alpha, model_resource=model_resource)
|
||||
is_loaded = True
|
||||
break
|
||||
if not is_loaded:
|
||||
print(f" Cannot load LoRA: {file_path}")
|
||||
print(f"Loading LoRA models from file: {file_path}")
|
||||
if len(state_dict) == 0:
|
||||
state_dict = load_state_dict(file_path)
|
||||
for model_name, model, model_path in zip(self.model_name, self.model, self.model_path):
|
||||
for lora in get_lora_loaders():
|
||||
match_results = lora.match(model, state_dict)
|
||||
if match_results is not None:
|
||||
print(f" Adding LoRA to {model_name} ({model_path}).")
|
||||
lora_prefix, model_resource = match_results
|
||||
lora.load(model, state_dict, lora_prefix, alpha=lora_alpha, model_resource=model_resource)
|
||||
break
|
||||
|
||||
|
||||
def load_model(self, file_path, model_names=None, device=None, torch_dtype=None):
|
||||
def load_model(self, file_path, model_names=None):
|
||||
print(f"Loading models from: {file_path}")
|
||||
if device is None: device = self.device
|
||||
if torch_dtype is None: torch_dtype = self.torch_dtype
|
||||
if isinstance(file_path, list):
|
||||
state_dict = {}
|
||||
for path in file_path:
|
||||
state_dict.update(load_state_dict(path))
|
||||
elif os.path.isfile(file_path):
|
||||
if os.path.isfile(file_path):
|
||||
state_dict = load_state_dict(file_path)
|
||||
else:
|
||||
state_dict = None
|
||||
@@ -408,7 +425,7 @@ class ModelManager:
|
||||
if model_detector.match(file_path, state_dict):
|
||||
model_names, models = model_detector.load(
|
||||
file_path, state_dict,
|
||||
device=device, torch_dtype=torch_dtype,
|
||||
device=self.device, torch_dtype=self.torch_dtype,
|
||||
allowed_model_names=model_names, model_manager=self
|
||||
)
|
||||
for model_name, model in zip(model_names, models):
|
||||
@@ -421,12 +438,12 @@ class ModelManager:
|
||||
print(f" We cannot detect the model type. No models are loaded.")
|
||||
|
||||
|
||||
def load_models(self, file_path_list, model_names=None, device=None, torch_dtype=None):
|
||||
def load_models(self, file_path_list, model_names=None):
|
||||
for file_path in file_path_list:
|
||||
self.load_model(file_path, model_names, device=device, torch_dtype=torch_dtype)
|
||||
self.load_model(file_path, model_names)
|
||||
|
||||
|
||||
def fetch_model(self, model_name, file_path=None, require_model_path=False, index=None):
|
||||
def fetch_model(self, model_name, file_path=None, require_model_path=False):
|
||||
fetched_models = []
|
||||
fetched_model_paths = []
|
||||
for model, model_path, model_name_ in zip(self.model, self.model_path, self.model_name):
|
||||
@@ -440,25 +457,12 @@ class ModelManager:
|
||||
return None
|
||||
if len(fetched_models) == 1:
|
||||
print(f"Using {model_name} from {fetched_model_paths[0]}.")
|
||||
model = fetched_models[0]
|
||||
path = fetched_model_paths[0]
|
||||
else:
|
||||
if index is None:
|
||||
model = fetched_models[0]
|
||||
path = fetched_model_paths[0]
|
||||
print(f"More than one {model_name} models are loaded in model manager: {fetched_model_paths}. Using {model_name} from {fetched_model_paths[0]}.")
|
||||
elif isinstance(index, int):
|
||||
model = fetched_models[:index]
|
||||
path = fetched_model_paths[:index]
|
||||
print(f"More than one {model_name} models are loaded in model manager: {fetched_model_paths}. Using {model_name} from {fetched_model_paths[:index]}.")
|
||||
else:
|
||||
model = fetched_models
|
||||
path = fetched_model_paths
|
||||
print(f"More than one {model_name} models are loaded in model manager: {fetched_model_paths}. Using {model_name} from {fetched_model_paths}.")
|
||||
print(f"More than one {model_name} models are loaded in model manager: {fetched_model_paths}. Using {model_name} from {fetched_model_paths[0]}.")
|
||||
if require_model_path:
|
||||
return model, path
|
||||
return fetched_models[0], fetched_model_paths[0]
|
||||
else:
|
||||
return model
|
||||
return fetched_models[0]
|
||||
|
||||
|
||||
def to(self, device):
|
||||
|
||||
@@ -1,161 +0,0 @@
|
||||
import torch
|
||||
from PIL import Image
|
||||
|
||||
|
||||
class NexusGenAutoregressiveModel(torch.nn.Module):
|
||||
def __init__(self, max_length=1024, max_pixels=262640):
|
||||
super(NexusGenAutoregressiveModel, self).__init__()
|
||||
from .nexus_gen_ar_model import Qwen2_5_VLForConditionalGeneration
|
||||
from transformers import Qwen2_5_VLConfig
|
||||
self.max_length = max_length
|
||||
self.max_pixels = max_pixels
|
||||
model_config = Qwen2_5_VLConfig(**{
|
||||
"_name_or_path": "DiffSynth-Studio/Nexus-GenV2",
|
||||
"architectures": [
|
||||
"Qwen2_5_VLForConditionalGeneration"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"auto_map": {
|
||||
"AutoConfig": "configuration_qwen2_5_vl.Qwen2_5_VLConfig",
|
||||
"AutoModel": "modeling_qwen2_5_vl.Qwen2_5_VLModel",
|
||||
"AutoModelForCausalLM": "modeling_qwen2_5_vl.Qwen2_5_VLForConditionalGeneration"
|
||||
},
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3584,
|
||||
"image_token_id": 151655,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 18944,
|
||||
"max_position_embeddings": 128000,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen2_5_vl",
|
||||
"num_attention_heads": 28,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
"pad_token_id": 151643,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": {
|
||||
"mrope_section": [
|
||||
16,
|
||||
24,
|
||||
24
|
||||
],
|
||||
"rope_type": "default",
|
||||
"type": "default"
|
||||
},
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": 32768,
|
||||
"tie_word_embeddings": False,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.49.0",
|
||||
"use_cache": False,
|
||||
"use_sliding_window": False,
|
||||
"video_token_id": 151656,
|
||||
"vision_config": {
|
||||
"hidden_size": 1280,
|
||||
"in_chans": 3,
|
||||
"model_type": "qwen2_5_vl",
|
||||
"spatial_patch_size": 14,
|
||||
"tokens_per_second": 2,
|
||||
"torch_dtype": "bfloat16"
|
||||
},
|
||||
"vision_end_token_id": 151653,
|
||||
"vision_start_token_id": 151652,
|
||||
"vision_token_id": 151654,
|
||||
"vocab_size": 152064
|
||||
})
|
||||
self.model = Qwen2_5_VLForConditionalGeneration(model_config)
|
||||
self.processor = None
|
||||
|
||||
|
||||
def load_processor(self, path):
|
||||
from .nexus_gen_ar_model import Qwen2_5_VLProcessor
|
||||
self.processor = Qwen2_5_VLProcessor.from_pretrained(path)
|
||||
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return NexusGenAutoregressiveModelStateDictConverter()
|
||||
|
||||
def bound_image(self, image, max_pixels=262640):
|
||||
from qwen_vl_utils import smart_resize
|
||||
resized_height, resized_width = smart_resize(
|
||||
image.height,
|
||||
image.width,
|
||||
max_pixels=max_pixels,
|
||||
)
|
||||
return image.resize((resized_width, resized_height))
|
||||
|
||||
def get_editing_msg(self, instruction):
|
||||
if '<image>' not in instruction:
|
||||
instruction = '<image> ' + instruction
|
||||
messages = [{"role":"user", "content":instruction}, {"role":"assistant", "content":"Here is the image: <image>"}]
|
||||
return messages
|
||||
|
||||
def get_generation_msg(self, instruction):
|
||||
instruction = "Generate an image according to the following description: {}".format(instruction)
|
||||
messages = [{"role":"user", "content":instruction}, {"role":"assistant", "content":"Here is an image based on the description: <image>"}]
|
||||
return messages
|
||||
|
||||
def forward(self, instruction, ref_image=None, num_img_tokens=81):
|
||||
"""
|
||||
Generate target embeddings for the given instruction and reference image.
|
||||
"""
|
||||
if ref_image is not None:
|
||||
messages = self.get_editing_msg(instruction)
|
||||
images = [self.bound_image(ref_image)] + [Image.new(mode='RGB', size=(252, 252), color=(255, 255, 255))]
|
||||
output_image_embeddings = self.get_target_embeddings(images, messages, self.processor, self.model, num_img_tokens)
|
||||
else:
|
||||
messages = self.get_generation_msg(instruction)
|
||||
images = [Image.new(mode='RGB', size=(252, 252), color=(255, 255, 255))]
|
||||
output_image_embeddings = self.get_target_embeddings(images, messages, self.processor, self.model, num_img_tokens)
|
||||
|
||||
return output_image_embeddings
|
||||
|
||||
def get_target_embeddings(self, images, messages, processor, model, num_img_tokens=81):
|
||||
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
|
||||
text = text.replace('<image>', '<|vision_start|><|image_pad|><|vision_end|>')
|
||||
inputs = processor(
|
||||
text=[text],
|
||||
images=images,
|
||||
padding=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
inputs = inputs.to(model.device)
|
||||
|
||||
input_embeds = model.model.embed_tokens(inputs['input_ids'])
|
||||
image_embeds = model.visual(inputs['pixel_values'], grid_thw=inputs['image_grid_thw'])
|
||||
ground_truth_image_embeds = image_embeds[-num_img_tokens:]
|
||||
input_image_embeds = image_embeds[:-num_img_tokens]
|
||||
|
||||
image_mask = inputs['input_ids'] == model.config.image_token_id
|
||||
indices = image_mask.cumsum(dim=1)
|
||||
input_image_mask = torch.logical_and(indices <= (image_embeds.shape[0] - ground_truth_image_embeds.shape[0]), image_mask)
|
||||
gt_image_mask = torch.logical_and(image_mask, ~input_image_mask)
|
||||
input_image_mask = input_image_mask.unsqueeze(-1).expand_as(input_embeds)
|
||||
input_embeds = input_embeds.masked_scatter(input_image_mask, input_image_embeds)
|
||||
|
||||
image_prefill_embeds = model.image_prefill_embeds(
|
||||
torch.arange(81, device=model.device).long()
|
||||
)
|
||||
input_embeds = input_embeds.masked_scatter(gt_image_mask.unsqueeze(-1).expand_as(input_embeds), image_prefill_embeds)
|
||||
|
||||
position_ids, _ = model.get_rope_index(
|
||||
inputs['input_ids'],
|
||||
inputs['image_grid_thw'],
|
||||
attention_mask=inputs['attention_mask'])
|
||||
position_ids = position_ids.contiguous()
|
||||
outputs = model(inputs_embeds=input_embeds, position_ids=position_ids, attention_mask=inputs['attention_mask'], return_dict=True)
|
||||
output_image_embeddings = outputs.image_embeddings[:, :-1, :]
|
||||
output_image_embeddings = output_image_embeddings[gt_image_mask[:, 1:]]
|
||||
return output_image_embeddings, input_image_embeds, inputs['image_grid_thw']
|
||||
|
||||
|
||||
class NexusGenAutoregressiveModelStateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
state_dict = {"model." + key: value for key, value in state_dict.items()}
|
||||
return state_dict
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,417 +0,0 @@
|
||||
import math
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from typing import Optional, Tuple
|
||||
|
||||
|
||||
|
||||
def rotate_half(x):
|
||||
"""Rotates half the hidden dims of the input."""
|
||||
x1 = x[..., : x.shape[-1] // 2]
|
||||
x2 = x[..., x.shape[-1] // 2 :]
|
||||
return torch.cat((-x2, x1), dim=-1)
|
||||
|
||||
|
||||
def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim=1):
|
||||
mrope_section = mrope_section * 2
|
||||
cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(
|
||||
unsqueeze_dim
|
||||
)
|
||||
sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(
|
||||
unsqueeze_dim
|
||||
)
|
||||
|
||||
q_embed = (q * cos) + (rotate_half(q) * sin)
|
||||
k_embed = (k * cos) + (rotate_half(k) * sin)
|
||||
return q_embed, k_embed
|
||||
|
||||
|
||||
class Qwen2_5_VLRotaryEmbedding(nn.Module):
|
||||
def __init__(self, config, device=None):
|
||||
super().__init__()
|
||||
# BC: "rope_type" was originally "type"
|
||||
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
|
||||
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
||||
else:
|
||||
self.rope_type = "default"
|
||||
self.max_seq_len_cached = config.max_position_embeddings
|
||||
self.original_max_seq_len = config.max_position_embeddings
|
||||
|
||||
self.config = config
|
||||
from transformers.modeling_rope_utils import _compute_default_rope_parameters
|
||||
self.rope_init_fn = _compute_default_rope_parameters
|
||||
|
||||
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
|
||||
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||||
self.original_inv_freq = self.inv_freq
|
||||
|
||||
|
||||
def _dynamic_frequency_update(self, position_ids, device):
|
||||
"""
|
||||
dynamic RoPE layers should recompute `inv_freq` in the following situations:
|
||||
1 - growing beyond the cached sequence length (allow scaling)
|
||||
2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
|
||||
"""
|
||||
seq_len = torch.max(position_ids) + 1
|
||||
if seq_len > self.max_seq_len_cached: # growth
|
||||
inv_freq, self.attention_scaling = self.rope_init_fn(
|
||||
self.config, device, seq_len=seq_len, **self.rope_kwargs
|
||||
)
|
||||
self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation
|
||||
self.max_seq_len_cached = seq_len
|
||||
|
||||
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
|
||||
self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
|
||||
self.max_seq_len_cached = self.original_max_seq_len
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(self, x, position_ids):
|
||||
if "dynamic" in self.rope_type:
|
||||
self._dynamic_frequency_update(position_ids, device=x.device)
|
||||
|
||||
# Core RoPE block. In contrast to other models, Qwen2_5_VL has different position ids for the grids
|
||||
# So we expand the inv_freq to shape (3, ...)
|
||||
inv_freq_expanded = self.inv_freq[None, None, :, None].float().expand(3, position_ids.shape[1], -1, 1)
|
||||
position_ids_expanded = position_ids[:, :, None, :].float() # shape (3, bs, 1, positions)
|
||||
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
|
||||
device_type = x.device.type
|
||||
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
|
||||
with torch.autocast(device_type=device_type, enabled=False):
|
||||
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(2, 3)
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
cos = emb.cos()
|
||||
sin = emb.sin()
|
||||
|
||||
# Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
|
||||
cos = cos * self.attention_scaling
|
||||
sin = sin * self.attention_scaling
|
||||
|
||||
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
||||
|
||||
|
||||
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
||||
"""
|
||||
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
||||
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
||||
"""
|
||||
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
||||
if n_rep == 1:
|
||||
return hidden_states
|
||||
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
||||
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
||||
|
||||
|
||||
class Qwen2_5_VLAttention(nn.Module):
|
||||
def __init__(self, config, layer_idx: Optional[int] = None):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.layer_idx = layer_idx
|
||||
|
||||
self.hidden_size = config.hidden_size
|
||||
self.num_heads = config.num_attention_heads
|
||||
self.head_dim = self.hidden_size // self.num_heads
|
||||
self.num_key_value_heads = config.num_key_value_heads
|
||||
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
||||
self.is_causal = True
|
||||
self.attention_dropout = config.attention_dropout
|
||||
self.rope_scaling = config.rope_scaling
|
||||
|
||||
if (self.head_dim * self.num_heads) != self.hidden_size:
|
||||
raise ValueError(
|
||||
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
||||
f" and `num_heads`: {self.num_heads})."
|
||||
)
|
||||
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
|
||||
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
|
||||
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
|
||||
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
|
||||
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
|
||||
query_states = self.q_proj(hidden_states)
|
||||
key_states = self.k_proj(hidden_states)
|
||||
value_states = self.v_proj(hidden_states)
|
||||
|
||||
query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
|
||||
key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
|
||||
value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
|
||||
|
||||
cos, sin = position_embeddings
|
||||
query_states, key_states = apply_multimodal_rotary_pos_emb(
|
||||
query_states, key_states, cos, sin, self.rope_scaling["mrope_section"]
|
||||
)
|
||||
|
||||
# repeat k/v heads if n_kv_heads < n_heads
|
||||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||||
|
||||
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||||
|
||||
# Fix precision issues in Qwen2-VL float16 inference
|
||||
# Replace inf values with zeros in attention weights to prevent NaN propagation
|
||||
if query_states.dtype == torch.float16:
|
||||
attn_weights = torch.where(torch.isinf(attn_weights), torch.zeros_like(attn_weights), attn_weights)
|
||||
|
||||
# upcast attention to fp32
|
||||
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
||||
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
|
||||
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
||||
raise ValueError(
|
||||
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
||||
f" {attn_output.size()}"
|
||||
)
|
||||
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
attn_output = attn_output.reshape(bsz, q_len, -1)
|
||||
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
return attn_output
|
||||
|
||||
|
||||
class Qwen2MLP(nn.Module):
|
||||
def __init__(self, config):
|
||||
super().__init__()
|
||||
from transformers.activations import ACT2FN
|
||||
self.config = config
|
||||
self.hidden_size = config.hidden_size
|
||||
self.intermediate_size = config.intermediate_size
|
||||
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||||
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||||
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
||||
self.act_fn = ACT2FN[config.hidden_act]
|
||||
|
||||
def forward(self, x):
|
||||
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
||||
return down_proj
|
||||
|
||||
|
||||
class Qwen2RMSNorm(nn.Module):
|
||||
def __init__(self, hidden_size, eps=1e-6):
|
||||
"""
|
||||
Qwen2RMSNorm is equivalent to T5LayerNorm
|
||||
"""
|
||||
super().__init__()
|
||||
self.weight = nn.Parameter(torch.ones(hidden_size))
|
||||
self.variance_epsilon = eps
|
||||
|
||||
def forward(self, hidden_states):
|
||||
input_dtype = hidden_states.dtype
|
||||
hidden_states = hidden_states.to(torch.float32)
|
||||
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
||||
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
||||
return self.weight * hidden_states.to(input_dtype)
|
||||
|
||||
def extra_repr(self):
|
||||
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
|
||||
|
||||
|
||||
class Qwen2_5_VLDecoderLayer(nn.Module):
|
||||
def __init__(self, config, layer_idx):
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
|
||||
self.self_attn = Qwen2_5_VLAttention(config, layer_idx)
|
||||
|
||||
self.mlp = Qwen2MLP(config)
|
||||
self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
|
||||
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
||||
|
||||
residual = hidden_states
|
||||
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
|
||||
# Self Attention
|
||||
hidden_states = self.self_attn(
|
||||
hidden_states=hidden_states,
|
||||
position_embeddings=position_embeddings,
|
||||
)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
# Fully Connected
|
||||
residual = hidden_states
|
||||
hidden_states = self.post_attention_layernorm(hidden_states)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class NexusGenImageEmbeddingMerger(nn.Module):
|
||||
def __init__(self, num_layers=1, out_channel=4096, expand_ratio=4, device='cpu'):
|
||||
super().__init__()
|
||||
from transformers import Qwen2_5_VLConfig
|
||||
from transformers.activations import ACT2FN
|
||||
config = Qwen2_5_VLConfig(**{
|
||||
"_name_or_path": "DiffSynth-Studio/Nexus-GenV2",
|
||||
"architectures": [
|
||||
"Qwen2_5_VLForConditionalGeneration"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"auto_map": {
|
||||
"AutoConfig": "configuration_qwen2_5_vl.Qwen2_5_VLConfig",
|
||||
"AutoModel": "modeling_qwen2_5_vl.Qwen2_5_VLModel",
|
||||
"AutoModelForCausalLM": "modeling_qwen2_5_vl.Qwen2_5_VLForConditionalGeneration"
|
||||
},
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3584,
|
||||
"image_token_id": 151655,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 18944,
|
||||
"max_position_embeddings": 128000,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen2_5_vl",
|
||||
"num_attention_heads": 28,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
"pad_token_id": 151643,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": {
|
||||
"mrope_section": [
|
||||
16,
|
||||
24,
|
||||
24
|
||||
],
|
||||
"rope_type": "default",
|
||||
"type": "default"
|
||||
},
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": 32768,
|
||||
"tie_word_embeddings": False,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.49.0",
|
||||
"use_cache": False,
|
||||
"use_sliding_window": False,
|
||||
"video_token_id": 151656,
|
||||
"vision_config": {
|
||||
"hidden_size": 1280,
|
||||
"in_chans": 3,
|
||||
"model_type": "qwen2_5_vl",
|
||||
"spatial_patch_size": 14,
|
||||
"tokens_per_second": 2,
|
||||
"torch_dtype": "bfloat16"
|
||||
},
|
||||
"vision_end_token_id": 151653,
|
||||
"vision_start_token_id": 151652,
|
||||
"vision_token_id": 151654,
|
||||
"vocab_size": 152064
|
||||
})
|
||||
self.config = config
|
||||
self.num_layers = num_layers
|
||||
self.layers = nn.ModuleList([Qwen2_5_VLDecoderLayer(config, layer_idx) for layer_idx in range(num_layers)])
|
||||
self.projector = nn.Sequential(Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps),
|
||||
nn.Linear(config.hidden_size, out_channel * expand_ratio),
|
||||
Qwen2RMSNorm(out_channel * expand_ratio, eps=config.rms_norm_eps),
|
||||
ACT2FN[config.hidden_act], nn.Linear(out_channel * expand_ratio, out_channel),
|
||||
Qwen2RMSNorm(out_channel, eps=config.rms_norm_eps))
|
||||
self.base_grid = torch.tensor([[1, 72, 72]], device=device)
|
||||
self.rotary_emb = Qwen2_5_VLRotaryEmbedding(config=config, device=device)
|
||||
|
||||
def get_position_ids(self, image_grid_thw):
|
||||
"""
|
||||
Generates position ids for the input embeddings grid.
|
||||
modified from the qwen2_vl mrope.
|
||||
"""
|
||||
batch_size = image_grid_thw.shape[0]
|
||||
spatial_merge_size = self.config.vision_config.spatial_merge_size
|
||||
t, h, w = (
|
||||
image_grid_thw[0][0],
|
||||
image_grid_thw[0][1],
|
||||
image_grid_thw[0][2],
|
||||
)
|
||||
llm_grid_t, llm_grid_h, llm_grid_w = (
|
||||
t.item(),
|
||||
h.item() // spatial_merge_size,
|
||||
w.item() // spatial_merge_size,
|
||||
)
|
||||
scale_h = self.base_grid[0][1].item() / h.item()
|
||||
scale_w = self.base_grid[0][2].item() / w.item()
|
||||
|
||||
range_tensor = torch.arange(llm_grid_t).view(-1, 1)
|
||||
expanded_range = range_tensor.expand(-1, llm_grid_h * llm_grid_w)
|
||||
time_tensor = expanded_range * self.config.vision_config.tokens_per_second
|
||||
t_index = time_tensor.long().flatten().to(image_grid_thw.device)
|
||||
h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten().to(image_grid_thw.device) * scale_h
|
||||
w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten().to(image_grid_thw.device) * scale_w
|
||||
# 3, B, L
|
||||
position_ids = torch.stack([t_index, h_index, w_index]).unsqueeze(0).repeat(batch_size, 1, 1).permute(1, 0, 2)
|
||||
return position_ids
|
||||
|
||||
def forward(self, embeds, embeds_grid, ref_embeds=None, ref_embeds_grid=None):
|
||||
position_ids = self.get_position_ids(embeds_grid)
|
||||
hidden_states = embeds
|
||||
if ref_embeds is not None:
|
||||
position_ids_ref_embeds = self.get_position_ids(ref_embeds_grid)
|
||||
position_ids = torch.cat((position_ids, position_ids_ref_embeds), dim=-1)
|
||||
hidden_states = torch.cat((embeds, ref_embeds), dim=1)
|
||||
|
||||
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
||||
for layer in self.layers:
|
||||
hidden_states = layer(hidden_states, position_embeddings)
|
||||
|
||||
hidden_states = self.projector(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return NexusGenMergerStateDictConverter()
|
||||
|
||||
|
||||
class NexusGenMergerStateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
return state_dict
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
merger_state_dict = {key.replace("embedding_merger.", ""): value for key, value in state_dict.items() if key.startswith('embedding_merger.')}
|
||||
return merger_state_dict
|
||||
|
||||
|
||||
class NexusGenAdapter(nn.Module):
|
||||
"""
|
||||
Adapter for Nexus-Gen generation decoder.
|
||||
"""
|
||||
def __init__(self, input_dim=3584, output_dim=4096):
|
||||
super(NexusGenAdapter, self).__init__()
|
||||
self.adapter = nn.Sequential(nn.Linear(input_dim, output_dim),
|
||||
nn.LayerNorm(output_dim), nn.ReLU(),
|
||||
nn.Linear(output_dim, output_dim),
|
||||
nn.LayerNorm(output_dim))
|
||||
|
||||
def forward(self, x):
|
||||
return self.adapter(x)
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return NexusGenAdapterStateDictConverter()
|
||||
|
||||
|
||||
class NexusGenAdapterStateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
return state_dict
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
adapter_state_dict = {key: value for key, value in state_dict.items() if key.startswith('adapter.')}
|
||||
return adapter_state_dict
|
||||
@@ -1,803 +0,0 @@
|
||||
# The code is revised from DiT
|
||||
import os
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
import math
|
||||
from safetensors.torch import load_file
|
||||
from typing import List, Optional, Tuple, Union
|
||||
import torch.utils.checkpoint
|
||||
from huggingface_hub import snapshot_download
|
||||
from transformers.modeling_outputs import BaseModelOutputWithPast
|
||||
from transformers import Phi3Config, Phi3Model
|
||||
from transformers.cache_utils import Cache, DynamicCache
|
||||
from transformers.utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class Phi3Transformer(Phi3Model):
|
||||
"""
|
||||
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`]
|
||||
We only modified the attention mask
|
||||
Args:
|
||||
config: Phi3Config
|
||||
"""
|
||||
def prefetch_layer(self, layer_idx: int, device: torch.device):
|
||||
"Starts prefetching the next layer cache"
|
||||
with torch.cuda.stream(self.prefetch_stream):
|
||||
# Prefetch next layer tensors to GPU
|
||||
for name, param in self.layers[layer_idx].named_parameters():
|
||||
param.data = param.data.to(device, non_blocking=True)
|
||||
|
||||
def evict_previous_layer(self, layer_idx: int):
|
||||
"Moves the previous layer cache to the CPU"
|
||||
prev_layer_idx = layer_idx - 1
|
||||
for name, param in self.layers[prev_layer_idx].named_parameters():
|
||||
param.data = param.data.to("cpu", non_blocking=True)
|
||||
|
||||
def get_offlaod_layer(self, layer_idx: int, device: torch.device):
|
||||
# init stream
|
||||
if not hasattr(self, "prefetch_stream"):
|
||||
self.prefetch_stream = torch.cuda.Stream()
|
||||
|
||||
# delete previous layer
|
||||
torch.cuda.current_stream().synchronize()
|
||||
self.evict_previous_layer(layer_idx)
|
||||
|
||||
# make sure the current layer is ready
|
||||
torch.cuda.synchronize(self.prefetch_stream)
|
||||
|
||||
# load next layer
|
||||
self.prefetch_layer((layer_idx + 1) % len(self.layers), device)
|
||||
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
offload_model: Optional[bool] = False,
|
||||
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
if (input_ids is None) ^ (inputs_embeds is not None):
|
||||
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
if use_cache:
|
||||
logger.warning_once(
|
||||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
||||
)
|
||||
use_cache = False
|
||||
|
||||
# kept for BC (non `Cache` `past_key_values` inputs)
|
||||
return_legacy_cache = False
|
||||
if use_cache and not isinstance(past_key_values, Cache):
|
||||
return_legacy_cache = True
|
||||
if past_key_values is None:
|
||||
past_key_values = DynamicCache()
|
||||
else:
|
||||
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
||||
logger.warning_once(
|
||||
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
|
||||
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
|
||||
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
|
||||
)
|
||||
|
||||
# if inputs_embeds is None:
|
||||
# inputs_embeds = self.embed_tokens(input_ids)
|
||||
|
||||
# if cache_position is None:
|
||||
# past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
||||
# cache_position = torch.arange(
|
||||
# past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
||||
# )
|
||||
# if position_ids is None:
|
||||
# position_ids = cache_position.unsqueeze(0)
|
||||
|
||||
if attention_mask is not None and attention_mask.dim() == 3:
|
||||
dtype = inputs_embeds.dtype
|
||||
min_dtype = torch.finfo(dtype).min
|
||||
attention_mask = (1 - attention_mask) * min_dtype
|
||||
attention_mask = attention_mask.unsqueeze(1).to(inputs_embeds.dtype)
|
||||
else:
|
||||
raise Exception("attention_mask parameter was unavailable or invalid")
|
||||
# causal_mask = self._update_causal_mask(
|
||||
# attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
||||
# )
|
||||
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
# decoder layers
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attns = () if output_attentions else None
|
||||
next_decoder_cache = None
|
||||
|
||||
layer_idx = -1
|
||||
for decoder_layer in self.layers:
|
||||
layer_idx += 1
|
||||
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
layer_outputs = self._gradient_checkpointing_func(
|
||||
decoder_layer.__call__,
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
position_ids,
|
||||
past_key_values,
|
||||
output_attentions,
|
||||
use_cache,
|
||||
cache_position,
|
||||
)
|
||||
else:
|
||||
if offload_model and not self.training:
|
||||
self.get_offlaod_layer(layer_idx, device=inputs_embeds.device)
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_values,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
cache_position=cache_position,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if use_cache:
|
||||
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
||||
|
||||
if output_attentions:
|
||||
all_self_attns += (layer_outputs[1],)
|
||||
|
||||
hidden_states = self.norm(hidden_states)
|
||||
|
||||
# add hidden states from the last decoder layer
|
||||
if output_hidden_states:
|
||||
print('************')
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
next_cache = next_decoder_cache if use_cache else None
|
||||
if return_legacy_cache:
|
||||
next_cache = next_cache.to_legacy_cache()
|
||||
|
||||
if not return_dict:
|
||||
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=next_cache,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attns,
|
||||
)
|
||||
|
||||
|
||||
def modulate(x, shift, scale):
|
||||
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
|
||||
|
||||
|
||||
class TimestepEmbedder(nn.Module):
|
||||
"""
|
||||
Embeds scalar timesteps into vector representations.
|
||||
"""
|
||||
def __init__(self, hidden_size, frequency_embedding_size=256):
|
||||
super().__init__()
|
||||
self.mlp = nn.Sequential(
|
||||
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
|
||||
nn.SiLU(),
|
||||
nn.Linear(hidden_size, hidden_size, bias=True),
|
||||
)
|
||||
self.frequency_embedding_size = frequency_embedding_size
|
||||
|
||||
@staticmethod
|
||||
def timestep_embedding(t, dim, max_period=10000):
|
||||
"""
|
||||
Create sinusoidal timestep embeddings.
|
||||
:param t: a 1-D Tensor of N indices, one per batch element.
|
||||
These may be fractional.
|
||||
:param dim: the dimension of the output.
|
||||
:param max_period: controls the minimum frequency of the embeddings.
|
||||
:return: an (N, D) Tensor of positional embeddings.
|
||||
"""
|
||||
# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
|
||||
half = dim // 2
|
||||
freqs = torch.exp(
|
||||
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
|
||||
).to(device=t.device)
|
||||
args = t[:, None].float() * freqs[None]
|
||||
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
|
||||
if dim % 2:
|
||||
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
|
||||
return embedding
|
||||
|
||||
def forward(self, t, dtype=torch.float32):
|
||||
t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
|
||||
t_emb = self.mlp(t_freq)
|
||||
return t_emb
|
||||
|
||||
|
||||
class FinalLayer(nn.Module):
|
||||
"""
|
||||
The final layer of DiT.
|
||||
"""
|
||||
def __init__(self, hidden_size, patch_size, out_channels):
|
||||
super().__init__()
|
||||
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
||||
self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
|
||||
self.adaLN_modulation = nn.Sequential(
|
||||
nn.SiLU(),
|
||||
nn.Linear(hidden_size, 2 * hidden_size, bias=True)
|
||||
)
|
||||
|
||||
def forward(self, x, c):
|
||||
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
|
||||
x = modulate(self.norm_final(x), shift, scale)
|
||||
x = self.linear(x)
|
||||
return x
|
||||
|
||||
|
||||
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=1):
|
||||
"""
|
||||
grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or
|
||||
[1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
|
||||
"""
|
||||
if isinstance(grid_size, int):
|
||||
grid_size = (grid_size, grid_size)
|
||||
|
||||
grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / interpolation_scale
|
||||
grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / interpolation_scale
|
||||
grid = np.meshgrid(grid_w, grid_h) # here w goes first
|
||||
grid = np.stack(grid, axis=0)
|
||||
|
||||
grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
|
||||
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
|
||||
if cls_token and extra_tokens > 0:
|
||||
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
|
||||
return pos_embed
|
||||
|
||||
|
||||
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
|
||||
assert embed_dim % 2 == 0
|
||||
|
||||
# use half of dimensions to encode grid_h
|
||||
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
|
||||
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
|
||||
|
||||
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
|
||||
return emb
|
||||
|
||||
|
||||
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
|
||||
"""
|
||||
embed_dim: output dimension for each position
|
||||
pos: a list of positions to be encoded: size (M,)
|
||||
out: (M, D)
|
||||
"""
|
||||
assert embed_dim % 2 == 0
|
||||
omega = np.arange(embed_dim // 2, dtype=np.float64)
|
||||
omega /= embed_dim / 2.
|
||||
omega = 1. / 10000**omega # (D/2,)
|
||||
|
||||
pos = pos.reshape(-1) # (M,)
|
||||
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
|
||||
|
||||
emb_sin = np.sin(out) # (M, D/2)
|
||||
emb_cos = np.cos(out) # (M, D/2)
|
||||
|
||||
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
|
||||
return emb
|
||||
|
||||
|
||||
class PatchEmbedMR(nn.Module):
|
||||
""" 2D Image to Patch Embedding
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
patch_size: int = 2,
|
||||
in_chans: int = 4,
|
||||
embed_dim: int = 768,
|
||||
bias: bool = True,
|
||||
):
|
||||
super().__init__()
|
||||
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.proj(x)
|
||||
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
|
||||
return x
|
||||
|
||||
|
||||
class OmniGenOriginalModel(nn.Module):
|
||||
"""
|
||||
Diffusion model with a Transformer backbone.
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
transformer_config: Phi3Config,
|
||||
patch_size=2,
|
||||
in_channels=4,
|
||||
pe_interpolation: float = 1.0,
|
||||
pos_embed_max_size: int = 192,
|
||||
):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = in_channels
|
||||
self.patch_size = patch_size
|
||||
self.pos_embed_max_size = pos_embed_max_size
|
||||
|
||||
hidden_size = transformer_config.hidden_size
|
||||
|
||||
self.x_embedder = PatchEmbedMR(patch_size, in_channels, hidden_size, bias=True)
|
||||
self.input_x_embedder = PatchEmbedMR(patch_size, in_channels, hidden_size, bias=True)
|
||||
|
||||
self.time_token = TimestepEmbedder(hidden_size)
|
||||
self.t_embedder = TimestepEmbedder(hidden_size)
|
||||
|
||||
self.pe_interpolation = pe_interpolation
|
||||
pos_embed = get_2d_sincos_pos_embed(hidden_size, pos_embed_max_size, interpolation_scale=self.pe_interpolation, base_size=64)
|
||||
self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=True)
|
||||
|
||||
self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)
|
||||
|
||||
self.initialize_weights()
|
||||
|
||||
self.llm = Phi3Transformer(config=transformer_config)
|
||||
self.llm.config.use_cache = False
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, model_name):
|
||||
if not os.path.exists(model_name):
|
||||
cache_folder = os.getenv('HF_HUB_CACHE')
|
||||
model_name = snapshot_download(repo_id=model_name,
|
||||
cache_dir=cache_folder,
|
||||
ignore_patterns=['flax_model.msgpack', 'rust_model.ot', 'tf_model.h5'])
|
||||
config = Phi3Config.from_pretrained(model_name)
|
||||
model = cls(config)
|
||||
if os.path.exists(os.path.join(model_name, 'model.safetensors')):
|
||||
print("Loading safetensors")
|
||||
ckpt = load_file(os.path.join(model_name, 'model.safetensors'))
|
||||
else:
|
||||
ckpt = torch.load(os.path.join(model_name, 'model.pt'), map_location='cpu')
|
||||
model.load_state_dict(ckpt)
|
||||
return model
|
||||
|
||||
def initialize_weights(self):
|
||||
assert not hasattr(self, "llama")
|
||||
|
||||
# Initialize transformer layers:
|
||||
def _basic_init(module):
|
||||
if isinstance(module, nn.Linear):
|
||||
torch.nn.init.xavier_uniform_(module.weight)
|
||||
if module.bias is not None:
|
||||
nn.init.constant_(module.bias, 0)
|
||||
self.apply(_basic_init)
|
||||
|
||||
# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
|
||||
w = self.x_embedder.proj.weight.data
|
||||
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
|
||||
nn.init.constant_(self.x_embedder.proj.bias, 0)
|
||||
|
||||
w = self.input_x_embedder.proj.weight.data
|
||||
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
|
||||
nn.init.constant_(self.x_embedder.proj.bias, 0)
|
||||
|
||||
|
||||
# Initialize timestep embedding MLP:
|
||||
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
|
||||
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
|
||||
nn.init.normal_(self.time_token.mlp[0].weight, std=0.02)
|
||||
nn.init.normal_(self.time_token.mlp[2].weight, std=0.02)
|
||||
|
||||
# Zero-out output layers:
|
||||
nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
|
||||
nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
|
||||
nn.init.constant_(self.final_layer.linear.weight, 0)
|
||||
nn.init.constant_(self.final_layer.linear.bias, 0)
|
||||
|
||||
def unpatchify(self, x, h, w):
|
||||
"""
|
||||
x: (N, T, patch_size**2 * C)
|
||||
imgs: (N, H, W, C)
|
||||
"""
|
||||
c = self.out_channels
|
||||
|
||||
x = x.reshape(shape=(x.shape[0], h//self.patch_size, w//self.patch_size, self.patch_size, self.patch_size, c))
|
||||
x = torch.einsum('nhwpqc->nchpwq', x)
|
||||
imgs = x.reshape(shape=(x.shape[0], c, h, w))
|
||||
return imgs
|
||||
|
||||
|
||||
def cropped_pos_embed(self, height, width):
|
||||
"""Crops positional embeddings for SD3 compatibility."""
|
||||
if self.pos_embed_max_size is None:
|
||||
raise ValueError("`pos_embed_max_size` must be set for cropping.")
|
||||
|
||||
height = height // self.patch_size
|
||||
width = width // self.patch_size
|
||||
if height > self.pos_embed_max_size:
|
||||
raise ValueError(
|
||||
f"Height ({height}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}."
|
||||
)
|
||||
if width > self.pos_embed_max_size:
|
||||
raise ValueError(
|
||||
f"Width ({width}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}."
|
||||
)
|
||||
|
||||
top = (self.pos_embed_max_size - height) // 2
|
||||
left = (self.pos_embed_max_size - width) // 2
|
||||
spatial_pos_embed = self.pos_embed.reshape(1, self.pos_embed_max_size, self.pos_embed_max_size, -1)
|
||||
spatial_pos_embed = spatial_pos_embed[:, top : top + height, left : left + width, :]
|
||||
# print(top, top + height, left, left + width, spatial_pos_embed.size())
|
||||
spatial_pos_embed = spatial_pos_embed.reshape(1, -1, spatial_pos_embed.shape[-1])
|
||||
return spatial_pos_embed
|
||||
|
||||
|
||||
def patch_multiple_resolutions(self, latents, padding_latent=None, is_input_images:bool=False):
|
||||
if isinstance(latents, list):
|
||||
return_list = False
|
||||
if padding_latent is None:
|
||||
padding_latent = [None] * len(latents)
|
||||
return_list = True
|
||||
patched_latents, num_tokens, shapes = [], [], []
|
||||
for latent, padding in zip(latents, padding_latent):
|
||||
height, width = latent.shape[-2:]
|
||||
if is_input_images:
|
||||
latent = self.input_x_embedder(latent)
|
||||
else:
|
||||
latent = self.x_embedder(latent)
|
||||
pos_embed = self.cropped_pos_embed(height, width)
|
||||
latent = latent + pos_embed
|
||||
if padding is not None:
|
||||
latent = torch.cat([latent, padding], dim=-2)
|
||||
patched_latents.append(latent)
|
||||
|
||||
num_tokens.append(pos_embed.size(1))
|
||||
shapes.append([height, width])
|
||||
if not return_list:
|
||||
latents = torch.cat(patched_latents, dim=0)
|
||||
else:
|
||||
latents = patched_latents
|
||||
else:
|
||||
height, width = latents.shape[-2:]
|
||||
if is_input_images:
|
||||
latents = self.input_x_embedder(latents)
|
||||
else:
|
||||
latents = self.x_embedder(latents)
|
||||
pos_embed = self.cropped_pos_embed(height, width)
|
||||
latents = latents + pos_embed
|
||||
num_tokens = latents.size(1)
|
||||
shapes = [height, width]
|
||||
return latents, num_tokens, shapes
|
||||
|
||||
|
||||
def forward(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, padding_latent=None, past_key_values=None, return_past_key_values=True, offload_model:bool=False):
|
||||
"""
|
||||
|
||||
"""
|
||||
input_is_list = isinstance(x, list)
|
||||
x, num_tokens, shapes = self.patch_multiple_resolutions(x, padding_latent)
|
||||
time_token = self.time_token(timestep, dtype=x[0].dtype).unsqueeze(1)
|
||||
|
||||
if input_img_latents is not None:
|
||||
input_latents, _, _ = self.patch_multiple_resolutions(input_img_latents, is_input_images=True)
|
||||
if input_ids is not None:
|
||||
condition_embeds = self.llm.embed_tokens(input_ids).clone()
|
||||
input_img_inx = 0
|
||||
for b_inx in input_image_sizes.keys():
|
||||
for start_inx, end_inx in input_image_sizes[b_inx]:
|
||||
condition_embeds[b_inx, start_inx: end_inx] = input_latents[input_img_inx]
|
||||
input_img_inx += 1
|
||||
if input_img_latents is not None:
|
||||
assert input_img_inx == len(input_latents)
|
||||
|
||||
input_emb = torch.cat([condition_embeds, time_token, x], dim=1)
|
||||
else:
|
||||
input_emb = torch.cat([time_token, x], dim=1)
|
||||
output = self.llm(inputs_embeds=input_emb, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, offload_model=offload_model)
|
||||
output, past_key_values = output.last_hidden_state, output.past_key_values
|
||||
if input_is_list:
|
||||
image_embedding = output[:, -max(num_tokens):]
|
||||
time_emb = self.t_embedder(timestep, dtype=x.dtype)
|
||||
x = self.final_layer(image_embedding, time_emb)
|
||||
latents = []
|
||||
for i in range(x.size(0)):
|
||||
latent = x[i:i+1, :num_tokens[i]]
|
||||
latent = self.unpatchify(latent, shapes[i][0], shapes[i][1])
|
||||
latents.append(latent)
|
||||
else:
|
||||
image_embedding = output[:, -num_tokens:]
|
||||
time_emb = self.t_embedder(timestep, dtype=x.dtype)
|
||||
x = self.final_layer(image_embedding, time_emb)
|
||||
latents = self.unpatchify(x, shapes[0], shapes[1])
|
||||
|
||||
if return_past_key_values:
|
||||
return latents, past_key_values
|
||||
return latents
|
||||
|
||||
@torch.no_grad()
|
||||
def forward_with_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model):
|
||||
self.llm.config.use_cache = use_kv_cache
|
||||
model_out, past_key_values = self.forward(x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, past_key_values=past_key_values, return_past_key_values=True, offload_model=offload_model)
|
||||
if use_img_cfg:
|
||||
cond, uncond, img_cond = torch.split(model_out, len(model_out) // 3, dim=0)
|
||||
cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond)
|
||||
model_out = [cond, cond, cond]
|
||||
else:
|
||||
cond, uncond = torch.split(model_out, len(model_out) // 2, dim=0)
|
||||
cond = uncond + cfg_scale * (cond - uncond)
|
||||
model_out = [cond, cond]
|
||||
|
||||
return torch.cat(model_out, dim=0), past_key_values
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def forward_with_separate_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model):
|
||||
self.llm.config.use_cache = use_kv_cache
|
||||
if past_key_values is None:
|
||||
past_key_values = [None] * len(attention_mask)
|
||||
|
||||
x = torch.split(x, len(x) // len(attention_mask), dim=0)
|
||||
timestep = timestep.to(x[0].dtype)
|
||||
timestep = torch.split(timestep, len(timestep) // len(input_ids), dim=0)
|
||||
|
||||
model_out, pask_key_values = [], []
|
||||
for i in range(len(input_ids)):
|
||||
temp_out, temp_pask_key_values = self.forward(x[i], timestep[i], input_ids[i], input_img_latents[i], input_image_sizes[i], attention_mask[i], position_ids[i], past_key_values=past_key_values[i], return_past_key_values=True, offload_model=offload_model)
|
||||
model_out.append(temp_out)
|
||||
pask_key_values.append(temp_pask_key_values)
|
||||
|
||||
if len(model_out) == 3:
|
||||
cond, uncond, img_cond = model_out
|
||||
cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond)
|
||||
model_out = [cond, cond, cond]
|
||||
elif len(model_out) == 2:
|
||||
cond, uncond = model_out
|
||||
cond = uncond + cfg_scale * (cond - uncond)
|
||||
model_out = [cond, cond]
|
||||
else:
|
||||
return model_out[0]
|
||||
|
||||
return torch.cat(model_out, dim=0), pask_key_values
|
||||
|
||||
|
||||
|
||||
class OmniGenTransformer(OmniGenOriginalModel):
|
||||
def __init__(self):
|
||||
config = {
|
||||
"_name_or_path": "Phi-3-vision-128k-instruct",
|
||||
"architectures": [
|
||||
"Phi3ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3072,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 8192,
|
||||
"max_position_embeddings": 131072,
|
||||
"model_type": "phi3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 32,
|
||||
"original_max_position_embeddings": 4096,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": {
|
||||
"long_factor": [
|
||||
1.0299999713897705,
|
||||
1.0499999523162842,
|
||||
1.0499999523162842,
|
||||
1.0799999237060547,
|
||||
1.2299998998641968,
|
||||
1.2299998998641968,
|
||||
1.2999999523162842,
|
||||
1.4499999284744263,
|
||||
1.5999999046325684,
|
||||
1.6499998569488525,
|
||||
1.8999998569488525,
|
||||
2.859999895095825,
|
||||
3.68999981880188,
|
||||
5.419999599456787,
|
||||
5.489999771118164,
|
||||
5.489999771118164,
|
||||
9.09000015258789,
|
||||
11.579999923706055,
|
||||
15.65999984741211,
|
||||
15.769999504089355,
|
||||
15.789999961853027,
|
||||
18.360000610351562,
|
||||
21.989999771118164,
|
||||
23.079999923706055,
|
||||
30.009998321533203,
|
||||
32.35000228881836,
|
||||
32.590003967285156,
|
||||
35.56000518798828,
|
||||
39.95000457763672,
|
||||
53.840003967285156,
|
||||
56.20000457763672,
|
||||
57.95000457763672,
|
||||
59.29000473022461,
|
||||
59.77000427246094,
|
||||
59.920005798339844,
|
||||
61.190006256103516,
|
||||
61.96000671386719,
|
||||
62.50000762939453,
|
||||
63.3700065612793,
|
||||
63.48000717163086,
|
||||
63.48000717163086,
|
||||
63.66000747680664,
|
||||
63.850006103515625,
|
||||
64.08000946044922,
|
||||
64.760009765625,
|
||||
64.80001068115234,
|
||||
64.81001281738281,
|
||||
64.81001281738281
|
||||
],
|
||||
"short_factor": [
|
||||
1.05,
|
||||
1.05,
|
||||
1.05,
|
||||
1.1,
|
||||
1.1,
|
||||
1.1,
|
||||
1.2500000000000002,
|
||||
1.2500000000000002,
|
||||
1.4000000000000004,
|
||||
1.4500000000000004,
|
||||
1.5500000000000005,
|
||||
1.8500000000000008,
|
||||
1.9000000000000008,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.1000000000000005,
|
||||
2.1000000000000005,
|
||||
2.2,
|
||||
2.3499999999999996,
|
||||
2.3499999999999996,
|
||||
2.3499999999999996,
|
||||
2.3499999999999996,
|
||||
2.3999999999999995,
|
||||
2.3999999999999995,
|
||||
2.6499999999999986,
|
||||
2.6999999999999984,
|
||||
2.8999999999999977,
|
||||
2.9499999999999975,
|
||||
3.049999999999997,
|
||||
3.049999999999997,
|
||||
3.049999999999997
|
||||
],
|
||||
"type": "su"
|
||||
},
|
||||
"rope_theta": 10000.0,
|
||||
"sliding_window": 131072,
|
||||
"tie_word_embeddings": False,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.38.1",
|
||||
"use_cache": True,
|
||||
"vocab_size": 32064,
|
||||
"_attn_implementation": "sdpa"
|
||||
}
|
||||
config = Phi3Config(**config)
|
||||
super().__init__(config)
|
||||
|
||||
|
||||
def forward(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, padding_latent=None, past_key_values=None, return_past_key_values=True, offload_model:bool=False):
|
||||
input_is_list = isinstance(x, list)
|
||||
x, num_tokens, shapes = self.patch_multiple_resolutions(x, padding_latent)
|
||||
time_token = self.time_token(timestep, dtype=x[0].dtype).unsqueeze(1)
|
||||
|
||||
if input_img_latents is not None:
|
||||
input_latents, _, _ = self.patch_multiple_resolutions(input_img_latents, is_input_images=True)
|
||||
if input_ids is not None:
|
||||
condition_embeds = self.llm.embed_tokens(input_ids).clone()
|
||||
input_img_inx = 0
|
||||
for b_inx in input_image_sizes.keys():
|
||||
for start_inx, end_inx in input_image_sizes[b_inx]:
|
||||
condition_embeds[b_inx, start_inx: end_inx] = input_latents[input_img_inx]
|
||||
input_img_inx += 1
|
||||
if input_img_latents is not None:
|
||||
assert input_img_inx == len(input_latents)
|
||||
|
||||
input_emb = torch.cat([condition_embeds, time_token, x], dim=1)
|
||||
else:
|
||||
input_emb = torch.cat([time_token, x], dim=1)
|
||||
output = self.llm(inputs_embeds=input_emb, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, offload_model=offload_model)
|
||||
output, past_key_values = output.last_hidden_state, output.past_key_values
|
||||
if input_is_list:
|
||||
image_embedding = output[:, -max(num_tokens):]
|
||||
time_emb = self.t_embedder(timestep, dtype=x.dtype)
|
||||
x = self.final_layer(image_embedding, time_emb)
|
||||
latents = []
|
||||
for i in range(x.size(0)):
|
||||
latent = x[i:i+1, :num_tokens[i]]
|
||||
latent = self.unpatchify(latent, shapes[i][0], shapes[i][1])
|
||||
latents.append(latent)
|
||||
else:
|
||||
image_embedding = output[:, -num_tokens:]
|
||||
time_emb = self.t_embedder(timestep, dtype=x.dtype)
|
||||
x = self.final_layer(image_embedding, time_emb)
|
||||
latents = self.unpatchify(x, shapes[0], shapes[1])
|
||||
|
||||
if return_past_key_values:
|
||||
return latents, past_key_values
|
||||
return latents
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def forward_with_separate_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model):
|
||||
self.llm.config.use_cache = use_kv_cache
|
||||
if past_key_values is None:
|
||||
past_key_values = [None] * len(attention_mask)
|
||||
|
||||
x = torch.split(x, len(x) // len(attention_mask), dim=0)
|
||||
timestep = timestep.to(x[0].dtype)
|
||||
timestep = torch.split(timestep, len(timestep) // len(input_ids), dim=0)
|
||||
|
||||
model_out, pask_key_values = [], []
|
||||
for i in range(len(input_ids)):
|
||||
temp_out, temp_pask_key_values = self.forward(x[i], timestep[i], input_ids[i], input_img_latents[i], input_image_sizes[i], attention_mask[i], position_ids[i], past_key_values=past_key_values[i], return_past_key_values=True, offload_model=offload_model)
|
||||
model_out.append(temp_out)
|
||||
pask_key_values.append(temp_pask_key_values)
|
||||
|
||||
if len(model_out) == 3:
|
||||
cond, uncond, img_cond = model_out
|
||||
cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond)
|
||||
model_out = [cond, cond, cond]
|
||||
elif len(model_out) == 2:
|
||||
cond, uncond = model_out
|
||||
cond = uncond + cfg_scale * (cond - uncond)
|
||||
model_out = [cond, cond]
|
||||
else:
|
||||
return model_out[0]
|
||||
|
||||
return torch.cat(model_out, dim=0), pask_key_values
|
||||
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return OmniGenTransformerStateDictConverter()
|
||||
|
||||
|
||||
|
||||
class OmniGenTransformerStateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
return state_dict
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
return state_dict
|
||||
@@ -1,63 +0,0 @@
|
||||
from .qwen_image_dit import QwenImageTransformerBlock, AdaLayerNorm, TimestepEmbeddings
|
||||
from einops import rearrange
|
||||
import torch
|
||||
|
||||
|
||||
|
||||
class QwenImageAccelerateAdapter(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
num_layers: int = 1,
|
||||
):
|
||||
super().__init__()
|
||||
self.proj_latents_in = torch.nn.Linear(64, 3072)
|
||||
self.time_text_embed = TimestepEmbeddings(256, 3072, diffusers_compatible_format=True, scale=1000, align_dtype_to_timestep=True)
|
||||
self.transformer_blocks = torch.nn.ModuleList(
|
||||
[
|
||||
QwenImageTransformerBlock(
|
||||
dim=3072,
|
||||
num_attention_heads=24,
|
||||
attention_head_dim=128,
|
||||
)
|
||||
for _ in range(num_layers)
|
||||
]
|
||||
)
|
||||
self.norm_out = AdaLayerNorm(3072, single=True)
|
||||
self.proj_out = torch.nn.Linear(3072, 64)
|
||||
self.proj_latents_out = torch.nn.Linear(64, 64)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
latents=None,
|
||||
image=None,
|
||||
text=None,
|
||||
image_rotary_emb=None,
|
||||
timestep=None,
|
||||
):
|
||||
latents = rearrange(latents, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
|
||||
image = image + self.proj_latents_in(latents)
|
||||
conditioning = self.time_text_embed(timestep, image.dtype)
|
||||
for block in self.transformer_blocks:
|
||||
text, image = block(
|
||||
image=image,
|
||||
text=text,
|
||||
temb=conditioning,
|
||||
image_rotary_emb=image_rotary_emb,
|
||||
)
|
||||
image = self.norm_out(image, conditioning)
|
||||
image = self.proj_out(image)
|
||||
image = image + self.proj_latents_out(latents)
|
||||
return image
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return QwenImageAccelerateAdapterStateDictConverter()
|
||||
|
||||
|
||||
|
||||
class QwenImageAccelerateAdapterStateDictConverter():
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
return state_dict
|
||||
@@ -1,458 +0,0 @@
|
||||
import torch, math
|
||||
import torch.nn as nn
|
||||
from typing import Tuple, Optional, Union, List
|
||||
from einops import rearrange
|
||||
from .sd3_dit import TimestepEmbeddings, RMSNorm
|
||||
from .flux_dit import AdaLayerNorm
|
||||
|
||||
try:
|
||||
import flash_attn_interface
|
||||
FLASH_ATTN_3_AVAILABLE = True
|
||||
except ModuleNotFoundError:
|
||||
FLASH_ATTN_3_AVAILABLE = False
|
||||
|
||||
|
||||
def qwen_image_flash_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, num_heads: int, attention_mask = None, enable_fp8_attention: bool = False):
|
||||
if FLASH_ATTN_3_AVAILABLE and attention_mask is None:
|
||||
if not enable_fp8_attention:
|
||||
q = rearrange(q, "b n s d -> b s n d", n=num_heads)
|
||||
k = rearrange(k, "b n s d -> b s n d", n=num_heads)
|
||||
v = rearrange(v, "b n s d -> b s n d", n=num_heads)
|
||||
x = flash_attn_interface.flash_attn_func(q, k, v)
|
||||
if isinstance(x, tuple):
|
||||
x = x[0]
|
||||
x = rearrange(x, "b s n d -> b s (n d)", n=num_heads)
|
||||
else:
|
||||
origin_dtype = q.dtype
|
||||
q_std, k_std, v_std = q.std(), k.std(), v.std()
|
||||
q, k, v = (q / q_std).to(torch.float8_e4m3fn), (k / k_std).to(torch.float8_e4m3fn), (v / v_std).to(torch.float8_e4m3fn)
|
||||
q = rearrange(q, "b n s d -> b s n d", n=num_heads)
|
||||
k = rearrange(k, "b n s d -> b s n d", n=num_heads)
|
||||
v = rearrange(v, "b n s d -> b s n d", n=num_heads)
|
||||
x = flash_attn_interface.flash_attn_func(q, k, v, softmax_scale=q_std * k_std / math.sqrt(q.size(-1)))
|
||||
if isinstance(x, tuple):
|
||||
x = x[0]
|
||||
x = x.to(origin_dtype) * v_std
|
||||
x = rearrange(x, "b s n d -> b s (n d)", n=num_heads)
|
||||
else:
|
||||
x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attention_mask)
|
||||
x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
|
||||
return x
|
||||
|
||||
|
||||
class ApproximateGELU(nn.Module):
|
||||
def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
|
||||
super().__init__()
|
||||
self.proj = nn.Linear(dim_in, dim_out, bias=bias)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.proj(x)
|
||||
return x * torch.sigmoid(1.702 * x)
|
||||
|
||||
def apply_rotary_emb_qwen(
|
||||
x: torch.Tensor,
|
||||
freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]]
|
||||
):
|
||||
x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
|
||||
x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3)
|
||||
return x_out.type_as(x)
|
||||
|
||||
|
||||
class QwenEmbedRope(nn.Module):
|
||||
def __init__(self, theta: int, axes_dim: list[int], scale_rope=False):
|
||||
super().__init__()
|
||||
self.theta = theta
|
||||
self.axes_dim = axes_dim
|
||||
pos_index = torch.arange(1024)
|
||||
neg_index = torch.arange(1024).flip(0) * -1 - 1
|
||||
self.pos_freqs = torch.cat([
|
||||
self.rope_params(pos_index, self.axes_dim[0], self.theta),
|
||||
self.rope_params(pos_index, self.axes_dim[1], self.theta),
|
||||
self.rope_params(pos_index, self.axes_dim[2], self.theta),
|
||||
], dim=1)
|
||||
self.neg_freqs = torch.cat([
|
||||
self.rope_params(neg_index, self.axes_dim[0], self.theta),
|
||||
self.rope_params(neg_index, self.axes_dim[1], self.theta),
|
||||
self.rope_params(neg_index, self.axes_dim[2], self.theta),
|
||||
], dim=1)
|
||||
self.rope_cache = {}
|
||||
self.scale_rope = scale_rope
|
||||
|
||||
def rope_params(self, index, dim, theta=10000):
|
||||
"""
|
||||
Args:
|
||||
index: [0, 1, 2, 3] 1D Tensor representing the position index of the token
|
||||
"""
|
||||
assert dim % 2 == 0
|
||||
freqs = torch.outer(
|
||||
index,
|
||||
1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float32).div(dim))
|
||||
)
|
||||
freqs = torch.polar(torch.ones_like(freqs), freqs)
|
||||
return freqs
|
||||
|
||||
def forward(self, video_fhw, txt_seq_lens, device):
|
||||
if self.pos_freqs.device != device:
|
||||
self.pos_freqs = self.pos_freqs.to(device)
|
||||
self.neg_freqs = self.neg_freqs.to(device)
|
||||
|
||||
if isinstance(video_fhw, list):
|
||||
video_fhw = video_fhw[0]
|
||||
frame, height, width = video_fhw
|
||||
rope_key = f"{frame}_{height}_{width}"
|
||||
|
||||
if rope_key not in self.rope_cache:
|
||||
seq_lens = frame * height * width
|
||||
freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
|
||||
freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
|
||||
freqs_frame = freqs_pos[0][:frame].view(frame, 1, 1, -1).expand(frame, height, width, -1)
|
||||
if self.scale_rope:
|
||||
freqs_height = torch.cat(
|
||||
[
|
||||
freqs_neg[1][-(height - height//2):],
|
||||
freqs_pos[1][:height//2]
|
||||
],
|
||||
dim=0
|
||||
)
|
||||
freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1)
|
||||
freqs_width = torch.cat(
|
||||
[
|
||||
freqs_neg[2][-(width - width//2):],
|
||||
freqs_pos[2][:width//2]
|
||||
],
|
||||
dim=0
|
||||
)
|
||||
freqs_width = freqs_width.view(1, 1, width, -1).expand(frame, height, width, -1)
|
||||
|
||||
else:
|
||||
freqs_height = freqs_pos[1][:height].view(1, height, 1, -1).expand(frame, height, width, -1)
|
||||
freqs_width = freqs_pos[2][:width].view(1, 1, width, -1).expand(frame, height, width, -1)
|
||||
|
||||
freqs = torch.cat([freqs_frame, freqs_height, freqs_width], dim=-1).reshape(seq_lens, -1)
|
||||
self.rope_cache[rope_key] = freqs.clone().contiguous()
|
||||
vid_freqs = self.rope_cache[rope_key]
|
||||
|
||||
if self.scale_rope:
|
||||
max_vid_index = max(height // 2, width // 2)
|
||||
else:
|
||||
max_vid_index = max(height, width)
|
||||
|
||||
max_len = max(txt_seq_lens)
|
||||
txt_freqs = self.pos_freqs[max_vid_index: max_vid_index + max_len, ...]
|
||||
return vid_freqs, txt_freqs
|
||||
|
||||
|
||||
class QwenFeedForward(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
dim_out: Optional[int] = None,
|
||||
dropout: float = 0.0,
|
||||
):
|
||||
super().__init__()
|
||||
inner_dim = int(dim * 4)
|
||||
self.net = nn.ModuleList([])
|
||||
self.net.append(ApproximateGELU(dim, inner_dim))
|
||||
self.net.append(nn.Dropout(dropout))
|
||||
self.net.append(nn.Linear(inner_dim, dim_out))
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:
|
||||
for module in self.net:
|
||||
hidden_states = module(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
class QwenDoubleStreamAttention(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim_a,
|
||||
dim_b,
|
||||
num_heads,
|
||||
head_dim,
|
||||
):
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = head_dim
|
||||
|
||||
self.to_q = nn.Linear(dim_a, dim_a)
|
||||
self.to_k = nn.Linear(dim_a, dim_a)
|
||||
self.to_v = nn.Linear(dim_a, dim_a)
|
||||
self.norm_q = RMSNorm(head_dim, eps=1e-6)
|
||||
self.norm_k = RMSNorm(head_dim, eps=1e-6)
|
||||
|
||||
self.add_q_proj = nn.Linear(dim_b, dim_b)
|
||||
self.add_k_proj = nn.Linear(dim_b, dim_b)
|
||||
self.add_v_proj = nn.Linear(dim_b, dim_b)
|
||||
self.norm_added_q = RMSNorm(head_dim, eps=1e-6)
|
||||
self.norm_added_k = RMSNorm(head_dim, eps=1e-6)
|
||||
|
||||
self.to_out = torch.nn.Sequential(nn.Linear(dim_a, dim_a))
|
||||
self.to_add_out = nn.Linear(dim_b, dim_b)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
image: torch.FloatTensor,
|
||||
text: torch.FloatTensor,
|
||||
image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
||||
attention_mask: Optional[torch.FloatTensor] = None,
|
||||
enable_fp8_attention: bool = False,
|
||||
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
|
||||
img_q, img_k, img_v = self.to_q(image), self.to_k(image), self.to_v(image)
|
||||
txt_q, txt_k, txt_v = self.add_q_proj(text), self.add_k_proj(text), self.add_v_proj(text)
|
||||
seq_txt = txt_q.shape[1]
|
||||
|
||||
img_q = rearrange(img_q, 'b s (h d) -> b h s d', h=self.num_heads)
|
||||
img_k = rearrange(img_k, 'b s (h d) -> b h s d', h=self.num_heads)
|
||||
img_v = rearrange(img_v, 'b s (h d) -> b h s d', h=self.num_heads)
|
||||
|
||||
txt_q = rearrange(txt_q, 'b s (h d) -> b h s d', h=self.num_heads)
|
||||
txt_k = rearrange(txt_k, 'b s (h d) -> b h s d', h=self.num_heads)
|
||||
txt_v = rearrange(txt_v, 'b s (h d) -> b h s d', h=self.num_heads)
|
||||
|
||||
img_q, img_k = self.norm_q(img_q), self.norm_k(img_k)
|
||||
txt_q, txt_k = self.norm_added_q(txt_q), self.norm_added_k(txt_k)
|
||||
|
||||
if image_rotary_emb is not None:
|
||||
img_freqs, txt_freqs = image_rotary_emb
|
||||
img_q = apply_rotary_emb_qwen(img_q, img_freqs)
|
||||
img_k = apply_rotary_emb_qwen(img_k, img_freqs)
|
||||
txt_q = apply_rotary_emb_qwen(txt_q, txt_freqs)
|
||||
txt_k = apply_rotary_emb_qwen(txt_k, txt_freqs)
|
||||
|
||||
joint_q = torch.cat([txt_q, img_q], dim=2)
|
||||
joint_k = torch.cat([txt_k, img_k], dim=2)
|
||||
joint_v = torch.cat([txt_v, img_v], dim=2)
|
||||
|
||||
joint_attn_out = qwen_image_flash_attention(joint_q, joint_k, joint_v, num_heads=joint_q.shape[1], attention_mask=attention_mask, enable_fp8_attention=enable_fp8_attention).to(joint_q.dtype)
|
||||
|
||||
txt_attn_output = joint_attn_out[:, :seq_txt, :]
|
||||
img_attn_output = joint_attn_out[:, seq_txt:, :]
|
||||
|
||||
img_attn_output = self.to_out(img_attn_output)
|
||||
txt_attn_output = self.to_add_out(txt_attn_output)
|
||||
|
||||
return img_attn_output, txt_attn_output
|
||||
|
||||
|
||||
class QwenImageTransformerBlock(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
num_attention_heads: int,
|
||||
attention_head_dim: int,
|
||||
eps: float = 1e-6,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.dim = dim
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.attention_head_dim = attention_head_dim
|
||||
|
||||
self.img_mod = nn.Sequential(
|
||||
nn.SiLU(),
|
||||
nn.Linear(dim, 6 * dim),
|
||||
)
|
||||
self.img_norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
|
||||
self.attn = QwenDoubleStreamAttention(
|
||||
dim_a=dim,
|
||||
dim_b=dim,
|
||||
num_heads=num_attention_heads,
|
||||
head_dim=attention_head_dim,
|
||||
)
|
||||
self.img_norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
|
||||
self.img_mlp = QwenFeedForward(dim=dim, dim_out=dim)
|
||||
|
||||
self.txt_mod = nn.Sequential(
|
||||
nn.SiLU(),
|
||||
nn.Linear(dim, 6 * dim, bias=True),
|
||||
)
|
||||
self.txt_norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
|
||||
self.txt_norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
|
||||
self.txt_mlp = QwenFeedForward(dim=dim, dim_out=dim)
|
||||
|
||||
def _modulate(self, x, mod_params):
|
||||
shift, scale, gate = mod_params.chunk(3, dim=-1)
|
||||
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1), gate.unsqueeze(1)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
image: torch.Tensor,
|
||||
text: torch.Tensor,
|
||||
temb: torch.Tensor,
|
||||
image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
enable_fp8_attention = False,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
|
||||
img_mod_attn, img_mod_mlp = self.img_mod(temb).chunk(2, dim=-1) # [B, 3*dim] each
|
||||
txt_mod_attn, txt_mod_mlp = self.txt_mod(temb).chunk(2, dim=-1) # [B, 3*dim] each
|
||||
|
||||
img_normed = self.img_norm1(image)
|
||||
img_modulated, img_gate = self._modulate(img_normed, img_mod_attn)
|
||||
|
||||
txt_normed = self.txt_norm1(text)
|
||||
txt_modulated, txt_gate = self._modulate(txt_normed, txt_mod_attn)
|
||||
|
||||
img_attn_out, txt_attn_out = self.attn(
|
||||
image=img_modulated,
|
||||
text=txt_modulated,
|
||||
image_rotary_emb=image_rotary_emb,
|
||||
attention_mask=attention_mask,
|
||||
enable_fp8_attention=enable_fp8_attention,
|
||||
)
|
||||
|
||||
image = image + img_gate * img_attn_out
|
||||
text = text + txt_gate * txt_attn_out
|
||||
|
||||
img_normed_2 = self.img_norm2(image)
|
||||
img_modulated_2, img_gate_2 = self._modulate(img_normed_2, img_mod_mlp)
|
||||
|
||||
txt_normed_2 = self.txt_norm2(text)
|
||||
txt_modulated_2, txt_gate_2 = self._modulate(txt_normed_2, txt_mod_mlp)
|
||||
|
||||
img_mlp_out = self.img_mlp(img_modulated_2)
|
||||
txt_mlp_out = self.txt_mlp(txt_modulated_2)
|
||||
|
||||
image = image + img_gate_2 * img_mlp_out
|
||||
text = text + txt_gate_2 * txt_mlp_out
|
||||
|
||||
return text, image
|
||||
|
||||
|
||||
class QwenImageDiT(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
num_layers: int = 60,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.pos_embed = QwenEmbedRope(theta=10000, axes_dim=[16,56,56], scale_rope=True)
|
||||
|
||||
self.time_text_embed = TimestepEmbeddings(256, 3072, diffusers_compatible_format=True, scale=1000, align_dtype_to_timestep=True)
|
||||
self.txt_norm = RMSNorm(3584, eps=1e-6)
|
||||
|
||||
self.img_in = nn.Linear(64, 3072)
|
||||
self.txt_in = nn.Linear(3584, 3072)
|
||||
|
||||
self.transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
QwenImageTransformerBlock(
|
||||
dim=3072,
|
||||
num_attention_heads=24,
|
||||
attention_head_dim=128,
|
||||
)
|
||||
for _ in range(num_layers)
|
||||
]
|
||||
)
|
||||
self.norm_out = AdaLayerNorm(3072, single=True)
|
||||
self.proj_out = nn.Linear(3072, 64)
|
||||
|
||||
|
||||
def process_entity_masks(self, latents, prompt_emb, prompt_emb_mask, entity_prompt_emb, entity_prompt_emb_mask, entity_masks, height, width, image, img_shapes):
|
||||
# prompt_emb
|
||||
all_prompt_emb = entity_prompt_emb + [prompt_emb]
|
||||
all_prompt_emb = [self.txt_in(self.txt_norm(local_prompt_emb)) for local_prompt_emb in all_prompt_emb]
|
||||
all_prompt_emb = torch.cat(all_prompt_emb, dim=1)
|
||||
|
||||
# image_rotary_emb
|
||||
txt_seq_lens = prompt_emb_mask.sum(dim=1).tolist()
|
||||
image_rotary_emb = self.pos_embed(img_shapes, txt_seq_lens, device=latents.device)
|
||||
entity_seq_lens = [emb_mask.sum(dim=1).tolist() for emb_mask in entity_prompt_emb_mask]
|
||||
entity_rotary_emb = [self.pos_embed(img_shapes, entity_seq_len, device=latents.device)[1] for entity_seq_len in entity_seq_lens]
|
||||
txt_rotary_emb = torch.cat(entity_rotary_emb + [image_rotary_emb[1]], dim=0)
|
||||
image_rotary_emb = (image_rotary_emb[0], txt_rotary_emb)
|
||||
|
||||
# attention_mask
|
||||
repeat_dim = latents.shape[1]
|
||||
max_masks = entity_masks.shape[1]
|
||||
entity_masks = entity_masks.repeat(1, 1, repeat_dim, 1, 1)
|
||||
entity_masks = [entity_masks[:, i, None].squeeze(1) for i in range(max_masks)]
|
||||
global_mask = torch.ones_like(entity_masks[0]).to(device=latents.device, dtype=latents.dtype)
|
||||
entity_masks = entity_masks + [global_mask]
|
||||
|
||||
N = len(entity_masks)
|
||||
batch_size = entity_masks[0].shape[0]
|
||||
seq_lens = [mask_.sum(dim=1).item() for mask_ in entity_prompt_emb_mask] + [prompt_emb_mask.sum(dim=1).item()]
|
||||
total_seq_len = sum(seq_lens) + image.shape[1]
|
||||
patched_masks = []
|
||||
for i in range(N):
|
||||
patched_mask = rearrange(entity_masks[i], "B C (H P) (W Q) -> B (H W) (C P Q)", H=height//16, W=width//16, P=2, Q=2)
|
||||
patched_masks.append(patched_mask)
|
||||
attention_mask = torch.ones((batch_size, total_seq_len, total_seq_len), dtype=torch.bool).to(device=entity_masks[0].device)
|
||||
|
||||
# prompt-image attention mask
|
||||
image_start = sum(seq_lens)
|
||||
image_end = total_seq_len
|
||||
cumsum = [0]
|
||||
for length in seq_lens:
|
||||
cumsum.append(cumsum[-1] + length)
|
||||
for i in range(N):
|
||||
prompt_start = cumsum[i]
|
||||
prompt_end = cumsum[i+1]
|
||||
image_mask = torch.sum(patched_masks[i], dim=-1) > 0
|
||||
image_mask = image_mask.unsqueeze(1).repeat(1, seq_lens[i], 1)
|
||||
# prompt update with image
|
||||
attention_mask[:, prompt_start:prompt_end, image_start:image_end] = image_mask
|
||||
# image update with prompt
|
||||
attention_mask[:, image_start:image_end, prompt_start:prompt_end] = image_mask.transpose(1, 2)
|
||||
# prompt-prompt attention mask, let the prompt tokens not attend to each other
|
||||
for i in range(N):
|
||||
for j in range(N):
|
||||
if i == j:
|
||||
continue
|
||||
start_i, end_i = cumsum[i], cumsum[i+1]
|
||||
start_j, end_j = cumsum[j], cumsum[j+1]
|
||||
attention_mask[:, start_i:end_i, start_j:end_j] = False
|
||||
|
||||
attention_mask = attention_mask.float()
|
||||
attention_mask[attention_mask == 0] = float('-inf')
|
||||
attention_mask[attention_mask == 1] = 0
|
||||
attention_mask = attention_mask.to(device=latents.device, dtype=latents.dtype).unsqueeze(1)
|
||||
|
||||
return all_prompt_emb, image_rotary_emb, attention_mask
|
||||
|
||||
def forward(
|
||||
self,
|
||||
latents=None,
|
||||
timestep=None,
|
||||
prompt_emb=None,
|
||||
prompt_emb_mask=None,
|
||||
height=None,
|
||||
width=None,
|
||||
):
|
||||
img_shapes = [(latents.shape[0], latents.shape[2]//2, latents.shape[3]//2)]
|
||||
txt_seq_lens = prompt_emb_mask.sum(dim=1).tolist()
|
||||
|
||||
image = rearrange(latents, "B C (H P) (W Q) -> B (H W) (P Q C)", H=height//16, W=width//16, P=2, Q=2)
|
||||
image = self.img_in(image)
|
||||
text = self.txt_in(self.txt_norm(prompt_emb))
|
||||
|
||||
conditioning = self.time_text_embed(timestep, image.dtype)
|
||||
|
||||
image_rotary_emb = self.pos_embed(img_shapes, txt_seq_lens, device=latents.device)
|
||||
|
||||
for block in self.transformer_blocks:
|
||||
text, image = block(
|
||||
image=image,
|
||||
text=text,
|
||||
temb=conditioning,
|
||||
image_rotary_emb=image_rotary_emb,
|
||||
)
|
||||
|
||||
image = self.norm_out(image, conditioning)
|
||||
image = self.proj_out(image)
|
||||
|
||||
latents = rearrange(image, "B (H W) (P Q C) -> B C (H P) (W Q)", H=height//16, W=width//16, P=2, Q=2)
|
||||
return image
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return QwenImageDiTStateDictConverter()
|
||||
|
||||
|
||||
|
||||
class QwenImageDiTStateDictConverter():
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
return state_dict
|
||||
@@ -1,255 +0,0 @@
|
||||
from transformers import Qwen2_5_VLModel
|
||||
import torch
|
||||
from typing import Optional, Union
|
||||
|
||||
|
||||
class QwenImageTextEncoder(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
from transformers import Qwen2_5_VLConfig
|
||||
config = Qwen2_5_VLConfig(**{
|
||||
"architectures": [
|
||||
"Qwen2_5_VLForConditionalGeneration"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3584,
|
||||
"image_token_id": 151655,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 18944,
|
||||
"max_position_embeddings": 128000,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen2_5_vl",
|
||||
"num_attention_heads": 28,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": {
|
||||
"mrope_section": [
|
||||
16,
|
||||
24,
|
||||
24
|
||||
],
|
||||
"rope_type": "default",
|
||||
"type": "default"
|
||||
},
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": 32768,
|
||||
"text_config": {
|
||||
"architectures": [
|
||||
"Qwen2_5_VLForConditionalGeneration"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3584,
|
||||
"image_token_id": None,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 18944,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 128000,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen2_5_vl_text",
|
||||
"num_attention_heads": 28,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": {
|
||||
"mrope_section": [
|
||||
16,
|
||||
24,
|
||||
24
|
||||
],
|
||||
"rope_type": "default",
|
||||
"type": "default"
|
||||
},
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": None,
|
||||
"torch_dtype": "float32",
|
||||
"use_cache": True,
|
||||
"use_sliding_window": False,
|
||||
"video_token_id": None,
|
||||
"vision_end_token_id": 151653,
|
||||
"vision_start_token_id": 151652,
|
||||
"vision_token_id": 151654,
|
||||
"vocab_size": 152064
|
||||
},
|
||||
"tie_word_embeddings": False,
|
||||
"torch_dtype": "float32",
|
||||
"transformers_version": "4.54.0",
|
||||
"use_cache": True,
|
||||
"use_sliding_window": False,
|
||||
"video_token_id": 151656,
|
||||
"vision_config": {
|
||||
"depth": 32,
|
||||
"fullatt_block_indexes": [
|
||||
7,
|
||||
15,
|
||||
23,
|
||||
31
|
||||
],
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 1280,
|
||||
"in_channels": 3,
|
||||
"in_chans": 3,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 3420,
|
||||
"model_type": "qwen2_5_vl",
|
||||
"num_heads": 16,
|
||||
"out_hidden_size": 3584,
|
||||
"patch_size": 14,
|
||||
"spatial_merge_size": 2,
|
||||
"spatial_patch_size": 14,
|
||||
"temporal_patch_size": 2,
|
||||
"tokens_per_second": 2,
|
||||
"torch_dtype": "float32",
|
||||
"window_size": 112
|
||||
},
|
||||
"vision_end_token_id": 151653,
|
||||
"vision_start_token_id": 151652,
|
||||
"vision_token_id": 151654,
|
||||
"vocab_size": 152064
|
||||
})
|
||||
self.model = Qwen2_5_VLModel(config)
|
||||
self.lm_head = torch.nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
pixel_values: Optional[torch.Tensor] = None,
|
||||
pixel_values_videos: Optional[torch.FloatTensor] = None,
|
||||
image_grid_thw: Optional[torch.LongTensor] = None,
|
||||
video_grid_thw: Optional[torch.LongTensor] = None,
|
||||
rope_deltas: Optional[torch.LongTensor] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
second_per_grid_ts: Optional[torch.Tensor] = None,
|
||||
logits_to_keep: Union[int, torch.Tensor] = 0,
|
||||
**kwargs,
|
||||
):
|
||||
r"""
|
||||
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||||
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
||||
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
||||
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
||||
image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
|
||||
The temporal, height and width of feature shape of each image in LLM.
|
||||
video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
|
||||
The temporal, height and width of feature shape of each video in LLM.
|
||||
rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
|
||||
The rope index difference between sequence length and multimodal rope.
|
||||
second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*):
|
||||
The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from PIL import Image
|
||||
>>> import requests
|
||||
>>> from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
|
||||
|
||||
>>> model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
|
||||
>>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
|
||||
|
||||
>>> messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image"},
|
||||
{"type": "text", "text": "What is shown in this image?"},
|
||||
],
|
||||
},
|
||||
]
|
||||
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
||||
>>> image = Image.open(requests.get(url, stream=True).raw)
|
||||
|
||||
>>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
||||
>>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
|
||||
|
||||
>>> # Generate
|
||||
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
||||
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||||
"The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
|
||||
```"""
|
||||
|
||||
output_attentions = False
|
||||
output_hidden_states = True
|
||||
|
||||
outputs = self.model(
|
||||
input_ids=input_ids,
|
||||
pixel_values=pixel_values,
|
||||
pixel_values_videos=pixel_values_videos,
|
||||
image_grid_thw=image_grid_thw,
|
||||
video_grid_thw=video_grid_thw,
|
||||
second_per_grid_ts=second_per_grid_ts,
|
||||
position_ids=position_ids,
|
||||
attention_mask=attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=True,
|
||||
cache_position=cache_position,
|
||||
**kwargs,
|
||||
)
|
||||
return outputs.hidden_states
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return QwenImageTextEncoderStateDictConverter()
|
||||
|
||||
|
||||
|
||||
class QwenImageTextEncoderStateDictConverter():
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
state_dict_ = {}
|
||||
for k, v in state_dict.items():
|
||||
if k.startswith("visual."):
|
||||
k = "model." + k
|
||||
elif k.startswith("model."):
|
||||
k = k.replace("model.", "model.language_model.")
|
||||
state_dict_[k] = v
|
||||
return state_dict_
|
||||
@@ -1,736 +0,0 @@
|
||||
import torch
|
||||
from typing import List, Optional, Tuple, Union
|
||||
from torch import nn
|
||||
|
||||
|
||||
CACHE_T = 2
|
||||
|
||||
class QwenImageCausalConv3d(torch.nn.Conv3d):
|
||||
r"""
|
||||
A custom 3D causal convolution layer with feature caching support.
|
||||
|
||||
This layer extends the standard Conv3D layer by ensuring causality in the time dimension and handling feature
|
||||
caching for efficient inference.
|
||||
|
||||
Args:
|
||||
in_channels (int): Number of channels in the input image
|
||||
out_channels (int): Number of channels produced by the convolution
|
||||
kernel_size (int or tuple): Size of the convolving kernel
|
||||
stride (int or tuple, optional): Stride of the convolution. Default: 1
|
||||
padding (int or tuple, optional): Zero-padding added to all three sides of the input. Default: 0
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_channels: int,
|
||||
out_channels: int,
|
||||
kernel_size: Union[int, Tuple[int, int, int]],
|
||||
stride: Union[int, Tuple[int, int, int]] = 1,
|
||||
padding: Union[int, Tuple[int, int, int]] = 0,
|
||||
) -> None:
|
||||
super().__init__(
|
||||
in_channels=in_channels,
|
||||
out_channels=out_channels,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
)
|
||||
|
||||
# Set up causal padding
|
||||
self._padding = (self.padding[2], self.padding[2], self.padding[1], self.padding[1], 2 * self.padding[0], 0)
|
||||
self.padding = (0, 0, 0)
|
||||
|
||||
def forward(self, x, cache_x=None):
|
||||
padding = list(self._padding)
|
||||
if cache_x is not None and self._padding[4] > 0:
|
||||
cache_x = cache_x.to(x.device)
|
||||
x = torch.cat([cache_x, x], dim=2)
|
||||
padding[4] -= cache_x.shape[2]
|
||||
x = torch.nn.functional.pad(x, padding)
|
||||
return super().forward(x)
|
||||
|
||||
|
||||
|
||||
class QwenImageRMS_norm(nn.Module):
|
||||
r"""
|
||||
A custom RMS normalization layer.
|
||||
|
||||
Args:
|
||||
dim (int): The number of dimensions to normalize over.
|
||||
channel_first (bool, optional): Whether the input tensor has channels as the first dimension.
|
||||
Default is True.
|
||||
images (bool, optional): Whether the input represents image data. Default is True.
|
||||
bias (bool, optional): Whether to include a learnable bias term. Default is False.
|
||||
"""
|
||||
|
||||
def __init__(self, dim: int, channel_first: bool = True, images: bool = True, bias: bool = False) -> None:
|
||||
super().__init__()
|
||||
broadcastable_dims = (1, 1, 1) if not images else (1, 1)
|
||||
shape = (dim, *broadcastable_dims) if channel_first else (dim,)
|
||||
|
||||
self.channel_first = channel_first
|
||||
self.scale = dim**0.5
|
||||
self.gamma = nn.Parameter(torch.ones(shape))
|
||||
self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.0
|
||||
|
||||
def forward(self, x):
|
||||
return torch.nn.functional.normalize(x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias
|
||||
|
||||
|
||||
|
||||
class QwenImageResidualBlock(nn.Module):
|
||||
r"""
|
||||
A custom residual block module.
|
||||
|
||||
Args:
|
||||
in_dim (int): Number of input channels.
|
||||
out_dim (int): Number of output channels.
|
||||
dropout (float, optional): Dropout rate for the dropout layer. Default is 0.0.
|
||||
non_linearity (str, optional): Type of non-linearity to use. Default is "silu".
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_dim: int,
|
||||
out_dim: int,
|
||||
dropout: float = 0.0,
|
||||
non_linearity: str = "silu",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.in_dim = in_dim
|
||||
self.out_dim = out_dim
|
||||
self.nonlinearity = torch.nn.SiLU()
|
||||
|
||||
# layers
|
||||
self.norm1 = QwenImageRMS_norm(in_dim, images=False)
|
||||
self.conv1 = QwenImageCausalConv3d(in_dim, out_dim, 3, padding=1)
|
||||
self.norm2 = QwenImageRMS_norm(out_dim, images=False)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.conv2 = QwenImageCausalConv3d(out_dim, out_dim, 3, padding=1)
|
||||
self.conv_shortcut = QwenImageCausalConv3d(in_dim, out_dim, 1) if in_dim != out_dim else nn.Identity()
|
||||
|
||||
def forward(self, x, feat_cache=None, feat_idx=[0]):
|
||||
# Apply shortcut connection
|
||||
h = self.conv_shortcut(x)
|
||||
|
||||
# First normalization and activation
|
||||
x = self.norm1(x)
|
||||
x = self.nonlinearity(x)
|
||||
|
||||
if feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
|
||||
|
||||
x = self.conv1(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = self.conv1(x)
|
||||
|
||||
# Second normalization and activation
|
||||
x = self.norm2(x)
|
||||
x = self.nonlinearity(x)
|
||||
|
||||
# Dropout
|
||||
x = self.dropout(x)
|
||||
|
||||
if feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
|
||||
|
||||
x = self.conv2(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = self.conv2(x)
|
||||
|
||||
# Add residual connection
|
||||
return x + h
|
||||
|
||||
|
||||
|
||||
class QwenImageAttentionBlock(nn.Module):
|
||||
r"""
|
||||
Causal self-attention with a single head.
|
||||
|
||||
Args:
|
||||
dim (int): The number of channels in the input tensor.
|
||||
"""
|
||||
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
|
||||
# layers
|
||||
self.norm = QwenImageRMS_norm(dim)
|
||||
self.to_qkv = nn.Conv2d(dim, dim * 3, 1)
|
||||
self.proj = nn.Conv2d(dim, dim, 1)
|
||||
|
||||
def forward(self, x):
|
||||
identity = x
|
||||
batch_size, channels, time, height, width = x.size()
|
||||
|
||||
x = x.permute(0, 2, 1, 3, 4).reshape(batch_size * time, channels, height, width)
|
||||
x = self.norm(x)
|
||||
|
||||
# compute query, key, value
|
||||
qkv = self.to_qkv(x)
|
||||
qkv = qkv.reshape(batch_size * time, 1, channels * 3, -1)
|
||||
qkv = qkv.permute(0, 1, 3, 2).contiguous()
|
||||
q, k, v = qkv.chunk(3, dim=-1)
|
||||
|
||||
# apply attention
|
||||
x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
|
||||
|
||||
x = x.squeeze(1).permute(0, 2, 1).reshape(batch_size * time, channels, height, width)
|
||||
|
||||
# output projection
|
||||
x = self.proj(x)
|
||||
|
||||
# Reshape back: [(b*t), c, h, w] -> [b, c, t, h, w]
|
||||
x = x.view(batch_size, time, channels, height, width)
|
||||
x = x.permute(0, 2, 1, 3, 4)
|
||||
|
||||
return x + identity
|
||||
|
||||
|
||||
|
||||
class QwenImageUpsample(nn.Upsample):
|
||||
r"""
|
||||
Perform upsampling while ensuring the output tensor has the same data type as the input.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor to be upsampled.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Upsampled tensor with the same data type as the input.
|
||||
"""
|
||||
|
||||
def forward(self, x):
|
||||
return super().forward(x.float()).type_as(x)
|
||||
|
||||
|
||||
|
||||
class QwenImageResample(nn.Module):
|
||||
r"""
|
||||
A custom resampling module for 2D and 3D data.
|
||||
|
||||
Args:
|
||||
dim (int): The number of input/output channels.
|
||||
mode (str): The resampling mode. Must be one of:
|
||||
- 'none': No resampling (identity operation).
|
||||
- 'upsample2d': 2D upsampling with nearest-exact interpolation and convolution.
|
||||
- 'upsample3d': 3D upsampling with nearest-exact interpolation, convolution, and causal 3D convolution.
|
||||
- 'downsample2d': 2D downsampling with zero-padding and convolution.
|
||||
- 'downsample3d': 3D downsampling with zero-padding, convolution, and causal 3D convolution.
|
||||
"""
|
||||
|
||||
def __init__(self, dim: int, mode: str) -> None:
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.mode = mode
|
||||
|
||||
# layers
|
||||
if mode == "upsample2d":
|
||||
self.resample = nn.Sequential(
|
||||
QwenImageUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, dim // 2, 3, padding=1)
|
||||
)
|
||||
elif mode == "upsample3d":
|
||||
self.resample = nn.Sequential(
|
||||
QwenImageUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, dim // 2, 3, padding=1)
|
||||
)
|
||||
self.time_conv = QwenImageCausalConv3d(dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
|
||||
|
||||
elif mode == "downsample2d":
|
||||
self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2)))
|
||||
elif mode == "downsample3d":
|
||||
self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2)))
|
||||
self.time_conv = QwenImageCausalConv3d(dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))
|
||||
|
||||
else:
|
||||
self.resample = nn.Identity()
|
||||
|
||||
def forward(self, x, feat_cache=None, feat_idx=[0]):
|
||||
b, c, t, h, w = x.size()
|
||||
if self.mode == "upsample3d":
|
||||
if feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
if feat_cache[idx] is None:
|
||||
feat_cache[idx] = "Rep"
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] != "Rep":
|
||||
# cache last frame of last two chunk
|
||||
cache_x = torch.cat(
|
||||
[feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2
|
||||
)
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] == "Rep":
|
||||
cache_x = torch.cat([torch.zeros_like(cache_x).to(cache_x.device), cache_x], dim=2)
|
||||
if feat_cache[idx] == "Rep":
|
||||
x = self.time_conv(x)
|
||||
else:
|
||||
x = self.time_conv(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
|
||||
x = x.reshape(b, 2, c, t, h, w)
|
||||
x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), 3)
|
||||
x = x.reshape(b, c, t * 2, h, w)
|
||||
t = x.shape[2]
|
||||
x = x.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
|
||||
x = self.resample(x)
|
||||
x = x.view(b, t, x.size(1), x.size(2), x.size(3)).permute(0, 2, 1, 3, 4)
|
||||
|
||||
if self.mode == "downsample3d":
|
||||
if feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
if feat_cache[idx] is None:
|
||||
feat_cache[idx] = x.clone()
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
cache_x = x[:, :, -1:, :, :].clone()
|
||||
x = self.time_conv(torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class QwenImageMidBlock(nn.Module):
|
||||
"""
|
||||
Middle block for WanVAE encoder and decoder.
|
||||
|
||||
Args:
|
||||
dim (int): Number of input/output channels.
|
||||
dropout (float): Dropout rate.
|
||||
non_linearity (str): Type of non-linearity to use.
|
||||
"""
|
||||
|
||||
def __init__(self, dim: int, dropout: float = 0.0, non_linearity: str = "silu", num_layers: int = 1):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
|
||||
# Create the components
|
||||
resnets = [QwenImageResidualBlock(dim, dim, dropout, non_linearity)]
|
||||
attentions = []
|
||||
for _ in range(num_layers):
|
||||
attentions.append(QwenImageAttentionBlock(dim))
|
||||
resnets.append(QwenImageResidualBlock(dim, dim, dropout, non_linearity))
|
||||
self.attentions = nn.ModuleList(attentions)
|
||||
self.resnets = nn.ModuleList(resnets)
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
def forward(self, x, feat_cache=None, feat_idx=[0]):
|
||||
# First residual block
|
||||
x = self.resnets[0](x, feat_cache, feat_idx)
|
||||
|
||||
# Process through attention and residual blocks
|
||||
for attn, resnet in zip(self.attentions, self.resnets[1:]):
|
||||
if attn is not None:
|
||||
x = attn(x)
|
||||
|
||||
x = resnet(x, feat_cache, feat_idx)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class QwenImageEncoder3d(nn.Module):
|
||||
r"""
|
||||
A 3D encoder module.
|
||||
|
||||
Args:
|
||||
dim (int): The base number of channels in the first layer.
|
||||
z_dim (int): The dimensionality of the latent space.
|
||||
dim_mult (list of int): Multipliers for the number of channels in each block.
|
||||
num_res_blocks (int): Number of residual blocks in each block.
|
||||
attn_scales (list of float): Scales at which to apply attention mechanisms.
|
||||
temperal_downsample (list of bool): Whether to downsample temporally in each block.
|
||||
dropout (float): Dropout rate for the dropout layers.
|
||||
non_linearity (str): Type of non-linearity to use.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim=128,
|
||||
z_dim=4,
|
||||
dim_mult=[1, 2, 4, 4],
|
||||
num_res_blocks=2,
|
||||
attn_scales=[],
|
||||
temperal_downsample=[True, True, False],
|
||||
dropout=0.0,
|
||||
non_linearity: str = "silu",
|
||||
):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.z_dim = z_dim
|
||||
self.dim_mult = dim_mult
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.attn_scales = attn_scales
|
||||
self.temperal_downsample = temperal_downsample
|
||||
self.nonlinearity = torch.nn.SiLU()
|
||||
|
||||
# dimensions
|
||||
dims = [dim * u for u in [1] + dim_mult]
|
||||
scale = 1.0
|
||||
|
||||
# init block
|
||||
self.conv_in = QwenImageCausalConv3d(3, dims[0], 3, padding=1)
|
||||
|
||||
# downsample blocks
|
||||
self.down_blocks = torch.nn.ModuleList([])
|
||||
for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
|
||||
# residual (+attention) blocks
|
||||
for _ in range(num_res_blocks):
|
||||
self.down_blocks.append(QwenImageResidualBlock(in_dim, out_dim, dropout))
|
||||
if scale in attn_scales:
|
||||
self.down_blocks.append(QwenImageAttentionBlock(out_dim))
|
||||
in_dim = out_dim
|
||||
|
||||
# downsample block
|
||||
if i != len(dim_mult) - 1:
|
||||
mode = "downsample3d" if temperal_downsample[i] else "downsample2d"
|
||||
self.down_blocks.append(QwenImageResample(out_dim, mode=mode))
|
||||
scale /= 2.0
|
||||
|
||||
# middle blocks
|
||||
self.mid_block = QwenImageMidBlock(out_dim, dropout, non_linearity, num_layers=1)
|
||||
|
||||
# output blocks
|
||||
self.norm_out = QwenImageRMS_norm(out_dim, images=False)
|
||||
self.conv_out = QwenImageCausalConv3d(out_dim, z_dim, 3, padding=1)
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
def forward(self, x, feat_cache=None, feat_idx=[0]):
|
||||
if feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
# cache last frame of last two chunk
|
||||
cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
|
||||
x = self.conv_in(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = self.conv_in(x)
|
||||
|
||||
## downsamples
|
||||
for layer in self.down_blocks:
|
||||
if feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
## middle
|
||||
x = self.mid_block(x, feat_cache, feat_idx)
|
||||
|
||||
## head
|
||||
x = self.norm_out(x)
|
||||
x = self.nonlinearity(x)
|
||||
if feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
# cache last frame of last two chunk
|
||||
cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
|
||||
x = self.conv_out(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = self.conv_out(x)
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class QwenImageUpBlock(nn.Module):
|
||||
"""
|
||||
A block that handles upsampling for the WanVAE decoder.
|
||||
|
||||
Args:
|
||||
in_dim (int): Input dimension
|
||||
out_dim (int): Output dimension
|
||||
num_res_blocks (int): Number of residual blocks
|
||||
dropout (float): Dropout rate
|
||||
upsample_mode (str, optional): Mode for upsampling ('upsample2d' or 'upsample3d')
|
||||
non_linearity (str): Type of non-linearity to use
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_dim: int,
|
||||
out_dim: int,
|
||||
num_res_blocks: int,
|
||||
dropout: float = 0.0,
|
||||
upsample_mode: Optional[str] = None,
|
||||
non_linearity: str = "silu",
|
||||
):
|
||||
super().__init__()
|
||||
self.in_dim = in_dim
|
||||
self.out_dim = out_dim
|
||||
|
||||
# Create layers list
|
||||
resnets = []
|
||||
# Add residual blocks and attention if needed
|
||||
current_dim = in_dim
|
||||
for _ in range(num_res_blocks + 1):
|
||||
resnets.append(QwenImageResidualBlock(current_dim, out_dim, dropout, non_linearity))
|
||||
current_dim = out_dim
|
||||
|
||||
self.resnets = nn.ModuleList(resnets)
|
||||
|
||||
# Add upsampling layer if needed
|
||||
self.upsamplers = None
|
||||
if upsample_mode is not None:
|
||||
self.upsamplers = nn.ModuleList([QwenImageResample(out_dim, mode=upsample_mode)])
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
def forward(self, x, feat_cache=None, feat_idx=[0]):
|
||||
"""
|
||||
Forward pass through the upsampling block.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor
|
||||
feat_cache (list, optional): Feature cache for causal convolutions
|
||||
feat_idx (list, optional): Feature index for cache management
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Output tensor
|
||||
"""
|
||||
for resnet in self.resnets:
|
||||
if feat_cache is not None:
|
||||
x = resnet(x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = resnet(x)
|
||||
|
||||
if self.upsamplers is not None:
|
||||
if feat_cache is not None:
|
||||
x = self.upsamplers[0](x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = self.upsamplers[0](x)
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class QwenImageDecoder3d(nn.Module):
|
||||
r"""
|
||||
A 3D decoder module.
|
||||
|
||||
Args:
|
||||
dim (int): The base number of channels in the first layer.
|
||||
z_dim (int): The dimensionality of the latent space.
|
||||
dim_mult (list of int): Multipliers for the number of channels in each block.
|
||||
num_res_blocks (int): Number of residual blocks in each block.
|
||||
attn_scales (list of float): Scales at which to apply attention mechanisms.
|
||||
temperal_upsample (list of bool): Whether to upsample temporally in each block.
|
||||
dropout (float): Dropout rate for the dropout layers.
|
||||
non_linearity (str): Type of non-linearity to use.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim=128,
|
||||
z_dim=4,
|
||||
dim_mult=[1, 2, 4, 4],
|
||||
num_res_blocks=2,
|
||||
attn_scales=[],
|
||||
temperal_upsample=[False, True, True],
|
||||
dropout=0.0,
|
||||
non_linearity: str = "silu",
|
||||
):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.z_dim = z_dim
|
||||
self.dim_mult = dim_mult
|
||||
self.num_res_blocks = num_res_blocks
|
||||
self.attn_scales = attn_scales
|
||||
self.temperal_upsample = temperal_upsample
|
||||
|
||||
self.nonlinearity = torch.nn.SiLU()
|
||||
|
||||
# dimensions
|
||||
dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
|
||||
scale = 1.0 / 2 ** (len(dim_mult) - 2)
|
||||
|
||||
# init block
|
||||
self.conv_in = QwenImageCausalConv3d(z_dim, dims[0], 3, padding=1)
|
||||
|
||||
# middle blocks
|
||||
self.mid_block = QwenImageMidBlock(dims[0], dropout, non_linearity, num_layers=1)
|
||||
|
||||
# upsample blocks
|
||||
self.up_blocks = nn.ModuleList([])
|
||||
for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
|
||||
# residual (+attention) blocks
|
||||
if i > 0:
|
||||
in_dim = in_dim // 2
|
||||
|
||||
# Determine if we need upsampling
|
||||
upsample_mode = None
|
||||
if i != len(dim_mult) - 1:
|
||||
upsample_mode = "upsample3d" if temperal_upsample[i] else "upsample2d"
|
||||
|
||||
# Create and add the upsampling block
|
||||
up_block = QwenImageUpBlock(
|
||||
in_dim=in_dim,
|
||||
out_dim=out_dim,
|
||||
num_res_blocks=num_res_blocks,
|
||||
dropout=dropout,
|
||||
upsample_mode=upsample_mode,
|
||||
non_linearity=non_linearity,
|
||||
)
|
||||
self.up_blocks.append(up_block)
|
||||
|
||||
# Update scale for next iteration
|
||||
if upsample_mode is not None:
|
||||
scale *= 2.0
|
||||
|
||||
# output blocks
|
||||
self.norm_out = QwenImageRMS_norm(out_dim, images=False)
|
||||
self.conv_out = QwenImageCausalConv3d(out_dim, 3, 3, padding=1)
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
def forward(self, x, feat_cache=None, feat_idx=[0]):
|
||||
## conv1
|
||||
if feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
# cache last frame of last two chunk
|
||||
cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
|
||||
x = self.conv_in(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = self.conv_in(x)
|
||||
|
||||
## middle
|
||||
x = self.mid_block(x, feat_cache, feat_idx)
|
||||
|
||||
## upsamples
|
||||
for up_block in self.up_blocks:
|
||||
x = up_block(x, feat_cache, feat_idx)
|
||||
|
||||
## head
|
||||
x = self.norm_out(x)
|
||||
x = self.nonlinearity(x)
|
||||
if feat_cache is not None:
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:, :, :].clone()
|
||||
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
|
||||
# cache last frame of last two chunk
|
||||
cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
|
||||
x = self.conv_out(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = self.conv_out(x)
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class QwenImageVAE(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
base_dim: int = 96,
|
||||
z_dim: int = 16,
|
||||
dim_mult: Tuple[int] = [1, 2, 4, 4],
|
||||
num_res_blocks: int = 2,
|
||||
attn_scales: List[float] = [],
|
||||
temperal_downsample: List[bool] = [False, True, True],
|
||||
dropout: float = 0.0,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.z_dim = z_dim
|
||||
self.temperal_downsample = temperal_downsample
|
||||
self.temperal_upsample = temperal_downsample[::-1]
|
||||
|
||||
self.encoder = QwenImageEncoder3d(
|
||||
base_dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout
|
||||
)
|
||||
self.quant_conv = QwenImageCausalConv3d(z_dim * 2, z_dim * 2, 1)
|
||||
self.post_quant_conv = QwenImageCausalConv3d(z_dim, z_dim, 1)
|
||||
|
||||
self.decoder = QwenImageDecoder3d(
|
||||
base_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout
|
||||
)
|
||||
|
||||
mean = [
|
||||
-0.7571,
|
||||
-0.7089,
|
||||
-0.9113,
|
||||
0.1075,
|
||||
-0.1745,
|
||||
0.9653,
|
||||
-0.1517,
|
||||
1.5508,
|
||||
0.4134,
|
||||
-0.0715,
|
||||
0.5517,
|
||||
-0.3632,
|
||||
-0.1922,
|
||||
-0.9497,
|
||||
0.2503,
|
||||
-0.2921,
|
||||
]
|
||||
std = [
|
||||
2.8184,
|
||||
1.4541,
|
||||
2.3275,
|
||||
2.6558,
|
||||
1.2196,
|
||||
1.7708,
|
||||
2.6052,
|
||||
2.0743,
|
||||
3.2687,
|
||||
2.1526,
|
||||
2.8652,
|
||||
1.5579,
|
||||
1.6382,
|
||||
1.1253,
|
||||
2.8251,
|
||||
1.9160,
|
||||
]
|
||||
self.mean = torch.tensor(mean).view(1, 16, 1, 1, 1)
|
||||
self.std = 1 / torch.tensor(std).view(1, 16, 1, 1, 1)
|
||||
|
||||
def encode(self, x, **kwargs):
|
||||
x = x.unsqueeze(2)
|
||||
x = self.encoder(x)
|
||||
x = self.quant_conv(x)
|
||||
x = x[:, :16]
|
||||
mean, std = self.mean.to(dtype=x.dtype, device=x.device), self.std.to(dtype=x.dtype, device=x.device)
|
||||
x = (x - mean) * std
|
||||
x = x.squeeze(2)
|
||||
return x
|
||||
|
||||
def decode(self, x, **kwargs):
|
||||
x = x.unsqueeze(2)
|
||||
mean, std = self.mean.to(dtype=x.dtype, device=x.device), self.std.to(dtype=x.dtype, device=x.device)
|
||||
x = x / std + mean
|
||||
x = self.post_quant_conv(x)
|
||||
x = self.decoder(x)
|
||||
x = x.squeeze(2)
|
||||
return x
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return QwenImageVAEStateDictConverter()
|
||||
|
||||
|
||||
|
||||
class QwenImageVAEStateDictConverter():
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
return state_dict
|
||||
@@ -1,168 +0,0 @@
|
||||
import torch
|
||||
|
||||
|
||||
class Qwen25VL_7b_Embedder(torch.nn.Module):
|
||||
def __init__(self, model_path, max_length=640, dtype=torch.bfloat16, device="cuda"):
|
||||
super(Qwen25VL_7b_Embedder, self).__init__()
|
||||
self.max_length = max_length
|
||||
self.dtype = dtype
|
||||
self.device = device
|
||||
|
||||
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
|
||||
|
||||
self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
|
||||
model_path,
|
||||
torch_dtype=dtype,
|
||||
).to(torch.cuda.current_device())
|
||||
|
||||
self.model.requires_grad_(False)
|
||||
self.processor = AutoProcessor.from_pretrained(
|
||||
model_path, min_pixels=256 * 28 * 28, max_pixels=324 * 28 * 28
|
||||
)
|
||||
|
||||
Qwen25VL_7b_PREFIX = '''Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:
|
||||
- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.
|
||||
- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.\n
|
||||
Here are examples of how to transform or refine prompts:
|
||||
- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.
|
||||
- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.\n
|
||||
Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:
|
||||
User Prompt:'''
|
||||
|
||||
self.prefix = Qwen25VL_7b_PREFIX
|
||||
|
||||
@staticmethod
|
||||
def from_pretrained(path, torch_dtype=torch.bfloat16, device="cuda"):
|
||||
return Qwen25VL_7b_Embedder(path, dtype=torch_dtype, device=device)
|
||||
|
||||
def forward(self, caption, ref_images):
|
||||
text_list = caption
|
||||
embs = torch.zeros(
|
||||
len(text_list),
|
||||
self.max_length,
|
||||
self.model.config.hidden_size,
|
||||
dtype=torch.bfloat16,
|
||||
device=torch.cuda.current_device(),
|
||||
)
|
||||
hidden_states = torch.zeros(
|
||||
len(text_list),
|
||||
self.max_length,
|
||||
self.model.config.hidden_size,
|
||||
dtype=torch.bfloat16,
|
||||
device=torch.cuda.current_device(),
|
||||
)
|
||||
masks = torch.zeros(
|
||||
len(text_list),
|
||||
self.max_length,
|
||||
dtype=torch.long,
|
||||
device=torch.cuda.current_device(),
|
||||
)
|
||||
input_ids_list = []
|
||||
attention_mask_list = []
|
||||
emb_list = []
|
||||
|
||||
def split_string(s):
|
||||
s = s.replace("“", '"').replace("”", '"').replace("'", '''"''') # use english quotes
|
||||
result = []
|
||||
in_quotes = False
|
||||
temp = ""
|
||||
|
||||
for idx,char in enumerate(s):
|
||||
if char == '"' and idx>155:
|
||||
temp += char
|
||||
if not in_quotes:
|
||||
result.append(temp)
|
||||
temp = ""
|
||||
|
||||
in_quotes = not in_quotes
|
||||
continue
|
||||
if in_quotes:
|
||||
if char.isspace():
|
||||
pass # have space token
|
||||
|
||||
result.append("“" + char + "”")
|
||||
else:
|
||||
temp += char
|
||||
|
||||
if temp:
|
||||
result.append(temp)
|
||||
|
||||
return result
|
||||
|
||||
for idx, (txt, imgs) in enumerate(zip(text_list, ref_images)):
|
||||
|
||||
messages = [{"role": "user", "content": []}]
|
||||
|
||||
messages[0]["content"].append({"type": "text", "text": f"{self.prefix}"})
|
||||
|
||||
messages[0]["content"].append({"type": "image", "image": imgs})
|
||||
|
||||
# 再添加 text
|
||||
messages[0]["content"].append({"type": "text", "text": f"{txt}"})
|
||||
|
||||
# Preparation for inference
|
||||
text = self.processor.apply_chat_template(
|
||||
messages, tokenize=False, add_generation_prompt=True, add_vision_id=True
|
||||
)
|
||||
|
||||
image_inputs = [imgs]
|
||||
|
||||
inputs = self.processor(
|
||||
text=[text],
|
||||
images=image_inputs,
|
||||
padding=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
|
||||
old_inputs_ids = inputs.input_ids
|
||||
text_split_list = split_string(text)
|
||||
|
||||
token_list = []
|
||||
for text_each in text_split_list:
|
||||
txt_inputs = self.processor(
|
||||
text=text_each,
|
||||
images=None,
|
||||
videos=None,
|
||||
padding=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
token_each = txt_inputs.input_ids
|
||||
if token_each[0][0] == 2073 and token_each[0][-1] == 854:
|
||||
token_each = token_each[:, 1:-1]
|
||||
token_list.append(token_each)
|
||||
else:
|
||||
token_list.append(token_each)
|
||||
|
||||
new_txt_ids = torch.cat(token_list, dim=1).to("cuda")
|
||||
|
||||
new_txt_ids = new_txt_ids.to(old_inputs_ids.device)
|
||||
|
||||
idx1 = (old_inputs_ids == 151653).nonzero(as_tuple=True)[1][0]
|
||||
idx2 = (new_txt_ids == 151653).nonzero(as_tuple=True)[1][0]
|
||||
inputs.input_ids = (
|
||||
torch.cat([old_inputs_ids[0, :idx1], new_txt_ids[0, idx2:]], dim=0)
|
||||
.unsqueeze(0)
|
||||
.to("cuda")
|
||||
)
|
||||
inputs.attention_mask = (inputs.input_ids > 0).long().to("cuda")
|
||||
outputs = self.model(
|
||||
input_ids=inputs.input_ids,
|
||||
attention_mask=inputs.attention_mask,
|
||||
pixel_values=inputs.pixel_values.to("cuda"),
|
||||
image_grid_thw=inputs.image_grid_thw.to("cuda"),
|
||||
output_hidden_states=True,
|
||||
)
|
||||
|
||||
emb = outputs["hidden_states"][-1]
|
||||
|
||||
embs[idx, : min(self.max_length, emb.shape[1] - 217)] = emb[0, 217:][
|
||||
: self.max_length
|
||||
]
|
||||
|
||||
masks[idx, : min(self.max_length, emb.shape[1] - 217)] = torch.ones(
|
||||
(min(self.max_length, emb.shape[1] - 217)),
|
||||
dtype=torch.long,
|
||||
device=torch.cuda.current_device(),
|
||||
)
|
||||
|
||||
return embs, masks
|
||||
@@ -5,26 +5,6 @@ from .tiler import TileWorker
|
||||
|
||||
|
||||
|
||||
class RMSNorm(torch.nn.Module):
|
||||
def __init__(self, dim, eps, elementwise_affine=True):
|
||||
super().__init__()
|
||||
self.eps = eps
|
||||
if elementwise_affine:
|
||||
self.weight = torch.nn.Parameter(torch.ones((dim,)))
|
||||
else:
|
||||
self.weight = None
|
||||
|
||||
def forward(self, hidden_states):
|
||||
input_dtype = hidden_states.dtype
|
||||
variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
|
||||
hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
|
||||
hidden_states = hidden_states.to(input_dtype)
|
||||
if self.weight is not None:
|
||||
hidden_states = hidden_states * self.weight
|
||||
return hidden_states
|
||||
|
||||
|
||||
|
||||
class PatchEmbed(torch.nn.Module):
|
||||
def __init__(self, patch_size=2, in_channels=16, embed_dim=1536, pos_embed_max_size=192):
|
||||
super().__init__()
|
||||
@@ -32,7 +12,7 @@ class PatchEmbed(torch.nn.Module):
|
||||
self.patch_size = patch_size
|
||||
|
||||
self.proj = torch.nn.Conv2d(in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size)
|
||||
self.pos_embed = torch.nn.Parameter(torch.zeros(1, self.pos_embed_max_size, self.pos_embed_max_size, embed_dim))
|
||||
self.pos_embed = torch.nn.Parameter(torch.zeros(1, self.pos_embed_max_size, self.pos_embed_max_size, 1536))
|
||||
|
||||
def cropped_pos_embed(self, height, width):
|
||||
height = height // self.patch_size
|
||||
@@ -50,30 +30,14 @@ class PatchEmbed(torch.nn.Module):
|
||||
return latent + pos_embed
|
||||
|
||||
|
||||
class DiffusersCompatibleTimestepProj(torch.nn.Module):
|
||||
def __init__(self, dim_in, dim_out):
|
||||
super().__init__()
|
||||
self.linear_1 = torch.nn.Linear(dim_in, dim_out)
|
||||
self.act = torch.nn.SiLU()
|
||||
self.linear_2 = torch.nn.Linear(dim_out, dim_out)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.linear_1(x)
|
||||
x = self.act(x)
|
||||
x = self.linear_2(x)
|
||||
return x
|
||||
|
||||
|
||||
class TimestepEmbeddings(torch.nn.Module):
|
||||
def __init__(self, dim_in, dim_out, computation_device=None, diffusers_compatible_format=False, scale=1, align_dtype_to_timestep=False):
|
||||
def __init__(self, dim_in, dim_out):
|
||||
super().__init__()
|
||||
self.time_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device, scale=scale, align_dtype_to_timestep=align_dtype_to_timestep)
|
||||
if diffusers_compatible_format:
|
||||
self.timestep_embedder = DiffusersCompatibleTimestepProj(dim_in, dim_out)
|
||||
else:
|
||||
self.timestep_embedder = torch.nn.Sequential(
|
||||
torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out)
|
||||
)
|
||||
self.time_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0)
|
||||
self.timestep_embedder = torch.nn.Sequential(
|
||||
torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out)
|
||||
)
|
||||
|
||||
def forward(self, timestep, dtype):
|
||||
time_emb = self.time_proj(timestep).to(dtype)
|
||||
@@ -83,11 +47,10 @@ class TimestepEmbeddings(torch.nn.Module):
|
||||
|
||||
|
||||
class AdaLayerNorm(torch.nn.Module):
|
||||
def __init__(self, dim, single=False, dual=False):
|
||||
def __init__(self, dim, single=False):
|
||||
super().__init__()
|
||||
self.single = single
|
||||
self.dual = dual
|
||||
self.linear = torch.nn.Linear(dim, dim * [[6, 2][single], 9][dual])
|
||||
self.linear = torch.nn.Linear(dim, dim * (2 if single else 6))
|
||||
self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
|
||||
|
||||
def forward(self, x, emb):
|
||||
@@ -96,12 +59,6 @@ class AdaLayerNorm(torch.nn.Module):
|
||||
scale, shift = emb.unsqueeze(1).chunk(2, dim=2)
|
||||
x = self.norm(x) * (1 + scale) + shift
|
||||
return x
|
||||
elif self.dual:
|
||||
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, shift_msa2, scale_msa2, gate_msa2 = emb.unsqueeze(1).chunk(9, dim=2)
|
||||
norm_x = self.norm(x)
|
||||
x = norm_x * (1 + scale_msa) + shift_msa
|
||||
norm_x2 = norm_x * (1 + scale_msa2) + shift_msa2
|
||||
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_x2, gate_msa2
|
||||
else:
|
||||
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.unsqueeze(1).chunk(6, dim=2)
|
||||
x = self.norm(x) * (1 + scale_msa) + shift_msa
|
||||
@@ -110,7 +67,7 @@ class AdaLayerNorm(torch.nn.Module):
|
||||
|
||||
|
||||
class JointAttention(torch.nn.Module):
|
||||
def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False, use_rms_norm=False):
|
||||
def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False):
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = head_dim
|
||||
@@ -123,38 +80,12 @@ class JointAttention(torch.nn.Module):
|
||||
if not only_out_a:
|
||||
self.b_to_out = torch.nn.Linear(dim_b, dim_b)
|
||||
|
||||
if use_rms_norm:
|
||||
self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
|
||||
self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
|
||||
self.norm_q_b = RMSNorm(head_dim, eps=1e-6)
|
||||
self.norm_k_b = RMSNorm(head_dim, eps=1e-6)
|
||||
else:
|
||||
self.norm_q_a = None
|
||||
self.norm_k_a = None
|
||||
self.norm_q_b = None
|
||||
self.norm_k_b = None
|
||||
|
||||
|
||||
def process_qkv(self, hidden_states, to_qkv, norm_q, norm_k):
|
||||
batch_size = hidden_states.shape[0]
|
||||
qkv = to_qkv(hidden_states)
|
||||
qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
|
||||
q, k, v = qkv.chunk(3, dim=1)
|
||||
if norm_q is not None:
|
||||
q = norm_q(q)
|
||||
if norm_k is not None:
|
||||
k = norm_k(k)
|
||||
return q, k, v
|
||||
|
||||
|
||||
def forward(self, hidden_states_a, hidden_states_b):
|
||||
batch_size = hidden_states_a.shape[0]
|
||||
|
||||
qa, ka, va = self.process_qkv(hidden_states_a, self.a_to_qkv, self.norm_q_a, self.norm_k_a)
|
||||
qb, kb, vb = self.process_qkv(hidden_states_b, self.b_to_qkv, self.norm_q_b, self.norm_k_b)
|
||||
q = torch.concat([qa, qb], dim=2)
|
||||
k = torch.concat([ka, kb], dim=2)
|
||||
v = torch.concat([va, vb], dim=2)
|
||||
qkv = torch.concat([self.a_to_qkv(hidden_states_a), self.b_to_qkv(hidden_states_b)], dim=1)
|
||||
qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
|
||||
q, k, v = qkv.chunk(3, dim=1)
|
||||
|
||||
hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v)
|
||||
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
|
||||
@@ -166,105 +97,16 @@ class JointAttention(torch.nn.Module):
|
||||
else:
|
||||
hidden_states_b = self.b_to_out(hidden_states_b)
|
||||
return hidden_states_a, hidden_states_b
|
||||
|
||||
|
||||
|
||||
class SingleAttention(torch.nn.Module):
|
||||
def __init__(self, dim_a, num_heads, head_dim, use_rms_norm=False):
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = head_dim
|
||||
|
||||
self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
|
||||
self.a_to_out = torch.nn.Linear(dim_a, dim_a)
|
||||
|
||||
if use_rms_norm:
|
||||
self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
|
||||
self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
|
||||
else:
|
||||
self.norm_q_a = None
|
||||
self.norm_k_a = None
|
||||
|
||||
|
||||
def process_qkv(self, hidden_states, to_qkv, norm_q, norm_k):
|
||||
batch_size = hidden_states.shape[0]
|
||||
qkv = to_qkv(hidden_states)
|
||||
qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
|
||||
q, k, v = qkv.chunk(3, dim=1)
|
||||
if norm_q is not None:
|
||||
q = norm_q(q)
|
||||
if norm_k is not None:
|
||||
k = norm_k(k)
|
||||
return q, k, v
|
||||
|
||||
|
||||
def forward(self, hidden_states_a):
|
||||
batch_size = hidden_states_a.shape[0]
|
||||
q, k, v = self.process_qkv(hidden_states_a, self.a_to_qkv, self.norm_q_a, self.norm_k_a)
|
||||
|
||||
hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v)
|
||||
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
|
||||
hidden_states = hidden_states.to(q.dtype)
|
||||
hidden_states = self.a_to_out(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
|
||||
class DualTransformerBlock(torch.nn.Module):
|
||||
def __init__(self, dim, num_attention_heads, use_rms_norm=False):
|
||||
super().__init__()
|
||||
self.norm1_a = AdaLayerNorm(dim, dual=True)
|
||||
self.norm1_b = AdaLayerNorm(dim)
|
||||
|
||||
self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm)
|
||||
self.attn2 = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm)
|
||||
|
||||
self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
|
||||
self.ff_a = torch.nn.Sequential(
|
||||
torch.nn.Linear(dim, dim*4),
|
||||
torch.nn.GELU(approximate="tanh"),
|
||||
torch.nn.Linear(dim*4, dim)
|
||||
)
|
||||
|
||||
self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
|
||||
self.ff_b = torch.nn.Sequential(
|
||||
torch.nn.Linear(dim, dim*4),
|
||||
torch.nn.GELU(approximate="tanh"),
|
||||
torch.nn.Linear(dim*4, dim)
|
||||
)
|
||||
|
||||
|
||||
def forward(self, hidden_states_a, hidden_states_b, temb):
|
||||
norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a, norm_hidden_states_a_2, gate_msa_a_2 = self.norm1_a(hidden_states_a, emb=temb)
|
||||
norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb)
|
||||
|
||||
# Attention
|
||||
attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b)
|
||||
|
||||
# Part A
|
||||
hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
|
||||
hidden_states_a = hidden_states_a + gate_msa_a_2 * self.attn2(norm_hidden_states_a_2)
|
||||
norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a
|
||||
hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a)
|
||||
|
||||
# Part B
|
||||
hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b
|
||||
norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b
|
||||
hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b)
|
||||
|
||||
return hidden_states_a, hidden_states_b
|
||||
|
||||
|
||||
|
||||
class JointTransformerBlock(torch.nn.Module):
|
||||
def __init__(self, dim, num_attention_heads, use_rms_norm=False, dual=False):
|
||||
def __init__(self, dim, num_attention_heads):
|
||||
super().__init__()
|
||||
self.norm1_a = AdaLayerNorm(dim, dual=dual)
|
||||
self.norm1_a = AdaLayerNorm(dim)
|
||||
self.norm1_b = AdaLayerNorm(dim)
|
||||
|
||||
self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm)
|
||||
if dual:
|
||||
self.attn2 = SingleAttention(dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm)
|
||||
self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads)
|
||||
|
||||
self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
|
||||
self.ff_a = torch.nn.Sequential(
|
||||
@@ -282,10 +124,7 @@ class JointTransformerBlock(torch.nn.Module):
|
||||
|
||||
|
||||
def forward(self, hidden_states_a, hidden_states_b, temb):
|
||||
if self.norm1_a.dual:
|
||||
norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a, norm_hidden_states_a_2, gate_msa_a_2 = self.norm1_a(hidden_states_a, emb=temb)
|
||||
else:
|
||||
norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb)
|
||||
norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb)
|
||||
norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb)
|
||||
|
||||
# Attention
|
||||
@@ -293,8 +132,6 @@ class JointTransformerBlock(torch.nn.Module):
|
||||
|
||||
# Part A
|
||||
hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
|
||||
if self.norm1_a.dual:
|
||||
hidden_states_a = hidden_states_a + gate_msa_a_2 * self.attn2(norm_hidden_states_a_2)
|
||||
norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a
|
||||
hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a)
|
||||
|
||||
@@ -308,12 +145,12 @@ class JointTransformerBlock(torch.nn.Module):
|
||||
|
||||
|
||||
class JointTransformerFinalBlock(torch.nn.Module):
|
||||
def __init__(self, dim, num_attention_heads, use_rms_norm=False):
|
||||
def __init__(self, dim, num_attention_heads):
|
||||
super().__init__()
|
||||
self.norm1_a = AdaLayerNorm(dim)
|
||||
self.norm1_b = AdaLayerNorm(dim, single=True)
|
||||
|
||||
self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, only_out_a=True, use_rms_norm=use_rms_norm)
|
||||
self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, only_out_a=True)
|
||||
|
||||
self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
|
||||
self.ff_a = torch.nn.Sequential(
|
||||
@@ -340,17 +177,15 @@ class JointTransformerFinalBlock(torch.nn.Module):
|
||||
|
||||
|
||||
class SD3DiT(torch.nn.Module):
|
||||
def __init__(self, embed_dim=1536, num_layers=24, use_rms_norm=False, num_dual_blocks=0, pos_embed_max_size=192):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.pos_embedder = PatchEmbed(patch_size=2, in_channels=16, embed_dim=embed_dim, pos_embed_max_size=pos_embed_max_size)
|
||||
self.time_embedder = TimestepEmbeddings(256, embed_dim)
|
||||
self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(2048, embed_dim), torch.nn.SiLU(), torch.nn.Linear(embed_dim, embed_dim))
|
||||
self.context_embedder = torch.nn.Linear(4096, embed_dim)
|
||||
self.blocks = torch.nn.ModuleList([JointTransformerBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm, dual=True) for _ in range(num_dual_blocks)]
|
||||
+ [JointTransformerBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm) for _ in range(num_layers-1-num_dual_blocks)]
|
||||
+ [JointTransformerFinalBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm)])
|
||||
self.norm_out = AdaLayerNorm(embed_dim, single=True)
|
||||
self.proj_out = torch.nn.Linear(embed_dim, 64)
|
||||
self.pos_embedder = PatchEmbed(patch_size=2, in_channels=16, embed_dim=1536, pos_embed_max_size=192)
|
||||
self.time_embedder = TimestepEmbeddings(256, 1536)
|
||||
self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(2048, 1536), torch.nn.SiLU(), torch.nn.Linear(1536, 1536))
|
||||
self.context_embedder = torch.nn.Linear(4096, 1536)
|
||||
self.blocks = torch.nn.ModuleList([JointTransformerBlock(1536, 24) for _ in range(23)] + [JointTransformerFinalBlock(1536, 24)])
|
||||
self.norm_out = AdaLayerNorm(1536, single=True)
|
||||
self.proj_out = torch.nn.Linear(1536, 64)
|
||||
|
||||
def tiled_forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size=128, tile_stride=64):
|
||||
# Due to the global positional embedding, we cannot implement layer-wise tiled forward.
|
||||
@@ -403,24 +238,6 @@ class SD3DiTStateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def infer_architecture(self, state_dict):
|
||||
embed_dim = state_dict["blocks.0.ff_a.0.weight"].shape[1]
|
||||
num_layers = 100
|
||||
while num_layers > 0 and f"blocks.{num_layers-1}.ff_a.0.bias" not in state_dict:
|
||||
num_layers -= 1
|
||||
use_rms_norm = "blocks.0.attn.norm_q_a.weight" in state_dict
|
||||
num_dual_blocks = 0
|
||||
while f"blocks.{num_dual_blocks}.attn2.a_to_out.bias" in state_dict:
|
||||
num_dual_blocks += 1
|
||||
pos_embed_max_size = state_dict["pos_embedder.pos_embed"].shape[1]
|
||||
return {
|
||||
"embed_dim": embed_dim,
|
||||
"num_layers": num_layers,
|
||||
"use_rms_norm": use_rms_norm,
|
||||
"num_dual_blocks": num_dual_blocks,
|
||||
"pos_embed_max_size": pos_embed_max_size
|
||||
}
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
rename_dict = {
|
||||
"context_embedder": "context_embedder",
|
||||
@@ -447,17 +264,12 @@ class SD3DiTStateDictConverter:
|
||||
"ff.net.2": "ff_a.2",
|
||||
"ff_context.net.0.proj": "ff_b.0",
|
||||
"ff_context.net.2": "ff_b.2",
|
||||
|
||||
"attn.norm_q": "attn.norm_q_a",
|
||||
"attn.norm_k": "attn.norm_k_a",
|
||||
"attn.norm_added_q": "attn.norm_q_b",
|
||||
"attn.norm_added_k": "attn.norm_k_b",
|
||||
}
|
||||
state_dict_ = {}
|
||||
for name, param in state_dict.items():
|
||||
if name in rename_dict:
|
||||
if name == "pos_embed.pos_embed":
|
||||
param = param.reshape((1, 192, 192, param.shape[-1]))
|
||||
param = param.reshape((1, 192, 192, 1536))
|
||||
state_dict_[rename_dict[name]] = param
|
||||
elif name.endswith(".weight") or name.endswith(".bias"):
|
||||
suffix = ".weight" if name.endswith(".weight") else ".bias"
|
||||
@@ -471,19 +283,7 @@ class SD3DiTStateDictConverter:
|
||||
if middle in rename_dict:
|
||||
name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
|
||||
state_dict_[name_] = param
|
||||
merged_keys = [name for name in state_dict_ if ".a_to_q." in name or ".b_to_q." in name]
|
||||
for key in merged_keys:
|
||||
param = torch.concat([
|
||||
state_dict_[key.replace("to_q", "to_q")],
|
||||
state_dict_[key.replace("to_q", "to_k")],
|
||||
state_dict_[key.replace("to_q", "to_v")],
|
||||
], dim=0)
|
||||
name = key.replace("to_q", "to_qkv")
|
||||
state_dict_.pop(key.replace("to_q", "to_q"))
|
||||
state_dict_.pop(key.replace("to_q", "to_k"))
|
||||
state_dict_.pop(key.replace("to_q", "to_v"))
|
||||
state_dict_[name] = param
|
||||
return state_dict_, self.infer_architecture(state_dict_)
|
||||
return state_dict_
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
rename_dict = {
|
||||
@@ -491,7 +291,478 @@ class SD3DiTStateDictConverter:
|
||||
"model.diffusion_model.context_embedder.weight": "context_embedder.weight",
|
||||
"model.diffusion_model.final_layer.linear.bias": "proj_out.bias",
|
||||
"model.diffusion_model.final_layer.linear.weight": "proj_out.weight",
|
||||
|
||||
"model.diffusion_model.joint_blocks.0.context_block.adaLN_modulation.1.bias": "blocks.0.norm1_b.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.0.context_block.adaLN_modulation.1.weight": "blocks.0.norm1_b.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.0.context_block.attn.proj.bias": "blocks.0.attn.b_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.0.context_block.attn.proj.weight": "blocks.0.attn.b_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.0.context_block.attn.qkv.bias": ['blocks.0.attn.b_to_q.bias', 'blocks.0.attn.b_to_k.bias', 'blocks.0.attn.b_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.0.context_block.attn.qkv.weight": ['blocks.0.attn.b_to_q.weight', 'blocks.0.attn.b_to_k.weight', 'blocks.0.attn.b_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.0.context_block.mlp.fc1.bias": "blocks.0.ff_b.0.bias",
|
||||
"model.diffusion_model.joint_blocks.0.context_block.mlp.fc1.weight": "blocks.0.ff_b.0.weight",
|
||||
"model.diffusion_model.joint_blocks.0.context_block.mlp.fc2.bias": "blocks.0.ff_b.2.bias",
|
||||
"model.diffusion_model.joint_blocks.0.context_block.mlp.fc2.weight": "blocks.0.ff_b.2.weight",
|
||||
"model.diffusion_model.joint_blocks.0.x_block.adaLN_modulation.1.bias": "blocks.0.norm1_a.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.0.x_block.adaLN_modulation.1.weight": "blocks.0.norm1_a.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.0.x_block.attn.proj.bias": "blocks.0.attn.a_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.0.x_block.attn.proj.weight": "blocks.0.attn.a_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.0.x_block.attn.qkv.bias": ['blocks.0.attn.a_to_q.bias', 'blocks.0.attn.a_to_k.bias', 'blocks.0.attn.a_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.0.x_block.attn.qkv.weight": ['blocks.0.attn.a_to_q.weight', 'blocks.0.attn.a_to_k.weight', 'blocks.0.attn.a_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.0.x_block.mlp.fc1.bias": "blocks.0.ff_a.0.bias",
|
||||
"model.diffusion_model.joint_blocks.0.x_block.mlp.fc1.weight": "blocks.0.ff_a.0.weight",
|
||||
"model.diffusion_model.joint_blocks.0.x_block.mlp.fc2.bias": "blocks.0.ff_a.2.bias",
|
||||
"model.diffusion_model.joint_blocks.0.x_block.mlp.fc2.weight": "blocks.0.ff_a.2.weight",
|
||||
"model.diffusion_model.joint_blocks.1.context_block.adaLN_modulation.1.bias": "blocks.1.norm1_b.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.1.context_block.adaLN_modulation.1.weight": "blocks.1.norm1_b.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.1.context_block.attn.proj.bias": "blocks.1.attn.b_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.1.context_block.attn.proj.weight": "blocks.1.attn.b_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.1.context_block.attn.qkv.bias": ['blocks.1.attn.b_to_q.bias', 'blocks.1.attn.b_to_k.bias', 'blocks.1.attn.b_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.1.context_block.attn.qkv.weight": ['blocks.1.attn.b_to_q.weight', 'blocks.1.attn.b_to_k.weight', 'blocks.1.attn.b_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.1.context_block.mlp.fc1.bias": "blocks.1.ff_b.0.bias",
|
||||
"model.diffusion_model.joint_blocks.1.context_block.mlp.fc1.weight": "blocks.1.ff_b.0.weight",
|
||||
"model.diffusion_model.joint_blocks.1.context_block.mlp.fc2.bias": "blocks.1.ff_b.2.bias",
|
||||
"model.diffusion_model.joint_blocks.1.context_block.mlp.fc2.weight": "blocks.1.ff_b.2.weight",
|
||||
"model.diffusion_model.joint_blocks.1.x_block.adaLN_modulation.1.bias": "blocks.1.norm1_a.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.1.x_block.adaLN_modulation.1.weight": "blocks.1.norm1_a.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.1.x_block.attn.proj.bias": "blocks.1.attn.a_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.1.x_block.attn.proj.weight": "blocks.1.attn.a_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.1.x_block.attn.qkv.bias": ['blocks.1.attn.a_to_q.bias', 'blocks.1.attn.a_to_k.bias', 'blocks.1.attn.a_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.1.x_block.attn.qkv.weight": ['blocks.1.attn.a_to_q.weight', 'blocks.1.attn.a_to_k.weight', 'blocks.1.attn.a_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.1.x_block.mlp.fc1.bias": "blocks.1.ff_a.0.bias",
|
||||
"model.diffusion_model.joint_blocks.1.x_block.mlp.fc1.weight": "blocks.1.ff_a.0.weight",
|
||||
"model.diffusion_model.joint_blocks.1.x_block.mlp.fc2.bias": "blocks.1.ff_a.2.bias",
|
||||
"model.diffusion_model.joint_blocks.1.x_block.mlp.fc2.weight": "blocks.1.ff_a.2.weight",
|
||||
"model.diffusion_model.joint_blocks.10.context_block.adaLN_modulation.1.bias": "blocks.10.norm1_b.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.10.context_block.adaLN_modulation.1.weight": "blocks.10.norm1_b.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.10.context_block.attn.proj.bias": "blocks.10.attn.b_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.10.context_block.attn.proj.weight": "blocks.10.attn.b_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.10.context_block.attn.qkv.bias": ['blocks.10.attn.b_to_q.bias', 'blocks.10.attn.b_to_k.bias', 'blocks.10.attn.b_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.10.context_block.attn.qkv.weight": ['blocks.10.attn.b_to_q.weight', 'blocks.10.attn.b_to_k.weight', 'blocks.10.attn.b_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.10.context_block.mlp.fc1.bias": "blocks.10.ff_b.0.bias",
|
||||
"model.diffusion_model.joint_blocks.10.context_block.mlp.fc1.weight": "blocks.10.ff_b.0.weight",
|
||||
"model.diffusion_model.joint_blocks.10.context_block.mlp.fc2.bias": "blocks.10.ff_b.2.bias",
|
||||
"model.diffusion_model.joint_blocks.10.context_block.mlp.fc2.weight": "blocks.10.ff_b.2.weight",
|
||||
"model.diffusion_model.joint_blocks.10.x_block.adaLN_modulation.1.bias": "blocks.10.norm1_a.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.10.x_block.adaLN_modulation.1.weight": "blocks.10.norm1_a.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.10.x_block.attn.proj.bias": "blocks.10.attn.a_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.10.x_block.attn.proj.weight": "blocks.10.attn.a_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.10.x_block.attn.qkv.bias": ['blocks.10.attn.a_to_q.bias', 'blocks.10.attn.a_to_k.bias', 'blocks.10.attn.a_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.10.x_block.attn.qkv.weight": ['blocks.10.attn.a_to_q.weight', 'blocks.10.attn.a_to_k.weight', 'blocks.10.attn.a_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.10.x_block.mlp.fc1.bias": "blocks.10.ff_a.0.bias",
|
||||
"model.diffusion_model.joint_blocks.10.x_block.mlp.fc1.weight": "blocks.10.ff_a.0.weight",
|
||||
"model.diffusion_model.joint_blocks.10.x_block.mlp.fc2.bias": "blocks.10.ff_a.2.bias",
|
||||
"model.diffusion_model.joint_blocks.10.x_block.mlp.fc2.weight": "blocks.10.ff_a.2.weight",
|
||||
"model.diffusion_model.joint_blocks.11.context_block.adaLN_modulation.1.bias": "blocks.11.norm1_b.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.11.context_block.adaLN_modulation.1.weight": "blocks.11.norm1_b.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.11.context_block.attn.proj.bias": "blocks.11.attn.b_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.11.context_block.attn.proj.weight": "blocks.11.attn.b_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.11.context_block.attn.qkv.bias": ['blocks.11.attn.b_to_q.bias', 'blocks.11.attn.b_to_k.bias', 'blocks.11.attn.b_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.11.context_block.attn.qkv.weight": ['blocks.11.attn.b_to_q.weight', 'blocks.11.attn.b_to_k.weight', 'blocks.11.attn.b_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.11.context_block.mlp.fc1.bias": "blocks.11.ff_b.0.bias",
|
||||
"model.diffusion_model.joint_blocks.11.context_block.mlp.fc1.weight": "blocks.11.ff_b.0.weight",
|
||||
"model.diffusion_model.joint_blocks.11.context_block.mlp.fc2.bias": "blocks.11.ff_b.2.bias",
|
||||
"model.diffusion_model.joint_blocks.11.context_block.mlp.fc2.weight": "blocks.11.ff_b.2.weight",
|
||||
"model.diffusion_model.joint_blocks.11.x_block.adaLN_modulation.1.bias": "blocks.11.norm1_a.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.11.x_block.adaLN_modulation.1.weight": "blocks.11.norm1_a.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.11.x_block.attn.proj.bias": "blocks.11.attn.a_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.11.x_block.attn.proj.weight": "blocks.11.attn.a_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.11.x_block.attn.qkv.bias": ['blocks.11.attn.a_to_q.bias', 'blocks.11.attn.a_to_k.bias', 'blocks.11.attn.a_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.11.x_block.attn.qkv.weight": ['blocks.11.attn.a_to_q.weight', 'blocks.11.attn.a_to_k.weight', 'blocks.11.attn.a_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.11.x_block.mlp.fc1.bias": "blocks.11.ff_a.0.bias",
|
||||
"model.diffusion_model.joint_blocks.11.x_block.mlp.fc1.weight": "blocks.11.ff_a.0.weight",
|
||||
"model.diffusion_model.joint_blocks.11.x_block.mlp.fc2.bias": "blocks.11.ff_a.2.bias",
|
||||
"model.diffusion_model.joint_blocks.11.x_block.mlp.fc2.weight": "blocks.11.ff_a.2.weight",
|
||||
"model.diffusion_model.joint_blocks.12.context_block.adaLN_modulation.1.bias": "blocks.12.norm1_b.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.12.context_block.adaLN_modulation.1.weight": "blocks.12.norm1_b.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.12.context_block.attn.proj.bias": "blocks.12.attn.b_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.12.context_block.attn.proj.weight": "blocks.12.attn.b_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.12.context_block.attn.qkv.bias": ['blocks.12.attn.b_to_q.bias', 'blocks.12.attn.b_to_k.bias', 'blocks.12.attn.b_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.12.context_block.attn.qkv.weight": ['blocks.12.attn.b_to_q.weight', 'blocks.12.attn.b_to_k.weight', 'blocks.12.attn.b_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.12.context_block.mlp.fc1.bias": "blocks.12.ff_b.0.bias",
|
||||
"model.diffusion_model.joint_blocks.12.context_block.mlp.fc1.weight": "blocks.12.ff_b.0.weight",
|
||||
"model.diffusion_model.joint_blocks.12.context_block.mlp.fc2.bias": "blocks.12.ff_b.2.bias",
|
||||
"model.diffusion_model.joint_blocks.12.context_block.mlp.fc2.weight": "blocks.12.ff_b.2.weight",
|
||||
"model.diffusion_model.joint_blocks.12.x_block.adaLN_modulation.1.bias": "blocks.12.norm1_a.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.12.x_block.adaLN_modulation.1.weight": "blocks.12.norm1_a.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.12.x_block.attn.proj.bias": "blocks.12.attn.a_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.12.x_block.attn.proj.weight": "blocks.12.attn.a_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.12.x_block.attn.qkv.bias": ['blocks.12.attn.a_to_q.bias', 'blocks.12.attn.a_to_k.bias', 'blocks.12.attn.a_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.12.x_block.attn.qkv.weight": ['blocks.12.attn.a_to_q.weight', 'blocks.12.attn.a_to_k.weight', 'blocks.12.attn.a_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.12.x_block.mlp.fc1.bias": "blocks.12.ff_a.0.bias",
|
||||
"model.diffusion_model.joint_blocks.12.x_block.mlp.fc1.weight": "blocks.12.ff_a.0.weight",
|
||||
"model.diffusion_model.joint_blocks.12.x_block.mlp.fc2.bias": "blocks.12.ff_a.2.bias",
|
||||
"model.diffusion_model.joint_blocks.12.x_block.mlp.fc2.weight": "blocks.12.ff_a.2.weight",
|
||||
"model.diffusion_model.joint_blocks.13.context_block.adaLN_modulation.1.bias": "blocks.13.norm1_b.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.13.context_block.adaLN_modulation.1.weight": "blocks.13.norm1_b.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.13.context_block.attn.proj.bias": "blocks.13.attn.b_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.13.context_block.attn.proj.weight": "blocks.13.attn.b_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.13.context_block.attn.qkv.bias": ['blocks.13.attn.b_to_q.bias', 'blocks.13.attn.b_to_k.bias', 'blocks.13.attn.b_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.13.context_block.attn.qkv.weight": ['blocks.13.attn.b_to_q.weight', 'blocks.13.attn.b_to_k.weight', 'blocks.13.attn.b_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.13.context_block.mlp.fc1.bias": "blocks.13.ff_b.0.bias",
|
||||
"model.diffusion_model.joint_blocks.13.context_block.mlp.fc1.weight": "blocks.13.ff_b.0.weight",
|
||||
"model.diffusion_model.joint_blocks.13.context_block.mlp.fc2.bias": "blocks.13.ff_b.2.bias",
|
||||
"model.diffusion_model.joint_blocks.13.context_block.mlp.fc2.weight": "blocks.13.ff_b.2.weight",
|
||||
"model.diffusion_model.joint_blocks.13.x_block.adaLN_modulation.1.bias": "blocks.13.norm1_a.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.13.x_block.adaLN_modulation.1.weight": "blocks.13.norm1_a.linear.weight",
|
||||
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||||
"model.diffusion_model.joint_blocks.6.x_block.adaLN_modulation.1.bias": "blocks.6.norm1_a.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.6.x_block.adaLN_modulation.1.weight": "blocks.6.norm1_a.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.6.x_block.attn.proj.bias": "blocks.6.attn.a_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.6.x_block.attn.proj.weight": "blocks.6.attn.a_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.6.x_block.attn.qkv.bias": ['blocks.6.attn.a_to_q.bias', 'blocks.6.attn.a_to_k.bias', 'blocks.6.attn.a_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.6.x_block.attn.qkv.weight": ['blocks.6.attn.a_to_q.weight', 'blocks.6.attn.a_to_k.weight', 'blocks.6.attn.a_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.6.x_block.mlp.fc1.bias": "blocks.6.ff_a.0.bias",
|
||||
"model.diffusion_model.joint_blocks.6.x_block.mlp.fc1.weight": "blocks.6.ff_a.0.weight",
|
||||
"model.diffusion_model.joint_blocks.6.x_block.mlp.fc2.bias": "blocks.6.ff_a.2.bias",
|
||||
"model.diffusion_model.joint_blocks.6.x_block.mlp.fc2.weight": "blocks.6.ff_a.2.weight",
|
||||
"model.diffusion_model.joint_blocks.7.context_block.adaLN_modulation.1.bias": "blocks.7.norm1_b.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.7.context_block.adaLN_modulation.1.weight": "blocks.7.norm1_b.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.7.context_block.attn.proj.bias": "blocks.7.attn.b_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.7.context_block.attn.proj.weight": "blocks.7.attn.b_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.7.context_block.attn.qkv.bias": ['blocks.7.attn.b_to_q.bias', 'blocks.7.attn.b_to_k.bias', 'blocks.7.attn.b_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.7.context_block.attn.qkv.weight": ['blocks.7.attn.b_to_q.weight', 'blocks.7.attn.b_to_k.weight', 'blocks.7.attn.b_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.7.context_block.mlp.fc1.bias": "blocks.7.ff_b.0.bias",
|
||||
"model.diffusion_model.joint_blocks.7.context_block.mlp.fc1.weight": "blocks.7.ff_b.0.weight",
|
||||
"model.diffusion_model.joint_blocks.7.context_block.mlp.fc2.bias": "blocks.7.ff_b.2.bias",
|
||||
"model.diffusion_model.joint_blocks.7.context_block.mlp.fc2.weight": "blocks.7.ff_b.2.weight",
|
||||
"model.diffusion_model.joint_blocks.7.x_block.adaLN_modulation.1.bias": "blocks.7.norm1_a.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.7.x_block.adaLN_modulation.1.weight": "blocks.7.norm1_a.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.7.x_block.attn.proj.bias": "blocks.7.attn.a_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.7.x_block.attn.proj.weight": "blocks.7.attn.a_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.7.x_block.attn.qkv.bias": ['blocks.7.attn.a_to_q.bias', 'blocks.7.attn.a_to_k.bias', 'blocks.7.attn.a_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.7.x_block.attn.qkv.weight": ['blocks.7.attn.a_to_q.weight', 'blocks.7.attn.a_to_k.weight', 'blocks.7.attn.a_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.7.x_block.mlp.fc1.bias": "blocks.7.ff_a.0.bias",
|
||||
"model.diffusion_model.joint_blocks.7.x_block.mlp.fc1.weight": "blocks.7.ff_a.0.weight",
|
||||
"model.diffusion_model.joint_blocks.7.x_block.mlp.fc2.bias": "blocks.7.ff_a.2.bias",
|
||||
"model.diffusion_model.joint_blocks.7.x_block.mlp.fc2.weight": "blocks.7.ff_a.2.weight",
|
||||
"model.diffusion_model.joint_blocks.8.context_block.adaLN_modulation.1.bias": "blocks.8.norm1_b.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.8.context_block.adaLN_modulation.1.weight": "blocks.8.norm1_b.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.8.context_block.attn.proj.bias": "blocks.8.attn.b_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.8.context_block.attn.proj.weight": "blocks.8.attn.b_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.8.context_block.attn.qkv.bias": ['blocks.8.attn.b_to_q.bias', 'blocks.8.attn.b_to_k.bias', 'blocks.8.attn.b_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.8.context_block.attn.qkv.weight": ['blocks.8.attn.b_to_q.weight', 'blocks.8.attn.b_to_k.weight', 'blocks.8.attn.b_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.8.context_block.mlp.fc1.bias": "blocks.8.ff_b.0.bias",
|
||||
"model.diffusion_model.joint_blocks.8.context_block.mlp.fc1.weight": "blocks.8.ff_b.0.weight",
|
||||
"model.diffusion_model.joint_blocks.8.context_block.mlp.fc2.bias": "blocks.8.ff_b.2.bias",
|
||||
"model.diffusion_model.joint_blocks.8.context_block.mlp.fc2.weight": "blocks.8.ff_b.2.weight",
|
||||
"model.diffusion_model.joint_blocks.8.x_block.adaLN_modulation.1.bias": "blocks.8.norm1_a.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.8.x_block.adaLN_modulation.1.weight": "blocks.8.norm1_a.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.8.x_block.attn.proj.bias": "blocks.8.attn.a_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.8.x_block.attn.proj.weight": "blocks.8.attn.a_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.8.x_block.attn.qkv.bias": ['blocks.8.attn.a_to_q.bias', 'blocks.8.attn.a_to_k.bias', 'blocks.8.attn.a_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.8.x_block.attn.qkv.weight": ['blocks.8.attn.a_to_q.weight', 'blocks.8.attn.a_to_k.weight', 'blocks.8.attn.a_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.8.x_block.mlp.fc1.bias": "blocks.8.ff_a.0.bias",
|
||||
"model.diffusion_model.joint_blocks.8.x_block.mlp.fc1.weight": "blocks.8.ff_a.0.weight",
|
||||
"model.diffusion_model.joint_blocks.8.x_block.mlp.fc2.bias": "blocks.8.ff_a.2.bias",
|
||||
"model.diffusion_model.joint_blocks.8.x_block.mlp.fc2.weight": "blocks.8.ff_a.2.weight",
|
||||
"model.diffusion_model.joint_blocks.9.context_block.adaLN_modulation.1.bias": "blocks.9.norm1_b.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.9.context_block.adaLN_modulation.1.weight": "blocks.9.norm1_b.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.9.context_block.attn.proj.bias": "blocks.9.attn.b_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.9.context_block.attn.proj.weight": "blocks.9.attn.b_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.9.context_block.attn.qkv.bias": ['blocks.9.attn.b_to_q.bias', 'blocks.9.attn.b_to_k.bias', 'blocks.9.attn.b_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.9.context_block.attn.qkv.weight": ['blocks.9.attn.b_to_q.weight', 'blocks.9.attn.b_to_k.weight', 'blocks.9.attn.b_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.9.context_block.mlp.fc1.bias": "blocks.9.ff_b.0.bias",
|
||||
"model.diffusion_model.joint_blocks.9.context_block.mlp.fc1.weight": "blocks.9.ff_b.0.weight",
|
||||
"model.diffusion_model.joint_blocks.9.context_block.mlp.fc2.bias": "blocks.9.ff_b.2.bias",
|
||||
"model.diffusion_model.joint_blocks.9.context_block.mlp.fc2.weight": "blocks.9.ff_b.2.weight",
|
||||
"model.diffusion_model.joint_blocks.9.x_block.adaLN_modulation.1.bias": "blocks.9.norm1_a.linear.bias",
|
||||
"model.diffusion_model.joint_blocks.9.x_block.adaLN_modulation.1.weight": "blocks.9.norm1_a.linear.weight",
|
||||
"model.diffusion_model.joint_blocks.9.x_block.attn.proj.bias": "blocks.9.attn.a_to_out.bias",
|
||||
"model.diffusion_model.joint_blocks.9.x_block.attn.proj.weight": "blocks.9.attn.a_to_out.weight",
|
||||
"model.diffusion_model.joint_blocks.9.x_block.attn.qkv.bias": ['blocks.9.attn.a_to_q.bias', 'blocks.9.attn.a_to_k.bias', 'blocks.9.attn.a_to_v.bias'],
|
||||
"model.diffusion_model.joint_blocks.9.x_block.attn.qkv.weight": ['blocks.9.attn.a_to_q.weight', 'blocks.9.attn.a_to_k.weight', 'blocks.9.attn.a_to_v.weight'],
|
||||
"model.diffusion_model.joint_blocks.9.x_block.mlp.fc1.bias": "blocks.9.ff_a.0.bias",
|
||||
"model.diffusion_model.joint_blocks.9.x_block.mlp.fc1.weight": "blocks.9.ff_a.0.weight",
|
||||
"model.diffusion_model.joint_blocks.9.x_block.mlp.fc2.bias": "blocks.9.ff_a.2.bias",
|
||||
"model.diffusion_model.joint_blocks.9.x_block.mlp.fc2.weight": "blocks.9.ff_a.2.weight",
|
||||
"model.diffusion_model.pos_embed": "pos_embedder.pos_embed",
|
||||
"model.diffusion_model.t_embedder.mlp.0.bias": "time_embedder.timestep_embedder.0.bias",
|
||||
"model.diffusion_model.t_embedder.mlp.0.weight": "time_embedder.timestep_embedder.0.weight",
|
||||
@@ -509,59 +780,19 @@ class SD3DiTStateDictConverter:
|
||||
"model.diffusion_model.final_layer.adaLN_modulation.1.weight": "norm_out.linear.weight",
|
||||
"model.diffusion_model.final_layer.adaLN_modulation.1.bias": "norm_out.linear.bias",
|
||||
}
|
||||
for i in range(40):
|
||||
rename_dict.update({
|
||||
f"model.diffusion_model.joint_blocks.{i}.context_block.adaLN_modulation.1.bias": f"blocks.{i}.norm1_b.linear.bias",
|
||||
f"model.diffusion_model.joint_blocks.{i}.context_block.adaLN_modulation.1.weight": f"blocks.{i}.norm1_b.linear.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.context_block.attn.proj.bias": f"blocks.{i}.attn.b_to_out.bias",
|
||||
f"model.diffusion_model.joint_blocks.{i}.context_block.attn.proj.weight": f"blocks.{i}.attn.b_to_out.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.context_block.attn.qkv.bias": [f'blocks.{i}.attn.b_to_q.bias', f'blocks.{i}.attn.b_to_k.bias', f'blocks.{i}.attn.b_to_v.bias'],
|
||||
f"model.diffusion_model.joint_blocks.{i}.context_block.attn.qkv.weight": [f'blocks.{i}.attn.b_to_q.weight', f'blocks.{i}.attn.b_to_k.weight', f'blocks.{i}.attn.b_to_v.weight'],
|
||||
f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc1.bias": f"blocks.{i}.ff_b.0.bias",
|
||||
f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc1.weight": f"blocks.{i}.ff_b.0.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc2.bias": f"blocks.{i}.ff_b.2.bias",
|
||||
f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc2.weight": f"blocks.{i}.ff_b.2.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.adaLN_modulation.1.bias": f"blocks.{i}.norm1_a.linear.bias",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.adaLN_modulation.1.weight": f"blocks.{i}.norm1_a.linear.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.attn.proj.bias": f"blocks.{i}.attn.a_to_out.bias",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.attn.proj.weight": f"blocks.{i}.attn.a_to_out.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.attn.qkv.bias": [f'blocks.{i}.attn.a_to_q.bias', f'blocks.{i}.attn.a_to_k.bias', f'blocks.{i}.attn.a_to_v.bias'],
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.attn.qkv.weight": [f'blocks.{i}.attn.a_to_q.weight', f'blocks.{i}.attn.a_to_k.weight', f'blocks.{i}.attn.a_to_v.weight'],
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc1.bias": f"blocks.{i}.ff_a.0.bias",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc1.weight": f"blocks.{i}.ff_a.0.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc2.bias": f"blocks.{i}.ff_a.2.bias",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc2.weight": f"blocks.{i}.ff_a.2.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.attn.ln_q.weight": f"blocks.{i}.attn.norm_q_a.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.attn.ln_k.weight": f"blocks.{i}.attn.norm_k_a.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.context_block.attn.ln_q.weight": f"blocks.{i}.attn.norm_q_b.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.context_block.attn.ln_k.weight": f"blocks.{i}.attn.norm_k_b.weight",
|
||||
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.ln_q.weight": f"blocks.{i}.attn2.norm_q_a.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.ln_k.weight": f"blocks.{i}.attn2.norm_k_a.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.qkv.weight": f"blocks.{i}.attn2.a_to_qkv.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.qkv.bias": f"blocks.{i}.attn2.a_to_qkv.bias",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.proj.weight": f"blocks.{i}.attn2.a_to_out.weight",
|
||||
f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.proj.bias": f"blocks.{i}.attn2.a_to_out.bias",
|
||||
})
|
||||
state_dict_ = {}
|
||||
for name in state_dict:
|
||||
if name in rename_dict:
|
||||
param = state_dict[name]
|
||||
if name == "model.diffusion_model.pos_embed":
|
||||
pos_embed_max_size = int(param.shape[1] ** 0.5 + 0.4)
|
||||
param = param.reshape((1, pos_embed_max_size, pos_embed_max_size, param.shape[-1]))
|
||||
if name.startswith("model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1."):
|
||||
param = torch.concat([param[1536:], param[:1536]], axis=0)
|
||||
elif name.startswith("model.diffusion_model.final_layer.adaLN_modulation.1."):
|
||||
param = torch.concat([param[1536:], param[:1536]], axis=0)
|
||||
elif name == "model.diffusion_model.pos_embed":
|
||||
param = param.reshape((1, 192, 192, 1536))
|
||||
if isinstance(rename_dict[name], str):
|
||||
state_dict_[rename_dict[name]] = param
|
||||
else:
|
||||
name_ = rename_dict[name][0].replace(".a_to_q.", ".a_to_qkv.").replace(".b_to_q.", ".b_to_qkv.")
|
||||
state_dict_[name_] = param
|
||||
extra_kwargs = self.infer_architecture(state_dict_)
|
||||
num_layers = extra_kwargs["num_layers"]
|
||||
for name in [
|
||||
f"blocks.{num_layers-1}.norm1_b.linear.weight", f"blocks.{num_layers-1}.norm1_b.linear.bias", "norm_out.linear.weight", "norm_out.linear.bias",
|
||||
]:
|
||||
param = state_dict_[name]
|
||||
dim = param.shape[0] // 2
|
||||
param = torch.concat([param[dim:], param[:dim]], axis=0)
|
||||
state_dict_[name] = param
|
||||
return state_dict_, self.infer_architecture(state_dict_)
|
||||
return state_dict_
|
||||
|
||||
@@ -2,18 +2,15 @@ import torch
|
||||
from transformers import T5EncoderModel, T5Config
|
||||
from .sd_text_encoder import SDTextEncoder
|
||||
from .sdxl_text_encoder import SDXLTextEncoder2, SDXLTextEncoder2StateDictConverter
|
||||
|
||||
|
||||
|
||||
class SD3TextEncoder1(SDTextEncoder):
|
||||
def __init__(self, vocab_size=49408):
|
||||
super().__init__(vocab_size=vocab_size)
|
||||
|
||||
def forward(self, input_ids, clip_skip=2, extra_mask=None):
|
||||
embeds = self.token_embedding(input_ids)
|
||||
embeds = embeds + self.position_embeds.to(dtype=embeds.dtype, device=input_ids.device)
|
||||
def forward(self, input_ids, clip_skip=2):
|
||||
embeds = self.token_embedding(input_ids) + self.position_embeds
|
||||
attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
|
||||
if extra_mask is not None:
|
||||
attn_mask[:, extra_mask[0]==0] = float("-inf")
|
||||
for encoder_id, encoder in enumerate(self.encoders):
|
||||
embeds = encoder(embeds, attn_mask=attn_mask)
|
||||
if encoder_id + clip_skip == len(self.encoders):
|
||||
@@ -325,11 +322,6 @@ class SD3TextEncoder1StateDictConverter:
|
||||
if name == "text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight":
|
||||
param = param.reshape((1, param.shape[0], param.shape[1]))
|
||||
state_dict_[rename_dict[name]] = param
|
||||
elif ("text_encoders.clip_l.transformer." + name) in rename_dict:
|
||||
param = state_dict[name]
|
||||
if name == "text_model.embeddings.position_embedding.weight":
|
||||
param = param.reshape((1, param.shape[0], param.shape[1]))
|
||||
state_dict_[rename_dict["text_encoders.clip_l.transformer." + name]] = param
|
||||
return state_dict_
|
||||
|
||||
|
||||
@@ -868,11 +860,6 @@ class SD3TextEncoder2StateDictConverter(SDXLTextEncoder2StateDictConverter):
|
||||
if name == "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight":
|
||||
param = param.reshape((1, param.shape[0], param.shape[1]))
|
||||
state_dict_[rename_dict[name]] = param
|
||||
elif ("text_encoders.clip_g.transformer." + name) in rename_dict:
|
||||
param = state_dict[name]
|
||||
if name == "text_model.embeddings.position_embedding.weight":
|
||||
param = param.reshape((1, param.shape[0], param.shape[1]))
|
||||
state_dict_[rename_dict["text_encoders.clip_g.transformer." + name]] = param
|
||||
return state_dict_
|
||||
|
||||
|
||||
|
||||
@@ -1,683 +0,0 @@
|
||||
from typing import Optional
|
||||
|
||||
import torch, math
|
||||
import torch.nn
|
||||
from einops import rearrange
|
||||
from torch import nn
|
||||
from functools import partial
|
||||
from einops import rearrange
|
||||
|
||||
|
||||
|
||||
def attention(q, k, v, attn_mask, mode="torch"):
|
||||
q = q.transpose(1, 2)
|
||||
k = k.transpose(1, 2)
|
||||
v = v.transpose(1, 2)
|
||||
x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
|
||||
x = rearrange(x, "b n s d -> b s (n d)")
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class MLP(nn.Module):
|
||||
"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
hidden_channels=None,
|
||||
out_features=None,
|
||||
act_layer=nn.GELU,
|
||||
norm_layer=None,
|
||||
bias=True,
|
||||
drop=0.0,
|
||||
use_conv=False,
|
||||
device=None,
|
||||
dtype=None,
|
||||
):
|
||||
super().__init__()
|
||||
out_features = out_features or in_channels
|
||||
hidden_channels = hidden_channels or in_channels
|
||||
bias = (bias, bias)
|
||||
drop_probs = (drop, drop)
|
||||
linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
|
||||
|
||||
self.fc1 = linear_layer(
|
||||
in_channels, hidden_channels, bias=bias[0], device=device, dtype=dtype
|
||||
)
|
||||
self.act = act_layer()
|
||||
self.drop1 = nn.Dropout(drop_probs[0])
|
||||
self.norm = (
|
||||
norm_layer(hidden_channels, device=device, dtype=dtype)
|
||||
if norm_layer is not None
|
||||
else nn.Identity()
|
||||
)
|
||||
self.fc2 = linear_layer(
|
||||
hidden_channels, out_features, bias=bias[1], device=device, dtype=dtype
|
||||
)
|
||||
self.drop2 = nn.Dropout(drop_probs[1])
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop1(x)
|
||||
x = self.norm(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop2(x)
|
||||
return x
|
||||
|
||||
|
||||
class TextProjection(nn.Module):
|
||||
"""
|
||||
Projects text embeddings. Also handles dropout for classifier-free guidance.
|
||||
|
||||
Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
|
||||
"""
|
||||
|
||||
def __init__(self, in_channels, hidden_size, act_layer, dtype=None, device=None):
|
||||
factory_kwargs = {"dtype": dtype, "device": device}
|
||||
super().__init__()
|
||||
self.linear_1 = nn.Linear(
|
||||
in_features=in_channels,
|
||||
out_features=hidden_size,
|
||||
bias=True,
|
||||
**factory_kwargs,
|
||||
)
|
||||
self.act_1 = act_layer()
|
||||
self.linear_2 = nn.Linear(
|
||||
in_features=hidden_size,
|
||||
out_features=hidden_size,
|
||||
bias=True,
|
||||
**factory_kwargs,
|
||||
)
|
||||
|
||||
def forward(self, caption):
|
||||
hidden_states = self.linear_1(caption)
|
||||
hidden_states = self.act_1(hidden_states)
|
||||
hidden_states = self.linear_2(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class TimestepEmbedder(nn.Module):
|
||||
"""
|
||||
Embeds scalar timesteps into vector representations.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size,
|
||||
act_layer,
|
||||
frequency_embedding_size=256,
|
||||
max_period=10000,
|
||||
out_size=None,
|
||||
dtype=None,
|
||||
device=None,
|
||||
):
|
||||
factory_kwargs = {"dtype": dtype, "device": device}
|
||||
super().__init__()
|
||||
self.frequency_embedding_size = frequency_embedding_size
|
||||
self.max_period = max_period
|
||||
if out_size is None:
|
||||
out_size = hidden_size
|
||||
|
||||
self.mlp = nn.Sequential(
|
||||
nn.Linear(
|
||||
frequency_embedding_size, hidden_size, bias=True, **factory_kwargs
|
||||
),
|
||||
act_layer(),
|
||||
nn.Linear(hidden_size, out_size, bias=True, **factory_kwargs),
|
||||
)
|
||||
nn.init.normal_(self.mlp[0].weight, std=0.02) # type: ignore
|
||||
nn.init.normal_(self.mlp[2].weight, std=0.02) # type: ignore
|
||||
|
||||
@staticmethod
|
||||
def timestep_embedding(t, dim, max_period=10000):
|
||||
"""
|
||||
Create sinusoidal timestep embeddings.
|
||||
|
||||
Args:
|
||||
t (torch.Tensor): a 1-D Tensor of N indices, one per batch element. These may be fractional.
|
||||
dim (int): the dimension of the output.
|
||||
max_period (int): controls the minimum frequency of the embeddings.
|
||||
|
||||
Returns:
|
||||
embedding (torch.Tensor): An (N, D) Tensor of positional embeddings.
|
||||
|
||||
.. ref_link: https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
|
||||
"""
|
||||
half = dim // 2
|
||||
freqs = torch.exp(
|
||||
-math.log(max_period)
|
||||
* torch.arange(start=0, end=half, dtype=torch.float32)
|
||||
/ half
|
||||
).to(device=t.device)
|
||||
args = t[:, None].float() * freqs[None]
|
||||
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
|
||||
if dim % 2:
|
||||
embedding = torch.cat(
|
||||
[embedding, torch.zeros_like(embedding[:, :1])], dim=-1
|
||||
)
|
||||
return embedding
|
||||
|
||||
def forward(self, t):
|
||||
t_freq = self.timestep_embedding(
|
||||
t, self.frequency_embedding_size, self.max_period
|
||||
).type(t.dtype) # type: ignore
|
||||
t_emb = self.mlp(t_freq)
|
||||
return t_emb
|
||||
|
||||
|
||||
def apply_gate(x, gate=None, tanh=False):
|
||||
"""AI is creating summary for apply_gate
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): input tensor.
|
||||
gate (torch.Tensor, optional): gate tensor. Defaults to None.
|
||||
tanh (bool, optional): whether to use tanh function. Defaults to False.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: the output tensor after apply gate.
|
||||
"""
|
||||
if gate is None:
|
||||
return x
|
||||
if tanh:
|
||||
return x * gate.unsqueeze(1).tanh()
|
||||
else:
|
||||
return x * gate.unsqueeze(1)
|
||||
|
||||
|
||||
class RMSNorm(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
elementwise_affine=True,
|
||||
eps: float = 1e-6,
|
||||
device=None,
|
||||
dtype=None,
|
||||
):
|
||||
"""
|
||||
Initialize the RMSNorm normalization layer.
|
||||
|
||||
Args:
|
||||
dim (int): The dimension of the input tensor.
|
||||
eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
|
||||
|
||||
Attributes:
|
||||
eps (float): A small value added to the denominator for numerical stability.
|
||||
weight (nn.Parameter): Learnable scaling parameter.
|
||||
|
||||
"""
|
||||
factory_kwargs = {"device": device, "dtype": dtype}
|
||||
super().__init__()
|
||||
self.eps = eps
|
||||
if elementwise_affine:
|
||||
self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs))
|
||||
|
||||
def _norm(self, x):
|
||||
"""
|
||||
Apply the RMSNorm normalization to the input tensor.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): The input tensor.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: The normalized tensor.
|
||||
|
||||
"""
|
||||
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Forward pass through the RMSNorm layer.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): The input tensor.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: The output tensor after applying RMSNorm.
|
||||
|
||||
"""
|
||||
output = self._norm(x.float()).type_as(x)
|
||||
if hasattr(self, "weight"):
|
||||
output = output * self.weight
|
||||
return output
|
||||
|
||||
|
||||
def get_norm_layer(norm_layer):
|
||||
"""
|
||||
Get the normalization layer.
|
||||
|
||||
Args:
|
||||
norm_layer (str): The type of normalization layer.
|
||||
|
||||
Returns:
|
||||
norm_layer (nn.Module): The normalization layer.
|
||||
"""
|
||||
if norm_layer == "layer":
|
||||
return nn.LayerNorm
|
||||
elif norm_layer == "rms":
|
||||
return RMSNorm
|
||||
else:
|
||||
raise NotImplementedError(f"Norm layer {norm_layer} is not implemented")
|
||||
|
||||
|
||||
def get_activation_layer(act_type):
|
||||
"""get activation layer
|
||||
|
||||
Args:
|
||||
act_type (str): the activation type
|
||||
|
||||
Returns:
|
||||
torch.nn.functional: the activation layer
|
||||
"""
|
||||
if act_type == "gelu":
|
||||
return lambda: nn.GELU()
|
||||
elif act_type == "gelu_tanh":
|
||||
return lambda: nn.GELU(approximate="tanh")
|
||||
elif act_type == "relu":
|
||||
return nn.ReLU
|
||||
elif act_type == "silu":
|
||||
return nn.SiLU
|
||||
else:
|
||||
raise ValueError(f"Unknown activation type: {act_type}")
|
||||
|
||||
class IndividualTokenRefinerBlock(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size,
|
||||
heads_num,
|
||||
mlp_width_ratio: str = 4.0,
|
||||
mlp_drop_rate: float = 0.0,
|
||||
act_type: str = "silu",
|
||||
qk_norm: bool = False,
|
||||
qk_norm_type: str = "layer",
|
||||
qkv_bias: bool = True,
|
||||
need_CA: bool = False,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
device: Optional[torch.device] = None,
|
||||
):
|
||||
factory_kwargs = {"device": device, "dtype": dtype}
|
||||
super().__init__()
|
||||
self.need_CA = need_CA
|
||||
self.heads_num = heads_num
|
||||
head_dim = hidden_size // heads_num
|
||||
mlp_hidden_dim = int(hidden_size * mlp_width_ratio)
|
||||
|
||||
self.norm1 = nn.LayerNorm(
|
||||
hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
|
||||
)
|
||||
self.self_attn_qkv = nn.Linear(
|
||||
hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs
|
||||
)
|
||||
qk_norm_layer = get_norm_layer(qk_norm_type)
|
||||
self.self_attn_q_norm = (
|
||||
qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
|
||||
if qk_norm
|
||||
else nn.Identity()
|
||||
)
|
||||
self.self_attn_k_norm = (
|
||||
qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
|
||||
if qk_norm
|
||||
else nn.Identity()
|
||||
)
|
||||
self.self_attn_proj = nn.Linear(
|
||||
hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs
|
||||
)
|
||||
|
||||
self.norm2 = nn.LayerNorm(
|
||||
hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
|
||||
)
|
||||
act_layer = get_activation_layer(act_type)
|
||||
self.mlp = MLP(
|
||||
in_channels=hidden_size,
|
||||
hidden_channels=mlp_hidden_dim,
|
||||
act_layer=act_layer,
|
||||
drop=mlp_drop_rate,
|
||||
**factory_kwargs,
|
||||
)
|
||||
|
||||
self.adaLN_modulation = nn.Sequential(
|
||||
act_layer(),
|
||||
nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs),
|
||||
)
|
||||
|
||||
if self.need_CA:
|
||||
self.cross_attnblock=CrossAttnBlock(hidden_size=hidden_size,
|
||||
heads_num=heads_num,
|
||||
mlp_width_ratio=mlp_width_ratio,
|
||||
mlp_drop_rate=mlp_drop_rate,
|
||||
act_type=act_type,
|
||||
qk_norm=qk_norm,
|
||||
qk_norm_type=qk_norm_type,
|
||||
qkv_bias=qkv_bias,
|
||||
**factory_kwargs,)
|
||||
# Zero-initialize the modulation
|
||||
nn.init.zeros_(self.adaLN_modulation[1].weight)
|
||||
nn.init.zeros_(self.adaLN_modulation[1].bias)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
c: torch.Tensor, # timestep_aware_representations + context_aware_representations
|
||||
attn_mask: torch.Tensor = None,
|
||||
y: torch.Tensor = None,
|
||||
):
|
||||
gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1)
|
||||
|
||||
norm_x = self.norm1(x)
|
||||
qkv = self.self_attn_qkv(norm_x)
|
||||
q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
|
||||
# Apply QK-Norm if needed
|
||||
q = self.self_attn_q_norm(q).to(v)
|
||||
k = self.self_attn_k_norm(k).to(v)
|
||||
|
||||
# Self-Attention
|
||||
attn = attention(q, k, v, mode="torch", attn_mask=attn_mask)
|
||||
|
||||
x = x + apply_gate(self.self_attn_proj(attn), gate_msa)
|
||||
|
||||
if self.need_CA:
|
||||
x = self.cross_attnblock(x, c, attn_mask, y)
|
||||
|
||||
# FFN Layer
|
||||
x = x + apply_gate(self.mlp(self.norm2(x)), gate_mlp)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
|
||||
|
||||
class CrossAttnBlock(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size,
|
||||
heads_num,
|
||||
mlp_width_ratio: str = 4.0,
|
||||
mlp_drop_rate: float = 0.0,
|
||||
act_type: str = "silu",
|
||||
qk_norm: bool = False,
|
||||
qk_norm_type: str = "layer",
|
||||
qkv_bias: bool = True,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
device: Optional[torch.device] = None,
|
||||
):
|
||||
factory_kwargs = {"device": device, "dtype": dtype}
|
||||
super().__init__()
|
||||
self.heads_num = heads_num
|
||||
head_dim = hidden_size // heads_num
|
||||
|
||||
self.norm1 = nn.LayerNorm(
|
||||
hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
|
||||
)
|
||||
self.norm1_2 = nn.LayerNorm(
|
||||
hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
|
||||
)
|
||||
self.self_attn_q = nn.Linear(
|
||||
hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs
|
||||
)
|
||||
self.self_attn_kv = nn.Linear(
|
||||
hidden_size, hidden_size*2, bias=qkv_bias, **factory_kwargs
|
||||
)
|
||||
qk_norm_layer = get_norm_layer(qk_norm_type)
|
||||
self.self_attn_q_norm = (
|
||||
qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
|
||||
if qk_norm
|
||||
else nn.Identity()
|
||||
)
|
||||
self.self_attn_k_norm = (
|
||||
qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
|
||||
if qk_norm
|
||||
else nn.Identity()
|
||||
)
|
||||
self.self_attn_proj = nn.Linear(
|
||||
hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs
|
||||
)
|
||||
|
||||
self.norm2 = nn.LayerNorm(
|
||||
hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
|
||||
)
|
||||
act_layer = get_activation_layer(act_type)
|
||||
|
||||
self.adaLN_modulation = nn.Sequential(
|
||||
act_layer(),
|
||||
nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs),
|
||||
)
|
||||
# Zero-initialize the modulation
|
||||
nn.init.zeros_(self.adaLN_modulation[1].weight)
|
||||
nn.init.zeros_(self.adaLN_modulation[1].bias)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
c: torch.Tensor, # timestep_aware_representations + context_aware_representations
|
||||
attn_mask: torch.Tensor = None,
|
||||
y: torch.Tensor=None,
|
||||
|
||||
):
|
||||
gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1)
|
||||
|
||||
norm_x = self.norm1(x)
|
||||
norm_y = self.norm1_2(y)
|
||||
q = self.self_attn_q(norm_x)
|
||||
q = rearrange(q, "B L (H D) -> B L H D", H=self.heads_num)
|
||||
kv = self.self_attn_kv(norm_y)
|
||||
k, v = rearrange(kv, "B L (K H D) -> K B L H D", K=2, H=self.heads_num)
|
||||
# Apply QK-Norm if needed
|
||||
q = self.self_attn_q_norm(q).to(v)
|
||||
k = self.self_attn_k_norm(k).to(v)
|
||||
|
||||
# Self-Attention
|
||||
attn = attention(q, k, v, mode="torch", attn_mask=attn_mask)
|
||||
|
||||
x = x + apply_gate(self.self_attn_proj(attn), gate_msa)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class IndividualTokenRefiner(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size,
|
||||
heads_num,
|
||||
depth,
|
||||
mlp_width_ratio: float = 4.0,
|
||||
mlp_drop_rate: float = 0.0,
|
||||
act_type: str = "silu",
|
||||
qk_norm: bool = False,
|
||||
qk_norm_type: str = "layer",
|
||||
qkv_bias: bool = True,
|
||||
need_CA:bool=False,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
device: Optional[torch.device] = None,
|
||||
):
|
||||
|
||||
factory_kwargs = {"device": device, "dtype": dtype}
|
||||
super().__init__()
|
||||
self.need_CA = need_CA
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
IndividualTokenRefinerBlock(
|
||||
hidden_size=hidden_size,
|
||||
heads_num=heads_num,
|
||||
mlp_width_ratio=mlp_width_ratio,
|
||||
mlp_drop_rate=mlp_drop_rate,
|
||||
act_type=act_type,
|
||||
qk_norm=qk_norm,
|
||||
qk_norm_type=qk_norm_type,
|
||||
qkv_bias=qkv_bias,
|
||||
need_CA=self.need_CA,
|
||||
**factory_kwargs,
|
||||
)
|
||||
for _ in range(depth)
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
c: torch.LongTensor,
|
||||
mask: Optional[torch.Tensor] = None,
|
||||
y:torch.Tensor=None,
|
||||
):
|
||||
self_attn_mask = None
|
||||
if mask is not None:
|
||||
batch_size = mask.shape[0]
|
||||
seq_len = mask.shape[1]
|
||||
mask = mask.to(x.device)
|
||||
# batch_size x 1 x seq_len x seq_len
|
||||
self_attn_mask_1 = mask.view(batch_size, 1, 1, seq_len).repeat(
|
||||
1, 1, seq_len, 1
|
||||
)
|
||||
# batch_size x 1 x seq_len x seq_len
|
||||
self_attn_mask_2 = self_attn_mask_1.transpose(2, 3)
|
||||
# batch_size x 1 x seq_len x seq_len, 1 for broadcasting of heads_num
|
||||
self_attn_mask = (self_attn_mask_1 & self_attn_mask_2).bool()
|
||||
# avoids self-attention weight being NaN for padding tokens
|
||||
self_attn_mask[:, :, :, 0] = True
|
||||
|
||||
|
||||
for block in self.blocks:
|
||||
x = block(x, c, self_attn_mask,y)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SingleTokenRefiner(torch.nn.Module):
|
||||
"""
|
||||
A single token refiner block for llm text embedding refine.
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
hidden_size,
|
||||
heads_num,
|
||||
depth,
|
||||
mlp_width_ratio: float = 4.0,
|
||||
mlp_drop_rate: float = 0.0,
|
||||
act_type: str = "silu",
|
||||
qk_norm: bool = False,
|
||||
qk_norm_type: str = "layer",
|
||||
qkv_bias: bool = True,
|
||||
need_CA:bool=False,
|
||||
attn_mode: str = "torch",
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
device: Optional[torch.device] = None,
|
||||
):
|
||||
factory_kwargs = {"device": device, "dtype": dtype}
|
||||
super().__init__()
|
||||
self.attn_mode = attn_mode
|
||||
self.need_CA = need_CA
|
||||
assert self.attn_mode == "torch", "Only support 'torch' mode for token refiner."
|
||||
|
||||
self.input_embedder = nn.Linear(
|
||||
in_channels, hidden_size, bias=True, **factory_kwargs
|
||||
)
|
||||
if self.need_CA:
|
||||
self.input_embedder_CA = nn.Linear(
|
||||
in_channels, hidden_size, bias=True, **factory_kwargs
|
||||
)
|
||||
|
||||
act_layer = get_activation_layer(act_type)
|
||||
# Build timestep embedding layer
|
||||
self.t_embedder = TimestepEmbedder(hidden_size, act_layer, **factory_kwargs)
|
||||
# Build context embedding layer
|
||||
self.c_embedder = TextProjection(
|
||||
in_channels, hidden_size, act_layer, **factory_kwargs
|
||||
)
|
||||
|
||||
self.individual_token_refiner = IndividualTokenRefiner(
|
||||
hidden_size=hidden_size,
|
||||
heads_num=heads_num,
|
||||
depth=depth,
|
||||
mlp_width_ratio=mlp_width_ratio,
|
||||
mlp_drop_rate=mlp_drop_rate,
|
||||
act_type=act_type,
|
||||
qk_norm=qk_norm,
|
||||
qk_norm_type=qk_norm_type,
|
||||
qkv_bias=qkv_bias,
|
||||
need_CA=need_CA,
|
||||
**factory_kwargs,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
t: torch.LongTensor,
|
||||
mask: Optional[torch.LongTensor] = None,
|
||||
y: torch.LongTensor=None,
|
||||
):
|
||||
timestep_aware_representations = self.t_embedder(t)
|
||||
|
||||
if mask is None:
|
||||
context_aware_representations = x.mean(dim=1)
|
||||
else:
|
||||
mask_float = mask.unsqueeze(-1) # [b, s1, 1]
|
||||
context_aware_representations = (x * mask_float).sum(
|
||||
dim=1
|
||||
) / mask_float.sum(dim=1)
|
||||
context_aware_representations = self.c_embedder(context_aware_representations)
|
||||
c = timestep_aware_representations + context_aware_representations
|
||||
|
||||
x = self.input_embedder(x)
|
||||
if self.need_CA:
|
||||
y = self.input_embedder_CA(y)
|
||||
x = self.individual_token_refiner(x, c, mask, y)
|
||||
else:
|
||||
x = self.individual_token_refiner(x, c, mask)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Qwen2Connector(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
# biclip_dim=1024,
|
||||
in_channels=3584,
|
||||
hidden_size=4096,
|
||||
heads_num=32,
|
||||
depth=2,
|
||||
need_CA=False,
|
||||
device=None,
|
||||
dtype=torch.bfloat16,
|
||||
):
|
||||
super().__init__()
|
||||
factory_kwargs = {"device": device, "dtype":dtype}
|
||||
|
||||
self.S =SingleTokenRefiner(in_channels=in_channels,hidden_size=hidden_size,heads_num=heads_num,depth=depth,need_CA=need_CA,**factory_kwargs)
|
||||
self.global_proj_out=nn.Linear(in_channels,768)
|
||||
|
||||
self.scale_factor = nn.Parameter(torch.zeros(1))
|
||||
with torch.no_grad():
|
||||
self.scale_factor.data += -(1 - 0.09)
|
||||
|
||||
def forward(self, x,t,mask):
|
||||
mask_float = mask.unsqueeze(-1) # [b, s1, 1]
|
||||
x_mean = (x * mask_float).sum(
|
||||
dim=1
|
||||
) / mask_float.sum(dim=1) * (1 + self.scale_factor.to(dtype=x.dtype, device=x.device))
|
||||
|
||||
global_out=self.global_proj_out(x_mean)
|
||||
encoder_hidden_states = self.S(x,t,mask)
|
||||
return encoder_hidden_states,global_out
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return Qwen2ConnectorStateDictConverter()
|
||||
|
||||
|
||||
class Qwen2ConnectorStateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
return state_dict
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
state_dict_ = {}
|
||||
for name, param in state_dict.items():
|
||||
if name.startswith("connector."):
|
||||
name_ = name[len("connector."):]
|
||||
state_dict_[name_] = param
|
||||
return state_dict_
|
||||
@@ -1,940 +0,0 @@
|
||||
# Copyright 2025 StepFun Inc. All Rights Reserved.
|
||||
#
|
||||
# Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
# of this software and associated documentation files (the "Software"), to deal
|
||||
# in the Software without restriction, including without limitation the rights
|
||||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
# copies of the Software, and to permit persons to whom the Software is
|
||||
# furnished to do so, subject to the following conditions:
|
||||
#
|
||||
# The above copyright notice and this permission notice shall be included in all
|
||||
# copies or substantial portions of the Software.
|
||||
# ==============================================================================
|
||||
from typing import Dict, Optional, Tuple, Union, List
|
||||
import torch, math
|
||||
from torch import nn
|
||||
from einops import rearrange, repeat
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
class RMSNorm(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
elementwise_affine=True,
|
||||
eps: float = 1e-6,
|
||||
device=None,
|
||||
dtype=None,
|
||||
):
|
||||
"""
|
||||
Initialize the RMSNorm normalization layer.
|
||||
|
||||
Args:
|
||||
dim (int): The dimension of the input tensor.
|
||||
eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
|
||||
|
||||
Attributes:
|
||||
eps (float): A small value added to the denominator for numerical stability.
|
||||
weight (nn.Parameter): Learnable scaling parameter.
|
||||
|
||||
"""
|
||||
factory_kwargs = {"device": device, "dtype": dtype}
|
||||
super().__init__()
|
||||
self.eps = eps
|
||||
if elementwise_affine:
|
||||
self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs))
|
||||
|
||||
def _norm(self, x):
|
||||
"""
|
||||
Apply the RMSNorm normalization to the input tensor.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): The input tensor.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: The normalized tensor.
|
||||
|
||||
"""
|
||||
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Forward pass through the RMSNorm layer.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): The input tensor.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: The output tensor after applying RMSNorm.
|
||||
|
||||
"""
|
||||
output = self._norm(x.float()).type_as(x)
|
||||
if hasattr(self, "weight"):
|
||||
output = output * self.weight
|
||||
return output
|
||||
|
||||
|
||||
ACTIVATION_FUNCTIONS = {
|
||||
"swish": nn.SiLU(),
|
||||
"silu": nn.SiLU(),
|
||||
"mish": nn.Mish(),
|
||||
"gelu": nn.GELU(),
|
||||
"relu": nn.ReLU(),
|
||||
}
|
||||
|
||||
|
||||
def get_activation(act_fn: str) -> nn.Module:
|
||||
"""Helper function to get activation function from string.
|
||||
|
||||
Args:
|
||||
act_fn (str): Name of activation function.
|
||||
|
||||
Returns:
|
||||
nn.Module: Activation function.
|
||||
"""
|
||||
|
||||
act_fn = act_fn.lower()
|
||||
if act_fn in ACTIVATION_FUNCTIONS:
|
||||
return ACTIVATION_FUNCTIONS[act_fn]
|
||||
else:
|
||||
raise ValueError(f"Unsupported activation function: {act_fn}")
|
||||
|
||||
|
||||
def get_timestep_embedding(
|
||||
timesteps: torch.Tensor,
|
||||
embedding_dim: int,
|
||||
flip_sin_to_cos: bool = False,
|
||||
downscale_freq_shift: float = 1,
|
||||
scale: float = 1,
|
||||
max_period: int = 10000,
|
||||
):
|
||||
"""
|
||||
This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
|
||||
|
||||
:param timesteps: a 1-D Tensor of N indices, one per batch element.
|
||||
These may be fractional.
|
||||
:param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
|
||||
embeddings. :return: an [N x dim] Tensor of positional embeddings.
|
||||
"""
|
||||
assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
|
||||
|
||||
half_dim = embedding_dim // 2
|
||||
exponent = -math.log(max_period) * torch.arange(
|
||||
start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
|
||||
)
|
||||
exponent = exponent / (half_dim - downscale_freq_shift)
|
||||
|
||||
emb = torch.exp(exponent)
|
||||
emb = timesteps[:, None].float() * emb[None, :]
|
||||
|
||||
# scale embeddings
|
||||
emb = scale * emb
|
||||
|
||||
# concat sine and cosine embeddings
|
||||
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
|
||||
|
||||
# flip sine and cosine embeddings
|
||||
if flip_sin_to_cos:
|
||||
emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
|
||||
|
||||
# zero pad
|
||||
if embedding_dim % 2 == 1:
|
||||
emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
|
||||
return emb
|
||||
|
||||
|
||||
class Timesteps(nn.Module):
|
||||
def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
|
||||
super().__init__()
|
||||
self.num_channels = num_channels
|
||||
self.flip_sin_to_cos = flip_sin_to_cos
|
||||
self.downscale_freq_shift = downscale_freq_shift
|
||||
|
||||
def forward(self, timesteps):
|
||||
t_emb = get_timestep_embedding(
|
||||
timesteps,
|
||||
self.num_channels,
|
||||
flip_sin_to_cos=self.flip_sin_to_cos,
|
||||
downscale_freq_shift=self.downscale_freq_shift,
|
||||
)
|
||||
return t_emb
|
||||
|
||||
|
||||
class TimestepEmbedding(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels: int,
|
||||
time_embed_dim: int,
|
||||
act_fn: str = "silu",
|
||||
out_dim: int = None,
|
||||
post_act_fn: Optional[str] = None,
|
||||
cond_proj_dim=None,
|
||||
sample_proj_bias=True
|
||||
):
|
||||
super().__init__()
|
||||
linear_cls = nn.Linear
|
||||
|
||||
self.linear_1 = linear_cls(
|
||||
in_channels,
|
||||
time_embed_dim,
|
||||
bias=sample_proj_bias,
|
||||
)
|
||||
|
||||
if cond_proj_dim is not None:
|
||||
self.cond_proj = linear_cls(
|
||||
cond_proj_dim,
|
||||
in_channels,
|
||||
bias=False,
|
||||
)
|
||||
else:
|
||||
self.cond_proj = None
|
||||
|
||||
self.act = get_activation(act_fn)
|
||||
|
||||
if out_dim is not None:
|
||||
time_embed_dim_out = out_dim
|
||||
else:
|
||||
time_embed_dim_out = time_embed_dim
|
||||
|
||||
self.linear_2 = linear_cls(
|
||||
time_embed_dim,
|
||||
time_embed_dim_out,
|
||||
bias=sample_proj_bias,
|
||||
)
|
||||
|
||||
if post_act_fn is None:
|
||||
self.post_act = None
|
||||
else:
|
||||
self.post_act = get_activation(post_act_fn)
|
||||
|
||||
def forward(self, sample, condition=None):
|
||||
if condition is not None:
|
||||
sample = sample + self.cond_proj(condition)
|
||||
sample = self.linear_1(sample)
|
||||
|
||||
if self.act is not None:
|
||||
sample = self.act(sample)
|
||||
|
||||
sample = self.linear_2(sample)
|
||||
|
||||
if self.post_act is not None:
|
||||
sample = self.post_act(sample)
|
||||
return sample
|
||||
|
||||
|
||||
class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
|
||||
def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False):
|
||||
super().__init__()
|
||||
|
||||
self.outdim = size_emb_dim
|
||||
self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
|
||||
self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
|
||||
|
||||
self.use_additional_conditions = use_additional_conditions
|
||||
if self.use_additional_conditions:
|
||||
self.additional_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
|
||||
self.resolution_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)
|
||||
self.nframe_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
|
||||
self.fps_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
|
||||
|
||||
def forward(self, timestep, resolution=None, nframe=None, fps=None):
|
||||
hidden_dtype = timestep.dtype
|
||||
|
||||
timesteps_proj = self.time_proj(timestep)
|
||||
timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D)
|
||||
|
||||
if self.use_additional_conditions:
|
||||
batch_size = timestep.shape[0]
|
||||
resolution_emb = self.additional_condition_proj(resolution.flatten()).to(hidden_dtype)
|
||||
resolution_emb = self.resolution_embedder(resolution_emb).reshape(batch_size, -1)
|
||||
nframe_emb = self.additional_condition_proj(nframe.flatten()).to(hidden_dtype)
|
||||
nframe_emb = self.nframe_embedder(nframe_emb).reshape(batch_size, -1)
|
||||
conditioning = timesteps_emb + resolution_emb + nframe_emb
|
||||
|
||||
if fps is not None:
|
||||
fps_emb = self.additional_condition_proj(fps.flatten()).to(hidden_dtype)
|
||||
fps_emb = self.fps_embedder(fps_emb).reshape(batch_size, -1)
|
||||
conditioning = conditioning + fps_emb
|
||||
else:
|
||||
conditioning = timesteps_emb
|
||||
|
||||
return conditioning
|
||||
|
||||
|
||||
class AdaLayerNormSingle(nn.Module):
|
||||
r"""
|
||||
Norm layer adaptive layer norm single (adaLN-single).
|
||||
|
||||
As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).
|
||||
|
||||
Parameters:
|
||||
embedding_dim (`int`): The size of each embedding vector.
|
||||
use_additional_conditions (`bool`): To use additional conditions for normalization or not.
|
||||
"""
|
||||
def __init__(self, embedding_dim: int, use_additional_conditions: bool = False, time_step_rescale=1000):
|
||||
super().__init__()
|
||||
|
||||
self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(
|
||||
embedding_dim, size_emb_dim=embedding_dim // 2, use_additional_conditions=use_additional_conditions
|
||||
)
|
||||
|
||||
self.silu = nn.SiLU()
|
||||
self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)
|
||||
|
||||
self.time_step_rescale = time_step_rescale ## timestep usually in [0, 1], we rescale it to [0,1000] for stability
|
||||
|
||||
def forward(
|
||||
self,
|
||||
timestep: torch.Tensor,
|
||||
added_cond_kwargs: Dict[str, torch.Tensor] = None,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
embedded_timestep = self.emb(timestep*self.time_step_rescale, **added_cond_kwargs)
|
||||
|
||||
out = self.linear(self.silu(embedded_timestep))
|
||||
|
||||
return out, embedded_timestep
|
||||
|
||||
|
||||
class PixArtAlphaTextProjection(nn.Module):
|
||||
"""
|
||||
Projects caption embeddings. Also handles dropout for classifier-free guidance.
|
||||
|
||||
Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
|
||||
"""
|
||||
|
||||
def __init__(self, in_features, hidden_size):
|
||||
super().__init__()
|
||||
self.linear_1 = nn.Linear(
|
||||
in_features,
|
||||
hidden_size,
|
||||
bias=True,
|
||||
)
|
||||
self.act_1 = nn.GELU(approximate="tanh")
|
||||
self.linear_2 = nn.Linear(
|
||||
hidden_size,
|
||||
hidden_size,
|
||||
bias=True,
|
||||
)
|
||||
|
||||
def forward(self, caption):
|
||||
hidden_states = self.linear_1(caption)
|
||||
hidden_states = self.act_1(hidden_states)
|
||||
hidden_states = self.linear_2(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def attn_processor(self, attn_type):
|
||||
if attn_type == 'torch':
|
||||
return self.torch_attn_func
|
||||
elif attn_type == 'parallel':
|
||||
return self.parallel_attn_func
|
||||
else:
|
||||
raise Exception('Not supported attention type...')
|
||||
|
||||
def torch_attn_func(
|
||||
self,
|
||||
q,
|
||||
k,
|
||||
v,
|
||||
attn_mask=None,
|
||||
causal=False,
|
||||
drop_rate=0.0,
|
||||
**kwargs
|
||||
):
|
||||
|
||||
if attn_mask is not None and attn_mask.dtype != torch.bool:
|
||||
attn_mask = attn_mask.to(q.dtype)
|
||||
|
||||
if attn_mask is not None and attn_mask.ndim == 3: ## no head
|
||||
n_heads = q.shape[2]
|
||||
attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1)
|
||||
|
||||
q, k, v = map(lambda x: rearrange(x, 'b s h d -> b h s d'), (q, k, v))
|
||||
if attn_mask is not None:
|
||||
attn_mask = attn_mask.to(q.device)
|
||||
x = torch.nn.functional.scaled_dot_product_attention(
|
||||
q, k, v, attn_mask=attn_mask, dropout_p=drop_rate, is_causal=causal
|
||||
)
|
||||
x = rearrange(x, 'b h s d -> b s h d')
|
||||
return x
|
||||
|
||||
|
||||
class RoPE1D:
|
||||
def __init__(self, freq=1e4, F0=1.0, scaling_factor=1.0):
|
||||
self.base = freq
|
||||
self.F0 = F0
|
||||
self.scaling_factor = scaling_factor
|
||||
self.cache = {}
|
||||
|
||||
def get_cos_sin(self, D, seq_len, device, dtype):
|
||||
if (D, seq_len, device, dtype) not in self.cache:
|
||||
inv_freq = 1.0 / (self.base ** (torch.arange(0, D, 2).float().to(device) / D))
|
||||
t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype)
|
||||
freqs = torch.einsum("i,j->ij", t, inv_freq).to(dtype)
|
||||
freqs = torch.cat((freqs, freqs), dim=-1)
|
||||
cos = freqs.cos() # (Seq, Dim)
|
||||
sin = freqs.sin()
|
||||
self.cache[D, seq_len, device, dtype] = (cos, sin)
|
||||
return self.cache[D, seq_len, device, dtype]
|
||||
|
||||
@staticmethod
|
||||
def rotate_half(x):
|
||||
x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
|
||||
return torch.cat((-x2, x1), dim=-1)
|
||||
|
||||
def apply_rope1d(self, tokens, pos1d, cos, sin):
|
||||
assert pos1d.ndim == 2
|
||||
cos = torch.nn.functional.embedding(pos1d, cos)[:, :, None, :]
|
||||
sin = torch.nn.functional.embedding(pos1d, sin)[:, :, None, :]
|
||||
return (tokens * cos) + (self.rotate_half(tokens) * sin)
|
||||
|
||||
def __call__(self, tokens, positions):
|
||||
"""
|
||||
input:
|
||||
* tokens: batch_size x ntokens x nheads x dim
|
||||
* positions: batch_size x ntokens (t position of each token)
|
||||
output:
|
||||
* tokens after applying RoPE2D (batch_size x ntokens x nheads x dim)
|
||||
"""
|
||||
D = tokens.size(3)
|
||||
assert positions.ndim == 2 # Batch, Seq
|
||||
cos, sin = self.get_cos_sin(D, int(positions.max()) + 1, tokens.device, tokens.dtype)
|
||||
tokens = self.apply_rope1d(tokens, positions, cos, sin)
|
||||
return tokens
|
||||
|
||||
|
||||
class RoPE3D(RoPE1D):
|
||||
def __init__(self, freq=1e4, F0=1.0, scaling_factor=1.0):
|
||||
super(RoPE3D, self).__init__(freq, F0, scaling_factor)
|
||||
self.position_cache = {}
|
||||
|
||||
def get_mesh_3d(self, rope_positions, bsz):
|
||||
f, h, w = rope_positions
|
||||
|
||||
if f"{f}-{h}-{w}" not in self.position_cache:
|
||||
x = torch.arange(f, device='cpu')
|
||||
y = torch.arange(h, device='cpu')
|
||||
z = torch.arange(w, device='cpu')
|
||||
self.position_cache[f"{f}-{h}-{w}"] = torch.cartesian_prod(x, y, z).view(1, f*h*w, 3).expand(bsz, -1, 3)
|
||||
return self.position_cache[f"{f}-{h}-{w}"]
|
||||
|
||||
def __call__(self, tokens, rope_positions, ch_split, parallel=False):
|
||||
"""
|
||||
input:
|
||||
* tokens: batch_size x ntokens x nheads x dim
|
||||
* rope_positions: list of (f, h, w)
|
||||
output:
|
||||
* tokens after applying RoPE2D (batch_size x ntokens x nheads x dim)
|
||||
"""
|
||||
assert sum(ch_split) == tokens.size(-1);
|
||||
|
||||
mesh_grid = self.get_mesh_3d(rope_positions, bsz=tokens.shape[0])
|
||||
out = []
|
||||
for i, (D, x) in enumerate(zip(ch_split, torch.split(tokens, ch_split, dim=-1))):
|
||||
cos, sin = self.get_cos_sin(D, int(mesh_grid.max()) + 1, tokens.device, tokens.dtype)
|
||||
|
||||
if parallel:
|
||||
pass
|
||||
else:
|
||||
mesh = mesh_grid[:, :, i].clone()
|
||||
x = self.apply_rope1d(x, mesh.to(tokens.device), cos, sin)
|
||||
out.append(x)
|
||||
|
||||
tokens = torch.cat(out, dim=-1)
|
||||
return tokens
|
||||
|
||||
|
||||
class SelfAttention(Attention):
|
||||
def __init__(self, hidden_dim, head_dim, bias=False, with_rope=True, with_qk_norm=True, attn_type='torch'):
|
||||
super().__init__()
|
||||
self.head_dim = head_dim
|
||||
self.n_heads = hidden_dim // head_dim
|
||||
|
||||
self.wqkv = nn.Linear(hidden_dim, hidden_dim*3, bias=bias)
|
||||
self.wo = nn.Linear(hidden_dim, hidden_dim, bias=bias)
|
||||
|
||||
self.with_rope = with_rope
|
||||
self.with_qk_norm = with_qk_norm
|
||||
if self.with_qk_norm:
|
||||
self.q_norm = RMSNorm(head_dim, elementwise_affine=True)
|
||||
self.k_norm = RMSNorm(head_dim, elementwise_affine=True)
|
||||
|
||||
if self.with_rope:
|
||||
self.rope_3d = RoPE3D(freq=1e4, F0=1.0, scaling_factor=1.0)
|
||||
self.rope_ch_split = [64, 32, 32]
|
||||
|
||||
self.core_attention = self.attn_processor(attn_type=attn_type)
|
||||
self.parallel = attn_type=='parallel'
|
||||
|
||||
def apply_rope3d(self, x, fhw_positions, rope_ch_split, parallel=True):
|
||||
x = self.rope_3d(x, fhw_positions, rope_ch_split, parallel)
|
||||
return x
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x,
|
||||
cu_seqlens=None,
|
||||
max_seqlen=None,
|
||||
rope_positions=None,
|
||||
attn_mask=None
|
||||
):
|
||||
xqkv = self.wqkv(x)
|
||||
xqkv = xqkv.view(*x.shape[:-1], self.n_heads, 3*self.head_dim)
|
||||
|
||||
xq, xk, xv = torch.split(xqkv, [self.head_dim]*3, dim=-1) ## seq_len, n, dim
|
||||
|
||||
if self.with_qk_norm:
|
||||
xq = self.q_norm(xq)
|
||||
xk = self.k_norm(xk)
|
||||
|
||||
if self.with_rope:
|
||||
xq = self.apply_rope3d(xq, rope_positions, self.rope_ch_split, parallel=self.parallel)
|
||||
xk = self.apply_rope3d(xk, rope_positions, self.rope_ch_split, parallel=self.parallel)
|
||||
|
||||
output = self.core_attention(
|
||||
xq,
|
||||
xk,
|
||||
xv,
|
||||
cu_seqlens=cu_seqlens,
|
||||
max_seqlen=max_seqlen,
|
||||
attn_mask=attn_mask
|
||||
)
|
||||
output = rearrange(output, 'b s h d -> b s (h d)')
|
||||
output = self.wo(output)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
class CrossAttention(Attention):
|
||||
def __init__(self, hidden_dim, head_dim, bias=False, with_qk_norm=True, attn_type='torch'):
|
||||
super().__init__()
|
||||
self.head_dim = head_dim
|
||||
self.n_heads = hidden_dim // head_dim
|
||||
|
||||
self.wq = nn.Linear(hidden_dim, hidden_dim, bias=bias)
|
||||
self.wkv = nn.Linear(hidden_dim, hidden_dim*2, bias=bias)
|
||||
self.wo = nn.Linear(hidden_dim, hidden_dim, bias=bias)
|
||||
|
||||
self.with_qk_norm = with_qk_norm
|
||||
if self.with_qk_norm:
|
||||
self.q_norm = RMSNorm(head_dim, elementwise_affine=True)
|
||||
self.k_norm = RMSNorm(head_dim, elementwise_affine=True)
|
||||
|
||||
self.core_attention = self.attn_processor(attn_type=attn_type)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
encoder_hidden_states: torch.Tensor,
|
||||
attn_mask=None
|
||||
):
|
||||
xq = self.wq(x)
|
||||
xq = xq.view(*xq.shape[:-1], self.n_heads, self.head_dim)
|
||||
|
||||
xkv = self.wkv(encoder_hidden_states)
|
||||
xkv = xkv.view(*xkv.shape[:-1], self.n_heads, 2*self.head_dim)
|
||||
|
||||
xk, xv = torch.split(xkv, [self.head_dim]*2, dim=-1) ## seq_len, n, dim
|
||||
|
||||
if self.with_qk_norm:
|
||||
xq = self.q_norm(xq)
|
||||
xk = self.k_norm(xk)
|
||||
|
||||
output = self.core_attention(
|
||||
xq,
|
||||
xk,
|
||||
xv,
|
||||
attn_mask=attn_mask
|
||||
)
|
||||
|
||||
output = rearrange(output, 'b s h d -> b s (h d)')
|
||||
output = self.wo(output)
|
||||
|
||||
return output
|
||||
|
||||
|
||||
class GELU(nn.Module):
|
||||
r"""
|
||||
GELU activation function with tanh approximation support with `approximate="tanh"`.
|
||||
|
||||
Parameters:
|
||||
dim_in (`int`): The number of channels in the input.
|
||||
dim_out (`int`): The number of channels in the output.
|
||||
approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation.
|
||||
bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
|
||||
"""
|
||||
|
||||
def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True):
|
||||
super().__init__()
|
||||
self.proj = nn.Linear(dim_in, dim_out, bias=bias)
|
||||
self.approximate = approximate
|
||||
|
||||
def gelu(self, gate: torch.Tensor) -> torch.Tensor:
|
||||
return torch.nn.functional.gelu(gate, approximate=self.approximate)
|
||||
|
||||
def forward(self, hidden_states):
|
||||
hidden_states = self.proj(hidden_states)
|
||||
hidden_states = self.gelu(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
inner_dim: Optional[int] = None,
|
||||
dim_out: Optional[int] = None,
|
||||
mult: int = 4,
|
||||
bias: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
inner_dim = dim*mult if inner_dim is None else inner_dim
|
||||
dim_out = dim if dim_out is None else dim_out
|
||||
self.net = nn.ModuleList([
|
||||
GELU(dim, inner_dim, approximate="tanh", bias=bias),
|
||||
nn.Identity(),
|
||||
nn.Linear(inner_dim, dim_out, bias=bias)
|
||||
])
|
||||
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:
|
||||
for module in self.net:
|
||||
hidden_states = module(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
def modulate(x, scale, shift):
|
||||
x = x * (1 + scale) + shift
|
||||
return x
|
||||
|
||||
|
||||
def gate(x, gate):
|
||||
x = gate * x
|
||||
return x
|
||||
|
||||
|
||||
class StepVideoTransformerBlock(nn.Module):
|
||||
r"""
|
||||
A basic Transformer block.
|
||||
|
||||
Parameters:
|
||||
dim (`int`): The number of channels in the input and output.
|
||||
num_attention_heads (`int`): The number of heads to use for multi-head attention.
|
||||
attention_head_dim (`int`): The number of channels in each head.
|
||||
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
|
||||
cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
|
||||
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
|
||||
num_embeds_ada_norm (:
|
||||
obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
|
||||
attention_bias (:
|
||||
obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
|
||||
only_cross_attention (`bool`, *optional*):
|
||||
Whether to use only cross-attention layers. In this case two cross attention layers are used.
|
||||
double_self_attention (`bool`, *optional*):
|
||||
Whether to use two self-attention layers. In this case no cross attention layers are used.
|
||||
upcast_attention (`bool`, *optional*):
|
||||
Whether to upcast the attention computation to float32. This is useful for mixed precision training.
|
||||
norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
|
||||
Whether to use learnable elementwise affine parameters for normalization.
|
||||
norm_type (`str`, *optional*, defaults to `"layer_norm"`):
|
||||
The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
|
||||
final_dropout (`bool` *optional*, defaults to False):
|
||||
Whether to apply a final dropout after the last feed-forward layer.
|
||||
attention_type (`str`, *optional*, defaults to `"default"`):
|
||||
The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
|
||||
positional_embeddings (`str`, *optional*, defaults to `None`):
|
||||
The type of positional embeddings to apply to.
|
||||
num_positional_embeddings (`int`, *optional*, defaults to `None`):
|
||||
The maximum number of positional embeddings to apply.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
attention_head_dim: int,
|
||||
norm_eps: float = 1e-5,
|
||||
ff_inner_dim: Optional[int] = None,
|
||||
ff_bias: bool = False,
|
||||
attention_type: str = 'parallel'
|
||||
):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.norm1 = nn.LayerNorm(dim, eps=norm_eps)
|
||||
self.attn1 = SelfAttention(dim, attention_head_dim, bias=False, with_rope=True, with_qk_norm=True, attn_type=attention_type)
|
||||
|
||||
self.norm2 = nn.LayerNorm(dim, eps=norm_eps)
|
||||
self.attn2 = CrossAttention(dim, attention_head_dim, bias=False, with_qk_norm=True, attn_type='torch')
|
||||
|
||||
self.ff = FeedForward(dim=dim, inner_dim=ff_inner_dim, dim_out=dim, bias=ff_bias)
|
||||
|
||||
self.scale_shift_table = nn.Parameter(torch.randn(6, dim) /dim**0.5)
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(
|
||||
self,
|
||||
q: torch.Tensor,
|
||||
kv: Optional[torch.Tensor] = None,
|
||||
timestep: Optional[torch.LongTensor] = None,
|
||||
attn_mask = None,
|
||||
rope_positions: list = None,
|
||||
) -> torch.Tensor:
|
||||
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
|
||||
torch.clone(chunk) for chunk in (self.scale_shift_table[None].to(dtype=q.dtype, device=q.device) + timestep.reshape(-1, 6, self.dim)).chunk(6, dim=1)
|
||||
)
|
||||
|
||||
scale_shift_q = modulate(self.norm1(q), scale_msa, shift_msa)
|
||||
|
||||
attn_q = self.attn1(
|
||||
scale_shift_q,
|
||||
rope_positions=rope_positions
|
||||
)
|
||||
|
||||
q = gate(attn_q, gate_msa) + q
|
||||
|
||||
attn_q = self.attn2(
|
||||
q,
|
||||
kv,
|
||||
attn_mask
|
||||
)
|
||||
|
||||
q = attn_q + q
|
||||
|
||||
scale_shift_q = modulate(self.norm2(q), scale_mlp, shift_mlp)
|
||||
|
||||
ff_output = self.ff(scale_shift_q)
|
||||
|
||||
q = gate(ff_output, gate_mlp) + q
|
||||
|
||||
return q
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
"""2D Image to Patch Embedding"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
patch_size=64,
|
||||
in_channels=3,
|
||||
embed_dim=768,
|
||||
layer_norm=False,
|
||||
flatten=True,
|
||||
bias=True,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.flatten = flatten
|
||||
self.layer_norm = layer_norm
|
||||
|
||||
self.proj = nn.Conv2d(
|
||||
in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
|
||||
)
|
||||
|
||||
def forward(self, latent):
|
||||
latent = self.proj(latent).to(latent.dtype)
|
||||
if self.flatten:
|
||||
latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC
|
||||
if self.layer_norm:
|
||||
latent = self.norm(latent)
|
||||
|
||||
return latent
|
||||
|
||||
|
||||
class StepVideoModel(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
num_attention_heads: int = 48,
|
||||
attention_head_dim: int = 128,
|
||||
in_channels: int = 64,
|
||||
out_channels: Optional[int] = 64,
|
||||
num_layers: int = 48,
|
||||
dropout: float = 0.0,
|
||||
patch_size: int = 1,
|
||||
norm_type: str = "ada_norm_single",
|
||||
norm_elementwise_affine: bool = False,
|
||||
norm_eps: float = 1e-6,
|
||||
use_additional_conditions: Optional[bool] = False,
|
||||
caption_channels: Optional[Union[int, List, Tuple]] = [6144, 1024],
|
||||
attention_type: Optional[str] = "torch",
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
# Set some common variables used across the board.
|
||||
self.inner_dim = num_attention_heads * attention_head_dim
|
||||
self.out_channels = in_channels if out_channels is None else out_channels
|
||||
|
||||
self.use_additional_conditions = use_additional_conditions
|
||||
|
||||
self.pos_embed = PatchEmbed(
|
||||
patch_size=patch_size,
|
||||
in_channels=in_channels,
|
||||
embed_dim=self.inner_dim,
|
||||
)
|
||||
|
||||
self.transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
StepVideoTransformerBlock(
|
||||
dim=self.inner_dim,
|
||||
attention_head_dim=attention_head_dim,
|
||||
attention_type=attention_type
|
||||
)
|
||||
for _ in range(num_layers)
|
||||
]
|
||||
)
|
||||
|
||||
# 3. Output blocks.
|
||||
self.norm_out = nn.LayerNorm(self.inner_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine)
|
||||
self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5)
|
||||
self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels)
|
||||
self.patch_size = patch_size
|
||||
|
||||
self.adaln_single = AdaLayerNormSingle(
|
||||
self.inner_dim, use_additional_conditions=self.use_additional_conditions
|
||||
)
|
||||
|
||||
if isinstance(caption_channels, int):
|
||||
caption_channel = caption_channels
|
||||
else:
|
||||
caption_channel, clip_channel = caption_channels
|
||||
self.clip_projection = nn.Linear(clip_channel, self.inner_dim)
|
||||
|
||||
self.caption_norm = nn.LayerNorm(caption_channel, eps=norm_eps, elementwise_affine=norm_elementwise_affine)
|
||||
|
||||
self.caption_projection = PixArtAlphaTextProjection(
|
||||
in_features=caption_channel, hidden_size=self.inner_dim
|
||||
)
|
||||
|
||||
self.parallel = attention_type=='parallel'
|
||||
|
||||
def patchfy(self, hidden_states):
|
||||
hidden_states = rearrange(hidden_states, 'b f c h w -> (b f) c h w')
|
||||
hidden_states = self.pos_embed(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
def prepare_attn_mask(self, encoder_attention_mask, encoder_hidden_states, q_seqlen):
|
||||
kv_seqlens = encoder_attention_mask.sum(dim=1).int()
|
||||
mask = torch.zeros([len(kv_seqlens), q_seqlen, max(kv_seqlens)], dtype=torch.bool, device=encoder_attention_mask.device)
|
||||
encoder_hidden_states = encoder_hidden_states[:,: max(kv_seqlens)]
|
||||
for i, kv_len in enumerate(kv_seqlens):
|
||||
mask[i, :, :kv_len] = 1
|
||||
return encoder_hidden_states, mask
|
||||
|
||||
|
||||
def block_forward(
|
||||
self,
|
||||
hidden_states,
|
||||
encoder_hidden_states=None,
|
||||
timestep=None,
|
||||
rope_positions=None,
|
||||
attn_mask=None,
|
||||
parallel=True
|
||||
):
|
||||
for block in tqdm(self.transformer_blocks, desc="Transformer blocks"):
|
||||
hidden_states = block(
|
||||
hidden_states,
|
||||
encoder_hidden_states,
|
||||
timestep=timestep,
|
||||
attn_mask=attn_mask,
|
||||
rope_positions=rope_positions
|
||||
)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
encoder_hidden_states: Optional[torch.Tensor] = None,
|
||||
encoder_hidden_states_2: Optional[torch.Tensor] = None,
|
||||
timestep: Optional[torch.LongTensor] = None,
|
||||
added_cond_kwargs: Dict[str, torch.Tensor] = None,
|
||||
encoder_attention_mask: Optional[torch.Tensor] = None,
|
||||
fps: torch.Tensor=None,
|
||||
return_dict: bool = False,
|
||||
):
|
||||
assert hidden_states.ndim==5; "hidden_states's shape should be (bsz, f, ch, h ,w)"
|
||||
|
||||
bsz, frame, _, height, width = hidden_states.shape
|
||||
height, width = height // self.patch_size, width // self.patch_size
|
||||
|
||||
hidden_states = self.patchfy(hidden_states)
|
||||
len_frame = hidden_states.shape[1]
|
||||
|
||||
if self.use_additional_conditions:
|
||||
added_cond_kwargs = {
|
||||
"resolution": torch.tensor([(height, width)]*bsz, device=hidden_states.device, dtype=hidden_states.dtype),
|
||||
"nframe": torch.tensor([frame]*bsz, device=hidden_states.device, dtype=hidden_states.dtype),
|
||||
"fps": fps
|
||||
}
|
||||
else:
|
||||
added_cond_kwargs = {}
|
||||
|
||||
timestep, embedded_timestep = self.adaln_single(
|
||||
timestep, added_cond_kwargs=added_cond_kwargs
|
||||
)
|
||||
|
||||
encoder_hidden_states = self.caption_projection(self.caption_norm(encoder_hidden_states))
|
||||
|
||||
if encoder_hidden_states_2 is not None and hasattr(self, 'clip_projection'):
|
||||
clip_embedding = self.clip_projection(encoder_hidden_states_2)
|
||||
encoder_hidden_states = torch.cat([clip_embedding, encoder_hidden_states], dim=1)
|
||||
|
||||
hidden_states = rearrange(hidden_states, '(b f) l d-> b (f l) d', b=bsz, f=frame, l=len_frame).contiguous()
|
||||
encoder_hidden_states, attn_mask = self.prepare_attn_mask(encoder_attention_mask, encoder_hidden_states, q_seqlen=frame*len_frame)
|
||||
|
||||
hidden_states = self.block_forward(
|
||||
hidden_states,
|
||||
encoder_hidden_states,
|
||||
timestep=timestep,
|
||||
rope_positions=[frame, height, width],
|
||||
attn_mask=attn_mask,
|
||||
parallel=self.parallel
|
||||
)
|
||||
|
||||
hidden_states = rearrange(hidden_states, 'b (f l) d -> (b f) l d', b=bsz, f=frame, l=len_frame)
|
||||
|
||||
embedded_timestep = repeat(embedded_timestep, 'b d -> (b f) d', f=frame).contiguous()
|
||||
|
||||
shift, scale = (self.scale_shift_table[None].to(dtype=embedded_timestep.dtype, device=embedded_timestep.device) + embedded_timestep[:, None]).chunk(2, dim=1)
|
||||
hidden_states = self.norm_out(hidden_states)
|
||||
# Modulation
|
||||
hidden_states = hidden_states * (1 + scale) + shift
|
||||
hidden_states = self.proj_out(hidden_states)
|
||||
|
||||
# unpatchify
|
||||
hidden_states = hidden_states.reshape(
|
||||
shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
|
||||
)
|
||||
|
||||
hidden_states = rearrange(hidden_states, 'n h w p q c -> n c h p w q')
|
||||
output = hidden_states.reshape(
|
||||
shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)
|
||||
)
|
||||
|
||||
output = rearrange(output, '(b f) c h w -> b f c h w', f=frame)
|
||||
|
||||
if return_dict:
|
||||
return {'x': output}
|
||||
return output
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return StepVideoDiTStateDictConverter()
|
||||
|
||||
|
||||
class StepVideoDiTStateDictConverter:
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
return state_dict
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
return state_dict
|
||||
|
||||
|
||||
|
||||
@@ -1,553 +0,0 @@
|
||||
# Copyright 2025 StepFun Inc. All Rights Reserved.
|
||||
#
|
||||
# Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
# of this software and associated documentation files (the "Software"), to deal
|
||||
# in the Software without restriction, including without limitation the rights
|
||||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
# copies of the Software, and to permit persons to whom the Software is
|
||||
# furnished to do so, subject to the following conditions:
|
||||
#
|
||||
# The above copyright notice and this permission notice shall be included in all
|
||||
# copies or substantial portions of the Software.
|
||||
# ==============================================================================
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from .stepvideo_dit import RMSNorm
|
||||
from safetensors.torch import load_file
|
||||
from transformers.modeling_utils import PretrainedConfig, PreTrainedModel
|
||||
from einops import rearrange
|
||||
import json
|
||||
from typing import List
|
||||
from functools import wraps
|
||||
import warnings
|
||||
|
||||
|
||||
|
||||
class EmptyInitOnDevice(torch.overrides.TorchFunctionMode):
|
||||
def __init__(self, device=None):
|
||||
self.device = device
|
||||
|
||||
def __torch_function__(self, func, types, args=(), kwargs=None):
|
||||
kwargs = kwargs or {}
|
||||
if getattr(func, '__module__', None) == 'torch.nn.init':
|
||||
if 'tensor' in kwargs:
|
||||
return kwargs['tensor']
|
||||
else:
|
||||
return args[0]
|
||||
if self.device is not None and func in torch.utils._device._device_constructors() and kwargs.get('device') is None:
|
||||
kwargs['device'] = self.device
|
||||
return func(*args, **kwargs)
|
||||
|
||||
|
||||
def with_empty_init(func):
|
||||
@wraps(func)
|
||||
def wrapper(*args, **kwargs):
|
||||
with EmptyInitOnDevice('cpu'):
|
||||
return func(*args, **kwargs)
|
||||
return wrapper
|
||||
|
||||
|
||||
|
||||
class LLaMaEmbedding(nn.Module):
|
||||
"""Language model embeddings.
|
||||
|
||||
Arguments:
|
||||
hidden_size: hidden size
|
||||
vocab_size: vocabulary size
|
||||
max_sequence_length: maximum size of sequence. This
|
||||
is used for positional embedding
|
||||
embedding_dropout_prob: dropout probability for embeddings
|
||||
init_method: weight initialization method
|
||||
num_tokentypes: size of the token-type embeddings. 0 value
|
||||
will ignore this embedding
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
cfg,
|
||||
):
|
||||
super().__init__()
|
||||
self.hidden_size = cfg.hidden_size
|
||||
self.params_dtype = cfg.params_dtype
|
||||
self.fp32_residual_connection = cfg.fp32_residual_connection
|
||||
self.embedding_weights_in_fp32 = cfg.embedding_weights_in_fp32
|
||||
self.word_embeddings = torch.nn.Embedding(
|
||||
cfg.padded_vocab_size, self.hidden_size,
|
||||
)
|
||||
self.embedding_dropout = torch.nn.Dropout(cfg.hidden_dropout)
|
||||
|
||||
def forward(self, input_ids):
|
||||
# Embeddings.
|
||||
if self.embedding_weights_in_fp32:
|
||||
self.word_embeddings = self.word_embeddings.to(torch.float32)
|
||||
embeddings = self.word_embeddings(input_ids)
|
||||
if self.embedding_weights_in_fp32:
|
||||
embeddings = embeddings.to(self.params_dtype)
|
||||
self.word_embeddings = self.word_embeddings.to(self.params_dtype)
|
||||
|
||||
# Data format change to avoid explicit transposes : [b s h] --> [s b h].
|
||||
embeddings = embeddings.transpose(0, 1).contiguous()
|
||||
|
||||
# If the input flag for fp32 residual connection is set, convert for float.
|
||||
if self.fp32_residual_connection:
|
||||
embeddings = embeddings.float()
|
||||
|
||||
# Dropout.
|
||||
embeddings = self.embedding_dropout(embeddings)
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
|
||||
class StepChatTokenizer:
|
||||
"""Step Chat Tokenizer"""
|
||||
|
||||
def __init__(
|
||||
self, model_file, name="StepChatTokenizer",
|
||||
bot_token="<|BOT|>", # Begin of Turn
|
||||
eot_token="<|EOT|>", # End of Turn
|
||||
call_start_token="<|CALL_START|>", # Call Start
|
||||
call_end_token="<|CALL_END|>", # Call End
|
||||
think_start_token="<|THINK_START|>", # Think Start
|
||||
think_end_token="<|THINK_END|>", # Think End
|
||||
mask_start_token="<|MASK_1e69f|>", # Mask start
|
||||
mask_end_token="<|UNMASK_1e69f|>", # Mask end
|
||||
):
|
||||
import sentencepiece
|
||||
|
||||
self._tokenizer = sentencepiece.SentencePieceProcessor(model_file=model_file)
|
||||
|
||||
self._vocab = {}
|
||||
self._inv_vocab = {}
|
||||
|
||||
self._special_tokens = {}
|
||||
self._inv_special_tokens = {}
|
||||
|
||||
self._t5_tokens = []
|
||||
|
||||
for idx in range(self._tokenizer.get_piece_size()):
|
||||
text = self._tokenizer.id_to_piece(idx)
|
||||
self._inv_vocab[idx] = text
|
||||
self._vocab[text] = idx
|
||||
|
||||
if self._tokenizer.is_control(idx) or self._tokenizer.is_unknown(idx):
|
||||
self._special_tokens[text] = idx
|
||||
self._inv_special_tokens[idx] = text
|
||||
|
||||
self._unk_id = self._tokenizer.unk_id()
|
||||
self._bos_id = self._tokenizer.bos_id()
|
||||
self._eos_id = self._tokenizer.eos_id()
|
||||
|
||||
for token in [
|
||||
bot_token, eot_token, call_start_token, call_end_token,
|
||||
think_start_token, think_end_token
|
||||
]:
|
||||
assert token in self._vocab, f"Token '{token}' not found in tokenizer"
|
||||
assert token in self._special_tokens, f"Token '{token}' is not a special token"
|
||||
|
||||
for token in [mask_start_token, mask_end_token]:
|
||||
assert token in self._vocab, f"Token '{token}' not found in tokenizer"
|
||||
|
||||
self._bot_id = self._tokenizer.piece_to_id(bot_token)
|
||||
self._eot_id = self._tokenizer.piece_to_id(eot_token)
|
||||
self._call_start_id = self._tokenizer.piece_to_id(call_start_token)
|
||||
self._call_end_id = self._tokenizer.piece_to_id(call_end_token)
|
||||
self._think_start_id = self._tokenizer.piece_to_id(think_start_token)
|
||||
self._think_end_id = self._tokenizer.piece_to_id(think_end_token)
|
||||
self._mask_start_id = self._tokenizer.piece_to_id(mask_start_token)
|
||||
self._mask_end_id = self._tokenizer.piece_to_id(mask_end_token)
|
||||
|
||||
self._underline_id = self._tokenizer.piece_to_id("\u2581")
|
||||
|
||||
@property
|
||||
def vocab(self):
|
||||
return self._vocab
|
||||
|
||||
@property
|
||||
def inv_vocab(self):
|
||||
return self._inv_vocab
|
||||
|
||||
@property
|
||||
def vocab_size(self):
|
||||
return self._tokenizer.vocab_size()
|
||||
|
||||
def tokenize(self, text: str) -> List[int]:
|
||||
return self._tokenizer.encode_as_ids(text)
|
||||
|
||||
def detokenize(self, token_ids: List[int]) -> str:
|
||||
return self._tokenizer.decode_ids(token_ids)
|
||||
|
||||
|
||||
class Tokens:
|
||||
def __init__(self, input_ids, cu_input_ids, attention_mask, cu_seqlens, max_seq_len) -> None:
|
||||
self.input_ids = input_ids
|
||||
self.attention_mask = attention_mask
|
||||
self.cu_input_ids = cu_input_ids
|
||||
self.cu_seqlens = cu_seqlens
|
||||
self.max_seq_len = max_seq_len
|
||||
def to(self, device):
|
||||
self.input_ids = self.input_ids.to(device)
|
||||
self.attention_mask = self.attention_mask.to(device)
|
||||
self.cu_input_ids = self.cu_input_ids.to(device)
|
||||
self.cu_seqlens = self.cu_seqlens.to(device)
|
||||
return self
|
||||
|
||||
class Wrapped_StepChatTokenizer(StepChatTokenizer):
|
||||
def __call__(self, text, max_length=320, padding="max_length", truncation=True, return_tensors="pt"):
|
||||
# [bos, ..., eos, pad, pad, ..., pad]
|
||||
self.BOS = 1
|
||||
self.EOS = 2
|
||||
self.PAD = 2
|
||||
out_tokens = []
|
||||
attn_mask = []
|
||||
if len(text) == 0:
|
||||
part_tokens = [self.BOS] + [self.EOS]
|
||||
valid_size = len(part_tokens)
|
||||
if len(part_tokens) < max_length:
|
||||
part_tokens += [self.PAD] * (max_length - valid_size)
|
||||
out_tokens.append(part_tokens)
|
||||
attn_mask.append([1]*valid_size+[0]*(max_length-valid_size))
|
||||
else:
|
||||
for part in text:
|
||||
part_tokens = self.tokenize(part)
|
||||
part_tokens = part_tokens[:(max_length - 2)] # leave 2 space for bos and eos
|
||||
part_tokens = [self.BOS] + part_tokens + [self.EOS]
|
||||
valid_size = len(part_tokens)
|
||||
if len(part_tokens) < max_length:
|
||||
part_tokens += [self.PAD] * (max_length - valid_size)
|
||||
out_tokens.append(part_tokens)
|
||||
attn_mask.append([1]*valid_size+[0]*(max_length-valid_size))
|
||||
|
||||
out_tokens = torch.tensor(out_tokens, dtype=torch.long)
|
||||
attn_mask = torch.tensor(attn_mask, dtype=torch.long)
|
||||
|
||||
# padding y based on tp size
|
||||
padded_len = 0
|
||||
padded_flag = True if padded_len > 0 else False
|
||||
if padded_flag:
|
||||
pad_tokens = torch.tensor([[self.PAD] * max_length], device=out_tokens.device)
|
||||
pad_attn_mask = torch.tensor([[1]*padded_len+[0]*(max_length-padded_len)], device=attn_mask.device)
|
||||
out_tokens = torch.cat([out_tokens, pad_tokens], dim=0)
|
||||
attn_mask = torch.cat([attn_mask, pad_attn_mask], dim=0)
|
||||
|
||||
# cu_seqlens
|
||||
cu_out_tokens = out_tokens.masked_select(attn_mask != 0).unsqueeze(0)
|
||||
seqlen = attn_mask.sum(dim=1).tolist()
|
||||
cu_seqlens = torch.cumsum(torch.tensor([0]+seqlen), 0).to(device=out_tokens.device,dtype=torch.int32)
|
||||
max_seq_len = max(seqlen)
|
||||
return Tokens(out_tokens, cu_out_tokens, attn_mask, cu_seqlens, max_seq_len)
|
||||
|
||||
|
||||
|
||||
def flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=True,
|
||||
return_attn_probs=False, tp_group_rank=0, tp_group_size=1):
|
||||
softmax_scale = q.size(-1) ** (-0.5) if softmax_scale is None else softmax_scale
|
||||
if hasattr(torch.ops.Optimus, "fwd"):
|
||||
results = torch.ops.Optimus.fwd(q, k, v, None, dropout_p, softmax_scale, causal, return_attn_probs, None, tp_group_rank, tp_group_size)[0]
|
||||
else:
|
||||
warnings.warn("Cannot load `torch.ops.Optimus.fwd`. Using `torch.nn.functional.scaled_dot_product_attention` instead.")
|
||||
results = torch.nn.functional.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), is_causal=True, scale=softmax_scale).transpose(1, 2)
|
||||
return results
|
||||
|
||||
|
||||
class FlashSelfAttention(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
attention_dropout=0.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.dropout_p = attention_dropout
|
||||
|
||||
|
||||
def forward(self, q, k, v, cu_seqlens=None, max_seq_len=None):
|
||||
if cu_seqlens is None:
|
||||
output = flash_attn_func(q, k, v, dropout_p=self.dropout_p)
|
||||
else:
|
||||
raise ValueError('cu_seqlens is not supported!')
|
||||
|
||||
return output
|
||||
|
||||
|
||||
|
||||
def safediv(n, d):
|
||||
q, r = divmod(n, d)
|
||||
assert r == 0
|
||||
return q
|
||||
|
||||
|
||||
class MultiQueryAttention(nn.Module):
|
||||
def __init__(self, cfg, layer_id=None):
|
||||
super().__init__()
|
||||
|
||||
self.head_dim = cfg.hidden_size // cfg.num_attention_heads
|
||||
self.max_seq_len = cfg.seq_length
|
||||
self.use_flash_attention = cfg.use_flash_attn
|
||||
assert self.use_flash_attention, 'FlashAttention is required!'
|
||||
|
||||
self.n_groups = cfg.num_attention_groups
|
||||
self.tp_size = 1
|
||||
self.n_local_heads = cfg.num_attention_heads
|
||||
self.n_local_groups = self.n_groups
|
||||
|
||||
self.wqkv = nn.Linear(
|
||||
cfg.hidden_size,
|
||||
cfg.hidden_size + self.head_dim * 2 * self.n_groups,
|
||||
bias=False,
|
||||
)
|
||||
self.wo = nn.Linear(
|
||||
cfg.hidden_size,
|
||||
cfg.hidden_size,
|
||||
bias=False,
|
||||
)
|
||||
|
||||
assert self.use_flash_attention, 'non-Flash attention not supported yet.'
|
||||
self.core_attention = FlashSelfAttention(attention_dropout=cfg.attention_dropout)
|
||||
|
||||
self.layer_id = layer_id
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
mask: Optional[torch.Tensor],
|
||||
cu_seqlens: Optional[torch.Tensor],
|
||||
max_seq_len: Optional[torch.Tensor],
|
||||
):
|
||||
seqlen, bsz, dim = x.shape
|
||||
xqkv = self.wqkv(x)
|
||||
|
||||
xq, xkv = torch.split(
|
||||
xqkv,
|
||||
(dim // self.tp_size,
|
||||
self.head_dim*2*self.n_groups // self.tp_size
|
||||
),
|
||||
dim=-1,
|
||||
)
|
||||
|
||||
# gather on 1st dimension
|
||||
xq = xq.view(seqlen, bsz, self.n_local_heads, self.head_dim)
|
||||
xkv = xkv.view(seqlen, bsz, self.n_local_groups, 2 * self.head_dim)
|
||||
xk, xv = xkv.chunk(2, -1)
|
||||
|
||||
# rotary embedding + flash attn
|
||||
xq = rearrange(xq, "s b h d -> b s h d")
|
||||
xk = rearrange(xk, "s b h d -> b s h d")
|
||||
xv = rearrange(xv, "s b h d -> b s h d")
|
||||
|
||||
q_per_kv = self.n_local_heads // self.n_local_groups
|
||||
if q_per_kv > 1:
|
||||
b, s, h, d = xk.size()
|
||||
if h == 1:
|
||||
xk = xk.expand(b, s, q_per_kv, d)
|
||||
xv = xv.expand(b, s, q_per_kv, d)
|
||||
else:
|
||||
''' To cover the cases where h > 1, we have
|
||||
the following implementation, which is equivalent to:
|
||||
xk = xk.repeat_interleave(q_per_kv, dim=-2)
|
||||
xv = xv.repeat_interleave(q_per_kv, dim=-2)
|
||||
but can avoid calling aten::item() that involves cpu.
|
||||
'''
|
||||
idx = torch.arange(q_per_kv * h, device=xk.device).reshape(q_per_kv, -1).permute(1, 0).flatten()
|
||||
xk = torch.index_select(xk.repeat(1, 1, q_per_kv, 1), 2, idx).contiguous()
|
||||
xv = torch.index_select(xv.repeat(1, 1, q_per_kv, 1), 2, idx).contiguous()
|
||||
|
||||
if self.use_flash_attention:
|
||||
output = self.core_attention(xq, xk, xv,
|
||||
cu_seqlens=cu_seqlens,
|
||||
max_seq_len=max_seq_len)
|
||||
# reduce-scatter only support first dimension now
|
||||
output = rearrange(output, "b s h d -> s b (h d)").contiguous()
|
||||
else:
|
||||
xq, xk, xv = [
|
||||
rearrange(x, "b s ... -> s b ...").contiguous()
|
||||
for x in (xq, xk, xv)
|
||||
]
|
||||
output = self.core_attention(xq, xk, xv, mask)
|
||||
output = self.wo(output)
|
||||
return output
|
||||
|
||||
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
cfg,
|
||||
dim: int,
|
||||
hidden_dim: int,
|
||||
layer_id: int,
|
||||
multiple_of: int=256,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
|
||||
def swiglu(x):
|
||||
x = torch.chunk(x, 2, dim=-1)
|
||||
return F.silu(x[0]) * x[1]
|
||||
self.swiglu = swiglu
|
||||
|
||||
self.w1 = nn.Linear(
|
||||
dim,
|
||||
2 * hidden_dim,
|
||||
bias=False,
|
||||
)
|
||||
self.w2 = nn.Linear(
|
||||
hidden_dim,
|
||||
dim,
|
||||
bias=False,
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.swiglu(self.w1(x))
|
||||
output = self.w2(x)
|
||||
return output
|
||||
|
||||
|
||||
|
||||
class TransformerBlock(nn.Module):
|
||||
def __init__(
|
||||
self, cfg, layer_id: int
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.n_heads = cfg.num_attention_heads
|
||||
self.dim = cfg.hidden_size
|
||||
self.head_dim = cfg.hidden_size // cfg.num_attention_heads
|
||||
self.attention = MultiQueryAttention(
|
||||
cfg,
|
||||
layer_id=layer_id,
|
||||
)
|
||||
|
||||
self.feed_forward = FeedForward(
|
||||
cfg,
|
||||
dim=cfg.hidden_size,
|
||||
hidden_dim=cfg.ffn_hidden_size,
|
||||
layer_id=layer_id,
|
||||
)
|
||||
self.layer_id = layer_id
|
||||
self.attention_norm = RMSNorm(
|
||||
cfg.hidden_size,
|
||||
eps=cfg.layernorm_epsilon,
|
||||
)
|
||||
self.ffn_norm = RMSNorm(
|
||||
cfg.hidden_size,
|
||||
eps=cfg.layernorm_epsilon,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.Tensor,
|
||||
mask: Optional[torch.Tensor],
|
||||
cu_seqlens: Optional[torch.Tensor],
|
||||
max_seq_len: Optional[torch.Tensor],
|
||||
):
|
||||
residual = self.attention.forward(
|
||||
self.attention_norm(x), mask,
|
||||
cu_seqlens, max_seq_len
|
||||
)
|
||||
h = x + residual
|
||||
ffn_res = self.feed_forward.forward(self.ffn_norm(h))
|
||||
out = h + ffn_res
|
||||
return out
|
||||
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config,
|
||||
max_seq_size=8192,
|
||||
):
|
||||
super().__init__()
|
||||
self.num_layers = config.num_layers
|
||||
self.layers = self._build_layers(config)
|
||||
|
||||
def _build_layers(self, config):
|
||||
layers = torch.nn.ModuleList()
|
||||
for layer_id in range(self.num_layers):
|
||||
layers.append(
|
||||
TransformerBlock(
|
||||
config,
|
||||
layer_id=layer_id + 1 ,
|
||||
)
|
||||
)
|
||||
return layers
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
cu_seqlens=None,
|
||||
max_seq_len=None,
|
||||
):
|
||||
|
||||
if max_seq_len is not None and not isinstance(max_seq_len, torch.Tensor):
|
||||
max_seq_len = torch.tensor(max_seq_len, dtype=torch.int32, device="cpu")
|
||||
|
||||
for lid, layer in enumerate(self.layers):
|
||||
hidden_states = layer(
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
cu_seqlens,
|
||||
max_seq_len,
|
||||
)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class Step1Model(PreTrainedModel):
|
||||
config_class=PretrainedConfig
|
||||
@with_empty_init
|
||||
def __init__(
|
||||
self,
|
||||
config,
|
||||
):
|
||||
super().__init__(config)
|
||||
self.tok_embeddings = LLaMaEmbedding(config)
|
||||
self.transformer = Transformer(config)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids=None,
|
||||
attention_mask=None,
|
||||
):
|
||||
|
||||
hidden_states = self.tok_embeddings(input_ids)
|
||||
|
||||
hidden_states = self.transformer(
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
)
|
||||
return hidden_states
|
||||
|
||||
|
||||
|
||||
class STEP1TextEncoder(torch.nn.Module):
|
||||
def __init__(self, model_dir, max_length=320):
|
||||
super(STEP1TextEncoder, self).__init__()
|
||||
self.max_length = max_length
|
||||
self.text_tokenizer = Wrapped_StepChatTokenizer(os.path.join(model_dir, 'step1_chat_tokenizer.model'))
|
||||
text_encoder = Step1Model.from_pretrained(model_dir)
|
||||
self.text_encoder = text_encoder.eval().to(torch.bfloat16)
|
||||
|
||||
@staticmethod
|
||||
def from_pretrained(path, torch_dtype=torch.bfloat16):
|
||||
model = STEP1TextEncoder(path).to(torch_dtype)
|
||||
return model
|
||||
|
||||
@torch.no_grad
|
||||
def forward(self, prompts, with_mask=True, max_length=None, device="cuda"):
|
||||
self.device = device
|
||||
with torch.no_grad(), torch.amp.autocast(dtype=torch.bfloat16, device_type=device):
|
||||
if type(prompts) is str:
|
||||
prompts = [prompts]
|
||||
|
||||
txt_tokens = self.text_tokenizer(
|
||||
prompts, max_length=max_length or self.max_length, padding="max_length", truncation=True, return_tensors="pt"
|
||||
)
|
||||
y = self.text_encoder(
|
||||
txt_tokens.input_ids.to(self.device),
|
||||
attention_mask=txt_tokens.attention_mask.to(self.device) if with_mask else None
|
||||
)
|
||||
y_mask = txt_tokens.attention_mask
|
||||
return y.transpose(0,1), y_mask
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -44,8 +44,6 @@ def get_timestep_embedding(
|
||||
downscale_freq_shift: float = 1,
|
||||
scale: float = 1,
|
||||
max_period: int = 10000,
|
||||
computation_device = None,
|
||||
align_dtype_to_timestep = False,
|
||||
):
|
||||
"""
|
||||
This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
|
||||
@@ -59,13 +57,11 @@ def get_timestep_embedding(
|
||||
|
||||
half_dim = embedding_dim // 2
|
||||
exponent = -math.log(max_period) * torch.arange(
|
||||
start=0, end=half_dim, dtype=torch.float32, device=timesteps.device if computation_device is None else computation_device
|
||||
start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
|
||||
)
|
||||
exponent = exponent / (half_dim - downscale_freq_shift)
|
||||
|
||||
emb = torch.exp(exponent).to(timesteps.device)
|
||||
if align_dtype_to_timestep:
|
||||
emb = emb.to(timesteps.dtype)
|
||||
emb = torch.exp(exponent)
|
||||
emb = timesteps[:, None].float() * emb[None, :]
|
||||
|
||||
# scale embeddings
|
||||
@@ -85,14 +81,11 @@ def get_timestep_embedding(
|
||||
|
||||
|
||||
class TemporalTimesteps(torch.nn.Module):
|
||||
def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, computation_device = None, scale=1, align_dtype_to_timestep=False):
|
||||
def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
|
||||
super().__init__()
|
||||
self.num_channels = num_channels
|
||||
self.flip_sin_to_cos = flip_sin_to_cos
|
||||
self.downscale_freq_shift = downscale_freq_shift
|
||||
self.computation_device = computation_device
|
||||
self.scale = scale
|
||||
self.align_dtype_to_timestep = align_dtype_to_timestep
|
||||
|
||||
def forward(self, timesteps):
|
||||
t_emb = get_timestep_embedding(
|
||||
@@ -100,9 +93,6 @@ class TemporalTimesteps(torch.nn.Module):
|
||||
self.num_channels,
|
||||
flip_sin_to_cos=self.flip_sin_to_cos,
|
||||
downscale_freq_shift=self.downscale_freq_shift,
|
||||
computation_device=self.computation_device,
|
||||
scale=self.scale,
|
||||
align_dtype_to_timestep=self.align_dtype_to_timestep,
|
||||
)
|
||||
return t_emb
|
||||
|
||||
|
||||
@@ -55,7 +55,7 @@ class TileWorker:
|
||||
|
||||
|
||||
def io_scale(self, model_output, tile_size):
|
||||
# Determine the size modification happened in forward_fn
|
||||
# Determine the size modification happend in forward_fn
|
||||
# We only consider the same scale on height and width.
|
||||
io_scale = model_output.shape[2] / tile_size
|
||||
return io_scale
|
||||
@@ -107,60 +107,6 @@ class TileWorker:
|
||||
|
||||
|
||||
|
||||
class FastTileWorker:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
|
||||
def build_mask(self, data, is_bound):
|
||||
_, _, H, W = data.shape
|
||||
h = repeat(torch.arange(H), "H -> H W", H=H, W=W)
|
||||
w = repeat(torch.arange(W), "W -> H W", H=H, W=W)
|
||||
border_width = (H + W) // 4
|
||||
pad = torch.ones_like(h) * border_width
|
||||
mask = torch.stack([
|
||||
pad if is_bound[0] else h + 1,
|
||||
pad if is_bound[1] else H - h,
|
||||
pad if is_bound[2] else w + 1,
|
||||
pad if is_bound[3] else W - w
|
||||
]).min(dim=0).values
|
||||
mask = mask.clip(1, border_width)
|
||||
mask = (mask / border_width).to(dtype=data.dtype, device=data.device)
|
||||
mask = rearrange(mask, "H W -> 1 H W")
|
||||
return mask
|
||||
|
||||
|
||||
def tiled_forward(self, forward_fn, model_input, tile_size, tile_stride, tile_device="cpu", tile_dtype=torch.float32, border_width=None):
|
||||
# Prepare
|
||||
B, C, H, W = model_input.shape
|
||||
border_width = int(tile_stride*0.5) if border_width is None else border_width
|
||||
weight = torch.zeros((1, 1, H, W), dtype=tile_dtype, device=tile_device)
|
||||
values = torch.zeros((B, C, H, W), dtype=tile_dtype, device=tile_device)
|
||||
|
||||
# Split tasks
|
||||
tasks = []
|
||||
for h in range(0, H, tile_stride):
|
||||
for w in range(0, W, tile_stride):
|
||||
if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W):
|
||||
continue
|
||||
h_, w_ = h + tile_size, w + tile_size
|
||||
if h_ > H: h, h_ = H - tile_size, H
|
||||
if w_ > W: w, w_ = W - tile_size, W
|
||||
tasks.append((h, h_, w, w_))
|
||||
|
||||
# Run
|
||||
for hl, hr, wl, wr in tasks:
|
||||
# Forward
|
||||
hidden_states_batch = forward_fn(hl, hr, wl, wr).to(dtype=tile_dtype, device=tile_device)
|
||||
|
||||
mask = self.build_mask(hidden_states_batch, is_bound=(hl==0, hr>=H, wl==0, wr>=W))
|
||||
values[:, :, hl:hr, wl:wr] += hidden_states_batch * mask
|
||||
weight[:, :, hl:hr, wl:wr] += mask
|
||||
values /= weight
|
||||
return values
|
||||
|
||||
|
||||
|
||||
class TileWorker2Dto3D:
|
||||
"""
|
||||
Process 3D tensors, but only enable TileWorker on 2D.
|
||||
|
||||
@@ -1,56 +1,7 @@
|
||||
import torch, os
|
||||
from safetensors import safe_open
|
||||
from contextlib import contextmanager
|
||||
import hashlib
|
||||
|
||||
@contextmanager
|
||||
def init_weights_on_device(device = torch.device("meta"), include_buffers :bool = False):
|
||||
|
||||
old_register_parameter = torch.nn.Module.register_parameter
|
||||
if include_buffers:
|
||||
old_register_buffer = torch.nn.Module.register_buffer
|
||||
|
||||
def register_empty_parameter(module, name, param):
|
||||
old_register_parameter(module, name, param)
|
||||
if param is not None:
|
||||
param_cls = type(module._parameters[name])
|
||||
kwargs = module._parameters[name].__dict__
|
||||
kwargs["requires_grad"] = param.requires_grad
|
||||
module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs)
|
||||
|
||||
def register_empty_buffer(module, name, buffer, persistent=True):
|
||||
old_register_buffer(module, name, buffer, persistent=persistent)
|
||||
if buffer is not None:
|
||||
module._buffers[name] = module._buffers[name].to(device)
|
||||
|
||||
def patch_tensor_constructor(fn):
|
||||
def wrapper(*args, **kwargs):
|
||||
kwargs["device"] = device
|
||||
return fn(*args, **kwargs)
|
||||
|
||||
return wrapper
|
||||
|
||||
if include_buffers:
|
||||
tensor_constructors_to_patch = {
|
||||
torch_function_name: getattr(torch, torch_function_name)
|
||||
for torch_function_name in ["empty", "zeros", "ones", "full"]
|
||||
}
|
||||
else:
|
||||
tensor_constructors_to_patch = {}
|
||||
|
||||
try:
|
||||
torch.nn.Module.register_parameter = register_empty_parameter
|
||||
if include_buffers:
|
||||
torch.nn.Module.register_buffer = register_empty_buffer
|
||||
for torch_function_name in tensor_constructors_to_patch.keys():
|
||||
setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name)))
|
||||
yield
|
||||
finally:
|
||||
torch.nn.Module.register_parameter = old_register_parameter
|
||||
if include_buffers:
|
||||
torch.nn.Module.register_buffer = old_register_buffer
|
||||
for torch_function_name, old_torch_function in tensor_constructors_to_patch.items():
|
||||
setattr(torch, torch_function_name, old_torch_function)
|
||||
|
||||
def load_state_dict_from_folder(file_path, torch_dtype=None):
|
||||
state_dict = {}
|
||||
@@ -62,16 +13,16 @@ def load_state_dict_from_folder(file_path, torch_dtype=None):
|
||||
return state_dict
|
||||
|
||||
|
||||
def load_state_dict(file_path, torch_dtype=None, device="cpu"):
|
||||
def load_state_dict(file_path, torch_dtype=None):
|
||||
if file_path.endswith(".safetensors"):
|
||||
return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device)
|
||||
return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype)
|
||||
else:
|
||||
return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype, device=device)
|
||||
return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype)
|
||||
|
||||
|
||||
def load_state_dict_from_safetensors(file_path, torch_dtype=None, device="cpu"):
|
||||
def load_state_dict_from_safetensors(file_path, torch_dtype=None):
|
||||
state_dict = {}
|
||||
with safe_open(file_path, framework="pt", device=str(device)) as f:
|
||||
with safe_open(file_path, framework="pt", device="cpu") as f:
|
||||
for k in f.keys():
|
||||
state_dict[k] = f.get_tensor(k)
|
||||
if torch_dtype is not None:
|
||||
@@ -79,8 +30,8 @@ def load_state_dict_from_safetensors(file_path, torch_dtype=None, device="cpu"):
|
||||
return state_dict
|
||||
|
||||
|
||||
def load_state_dict_from_bin(file_path, torch_dtype=None, device="cpu"):
|
||||
state_dict = torch.load(file_path, map_location=device, weights_only=True)
|
||||
def load_state_dict_from_bin(file_path, torch_dtype=None):
|
||||
state_dict = torch.load(file_path, map_location="cpu")
|
||||
if torch_dtype is not None:
|
||||
for i in state_dict:
|
||||
if isinstance(state_dict[i], torch.Tensor):
|
||||
@@ -143,40 +94,3 @@ def search_for_files(folder, extensions):
|
||||
files.append(folder)
|
||||
break
|
||||
return files
|
||||
|
||||
|
||||
def convert_state_dict_keys_to_single_str(state_dict, with_shape=True):
|
||||
keys = []
|
||||
for key, value in state_dict.items():
|
||||
if isinstance(key, str):
|
||||
if isinstance(value, torch.Tensor):
|
||||
if with_shape:
|
||||
shape = "_".join(map(str, list(value.shape)))
|
||||
keys.append(key + ":" + shape)
|
||||
keys.append(key)
|
||||
elif isinstance(value, dict):
|
||||
keys.append(key + "|" + convert_state_dict_keys_to_single_str(value, with_shape=with_shape))
|
||||
keys.sort()
|
||||
keys_str = ",".join(keys)
|
||||
return keys_str
|
||||
|
||||
|
||||
def split_state_dict_with_prefix(state_dict):
|
||||
keys = sorted([key for key in state_dict if isinstance(key, str)])
|
||||
prefix_dict = {}
|
||||
for key in keys:
|
||||
prefix = key if "." not in key else key.split(".")[0]
|
||||
if prefix not in prefix_dict:
|
||||
prefix_dict[prefix] = []
|
||||
prefix_dict[prefix].append(key)
|
||||
state_dicts = []
|
||||
for prefix, keys in prefix_dict.items():
|
||||
sub_state_dict = {key: state_dict[key] for key in keys}
|
||||
state_dicts.append(sub_state_dict)
|
||||
return state_dicts
|
||||
|
||||
|
||||
def hash_state_dict_keys(state_dict, with_shape=True):
|
||||
keys_str = convert_state_dict_keys_to_single_str(state_dict, with_shape=with_shape)
|
||||
keys_str = keys_str.encode(encoding="UTF-8")
|
||||
return hashlib.md5(keys_str).hexdigest()
|
||||
@@ -1,202 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from einops import rearrange
|
||||
import os
|
||||
from typing_extensions import Literal
|
||||
|
||||
class SimpleAdapter(nn.Module):
|
||||
def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1):
|
||||
super(SimpleAdapter, self).__init__()
|
||||
|
||||
# Pixel Unshuffle: reduce spatial dimensions by a factor of 8
|
||||
self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8)
|
||||
|
||||
# Convolution: reduce spatial dimensions by a factor
|
||||
# of 2 (without overlap)
|
||||
self.conv = nn.Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0)
|
||||
|
||||
# Residual blocks for feature extraction
|
||||
self.residual_blocks = nn.Sequential(
|
||||
*[ResidualBlock(out_dim) for _ in range(num_residual_blocks)]
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
# Reshape to merge the frame dimension into batch
|
||||
bs, c, f, h, w = x.size()
|
||||
x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w)
|
||||
|
||||
# Pixel Unshuffle operation
|
||||
x_unshuffled = self.pixel_unshuffle(x)
|
||||
|
||||
# Convolution operation
|
||||
x_conv = self.conv(x_unshuffled)
|
||||
|
||||
# Feature extraction with residual blocks
|
||||
out = self.residual_blocks(x_conv)
|
||||
|
||||
# Reshape to restore original bf dimension
|
||||
out = out.view(bs, f, out.size(1), out.size(2), out.size(3))
|
||||
|
||||
# Permute dimensions to reorder (if needed), e.g., swap channels and feature frames
|
||||
out = out.permute(0, 2, 1, 3, 4)
|
||||
|
||||
return out
|
||||
|
||||
def process_camera_coordinates(
|
||||
self,
|
||||
direction: Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown"],
|
||||
length: int,
|
||||
height: int,
|
||||
width: int,
|
||||
speed: float = 1/54,
|
||||
origin=(0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0)
|
||||
):
|
||||
if origin is None:
|
||||
origin = (0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0)
|
||||
coordinates = generate_camera_coordinates(direction, length, speed, origin)
|
||||
plucker_embedding = process_pose_file(coordinates, width, height)
|
||||
return plucker_embedding
|
||||
|
||||
|
||||
|
||||
class ResidualBlock(nn.Module):
|
||||
def __init__(self, dim):
|
||||
super(ResidualBlock, self).__init__()
|
||||
self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.conv2 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
|
||||
|
||||
def forward(self, x):
|
||||
residual = x
|
||||
out = self.relu(self.conv1(x))
|
||||
out = self.conv2(out)
|
||||
out += residual
|
||||
return out
|
||||
|
||||
class Camera(object):
|
||||
"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
|
||||
"""
|
||||
def __init__(self, entry):
|
||||
fx, fy, cx, cy = entry[1:5]
|
||||
self.fx = fx
|
||||
self.fy = fy
|
||||
self.cx = cx
|
||||
self.cy = cy
|
||||
w2c_mat = np.array(entry[7:]).reshape(3, 4)
|
||||
w2c_mat_4x4 = np.eye(4)
|
||||
w2c_mat_4x4[:3, :] = w2c_mat
|
||||
self.w2c_mat = w2c_mat_4x4
|
||||
self.c2w_mat = np.linalg.inv(w2c_mat_4x4)
|
||||
|
||||
def get_relative_pose(cam_params):
|
||||
"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
|
||||
"""
|
||||
abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
|
||||
abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
|
||||
cam_to_origin = 0
|
||||
target_cam_c2w = np.array([
|
||||
[1, 0, 0, 0],
|
||||
[0, 1, 0, -cam_to_origin],
|
||||
[0, 0, 1, 0],
|
||||
[0, 0, 0, 1]
|
||||
])
|
||||
abs2rel = target_cam_c2w @ abs_w2cs[0]
|
||||
ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
|
||||
ret_poses = np.array(ret_poses, dtype=np.float32)
|
||||
return ret_poses
|
||||
|
||||
def custom_meshgrid(*args):
|
||||
# torch>=2.0.0 only
|
||||
return torch.meshgrid(*args, indexing='ij')
|
||||
|
||||
|
||||
def ray_condition(K, c2w, H, W, device):
|
||||
"""Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
|
||||
"""
|
||||
# c2w: B, V, 4, 4
|
||||
# K: B, V, 4
|
||||
|
||||
B = K.shape[0]
|
||||
|
||||
j, i = custom_meshgrid(
|
||||
torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
|
||||
torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
|
||||
)
|
||||
i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW]
|
||||
j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW]
|
||||
|
||||
fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1
|
||||
|
||||
zs = torch.ones_like(i) # [B, HxW]
|
||||
xs = (i - cx) / fx * zs
|
||||
ys = (j - cy) / fy * zs
|
||||
zs = zs.expand_as(ys)
|
||||
|
||||
directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3
|
||||
directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3
|
||||
|
||||
rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, 3, HW
|
||||
rays_o = c2w[..., :3, 3] # B, V, 3
|
||||
rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, 3, HW
|
||||
# c2w @ dirctions
|
||||
rays_dxo = torch.linalg.cross(rays_o, rays_d)
|
||||
plucker = torch.cat([rays_dxo, rays_d], dim=-1)
|
||||
plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6
|
||||
# plucker = plucker.permute(0, 1, 4, 2, 3)
|
||||
return plucker
|
||||
|
||||
|
||||
def process_pose_file(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu', return_poses=False):
|
||||
if return_poses:
|
||||
return cam_params
|
||||
else:
|
||||
cam_params = [Camera(cam_param) for cam_param in cam_params]
|
||||
|
||||
sample_wh_ratio = width / height
|
||||
pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed
|
||||
|
||||
if pose_wh_ratio > sample_wh_ratio:
|
||||
resized_ori_w = height * pose_wh_ratio
|
||||
for cam_param in cam_params:
|
||||
cam_param.fx = resized_ori_w * cam_param.fx / width
|
||||
else:
|
||||
resized_ori_h = width / pose_wh_ratio
|
||||
for cam_param in cam_params:
|
||||
cam_param.fy = resized_ori_h * cam_param.fy / height
|
||||
|
||||
intrinsic = np.asarray([[cam_param.fx * width,
|
||||
cam_param.fy * height,
|
||||
cam_param.cx * width,
|
||||
cam_param.cy * height]
|
||||
for cam_param in cam_params], dtype=np.float32)
|
||||
|
||||
K = torch.as_tensor(intrinsic)[None] # [1, 1, 4]
|
||||
c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere
|
||||
c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4]
|
||||
plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W
|
||||
plucker_embedding = plucker_embedding[None]
|
||||
plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0]
|
||||
return plucker_embedding
|
||||
|
||||
|
||||
|
||||
def generate_camera_coordinates(
|
||||
direction: Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown"],
|
||||
length: int,
|
||||
speed: float = 1/54,
|
||||
origin=(0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0)
|
||||
):
|
||||
coordinates = [list(origin)]
|
||||
while len(coordinates) < length:
|
||||
coor = coordinates[-1].copy()
|
||||
if "Left" in direction:
|
||||
coor[9] += speed
|
||||
if "Right" in direction:
|
||||
coor[9] -= speed
|
||||
if "Up" in direction:
|
||||
coor[13] += speed
|
||||
if "Down" in direction:
|
||||
coor[13] -= speed
|
||||
coordinates.append(coor)
|
||||
return coordinates
|
||||
@@ -1,718 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import math
|
||||
from typing import Tuple, Optional
|
||||
from einops import rearrange
|
||||
from .utils import hash_state_dict_keys
|
||||
from .wan_video_camera_controller import SimpleAdapter
|
||||
try:
|
||||
import flash_attn_interface
|
||||
FLASH_ATTN_3_AVAILABLE = True
|
||||
except ModuleNotFoundError:
|
||||
FLASH_ATTN_3_AVAILABLE = False
|
||||
|
||||
try:
|
||||
import flash_attn
|
||||
FLASH_ATTN_2_AVAILABLE = True
|
||||
except ModuleNotFoundError:
|
||||
FLASH_ATTN_2_AVAILABLE = False
|
||||
|
||||
try:
|
||||
from sageattention import sageattn
|
||||
SAGE_ATTN_AVAILABLE = True
|
||||
except ModuleNotFoundError:
|
||||
SAGE_ATTN_AVAILABLE = False
|
||||
|
||||
|
||||
def flash_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, num_heads: int, compatibility_mode=False):
|
||||
if compatibility_mode:
|
||||
q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
|
||||
k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
|
||||
v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
|
||||
x = F.scaled_dot_product_attention(q, k, v)
|
||||
x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
|
||||
elif FLASH_ATTN_3_AVAILABLE:
|
||||
q = rearrange(q, "b s (n d) -> b s n d", n=num_heads)
|
||||
k = rearrange(k, "b s (n d) -> b s n d", n=num_heads)
|
||||
v = rearrange(v, "b s (n d) -> b s n d", n=num_heads)
|
||||
x = flash_attn_interface.flash_attn_func(q, k, v)
|
||||
if isinstance(x,tuple):
|
||||
x = x[0]
|
||||
x = rearrange(x, "b s n d -> b s (n d)", n=num_heads)
|
||||
elif FLASH_ATTN_2_AVAILABLE:
|
||||
q = rearrange(q, "b s (n d) -> b s n d", n=num_heads)
|
||||
k = rearrange(k, "b s (n d) -> b s n d", n=num_heads)
|
||||
v = rearrange(v, "b s (n d) -> b s n d", n=num_heads)
|
||||
x = flash_attn.flash_attn_func(q, k, v)
|
||||
x = rearrange(x, "b s n d -> b s (n d)", n=num_heads)
|
||||
elif SAGE_ATTN_AVAILABLE:
|
||||
q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
|
||||
k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
|
||||
v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
|
||||
x = sageattn(q, k, v)
|
||||
x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
|
||||
else:
|
||||
q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
|
||||
k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
|
||||
v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
|
||||
x = F.scaled_dot_product_attention(q, k, v)
|
||||
x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
|
||||
return x
|
||||
|
||||
|
||||
def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor):
|
||||
return (x * (1 + scale) + shift)
|
||||
|
||||
|
||||
def sinusoidal_embedding_1d(dim, position):
|
||||
sinusoid = torch.outer(position.type(torch.float64), torch.pow(
|
||||
10000, -torch.arange(dim//2, dtype=torch.float64, device=position.device).div(dim//2)))
|
||||
x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
|
||||
return x.to(position.dtype)
|
||||
|
||||
|
||||
def precompute_freqs_cis_3d(dim: int, end: int = 1024, theta: float = 10000.0):
|
||||
# 3d rope precompute
|
||||
f_freqs_cis = precompute_freqs_cis(dim - 2 * (dim // 3), end, theta)
|
||||
h_freqs_cis = precompute_freqs_cis(dim // 3, end, theta)
|
||||
w_freqs_cis = precompute_freqs_cis(dim // 3, end, theta)
|
||||
return f_freqs_cis, h_freqs_cis, w_freqs_cis
|
||||
|
||||
|
||||
def precompute_freqs_cis(dim: int, end: int = 1024, theta: float = 10000.0):
|
||||
# 1d rope precompute
|
||||
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)
|
||||
[: (dim // 2)].double() / dim))
|
||||
freqs = torch.outer(torch.arange(end, device=freqs.device), freqs)
|
||||
freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
|
||||
return freqs_cis
|
||||
|
||||
|
||||
def rope_apply(x, freqs, num_heads):
|
||||
x = rearrange(x, "b s (n d) -> b s n d", n=num_heads)
|
||||
x_out = torch.view_as_complex(x.to(torch.float64).reshape(
|
||||
x.shape[0], x.shape[1], x.shape[2], -1, 2))
|
||||
x_out = torch.view_as_real(x_out * freqs).flatten(2)
|
||||
return x_out.to(x.dtype)
|
||||
|
||||
|
||||
class RMSNorm(nn.Module):
|
||||
def __init__(self, dim, eps=1e-5):
|
||||
super().__init__()
|
||||
self.eps = eps
|
||||
self.weight = nn.Parameter(torch.ones(dim))
|
||||
|
||||
def norm(self, x):
|
||||
return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
|
||||
|
||||
def forward(self, x):
|
||||
dtype = x.dtype
|
||||
return self.norm(x.float()).to(dtype) * self.weight
|
||||
|
||||
|
||||
class AttentionModule(nn.Module):
|
||||
def __init__(self, num_heads):
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
|
||||
def forward(self, q, k, v):
|
||||
x = flash_attention(q=q, k=k, v=v, num_heads=self.num_heads)
|
||||
return x
|
||||
|
||||
|
||||
class SelfAttention(nn.Module):
|
||||
def __init__(self, dim: int, num_heads: int, eps: float = 1e-6):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim // num_heads
|
||||
|
||||
self.q = nn.Linear(dim, dim)
|
||||
self.k = nn.Linear(dim, dim)
|
||||
self.v = nn.Linear(dim, dim)
|
||||
self.o = nn.Linear(dim, dim)
|
||||
self.norm_q = RMSNorm(dim, eps=eps)
|
||||
self.norm_k = RMSNorm(dim, eps=eps)
|
||||
|
||||
self.attn = AttentionModule(self.num_heads)
|
||||
|
||||
def forward(self, x, freqs):
|
||||
q = self.norm_q(self.q(x))
|
||||
k = self.norm_k(self.k(x))
|
||||
v = self.v(x)
|
||||
q = rope_apply(q, freqs, self.num_heads)
|
||||
k = rope_apply(k, freqs, self.num_heads)
|
||||
x = self.attn(q, k, v)
|
||||
return self.o(x)
|
||||
|
||||
|
||||
class CrossAttention(nn.Module):
|
||||
def __init__(self, dim: int, num_heads: int, eps: float = 1e-6, has_image_input: bool = False):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim // num_heads
|
||||
|
||||
self.q = nn.Linear(dim, dim)
|
||||
self.k = nn.Linear(dim, dim)
|
||||
self.v = nn.Linear(dim, dim)
|
||||
self.o = nn.Linear(dim, dim)
|
||||
self.norm_q = RMSNorm(dim, eps=eps)
|
||||
self.norm_k = RMSNorm(dim, eps=eps)
|
||||
self.has_image_input = has_image_input
|
||||
if has_image_input:
|
||||
self.k_img = nn.Linear(dim, dim)
|
||||
self.v_img = nn.Linear(dim, dim)
|
||||
self.norm_k_img = RMSNorm(dim, eps=eps)
|
||||
|
||||
self.attn = AttentionModule(self.num_heads)
|
||||
|
||||
def forward(self, x: torch.Tensor, y: torch.Tensor):
|
||||
if self.has_image_input:
|
||||
img = y[:, :257]
|
||||
ctx = y[:, 257:]
|
||||
else:
|
||||
ctx = y
|
||||
q = self.norm_q(self.q(x))
|
||||
k = self.norm_k(self.k(ctx))
|
||||
v = self.v(ctx)
|
||||
x = self.attn(q, k, v)
|
||||
if self.has_image_input:
|
||||
k_img = self.norm_k_img(self.k_img(img))
|
||||
v_img = self.v_img(img)
|
||||
y = flash_attention(q, k_img, v_img, num_heads=self.num_heads)
|
||||
x = x + y
|
||||
return self.o(x)
|
||||
|
||||
|
||||
class GateModule(nn.Module):
|
||||
def __init__(self,):
|
||||
super().__init__()
|
||||
|
||||
def forward(self, x, gate, residual):
|
||||
return x + gate * residual
|
||||
|
||||
class DiTBlock(nn.Module):
|
||||
def __init__(self, has_image_input: bool, dim: int, num_heads: int, ffn_dim: int, eps: float = 1e-6):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.ffn_dim = ffn_dim
|
||||
|
||||
self.self_attn = SelfAttention(dim, num_heads, eps)
|
||||
self.cross_attn = CrossAttention(
|
||||
dim, num_heads, eps, has_image_input=has_image_input)
|
||||
self.norm1 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False)
|
||||
self.norm2 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False)
|
||||
self.norm3 = nn.LayerNorm(dim, eps=eps)
|
||||
self.ffn = nn.Sequential(nn.Linear(dim, ffn_dim), nn.GELU(
|
||||
approximate='tanh'), nn.Linear(ffn_dim, dim))
|
||||
self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
|
||||
self.gate = GateModule()
|
||||
|
||||
def forward(self, x, context, t_mod, freqs):
|
||||
has_seq = len(t_mod.shape) == 4
|
||||
chunk_dim = 2 if has_seq else 1
|
||||
# msa: multi-head self-attention mlp: multi-layer perceptron
|
||||
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
|
||||
self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(6, dim=chunk_dim)
|
||||
if has_seq:
|
||||
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
|
||||
shift_msa.squeeze(2), scale_msa.squeeze(2), gate_msa.squeeze(2),
|
||||
shift_mlp.squeeze(2), scale_mlp.squeeze(2), gate_mlp.squeeze(2),
|
||||
)
|
||||
input_x = modulate(self.norm1(x), shift_msa, scale_msa)
|
||||
x = self.gate(x, gate_msa, self.self_attn(input_x, freqs))
|
||||
x = x + self.cross_attn(self.norm3(x), context)
|
||||
input_x = modulate(self.norm2(x), shift_mlp, scale_mlp)
|
||||
x = self.gate(x, gate_mlp, self.ffn(input_x))
|
||||
return x
|
||||
|
||||
|
||||
class MLP(torch.nn.Module):
|
||||
def __init__(self, in_dim, out_dim, has_pos_emb=False):
|
||||
super().__init__()
|
||||
self.proj = torch.nn.Sequential(
|
||||
nn.LayerNorm(in_dim),
|
||||
nn.Linear(in_dim, in_dim),
|
||||
nn.GELU(),
|
||||
nn.Linear(in_dim, out_dim),
|
||||
nn.LayerNorm(out_dim)
|
||||
)
|
||||
self.has_pos_emb = has_pos_emb
|
||||
if has_pos_emb:
|
||||
self.emb_pos = torch.nn.Parameter(torch.zeros((1, 514, 1280)))
|
||||
|
||||
def forward(self, x):
|
||||
if self.has_pos_emb:
|
||||
x = x + self.emb_pos.to(dtype=x.dtype, device=x.device)
|
||||
return self.proj(x)
|
||||
|
||||
|
||||
class Head(nn.Module):
|
||||
def __init__(self, dim: int, out_dim: int, patch_size: Tuple[int, int, int], eps: float):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.patch_size = patch_size
|
||||
self.norm = nn.LayerNorm(dim, eps=eps, elementwise_affine=False)
|
||||
self.head = nn.Linear(dim, out_dim * math.prod(patch_size))
|
||||
self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)
|
||||
|
||||
def forward(self, x, t_mod):
|
||||
if len(t_mod.shape) == 3:
|
||||
shift, scale = (self.modulation.unsqueeze(0).to(dtype=t_mod.dtype, device=t_mod.device) + t_mod.unsqueeze(2)).chunk(2, dim=2)
|
||||
x = (self.head(self.norm(x) * (1 + scale.squeeze(2)) + shift.squeeze(2)))
|
||||
else:
|
||||
shift, scale = (self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(2, dim=1)
|
||||
x = (self.head(self.norm(x) * (1 + scale) + shift))
|
||||
return x
|
||||
|
||||
|
||||
class WanModel(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
in_dim: int,
|
||||
ffn_dim: int,
|
||||
out_dim: int,
|
||||
text_dim: int,
|
||||
freq_dim: int,
|
||||
eps: float,
|
||||
patch_size: Tuple[int, int, int],
|
||||
num_heads: int,
|
||||
num_layers: int,
|
||||
has_image_input: bool,
|
||||
has_image_pos_emb: bool = False,
|
||||
has_ref_conv: bool = False,
|
||||
add_control_adapter: bool = False,
|
||||
in_dim_control_adapter: int = 24,
|
||||
seperated_timestep: bool = False,
|
||||
require_vae_embedding: bool = True,
|
||||
require_clip_embedding: bool = True,
|
||||
fuse_vae_embedding_in_latents: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.freq_dim = freq_dim
|
||||
self.has_image_input = has_image_input
|
||||
self.patch_size = patch_size
|
||||
self.seperated_timestep = seperated_timestep
|
||||
self.require_vae_embedding = require_vae_embedding
|
||||
self.require_clip_embedding = require_clip_embedding
|
||||
self.fuse_vae_embedding_in_latents = fuse_vae_embedding_in_latents
|
||||
|
||||
self.patch_embedding = nn.Conv3d(
|
||||
in_dim, dim, kernel_size=patch_size, stride=patch_size)
|
||||
self.text_embedding = nn.Sequential(
|
||||
nn.Linear(text_dim, dim),
|
||||
nn.GELU(approximate='tanh'),
|
||||
nn.Linear(dim, dim)
|
||||
)
|
||||
self.time_embedding = nn.Sequential(
|
||||
nn.Linear(freq_dim, dim),
|
||||
nn.SiLU(),
|
||||
nn.Linear(dim, dim)
|
||||
)
|
||||
self.time_projection = nn.Sequential(
|
||||
nn.SiLU(), nn.Linear(dim, dim * 6))
|
||||
self.blocks = nn.ModuleList([
|
||||
DiTBlock(has_image_input, dim, num_heads, ffn_dim, eps)
|
||||
for _ in range(num_layers)
|
||||
])
|
||||
self.head = Head(dim, out_dim, patch_size, eps)
|
||||
head_dim = dim // num_heads
|
||||
self.freqs = precompute_freqs_cis_3d(head_dim)
|
||||
|
||||
if has_image_input:
|
||||
self.img_emb = MLP(1280, dim, has_pos_emb=has_image_pos_emb) # clip_feature_dim = 1280
|
||||
if has_ref_conv:
|
||||
self.ref_conv = nn.Conv2d(16, dim, kernel_size=(2, 2), stride=(2, 2))
|
||||
self.has_image_pos_emb = has_image_pos_emb
|
||||
self.has_ref_conv = has_ref_conv
|
||||
if add_control_adapter:
|
||||
self.control_adapter = SimpleAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:])
|
||||
else:
|
||||
self.control_adapter = None
|
||||
|
||||
def patchify(self, x: torch.Tensor, control_camera_latents_input: Optional[torch.Tensor] = None):
|
||||
x = self.patch_embedding(x)
|
||||
if self.control_adapter is not None and control_camera_latents_input is not None:
|
||||
y_camera = self.control_adapter(control_camera_latents_input)
|
||||
x = [u + v for u, v in zip(x, y_camera)]
|
||||
x = x[0].unsqueeze(0)
|
||||
grid_size = x.shape[2:]
|
||||
x = rearrange(x, 'b c f h w -> b (f h w) c').contiguous()
|
||||
return x, grid_size # x, grid_size: (f, h, w)
|
||||
|
||||
def unpatchify(self, x: torch.Tensor, grid_size: torch.Tensor):
|
||||
return rearrange(
|
||||
x, 'b (f h w) (x y z c) -> b c (f x) (h y) (w z)',
|
||||
f=grid_size[0], h=grid_size[1], w=grid_size[2],
|
||||
x=self.patch_size[0], y=self.patch_size[1], z=self.patch_size[2]
|
||||
)
|
||||
|
||||
def forward(self,
|
||||
x: torch.Tensor,
|
||||
timestep: torch.Tensor,
|
||||
context: torch.Tensor,
|
||||
clip_feature: Optional[torch.Tensor] = None,
|
||||
y: Optional[torch.Tensor] = None,
|
||||
use_gradient_checkpointing: bool = False,
|
||||
use_gradient_checkpointing_offload: bool = False,
|
||||
**kwargs,
|
||||
):
|
||||
t = self.time_embedding(
|
||||
sinusoidal_embedding_1d(self.freq_dim, timestep))
|
||||
t_mod = self.time_projection(t).unflatten(1, (6, self.dim))
|
||||
context = self.text_embedding(context)
|
||||
|
||||
if self.has_image_input:
|
||||
x = torch.cat([x, y], dim=1) # (b, c_x + c_y, f, h, w)
|
||||
clip_embdding = self.img_emb(clip_feature)
|
||||
context = torch.cat([clip_embdding, context], dim=1)
|
||||
|
||||
x, (f, h, w) = self.patchify(x)
|
||||
|
||||
freqs = torch.cat([
|
||||
self.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
|
||||
self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
|
||||
self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
|
||||
], dim=-1).reshape(f * h * w, 1, -1).to(x.device)
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
return module(*inputs)
|
||||
return custom_forward
|
||||
|
||||
for block in self.blocks:
|
||||
if self.training and use_gradient_checkpointing:
|
||||
if use_gradient_checkpointing_offload:
|
||||
with torch.autograd.graph.save_on_cpu():
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
x, context, t_mod, freqs,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
x, context, t_mod, freqs,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
x = block(x, context, t_mod, freqs)
|
||||
|
||||
x = self.head(x, t)
|
||||
x = self.unpatchify(x, (f, h, w))
|
||||
return x
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return WanModelStateDictConverter()
|
||||
|
||||
|
||||
class WanModelStateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
rename_dict = {
|
||||
"blocks.0.attn1.norm_k.weight": "blocks.0.self_attn.norm_k.weight",
|
||||
"blocks.0.attn1.norm_q.weight": "blocks.0.self_attn.norm_q.weight",
|
||||
"blocks.0.attn1.to_k.bias": "blocks.0.self_attn.k.bias",
|
||||
"blocks.0.attn1.to_k.weight": "blocks.0.self_attn.k.weight",
|
||||
"blocks.0.attn1.to_out.0.bias": "blocks.0.self_attn.o.bias",
|
||||
"blocks.0.attn1.to_out.0.weight": "blocks.0.self_attn.o.weight",
|
||||
"blocks.0.attn1.to_q.bias": "blocks.0.self_attn.q.bias",
|
||||
"blocks.0.attn1.to_q.weight": "blocks.0.self_attn.q.weight",
|
||||
"blocks.0.attn1.to_v.bias": "blocks.0.self_attn.v.bias",
|
||||
"blocks.0.attn1.to_v.weight": "blocks.0.self_attn.v.weight",
|
||||
"blocks.0.attn2.norm_k.weight": "blocks.0.cross_attn.norm_k.weight",
|
||||
"blocks.0.attn2.norm_q.weight": "blocks.0.cross_attn.norm_q.weight",
|
||||
"blocks.0.attn2.to_k.bias": "blocks.0.cross_attn.k.bias",
|
||||
"blocks.0.attn2.to_k.weight": "blocks.0.cross_attn.k.weight",
|
||||
"blocks.0.attn2.to_out.0.bias": "blocks.0.cross_attn.o.bias",
|
||||
"blocks.0.attn2.to_out.0.weight": "blocks.0.cross_attn.o.weight",
|
||||
"blocks.0.attn2.to_q.bias": "blocks.0.cross_attn.q.bias",
|
||||
"blocks.0.attn2.to_q.weight": "blocks.0.cross_attn.q.weight",
|
||||
"blocks.0.attn2.to_v.bias": "blocks.0.cross_attn.v.bias",
|
||||
"blocks.0.attn2.to_v.weight": "blocks.0.cross_attn.v.weight",
|
||||
"blocks.0.ffn.net.0.proj.bias": "blocks.0.ffn.0.bias",
|
||||
"blocks.0.ffn.net.0.proj.weight": "blocks.0.ffn.0.weight",
|
||||
"blocks.0.ffn.net.2.bias": "blocks.0.ffn.2.bias",
|
||||
"blocks.0.ffn.net.2.weight": "blocks.0.ffn.2.weight",
|
||||
"blocks.0.norm2.bias": "blocks.0.norm3.bias",
|
||||
"blocks.0.norm2.weight": "blocks.0.norm3.weight",
|
||||
"blocks.0.scale_shift_table": "blocks.0.modulation",
|
||||
"condition_embedder.text_embedder.linear_1.bias": "text_embedding.0.bias",
|
||||
"condition_embedder.text_embedder.linear_1.weight": "text_embedding.0.weight",
|
||||
"condition_embedder.text_embedder.linear_2.bias": "text_embedding.2.bias",
|
||||
"condition_embedder.text_embedder.linear_2.weight": "text_embedding.2.weight",
|
||||
"condition_embedder.time_embedder.linear_1.bias": "time_embedding.0.bias",
|
||||
"condition_embedder.time_embedder.linear_1.weight": "time_embedding.0.weight",
|
||||
"condition_embedder.time_embedder.linear_2.bias": "time_embedding.2.bias",
|
||||
"condition_embedder.time_embedder.linear_2.weight": "time_embedding.2.weight",
|
||||
"condition_embedder.time_proj.bias": "time_projection.1.bias",
|
||||
"condition_embedder.time_proj.weight": "time_projection.1.weight",
|
||||
"patch_embedding.bias": "patch_embedding.bias",
|
||||
"patch_embedding.weight": "patch_embedding.weight",
|
||||
"scale_shift_table": "head.modulation",
|
||||
"proj_out.bias": "head.head.bias",
|
||||
"proj_out.weight": "head.head.weight",
|
||||
}
|
||||
state_dict_ = {}
|
||||
for name, param in state_dict.items():
|
||||
if name in rename_dict:
|
||||
state_dict_[rename_dict[name]] = param
|
||||
else:
|
||||
name_ = ".".join(name.split(".")[:1] + ["0"] + name.split(".")[2:])
|
||||
if name_ in rename_dict:
|
||||
name_ = rename_dict[name_]
|
||||
name_ = ".".join(name_.split(".")[:1] + [name.split(".")[1]] + name_.split(".")[2:])
|
||||
state_dict_[name_] = param
|
||||
if hash_state_dict_keys(state_dict) == "cb104773c6c2cb6df4f9529ad5c60d0b":
|
||||
config = {
|
||||
"model_type": "t2v",
|
||||
"patch_size": (1, 2, 2),
|
||||
"text_len": 512,
|
||||
"in_dim": 16,
|
||||
"dim": 5120,
|
||||
"ffn_dim": 13824,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 16,
|
||||
"num_heads": 40,
|
||||
"num_layers": 40,
|
||||
"window_size": (-1, -1),
|
||||
"qk_norm": True,
|
||||
"cross_attn_norm": True,
|
||||
"eps": 1e-6,
|
||||
}
|
||||
else:
|
||||
config = {}
|
||||
return state_dict_, config
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
state_dict = {name: param for name, param in state_dict.items() if not name.startswith("vace")}
|
||||
if hash_state_dict_keys(state_dict) == "9269f8db9040a9d860eaca435be61814":
|
||||
config = {
|
||||
"has_image_input": False,
|
||||
"patch_size": [1, 2, 2],
|
||||
"in_dim": 16,
|
||||
"dim": 1536,
|
||||
"ffn_dim": 8960,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 16,
|
||||
"num_heads": 12,
|
||||
"num_layers": 30,
|
||||
"eps": 1e-6
|
||||
}
|
||||
elif hash_state_dict_keys(state_dict) == "aafcfd9672c3a2456dc46e1cb6e52c70":
|
||||
config = {
|
||||
"has_image_input": False,
|
||||
"patch_size": [1, 2, 2],
|
||||
"in_dim": 16,
|
||||
"dim": 5120,
|
||||
"ffn_dim": 13824,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 16,
|
||||
"num_heads": 40,
|
||||
"num_layers": 40,
|
||||
"eps": 1e-6
|
||||
}
|
||||
elif hash_state_dict_keys(state_dict) == "6bfcfb3b342cb286ce886889d519a77e":
|
||||
config = {
|
||||
"has_image_input": True,
|
||||
"patch_size": [1, 2, 2],
|
||||
"in_dim": 36,
|
||||
"dim": 5120,
|
||||
"ffn_dim": 13824,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 16,
|
||||
"num_heads": 40,
|
||||
"num_layers": 40,
|
||||
"eps": 1e-6
|
||||
}
|
||||
elif hash_state_dict_keys(state_dict) == "6d6ccde6845b95ad9114ab993d917893":
|
||||
config = {
|
||||
"has_image_input": True,
|
||||
"patch_size": [1, 2, 2],
|
||||
"in_dim": 36,
|
||||
"dim": 1536,
|
||||
"ffn_dim": 8960,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 16,
|
||||
"num_heads": 12,
|
||||
"num_layers": 30,
|
||||
"eps": 1e-6
|
||||
}
|
||||
elif hash_state_dict_keys(state_dict) == "6bfcfb3b342cb286ce886889d519a77e":
|
||||
config = {
|
||||
"has_image_input": True,
|
||||
"patch_size": [1, 2, 2],
|
||||
"in_dim": 36,
|
||||
"dim": 5120,
|
||||
"ffn_dim": 13824,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 16,
|
||||
"num_heads": 40,
|
||||
"num_layers": 40,
|
||||
"eps": 1e-6
|
||||
}
|
||||
elif hash_state_dict_keys(state_dict) == "349723183fc063b2bfc10bb2835cf677":
|
||||
# 1.3B PAI control
|
||||
config = {
|
||||
"has_image_input": True,
|
||||
"patch_size": [1, 2, 2],
|
||||
"in_dim": 48,
|
||||
"dim": 1536,
|
||||
"ffn_dim": 8960,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 16,
|
||||
"num_heads": 12,
|
||||
"num_layers": 30,
|
||||
"eps": 1e-6
|
||||
}
|
||||
elif hash_state_dict_keys(state_dict) == "efa44cddf936c70abd0ea28b6cbe946c":
|
||||
# 14B PAI control
|
||||
config = {
|
||||
"has_image_input": True,
|
||||
"patch_size": [1, 2, 2],
|
||||
"in_dim": 48,
|
||||
"dim": 5120,
|
||||
"ffn_dim": 13824,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 16,
|
||||
"num_heads": 40,
|
||||
"num_layers": 40,
|
||||
"eps": 1e-6
|
||||
}
|
||||
elif hash_state_dict_keys(state_dict) == "3ef3b1f8e1dab83d5b71fd7b617f859f":
|
||||
config = {
|
||||
"has_image_input": True,
|
||||
"patch_size": [1, 2, 2],
|
||||
"in_dim": 36,
|
||||
"dim": 5120,
|
||||
"ffn_dim": 13824,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 16,
|
||||
"num_heads": 40,
|
||||
"num_layers": 40,
|
||||
"eps": 1e-6,
|
||||
"has_image_pos_emb": True
|
||||
}
|
||||
elif hash_state_dict_keys(state_dict) == "70ddad9d3a133785da5ea371aae09504":
|
||||
# 1.3B PAI control v1.1
|
||||
config = {
|
||||
"has_image_input": True,
|
||||
"patch_size": [1, 2, 2],
|
||||
"in_dim": 48,
|
||||
"dim": 1536,
|
||||
"ffn_dim": 8960,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 16,
|
||||
"num_heads": 12,
|
||||
"num_layers": 30,
|
||||
"eps": 1e-6,
|
||||
"has_ref_conv": True
|
||||
}
|
||||
elif hash_state_dict_keys(state_dict) == "26bde73488a92e64cc20b0a7485b9e5b":
|
||||
# 14B PAI control v1.1
|
||||
config = {
|
||||
"has_image_input": True,
|
||||
"patch_size": [1, 2, 2],
|
||||
"in_dim": 48,
|
||||
"dim": 5120,
|
||||
"ffn_dim": 13824,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 16,
|
||||
"num_heads": 40,
|
||||
"num_layers": 40,
|
||||
"eps": 1e-6,
|
||||
"has_ref_conv": True
|
||||
}
|
||||
elif hash_state_dict_keys(state_dict) == "ac6a5aa74f4a0aab6f64eb9a72f19901":
|
||||
# 1.3B PAI control-camera v1.1
|
||||
config = {
|
||||
"has_image_input": True,
|
||||
"patch_size": [1, 2, 2],
|
||||
"in_dim": 32,
|
||||
"dim": 1536,
|
||||
"ffn_dim": 8960,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 16,
|
||||
"num_heads": 12,
|
||||
"num_layers": 30,
|
||||
"eps": 1e-6,
|
||||
"has_ref_conv": False,
|
||||
"add_control_adapter": True,
|
||||
"in_dim_control_adapter": 24,
|
||||
}
|
||||
elif hash_state_dict_keys(state_dict) == "b61c605c2adbd23124d152ed28e049ae":
|
||||
# 14B PAI control-camera v1.1
|
||||
config = {
|
||||
"has_image_input": True,
|
||||
"patch_size": [1, 2, 2],
|
||||
"in_dim": 32,
|
||||
"dim": 5120,
|
||||
"ffn_dim": 13824,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 16,
|
||||
"num_heads": 40,
|
||||
"num_layers": 40,
|
||||
"eps": 1e-6,
|
||||
"has_ref_conv": False,
|
||||
"add_control_adapter": True,
|
||||
"in_dim_control_adapter": 24,
|
||||
}
|
||||
elif hash_state_dict_keys(state_dict) == "1f5ab7703c6fc803fdded85ff040c316":
|
||||
# Wan-AI/Wan2.2-TI2V-5B
|
||||
config = {
|
||||
"has_image_input": False,
|
||||
"patch_size": [1, 2, 2],
|
||||
"in_dim": 48,
|
||||
"dim": 3072,
|
||||
"ffn_dim": 14336,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 48,
|
||||
"num_heads": 24,
|
||||
"num_layers": 30,
|
||||
"eps": 1e-6,
|
||||
"seperated_timestep": True,
|
||||
"require_clip_embedding": False,
|
||||
"require_vae_embedding": False,
|
||||
"fuse_vae_embedding_in_latents": True,
|
||||
}
|
||||
elif hash_state_dict_keys(state_dict) == "5b013604280dd715f8457c6ed6d6a626":
|
||||
# Wan-AI/Wan2.2-I2V-A14B
|
||||
config = {
|
||||
"has_image_input": False,
|
||||
"patch_size": [1, 2, 2],
|
||||
"in_dim": 36,
|
||||
"dim": 5120,
|
||||
"ffn_dim": 13824,
|
||||
"freq_dim": 256,
|
||||
"text_dim": 4096,
|
||||
"out_dim": 16,
|
||||
"num_heads": 40,
|
||||
"num_layers": 40,
|
||||
"eps": 1e-6,
|
||||
"require_clip_embedding": False,
|
||||
}
|
||||
else:
|
||||
config = {}
|
||||
return state_dict, config
|
||||
@@ -1,902 +0,0 @@
|
||||
"""
|
||||
Concise re-implementation of
|
||||
``https://github.com/openai/CLIP'' and
|
||||
``https://github.com/mlfoundations/open_clip''.
|
||||
"""
|
||||
import math
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torchvision.transforms as T
|
||||
from .wan_video_dit import flash_attention
|
||||
|
||||
|
||||
class SelfAttention(nn.Module):
|
||||
|
||||
def __init__(self, dim, num_heads, dropout=0.1, eps=1e-5):
|
||||
assert dim % num_heads == 0
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim // num_heads
|
||||
self.eps = eps
|
||||
|
||||
# layers
|
||||
self.q = nn.Linear(dim, dim)
|
||||
self.k = nn.Linear(dim, dim)
|
||||
self.v = nn.Linear(dim, dim)
|
||||
self.o = nn.Linear(dim, dim)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
def forward(self, x, mask):
|
||||
"""
|
||||
x: [B, L, C].
|
||||
"""
|
||||
b, s, c, n, d = *x.size(), self.num_heads, self.head_dim
|
||||
|
||||
# compute query, key, value
|
||||
q = self.q(x).reshape(b, s, n, d).permute(0, 2, 1, 3)
|
||||
k = self.k(x).reshape(b, s, n, d).permute(0, 2, 1, 3)
|
||||
v = self.v(x).reshape(b, s, n, d).permute(0, 2, 1, 3)
|
||||
|
||||
# compute attention
|
||||
p = self.dropout.p if self.training else 0.0
|
||||
x = F.scaled_dot_product_attention(q, k, v, mask, p)
|
||||
x = x.permute(0, 2, 1, 3).reshape(b, s, c)
|
||||
|
||||
# output
|
||||
x = self.o(x)
|
||||
x = self.dropout(x)
|
||||
return x
|
||||
|
||||
|
||||
class AttentionBlock(nn.Module):
|
||||
|
||||
def __init__(self, dim, num_heads, post_norm, dropout=0.1, eps=1e-5):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.post_norm = post_norm
|
||||
self.eps = eps
|
||||
|
||||
# layers
|
||||
self.attn = SelfAttention(dim, num_heads, dropout, eps)
|
||||
self.norm1 = nn.LayerNorm(dim, eps=eps)
|
||||
self.ffn = nn.Sequential(
|
||||
nn.Linear(dim, dim * 4), nn.GELU(), nn.Linear(dim * 4, dim),
|
||||
nn.Dropout(dropout))
|
||||
self.norm2 = nn.LayerNorm(dim, eps=eps)
|
||||
|
||||
def forward(self, x, mask):
|
||||
if self.post_norm:
|
||||
x = self.norm1(x + self.attn(x, mask))
|
||||
x = self.norm2(x + self.ffn(x))
|
||||
else:
|
||||
x = x + self.attn(self.norm1(x), mask)
|
||||
x = x + self.ffn(self.norm2(x))
|
||||
return x
|
||||
|
||||
|
||||
class XLMRoberta(nn.Module):
|
||||
"""
|
||||
XLMRobertaModel with no pooler and no LM head.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
vocab_size=250002,
|
||||
max_seq_len=514,
|
||||
type_size=1,
|
||||
pad_id=1,
|
||||
dim=1024,
|
||||
num_heads=16,
|
||||
num_layers=24,
|
||||
post_norm=True,
|
||||
dropout=0.1,
|
||||
eps=1e-5):
|
||||
super().__init__()
|
||||
self.vocab_size = vocab_size
|
||||
self.max_seq_len = max_seq_len
|
||||
self.type_size = type_size
|
||||
self.pad_id = pad_id
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.num_layers = num_layers
|
||||
self.post_norm = post_norm
|
||||
self.eps = eps
|
||||
|
||||
# embeddings
|
||||
self.token_embedding = nn.Embedding(vocab_size, dim, padding_idx=pad_id)
|
||||
self.type_embedding = nn.Embedding(type_size, dim)
|
||||
self.pos_embedding = nn.Embedding(max_seq_len, dim, padding_idx=pad_id)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
# blocks
|
||||
self.blocks = nn.ModuleList([
|
||||
AttentionBlock(dim, num_heads, post_norm, dropout, eps)
|
||||
for _ in range(num_layers)
|
||||
])
|
||||
|
||||
# norm layer
|
||||
self.norm = nn.LayerNorm(dim, eps=eps)
|
||||
|
||||
def forward(self, ids):
|
||||
"""
|
||||
ids: [B, L] of torch.LongTensor.
|
||||
"""
|
||||
b, s = ids.shape
|
||||
mask = ids.ne(self.pad_id).long()
|
||||
|
||||
# embeddings
|
||||
x = self.token_embedding(ids) + \
|
||||
self.type_embedding(torch.zeros_like(ids)) + \
|
||||
self.pos_embedding(self.pad_id + torch.cumsum(mask, dim=1) * mask)
|
||||
if self.post_norm:
|
||||
x = self.norm(x)
|
||||
x = self.dropout(x)
|
||||
|
||||
# blocks
|
||||
mask = torch.where(
|
||||
mask.view(b, 1, 1, s).gt(0), 0.0,
|
||||
torch.finfo(x.dtype).min)
|
||||
for block in self.blocks:
|
||||
x = block(x, mask)
|
||||
|
||||
# output
|
||||
if not self.post_norm:
|
||||
x = self.norm(x)
|
||||
return x
|
||||
|
||||
|
||||
def xlm_roberta_large(pretrained=False,
|
||||
return_tokenizer=False,
|
||||
device='cpu',
|
||||
**kwargs):
|
||||
"""
|
||||
XLMRobertaLarge adapted from Huggingface.
|
||||
"""
|
||||
# params
|
||||
cfg = dict(
|
||||
vocab_size=250002,
|
||||
max_seq_len=514,
|
||||
type_size=1,
|
||||
pad_id=1,
|
||||
dim=1024,
|
||||
num_heads=16,
|
||||
num_layers=24,
|
||||
post_norm=True,
|
||||
dropout=0.1,
|
||||
eps=1e-5)
|
||||
cfg.update(**kwargs)
|
||||
|
||||
# init model
|
||||
if pretrained:
|
||||
from sora import DOWNLOAD_TO_CACHE
|
||||
|
||||
# init a meta model
|
||||
with torch.device('meta'):
|
||||
model = XLMRoberta(**cfg)
|
||||
|
||||
# load checkpoint
|
||||
model.load_state_dict(
|
||||
torch.load(
|
||||
DOWNLOAD_TO_CACHE('models/xlm_roberta/xlm_roberta_large.pth'),
|
||||
map_location=device),
|
||||
assign=True)
|
||||
else:
|
||||
# init a model on device
|
||||
with torch.device(device):
|
||||
model = XLMRoberta(**cfg)
|
||||
|
||||
# init tokenizer
|
||||
if return_tokenizer:
|
||||
from sora.data import HuggingfaceTokenizer
|
||||
tokenizer = HuggingfaceTokenizer(
|
||||
name='xlm-roberta-large',
|
||||
seq_len=model.text_len,
|
||||
clean='whitespace')
|
||||
return model, tokenizer
|
||||
else:
|
||||
return model
|
||||
|
||||
|
||||
|
||||
def pos_interpolate(pos, seq_len):
|
||||
if pos.size(1) == seq_len:
|
||||
return pos
|
||||
else:
|
||||
src_grid = int(math.sqrt(pos.size(1)))
|
||||
tar_grid = int(math.sqrt(seq_len))
|
||||
n = pos.size(1) - src_grid * src_grid
|
||||
return torch.cat([
|
||||
pos[:, :n],
|
||||
F.interpolate(
|
||||
pos[:, n:].float().reshape(1, src_grid, src_grid, -1).permute(
|
||||
0, 3, 1, 2),
|
||||
size=(tar_grid, tar_grid),
|
||||
mode='bicubic',
|
||||
align_corners=False).flatten(2).transpose(1, 2)
|
||||
],
|
||||
dim=1)
|
||||
|
||||
|
||||
class QuickGELU(nn.Module):
|
||||
|
||||
def forward(self, x):
|
||||
return x * torch.sigmoid(1.702 * x)
|
||||
|
||||
|
||||
class LayerNorm(nn.LayerNorm):
|
||||
|
||||
def forward(self, x):
|
||||
return super().forward(x).type_as(x)
|
||||
|
||||
|
||||
class SelfAttention(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
dim,
|
||||
num_heads,
|
||||
causal=False,
|
||||
attn_dropout=0.0,
|
||||
proj_dropout=0.0):
|
||||
assert dim % num_heads == 0
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim // num_heads
|
||||
self.causal = causal
|
||||
self.attn_dropout = attn_dropout
|
||||
self.proj_dropout = proj_dropout
|
||||
|
||||
# layers
|
||||
self.to_qkv = nn.Linear(dim, dim * 3)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
x: [B, L, C].
|
||||
"""
|
||||
# compute query, key, value
|
||||
q, k, v = self.to_qkv(x).chunk(3, dim=-1)
|
||||
|
||||
# compute attention
|
||||
x = flash_attention(q, k, v, num_heads=self.num_heads, compatibility_mode=True)
|
||||
|
||||
# output
|
||||
x = self.proj(x)
|
||||
x = F.dropout(x, self.proj_dropout, self.training)
|
||||
return x
|
||||
|
||||
|
||||
class SwiGLU(nn.Module):
|
||||
|
||||
def __init__(self, dim, mid_dim):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.mid_dim = mid_dim
|
||||
|
||||
# layers
|
||||
self.fc1 = nn.Linear(dim, mid_dim)
|
||||
self.fc2 = nn.Linear(dim, mid_dim)
|
||||
self.fc3 = nn.Linear(mid_dim, dim)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.silu(self.fc1(x)) * self.fc2(x)
|
||||
x = self.fc3(x)
|
||||
return x
|
||||
|
||||
|
||||
class AttentionBlock(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
dim,
|
||||
mlp_ratio,
|
||||
num_heads,
|
||||
post_norm=False,
|
||||
causal=False,
|
||||
activation='quick_gelu',
|
||||
attn_dropout=0.0,
|
||||
proj_dropout=0.0,
|
||||
norm_eps=1e-5):
|
||||
assert activation in ['quick_gelu', 'gelu', 'swi_glu']
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.mlp_ratio = mlp_ratio
|
||||
self.num_heads = num_heads
|
||||
self.post_norm = post_norm
|
||||
self.causal = causal
|
||||
self.norm_eps = norm_eps
|
||||
|
||||
# layers
|
||||
self.norm1 = LayerNorm(dim, eps=norm_eps)
|
||||
self.attn = SelfAttention(dim, num_heads, causal, attn_dropout,
|
||||
proj_dropout)
|
||||
self.norm2 = LayerNorm(dim, eps=norm_eps)
|
||||
if activation == 'swi_glu':
|
||||
self.mlp = SwiGLU(dim, int(dim * mlp_ratio))
|
||||
else:
|
||||
self.mlp = nn.Sequential(
|
||||
nn.Linear(dim, int(dim * mlp_ratio)),
|
||||
QuickGELU() if activation == 'quick_gelu' else nn.GELU(),
|
||||
nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout))
|
||||
|
||||
def forward(self, x):
|
||||
if self.post_norm:
|
||||
x = x + self.norm1(self.attn(x))
|
||||
x = x + self.norm2(self.mlp(x))
|
||||
else:
|
||||
x = x + self.attn(self.norm1(x))
|
||||
x = x + self.mlp(self.norm2(x))
|
||||
return x
|
||||
|
||||
|
||||
class AttentionPool(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
dim,
|
||||
mlp_ratio,
|
||||
num_heads,
|
||||
activation='gelu',
|
||||
proj_dropout=0.0,
|
||||
norm_eps=1e-5):
|
||||
assert dim % num_heads == 0
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.mlp_ratio = mlp_ratio
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim // num_heads
|
||||
self.proj_dropout = proj_dropout
|
||||
self.norm_eps = norm_eps
|
||||
|
||||
# layers
|
||||
gain = 1.0 / math.sqrt(dim)
|
||||
self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim))
|
||||
self.to_q = nn.Linear(dim, dim)
|
||||
self.to_kv = nn.Linear(dim, dim * 2)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.norm = LayerNorm(dim, eps=norm_eps)
|
||||
self.mlp = nn.Sequential(
|
||||
nn.Linear(dim, int(dim * mlp_ratio)),
|
||||
QuickGELU() if activation == 'quick_gelu' else nn.GELU(),
|
||||
nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout))
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
x: [B, L, C].
|
||||
"""
|
||||
b, s, c, n, d = *x.size(), self.num_heads, self.head_dim
|
||||
|
||||
# compute query, key, value
|
||||
q = self.to_q(self.cls_embedding).view(1, 1, n*d).expand(b, -1, -1)
|
||||
k, v = self.to_kv(x).chunk(2, dim=-1)
|
||||
|
||||
# compute attention
|
||||
x = flash_attention(q, k, v, num_heads=self.num_heads, compatibility_mode=True)
|
||||
x = x.reshape(b, 1, c)
|
||||
|
||||
# output
|
||||
x = self.proj(x)
|
||||
x = F.dropout(x, self.proj_dropout, self.training)
|
||||
|
||||
# mlp
|
||||
x = x + self.mlp(self.norm(x))
|
||||
return x[:, 0]
|
||||
|
||||
|
||||
class VisionTransformer(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
image_size=224,
|
||||
patch_size=16,
|
||||
dim=768,
|
||||
mlp_ratio=4,
|
||||
out_dim=512,
|
||||
num_heads=12,
|
||||
num_layers=12,
|
||||
pool_type='token',
|
||||
pre_norm=True,
|
||||
post_norm=False,
|
||||
activation='quick_gelu',
|
||||
attn_dropout=0.0,
|
||||
proj_dropout=0.0,
|
||||
embedding_dropout=0.0,
|
||||
norm_eps=1e-5):
|
||||
if image_size % patch_size != 0:
|
||||
print(
|
||||
'[WARNING] image_size is not divisible by patch_size',
|
||||
flush=True)
|
||||
assert pool_type in ('token', 'token_fc', 'attn_pool')
|
||||
out_dim = out_dim or dim
|
||||
super().__init__()
|
||||
self.image_size = image_size
|
||||
self.patch_size = patch_size
|
||||
self.num_patches = (image_size // patch_size)**2
|
||||
self.dim = dim
|
||||
self.mlp_ratio = mlp_ratio
|
||||
self.out_dim = out_dim
|
||||
self.num_heads = num_heads
|
||||
self.num_layers = num_layers
|
||||
self.pool_type = pool_type
|
||||
self.post_norm = post_norm
|
||||
self.norm_eps = norm_eps
|
||||
|
||||
# embeddings
|
||||
gain = 1.0 / math.sqrt(dim)
|
||||
self.patch_embedding = nn.Conv2d(
|
||||
3,
|
||||
dim,
|
||||
kernel_size=patch_size,
|
||||
stride=patch_size,
|
||||
bias=not pre_norm)
|
||||
if pool_type in ('token', 'token_fc'):
|
||||
self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim))
|
||||
self.pos_embedding = nn.Parameter(gain * torch.randn(
|
||||
1, self.num_patches +
|
||||
(1 if pool_type in ('token', 'token_fc') else 0), dim))
|
||||
self.dropout = nn.Dropout(embedding_dropout)
|
||||
|
||||
# transformer
|
||||
self.pre_norm = LayerNorm(dim, eps=norm_eps) if pre_norm else None
|
||||
self.transformer = nn.Sequential(*[
|
||||
AttentionBlock(dim, mlp_ratio, num_heads, post_norm, False,
|
||||
activation, attn_dropout, proj_dropout, norm_eps)
|
||||
for _ in range(num_layers)
|
||||
])
|
||||
self.post_norm = LayerNorm(dim, eps=norm_eps)
|
||||
|
||||
# head
|
||||
if pool_type == 'token':
|
||||
self.head = nn.Parameter(gain * torch.randn(dim, out_dim))
|
||||
elif pool_type == 'token_fc':
|
||||
self.head = nn.Linear(dim, out_dim)
|
||||
elif pool_type == 'attn_pool':
|
||||
self.head = AttentionPool(dim, mlp_ratio, num_heads, activation,
|
||||
proj_dropout, norm_eps)
|
||||
|
||||
def forward(self, x, interpolation=False, use_31_block=False):
|
||||
b = x.size(0)
|
||||
|
||||
# embeddings
|
||||
x = self.patch_embedding(x).flatten(2).permute(0, 2, 1)
|
||||
if self.pool_type in ('token', 'token_fc'):
|
||||
x = torch.cat([self.cls_embedding.expand(b, -1, -1).to(dtype=x.dtype, device=x.device), x], dim=1)
|
||||
if interpolation:
|
||||
e = pos_interpolate(self.pos_embedding, x.size(1))
|
||||
else:
|
||||
e = self.pos_embedding
|
||||
e = e.to(dtype=x.dtype, device=x.device)
|
||||
x = self.dropout(x + e)
|
||||
if self.pre_norm is not None:
|
||||
x = self.pre_norm(x)
|
||||
|
||||
# transformer
|
||||
if use_31_block:
|
||||
x = self.transformer[:-1](x)
|
||||
return x
|
||||
else:
|
||||
x = self.transformer(x)
|
||||
return x
|
||||
|
||||
|
||||
class CLIP(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
embed_dim=512,
|
||||
image_size=224,
|
||||
patch_size=16,
|
||||
vision_dim=768,
|
||||
vision_mlp_ratio=4,
|
||||
vision_heads=12,
|
||||
vision_layers=12,
|
||||
vision_pool='token',
|
||||
vision_pre_norm=True,
|
||||
vision_post_norm=False,
|
||||
vocab_size=49408,
|
||||
text_len=77,
|
||||
text_dim=512,
|
||||
text_mlp_ratio=4,
|
||||
text_heads=8,
|
||||
text_layers=12,
|
||||
text_causal=True,
|
||||
text_pool='argmax',
|
||||
text_head_bias=False,
|
||||
logit_bias=None,
|
||||
activation='quick_gelu',
|
||||
attn_dropout=0.0,
|
||||
proj_dropout=0.0,
|
||||
embedding_dropout=0.0,
|
||||
norm_eps=1e-5):
|
||||
super().__init__()
|
||||
self.embed_dim = embed_dim
|
||||
self.image_size = image_size
|
||||
self.patch_size = patch_size
|
||||
self.vision_dim = vision_dim
|
||||
self.vision_mlp_ratio = vision_mlp_ratio
|
||||
self.vision_heads = vision_heads
|
||||
self.vision_layers = vision_layers
|
||||
self.vision_pool = vision_pool
|
||||
self.vision_pre_norm = vision_pre_norm
|
||||
self.vision_post_norm = vision_post_norm
|
||||
self.vocab_size = vocab_size
|
||||
self.text_len = text_len
|
||||
self.text_dim = text_dim
|
||||
self.text_mlp_ratio = text_mlp_ratio
|
||||
self.text_heads = text_heads
|
||||
self.text_layers = text_layers
|
||||
self.text_causal = text_causal
|
||||
self.text_pool = text_pool
|
||||
self.text_head_bias = text_head_bias
|
||||
self.norm_eps = norm_eps
|
||||
|
||||
# models
|
||||
self.visual = VisionTransformer(
|
||||
image_size=image_size,
|
||||
patch_size=patch_size,
|
||||
dim=vision_dim,
|
||||
mlp_ratio=vision_mlp_ratio,
|
||||
out_dim=embed_dim,
|
||||
num_heads=vision_heads,
|
||||
num_layers=vision_layers,
|
||||
pool_type=vision_pool,
|
||||
pre_norm=vision_pre_norm,
|
||||
post_norm=vision_post_norm,
|
||||
activation=activation,
|
||||
attn_dropout=attn_dropout,
|
||||
proj_dropout=proj_dropout,
|
||||
embedding_dropout=embedding_dropout,
|
||||
norm_eps=norm_eps)
|
||||
self.textual = TextTransformer(
|
||||
vocab_size=vocab_size,
|
||||
text_len=text_len,
|
||||
dim=text_dim,
|
||||
mlp_ratio=text_mlp_ratio,
|
||||
out_dim=embed_dim,
|
||||
num_heads=text_heads,
|
||||
num_layers=text_layers,
|
||||
causal=text_causal,
|
||||
pool_type=text_pool,
|
||||
head_bias=text_head_bias,
|
||||
activation=activation,
|
||||
attn_dropout=attn_dropout,
|
||||
proj_dropout=proj_dropout,
|
||||
embedding_dropout=embedding_dropout,
|
||||
norm_eps=norm_eps)
|
||||
self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([]))
|
||||
if logit_bias is not None:
|
||||
self.logit_bias = nn.Parameter(logit_bias * torch.ones([]))
|
||||
|
||||
# initialize weights
|
||||
self.init_weights()
|
||||
|
||||
def forward(self, imgs, txt_ids):
|
||||
"""
|
||||
imgs: [B, 3, H, W] of torch.float32.
|
||||
- mean: [0.48145466, 0.4578275, 0.40821073]
|
||||
- std: [0.26862954, 0.26130258, 0.27577711]
|
||||
txt_ids: [B, L] of torch.long. Encoded by data.CLIPTokenizer.
|
||||
"""
|
||||
xi = self.visual(imgs)
|
||||
xt = self.textual(txt_ids)
|
||||
return xi, xt
|
||||
|
||||
def init_weights(self):
|
||||
# embeddings
|
||||
nn.init.normal_(self.textual.token_embedding.weight, std=0.02)
|
||||
nn.init.normal_(self.visual.patch_embedding.weight, std=0.1)
|
||||
|
||||
# attentions
|
||||
for modality in ['visual', 'textual']:
|
||||
dim = self.vision_dim if modality == 'visual' else self.text_dim
|
||||
transformer = getattr(self, modality).transformer
|
||||
proj_gain = (1.0 / math.sqrt(dim)) * (
|
||||
1.0 / math.sqrt(2 * len(transformer)))
|
||||
attn_gain = 1.0 / math.sqrt(dim)
|
||||
mlp_gain = 1.0 / math.sqrt(2.0 * dim)
|
||||
for block in transformer:
|
||||
nn.init.normal_(block.attn.to_qkv.weight, std=attn_gain)
|
||||
nn.init.normal_(block.attn.proj.weight, std=proj_gain)
|
||||
nn.init.normal_(block.mlp[0].weight, std=mlp_gain)
|
||||
nn.init.normal_(block.mlp[2].weight, std=proj_gain)
|
||||
|
||||
def param_groups(self):
|
||||
groups = [{
|
||||
'params': [
|
||||
p for n, p in self.named_parameters()
|
||||
if 'norm' in n or n.endswith('bias')
|
||||
],
|
||||
'weight_decay': 0.0
|
||||
}, {
|
||||
'params': [
|
||||
p for n, p in self.named_parameters()
|
||||
if not ('norm' in n or n.endswith('bias'))
|
||||
]
|
||||
}]
|
||||
return groups
|
||||
|
||||
|
||||
class XLMRobertaWithHead(XLMRoberta):
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.out_dim = kwargs.pop('out_dim')
|
||||
super().__init__(**kwargs)
|
||||
|
||||
# head
|
||||
mid_dim = (self.dim + self.out_dim) // 2
|
||||
self.head = nn.Sequential(
|
||||
nn.Linear(self.dim, mid_dim, bias=False), nn.GELU(),
|
||||
nn.Linear(mid_dim, self.out_dim, bias=False))
|
||||
|
||||
def forward(self, ids):
|
||||
# xlm-roberta
|
||||
x = super().forward(ids)
|
||||
|
||||
# average pooling
|
||||
mask = ids.ne(self.pad_id).unsqueeze(-1).to(x)
|
||||
x = (x * mask).sum(dim=1) / mask.sum(dim=1)
|
||||
|
||||
# head
|
||||
x = self.head(x)
|
||||
return x
|
||||
|
||||
|
||||
class XLMRobertaCLIP(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
embed_dim=1024,
|
||||
image_size=224,
|
||||
patch_size=14,
|
||||
vision_dim=1280,
|
||||
vision_mlp_ratio=4,
|
||||
vision_heads=16,
|
||||
vision_layers=32,
|
||||
vision_pool='token',
|
||||
vision_pre_norm=True,
|
||||
vision_post_norm=False,
|
||||
activation='gelu',
|
||||
vocab_size=250002,
|
||||
max_text_len=514,
|
||||
type_size=1,
|
||||
pad_id=1,
|
||||
text_dim=1024,
|
||||
text_heads=16,
|
||||
text_layers=24,
|
||||
text_post_norm=True,
|
||||
text_dropout=0.1,
|
||||
attn_dropout=0.0,
|
||||
proj_dropout=0.0,
|
||||
embedding_dropout=0.0,
|
||||
norm_eps=1e-5):
|
||||
super().__init__()
|
||||
self.embed_dim = embed_dim
|
||||
self.image_size = image_size
|
||||
self.patch_size = patch_size
|
||||
self.vision_dim = vision_dim
|
||||
self.vision_mlp_ratio = vision_mlp_ratio
|
||||
self.vision_heads = vision_heads
|
||||
self.vision_layers = vision_layers
|
||||
self.vision_pre_norm = vision_pre_norm
|
||||
self.vision_post_norm = vision_post_norm
|
||||
self.activation = activation
|
||||
self.vocab_size = vocab_size
|
||||
self.max_text_len = max_text_len
|
||||
self.type_size = type_size
|
||||
self.pad_id = pad_id
|
||||
self.text_dim = text_dim
|
||||
self.text_heads = text_heads
|
||||
self.text_layers = text_layers
|
||||
self.text_post_norm = text_post_norm
|
||||
self.norm_eps = norm_eps
|
||||
|
||||
# models
|
||||
self.visual = VisionTransformer(
|
||||
image_size=image_size,
|
||||
patch_size=patch_size,
|
||||
dim=vision_dim,
|
||||
mlp_ratio=vision_mlp_ratio,
|
||||
out_dim=embed_dim,
|
||||
num_heads=vision_heads,
|
||||
num_layers=vision_layers,
|
||||
pool_type=vision_pool,
|
||||
pre_norm=vision_pre_norm,
|
||||
post_norm=vision_post_norm,
|
||||
activation=activation,
|
||||
attn_dropout=attn_dropout,
|
||||
proj_dropout=proj_dropout,
|
||||
embedding_dropout=embedding_dropout,
|
||||
norm_eps=norm_eps)
|
||||
self.textual = None
|
||||
self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([]))
|
||||
|
||||
def forward(self, imgs, txt_ids):
|
||||
"""
|
||||
imgs: [B, 3, H, W] of torch.float32.
|
||||
- mean: [0.48145466, 0.4578275, 0.40821073]
|
||||
- std: [0.26862954, 0.26130258, 0.27577711]
|
||||
txt_ids: [B, L] of torch.long.
|
||||
Encoded by data.CLIPTokenizer.
|
||||
"""
|
||||
xi = self.visual(imgs)
|
||||
xt = self.textual(txt_ids)
|
||||
return xi, xt
|
||||
|
||||
def param_groups(self):
|
||||
groups = [{
|
||||
'params': [
|
||||
p for n, p in self.named_parameters()
|
||||
if 'norm' in n or n.endswith('bias')
|
||||
],
|
||||
'weight_decay': 0.0
|
||||
}, {
|
||||
'params': [
|
||||
p for n, p in self.named_parameters()
|
||||
if not ('norm' in n or n.endswith('bias'))
|
||||
]
|
||||
}]
|
||||
return groups
|
||||
|
||||
|
||||
def _clip(pretrained=False,
|
||||
pretrained_name=None,
|
||||
model_cls=CLIP,
|
||||
return_transforms=False,
|
||||
return_tokenizer=False,
|
||||
tokenizer_padding='eos',
|
||||
dtype=torch.float32,
|
||||
device='cpu',
|
||||
**kwargs):
|
||||
# init model
|
||||
if pretrained and pretrained_name:
|
||||
from sora import BUCKET, DOWNLOAD_TO_CACHE
|
||||
|
||||
# init a meta model
|
||||
with torch.device('meta'):
|
||||
model = model_cls(**kwargs)
|
||||
|
||||
# checkpoint path
|
||||
checkpoint = f'models/clip/{pretrained_name}'
|
||||
if dtype in (torch.float16, torch.bfloat16):
|
||||
suffix = '-' + {
|
||||
torch.float16: 'fp16',
|
||||
torch.bfloat16: 'bf16'
|
||||
}[dtype]
|
||||
if object_exists(BUCKET, f'{checkpoint}{suffix}.pth'):
|
||||
checkpoint = f'{checkpoint}{suffix}'
|
||||
checkpoint += '.pth'
|
||||
|
||||
# load
|
||||
model.load_state_dict(
|
||||
torch.load(DOWNLOAD_TO_CACHE(checkpoint), map_location=device),
|
||||
assign=True,
|
||||
strict=False)
|
||||
else:
|
||||
# init a model on device
|
||||
with torch.device(device):
|
||||
model = model_cls(**kwargs)
|
||||
|
||||
# set device
|
||||
output = (model,)
|
||||
|
||||
# init transforms
|
||||
if return_transforms:
|
||||
# mean and std
|
||||
if 'siglip' in pretrained_name.lower():
|
||||
mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]
|
||||
else:
|
||||
mean = [0.48145466, 0.4578275, 0.40821073]
|
||||
std = [0.26862954, 0.26130258, 0.27577711]
|
||||
|
||||
# transforms
|
||||
transforms = T.Compose([
|
||||
T.Resize((model.image_size, model.image_size),
|
||||
interpolation=T.InterpolationMode.BICUBIC),
|
||||
T.ToTensor(),
|
||||
T.Normalize(mean=mean, std=std)
|
||||
])
|
||||
output += (transforms,)
|
||||
|
||||
# init tokenizer
|
||||
if return_tokenizer:
|
||||
from sora import data
|
||||
if 'siglip' in pretrained_name.lower():
|
||||
tokenizer = data.HuggingfaceTokenizer(
|
||||
name=f'timm/{pretrained_name}',
|
||||
seq_len=model.text_len,
|
||||
clean='canonicalize')
|
||||
elif 'xlm' in pretrained_name.lower():
|
||||
tokenizer = data.HuggingfaceTokenizer(
|
||||
name='xlm-roberta-large',
|
||||
seq_len=model.max_text_len - 2,
|
||||
clean='whitespace')
|
||||
elif 'mba' in pretrained_name.lower():
|
||||
tokenizer = data.HuggingfaceTokenizer(
|
||||
name='facebook/xlm-roberta-xl',
|
||||
seq_len=model.max_text_len - 2,
|
||||
clean='whitespace')
|
||||
else:
|
||||
tokenizer = data.CLIPTokenizer(
|
||||
seq_len=model.text_len, padding=tokenizer_padding)
|
||||
output += (tokenizer,)
|
||||
return output[0] if len(output) == 1 else output
|
||||
|
||||
|
||||
def clip_xlm_roberta_vit_h_14(
|
||||
pretrained=False,
|
||||
pretrained_name='open-clip-xlm-roberta-large-vit-huge-14',
|
||||
**kwargs):
|
||||
cfg = dict(
|
||||
embed_dim=1024,
|
||||
image_size=224,
|
||||
patch_size=14,
|
||||
vision_dim=1280,
|
||||
vision_mlp_ratio=4,
|
||||
vision_heads=16,
|
||||
vision_layers=32,
|
||||
vision_pool='token',
|
||||
activation='gelu',
|
||||
vocab_size=250002,
|
||||
max_text_len=514,
|
||||
type_size=1,
|
||||
pad_id=1,
|
||||
text_dim=1024,
|
||||
text_heads=16,
|
||||
text_layers=24,
|
||||
text_post_norm=True,
|
||||
text_dropout=0.1,
|
||||
attn_dropout=0.0,
|
||||
proj_dropout=0.0,
|
||||
embedding_dropout=0.0)
|
||||
cfg.update(**kwargs)
|
||||
return _clip(pretrained, pretrained_name, XLMRobertaCLIP, **cfg)
|
||||
|
||||
|
||||
class WanImageEncoder(torch.nn.Module):
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
# init model
|
||||
self.model, self.transforms = clip_xlm_roberta_vit_h_14(
|
||||
pretrained=False,
|
||||
return_transforms=True,
|
||||
return_tokenizer=False,
|
||||
dtype=torch.float32,
|
||||
device="cpu")
|
||||
|
||||
def encode_image(self, videos):
|
||||
# preprocess
|
||||
size = (self.model.image_size,) * 2
|
||||
videos = torch.cat([
|
||||
F.interpolate(
|
||||
u,
|
||||
size=size,
|
||||
mode='bicubic',
|
||||
align_corners=False) for u in videos
|
||||
])
|
||||
videos = self.transforms.transforms[-1](videos.mul_(0.5).add_(0.5))
|
||||
|
||||
# forward
|
||||
dtype = next(iter(self.model.visual.parameters())).dtype
|
||||
videos = videos.to(dtype)
|
||||
out = self.model.visual(videos, use_31_block=True)
|
||||
return out
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return WanImageEncoderStateDictConverter()
|
||||
|
||||
|
||||
class WanImageEncoderStateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
return state_dict
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
state_dict_ = {}
|
||||
for name, param in state_dict.items():
|
||||
if name.startswith("textual."):
|
||||
continue
|
||||
name = "model." + name
|
||||
state_dict_[name] = param
|
||||
return state_dict_
|
||||
|
||||
@@ -1,44 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from .wan_video_dit import sinusoidal_embedding_1d
|
||||
|
||||
|
||||
|
||||
class WanMotionControllerModel(torch.nn.Module):
|
||||
def __init__(self, freq_dim=256, dim=1536):
|
||||
super().__init__()
|
||||
self.freq_dim = freq_dim
|
||||
self.linear = nn.Sequential(
|
||||
nn.Linear(freq_dim, dim),
|
||||
nn.SiLU(),
|
||||
nn.Linear(dim, dim),
|
||||
nn.SiLU(),
|
||||
nn.Linear(dim, dim * 6),
|
||||
)
|
||||
|
||||
def forward(self, motion_bucket_id):
|
||||
emb = sinusoidal_embedding_1d(self.freq_dim, motion_bucket_id * 10)
|
||||
emb = self.linear(emb)
|
||||
return emb
|
||||
|
||||
def init(self):
|
||||
state_dict = self.linear[-1].state_dict()
|
||||
state_dict = {i: state_dict[i] * 0 for i in state_dict}
|
||||
self.linear[-1].load_state_dict(state_dict)
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return WanMotionControllerModelDictConverter()
|
||||
|
||||
|
||||
|
||||
class WanMotionControllerModelDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
return state_dict
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
return state_dict
|
||||
|
||||
@@ -1,269 +0,0 @@
|
||||
import math
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
def fp16_clamp(x):
|
||||
if x.dtype == torch.float16 and torch.isinf(x).any():
|
||||
clamp = torch.finfo(x.dtype).max - 1000
|
||||
x = torch.clamp(x, min=-clamp, max=clamp)
|
||||
return x
|
||||
|
||||
|
||||
class GELU(nn.Module):
|
||||
|
||||
def forward(self, x):
|
||||
return 0.5 * x * (1.0 + torch.tanh(
|
||||
math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
|
||||
|
||||
|
||||
class T5LayerNorm(nn.Module):
|
||||
|
||||
def __init__(self, dim, eps=1e-6):
|
||||
super(T5LayerNorm, self).__init__()
|
||||
self.dim = dim
|
||||
self.eps = eps
|
||||
self.weight = nn.Parameter(torch.ones(dim))
|
||||
|
||||
def forward(self, x):
|
||||
x = x * torch.rsqrt(x.float().pow(2).mean(dim=-1, keepdim=True) +
|
||||
self.eps)
|
||||
if self.weight.dtype in [torch.float16, torch.bfloat16]:
|
||||
x = x.type_as(self.weight)
|
||||
return self.weight * x
|
||||
|
||||
|
||||
class T5Attention(nn.Module):
|
||||
|
||||
def __init__(self, dim, dim_attn, num_heads, dropout=0.1):
|
||||
assert dim_attn % num_heads == 0
|
||||
super(T5Attention, self).__init__()
|
||||
self.dim = dim
|
||||
self.dim_attn = dim_attn
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim_attn // num_heads
|
||||
|
||||
# layers
|
||||
self.q = nn.Linear(dim, dim_attn, bias=False)
|
||||
self.k = nn.Linear(dim, dim_attn, bias=False)
|
||||
self.v = nn.Linear(dim, dim_attn, bias=False)
|
||||
self.o = nn.Linear(dim_attn, dim, bias=False)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
def forward(self, x, context=None, mask=None, pos_bias=None):
|
||||
"""
|
||||
x: [B, L1, C].
|
||||
context: [B, L2, C] or None.
|
||||
mask: [B, L2] or [B, L1, L2] or None.
|
||||
"""
|
||||
# check inputs
|
||||
context = x if context is None else context
|
||||
b, n, c = x.size(0), self.num_heads, self.head_dim
|
||||
|
||||
# compute query, key, value
|
||||
q = self.q(x).view(b, -1, n, c)
|
||||
k = self.k(context).view(b, -1, n, c)
|
||||
v = self.v(context).view(b, -1, n, c)
|
||||
|
||||
# attention bias
|
||||
attn_bias = x.new_zeros(b, n, q.size(1), k.size(1))
|
||||
if pos_bias is not None:
|
||||
attn_bias += pos_bias
|
||||
if mask is not None:
|
||||
assert mask.ndim in [2, 3]
|
||||
mask = mask.view(b, 1, 1,
|
||||
-1) if mask.ndim == 2 else mask.unsqueeze(1)
|
||||
attn_bias.masked_fill_(mask == 0, torch.finfo(x.dtype).min)
|
||||
|
||||
# compute attention (T5 does not use scaling)
|
||||
attn = torch.einsum('binc,bjnc->bnij', q, k) + attn_bias
|
||||
attn = F.softmax(attn.float(), dim=-1).type_as(attn)
|
||||
x = torch.einsum('bnij,bjnc->binc', attn, v)
|
||||
|
||||
# output
|
||||
x = x.reshape(b, -1, n * c)
|
||||
x = self.o(x)
|
||||
x = self.dropout(x)
|
||||
return x
|
||||
|
||||
|
||||
class T5FeedForward(nn.Module):
|
||||
|
||||
def __init__(self, dim, dim_ffn, dropout=0.1):
|
||||
super(T5FeedForward, self).__init__()
|
||||
self.dim = dim
|
||||
self.dim_ffn = dim_ffn
|
||||
|
||||
# layers
|
||||
self.gate = nn.Sequential(nn.Linear(dim, dim_ffn, bias=False), GELU())
|
||||
self.fc1 = nn.Linear(dim, dim_ffn, bias=False)
|
||||
self.fc2 = nn.Linear(dim_ffn, dim, bias=False)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x) * self.gate(x)
|
||||
x = self.dropout(x)
|
||||
x = self.fc2(x)
|
||||
x = self.dropout(x)
|
||||
return x
|
||||
|
||||
|
||||
class T5SelfAttention(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
dim,
|
||||
dim_attn,
|
||||
dim_ffn,
|
||||
num_heads,
|
||||
num_buckets,
|
||||
shared_pos=True,
|
||||
dropout=0.1):
|
||||
super(T5SelfAttention, self).__init__()
|
||||
self.dim = dim
|
||||
self.dim_attn = dim_attn
|
||||
self.dim_ffn = dim_ffn
|
||||
self.num_heads = num_heads
|
||||
self.num_buckets = num_buckets
|
||||
self.shared_pos = shared_pos
|
||||
|
||||
# layers
|
||||
self.norm1 = T5LayerNorm(dim)
|
||||
self.attn = T5Attention(dim, dim_attn, num_heads, dropout)
|
||||
self.norm2 = T5LayerNorm(dim)
|
||||
self.ffn = T5FeedForward(dim, dim_ffn, dropout)
|
||||
self.pos_embedding = None if shared_pos else T5RelativeEmbedding(
|
||||
num_buckets, num_heads, bidirectional=True)
|
||||
|
||||
def forward(self, x, mask=None, pos_bias=None):
|
||||
e = pos_bias if self.shared_pos else self.pos_embedding(
|
||||
x.size(1), x.size(1))
|
||||
x = fp16_clamp(x + self.attn(self.norm1(x), mask=mask, pos_bias=e))
|
||||
x = fp16_clamp(x + self.ffn(self.norm2(x)))
|
||||
return x
|
||||
|
||||
|
||||
class T5RelativeEmbedding(nn.Module):
|
||||
|
||||
def __init__(self, num_buckets, num_heads, bidirectional, max_dist=128):
|
||||
super(T5RelativeEmbedding, self).__init__()
|
||||
self.num_buckets = num_buckets
|
||||
self.num_heads = num_heads
|
||||
self.bidirectional = bidirectional
|
||||
self.max_dist = max_dist
|
||||
|
||||
# layers
|
||||
self.embedding = nn.Embedding(num_buckets, num_heads)
|
||||
|
||||
def forward(self, lq, lk):
|
||||
device = self.embedding.weight.device
|
||||
# rel_pos = torch.arange(lk).unsqueeze(0).to(device) - \
|
||||
# torch.arange(lq).unsqueeze(1).to(device)
|
||||
rel_pos = torch.arange(lk, device=device).unsqueeze(0) - \
|
||||
torch.arange(lq, device=device).unsqueeze(1)
|
||||
rel_pos = self._relative_position_bucket(rel_pos)
|
||||
rel_pos_embeds = self.embedding(rel_pos)
|
||||
rel_pos_embeds = rel_pos_embeds.permute(2, 0, 1).unsqueeze(
|
||||
0) # [1, N, Lq, Lk]
|
||||
return rel_pos_embeds.contiguous()
|
||||
|
||||
def _relative_position_bucket(self, rel_pos):
|
||||
# preprocess
|
||||
if self.bidirectional:
|
||||
num_buckets = self.num_buckets // 2
|
||||
rel_buckets = (rel_pos > 0).long() * num_buckets
|
||||
rel_pos = torch.abs(rel_pos)
|
||||
else:
|
||||
num_buckets = self.num_buckets
|
||||
rel_buckets = 0
|
||||
rel_pos = -torch.min(rel_pos, torch.zeros_like(rel_pos))
|
||||
|
||||
# embeddings for small and large positions
|
||||
max_exact = num_buckets // 2
|
||||
rel_pos_large = max_exact + (torch.log(rel_pos.float() / max_exact) /
|
||||
math.log(self.max_dist / max_exact) *
|
||||
(num_buckets - max_exact)).long()
|
||||
rel_pos_large = torch.min(
|
||||
rel_pos_large, torch.full_like(rel_pos_large, num_buckets - 1))
|
||||
rel_buckets += torch.where(rel_pos < max_exact, rel_pos, rel_pos_large)
|
||||
return rel_buckets
|
||||
|
||||
def init_weights(m):
|
||||
if isinstance(m, T5LayerNorm):
|
||||
nn.init.ones_(m.weight)
|
||||
elif isinstance(m, T5FeedForward):
|
||||
nn.init.normal_(m.gate[0].weight, std=m.dim**-0.5)
|
||||
nn.init.normal_(m.fc1.weight, std=m.dim**-0.5)
|
||||
nn.init.normal_(m.fc2.weight, std=m.dim_ffn**-0.5)
|
||||
elif isinstance(m, T5Attention):
|
||||
nn.init.normal_(m.q.weight, std=(m.dim * m.dim_attn)**-0.5)
|
||||
nn.init.normal_(m.k.weight, std=m.dim**-0.5)
|
||||
nn.init.normal_(m.v.weight, std=m.dim**-0.5)
|
||||
nn.init.normal_(m.o.weight, std=(m.num_heads * m.dim_attn)**-0.5)
|
||||
elif isinstance(m, T5RelativeEmbedding):
|
||||
nn.init.normal_(
|
||||
m.embedding.weight, std=(2 * m.num_buckets * m.num_heads)**-0.5)
|
||||
|
||||
|
||||
class WanTextEncoder(torch.nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
vocab=256384,
|
||||
dim=4096,
|
||||
dim_attn=4096,
|
||||
dim_ffn=10240,
|
||||
num_heads=64,
|
||||
num_layers=24,
|
||||
num_buckets=32,
|
||||
shared_pos=False,
|
||||
dropout=0.1):
|
||||
super(WanTextEncoder, self).__init__()
|
||||
self.dim = dim
|
||||
self.dim_attn = dim_attn
|
||||
self.dim_ffn = dim_ffn
|
||||
self.num_heads = num_heads
|
||||
self.num_layers = num_layers
|
||||
self.num_buckets = num_buckets
|
||||
self.shared_pos = shared_pos
|
||||
|
||||
# layers
|
||||
self.token_embedding = vocab if isinstance(vocab, nn.Embedding) \
|
||||
else nn.Embedding(vocab, dim)
|
||||
self.pos_embedding = T5RelativeEmbedding(
|
||||
num_buckets, num_heads, bidirectional=True) if shared_pos else None
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.blocks = nn.ModuleList([
|
||||
T5SelfAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets,
|
||||
shared_pos, dropout) for _ in range(num_layers)
|
||||
])
|
||||
self.norm = T5LayerNorm(dim)
|
||||
|
||||
# initialize weights
|
||||
self.apply(init_weights)
|
||||
|
||||
def forward(self, ids, mask=None):
|
||||
x = self.token_embedding(ids)
|
||||
x = self.dropout(x)
|
||||
e = self.pos_embedding(x.size(1),
|
||||
x.size(1)) if self.shared_pos else None
|
||||
for block in self.blocks:
|
||||
x = block(x, mask, pos_bias=e)
|
||||
x = self.norm(x)
|
||||
x = self.dropout(x)
|
||||
return x
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return WanTextEncoderStateDictConverter()
|
||||
|
||||
|
||||
class WanTextEncoderStateDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_diffusers(self, state_dict):
|
||||
return state_dict
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
return state_dict
|
||||
@@ -1,113 +0,0 @@
|
||||
import torch
|
||||
from .wan_video_dit import DiTBlock
|
||||
from .utils import hash_state_dict_keys
|
||||
|
||||
class VaceWanAttentionBlock(DiTBlock):
|
||||
def __init__(self, has_image_input, dim, num_heads, ffn_dim, eps=1e-6, block_id=0):
|
||||
super().__init__(has_image_input, dim, num_heads, ffn_dim, eps=eps)
|
||||
self.block_id = block_id
|
||||
if block_id == 0:
|
||||
self.before_proj = torch.nn.Linear(self.dim, self.dim)
|
||||
self.after_proj = torch.nn.Linear(self.dim, self.dim)
|
||||
|
||||
def forward(self, c, x, context, t_mod, freqs):
|
||||
if self.block_id == 0:
|
||||
c = self.before_proj(c) + x
|
||||
all_c = []
|
||||
else:
|
||||
all_c = list(torch.unbind(c))
|
||||
c = all_c.pop(-1)
|
||||
c = super().forward(c, context, t_mod, freqs)
|
||||
c_skip = self.after_proj(c)
|
||||
all_c += [c_skip, c]
|
||||
c = torch.stack(all_c)
|
||||
return c
|
||||
|
||||
|
||||
class VaceWanModel(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
vace_layers=(0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28),
|
||||
vace_in_dim=96,
|
||||
patch_size=(1, 2, 2),
|
||||
has_image_input=False,
|
||||
dim=1536,
|
||||
num_heads=12,
|
||||
ffn_dim=8960,
|
||||
eps=1e-6,
|
||||
):
|
||||
super().__init__()
|
||||
self.vace_layers = vace_layers
|
||||
self.vace_in_dim = vace_in_dim
|
||||
self.vace_layers_mapping = {i: n for n, i in enumerate(self.vace_layers)}
|
||||
|
||||
# vace blocks
|
||||
self.vace_blocks = torch.nn.ModuleList([
|
||||
VaceWanAttentionBlock(has_image_input, dim, num_heads, ffn_dim, eps, block_id=i)
|
||||
for i in self.vace_layers
|
||||
])
|
||||
|
||||
# vace patch embeddings
|
||||
self.vace_patch_embedding = torch.nn.Conv3d(vace_in_dim, dim, kernel_size=patch_size, stride=patch_size)
|
||||
|
||||
def forward(
|
||||
self, x, vace_context, context, t_mod, freqs,
|
||||
use_gradient_checkpointing: bool = False,
|
||||
use_gradient_checkpointing_offload: bool = False,
|
||||
):
|
||||
c = [self.vace_patch_embedding(u.unsqueeze(0)) for u in vace_context]
|
||||
c = [u.flatten(2).transpose(1, 2) for u in c]
|
||||
c = torch.cat([
|
||||
torch.cat([u, u.new_zeros(1, x.shape[1] - u.size(1), u.size(2))],
|
||||
dim=1) for u in c
|
||||
])
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
return module(*inputs)
|
||||
return custom_forward
|
||||
|
||||
for block in self.vace_blocks:
|
||||
if use_gradient_checkpointing_offload:
|
||||
with torch.autograd.graph.save_on_cpu():
|
||||
c = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
c, x, context, t_mod, freqs,
|
||||
use_reentrant=False,
|
||||
)
|
||||
elif use_gradient_checkpointing:
|
||||
c = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
c, x, context, t_mod, freqs,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
c = block(c, x, context, t_mod, freqs)
|
||||
hints = torch.unbind(c)[:-1]
|
||||
return hints
|
||||
|
||||
@staticmethod
|
||||
def state_dict_converter():
|
||||
return VaceWanModelDictConverter()
|
||||
|
||||
|
||||
class VaceWanModelDictConverter:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def from_civitai(self, state_dict):
|
||||
state_dict_ = {name: param for name, param in state_dict.items() if name.startswith("vace")}
|
||||
if hash_state_dict_keys(state_dict_) == '3b2726384e4f64837bdf216eea3f310d': # vace 14B
|
||||
config = {
|
||||
"vace_layers": (0, 5, 10, 15, 20, 25, 30, 35),
|
||||
"vace_in_dim": 96,
|
||||
"patch_size": (1, 2, 2),
|
||||
"has_image_input": False,
|
||||
"dim": 5120,
|
||||
"num_heads": 40,
|
||||
"ffn_dim": 13824,
|
||||
"eps": 1e-06,
|
||||
}
|
||||
else:
|
||||
config = {}
|
||||
return state_dict_, config
|
||||
File diff suppressed because it is too large
Load Diff
@@ -7,9 +7,5 @@ from .hunyuan_image import HunyuanDiTImagePipeline
|
||||
from .svd_video import SVDVideoPipeline
|
||||
from .flux_image import FluxImagePipeline
|
||||
from .cog_video import CogVideoPipeline
|
||||
from .omnigen_image import OmnigenImagePipeline
|
||||
from .pipeline_runner import SDVideoPipelineRunner
|
||||
from .hunyuan_video import HunyuanVideoPipeline
|
||||
from .step_video import StepVideoPipeline
|
||||
from .wan_video import WanVideoPipeline
|
||||
KolorsImagePipeline = SDXLImagePipeline
|
||||
|
||||
@@ -1,32 +1,19 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from torchvision.transforms import GaussianBlur
|
||||
|
||||
|
||||
|
||||
class BasePipeline(torch.nn.Module):
|
||||
|
||||
def __init__(self, device="cuda", torch_dtype=torch.float16, height_division_factor=64, width_division_factor=64):
|
||||
def __init__(self, device="cuda", torch_dtype=torch.float16):
|
||||
super().__init__()
|
||||
self.device = device
|
||||
self.torch_dtype = torch_dtype
|
||||
self.height_division_factor = height_division_factor
|
||||
self.width_division_factor = width_division_factor
|
||||
self.cpu_offload = False
|
||||
self.model_names = []
|
||||
|
||||
|
||||
def check_resize_height_width(self, height, width):
|
||||
if height % self.height_division_factor != 0:
|
||||
height = (height + self.height_division_factor - 1) // self.height_division_factor * self.height_division_factor
|
||||
print(f"The height cannot be evenly divided by {self.height_division_factor}. We round it up to {height}.")
|
||||
if width % self.width_division_factor != 0:
|
||||
width = (width + self.width_division_factor - 1) // self.width_division_factor * self.width_division_factor
|
||||
print(f"The width cannot be evenly divided by {self.width_division_factor}. We round it up to {width}.")
|
||||
return height, width
|
||||
|
||||
|
||||
def preprocess_image(self, image):
|
||||
image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0)
|
||||
return image
|
||||
@@ -48,30 +35,21 @@ class BasePipeline(torch.nn.Module):
|
||||
return video
|
||||
|
||||
|
||||
def merge_latents(self, value, latents, masks, scales, blur_kernel_size=33, blur_sigma=10.0):
|
||||
if len(latents) > 0:
|
||||
blur = GaussianBlur(kernel_size=blur_kernel_size, sigma=blur_sigma)
|
||||
height, width = value.shape[-2:]
|
||||
weight = torch.ones_like(value)
|
||||
for latent, mask, scale in zip(latents, masks, scales):
|
||||
mask = self.preprocess_image(mask.resize((width, height))).mean(dim=1, keepdim=True) > 0
|
||||
mask = mask.repeat(1, latent.shape[1], 1, 1).to(dtype=latent.dtype, device=latent.device)
|
||||
mask = blur(mask)
|
||||
value += latent * mask * scale
|
||||
weight += mask * scale
|
||||
value /= weight
|
||||
def merge_latents(self, value, latents, masks, scales):
|
||||
height, width = value.shape[-2:]
|
||||
weight = torch.ones_like(value)
|
||||
for latent, mask, scale in zip(latents, masks, scales):
|
||||
mask = self.preprocess_image(mask.resize((width, height))).mean(dim=1, keepdim=True) > 0
|
||||
mask = mask.repeat(1, latent.shape[1], 1, 1)
|
||||
value[mask] += latent[mask] * scale
|
||||
weight[mask] += scale
|
||||
value /= weight
|
||||
return value
|
||||
|
||||
|
||||
def control_noise_via_local_prompts(self, prompt_emb_global, prompt_emb_locals, masks, mask_scales, inference_callback, special_kwargs=None, special_local_kwargs_list=None):
|
||||
if special_kwargs is None:
|
||||
noise_pred_global = inference_callback(prompt_emb_global)
|
||||
else:
|
||||
noise_pred_global = inference_callback(prompt_emb_global, special_kwargs)
|
||||
if special_local_kwargs_list is None:
|
||||
noise_pred_locals = [inference_callback(prompt_emb_local) for prompt_emb_local in prompt_emb_locals]
|
||||
else:
|
||||
noise_pred_locals = [inference_callback(prompt_emb_local, special_kwargs) for prompt_emb_local, special_kwargs in zip(prompt_emb_locals, special_local_kwargs_list)]
|
||||
def control_noise_via_local_prompts(self, prompt_emb_global, prompt_emb_locals, masks, mask_scales, inference_callback):
|
||||
noise_pred_global = inference_callback(prompt_emb_global)
|
||||
noise_pred_locals = [inference_callback(prompt_emb_local) for prompt_emb_local in prompt_emb_locals]
|
||||
noise_pred = self.merge_latents(noise_pred_global, noise_pred_locals, masks, mask_scales)
|
||||
return noise_pred
|
||||
|
||||
@@ -87,11 +65,9 @@ class BasePipeline(torch.nn.Module):
|
||||
mask_scales += [100.0] * len(extended_prompt_dict.get("masks", []))
|
||||
return prompt, local_prompts, masks, mask_scales
|
||||
|
||||
|
||||
def enable_cpu_offload(self):
|
||||
self.cpu_offload = True
|
||||
|
||||
|
||||
def load_models_to_device(self, loadmodel_names=[]):
|
||||
# only load models to device if cpu_offload is enabled
|
||||
if not self.cpu_offload:
|
||||
@@ -101,27 +77,11 @@ class BasePipeline(torch.nn.Module):
|
||||
if model_name not in loadmodel_names:
|
||||
model = getattr(self, model_name)
|
||||
if model is not None:
|
||||
if hasattr(model, "vram_management_enabled") and model.vram_management_enabled:
|
||||
for module in model.modules():
|
||||
if hasattr(module, "offload"):
|
||||
module.offload()
|
||||
else:
|
||||
model.cpu()
|
||||
model.cpu()
|
||||
# load the needed models to device
|
||||
for model_name in loadmodel_names:
|
||||
model = getattr(self, model_name)
|
||||
if model is not None:
|
||||
if hasattr(model, "vram_management_enabled") and model.vram_management_enabled:
|
||||
for module in model.modules():
|
||||
if hasattr(module, "onload"):
|
||||
module.onload()
|
||||
else:
|
||||
model.to(self.device)
|
||||
model.to(self.device)
|
||||
# fresh the cuda cache
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
|
||||
def generate_noise(self, shape, seed=None, device="cpu", dtype=torch.float16):
|
||||
generator = None if seed is None else torch.Generator(device).manual_seed(seed)
|
||||
noise = torch.randn(shape, generator=generator, device=device, dtype=dtype)
|
||||
return noise
|
||||
|
||||
@@ -13,7 +13,7 @@ from einops import rearrange
|
||||
class CogVideoPipeline(BasePipeline):
|
||||
|
||||
def __init__(self, device="cuda", torch_dtype=torch.float16):
|
||||
super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16)
|
||||
super().__init__(device=device, torch_dtype=torch_dtype)
|
||||
self.scheduler = EnhancedDDIMScheduler(rescale_zero_terminal_snr=True, prediction_type="v_prediction")
|
||||
self.prompter = CogPrompter()
|
||||
# models
|
||||
@@ -73,12 +73,9 @@ class CogVideoPipeline(BasePipeline):
|
||||
tiled=False,
|
||||
tile_size=(60, 90),
|
||||
tile_stride=(30, 45),
|
||||
seed=None,
|
||||
progress_bar_cmd=tqdm,
|
||||
progress_bar_st=None,
|
||||
):
|
||||
height, width = self.check_resize_height_width(height, width)
|
||||
|
||||
# Tiler parameters
|
||||
tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
|
||||
|
||||
@@ -86,8 +83,7 @@ class CogVideoPipeline(BasePipeline):
|
||||
self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength)
|
||||
|
||||
# Prepare latent tensors
|
||||
noise = self.generate_noise((1, 16, num_frames // 4 + 1, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype)
|
||||
|
||||
noise = torch.randn((1, 16, num_frames // 4 + 1, height//8, width//8), device="cpu", dtype=self.torch_dtype)
|
||||
if denoising_strength == 1.0:
|
||||
latents = noise.clone()
|
||||
else:
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user