lora retrieval

cd0319-ImportError-libX11.so.6

.github/workflows/publish.yaml

@@ -0,0 +1,29 @@
+name: release
+
+on:
+  push:
+    tags:
+      - 'v**'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}-publish
+  cancel-in-progress: true
+
+jobs:
+  build-n-publish:
+    runs-on: ubuntu-20.04
+    #if: startsWith(github.event.ref, 'refs/tags')
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.10'
+      - name: Install wheel
+        run: pip install wheel && pip install -r requirements.txt
+      - name: Build DiffSynth
+        run: python setup.py sdist bdist_wheel
+      - name: Publish package to PyPI
+        run: |
+          pip install twine
+          twine upload dist/* --skip-existing -u __token__ -p ${{ secrets.PYPI_API_TOKEN }}

README.md

@@ -1,92 +1,184 @@
 # DiffSynth Studio
+[![PyPI](https://img.shields.io/pypi/v/DiffSynth)](https://pypi.org/project/DiffSynth/)
+[![license](https://img.shields.io/github/license/modelscope/DiffSynth-Studio.svg)](https://github.com/modelscope/DiffSynth-Studio/blob/master/LICENSE)
+[![open issues](https://isitmaintained.com/badge/open/modelscope/DiffSynth-Studio.svg)](https://github.com/modelscope/DiffSynth-Studio/issues)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/modelscope/DiffSynth-Studio.svg)](https://GitHub.com/modelscope/DiffSynth-Studio/pull/)
+[![GitHub latest commit](https://badgen.net/github/last-commit/modelscope/DiffSynth-Studio)](https://GitHub.com/modelscope/DiffSynth-Studio/commit/)
+
+<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>
+
+Document: https://diffsynth-studio.readthedocs.io/zh-cn/latest/index.html
 
 ## Introduction
 
 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!
 
-## Roadmap
+Until now, DiffSynth Studio has supported the following models:
+
+* [Wan-Video](https://github.com/Wan-Video/Wan2.1)
+* [StepVideo](https://github.com/stepfun-ai/Step-Video-T2V)
+* [HunyuanVideo](https://github.com/Tencent/HunyuanVideo), [HunyuanVideo-I2V]()
+* [CogVideoX](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
+- **March 25, 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).
+
+- **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
+  - Video editing
+  - Self-upscaling
+  - Video interpolation
+
+- **August 22, 2024.** We have implemented an interesting painter that supports all text-to-image models. Now you can create stunning images using the painter, with assistance from AI!
+  - Use it in our [WebUI](#usage-in-webui).
+
+- **August 21, 2024.** FLUX is supported in DiffSynth-Studio.
+  - 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.
+  - [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).
+  - Technical report is released on [arXiv](https://arxiv.org/abs/2406.14130).
+  - You can try ExVideo in this [Demo](https://huggingface.co/spaces/modelscope/ExVideo-SVD-128f-v1)!
+
+- **June 13, 2024.** DiffSynth Studio is transferred to ModelScope. The developers have transitioned from "I" to "we". Of course, I will still participate in development and maintenance.
+
+- **Jan 29, 2024.** We propose Diffutoon, a fantastic solution for toon shading.
+  - [Project Page](https://ecnu-cilab.github.io/DiffutoonProjectPage/)
+  - The source codes are released in this project.
+  - The technical report (IJCAI 2024) is released on [arXiv](https://arxiv.org/abs/2401.16224).
+
+- **Dec 8, 2023.** We decide to develop a new Project, aiming to release the potential of diffusion models, especially in video synthesis. The development of this project is started.
+
+- **Nov 15, 2023.** We propose FastBlend, a powerful video deflickering algorithm.
+  - The sd-webui extension is released on [GitHub](https://github.com/Artiprocher/sd-webui-fastblend).
+  - Demo videos are shown on Bilibili, including three tasks.
+    - [Video deflickering](https://www.bilibili.com/video/BV1d94y1W7PE)
+    - [Video interpolation](https://www.bilibili.com/video/BV1Lw411m71p)
+    - [Image-driven video rendering](https://www.bilibili.com/video/BV1RB4y1Z7LF)
+  - The technical report is released on [arXiv](https://arxiv.org/abs/2311.09265).
+  - An unofficial ComfyUI extension developed by other users is released on [GitHub](https://github.com/AInseven/ComfyUI-fastblend).
+
+- **Oct 1, 2023.** We release an early version of this project, namely FastSDXL. A try for building a diffusion engine.
+  - The source codes are released on [GitHub](https://github.com/Artiprocher/FastSDXL).
+  - FastSDXL includes a trainable OLSS scheduler for efficiency improvement.
+    - The original repo of OLSS is [here](https://github.com/alibaba/EasyNLP/tree/master/diffusion/olss_scheduler).
+    - The technical report (CIKM 2023) is released on [arXiv](https://arxiv.org/abs/2305.14677).
+    - A demo video is shown on [Bilibili](https://www.bilibili.com/video/BV1w8411y7uj).
+    - Since OLSS requires additional training, we don't implement it in this project.
+
+- **Aug 29, 2023.** We propose DiffSynth, a video synthesis framework.
+  - [Project Page](https://ecnu-cilab.github.io/DiffSynth.github.io/).
+  - 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).
 
-* Aug 29, 2023. We propose DiffSynth, a video synthesis framework.
-    * [Project Page](https://ecnu-cilab.github.io/DiffSynth.github.io/).
-    * 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).
-* Oct 1, 2023. We release an early version of this project, namely FastSDXL. A try for building a diffusion engine.
-    * The source codes are released on [GitHub](https://github.com/Artiprocher/FastSDXL).
-    * FastSDXL includes a trainable OLSS scheduler for efficiency improvement.
-        * The original repo of OLSS is [here](https://github.com/alibaba/EasyNLP/tree/master/diffusion/olss_scheduler).
-        * The technical report (CIKM 2023) is released on [arXiv](https://arxiv.org/abs/2305.14677).
-        * A demo video is shown on [Bilibili](https://www.bilibili.com/video/BV1w8411y7uj).
-        * Since OLSS requires additional training, we don't implement it in this project.
-* Nov 15, 2023. We propose FastBlend, a powerful video deflickering algorithm.
-    * The sd-webui extension is released on [GitHub](https://github.com/Artiprocher/sd-webui-fastblend).
-    * Demo videos are shown on Bilibili, including three tasks.
-        * [Video deflickering](https://www.bilibili.com/video/BV1d94y1W7PE)
-        * [Video interpolation](https://www.bilibili.com/video/BV1Lw411m71p)
-        * [Image-driven video rendering](https://www.bilibili.com/video/BV1RB4y1Z7LF)
-    * The technical report is released on [arXiv](https://arxiv.org/abs/2311.09265).
-    * An unofficial ComfyUI extension developed by other users is released on [GitHub](https://github.com/AInseven/ComfyUI-fastblend).
-* Dec 8, 2023. We decide to develop a new Project, aiming to release the potential of diffusion models, especially in video synthesis. The development of this project is started.
-* Jan 29, 2024. We propose Diffutoon, a fantastic solution for toon shading.
-    * [Project Page](https://ecnu-cilab.github.io/DiffutoonProjectPage/).
-    * The source codes are released in this project.
-    * The technical report (IJCAI 2024) is released on [arXiv](https://arxiv.org/abs/2401.16224).
-* June 13, 2024. DiffSynth Studio is transfered to ModelScope. The developers have transitioned from "I" to "we". Of course, I will still participate in development and maintenance.
-* 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).
-    * Technical report is released on [arXiv](https://arxiv.org/abs/2406.14130).
-* Until now, DiffSynth Studio has supported the following models:
-    * [Stable Diffusion](https://huggingface.co/runwayml/stable-diffusion-v1-5)
-    * [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
-    * [ControlNet](https://github.com/lllyasviel/ControlNet)
-    * [AnimateDiff](https://github.com/guoyww/animatediff/)
-    * [Ip-Adapter](https://github.com/tencent-ailab/IP-Adapter)
-    * [ESRGAN](https://github.com/xinntao/ESRGAN)
-    * [RIFE](https://github.com/hzwer/ECCV2022-RIFE)
-    * [Hunyuan-DiT](https://github.com/Tencent/HunyuanDiT)
-    * [Stable Video Diffusion](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt)
-    * [ExVideo](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1)
 
 ## Installation
 
-Create Python environment:
+Install from source code (recommended):
 
 ```
-conda env create -f environment.yml
+git clone https://github.com/modelscope/DiffSynth-Studio.git
+cd DiffSynth-Studio
+pip install -e .
 ```
 
-We find that sometimes `conda` cannot install `cupy` correctly, please install it manually. See [this document](https://docs.cupy.dev/en/stable/install.html) for more details.
-
-Enter the Python environment:
+Or install from pypi (There is a delay in the update. If you want to experience the latest features, please do not use this installation method.):
 
 ```
-conda activate DiffSynthStudio
+pip install diffsynth
 ```
 
+If you encounter issues during installation, it may be caused by the packages we depend on. Please refer to the documentation of the package that caused the problem.
+
+* [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)
+
 ## Usage (in Python code)
 
 The Python examples are in [`examples`](./examples/). We provide an overview here.
 
-### Long Video Synthesis
+### Download Models
 
-We trained an extended video synthesis model, which can generate 128 frames. [`examples/ExVideo`](./examples/ExVideo/)
+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 extended video synthesis models, which can generate 128 frames. [`examples/ExVideo`](./examples/ExVideo/)
 
 https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc
 
-### Image Synthesis
+https://github.com/user-attachments/assets/321ee04b-8c17-479e-8a95-8cbcf21f8d7e
 
-Generate high-resolution images, by breaking the limitation of diffusion models! [`examples/image_synthesis`](./examples/image_synthesis/)
-
-|512*512|1024*1024|2048*2048|4096*4096|
-|-|-|-|-|
-|![512](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/55f679e9-7445-4605-9315-302e93d11370)|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/6fc84611-8da6-4a1f-8fee-9a34eba3b4a5)|![2048](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/9087a73c-9164-4c58-b2a0-effc694143fb)|![4096](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/edee9e71-fc39-4d1c-9ca9-fa52002c67ac)|
-
-|1024*1024|2048*2048|
-|-|-|
-|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/67687748-e738-438c-aee5-96096f09ac90)|![2048](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/584186bc-9855-4140-878e-99541f9a757f)|
-
-### Toon Shading
+#### Toon Shading
 
 Render realistic videos in a flatten style and enable video editing features. [`examples/Diffutoon`](./examples/Diffutoon/)
 
@@ -94,32 +186,60 @@ https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-47
 
 https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/20528af5-5100-474a-8cdc-440b9efdd86c
 
-### Video Stylization
+#### Video Stylization
 
 Video stylization without video models. [`examples/diffsynth`](./examples/diffsynth/)
 
 https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea
 
-### Chinese Models
+### Image Synthesis
 
-Use Hunyuan-DiT to generate images with Chinese prompts. We also support LoRA fine-tuning of this model. [`examples/hunyuan_dit`](./examples/hunyuan_dit/)
+Generate high-resolution images, by breaking the limitation of diffusion models! [`examples/image_synthesis`](./examples/image_synthesis/).
 
-Prompt: 少女手捧鲜花，坐在公园的长椅上，夕阳的余晖洒在少女的脸庞，整个画面充满诗意的美感
+LoRA fine-tuning is supported in [`examples/train`](./examples/train/).
 
-|1024x1024|2048x2048 (highres-fix)|
+|FLUX|Stable Diffusion 3|
 |-|-|
-|![image_1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/2b6528cf-a229-46e9-b7dd-4a9475b07308)|![image_2048](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/11d264ec-966b-45c9-9804-74b60428b866)|
+|![image_1024_cfg](https://github.com/user-attachments/assets/984561e9-553d-4952-9443-79ce144f379f)|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4df346db-6f91-420a-b4c1-26e205376098)|
 
-Prompt: 一只小狗蹦蹦跳跳，周围是姹紫嫣红的鲜花，远处是山脉
-
-|Without LoRA|With LoRA|
+|Kolors|Hunyuan-DiT|
 |-|-|
-|![image_without_lora](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/1aa21de5-a992-4b66-b14f-caa44e08876e)|![image_with_lora](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/83a0a41a-691f-4610-8e7b-d8e17c50a282)|
+|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/53ef6f41-da11-4701-8665-9f64392607bf)|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/60b022c8-df3f-4541-95ab-bf39f2fa8bb5)|
+
+|Stable Diffusion|Stable Diffusion XL|
+|-|-|
+|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/6fc84611-8da6-4a1f-8fee-9a34eba3b4a5)|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/67687748-e738-438c-aee5-96096f09ac90)|
 
 ## 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
+
 ```
-python -m streamlit run DiffSynth_Studio.py
+pip install gradio
+```
+
+```
+python apps/gradio/DiffSynth_Studio.py
+```
+
+![20240822102002](https://github.com/user-attachments/assets/59613157-de51-4109-99b3-97cbffd88076)
+
+* `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

apps/gradio/DiffSynth_Studio.py

@@ -0,0 +1,252 @@
+import gradio as gr
+from diffsynth import ModelManager, SDImagePipeline, SDXLImagePipeline, SD3ImagePipeline, HunyuanDiTImagePipeline, FluxImagePipeline
+import os, torch
+from PIL import Image
+import numpy as np
+
+
+config = {
+    "model_config": {
+        "Stable Diffusion": {
+            "model_folder": "models/stable_diffusion",
+            "pipeline_class": SDImagePipeline,
+            "default_parameters": {
+                "cfg_scale": 7.0,
+                "height": 512,
+                "width": 512,
+            }
+        },
+        "Stable Diffusion XL": {
+            "model_folder": "models/stable_diffusion_xl",
+            "pipeline_class": SDXLImagePipeline,
+            "default_parameters": {
+                "cfg_scale": 7.0,
+            }
+        },
+        "Stable Diffusion 3": {
+            "model_folder": "models/stable_diffusion_3",
+            "pipeline_class": SD3ImagePipeline,
+            "default_parameters": {
+                "cfg_scale": 7.0,
+            }
+        },
+        "Stable Diffusion XL Turbo": {
+            "model_folder": "models/stable_diffusion_xl_turbo",
+            "pipeline_class": SDXLImagePipeline,
+            "default_parameters": {
+                "negative_prompt": "",
+                "cfg_scale": 1.0,
+                "num_inference_steps": 1,
+                "height": 512,
+                "width": 512,
+            }
+        },
+        "Kolors": {
+            "model_folder": "models/kolors",
+            "pipeline_class": SDXLImagePipeline,
+            "default_parameters": {
+                "cfg_scale": 7.0,
+            }
+        },
+        "HunyuanDiT": {
+            "model_folder": "models/HunyuanDiT",
+            "pipeline_class": HunyuanDiTImagePipeline,
+            "default_parameters": {
+                "cfg_scale": 7.0,
+            }
+        },
+        "FLUX": {
+            "model_folder": "models/FLUX",
+            "pipeline_class": FluxImagePipeline,
+            "default_parameters": {
+                "cfg_scale": 1.0,
+            }
+        }
+    },
+    "max_num_painter_layers": 8,
+    "max_num_model_cache": 1,
+}
+
+
+def load_model_list(model_type):
+    if model_type is None:
+        return []
+    folder = config["model_config"][model_type]["model_folder"]
+    file_list = [i for i in os.listdir(folder) if i.endswith(".safetensors")]
+    if model_type in ["HunyuanDiT", "Kolors", "FLUX"]:
+        file_list += [i for i in os.listdir(folder) if os.path.isdir(os.path.join(folder, i))]
+    file_list = sorted(file_list)
+    return file_list
+
+
+def load_model(model_type, model_path):
+    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()
+    if model_type == "HunyuanDiT":
+        model_manager.load_models([
+            os.path.join(model_path, "clip_text_encoder/pytorch_model.bin"),
+            os.path.join(model_path, "mt5/pytorch_model.bin"),
+            os.path.join(model_path, "model/pytorch_model_ema.pt"),
+            os.path.join(model_path, "sdxl-vae-fp16-fix/diffusion_pytorch_model.bin"),
+        ])
+    elif model_type == "Kolors":
+        model_manager.load_models([
+            os.path.join(model_path, "text_encoder"),
+            os.path.join(model_path, "unet/diffusion_pytorch_model.safetensors"),
+            os.path.join(model_path, "vae/diffusion_pytorch_model.safetensors"),
+        ])
+    elif model_type == "FLUX":
+        model_manager.torch_dtype = torch.bfloat16
+        file_list = [
+            os.path.join(model_path, "text_encoder/model.safetensors"),
+            os.path.join(model_path, "text_encoder_2"),
+        ]
+        for file_name in os.listdir(model_path):
+            if file_name.endswith(".safetensors"):
+                file_list.append(os.path.join(model_path, file_name))
+        model_manager.load_models(file_list)
+    else:
+        model_manager.load_model(model_path)
+    pipe = config["model_config"][model_type]["pipeline_class"].from_model_manager(model_manager)
+    while len(model_dict) + 1 > config["max_num_model_cache"]:
+        key = next(iter(model_dict.keys()))
+        model_manager_to_release, _ = model_dict[key]
+        model_manager_to_release.to("cpu")
+        del model_dict[key]
+        torch.cuda.empty_cache()
+    model_dict[model_key] = model_manager, pipe
+    return model_manager, pipe
+
+
+model_dict = {}
+
+with gr.Blocks() as app:
+    gr.Markdown("# DiffSynth-Studio Painter")
+    with gr.Row():
+        with gr.Column(scale=382, min_width=100):
+
+            with gr.Accordion(label="Model"):
+                model_type = gr.Dropdown(choices=[i for i in config["model_config"]], label="Model type")
+                model_path = gr.Dropdown(choices=[], interactive=True, label="Model path")
+
+                @gr.on(inputs=model_type, outputs=model_path, triggers=model_type.change)
+                def model_type_to_model_path(model_type):
+                    return gr.Dropdown(choices=load_model_list(model_type))
+                
+            with gr.Accordion(label="Prompt"):
+                prompt = gr.Textbox(label="Prompt", lines=3)
+                negative_prompt = gr.Textbox(label="Negative prompt", lines=1)
+                cfg_scale = gr.Slider(minimum=1.0, maximum=10.0, value=7.0, step=0.1, interactive=True, label="Classifier-free guidance scale")
+                embedded_guidance = gr.Slider(minimum=0.0, maximum=10.0, value=0.0, step=0.1, interactive=True, label="Embedded guidance scale (only for FLUX)")
+            
+            with gr.Accordion(label="Image"):
+                num_inference_steps = gr.Slider(minimum=1, maximum=100, value=20, step=1, interactive=True, label="Inference steps")
+                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.Column():
+                    use_fixed_seed = gr.Checkbox(value=True, interactive=False, label="Use fixed seed")
+                    seed = gr.Number(minimum=0, maximum=10**9, value=0, interactive=True, label="Random seed", show_label=False)
+
+            @gr.on(
+                inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width],
+                outputs=[prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width],
+                triggers=model_path.change
+            )
+            def model_path_to_default_params(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width):
+                load_model(model_type, model_path)
+                cfg_scale = config["model_config"][model_type]["default_parameters"].get("cfg_scale", cfg_scale)
+                embedded_guidance = config["model_config"][model_type]["default_parameters"].get("embedded_guidance", embedded_guidance)
+                num_inference_steps = config["model_config"][model_type]["default_parameters"].get("num_inference_steps", num_inference_steps)
+                height = config["model_config"][model_type]["default_parameters"].get("height", height)
+                width = config["model_config"][model_type]["default_parameters"].get("width", width)
+                return prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width
+                
+
+        with gr.Column(scale=618, min_width=100):
+            with gr.Accordion(label="Painter"):
+                enable_local_prompt_list = []
+                local_prompt_list = []
+                mask_scale_list = []
+                canvas_list = []
+                for painter_layer_id in range(config["max_num_painter_layers"]):
+                    with gr.Tab(label=f"Layer {painter_layer_id}"):
+                        enable_local_prompt = gr.Checkbox(label="Enable", value=False, key=f"enable_local_prompt_{painter_layer_id}")
+                        local_prompt = gr.Textbox(label="Local prompt", key=f"local_prompt_{painter_layer_id}")
+                        mask_scale = gr.Slider(minimum=0.0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Mask scale", key=f"mask_scale_{painter_layer_id}")
+                        canvas = gr.ImageEditor(canvas_size=(512, 1), sources=None, layers=False, interactive=True, image_mode="RGBA",
+                                                brush=gr.Brush(default_size=100, default_color="#000000", colors=["#000000"]),
+                                                label="Painter", key=f"canvas_{painter_layer_id}")
+                        @gr.on(inputs=[height, width, canvas], outputs=canvas, triggers=[height.change, width.change, canvas.clear, enable_local_prompt.change], 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
+                        
+                        enable_local_prompt_list.append(enable_local_prompt)
+                        local_prompt_list.append(local_prompt)
+                        mask_scale_list.append(mask_scale)
+                        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():
+                        output_to_input_button = gr.Button(value="Set as input image")
+                painter_background = gr.State(None)
+                input_background = gr.State(None)
+                @gr.on(
+                    inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, seed] + enable_local_prompt_list + local_prompt_list + mask_scale_list + canvas_list,
+                    outputs=[output_image],
+                    triggers=run_button.click
+                )
+                def generate_image(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, seed, *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
+                    enable_local_prompt_list, local_prompt_list, mask_scale_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"]],
+                        args[2 * config["max_num_painter_layers"]: 3 * config["max_num_painter_layers"]],
+                        args[3 * config["max_num_painter_layers"]: 4 * config["max_num_painter_layers"]]
+                    )
+                    local_prompts, masks, mask_scales = [], [], []
+                    for enable_local_prompt, local_prompt, mask_scale, canvas in zip(
+                        enable_local_prompt_list, local_prompt_list, mask_scale_list, canvas_list
+                    ):
+                        if enable_local_prompt:
+                            local_prompts.append(local_prompt)
+                            masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB"))
+                            mask_scales.append(mask_scale)
+                    input_params.update({
+                        "local_prompts": local_prompts,
+                        "masks": masks,
+                        "mask_scales": mask_scales,
+                    })
+                    torch.manual_seed(seed)
+                    image = pipe(**input_params)
+                    return image
+                
+                @gr.on(inputs=[output_image] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click)
+                def send_output_to_painter_background(output_image, *canvas_list):
+                    for canvas in canvas_list:
+                        h, w = canvas["background"].shape[:2]
+                        canvas["background"] = output_image.resize((w, h))
+                    return tuple(canvas_list)
+app.launch()

apps/gradio/entity_level_control.py

@@ -0,0 +1,390 @@
+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()

apps/streamlit/DiffSynth_Studio.py

@@ -1,7 +1,7 @@
 # Set web page format
 import streamlit as st
 st.set_page_config(layout="wide")
-# Diasble virtual VRAM on windows system
+# Disable virtual VRAM on windows system
 import torch
 torch.cuda.set_per_process_memory_fraction(0.999, 0)
 

apps/streamlit/pages/1_Image_Creator.py

@@ -1,11 +1,11 @@
-import torch, os, io
+import torch, os, io, json, time
 import numpy as np
 from PIL import Image
 import streamlit as st
 st.set_page_config(layout="wide")
 from streamlit_drawable_canvas import st_canvas
 from diffsynth.models import ModelManager
-from diffsynth.pipelines import SDImagePipeline, SDXLImagePipeline, HunyuanDiTImagePipeline
+from diffsynth.pipelines import SDImagePipeline, SDXLImagePipeline, SD3ImagePipeline, HunyuanDiTImagePipeline, FluxImagePipeline
 from diffsynth.data.video import crop_and_resize
 
 
@@ -20,6 +20,11 @@ config = {
         "pipeline_class": SDXLImagePipeline,
         "fixed_parameters": {}
     },
+    "Stable Diffusion 3": {
+        "model_folder": "models/stable_diffusion_3",
+        "pipeline_class": SD3ImagePipeline,
+        "fixed_parameters": {}
+    },
     "Stable Diffusion XL Turbo": {
         "model_folder": "models/stable_diffusion_xl_turbo",
         "pipeline_class": SDXLImagePipeline,
@@ -31,6 +36,11 @@ config = {
             "width": 512,
         }
     },
+    "Kolors": {
+        "model_folder": "models/kolors",
+        "pipeline_class": SDXLImagePipeline,
+        "fixed_parameters": {}
+    },
     "HunyuanDiT": {
         "model_folder": "models/HunyuanDiT",
         "pipeline_class": HunyuanDiTImagePipeline,
@@ -39,13 +49,20 @@ config = {
             "width": 1024,
         }
     },
+    "FLUX": {
+        "model_folder": "models/FLUX",
+        "pipeline_class": FluxImagePipeline,
+        "fixed_parameters": {
+            "cfg_scale": 1.0,
+        }
+    }
 }
 
 
 def load_model_list(model_type):
     folder = config[model_type]["model_folder"]
     file_list = [i for i in os.listdir(folder) if i.endswith(".safetensors")]
-    if model_type == "HunyuanDiT":
+    if model_type in ["HunyuanDiT", "Kolors", "FLUX"]:
         file_list += [i for i in os.listdir(folder) if os.path.isdir(os.path.join(folder, i))]
     file_list = sorted(file_list)
     return file_list
@@ -69,6 +86,22 @@ def load_model(model_type, model_path):
             os.path.join(model_path, "model/pytorch_model_ema.pt"),
             os.path.join(model_path, "sdxl-vae-fp16-fix/diffusion_pytorch_model.bin"),
         ])
+    elif model_type == "Kolors":
+        model_manager.load_models([
+            os.path.join(model_path, "text_encoder"),
+            os.path.join(model_path, "unet/diffusion_pytorch_model.safetensors"),
+            os.path.join(model_path, "vae/diffusion_pytorch_model.safetensors"),
+        ])
+    elif model_type == "FLUX":
+        model_manager.torch_dtype = torch.bfloat16
+        file_list = [
+            os.path.join(model_path, "text_encoder/model.safetensors"),
+            os.path.join(model_path, "text_encoder_2"),
+        ]
+        for file_name in os.listdir(model_path):
+            if file_name.endswith(".safetensors"):
+                file_list.append(os.path.join(model_path, file_name))
+        model_manager.load_models(file_list)
     else:
         model_manager.load_model(model_path)
     pipeline = config[model_type]["pipeline_class"].from_model_manager(model_manager)
@@ -239,6 +272,48 @@ with column_input:
                     key="canvas"
                 )
 
+    num_painter_layer = st.number_input("Number of painter layers", min_value=0, max_value=10, step=1, value=0)
+    local_prompts, masks, mask_scales = [], [], []
+    white_board = Image.fromarray(np.ones((512, 512, 3), dtype=np.uint8) * 255)
+    painter_layers_json_data = []
+    for painter_tab_id in range(num_painter_layer):
+        with st.expander(f"Painter layer {painter_tab_id}", expanded=True):
+            enable_local_prompt = st.checkbox(f"Enable prompt {painter_tab_id}", value=True)
+            local_prompt = st.text_area(f"Prompt {painter_tab_id}")
+            mask_scale = st.slider(f"Mask scale {painter_tab_id}", min_value=0.0, max_value=3.0, value=1.0)
+            stroke_width = st.slider(f"Stroke width {painter_tab_id}", min_value=1, max_value=300, value=100)
+            canvas_result_local = st_canvas(
+                fill_color="#000000",
+                stroke_width=stroke_width,
+                stroke_color="#000000",
+                background_color="rgba(255, 255, 255, 0)",
+                background_image=white_board,
+                update_streamlit=True,
+                height=512,
+                width=512,
+                drawing_mode="freedraw",
+                key=f"canvas_{painter_tab_id}"
+            )
+            if canvas_result_local.json_data is not None:
+                painter_layers_json_data.append(canvas_result_local.json_data.copy())
+                painter_layers_json_data[-1]["prompt"] = local_prompt
+            if enable_local_prompt:
+                local_prompts.append(local_prompt)
+                if canvas_result_local.image_data is not None:
+                    mask = apply_stroke_to_image(canvas_result_local.image_data, white_board)
+                else:
+                    mask = white_board
+                mask = Image.fromarray(255 - np.array(mask))
+                masks.append(mask)
+                mask_scales.append(mask_scale)
+    save_painter_layers = st.button("Save painter layers")
+    if save_painter_layers:
+        os.makedirs("data/painter_layers", exist_ok=True)
+        json_file_path = f"data/painter_layers/{time.time_ns()}.json"
+        with open(json_file_path, "w") as f:
+            json.dump(painter_layers_json_data, f, indent=4)
+            st.markdown(f"Painter layers are saved in {json_file_path}.")
+
 
 with column_output:
     run_button = st.button("Generate image", type="primary")
@@ -266,6 +341,7 @@ with column_output:
                 progress_bar_st = st.progress(0.0)
                 image = pipeline(
                     prompt, negative_prompt=negative_prompt,
+                    local_prompts=local_prompts, masks=masks, mask_scales=mask_scales,
                     cfg_scale=cfg_scale, num_inference_steps=num_inference_steps,
                     height=height, width=width,
                     input_image=input_image, denoising_strength=denoising_strength,

diffsynth/__init__.py

@@ -1,6 +1,6 @@
 from .data import *
 from .models import *
-from .prompts import *
+from .prompters import *
 from .schedulers import *
 from .pipelines import *
 from .controlnets import *

diffsynth/configs/model_config.py

@@ -0,0 +1,777 @@
+from typing_extensions import Literal, TypeAlias
+
+from ..models.sd_text_encoder import SDTextEncoder
+from ..models.sd_unet import SDUNet
+from ..models.sd_vae_encoder import SDVAEEncoder
+from ..models.sd_vae_decoder import SDVAEDecoder
+
+from ..models.sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2
+from ..models.sdxl_unet import SDXLUNet
+from ..models.sdxl_vae_decoder import SDXLVAEDecoder
+from ..models.sdxl_vae_encoder import SDXLVAEEncoder
+
+from ..models.sd3_text_encoder import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3
+from ..models.sd3_dit import SD3DiT
+from ..models.sd3_vae_decoder import SD3VAEDecoder
+from ..models.sd3_vae_encoder import SD3VAEEncoder
+
+from ..models.sd_controlnet import SDControlNet
+from ..models.sdxl_controlnet import SDXLControlNetUnion
+
+from ..models.sd_motion import SDMotionModel
+from ..models.sdxl_motion import SDXLMotionModel
+
+from ..models.svd_image_encoder import SVDImageEncoder
+from ..models.svd_unet import SVDUNet
+from ..models.svd_vae_decoder import SVDVAEDecoder
+from ..models.svd_vae_encoder import SVDVAEEncoder
+
+from ..models.sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder
+from ..models.sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder
+
+from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder
+from ..models.hunyuan_dit import HunyuanDiT
+
+from ..models.flux_dit import FluxDiT
+from ..models.flux_text_encoder import FluxTextEncoder2
+from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder
+from ..models.flux_controlnet import FluxControlNet
+from ..models.flux_ipadapter import FluxIpAdapter
+
+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
+
+
+model_loader_configs = [
+    # These configs are provided for detecting model type automatically.
+    # The format is (state_dict_keys_hash, state_dict_keys_hash_with_shape, model_names, model_classes, model_resource)
+    (None, "091b0e30e77c76626b3ba62acdf95343", ["sd_controlnet"], [SDControlNet], "civitai"),
+    (None, "4a6c8306a27d916dea81263c8c88f450", ["hunyuan_dit_clip_text_encoder"], [HunyuanDiTCLIPTextEncoder], "civitai"),
+    (None, "f4aec400fe394297961218c768004521", ["hunyuan_dit"], [HunyuanDiT], "civitai"),
+    (None, "9e6e58043a5a2e332803ed42f6ee7181", ["hunyuan_dit_t5_text_encoder"], [HunyuanDiTT5TextEncoder], "civitai"),
+    (None, "13115dd45a6e1c39860f91ab073b8a78", ["sdxl_vae_encoder", "sdxl_vae_decoder"], [SDXLVAEEncoder, SDXLVAEDecoder], "diffusers"),
+    (None, "d78aa6797382a6d455362358a3295ea9", ["sd_ipadapter_clip_image_encoder"], [IpAdapterCLIPImageEmbedder], "diffusers"),
+    (None, "e291636cc15e803186b47404262ef812", ["sd_ipadapter"], [SDIpAdapter], "civitai"),
+    (None, "399c81f2f8de8d1843d0127a00f3c224", ["sdxl_ipadapter_clip_image_encoder"], [IpAdapterXLCLIPImageEmbedder], "diffusers"),
+    (None, "a64eac9aa0db4b9602213bc0131281c7", ["sdxl_ipadapter"], [SDXLIpAdapter], "civitai"),
+    (None, "52817e4fdd89df154f02749ca6f692ac", ["sdxl_unet"], [SDXLUNet], "diffusers"),
+    (None, "03343c606f16d834d6411d0902b53636", ["sd_text_encoder", "sd_unet", "sd_vae_decoder", "sd_vae_encoder"], [SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder], "civitai"),
+    (None, "d4ba77a7ece070679b4a987f58f201e9", ["sd_text_encoder"], [SDTextEncoder], "civitai"),
+    (None, "d0c89e55c5a57cf3981def0cb1c9e65a", ["sd_vae_decoder", "sd_vae_encoder"], [SDVAEDecoder, SDVAEEncoder], "civitai"),
+    (None, "3926bf373b39a67eeafd7901478a47a7", ["sd_unet"], [SDUNet], "civitai"),
+    (None, "1e0c39ec176b9007c05f76d52b554a4d", ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3TextEncoder1, SD3TextEncoder2, SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
+    (None, "d9e0290829ba8d98e28e1a2b1407db4a", ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_text_encoder_3", "sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3, SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"),
+    (None, "5072d0b24e406b49507abe861cf97691", ["sd3_text_encoder_3"], [SD3TextEncoder3], "civitai"),
+    (None, "4cf64a799d04260df438c6f33c9a047e", ["sdxl_text_encoder", "sdxl_text_encoder_2", "sdxl_unet", "sdxl_vae_decoder", "sdxl_vae_encoder"], [SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder], "civitai"),
+    (None, "d9b008a867c498ab12ad24042eff8e3f", ["sdxl_text_encoder", "sdxl_text_encoder_2", "sdxl_unet", "sdxl_vae_decoder", "sdxl_vae_encoder"], [SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder], "civitai"), # SDXL-Turbo
+    (None, "025bb7452e531a3853d951d77c63f032", ["sdxl_text_encoder", "sdxl_text_encoder_2"], [SDXLTextEncoder, SDXLTextEncoder2], "civitai"),
+    (None, "298997b403a4245c04102c9f36aac348", ["sdxl_unet"], [SDXLUNet], "civitai"),
+    (None, "2a07abce74b4bdc696b76254ab474da6", ["svd_image_encoder", "svd_unet", "svd_vae_decoder", "svd_vae_encoder"], [SVDImageEncoder, SVDUNet, SVDVAEDecoder, SVDVAEEncoder], "civitai"),
+    (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, "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, "605c56eab23e9e2af863ad8f0813a25d", ["flux_dit"], [FluxDiT], "diffusers"),
+    (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, "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, "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"),
+]
+huggingface_model_loader_configs = [
+    # These configs are provided for detecting model type automatically.
+    # The format is (architecture_in_huggingface_config, huggingface_lib, model_name, redirected_architecture)
+    ("ChatGLMModel", "diffsynth.models.kolors_text_encoder", "kolors_text_encoder", None),
+    ("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),
+    ("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"),
+]
+patch_model_loader_configs = [
+    # These configs are provided for detecting model type automatically.
+    # The format is (state_dict_keys_hash_with_shape, model_name, model_class, extra_kwargs)
+    ("9a4ab6869ac9b7d6e31f9854e397c867", ["svd_unet"], [SVDUNet], {"add_positional_conv": 128}),
+]
+
+preset_models_on_huggingface = {
+    "HunyuanDiT": [
+        ("Tencent-Hunyuan/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"),
+        ("Tencent-Hunyuan/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"),
+        ("Tencent-Hunyuan/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"),
+        ("Tencent-Hunyuan/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"),
+    ],
+    "stable-video-diffusion-img2vid-xt": [
+        ("stabilityai/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"),
+    ],
+    "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"),
+        ("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"),
+    ],
+    # FLUX
+    "FLUX.1-dev": [
+        ("black-forest-labs/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"),
+        ("black-forest-labs/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+        ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+        ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+        ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"),
+        ("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
+    "HunyuanDiT": [
+        ("modelscope/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"),
+        ("modelscope/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"),
+        ("modelscope/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"),
+        ("modelscope/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"),
+    ],
+    # Stable Video Diffusion
+    "stable-video-diffusion-img2vid-xt": [
+        ("AI-ModelScope/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"),
+    ],
+    # ExVideo
+    "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"),
+    ],
+    "DreamShaper_8": [
+        ("sd_lora/dreamshaper_8", "dreamshaper_8.safetensors", "models/stable_diffusion"),
+    ],
+    "AingDiffusion_v12": [
+        ("sd_lora/aingdiffusion_v12", "aingdiffusion_v12.safetensors", "models/stable_diffusion"),
+    ],
+    "Flat2DAnimerge_v45Sharp": [
+        ("sd_lora/Flat-2D-Animerge", "flat2DAnimerge_v45Sharp.safetensors", "models/stable_diffusion"),
+    ],
+    # Textual Inversion
+    "TextualInversion_VeryBadImageNegative_v1.3": [
+        ("sd_lora/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"),
+    ],
+    # Stable Diffusion XL
+    "StableDiffusionXL_v1": [
+        ("AI-ModelScope/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"),
+    ],
+    "BluePencilXL_v200": [
+        ("sd_lora/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"),
+    ],
+    "StableDiffusionXL_Turbo": [
+        ("AI-ModelScope/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"),
+    ],
+    "SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0": [
+        ("sd_lora/zyd232_ChineseInkStyle_SDXL_v1_0", "zyd232_ChineseInkStyle_SDXL_v1_0.safetensors", "models/lora"),
+    ],
+    # Stable Diffusion 3
+    "StableDiffusion3": [
+        ("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"),
+    ],
+    "StableDiffusion3_without_T5": [
+        ("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"),
+    ],
+    # ControlNet
+    "ControlNet_v11f1p_sd15_depth": [
+        ("AI-ModelScope/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"),
+        ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators")
+    ],
+    "ControlNet_v11p_sd15_softedge": [
+        ("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"),
+        ("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators")
+    ],
+    "ControlNet_v11f1e_sd15_tile": [
+        ("AI-ModelScope/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet")
+    ],
+    "ControlNet_v11p_sd15_lineart": [
+        ("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"),
+        ("sd_lora/Annotators", "sk_model.pth", "models/Annotators"),
+        ("sd_lora/Annotators", "sk_model2.pth", "models/Annotators")
+    ],
+    "ControlNet_union_sdxl_promax": [
+        ("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"),
+    ],
+    "AnimateDiff_xl_beta": [
+        ("Shanghai_AI_Laboratory/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"),
+    ],
+    # RIFE
+    "RIFE": [
+        ("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",
+        ],
+    },
+    # 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",
+        ],
+    },
+    # 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",
+        ],
+    },
+    # 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",
+        ],
+    },
+    # IP-Adapter
+    "IP-Adapter-SD": [
+        ("AI-ModelScope/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"),
+        ("AI-ModelScope/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"),
+    ],
+    "IP-Adapter-SDXL": [
+        ("AI-ModelScope/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"),
+        ("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",
+        ],
+    },
+    "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"),
+    ],
+    "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",
+        ],
+    },
+    # ESRGAN
+    "ESRGAN_x4": [
+        ("AI-ModelScope/Real-ESRGAN", "RealESRGAN_x4.pth", "models/ESRGAN"),
+    ],
+    # RIFE
+    "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"),
+    ],
+    "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",
+    "Flat2DAnimerge_v45Sharp",
+    "TextualInversion_VeryBadImageNegative_v1.3",
+    "StableDiffusionXL_v1",
+    "BluePencilXL_v200",
+    "StableDiffusionXL_Turbo",
+    "ControlNet_v11f1p_sd15_depth",
+    "ControlNet_v11p_sd15_softedge",
+    "ControlNet_v11f1e_sd15_tile",
+    "ControlNet_v11p_sd15_lineart",
+    "AnimateDiff_v2",
+    "AnimateDiff_xl_beta",
+    "RIFE",
+    "BeautifulPrompt",
+    "opus-mt-zh-en",
+    "IP-Adapter-SD",
+    "IP-Adapter-SDXL",
+    "StableDiffusion3",
+    "StableDiffusion3_without_T5",
+    "Kolors",
+    "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",
+    "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",
+]

diffsynth/controlnets/__init__.py

@@ -1,2 +1,2 @@
-from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager
+from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager, FluxMultiControlNetManager
 from .processors import Annotator

diffsynth/controlnets/controlnet_unit.py

@@ -4,10 +4,11 @@ from .processors import Processor_id
 
 
 class ControlNetConfigUnit:
-    def __init__(self, processor_id: Processor_id, model_path, scale=1.0):
+    def __init__(self, processor_id: Processor_id, model_path, scale=1.0, skip_processor=False):
         self.processor_id = processor_id
         self.model_path = model_path
         self.scale = scale
+        self.skip_processor = skip_processor
 
 
 class ControlNetUnit:
@@ -23,6 +24,16 @@ class MultiControlNetManager:
         self.models = [unit.model for unit in controlnet_units]
         self.scales = [unit.scale for unit in controlnet_units]
 
+    def cpu(self):
+        for model in self.models:
+            model.cpu()
+
+    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:
             processed_image = [processor(image) for processor in self.processors]
@@ -37,13 +48,14 @@ class MultiControlNetManager:
     def __call__(
         self,
         sample, timestep, encoder_hidden_states, conditionings,
-        tiled=False, tile_size=64, tile_stride=32
+        tiled=False, tile_size=64, tile_stride=32, **kwargs
     ):
         res_stack = None
-        for conditioning, model, scale in zip(conditionings, self.models, self.scales):
+        for processor, conditioning, model, scale in zip(self.processors, conditionings, self.models, self.scales):
             res_stack_ = model(
-                sample, timestep, encoder_hidden_states, conditioning,
-                tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
+                sample, timestep, encoder_hidden_states, conditioning, **kwargs,
+                tiled=tiled, tile_size=tile_size, tile_stride=tile_stride,
+                processor_id=processor.processor_id
             )
             res_stack_ = [res * scale for res in res_stack_]
             if res_stack is None:
@@ -51,3 +63,29 @@ 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

diffsynth/controlnets/processors.py

@@ -1,39 +1,50 @@
 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", "tile"
+    "canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "normal", "tile", "none", "inpaint"
 ]
 
 class Annotator:
-    def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None):
-        if processor_id == "canny":
-            self.processor = CannyDetector()
-        elif processor_id == "depth":
-            self.processor = MidasDetector.from_pretrained(model_path).to("cuda")
-        elif processor_id == "softedge":
-            self.processor = HEDdetector.from_pretrained(model_path).to("cuda")
-        elif processor_id == "lineart":
-            self.processor = LineartDetector.from_pretrained(model_path).to("cuda")
-        elif processor_id == "lineart_anime":
-            self.processor = LineartAnimeDetector.from_pretrained(model_path).to("cuda")
-        elif processor_id == "openpose":
-            self.processor = OpenposeDetector.from_pretrained(model_path).to("cuda")
-        elif processor_id == "tile":
-            self.processor = None
+    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:
-            raise ValueError(f"Unsupported processor_id: {processor_id}")
-        
+            self.processor = None
+
         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"):
 
-    def __call__(self, image):
+            self.processor.model.to(device)
+
+    def __call__(self, image, mask=None):
         width, height = image.size
         if self.processor_id == "openpose":
             kwargs = {

diffsynth/data/simple_text_image.py

@@ -0,0 +1,41 @@
+import torch, os, torchvision
+from torchvision import transforms
+import pandas as pd
+from PIL import Image
+
+
+
+class TextImageDataset(torch.utils.data.Dataset):
+    def __init__(self, dataset_path, steps_per_epoch=10000, height=1024, width=1024, center_crop=True, random_flip=False):
+        self.steps_per_epoch = steps_per_epoch
+        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.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)),
+                transforms.RandomHorizontalFlip() if random_flip else transforms.Lambda(lambda x: x),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+
+
+    def __getitem__(self, index):
+        data_id = torch.randint(0, len(self.path), (1,))[0]
+        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}
+
+
+    def __len__(self):
+        return self.steps_per_epoch

diffsynth/data/video.py

@@ -135,8 +135,8 @@ class VideoData:
             frame.save(os.path.join(folder, f"{i}.png"))
 
 
-def save_video(frames, save_path, fps, quality=9):
-    writer = imageio.get_writer(save_path, fps=fps, quality=quality)
+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)
     for frame in tqdm(frames, desc="Saving video"):
         frame = np.array(frame)
         writer.append_data(frame)

diffsynth/extensions/ESRGAN/__init__.py

@@ -41,7 +41,7 @@ class RRDB(torch.nn.Module):
 
 class RRDBNet(torch.nn.Module):
 
-    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32):
+    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, **kwargs):
         super(RRDBNet, self).__init__()
         self.conv_first = torch.nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
         self.body = torch.torch.nn.Sequential(*[RRDB(num_feat=num_feat, num_grow_ch=num_grow_ch) for _ in range(num_block)])
@@ -65,6 +65,21 @@ class RRDBNet(torch.nn.Module):
         feat = self.lrelu(self.conv_up2(feat))
         out = self.conv_last(self.lrelu(self.conv_hr(feat)))
         return out
+    
+    @staticmethod
+    def state_dict_converter():
+        return RRDBNetStateDictConverter()
+    
+
+class RRDBNetStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict, {"upcast_to_float32": True}
+    
+    def from_civitai(self, state_dict):
+        return state_dict, {"upcast_to_float32": True}
 
 
 class ESRGAN(torch.nn.Module):
@@ -73,12 +88,8 @@ class ESRGAN(torch.nn.Module):
         self.model = model
 
     @staticmethod
-    def from_pretrained(model_path):
-        model = RRDBNet()
-        state_dict = torch.load(model_path, map_location="cpu")["params_ema"]
-        model.load_state_dict(state_dict)
-        model.eval()
-        return ESRGAN(model)
+    def from_model_manager(model_manager):
+        return ESRGAN(model_manager.fetch_model("esrgan"))
 
     def process_image(self, image):
         image = torch.Tensor(np.array(image, dtype=np.float32) / 255).permute(2, 0, 1)
@@ -96,6 +107,12 @@ 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)
 
@@ -115,4 +132,6 @@ 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

diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py

@@ -0,0 +1 @@
+from .blip_pretrain import *

diffsynth/extensions/ImageQualityMetric/BLIP/blip.py

@@ -0,0 +1,77 @@
+'''
+ * 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
+    

diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py

@@ -0,0 +1,44 @@
+'''
+ * 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)
+

diffsynth/extensions/ImageQualityMetric/BLIP/med.py

@@ -0,0 +1,947 @@
+'''
+ * 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.warn(
+                        "`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

diffsynth/extensions/ImageQualityMetric/BLIP/vit.py

@@ -0,0 +1,301 @@
+'''
+ * 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

diffsynth/extensions/ImageQualityMetric/__init__.py

@@ -0,0 +1,148 @@
+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

diffsynth/extensions/ImageQualityMetric/aesthetic.py

@@ -0,0 +1,148 @@
+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}")

diffsynth/extensions/ImageQualityMetric/clip.py

@@ -0,0 +1,97 @@
+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.")

diffsynth/extensions/ImageQualityMetric/config.py

@@ -0,0 +1,23 @@
+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")
+}

diffsynth/extensions/ImageQualityMetric/hps.py

@@ -0,0 +1,118 @@
+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}")

diffsynth/extensions/ImageQualityMetric/imagereward.py

@@ -0,0 +1,212 @@
+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)

diffsynth/extensions/ImageQualityMetric/mps.py

@@ -0,0 +1,129 @@
+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.")

diffsynth/extensions/ImageQualityMetric/open_clip/__init__.py

@@ -0,0 +1,14 @@
+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

diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py

@@ -0,0 +1,458 @@
+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,
+    }

diffsynth/extensions/ImageQualityMetric/open_clip/constants.py

@@ -0,0 +1,2 @@
+OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
+OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)

diffsynth/extensions/ImageQualityMetric/open_clip/factory.py

@@ -0,0 +1,433 @@
+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

diffsynth/extensions/ImageQualityMetric/open_clip/hf_configs.py

@@ -0,0 +1,45 @@
+# 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",
+    },
+}

diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py

@@ -0,0 +1,176 @@
+""" 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

diffsynth/extensions/ImageQualityMetric/open_clip/loss.py

@@ -0,0 +1,270 @@
+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

diffsynth/extensions/ImageQualityMetric/open_clip/model.py

@@ -0,0 +1,461 @@
+""" 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

diffsynth/extensions/ImageQualityMetric/open_clip/model_configs/ViT-H-14.json

@@ -0,0 +1,17 @@
+{
+    "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
+    }
+}

diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py

@@ -0,0 +1,181 @@
+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

diffsynth/extensions/ImageQualityMetric/open_clip/openai.py

@@ -0,0 +1,144 @@
+""" 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

diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py

@@ -0,0 +1,376 @@
+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

diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py

@@ -0,0 +1,243 @@
+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.')

diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py

@@ -0,0 +1,127 @@
+""" 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

diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py

@@ -0,0 +1,211 @@
+""" 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

diffsynth/extensions/ImageQualityMetric/open_clip/transform.py

@@ -0,0 +1,216 @@
+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

diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py

@@ -0,0 +1,727 @@
+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

diffsynth/extensions/ImageQualityMetric/open_clip/utils.py

@@ -0,0 +1,60 @@
+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)

diffsynth/extensions/ImageQualityMetric/open_clip/version.py

@@ -0,0 +1 @@
+__version__ = '2.16.0'

diffsynth/extensions/ImageQualityMetric/pickscore.py

@@ -0,0 +1,112 @@
+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}")

diffsynth/extensions/ImageQualityMetric/trainer/__init__.py

@@ -0,0 +1 @@
+from .models import *

diffsynth/extensions/ImageQualityMetric/trainer/models/__init__.py

@@ -0,0 +1,3 @@
+from .base_model import *
+from .clip_model import *
+from .cross_modeling import *

diffsynth/extensions/ImageQualityMetric/trainer/models/base_model.py

@@ -0,0 +1,7 @@
+from dataclasses import dataclass
+
+
+
+@dataclass
+class BaseModelConfig:
+    pass

diffsynth/extensions/ImageQualityMetric/trainer/models/clip_model.py

@@ -0,0 +1,146 @@
+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)
+

diffsynth/extensions/ImageQualityMetric/trainer/models/cross_modeling.py

@@ -0,0 +1,292 @@
+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

diffsynth/extensions/RIFE/__init__.py

@@ -58,7 +58,7 @@ class IFBlock(nn.Module):
 
 
 class IFNet(nn.Module):
-    def __init__(self):
+    def __init__(self, **kwargs):
         super(IFNet, self).__init__()
         self.block0 = IFBlock(7+4, c=90)
         self.block1 = IFBlock(7+4, c=90)
@@ -99,7 +99,8 @@ class IFNet(nn.Module):
             merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])    
         return flow_list, mask_list[2], merged
     
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return IFNetStateDictConverter()
 
 
@@ -112,7 +113,7 @@ class IFNetStateDictConverter:
         return state_dict_
     
     def from_civitai(self, state_dict):
-        return self.from_diffusers(state_dict)
+        return self.from_diffusers(state_dict), {"upcast_to_float32": True}
 
 
 class RIFEInterpolater:
@@ -124,7 +125,7 @@ class RIFEInterpolater:
 
     @staticmethod
     def from_model_manager(model_manager):
-        return RIFEInterpolater(model_manager.RIFE, device=model_manager.device)
+        return RIFEInterpolater(model_manager.fetch_model("rife"), device=model_manager.device)
 
     def process_image(self, image):
         width, height = image.size
@@ -202,7 +203,7 @@ class RIFESmoother(RIFEInterpolater):
 
     @staticmethod
     def from_model_manager(model_manager):
-        return RIFESmoother(model_manager.RIFE, device=model_manager.device)
+        return RIFEInterpolater(model_manager.fetch_model("rife"), device=model_manager.device)
     
     def process_tensors(self, input_tensor, scale=1.0, batch_size=4):
         output_tensor = []

diffsynth/models/__init__.py

@@ -1,482 +1 @@
-import torch, os
-from safetensors import safe_open
-
-from .sd_text_encoder import SDTextEncoder
-from .sd_unet import SDUNet
-from .sd_vae_encoder import SDVAEEncoder
-from .sd_vae_decoder import SDVAEDecoder
-from .sd_lora import SDLoRA
-
-from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2
-from .sdxl_unet import SDXLUNet
-from .sdxl_vae_decoder import SDXLVAEDecoder
-from .sdxl_vae_encoder import SDXLVAEEncoder
-
-from .sd_controlnet import SDControlNet
-
-from .sd_motion import SDMotionModel
-from .sdxl_motion import SDXLMotionModel
-
-from .svd_image_encoder import SVDImageEncoder
-from .svd_unet import SVDUNet
-from .svd_vae_decoder import SVDVAEDecoder
-from .svd_vae_encoder import SVDVAEEncoder
-
-from .sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder
-from .sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder
-
-from .hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder
-from .hunyuan_dit import HunyuanDiT
-
-
-class ModelManager:
-    def __init__(self, torch_dtype=torch.float16, device="cuda"):
-        self.torch_dtype = torch_dtype
-        self.device = device
-        self.model = {}
-        self.model_path = {}
-        self.textual_inversion_dict = {}
-
-    def is_stable_video_diffusion(self, state_dict):
-        param_name = "model.diffusion_model.output_blocks.9.1.time_stack.0.norm_in.weight"
-        return param_name in state_dict
-
-    def is_RIFE(self, state_dict):
-        param_name = "block_tea.convblock3.0.1.weight"
-        return param_name in state_dict or ("module." + param_name) in state_dict
-
-    def is_beautiful_prompt(self, state_dict):
-        param_name = "transformer.h.9.self_attention.query_key_value.weight"
-        return param_name in state_dict
-
-    def is_stabe_diffusion_xl(self, state_dict):
-        param_name = "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight"
-        return param_name in state_dict
-
-    def is_stable_diffusion(self, state_dict):
-        if self.is_stabe_diffusion_xl(state_dict):
-            return False
-        param_name = "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.weight"
-        return param_name in state_dict
-    
-    def is_controlnet(self, state_dict):
-        param_name = "control_model.time_embed.0.weight"
-        param_name_2 = "mid_block.resnets.1.time_emb_proj.weight" # For controlnets in diffusers format
-        return param_name in state_dict or param_name_2 in state_dict
-    
-    def is_animatediff(self, state_dict):
-        param_name = "mid_block.motion_modules.0.temporal_transformer.proj_out.weight"
-        return param_name in state_dict
-    
-    def is_animatediff_xl(self, state_dict):
-        param_name = "up_blocks.2.motion_modules.2.temporal_transformer.transformer_blocks.0.ff_norm.weight"
-        return param_name in state_dict
-    
-    def is_sd_lora(self, state_dict):
-        param_name = "lora_unet_up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2.lora_up.weight"
-        return param_name in state_dict
-    
-    def is_translator(self, state_dict):
-        param_name = "model.encoder.layers.5.self_attn_layer_norm.weight"
-        return param_name in state_dict and len(state_dict) == 254
-    
-    def is_ipadapter(self, state_dict):
-        return "image_proj" in state_dict and "ip_adapter" in state_dict and state_dict["image_proj"]["proj.weight"].shape == torch.Size([3072, 1024])
-    
-    def is_ipadapter_image_encoder(self, state_dict):
-        param_name = "vision_model.encoder.layers.31.self_attn.v_proj.weight"
-        return param_name in state_dict and len(state_dict) == 521
-    
-    def is_ipadapter_xl(self, state_dict):
-        return "image_proj" in state_dict and "ip_adapter" in state_dict and state_dict["image_proj"]["proj.weight"].shape == torch.Size([8192, 1280])
-    
-    def is_ipadapter_xl_image_encoder(self, state_dict):
-        param_name = "vision_model.encoder.layers.47.self_attn.v_proj.weight"
-        return param_name in state_dict and len(state_dict) == 777
-    
-    def is_hunyuan_dit_clip_text_encoder(self, state_dict):
-        param_name = "bert.encoder.layer.23.attention.output.dense.weight"
-        return param_name in state_dict
-    
-    def is_hunyuan_dit_t5_text_encoder(self, state_dict):
-        param_name = "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight"
-        return param_name in state_dict
-    
-    def is_hunyuan_dit(self, state_dict):
-        param_name = "final_layer.adaLN_modulation.1.weight"
-        return param_name in state_dict
-    
-    def is_diffusers_vae(self, state_dict):
-        param_name = "quant_conv.weight"
-        return param_name in state_dict
-    
-    def is_ExVideo_StableVideoDiffusion(self, state_dict):
-        param_name = "blocks.185.positional_embedding.embeddings"
-        return param_name in state_dict
-    
-    def load_stable_video_diffusion(self, state_dict, components=None, file_path="", add_positional_conv=None):
-        component_dict = {
-            "image_encoder": SVDImageEncoder,
-            "unet": SVDUNet,
-            "vae_decoder": SVDVAEDecoder,
-            "vae_encoder": SVDVAEEncoder,
-        }
-        if components is None:
-            components = ["image_encoder", "unet", "vae_decoder", "vae_encoder"]
-        for component in components:
-            if component == "unet":
-                self.model[component] = component_dict[component](add_positional_conv=add_positional_conv)
-                self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict, add_positional_conv=add_positional_conv), strict=False)
-            else:
-                self.model[component] = component_dict[component]()
-                self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
-            self.model[component].to(self.torch_dtype).to(self.device)
-            self.model_path[component] = file_path
-    
-    def load_stable_diffusion(self, state_dict, components=None, file_path=""):
-        component_dict = {
-            "text_encoder": SDTextEncoder,
-            "unet": SDUNet,
-            "vae_decoder": SDVAEDecoder,
-            "vae_encoder": SDVAEEncoder,
-            "refiner": SDXLUNet,
-        }
-        if components is None:
-            components = ["text_encoder", "unet", "vae_decoder", "vae_encoder"]
-        for component in components:
-            if component == "text_encoder":
-                # Add additional token embeddings to text encoder
-                token_embeddings = [state_dict["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"]]
-                for keyword in self.textual_inversion_dict:
-                    _, embeddings = self.textual_inversion_dict[keyword]
-                    token_embeddings.append(embeddings.to(dtype=token_embeddings[0].dtype))
-                token_embeddings = torch.concat(token_embeddings, dim=0)
-                state_dict["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"] = token_embeddings
-                self.model[component] = component_dict[component](vocab_size=token_embeddings.shape[0])
-                self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
-                self.model[component].to(self.torch_dtype).to(self.device)
-            else:
-                self.model[component] = component_dict[component]()
-                self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
-                self.model[component].to(self.torch_dtype).to(self.device)
-            self.model_path[component] = file_path
-
-    def load_stable_diffusion_xl(self, state_dict, components=None, file_path=""):
-        component_dict = {
-            "text_encoder": SDXLTextEncoder,
-            "text_encoder_2": SDXLTextEncoder2,
-            "unet": SDXLUNet,
-            "vae_decoder": SDXLVAEDecoder,
-            "vae_encoder": SDXLVAEEncoder,
-        }
-        if components is None:
-            components = ["text_encoder", "text_encoder_2", "unet", "vae_decoder", "vae_encoder"]
-        for component in components:
-            self.model[component] = component_dict[component]()
-            self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
-            if component in ["vae_decoder", "vae_encoder"]:
-                # These two model will output nan when float16 is enabled.
-                # The precision problem happens in the last three resnet blocks.
-                # I do not know how to solve this problem.
-                self.model[component].to(torch.float32).to(self.device)
-            else:
-                self.model[component].to(self.torch_dtype).to(self.device)
-            self.model_path[component] = file_path
-
-    def load_controlnet(self, state_dict, file_path=""):
-        component = "controlnet"
-        if component not in self.model:
-            self.model[component] = []
-            self.model_path[component] = []
-        model = SDControlNet()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component].append(model)
-        self.model_path[component].append(file_path)
-
-    def load_animatediff(self, state_dict, file_path=""):
-        component = "motion_modules"
-        model = SDMotionModel()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_animatediff_xl(self, state_dict, file_path=""):
-        component = "motion_modules_xl"
-        model = SDXLMotionModel()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_beautiful_prompt(self, state_dict, file_path=""):
-        component = "beautiful_prompt"
-        from transformers import AutoModelForCausalLM
-        model_folder = os.path.dirname(file_path)
-        model = AutoModelForCausalLM.from_pretrained(
-            model_folder, state_dict=state_dict, local_files_only=True, torch_dtype=self.torch_dtype
-        ).to(self.device).eval()
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_RIFE(self, state_dict, file_path=""):
-        component = "RIFE"
-        from ..extensions.RIFE import IFNet
-        model = IFNet().eval()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(torch.float32).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_sd_lora(self, state_dict, alpha):
-        SDLoRA().add_lora_to_text_encoder(self.model["text_encoder"], state_dict, alpha=alpha, device=self.device)
-        SDLoRA().add_lora_to_unet(self.model["unet"], state_dict, alpha=alpha, device=self.device)
-
-    def load_translator(self, state_dict, file_path=""):
-        # This model is lightweight, we do not place it on GPU.
-        component = "translator"
-        from transformers import AutoModelForSeq2SeqLM
-        model_folder = os.path.dirname(file_path)
-        model = AutoModelForSeq2SeqLM.from_pretrained(model_folder).eval()
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_ipadapter(self, state_dict, file_path=""):
-        component = "ipadapter"
-        model = SDIpAdapter()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_ipadapter_image_encoder(self, state_dict, file_path=""):
-        component = "ipadapter_image_encoder"
-        model = IpAdapterCLIPImageEmbedder()
-        model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_ipadapter_xl(self, state_dict, file_path=""):
-        component = "ipadapter_xl"
-        model = SDXLIpAdapter()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_ipadapter_xl_image_encoder(self, state_dict, file_path=""):
-        component = "ipadapter_xl_image_encoder"
-        model = IpAdapterXLCLIPImageEmbedder()
-        model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_hunyuan_dit_clip_text_encoder(self, state_dict, file_path=""):
-        component = "hunyuan_dit_clip_text_encoder"
-        model = HunyuanDiTCLIPTextEncoder()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_hunyuan_dit_t5_text_encoder(self, state_dict, file_path=""):
-        component = "hunyuan_dit_t5_text_encoder"
-        model = HunyuanDiTT5TextEncoder()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_hunyuan_dit(self, state_dict, file_path=""):
-        component = "hunyuan_dit"
-        model = HunyuanDiT()
-        model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_diffusers_vae(self, state_dict, file_path=""):
-        # TODO: detect SD and SDXL
-        component = "vae_encoder"
-        model = SDXLVAEEncoder()
-        model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-        component = "vae_decoder"
-        model = SDXLVAEDecoder()
-        model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict))
-        model.to(self.torch_dtype).to(self.device)
-        self.model[component] = model
-        self.model_path[component] = file_path
-
-    def load_ExVideo_StableVideoDiffusion(self, state_dict, file_path=""):
-        unet_state_dict = self.model["unet"].state_dict()
-        self.model["unet"].to("cpu")
-        del self.model["unet"]
-        add_positional_conv = state_dict["blocks.185.positional_embedding.embeddings"].shape[0]
-        self.model["unet"] = SVDUNet(add_positional_conv=add_positional_conv)
-        self.model["unet"].load_state_dict(unet_state_dict, strict=False)
-        self.model["unet"].load_state_dict(state_dict, strict=False)
-        self.model["unet"].to(self.torch_dtype).to(self.device)
-
-    def search_for_embeddings(self, state_dict):
-        embeddings = []
-        for k in state_dict:
-            if isinstance(state_dict[k], torch.Tensor):
-                embeddings.append(state_dict[k])
-            elif isinstance(state_dict[k], dict):
-                embeddings += self.search_for_embeddings(state_dict[k])
-        return embeddings
-
-    def load_textual_inversions(self, folder):
-        # Store additional tokens here
-        self.textual_inversion_dict = {}
-
-        # Load every textual inversion file
-        for file_name in os.listdir(folder):
-            if file_name.endswith(".txt"):
-                continue
-            keyword = os.path.splitext(file_name)[0]
-            state_dict = load_state_dict(os.path.join(folder, file_name))
-
-            # Search for embeddings
-            for embeddings in self.search_for_embeddings(state_dict):
-                if len(embeddings.shape) == 2 and embeddings.shape[1] == 768:
-                    tokens = [f"{keyword}_{i}" for i in range(embeddings.shape[0])]
-                    self.textual_inversion_dict[keyword] = (tokens, embeddings)
-                    break
-        
-    def load_model(self, file_path, components=None, lora_alphas=[]):
-        state_dict = load_state_dict(file_path, torch_dtype=self.torch_dtype)
-        if self.is_stable_video_diffusion(state_dict):
-            self.load_stable_video_diffusion(state_dict, file_path=file_path)
-        elif self.is_animatediff(state_dict):
-            self.load_animatediff(state_dict, file_path=file_path)
-        elif self.is_animatediff_xl(state_dict):
-            self.load_animatediff_xl(state_dict, file_path=file_path)
-        elif self.is_controlnet(state_dict):
-            self.load_controlnet(state_dict, file_path=file_path)
-        elif self.is_stabe_diffusion_xl(state_dict):
-            self.load_stable_diffusion_xl(state_dict, components=components, file_path=file_path)
-        elif self.is_stable_diffusion(state_dict):
-            self.load_stable_diffusion(state_dict, components=components, file_path=file_path)
-        elif self.is_sd_lora(state_dict):
-            self.load_sd_lora(state_dict, alpha=lora_alphas.pop(0))
-        elif self.is_beautiful_prompt(state_dict):
-            self.load_beautiful_prompt(state_dict, file_path=file_path)
-        elif self.is_RIFE(state_dict):
-            self.load_RIFE(state_dict, file_path=file_path)
-        elif self.is_translator(state_dict):
-            self.load_translator(state_dict, file_path=file_path)
-        elif self.is_ipadapter(state_dict):
-            self.load_ipadapter(state_dict, file_path=file_path)
-        elif self.is_ipadapter_image_encoder(state_dict):
-            self.load_ipadapter_image_encoder(state_dict, file_path=file_path)
-        elif self.is_ipadapter_xl(state_dict):
-            self.load_ipadapter_xl(state_dict, file_path=file_path)
-        elif self.is_ipadapter_xl_image_encoder(state_dict):
-            self.load_ipadapter_xl_image_encoder(state_dict, file_path=file_path)
-        elif self.is_hunyuan_dit_clip_text_encoder(state_dict):
-            self.load_hunyuan_dit_clip_text_encoder(state_dict, file_path=file_path)
-        elif self.is_hunyuan_dit_t5_text_encoder(state_dict):
-            self.load_hunyuan_dit_t5_text_encoder(state_dict, file_path=file_path)
-        elif self.is_hunyuan_dit(state_dict):
-            self.load_hunyuan_dit(state_dict, file_path=file_path)
-        elif self.is_diffusers_vae(state_dict):
-            self.load_diffusers_vae(state_dict, file_path=file_path)
-        elif self.is_ExVideo_StableVideoDiffusion(state_dict):
-            self.load_ExVideo_StableVideoDiffusion(state_dict, file_path=file_path)
-
-    def load_models(self, file_path_list, lora_alphas=[]):
-        for file_path in file_path_list:
-            self.load_model(file_path, lora_alphas=lora_alphas)
-        
-    def to(self, device):
-        for component in self.model:
-            if isinstance(self.model[component], list):
-                for model in self.model[component]:
-                    model.to(device)
-            else:
-                self.model[component].to(device)
-        torch.cuda.empty_cache()
-
-    def get_model_with_model_path(self, model_path):
-        for component in self.model_path:
-            if isinstance(self.model_path[component], str):
-                if os.path.samefile(self.model_path[component], model_path):
-                    return self.model[component]
-            elif isinstance(self.model_path[component], list):
-                for i, model_path_ in enumerate(self.model_path[component]):
-                    if os.path.samefile(model_path_, model_path):
-                        return self.model[component][i]
-        raise ValueError(f"Please load model {model_path} before you use it.")
-    
-    def __getattr__(self, __name):
-        if __name in self.model:
-            return self.model[__name]
-        else:
-            return super.__getattribute__(__name)
-
-
-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)
-    else:
-        return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype)
-
-
-def load_state_dict_from_safetensors(file_path, torch_dtype=None):
-    state_dict = {}
-    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:
-                state_dict[k] = state_dict[k].to(torch_dtype)
-    return state_dict
-
-
-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):
-                state_dict[i] = state_dict[i].to(torch_dtype)
-    return state_dict
-
-
-def search_parameter(param, state_dict):
-    for name, param_ in state_dict.items():
-        if param.numel() == param_.numel():
-            if param.shape == param_.shape:
-                if torch.dist(param, param_) < 1e-6:
-                    return name
-            else:
-                if torch.dist(param.flatten(), param_.flatten()) < 1e-6:
-                    return name
-    return None
-
-
-def build_rename_dict(source_state_dict, target_state_dict, split_qkv=False):
-    matched_keys = set()
-    with torch.no_grad():
-        for name in source_state_dict:
-            rename = search_parameter(source_state_dict[name], target_state_dict)
-            if rename is not None:
-                print(f'"{name}": "{rename}",')
-                matched_keys.add(rename)
-            elif split_qkv and len(source_state_dict[name].shape)>=1 and source_state_dict[name].shape[0]%3==0:
-                length = source_state_dict[name].shape[0] // 3
-                rename = []
-                for i in range(3):
-                    rename.append(search_parameter(source_state_dict[name][i*length: i*length+length], target_state_dict))
-                if None not in rename:
-                    print(f'"{name}": {rename},')
-                    for rename_ in rename:
-                        matched_keys.add(rename_)
-    for name in target_state_dict:
-        if name not in matched_keys:
-            print("Cannot find", name, target_state_dict[name].shape)
+from .model_manager import *

diffsynth/models/cog_dit.py

@@ -0,0 +1,408 @@
+import torch
+from einops import rearrange, repeat
+from .sd3_dit import TimestepEmbeddings
+from .attention import Attention
+from .utils import load_state_dict_from_folder
+from .tiler import TileWorker2Dto3D
+import numpy as np
+
+
+
+class CogPatchify(torch.nn.Module):
+    def __init__(self, dim_in, dim_out, patch_size) -> None:
+        super().__init__()
+        self.proj = torch.nn.Conv3d(dim_in, dim_out, kernel_size=(1, patch_size, patch_size), stride=(1, patch_size, patch_size))
+
+    def forward(self, hidden_states):
+        hidden_states = self.proj(hidden_states)
+        hidden_states = rearrange(hidden_states, "B C T H W -> B (T H W) C")
+        return hidden_states
+    
+
+
+class CogAdaLayerNorm(torch.nn.Module):
+    def __init__(self, dim, dim_cond, single=False):
+        super().__init__()
+        self.single = single
+        self.linear = torch.nn.Linear(dim_cond, dim * (2 if single else 6))
+        self.norm = torch.nn.LayerNorm(dim, elementwise_affine=True, eps=1e-5)
+
+
+    def forward(self, hidden_states, prompt_emb, emb):
+        emb = self.linear(torch.nn.functional.silu(emb))
+        if self.single:
+            shift, scale = emb.unsqueeze(1).chunk(2, dim=2)
+            hidden_states = self.norm(hidden_states) * (1 + scale) + shift
+            return hidden_states
+        else:
+            shift_a, scale_a, gate_a, shift_b, scale_b, gate_b = emb.unsqueeze(1).chunk(6, dim=2)
+            hidden_states = self.norm(hidden_states) * (1 + scale_a) + shift_a
+            prompt_emb = self.norm(prompt_emb) * (1 + scale_b) + shift_b
+            return hidden_states, prompt_emb, gate_a, gate_b
+
+
+
+class CogDiTBlock(torch.nn.Module):
+    def __init__(self, dim, dim_cond, num_heads):
+        super().__init__()
+        self.norm1 = CogAdaLayerNorm(dim, dim_cond)
+        self.attn1 = Attention(q_dim=dim, num_heads=48, head_dim=dim//num_heads, bias_q=True, bias_kv=True, bias_out=True)
+        self.norm_q = torch.nn.LayerNorm((dim//num_heads,), eps=1e-06, elementwise_affine=True)
+        self.norm_k = torch.nn.LayerNorm((dim//num_heads,), eps=1e-06, elementwise_affine=True)
+
+        self.norm2 = CogAdaLayerNorm(dim, dim_cond)
+        self.ff = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+    
+
+    def apply_rotary_emb(self, x, freqs_cis):
+        cos, sin = freqs_cis  # [S, D]
+        cos = cos[None, None]
+        sin = sin[None, None]
+        cos, sin = cos.to(x.device), sin.to(x.device)
+        x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
+        x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+        out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
+        return out
+    
+
+    def process_qkv(self, q, k, v, image_rotary_emb, text_seq_length):
+        q = self.norm_q(q)
+        k = self.norm_k(k)
+        q[:, :, text_seq_length:] = self.apply_rotary_emb(q[:, :, text_seq_length:], image_rotary_emb)
+        k[:, :, text_seq_length:] = self.apply_rotary_emb(k[:, :, text_seq_length:], image_rotary_emb)
+        return q, k, v
+        
+
+    def forward(self, hidden_states, prompt_emb, time_emb, image_rotary_emb):
+        # Attention
+        norm_hidden_states, norm_encoder_hidden_states, gate_a, gate_b = self.norm1(
+            hidden_states, prompt_emb, time_emb
+        )
+        attention_io = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
+        attention_io = self.attn1(
+            attention_io,
+            qkv_preprocessor=lambda q, k, v: self.process_qkv(q, k, v, image_rotary_emb, prompt_emb.shape[1])
+        )
+
+        hidden_states = hidden_states + gate_a * attention_io[:, prompt_emb.shape[1]:]
+        prompt_emb = prompt_emb + gate_b * attention_io[:, :prompt_emb.shape[1]]
+
+        # Feed forward
+        norm_hidden_states, norm_encoder_hidden_states, gate_a, gate_b = self.norm2(
+            hidden_states, prompt_emb, time_emb
+        )
+        ff_io = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
+        ff_io = self.ff(ff_io)
+
+        hidden_states = hidden_states + gate_a * ff_io[:, prompt_emb.shape[1]:]
+        prompt_emb = prompt_emb + gate_b * ff_io[:, :prompt_emb.shape[1]]
+
+        return hidden_states, prompt_emb
+
+
+
+class CogDiT(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.patchify = CogPatchify(16, 3072, 2)
+        self.time_embedder = TimestepEmbeddings(3072, 512)
+        self.context_embedder = torch.nn.Linear(4096, 3072)
+        self.blocks = torch.nn.ModuleList([CogDiTBlock(3072, 512, 48) for _ in range(42)])
+        self.norm_final = torch.nn.LayerNorm((3072,), eps=1e-05, elementwise_affine=True)
+        self.norm_out = CogAdaLayerNorm(3072, 512, single=True)
+        self.proj_out = torch.nn.Linear(3072, 64, bias=True)
+
+
+    def get_resize_crop_region_for_grid(self, src, tgt_width, tgt_height):
+        tw = tgt_width
+        th = tgt_height
+        h, w = src
+        r = h / w
+        if r > (th / tw):
+            resize_height = th
+            resize_width = int(round(th / h * w))
+        else:
+            resize_width = tw
+            resize_height = int(round(tw / w * h))
+
+        crop_top = int(round((th - resize_height) / 2.0))
+        crop_left = int(round((tw - resize_width) / 2.0))
+
+        return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
+    
+
+    def get_3d_rotary_pos_embed(
+        self, embed_dim, crops_coords, grid_size, temporal_size, theta: int = 10000, use_real: bool = True
+    ):
+        start, stop = crops_coords
+        grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32)
+        grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32)
+        grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
+
+        # Compute dimensions for each axis
+        dim_t = embed_dim // 4
+        dim_h = embed_dim // 8 * 3
+        dim_w = embed_dim // 8 * 3
+
+        # Temporal frequencies
+        freqs_t = 1.0 / (theta ** (torch.arange(0, dim_t, 2).float() / dim_t))
+        grid_t = torch.from_numpy(grid_t).float()
+        freqs_t = torch.einsum("n , f -> n f", grid_t, freqs_t)
+        freqs_t = freqs_t.repeat_interleave(2, dim=-1)
+
+        # Spatial frequencies for height and width
+        freqs_h = 1.0 / (theta ** (torch.arange(0, dim_h, 2).float() / dim_h))
+        freqs_w = 1.0 / (theta ** (torch.arange(0, dim_w, 2).float() / dim_w))
+        grid_h = torch.from_numpy(grid_h).float()
+        grid_w = torch.from_numpy(grid_w).float()
+        freqs_h = torch.einsum("n , f -> n f", grid_h, freqs_h)
+        freqs_w = torch.einsum("n , f -> n f", grid_w, freqs_w)
+        freqs_h = freqs_h.repeat_interleave(2, dim=-1)
+        freqs_w = freqs_w.repeat_interleave(2, dim=-1)
+
+        # Broadcast and concatenate tensors along specified dimension
+        def broadcast(tensors, dim=-1):
+            num_tensors = len(tensors)
+            shape_lens = {len(t.shape) for t in tensors}
+            assert len(shape_lens) == 1, "tensors must all have the same number of dimensions"
+            shape_len = list(shape_lens)[0]
+            dim = (dim + shape_len) if dim < 0 else dim
+            dims = list(zip(*(list(t.shape) for t in tensors)))
+            expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim]
+            assert all(
+                [*(len(set(t[1])) <= 2 for t in expandable_dims)]
+            ), "invalid dimensions for broadcastable concatenation"
+            max_dims = [(t[0], max(t[1])) for t in expandable_dims]
+            expanded_dims = [(t[0], (t[1],) * num_tensors) for t in max_dims]
+            expanded_dims.insert(dim, (dim, dims[dim]))
+            expandable_shapes = list(zip(*(t[1] for t in expanded_dims)))
+            tensors = [t[0].expand(*t[1]) for t in zip(tensors, expandable_shapes)]
+            return torch.cat(tensors, dim=dim)
+
+        freqs = broadcast((freqs_t[:, None, None, :], freqs_h[None, :, None, :], freqs_w[None, None, :, :]), dim=-1)
+
+        t, h, w, d = freqs.shape
+        freqs = freqs.view(t * h * w, d)
+
+        # Generate sine and cosine components
+        sin = freqs.sin()
+        cos = freqs.cos()
+
+        if use_real:
+            return cos, sin
+        else:
+            freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+            return freqs_cis
+    
+
+    def prepare_rotary_positional_embeddings(
+        self,
+        height: int,
+        width: int,
+        num_frames: int,
+        device: torch.device,
+    ):
+        grid_height = height // 2
+        grid_width = width // 2
+        base_size_width = 720 // (8 * 2)
+        base_size_height = 480 // (8 * 2)
+
+        grid_crops_coords = self.get_resize_crop_region_for_grid(
+            (grid_height, grid_width), base_size_width, base_size_height
+        )
+        freqs_cos, freqs_sin = self.get_3d_rotary_pos_embed(
+            embed_dim=64,
+            crops_coords=grid_crops_coords,
+            grid_size=(grid_height, grid_width),
+            temporal_size=num_frames,
+            use_real=True,
+        )
+
+        freqs_cos = freqs_cos.to(device=device)
+        freqs_sin = freqs_sin.to(device=device)
+        return freqs_cos, freqs_sin
+
+
+    def unpatchify(self, hidden_states, height, width):
+        hidden_states = rearrange(hidden_states, "B (T H W) (C P Q) -> B C T (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2)
+        return hidden_states
+    
+
+    def build_mask(self, T, H, W, dtype, device, is_bound):
+        t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W)
+        h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W)
+        w = repeat(torch.arange(W), "W -> T H W", T=T, 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 t + 1,
+            pad if is_bound[1] else T - t,
+            pad if is_bound[2] else h + 1,
+            pad if is_bound[3] else H - h,
+            pad if is_bound[4] else w + 1,
+            pad if is_bound[5] else W - w
+        ]).min(dim=0).values
+        mask = mask.clip(1, border_width)
+        mask = (mask / border_width).to(dtype=dtype, device=device)
+        mask = rearrange(mask, "T H W -> 1 1 T H W")
+        return mask
+    
+
+    def tiled_forward(self, hidden_states, timestep, prompt_emb, tile_size=(60, 90), tile_stride=(30, 45)):
+        B, C, T, H, W = hidden_states.shape
+        value = torch.zeros((B, C, T, H, W), dtype=hidden_states.dtype, device=hidden_states.device)
+        weight = torch.zeros((B, C, T, H, W), dtype=hidden_states.dtype, device=hidden_states.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_ = max(H - tile_size, 0), H
+                if w_ > W: w, w_ = max(W - tile_size, 0), W
+                tasks.append((h, h_, w, w_))
+
+        # Run
+        for hl, hr, wl, wr in tasks:
+            mask = self.build_mask(
+                value.shape[2], (hr-hl), (wr-wl),
+                hidden_states.dtype, hidden_states.device,
+                is_bound=(True, True, hl==0, hr>=H, wl==0, wr>=W)
+            )
+            model_output = self.forward(hidden_states[:, :, :, hl:hr, wl:wr], timestep, prompt_emb)
+            value[:, :, :, hl:hr, wl:wr] += model_output * mask
+            weight[:, :, :, hl:hr, wl:wr] += mask
+        value = value / weight
+
+        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):
+        if tiled:
+            return TileWorker2Dto3D().tiled_forward(
+                forward_fn=lambda x: self.forward(x, timestep, prompt_emb),
+                model_input=hidden_states,
+                tile_size=tile_size, tile_stride=tile_stride,
+                tile_device=hidden_states.device, tile_dtype=hidden_states.dtype,
+                computation_device=self.context_embedder.weight.device, computation_dtype=self.context_embedder.weight.dtype
+            )
+        num_frames, height, width = hidden_states.shape[-3:]
+        if image_rotary_emb is None:
+            image_rotary_emb = self.prepare_rotary_positional_embeddings(height, width, num_frames, device=self.context_embedder.weight.device)
+        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 = torch.cat([prompt_emb, hidden_states], dim=1)
+        hidden_states = self.norm_final(hidden_states)
+        hidden_states = hidden_states[:, prompt_emb.shape[1]:]
+        hidden_states = self.norm_out(hidden_states, prompt_emb, time_emb)
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = self.unpatchify(hidden_states, height, width)
+
+        return hidden_states
+    
+
+    @staticmethod
+    def state_dict_converter():
+        return CogDiTStateDictConverter()
+    
+
+    @staticmethod
+    def from_pretrained(file_path, torch_dtype=torch.bfloat16):
+        model = CogDiT().to(torch_dtype)
+        state_dict = load_state_dict_from_folder(file_path, torch_dtype=torch_dtype)
+        state_dict = CogDiT.state_dict_converter().from_diffusers(state_dict)
+        model.load_state_dict(state_dict)
+        return model
+
+
+
+class CogDiTStateDictConverter:
+    def __init__(self):
+        pass
+
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "patch_embed.proj.weight": "patchify.proj.weight",
+            "patch_embed.proj.bias": "patchify.proj.bias",
+            "patch_embed.text_proj.weight": "context_embedder.weight",
+            "patch_embed.text_proj.bias": "context_embedder.bias",
+            "time_embedding.linear_1.weight": "time_embedder.timestep_embedder.0.weight",
+            "time_embedding.linear_1.bias": "time_embedder.timestep_embedder.0.bias",
+            "time_embedding.linear_2.weight": "time_embedder.timestep_embedder.2.weight",
+            "time_embedding.linear_2.bias": "time_embedder.timestep_embedder.2.bias",
+
+            "norm_final.weight": "norm_final.weight",
+            "norm_final.bias": "norm_final.bias",
+            "norm_out.linear.weight": "norm_out.linear.weight",
+            "norm_out.linear.bias": "norm_out.linear.bias",
+            "norm_out.norm.weight": "norm_out.norm.weight",
+            "norm_out.norm.bias": "norm_out.norm.bias",
+            "proj_out.weight": "proj_out.weight",
+            "proj_out.bias": "proj_out.bias",
+        }
+        suffix_dict = {
+            "norm1.linear.weight": "norm1.linear.weight",
+            "norm1.linear.bias": "norm1.linear.bias",
+            "norm1.norm.weight": "norm1.norm.weight",
+            "norm1.norm.bias": "norm1.norm.bias",
+            "attn1.norm_q.weight": "norm_q.weight",
+            "attn1.norm_q.bias": "norm_q.bias",
+            "attn1.norm_k.weight": "norm_k.weight",
+            "attn1.norm_k.bias": "norm_k.bias",
+            "attn1.to_q.weight": "attn1.to_q.weight",
+            "attn1.to_q.bias": "attn1.to_q.bias",
+            "attn1.to_k.weight": "attn1.to_k.weight",
+            "attn1.to_k.bias": "attn1.to_k.bias",
+            "attn1.to_v.weight": "attn1.to_v.weight",
+            "attn1.to_v.bias": "attn1.to_v.bias",
+            "attn1.to_out.0.weight": "attn1.to_out.weight",
+            "attn1.to_out.0.bias": "attn1.to_out.bias",
+            "norm2.linear.weight": "norm2.linear.weight",
+            "norm2.linear.bias": "norm2.linear.bias",
+            "norm2.norm.weight": "norm2.norm.weight",
+            "norm2.norm.bias": "norm2.norm.bias",
+            "ff.net.0.proj.weight": "ff.0.weight",
+            "ff.net.0.proj.bias": "ff.0.bias",
+            "ff.net.2.weight": "ff.2.weight",
+            "ff.net.2.bias": "ff.2.bias",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                if name == "patch_embed.proj.weight":
+                    param = param.unsqueeze(2)
+                state_dict_[rename_dict[name]] = param
+            else:
+                names = name.split(".")
+                if names[0] == "transformer_blocks":
+                    suffix = ".".join(names[2:])
+                    state_dict_[f"blocks.{names[1]}." + suffix_dict[suffix]] = param
+        return state_dict_
+    
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/cog_vae.py

@@ -0,0 +1,518 @@
+import torch
+from einops import rearrange, repeat
+from .tiler import TileWorker2Dto3D
+
+
+
+class Downsample3D(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 3,
+        stride: int = 2,
+        padding: int = 0,
+        compress_time: bool = False,
+    ):
+        super().__init__()
+
+        self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
+        self.compress_time = compress_time
+
+    def forward(self, x: torch.Tensor, xq: torch.Tensor) -> torch.Tensor:
+        if self.compress_time:
+            batch_size, channels, frames, height, width = x.shape
+
+            # (batch_size, channels, frames, height, width) -> (batch_size, height, width, channels, frames) -> (batch_size * height * width, channels, frames)
+            x = x.permute(0, 3, 4, 1, 2).reshape(batch_size * height * width, channels, frames)
+
+            if x.shape[-1] % 2 == 1:
+                x_first, x_rest = x[..., 0], x[..., 1:]
+                if x_rest.shape[-1] > 0:
+                    # (batch_size * height * width, channels, frames - 1) -> (batch_size * height * width, channels, (frames - 1) // 2)
+                    x_rest = torch.nn.functional.avg_pool1d(x_rest, kernel_size=2, stride=2)
+
+                x = torch.cat([x_first[..., None], x_rest], dim=-1)
+                # (batch_size * height * width, channels, (frames // 2) + 1) -> (batch_size, height, width, channels, (frames // 2) + 1) -> (batch_size, channels, (frames // 2) + 1, height, width)
+                x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2)
+            else:
+                # (batch_size * height * width, channels, frames) -> (batch_size * height * width, channels, frames // 2)
+                x = torch.nn.functional.avg_pool1d(x, kernel_size=2, stride=2)
+                # (batch_size * height * width, channels, frames // 2) -> (batch_size, height, width, channels, frames // 2) -> (batch_size, channels, frames // 2, height, width)
+                x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2)
+
+        # Pad the tensor
+        pad = (0, 1, 0, 1)
+        x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+        batch_size, channels, frames, height, width = x.shape
+        # (batch_size, channels, frames, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size * frames, channels, height, width)
+        x = x.permute(0, 2, 1, 3, 4).reshape(batch_size * frames, channels, height, width)
+        x = self.conv(x)
+        # (batch_size * frames, channels, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size, channels, frames, height, width)
+        x = x.reshape(batch_size, frames, x.shape[1], x.shape[2], x.shape[3]).permute(0, 2, 1, 3, 4)
+        return x
+
+
+
+class Upsample3D(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 3,
+        stride: int = 1,
+        padding: int = 1,
+        compress_time: bool = False,
+    ) -> None:
+        super().__init__()
+        self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
+        self.compress_time = compress_time
+
+    def forward(self, inputs: torch.Tensor, xq: torch.Tensor) -> torch.Tensor:
+        if self.compress_time:
+            if inputs.shape[2] > 1 and inputs.shape[2] % 2 == 1:
+                # split first frame
+                x_first, x_rest = inputs[:, :, 0], inputs[:, :, 1:]
+
+                x_first = torch.nn.functional.interpolate(x_first, scale_factor=2.0)
+                x_rest = torch.nn.functional.interpolate(x_rest, scale_factor=2.0)
+                x_first = x_first[:, :, None, :, :]
+                inputs = torch.cat([x_first, x_rest], dim=2)
+            elif inputs.shape[2] > 1:
+                inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0)
+            else:
+                inputs = inputs.squeeze(2)
+                inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0)
+                inputs = inputs[:, :, None, :, :]
+        else:
+            # only interpolate 2D
+            b, c, t, h, w = inputs.shape
+            inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
+            inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0)
+            inputs = inputs.reshape(b, t, c, *inputs.shape[2:]).permute(0, 2, 1, 3, 4)
+
+        b, c, t, h, w = inputs.shape
+        inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
+        inputs = self.conv(inputs)
+        inputs = inputs.reshape(b, t, *inputs.shape[1:]).permute(0, 2, 1, 3, 4)
+
+        return inputs
+
+
+
+class CogVideoXSpatialNorm3D(torch.nn.Module):
+    def __init__(self, f_channels, zq_channels, groups):
+        super().__init__()
+        self.norm_layer = torch.nn.GroupNorm(num_channels=f_channels, num_groups=groups, eps=1e-6, affine=True)
+        self.conv_y = torch.nn.Conv3d(zq_channels, f_channels, kernel_size=1, stride=1)
+        self.conv_b = torch.nn.Conv3d(zq_channels, f_channels, kernel_size=1, stride=1)
+
+
+    def forward(self, f: torch.Tensor, zq: torch.Tensor) -> torch.Tensor:
+        if f.shape[2] > 1 and f.shape[2] % 2 == 1:
+            f_first, f_rest = f[:, :, :1], f[:, :, 1:]
+            f_first_size, f_rest_size = f_first.shape[-3:], f_rest.shape[-3:]
+            z_first, z_rest = zq[:, :, :1], zq[:, :, 1:]
+            z_first = torch.nn.functional.interpolate(z_first, size=f_first_size)
+            z_rest = torch.nn.functional.interpolate(z_rest, size=f_rest_size)
+            zq = torch.cat([z_first, z_rest], dim=2)
+        else:
+            zq = torch.nn.functional.interpolate(zq, size=f.shape[-3:])
+
+        norm_f = self.norm_layer(f)
+        new_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
+        return new_f
+
+
+
+class Resnet3DBlock(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, spatial_norm_dim, groups, eps=1e-6, use_conv_shortcut=False):
+        super().__init__()
+        self.nonlinearity = torch.nn.SiLU()
+        if spatial_norm_dim is None:
+            self.norm1 = torch.nn.GroupNorm(num_channels=in_channels, num_groups=groups, eps=eps)
+            self.norm2 = torch.nn.GroupNorm(num_channels=out_channels, num_groups=groups, eps=eps)
+        else:
+            self.norm1 = CogVideoXSpatialNorm3D(in_channels, spatial_norm_dim, groups)
+            self.norm2 = CogVideoXSpatialNorm3D(out_channels, spatial_norm_dim, groups)
+
+        self.conv1 = CachedConv3d(in_channels, out_channels, kernel_size=3, padding=(0, 1, 1))
+
+        self.conv2 = CachedConv3d(out_channels, out_channels, kernel_size=3, padding=(0, 1, 1))
+
+        if in_channels != out_channels:
+            if use_conv_shortcut:
+                self.conv_shortcut = CachedConv3d(in_channels, out_channels, kernel_size=3, padding=(0, 1, 1))
+            else:
+                self.conv_shortcut = torch.nn.Conv3d(in_channels, out_channels, kernel_size=1)
+        else:
+            self.conv_shortcut = lambda x: x
+
+
+    def forward(self, hidden_states, zq):
+        residual = hidden_states
+
+        hidden_states = self.norm1(hidden_states, zq) if isinstance(self.norm1, CogVideoXSpatialNorm3D) else self.norm1(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv1(hidden_states)
+
+        hidden_states = self.norm2(hidden_states, zq) if isinstance(self.norm2, CogVideoXSpatialNorm3D) else self.norm2(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+
+        hidden_states = hidden_states + self.conv_shortcut(residual)
+
+        return hidden_states
+    
+
+
+class CachedConv3d(torch.nn.Conv3d):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
+        super().__init__(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
+        self.cached_tensor = None
+
+
+    def clear_cache(self):
+        self.cached_tensor = None
+    
+
+    def forward(self, input: torch.Tensor, use_cache = True) -> torch.Tensor:
+        if use_cache:
+            if self.cached_tensor is None:
+                self.cached_tensor = torch.concat([input[:, :, :1]] * 2, dim=2)
+            input = torch.concat([self.cached_tensor, input], dim=2)
+            self.cached_tensor = input[:, :, -2:]
+        return super().forward(input)
+
+
+
+class CogVAEDecoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.7
+        self.conv_in = CachedConv3d(16, 512, kernel_size=3, stride=1, padding=(0, 1, 1))
+
+        self.blocks = torch.nn.ModuleList([
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Upsample3D(512, 512, compress_time=True),
+            Resnet3DBlock(512, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Upsample3D(256, 256, compress_time=True),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Upsample3D(256, 256, compress_time=False),
+            Resnet3DBlock(256, 128, 16, 32),
+            Resnet3DBlock(128, 128, 16, 32),
+            Resnet3DBlock(128, 128, 16, 32),
+            Resnet3DBlock(128, 128, 16, 32),
+        ])
+
+        self.norm_out = CogVideoXSpatialNorm3D(128, 16, 32)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = CachedConv3d(128, 3, kernel_size=3, stride=1, padding=(0, 1, 1))
+
+
+    def forward(self, sample):
+        sample = sample / self.scaling_factor
+        hidden_states = self.conv_in(sample)
+
+        for block in self.blocks:
+            hidden_states = block(hidden_states, sample)
+        
+        hidden_states = self.norm_out(hidden_states, sample)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+    
+
+    def decode_video(self, sample, tiled=True, tile_size=(60, 90), tile_stride=(30, 45), progress_bar=lambda x:x):
+        if tiled:
+            B, C, T, H, W = sample.shape
+            return TileWorker2Dto3D().tiled_forward(
+                forward_fn=lambda x: self.decode_small_video(x),
+                model_input=sample,
+                tile_size=tile_size, tile_stride=tile_stride,
+                tile_device=sample.device, tile_dtype=sample.dtype,
+                computation_device=sample.device, computation_dtype=sample.dtype,
+                scales=(3/16, (T//2*8+T%2)/T, 8, 8),
+                progress_bar=progress_bar
+            )
+        else:
+            return self.decode_small_video(sample)
+    
+
+    def decode_small_video(self, sample):
+        B, C, T, H, W = sample.shape
+        computation_device = self.conv_in.weight.device
+        computation_dtype = self.conv_in.weight.dtype
+        value = []
+        for i in range(T//2):
+            tl = i*2 + T%2 - (T%2 and i==0)
+            tr = i*2 + 2 + T%2
+            model_input = sample[:, :, tl: tr, :, :].to(dtype=computation_dtype, device=computation_device)
+            model_output = self.forward(model_input).to(dtype=sample.dtype, device=sample.device)
+            value.append(model_output)
+        value = torch.concat(value, dim=2)
+        for name, module in self.named_modules():
+            if isinstance(module, CachedConv3d):
+                module.clear_cache()
+        return value
+    
+
+    @staticmethod
+    def state_dict_converter():
+        return CogVAEDecoderStateDictConverter()
+    
+
+
+class CogVAEEncoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.7
+        self.conv_in = CachedConv3d(3, 128, kernel_size=3, stride=1, padding=(0, 1, 1))
+
+        self.blocks = torch.nn.ModuleList([
+            Resnet3DBlock(128, 128, None, 32),
+            Resnet3DBlock(128, 128, None, 32),
+            Resnet3DBlock(128, 128, None, 32),
+            Downsample3D(128, 128, compress_time=True),
+            Resnet3DBlock(128, 256, None, 32),
+            Resnet3DBlock(256, 256, None, 32),
+            Resnet3DBlock(256, 256, None, 32),
+            Downsample3D(256, 256, compress_time=True),
+            Resnet3DBlock(256, 256, None, 32),
+            Resnet3DBlock(256, 256, None, 32),
+            Resnet3DBlock(256, 256, None, 32),
+            Downsample3D(256, 256, compress_time=False),
+            Resnet3DBlock(256, 512, None, 32),
+            Resnet3DBlock(512, 512, None, 32),
+            Resnet3DBlock(512, 512, None, 32),
+            Resnet3DBlock(512, 512, None, 32),
+            Resnet3DBlock(512, 512, None, 32),
+        ])
+
+        self.norm_out = torch.nn.GroupNorm(32, 512, eps=1e-06, affine=True)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = CachedConv3d(512, 32, kernel_size=3, stride=1, padding=(0, 1, 1))
+
+
+    def forward(self, sample):
+        hidden_states = self.conv_in(sample)
+
+        for block in self.blocks:
+            hidden_states = block(hidden_states, sample)
+        
+        hidden_states = self.norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)[:, :16]
+        hidden_states = hidden_states * self.scaling_factor
+
+        return hidden_states
+    
+
+    def encode_video(self, sample, tiled=True, tile_size=(60, 90), tile_stride=(30, 45), progress_bar=lambda x:x):
+        if tiled:
+            B, C, T, H, W = sample.shape
+            return TileWorker2Dto3D().tiled_forward(
+                forward_fn=lambda x: self.encode_small_video(x),
+                model_input=sample,
+                tile_size=(i * 8 for i in tile_size), tile_stride=(i * 8 for i in tile_stride),
+                tile_device=sample.device, tile_dtype=sample.dtype,
+                computation_device=sample.device, computation_dtype=sample.dtype,
+                scales=(16/3, (T//4+T%2)/T, 1/8, 1/8),
+                progress_bar=progress_bar
+            )
+        else:
+            return self.encode_small_video(sample)
+    
+
+    def encode_small_video(self, sample):
+        B, C, T, H, W = sample.shape
+        computation_device = self.conv_in.weight.device
+        computation_dtype = self.conv_in.weight.dtype
+        value = []
+        for i in range(T//8):
+            t = i*8 + T%2 - (T%2 and i==0)
+            t_ = i*8 + 8 + T%2
+            model_input = sample[:, :, t: t_, :, :].to(dtype=computation_dtype, device=computation_device)
+            model_output = self.forward(model_input).to(dtype=sample.dtype, device=sample.device)
+            value.append(model_output)
+        value = torch.concat(value, dim=2)
+        for name, module in self.named_modules():
+            if isinstance(module, CachedConv3d):
+                module.clear_cache()
+        return value
+    
+
+    @staticmethod
+    def state_dict_converter():
+        return CogVAEEncoderStateDictConverter()
+
+
+
+class CogVAEEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "encoder.conv_in.conv.weight": "conv_in.weight",
+            "encoder.conv_in.conv.bias": "conv_in.bias",
+            "encoder.down_blocks.0.downsamplers.0.conv.weight": "blocks.3.conv.weight",
+            "encoder.down_blocks.0.downsamplers.0.conv.bias": "blocks.3.conv.bias",
+            "encoder.down_blocks.1.downsamplers.0.conv.weight": "blocks.7.conv.weight",
+            "encoder.down_blocks.1.downsamplers.0.conv.bias": "blocks.7.conv.bias",
+            "encoder.down_blocks.2.downsamplers.0.conv.weight": "blocks.11.conv.weight",
+            "encoder.down_blocks.2.downsamplers.0.conv.bias": "blocks.11.conv.bias",
+            "encoder.norm_out.weight": "norm_out.weight",
+            "encoder.norm_out.bias": "norm_out.bias",
+            "encoder.conv_out.conv.weight": "conv_out.weight",
+            "encoder.conv_out.conv.bias": "conv_out.bias",
+        }
+        prefix_dict = {
+            "encoder.down_blocks.0.resnets.0.": "blocks.0.",
+            "encoder.down_blocks.0.resnets.1.": "blocks.1.",
+            "encoder.down_blocks.0.resnets.2.": "blocks.2.",
+            "encoder.down_blocks.1.resnets.0.": "blocks.4.",
+            "encoder.down_blocks.1.resnets.1.": "blocks.5.",
+            "encoder.down_blocks.1.resnets.2.": "blocks.6.",
+            "encoder.down_blocks.2.resnets.0.": "blocks.8.",
+            "encoder.down_blocks.2.resnets.1.": "blocks.9.",
+            "encoder.down_blocks.2.resnets.2.": "blocks.10.",
+            "encoder.down_blocks.3.resnets.0.": "blocks.12.",
+            "encoder.down_blocks.3.resnets.1.": "blocks.13.",
+            "encoder.down_blocks.3.resnets.2.": "blocks.14.",
+            "encoder.mid_block.resnets.0.": "blocks.15.",
+            "encoder.mid_block.resnets.1.": "blocks.16.",
+        }
+        suffix_dict = {
+            "norm1.norm_layer.weight": "norm1.norm_layer.weight",
+            "norm1.norm_layer.bias": "norm1.norm_layer.bias",
+            "norm1.conv_y.conv.weight": "norm1.conv_y.weight",
+            "norm1.conv_y.conv.bias": "norm1.conv_y.bias",
+            "norm1.conv_b.conv.weight": "norm1.conv_b.weight",
+            "norm1.conv_b.conv.bias": "norm1.conv_b.bias",
+            "norm2.norm_layer.weight": "norm2.norm_layer.weight",
+            "norm2.norm_layer.bias": "norm2.norm_layer.bias",
+            "norm2.conv_y.conv.weight": "norm2.conv_y.weight",
+            "norm2.conv_y.conv.bias": "norm2.conv_y.bias",
+            "norm2.conv_b.conv.weight": "norm2.conv_b.weight",
+            "norm2.conv_b.conv.bias": "norm2.conv_b.bias",
+            "conv1.conv.weight": "conv1.weight",
+            "conv1.conv.bias": "conv1.bias",
+            "conv2.conv.weight": "conv2.weight",
+            "conv2.conv.bias": "conv2.bias",
+            "conv_shortcut.weight": "conv_shortcut.weight",
+            "conv_shortcut.bias": "conv_shortcut.bias",
+            "norm1.weight": "norm1.weight",
+            "norm1.bias": "norm1.bias",
+            "norm2.weight": "norm2.weight",
+            "norm2.bias": "norm2.bias",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                state_dict_[rename_dict[name]] = param
+            else:
+                for prefix in prefix_dict:
+                    if name.startswith(prefix):
+                        suffix = name[len(prefix):]
+                        state_dict_[prefix_dict[prefix] + suffix_dict[suffix]] = param
+        return state_dict_
+    
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
+
+
+
+class CogVAEDecoderStateDictConverter:
+    def __init__(self):
+        pass
+
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "decoder.conv_in.conv.weight": "conv_in.weight",
+            "decoder.conv_in.conv.bias": "conv_in.bias",
+            "decoder.up_blocks.0.upsamplers.0.conv.weight": "blocks.6.conv.weight",
+            "decoder.up_blocks.0.upsamplers.0.conv.bias": "blocks.6.conv.bias",
+            "decoder.up_blocks.1.upsamplers.0.conv.weight": "blocks.11.conv.weight",
+            "decoder.up_blocks.1.upsamplers.0.conv.bias": "blocks.11.conv.bias",
+            "decoder.up_blocks.2.upsamplers.0.conv.weight": "blocks.16.conv.weight",
+            "decoder.up_blocks.2.upsamplers.0.conv.bias": "blocks.16.conv.bias",
+            "decoder.norm_out.norm_layer.weight": "norm_out.norm_layer.weight",
+            "decoder.norm_out.norm_layer.bias": "norm_out.norm_layer.bias",
+            "decoder.norm_out.conv_y.conv.weight": "norm_out.conv_y.weight",
+            "decoder.norm_out.conv_y.conv.bias": "norm_out.conv_y.bias",
+            "decoder.norm_out.conv_b.conv.weight": "norm_out.conv_b.weight",
+            "decoder.norm_out.conv_b.conv.bias": "norm_out.conv_b.bias",
+            "decoder.conv_out.conv.weight": "conv_out.weight",
+            "decoder.conv_out.conv.bias": "conv_out.bias"
+        }
+        prefix_dict = {
+            "decoder.mid_block.resnets.0.": "blocks.0.",
+            "decoder.mid_block.resnets.1.": "blocks.1.",
+            "decoder.up_blocks.0.resnets.0.": "blocks.2.",
+            "decoder.up_blocks.0.resnets.1.": "blocks.3.",
+            "decoder.up_blocks.0.resnets.2.": "blocks.4.",
+            "decoder.up_blocks.0.resnets.3.": "blocks.5.",
+            "decoder.up_blocks.1.resnets.0.": "blocks.7.",
+            "decoder.up_blocks.1.resnets.1.": "blocks.8.",
+            "decoder.up_blocks.1.resnets.2.": "blocks.9.",
+            "decoder.up_blocks.1.resnets.3.": "blocks.10.",
+            "decoder.up_blocks.2.resnets.0.": "blocks.12.",
+            "decoder.up_blocks.2.resnets.1.": "blocks.13.",
+            "decoder.up_blocks.2.resnets.2.": "blocks.14.",
+            "decoder.up_blocks.2.resnets.3.": "blocks.15.",
+            "decoder.up_blocks.3.resnets.0.": "blocks.17.",
+            "decoder.up_blocks.3.resnets.1.": "blocks.18.",
+            "decoder.up_blocks.3.resnets.2.": "blocks.19.",
+            "decoder.up_blocks.3.resnets.3.": "blocks.20.",
+        }
+        suffix_dict = {
+            "norm1.norm_layer.weight": "norm1.norm_layer.weight",
+            "norm1.norm_layer.bias": "norm1.norm_layer.bias",
+            "norm1.conv_y.conv.weight": "norm1.conv_y.weight",
+            "norm1.conv_y.conv.bias": "norm1.conv_y.bias",
+            "norm1.conv_b.conv.weight": "norm1.conv_b.weight",
+            "norm1.conv_b.conv.bias": "norm1.conv_b.bias",
+            "norm2.norm_layer.weight": "norm2.norm_layer.weight",
+            "norm2.norm_layer.bias": "norm2.norm_layer.bias",
+            "norm2.conv_y.conv.weight": "norm2.conv_y.weight",
+            "norm2.conv_y.conv.bias": "norm2.conv_y.bias",
+            "norm2.conv_b.conv.weight": "norm2.conv_b.weight",
+            "norm2.conv_b.conv.bias": "norm2.conv_b.bias",
+            "conv1.conv.weight": "conv1.weight",
+            "conv1.conv.bias": "conv1.bias",
+            "conv2.conv.weight": "conv2.weight",
+            "conv2.conv.bias": "conv2.bias",
+            "conv_shortcut.weight": "conv_shortcut.weight",
+            "conv_shortcut.bias": "conv_shortcut.bias",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                state_dict_[rename_dict[name]] = param
+            else:
+                for prefix in prefix_dict:
+                    if name.startswith(prefix):
+                        suffix = name[len(prefix):]
+                        state_dict_[prefix_dict[prefix] + suffix_dict[suffix]] = param
+        return state_dict_
+    
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
+

diffsynth/models/downloader.py

@@ -0,0 +1,111 @@
+from huggingface_hub import hf_hub_download
+from modelscope import snapshot_download
+import os, shutil
+from typing_extensions import Literal, TypeAlias
+from typing import List
+from ..configs.model_config import preset_models_on_huggingface, preset_models_on_modelscope, Preset_model_id
+
+
+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}.")
+    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]))
+
+
+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}.")
+    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]))
+
+
+Preset_model_website: TypeAlias = Literal[
+    "HuggingFace",
+    "ModelScope",
+]
+website_to_preset_models = {
+    "HuggingFace": preset_models_on_huggingface,
+    "ModelScope": preset_models_on_modelscope,
+}
+website_to_download_fn = {
+    "HuggingFace": download_from_huggingface,
+    "ModelScope": download_from_modelscope,
+}
+
+
+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:
+                    # 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)
+                        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

diffsynth/models/flux_controlnet.py

@@ -0,0 +1,327 @@
+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}
+        else:
+            extra_kwargs = {}
+        return state_dict_, extra_kwargs
+    
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/flux_dit.py

@@ -0,0 +1,861 @@
+import torch
+from .sd3_dit import TimestepEmbeddings, AdaLayerNorm, RMSNorm
+from einops import rearrange
+from .tiler import TileWorker
+from .utils import init_weights_on_device
+
+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):
+    def __init__(self, dim, theta, axes_dim):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+
+    def rope(self, pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor:
+        assert dim % 2 == 0, "The dimension must be even."
+
+        scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
+        omega = 1.0 / (theta**scale)
+
+        batch_size, seq_length = pos.shape
+        out = torch.einsum("...n,d->...nd", pos, omega)
+        cos_out = torch.cos(out)
+        sin_out = torch.sin(out)
+
+        stacked_out = torch.stack([cos_out, -sin_out, sin_out, cos_out], dim=-1)
+        out = stacked_out.view(batch_size, -1, dim // 2, 2, 2)
+        return out.float()
+
+
+    def forward(self, ids):
+        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 AdaLayerNorm(torch.nn.Module):
+    def __init__(self, dim, single=False, dual=False):
+        super().__init__()
+        self.single = single
+        self.dual = dual
+        self.linear = torch.nn.Linear(dim, dim * [[6, 2][single], 9][dual])
+        self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb, **kwargs):
+        emb = self.linear(torch.nn.functional.silu(emb),**kwargs)
+        if self.single:
+            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
+            return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+class FluxJointAttention(torch.nn.Module):
+    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
+        self.only_out_a = only_out_a
+
+        self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
+        self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3)
+
+        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)
+
+        self.a_to_out = torch.nn.Linear(dim_a, dim_a)
+        if not only_out_a:
+            self.b_to_out = torch.nn.Linear(dim_b, dim_b)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        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, **kwargs):
+        batch_size = hidden_states_a.shape[0]
+
+        # Part A
+        qkv_a = self.a_to_qkv(hidden_states_a,**kwargs)
+        qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q_a, k_a, v_a = qkv_a.chunk(3, dim=1)
+        q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a)
+
+        # Part B
+        qkv_b = self.b_to_qkv(hidden_states_b,**kwargs)
+        qkv_b = qkv_b.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q_b, k_b, v_b = qkv_b.chunk(3, dim=1)
+        q_b, k_b = self.norm_q_b(q_b), self.norm_k_b(k_b)
+
+        q = torch.concat([q_b, q_a], dim=2)
+        k = torch.concat([k_b, k_a], dim=2)
+        v = torch.concat([v_b, v_a], dim=2)
+
+        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 = 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,**kwargs)
+        if self.only_out_a:
+            return hidden_states_a
+        else:
+            hidden_states_b = self.b_to_out(hidden_states_b,**kwargs)
+            return hidden_states_a, hidden_states_b
+
+class AutoSequential(torch.nn.Sequential):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+    def forward(self, input, **kwargs):
+        for module in self:
+            
+            if isinstance(module, torch.nn.Linear):
+                # print("##"*10)
+                input = module(input, **kwargs)
+            else:
+                input = module(input)
+        return input
+
+
+class FluxJointTransformerBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads):
+        super().__init__()
+        self.norm1_a = AdaLayerNorm(dim)
+        self.norm1_b = AdaLayerNorm(dim)
+
+        self.attn = FluxJointAttention(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(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+        # self.ff_a = AutoSequential(
+        #     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)
+        )
+        # self.ff_b = AutoSequential(
+        #     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, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None, **kwargs):
+        norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb, **kwargs)
+        norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb, **kwargs)
+
+        # 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, **kwargs)
+
+        # Part A
+        hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
+        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 FluxSingleAttention(torch.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.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
+
+        self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
+        self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        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, image_rotary_emb, **kwargs):
+        batch_size = hidden_states.shape[0]
+
+        qkv_a = self.a_to_qkv(hidden_states,**kwargs)
+        qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q_a, k_a, v = qkv_a.chunk(3, dim=1)
+        q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a)
+
+        q, k = self.apply_rope(q_a, k_a, image_rotary_emb)
+
+        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)
+        return hidden_states
+
+
+
+class AdaLayerNormSingle(torch.nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.silu = torch.nn.SiLU()
+        self.linear = torch.nn.Linear(dim, 3 * dim, bias=True)
+        self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+
+    def forward(self, x, emb, **kwargs):
+        emb = self.linear(self.silu(emb),**kwargs)
+        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):
+    def __init__(self, dim, num_attention_heads):
+        super().__init__()
+        self.num_heads = num_attention_heads
+        self.head_dim = dim // num_attention_heads
+        self.dim = dim
+
+        self.norm = AdaLayerNormSingle(dim)
+        self.to_qkv_mlp = torch.nn.Linear(dim, dim * (3 + 4))
+        self.norm_q_a = RMSNorm(self.head_dim, eps=1e-6)
+        self.norm_k_a = RMSNorm(self.head_dim, eps=1e-6)
+
+        self.proj_out = torch.nn.Linear(dim * 5, dim)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        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, **kwargs):
+        batch_size = hidden_states.shape[0]
+
+        qkv = hidden_states.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q, k, v = qkv.chunk(3, dim=1)
+        q, k = self.norm_q_a(q), self.norm_k_a(k)
+
+        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 = 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, **kwargs):
+        residual = hidden_states_a
+        norm_hidden_states, gate = self.norm(hidden_states_a, emb=temb, **kwargs)
+        hidden_states_a = self.to_qkv_mlp(norm_hidden_states, **kwargs)
+        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, **kwargs)
+        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, **kwargs)
+        hidden_states_a = residual + hidden_states_a
+
+        return hidden_states_a, hidden_states_b
+
+
+
+class AdaLayerNormContinuous(torch.nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.silu = torch.nn.SiLU()
+        self.linear = torch.nn.Linear(dim, dim * 2, bias=True)
+        self.norm = torch.nn.LayerNorm(dim, eps=1e-6, elementwise_affine=False)
+
+    def forward(self, x, conditioning, **kwargs):
+        emb = self.linear(self.silu(conditioning),**kwargs)
+        scale, shift = torch.chunk(emb, 2, dim=1)
+        x = self.norm(x) * (1 + scale)[:, None] + shift[:, None]
+        return x
+
+
+class FluxDiT(torch.nn.Module):
+    def __init__(self, disable_guidance_embedder=False):
+        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.pooled_text_embedder = AutoSequential(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(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)
+
+
+    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 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
+        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 tiled_forward(
+        self,
+        hidden_states,
+        timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids,
+        tile_size=128, tile_stride=64,
+        **kwargs
+    ):
+        # Due to the global positional embedding, we cannot implement layer-wise tiled forward.
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x, timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None),
+            hidden_states,
+            tile_size,
+            tile_stride,
+            tile_device=hidden_states.device,
+            tile_dtype=hidden_states.dtype
+        )
+        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 = hidden_states.shape[1]
+        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,
+        use_gradient_checkpointing=False,
+        **kwargs
+    ):
+        if tiled:
+            return self.tiled_forward(
+                hidden_states,
+                timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids,
+                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)
+
+        height, width = hidden_states.shape[-2:]
+        hidden_states = self.patchify(hidden_states)
+        hidden_states = self.x_embedder(hidden_states,**kwargs)
+
+        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, **kwargs)
+            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, **kwargs,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask, **kwargs)
+
+        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, **kwargs,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask, **kwargs)
+        hidden_states = hidden_states[:, prompt_emb.shape[1]:]
+
+        hidden_states = self.final_norm_out(hidden_states, conditioning, **kwargs)
+        hidden_states = self.final_proj_out(hidden_states, **kwargs)
+        hidden_states = self.unpatchify(hidden_states, height, width)
+
+        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:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, 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"
+                if "lora_B" in name:
+                    suffix = ".lora_B" + suffix
+                if "lora_A" in name:
+                    suffix = ".lora_A" + suffix
+                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:
+                        pass
+                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:
+                    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:
+                    # print('$$'*10)
+                    # mlp_name = name.replace(".a_to_q.", ".proj_in_besides_attn.")
+                    # print(f'mlp name: {mlp_name}')
+                    # print(f'mlp shape: {mlp.shape}')
+                    state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn."))
+                # print(f'mlp shape: {mlp.shape}')
+                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:
+                    # create zreo matrix
+                    d, r = state_dict_[name].shape
+                    # print('--'*10)
+                    # print(d, r)
+                    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_
+
+    def from_civitai(self, 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",
+            "time_in.out_layer.bias": "time_embedder.timestep_embedder.2.bias",
+            "time_in.out_layer.weight": "time_embedder.timestep_embedder.2.weight",
+            "txt_in.bias": "context_embedder.bias",
+            "txt_in.weight": "context_embedder.weight",
+            "vector_in.in_layer.bias": "pooled_text_embedder.0.bias",
+            "vector_in.in_layer.weight": "pooled_text_embedder.0.weight",
+            "vector_in.out_layer.bias": "pooled_text_embedder.2.bias",
+            "vector_in.out_layer.weight": "pooled_text_embedder.2.weight",
+            "final_layer.linear.bias": "final_proj_out.bias",
+            "final_layer.linear.weight": "final_proj_out.weight",
+            "guidance_in.in_layer.bias": "guidance_embedder.timestep_embedder.0.bias",
+            "guidance_in.in_layer.weight": "guidance_embedder.timestep_embedder.0.weight",
+            "guidance_in.out_layer.bias": "guidance_embedder.timestep_embedder.2.bias",
+            "guidance_in.out_layer.weight": "guidance_embedder.timestep_embedder.2.weight",
+            "img_in.bias": "x_embedder.bias",
+            "img_in.weight": "x_embedder.weight",
+            "final_layer.adaLN_modulation.1.weight": "final_norm_out.linear.weight",
+            "final_layer.adaLN_modulation.1.bias": "final_norm_out.linear.bias",
+        }
+        suffix_rename_dict = {
+            "img_attn.norm.key_norm.scale": "attn.norm_k_a.weight",
+            "img_attn.norm.query_norm.scale": "attn.norm_q_a.weight",
+            "img_attn.proj.bias": "attn.a_to_out.bias",
+            "img_attn.proj.weight": "attn.a_to_out.weight",
+            "img_attn.qkv.bias": "attn.a_to_qkv.bias",
+            "img_attn.qkv.weight": "attn.a_to_qkv.weight",
+            "img_mlp.0.bias": "ff_a.0.bias",
+            "img_mlp.0.weight": "ff_a.0.weight",
+            "img_mlp.2.bias": "ff_a.2.bias",
+            "img_mlp.2.weight": "ff_a.2.weight",
+            "img_mod.lin.bias": "norm1_a.linear.bias",
+            "img_mod.lin.weight": "norm1_a.linear.weight",
+            "txt_attn.norm.key_norm.scale": "attn.norm_k_b.weight",
+            "txt_attn.norm.query_norm.scale": "attn.norm_q_b.weight",
+            "txt_attn.proj.bias": "attn.b_to_out.bias",
+            "txt_attn.proj.weight": "attn.b_to_out.weight",
+            "txt_attn.qkv.bias": "attn.b_to_qkv.bias",
+            "txt_attn.qkv.weight": "attn.b_to_qkv.weight",
+            "txt_mlp.0.bias": "ff_b.0.bias",
+            "txt_mlp.0.weight": "ff_b.0.weight",
+            "txt_mlp.2.bias": "ff_b.2.bias",
+            "txt_mlp.2.weight": "ff_b.2.weight",
+            "txt_mod.lin.bias": "norm1_b.linear.bias",
+            "txt_mod.lin.weight": "norm1_b.linear.weight",
+
+            "linear1.bias": "to_qkv_mlp.bias",
+            "linear1.weight": "to_qkv_mlp.weight",
+            "linear2.bias": "proj_out.bias",
+            "linear2.weight": "proj_out.weight",
+            "modulation.lin.bias": "norm.linear.bias",
+            "modulation.lin.weight": "norm.linear.weight",
+            "norm.key_norm.scale": "norm_k_a.weight",
+            "norm.query_norm.scale": "norm_q_a.weight",
+        }
+        state_dict_ = {}
+
+
+        for name, param in state_dict.items():
+            # match lora load
+            l_name = ''
+            if 'lora_A' in name :
+                l_name = 'lora_A'
+            if 'lora_B' in name :
+                l_name = 'lora_B'
+            if l_name != '':
+                name = name.replace(l_name+'.', '')
+            
+
+            if name.startswith("model.diffusion_model."):
+                name = name[len("model.diffusion_model."):]
+            names = name.split(".")
+            if name in rename_dict:
+                rename = rename_dict[name]
+                if name.startswith("final_layer.adaLN_modulation.1."):
+                    if l_name == 'lora_A':
+                        param = torch.concat([param[:,3072:], param[:,:3072]], dim=1)
+                    else:
+                        param = torch.concat([param[3072:], param[:3072]], dim=0)
+                if l_name != '':
+                    state_dict_[rename.replace('weight',l_name+'.weight')] = param
+                else:
+                    state_dict_[rename] = param
+               
+            elif names[0] == "double_blocks":
+                rename = f"blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])]
+                if l_name != '':
+                    state_dict_[rename.replace('weight',l_name+'.weight')] = param
+                else:
+                    state_dict_[rename] = param
+
+            elif names[0] == "single_blocks":
+                if ".".join(names[2:]) in suffix_rename_dict:
+                    rename = f"single_blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])]
+                    if l_name != '':
+                        state_dict_[rename.replace('weight',l_name+'.weight')] = param
+                    else:
+                        state_dict_[rename] = param
+            else:
+                pass
+        if "guidance_embedder.timestep_embedder.0.weight" not in state_dict_:
+            return state_dict_, {"disable_guidance_embedder": True}
+        else:
+            return state_dict_

diffsynth/models/flux_ipadapter.py

@@ -0,0 +1,94 @@
+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)

diffsynth/models/flux_text_encoder.py

@@ -0,0 +1,32 @@
+import torch
+from transformers import T5EncoderModel, T5Config
+from .sd_text_encoder import SDTextEncoder
+
+
+
+class FluxTextEncoder2(T5EncoderModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.eval()
+
+    def forward(self, input_ids):
+        outputs = super().forward(input_ids=input_ids)
+        prompt_emb = outputs.last_hidden_state
+        return prompt_emb
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxTextEncoder2StateDictConverter()
+
+
+
+class FluxTextEncoder2StateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = state_dict
+        return state_dict_
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/flux_vae.py

@@ -0,0 +1,303 @@
+from .sd3_vae_encoder import SD3VAEEncoder, SDVAEEncoderStateDictConverter
+from .sd3_vae_decoder import SD3VAEDecoder, SDVAEDecoderStateDictConverter
+
+
+class FluxVAEEncoder(SD3VAEEncoder):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.3611
+        self.shift_factor = 0.1159
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxVAEEncoderStateDictConverter()
+
+
+class FluxVAEDecoder(SD3VAEDecoder):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.3611
+        self.shift_factor = 0.1159
+    
+    @staticmethod
+    def state_dict_converter():
+        return FluxVAEDecoderStateDictConverter()
+
+
+class FluxVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter):
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "encoder.conv_in.bias": "conv_in.bias",
+            "encoder.conv_in.weight": "conv_in.weight",
+            "encoder.conv_out.bias": "conv_out.bias",
+            "encoder.conv_out.weight": "conv_out.weight",
+            "encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias",
+            "encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight",
+            "encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias",
+            "encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight",
+            "encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias",
+            "encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight",
+            "encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias",
+            "encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight",
+            "encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias",
+            "encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight",
+            "encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias",
+            "encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight",
+            "encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias",
+            "encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight",
+            "encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias",
+            "encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight",
+            "encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias",
+            "encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight",
+            "encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias",
+            "encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight",
+            "encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias",
+            "encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight",
+            "encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias",
+            "encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight",
+            "encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias",
+            "encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight",
+            "encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias",
+            "encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight",
+            "encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias",
+            "encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight",
+            "encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias",
+            "encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight",
+            "encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias",
+            "encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight",
+            "encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias",
+            "encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight",
+            "encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias",
+            "encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight",
+            "encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias",
+            "encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight",
+            "encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias",
+            "encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight",
+            "encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias",
+            "encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight",
+            "encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias",
+            "encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight",
+            "encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias",
+            "encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight",
+            "encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias",
+            "encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight",
+            "encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias",
+            "encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight",
+            "encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias",
+            "encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight",
+            "encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias",
+            "encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight",
+            "encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias",
+            "encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight",
+            "encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias",
+            "encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight",
+            "encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias",
+            "encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight",
+            "encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias",
+            "encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight",
+            "encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias",
+            "encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight",
+            "encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias",
+            "encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight",
+            "encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias",
+            "encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight",
+            "encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias",
+            "encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight",
+            "encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias",
+            "encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight",
+            "encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias",
+            "encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight",
+            "encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias",
+            "encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight",
+            "encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias",
+            "encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight",
+            "encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias",
+            "encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight",
+            "encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias",
+            "encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight",
+            "encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias",
+            "encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight",
+            "encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias",
+            "encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight",
+            "encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias",
+            "encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight",
+            "encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias",
+            "encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight",
+            "encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias",
+            "encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight",
+            "encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias",
+            "encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight",
+            "encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias",
+            "encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight",
+            "encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias",
+            "encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight",
+            "encoder.norm_out.bias": "conv_norm_out.bias",
+            "encoder.norm_out.weight": "conv_norm_out.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if "transformer_blocks" in rename_dict[name]:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
+    
+
+
+class FluxVAEDecoderStateDictConverter(SDVAEDecoderStateDictConverter):
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "decoder.conv_in.bias": "conv_in.bias",
+            "decoder.conv_in.weight": "conv_in.weight",
+            "decoder.conv_out.bias": "conv_out.bias",
+            "decoder.conv_out.weight": "conv_out.weight",
+            "decoder.mid.attn_1.k.bias": "blocks.1.transformer_blocks.0.to_k.bias",
+            "decoder.mid.attn_1.k.weight": "blocks.1.transformer_blocks.0.to_k.weight",
+            "decoder.mid.attn_1.norm.bias": "blocks.1.norm.bias",
+            "decoder.mid.attn_1.norm.weight": "blocks.1.norm.weight",
+            "decoder.mid.attn_1.proj_out.bias": "blocks.1.transformer_blocks.0.to_out.bias",
+            "decoder.mid.attn_1.proj_out.weight": "blocks.1.transformer_blocks.0.to_out.weight",
+            "decoder.mid.attn_1.q.bias": "blocks.1.transformer_blocks.0.to_q.bias",
+            "decoder.mid.attn_1.q.weight": "blocks.1.transformer_blocks.0.to_q.weight",
+            "decoder.mid.attn_1.v.bias": "blocks.1.transformer_blocks.0.to_v.bias",
+            "decoder.mid.attn_1.v.weight": "blocks.1.transformer_blocks.0.to_v.weight",
+            "decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias",
+            "decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight",
+            "decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias",
+            "decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight",
+            "decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias",
+            "decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight",
+            "decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias",
+            "decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight",
+            "decoder.mid.block_2.conv1.bias": "blocks.2.conv1.bias",
+            "decoder.mid.block_2.conv1.weight": "blocks.2.conv1.weight",
+            "decoder.mid.block_2.conv2.bias": "blocks.2.conv2.bias",
+            "decoder.mid.block_2.conv2.weight": "blocks.2.conv2.weight",
+            "decoder.mid.block_2.norm1.bias": "blocks.2.norm1.bias",
+            "decoder.mid.block_2.norm1.weight": "blocks.2.norm1.weight",
+            "decoder.mid.block_2.norm2.bias": "blocks.2.norm2.bias",
+            "decoder.mid.block_2.norm2.weight": "blocks.2.norm2.weight",
+            "decoder.norm_out.bias": "conv_norm_out.bias",
+            "decoder.norm_out.weight": "conv_norm_out.weight",
+            "decoder.up.0.block.0.conv1.bias": "blocks.15.conv1.bias",
+            "decoder.up.0.block.0.conv1.weight": "blocks.15.conv1.weight",
+            "decoder.up.0.block.0.conv2.bias": "blocks.15.conv2.bias",
+            "decoder.up.0.block.0.conv2.weight": "blocks.15.conv2.weight",
+            "decoder.up.0.block.0.nin_shortcut.bias": "blocks.15.conv_shortcut.bias",
+            "decoder.up.0.block.0.nin_shortcut.weight": "blocks.15.conv_shortcut.weight",
+            "decoder.up.0.block.0.norm1.bias": "blocks.15.norm1.bias",
+            "decoder.up.0.block.0.norm1.weight": "blocks.15.norm1.weight",
+            "decoder.up.0.block.0.norm2.bias": "blocks.15.norm2.bias",
+            "decoder.up.0.block.0.norm2.weight": "blocks.15.norm2.weight",
+            "decoder.up.0.block.1.conv1.bias": "blocks.16.conv1.bias",
+            "decoder.up.0.block.1.conv1.weight": "blocks.16.conv1.weight",
+            "decoder.up.0.block.1.conv2.bias": "blocks.16.conv2.bias",
+            "decoder.up.0.block.1.conv2.weight": "blocks.16.conv2.weight",
+            "decoder.up.0.block.1.norm1.bias": "blocks.16.norm1.bias",
+            "decoder.up.0.block.1.norm1.weight": "blocks.16.norm1.weight",
+            "decoder.up.0.block.1.norm2.bias": "blocks.16.norm2.bias",
+            "decoder.up.0.block.1.norm2.weight": "blocks.16.norm2.weight",
+            "decoder.up.0.block.2.conv1.bias": "blocks.17.conv1.bias",
+            "decoder.up.0.block.2.conv1.weight": "blocks.17.conv1.weight",
+            "decoder.up.0.block.2.conv2.bias": "blocks.17.conv2.bias",
+            "decoder.up.0.block.2.conv2.weight": "blocks.17.conv2.weight",
+            "decoder.up.0.block.2.norm1.bias": "blocks.17.norm1.bias",
+            "decoder.up.0.block.2.norm1.weight": "blocks.17.norm1.weight",
+            "decoder.up.0.block.2.norm2.bias": "blocks.17.norm2.bias",
+            "decoder.up.0.block.2.norm2.weight": "blocks.17.norm2.weight",
+            "decoder.up.1.block.0.conv1.bias": "blocks.11.conv1.bias",
+            "decoder.up.1.block.0.conv1.weight": "blocks.11.conv1.weight",
+            "decoder.up.1.block.0.conv2.bias": "blocks.11.conv2.bias",
+            "decoder.up.1.block.0.conv2.weight": "blocks.11.conv2.weight",
+            "decoder.up.1.block.0.nin_shortcut.bias": "blocks.11.conv_shortcut.bias",
+            "decoder.up.1.block.0.nin_shortcut.weight": "blocks.11.conv_shortcut.weight",
+            "decoder.up.1.block.0.norm1.bias": "blocks.11.norm1.bias",
+            "decoder.up.1.block.0.norm1.weight": "blocks.11.norm1.weight",
+            "decoder.up.1.block.0.norm2.bias": "blocks.11.norm2.bias",
+            "decoder.up.1.block.0.norm2.weight": "blocks.11.norm2.weight",
+            "decoder.up.1.block.1.conv1.bias": "blocks.12.conv1.bias",
+            "decoder.up.1.block.1.conv1.weight": "blocks.12.conv1.weight",
+            "decoder.up.1.block.1.conv2.bias": "blocks.12.conv2.bias",
+            "decoder.up.1.block.1.conv2.weight": "blocks.12.conv2.weight",
+            "decoder.up.1.block.1.norm1.bias": "blocks.12.norm1.bias",
+            "decoder.up.1.block.1.norm1.weight": "blocks.12.norm1.weight",
+            "decoder.up.1.block.1.norm2.bias": "blocks.12.norm2.bias",
+            "decoder.up.1.block.1.norm2.weight": "blocks.12.norm2.weight",
+            "decoder.up.1.block.2.conv1.bias": "blocks.13.conv1.bias",
+            "decoder.up.1.block.2.conv1.weight": "blocks.13.conv1.weight",
+            "decoder.up.1.block.2.conv2.bias": "blocks.13.conv2.bias",
+            "decoder.up.1.block.2.conv2.weight": "blocks.13.conv2.weight",
+            "decoder.up.1.block.2.norm1.bias": "blocks.13.norm1.bias",
+            "decoder.up.1.block.2.norm1.weight": "blocks.13.norm1.weight",
+            "decoder.up.1.block.2.norm2.bias": "blocks.13.norm2.bias",
+            "decoder.up.1.block.2.norm2.weight": "blocks.13.norm2.weight",
+            "decoder.up.1.upsample.conv.bias": "blocks.14.conv.bias",
+            "decoder.up.1.upsample.conv.weight": "blocks.14.conv.weight",
+            "decoder.up.2.block.0.conv1.bias": "blocks.7.conv1.bias",
+            "decoder.up.2.block.0.conv1.weight": "blocks.7.conv1.weight",
+            "decoder.up.2.block.0.conv2.bias": "blocks.7.conv2.bias",
+            "decoder.up.2.block.0.conv2.weight": "blocks.7.conv2.weight",
+            "decoder.up.2.block.0.norm1.bias": "blocks.7.norm1.bias",
+            "decoder.up.2.block.0.norm1.weight": "blocks.7.norm1.weight",
+            "decoder.up.2.block.0.norm2.bias": "blocks.7.norm2.bias",
+            "decoder.up.2.block.0.norm2.weight": "blocks.7.norm2.weight",
+            "decoder.up.2.block.1.conv1.bias": "blocks.8.conv1.bias",
+            "decoder.up.2.block.1.conv1.weight": "blocks.8.conv1.weight",
+            "decoder.up.2.block.1.conv2.bias": "blocks.8.conv2.bias",
+            "decoder.up.2.block.1.conv2.weight": "blocks.8.conv2.weight",
+            "decoder.up.2.block.1.norm1.bias": "blocks.8.norm1.bias",
+            "decoder.up.2.block.1.norm1.weight": "blocks.8.norm1.weight",
+            "decoder.up.2.block.1.norm2.bias": "blocks.8.norm2.bias",
+            "decoder.up.2.block.1.norm2.weight": "blocks.8.norm2.weight",
+            "decoder.up.2.block.2.conv1.bias": "blocks.9.conv1.bias",
+            "decoder.up.2.block.2.conv1.weight": "blocks.9.conv1.weight",
+            "decoder.up.2.block.2.conv2.bias": "blocks.9.conv2.bias",
+            "decoder.up.2.block.2.conv2.weight": "blocks.9.conv2.weight",
+            "decoder.up.2.block.2.norm1.bias": "blocks.9.norm1.bias",
+            "decoder.up.2.block.2.norm1.weight": "blocks.9.norm1.weight",
+            "decoder.up.2.block.2.norm2.bias": "blocks.9.norm2.bias",
+            "decoder.up.2.block.2.norm2.weight": "blocks.9.norm2.weight",
+            "decoder.up.2.upsample.conv.bias": "blocks.10.conv.bias",
+            "decoder.up.2.upsample.conv.weight": "blocks.10.conv.weight",
+            "decoder.up.3.block.0.conv1.bias": "blocks.3.conv1.bias",
+            "decoder.up.3.block.0.conv1.weight": "blocks.3.conv1.weight",
+            "decoder.up.3.block.0.conv2.bias": "blocks.3.conv2.bias",
+            "decoder.up.3.block.0.conv2.weight": "blocks.3.conv2.weight",
+            "decoder.up.3.block.0.norm1.bias": "blocks.3.norm1.bias",
+            "decoder.up.3.block.0.norm1.weight": "blocks.3.norm1.weight",
+            "decoder.up.3.block.0.norm2.bias": "blocks.3.norm2.bias",
+            "decoder.up.3.block.0.norm2.weight": "blocks.3.norm2.weight",
+            "decoder.up.3.block.1.conv1.bias": "blocks.4.conv1.bias",
+            "decoder.up.3.block.1.conv1.weight": "blocks.4.conv1.weight",
+            "decoder.up.3.block.1.conv2.bias": "blocks.4.conv2.bias",
+            "decoder.up.3.block.1.conv2.weight": "blocks.4.conv2.weight",
+            "decoder.up.3.block.1.norm1.bias": "blocks.4.norm1.bias",
+            "decoder.up.3.block.1.norm1.weight": "blocks.4.norm1.weight",
+            "decoder.up.3.block.1.norm2.bias": "blocks.4.norm2.bias",
+            "decoder.up.3.block.1.norm2.weight": "blocks.4.norm2.weight",
+            "decoder.up.3.block.2.conv1.bias": "blocks.5.conv1.bias",
+            "decoder.up.3.block.2.conv1.weight": "blocks.5.conv1.weight",
+            "decoder.up.3.block.2.conv2.bias": "blocks.5.conv2.bias",
+            "decoder.up.3.block.2.conv2.weight": "blocks.5.conv2.weight",
+            "decoder.up.3.block.2.norm1.bias": "blocks.5.norm1.bias",
+            "decoder.up.3.block.2.norm1.weight": "blocks.5.norm1.weight",
+            "decoder.up.3.block.2.norm2.bias": "blocks.5.norm2.bias",
+            "decoder.up.3.block.2.norm2.weight": "blocks.5.norm2.weight",
+            "decoder.up.3.upsample.conv.bias": "blocks.6.conv.bias",
+            "decoder.up.3.upsample.conv.weight": "blocks.6.conv.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if "transformer_blocks" in rename_dict[name]:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_

diffsynth/models/hunyuan_dit.py

@@ -1,5 +1,4 @@
 from .attention import Attention
-from .tiler import TileWorker
 from einops import repeat, rearrange
 import math
 import torch
@@ -399,7 +398,8 @@ class HunyuanDiT(torch.nn.Module):
         hidden_states, _ = hidden_states.chunk(2, dim=1)
         return hidden_states
     
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return HunyuanDiTStateDictConverter()
 
 

diffsynth/models/hunyuan_dit_text_encoder.py

@@ -79,7 +79,8 @@ class HunyuanDiTCLIPTextEncoder(BertModel):
             prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean
         return prompt_emb
 
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return HunyuanDiTCLIPTextEncoderStateDictConverter()
 
 
@@ -131,7 +132,8 @@ class HunyuanDiTT5TextEncoder(T5EncoderModel):
             prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean
         return prompt_emb
     
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return HunyuanDiTT5TextEncoderStateDictConverter()
 
 

diffsynth/models/hunyuan_video_dit.py

@@ -0,0 +1,920 @@
+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_

diffsynth/models/hunyuan_video_text_encoder.py

@@ -0,0 +1,68 @@
+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

diffsynth/models/hunyuan_video_vae_decoder.py

@@ -0,0 +1,507 @@
+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_

diffsynth/models/hunyuan_video_vae_encoder.py

@@ -0,0 +1,307 @@
+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_

diffsynth/models/lora.py

@@ -0,0 +1,472 @@
+import torch
+from .sd_unet import SDUNet
+from .sdxl_unet import SDXLUNet
+from .sd_text_encoder import SDTextEncoder
+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
+
+
+
+class LoRAFromCivitai:
+    def __init__(self):
+        self.supported_model_classes = []
+        self.lora_prefix = []
+        self.renamed_lora_prefix = {}
+        self.special_keys = {}
+
+
+    def convert_state_dict(self, state_dict, lora_prefix="lora_unet_", alpha=1.0):
+        for key in state_dict:
+            if ".lora_up" in key:
+                return self.convert_state_dict_up_down(state_dict, lora_prefix, alpha)
+        return self.convert_state_dict_AB(state_dict, lora_prefix, alpha)
+
+    def convert_state_name(self, state_dict, lora_prefix="lora_unet_", alpha=1.0):
+        for key in state_dict:
+            if ".lora_up" in key:
+                return self.convert_state_name_up_down(state_dict, lora_prefix, alpha)
+        return self.convert_state_name_AB(state_dict, lora_prefix, alpha)
+
+
+    def convert_state_dict_up_down(self, state_dict, lora_prefix="lora_unet_", alpha=1.0):
+        renamed_lora_prefix = self.renamed_lora_prefix.get(lora_prefix, "")
+        state_dict_ = {}
+        for key in state_dict:
+            if ".lora_up" not in key:
+                continue
+            if not key.startswith(lora_prefix):
+                continue
+            weight_up = state_dict[key].to(device="cuda", dtype=torch.float16)
+            weight_down = state_dict[key.replace(".lora_up", ".lora_down")].to(device="cuda", dtype=torch.float16)
+            if len(weight_up.shape) == 4:
+                weight_up = weight_up.squeeze(3).squeeze(2).to(torch.float32)
+                weight_down = weight_down.squeeze(3).squeeze(2).to(torch.float32)
+                lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
+            else:
+                lora_weight = alpha * torch.mm(weight_up, weight_down)
+            target_name = key.split(".")[0].replace(lora_prefix, renamed_lora_prefix).replace("_", ".") + ".weight"
+            for special_key in self.special_keys:
+                target_name = target_name.replace(special_key, self.special_keys[special_key])
+            state_dict_[target_name] = lora_weight.cpu()
+        return state_dict_
+    
+
+    def convert_state_name_up_down(self, state_dict, lora_prefix="lora_unet_", alpha=1.0):
+        renamed_lora_prefix = self.renamed_lora_prefix.get(lora_prefix, "")
+        state_dict_ = {}
+        for key in state_dict:
+            if ".lora_up" not in key:
+                continue
+            if not key.startswith(lora_prefix):
+                continue
+            weight_up = state_dict[key].to(device="cuda", dtype=torch.float16)
+            weight_down = state_dict[key.replace(".lora_up", ".lora_down")].to(device="cuda", dtype=torch.float16)
+            if len(weight_up.shape) == 4:
+                weight_up = weight_up.squeeze(3).squeeze(2).to(torch.float32)
+                weight_down = weight_down.squeeze(3).squeeze(2).to(torch.float32)
+            target_name = key.split(".")[0].replace(lora_prefix, renamed_lora_prefix).replace("_", ".") + ".weight"
+            for special_key in self.special_keys:
+                target_name = target_name.replace(special_key, self.special_keys[special_key])
+            
+            state_dict_[target_name.replace(".weight",".lora_B.weight")] = weight_up.cpu()
+            state_dict_[target_name.replace(".weight",".lora_A.weight")] = weight_down.cpu()
+        return state_dict_
+    
+
+    def convert_state_dict_AB(self, state_dict, lora_prefix="", alpha=1.0, device="cuda", torch_dtype=torch.float16):
+        state_dict_ = {}
+
+        for key in state_dict:
+            if ".lora_B." not in key:
+                continue
+            if not key.startswith(lora_prefix):
+                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(".")
+            keys.pop(keys.index("lora_B"))
+            
+            target_name = ".".join(keys)
+           
+            target_name = target_name[len(lora_prefix):]
+           
+            state_dict_[target_name] = lora_weight.cpu()
+        return state_dict_
+    
+    def convert_state_name_AB(self, state_dict, lora_prefix="", alpha=1.0, device="cuda", torch_dtype=torch.float16):
+        state_dict_ = {}
+ 
+        for key in state_dict:
+            if ".lora_B." not in key:
+                continue
+            if not key.startswith(lora_prefix):
+                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)
+
+            keys = key.split(".")
+            keys.pop(keys.index("lora_B"))
+
+            target_name = ".".join(keys)
+
+            target_name = target_name[len(lora_prefix):]
+
+            state_dict_[target_name.replace(".weight",".lora_B.weight")] = weight_up.cpu()
+            state_dict_[target_name.replace(".weight",".lora_A.weight")] = weight_down.cpu()
+        return state_dict_
+
+    def load(self, model, state_dict_lora, lora_prefix, alpha=1.0, model_resource=None):
+        state_dict_model = model.state_dict()
+        state_dict_lora = self.convert_state_dict(state_dict_lora, lora_prefix=lora_prefix, alpha=alpha)
+        if model_resource == "diffusers":
+            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)
+    
+
+    def match(self, model, state_dict_lora):
+        for lora_prefix, model_class in zip(self.lora_prefix, self.supported_model_classes):
+            if not isinstance(model, model_class):
+                continue
+            # print(f'lora_prefix: {lora_prefix}')
+            state_dict_model = model.state_dict()
+            for model_resource in ["diffusers", "civitai"]:
+                try:
+                    state_dict_lora_ = self.convert_state_dict(state_dict_lora, lora_prefix=lora_prefix, alpha=1.0)
+                    # print(f'after convert_state_dict lora state_dict:{state_dict_lora_.keys()}')
+                    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_)
+                    # print(f'after converter_fn lora state_dict:{state_dict_lora_.keys()}')
+                    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_:
+                        if name not in state_dict_model:
+                            break
+                    else:
+                        return lora_prefix, model_resource
+                except:
+                    pass
+        return None
+
+    def get_converted_lora_state_dict(self, model, state_dict_lora):
+        for lora_prefix, model_class in zip(self.lora_prefix, self.supported_model_classes):
+            if not isinstance(model, model_class):
+                continue
+
+            state_dict_model = model.state_dict()
+            for model_resource in ["diffusers","civitai"]:
+                try:
+                    state_dict_lora_ = self.convert_state_name(state_dict_lora, lora_prefix=lora_prefix, alpha=1.0)
+ 
+                    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
+                    # return state_dict_lora_
+                    for name in state_dict_lora_:
+                        if name.replace('.lora_B','').replace('.lora_A','') not in state_dict_model:
+                            print(f"   lora's {name} is not in model.")
+                            break
+                    else:
+                        return state_dict_lora_
+                except Exception as e:
+                    print(f"error   {str(e)}")
+        return None
+
+class SDLoRAFromCivitai(LoRAFromCivitai):
+    def __init__(self):
+        super().__init__()
+        self.supported_model_classes = [SDUNet, SDTextEncoder]
+        self.lora_prefix = ["lora_unet_", "lora_te_"]
+        self.special_keys = {
+            "down.blocks": "down_blocks",
+            "up.blocks": "up_blocks",
+            "mid.block": "mid_block",
+            "proj.in": "proj_in",
+            "proj.out": "proj_out",
+            "transformer.blocks": "transformer_blocks",
+            "to.q": "to_q",
+            "to.k": "to_k",
+            "to.v": "to_v",
+            "to.out": "to_out",
+            "text.model": "text_model",
+            "self.attn.q.proj": "self_attn.q_proj",
+            "self.attn.k.proj": "self_attn.k_proj",
+            "self.attn.v.proj": "self_attn.v_proj",
+            "self.attn.out.proj": "self_attn.out_proj",
+            "input.blocks": "model.diffusion_model.input_blocks",
+            "middle.block": "model.diffusion_model.middle_block",
+            "output.blocks": "model.diffusion_model.output_blocks",
+        }
+
+
+class SDXLLoRAFromCivitai(LoRAFromCivitai):
+    def __init__(self):
+        super().__init__()
+        self.supported_model_classes = [SDXLUNet, SDXLTextEncoder, SDXLTextEncoder2]
+        self.lora_prefix = ["lora_unet_", "lora_te1_", "lora_te2_"]
+        self.renamed_lora_prefix = {"lora_te2_": "2"}
+        self.special_keys = {
+            "down.blocks": "down_blocks",
+            "up.blocks": "up_blocks",
+            "mid.block": "mid_block",
+            "proj.in": "proj_in",
+            "proj.out": "proj_out",
+            "transformer.blocks": "transformer_blocks",
+            "to.q": "to_q",
+            "to.k": "to_k",
+            "to.v": "to_v",
+            "to.out": "to_out",
+            "text.model": "conditioner.embedders.0.transformer.text_model",
+            "self.attn.q.proj": "self_attn.q_proj",
+            "self.attn.k.proj": "self_attn.k_proj",
+            "self.attn.v.proj": "self_attn.v_proj",
+            "self.attn.out.proj": "self_attn.out_proj",
+            "input.blocks": "model.diffusion_model.input_blocks",
+            "middle.block": "model.diffusion_model.middle_block",
+            "output.blocks": "model.diffusion_model.output_blocks",
+            "2conditioner.embedders.0.transformer.text_model.encoder.layers": "text_model.encoder.layers"
+        }
+        
+
+class FluxLoRAFromCivitai(LoRAFromCivitai):
+    def __init__(self):
+        super().__init__()
+        self.supported_model_classes = [FluxDiT, FluxDiT]
+        self.lora_prefix = ["lora_unet_", "transformer."]
+        self.renamed_lora_prefix = {}
+        self.special_keys = {
+            "single.blocks": "single_blocks",
+            "double.blocks": "double_blocks",
+            "img.attn": "img_attn",
+            "img.mlp": "img_mlp",
+            "img.mod": "img_mod",
+            "txt.attn": "txt_attn",
+            "txt.mlp": "txt_mlp",
+            "txt.mod": "txt_mod",
+        }
+
+
+class GeneralLoRAFromPeft:
+    def __init__(self):
+        self.supported_model_classes = [SDUNet, SDXLUNet, SD3DiT, HunyuanDiT, FluxDiT, CogDiT, WanModel]
+
+
+    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)
+        if torch_dtype == torch.float8_e4m3fn:
+            torch_dtype = torch.float32
+        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"))
+            target_name = ".".join(keys)
+            if target_name.startswith("diffusion_model."):
+                target_name = target_name[len("diffusion_model."):]
+            if target_name not in target_state_dict:
+                return {}
+            state_dict_[target_name] = lora_weight.cpu()
+        return state_dict_
+    
+
+    def load(self, model, state_dict_lora, lora_prefix="", alpha=1.0, model_resource=""):
+        state_dict_model = model.state_dict()
+        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:
+                if state_dict_model[name].dtype == torch.float8_e4m3fn:
+                    weight = state_dict_model[name].to(torch.float32)
+                    lora_weight = state_dict_lora[name].to(
+                        dtype=torch.float32,
+                        device=state_dict_model[name].device
+                    )
+                    state_dict_model[name] = (weight + lora_weight).to(
+                        dtype=state_dict_model[name].dtype,
+                        device=state_dict_model[name].device
+                    )
+                else:
+                    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)
+    
+
+    def match(self, model, state_dict_lora):
+        for model_class in self.supported_model_classes:
+            if not isinstance(model, model_class):
+                continue
+            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
+    
+
+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=1.0):
+        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:
+                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"):
+                state_dict_[rename.replace("lora_up.weight", "alpha")] = torch.tensor((alpha,))[0]
+        return state_dict_
+    
+    @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_
+    
+
+def get_lora_loaders():
+    return [SDLoRAFromCivitai(), SDXLLoRAFromCivitai(), FluxLoRAFromCivitai(), HunyuanVideoLoRAFromCivitai(), GeneralLoRAFromPeft()]

diffsynth/models/model_manager.py

@@ -0,0 +1,454 @@
+import os, torch, json, importlib
+from typing import List
+
+from .downloader import download_models, download_customized_models, Preset_model_id, Preset_model_website
+
+from .sd_text_encoder import SDTextEncoder
+from .sd_unet import SDUNet
+from .sd_vae_encoder import SDVAEEncoder
+from .sd_vae_decoder import SDVAEDecoder
+from .lora import get_lora_loaders
+
+from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2
+from .sdxl_unet import SDXLUNet
+from .sdxl_vae_decoder import SDXLVAEDecoder
+from .sdxl_vae_encoder import SDXLVAEEncoder
+
+from .sd3_text_encoder import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3
+from .sd3_dit import SD3DiT
+from .sd3_vae_decoder import SD3VAEDecoder
+from .sd3_vae_encoder import SD3VAEEncoder
+
+from .sd_controlnet import SDControlNet
+from .sdxl_controlnet import SDXLControlNetUnion
+
+from .sd_motion import SDMotionModel
+from .sdxl_motion import SDXLMotionModel
+
+from .svd_image_encoder import SVDImageEncoder
+from .svd_unet import SVDUNet
+from .svd_vae_decoder import SVDVAEDecoder
+from .svd_vae_encoder import SVDVAEEncoder
+
+from .sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder
+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_vae import FluxVAEEncoder, FluxVAEDecoder
+from .flux_ipadapter import FluxIpAdapter
+
+from .cog_vae import CogVAEEncoder, CogVAEDecoder
+from .cog_dit import CogDiT
+
+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
+
+
+def load_model_from_single_file(state_dict, model_names, model_classes, model_resource, torch_dtype, device):
+    loaded_model_names, loaded_models = [], []
+    for model_name, model_class in zip(model_names, model_classes):
+        print(f"    model_name: {model_name} model_class: {model_class.__name__}")
+        state_dict_converter = model_class.state_dict_converter()
+        if model_resource == "civitai":
+            state_dict_results = state_dict_converter.from_civitai(state_dict)
+        elif model_resource == "diffusers":
+            state_dict_results = state_dict_converter.from_diffusers(state_dict)
+        if isinstance(state_dict_results, tuple):
+            model_state_dict, extra_kwargs = state_dict_results
+            print(f"        This model is initialized with extra kwargs: {extra_kwargs}")
+        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)
+        loaded_model_names.append(model_name)
+        loaded_models.append(model)
+    return loaded_model_names, loaded_models
+
+
+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)
+        if torch_dtype == torch.float16 and hasattr(model, "half"):
+            model = model.half()
+        try:
+            model = model.to(device=device)
+        except:
+            pass
+        loaded_model_names.append(model_name)
+        loaded_models.append(model)
+    return loaded_model_names, loaded_models
+
+
+def load_single_patch_model_from_single_file(state_dict, model_name, model_class, base_model, extra_kwargs, torch_dtype, device):
+    print(f"    model_name: {model_name} model_class: {model_class.__name__} extra_kwargs: {extra_kwargs}")
+    base_state_dict = base_model.state_dict()
+    base_model.to("cpu")
+    del base_model
+    model = model_class(**extra_kwargs)
+    model.load_state_dict(base_state_dict, strict=False)
+    model.load_state_dict(state_dict, strict=False)
+    model.to(dtype=torch_dtype, device=device)
+    return model
+
+
+def load_patch_model_from_single_file(state_dict, model_names, model_classes, extra_kwargs, model_manager, torch_dtype, device):
+    loaded_model_names, loaded_models = [], []
+    for model_name, model_class in zip(model_names, model_classes):
+        while True:
+            for model_id in range(len(model_manager.model)):
+                base_model_name = model_manager.model_name[model_id]
+                if base_model_name == model_name:
+                    base_model_path = model_manager.model_path[model_id]
+                    base_model = model_manager.model[model_id]
+                    print(f"    Adding patch model to {base_model_name} ({base_model_path})")
+                    patched_model = load_single_patch_model_from_single_file(
+                        state_dict, model_name, model_class, base_model, extra_kwargs, torch_dtype, device)
+                    loaded_model_names.append(base_model_name)
+                    loaded_models.append(patched_model)
+                    model_manager.model.pop(model_id)
+                    model_manager.model_path.pop(model_id)
+                    model_manager.model_name.pop(model_id)
+                    break
+            else:
+                break
+    return loaded_model_names, loaded_models
+
+
+
+class ModelDetectorTemplate:
+    def __init__(self):
+        pass
+
+    def match(self, file_path="", state_dict={}):
+        return False
+    
+    def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs):
+        return [], []
+    
+
+
+class ModelDetectorFromSingleFile:
+    def __init__(self, model_loader_configs=[]):
+        self.keys_hash_with_shape_dict = {}
+        self.keys_hash_dict = {}
+        for metadata in model_loader_configs:
+            self.add_model_metadata(*metadata)
+
+
+    def add_model_metadata(self, keys_hash, keys_hash_with_shape, model_names, model_classes, model_resource):
+        self.keys_hash_with_shape_dict[keys_hash_with_shape] = (model_names, model_classes, model_resource)
+        if keys_hash is not None:
+            self.keys_hash_dict[keys_hash] = (model_names, model_classes, model_resource)
+
+
+    def match(self, file_path="", state_dict={}):
+        if isinstance(file_path, str) and os.path.isdir(file_path):
+            return False
+        if len(state_dict) == 0:
+            state_dict = load_state_dict(file_path)
+        keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True)
+        if keys_hash_with_shape in self.keys_hash_with_shape_dict:
+            return True
+        keys_hash = hash_state_dict_keys(state_dict, with_shape=False)
+        if keys_hash in self.keys_hash_dict:
+            return True
+        return False
+
+
+    def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs):
+        if len(state_dict) == 0:
+            state_dict = load_state_dict(file_path)
+
+        # Load models with strict matching
+        keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True)
+        if keys_hash_with_shape in self.keys_hash_with_shape_dict:
+            model_names, model_classes, model_resource = self.keys_hash_with_shape_dict[keys_hash_with_shape]
+            loaded_model_names, loaded_models = load_model_from_single_file(state_dict, model_names, model_classes, model_resource, torch_dtype, device)
+            return loaded_model_names, loaded_models
+
+        # Load models without strict matching
+        # (the shape of parameters may be inconsistent, and the state_dict_converter will modify the model architecture)
+        keys_hash = hash_state_dict_keys(state_dict, with_shape=False)
+        if keys_hash in self.keys_hash_dict:
+            model_names, model_classes, model_resource = self.keys_hash_dict[keys_hash]
+            loaded_model_names, loaded_models = load_model_from_single_file(state_dict, model_names, model_classes, model_resource, torch_dtype, device)
+            return loaded_model_names, loaded_models
+
+        return loaded_model_names, loaded_models
+
+
+
+class ModelDetectorFromSplitedSingleFile(ModelDetectorFromSingleFile):
+    def __init__(self, model_loader_configs=[]):
+        super().__init__(model_loader_configs)
+
+
+    def match(self, file_path="", state_dict={}):
+        if isinstance(file_path, str) and os.path.isdir(file_path):
+            return False
+        if len(state_dict) == 0:
+            state_dict = load_state_dict(file_path)
+        splited_state_dict = split_state_dict_with_prefix(state_dict)
+        for sub_state_dict in splited_state_dict:
+            if super().match(file_path, sub_state_dict):
+                return True
+        return False
+
+
+    def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs):
+        # Split the state_dict and load from each component
+        splited_state_dict = split_state_dict_with_prefix(state_dict)
+        valid_state_dict = {}
+        for sub_state_dict in splited_state_dict:
+            if super().match(file_path, sub_state_dict):
+                valid_state_dict.update(sub_state_dict)
+        if super().match(file_path, valid_state_dict):
+            loaded_model_names, loaded_models = super().load(file_path, valid_state_dict, device, torch_dtype)
+        else:
+            loaded_model_names, loaded_models = [], []
+            for sub_state_dict in splited_state_dict:
+                if super().match(file_path, sub_state_dict):
+                    loaded_model_names_, loaded_models_ = super().load(file_path, valid_state_dict, device, torch_dtype)
+                    loaded_model_names += loaded_model_names_
+                    loaded_models += loaded_models_
+        return loaded_model_names, loaded_models
+    
+
+
+class ModelDetectorFromHuggingfaceFolder:
+    def __init__(self, model_loader_configs=[]):
+        self.architecture_dict = {}
+        for metadata in model_loader_configs:
+            self.add_model_metadata(*metadata)
+
+
+    def add_model_metadata(self, architecture, huggingface_lib, model_name, redirected_architecture):
+        self.architecture_dict[architecture] = (huggingface_lib, model_name, redirected_architecture)
+
+
+    def match(self, file_path="", state_dict={}):
+        if not isinstance(file_path, str) or os.path.isfile(file_path):
+            return False
+        file_list = os.listdir(file_path)
+        if "config.json" not in file_list:
+            return False
+        with open(os.path.join(file_path, "config.json"), "r") as f:
+            config = json.load(f)
+        if "architectures" not in config and "_class_name" not in config:
+            return False
+        return True
+
+
+    def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs):
+        with open(os.path.join(file_path, "config.json"), "r") as f:
+            config = json.load(f)
+        loaded_model_names, loaded_models = [], []
+        architectures = config["architectures"] if "architectures" in config else [config["_class_name"]]
+        for architecture in architectures:
+            huggingface_lib, model_name, redirected_architecture = self.architecture_dict[architecture]
+            if redirected_architecture is not None:
+                architecture = redirected_architecture
+            model_class = importlib.import_module(huggingface_lib).__getattribute__(architecture)
+            loaded_model_names_, loaded_models_ = load_model_from_huggingface_folder(file_path, [model_name], [model_class], torch_dtype, device)
+            loaded_model_names += loaded_model_names_
+            loaded_models += loaded_models_
+        return loaded_model_names, loaded_models
+    
+
+
+class ModelDetectorFromPatchedSingleFile:
+    def __init__(self, model_loader_configs=[]):
+        self.keys_hash_with_shape_dict = {}
+        for metadata in model_loader_configs:
+            self.add_model_metadata(*metadata)
+
+
+    def add_model_metadata(self, keys_hash_with_shape, model_name, model_class, extra_kwargs):
+        self.keys_hash_with_shape_dict[keys_hash_with_shape] = (model_name, model_class, extra_kwargs)
+
+
+    def match(self, file_path="", state_dict={}):
+        if not isinstance(file_path, str) or os.path.isdir(file_path):
+            return False
+        if len(state_dict) == 0:
+            state_dict = load_state_dict(file_path)
+        keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True)
+        if keys_hash_with_shape in self.keys_hash_with_shape_dict:
+            return True
+        return False
+
+
+    def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, model_manager=None, **kwargs):
+        if len(state_dict) == 0:
+            state_dict = load_state_dict(file_path)
+
+        # Load models with strict matching
+        loaded_model_names, loaded_models = [], []
+        keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True)
+        if keys_hash_with_shape in self.keys_hash_with_shape_dict:
+            model_names, model_classes, extra_kwargs = self.keys_hash_with_shape_dict[keys_hash_with_shape]
+            loaded_model_names_, loaded_models_ = load_patch_model_from_single_file(
+                state_dict, model_names, model_classes, extra_kwargs, model_manager, torch_dtype, device)
+            loaded_model_names += loaded_model_names_
+            loaded_models += loaded_models_
+        return loaded_model_names, loaded_models
+
+
+
+class ModelManager:
+    def __init__(
+        self,
+        torch_dtype=torch.float16,
+        device="cuda",
+        model_id_list: List[Preset_model_id] = [],
+        downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"],
+        file_path_list: List[str] = [],
+    ):
+        self.torch_dtype = torch_dtype
+        self.device = device
+        self.model = []
+        self.model_path = []
+        self.model_name = []
+        downloaded_files = download_models(model_id_list, downloading_priority) if len(model_id_list) > 0 else []
+        self.model_detector = [
+            ModelDetectorFromSingleFile(model_loader_configs),
+            ModelDetectorFromSplitedSingleFile(model_loader_configs),
+            ModelDetectorFromHuggingfaceFolder(huggingface_model_loader_configs),
+            ModelDetectorFromPatchedSingleFile(patch_model_loader_configs),
+        ]
+        self.load_models(downloaded_files + file_path_list)
+
+
+    def load_model_from_single_file(self, file_path="", state_dict={}, model_names=[], model_classes=[], model_resource=None):
+        print(f"Loading models from file: {file_path}")
+        if len(state_dict) == 0:
+            state_dict = load_state_dict(file_path)
+        model_names, models = load_model_from_single_file(state_dict, model_names, model_classes, model_resource, self.torch_dtype, self.device)
+        for model_name, model in zip(model_names, models):
+            self.model.append(model)
+            self.model_path.append(file_path)
+            self.model_name.append(model_name)
+        print(f"    The following models are loaded: {model_names}.")
+
+
+    def load_model_from_huggingface_folder(self, file_path="", model_names=[], model_classes=[]):
+        print(f"Loading models from folder: {file_path}")
+        model_names, models = load_model_from_huggingface_folder(file_path, model_names, model_classes, self.torch_dtype, self.device)
+        for model_name, model in zip(model_names, models):
+            self.model.append(model)
+            self.model_path.append(file_path)
+            self.model_name.append(model_name)
+        print(f"    The following models are loaded: {model_names}.")
+
+
+    def load_patch_model_from_single_file(self, file_path="", state_dict={}, model_names=[], model_classes=[], extra_kwargs={}):
+        print(f"Loading patch models from file: {file_path}")
+        model_names, models = load_patch_model_from_single_file(
+            state_dict, model_names, model_classes, extra_kwargs, self, self.torch_dtype, self.device)
+        for model_name, model in zip(model_names, models):
+            self.model.append(model)
+            self.model_path.append(file_path)
+            self.model_name.append(model_name)
+        print(f"    The following patched models are loaded: {model_names}.")
+
+
+    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}")
+
+
+    def load_model(self, file_path, model_names=None, device=None, torch_dtype=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):
+            state_dict = load_state_dict(file_path)
+        else:
+            state_dict = None
+        for model_detector in self.model_detector:
+            if model_detector.match(file_path, state_dict):
+                model_names, models = model_detector.load(
+                    file_path, state_dict,
+                    device=device, torch_dtype=torch_dtype,
+                    allowed_model_names=model_names, model_manager=self
+                )
+                for model_name, model in zip(model_names, models):
+                    self.model.append(model)
+                    self.model_path.append(file_path)
+                    self.model_name.append(model_name)
+                print(f"    The following models are loaded: {model_names}.")
+                break
+        else:
+            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):
+        for file_path in file_path_list:
+            self.load_model(file_path, model_names, device=device, torch_dtype=torch_dtype)
+
+    
+    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):
+            if file_path is not None and file_path != model_path:
+                continue
+            if model_name == model_name_:
+                fetched_models.append(model)
+                fetched_model_paths.append(model_path)
+        if len(fetched_models) == 0:
+            print(f"No {model_name} models available.")
+            return None
+        if len(fetched_models) == 1:
+            print(f"Using {model_name} from {fetched_model_paths[0]}.")
+        else:
+            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 fetched_models[0], fetched_model_paths[0]
+        else:
+            return fetched_models[0]
+        
+
+    def to(self, device):
+        for model in self.model:
+            model.to(device)
+

diffsynth/models/omnigen.py

@@ -0,0 +1,803 @@
+# 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

diffsynth/models/sd3_dit.py

@@ -0,0 +1,551 @@
+import torch
+from einops import rearrange
+from .svd_unet import TemporalTimesteps
+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__()
+        self.pos_embed_max_size = pos_embed_max_size
+        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))
+
+    def cropped_pos_embed(self, height, width):
+        height = height // self.patch_size
+        width = width // self.patch_size
+        top = (self.pos_embed_max_size - height) // 2
+        left = (self.pos_embed_max_size - width) // 2
+        spatial_pos_embed = self.pos_embed[:, top : top + height, left : left + width, :].flatten(1, 2)
+        return spatial_pos_embed
+
+    def forward(self, latent):
+        height, width = latent.shape[-2:]
+        latent = self.proj(latent)
+        latent = latent.flatten(2).transpose(1, 2)
+        pos_embed = self.cropped_pos_embed(height, width)
+        return latent + pos_embed
+
+
+
+class TimestepEmbeddings(torch.nn.Module):
+    def __init__(self, dim_in, dim_out, computation_device=None):
+        super().__init__()
+        self.time_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device)
+        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)
+        time_emb = self.timestep_embedder(time_emb)
+        return time_emb
+
+
+
+class AdaLayerNorm(torch.nn.Module):
+    def __init__(self, dim, single=False, dual=False):
+        super().__init__()
+        self.single = single
+        self.dual = dual
+        self.linear = torch.nn.Linear(dim, dim * [[6, 2][single], 9][dual])
+        self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb):
+        emb = self.linear(torch.nn.functional.silu(emb))
+        if self.single:
+            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
+            return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+
+class JointAttention(torch.nn.Module):
+    def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False, use_rms_norm=False):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        self.only_out_a = only_out_a
+
+        self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
+        self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3)
+
+        self.a_to_out = torch.nn.Linear(dim_a, dim_a)
+        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)
+
+        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_a, hidden_states_b = hidden_states[:, :hidden_states_a.shape[1]], hidden_states[:, hidden_states_a.shape[1]:]
+        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 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):
+        super().__init__()
+        self.norm1_a = AdaLayerNorm(dim, dual=dual)
+        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.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):
+        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_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
+        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)
+
+        # 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 JointTransformerFinalBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads, use_rms_norm=False):
+        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.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)
+        )
+
+
+    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 = self.norm1_a(hidden_states_a, emb=temb)
+        norm_hidden_states_b = self.norm1_b(hidden_states_b, emb=temb)
+
+        # Attention
+        attn_output_a = self.attn(norm_hidden_states_a, norm_hidden_states_b)
+
+        # Part A
+        hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
+        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)
+
+        return hidden_states_a, hidden_states_b
+
+
+
+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):
+        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)
+
+    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.
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x, timestep, prompt_emb, pooled_prompt_emb),
+            hidden_states,
+            tile_size,
+            tile_stride,
+            tile_device=hidden_states.device,
+            tile_dtype=hidden_states.dtype
+        )
+        return hidden_states
+
+    def forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tiled=False, tile_size=128, tile_stride=64, use_gradient_checkpointing=False):
+        if tiled:
+            return self.tiled_forward(hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size, tile_stride)
+        conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb)
+        prompt_emb = self.context_embedder(prompt_emb)
+
+        height, width = hidden_states.shape[-2:]
+        hidden_states = self.pos_embedder(hidden_states)
+
+        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,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning)
+        
+        hidden_states = self.norm_out(hidden_states, conditioning)
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = rearrange(hidden_states, "B (H W) (P Q C) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2)
+        return hidden_states
+        
+    @staticmethod
+    def state_dict_converter():
+        return SD3DiTStateDictConverter()
+
+
+
+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",
+            "pos_embed.pos_embed": "pos_embedder.pos_embed",
+            "pos_embed.proj": "pos_embedder.proj",
+            "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.text_embedder.linear_1": "pooled_text_embedder.0",
+            "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
+            "norm_out.linear": "norm_out.linear",
+            "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",
+        }
+        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]))
+                state_dict_[rename_dict[name]] = param
+            elif name.endswith(".weight") or name.endswith(".bias"):
+                suffix = ".weight" if name.endswith(".weight") else ".bias"
+                prefix = name[:-len(suffix)]
+                if prefix in rename_dict:
+                    state_dict_[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
+        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_)
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "model.diffusion_model.context_embedder.bias": "context_embedder.bias",
+            "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.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",
+            "model.diffusion_model.t_embedder.mlp.2.bias": "time_embedder.timestep_embedder.2.bias",
+            "model.diffusion_model.t_embedder.mlp.2.weight": "time_embedder.timestep_embedder.2.weight",
+            "model.diffusion_model.x_embedder.proj.bias": "pos_embedder.proj.bias",
+            "model.diffusion_model.x_embedder.proj.weight": "pos_embedder.proj.weight",
+            "model.diffusion_model.y_embedder.mlp.0.bias": "pooled_text_embedder.0.bias",
+            "model.diffusion_model.y_embedder.mlp.0.weight": "pooled_text_embedder.0.weight",
+            "model.diffusion_model.y_embedder.mlp.2.bias": "pooled_text_embedder.2.bias",
+            "model.diffusion_model.y_embedder.mlp.2.weight": "pooled_text_embedder.2.weight",
+            
+            "model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1.weight": "blocks.23.norm1_b.linear.weight",
+            "model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1.bias": "blocks.23.norm1_b.linear.bias",
+            "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 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_)

diffsynth/models/sd3_vae_decoder.py

@@ -0,0 +1,81 @@
+import torch
+from .sd_vae_decoder import VAEAttentionBlock, SDVAEDecoderStateDictConverter
+from .sd_unet import ResnetBlock, UpSampler
+from .tiler import TileWorker
+
+
+
+class SD3VAEDecoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 1.5305 # Different from SD 1.x
+        self.shift_factor = 0.0609 # Different from SD 1.x
+        self.conv_in = torch.nn.Conv2d(16, 512, kernel_size=3, padding=1) # Different from SD 1.x
+
+        self.blocks = torch.nn.ModuleList([
+            # UNetMidBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            UpSampler(256),
+            # UpDecoderBlock2D
+            ResnetBlock(256, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-6)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1)
+    
+    def tiled_forward(self, sample, tile_size=64, tile_stride=32):
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x),
+            sample,
+            tile_size,
+            tile_stride,
+            tile_device=sample.device,
+            tile_dtype=sample.dtype
+        )
+        return hidden_states
+
+    def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
+        # For VAE Decoder, we do not need to apply the tiler on each layer.
+        if tiled:
+            return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
+
+        # 1. pre-process
+        hidden_states = sample / self.scaling_factor + self.shift_factor
+        hidden_states = self.conv_in(hidden_states)
+        time_emb = None
+        text_emb = None
+        res_stack = None
+
+        # 2. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        
+        # 3. output
+        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
+    
+    @staticmethod
+    def state_dict_converter():
+        return SDVAEDecoderStateDictConverter()

diffsynth/models/sd3_vae_encoder.py

@@ -0,0 +1,95 @@
+import torch
+from .sd_unet import ResnetBlock, DownSampler
+from .sd_vae_encoder import VAEAttentionBlock, SDVAEEncoderStateDictConverter
+from .tiler import TileWorker
+from einops import rearrange
+
+
+class SD3VAEEncoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 1.5305 # Different from SD 1.x
+        self.shift_factor = 0.0609 # Different from SD 1.x
+        self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1)
+
+        self.blocks = torch.nn.ModuleList([
+            # DownEncoderBlock2D
+            ResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            DownSampler(128, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(128, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            DownSampler(256, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(256, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            DownSampler(512, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            # UNetMidBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(512, 32, kernel_size=3, padding=1)
+
+    def tiled_forward(self, sample, tile_size=64, tile_stride=32):
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x),
+            sample,
+            tile_size,
+            tile_stride,
+            tile_device=sample.device,
+            tile_dtype=sample.dtype
+        )
+        return hidden_states
+
+    def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
+        # For VAE Decoder, we do not need to apply the tiler on each layer.
+        if tiled:
+            return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
+        
+        # 1. pre-process
+        hidden_states = self.conv_in(sample)
+        time_emb = None
+        text_emb = None
+        res_stack = None
+
+        # 2. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        
+        # 3. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+        hidden_states = hidden_states[:, :16]
+        hidden_states = (hidden_states - self.shift_factor) * self.scaling_factor
+
+        return hidden_states
+    
+    def encode_video(self, sample, batch_size=8):
+        B = sample.shape[0]
+        hidden_states = []
+
+        for i in range(0, sample.shape[2], batch_size):
+
+            j = min(i + batch_size, sample.shape[2])
+            sample_batch = rearrange(sample[:,:,i:j], "B C T H W -> (B T) C H W")
+
+            hidden_states_batch = self(sample_batch)
+            hidden_states_batch = rearrange(hidden_states_batch, "(B T) C H W -> B C T H W", B=B)
+
+            hidden_states.append(hidden_states_batch)
+        
+        hidden_states = torch.concat(hidden_states, dim=2)
+        return hidden_states
+    
+    @staticmethod
+    def state_dict_converter():
+        return SDVAEEncoderStateDictConverter()

diffsynth/models/sd_controlnet.py

@@ -97,9 +97,10 @@ class SDControlNet(torch.nn.Module):
         self,
         sample, timestep, encoder_hidden_states, conditioning,
         tiled=False, tile_size=64, tile_stride=32,
+        **kwargs
     ):
         # 1. time
-        time_emb = self.time_proj(timestep[None]).to(sample.dtype)
+        time_emb = self.time_proj(timestep).to(sample.dtype)
         time_emb = self.time_embedding(time_emb)
         time_emb = time_emb.repeat(sample.shape[0], 1)
 
@@ -134,7 +135,8 @@ class SDControlNet(torch.nn.Module):
 
         return controlnet_res_stack
 
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SDControlNetStateDictConverter()
 
 

diffsynth/models/sd_ipadapter.py

@@ -29,7 +29,7 @@ class SDIpAdapter(torch.nn.Module):
 
     def set_less_adapter(self):
         # IP-Adapter for SD v1.5 doesn't support this feature.
-        self.set_full_adapter(self)
+        self.set_full_adapter()
 
     def forward(self, hidden_states, scale=1.0):
         hidden_states = self.image_proj(hidden_states)
@@ -47,7 +47,8 @@ class SDIpAdapter(torch.nn.Module):
             }
         return ip_kv_dict
 
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SDIpAdapterStateDictConverter()
 
 

diffsynth/models/sd_lora.py

@@ -1,60 +0,0 @@
-import torch
-from .sd_unet import SDUNetStateDictConverter, SDUNet
-from .sd_text_encoder import SDTextEncoderStateDictConverter, SDTextEncoder
-
-
-class SDLoRA:
-    def __init__(self):
-        pass
-
-    def convert_state_dict(self, state_dict, lora_prefix="lora_unet_", alpha=1.0, device="cuda"):
-        special_keys = {
-            "down.blocks": "down_blocks",
-            "up.blocks": "up_blocks",
-            "mid.block": "mid_block",
-            "proj.in": "proj_in",
-            "proj.out": "proj_out",
-            "transformer.blocks": "transformer_blocks",
-            "to.q": "to_q",
-            "to.k": "to_k",
-            "to.v": "to_v",
-            "to.out": "to_out",
-        }
-        state_dict_ = {}
-        for key in state_dict:
-            if ".lora_up" not in key:
-                continue
-            if not key.startswith(lora_prefix):
-                continue
-            weight_up = state_dict[key].to(device="cuda", dtype=torch.float16)
-            weight_down = state_dict[key.replace(".lora_up", ".lora_down")].to(device="cuda", dtype=torch.float16)
-            if len(weight_up.shape) == 4:
-                weight_up = weight_up.squeeze(3).squeeze(2).to(torch.float32)
-                weight_down = weight_down.squeeze(3).squeeze(2).to(torch.float32)
-                lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
-            else:
-                lora_weight = alpha * torch.mm(weight_up, weight_down)
-            target_name = key.split(".")[0].replace("_", ".")[len(lora_prefix):] + ".weight"
-            for special_key in special_keys:
-                target_name = target_name.replace(special_key, special_keys[special_key])
-            state_dict_[target_name] = lora_weight.cpu()
-        return state_dict_
-    
-    def add_lora_to_unet(self, unet: SDUNet, state_dict_lora, alpha=1.0, device="cuda"):
-        state_dict_unet = unet.state_dict()
-        state_dict_lora = self.convert_state_dict(state_dict_lora, lora_prefix="lora_unet_", alpha=alpha, device=device)
-        state_dict_lora = SDUNetStateDictConverter().from_diffusers(state_dict_lora)
-        if len(state_dict_lora) > 0:
-            for name in state_dict_lora:
-                state_dict_unet[name] += state_dict_lora[name].to(device=device)
-            unet.load_state_dict(state_dict_unet)
-
-    def add_lora_to_text_encoder(self, text_encoder: SDTextEncoder, state_dict_lora, alpha=1.0, device="cuda"):
-        state_dict_text_encoder = text_encoder.state_dict()
-        state_dict_lora = self.convert_state_dict(state_dict_lora, lora_prefix="lora_te_", alpha=alpha, device=device)
-        state_dict_lora = SDTextEncoderStateDictConverter().from_diffusers(state_dict_lora)
-        if len(state_dict_lora) > 0:
-            for name in state_dict_lora:
-                state_dict_text_encoder[name] += state_dict_lora[name].to(device=device)
-            text_encoder.load_state_dict(state_dict_text_encoder)
-

diffsynth/models/sd_motion.py

@@ -144,7 +144,8 @@ class SDMotionModel(torch.nn.Module):
     def forward(self):
         pass
 
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SDMotionModelStateDictConverter()
 
 

diffsynth/models/sd_text_encoder.py

@@ -71,7 +71,8 @@ class SDTextEncoder(torch.nn.Module):
         embeds = self.final_layer_norm(embeds)
         return embeds
     
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SDTextEncoderStateDictConverter()
 
 

diffsynth/models/sd_unet.py

@@ -323,7 +323,7 @@ class SDUNet(torch.nn.Module):
 
     def forward(self, sample, timestep, encoder_hidden_states, **kwargs):
         # 1. time
-        time_emb = self.time_proj(timestep[None]).to(sample.dtype)
+        time_emb = self.time_proj(timestep).to(sample.dtype)
         time_emb = self.time_embedding(time_emb)
 
         # 2. pre-process
@@ -342,7 +342,8 @@ class SDUNet(torch.nn.Module):
 
         return hidden_states
     
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SDUNetStateDictConverter()
 
 

diffsynth/models/sd_vae_decoder.py

@@ -90,6 +90,8 @@ class SDVAEDecoder(torch.nn.Module):
         return hidden_states
 
     def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
+        original_dtype = sample.dtype
+        sample = sample.to(dtype=next(iter(self.parameters())).dtype)
         # For VAE Decoder, we do not need to apply the tiler on each layer.
         if tiled:
             return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
@@ -110,10 +112,12 @@ class SDVAEDecoder(torch.nn.Module):
         hidden_states = self.conv_norm_out(hidden_states)
         hidden_states = self.conv_act(hidden_states)
         hidden_states = self.conv_out(hidden_states)
+        hidden_states = hidden_states.to(original_dtype)
 
         return hidden_states
     
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SDVAEDecoderStateDictConverter()
     
 

diffsynth/models/sd_vae_encoder.py

@@ -50,6 +50,8 @@ class SDVAEEncoder(torch.nn.Module):
         return hidden_states
 
     def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
+        original_dtype = sample.dtype
+        sample = sample.to(dtype=next(iter(self.parameters())).dtype)
         # For VAE Decoder, we do not need to apply the tiler on each layer.
         if tiled:
             return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
@@ -71,6 +73,7 @@ class SDVAEEncoder(torch.nn.Module):
         hidden_states = self.quant_conv(hidden_states)
         hidden_states = hidden_states[:, :4]
         hidden_states *= self.scaling_factor
+        hidden_states = hidden_states.to(original_dtype)
 
         return hidden_states
     
@@ -91,7 +94,8 @@ class SDVAEEncoder(torch.nn.Module):
         hidden_states = torch.concat(hidden_states, dim=2)
         return hidden_states
     
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SDVAEEncoderStateDictConverter()
     
 

diffsynth/models/sdxl_controlnet.py

@@ -0,0 +1,318 @@
+import torch
+from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler
+from .sdxl_unet import SDXLUNet
+from .tiler import TileWorker
+from .sd_controlnet import ControlNetConditioningLayer
+from collections import OrderedDict
+
+
+
+class QuickGELU(torch.nn.Module):
+
+    def forward(self, x: torch.Tensor):
+        return x * torch.sigmoid(1.702 * x)
+
+
+
+class ResidualAttentionBlock(torch.nn.Module):
+
+    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
+        super().__init__()
+
+        self.attn = torch.nn.MultiheadAttention(d_model, n_head)
+        self.ln_1 = torch.nn.LayerNorm(d_model)
+        self.mlp = torch.nn.Sequential(OrderedDict([
+            ("c_fc", torch.nn.Linear(d_model, d_model * 4)),
+            ("gelu", QuickGELU()),
+            ("c_proj", torch.nn.Linear(d_model * 4, d_model))
+        ]))
+        self.ln_2 = torch.nn.LayerNorm(d_model)
+        self.attn_mask = attn_mask
+
+    def attention(self, x: torch.Tensor):
+        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
+        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
+
+    def forward(self, x: torch.Tensor):
+        x = x + self.attention(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+
+class SDXLControlNetUnion(torch.nn.Module):
+    def __init__(self, global_pool=False):
+        super().__init__()
+        self.time_proj = Timesteps(320)
+        self.time_embedding = torch.nn.Sequential(
+            torch.nn.Linear(320, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.add_time_proj = Timesteps(256)
+        self.add_time_embedding = torch.nn.Sequential(
+            torch.nn.Linear(2816, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.control_type_proj = Timesteps(256)
+        self.control_type_embedding = torch.nn.Sequential(
+            torch.nn.Linear(256 * 8, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
+
+        self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320))
+        self.controlnet_transformer = ResidualAttentionBlock(320, 8)
+        self.task_embedding = torch.nn.Parameter(torch.randn(8, 320))
+        self.spatial_ch_projs = torch.nn.Linear(320, 320)
+
+        self.blocks = torch.nn.ModuleList([
+            # DownBlock2D
+            ResnetBlock(320, 320, 1280),
+            PushBlock(),
+            ResnetBlock(320, 320, 1280),
+            PushBlock(),
+            DownSampler(320),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(320, 640, 1280),
+            AttentionBlock(10, 64, 640, 2, 2048),
+            PushBlock(),
+            ResnetBlock(640, 640, 1280),
+            AttentionBlock(10, 64, 640, 2, 2048),
+            PushBlock(),
+            DownSampler(640),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(640, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            PushBlock(),
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            PushBlock(),
+            # UNetMidBlock2DCrossAttn
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            ResnetBlock(1280, 1280, 1280),
+            PushBlock()
+        ])
+
+        self.controlnet_blocks = torch.nn.ModuleList([
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
+        ])
+
+        self.global_pool = global_pool
+
+        # 0 -- openpose
+        # 1 -- depth
+        # 2 -- hed/pidi/scribble/ted
+        # 3 -- canny/lineart/anime_lineart/mlsd
+        # 4 -- normal
+        # 5 -- segment
+        # 6 -- tile
+        # 7 -- repaint
+        self.task_id = {
+            "openpose": 0,
+            "depth": 1,
+            "softedge": 2,
+            "canny": 3,
+            "lineart": 3,
+            "lineart_anime": 3,
+            "tile": 6,
+            "inpaint": 7
+        }
+
+
+    def fuse_condition_to_input(self, hidden_states, task_id, conditioning):
+        controlnet_cond = self.controlnet_conv_in(conditioning)
+        feat_seq = torch.mean(controlnet_cond, dim=(2, 3))
+        feat_seq = feat_seq + self.task_embedding[task_id]
+        x = torch.stack([feat_seq, torch.mean(hidden_states, dim=(2, 3))], dim=1)
+        x = self.controlnet_transformer(x)
+
+        alpha = self.spatial_ch_projs(x[:,0]).unsqueeze(-1).unsqueeze(-1)
+        controlnet_cond_fuser = controlnet_cond + alpha
+
+        hidden_states = hidden_states + controlnet_cond_fuser
+        return hidden_states
+    
+
+    def forward(
+        self,
+        sample, timestep, encoder_hidden_states,
+        conditioning, processor_id, add_time_id, add_text_embeds,
+        tiled=False, tile_size=64, tile_stride=32,
+        unet:SDXLUNet=None,
+        **kwargs
+    ):
+        task_id = self.task_id[processor_id]
+
+        # 1. time
+        t_emb = self.time_proj(timestep).to(sample.dtype)
+        t_emb = self.time_embedding(t_emb)
+        
+        time_embeds = self.add_time_proj(add_time_id)
+        time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1))
+        add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1)
+        add_embeds = add_embeds.to(sample.dtype)
+        if unet is not None and unet.is_kolors:
+            add_embeds = unet.add_time_embedding(add_embeds)
+        else:
+            add_embeds = self.add_time_embedding(add_embeds)
+
+        control_type = torch.zeros((sample.shape[0], 8), dtype=sample.dtype, device=sample.device)
+        control_type[:, task_id] = 1
+        control_embeds = self.control_type_proj(control_type.flatten())
+        control_embeds = control_embeds.reshape((sample.shape[0], -1))
+        control_embeds = control_embeds.to(sample.dtype)
+        control_embeds = self.control_type_embedding(control_embeds)
+        time_emb = t_emb + add_embeds + control_embeds
+
+        # 2. pre-process
+        height, width = sample.shape[2], sample.shape[3]
+        hidden_states = self.conv_in(sample)
+        hidden_states = self.fuse_condition_to_input(hidden_states, task_id, conditioning)
+        text_emb = encoder_hidden_states
+        if unet is not None and unet.is_kolors:
+            text_emb = unet.text_intermediate_proj(text_emb)
+        res_stack = [hidden_states]
+
+        # 3. blocks
+        for i, block in enumerate(self.blocks):
+            if tiled and not isinstance(block, PushBlock):
+                _, _, inter_height, _ = hidden_states.shape
+                resize_scale = inter_height / height
+                hidden_states = TileWorker().tiled_forward(
+                    lambda x: block(x, time_emb, text_emb, res_stack)[0],
+                    hidden_states,
+                    int(tile_size * resize_scale),
+                    int(tile_stride * resize_scale),
+                    tile_device=hidden_states.device,
+                    tile_dtype=hidden_states.dtype
+                )
+            else:
+                hidden_states, _, _, _ = block(hidden_states, time_emb, text_emb, res_stack)
+
+        # 4. ControlNet blocks
+        controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)]
+
+        # pool
+        if self.global_pool:
+            controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack]
+
+        return controlnet_res_stack
+
+    @staticmethod
+    def state_dict_converter():
+        return SDXLControlNetUnionStateDictConverter()
+
+
+
+class SDXLControlNetUnionStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        # architecture
+        block_types = [
+            "ResnetBlock", "PushBlock", "ResnetBlock", "PushBlock", "DownSampler", "PushBlock",
+            "ResnetBlock", "AttentionBlock", "PushBlock", "ResnetBlock", "AttentionBlock", "PushBlock", "DownSampler", "PushBlock",
+            "ResnetBlock", "AttentionBlock", "PushBlock", "ResnetBlock", "AttentionBlock", "PushBlock",
+            "ResnetBlock", "AttentionBlock", "ResnetBlock", "PushBlock"
+        ]
+
+        # controlnet_rename_dict
+        controlnet_rename_dict = {
+            "controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight",
+            "controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias",
+            "controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight",
+            "controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias",
+            "controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight",
+            "controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias",
+            "controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight",
+            "controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias",
+            "controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight",
+            "controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias",
+            "controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight",
+            "controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias",
+            "controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight",
+            "controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias",
+            "controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight",
+            "controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias",
+            "control_add_embedding.linear_1.weight": "control_type_embedding.0.weight",
+            "control_add_embedding.linear_1.bias": "control_type_embedding.0.bias",
+            "control_add_embedding.linear_2.weight": "control_type_embedding.2.weight",
+            "control_add_embedding.linear_2.bias": "control_type_embedding.2.bias",
+        }
+
+        # Rename each parameter
+        name_list = sorted([name for name in state_dict])
+        rename_dict = {}
+        block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1}
+        last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""}
+        for name in name_list:
+            names = name.split(".")
+            if names[0] in ["conv_in", "conv_norm_out", "conv_out", "task_embedding", "spatial_ch_projs"]:
+                pass
+            elif name in controlnet_rename_dict:
+                names = controlnet_rename_dict[name].split(".")
+            elif names[0] == "controlnet_down_blocks":
+                names[0] = "controlnet_blocks"
+            elif names[0] == "controlnet_mid_block":
+                names = ["controlnet_blocks", "9", names[-1]]
+            elif names[0] in ["time_embedding", "add_embedding"]:
+                if names[0] == "add_embedding":
+                    names[0] = "add_time_embedding"
+                names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]]
+            elif names[0] == "control_add_embedding":
+                names[0] = "control_type_embedding"
+            elif names[0] == "transformer_layes":
+                names[0] = "controlnet_transformer"
+                names.pop(1)
+            elif names[0] in ["down_blocks", "mid_block", "up_blocks"]:
+                if names[0] == "mid_block":
+                    names.insert(1, "0")
+                block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]]
+                block_type_with_id = ".".join(names[:4])
+                if block_type_with_id != last_block_type_with_id[block_type]:
+                    block_id[block_type] += 1
+                last_block_type_with_id[block_type] = block_type_with_id
+                while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
+                    block_id[block_type] += 1
+                block_type_with_id = ".".join(names[:4])
+                names = ["blocks", str(block_id[block_type])] + names[4:]
+                if "ff" in names:
+                    ff_index = names.index("ff")
+                    component = ".".join(names[ff_index:ff_index+3])
+                    component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component]
+                    names = names[:ff_index] + [component] + names[ff_index+3:]
+                if "to_out" in names:
+                    names.pop(names.index("to_out") + 1)
+            else:
+                print(name, state_dict[name].shape)
+                # raise ValueError(f"Unknown parameters: {name}")
+            rename_dict[name] = ".".join(names)
+
+        # Convert state_dict
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name not in rename_dict:
+                continue
+            if ".proj_in." in name or ".proj_out." in name:
+                param = param.squeeze()
+            state_dict_[rename_dict[name]] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)

diffsynth/models/sdxl_ipadapter.py

@@ -96,7 +96,8 @@ class SDXLIpAdapter(torch.nn.Module):
             }
         return ip_kv_dict
 
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SDXLIpAdapterStateDictConverter()
 
 

diffsynth/models/sdxl_motion.py

@@ -49,7 +49,8 @@ class SDXLMotionModel(torch.nn.Module):
     def forward(self):
         pass
 
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SDMotionModelStateDictConverter()
 
 

diffsynth/models/sdxl_text_encoder.py

@@ -36,7 +36,8 @@ class SDXLTextEncoder(torch.nn.Module):
                 break
         return embeds
     
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SDXLTextEncoderStateDictConverter()
     
 
@@ -80,7 +81,8 @@ class SDXLTextEncoder2(torch.nn.Module):
         pooled_embeds = self.text_projection(pooled_embeds)
         return pooled_embeds, hidden_states
     
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SDXLTextEncoder2StateDictConverter()
 
 

diffsynth/models/sdxl_unet.py

@@ -3,7 +3,7 @@ from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, PopBlock
 
 
 class SDXLUNet(torch.nn.Module):
-    def __init__(self):
+    def __init__(self, is_kolors=False):
         super().__init__()
         self.time_proj = Timesteps(320)
         self.time_embedding = torch.nn.Sequential(
@@ -13,11 +13,12 @@ class SDXLUNet(torch.nn.Module):
         )
         self.add_time_proj = Timesteps(256)
         self.add_time_embedding = torch.nn.Sequential(
-            torch.nn.Linear(2816, 1280),
+            torch.nn.Linear(5632 if is_kolors else 2816, 1280),
             torch.nn.SiLU(),
             torch.nn.Linear(1280, 1280)
         )
         self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
+        self.text_intermediate_proj = torch.nn.Linear(4096, 2048) if is_kolors else None
 
         self.blocks = torch.nn.ModuleList([
             # DownBlock2D
@@ -82,13 +83,17 @@ class SDXLUNet(torch.nn.Module):
         self.conv_act = torch.nn.SiLU()
         self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1)
 
+        self.is_kolors = is_kolors
+
     def forward(
         self,
         sample, timestep, encoder_hidden_states, add_time_id, add_text_embeds,
-        tiled=False, tile_size=64, tile_stride=8, **kwargs
+        tiled=False, tile_size=64, tile_stride=8,
+        use_gradient_checkpointing=False,
+        **kwargs
     ):
         # 1. time
-        t_emb = self.time_proj(timestep[None]).to(sample.dtype)
+        t_emb = self.time_proj(timestep).to(sample.dtype)
         t_emb = self.time_embedding(t_emb)
         
         time_embeds = self.add_time_proj(add_time_id)
@@ -102,15 +107,26 @@ class SDXLUNet(torch.nn.Module):
         # 2. pre-process
         height, width = sample.shape[2], sample.shape[3]
         hidden_states = self.conv_in(sample)
-        text_emb = encoder_hidden_states
+        text_emb = encoder_hidden_states if self.text_intermediate_proj is None else self.text_intermediate_proj(encoder_hidden_states)
         res_stack = [hidden_states]
         
         # 3. blocks
+        def create_custom_forward(module):
+            def custom_forward(*inputs):
+                return module(*inputs)
+            return custom_forward
         for i, block in enumerate(self.blocks):
-            hidden_states, time_emb, text_emb, res_stack = block(
-                hidden_states, time_emb, text_emb, res_stack,
-                tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
-            )
+            if self.training and use_gradient_checkpointing and not (isinstance(block, PushBlock) or isinstance(block, PopBlock)):
+                hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states, time_emb, text_emb, res_stack,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, time_emb, text_emb, res_stack = block(
+                    hidden_states, time_emb, text_emb, res_stack,
+                    tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
+                )
         
         # 4. output
         hidden_states = self.conv_norm_out(hidden_states)
@@ -119,7 +135,8 @@ class SDXLUNet(torch.nn.Module):
 
         return hidden_states
     
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SDXLUNetStateDictConverter()
 
 
@@ -148,6 +165,8 @@ class SDXLUNetStateDictConverter:
             names = name.split(".")
             if names[0] in ["conv_in", "conv_norm_out", "conv_out"]:
                 pass
+            elif names[0] in ["encoder_hid_proj"]:
+                names[0] = "text_intermediate_proj"
             elif names[0] in ["time_embedding", "add_embedding"]:
                 if names[0] == "add_embedding":
                     names[0] = "add_time_embedding"
@@ -181,7 +200,10 @@ class SDXLUNetStateDictConverter:
             if ".proj_in." in name or ".proj_out." in name:
                 param = param.squeeze()
             state_dict_[rename_dict[name]] = param
-        return state_dict_
+        if "text_intermediate_proj.weight" in state_dict_:
+            return state_dict_, {"is_kolors": True}
+        else:
+            return state_dict_
     
     def from_civitai(self, state_dict):
         rename_dict = {
@@ -1873,4 +1895,7 @@ class SDXLUNetStateDictConverter:
                 if ".proj_in." in name or ".proj_out." in name:
                     param = param.squeeze()
                 state_dict_[rename_dict[name]] = param
-        return state_dict_
+        if "text_intermediate_proj.weight" in state_dict_:
+            return state_dict_, {"is_kolors": True}
+        else:
+            return state_dict_

diffsynth/models/sdxl_vae_decoder.py

@@ -2,14 +2,23 @@ from .sd_vae_decoder import SDVAEDecoder, SDVAEDecoderStateDictConverter
 
 
 class SDXLVAEDecoder(SDVAEDecoder):
-    def __init__(self):
+    def __init__(self, upcast_to_float32=True):
         super().__init__()
         self.scaling_factor = 0.13025
 
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SDXLVAEDecoderStateDictConverter()
     
 
 class SDXLVAEDecoderStateDictConverter(SDVAEDecoderStateDictConverter):
     def __init__(self):
         super().__init__()
+
+    def from_diffusers(self, state_dict):
+        state_dict = super().from_diffusers(state_dict)
+        return state_dict, {"upcast_to_float32": True}
+    
+    def from_civitai(self, state_dict):
+        state_dict = super().from_civitai(state_dict)
+        return state_dict, {"upcast_to_float32": True}

diffsynth/models/sdxl_vae_encoder.py

@@ -2,14 +2,23 @@ from .sd_vae_encoder import SDVAEEncoderStateDictConverter, SDVAEEncoder
 
 
 class SDXLVAEEncoder(SDVAEEncoder):
-    def __init__(self):
+    def __init__(self, upcast_to_float32=True):
         super().__init__()
         self.scaling_factor = 0.13025
     
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SDXLVAEEncoderStateDictConverter()
 
 
 class SDXLVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter):
     def __init__(self):
         super().__init__()
+
+    def from_diffusers(self, state_dict):
+        state_dict = super().from_diffusers(state_dict)
+        return state_dict, {"upcast_to_float32": True}
+    
+    def from_civitai(self, state_dict):
+        state_dict = super().from_civitai(state_dict)
+        return state_dict, {"upcast_to_float32": True}

diffsynth/models/stepvideo_dit.py

@@ -0,0 +1,940 @@
+# 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
+
+    
+    

diffsynth/models/stepvideo_text_encoder.py

@@ -0,0 +1,553 @@
+# 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
+

diffsynth/models/svd_image_encoder.py

@@ -44,7 +44,8 @@ class SVDImageEncoder(torch.nn.Module):
         embeds = self.visual_projection(embeds)
         return embeds
 
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SVDImageEncoderStateDictConverter()
 
 

diffsynth/models/svd_unet.py

@@ -44,6 +44,7 @@ def get_timestep_embedding(
     downscale_freq_shift: float = 1,
     scale: float = 1,
     max_period: int = 10000,
+    computation_device = None,
 ):
     """
     This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
@@ -57,11 +58,11 @@ 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
+        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device if computation_device is None else computation_device
     )
     exponent = exponent / (half_dim - downscale_freq_shift)
 
-    emb = torch.exp(exponent)
+    emb = torch.exp(exponent).to(timesteps.device)
     emb = timesteps[:, None].float() * emb[None, :]
 
     # scale embeddings
@@ -81,11 +82,12 @@ def get_timestep_embedding(
 
 
 class TemporalTimesteps(torch.nn.Module):
-    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
+    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, computation_device = None):
         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
 
     def forward(self, timesteps):
         t_emb = get_timestep_embedding(
@@ -93,6 +95,7 @@ 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,
         )
         return t_emb
     
@@ -407,7 +410,8 @@ class SVDUNet(torch.nn.Module):
 
         return hidden_states
     
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SVDUNetStateDictConverter()
     
 

diffsynth/models/svd_vae_decoder.py

@@ -199,7 +199,8 @@ class SVDVAEDecoder(torch.nn.Module):
         return values
     
     
-    def state_dict_converter(self):
+    @staticmethod
+    def state_dict_converter():
         return SVDVAEDecoderStateDictConverter()