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Artiprocher
2025-11-07 19:30:03 +08:00
parent 74f8181f93
commit bdedd46d4c
12 changed files with 677 additions and 20 deletions
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2
diffsynth/diffusion/parsers.py
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@@ -10,9 +10,9 @@ def add_dataset_base_config(parser: argparse.ArgumentParser):
return parser
def add_image_size_config(parser: argparse.ArgumentParser):
parser.add_argument("--max_pixels", type=int, default=1024*1024, help="Maximum number of pixels per frame, used for dynamic resolution..")
parser.add_argument("--height", type=int, default=None, help="Height of images. Leave `height` and `width` empty to enable dynamic resolution.")
parser.add_argument("--width", type=int, default=None, help="Width of images. Leave `height` and `width` empty to enable dynamic resolution.")
parser.add_argument("--max_pixels", type=int, default=1024*1024, help="Maximum number of pixels per frame, used for dynamic resolution.")
return parser
def add_model_config(parser: argparse.ArgumentParser):

1
docs/API_Reference/core/data.md
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@@ -98,6 +98,7 @@ image,prompt
image_1.jpg,"a dog"
image_2.jpg,"a cat"
```
* `json` 格式：可读性高、支持列表数据、内存占用大
```json

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docs/API_Reference/core/loader.md
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@@ -21,8 +21,11 @@ config = ModelConfig(
)
# Download models
config.download_if_necessary()
print(config.path)
```
调用 `download_if_necessary` 后，模型会自动下载，并将路径返回到 `config.path` 中。
### 从本地路径加载模型
如果从本地路径加载模型，则需要填入 `path`：
@@ -46,6 +49,10 @@ config = ModelConfig(path=[
])
```
### 显存管理配置
`ModelConfig` 也包含了显存管理配置信息，详见[显存管理](/docs/Pipeline_Usage/VRAM_management.md#更多使用方式)。
## 模型文件加载
`diffsynth.core.loader` 提供了统一的 `load_state_dict`，用于加载模型文件中的 state dict。

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docs/Model_Details/FLUX.md Normal file
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docs/Model_Details/Overview.md Normal file
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@@ -0,0 +1,289 @@
# 模型目录
## Qwen-Image
文档：[./Qwen-Image.md](./Qwen-Image.md)
<details>
<summary>效果一览</summary>
![Image](https://github.com/user-attachments/assets/738078d8-8749-4a53-a046-571861541924)
</details>
<details>
<summary>快速开始</summary>
```python
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
from PIL import Image
import torch
pipe = QwenImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
)
prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
image = pipe(
prompt, seed=0, num_inference_steps=40,
# edit_image=Image.open("xxx.jpg").resize((1328, 1328)) # For Qwen-Image-Edit
)
image.save("image.jpg")
```
</details>
<details>
<summary>模型血缘</summary>
```mermaid
graph LR;
Qwen/Qwen-Image-->Qwen/Qwen-Image-Edit;
Qwen/Qwen-Image-Edit-->Qwen/Qwen-Image-Edit-2509;
Qwen/Qwen-Image-->EliGen-Series;
EliGen-Series-->DiffSynth-Studio/Qwen-Image-EliGen;
DiffSynth-Studio/Qwen-Image-EliGen-->DiffSynth-Studio/Qwen-Image-EliGen-V2;
EliGen-Series-->DiffSynth-Studio/Qwen-Image-EliGen-Poster;
Qwen/Qwen-Image-->Distill-Series;
Distill-Series-->DiffSynth-Studio/Qwen-Image-Distill-Full;
Distill-Series-->DiffSynth-Studio/Qwen-Image-Distill-LoRA;
Qwen/Qwen-Image-->ControlNet-Series;
ControlNet-Series-->Blockwise-ControlNet-Series;
Blockwise-ControlNet-Series-->DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny;
Blockwise-ControlNet-Series-->DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth;
Blockwise-ControlNet-Series-->DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint;
ControlNet-Series-->DiffSynth-Studio/Qwen-Image-In-Context-Control-Union;
Qwen/Qwen-Image-->DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix;
```
</details>
|模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
|-|-|-|-|-|-|-|
|[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](/examples/qwen_image/model_inference/Qwen-Image.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image.py)|
|[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py)|
|[Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py)|
|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
|[DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
|[DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py)|
|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py)|
|[DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)|[code](/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py)|
|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny)|[code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py)|
|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth)|[code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py)|
|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)|[code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|
|[DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union)|[code](/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py)|
|[DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py)|-|-|-|-|
## FLUX 系列
文档：[./FLUX.md](./FLUX.md)
<details>
<summary>效果一览</summary>
![Image](https://github.com/user-attachments/assets/c01258e2-f251-441a-aa1e-ebb22f02594d)
</details>
<details>
<summary>快速开始</summary>
```python
import torch
from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
pipe = FluxImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
],
)
image = pipe(prompt="a cat", seed=0)
image.save("image.jpg")
```
</details>
<details>
<summary>模型血缘</summary>
```mermaid
graph LR;
FLUX.1-Series-->black-forest-labs/FLUX.1-dev;
FLUX.1-Series-->black-forest-labs/FLUX.1-Krea-dev;
FLUX.1-Series-->black-forest-labs/FLUX.1-Kontext-dev;
black-forest-labs/FLUX.1-dev-->FLUX.1-dev-ControlNet-Series;
FLUX.1-dev-ControlNet-Series-->alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta;
FLUX.1-dev-ControlNet-Series-->InstantX/FLUX.1-dev-Controlnet-Union-alpha;
FLUX.1-dev-ControlNet-Series-->jasperai/Flux.1-dev-Controlnet-Upscaler;
black-forest-labs/FLUX.1-dev-->InstantX/FLUX.1-dev-IP-Adapter;
black-forest-labs/FLUX.1-dev-->ByteDance/InfiniteYou;
black-forest-labs/FLUX.1-dev-->DiffSynth-Studio/Eligen;
black-forest-labs/FLUX.1-dev-->DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev;
black-forest-labs/FLUX.1-dev-->DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev;
black-forest-labs/FLUX.1-dev-->ostris/Flex.2-preview;
black-forest-labs/FLUX.1-dev-->stepfun-ai/Step1X-Edit;
Qwen/Qwen2.5-VL-7B-Instruct-->stepfun-ai/Step1X-Edit;
black-forest-labs/FLUX.1-dev-->DiffSynth-Studio/Nexus-GenV2;
Qwen/Qwen2.5-VL-7B-Instruct-->DiffSynth-Studio/Nexus-GenV2;
```
</details>
|模型 ID|额外参数|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
|-|-|-|-|-|-|-|-|
|[black-forest-labs/FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev)||[code](/examples/flux/model_inference/FLUX.1-dev.py)|[code](/examples/flux/model_inference_low_vram/FLUX.1-dev.py)|[code](/examples/flux/model_training/full/FLUX.1-dev.sh)|[code](/examples/flux/model_training/validate_full/FLUX.1-dev.py)|[code](/examples/flux/model_training/lora/FLUX.1-dev.sh)|[code](/examples/flux/model_training/validate_lora/FLUX.1-dev.py)|
|[black-forest-labs/FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev)||[code](/examples/flux/model_inference/FLUX.1-Krea-dev.py)|[code](/examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py)|[code](/examples/flux/model_training/full/FLUX.1-Krea-dev.sh)|[code](/examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py)|[code](/examples/flux/model_training/lora/FLUX.1-Krea-dev.sh)|[code](/examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py)|
|[black-forest-labs/FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev)|`kontext_images`|[code](/examples/flux/model_inference/FLUX.1-Kontext-dev.py)|[code](/examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py)|[code](/examples/flux/model_training/full/FLUX.1-Kontext-dev.sh)|[code](/examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py)|[code](/examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh)|[code](/examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py)|
|[alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta)|`controlnet_inputs`|[code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|
|[InstantX/FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha)|`controlnet_inputs`|[code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py)|
|[jasperai/Flux.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler)|`controlnet_inputs`|[code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py)|[code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py)|[code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py)|[code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py)|
|[InstantX/FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter)|`ipadapter_images`, `ipadapter_scale`|[code](/examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py)|[code](/examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py)|[code](/examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh)|[code](/examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py)|[code](/examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh)|[code](/examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py)|
|[ByteDance/InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou)|`infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs`|[code](/examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py)|[code](/examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py)|[code](/examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh)|[code](/examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py)|[code](/examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh)|[code](/examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py)|
|[DiffSynth-Studio/Eligen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen)|`eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint`|[code](/examples/flux/model_inference/FLUX.1-dev-EliGen.py)|[code](/examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py)|-|-|[code](/examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh)|[code](/examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py)|
|[DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev)|`lora_encoder_inputs`, `lora_encoder_scale`|[code](/examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py)|[code](/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py)|[code](/examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh)|[code](/examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py)|-|-|
|[DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev)||[code](/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py)|-|-|-|-|-|
|[stepfun-ai/Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit)|`step1x_reference_image`|[code](/examples/flux/model_inference/Step1X-Edit.py)|[code](/examples/flux/model_inference_low_vram/Step1X-Edit.py)|[code](/examples/flux/model_training/full/Step1X-Edit.sh)|[code](/examples/flux/model_training/validate_full/Step1X-Edit.py)|[code](/examples/flux/model_training/lora/Step1X-Edit.sh)|[code](/examples/flux/model_training/validate_lora/Step1X-Edit.py)|
|[ostris/Flex.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview)|`flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop`|[code](/examples/flux/model_inference/FLEX.2-preview.py)|[code](/examples/flux/model_inference_low_vram/FLEX.2-preview.py)|[code](/examples/flux/model_training/full/FLEX.2-preview.sh)|[code](/examples/flux/model_training/validate_full/FLEX.2-preview.py)|[code](/examples/flux/model_training/lora/FLEX.2-preview.sh)|[code](/examples/flux/model_training/validate_lora/FLEX.2-preview.py)|
|[DiffSynth-Studio/Nexus-GenV2](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2)|`nexus_gen_reference_image`|[code](/examples/flux/model_inference/Nexus-Gen-Editing.py)|[code](/examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py)|[code](/examples/flux/model_training/full/Nexus-Gen.sh)|[code](/examples/flux/model_training/validate_full/Nexus-Gen.py)|[code](/examples/flux/model_training/lora/Nexus-Gen.sh)|[code](/examples/flux/model_training/validate_lora/Nexus-Gen.py)|
## Wan 系列
文档：[./Wan.md](./Wan.md)
<details>
<summary>效果一览</summary>
https://github.com/user-attachments/assets/1d66ae74-3b02-40a9-acc3-ea95fc039314
</details>
<details>
<summary>快速开始</summary>
```python
import torch
from diffsynth import save_video
from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
pipe = WanVideoPipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
],
)
pipe.enable_vram_management()
video = pipe(
prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。",
negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
seed=0, tiled=True,
)
save_video(video, "video1.mp4", fps=15, quality=5)
```
</details>
<details>
<summary>模型血缘</summary>
```mermaid
graph LR;
Wan-Series-->Wan2.1-Series;
Wan-Series-->Wan2.2-Series;
Wan2.1-Series-->Wan-AI/Wan2.1-T2V-1.3B;
Wan2.1-Series-->Wan-AI/Wan2.1-T2V-14B;
Wan-AI/Wan2.1-T2V-14B-->Wan-AI/Wan2.1-I2V-14B-480P;
Wan-AI/Wan2.1-I2V-14B-480P-->Wan-AI/Wan2.1-I2V-14B-720P;
Wan-AI/Wan2.1-T2V-14B-->Wan-AI/Wan2.1-FLF2V-14B-720P;
Wan-AI/Wan2.1-T2V-1.3B-->iic/VACE-Wan2.1-1.3B-Preview;
iic/VACE-Wan2.1-1.3B-Preview-->Wan-AI/Wan2.1-VACE-1.3B;
Wan-AI/Wan2.1-T2V-14B-->Wan-AI/Wan2.1-VACE-14B;
Wan-AI/Wan2.1-T2V-1.3B-->Wan2.1-Fun-1.3B-Series;
Wan2.1-Fun-1.3B-Series-->PAI/Wan2.1-Fun-1.3B-InP;
Wan2.1-Fun-1.3B-Series-->PAI/Wan2.1-Fun-1.3B-Control;
Wan-AI/Wan2.1-T2V-14B-->Wan2.1-Fun-14B-Series;
Wan2.1-Fun-14B-Series-->PAI/Wan2.1-Fun-14B-InP;
Wan2.1-Fun-14B-Series-->PAI/Wan2.1-Fun-14B-Control;
Wan-AI/Wan2.1-T2V-1.3B-->Wan2.1-Fun-V1.1-1.3B-Series;
Wan2.1-Fun-V1.1-1.3B-Series-->PAI/Wan2.1-Fun-V1.1-1.3B-Control;
Wan2.1-Fun-V1.1-1.3B-Series-->PAI/Wan2.1-Fun-V1.1-1.3B-InP;
Wan2.1-Fun-V1.1-1.3B-Series-->PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera;
Wan-AI/Wan2.1-T2V-14B-->Wan2.1-Fun-V1.1-14B-Series;
Wan2.1-Fun-V1.1-14B-Series-->PAI/Wan2.1-Fun-V1.1-14B-Control;
Wan2.1-Fun-V1.1-14B-Series-->PAI/Wan2.1-Fun-V1.1-14B-InP;
Wan2.1-Fun-V1.1-14B-Series-->PAI/Wan2.1-Fun-V1.1-14B-Control-Camera;
Wan-AI/Wan2.1-T2V-1.3B-->DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1;
Wan-AI/Wan2.1-T2V-14B-->krea/krea-realtime-video;
Wan-AI/Wan2.1-T2V-14B-->meituan-longcat/LongCat-Video;
Wan-AI/Wan2.1-I2V-14B-720P-->ByteDance/Video-As-Prompt-Wan2.1-14B;
Wan-AI/Wan2.1-T2V-14B-->Wan-AI/Wan2.2-Animate-14B;
Wan-AI/Wan2.1-T2V-14B-->Wan-AI/Wan2.2-S2V-14B;
Wan2.2-Series-->Wan-AI/Wan2.2-T2V-A14B;
Wan2.2-Series-->Wan-AI/Wan2.2-I2V-A14B;
Wan2.2-Series-->Wan-AI/Wan2.2-TI2V-5B;
Wan-AI/Wan2.2-T2V-A14B-->Wan2.2-Fun-Series;
Wan2.2-Fun-Series-->PAI/Wan2.2-VACE-Fun-A14B;
Wan2.2-Fun-Series-->PAI/Wan2.2-Fun-A14B-InP;
Wan2.2-Fun-Series-->PAI/Wan2.2-Fun-A14B-Control;
Wan2.2-Fun-Series-->PAI/Wan2.2-Fun-A14B-Control-Camera;
```
</details>
|模型 ID|额外参数|推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
|-|-|-|-|-|-|-|
|[Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)||[code](/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py)|
|[Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)||[code](/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py)|
|[Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)|`input_image`|[code](/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py)|
|[Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)|`input_image`|[code](/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py)|
|[Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P)|`input_image`, `end_image`|[code](/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py)|
|[iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview)|`vace_control_video`, `vace_reference_image`|[code](/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py)|
|[Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)|`vace_control_video`, `vace_reference_image`|[code](/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py)|
|[Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)|`vace_control_video`, `vace_reference_image`|[code](/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py)|
|[PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP)|`input_image`, `end_image`|[code](/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py)|
|[PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control)|`control_video`|[code](/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py)|
|[PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP)|`input_image`, `end_image`|[code](/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py)|
|[PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control)|`control_video`|[code](/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py)|
|[PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control)|`control_video`, `reference_image`|[code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py)|
|[PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control)|`control_video`, `reference_image`|[code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](/examples/wanvideo/examples/wanmodel_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py)|
|[PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP)|`input_image`, `end_image`|[code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py)|
|[PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP)|`input_image`, `end_image`|[code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py)|
|[PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera)|`control_camera_video`, `input_image`|[code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|
|[PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera)|`control_camera_video`, `input_image`|[code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|
|[DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1)|`motion_bucket_id`|[code](/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py)|[code](/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py)|[code](/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py)|
|[krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video)||[code](/examples/wanvideo/model_inference/krea-realtime-video.py)|[code](/examples/wanvideo/model_training/full/krea-realtime-video.sh)|[code](/examples/wanvideo/model_training/validate_full/krea-realtime-video.py)|[code](/examples/wanvideo/model_training/lora/krea-realtime-video.sh)|[code](/examples/wanvideo/model_training/validate_lora/krea-realtime-video.py)|
|[meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video)|`longcat_video`|[code](/examples/wanvideo/model_inference/LongCat-Video.py)|[code](/examples/wanvideo/model_training/full/LongCat-Video.sh)|[code](/examples/wanvideo/model_training/validate_full/LongCat-Video.py)|[code](/examples/wanvideo/model_training/lora/LongCat-Video.sh)|[code](/examples/wanvideo/model_training/validate_lora/LongCat-Video.py)|
|[ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B)|`vap_video`, `vap_prompt`|[code](/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py)|[code](/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh)|[code](/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py)|[code](/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh)|[code](/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py)|
|[Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)||[code](/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py)|
|[Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)|`input_image`|[code](/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py)|
|[Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)|`input_image`|[code](/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py)|
|[Wan-AI/Wan2.2-Animate-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-Animate-14B)|`input_image`, `animate_pose_video`, `animate_face_video`, `animate_inpaint_video`, `animate_mask_video`|[code](/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py)|
|[Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B)|`input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video`|[code](/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py)|
|[PAI/Wan2.2-VACE-Fun-A14B](https://www.modelscope.cn/models/PAI/Wan2.2-VACE-Fun-A14B)|`vace_control_video`, `vace_reference_image`|[code](/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py)|
|[PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP)|`input_image`, `end_image`|[code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py)|
|[PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control)|`control_video`, `reference_image`|[code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py)|
|[PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera)|`control_camera_video`, `input_image`|[code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py)|

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# 模型推理
本文档以 Qwen-Image 模型为例，介绍如何使用 `DiffSynth-Studio` 进行模型推理。
## 加载模型
模型通过 `from_pretrained` 加载：
```python
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
import torch
pipe = QwenImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
)
```
其中 `torch_dtype` 和 `device` 是计算精度和计算设备（不是模型的精度和设备）。`model_configs` 可通过多种方式配置模型路径，关于本项目内部是如何加载模型的，请参考 [`diffsynth.core.loader`](/docs/API_Reference/core/loader.md)。
<details>
<summary>从远程下载模型并加载</summary>
> `DiffSynth-Studio` 默认从[魔搭社区](https://www.modelscope.cn/)下载并加载模型，需填写 `model_id` 和 `origin_file_pattern`，例如
>
> ```python
> ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
> ```
>
> 模型文件默认下载到 `./models` 路径，该路径可通过[环境变量 DIFFSYNTH_MODEL_BASE_PATH](/docs/Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path) 修改。
>
> 默认情况下，即使模型已经下载完毕，程序仍会向远程查询是否有遗漏文件，如果要完全关闭远程请求，请将[环境变量 DIFFSYNTH_SKIP_DOWNLOAD](/docs/Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) 设置为 `True`。
</details>
<details>
<summary>从本地文件路径加载模型</summary>
> 填写 `path`，例如
>
> ```python
> ModelConfig(path="models/xxx.safetensors")
> ```
>
> 对于从多个文件加载的模型，使用列表即可，例如
>
> ```python
> ModelConfig(path=[
> "models/Qwen/Qwen-Image/text_encoder/model-00001-of-00004.safetensors",
> "models/Qwen/Qwen-Image/text_encoder/model-00002-of-00004.safetensors",
> "models/Qwen/Qwen-Image/text_encoder/model-00003-of-00004.safetensors",
> "models/Qwen/Qwen-Image/text_encoder/model-00004-of-00004.safetensors",
> ])
> ```
</details>
## 启动推理
输入提示词，即可启动推理过程，生成一张图片。
```python
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
import torch
pipe = QwenImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
)
prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
image = pipe(prompt, seed=0, num_inference_steps=40)
image.save("image.jpg")
```
每个模型 `Pipeline` 的输入参数不同，请参考各模型的文档。
如果模型参数量太大，导致显存不足，请开启[显存管理](./VRAM_management.md)。

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# 模型训练
本文档介绍如何使用 `DiffSynth-Studio` 进行模型训练。
## 脚本参数
训练脚本通常包含以下参数：
* 数据集基础配置
* `--dataset_base_path`: 数据集的根目录。
* `--dataset_metadata_path`: 数据集的元数据文件路径。
* `--dataset_repeat`: 每个 epoch 中数据集重复的次数。
* `--dataset_num_workers`: 每个 Dataloder 的进程数量。
* `--data_file_keys`: 元数据中需要加载的字段名称，通常是图像或视频文件的路径，以 `,` 分隔。
* 模型加载配置
* `--model_paths`: 要加载的模型路径。JSON 格式。
* `--model_id_with_origin_paths`: 带原始路径的模型 ID，例如 `"Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors"`。用逗号分隔。
* `--extra_inputs`: 模型 Pipeline 所需的额外输入参数，例如训练图像编辑模型 Qwen-Image-Edit 时需要额外参数 `edit_image`，以 `,` 分隔。
* `--fp8_models`：以 FP8 格式加载的模型，格式与 `--model_paths` 或 `--model_id_with_origin_paths` 一致，目前仅支持参数不被梯度更新的模型（不需要梯度回传，或梯度仅更新其 LoRA）。
* 训练基础配置
* `--learning_rate`: 学习率。
* `--num_epochs`: 轮数（Epoch）。
* `--trainable_models`: 可训练的模型，例如 `dit`、`vae`、`text_encoder`。
* `--find_unused_parameters`: DDP 训练中是否存在未使用的参数，少数模型包含不参与梯度计算的冗余参数，需开启这一设置避免在多 GPU 训练中报错。
* `--weight_decay`：权重衰减大小，详见 [torch.optim.AdamW](https://docs.pytorch.org/docs/stable/generated/torch.optim.AdamW.html)。
* `--task`: 训练任务，默认为 `sft`，部分模型支持更多训练模式，请参考每个特定模型的文档。
* 输出配置
* `--output_path`: 模型保存路径。
* `--remove_prefix_in_ckpt`: 在模型文件的 state dict 中移除前缀。
* `--save_steps`: 保存模型的训练步数间隔，若此参数留空，则每个 epoch 保存一次。
* LoRA 配置
* `--lora_base_model`: LoRA 添加到哪个模型上。
* `--lora_target_modules`: LoRA 添加到哪些层上。
* `--lora_rank`: LoRA 的秩（Rank）。
* `--lora_checkpoint`: LoRA 检查点的路径。如果提供此路径，LoRA 将从此检查点加载。
* `--preset_lora_path`: 预置 LoRA 检查点路径，如果提供此路径，这一 LoRA 将会以融入基础模型的形式加载。此参数用于 LoRA 差分训练。
* `--preset_lora_model`: 预置 LoRA 融入的模型，例如 `dit`。
* 梯度配置
* `--use_gradient_checkpointing`: 是否启用 gradient checkpointing。
* `--use_gradient_checkpointing_offload`: 是否将 gradient checkpointing 卸载到内存中。
* `--gradient_accumulation_steps`: 梯度累积步数。
* 图像宽高配置（适用于图像生成模型和视频生成模型）
* `--height`: 图像或视频的高度。将 `height` 和 `width` 留空以启用动态分辨率。
* `--width`: 图像或视频的宽度。将 `height` 和 `width` 留空以启用动态分辨率。
* `--max_pixels`: 图像或视频帧的最大像素面积，当启用动态分辨率时，分辨率大于这个数值的图片都会被缩小，分辨率小于这个数值的图片保持不变。
部分模型的训练脚本还包含额外的参数，详见各模型的文档。
## 准备数据集
`DiffSynth-Studio` 采用通用数据集格式，数据集包含一系列数据文件（图像、视频等），以及标注元数据的文件，我们建议您这样组织数据集文件：
```
data/example_image_dataset/
├── metadata.csv
├── image_1.jpg
└── image_2.jpg
```
其中 `image_1.jpg`、`image_2.jpg` 为训练用图像数据，`metadata.csv` 为元数据列表，例如
```
image,prompt
image_1.jpg,"a dog"
image_2.jpg,"a cat"
```
我们构建了样例数据集，以方便您进行测试。了解通用数据集架构是如何实现的，请参考 [`diffsynth.core.data`](/docs/API_Reference/core/data.md)。
<details>
<summary>样例图像数据集</summary>
> ```shell
> modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
> ```
>
> 适用于 Qwen-Image、FLUX 等图像生成模型的训练。
</details>
<details>
<summary>样例视频数据集</summary>
> ```shell
> modelscope download --dataset DiffSynth-Studio/example_video_dataset --local_dir ./data/example_video_dataset
> ```
>
> 适用于 Wan 等视频生成模型的训练。
</details>
## 加载模型
类似于[推理时的模型加载](./Model_Inference.md#加载模型)，我们支持多种方式配置模型路径，两种方式是可以混用的。
<details>
<summary>从远程下载模型并加载</summary>
> 如果在推理时我们通过以下设置加载模型
>
> ```python
> model_configs=[
> ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
> ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
> ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
> ]
> ```
>
> 那么在训练时，填入以下参数即可加载对应的模型。
>
> ```shell
> --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors"
> ```
>
> 模型文件默认下载到 `./models` 路径，该路径可通过[环境变量 DIFFSYNTH_MODEL_BASE_PATH](/docs/Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path) 修改。
>
> 默认情况下，即使模型已经下载完毕，程序仍会向远程查询是否有遗漏文件，如果要完全关闭远程请求，请将[环境变量 DIFFSYNTH_SKIP_DOWNLOAD](/docs/Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) 设置为 `True`。
</details>
<details>
<summary>从本地文件路径加载模型</summary>
> 如果从本地文件加载模型，例如推理时
>
> ```python
> model_configs=[
> ModelConfig([
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00001-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00002-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00003-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00004-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00005-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00006-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00007-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00008-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00009-of-00009.safetensors"
> ]),
> ModelConfig([
> "models/Qwen/Qwen-Image/text_encoder/model-00001-of-00004.safetensors",
> "models/Qwen/Qwen-Image/text_encoder/model-00002-of-00004.safetensors",
> "models/Qwen/Qwen-Image/text_encoder/model-00003-of-00004.safetensors",
> "models/Qwen/Qwen-Image/text_encoder/model-00004-of-00004.safetensors"
> ]),
> ModelConfig("models/Qwen/Qwen-Image/vae/diffusion_pytorch_model.safetensors")
> ]
> ```
>
> 那么训练时需设置为
>
> ```shell
> --model_paths '[
> [
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00001-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00002-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00003-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00004-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00005-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00006-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00007-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00008-of-00009.safetensors",
> "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00009-of-00009.safetensors"
> ],
> [
> "models/Qwen/Qwen-Image/text_encoder/model-00001-of-00004.safetensors",
> "models/Qwen/Qwen-Image/text_encoder/model-00002-of-00004.safetensors",
> "models/Qwen/Qwen-Image/text_encoder/model-00003-of-00004.safetensors",
> "models/Qwen/Qwen-Image/text_encoder/model-00004-of-00004.safetensors"
> ],
> "models/Qwen/Qwen-Image/vae/diffusion_pytorch_model.safetensors"
> ]' \
> ```
>
> 请注意，`--model_paths` 是 json 格式，其中不能出现多余的 `,`，否则无法被正常解析。
</details>
## 设置可训练模块
训练框架支持任意模型的训练，以 Qwen-Image 为例，若全量训练其中的 DiT 模型，则需设置为
```shell
--trainable_models "dit"
```
若训练 DiT 模型的 LoRA，则需设置
```shell
--lora_base_model dit --lora_target_modules "to_q,to_k,to_v" --lora_rank 32
```
我们希望给技术探索留下足够的发挥空间，因此框架支持同时训练任意多个模块，例如同时训练 text encoder、controlnet，以及 DiT 的 LoRA：
```shell
--trainable_models "text_encoder,controlnet" --lora_base_model dit --lora_target_modules "to_q,to_k,to_v" --lora_rank 32
```
此外，由于训练脚本中加载了多个模块（text encoder、dit、vae 等），保存模型文件时需要移除前缀，例如在全量训练 DiT 部分或者训练 DiT 部分的 LoRA 模型时，请设置 `--remove_prefix_in_ckpt pipe.dit.`。如果多个模块同时训练，则需开发者在训练完成后自行编写代码拆分模型文件中的 state dict。
## 启动训练程序
训练框架基于 [`accelerate`](https://huggingface.co/docs/accelerate/index) 构建，训练命令按照如下格式编写：
```shell
accelerate launch xxx/train.py \
--xxx yyy \
--xxxx yyyy
```
我们为每个模型编写了预置的训练脚本，详见各模型的文档。
默认情况下，`accelerate` 会按照 `~/.cache/huggingface/accelerate/default_config.yaml` 的配置进行训练，使用 `accelerate config` 可在终端交互式地配置，包括多 GPU 训练、[`DeepSpeed`](https://www.deepspeed.ai/) 等。
我们为部分模型提供了推荐的 `accelerate` 配置文件，可通过 `--config_file` 设置，例如 Qwen-Image 模型的全量训练：
```shell
accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \
--dataset_base_path data/example_image_dataset \
--dataset_metadata_path data/example_image_dataset/metadata.csv \
--max_pixels 1048576 \
--dataset_repeat 50 \
--model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \
--learning_rate 1e-5 \
--num_epochs 2 \
--remove_prefix_in_ckpt "pipe.dit." \
--output_path "./models/train/Qwen-Image_full" \
--trainable_models "dit" \
--use_gradient_checkpointing \
--find_unused_parameters
```
## 训练注意事项
* 数据集的元数据除 `csv` 格式外，还支持 `json`、`jsonl` 格式，关于如何选择最佳的元数据格式，请参考[](/docs/API_Reference/core/data.md#元数据)
* 通常训练效果与训练步数强相关，与 epoch 数量弱相关，因此我们更推荐使用参数 `--save_steps` 按训练步数间隔来保存模型文件。
* 当数据量 * `dataset_repeat` 超过 $10^9$ 时，我们观测到数据集的速度明显变慢，这似乎是 `PyTorch` 的 bug，我们尚不确定新版本的 `PyTorch` 是否已经修复了这一问题。
* 学习率 `--learning_rate` 在 LoRA 训练中建议设置为 `1e-4`，在全量训练中建议设置为 `1e-5`。
* 训练框架不支持 batch size > 1，原因是复杂的，详见 [Q&A: 为什么训练框架不支持 batch size > 1？](/docs/QA.md#为什么训练框架不支持-batch-size--1)
* 少数模型包含冗余参数，例如 Qwen-Image 的 DiT 部分最后一层的文本编码部分，在训练这些模型时，需设置 `--find_unused_parameters` 避免在多 GPU 训练中报错。出于对开源社区模型兼容性的考虑，我们不打算删除这些冗余参数。
* Diffusion 模型的损失函数值与实际效果的关系不大，因此我们在训练过程中不会记录损失函数值。我们建议把 `--num_epochs` 设置为足够大的数值，边训边测，直至效果收敛后手动关闭训练程序。
* `--use_gradient_checkpointing` 通常是开启的，除非 GPU 显存足够；`--use_gradient_checkpointing_offload` 则按需开启，详见 [`diffsynth.core.gradient`](/docs/API_Reference/core/gradient.md)。

9
docs/Pipeline_Usage/Quick_Start.md → docs/Pipeline_Usage/Setup.md
View File

@@ -1,6 +1,4 @@
# 快速开始
## 安装
# 安装依赖
从源码安装（推荐）：
@@ -10,9 +8,6 @@ cd DiffSynth-Studio
pip install -e .
```
<details>
<summary>其他安装方式</summary>
从 pypi 安装（存在版本更新延迟，如需使用最新功能，请从源码安装）
```
@@ -24,5 +19,3 @@ pip install diffsynth
* [torch](https://pytorch.org/get-started/locally/)
* [sentencepiece](https://github.com/google/sentencepiece)
* [cmake](https://cmake.org)
</details>

7
docs/QA.md Normal file
View File

@@ -0,0 +1,7 @@
# 常见问题
## 为什么训练框架不支持 batch size > 1？
## 为什么不删除某些模型中的冗余参数？
## 为什么 FP8 量化没有任何加速效果？

46
docs/Overview.md → docs/README.md
View File

@@ -1,18 +1,36 @@
# DiffSynth-Studio 文档
`DiffSynth-Studio` 旨在构建一个通用的 Diffusion 模型框架，支持主流 Diffusion 模型的推理和训练，孵化模型技术的创新成果。
欢迎来到 Diffusion 模型的魔法世界！`DiffSynth-Studio` 是由[魔搭社区](https://www.modelscope.cn/)团队开发和维护的开源 Diffusion 模型引擎。我们期望构建一个通用的 Diffusion 模型框架，以框架建设孵化技术创新，凝聚开源社区的力量，探索生成式模型技术的边界！
## Section 1: 上手使用
本节介绍 `DiffSynth-Studio` 的基本使用方式，包括如何启用显存管理从而在极低显存的 GPU 上进行推理，以及如何训练任意基础模型、LoRA、ControlNet 等模型。
* [快速开始](./Pipeline_Usage/Quick_Start.md)【TODO】
* [模型推理](./Pipeline_Usage/Model_Inference.md)【TODO】
* [安装依赖](./Pipeline_Usage/Setup.md)
* [模型推理](./Pipeline_Usage/Model_Inference.md)
* [显存管理](./Pipeline_Usage/VRAM_management.md)
* [模型训练](./Pipeline_Usage/Model_Training.md)【TODO】
* [模型训练](./Pipeline_Usage/Model_Training.md)
* [环境变量](./Pipeline_Usage/Environment_Variables.md)
## Section 2: 模型接入
## Section 2: 模型详解
本节介绍 `DiffSynth-Studio` 所支持的 Diffusion 模型，部分模型 Pipeline 具备可控生成、并行加速等特色功能。
* [模型目录](./Model_Details/Overview.md)
* [Qwen-Image](./Model_Details/Qwen-Image.md)【TODO】
* [FLUX](./Model_Details/FLUX.md)【TODO】
* [Wan](./Model_Details/Wan.md)【TODO】
## Section 3: 特殊训练
本节介绍 `DiffSynth-Studio` 所支持的特殊训练模式
* FP8 训练
* 端到端蒸馏训练
* 差分 LoRA 训练
* 拆分训练
## Section 3: 模型接入
本节介绍如何将模型接入 `DiffSynth-Studio` 从而使用框架基础功能，帮助开发者为本项目提供新模型的支持，或进行私有化模型的推理和训练。
@@ -21,7 +39,7 @@
* [接入细粒度显存管理](./Developer_Guide/Enabling_VRAM_management.md)
* [接入模型训练](./Developer_Guide/Training_Diffusion_Models.md)
## Section 3: API 参考
## Section 4: API 参考
本节介绍 `DiffSynth-Studio` 中的独立核心模块 `diffsynth.core`，介绍内部的功能是如何设计和运作的，开发者如有需要，可将其中的功能模块用于其他代码库的开发中。
@@ -31,11 +49,17 @@
* [`diffsynth.core.loader`](./API_Reference/core/loader.md): 模型下载与加载
* [`diffsynth.core.vram`](./API_Reference/core/vram.md): 显存管理
## Section 4: 学术导引
## Section 5: 学术导引
本节介绍如何利用 `DiffSynth-Studio` 训练新的模型，帮助科研工作者探索新的模型技术。
* 从零开始训练模型【TODO】
* 推理改进优化技术【TODO】
* 设计可控生成模型【TODO】
* 创建新的训练范式【TODO】
* 从零开始训练模型【coming soon】
* 推理改进优化技术【coming soon】
* 设计可控生成模型【coming soon】
* 创建新的训练范式【coming soon】
## Section 6: 常见问题
本节总结了开发者常见的问题，如果你在使用和开发中遇到了问题，请参考本节内容，如果仍无法解决，请到 GitHub 上给我们提 issue。
* [常见问题](./QA.md)【TODO】