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Hong Zhang
2026-02-10 19:51:04 +08:00
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16
docs/en/Model_Details/FLUX.md
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@@ -14,7 +14,7 @@ cd DiffSynth-Studio
pip install -e .
```
For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
## Quick Start
@@ -98,14 +98,14 @@ graph LR;
Special Training Scripts:
* Differential LoRA Training: [doc](/docs/en/Training/Differential_LoRA.md), [code](/examples/flux/model_training/special/differential_training/)
* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md), [code](/examples/flux/model_training/special/fp8_training/)
* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/flux/model_training/special/split_training/)
* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/flux/model_training/lora/FLUX.1-dev-Distill-LoRA.sh)
* Differential LoRA Training: [doc](../Training/Differential_LoRA.md), [code](/examples/flux/model_training/special/differential_training/)
* FP8 Precision Training: [doc](../Training/FP8_Precision.md), [code](/examples/flux/model_training/special/fp8_training/)
* Two-stage Split Training: [doc](../Training/Split_Training.md), [code](/examples/flux/model_training/special/split_training/)
* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md), [code](/examples/flux/model_training/lora/FLUX.1-dev-Distill-LoRA.sh)
## Model Inference
Models are loaded via `FluxImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
Models are loaded via `FluxImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).
Input parameters for `FluxImagePipeline` inference include:
@@ -143,7 +143,7 @@ Input parameters for `FluxImagePipeline` inference include:
* `flex_control_stop`: Flex model control stop timestep.
* `nexus_gen_reference_image`: Nexus-Gen model reference image.
If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
## Model Training
@@ -198,4 +198,4 @@ We have built a sample image dataset for your testing. You can download this dat
modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
```
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).

16
docs/en/Model_Details/FLUX2.md
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@@ -21,7 +21,7 @@ cd DiffSynth-Studio
pip install -e .
```
For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
## Quick Start
@@ -69,14 +69,14 @@ image.save("image.jpg")
Special Training Scripts:
* Differential LoRA Training: [doc](/docs/en/Training/Differential_LoRA.md)
* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md)
* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md)
* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md)
* Differential LoRA Training: [doc](../Training/Differential_LoRA.md)
* FP8 Precision Training: [doc](../Training/FP8_Precision.md)
* Two-stage Split Training: [doc](../Training/Split_Training.md)
* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md)
## Model Inference
Models are loaded via `Flux2ImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
Models are loaded via `Flux2ImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).
Input parameters for `Flux2ImagePipeline` inference include:
@@ -95,7 +95,7 @@ Input parameters for `Flux2ImagePipeline` inference include:
* `tile_stride`: Tile stride during VAE encoding/decoding stages, default is 64, only effective when `tiled=True`, must be less than or equal to `tile_size`.
* `progress_bar_cmd`: Progress bar, default is `tqdm.tqdm`. Can be disabled by setting to `lambda x:x`.
If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
## Model Training
@@ -148,4 +148,4 @@ We have built a sample image dataset for your testing. You can download this dat
modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
```
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).

6
docs/en/Model_Details/LTX-2.md
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@@ -12,7 +12,7 @@ cd DiffSynth-Studio
pip install -e .
```
For more information about installation, please refer to [Installation Dependencies](/docs/en/Pipeline_Usage/Setup.md).
For more information about installation, please refer to [Installation Dependencies](../Pipeline_Usage/Setup.md).
## Quick Start
@@ -83,7 +83,7 @@ write_video_audio_ltx2(
## Model Inference
Models are loaded through `LTX2AudioVideoPipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models) for details.
Models are loaded through `LTX2AudioVideoPipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models) for details.
Input parameters for `LTX2AudioVideoPipeline` inference include:
@@ -109,7 +109,7 @@ Input parameters for `LTX2AudioVideoPipeline` inference include:
* `use_distilled_pipeline`: Whether to use distilled pipeline, default is `False`.
* `progress_bar_cmd`: Progress bar, default is `tqdm.tqdm`. Can be set to `lambda x:x` to hide the progress bar.
If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the previous "Supported Inference Scripts" section.
If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the previous "Supported Inference Scripts" section.
## Model Training

12
docs/en/Model_Details/Overview.md
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@@ -2,7 +2,7 @@
## Qwen-Image
Documentation: [./Qwen-Image.md](/docs/en/Model_Details/Qwen-Image.md)
Documentation: [./Qwen-Image.md](../Model_Details/Qwen-Image.md)
<details>
@@ -85,7 +85,7 @@ graph LR;
## FLUX Series
Documentation: [./FLUX.md](/docs/en/Model_Details/FLUX.md)
Documentation: [./FLUX.md](../Model_Details/FLUX.md)
<details>
@@ -166,7 +166,7 @@ graph LR;
## Wan Series
Documentation: [./Wan.md](/docs/en/Model_Details/Wan.md)
Documentation: [./Wan.md](../Model_Details/Wan.md)
<details>
@@ -286,6 +286,6 @@ graph LR;
| [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) |
* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md), [code](/examples/wanvideo/model_training/special/fp8_training/)
* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/wanvideo/model_training/special/split_training/)
* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/wanvideo/model_training/special/direct_distill/)
* FP8 Precision Training: [doc](../Training/FP8_Precision.md), [code](/examples/wanvideo/model_training/special/fp8_training/)
* Two-stage Split Training: [doc](../Training/Split_Training.md), [code](/examples/wanvideo/model_training/special/split_training/)
* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md), [code](/examples/wanvideo/model_training/special/direct_distill/)

16
docs/en/Model_Details/Qwen-Image.md
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@@ -14,7 +14,7 @@ cd DiffSynth-Studio
pip install -e .
```
For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
## Quick Start
@@ -102,10 +102,10 @@ graph LR;
Special Training Scripts:
* Differential LoRA Training: [doc](/docs/en/Training/Differential_LoRA.md), [code](/examples/qwen_image/model_training/special/differential_training/)
* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md), [code](/examples/qwen_image/model_training/special/fp8_training/)
* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/qwen_image/model_training/special/split_training/)
* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)
* Differential LoRA Training: [doc](../Training/Differential_LoRA.md), [code](/examples/qwen_image/model_training/special/differential_training/)
* FP8 Precision Training: [doc](../Training/FP8_Precision.md), [code](/examples/qwen_image/model_training/special/fp8_training/)
* Two-stage Split Training: [doc](../Training/Split_Training.md), [code](/examples/qwen_image/model_training/special/split_training/)
* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md), [code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)
DeepSpeed ZeRO Stage 3 Training: The Qwen-Image series models support DeepSpeed ZeRO Stage 3 training, which partitions the model across multiple GPUs. Taking full parameter training of the Qwen-Image model as an example, the following modifications are required:
@@ -114,7 +114,7 @@ DeepSpeed ZeRO Stage 3 Training: The Qwen-Image series models support DeepSpeed
## Model Inference
Models are loaded via `QwenImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
Models are loaded via `QwenImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).
Input parameters for `QwenImagePipeline` inference include:
@@ -145,7 +145,7 @@ Input parameters for `QwenImagePipeline` inference include:
* `tile_stride`: Tile stride during VAE encoding/decoding stages, default is 64, only effective when `tiled=True`, must be less than or equal to `tile_size`.
* `progress_bar_cmd`: Progress bar, default is `tqdm.tqdm`. Can be disabled by setting to `lambda x:x`.
If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
## Model Training
@@ -199,4 +199,4 @@ We have built a sample image dataset for your testing. You can download this dat
modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
```
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).

14
docs/en/Model_Details/Wan.md
View File

@@ -14,7 +14,7 @@ cd DiffSynth-Studio
pip install -e .
```
For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
## Quick Start
@@ -138,9 +138,9 @@ graph LR;
| [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) |
* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md), [code](/examples/wanvideo/model_training/special/fp8_training/)
* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/wanvideo/model_training/special/split_training/)
* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/wanvideo/model_training/special/direct_distill/)
* FP8 Precision Training: [doc](../Training/FP8_Precision.md), [code](/examples/wanvideo/model_training/special/fp8_training/)
* Two-stage Split Training: [doc](../Training/Split_Training.md), [code](/examples/wanvideo/model_training/special/split_training/)
* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md), [code](/examples/wanvideo/model_training/special/direct_distill/)
DeepSpeed ZeRO Stage 3 Training: The Wan series models support DeepSpeed ZeRO Stage 3 training, which partitions the model across multiple GPUs. Taking full parameter training of the Wan2.1-T2V-14B model as an example, the following modifications are required:
@@ -149,7 +149,7 @@ DeepSpeed ZeRO Stage 3 Training: The Wan series models support DeepSpeed ZeRO St
## Model Inference
Models are loaded via `WanVideoPipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
Models are loaded via `WanVideoPipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).
Input parameters for `WanVideoPipeline` inference include:
@@ -199,7 +199,7 @@ Input parameters for `WanVideoPipeline` inference include:
* `tea_cache_model_id`: Model ID used by TeaCache.
* `progress_bar_cmd`: Progress bar, default is `tqdm.tqdm`. Can be disabled by setting to `lambda x:x`.
If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
## Model Training
@@ -254,4 +254,4 @@ We have built a sample video dataset for your testing. You can download this dat
modelscope download --dataset DiffSynth-Studio/example_video_dataset --local_dir ./data/example_video_dataset
```
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).

10
docs/en/Model_Details/Z-Image.md
View File

@@ -12,7 +12,7 @@ cd DiffSynth-Studio
pip install -e .
```
For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
## Quick Start
@@ -61,12 +61,12 @@ image.save("image.jpg")
Special Training Scripts:
* Differential LoRA Training: [doc](/docs/en/Training/Differential_LoRA.md), [code](/examples/z_image/model_training/special/differential_training/)
* Differential LoRA Training: [doc](../Training/Differential_LoRA.md), [code](/examples/z_image/model_training/special/differential_training/)
* Trajectory Imitation Distillation Training (Experimental Feature): [code](/examples/z_image/model_training/special/trajectory_imitation/)
## Model Inference
Models are loaded via `ZImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
Models are loaded via `ZImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).
Input parameters for `ZImagePipeline` inference include:
@@ -84,7 +84,7 @@ Input parameters for `ZImagePipeline` inference include:
* `edit_image`: Edit images for image editing models, supporting multiple images.
* `positive_only_lora`: LoRA weights used only in positive prompts.
If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
## Model Training
@@ -137,7 +137,7 @@ We have built a sample image dataset for your testing. You can download this dat
modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
```
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
Training Tips: