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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/).