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Qwen-Image-Distill-LoRA

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2025-08-11 15:01:21 +08:00
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examples/qwen_image/README.md
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@@ -44,6 +44,7 @@ image.save("image.jpg")
|-|-|-|-|-|-|
|[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image )|[code](./model_inference/Qwen-Image.py)|[code](./model_training/full/Qwen-Image.sh)|[code](./model_training/validate_full/Qwen-Image.py)|[code](./model_training/lora/Qwen-Image.sh)|[code](./model_training/validate_lora/Qwen-Image.py)|
|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./model_inference/Qwen-Image-Distill-Full.py)|[code](./model_training/full/Qwen-Image-Distill-Full.sh)|[code](./model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./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](./model_inference/Qwen-Image-Distill-LoRA.py)|-|-|-|-|
|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](./model_inference/Qwen-Image-EliGen.py)|-|-|[code](./model_training/lora/Qwen-Image-EliGen.sh)|[code](./model_training/validate_lora/Qwen-Image-EliGen.py)|
## Model Inference
@@ -178,6 +179,9 @@ After enabling VRAM management, the framework will automatically choose a memory
* GPUs that support FP8 operations (e.g., H200, etc.): Please install [Flash Attention 3](https://github.com/Dao-AILab/flash-attention). Otherwise, FP8 acceleration will only apply to Linear layers.
* Faster inference but higher VRAM usage: Use [./accelerate/Qwen-Image-FP8.py](./accelerate/Qwen-Image-FP8.py)
* Slightly slower inference but lower VRAM usage: Use [./accelerate/Qwen-Image-FP8-offload.py](./accelerate/Qwen-Image-FP8-offload.py)
* Distillation acceleration: We trained two distillation models for fast inference at `cfg_scale=1` and `num_inference_steps=15`.
* [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full): Full distillation version. Better image quality but lower LoRA compatibility. Use [./model_inference/Qwen-Image-Distill-Full.py](./model_inference/Qwen-Image-Distill-Full.py).
* [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA): LoRA distillation version. Slightly lower image quality but better LoRA compatibility. Use [./model_inference/Qwen-Image-Distill-LoRA.py](./model_inference/Qwen-Image-Distill-LoRA.py).
</details>

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examples/qwen_image/README_zh.md
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@@ -44,6 +44,7 @@ image.save("image.jpg")
|-|-|-|-|-|-|
|[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](./model_inference/Qwen-Image.py)|[code](./model_training/full/Qwen-Image.sh)|[code](./model_training/validate_full/Qwen-Image.py)|[code](./model_training/lora/Qwen-Image.sh)|[code](./model_training/validate_lora/Qwen-Image.py)|
|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./model_inference/Qwen-Image-Distill-Full.py)|[code](./model_training/full/Qwen-Image-Distill-Full.sh)|[code](./model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./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](./model_inference/Qwen-Image-Distill-LoRA.py)|-|-|-|-|
|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](./model_inference/Qwen-Image-EliGen.py)|-|-|[code](./model_training/lora/Qwen-Image-EliGen.sh)|[code](./model_training/validate_lora/Qwen-Image-EliGen.py)|
## 模型推理
@@ -178,6 +179,9 @@ FP8 量化能够大幅度减少显存占用，但不会加速，部分模型在
* GPU 支持 FP8 运算（例如 H200 等）：请安装 [Flash Attention 3](https://github.com/Dao-AILab/flash-attention)，否则 FP8 加速仅对 Linear 层生效
* 更快的速度，但更大的显存：请使用 [./accelerate/Qwen-Image-FP8.py](./accelerate/Qwen-Image-FP8.py)
* 稍慢的速度，但更小的显存：请使用 [./accelerate/Qwen-Image-FP8-offload.py](./accelerate/Qwen-Image-FP8-offload.py)
* 蒸馏加速：我们训练了两个蒸馏加速模型，可以在 `cfg_scale=1` 和 `num_inference_steps=15` 设置下进行快速推理
* [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)：全量蒸馏训练版本，更好的生成效果，稍差的 LoRA 兼容性，请使用 [./model_inference/Qwen-Image-Distill-Full.py](./model_inference/Qwen-Image-Distill-Full.py)
* [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)：LoRA 蒸馏训练版本，稍差的生成效果，更好的 LoRA 兼容性，请使用 [./model_inference/Qwen-Image-Distill-LoRA.py](./model_inference/Qwen-Image-Distill-LoRA.py)
</details>

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examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py Normal file
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@@ -0,0 +1,20 @@
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
from modelscope import snapshot_download
import torch
snapshot_download("DiffSynth-Studio/Qwen-Image-Distill-LoRA", local_dir="DiffSynth-Studio/Qwen-Image-Distill-LoRA")
pipe = QwenImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
)
pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Distill-LoRA/model.safetensors")
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=15, cfg_scale=1)
image.save("image.jpg")