本地布署Qwen-Image全量蒸馏加速模型

一，#本机环境检查

执行nvidia-smi，查看右上角。验证显卡驱动已安装最高支持的版本。

nvidia-smi

执行nvcc -V验证cuda

nvcc -V

执行conda --version验证conda版本

conda --version

#列出所有已创建的Conda 环境​​：

conda env list
或
conda info --envs

#若存在，先删除已存在环境

conda env remove -n diffusers_qwen_image

#创建新环境

conda create -n diffusers_qwen_image python=3.10

#激活环境

conda activate diffusers_qwen_image

 

二，依赖库安装

#下载diffsynth

git clone https://github.com/modelscope/DiffSynth-Studio.git

#安装diffsynth

cd DiffSynth-Studio
pip install .

​​#验证diffsynth库是否安装成功

python3 -c "import diffsynth; print('diffsynth导入成功，版本:', diffsynth.__version__)"

 

 

#运行文生图

CUDA_VISIBLE_DEVICES=4,5,6,7 python3 -c "
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
import torchpipe = QwenImagePipeline.from_pretrained(torch_dtype=torch.bfloat16,device='cuda',model_configs=[ModelConfig(model_id='DiffSynth-Studio/Qwen-Image-Distill-Full', origin_file_pattern='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=15, cfg_scale=1)
image.save('image.jpg')
"