Development of integrated solutions
There are three key phases of technology deployment:
environmental preparation
- Install Python 3.8+ and CUDA 11.7 (required for GPU acceleration)
- Dependency library installation command:
pip install torch transformers diffusers accelerate
Core Code Implementation
Sample code showing key parameter configuration:
pipeline = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit", torch_dtype=torch.float16)
pipeline.to("cuda")
inputs = {
"image": Image.open("input.jpg"),
"prompt": "将沙发材质改为真皮",
"true_cfg_scale": 7.0, # 控制修改强度（3-10）
"negative_prompt": "模糊,低质量", # 排除不想要的效果
"num_inference_steps": 30
}
Optimization Recommendations
- Batch mode: use torch.DataLoader acceleration for large number of images
- Memory management: use pipe.enable_model_cpu_offload() to relieve video memory pressure
- Quantitative Deployment: Add load_in_8bit=True parameter for video memory optimization
This answer comes from the articleQwen-Image-Edit: an AI model for editing images based on textual commandsThe
May not be reproduced without permission:AI productivity tools " How can developers deploy the Qwen-Image-Edit model locally via Python code?
