Image Quality Enhancement Program
For image models such as FLUX, the output can be optimized by the following methods:
- parameterization: Increase num_inference_steps (suggest 30-50), use higher resolution (1024×1024)
- Prompt Optimization: Add style keywords (e.g. "4K details") and negative alerts
- Model Selection: Try different versions of the FLUX model (checkpoint)
Typical High Quality Configurations::
image = pipe(
prompt="高清摄影作品，(最佳画质)，(大师级光影)",
negative_prompt="模糊，低清，畸变",
width=1024,
num_inference_steps=50,
guidance_scale=7.5
)
Caveats:
- High quality settings increase generation time by 20-301 TP3T
- Graphics memory usage is proportional to resolution squared
- Some LoRA models may introduce stylistic bias
This answer comes from the articleDiffSynth-Engine: Open Source Engine for Low-Existing Deployments of FLUX, Wan 2.1The
May not be reproduced without permission:AI productivity tools " How to optimize the quality of images generated by DiffSynth-Engine?
