Solution: Optimization of CFG technology and parameter tuning
To improve the text matching of Stable Diffusion model, CFG-Zero-star is mainly realized by the following methods:
- Enabling CFG-Zero-star optimization: set in demo.py
use_cfg_zero_star = TrueThis feature improves the classifier free bootstrapping strategy and enhances the relevance of the generated content to the cue words. - Adjusting the intensity of guidance: Modification
guidance_scaleparameter (default 4.0, range 1-20), the higher the value the tighter the text control, but too high may lead to image quality degradation - Increase Reasoning Steps: By upgrading
num_inference_stepsvalue (default 28) gives the model more time for iterative optimization - Use of high-quality base models: Optimized models such as Stable Diffusion 3 or SD3.5 are recommended and can be downloaded from Hugging Face
Practical steps: 1) Configure the virtual environment and project dependencies 2) Use clear, specific descriptions in the prompts 3) Test with the default parameters first, then gradually adjust guidance_scale 4) Compare the difference in effect of enabling/disabling CFG-Zero-star
This answer comes from the articleCFG-Zero-star: An Open Source Tool for Improving Image and Video Generation QualityThe
May not be reproduced without permission:AI productivity tools " How to Improve Text Matching in Stable Diffusion Model Generated Images Using CFG-Zero-star?
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