Precision control method
For natural language image editing scenarios, the following combination of parameter optimization is recommended:
- timing control::
start_timestep=0.4: Preserve more of the structural features of the original imageend_timestep=0.15: Avoid excessive modification of HF details
- Cue word engineering::
- Use bracket weighting: e.g. "(moon:1.3) in (dark sky:0.8)"
- Add negative tips:
negative_prompt="blurry, deformed"
- hybrid control::
- Combined with PuLID's
identity_strength=0.5 - Setting when loading face LoRA
alpha=0.7
- Combined with PuLID's
Example of a typical workflow:
1. Loading the FLUX.1-Kontext-dev base model
2. Add ControlNet preprocessing nodes to extract the edge map
3. Enter "change hairstyle to curly" in the NunchakuKontextEditor node.
4. Settingsmask_dilation=8Control of the area of influence
Measured data shows that this solution can realize the editing task of 512×512 resolution on RTX 3060 in about 22 seconds, compared with the native 16-bit model editing accuracy gap <5%
This answer comes from the articleNunchaku: an inference tool for efficiently running FLUX.1 and SANA 4-bit quantization modelsThe
May not be reproduced without permission:AI productivity tools " How to improve the accuracy of image editing with FLUX.1-Kontext-dev?
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