SynthLight ensures detail retention through two key technologies:
- Inference Sampling without Classifier Guidance: At each denoising step of the diffusion process, the model cross-references the low-frequency information of the original image so that it receives high-level guidance from the new lighting conditions without destroying the original facial details
- Multi-scale feature fusion: The network architecture includes a specialized skip-connection module that passes local texture features extracted by the encoder (e.g., eyelashes, lip lines) directly to the decoder
Data from the technical paper shows that this design allows SynthLight to improve the LPIPS perceived similarity metric over the baseline model by 231 TP3 T. In practice, users will notice that character-specific micro-features such as moles and scars are retained intact, even when the direction of light is changed drastically.
This answer comes from the articleSynthLight: natural light rendering of portrait images (unreleased)The
May not be reproduced without permission:AI productivity tools " How does SynthLight's diffusion model ensure that no detail of the original portrait is lost during processing?
