SynthLight's Core Technology Architecture
SynthLight uses the current state-of-the-art diffusion modeling technology architecture optimized specifically for portrait relighting tasks. The tool is able to re-render the input portrait photos with high quality lighting effects by means of a deep learning model. Diffusion modeling has a significant advantage in the field of image generation, gradually removing noise and reconstructing the image, which allows SynthLight to generate more natural and realistic lighting effects. The tool also integrates specialized datasets generated by the physical rendering engine for training, ensuring that lighting transitions are accurately simulated while maintaining the identity of the subject in all lighting conditions.
- Using the denoising diffusion probability model (DDPM) framework
- Using the U-Net architecture as a core neural network
- Integration of a physical rendering engine (LightStage) to generate training data
- Support for inference sampling without classifier guidance
Compared with the traditional image processing methods, this diffusion model-based technical solution can better preserve the detailed features of the original image while realizing a more natural lighting effect conversion.
This answer comes from the articleSynthLight: natural light rendering of portrait images (unreleased)The
