  Image retouching aims to enhance the visual quality of photos. Considering
the different aesthetic preferences of users, the target of retouching is
subjective. However, current retouching methods mostly adopt deterministic
models, which not only neglects the style diversity in the expert-retouched
results and tends to learn an average style during training, but also lacks
sample diversity during inference. In this paper, we propose a diffusion-based
method, named DiffRetouch. Thanks to the excellent distribution modeling
ability of diffusion, our method can capture the complex fine-retouched
distribution covering various visual-pleasing styles in the training data.
Moreover, four image attributes are made adjustable to provide a user-friendly
editing mechanism. By adjusting these attributes in specified ranges, users are
allowed to customize preferred styles within the learned fine-retouched
distribution. Additionally, the affine bilateral grid and contrastive learning
scheme are introduced to handle the problem of texture distortion and control
insensitivity respectively. Extensive experiments have demonstrated the
superior performance of our method on visually appealing and sample diversity.
The code will be made available to the community.
