Color Conditional Generation with Sliced Wasserstein Guidance
This addresses the challenge of color-conditional image generation for AI and creative applications, offering an incremental improvement over existing methods.
The paper tackles the problem of generating images with semantically meaningful colors conditioned on a reference color distribution, proposing SW-Guidance, a training-free method that modifies diffusion model sampling using the Sliced 1-Wasserstein distance, which outperforms state-of-the-art techniques in color similarity while maintaining semantic coherence.
We propose SW-Guidance, a training-free approach for image generation conditioned on the color distribution of a reference image. While it is possible to generate an image with fixed colors by first creating an image from a text prompt and then applying a color style transfer method, this approach often results in semantically meaningless colors in the generated image. Our method solves this problem by modifying the sampling process of a diffusion model to incorporate the differentiable Sliced 1-Wasserstein distance between the color distribution of the generated image and the reference palette. Our method outperforms state-of-the-art techniques for color-conditional generation in terms of color similarity to the reference, producing images that not only match the reference colors but also maintain semantic coherence with the original text prompt. Our source code is available at https://github.com/alobashev/sw-guidance/.