Color Conditional Generation with Sliced Wasserstein Guidance

• URL: http://arxiv.org/abs/2503.19034v1
• Date: Mon, 24 Mar 2025 18:06:03 GMT
• Title: Color Conditional Generation with Sliced Wasserstein Guidance
• Authors: Alexander Lobashev, Maria Larchenko, Dmitry Guskov,
• Abstract summary: SW-Guidance is a training-free approach for image generation conditioned on the color distribution of a reference image.<n>Our method outperforms state-of-the-art techniques for color-conditional generation in terms of color similarity to the reference.
• Score: 44.99833362998488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/.

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