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 this https URL.
Abstract (translated)
我们提出了一种名为SW-Guidance的方法，这是一种无需训练的图像生成方法，可以根据参考图像的颜色分布进行条件化。尽管可以通过先从文本提示创建图像然后再应用颜色风格迁移方法来生成固定颜色的图像，但这种方法通常会导致在生成的图像中出现语义上无意义的颜色。我们的方法通过修改扩散模型的采样过程来解决这一问题，该过程引入了生成图像与参考调色板之间可微分的切片1-沃瑟斯坦距离（Sliced 1-Wasserstein distance）。我们的方法在颜色相似性方面优于最先进的颜色条件生成技术，并且不仅匹配参考颜色，还能保持与原始文本提示的语义一致性。我们源代码的链接在此：[此URL]。（请将"[此URL]"替换为实际提供的具体网址）
URL
https://arxiv.org/abs/2503.19034
