This study addresses the challenge of, without training or fine-tuning, controlling the global color aspect of images generated with a diffusion model. We rewrite the guidance equations to ensure that the outputs are closer to a known color map, and this without hindering the quality of the generation. Our method leads to new guidance equations. We show in the color guidance context that, the scaling of the guidance should not decrease but remains high throughout the diffusion process. In a second contribution, our guidance is applied in a compression framework, we combine both semantic and general color information on the image to decode the images at low cost. We show that our method is effective at improving fidelity and realism of compressed images at extremely low bit rates, when compared to other classical or more semantic oriented approaches.

该研究提出了一个在不使用训练或调整技术的情况下，控制扩散模型生成图像中全局颜色方面的挑战。通过修改指导方程，确保输出更接近已知的颜色映射，同时不影响生成图像的质量。该方法得到了新的指导方程。研究表明，在颜色指导上，指导的缩放不应该随着扩散过程的进行而减少，而应始终保持较高的缩放比例。作为第二项贡献，我们将指导方法应用于压缩框架，将图像的语义信息和一般颜色信息结合起来，以实现低成本的图像解码。与其他经典或更语义导向的方法相比，研究表明我们的方法在极低比特率下改善了压缩图像的保真度和逼真度。

扩散模型中的精细颜色引导及其在极低比特率图像压缩中的应用