This study addresses the challenge of controlling the global color aspect of images generated by a diffusion model without training or fine-tuning. We rewrite the guidance equations to ensure that the outputs are closer to a known color map, without compromising the quality of the generation. Our method results in new guidance equations. In the context of color guidance, we show that the scaling of the guidance should not decrease but rather increase throughout the diffusion process. In a second contribution, our guidance is applied in a compression framework, where we combine both semantic and general color information of the image to decode at very low cost. We show that our method is effective in improving the fidelity and realism of compressed images at extremely low bit rates ($10^{-2}$bpp), performing better on these criteria when compared to other classical or more semantically oriented approaches. The implementation of our method is available on gitlab athttps://gitlab.inria.fr/tbordin/color-guidance.

Coding algorithms are usually designed to pixel-wisely reconstruct images, which limits the expected gains in terms of compression. In this work, we introduce a semantic compressed representation for images: CoCliCo. We encode the inputs into a CLIP latent vector and a tiny color map, and we use a conditional diffusion model for reconstruction. When compared to the most recent traditional and generative coders, our approach reaches drastic compression gains while keeping most of the high-level information and a good level of realism.

We propose a framework for image compression in which the fidelity criterion is replaced by a semantic and quality preservation objective. Encoding the image thus becomes a simple extraction of semantic, enabling to reach drastic compression ratio. The decoding side is handled by a generative model relying on the diffusion process for the reconstruction of images. We first propose to describe the semantic using low resolution segmentation maps as guide. We further improve the generation, introducing colors map guidance without retraining the generative decoder. We show that it is possible to produce images of high visual quality with preserved semantic at extremely low bitrates when compared with classical codecs.