  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.
