Retinex-Diffusion: On Controlling Illumination Conditions in Diffusion Models via Retinex Theory
<|reference_start|>Retinex-Diffusion: On Controlling Illumination Conditions in Diffusion Models via Retinex Theory: This paper introduces a novel approach to illumination manipulation in diffusion models, addressing the gap in conditional image generation with a focus on lighting conditions. We conceptualize the diffusion model as a black-box image render and strategically decompose its energy function in alignment with the image formation model. Our method effectively separates and controls illumination-related properties during the generative process. It generates images with realistic illumination effects, including cast shadow, soft shadow, and inter-reflections. Remarkably, it achieves this without the necessity for learning intrinsic decomposition, finding directions in latent space, or undergoing additional training with new datasets.<|reference_end|>
@article{xing2024retinex-diffusion:,
    title={Retinex-Diffusion: On Controlling Illumination Conditions in Diffusion
  Models via Retinex Theory},
    author={Xiaoyan Xing, Vincent Tao Hu, Jan Hendrik Metzen, Konrad Groh, Sezer
  Karaoglu, Theo Gevers},
    journal={arXiv preprint arXiv:2407.20785},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20785},
    primaryClass={cs.CV}
}