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arXiv · 2407.20785

Retinex-Diffusion: On Controlling Illumination Conditions in Diffusion Models via Retinex Theory

Xiaoyan Xing·Vincent Tao Hu·Jan Hendrik Metzen·Konrad Groh·Sezer Karaoglu·Theo Gevers
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Abstract

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.

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Xiaoyan Xing, Vincent Tao Hu, Jan Hendrik Metzen, Konrad Groh, Sezer Karaoglu, Theo Gevers. 2024-07-29. Retinex-Diffusion: On Controlling Illumination Conditions in Diffusion Models via Retinex Theory. https://arxiv.org/abs/2407.20785

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