Learning Illumination Control in Diffusion Models

ArXi:2604.24877v1 Announce Type: cross Controlling illumination in images is essential for photography and visual content creation. While closed-source models have nstrated impressive illumination control, open-source alternatives either require heavy control inputs like depth maps or do not release their data and code. We present a fully open-source and reproducible pipeline for learning illumination control in diffusion models. Our approach builds a data engine that transforms well-lit images into supervised.