LGTM: Training-Free Light-Guided Text-to-Image Diffusion Model via Initial Noise Manipulation

ArXi:2603.24086v1 Announce Type: new Diffusion models have nstrated high-quality performance in conditional text-to-image generation, particularly with structural cues such as edges, layouts, and depth. However, lighting conditions have received limited attention and remain difficult to control within the generative process. Existing methods handle lighting through a two-stage pipeline that relights images after generation, which is inefficient. Moreover, they rely on fine-tuning with large datasets and heavy computation, limiting their adaptability to new models and tasks.