Edge‐preserving noise for diffusion models
Jente Vandersanden, Sascha Holl, Xingchang Huang, Gurprit Singh · Computer Graphics Forum · 2026
Abstract Classical diffusion models typically rely on isotropic Gaussian noise, treating all regions uniformly and overlooking structural information important for high‐quality generation. We introduce an edge‐preserving diffusion process that generalizes isotropic models via a hybrid noise scheme with an edge‐aware scheduler that smoothly transitions from edge‐preserving to isotropic noise. This enables the model to capture fine structural details while generally maintaining global performance. We evaluate the impact of structure‐aware noise in both diffusion and flow‐matching frameworks, and show that existing isotropic models can be efficiently fine‐tuned with edge‐preserving noise, making our framework practical for adapting pre‐trained systems. Beyond unconditional generation, our method particularly shows improvements in structure‐guided tasks such as stroke‐to‐image synthesis, improving robustness and perceptual quality, as evidenced by consistent improvements across FID, KID, and CLIP‐score.