By tailoring noise schedules to each image's spectral content, you can generate higher-quality images with fewer denoising steps, making diffusion models faster and more efficient.
This paper proposes a smarter way to design noise schedules for diffusion models by analyzing the spectral properties of images. Instead of using the same handcrafted noise schedule for all images, the method creates custom schedules for each image that eliminate unnecessary denoising steps, improving generation quality especially when using fewer sampling steps.