Iteratively denoising random noise produces images as good as GANs but with better training stability — the foundation for Stable Diffusion, DALL-E 2, and modern image generation.
Demonstrates that diffusion models — which learn to reverse a gradual noising process — can generate high-quality images competitive with GANs. Establishes the theoretical and practical foundation for the diffusion model paradigm.