Denoising Diffusion Models are generative AI frameworks that synthesize images from noise through an iterative denoising process. They are celebrated for their exceptional image generation capabilities and diversity, largely attributed to text- or class-conditional guidance methods, including classifier guidance and classifier-free guidance. These models have been notably successful in creating diverse, high-quality images. Recent studies […] The post Self-Attention Guidance: Improving Sample Quality of Diffusion Models appeared first on Unite.AI.