On the Robustness of Distribution Support under Diffusion Guidance
Researchers have established a theoretical foundation for the effectiveness of diffusion guidance in generative models. Their work demonstrates that guided diffusion processes, when provided with exact score functions, consistently generate samples that remain close to the target distribution's support. This robustness ensures that generated samples are structurally plausible and suitable for downstream applications. AI
IMPACT Provides a theoretical understanding of diffusion guidance, potentially improving the reliability and plausibility of generated samples in diffusion models.