YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy

Organizes papers into hierarchical taxonomies across algorithm enhancements (sampling acceleration, likelihood optimization, manifold/discrete data handling) and applications spanning computer vision, NLP, time-series, multimodal generation, and molecular modeling. Tracks connections between diffusion models and other generative frameworks (VAEs, GANs, normalizing flows, energy-based models), with coverage of emerging integration patterns with large language models and reinforcement learning approaches. Maintains synchronized updates with an ACM Computing Surveys publication to reflect the rapidly evolving diffusion model landscape.

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