Quotient-space diffusion models reduce learning complexity for symmetric generative tasks by formally accounting for group symmetries, enabling better molecular and protein structure generation without learning redundant symmetric variations.
This paper introduces a mathematical framework for diffusion models that accounts for symmetries in generative tasks, particularly molecular structure generation. By modeling distributions on quotient spaces (which treat symmetric objects as equivalent), the approach simplifies learning compared to existing symmetry-aware methods and guarantees correct sampling of target distributions.