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Westlake University
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Low generative perplexity in diffusion models often masks excessive repetition, with a simple fix cutting repetition to human levels while being 1.5–5x cheaper.
Ditch the equivariant constraints: canonicalization lets you train simpler, faster diffusion models that actually *outperform* equivariant architectures for symmetric generative tasks like 3D molecule design.
