Nearly d-linear convergence bounds for diffusion models via stochastic localization
Denoising diffusions are a powerful method to generate approximate samples from high-dimensional data distributions. Recent results provide polynomial bounds on their convergence rate, assuming L 2 -accurate scores. Until now, the tightest bounds were either superlinear in the data dimension or requ...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
OpenReview
2024
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