High-accuracy and dimension-free sampling with diffusions
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A new solver for diffusion models has been developed that significantly enhances sampling efficiency. Unlike previous methods, which required polynomially scaling iterations with accuracy, this approach achieves a polylogarithmic scaling in accuracy ($1/\varepsilon$), providing a high-accuracy guarantee while being dimension-independent, relying only on the effective radius of the target distribution's support.
New Solver Enhances Efficiency of Diffusion Models in Sampling
A groundbreaking solver has emerged that significantly improves sampling efficiency from complex multi-modal distributions in diffusion models. This approach combines low-degree approximation and the collocation method to tackle the long-standing issue of iteration complexity in diffusion model discretization.
The new solver exhibits polylogarithmic scaling in relation to the inverse accuracy, 1/ε, contrasting with previous methods that scaled polynomially. It offers the first high-accuracy guarantee for diffusion-based samplers using approximate access to the scores of the data distribution. Notably, its complexity is independent of the ambient dimension and influenced solely by the effective radius of the target distribution's support.
This development could lead to more efficient sampling techniques in the research and application of diffusion models.
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📰 Original Source: https://arxiv.org/abs/2601.10708v1
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