arXiv:2605.22723v1 Announce Type: new Abstract: A central error measure in Gaussian DDPMs is the path-space KL divergence between the exact reverse chain and the learned Gaussian reverse process. This quantity is especially relevant for procedures such as classifier guidance, which perturb the entire reverse trajectory rather than only the terminal sample. Prior analyses show that standard isotropic reverse covariances suffer an unavoidable $\Omega(1/T)$ path-KL error as the number of denoising steps $T$ grows. We show that matching the full posterior covariance breaks this barrier, yielding a
This research addresses a fundamental limitation in Diffusion Models (DDPMs) that is becoming critical as these models are deployed for more complex tasks requiring precision in generative outcomes.
Improving the accuracy and efficiency of DDPMs directly impacts the scalability and real-world applicability of generative AI, particularly for high-fidelity tasks like classifier guidance, which is crucial for controllable generation.
By matching the full posterior covariance, DDPMs can achieve significantly lower path-KL error, potentially leading to more stable, higher-quality, and computationally efficient generative processes.
- · AI researchers and developers
- · Companies using generative AI for high-fidelity outputs
- · Generative AI infrastructure providers
- · Generative models reliant on less sophisticated covariance handling
More accurate and stable Diffusion Models, especially for guided generation.
Accelerated development and adoption of generative AI in fields requiring precise control and high quality.
Reduced compute costs and faster inference for advanced generative tasks, making AI more accessible and powerful.
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Read at arXiv cs.LG