diffusion-models neural-networks machine-learning generative-models

Learning the Integral of a Diffusion Model

sander.ai

May 6, 2026

83 min read

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Summary

Sampling from a diffusion model involves an iterative process where a denoiser estimates the tangent direction to a path through input space. Neural networks can be trained to directly predict the integral that transforms samples from a simple noise distribution into samples from a target distribution.

Key Takeaways

Flow maps enable faster sampling from diffusion models by predicting any point on a path from any other point on that same path.
The development of flow maps builds on the principles of diffusion models, which define a bijection between noise and data with unique, non-crossing paths.
Various sampling algorithms for diffusion models exist, categorized into stochastic and deterministic methods.
Flow maps facilitate improved sampling steerability and more efficient reward-based learning in addition to faster sampling.

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