# Stanford's Spectral Progressive Diffusion Delivers Up to 7× Speedup on FLUX.1-dev

_Research · published 2026-05-21_

Stanford researchers have introduced Spectral Progressive Diffusion, a framework that exploits a known property of diffusion models — that low-frequency content is generated early in the denoising process and high-frequency detail emerges later — to progressively increase resolution only when it is needed. By avoiding expensive high-resolution computation during noise-dominated timesteps, the method cuts redundant self-attention work without requiring architectural changes to the underlying model.

In testing, the training-free version of the framework achieves up to 7.09× wall-clock speedup and a 7.36× FLOPs reduction on FLUX.1-dev for image generation, while a lightweight fine-tuning variant further improves quality on models such as Z-Image and PixelGen. The approach also extends to video: applied to WAN 2.1, it delivers more than 2× speedup while maintaining high-fidelity VBench scores. Code and a ComfyUI demo have been released alongside the arXiv paper.

## Sources
- [howardxiao.ca](https://howardxiao.ca/speed/)
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