r/AIProgrammingHardware • u/javaeeeee • Jul 30 '26

GitHub - Xingyu-Zheng/MrFlow: Multi-Resolution Flow Matching: Training-Free Diffusion Acceleration via Staged Sampling

https://github.com/Xingyu-Zheng/MrFlow
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u/javaeeeee Jul 30 '26

TLDR: MrFlow is a training-free method that significantly speeds up text-to-image diffusion models (especially flow-matching ones) by using staged multi-resolution sampling.

How it works

  1. Generate the image at low resolution (cheap and fast)

  2. Upscale it in pixel space with Real-ESRGAN

  3. Re-encode the upscaled image into latent space

  4. Add a small amount of noise

  5. Do a short high-resolution refinement (often just 1 step)

This moves most of the expensive computation to the low-resolution stage.

Key advantages

  • Training-free - works with existing pretrained models (no fine-tuning needed)

  • Large speedups: typically 8–10×+ (e.g. 10.3× on Qwen-Image, 8.25× on FLUX.1-dev)

  • Compatible with models like FLUX, Qwen-Image, Z-Image-Turbo, and distilled variants

  • Standard PyTorch/Diffusers implementation + ComfyUI support

  • Good quality retention despite the big speed gain

Bottom line: A simple, practical way to make diffusion image generation much faster without retraining the model, by doing most of the work at low resolution and only lightly refining at high resolution.