Multi-Resolution Flow Matching: Training-Free Diffusion Acceleration via Staged Sampling
Summary
MrFlow is a training-free multi-resolution acceleration strategy for flow-matching text-to-image models that combines low-resolution generation with pixel-space super-resolution and noise injection, achieving up to 25x end-to-end speedup without training or runtime modifications.
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Paper page - Multi-Resolution Flow Matching: Training-Free Diffusion Acceleration via Staged Sampling
Source: https://huggingface.co/papers/2607.01642
Abstract
MrFlow accelerates text-to-image diffusion by combining low-resolution generation with pixel-space super-resolution and noise injection, achieving up to 25x speedup without training or runtime modifications.
Hardware-agnostic strategies for acceleratingtext-to-image diffusion, such astimestep distillationand feature caching, can reduce inference time without custom kernels or system-level optimization. Among them,multi-resolution generationstrategies have recently received broad attention, attaining more than 5x speedup without any training. However, the design of performing upsampling in the latent space, together with the selective modification of partial regions, causes these methods to exhibit noticeable blurring or artifacts. To this end, we propose MrFlow, a training-free multi-resolution acceleration strategy for pretrainedflow-matching modelsbuilt upon astaged low-to-high-resolution pipeline. MrFlow first rapidly generates the main structure at low resolution, then performssuper-resolutionin the pixel space using a lightweightpretrained GAN-based model, subsequently injects low-strength noise to enable high-frequency resampling, and finally refines the details at high resolution. Quantitative and qualitative results on FLUX.1-dev and Qwen-Image show that MrFlow exploits thequadratic token reductionand reduced step requirement of low-resolution sampling to achieve 10x end-to-end acceleration while keeping OneIG within a 1% gap relative to that before acceleration, significantly surpassing other training-free acceleration strategies, and requiring no training or runtime dynamic identification whatsoever. MrFlow can further be directly combined orthogonally with pre-trainedtimestep distillationstrategies, achieving even higher generation acceleration of up to 25x.
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