This repository provides the implementation of MrFlow, a training-free staged sampling method for accelerating pretrained flow-matching text-to-image diffusion models.
MrFlow first samples a low-resolution image, upsamples the decoded result in pixel space with Real-ESRGAN, re-encodes the upsampled image, injects scheduler-consistent low-strength noise, and performs a short high-resolution refinement. The pipeline shifts most denoising cost from expensive high-resolution sampling to cheaper low-resolution sampling while preserving local detail quality.
MrFlow framework
✨ Highlights
Training-free deployment. No finetuning, learned upsampler, or model-specific retraining is required.
No custom kernels. The implementation uses standard PyTorch, Diffusers pipelines, and scheduler controls.
Strong aggressive-speed regime. MrFlow reaches more than 10x end-to-end speedup on Qwen-Image while preserving visual quality.
Works with distilled models. The same pipeline can be combined with pretrained timestep-distilled models such as Pi-Flow and FLUX-schnell.
Compact staged design. The implementation transfers across Qwen-Image, FLUX.1-dev, FLUX.2 Klein, and Z-Image families.
[2026/07] 🔥 The MrFlow paper is available on arXiv, and the source code is released.
🛠️ Installation
Create a Diffusers-compatible environment for the target backbone. The demos use:
PyTorch
Diffusers
Transformers
Real-ESRGAN
MrFlow uses Real-ESRGAN for x2 pixel-space super-resolution. Install Real-ESRGAN from the official project and download the x2 weights:
https://github.com/xinntao/Real-ESRGAN
The scripts contain placeholder checkpoint paths. Replace them with local paths to the pretrained text-to-image model and Real-ESRGAN x2 weights before running.
🚀 Quick Start
The repository root keeps only two minimal reference scripts plus the shared scheduler helper:
Script
Model
Setting
Output
qwen_image_mrflow.py
Qwen-Image
MrFlow 12plus1
outputs/qwen_image_mrflow_12plus1/
flux1_mrflow.py
FLUX.1-dev
MrFlow 12plus1
outputs/flux1_mrflow_12plus1/
Edit the checkpoint paths at the top of each script:
stage3_refined.png: final high-resolution refined image.
⚙️ Core Settings
Setting
Low-resolution steps
Refinement steps
Direct sigma
Typical use
12plus1
12
1
0.12
Aggressive acceleration.
20plus1
20
1
0.12
Higher-quality operating point.
The high-resolution refinement uses an explicit direct-sigma schedule. For example, 12plus1 denotes 12 low-resolution denoising steps followed by one high-resolution step from sigma=0.12 to 0.
📦 Supported Demos
Parameterized variants and additional model-family demos are available in examples/.
Script
Backbone
Notes
examples/flux1_mrflow.py
FLUX.1-dev
Training-free MrFlow.
examples/flux1_piflow_mrflow.py
FLUX.1-dev + Pi-Flow
Combines MrFlow with distilled weights.
examples/qwen_image_mrflow.py
Qwen-Image
Training-free MrFlow.
examples/qwen_image_piflow_mrflow.py
Qwen-Image + Pi-Flow
Combines MrFlow with distilled weights.
examples/flux2_mrflow.py
FLUX.2 Klein
Base and non-base variants.
examples/zimage_turbo_mrflow.py
Z-Image-Turbo
Reduced-step model plus MrFlow refinement.
Run all configured examples with:
bash examples/run_examples.sh
See examples/README.md for command-line usage, FLUX.2 Klein presets, Z-Image-Turbo refinement defaults, and output filename conventions.
Pi-Flow examples are optional and require a separate local checkout of LakonLab. Set LAKONLAB_ROOT to that checkout before running the Pi-Flow scripts.
🧩 ComfyUI Plugin
MrFlow ComfyUI Plugin
The repository also includes ComfyUI-MrFlow/, a ComfyUI custom-node extension for Qwen-oriented MrFlow workflows. It provides helper nodes, editable workflow and API JSON examples, a reusable subgraph, and a model-link helper for split Qwen-Image bundles.
To use it, place or symlink ComfyUI-MrFlow/ into ComfyUI/custom_nodes/, restart ComfyUI, and open ComfyUI-MrFlow/examples/qwen_mrflow_workflow.json or load ComfyUI-MrFlow/subgraphs/qwen_mrflow.json.
🖼️ Results
Qwen-Image generation examples. With 12 low-resolution steps and one high-resolution refinement step, MrFlow produces diverse 1024-resolution samples on Qwen-Image while reaching above 10x end-to-end speedup.
MrFlow Qwen-Image samples
Accuracy-efficiency trade-off. On FLUX.1-dev and Qwen-Image, MrFlow offers a flexible trade-off between generation quality and measured end-to-end speedup, and remains effective where other training-free strategies degrade sharply.
MrFlow trade-off curve
Runtime breakdown. For Qwen-Image 12plus1, measured end-to-end latency is 4.77s versus 49.32s for native 50-step inference. The main cost is shifted from high-resolution sampling to cheaper low-resolution sampling, while SR and VAE overhead remain small.
MrFlow runtime breakdown
📊 Representative Numbers
Backbone
Setting
End-to-end speedup
FLUX.1-dev
12 + 1
8.25x
Qwen-Image
12 + 1
10.3x
FLUX.2 Klein Base 9B
12 + 1
8.79x
Z-Image-Turbo
8 + 1
21.0x
Qwen-Image + Pi-Flow
4 + 1
up to 25x
Speedups are measured end to end, including text encoding, VAE encode/decode, super-resolution, noise preparation, and diffusion forward passes.
1@misc{zheng2026multiresolutionflowmatchingtrainingfree,
2 title={Multi-Resolution Flow Matching: Training-Free Diffusion Acceleration via Staged Sampling},
3 author={Xingyu Zheng and Xianglong Liu and Yifu Ding and Weilun Feng and Junqing Lin and Jinyang Guo and Haotong Qin},
4 year={2026},
5 eprint={2607.01642},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV},
8 url={https://arxiv.org/abs/2607.01642},
9}
🙏 Acknowledgements
This implementation builds on the Diffusers ecosystem and uses Real-ESRGAN for pixel-space super-resolution.
