Instructions to use frozen2001/RFMSR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use frozen2001/RFMSR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("frozen2001/RFMSR", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| pipeline_tag: image-to-image | |
| # RFMSR: Residual Flow Matching for Image Super-Resolution | |
| This repository contains the weights for **RFMSR** (Residual Flow Matching for Image Super-Resolution), a vision-only super-resolution framework that utilizes a residual flow design centering the source distribution at the low-quality (LQ) latent to preserve structural priors. | |
| [π Paper](https://huggingface.co/papers/2607.12753) | [π» GitHub Repository](https://github.com/Faze-Hsw/RFMSR) | |
| ## Method Overview | |
| RFMSR centers the source distribution at the LR latent, reducing the transport distance and preserving structural priors throughout the flow trajectory. It uses a two-phase training strategy to achieve high-quality single-step generation without sacrificing multi-step refinement. | |
| <p align="center"> | |
| <img src="https://raw.githubusercontent.com/Faze-Hsw/RFMSR/main/assets/overview.png" alt="RFMSR overview" width="100%"> | |
| </p> | |
| ## Pretrained Weights | |
| This repository hosts the following model checkpoints: | |
| - `rfmsr.safetensors` β Phase I: Multi-step Flow Matching model (15-step recommended) | |
| - `rfmsr_os.safetensors` β Phase II: One-step model | |
| - `rfmsr_consistency.safetensors` β Consistency distillation: One-step model | |
| ## Quick Start (Inference) | |
| To run inference, please clone the official GitHub repository and install the dependencies: | |
| ```bash | |
| git clone https://github.com/Faze-Hsw/RFMSR.git | |
| cd RFMSR | |
| conda create -n rfmsr python=3.12 -y | |
| conda activate rfmsr | |
| pip install -r requirements.txt | |
| ``` | |
| Download the checkpoints via `huggingface-cli`: | |
| ```bash | |
| pip install huggingface_hub | |
| huggingface-cli download frozen2001/RFMSR ckpts/ --local-dir . --local-dir-use-symlinks False | |
| ``` | |
| Run super-resolution inference on your images: | |
| ```bash | |
| # One-step inference (fast, recommended) | |
| python infer_rfmsr.py --input input.png --steps 1 | |
| # Multi-step inference (higher quality) | |
| python infer_rfmsr.py --input input.png --steps 15 | |
| ``` | |
| ## Citation | |
| If you find this work useful, please cite: | |
| ```bibtex | |
| @misc{huang2026rfmsrresidualflowmatching, | |
| title={RFMSR: Residual Flow Matching for Image Super-Resolution}, | |
| author={Shuwei Huang and Tianyao Luo and Jicheng Liu and Daizong Liu and Pan Zhou}, | |
| year={2026}, | |
| eprint={2607.12753}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV}, | |
| url={https://arxiv.org/abs/2607.12753}, | |
| } | |
| ``` |