One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation

[Jianze Li], Jiezhang Cao, Yong Guo, Wenbo Li, and Yulun Zhang*, "One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation", ICML, 2025

[[project]] [arXiv] [supplementary material] [pretrained models]

🔥🔥🔥 News

• 2025-02-03: This repo is released.

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Abstract: Diffusion models (DMs) have significantly advanced the development of real-world image super-resolution (Real-ISR), but the computational cost of multi-step diffusion models limits their application. One-step diffusion models generate high-quality images in a one sampling step, greatly reducing computational overhead and inference latency. However, most existing one-step diffusion methods are constrained by the performance of the teacher model, where poor teacher performance results in image artifacts. To address this limitation, we propose FluxSR, a novel one-step diffusion Real-ISR technique based on flow matching models. We use the state-of-the-art diffusion model FLUX.1-dev as both the teacher model and the base model. First, we introduce Flow Trajectory Distillation (FTD) to distill a multi-step flow matching model into a one-step Real-ISR. Second, to improve image realism and address high-frequency artifact issues in generated images, we propose TV-LPIPS as a perceptual loss and introduce Attention Diversification Loss (ADL) as a regularization term to reduce token similarity in transformer, thereby eliminating high-frequency artifacts. Comprehensive experiments demonstrate that our method outperforms existing one-step diffusion-based Real-ISR methods.

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Pipeline

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🔖 TODO

• Release testing code and pre-trained models.
• Release training code.
• Release pre-trained models.
• Provide HuggingFace demo.

🔗 Contents

1. Models
2. Training
3. Testing
4. Results
5. Citation
6. Acknowledgements

🔎 Results

We achieve impressive performance on Real-world Image Super-Resolution. The full results could be downloaded here: Google Drive

Quantitative Results (click to expand)

• Results in Table 1 of the main paper

• Results in Table 2 (RealSet65 testset) of the main paper

• Quantitative results (×4) on the Real-ISR testset with ground truth.

Datasets	PSNR ↑	SSIM ↑	LPIPS ↓	DISTS ↓	MUSIQ ↑	MANIQA ↑	TOPIQ ↑	QAlign ↑
RealSR	24.83	0.7175	0.3200	0.1910	68.95	0.5335	0.6699	4.3781
DRealSR	25.92	0.7592	0.3418	0.1628	37.82	0.5310	-	4.3356

• Quantitative results (×4) on the Real-ISR testset without ground truth.

Datasets	MUSIQ ↑	MANIQA ↑	TOPIQ ↑	QAlign ↑
RealLR200	71.60	0.5588	0.6814	4.4004
RealLQ250	72.65	0.5490	0.6848	4.4077

Qualitative Results (click to expand)

• Results in Figure 5 of the main paper

📎 Citation

If you find the code helpful in your research or work, please cite the following paper(s).

@inproceedings{li2025one,
  title={One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation},
  author={Li, Jianze and Cao, Jiezhang and Guo, Yong and Li, Wenbo and Zhang, Yulun},
  booktitle={ICML},
  year={2025}
}

💡 Acknowledgements

This project is based on FLUX.