Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation
Authors: Jianze Li, Jiezhang Cao, Yong Guo, Wenbo Li, Yulun Zhang
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments demonstrate that our method outperforms existing one-step diffusion-based Real-ISR methods. The code and model will be released at https://github. com/Jianze Li-114/Flux SR. 5. Experiments 5.1. Experimental Settings Training Datasets. 5.2. Comparison with State-of-the-Art Methods 5.3. Ablation Study |
| Researcher Affiliation | Collaboration | Jianze Li * 1 Shanghai Jiao Tong University Jiezhang Cao * 2 Harvard University Yong Guo 3 Huawei Consumer Business Group Wenbo Li 4 Huawei Noah s Ark Lab. Correspondence to: Yulun Zhang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Flux SR Training Procedure |
| Open Source Code | No | The code and model will be released at https://github. com/Jianze Li-114/Flux SR. |
| Open Datasets | Yes | We evaluate our model on the synthetic dataset DIV2K-val (Agustsson & Timofte, 2017) and two real datasets: Real SR (Cai et al., 2019) and Real Set65 (Yue et al., 2024). |
| Dataset Splits | Yes | Test Datasets. We evaluate our model on the synthetic dataset DIV2K-val (Agustsson & Timofte, 2017) and two real datasets: Real SR (Cai et al., 2019) and Real Set65 (Yue et al., 2024). From DIV2K-val, we use the Real ESRGAN degradation pipeline to generate corresponding LR images. On the these datasets, we evaluate using full-size images to assess the model s performance in real-world scenarios. |
| Hardware Specification | No | The paper mentions hardware in the context of limitations for other methods, not for its own experimental setup: "For example, we find that even a server with 8 A800-80GB GPUs cannot satisfy the memory requirement of this distillation if we directly apply the popular one-step distillation method OSEDiff (Wu et al., 2024a) on top of Flux.1-dev (Labs, 2023)." No specific hardware details are provided for the authors' experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software libraries, programming languages, or tools used in the experiments. |
| Experiment Setup | Yes | In this section, we use Real SR as the test dataset. The training iterations are set to 30k. Other settings remain consistent with those mentioned in Sec. 5.1. |