Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs
Authors: qinpeng cui, yixuan liu, Xinyi Zhang, Qiqi Bao, Qingmin Liao, liwang Amd, Lu Tian, Zicheng Liu, Zhongdao Wang, Emad Barsoum
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate that our proposed method achieves state-of-the-art performance on synthetic and real-world datasets, while notably requiring only 5 sampling steps. |
| Researcher Affiliation | Collaboration | 1Advanced Micro Devices Inc. 2Tsinghua University |
| Pseudocode | Yes | Appendix B Pseudocode |
| Open Source Code | Yes | Code: https://github.com/AMD-AIG-AIMA/Do SSR |
| Open Datasets | Yes | For training, we train our Do SSR using a variety of datasets including DIV2K [1], DIV8K [15], Flickr2K [43], and OST [47]. |
| Dataset Splits | Yes | For synthetic data, we randomly crop 3K patches with a resolution of 512 512 from the DIV2K validation set [1] |
| Hardware Specification | Yes | The latency is calculated on the 4 SR task for 128 128 LR images with V100 GPU. |
| Software Dependencies | No | The paper mentions 'Stable Diffusion 2.1-base3' and 'Adam optimizer [20]' but does not provide specific software versions for key dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | The model is fine-tuned for 50k iterations using the Adam optimizer [20], with a batch size of 32 and a learning rate set to 5 10 5, on 512 512 resolution images. |