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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..

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 | Venue PDF | 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.