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..
PromptFix: You Prompt and We Fix the Photo
Authors: yongsheng yu, Ziyun Zeng, Hang Hua, Jianlong Fu, Jiebo Luo
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that Prompt Fix outperforms previous methods in various image-processing tasks. |
| Researcher Affiliation | Collaboration | 1University of Rochester, 2Microsoft Research |
| Pseudocode | Yes | Algorithm 1 High-frequency Guidance Sampling. |
| Open Source Code | Yes | The dataset and code are available at https://www.yongshengyu.com/Prompt Fix-Page. |
| Open Datasets | Yes | The dataset and code are available at https://www.yongshengyu.com/Prompt Fix-Page. The dataset is available at https://huggingface.co/datasets/yeates/ Promptfix Data. |
| Dataset Splits | Yes | For the test set, we randomly select 300 image pairs for each task. We construct the validation dataset with 200 images, and each image contains 3 restoration tasks... |
| Hardware Specification | Yes | We train Prompt Fix for 46 epochs on 32 NVIDIA V100 GPUs |
| Software Dependencies | No | The paper mentions specific backbone models like Stable Diffusion 1.5 and Intern VL2 [15], but does not provide version numbers for general software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We train Prompt Fix for 46 epochs on 32 NVIDIA V100 GPUs, employing a learning rate of 1 10 4 with the Adam optimizer. The training input resolution is set to 512 512... we randomly drop the input image latent, instruction, and auxiliary prompt with a probability of 0.075 during training. The hyperparameter λ for the time-scale weight in Algorithm 1 is empirically set to 0.001. |