Efficient Conditional Diffusion Model with Probability Flow Sampling for Image Super-resolution
Authors: Yutao Yuan, Chun Yuan
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on DIV2K, Image Net, and Celeb A demonstrate that our method achieves higher super-resolution quality than existing diffusion-based image super-resolution methods while having lower time consumption. |
| Researcher Affiliation | Academia | Yutao Yuan, Chun Yuan Tsinghua University yuanyt21@mails.tsinghua.edu.cn, yuanc@sz.tsinghua.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks clearly labeled as such. |
| Open Source Code | Yes | Our code is available at https://github.com/Yuan-Yutao/ECDP. |
| Open Datasets | Yes | For general image super-resolution (4 ), we evaluate the performance of various methods on two datasets, DIV2K (Agustsson and Timofte 2017) and Image Net (Russakovsky et al. 2015). ... For face image super-resolution (8 ), we train and evaluate on Celeb A. |
| Dataset Splits | No | The paper describes data usage for training and evaluation (e.g., 'trained using random HR image crops', 'evaluated with full-size images', 'center cropped and resized'), but does not explicitly provide quantitative train/validation/test dataset splits (e.g., percentages or exact counts) or reference to standard numerical splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components and architectures like 'U-Net architecture', 'Big GAN residual blocks', 'VGG network', and 'Runge Kutta ODE solver', but does not provide specific version numbers for any of these or other software dependencies. |
| Experiment Setup | Yes | In our experiments, we set β(t) in our forward process (5) to a linear function increasing from β(0) = 0.1 and β(T) = 20, matching the settings in VP-SDE (Song et al. 2021). ... To generate HR images, we perform probability flow sampling using a standard Runge Kutta ODE solver with absolute and relative error tolerance of 10^-4. |