Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution
Authors: Yiyang Ma, Huan Yang, Wenhan Yang, Jianlong Fu, Jiaying Liu
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on both bicubic-SR and real-SR degradation settings. For bicubic-SR, we train a vanilla diffusion-based SR model which simply concatenates LR images with noisy images xt as the architecture proposed in SR3 (Saharia et al., 2022c). For real-SR, we apply our method to Stable SR (Wang et al., 2023), which finetunes pre-trained Stable Diffusion (Rombach et al., 2022) on real-SR data. Experiments show that the quality of SR images sampled by few-step diffusion ODE samplers with our explored BC x T significantly outperforms the quality of results sampled by existing methods owning the same architecture. |
| Researcher Affiliation | Collaboration | 1Wangxuan Institute of Computer Technology, Peking University, 2Microsoft Research, 3Pengcheng Laboratory |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides links to the source codes of 'compared methods' (third-party baselines) but does not provide concrete access to their own source code for the methodology described in this paper. |
| Open Datasets | Yes | For bicubic-SR, we train the model on the widely-used dataset DF2k (Agustsson & Timofte, 2017; Lim et al., 2017)... To test the performances of bicubic-SR, we use 3 different datasets containing DIV2k-test (Agustsson & Timofte, 2017), Urban100 (Huang et al., 2015), B100 (Martin et al., 2001). |
| Dataset Splits | No | The paper specifies the datasets used for training and testing, and how test data was prepared, but does not explicitly provide information about a validation split for their model's training process. |
| Hardware Specification | Yes | The total training cost is about 2000 Tesla V100 GPU hours. |
| Software Dependencies | No | The paper mentions 'torchvision (Py Torch-Contributors, 2017)' but does not provide specific version numbers for PyTorch or other key software dependencies. |
| Experiment Setup | Yes | We first train the model for 2M iterations with a batch size of 16, then train the model for another 1M iterations with a batch size of 64, ensuring the convergence of our model. We use Adam optimizer (Kingma & Ba, 2015) during the whole training process and use mixed precision to accelerate training. In practice, the R and K are set to 300 and 1,000 respectively for both bicubic-SR and real-SR. |