Boosting Diffusion Models with an Adaptive Momentum Sampler
Authors: Xiyu Wang, Anh-Dung Dinh, Daochang Liu, Chang Xu
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results on multiple benchmarks demonstrate that our proposed reverse sampler yields remarkable improvements over different baselines. |
| Researcher Affiliation | Academia | Xiyu Wang, Anh-Dung Dinh, Daochang Liu, Chang Xu School of Computer Science, Faculty of Engineering, The University of Sydney, Australia xiyuwang.usyd@gmail.com, adin6536@uni.sydney.edu.au, daochang.liu@sydney.edu.au, c.xu@sydney.edu.au |
| Pseudocode | Yes | Algorithm 1 illustrates the adaptive momentum sampler process. |
| Open Source Code | Yes | The code is publicly available at github.com/Shiny Gua/DPMs-with-Adam |
| Open Datasets | Yes | We utilize CIFAR10 [Krizhevsky et al., 2009] (32 × 32), Celeb A [Liu et al., 2018] (64 × 64), Image Net [Deng et al., 2009] (64 × 64), LSUN [Yu et al., 2015] (256 × 256) and Celeb A-HQ [Karras et al., 2017] (256 × 256) in experiments. |
| Dataset Splits | No | The paper states the datasets used and sampling steps, but does not specify train/validation/test splits, absolute sample counts for splits, or cross-validation setup for reproducing the data partitioning. |
| Hardware Specification | Yes | Our experiments run on one node with 8 NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper discusses various models and frameworks (DDIM, DDPM, Analytic-DPM, LDM) and states that pre-trained models are used, but it does not provide specific version numbers for software dependencies such as programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | The sampling steps are set to 4000 on Image Net and 1000 on other datasets. For the Eq. 5, we select three η values... All the hyperparameters for the sampling process are presented in Appendix. Also, Table 4 investigates the effects of b and c hyperparameters. |