Denoising Diffusion Step-aware Models
Authors: Shuai Yang, Yukang Chen, Luozhou Wang, Shu Liu, Ying-Cong Chen
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations demonstrate that DDSM achieves computational savings of 49% for CIFAR-10, 61% for Celeb A-HQ, 59% for LSUN-bedroom, 71% for AFHQ, and 76% for Image Net, all without compromising the generation quality. |
| Researcher Affiliation | Collaboration | Shuai Yang 1,3 Yukang Chen 2 Luozhou Wang 1,3 Shu Liu 4 Yingcong Chen 1,5 1HKUST(GZ) 2CUHK 3HKUST(GZ) Smart More Joint Lab 4Smart More 5HKUST |
| Pseudocode | Yes | Algorithm 1 DDSM Training; Algorithm 2 DDSM Evolutionary Searching |
| Open Source Code | Yes | Our code and models are available at https://github.com/EnVision-Research/DDSM. |
| Open Datasets | Yes | We conduct experiments on five image datasets on different domains, ranging from small scale to large scale. They are CIFAR10 (Krizhevsky et al., 2009), Celeb A-HQ (64x64, 128x128) (Liu et al., 2015), LSUN-bedroom (Yu et al., 2016), AFHQ (Choi et al., 2020), and Image Net (Deng et al., 2009). |
| Dataset Splits | No | No explicit statement providing percentages or sample counts for training, validation, and test splits was found, although training data is mentioned. For the CIFAR-10 dataset, we utilized the standard split of 50,000 images designated for training. For the Celeb A-HQ-64, AFHQ, and LSUN-bedroom datasets, we adhered to the typical training data splits. |
| Hardware Specification | Yes | The GPU latency is the time cost of generating one image with a single NVIDIA RTX 3090. |
| Software Dependencies | No | No specific version numbers for software dependencies (e.g., pymoo, pytorch) are provided, only the names of the tools and their reference papers. |
| Experiment Setup | Yes | Regarding the sizes of the sub-networks, we offer seven different options, corresponding to 2/8 of the original ADM’s width... For the search parameters, the process encompasses a total of 10 iterations, with each iteration involving a population of 50, and maintaining a mutation rate of 0.001... For CIFAR-10, we set the weight parameter to 0.1 to favor higher image quality, while for Celeb A, the FLOPs weight parameter is adjusted to 0.25. |