OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models
Authors: Enshu Liu, Xuefei Ning, Zinan Lin, Huazhong Yang, Yu Wang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that OMS-DPM can find model schedules that improve generation quality and speed than prior state-of-the-art methods across CIFAR-10, Celeb A, Image Net, and LSUN datasets. When applied to the public checkpoints of the Stable Diffusion model, we are able to accelerate the sampling by 2 while maintaining the generation quality. Sec. 5: We experimentally validate OMS-DPM across a wide range of datasets, including CIFAR-10, Celeb A, Image Net-64 and LSUN-Church, and show that OMSDPM can achieve significantly better trade-offs on generation quality and speed than the baselines (Fig. 1). |
| Researcher Affiliation | Collaboration | 1Department of Electronic Engineering, Tsinghua University, Beijing, China 2Microsoft Research, Redmond, Washinton, U.S.A. |
| Pseudocode | Yes | Our complete search flow is shown at Algorithm 1. Time cost budget C should be given in advance. |
| Open Source Code | Yes | We have open-sourced our code at https://github.com/ jsttlgdkycy/OMS-DPM to allow the community to use OMS-DPM. |
| Open Datasets | Yes | We demonstrate that OMS-DPM can find model schedules that improve generation quality and speed than prior state-of-the-art methods across CIFAR-10, Celeb A, Image Net, and LSUN datasets. |
| Dataset Splits | No | The paper mentions 'training set' and 'validation set' in the context of splitting data for predictor training ('We split our generated schedule-FID dataset into two parts: training set and validation set.'), but it does not provide specific training/validation/test split percentages or sample counts for the main datasets (CIFAR-10, Celeb A, etc.) used to train the DPM models themselves. |
| Hardware Specification | Yes | The horizontal axis is the time cost of generating a batch of images evaluated on a single NVIDIA A100 GPU (10 NFEs is approximately equivalent to 1400ms latency). |
| Software Dependencies | No | The paper does not explicitly provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | All models are trained with 128 batch size for 800k iterations, with a learning rate of 2 10 4. We use 0.1 as dropout ratio and 0.9999 as EMA rate. |