Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models
Authors: Enshu Liu, Xuefei Ning, Zinan Lin, Huazhong Yang, Yu Wang
ICML 2023 | Venue PDF | 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. |