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..
Fast Sampling of Diffusion Models with Exponential Integrator
Authors: Qinsheng Zhang, Yongxin Chen
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments to validate the efficacy of DEIS. For instance, with a pre-trained model (Song et al., 2020b), DEIS is able to reach 4.17 FID with 10 NFEs, and 2.86 FID with 20 NFEs on CIFAR10. |
| Researcher Affiliation | Academia | Qinsheng Zhang Georgia Institute of Technology EMAIL Yongxin Chen Georgia Institute of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1 t AB-DEIS |
| Open Source Code | Yes | Project page and code: https://qsh-zh.github.io/deis. |
| Open Datasets | Yes | For instance, with a pre-trained model (Song et al., 2020b), DEIS is able to reach 4.17 FID with 10 NFEs, and 2.86 FID with 20 NFEs on CIFAR10. (Abstract), 64 64 Celeb A (Liu et al., 2015) with pre-trained model from Song et al. (2020a), class-conditioned 64 64 Image Net (Deng et al., 2009) with pre-trained model (Dhariwal & Nichol, 2021) |
| Dataset Splits | No | No specific dataset split information (percentages, sample counts, or explicit methodology) was found. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) were provided for the experimental setup. |
| Software Dependencies | No | We implemented our approach in Jax and Py Torch. |
| Experiment Setup | Yes | Due to numerical issues, we set ending time t0 in DMs during sampling a non-zero number. Song et al. (2020b) suggests t0 = 10-3 for VPSDE and t0 = 10-5 for VESDE. One such option is the quadratic timestep suggested in (Song et al., 2020a) |