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..
Parallel Sampling of Diffusion Models
Authors: Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, Nima Anari
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment with our method Para Di GMS on a suite of robotic control tasks [4] including Square [41], Push T, Franka Kitchen [7], and high-dimensional image generation models including Stable Diffusion-v2 [24] and LSUN Church and Bedroom [39]. |
| Researcher Affiliation | Academia | Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, Nima Anari Computer Science, Stanford University EMAIL |
| Pseudocode | Yes | In Algorithm 1 we present the complete procedure of Para Di GMS, incorporating sliding window over a batch, up-front sampling of noise, and tolerance of Picard iterations (Fig. 3). |
| Open Source Code | Yes | Code for our paper can be found at https://github.com/Andy Shih12/paradigms |
| Open Datasets | Yes | We experiment with our method Para Di GMS on a suite of robotic control tasks [4] including Square [41], Push T, Franka Kitchen [7], and high-dimensional image generation models including Stable Diffusion-v2 [24] and LSUN Church and Bedroom [39]. |
| Dataset Splits | No | The paper mentions 'evaluation episodes' and total sample counts for metrics (e.g., '5000 samples' for FID score), but it does not specify explicit train/validation/test splits with percentages or sample counts for dataset partitioning. |
| Hardware Specification | Yes | on a single A40 GPU. |
| Software Dependencies | No | The paper mentions 'PyTorch' and 'Diffusers library' but does not specify version numbers for these software components or any other ancillary software. |
| Experiment Setup | Yes | Each environment uses a prediction horizon of 16, and replanning horizon 8. The DDPM scheduler in Diffusion Policy [4] uses 100 step discretization, and the DDIM/DPMSolver schedulers use 15 step discretization. We use tolerance 5e-1 for DDPM and 1e-3 for DDIM. |