Parallel Sampling of Diffusion Models
Authors: Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, Nima Anari
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 {andyshih,belkhale,ermon,dorsa,anari}@cs.stanford.edu |
| 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. |