Denoising Diffusion Implicit Models
Authors: Jiaming Song, Chenlin Meng, Stefano Ermon
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we show that DDIMs outperform DDPMs in terms of image generation when fewer iterations are considered, giving speed ups of 10 to 100 over the original DDPM generation process. |
| Researcher Affiliation | Academia | Jiaming Song, Chenlin Meng & Stefano Ermon Stanford University {tsong,chenlin,ermon}@cs.stanford.edu |
| Pseudocode | No | The paper describes procedures using mathematical equations and derivations, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide explicit statements about the release of its own source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | We consider 4 image datasets with various resolutions: CIFAR10 (32 32, unconditional), Celeb A (64 64), LSUN Bedroom (256 256) and LSUN Church (256 256). |
| Dataset Splits | No | The paper mentions using specific datasets and a test set for evaluation, but does not provide explicit details about the training, validation, and test dataset splits (e.g., percentages, sample counts, or a clear splitting methodology). |
| Hardware Specification | Yes | For example, it takes around 20 hours to sample 50k images of size 32 32 from a DDPM, but less than a minute to do so from a GAN on a Nvidia 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions software components like 'U-Net' and 'Wide Res Net' but does not provide specific version numbers for these or any other software dependencies, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | For all datasets, we set the hyperparameters α according to the heuristic in (Ho et al., 2020) to make the results directly comparable. We use the same model for each dataset, and only compare the performance of different generative processes. For CIFAR10, Bedroom and Church, we obtain the pretrained checkpoints from the original DDPM implementation; for Celeb A, we trained our own model using the denoising objective L1. |