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..
Deep Equilibrium Approaches to Diffusion Models
Authors: Ashwini Pokle, Zhengyang Geng, J. Zico Kolter
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our method s strong performance across several datasets, including CIFAR10, Celeb A, and LSUN Bedroom and Churches.1 |
| Researcher Affiliation | Collaboration | Ashwini Pokle Carnegie Mellon University EMAIL Zhengyang Geng Carnegie Mellon University EMAIL Zico Kolter Carnegie Mellon University Bosch Center for AI EMAIL |
| Pseudocode | Yes | Algorithm 1 A naive algorithm to invert DDIM ... Algorithm 2 Inverting DDIM with DEQ |
| Open Source Code | Yes | Code is available at https://github.com/ashwinipokle/deq-ddim |
| Open Datasets | Yes | We consider four datasets that have images of different resolutions for our experiments: CIFAR10 (32 32) [46], Celeb A (64 64) [52], LSUN Bedroom (256 256) and LSUN Outdoor Church (256 256) [76]. |
| Dataset Splits | No | The paper mentions using well-known datasets (CIFAR10, Celeb A, LSUN) and refers to 'additional experimental details in the Appendix A', but the provided text does not explicitly state the training/validation/test splits, percentages, or sample counts used for these datasets. |
| Hardware Specification | Yes | All the experiments have been performed on NVIDIA RTX A6000 GPUs. |
| Software Dependencies | No | The paper mentions 'PyTorch-style pseudocode' and 'modern autograd packages' but does not specify exact version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For all the experiments, we use Anderson acceleration as the default fixed point solver. While training DEQs for model inversion, we use the 1-step gradient Eq. (18) to compute the backward pass. The damping factor for 1-step gradient is set to 0.1. For the forward pass of DEQ, we run Anderson solver for a maximum of 15 steps for each image. |