Deep Equilibrium Approaches to Diffusion Models
Authors: Ashwini Pokle, Zhengyang Geng, J. Zico Kolter
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 apokle@cs.cmu.edu Zhengyang Geng Carnegie Mellon University zgeng2@cs.cmu.edu Zico Kolter Carnegie Mellon University Bosch Center for AI zkolter@cs.cmu.edu |
| 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. |