Diffusion Models as Plug-and-Play Priors
Authors: Alexandros Graikos, Nikolay Malkin, Nebojsa Jojic, Dimitris Samaras
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We first explore the idea of generating conditional samples from an unconditional diffusion model on MNIST. We train the DDPM model of [7] on MNIST digits and experiment with different sets of constraints log c(x, y) to generate samples with specific attributes. |
| Researcher Affiliation | Collaboration | Alexandros Graikos Stony Brook University Stony Brook, NY agraikos@cs.stonybrook.edu Nikolay Malkin Mila, Université de Montréal Montréal, QC, Canada nikolay.malkin@mila.quebec Nebojsa Jojic Microsoft Research Redmond, WA jojic@microsoft.com Dimitris Samaras Stony Brook University Stony Brook, NY samaras@cs.stonybrook.edu |
| Pseudocode | Yes | Algorithm 1 Inferring a point estimate of p(x|y) δ(x η), under a DDPM prior and constraint. |
| Open Source Code | Yes | The code is available at https://github.com/Alex Graikos/diffusion_priors. |
| Open Datasets | Yes | We train the DDPM model of [7] on MNIST digit images and experiment with different sets of constraints log c(x, y) to generate samples with specific attributes. We utilize the pretrained DDPM network on FFHQ-256 [19] from [3] and a pretrained Res Net-18 face attribute classifier on Celeb A [25]. For this purpose, we use the Enviro Atlas dataset [32]. We use a dataset of Euclidean TSPs, with ground truth tours obtained by a state-of-the-art TSP solver [10], from [23]. |
| Dataset Splits | No | The main text does not explicitly provide specific percentages, counts, or methods for training/validation/test dataset splits. While the checklist indicates this information is in the Appendix, it is not present in the provided paper text. |
| Hardware Specification | No | The main text of the paper does not specify the exact hardware used (e.g., specific GPU or CPU models). The checklist indicates this information can be found in the Appendix, which is not provided. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). While deep learning frameworks are implied by the models used, no explicit versioning is provided in the main text. |
| Experiment Setup | Yes | Algorithm 1 outlines key experimental setup details: 'input pretrained DDPM ϵθ, auxiliary data y, constraint c, time schedule (ti)T i=1, learning rate λ'. It also specifies 'Initialize x N(0; I)' and shows the update rule including the learning rate: 'x x λ x[ ϵ ϵθ(xti, ti) 2 2 log c(x, y)]'. |