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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Interpreting and Improving Diffusion Models from an Optimization Perspective
Authors: Frank Permenter, Chenyang Yuan
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 27,177 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,055 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. Experiments |
| Researcher Affiliation | Industry | 1Toyota Research Institute, Cambridge, Massachusetts, USA. Correspondence to: Chenyang Yuan <EMAIL>, Frank Permenter <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 DDIM sampler (Song et al., 2020a) Require: (σN, . . . , σ0), x N N(0, I), ϵθ Ensure: Compute x0 with N evaluations of ϵθ for t = N, . . . , 1 do xt 1 xt + (σt 1 σt)ϵθ(xt, σt) return x0 |
| Open Source Code | Yes | Code for the experiments is available at https: //github.com/Toyota Research Institute/ gradient-estimation-sampler |
| Open Datasets | Yes | We use denoisers from (Ho et al., 2020; Song et al., 2020a) that were pretrained on the CIFAR10 (32x32) and Celeb A (64x64) datasets (Krizhevsky et al., 2009; Liu et al., 2015). |
| Dataset Splits | No | The paper mentions using 'training images' and evaluating on the 'MS COCO validation set', but does not provide specific details on train/validation/test splits for the datasets used to train or evaluate their models. |
| Hardware Specification | Yes | All the experiments were run on a single Nvidia RTX 4090 GPU. |
| Software Dependencies | Yes | We also use Stable Diffusion 2.1 provided in https://huggingface.co/stabilityai/stable-diffusion-2-1. |
| Experiment Setup | Yes | For the CIFAR-10 and Celeb A models, we choose σ1 = q σDDIM(N) 1 and σ0 = 0.01. For CIFAR-10 N = 5, 10, 20, 50 we choose σN = 40 and for Celeb A N = 5, 10, 20, 50 we choose σN = 40, 80, 100, 120 respectively. For Stable Diffusion, we use the same sigma schedule as that in DDIM. ... We found that setting γ = 2 works well for N < 20; for larger N slightly increasing γ also improves sample quality (see Appendix E for more details). |