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..
Diffusion Rejection Sampling
Authors: Byeonghu Na, Yeongmin Kim, Minsang Park, Donghyeok Shin, Wanmo Kang, Il-Chul Moon
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate the state-of-the-art performance of Diff RS on the benchmark datasets and the effectiveness of Diff RS for fast diffusion samplers and large-scale text-to-image diffusion models. |
| Researcher Affiliation | Collaboration | 1Department of Industrial & Systems Engineering, KAIST, Daejeon, Republic of Korea 2summary.ai, Daejeon, Republic of Korea. Correspondence to: Il-Chul Moon <EMAIL>, Byeonghu Na <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 One Step Diff RS (t, xt+1, Lt+1), Algorithm 2 Re-initialization(t + 1, xt), Algorithm 3 Diffusion Rejection Sampling (Diff RS) |
| Open Source Code | Yes | Our code is available at https: //github.com/aailabkaist/Diff RS. |
| Open Datasets | Yes | CIFAR-10 (Krizhevsky, 2009), and Image Net 64 64 and 256 256 (Deng et al., 2009)., COCO (Lin et al., 2014) |
| Dataset Splits | No | The paper uses standard benchmark datasets like CIFAR-10 and ImageNet, which typically have predefined splits. However, it does not explicitly state the specific train/validation/test split percentages, sample counts, or the methodology for creating these splits within the paper for its experiments. |
| Hardware Specification | Yes | Our discriminator is trained on a single NVIDIA Ge Force RTX 4090 GPU using CUDA 11.8 and Py Torch 1.12 versions., For the benchmark datasets, we utilize a single NVIDIA Ge Force RTX 4090 GPU, CUDA 11.8, and Py Torch 1.12. For the text-to-image generation, we use a single NVIDIA L40S GPU with CUDA 11.8 and Py Torch 2.1. |
| Software Dependencies | Yes | Our discriminator is trained on a single NVIDIA Ge Force RTX 4090 GPU using CUDA 11.8 and Py Torch 1.12 versions., For the benchmark datasets, we utilize a single NVIDIA Ge Force RTX 4090 GPU, CUDA 11.8, and Py Torch 1.12. For the text-to-image generation, we use a single NVIDIA L40S GPU with CUDA 11.8 and Py Torch 2.1. |
| Experiment Setup | Yes | Table 5. Configurations of the discriminator., Batch size 128, # Epoch 60, Table 6. Configuration details for each experimental result., Rejection percentile γ, Max. iteration K |