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..
simple diffusion: End-to-end diffusion for high resolution images
Authors: Emiel Hoogeboom, Jonathan Heek, Tim Salimans
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Combining these simple yet effective techniques, we achieve stateof-the-art on image generation among diffusion models without sampling modi๏ฌers on Image Net. |
| Researcher Affiliation | Industry | 1Google Research, Brain Team, Amsterdam, Netherlands. |
| Pseudocode | Yes | Appendix B.2.1. PSEUDO-CODE FOR U-VIT MODULES |
| Open Source Code | No | The paper does not include an unambiguous statement or a direct link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The paper refers to using "Image Net" and "MSCOCO" datasets, which are well-known public datasets used in machine learning. |
| Dataset Splits | No | The paper mentions "train and eval data splits" but does not explicitly specify a distinct validation split with percentages, counts, or references to predefined validation sets. |
| Hardware Specification | Yes | The smaller U-Net models can be trained on 64 TPUv2 devices... The large U-Vi T models are all trained using 128 TPUv4 devices... |
| Software Dependencies | No | The paper mentions software like JAX and Flax but does not provide specific version numbers for these or any other ancillary software components used in the experiments. |
| Experiment Setup | Yes | Specific settings for the UNet on Image Net 128 experiment: base_channels =128, emb_channels =1024 , (for diffusion time , image class) channel_multiplier =[1, 2, 4, 8, 8], num_res_blocks =[3, 4, 4, 12, 4], (unless noted otherwise) attn_resolutions =[8, 16], num_heads =4, dropout_from_resolution =16, (unless noted otherwise) dropout =0.1, patching_type= none schedule ={ name : cosine_shifted , shift : 64} (unless noted otherwise) num_train_steps =1 _500_000 |