simple diffusion: End-to-end diffusion for high resolution images
Authors: Emiel Hoogeboom, Jonathan Heek, Tim Salimans
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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 modiļ¬ers 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 |