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..
EM Distillation for One-step Diffusion Models
Authors: Sirui Xie, Zhisheng Xiao, Diederik Kingma, Tingbo Hou, Ying Nian Wu, Kevin P. Murphy, Tim Salimans, Ben Poole, Ruiqi Gao
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | EMD outperforms existing one-step generative methods in terms of FID scores on Image Net-64 and Image Net-128, and compares favorably with prior work on distilling text-to-image diffusion models. |
| Researcher Affiliation | Collaboration | 1Google Deep Mind 2Google Research 3UCLA |
| Pseudocode | Yes | Algorithm 1: EM Distillation |
| Open Source Code | No | We have not open sourced the model or code, but our approach is data-free so no training data is required. We also provide implementation details in the appendix that we hope are sufficient for reproducing our results. |
| Open Datasets | Yes | We employ EMD to learn one-step image generators on Image Net 64 64, Image Net 128 128 [60] and text-to-image generation. |
| Dataset Splits | No | The paper does not explicitly state details about the validation dataset split (e.g., percentages, sample counts, or explicit mention of a validation set used in their specific experiments), beyond referencing the overall datasets. |
| Hardware Specification | Yes | We run the distillation training for 300k steps (roughly 8 days) on 64 TPU-v4. We run the distillation training for 200k steps (roughly 10 days) on 128 TPU-v5p. Our method, EMD-8, trained on 256 TPU-v5e for 5 hours (5000 steps)... |
| Software Dependencies | No | The paper discusses software components and models (e.g., Stable Diffusion v1.5, Adam optimizer) but does not list specific version numbers for software dependencies required for replication. |
| Experiment Setup | Yes | We list other hyperparameters in Table 7. We list other hyperparameters in Table 8. We list other hyperparameters in Table 9. |