Elucidating the design space of classifier-guided diffusion generation
Authors: Jiajun Ma, Tianyang Hu, Wenjia Wang, Jiacheng Sun
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on Image Net validate our proposed method, showing that state-of-the-art (SOTA) diffusion models (DDPM, EDM, Di T) can be further improved (up to 20%) using off-the-shelf classifiers with barely any extra computational cost. |
| Researcher Affiliation | Collaboration | Jiajun Ma1,2, Tianyang Hu3 , Wenjia Wang1,2, Jiacheng Sun3 1Hong Kong University of Science and Technology 2Hong Kong University of Science and Technology (Guangzhou) 3Huawei Noah s Ark Lab |
| Pseudocode | Yes | The guided sampling algorithm is outlined in Algorithm 1 and the hyper-parameter settings can be found in Appendix C.3. |
| Open Source Code | Yes | The code is available at https://github.com/Alex Ma OLS/Elu CD/tree/main. |
| Open Datasets | Yes | Extensive experiments on Image Net validate our proposed method... All models generate 50,000 Image Net 128x128 samples with 250 DDPM steps. |
| Dataset Splits | No | The paper mentions generating samples for “evaluation” (e.g., “We generate 50,000 Image Net 128x128 samples for evaluation.”), which implies a testing phase, but does not provide specific training/validation splits or percentages for the datasets used. |
| Hardware Specification | Yes | Sampling time is recorded as GPU hours on NVIDIA V100. |
| Software Dependencies | No | The paper mentions “Pytorch Res Net checkpoints” and “PyTorch Res Net-50 and Res Net-101”, but does not provide specific version numbers for PyTorch or other software dependencies. |
| Experiment Setup | No | The paper states that “the hyper-parameter settings can be found in Appendix C.3.” but does not provide specific hyperparameter values or training configurations in the main text. |