Directly Denoising Diffusion Models
Authors: Dan Zhang, Jingjing Wang, Feng Luo
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we demonstrate the effectiveness of DDDMs across various image datasets including CIFAR-10 (Krizhevsky et al., 2009), and Image Net 64x64 (Deng et al., 2009), and observe comparable results to current state-of-the-art methods. Our model achieves FID scores of 2.57 and 2.33 on CIFAR-10 in one-step and two-step sampling respectively. By extending the sampling to 1000 steps, we further reduce FID score to 1.79. |
| Researcher Affiliation | Academia | 1School of Computing, Clemson University, USA. |
| Pseudocode | Yes | Algorithm 1 Training Algorithm 2 Sampling |
| Open Source Code | Yes | Our code is available at https://github.com/ The Luo Feng Lab/DDDM. |
| Open Datasets | Yes | To evaluate our method for image generation, we train several DDDMs on CIFAR-10 (Krizhevsky et al., 2009) and Image Net 64x64 (Deng et al., 2009) and benchmark their performance with competing methods in the literature. |
| Dataset Splits | No | The paper states that FID is computed between 50K generated samples and the whole training set, but does not specify the explicit train/validation/test dataset splits used for their model training and evaluation. |
| Hardware Specification | Yes | All models are trained on 8 Nvidia A100 GPUs. |
| Software Dependencies | No | The paper mentions using 'Adam' for experiments but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | For CIFAR-10, we set T = 1000 for baseline model and train the model for 1000 epochs with a constant learning rate of 0.0002 and batch size of 1024. ... For Image Net 64x64, ... we train the model for 520 epochs with a constant learning rate of 0.0001 and batch size of 1024. We use an exponential moving average (EMA) of the weights during training with a decay factor of 0.9999 for all the experiments. |