On Inference Stability for Diffusion Models
Authors: Viet Nguyen, Giang Vu, Tung Nguyen Thanh, Khoat Than, Toan Tran
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several benchmark datasets including CIFAR10, Celeb A, and Celeb A-HQ consistently show a remarkable improvement of our proposed method regarding the image generalization quality measured by FID and Inception Score compared to several DPM baselines. |
| Researcher Affiliation | Collaboration | Viet Nguyen1* , Giang Vu2,3*, Tung Nguyen Thanh2,3, Khoat Than2 , Toan Tran1 1Vin AI Research, Vietnam 2Hanoi University of Science and Technology, Vietnam 3 Viettel Group, Vietnam |
| Pseudocode | Yes | Algorithm 1: Conventional training Algorithm 2: Sampling Algorithm 3: Sequence-aware training |
| Open Source Code | Yes | Our code and pre-trained checkpoints are available at https://github.com/Vin AIResearch/SA-DPM. |
| Open Datasets | Yes | CIFAR10 32 32 (Krizhevsky 2012), Celeb A 64 64 (Liu et al. 2015) and one higher-resolution dataset Celeb A-HQ 256 256 (Karras et al. 2018). |
| Dataset Splits | No | The paper does not provide specific details on training/validation/test dataset splits, percentages, or sample counts. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | The sampling timesteps for all the datasets are set to {10, 50, 100, 200, 1000}. ... we used the SA-2-DPM with the weight λ of Lsa set to 1... λ {0.5, 1, 2} for SA-2-DPM, λ {0.3, 0.6, 1.5} for SA-3-DPM and λ {0.2, 0.4} for SA-4-DPM. |