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..
On Inference Stability for Diffusion Models
Authors: Viet Nguyen, Giang Vu, Tung Nguyen Thanh, Khoat Than, Toan Tran
AAAI 2024 | Venue PDF | 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. |