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..
AID: Attention Interpolation of Text-to-Image Diffusion
Authors: He Qiyuan, Jinghao Wang, Ziwei Liu, Angela Yao
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our method achieves greater consistency, smoothness, and efficiency in condition-based interpolation, aligning closely with human preferences. |
| Researcher Affiliation | Academia | Qiyuan He1 Jinghao Wang2 Ziwei Liu2 Angela Yao1, 1National University of Singapore 2S-Lab, Nanyang Technological University |
| Pseudocode | Yes | Algorithm 1 Exploration with Beta prior and Algorithm 2 Search smoothest sequence are presented in Appendix D. |
| Open Source Code | Yes | Our code and demo are available at https://qyh00.github.io/attention-interpolation-diffusion/. |
| Open Datasets | Yes | Our proposed framework is evaluated using corpora from CIFAR-10 [22] and the LAION-Aesthetics dataset from the larger LAION-5B collection [39]. |
| Dataset Splits | No | The paper describes sampling methods for trials and iterations but does not explicitly provide training, validation, or test dataset splits for model evaluation. |
| Hardware Specification | Yes | All quantitative and qualitative experiments presented in this work are conducted on a single H100 GPU and Float16 precision. |
| Software Dependencies | Yes | We use Stable Diffusion 1.4 [35] as the base model to implement our attention interpolation mechanism for quantitative evaluation. |
| Experiment Setup | Yes | In all experiments, a 512 × 512 image is generated with the DDIM Scheduler [42] and DPM Scheduler [26] within 25 timesteps. In terms of Bayesian optimization on α and β in the beta prior to applying our selection approach, we set the smoothness of the interpolation sequence as the objective target, [1, 15] as the range of both hyperparameters, 9 fixed exploration where α and β are chosen from {10, 12, 14}, and 15 iterations to optimize. |