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..
Optimizing Prompts for Text-to-Image Generation
Authors: Yaru Hao, Zewen Chi, Li Dong, Furu Wei
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on Stable Diffusion show that our method outperforms manual prompt engineering in terms of both automatic metrics and human preference ratings. |
| Researcher Affiliation | Industry | Yaru Hao , Zewen Chi , Li Dong, Furu Wei Microsoft Research https://github.com/microsoft/LMOps |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The pretrained checkpoints are available at https://aka.ms/promptist. The demo can be found at https://aka.ms/promptist-demo. The paper does not explicitly state that the source code for the methodology is released. |
| Open Datasets | Yes | We use three types of data: (1) in-domain prompts from Diffusion DB [Wang et al., 2022]... (2) out-of-domain image captions from COCO dataset [Chen et al., 2015], (3) image labels from Image Net-21k [Deng et al., 2009]... |
| Dataset Splits | No | The paper mentions 'validation loss' during fine-tuning, but does not provide explicit train/validation/test dataset splits (e.g., percentages or counts) for the datasets used. |
| Hardware Specification | Yes | Our experiments are implemented on V100 (32GB) GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as Python, PyTorch, or other libraries used in the implementation. |
| Experiment Setup | Yes | We use a batch size of 256, a learning rate of 5e-5, and a max length of 512. We finetune the model for 15k steps... We train the policy for 12k episodes, four PPO epochs per batch with one minibatch each, with a batch size of 256 and a constant learning rate of 5e-5. The value loss coefficient and the KL reward coefficient are kept at 2.3 and 0.2 respectively. |