MAPS-research/GEMRec-Roster

GEMRec-18k -- Roster

This is the official model checkpoint metadata dataset for the paper Towards Personalized Prompt-Model Retrieval for Generative Recommendation.

Dataset Intro

GEMRec-18K is a prompt-model interaction dataset with 18K images generated by 200 publicly-available generative models paired with a diverse set of 90 textual prompts. We randomly sampled a subset of 197 models from the full set of models (all finetuned from Stable Diffusion) on Civitai according to the popularity distribution (i.e., download counts) and added 3 original Stable Diffusion checkpoints (v1.4, v1.5, v2.1) from HuggingFace. All the model checkpoints have been converted to the Diffusers format. The textual prompts were drawn from three sources: 60 prompts were sampled from Parti Prompts; 10 prompts were sampled from Civitai by popularity; we also handcrafted 10 prompts following the prompting guide from DreamStudio, and then extended them to 20 by creating a shortened and simplified version following the tips from Midjourney. The textual prompts were classified into 12 categories: abstract, animal, architecture, art, artifact, food, illustration, people, produce & plant, scenery, vehicle, and world knowledge.

Links

Dataset

• GEMRec-Promptbook: The full version of our GemRec-18k dataset (images & metadata).
• GEMRec-Metadata: The pruned version of our GemRec-18k dataset (metadata only).
• GEMRec-Roster: The metadata for the 200 model checkpoints fetched from Civitai.

Space

• GEMRec-Gallery: Our web application for browsing and comparing the generated images.

Github Code

• GEMRec

Acknowledgement

This work was supported through the NYU High Performance Computing resources, services, and staff expertise.

Citation

If you find our work helpful, please consider cite it as follows:

@article{guo2023towards,
  title={Towards Personalized Prompt-Model Retrieval for Generative Recommendation},
  author={Guo, Yuanhe and Liu, Haoming and Wen, Hongyi},
  journal={arXiv preprint arXiv:2308.02205},
  year={2023}
}