Datasets:
dataset_info:
features:
- name: tag
dtype: string
- name: model_name
dtype: string
- name: model_id
dtype: int64
- name: modelVersion_name
dtype: string
- name: modelVersion_id
dtype: int64
- name: modelVersion_url
dtype: string
- name: modelVersion_trainedWords
dtype: string
- name: model_download_count
dtype: int64
- name: baseModel
dtype: string
splits:
- name: train
num_bytes: 36188
num_examples: 200
download_size: 22662
dataset_size: 36188
license: openrail
task_categories:
- text-to-image
language:
- en
tags:
- art
- diffusers
size_categories:
- n<1K
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-ModelCofferGallery: Our web application for browsing and comparing the generated images.
Github Code
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}
}