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metadata
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

Space

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}
}