README.md

metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: image_id
      dtype: string
    - name: tag
      dtype: string
    - name: model_id
      dtype: int64
    - name: modelVersion_id
      dtype: int64
    - name: prompt_id
      dtype: int64
    - name: size
      dtype: string
    - name: seed
      dtype: int64
    - name: prompt
      dtype: string
    - name: negativePrompt
      dtype: string
    - name: cfgScale
      dtype: int64
    - name: sampler
      dtype: string
    - name: note
      dtype: string
    - name: nsfw_score
      dtype: float64
    - name: mcos_score
      dtype: float64
    - name: clip_score
      dtype: float64
    - name: norm_clip
      dtype: float64
    - name: norm_mcos
      dtype: float64
    - name: norm_nsfw
      dtype: float64
    - name: norm_pop
      dtype: float64
  splits:
    - name: train
      num_bytes: 10373652334
      num_examples: 18000
  download_size: 9873105007
  dataset_size: 10373652334
task_categories:
  - text-to-image
language:
  - en
tags:
  - art
  - stable diffusion
  - diffusers
size_categories:
  - 10K<n<100K
license: openrail

GEMRec-18k -- Prompt Book

This is the official image 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}
}