|
--- |
|
dataset_info: |
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features: |
|
- name: image_id |
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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: 7955010 |
|
num_examples: 18000 |
|
download_size: 0 |
|
dataset_size: 7955010 |
|
license: openrail |
|
task_categories: |
|
- text-to-image |
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language: |
|
- en |
|
tags: |
|
- art |
|
- stable diffusion |
|
- diffusers |
|
size_categories: |
|
- 10K<n<100K |
|
--- |
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# GEMRec-18k -- Metadata |
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This is the official image metadata dataset for the paper [Towards Personalized Prompt-Model Retrieval for Generative Recommendation](https://github.com/MAPS-research/GEMRec). |
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|
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## Dataset Intro |
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`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](https://civitai.com/) 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](https://huggingface.co/docs/diffusers/index) format. The textual prompts were drawn from three sources: 60 prompts were sampled from [Parti Prompts](https://github.com/google-research/parti); 10 prompts were sampled from [Civitai](https://civitai.com/) by popularity; we also handcrafted 10 prompts following the prompting guide from [DreamStudio](https://beta.dreamstudio.ai/prompt-guide), and then extended them to 20 by creating a shortened and simplified version following the tips from [Midjourney](https://docs.midjourney.com/docs/prompts). The textual prompts were classified into 12 categories: abstract, animal, architecture, art, artifact, food, illustration, people, produce & plant, scenery, vehicle, and world knowledge. |
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## Links |
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#### Dataset |
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- [GEMRec-Promptbook](https://huggingface.co/datasets/MAPS-research/GEMRec-PromptBook): The full version of our GemRec-18k dataset (images & metadata). |
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- [GEMRec-Metadata](https://huggingface.co/datasets/MAPS-research/GEMRec-Metadata): The pruned version of our GemRec-18k dataset (metadata only). |
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- [GEMRec-Roster](https://huggingface.co/datasets/MAPS-research/GEMRec-Roster): The metadata for the 200 model checkpoints fetched from [Civitai](https://civitai.com/). |
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#### Space |
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- [GEMRec-Gallery](https://huggingface.co/spaces/MAPS-research/GEMRec-Gallery): Our web application for browsing and comparing the generated images. |
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#### Github Code |
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- [GEMRec](https://github.com/MAPS-research/GEMRec) |
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|
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## Acknowledgement |
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This work was supported through the NYU High Performance Computing resources, services, and staff expertise. |
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|
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## Citation |
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If you find our work helpful, please consider cite it as follows: |
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```bibtex |
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@article{guo2023towards, |
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title={Towards Personalized Prompt-Model Retrieval for Generative Recommendation}, |
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author={Guo, Yuanhe and Liu, Haoming and Wen, Hongyi}, |
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journal={arXiv preprint arXiv:2308.02205}, |
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year={2023} |
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} |
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``` |
