| --- |
| 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](https://github.com/MAPS-research/GEMRec). |
|
|
| ## 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](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. |
|
|
| ## Links |
| #### Dataset |
| - [GEMRec-Promptbook](https://huggingface.co/datasets/MAPS-research/GEMRec-PromptBook): The full version of our GemRec-18k dataset (images & metadata). |
| - [GEMRec-Metadata](https://huggingface.co/datasets/MAPS-research/GEMRec-Metadata): The pruned version of our GemRec-18k dataset (metadata only). |
| - [GEMRec-Roster](https://huggingface.co/datasets/MAPS-research/GEMRec-Roster): The metadata for the 200 model checkpoints fetched from [Civitai](https://civitai.com/). |
|
|
| #### Space |
| - [GEMRec-Gallery](https://huggingface.co/spaces/MAPS-research/GEMRec-Gallery): Our web application for browsing and comparing the generated images. |
|
|
| #### Github Code |
| - [GEMRec](https://github.com/MAPS-research/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: |
| ```bibtex |
| @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} |
| } |
| ``` |
