| | --- |
| | 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} |
| | } |
| | ``` |
