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Benchmarking Spatial Relationships in Text-to-Image Generation
Tejas Gokhale, Hamid Palangi, Besmira Nushi, Vibhav Vineet, Eric Horvitz, Ece Kamar, Chitta Baral, Yezhou Yang
- We introduce a large-scale challenge dataset SR2D that contains sentences describing two objects and the spatial relationship between them.
- We introduce a metric called VISOR (short for VerifyIng Spatial Object Relationships) to quantify spatial reasoning performance.
- VISOR and SR2D can be used off-the-shelf with any text-to-image model.
SR2D Dataset
Our dataset is hosted as here. This contains
- The text prompt dataset in
.json
format (text_spatial_rel_phrases.json
) - Images generated using 7 models (GLIDE, CogView2, DALLE-mini, Stable Diffusion, GLIDE + Stable Diffusion + CDM, and Stable Diffusion v2.1)
Alternatively, the text prompt dataset can also accessed from text_spatial_rel_phrases.json
. It contains all examples from the current version of the dataset (31680 text prompts) accompanied by the corresponding metadata.
This dataset can also be generated by running the script python create_spatial_phrases.py
GitHub repository
The GitHub repository for VISOR contains code for generating images with prompts from the SR2D dataset and evaluating the generated images using VISOR.
References
Code for text-to-image generation:
- GLIDE: https://github.com/openai/glide-text2im
- DALLE-mini: https://github.com/borisdayma/dalle-mini
- CogView2: https://github.com/THUDM/CogView2
- Stable Diffusion: https://github.com/CompVis/stable-diffusion
- Composable Diffusion Models: https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
- OpenAI API for DALLE-2: https://openai.com/api/
Citation
If you find SR2D or VISOR useful in your research, please use the following citation:
@article{gokhale2022benchmarking,
title={Benchmarking Spatial Relationships in Text-to-Image Generation},
author={Gokhale, Tejas and Palangi, Hamid and Nushi, Besmira and Vineet, Vibhav and Horvitz, Eric and Kamar, Ece and Baral, Chitta and Yang, Yezhou},
journal={arXiv preprint arXiv:2212.10015},
year={2022}
}
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