We present a framework for understanding and extracting style descriptors from images. Our framework comprises a new dataset curated using the insight that style is a subjective property of an image that captures complex yet meaningful interactions of factors including but not limited to colors, textures, shapes, etc.We also propose a method to extract style descriptors that can be used to attribute style of a generated image to the images used in the training dataset of a text-to-image mode
The checkpoint is for ViT-Large model
If you find our model, codebase or dataset beneficial, please consider citing our work:
@article{somepalli2024measuring,
title={Measuring Style Similarity in Diffusion Models},
author={Somepalli, Gowthami and Gupta, Anubhav and Gupta, Kamal and Palta, Shramay and Goldblum, Micah and Geiping, Jonas and Shrivastava, Abhinav and Goldstein, Tom},
journal={arXiv preprint arXiv:2404.01292},
year={2024}
}
We present a framework for understanding and extracting style descriptors from images. Our framework comprises a new dataset curated using the insight that style is a subjective property of an image that captures complex yet meaningful interactions of factors including but not limited to colors, textures, shapes, etc.We also propose a method to extract style descriptors that can be used to attribute style of a generated image to the images used in the training dataset of a text-to-image mode
The checkpoint is for ViT-Large model
If you find our model, codebase or dataset beneficial, please consider citing our work:
@article{somepalli2024measuring,
title={Measuring Style Similarity in Diffusion Models},
author={Somepalli, Gowthami and Gupta, Anubhav and Gupta, Kamal and Palta, Shramay and Goldblum, Micah and Geiping, Jonas and Shrivastava, Abhinav and Goldstein, Tom},
journal={arXiv preprint arXiv:2404.01292},
year={2024}
}