Interpreting the Weight Space of Customized Diffusion Models
<|reference_start|>Interpreting the Weight Space of Customized Diffusion Models: We investigate the space of weights spanned by a large collection of customized diffusion models. We populate this space by creating a dataset of over 60,000 models, each of which is a base model fine-tuned to insert a different person's visual identity. We model the underlying manifold of these weights as a subspace, which we term weights2weights. We demonstrate three immediate applications of this space -- sampling, editing, and inversion. First, as each point in the space corresponds to an identity, sampling a set of weights from it results in a model encoding a novel identity. Next, we find linear directions in this space corresponding to semantic edits of the identity (e.g., adding a beard). These edits persist in appearance across generated samples. Finally, we show that inverting a single image into this space reconstructs a realistic identity, even if the input image is out of distribution (e.g., a painting). Our results indicate that the weight space of fine-tuned diffusion models behaves as an interpretable latent space of identities.<|reference_end|>
@article{dravid2024interpreting,
    title={Interpreting the Weight Space of Customized Diffusion Models},
    author={Amil Dravid, Yossi Gandelsman, Kuan-Chieh Wang, Rameen Abdal, Gordon
  Wetzstein, Alexei A. Efros, Kfir Aberman},
    journal={arXiv preprint arXiv:2406.09413},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09413},
    primaryClass={cs.CV cs.GR cs.LG}
}