Interpreting the Weight Space of Customized Diffusion Models

Understand

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 that result in new diffusion models -- sampling, editing, and inversion.