Diffusion Self-Distillation for Zero-Shot Customized Image Generation
Authors: name: Shengqu Cai, name: Eric Chan, name: Yunzhi Zhang, name: Leonidas Guibas, name: Jiajun Wu, name: Gordon Wetzstein

Text-to-image diffusion models produce impressive results but are frustrating
tools for artists who desire fine-grained control. For example, a common use
case is to create images of a specific instance in novel contexts, i.e.,
"identity-preserving generation". This setting, along with many other tasks
(e.g., relighting), is a natural fit for image+text-conditional generative
models. However, there is insufficient high-quality paired data to train such a
model directly. We propose Diffusion Self-Distillation, a method for using a
pre-trained text-to-image model to generate its own dataset for
text-conditioned image-to-image tasks. We first leverage a text-to-image
diffusion model's in-context generation ability to create grids of images and
curate a large paired dataset with the help of a Visual-Language Model. We then
fine-tune the text-to-image model into a text+image-to-image model using the
curated paired dataset. We demonstrate that Diffusion Self-Distillation
outperforms existing zero-shot methods and is competitive with per-instance
tuning techniques on a wide range of identity-preservation generation tasks,
without requiring test-time optimization.