  Large text-to-image diffusion models have impressive capabilities in
generating photorealistic images from text prompts. How to effectively guide or
control these powerful models to perform different downstream tasks becomes an
important open problem. To tackle this challenge, we introduce a principled
finetuning method -- Orthogonal Finetuning (OFT), for adapting text-to-image
diffusion models to downstream tasks. Unlike existing methods, OFT can provably
preserve hyperspherical energy which characterizes the pairwise neuron
relationship on the unit hypersphere. We find that this property is crucial for
preserving the semantic generation ability of text-to-image diffusion models.
To improve finetuning stability, we further propose Constrained Orthogonal
Finetuning (COFT) which imposes an additional radius constraint to the
hypersphere. Specifically, we consider two important finetuning text-to-image
tasks: subject-driven generation where the goal is to generate subject-specific
images given a few images of a subject and a text prompt, and controllable
generation where the goal is to enable the model to take in additional control
signals. We empirically show that our OFT framework outperforms existing
methods in generation quality and convergence speed.
