Unconditional Priors Matter! Improving Conditional Generation of
  Fine-Tuned Diffusion Models
Authors: name: Prin Phunyaphibarn, name: Phillip Y. Lee, name: Jaihoon Kim, name: Minhyuk Sung

Classifier-Free Guidance (CFG) is a fundamental technique in training
conditional diffusion models. The common practice for CFG-based training is to
use a single network to learn both conditional and unconditional noise
prediction, with a small dropout rate for conditioning. However, we observe
that the joint learning of unconditional noise with limited bandwidth in
training results in poor priors for the unconditional case. More importantly,
these poor unconditional noise predictions become a serious reason for
degrading the quality of conditional generation. Inspired by the fact that most
CFG-based conditional models are trained by fine-tuning a base model with
better unconditional generation, we first show that simply replacing the
unconditional noise in CFG with that predicted by the base model can
significantly improve conditional generation. Furthermore, we show that a
diffusion model other than the one the fine-tuned model was trained on can be
used for unconditional noise replacement. We experimentally verify our claim
with a range of CFG-based conditional models for both image and video
generation, including Zero-1-to-3, Versatile Diffusion, DiT, DynamiCrafter, and
InstructPix2Pix.