Conditional Generation from Unconditional Diffusion Models using
  Denoiser Representations
Alexandros Graikos Srikar Yellapragada Dimitris Samaras
Denoising diffusion models have gained popularity as a generative modeling
technique for producing high-quality and diverse images. Applying these models
to downstream tasks requires conditioning, which can take the form of text,
class labels, or other forms of guidance. However, providing conditioning
information to these models can be challenging, particularly when annotations
are scarce or imprecise. In this paper, we propose adapting pre-trained
unconditional diffusion models to new conditions using the learned internal
representations of the denoiser network. We demonstrate the effectiveness of
our approach on various conditional generation tasks, including
attribute-conditioned generation and mask-conditioned generation. Additionally,
we show that augmenting the Tiny ImageNet training set with synthetic images
generated by our approach improves the classification accuracy of ResNet
baselines by up to 8%. Our approach provides a powerful and flexible way to
adapt diffusion models to new conditions and generate high-quality augmented
data for various conditional generation tasks.