Conditional Generation from Unconditional Diffusion Models using Denoiser Representations

Understand

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.