Denoising Diffusion Probabilistic Models, Jonathan Ho, Ajay Jain, Pieter Abbeel, 2020arXivDOI: 10.48550/arXiv.2006.11239 - Introduces the foundational Denoising Diffusion Probabilistic Models (DDPM) framework, outlining the U-Net architecture used for noise prediction. This paper establishes the base architecture that conditional diffusion models adapt.
High-Resolution Image Synthesis with Latent Diffusion Models, Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer, 2022CVPR 2022DOI: 10.48550/arXiv.2112.10752 - Introduces Latent Diffusion Models (LDMs), which significantly reduce computational costs by operating in a latent space and utilize cross-attention mechanisms for integrating high-dimensional conditioning, such as text embeddings, into the U-Net architecture.