generative models — papers

AIIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022 · Dec 2021 Open access

High-Resolution Image Synthesis with Latent Diffusion Models

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser and Björn Ommer

The paper proposes latent diffusion models (LDMs), which apply the diffusion process in the compressed latent space of a pretrained autoencoder rather than directly in pixel space, greatly reducing compute. A cross-attention conditioning mechanism enables flexible inputs such as text and bounding boxes for tasks including text-to-image generation, inpainting, and super-resolution. LDMs achieve strong or state-of-the-art results across these tasks while being far more efficient to train and sample, and this architecture underlies Stable Diffusion.

latent diffusion image synthesis generative models computer vision

AIAdvances in Neural Information Processing Systems 33 (NeurIPS 2020) · Jun 2020 Open access

Denoising Diffusion Probabilistic Models

Jonathan Ho, Ajay Jain and Pieter Abbeel

The paper introduces denoising diffusion probabilistic models (DDPMs), a class of latent-variable generative models trained to reverse a fixed Gaussian noising process. It establishes a connection between diffusion models and denoising score matching with Langevin dynamics, and proposes a simplified, reweighted training objective. The resulting models produce high-quality image samples, achieving competitive log-likelihoods and a strong FID on CIFAR-10.

diffusion models generative models image synthesis deep learning