736 citations · 1.4k across the 12 of their papers we have counts for
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Adversarial Diffusion Distillation
Axel Sauer, Dominik Lorenz, Andreas Blattmann +1
We introduce Adversarial Diffusion Distillation (ADD), a novel training approach that efficiently samples large-scale foundational image diffusion models in just 1-4 steps while ma…
Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
Andreas Blattmann, Tim Dockhorn, Sumith Kulal +9
We present Stable Video Diffusion - a latent video diffusion model for high-resolution, state-of-the-art text-to-video and image-to-video generation. Recently, latent diffusion mod…
SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
Dustin Podell, Zion English, Kyle Lacey +5
We present SDXL, a latent diffusion model for text-to-image synthesis. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The inc…
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
Andreas Blattmann, Robin Rombach, Huan Ling +4
Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent s…