SDXL: Improving Latent Diffusion Models for High-Resolution Image
  Synthesis
Authors: name: Dustin Podell, name: Zion English, name: Kyle Lacey, name: Andreas Blattmann, name: Tim Dockhorn, name: Jonas Müller, name: Joe Penna, name: Robin Rombach

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 increase of model parameters is mainly due to more
attention blocks and a larger cross-attention context as SDXL uses a second
text encoder. We design multiple novel conditioning schemes and train SDXL on
multiple aspect ratios. We also introduce a refinement model which is used to
improve the visual fidelity of samples generated by SDXL using a post-hoc
image-to-image technique. We demonstrate that SDXL shows drastically improved
performance compared the previous versions of Stable Diffusion and achieves
results competitive with those of black-box state-of-the-art image generators.
In the spirit of promoting open research and fostering transparency in large
model training and evaluation, we provide access to code and model weights at
https://github.com/Stability-AI/generative-models