arXiv:2307.01952cs.CV
SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
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Abstract
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
It was also solved by the even easier approach of aspect ratio conditioning, where you just pass in the dimensions of the crop to the NN like SDXL: https://arxiv.org/pdf/2307.01952#page=3&org=stability
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