SDXL: Improving Latent Diffusion Models for High-Resolution Image
  Synthesis
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
64a6318d711fc67c6a7d7896
Dustin Podell
claimed_verified
2023-07-06T09:36:15.572Z
63055631acc17ce4ad369a37
https://cdn-avatars.huggingface.co/v1/production/uploads/1676933320376-63055631acc17ce4ad369a37.png
Dustin Podell
user
TwoDukes
64a6318d711fc67c6a7d7897
Zion English
64a6318d711fc67c6a7d7898
Kyle Lacey
admin_assigned
2023-07-06T12:54:28.241Z
6318c908acffc70bd4deebec
https://cdn-avatars.huggingface.co/v1/production/uploads/6318c908acffc70bd4deebec/2I88otvT1rDot_22yYPIi.png
TwoPerCent
user
TwoPerCent
64a6318d711fc67c6a7d7899
Andreas Blattmann
admin_assigned
2023-07-06T12:54:47.300Z
635fbc957c05eb9f5997b568
/avatars/0bf85960fa7ad445b0abb2d70c9d5721.svg
Andreas Blattmann
user
ablattmann
64a6318d711fc67c6a7d789a
Tim Dockhorn
admin_assigned
2023-07-06T12:55:02.610Z
633f7a8f4be90e06da248e0f
https://cdn-avatars.huggingface.co/v1/production/uploads/633f7a8f4be90e06da248e0f/Kf-vMGCSIar8UGezk-TK9.jpeg
Tim Dockhorn
user
timudk
64a6318d711fc67c6a7d789b
Jonas Müller
admin_assigned
2023-07-06T12:55:34.083Z
64510d6304397681bcf9725b
/avatars/acb0f7305807539f892472ef4e847310.svg
Jonas Müller
user
jaymueller
64a6318d711fc67c6a7d789c
Joe Penna
claimed_verified
2023-07-06T12:46:37.612Z
63055f6d99870e13d3dece08
/avatars/eabc15338aaf92222f2390c533149369.svg
Joe Penna
user
MysteryGuitarMan
64a6318d711fc67c6a7d789d
Robin Rombach
admin_assigned
2023-07-06T12:55:54.651Z
62cfefa74b3e8dc1e32e38bf
https://cdn-avatars.huggingface.co/v1/production/uploads/1657794439283-noauth.png
Robin Rombach
user
rromb
SDXL: Improving Latent Diffusion Models for High-Resolution Image
  Synthesis
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
SDXL, a latent diffusion model using a larger UNet with additional text encoders and attention mechanisms, improves text-to-image synthesis significantly.
latent diffusion model UNet attention blocks cross-attention context text encoder conditioning schemes refinement model image-to-image technique visual fidelity