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Latest Advances in Stable Diffusion Technologyby@synthesizing

Latest Advances in Stable Diffusion Technology

by SynthesizingOctober 4th, 2024
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The Stability AI Applied Research team introduces advancements in Stable Diffusion technology, focusing on improved architecture, micro-conditioning, and multi-aspect training. The paper discusses various enhancements, outlines future research directions, and provides a comprehensive comparison with existing models and methodologies.
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Authors:

(1) Dustin Podell, Stability AI, Applied Research;

(2) Zion English, Stability AI, Applied Research;

(3) Kyle Lacey, Stability AI, Applied Research;

(4) Andreas Blattmann, Stability AI, Applied Research;

(5) Tim Dockhorn, Stability AI, Applied Research;

(6) Jonas Müller, Stability AI, Applied Research;

(7) Joe Penna, Stability AI, Applied Research;

(8) Robin Rombach, Stability AI, Applied Research.

Abstract and 1 Introduction

2 Improving Stable Diffusion

2.1 Architecture & Scale

2.2 Micro-Conditioning

2.3 Multi-Aspect Training

2.4 Improved Autoencoder and 2.5 Putting Everything Together

3 Future Work


Appendix

A Acknowledgements

B Limitations

C Diffusion Models

D Comparison to the State of the Art

E Comparison to Midjourney v5.1

F On FID Assessment of Generative Text-Image Foundation Models

G Additional Comparison between Single- and Two-Stage SDXL pipeline

References

References

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This paper is available on arxiv under CC BY 4.0 DEED license.