Modular Enhancements for Stable Diffusion Architecture

:::info 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.

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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

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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

In this section we present our improvements for the Stable Diffusion architecture. These are modular, and can be used individually or together to extend any model. Although the following strategies are implemented as extensions to latent diffusion models (LDMs) [38], most of them are also applicable to their pixel-space counterparts.

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

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