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. Table of Links 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 C Diffusion Models Sampling. In practice, this iterative denoising process explained above can be implemented through the numerical simulation of the Probability Flow ordinary differential equation (ODE) [47] Classifier-free guidance. Classifier-free guidance [13] is a technique to guide the iterative sampling process of a DM towards a conditioning signal c by mixing the predictions of a conditional and an unconditional model where w ≥ 0 is the guidance strength. In practice, the unconditional model can be trained jointly alongside the conditional model in a single network by randomly replacing the conditional signal c with a null embedding in Eq. (3), e.g., 10% of the time [13]. Classifier-free guidance is widely used to improve the sampling quality, trading for diversity, of text-to-image DMs [30, 38] This paper is available on arxiv under CC BY 4.0 DEED license. 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. Authors: 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. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Improving Stable Diffusion 2 Improving Stable Diffusion 2.1 Architecture & Scale 2.1 Architecture & Scale 2.2 Micro-Conditioning 2.2 Micro-Conditioning 2.3 Multi-Aspect Training 2.3 Multi-Aspect Training 2.4 Improved Autoencoder and 2.5 Putting Everything Together 2.4 Improved Autoencoder and 2.5 Putting Everything Together 3 Future Work 3 Future Work Appendix Appendix A Acknowledgements A Acknowledgements B Limitations B Limitations C Diffusion Models C Diffusion Models D Comparison to the State of the Art D Comparison to the State of the Art E Comparison to Midjourney v5.1 E Comparison to Midjourney v5.1 F On FID Assessment of Generative Text-Image Foundation Models F On FID Assessment of Generative Text-Image Foundation Models G Additional Comparison between Single- and Two-Stage SDXL pipeline G Additional Comparison between Single- and Two-Stage SDXL pipeline References References C Diffusion Models Sampling . In practice, this iterative denoising process explained above can be implemented through the numerical simulation of the Probability Flow ordinary differential equation (ODE) [47] Sampling Classifier-free guidance. Classifier-free guidance [13] is a technique to guide the iterative sampling process of a DM towards a conditioning signal c by mixing the predictions of a conditional and an unconditional model Classifier-free guidance. where w ≥ 0 is the guidance strength. In practice, the unconditional model can be trained jointly alongside the conditional model in a single network by randomly replacing the conditional signal c with a null embedding in Eq. (3), e.g., 10% of the time [13]. Classifier-free guidance is widely used to improve the sampling quality, trading for diversity, of text-to-image DMs [30, 38] This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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