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Computer Science > Computer Vision and Pattern Recognition

arXiv:2112.10752 (cs)
[Submitted on 20 Dec 2021 (v1), last revised 13 Apr 2022 (this version, v2)]

Title:High-Resolution Image Synthesis with Latent Diffusion Models

Authors:Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer
View a PDF of the paper titled High-Resolution Image Synthesis with Latent Diffusion Models, by Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Bj\"orn Ommer
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Abstract:By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs. Code is available at this https URL .
Comments: CVPR 2022
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2112.10752 [cs.CV]
  (or arXiv:2112.10752v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2112.10752
arXiv-issued DOI via DataCite

Submission history

From: Robin Rombach [view email]
[v1] Mon, 20 Dec 2021 18:55:25 UTC (46,150 KB)
[v2] Wed, 13 Apr 2022 11:38:44 UTC (38,971 KB)
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