StableDiffusion/CITATION.cff
2023-03-27 11:36:59 +07:00

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cff-version: 1.2.0
message: If you use this software, please cite it using these metadata.
title: stablediffusion
authors:
- family-names: Rombach
given-names: Robin
- family-names: Blattmann
given-names: Andreas
- family-names: Lorenz
given-names: Dominik
- family-names: Esser
given-names: Patrick
- family-names: Ommer
given-names: Björn
year: 2021
doi: 10.48550/arXiv.2112.10752
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.
input:
- format: arXiv
id: 2112.10752
type: article
url: https://arxiv.org/abs/2112.10752
output:
- format: PDF
url: https://arxiv.org/pdf/2112.10752.pdf
preferred-citation:
type: article
authors:
- family-names: Rombach
given-names: Robin
- family-names: Blattmann
given-names: Andreas
- family-names: Lorenz
given-names: Dominik
- family-names: Esser
given-names: Patrick
- family-names: Ommer
given-names: Björn
doi: "10.48550/arXiv.2112.10752"
eprint: "2112.10752"
archivePrefix: arXiv
primaryClass: cs.CV
title: High-Resolution Image Synthesis with Latent Diffusion Models
year: 2021