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Inversion-based style transfer with diffusion models
The artistic style within a painting is the means of expression, which includes not only the
painting material, colors, and brushstrokes, but also the high-level attributes, including …
painting material, colors, and brushstrokes, but also the high-level attributes, including …
Stylediffusion: Controllable disentangled style transfer via diffusion models
Content and style (CS) disentanglement is a fundamental problem and critical challenge of
style transfer. Existing approaches based on explicit definitions (eg, Gram matrix) or implicit …
style transfer. Existing approaches based on explicit definitions (eg, Gram matrix) or implicit …
[ספר][B] Understanding deep learning
SJD Prince - 2023 - books.google.com
An authoritative, accessible, and up-to-date treatment of deep learning that strikes a
pragmatic middle ground between theory and practice. Deep learning is a fast-moving field …
pragmatic middle ground between theory and practice. Deep learning is a fast-moving field …
Arf: Artistic radiance fields
We present a method for transferring the artistic features of an arbitrary style image to a 3D
scene. Previous methods that perform 3D stylization on point clouds or meshes are sensitive …
scene. Previous methods that perform 3D stylization on point clouds or meshes are sensitive …
Style injection in diffusion: A training-free approach for adapting large-scale diffusion models for style transfer
Despite the impressive generative capabilities of diffusion models existing diffusion model-
based style transfer methods require inference-stage optimization (eg fine-tuning or textual …
based style transfer methods require inference-stage optimization (eg fine-tuning or textual …
Stytr2: Image style transfer with transformers
The goal of image style transfer is to render an image with artistic features guided by a style
reference while maintaining the original content. Owing to the locality in convolutional neural …
reference while maintaining the original content. Owing to the locality in convolutional neural …
Domain enhanced arbitrary image style transfer via contrastive learning
In this work, we tackle the challenging problem of arbitrary image style transfer using a novel
style feature representation learning method. A suitable style representation, as a key …
style feature representation learning method. A suitable style representation, as a key …
Artistic style transfer with internal-external learning and contrastive learning
Although existing artistic style transfer methods have achieved significant improvement with
deep neural networks, they still suffer from artifacts such as disharmonious colors and …
deep neural networks, they still suffer from artifacts such as disharmonious colors and …
Ccpl: Contrastive coherence preserving loss for versatile style transfer
In this paper, we aim to devise a universally versatile style transfer method capable of
performing artistic, photo-realistic, and video style transfer jointly, without seeing videos …
performing artistic, photo-realistic, and video style transfer jointly, without seeing videos …
Deadiff: An efficient stylization diffusion model with disentangled representations
The diffusion-based text-to-image model harbors immense potential in transferring reference
style. However current encoder-based approaches significantly impair the text controllability …
style. However current encoder-based approaches significantly impair the text controllability …