Torch, TF, Lasagne code for audio style transfer.

http://dmitryulyanov.github.io/audio-texture-synthesis-and-style-transfer/

#dl #audio #styletransfer #torch #tf #lasagne

​​Real-Time Patch-Based Stylization of Portraits Using Generative Adversarial Network

Face photo stylization from #Snap research team. Rather fast solution with demo available.

Demo: http://facestyle.org/#
Paper: https://dcgi.fel.cvut.cz/home/sykorad/Futschik19-NPAR.pdf
YouTube: https://www.youtube.com/watch?v=G3nwTSd3_XA

#GAN #DL #Styletransfer

​​Domain-Aware Universal Style Transfer

Style transfer aims to reproduce content images with the styles from reference images. Modern style transfer methods can successfully apply arbitrary styles to images in either an artistic or a photo-realistic way. However, due to their structural limitations, they can do it only within a specific domain: the degrees of content preservation and stylization depends on a predefined target domain. As a result, both photo-realistic and artistic models have difficulty in performing the desired style transfer for the other domain.

The authors propose Domain-aware Style Transfer Networks (DSTN) that transfer not only the style but also the property of domain (i.e., domainness) from a given reference image. Furthermore, they design a novel domainess indicator (based on the texture and structural features) and introduce a unified framework with domain-aware skip connection to adaptively transfer the stroke and palette to the input contents guided by the domainness indicator.

Extensive experiments validate that their model produces better qualitative results and outperforms previous methods in terms of proxy metrics on both artistic and photo-realistic stylizations.

Paper: https://arxiv.org/abs/2108.04441

Code: https://github.com/Kibeom-Hong/Domain-Aware-Style-Transfer

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-dstn

#deeplearning #cv #styletransfer

​​A Recipe For Arbitrary Text Style Transfer with Large Language Models

Text style transfer is rewriting text to incorporate additional or alternative stylistic elements while preserving the overall semantics and structure.

Large language models are trained only for continuation, but recently many approaches showed that it is possible to perform other NLP tasks by expressing them as prompts that encourage the model to output the desired answer as the continuation.

The authors present a new prompting method (augmented zero-shot learning), which frames style transfer as a sentence rewriting task and requires only natural language instruction.

There are many great examples in the paper and on the project page - both formal and informal.
For example, "include the word "oregano"" and "in the style of a pirate".

Paper: https://arxiv.org/abs/2109.03910
Code: https://storage.googleapis.com/style-transfer-paper-123/index.html

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-llmdialog

#deeplearning #nlp #styletransfer

​​StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis

In this paper, the authors propose StyleGAN-T, a model designed for large-scale text-to-image synthesis. With its large capacity, stable training on diverse datasets, strong text alignment, and controllable variation-text alignment tradeoff, StyleGAN-T outperforms previous GANs and even surpasses distilled diffusion models, the previous frontrunners in fast text-to-image synthesis in terms of sample quality and speed.

StyleGAN-T achieves a better zero-shot MS COCO FID than current state of-the-art diffusion models at a resolution of 64×64. At 256×256, StyleGAN-T halves the zero-shot FID previously achieved by a GAN but continues to trail SOTA diffusion models.

Paper: https://arxiv.org/abs/2301.09515

Project link: https://sites.google.com/view/stylegan-t?pli=1

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-stylegan-t

#deeplearning #cv #gan #styletransfer