Fast style transfer

fast-neural-style. This is the code for the paper. Perceptual Losses for Real-Time Style Transfer and Super-Resolution Justin Johnson, Alexandre Alahi, Li Fei-Fei Presented at ECCV 2016. The paper builds on A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge by training feedforward neural networks that apply artistic styles to images Arbitrary Image Stylization under TensorFlow Hub is a module that can perform fast artistic style transfer that may work on arbitrary painting styles. By now, you already know what Neural Style Transfer is Fast Style Transfer in TensorFlow 2 This is an implementation of Fast-Style-Transfer on Python 3 and Tensorflow 2. The neural network is a combination of Gatys' A Neural Algorithm of Artistic Style, Johnson's Perceptual Losses for Real-Time Style Transfer and Super-Resolution, and Ulyanov's Instance Normalization Fast and Restricted Style Transfer In order to speed up the above process, a feed-forward convolutional network, termed a style transfer network T [R2], is introduced to learn the transformation. It takes as input a content image c and outputs the pastiche image p directly Fast Style Transfer API. 162 ∙ share This is a much faster implementation of Neural Style accomplished by pre-training on specific style examples. Content Style url upload file uploa

In this post, I will go over a fascinating technique known as Style Transfer. At the end of this experiment, we'll literally end up creating our one pieces of art, stealing the brush from the hands of Picasso, Monet, and Van Gogh and painting novel masterpieces on our own! As it has been the case for my last few posts, also for this one, the inspiration has come from fast.ai. In the 13th. Fast Style Transfer. A tensorflow implementation of fast style transfer described in the papers: Perceptual Losses for Real-Time Style Transfer and Super-Resolution by Johnson; Instance Normalization by Ulyanov; I recommend you to check my previous implementation of A Neural Algorithm of Artistic Style (Neural style) in here, since implementation in here is almost similar to it Fast and Restricted Style Transfer. Real-Time Image Style Transfer using Feed-Forward Networks . Mayank Agarwal. Follow. Aug 21 · 2 min read. In their seminal work, Image Style Transfer Using Convolutional Neural Networks, Gatys et al.[R1] demonstrate the efficacy of CNNs in separating and re-combining image content and style to create composite artistic images. Using feature. Style transfer, deep learning, feature transform. Contribute to NVIDIA/FastPhotoStyle development by creating an account on GitHub Fast Style Transfer recomposes images in the style of other images. This is a demo of Fast Style Transfer in ml5. You can choose different images, upload your own image, or turn on your webcam as an input, and choose a style in the middle, the output image will appear on the right

This topic demonstrates how to run the Neural Style Transfer sample application, which performs inference of style transfer models. NOTE: The OpenVINO™ toolkit does not include a pre-trained model to run the Neural Style Transfer sample.A public model from the Zhaw's Neural Style Transfer repository can be used. Read the Converting a Style Transfer Model from MXNet* topic from the Model. Fast Style Transfer Example. INPUT . Upload an image. Use my webcam. Transfer My Image / Video. Click this button to start transfering your own image or video. If you are using webcam, you might need to wait for 3s / frame. STYLE. OUTPUT. Built by Yining Shi with ML5. View Code on GitHub. Credits: The code and models are based on deeplearn.js demo by reiinakano. Fast style transfer (https://github.com/lengstrom/fast-style-transfer/) in Tensorflow IN/OUT to TouchDesigner almost in realtime. I'm open 640x480 borderless.. Train Fast Style Transfer Network. Open Live Script. This example shows how to train a network to transfer the style of an image to a second image. It is based on the architecture defined in [1]. This example is similar to Neural Style Transfer Using Deep Learning, but it works faster once you have trained the network on a style image S. This is because, to obtain the stylized image Y you only. We show results on image style transfer, where a feed-forward network is trained to solve the optimization problem proposed by Gatys et al. in real-time. Compared to the optimization-based method, our network gives similar qualitative results but is three orders of magnitude faster. We also experiment with single-image super-resolution, where replacing a per-pixel loss with a perceptual loss.

Gatys et al. 2016, Image Style Transfer Using Convolutional Neural Networks; Novak and Nikulin 2016, Improving the Neural Algorithm of Artistic Style; Ulyanov et al. 2016a, Texture Networks: Feed-forward Synthesis of Textures and Stylized Images; Ulyanov et al. 2016b, Instance Normalization: The Missing Ingredient for Fast Stylizatio Neural-Style, or Neural-Transfer, allows you to take an image and reproduce it with a new artistic style. The algorithm takes three images, an input image, a content-image, and a style-image, and changes the input to resemble the content of the content-image and the artistic style of the style-image. Underlying Principle¶ The principle is simple: we define two distances, one for the content. Fast Style Transfer for Arbitrary Styles. View on TensorFlow.org: Run in Google Colab: View on GitHub: Download notebook: See TF Hub model: Based on the model code in magenta and the publication: Exploring the structure of a real-time, arbitrary neural artistic stylization network. Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, Jonathon Shlens, Proceedings of the British. Fast Style Transfer for Arbitrary Styles. Setup. Define image loading and visualization functions Load example images. Import TF-Hub module. Demonstrate image stylization. Let's try it on more images. To Run: Load more images Specify the main content image and the style you want to use. Section . Filter code snippets . Insert. View source notebook. Connecting to a runtime to enable file. Style Transfer. Before we go to our Style Transfer application, let's clarify what we are striving to achieve. Let's define a style transfer as a process of modifying the style of an image while still preserving its content. Given an input image and a style image, we can compute an output image with the original content but a new style

GitHub - jcjohnson/fast-neural-style: Feedforward style

  1. 27 Jul 2016 • lengstrom/fast-style-transfer • It this paper we revisit the fast stylization method introduced in Ulyanov et. IMAGE GENERATION IMAGE STYLIZATION. 9,409. Paper Code Preserving Color in Neural Artistic Style Transfer. 19 Jun 2016 • cysmith/neural-style.
  2. Fast Style Transfer using TF-Hub This tutorial demonstrates the original style-transfer algorithm, which optimizes the image content to a particular style. Before getting into the details, let's see how the TensorFlow Hub model does this
  3. The efficiency of fast style transfer makes it widely applicable to many real-world applications. We use our style transfer network to generate stylized images for facial expression sequences in . As shown in Fig. 15, the stylized images preserve the emotion well. This can be particularly useful in applications like auto-generation of comic books. We can transfer images of different people.
  4. Fast Style Transfer for Arbitrary Styles. Setup. Define image loading and visualization functions Load example images. Import TF-Hub module. Demonstrate image stylization. Let's try it on more images. To Run: Load more images Specify the main content image and the style you want to use. Sección . Insertar. Ver cuaderno fuente. Conectarse a un entorno de ejecución permite la exploración de.

The input to the model is an image, and the output is a stylized image. The model is based on the Pytorch Fast Neural Style Transfer Example Style Transfer. Neural Style Transfer is an algorithm for combining the content of one image with the style of another image using convolutional neural networks. Here's an example that maps the artistic style of The Starry Night onto a night-time photograph of the Stanford campus Fast style transfer also uses deep neural networks but trains a standalone model to transform any image in a single, feed-forward pass. Trained models can stylize any image with just one iteration through the network, rather than thousands This is a demo app showing off TensorFire's ability to run the style-transfer neural network in your browser as fast as CPU TensorFlow on a desktop. You can learn more about TensorFire and what makes it fast (spoiler: WebGL) on the Project Page. You can also sign up to get notified when we publish new demos or launch. We won't spam you or give your email to third parties. Sucessfully signed up.

As an example of the kind of things you'll be building with deep learning models, here is a really fun project, fast style transfer. Style transfer allows you to take famous paintings, and recreate your own images in their styles! The network learns the underlying techniques of those paintings and figures out how to apply them on its own Fast neural style transfer. Since I already wrote a blog post about fast neural style transfer I'll just sum up the main idea here quickly. Taking a transform network and a loss network jointly where only the weights of the transform network is updated. After convergence the transfrom model can be used in a feed-forward manner to generate stilizied images. Feature loss. To make the content. This is an implementation of the Fast Neural Style Transfer algorithm running purely on the browser using the Deeplearn.JS library. Basically, a neural network attempts to draw one picture, the Content, in the style of another, the Style. Is my data safe? Can you see my webcam pics? Your data and pictures here never leave your computer! In fact, this is one of the main advantages of running. Neural style transfer Setup Import and configure modules Visualize the input Fast Style Transfer using TF-Hub Define content and style representations Intermediate layers for style and content Build the model Calculate style Extract style and content Run gradient descent Total variation loss Re-run the optimization Learn mor Neural style transfer is an optimization technique used to take three images, a content image, a style reference image (such as an artwork by a famous painter), and the input image you want to..

Fast Neural Style Transfer in 5 Minutes with TensorFlow

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  3. Fast Neural Style Transfer in Julia Counts 23 stargazers 1 issues 1 forks 1 contributors Readme FastStyleTransfer. NOTE: This version works for Julia 0.6. An update to support Julia 1.0 and latest Flux, is WIP. Check the julia-1.0 branch for latest updates. It contains working code which needs to be trained. The models for the last release will not work in julia-1.0 branch. But soon new and.
  4. The basis of this tutorial comes from Prisma Lab's blog and their PyTorch approach. However, we will use TensorFlow for the models and specifically, Fast Style Transfer by Logan Engstrom.
  5. Real-time style transfer. In March 2016 a group of researchers from Stanford University published a paper which outlined a method for achieving real-time style transfer. They were able to train a neural network to apply a single style to any given content image. Given this ability, a different network could be trained for each different style.
  6. Title: Fast Patch-based Style Transfer of Arbitrary Style. Authors: Tian Qi Chen, Mark Schmidt. Download PDF Abstract: Artistic style transfer is an image synthesis problem where the content of an image is reproduced with the style of another. Recent works show that a visually appealing style transfer can be achieved by using the hidden activations of a pretrained convolutional neural network.

There are now a bunch of off-the-shelf tools for training artistic style transfer models and thousands of open source implementations.Most use a variation of the network architecture described by Johnson et al to perform fast, feed-forward stylization.. As a result, the majority of the style transfer models you find are the same size: 7MB. That's not an unreasonably large asset to add to. Demonstration purpose for the paper Fast Neural Style Transfer for Video. Clips appear in the video are respectively Sing (2016) 2001: A Space Odyssey (1968) Minions (2015 Fast Style Transfer in TensorFlow (lengstrom/fast-style-transfer) https:// github.com / lengstrom/ fast-style-transfer (je m'excuse par avance auprès de @philippe_de_jonckheere qui va trouver ça atroce) Add styles from famous paintings to any photo in a fraction of a second! You can even style videos

GitHub - cryu854/FastStyle: Fast-Style-Transfer in

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  2. Neural Style Transfer (NST) refers to a class of software algorithms that manipulate digital images, or videos, in order to adopt the appearance or visual style of another image. NST algorithms are characterized by their use of deep neural networks for the sake of image transformation. Common uses for NST are the creation of artificial artwork from photographs, for example by transferring the.
  3. Fast Neural Style Transfer for Motion Data Daniel Holden and Ikhsanul Habibie University of Edinburgh Ikuo Kusajima University of Tokyo Taku Komura University of Edinburgh M otion style transfer is a technique for converting an actor's motion into that of a different character, such as one that is old, depressed, happy, or hurt. Automat-ing this process can save animators time because it.
  4. How to implement the Style Transfer algorithm in TensorFlow for combining the style and content of two images. https://github.com/Hvass-Labs/TensorFlow-Tutor..
  5. cd to the fast-style-transfer directory, then run the setup.sh script to download the pre-trained VGG-19 model file as well as the MS COCO training dataset, which we mentioned in the previous chapter - note that it can take several hours to download the large files. Run the following commands to create checkpoint files with training using a style image named starry_night.jpg and a content.
  6. Some video style transfer models have succeeded in improving temporal consistency, yet they fail to guarantee fast processing speed and nice perceptual style quality at the same time

Fast and Less Restricted Style Transfer by Mayank

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  2. lengstrom/fast-style-transfer. At first you need to pre-train a model yourself, or you can use some published models from here, just put them into the models directory. this command will train a network based on jf.jpg style reference, will output it into ch_jf directory, will use test.jpg as a testing image (for in-progress checking, which will be put into test directory), will use train2014.
  3. g real-time style transfer for multiple styles using one network. In this section we will look at two techniques that can achieve mix fast style transfer. Both techniques stem from the intuition that many styles probably share some degree of computa- tion, and that this sharing is thrown away by training Nnet-works from.
  4. Fast artistic style transfer for videos Download as .zip Download as .tar.gz View on GitHub. Abstract. Recently, research about artistic style transfer, which trains computers to be artists, become popular. Gatys et al. turned this task into an optimization problem and utilized convolution neural network to solve this problem. However, this method for image stylization doesn't work well for.

Video: Fast Style Transfer API DeepA

Introduction. Machine learning, or ML, is a subfield of AI focused on algorithms that learn models from data. Let's look at a practical application of machine learning in the field of Computer Vision called neural style transfer.In 2015, researchers used deep learning techniques to create an algorithm that mixed the content of one image with the artistic style of another fast-style-transfer 初探. 一、训练一个新的画风迁移网络. nohup python style.py --checkpoint-dir ./checkpoint/rain-princess --style ./rain-princess.jpg. This implementation of neural style transfer uses TensorFlow and Python instead of Lua. All of it works on Windows without additional trouble. First install Python 3.5 64-bit. Once you're done with that you will be able to use pip3 in the terminal to install packages. To get TensorFlow for CPU only use pip3 install --upgrade tensorflow For GPU support use (only get one of them) pip3 install. With the Style Transfer API, you'll be able to transform your photos. There are 4 simple steps to follow in your camera code (in our sample app, see MainActivity.java to follow along): First, get a FritzVisionStylePredictor by specifying the output style you'd like to achieve (you can see the different options in the official documentation )

Style Transfer with fast

  1. PyTorch on TPUs: Fast Neural Style Transfer. This notebook lets you run a pre-trained fast neural style transfer network implemented in PyTorch on a Cloud TPU. You can combine pictures and styles to create fun new images. You can learn more about fast neural style transfer from its implementation here or the original paper, available here. This notebook loads PyTorch and stores the network on.
  2. propose a new end-to-end model for photorealistic style transfer that is both fast and inherently generates photorealistic results. The core of our approach is a feed-forward neural network that learns local edge-aware a ne transforms that automatically obey the photorealism constraint. When trained on a diverse set of images and a variety of styles, our model can robustly apply style transfer.
  3. Fast (Feedforward) Style Transfer Perceptual Losses for Real-Time Style Transfer and Super-Resolution is the second paper in my reading series for Neural Style Transfer, where Johnson et al. built on the work of Gatys et al. and used feedforward networks to stylize image order of magnitudes faster than the previous optimization approach
  4. The authors of the original Neural Style Transfer paper. The authors of the paper introducing Real-Time Style Transfer. The author of the fast-style-transfer Github repository. The authors of Deeplearn.JS; To restore the repository, download the bundle reiinakano-fast-style-transfer-deeplearnjs_-_2017-10-02_15-16-10.bundle and run
  5. A more desirable solution would be to consider a model that can already perform fast style transfer on any pair of content and style, and port that to the browser. The Model. After searching related literature for a while, I stumbled upon a paper by Ghiasi, et. al. called Exploring the structure of a real-time, arbitrary neural artistic stylization network. This paper extends Dumoulin, et. al.

GitHub - hwalsuklee/tensorflow-fast-style-transfer: A

Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the. our work, [26,27] also propose feed-forward approaches for fast style transfer. Image super-resolution. Image super-resolution is a classic problem for which a variety of techniques have been developed. Yang et al. [28] provide an ex-haustive evaluation of the prevailing techniques prior to the widespread adoption of convolutional neural networks. They group super-resolution techniques into. According to the paper Image Style Transfer Using Convolutional Neural Networks, it employs a VGG-19 CNN architecture for extracting both the content and style features from the content and style images respectively. To get the content features, the second convolutional layer from the fourth block (of convolutional layers) is used. For convenience, the authors of the paper named it to be conv4. We show results on image style transfer, where a feed-forward network is trained to solve the optimization problem proposed by Gatys et al in real-time. Compared to the optimization-based method, our network gives similar qualitative results but is three orders of magnitude faster. We also experiment with single-image super-resolution, where replacing a per-pixel loss with a perceptual loss.

Fast and Restricted Style Transfer by Mayank Agarwal

Fast video multi-style transfer. / Gao, Wei; Li, Yijun; Yin, Yihang; Yang, Ming Hsuan. Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 3211-3219 9093420 (Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020). Research output: Chapter in Book/Report. Conditional Fast Style Transfer Network Keiji Yanai and Ryosuke Tanno The University of Electro-Communications, Tokyo 1-5-1 Chofugaoka Chofu-shi, Tokyo 182-8585 yanai@cs.uec.ac.jp,tanno-r@mm.inf.uec.ac.jp ABSTRACT In this paper, we propose a conditional fast neural style transfer network. We extend the network proposed as a fast neural style transfer network by Johnson et al. [8] so that the. Learning Linear Transformations for Fast Image and Video Style Transfer Xueting Li∗1, Sifei Liu∗2, Jan Kautz2, and Ming-Hsuan Yang1,3 1University of California, Merced, 2NVIDIA, 3Google Cloud Abstract Given a random pair of images, a universal style trans-fer method extracts the feel from a reference image to syn- thesize an output based on the look of a content image. Recent algorithms.


GitHub - NVIDIA/FastPhotoStyle: Style transfer, deep

Recently, in the community of Neural Style Transfer, several algorithms are proposed to transfer an artistic style in real-time, which is known as Fast Style Transfer. However, controlling the stroke size in stylized results still remains an open challenge. To achieve controllable stroke sizes, several attempts were made including training multiple models and resizing the input image in a. Hi there, I am working on a similar fast neural style transfer that is also based on Ghiasi's paper, but I have a difficulty implementing it, thought you might help me out. In the paper, the style embedding vector is used to influence the transformer network via conditional instance normalization (CIN). Specifically, that vector is somehow used to produce the two parameters in the CIN formula. The Fast Style Transfer methods have been recently proposed to transfer a photograph to an artistic style in real-time. This task involves controlling the stroke size in the stylized results, which remains an open challenge. In this paper, we present a stroke controllable style transfer network that can achieve continuous and spatial stroke size control. By analyzing the factors that influence. Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artwork Decoder Network Over Lightweight Reconstructed Feature for Fast Semantic Style Transfer 2. 涂鸦变油

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Fast Style Transfer — Yining Sh

In this section, we'll show you how to train models using the fast neural-style transfer algorithm with TensorFlow Convolutional neural networks for artistic style transfer 31 Mar 2017 — 52 min read . There's an amazing app out right now called Prisma that transforms your photos into works of art using the styles of famous artwork and motifs. The app performs this style transfer with the help of a branch of machine learning called convolutional neural networks

Neural Style Transfer C++ Sample - OpenVINO™ Toolki

While style transfer tends to play fast and loose with these edges, shifting them back and forth as it pleases, photo style transfer preserves them. There are limits to the technique, of course. Fast and Restricted Style Transfer. Real-Time Image Style Transfer using Feed-Forward Networks. In their seminal work, Image Style Transfer Using Convolutional Neural Networks, Gatys et al. In their seminal work, Image Style Transfer Using Convolutional Neural Networks, Gatys et al.[R1]demonstrate the efficacy of CNNs in separating and re-combining image content and style to create. Unlike previous approaches to fast style transfer, we feel that this method of modeling multiple styles at the same time opens the door to exciting new ways for users to interact with style transfer algorithms, not only allowing the freedom to create new styles based on the mixture of several others, but to do it in real-time. Stay tuned for a future post on the Magenta blog, in which we will. Tensorflow implementation of fast neural style transfer. neuralart An implementation of the paper 'A Neural Algorithm of Artistic Style'. neural-style Torch implementation of neural style algorithm ss-gan Style and Structure GAN (ECCV 2016) fast-neural-style Feedforward style transfer art-DCGAN Modified implementation of DCGAN focused on generative art. CNNMRF code for paper Combining Markov.

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Fast Style Transfer Simple Example - GitHub Page

Super fast color transfer between images. by Adrian Rosebrock on June 30, 2014. About a month ago, I spent a morning down at the beach, walking along the sand, letting the crisp, cold water lap against my feet. It was tranquil, relaxing. I then took out my iPhone and snapped a few photos of the ocean and clouds passing by. You know, something to remember the moment by. Because I knew that as. Neural Art. A Neural Algorithm of Artistic Style. arxiv: http://arxiv.org/abs/1508.06576 gitxiv: http://gitxiv.com/posts/jG46ukGod8R7Rdtud/a-neural-algorithm-of.

Fast style transfer (Tensorflow) in/out Touchdesigner

Image style transfer is an emerging technique based on deep learning, which takes advantage of the impressive feature extraction of convolutional neural networks (CNN). The extraction of high-level features of images makes the separation of style information and image content possible. Image style conversion technique aims to learn the style characteristics of various paintings, and then apply. Fast and Restricted Style Transfer Aug-25-2020, 06:05:58 GMT - #artificialintelligence In their seminal work, Image Style Transfer Using Convolutional Neural Networks, Gatys et al.[R1] demonstrate the efficacy of CNNs in separating and re-combining image content and style to create composite artistic images Fast Style Transfer. My first post on style transfer covered the technique introduced by Gatsy et al. Whilst impressive, the whole process of image generation takes time because it requires solving an optimization problem. This page uses the approach introduced by Johnson et al in this paper which allows us to transfer the style in realtime Controlling stroke size in Fast Style Transfer remains a difficult task. So far, only a few attempts have been made towards it, and they still exhibit several deficiencies regarding efficiency, flexibility, and diversity. In this paper, we aim to tackle these problems and propose a recurrent convolutional neural subnetwork, which we call recurrent stroke-pyramid, to control the stroke size in. DNN: Style Transfer. Maps the artistic style of various pieces of artwork onto input image. This demo is used to run style transfer models using OpenCV. It combines the content of one image with the style of another using convolutional neural networks

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Where can I learn more about neural style transfer? If you're interested in learning more about neural style transfer, including the history, theory, and implementing your own custom neural style transfer pipeline with Keras, I would suggest you take a look at my book, Deep Learning for Computer Vision with Python: Inside the book I discuss the Gatys et al. method in detail, including fully. Abstract Controlling stroke size in Fast Style Transfer remains a difficult task. So far, only a few attempts have been made towards it, and they still exhibit several deficiencies regarding effici.. $ cd fast-style-transfer. Download this Fast Styles Transfer Models from google drive. Add the downloaded folder to your fast-style-transfer directory. Add the image that you want to style to your fast-style-transfer directory. Your image should be smaller than 200 KB to finish in a decent amount of time. Stil from the fast-style-transfer. Put it in the fast-style-transfer folder. A checkpoint file is a model that already has tuned parameters. By using this checkpoint file, we won't need to train the model and can get straight to applying it. Copy the image you want to style into the fast-style-transfer folder. In your terminal, navigate to the fast-style-transfer folder and enter python evaluate.py --checkpoint ./rain. The two different implementations also have slightly different sets of features; neural_style.lua supports multiple style images and color-preserving style transfer, which are not implemented in slow_neural_style.lua. On the other hand slow_neural_style.lua should work with a much larger set of CNNs than neural_style.lua; I know for sure that it works with ResNets, and it should also work with. Style transfer for video. One solution to the problems with the original method is suggested in a subsequent paper, by Manuel Ruder, Alexey Dosovitskiy, and Thomas Brox titled Artistic style.

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