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In the paper Texture Networks: Feed-forward Synthesis of Textures and Stylized Images we describe a faster way to generate textures and stylize images. It requires learning a feedforward generator with a loss function proposed by Gatys et al.. When the model is trained, a texture sample or stylized image of any size can be generated instantly. Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis presents a better architectural design for the generator network. By switching batch_norm to Instance Norm we facilitate the learning process resulting in much better quality.
This is a TensorFlow reimplementation of Vadim's Lasagne code for style transfer algorithm for audio, which uses convolutions with random weights to represent audio features. To listen to examples go to the blog post. Also check out Torch implementation.
This is my try on drawing with neural networks, which is faster than Alex J. Champandard's version, and similar in quality. This approach is based on neural artistic style method (L. Gatys), whereas Alex's version uses CNN+MRF approach of Chuan Li. It takes several minutes to redraw Renoir example using GPU and it will easily fit in 4GB GPUs. If you were able to work with Justin Johnson's code for artistic style then this code should work for you too.
This is a Keras implementation of A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge. Neural Styler lets you create artistic images by combining a base picture with the style of another. For example, the images above show multiple iterations of the Chicago skyline combined with Edvard Munch's The Scream.
This is an extension of texture synthesis and style transfer method of Leon Gatys et al. based on Justin Johnson's code for neural style transfer. To listen to examples go to the blog post. Almost identical Lasagne implementation by Vadim Lebedev can be found here. Also check out TensorFlow implementation.
This repository merges fast-neural-doodle and and Texture Networks. Read the blog post for the details on the doodle algorithm and the paper to learn more about texture networks. You can find an online demo at likemo.net.
This is a PyTorch implementation of the paper A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. The code is based on Justin Johnson's Neural-Style. By resizing the style image before extracting style features, we can control the types of artistic features that are transfered from the style image; you can control this behavior with the -style_scale flag. Below we see three examples of rendering the Golden Gate Bridge in the style of The Starry Night. From left to right, -style_scale is 2.0, 1.0, and 0.5.
Creating a larger Neural-Style images through automated tiling. The idea for the script comes from a combination of techniques discovered by SwoosHkiD/bododge and ProGamerGov which was posted on the Neural-Style Wiki.
Tools made for usage alongside artistic style transfer projects based on the Controlling Perceptual Factors in Neural Style Transfer research paper by Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, Aaron Hertzmann, and Eli Shechtman. In-depth information about how to perform Scale Control and Color Control, including the Neural-Style parameters used in the examples, can be found on the wiki. The Color Control feature is broken down into two different features known as Luminance-Only Style Transfer, and Color Matching. The Scale Control feature focuses on separating style image content/shapes, and style image textures.