![]() ![]() To ensure this, and to give strong privacy guarantees when the training data is sensitive, it is possible to use techniques based on the theory of differential privacy. Ideally, the parameters of trained machine-learning models should encode general patterns rather than facts about specific training examples. Modern machine learning is increasingly applied to create amazing new technologies and user experiences, many of which involve training machines to learn responsibly from sensitive data, such as personal photos or email. Today, we’re excited to announce TensorFlow Privacy ( GitHub), an open source library that makes it easier not only for developers to train machine-learning models with privacy, but also for researchers to advance the state of the art in machine learning with strong privacy guarantees. Thanks to the contributors of this project.Posted by Carey Radebaugh (Product Manager) and Ulfar Erlingsson (Research Scientist) This code is based on the CartoonGAN-Tensorflow and Anime-Sketch-Coloring-with-Swish-Gated-Residual-UNet. Xin Chen, Gang Liu, Jie Chen Acknowledgment Regarding the request for commercial use, please contact us via email to help you obtain the authorization letter. Permission is granted to use the AnimeGAN given that you agree to my license terms. This repo is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications. □ pictures from the paper - AnimeGAN: a novel lightweight GAN for photo animation python get_generator_ckpt.py -checkpoint_dir. python train.py -dataset Hayao -epoch 101 -init_epoch 5 5. python edge_smooth.py -dataset Hayao -img_size 256 4. python video2anime.py -video video/input/お花見.mp4 -checkpoint_dir. python test.py -checkpoint_dir checkpoint/generator_Hayao_weight -test_dir dataset/test/real -style_name H 2. Use new high-quality style data, which come from BD movies as much as possible. ![]() ![]() Further reduce the number of parameters of the generator network.Ĥ. It is easy to train and directly achieve the effects in the paper.ģ. Solve the problem of high-frequency artifacts in the generated image.Ģ. The improvement directions of AnimeGANv2 mainly include the following 4 points:ġ. ![]() The generated stylized images will be affected by the overall brightness and tone of the style data, so try not to select the anime images of night as the style data, and it is necessary to make an exposure compensation for the overall style data to promote the consistency of brightness and darkness of the entire style data.ĪnimeGANv2 has been released and can be accessed here.In order to obtain a better face animation effect, when using 2 images as data pairs for training, it is suggested that the faces in the photos and the faces in the anime style data should be consistent in terms of gender as much as possible.since the real photos in the training set are all landscape photos, if you want to stylize the photos with people as the main body, you may as well add at least 3000 photos of people in the training set and retrain to obtain a new model.Online access: Be grateful to for developing an online access project, you can implement photo animation through a browser without installing anything, click here to have a try.AnimeGANv2, the improved version of AnimeGAN.If you like what I'm doing you can tip me on patreon. The paper can be accessed here or on the website. A Tensorflow implementation of AnimeGAN for fast photo animation ! 日本語 ![]()
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