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neural-style in TensorFlow is a TensorFlow implementation of neural style transfer. It creates a new image by combining the content of one image with the artistic style of one or more style images. The project uses TensorFlow automatic differentiation and the Adam optimizer rather than the original L-BFGS approach. Users can run it from the command line by providing a content image, style image inputs, and an output path. It supports checkpoint outputs, iteration control, style blending, and hyperparameter tuning for content weight, style weight, and learning rate. Overall, it is a focused research-style image generation tool for experimenting with artistic transfer and visual optimization.
Features
- TensorFlow-based neural style transfer
- Content image and style image input support
- Command-line image generation workflow
- Style blending with multiple style images
- Checkpoint image output during optimization
- Tunable iterations, style weight, content weight, and learning rate
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GNU General Public License version 3.0 (GPLv3)Follow neural-style in TensorFlow
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