ComfyUI-Fast-Style-Transfer

A ComfyUI node for fast neural style transfer

CommonProductImageNeural Style TransferImage Processing

ComfyUI-Fast-Style-Transfer is a rapid neural style transfer plugin developed based on the PyTorch framework. It allows users to achieve image style conversion through simple operations. This plugin is based on the fast-neural-style-pytorch project and currently only ports the basic inference functionality. Users can customize styles and achieve unique style transfer effects by training their own models.

Neural style transfer technology based on PyTorch
Supports custom style transfer
users can train their own models
Provides basic inference functionality for easy application in real projects
Requires downloading VGG-16 model and MS COCO training dataset
Operates through the ComfyUI interface
simplifying the training and inference process
Supports adjusting batch_size to accommodate different hardware configurations
Allows saving intermediate and final models during training
facilitating selection and testing

This plugin is aimed at developers and enthusiasts in the field of image processing who are interested in image style transfer technology and want to achieve personalized image effects through simple operations. It's ideal for those who want to quickly implement style transfer without delving into the intricacies of underlying algorithms.

User A uses the plugin to add a Van Gogh style artistic effect to their own photography.
Designer B leverages the plugin to quickly generate a series of images with a unified style for a design project.
Developer C integrates the plugin into their own application
providing users with an online style transfer service.

1. Clone the project locally: Clone ComfyUI-Fast-Style-Transfer to your local custom node folder using the git command.
2. Download dependency files: Download the VGG-16 model and MS COCO training dataset and place them in the specified folder.
3. Configure training parameters: Load the TrainFastStyleTransfer node in ComfyUI and adjust parameters like batch_size based on your hardware configuration.
4. Select style image: Use the 'load_image' node to load the style image and add it to the style image list of the TrainFastStyleTransfer node.
5. Start training: Once all parameters are set

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