ICDaeLST: Intensity-Controllable Detail Attention-enhanced for Lightweight Fast Style Transfer
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The mainstream style transfer methods usually use pre-trained deep convolutional neural network (VGG) models as encoders, or use more complex model structures to achieve better style transfer effects. This leads to extremely slow processing speeds for practical tasks due to limited resources or higher resolution image processing, such as 4K images, severely hindering the practical application value of style transfer models. We introduce a lightweight and fast styletransfer model with controllable detail attention enhancement, named ICDaeLST. The model adopts a minimal, shallow, and small architecture, forming a very compact lightweight model for efficient forward inference. Although its structure is simple and has limited parameters, we achieve better overall color and texture structure matching by introducing a style discriminator, design additional global semantic invariance loss to preserve the semantic and structural information of the content image from a high-level global perspective, and design a shallow detail attention enhancement module to preserve the detail information of the content image from a low-level detail perspective. We also achieve controllable intensity during inference for the first time (adjusting the degree of detail retention and texture structure transfer based on subjective judgment) to meet different users' subjective evaluation of stylization effects. Compared with the current best-performing and most lightweight models, our model achieves better style transfer quality and better content structure and detail retention, while having a smaller model size (17-250 times smaller) and faster speed (0.26-6.5 times faster), and achieves the fastest processing speed of 0.38s on 4K high-resolution images.
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