Efficient Image Style Transfer Algorithm Combining Attention Guidance and Diffusion-GAN Synergy Mechanism

In response to the challenges and shortcomings of existing image style transfer methods in terms of retaining style expression details and content structure consistency, this study proposes a stable image style transfer algorithm that integrates the attention guidance mechanism and the Diffusion-GAN collaborative mechanism. The proposed algorithm mainly includes three core modules: First, the model is designed based on the guidance mechanism of channel attention. This mechanism can adaptively adjust the saliency of style and content features to enhance the style texture retention ability and content semantic consistency; second, a collaborative generation framework of diffusion model and generative adversarial network is constructed. The model uses the stable step-by-step de-noising generation process of the diffusion model to achieve continuous modeling of style fusion. In order to improve the reconstruction efficiency, the feedback of the GAN discriminator is combined to enhance the realism and artistic expression of the image; finally, feature compression and progressive reconstruction strategies are introduced for optimization. This operation can effectively reduce the computational complexity while ensuring image quality. The corresponding experiments of this study were conducted on a public data-set. The results show that the proposed algorithm can obtain style transfer images with more artistic expression and stability while maintaining high-fidelity content reconstruction.