InstantStyle-Plus is a method for style transfer in text-to-image generation that prioritizes content preservation while integrating target styles. The method decomposes the style transfer task into three core elements: style, spatial structure, and semantic content. It introduces an approach that maintains the original content's integrity while seamlessly integrating the target style. The method utilizes the cutting-edge InstantStyle framework for efficient style injection and incorporates an inverted content latent noise and a versatile plug-and-play tile ControlNet to preserve the original image's intrinsic layout. A global semantic adapter is also used to enhance the semantic content's fidelity. A style extractor is employed as a discriminator to provide supplementary style guidance. The method is optimization-free and has been tested on various content images, demonstrating its robustness and versatility in adapting to diverse content. The method is compared to previous methods, showing superior results in balancing stylistic effects and content preservation. The method also includes an ablation study that highlights the importance of each component in preserving spatial structure and semantic content. The method is implemented with Stable Diffusion XL and has been shown to produce high-quality results in various experiments. The method is also evaluated for its ability to maintain semantic integrity and avoid semantic drift in scenarios where the textual prompt is absent or insufficient. The method has limitations, including the time-consuming inversion process and the need for substantial VRAM for style guidance. Future work includes developing a more elegant framework for injecting style without compromising content integrity during training.InstantStyle-Plus is a method for style transfer in text-to-image generation that prioritizes content preservation while integrating target styles. The method decomposes the style transfer task into three core elements: style, spatial structure, and semantic content. It introduces an approach that maintains the original content's integrity while seamlessly integrating the target style. The method utilizes the cutting-edge InstantStyle framework for efficient style injection and incorporates an inverted content latent noise and a versatile plug-and-play tile ControlNet to preserve the original image's intrinsic layout. A global semantic adapter is also used to enhance the semantic content's fidelity. A style extractor is employed as a discriminator to provide supplementary style guidance. The method is optimization-free and has been tested on various content images, demonstrating its robustness and versatility in adapting to diverse content. The method is compared to previous methods, showing superior results in balancing stylistic effects and content preservation. The method also includes an ablation study that highlights the importance of each component in preserving spatial structure and semantic content. The method is implemented with Stable Diffusion XL and has been shown to produce high-quality results in various experiments. The method is also evaluated for its ability to maintain semantic integrity and avoid semantic drift in scenarios where the textual prompt is absent or insufficient. The method has limitations, including the time-consuming inversion process and the need for substantial VRAM for style guidance. Future work includes developing a more elegant framework for injecting style without compromising content integrity during training.