InstantStyle-Plus is an advanced method for style transfer in text-to-image generation, designed to maintain the integrity of the original content while effectively incorporating the target style. The authors decompose the style transfer task into three core elements: style, spatial structure, and semantic content. They introduce InstantStyle-Plus, which prioritizes content preservation through an efficient and lightweight process, utilizing the InstantStyle framework. Key techniques include: 1. **Style Injection**: Utilizing cross-attention mechanisms to selectively inject reference style features into style-specific blocks. 2. **Spatial Structure Preservation**: Starting with inverted content latent noise and using a Tile ControlNet to maintain the spatial composition of the original image. 3. **Semantic Content Preservation**: Incorporating a global image adapter to enhance the semantic fidelity of the generated image. To balance content and style, a style extractor (CSD model) is used as a discriminator to provide additional style guidance. The method is evaluated using a pre-experimental approach, focusing on the practical utility of existing techniques rather than creating a new framework. The results demonstrate superior performance in preserving content while enhancing stylistic effects compared to previous methods. The authors also discuss limitations and future work, including the need for more efficient inversion processes and further exploration of Tile ControlNet's capabilities.InstantStyle-Plus is an advanced method for style transfer in text-to-image generation, designed to maintain the integrity of the original content while effectively incorporating the target style. The authors decompose the style transfer task into three core elements: style, spatial structure, and semantic content. They introduce InstantStyle-Plus, which prioritizes content preservation through an efficient and lightweight process, utilizing the InstantStyle framework. Key techniques include: 1. **Style Injection**: Utilizing cross-attention mechanisms to selectively inject reference style features into style-specific blocks. 2. **Spatial Structure Preservation**: Starting with inverted content latent noise and using a Tile ControlNet to maintain the spatial composition of the original image. 3. **Semantic Content Preservation**: Incorporating a global image adapter to enhance the semantic fidelity of the generated image. To balance content and style, a style extractor (CSD model) is used as a discriminator to provide additional style guidance. The method is evaluated using a pre-experimental approach, focusing on the practical utility of existing techniques rather than creating a new framework. The results demonstrate superior performance in preserving content while enhancing stylistic effects compared to previous methods. The authors also discuss limitations and future work, including the need for more efficient inversion processes and further exploration of Tile ControlNet's capabilities.