B-LoRA is a method for implicitly separating the style and content of a single image, enabling various image stylization tasks. The method leverages LoRA (Low-Rank Adaptation) to train two specific transformer blocks (B-LoRAs) in the SDXL model, which allows for the separation of style and content components. By jointly learning the LoRA weights of these two blocks, B-LoRA achieves a style-content separation that cannot be achieved by training each B-LoRA independently. This approach reduces overfitting and allows for efficient style manipulation. Once trained, the B-LoRAs can be used as independent components for various stylization tasks, including image style transfer, text-based image stylization, consistent style generation, and style-content mixing. The method is efficient, requiring only a single image for training, and allows for the reuse of learned styles and contents without additional training. B-LoRA is compared to other methods such as ZipLoRA and StyleDrop, and is shown to perform better in terms of style alignment and content preservation. The method is also evaluated through user studies, where it is preferred over alternative approaches. However, the method has limitations, such as the potential inclusion of color in the style component and the inability to adequately capture content in complex scenes. Future work includes exploring further separation techniques within LoRA fine-tuning to achieve more concrete separation into sub-components.B-LoRA is a method for implicitly separating the style and content of a single image, enabling various image stylization tasks. The method leverages LoRA (Low-Rank Adaptation) to train two specific transformer blocks (B-LoRAs) in the SDXL model, which allows for the separation of style and content components. By jointly learning the LoRA weights of these two blocks, B-LoRA achieves a style-content separation that cannot be achieved by training each B-LoRA independently. This approach reduces overfitting and allows for efficient style manipulation. Once trained, the B-LoRAs can be used as independent components for various stylization tasks, including image style transfer, text-based image stylization, consistent style generation, and style-content mixing. The method is efficient, requiring only a single image for training, and allows for the reuse of learned styles and contents without additional training. B-LoRA is compared to other methods such as ZipLoRA and StyleDrop, and is shown to perform better in terms of style alignment and content preservation. The method is also evaluated through user studies, where it is preferred over alternative approaches. However, the method has limitations, such as the potential inclusion of color in the style component and the inability to adequately capture content in complex scenes. Future work includes exploring further separation techniques within LoRA fine-tuning to achieve more concrete separation into sub-components.