Shot Stylization

Shot stylization focuses on efficiently transferring artistic styles to images using minimal training data, aiming to create high-quality stylized images with diverse styles and preserved structural integrity. Current research emphasizes the use of diffusion models and GANs, often incorporating pre-trained encoders and leveraging techniques like Ada-adapters and Style Transformation Networks to achieve one-shot or zero-shot stylization. This field is significant for its potential to democratize access to advanced image stylization tools, impacting creative applications and accelerating research in generative modeling by reducing the computational burden of training large models.