Abstract

Text-to-image generation has seen remarkable progress with the emergence of deep learning models like Stable Diffusion.These models allow for high customized image creation. However, conventional approaches often require extensive computational resources and subject-specific fine- tuning, limiting their scalability and accessibility. Instant Imager eliminates this need by leveraging in-context learning, enabling the model to replicate the capabilities of numerous subject models. This innovation allows for the instant creation of high-flexibility and creative potential. The framework’s efficiency is evident in its ability to generate customized images 10 times faster than the conventional optimization methods, while delivering user specific number of images with superior quality. Evolutions on stable and stable-diffusion-sdxl-turbo datasets highlight its performance, consistency outperforming exis confirmed by generation process. In addition to speed and quality, Instant Imager streamlines the image generation process, making it an essential tool for artists, designers, and content creators