  In recent years, significant progress has been made in the development of
text-to-image generation models. However, these models still face limitations
when it comes to achieving full controllability during the generation process.
Often, specific training or the use of limited models is required, and even
then, they have certain restrictions. To address these challenges, A two-stage
method that effectively combines controllability and high quality in the
generation of images is proposed. This approach leverages the expertise of
pre-trained models to achieve precise control over the generated images, while
also harnessing the power of diffusion models to achieve state-of-the-art
quality. By separating controllability from high quality, This method achieves
outstanding results. It is compatible with both latent and image space
diffusion models, ensuring versatility and flexibility. Moreover, This approach
consistently produces comparable outcomes to the current state-of-the-art
methods in the field. Overall, This proposed method represents a significant
advancement in text-to-image generation, enabling improved controllability
without compromising on the quality of the generated images.
