TCIG: Two-Stage Controlled Image Generation with Quality Enhancement through Diffusion
This addresses the limitation of controllability in text-to-image generation for users needing precise image control, though it appears incremental as it builds on existing pre-trained and diffusion models.
The paper tackles the problem of achieving full controllability in text-to-image generation without compromising quality, proposing a two-stage method that separates controllability from high-quality generation and achieves results comparable to state-of-the-art methods.
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.