TCIG: Two-Stage Controlled Image Generation with Quality Enhancement through Diffusion

• URL: http://arxiv.org/abs/2403.01212v1
• Date: Sat, 2 Mar 2024 13:59:02 GMT
• Title: TCIG: Two-Stage Controlled Image Generation with Quality Enhancement through Diffusion
• Authors: Salaheldin Mohamed
• Abstract summary: A two-stage method that combines controllability and high quality in the generation of images is proposed. By separating controllability from high quality, This method achieves outstanding results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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, spe- cific 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 effec- tively 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 flexibil- ity. Moreover, This approach consistently produces comparable outcomes to the current state-of-the-art methods in the field. Overall, This proposed method rep- resents a significant advancement in text-to-image generation, enabling improved controllability without compromising on the quality of the generated images.

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