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

Salaheldin Mohamed

TL;DR

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.

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, 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.

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

TL;DR

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.

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, 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.
Paper Structure (7 sections, 2 equations, 5 figures, 1 table)

This paper contains 7 sections, 2 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. First stage
  5. Second stage
  6. Experiments
  7. Conclusion

Figures (5)

  • Figure 1: Two stage generation, the first stage generates a perfectly contorlled image and second stage for producing high quality final output.
  • Figure 2: Two stage generation, the first column is the input (text prompt + segmentation masks), second column is two sample outputs of the first stage, and third column is two outputs of the second stage from the two inputs of the second stage respectively.
  • Figure 3: A comparison between this method (TCIG) and others (2206.02779, bartal2023multidiffusion, rombach2022highresolution). First row contains the input segmentation maps and it's text description prompt, last two rows are two samples of TCIG. figure was adapted from bartal2023multidiffusion.
  • Figure 4: First stage, through the guidance of segmentation models and the CLIP network (radford2021learning), a controlled image is produced
  • Figure 5: sample outputs of TCIG, First row contains the input segmentation maps and it's text description prompt, second row contains the final output