Core Concepts
A two-stage method is proposed to combine controllability and high quality in image generation by leveraging pre-trained models and diffusion models, achieving outstanding results.
Abstract
In recent years, advancements have been made in text-to-image generation models, but challenges persist in achieving full controllability. The proposed two-stage method separates controllability from high quality, utilizing pre-trained models and diffusion models. This approach ensures precise control over generated images while maintaining state-of-the-art quality. By dividing the generation process into two stages, the method achieves exceptional results comparable to current top methods in the field. The model's flexibility allows compatibility with both latent and image space diffusion models, offering improved controllability without compromising on image quality.
Stats
Specific training or limited models are required for achieving full controllability during image generation.
The proposed method achieves results comparable to state-of-the-art models.
TCIG outperforms previous solutions in terms of controllability and overall performance.
IoU metric comparison shows TCIG performing better than other methods on the COCO dataset.
Quotes
"By combining the power of a pre-trained segmentation model and a diffusion text-to-image model, TCIG enables the generation of controlled images from both text and segmentation mask inputs."
"This two-stage approach combines the strengths of both models, providing a powerful and controllable image generation method that rivals state-of-the-art models."