TextDiffuser-RL: Efficient and Robust Text Layout Optimization for High-Fidelity Text-to-Image Synthesis

Text-embedded image generation plays a critical role in industries such as graphic design, advertising, and digital content creation. Text-to-Image generation methods leveraging diffusion models, such as TextDiffuser-2, have demonstrated promising results in producing images with embedded text. TextDiffuser-2 effectively generates bounding box layouts that guide the rendering of visual text, achieving high fidelity and coherence. However, existing approaches often rely on resource-intensive processes and are limited in their ability to run efficiently on both CPU and GPU platforms. To address these challenges, we propose a novel two-stage pipeline that integrates reinforcement learning (RL) for rapid and optimized text layout generation with a diffusion-based image synthesis model. Our RL-based approach significantly accelerates the bounding box prediction step while reducing overlaps, allowing the system to run efficiently on both CPUs and GPUs. Extensive evaluations demonstrate that our framework maintains or surpasses TextDiffuser-2's quality in text placement and image synthesis, with markedly faster runtime and increased flexibility. Extensive evaluations demonstrate that our framework maintains or surpasses TextDiffuser-2's quality in text placement and image synthesis, with markedly faster runtime and increased flexibility. Our approach has been evaluated on the MARIOEval benchmark, achieving OCR and CLIPScore metrics close to state-of-the-art models, while being 97.64% more faster and requiring only 2MB of memory to run.

TWIG: Two-Step Image Generation using Segmentation Masks in Diffusion Models

In today's age of social media and marketing, copyright issues can be a major roadblock to the free sharing of images. Generative AI models have made it possible to create high-quality images, but concerns about copyright infringement are a hindrance to their abundant use. As these models use data from training images to generate new ones, it is often a daunting task to ensure they do not violate intellectual property rights. Some AI models have even been noted to directly copy copyrighted images, a problem often referred to as source copying. Traditional copyright protection measures such as watermarks and metadata have also proven to be futile in this regard. To address this issue, we propose a novel two-step image generation model inspired by the conditional diffusion model. The first step involves creating an image segmentation mask for some prompt-based generated images. This mask embodies the shape of the image. Thereafter, the diffusion model is asked to generate the image anew while avoiding the shape in question. This approach shows a decrease in structural similarity from the training image, i.e. we are able to avoid the source copying problem using this approach without expensive retraining of the model or user-centered prompt generation techniques. This makes our approach the most computationally inexpensive approach to avoiding both copyright infringement and source copying for diffusion model-based image generation.