Improving Diffusion Models for Scene Text Editing with Dual Encoders (2304.05568v1)

Abstract: Scene text editing is a challenging task that involves modifying or inserting specified texts in an image while maintaining its natural and realistic appearance. Most previous approaches to this task rely on style-transfer models that crop out text regions and feed them into image transfer models, such as GANs. However, these methods are limited in their ability to change text style and are unable to insert texts into images. Recent advances in diffusion models have shown promise in overcoming these limitations with text-conditional image editing. However, our empirical analysis reveals that state-of-the-art diffusion models struggle with rendering correct text and controlling text style. To address these problems, we propose DIFFSTE to improve pre-trained diffusion models with a dual encoder design, which includes a character encoder for better text legibility and an instruction encoder for better style control. An instruction tuning framework is introduced to train our model to learn the mapping from the text instruction to the corresponding image with either the specified style or the style of the surrounding texts in the background. Such a training method further brings our method the zero-shot generalization ability to the following three scenarios: generating text with unseen font variation, e.g., italic and bold, mixing different fonts to construct a new font, and using more relaxed forms of natural language as the instructions to guide the generation task. We evaluate our approach on five datasets and demonstrate its superior performance in terms of text correctness, image naturalness, and style controllability. Our code is publicly available. https://github.com/UCSB-NLP-Chang/DiffSTE

Improving Diffusion Models for Scene Text Editing with Dual Encoders

The paper presented by Jiabao Ji et al. focuses on enhancing the capabilities of diffusion models for scene text editing by integrating a dual encoder architecture, known as DiffSte. This approach addresses two primary challenges with existing diffusion models regarding scene text editing: the accuracy of text rendering and the controllability of text style based on given instructions.

Diffusion models have demonstrated a strong capability in generating high-quality images, often rivaling or surpassing generative adversarial networks (GANs) in producing realistic visuals. However, applying these models to scenarios demanding precise text insertion and edits, such as scene text editing, unveils significant weaknesses. Specifically, these models struggle with consistently correct text spelling and precise style adherence based on instruction, which are critical for applications like augmented reality translation or text image synthesis.

Key Contributions

1. Dual Encoder Design: The paper introduces a novel dual encoder setup that distinguishes itself from traditional single encoder architectures, aiming to enhance text rendering and style control. This design comprises:
   • Instruction Encoder: Retains the function of the existing CLIP encoder within stable diffusion models, tasked with processing broader contextual instructions regarding text edits.
   • Character Encoder: A dedicated component specifically engineered to encapsulate character-level information, fortifying spelling accuracy by providing direct access to individual character embeddings.
2. Instruction Tuning Framework: This framework improves model training processes by instructing the modified diffusion models using varied synthetic and real-world datasets, thereby enhancing the text and style generation capabilities. Text instructions are leveraged to relay detailed specifications about desired edits, including style constraints or relaxed natural language directions.
3. Zero-Shot Generalization: The DiffSte model exhibits commendable zero-shot generalization, efficiently generating unseen font variations and adapting compound font styles. This quality indicates a robust training foundation and architecture capable of handling diverse and unanticipated text editing scenarios.

Performance and Results

The proposed model was evaluated on both synthetic and real-world datasets, including ArT, COCOText, TextOCR, and ICDAR13. The results clearly indicate a substantial improvement across several metrics:

• Text Correctness: DiffSte shows marked enhancement, reflected in OCR metrics and human evaluations, over existing GAN-based models and non-modified diffusion baselines.
• Image Naturalness: Extensive human preference tests reveal DiffSte maintains significantly higher naturalness, achieving seamless integrations of edited areas without detectable boundaries or artifacts.
• Style Controllability: Under style-conditional scenarios, the model provides significant improvements in font correctness, despite GAN-based methods benefiting from direct access to style images.

Implications and Future Directions

The introduction of DiffSte and its components signifies a step forward in the practical application of diffusion models for text editing tasks, overcoming existing limitations regarding text accuracy and style alignment. This paper’s findings suggest further potential for tuning diffusion models with diverse instruction sets, likely improving generalized applicability in AI-driven creativity tasks such as graphic design, document preparation, and AR applications.

Moving forward, exploring the scalability of such dual encoder architectures across other domains beyond scene text editing might help refine these models further. Additionally, integrating DiffSte into platforms allowing real-time natural language interactions could unlock novel AI applications in user-centric generative arts and interactive media environments. Continued exploration of cross-modal encoder designs is necessary for broader AI development, aligning computational model outputs even closer with intuitive human requirements in digital spaces.