DiffUTE: Universal Text Editing Diffusion Model (2305.10825v3)

Abstract: Diffusion model based language-guided image editing has achieved great success recently. However, existing state-of-the-art diffusion models struggle with rendering correct text and text style during generation. To tackle this problem, we propose a universal self-supervised text editing diffusion model (DiffUTE), which aims to replace or modify words in the source image with another one while maintaining its realistic appearance. Specifically, we build our model on a diffusion model and carefully modify the network structure to enable the model for drawing multilingual characters with the help of glyph and position information. Moreover, we design a self-supervised learning framework to leverage large amounts of web data to improve the representation ability of the model. Experimental results show that our method achieves an impressive performance and enables controllable editing on in-the-wild images with high fidelity. Our code will be avaliable in \url{https://github.com/chenhaoxing/DiffUTE}.

• The paper introduces DiffUTE, a self-supervised diffusion model that integrates glyph information and text position cues to enhance multilingual text editing in images.
• It employs a progressive training strategy and interactive LLM integration, enabling precise and high-fidelity text modifications without explicit masks.
• Experimental results demonstrate DiffUTE’s superiority, outperforming DiffSTE by over 11 percentage points in OCR accuracy and human-assessed correctness.

An Analysis of DiffUTE: Universal Text Editing Diffusion Model

The paper introduces DiffUTE, a self-supervised diffusion model designed for text editing in images, specifically focusing on rendering multilingual text with high fidelity while maintaining realistic appearance. Traditional diffusion models have struggled with accurately generating text and maintaining text style in image editing applications. This paper proposes several innovative solutions to address these challenges and demonstrates the effectiveness of DiffUTE in multiple experimental scenarios.

Technical Innovations

DiffUTE incorporates several key innovations to enhance the text editing capabilities of diffusion models:

1. Modified Network Structure: The model integrates character glyphs and text position information as auxiliary inputs. This modification improves the model's ability to render diverse multilingual characters accurately. By explicitly providing glyph information, the model can exercise fine-grained control over character generation.
2. Self-supervised Framework: To circumvent the lack of extensive paired datasets required for supervised learning, the authors introduce a self-supervised learning framework. It leverages large quantities of web data, enabling the model to learn effective representations for text editing without needing manual annotations.
3. Progressive Training Strategy (PTT): A novel PTT strategy improves VAE's ability to reconstruct text regions by progressively increasing the training image size. This approach is crucial for maintaining the fidelity of fine-tuned textual details that are often lost during the VAE compression in traditional diffusion approaches.
4. Interactive Editing with LLM: The integration of a LLM, ChatGLM, facilitates interaction by allowing users to input natural language instructions for text editing tasks. This approach enhances usability by eliminating the need for explicit masks, instead letting the LLM interpret user requests for precise text modifications.

Empirical Results

The experimental evaluation underscores the enhanced performance of DiffUTE over existing methods in generating high-quality text in a variety of fonts, orientations, and languages. The paper provides robust numerical evidence demonstrating DiffUTE's superiority in terms of OCR accuracy and human-assessed correctness (Cor). On average, DiffUTE outperformed the next best method, DiffSTE, in OCR accuracy by over 11 percentage points, with similar gains in Cor metrics across multiple datasets.

Furthermore, the ablation studies validate the contributions of each component within the framework—particularly the impact of fine-grained control utilizing position and glyph guidance. Without these improvements, the text generation accuracy significantly declines, evidencing their critical role in the model's success.

Implications and Future Directions

The development of DiffUTE has both practical and theoretical implications. Practically, it offers a robust tool for high-fidelity text editing in applications such as advertising, augmented reality, and document processing. Theoretically, it contributes to our understanding of how character-level features and positional encodings can be effectively utilized within generative models to manage complex editing tasks.

Future developments might focus on enhancing the scalability of the model to handle more extensive and more complex text editing scenarios — such as editing entire paragraphs of text within a document. Additionally, the integration with LLMs opens avenues for more sophisticated human-computer interaction models, making automated editing tools more intuitive and accessible. Moreover, addressing the challenge of handling increased spatial complexity with growing text length in images remains a promising direction for further research.