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UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion Models (2312.04884v1)

Published 8 Dec 2023 in cs.CV
UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion Models

Abstract: Text-to-Image (T2I) generation methods based on diffusion model have garnered significant attention in the last few years. Although these image synthesis methods produce visually appealing results, they frequently exhibit spelling errors when rendering text within the generated images. Such errors manifest as missing, incorrect or extraneous characters, thereby severely constraining the performance of text image generation based on diffusion models. To address the aforementioned issue, this paper proposes a novel approach for text image generation, utilizing a pre-trained diffusion model (i.e., Stable Diffusion [27]). Our approach involves the design and training of a light-weight character-level text encoder, which replaces the original CLIP encoder and provides more robust text embeddings as conditional guidance. Then, we fine-tune the diffusion model using a large-scale dataset, incorporating local attention control under the supervision of character-level segmentation maps. Finally, by employing an inference stage refinement process, we achieve a notably high sequence accuracy when synthesizing text in arbitrarily given images. Both qualitative and quantitative results demonstrate the superiority of our method to the state of the art. Furthermore, we showcase several potential applications of the proposed UDiffText, including text-centric image synthesis, scene text editing, etc. Code and model will be available at https://github.com/ZYM-PKU/UDiffText .

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UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion Models

The research paper titled "UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion Models" introduces a novel method aimed at addressing the prevalent issues of text inaccuracies within Text-to-Image (T2I) generation models. The authors present a meticulous approach leveraging character-aware diffusion models, significantly enhancing the sequence accuracy of text rendering in synthesized images. The primary achievement of the UDiffText framework is its ability to synthesize text with high precision across various visual contexts, overcoming the common challenges faced by extant diffusion-based T2I models such as Stable Diffusion.

Technical Advancements

The researchers pinpoint a critical flaw in existing T2I models: the inadequacy of character-level information during the generation process. To combat this, they propose replacing the original CLIP text encoder with a highly efficient character-level text encoder. This substitution forms the bedrock of their approach by providing robust and discriminative text embeddings. The choice of using a character-level encoder facilitates the production of precise character-aware embeddings, essential for enhancing the syntactic integrity of text rendered in images.

The authors further introduce an innovative training strategy that combines denoising score matching (DSM) loss with local attention and scene text recognition losses. This integration is crucial for constraining the model to better attend to text regions during synthesis. By applying character-level segmentation maps as external supervision, the model effectively learns to align its attention with the structural boundaries of characters, thereby improving consistency and accuracy in text rendering.

Evaluation Metrics and Results

To substantiate their claims, the paper presents comprehensive quantitative and qualitative evaluations across multiple datasets, such as SynthText, LAION-OCR, ICDAR13, and TextSeg. Notably, the UDiffText framework achieves high sequence accuracy rates, significantly outperforming state-of-the-art alternatives like TextDiffuser and DiffSTE. For example, UDiffText shows remarkable performance improvements in sequence accuracy, reaching 94% on ICDAR13 datasets for text reconstruction. Furthermore, the method demonstrates lower Fréchet Inception Distance (FID) and LPIPS scores, indicating superior visual coherence and image quality.

The authors do not stop at reconstruction tasks but explore a range of applications, such as scene text editing and high-accuracy T2I generation. Through quantitative evaluations, they substantiate that their model effectively corrects text rendering errors typically seen in the output of diffusion models like DALL-E 3.

Implications and Future Prospects

The implications of this research are profound in both practical and theoretical contexts. Practically, the UDiffText framework sets a new standard for text generation in images, especially in applications requiring high precision text such as digital graphic design and automated content creation. Theoretically, it highlights the importance of character-level processing in multimodal models, prompting further research on enhancing the granularity at which models interpret and generate text.

Looking forward, the research presents opportunities to expand upon UDiffText by addressing its limitations, such as handling longer text sequences and enhancing performance across simpler backgrounds. The paper implicitly opens avenues for the integration of more nuanced character structural representations, potentially further refining text rendering capabilities.

In conclusion, UDiffText offers a significant advancement in the domain of text-integrated image generation. The research contributes a key innovation with the character-level focus for better textual integrity in image synthesis, thereby enhancing the fidelity and applicability of diffusion models in computational tasks. This work not only alleviates existing challenges within T2I models but also sets the stage for future developments in AI-driven text-image synthesis.

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Authors (2)
  1. Yiming Zhao (50 papers)
  2. Zhouhui Lian (36 papers)
Citations (20)
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