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Taiyi-Diffusion-XL: Advancing Bilingual Text-to-Image Generation with Large Vision-Language Model Support (2401.14688v3)

Published 26 Jan 2024 in cs.CL

Abstract: Recent advancements in text-to-image models have significantly enhanced image generation capabilities, yet a notable gap of open-source models persists in bilingual or Chinese language support. To address this need, we present Taiyi-Diffusion-XL, a new Chinese and English bilingual text-to-image model which is developed by extending the capabilities of CLIP and Stable-Diffusion-XL through a process of bilingual continuous pre-training. This approach includes the efficient expansion of vocabulary by integrating the most frequently used Chinese characters into CLIP's tokenizer and embedding layers, coupled with an absolute position encoding expansion. Additionally, we enrich text prompts by large vision-LLM, leading to better images captions and possess higher visual quality. These enhancements are subsequently applied to downstream text-to-image models. Our empirical results indicate that the developed CLIP model excels in bilingual image-text retrieval.Furthermore, the bilingual image generation capabilities of Taiyi-Diffusion-XL surpass previous models. This research leads to the development and open-sourcing of the Taiyi-Diffusion-XL model, representing a notable advancement in the field of image generation, particularly for Chinese language applications. This contribution is a step forward in addressing the need for more diverse language support in multimodal research. The model and demonstration are made publicly available at \href{https://huggingface.co/IDEA-CCNL/Taiyi-Stable-Diffusion-XL-3.5B/}, fostering further research and collaboration in this domain.

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Introduction

The field of text-to-image generation has recently witnessed the introduction of Taiyi-Diffusion-XL, a model that displays a significant leap in bilingual text-to-image synthesis. This model pivots from the norm where text-to-image generation is predominantly English-centric, requiring the cumbersome step of translating from other languages into English to utilize such advanced models. The Taiyi-Diffusion-XL overcomes these barriers by efficiently encoding and generating images from both Chinese and English text prompts, maintaining fidelity to each language's cultural and linguistic nuances.

Methodological Innovations

The development of Taiyi-Diffusion-XL involved multifaceted enhancements to pre-training approaches traditionally seen in models like CLIP. The two-phase methodology includes refining the dataset preparation with images paired with high-quality, detailed text descriptions. For CLIP model training, Taiyi-Diffusion-XL initializes with an English pre-trained version and adapts it using a bilingual dataset, notably improving the retrieval abilities in both languages. The subsequent Taiyi-XL training rests on a time-conditional UNet architecture and a loss function designed for multi-resolution denoising training processes. The implementation goes beyond the norm to address the complexities of bilingual datasets, resulting in a robust model that efficiently generates images from detailed textual prompts in both English and Chinese.

Empirical Validation

Extensive empirical analysis underscores Taiyi-Diffusion-XL's superiority over existing models. It achieves leading performance metrics in image-text retrieval and image generation quality, according to evaluations like CLIP Similarity, Inception Score, and Fréchet Inception Distance. These results emerge from exhaustive comparisons with benchmark models in bilingual text-to-image synthesis.

Implications and Future Research

Taiyi-Diffusion-XL represents a substantial contribution to the field of AI and multimedia generation, emphasizing the importance of inclusivity in language support. It paves the way for further studies in areas necessitating deep comprehension of bilingual textual descriptions for accurate image generation. By making the Taiyi-Diffusion-XL model openly available to researchers and developers, it invites extensive collaboration, with potential ramifications across numerous domains where bilingual multi-modal AI can be leveraged.

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Authors (9)
  1. Xiaojun Wu (94 papers)
  2. Dixiang Zhang (7 papers)
  3. Ruyi Gan (14 papers)
  4. Junyu Lu (32 papers)
  5. Ziwei Wu (19 papers)
  6. Renliang Sun (17 papers)
  7. Jiaxing Zhang (39 papers)
  8. Pingjian Zhang (9 papers)
  9. Yan Song (91 papers)
Citations (3)
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