LDGen: Enhancing Text-to-Image Synthesis via Large Language Model-Driven
  Language Representation
In this paper, we introduce LDGen, a novel method for integrating large
language models (LLMs) into existing text-to-image diffusion models while
minimizing computational demands. Traditional text encoders, such as CLIP and
T5, exhibit limitations in multilingual processing, hindering image generation
across diverse languages. We address these challenges by leveraging the
advanced capabilities of LLMs. Our approach employs a language representation
strategy that applies hierarchical caption optimization and human instruction
techniques to derive precise semantic information,. Subsequently, we
incorporate a lightweight adapter and a cross-modal refiner to facilitate
efficient feature alignment and interaction between LLMs and image features.
LDGen reduces training time and enables zero-shot multilingual image
generation. Experimental results indicate that our method surpasses baseline
models in both prompt adherence and image aesthetic quality, while seamlessly
supporting multiple languages. Project page: https://zrealli.github.io/LDGen.
67befd09afb202a5b7518572
Pengzhi Li
claimed_verified
2025-02-26T15:37:26.179Z
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Li
user
Kinpz
67befd09afb202a5b7518573
Pengfei Yu
67befd09afb202a5b7518574
Zide Liu
admin_assigned
2025-02-28T13:08:16.002Z
6431974f034ecbefddd4b463
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刘自得
user
zideliu
67befd09afb202a5b7518575
Wei He
67befd09afb202a5b7518576
Xuhao Pan
admin_assigned
2025-02-28T13:09:11.684Z
64478f36a478b20f17543240
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Pan Xuhao
user
qingtian0
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Xudong Rao
67befd09afb202a5b7518578
Tao Wei
67befd09afb202a5b7518579
Wei Chen
admin_assigned
2025-02-28T13:09:29.481Z
632acc6b7fb39c2b6351d84e
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wei chen
user
weichen
LDGen: Enhancing Text-to-Image Synthesis via Large Language Model-Driven
  Language Representation
In this paper, we introduce LDGen, a novel method for integrating large
language models (LLMs) into existing text-to-image diffusion models while
minimizing computational demands. Traditional text encoders, such as CLIP and
T5, exhibit limitations in multilingual processing, hindering image generation
across diverse languages. We address these challenges by leveraging the
advanced capabilities of LLMs. Our approach employs a language representation
strategy that applies hierarchical caption optimization and human instruction
techniques to derive precise semantic information,. Subsequently, we
incorporate a lightweight adapter and a cross-modal refiner to facilitate
efficient feature alignment and interaction between LLMs and image features.
LDGen reduces training time and enables zero-shot multilingual image
generation. Experimental results indicate that our method surpasses baseline
models in both prompt adherence and image aesthetic quality, while seamlessly
supporting multiple languages. Project page: https://zrealli.github.io/LDGen.
LDGen integrates large language models into text-to-image diffusion models to achieve efficient zero-shot multilingual image generation with improved prompt adherence and aesthetic quality.
LLMs text-to-image diffusion models CLIP T5 multilingual processing language representation hierarchical caption optimization human instruction lightweight adapter cross-modal refiner feature alignment zero-shot multilingual image generation prompt adherence image aesthetic quality