GlyphDraw2: Automatic Generation of Complex Glyph Posters with Diffusion Models and Large Language Models
<|reference_start|>GlyphDraw2: Automatic Generation of Complex Glyph Posters with Diffusion Models and Large Language Models: Posters play a crucial role in marketing and advertising by enhancing visual communication and brand visibility, making significant contributions to industrial design. With the latest advancements in controllable T2I diffusion models, increasing research has focused on rendering text within synthesized images. Despite improvements in text rendering accuracy, the field of automatic poster generation remains underexplored. In this paper, we propose an automatic poster generation framework with text rendering capabilities leveraging LLMs, utilizing a triple-cross attention mechanism based on alignment learning. This framework aims to create precise poster text within a detailed contextual background. Additionally, the framework supports controllable fonts, adjustable image resolution, and the rendering of posters with descriptions and text in both English and Chinese.Furthermore, we introduce a high-resolution font dataset and a poster dataset with resolutions exceeding 1024 pixels. Our approach leverages the SDXL architecture. Extensive experiments validate our method's capability in generating poster images with complex and contextually rich backgrounds.Codes is available at https://github.com/OPPO-Mente-Lab/GlyphDraw2.<|reference_end|>
@article{ma2024glyphdraw2:,
    title={GlyphDraw2: Automatic Generation of Complex Glyph Posters with Diffusion
  Models and Large Language Models},
    author={Jian Ma, Yonglin Deng, Chen Chen, Haonan Lu, Zhenyu Yang},
    journal={arXiv preprint arXiv:2407.02252},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02252},
    primaryClass={cs.CV}
}