  Posters play a crucial role in marketing and advertising, contributing
significantly to industrial design by enhancing visual communication and brand
visibility. With recent advances in controllable text-to-image diffusion
models, more concise research is now focusing on rendering text within
synthetic images. Despite improvements in text rendering accuracy, the field of
end-to-end poster generation remains underexplored. This complex task involves
striking a balance between text rendering accuracy and automated layout to
produce high-resolution images with variable aspect ratios. To tackle this
challenge, we propose an end-to-end text rendering framework employing a triple
cross-attention mechanism rooted in align learning, designed to create precise
poster text within detailed contextual backgrounds. Additionally, we introduce
a high-resolution dataset that exceeds 1024 pixels in image resolution. Our
approach leverages the SDXL architecture. Extensive experiments validate the
ability of our method to generate poster images featuring intricate and
contextually rich backgrounds. Codes will be available at
https://github.com/OPPO-Mente-Lab/GlyphDraw2.
