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Recent breakthroughs in the field of language-guided image generation haveyielded impressive achievements, enabling the creation of high-quality anddiverse images based on user instructions.Although the synthesis performance isfascinating, one significant limitation of current image generation models istheir insufficient ability to generate text coherently within images,particularly for complex glyph structures like Chinese characters. To addressthis problem, we introduce GlyphDraw, a general learning framework aiming toendow image generation models with the capacity to generate images coherentlyembedded with text for any specific language.We first sophisticatedly designthe image-text dataset's construction strategy, then build our modelspecifically on a diffusion-based image generator and carefully modify thenetwork structure to allow the model to learn drawing language characters withthe help of glyph and position information.Furthermore, we maintain the model'sopen-domain image synthesis capability by preventing catastrophic forgetting byusing parameter-efficient fine-tuning techniques.Extensive qualitative andquantitative experiments demonstrate that our method not only produces accuratelanguage characters as in prompts, but also seamlessly blends the generatedtext into the background.Please refer to our\href{https://1073521013.github.io/glyph-draw.github.io/}{project page}.\end{abstract}

GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation