teja00/CTIG-Diffusion
This implementation is based on the paper titled "Conditional Text Image Generation with Diffusion Models," which can be found at arXiv:2306.10804v1.
This project helps graphic designers, content creators, and marketing professionals create or modify text-based images with precise control. You can input existing images, text descriptions, or style preferences to generate new text images, enhance current ones with additional text, or repair damaged text in visuals. This is useful for anyone needing to produce high-quality, customized text graphics for various applications.
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Use this if you need to generate new text images from scratch, augment existing images with text, recover corrupted text images, or imitate the style of a reference text image.
Not ideal if you are looking for a tool to generate entire scenes or objects rather than focusing specifically on text within images.
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Language
Python
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Last pushed
Jun 05, 2025
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