  This paper introduces AnyTrans, an all-encompassing framework for the
task-Translate AnyText in the Image (TATI), which includes multilingual text
translation and text fusion within images. Our framework leverages the
strengths of large-scale models, such as Large Language Models (LLMs) and
text-guided diffusion models, to incorporate contextual cues from both textual
and visual elements during translation. The few-shot learning capability of
LLMs allows for the translation of fragmented texts by considering the overall
context. Meanwhile, the advanced inpainting and editing abilities of diffusion
models make it possible to fuse translated text seamlessly into the original
image while preserving its style and realism. Additionally, our framework can
be constructed entirely using open-source models and requires no training,
making it highly accessible and easily expandable. To encourage advancement in
the TATI task, we have meticulously compiled a test dataset called MTIT6, which
consists of multilingual text image translation data from six language pairs.
