  Text-to-image generation models often struggle with key element loss or
semantic confusion in tasks involving Chinese classical poetry.Addressing this
issue through fine-tuning models needs considerable training costs.
Additionally, manual prompts for re-diffusion adjustments need professional
knowledge. To solve this problem, we propose Poetry2Image, an iterative
correction framework for images generated from Chinese classical poetry.
Utilizing an external poetry dataset, Poetry2Image establishes an automated
feedback and correction loop, which enhances the alignment between poetry and
image through image generation models and subsequent re-diffusion modifications
suggested by large language models (LLM). Using a test set of 200 sentences of
Chinese classical poetry, the proposed method--when integrated with five
popular image generation models--achieves an average element completeness of
70.63%, representing an improvement of 25.56% over direct image generation. In
tests of semantic correctness, our method attains an average semantic
consistency of 80.09%. The study not only promotes the dissemination of ancient
poetry culture but also offers a reference for similar non-fine-tuning methods
to enhance LLM generation.
