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arXiv · 2412.10429

GPTDrawer: Enhancing Visual Synthesis through ChatGPT

Kun Li·Xinwei Chen·Tianyou Song·Hansong Zhang·Wenzhe Zhang·Qing Shan
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Abstract

In the burgeoning field of AI-driven image generation, the quest for precision and relevance in response to textual prompts remains paramount. This paper introduces GPTDrawer, an innovative pipeline that leverages the generative prowess of GPT-based models to enhance the visual synthesis process. Our methodology employs a novel algorithm that iteratively refines input prompts using keyword extraction, semantic analysis, and image-text congruence evaluation. By integrating ChatGPT for natural language processing and Stable Diffusion for image generation, GPTDrawer produces a batch of images that undergo successive refinement cycles, guided by cosine similarity metrics until a threshold of semantic alignment is attained. The results demonstrate a marked improvement in the fidelity of images generated in accordance with user-defined prompts, showcasing the system's ability to interpret and visualize complex semantic constructs. The implications of this work extend to various applications, from creative arts to design automation, setting a new benchmark for AI-assisted creative processes.

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Kun Li, Xinwei Chen, Tianyou Song, Hansong Zhang, Wenzhe Zhang, Qing Shan. 2024-12-11. GPTDrawer: Enhancing Visual Synthesis through ChatGPT. https://arxiv.org/abs/2412.10429

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