PromptCoT: Align Prompt Distribution via Adapted Chain-of-Thought

Junyi Yao, Yijiang Liu, Zhen Dong, Mingfei Guo, Helan Hu, Kurt Keutzer, Li Du, Daquan Zhou, Shanghang Zhang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 7027-7037

Diffusion-based generative models have exhibited remarkable capability in the production of high-fidelity visual content such as images and videos. However their performance is significantly contingent upon the quality of textual inputs commonly referred to as "prompts". The process of traditional prompt engineering while effective necessitates empirical expertise and poses challenges for inexperienced users. In this paper we introduce PromptCoT an innovative enhancer that autonomously refines prompts for users. PromptCoT is designed based on the observation that prompts which resemble the textual information of high-quality images in the training set often lead to superior generation performance. Therefore we fine-tune the pre-trained Large Language Models (LLM) using a curated text dataset that solely comprises descriptions of high-quality visual content. By doing so the LLM can capture the distribution of high-quality training texts enabling it to generate aligned continuations and revisions to boost the original texts. Nonetheless one drawback of pre-trained LLMs is their tendency to generate extraneous or irrelevant information. We employ the Chain-of-Thought (CoT) mechanism to improve the alignment between the original text prompts and their refined versions. CoT can extract and amalgamate crucial information from the aligned continuation and revision enabling reasonable inferences based on the contextual cues to produce a more comprehensive and nuanced final output. Considering computational efficiency instead of allocating a dedicated LLM for prompt enhancement to each individual model or dataset we integrate adapters that facilitate dataset-specific adaptation leveraging a shared pre-trained LLM as the foundation for this process. With independent fine-tuning of these adapters we can adapt PromptCoT to new datasets while minimally increasing training costs and memory usage. We evaluate the effectiveness of PromptCoT by assessing its performance on widely-used latent diffusion models for image and video generation. The results demonstrate significant improvements in key performance metrics.