Dynamic Prompt Optimizing for Text-to-Image Generation (2404.04095v1)

Abstract: Text-to-image generative models, specifically those based on diffusion models like Imagen and Stable Diffusion, have made substantial advancements. Recently, there has been a surge of interest in the delicate refinement of text prompts. Users assign weights or alter the injection time steps of certain words in the text prompts to improve the quality of generated images. However, the success of fine-control prompts depends on the accuracy of the text prompts and the careful selection of weights and time steps, which requires significant manual intervention. To address this, we introduce the \textbf{P}rompt \textbf{A}uto-\textbf{E}diting (PAE) method. Besides refining the original prompts for image generation, we further employ an online reinforcement learning strategy to explore the weights and injection time steps of each word, leading to the dynamic fine-control prompts. The reward function during training encourages the model to consider aesthetic score, semantic consistency, and user preferences. Experimental results demonstrate that our proposed method effectively improves the original prompts, generating visually more appealing images while maintaining semantic alignment. Code is available at https://github.com/Mowenyii/PAE.

An Overview of "Dynamic Prompt Optimizing for Text-to-Image Generation"

The paper "Dynamic Prompt Optimizing for Text-to-Image Generation" addresses a crucial challenge in the domain of text-to-image generation: optimizing text prompts to achieve improved image quality and semantic alignment without extensive manual input. This work is a methodical investigation into the automatization of prompt refinement, integrating reinforcement learning within text-to-image models, which have seen significant advancements primarily through diffusion techniques, such as those employed by Stable Diffusion and Imagen.

Research Context and Approach

The challenge tackled is the sensitivity of these generative models to the length and structure of input text prompts, which can lead to varied outcomes even for prompts conveying similar meanings. This sensitivity necessitates a nuanced approach for prompt optimization, a task traditionally reliant on manual methods that are labor-intensive and often inefficient.

The paper introduces a novel methodology termed Prompt Auto-Editing (PAE), which builds on this manual practice through automatized refinement processes. The method comprises an integration of reinforcement learning strategies to dynamically adjust the prompt configurations by exploring variables such as word weights and injection time steps — processes that are traditionally managed via heuristic adjustments by users. The optimization goal is to enhance the aesthetic appeal, semantic consistency, and alignment with user preferences in the generated images.

Methodological Framework

The PAE framework is made up of a two-stage training process, focusing on both static and dynamic aspects of prompt engineering:

1. Prompt Refinement via Supervised Fine-Tuning: This stage utilizes a fine-tuned LLM, based initially on GPT-2, to enrich user input prompts by appending effective modifiers. A confidence score filters publicly available datasets, ensuring only the highest quality prompt-image pairs are selected for training.
2. Dynamic Fine-Control via Reinforcement Learning: The second phase employs a dynamic approach by utilizing Reinforcement Learning (RL) to extend the model’s capabilities to dynamically assign importance to specific prompt words and adjust effect time-ranges in the diffusion process. This is supported by a reward function taking into account the aesthetic quality, semantic consistency, and user preferences.

Experimental Evaluation

Experimental evidence in the paper validates the efficiency of the PAE using datasets such as Lexica.art and DiffusionDB. PAE was tested for its ability to outperform existing methods, notably achieving higher aesthetic scores while maintaining strong human preferences, as indicated by PickScore. Furthermore, its application on the COCO dataset demonstrates its robustness and capacity to generalize beyond the trained domains, a key feature for practical adoption.

Implications and Future Work

PAE offers notable implications for both the theoretical understanding and practical applications in AI-driven content generation. Theoretically, it propels our understanding of prompt optimization's impact on generative model outputs, suggesting a shift towards more generalized and adaptable modeling frameworks. Practically, it reduces the reliance on manual prompt engineering, thereby improving user efficiency and broadening the applicability of text-to-image models across diverse industries, from media and entertainment to online content creation.

The findings prompt further exploration into integrating newer LLM architectures and more sophisticated RL frameworks. Future studies could also explore embedding user-specific preference models directly into the generation pipeline, thereby ensuring that outputs not only meet general aesthetic standards but also align with individual user or industry-specific tastes and requirements.

In summary, this work contributes an innovative, automated approach to prompt refinement, crucially enhancing the intersection of language processing and image generation within AI systems, and setting a foundation for future advancements in the field.