Preference Adaptive and Sequential Text-to-Image Generation (2412.10419v2)

Abstract: We address the problem of interactive text-to-image (T2I) generation, designing a reinforcement learning (RL) agent which iteratively improves a set of generated images for a user through a sequence of prompt expansions. Using human raters, we create a novel dataset of sequential preferences, which we leverage, together with large-scale open-source (non-sequential) datasets. We construct user-preference and user-choice models using an EM strategy and identify varying user preference types. We then leverage a large multimodal LLM (LMM) and a value-based RL approach to suggest an adaptive and diverse slate of prompt expansions to the user. Our Preference Adaptive and Sequential Text-to-image Agent (PASTA) extends T2I models with adaptive multi-turn capabilities, fostering collaborative co-creation and addressing uncertainty or underspecification in a user's intent. We evaluate PASTA using human raters, showing significant improvement compared to baseline methods. We also open-source our sequential rater dataset and simulated user-rater interactions to support future research in user-centric multi-turn T2I systems.

• The paper introduces PASTA (Personalized And Sequential Text-to-image Agent), an AI framework that extends traditional text-to-image models to enable personalized image generation through multiple interactive user turns.
• PASTA integrates user preference modeling, utilizes large multimodal models for prompt expansion, and employs a value-driven reinforcement learning approach using implicit Q-Learning for sequential decision making.
• Empirical evaluation demonstrates PASTA's improvement over baselines in human studies and synthetic tests, and the authors release a valuable dataset of sequential interactions to support future research.

Personalized and Sequential Text-to-Image Generation: An Analytical Overview

The paper "Personalized and Sequential Text-to-Image Generation" by Nabati et al. addresses the challenge of generating personalized images from text prompts through multiple interactive iterations, a problem that sits at the intersection of reinforcement learning (RL) and large multimodal models (LMMs). It introduces the Personalized And Sequential Text-to-image Agent (PASTA), which effectively extends the capabilities of traditional text-to-image (T2I) models to operate in a multi-turn setting. The research suggests that single-turn T2I models often fail to capture the user's nuanced and evolving visual intent, especially for complex or abstract concepts. By bringing in personalization and interactive feedback, PASTA aims to iteratively refine image generation to better align with user preferences.

Core Methodology and Contributions

PASTA is built on a robust framework that employs user preference modeling, prompt expansion via LMMs, and a value-based RL approach to personalize the T2I process. The primary technical contributions can be outlined as follows:

1. Data Collection and User Modeling: The paper emphasizes the collection of sequential user interaction data using human raters to build a foundational understanding of user dynamics in multi-turn dialogue. They complement this with a simulated dataset, generated through a user model that employs an EM-based strategy to discern different user preference types from both sequential and single-turn datasets.
2. Integration with Large Multimodal Models: The researchers leverage a large multimodal LLM as a backbone for generating diverse and personalized prompt expansions, which are then refined through user input over multiple turns. The LMM serves as both a candidate generator and a preliminary filter for prompt selection.
3. Reinforcement Learning for Sequential Decision Making: PASTA adopts a value-driven RL paradigm, where the candidate selector policy utilizes a state-action value function to iterate over selected prompts, aiming to maximize cumulative user satisfaction. They employ implicit Q-Learning to circumvent potential overestimation biases in offline RL settings, focusing on long-term user engagement.
4. Empirical Evaluation and Open Dataset: Evaluation of PASTA is conducted through human rater studies alongside synthetic user interactions, demonstrating marked improvement over baseline methods. The authors release a dataset containing the sequential interactions collected as well as the generated synthetic trajectories to facilitate future research.

Practical and Theoretical Implications

The practical implications of this research are profound for domains requiring high levels of creativity and personalization in visual content, such as digital content creation, personalized marketing, and user-centered design in virtual environments. Theoretically, the work advances the field's understanding of integrating RL with multimodal models to address complex user interaction problems. It also contributes to ongoing discussions on the efficacy of interactive and iterative approaches in AI to handle underspecified intent in user prompts.

Future Developments

This research establishes a framework for future explorations into more nuanced user preference modeling, potentially incorporating real-time feedback mechanisms for adaptive learning. Additionally, the concept of using LMMs in an interactive loop with reinforcement signals could be extended to other domains, such as conversational agents or interactive storytelling systems.

In sum, the PASTA framework represents a sophisticated foray into personalized AI-driven co-creation, pushing the boundaries of what is possible in text-to-image synthesis and setting the stage for further advancements in the domain. The combination of sequential RL, user modeling, and LMMs presents a promising pathway for enhancing engagement and satisfaction in user-AI interactions.