让AI理解用户逐步修改需求，动态优化图像生成。
Preference Adaptive and Sequential Text-to-Image Generation
- 用强化学习根据用户反馈迭代优化提示词
- 通过人类评分构建序列偏好数据集，提升交互体验
- 适合需要多轮协作的创意设计场景
针对交互式文本到图像生成问题，我们设计了一个强化学习代理，通过一系列提示词扩展迭代改进图像以满足用户需求。利用人工评分者构建了新的序列偏好数据集，并结合大规模开源非序列数据集进行训练。采用期望最大化（EM）策略构建用户偏好与选择模型，识别出不同的用户偏好类型。随后，利用大型多模态语言模型（LMM）和基于价值的强化学习方法，为用户提供自适应且多样化的提示词扩展建议。所提出的偏好自适应与序列化文生图代理（PASTA）赋予文生图模型多轮交互能力，促进人机协同创作，缓解用户意图模糊或不完整的问题。通过人类评估验证，PASTA显著优于基线方法。同时，我们开源了序列评分者数据集及模拟用户-评分者交互环境，以支持未来面向用户的多轮文生图系统研究。
原文摘要 · Abstract (English)
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 language model (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.
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