CARINOX：推理时间缩放与基于类别感知的奖励驱动初始噪声优化与探索
CARINOX: Inference-time Scaling with Category-Aware Reward-based Initial Noise Optimization and Exploration
AI总结：
本文提出CARINOX框架，结合噪声优化与探索，通过基于人类判断的奖励选择提升文本到图像扩散模型的对齐性能，在两个基准测试中分别提升16%和11%。
摘要：
文本到图像扩散模型，如Stable Diffusion，能够生成高质量和多样的图像，但常常在复杂对象关系、属性或空间安排的组成对齐上表现不佳。最近的推理时间方法通过奖励函数指导初始噪声的优化或探索来解决这一问题，但单独使用时存在固有局限：优化可能因初始化不佳或搜索轨迹不利而停滞，探索则可能需要大量样本才能找到满意输出。我们的分析进一步表明，单一奖励指标或随意组合无法可靠捕捉所有组成性方面，导致指导弱或不一致。为此，我们提出了Category-Aware Reward-based Initial Noise Optimization and Exploration (CARINOX)，一个结合噪声优化与探索的统一框架，基于与人类判断相关的奖励选择过程。在两个互补的基准测试中评估，CARINOX在T2I-CompBench++上平均对齐分数提高16%，在HRS基准上提高11%，在所有主要类别中均优于最先进的优化和探索方法，同时保持图像质量和多样性。项目页面可访问https://amirkasaei.com/carinox/。
英文摘要：
Text-to-image diffusion models, such as Stable Diffusion, can produce high-quality and diverse images but often fail to achieve compositional alignment, particularly when prompts describe complex object relationships, attributes, or spatial arrangements. Recent inference-time approaches address this by optimizing or exploring the initial noise under the guidance of reward functions that score text-image alignment without requiring model fine-tuning. While promising, each strategy has intrinsic limitations when used alone: optimization can stall due to poor initialization or unfavorable search trajectories, whereas exploration may require a prohibitively large number of samples to locate a satisfactory output. Our analysis further shows that neither single reward metrics nor ad-hoc combinations reliably capture all aspects of compositionality, leading to weak or inconsistent guidance. To overcome these challenges, we present Category-Aware Reward-based Initial Noise Optimization and EXploration (CARINOX), a unified framework that combines noise optimization and exploration with a principled reward selection procedure grounded in correlation with human judgments. Evaluations on two complementary benchmarks covering diverse compositional challenges show that CARINOX raises average alignment scores by approximately 19% on both T2I-CompBench++ and HRS, consistently outperforming state-of-the-art optimization and exploration-based methods across all major categories, while preserving image quality and diversity. The project page is available at https://amirkasaei.com/carinox/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
