标题： OptiPrune：通过注意力引导的噪声和动态标记选择提升提示-图像一致性 显示英文标题

标题： OptiPrune: Boosting Prompt-Image Consistency with Attention-Guided Noise and Dynamic Token Selection

摘要： 文本到图像扩散模型常常难以在生成的图像和文本提示之间实现准确的语义对齐，同时保持在资源受限硬件上的效率。 现有的方法要么通过噪声优化产生大量的计算开销，要么通过激进地剪枝标记来牺牲语义保真度。 在本工作中，我们提出了OptiPrune，一个统一的框架，结合了感知分布的初始噪声优化与基于相似性的标记剪枝，以同时解决这两个挑战。 具体来说，(1) 我们引入了一个由注意力得分引导的感知分布噪声优化模块，以引导初始潜在噪声朝向语义上有意义的区域，减轻诸如主体忽略和特征纠缠等问题；(2) 我们设计了一种硬件高效的标记剪枝策略，通过逐块相似性选择有代表性的基础标记，注入随机性以增强泛化能力，并在注意力操作前通过最大相似性复制恢复被剪枝的标记。 我们的方法在噪声优化过程中保留了高斯先验，并实现了高效的推理而不牺牲对齐质量。 在包括Animal-Animal在内的基准数据集上的实验表明，OptiPrune在显著降低计算成本的情况下实现了最先进的提示图像一致性。 显示英文摘要

摘要： Text-to-image diffusion models often struggle to achieve accurate semantic alignment between generated images and text prompts while maintaining efficiency for deployment on resource-constrained hardware. Existing approaches either incur substantial computational overhead through noise optimization or compromise semantic fidelity by aggressively pruning tokens. In this work, we propose OptiPrune, a unified framework that combines distribution-aware initial noise optimization with similarity-based token pruning to address both challenges simultaneously. Specifically, (1) we introduce a distribution-aware noise optimization module guided by attention scores to steer the initial latent noise toward semantically meaningful regions, mitigating issues such as subject neglect and feature entanglement; (2) we design a hardware-efficient token pruning strategy that selects representative base tokens via patch-wise similarity, injects randomness to enhance generalization, and recovers pruned tokens using maximum similarity copying before attention operations. Our method preserves the Gaussian prior during noise optimization and enables efficient inference without sacrificing alignment quality. Experiments on benchmark datasets, including Animal-Animal, demonstrate that OptiPrune achieves state-of-the-art prompt-image consistency with significantly reduced computational cost.