标题： 通过对比噪声优化的多样化文本到图像生成 显示英文标题

标题： Diverse Text-to-Image Generation via Contrastive Noise Optimization

摘要： 文本到图像（T2I）扩散模型在生成高保真图像方面表现出色，这主要得益于文本引导的推理。然而，这种优势通常伴随着一个关键缺点：多样性有限，因为输出在强文本引导下往往陷入相似的模式。现有方法通常在推理过程中优化中间潜在变量或文本条件，但这些方法仅带来适度的改进，或者对超参数调整仍很敏感。在本工作中，我们引入了对比噪声优化，这是一种简单而有效的方法，从不同的角度解决了多样性问题。与之前适应中间潜在变量的技术不同，我们的方法通过塑造初始噪声来促进多样化的输出。具体来说，我们在Tweedie数据空间中定义了一个对比损失，并优化一批噪声潜在变量。我们的对比优化使批次内的实例相互排斥，以最大化多样性，同时保持它们与参考样本的锚定关系，以保留保真度。我们进一步提供了对这种预处理机制的理论见解，以证明其有效性。在多个T2I骨干模型上的广泛实验表明，我们的方法在保持对超参数选择鲁棒性的同时，实现了更优的质量-多样性帕累托前沿。 显示英文摘要

摘要： Text-to-image (T2I) diffusion models have demonstrated impressive performance in generating high-fidelity images, largely enabled by text-guided inference. However, this advantage often comes with a critical drawback: limited diversity, as outputs tend to collapse into similar modes under strong text guidance. Existing approaches typically optimize intermediate latents or text conditions during inference, but these methods deliver only modest gains or remain sensitive to hyperparameter tuning. In this work, we introduce Contrastive Noise Optimization, a simple yet effective method that addresses the diversity issue from a distinct perspective. Unlike prior techniques that adapt intermediate latents, our approach shapes the initial noise to promote diverse outputs. Specifically, we develop a contrastive loss defined in the Tweedie data space and optimize a batch of noise latents. Our contrastive optimization repels instances within the batch to maximize diversity while keeping them anchored to a reference sample to preserve fidelity. We further provide theoretical insights into the mechanism of this preprocessing to substantiate its effectiveness. Extensive experiments across multiple T2I backbones demonstrate that our approach achieves a superior quality-diversity Pareto frontier while remaining robust to hyperparameter choices.