为时未晚：面向已训练扩散模型坍塌恢复的噪声优化
It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Models
对已训练好的扩散模型，只需在采样阶段优化初始噪声（并考虑噪声的频率特性）就能缓解模式坍塌、提升生成多样性，无需重训或额外引导网络；做文生图多样性、少样本采样策略的人可以直接复用这一噪声优化目标。
原文依据 a simple noise optimization objective can mitigate mode collapse while preserving the fidelity of the base model
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论当代文生图模型表现出令人惊讶程度的模式坍塌，这一点在给定同一文本提示进行多次采样时即可观察到。此前的工作试图通过引导机制操控模型，或生成大量候选样本再加以精炼来应对该问题。本工作采取不同方向，旨在通过噪声优化实现生成的多样性。具体而言，我们表明一个简单的噪声优化目标即可缓解模式坍塌，同时保持基模型的保真度。我们还分析了噪声的频率特性，并表明具有不同频率分布的替代噪声初始化能够同时改善优化与搜索。实验表明，噪声优化在生成质量与多样性两方面均取得更优结果。
英文原文摘要 864 字符
Contemporary text-to-image models exhibit a surprising degree of mode collapse, as can be seen when sampling several images given the same text prompt. Previous work has attempted to address this issue by steering the model using guidance mechanisms, or by generating a large pool of candidates and refining them. In this work, we take a different direction and aim for diversity in generations via noise optimization. Specifically, we show that a simple noise optimization objective can mitigate mode collapse while preserving the fidelity of the base model. We also analyze the frequency characteristics of the noise and show that alternative noise initializations with different frequency profiles can improve both optimization and search. Our experiments demonstrate that noise optimization yields superior results in terms of generation quality and diversity.