通过优化初始噪声提升图像生成质量与提示匹配度。
Noise-Level Diffusion Guidance: Well Begun is Half Done
- 不依赖额外数据或网络，直接调整初始噪声以对齐引导信号。
- 在五个基准上均提升图像质量和提示遵循度，且计算开销低。
- 适合希望无需训练即可增强现有扩散模型的开发者使用。
扩散模型在图像生成中已达到顶尖水平，但随机高斯噪声作为起始条件会影响最终输出，导致图像质量与提示一致性波动。现有噪声等级优化方法通常依赖额外数据构建、辅助网络或基于反向传播的优化，实用性受限。本文提出噪声等级引导（NLG），一种简单、高效且通用的噪声等级优化方法，通过提升初始噪声与通用引导信号对齐的可能性来优化初始噪声，无需额外训练数据、辅助网络或反向传播。该方法可统一应用于条件与非条件扩散模型，兼容多种扩散层级引导形式。在五个标准基准上的大量实验表明，本方法显著提升生成质量与输入条件遵循度。通过无缝集成现有引导方法并保持计算效率，NLG成为扩散模型实用且可扩展的增强方案。代码见：https://github.com/harveymannering/NoiseLevelGuidance。
原文摘要 · Abstract (English)
Diffusion models have achieved state-of-the-art image generation. However, the random Gaussian noise used to start the diffusion process influences the final output, causing variations in image quality and prompt adherence. Existing noise-level optimization approaches generally rely on extra dataset construction, additional networks, or backpropagation-based optimization, limiting their practicality. In this paper, we propose Noise Level Guidance (NLG), a simple, efficient, and general noise-level optimization approach that refines initial noise by increasing the likelihood of its alignment with general guidance - requiring no additional training data, auxiliary networks, or backpropagation. The proposed NLG approach provides a unified framework generalizable to both conditional and unconditional diffusion models, accommodating various forms of diffusion-level guidance. Extensive experiments on five standard benchmarks demonstrate that our approach enhances output generation quality and input condition adherence. By seamlessly integrating with existing guidance methods while maintaining computational efficiency, our method establishes NLG as a practical and scalable enhancement to diffusion models. Code can be found at https://github.com/harveymannering/NoiseLevelGuidance.
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