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这个项目是一种基于扩散反转的图像超分辨率(SR)新技术,旨在利用大型预训练扩散模型中包含的丰富图像先验来提高SR性能。该方法设计了一种"部分噪声预测"策略来构建扩散模型的中间状态,作为采样起点。核心是一个深度噪声预测器,用于估计前向扩散过程的最佳噪声图。训练好后,这个噪声预测器可用于部分地沿扩散轨迹初始化采样过程,生成所需的高分辨率结果。与现有方法相比,该方法提供了一种灵活高效的采样机制,支持任意数量的采样步骤,从1步到5步不等。即使只使用1个采样步骤,该方法也能展现出优于或可媲美最新最佳方法的性能。 【主要功能】 提出了一种基于扩散反转的图像超分辨率新技术,利用预训练扩散模型的丰富图像先验 设计了"部分噪声预测"策略,构建扩散模型的中间状态作为采样起点 开发了深度噪声预测器,估计前向扩散过程的最佳噪声图 提供了灵活高效的采样机制,支持任意数量的采样步骤 即使只使用1个采样步骤,也能达到优于或可媲美最新最佳方法的性能 【技术栈】 Python 3.10 PyTorch 2.4.0
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