DiffI2I:图像对图像翻译的高效扩散模型
概括
扩散模型 (DM) 在图像对图像翻译 (I2I) 中遇到了困难. 我们的新框架DiffI2I使用紧的先前表示来实现高效和准确的I2I任务,通过减少计算实现最先进的结果.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 扩散模型 (DM) 是图像合成的最新技术,但在图像到图像转换 (I2I) 任务中存在局限性.
- 现有的I2I的DM通常需要大量的代和大型模型,导致人工制造和低效率,特别是对于像超分辨率这样的任务,要求对基本真相 (GT) 图像保持忠实.
研究的目的:
- 提出一个新的扩散模型 (DM) 框架,DiffI2I,设计用于高效和高性能图像到图像转换 (I2I).
- 通过引入一种方法来解决传统DM在I2I任务中的局限性,该方法可以在降低计算成本的情况下产生准确的结果.
主要方法:
- DiffI2I 集成了三个组件:一个紧的 I2I 前提取网络 (CPEN),一个动态的 I2I 变压器 (DI2Iformer) 和一个消除噪音的网络.
- 采用了两阶段的培训过程:使用CPEN捕获一个紧的I2I先前表示 (IPR) 的预训练,以及扩散模型 (DM) 培训,DM从输入图像中估计了IPR.
主要成果:
- 与传统的DM相比,DiffI2I中的紧的IPR可以实现更准确的结果.
- DiffI2I使用更轻的无噪网络,需要更少的代,大大降低了计算负担.
- 在各种I2I任务中进行了广泛的实验,证明了最先进的性能.
结论:
- DiffI2I为图像对图像翻译 (I2I) 任务提供了一个简单,高效和强大的解决方案.
- 拟议的框架实现了卓越的性能,同时大幅降低了计算要求,使先进的I2I可访问.
- 在将扩散模型 (DM) 应用于实际的图像到图像翻译挑战方面,DiffI2I代表了重大进展.
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