一个扩散模型翻译器,用于高效的图像对图像翻译
概括
本研究介绍了一种高效的扩散模型翻译器 (DMT),用于图像对图像的翻译. 我们的方法加快了扩散模型的实际应用,如风格化和色彩化.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 扩散模型越来越多地用于图像对图像的翻译 (I2I).
- 现有的方法是耗时的,因为在每一个 denoising 步骤中进行代的改进.
- 需要更高效的I2I翻译技术.
研究的目的:
- 为图像对图像的翻译提出一个高效的扩散模型翻译器 (DMT).
- 在扩散模型中的中间步骤中为域移动提供理论理由.
- 开发一种自动选择域名转移最佳时间步骤的策略.
主要方法:
- 配备一个扩散模型与轻量级翻译器 (DMT).
- 从理论上证明在中间扩散阶段的域移动是合理的.
- 为域名转移提出自动时间步骤选择策略.
主要成果:
- 拟议的DMT实现了高效和高质量的图像对图像翻译.
- 该方法在各种应用中表现出有效性,包括造型,色彩,细分到图像和草图到图像.
- 在翻译质量和计算效率上,DMT在翻译质量和计算效率上都优于现有方法.
结论:
- 扩散模型翻译器 (DMT) 在高效的图像对图像翻译方面取得了重大进展.
- 理论框架和自动时间步骤选择提高了基于扩散的I2I的实用性和性能.
- 这种方法是广泛适用的,并改进了当前最先进的方法.
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