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免费MD:无培训的多域文本到图像生成与任何控制

Mingwen Shao¹, Chang Liu², Xiang Lv²

¹School of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China; Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology, Shenzhen, 518107, China.

Neural networks : the official journal of the International Neural Network Society

|March 15, 2026

概括

FreeMD通过分离文本和结构控制来增强文本到图像生成. 这种新的方法提高了语义对准与提示,并通过使用多域指导确保了更好的结构一致性.

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:

扩散模型推进可控制的文本到图像生成.
现有的方法在语义对齐和结构一致性方面扎,原因是结合的控制和有限的域重点.

研究的目的:

推出FreeMD,一种无需培训的方法,用于改进文本到图像生成.
为了增强语义对齐与文本提示,并实现优越的结构一致性.

主要方法:

提出FreeMD,一种无需培训的多域文本到图像生成方法.
引入了独立的外观和结构指导分支.
采用了多域指导,将空间和频率域相结合,用于结构控制.

主要成果:

FreeMD实现了更好的语义对齐与文本提示.
该方法证明了与控制信号的优异结构一致性.
实验表明FreeMD在可控性和发电质量方面优于现有方法.

结论:

FreeMD有效地解了文本和结构控制,以改进文本到图像生成.
多领域指导策略提高了语义准确性和结构连贯性.
FreeMD为各种生成模型和下游任务提供了一个插件解决方案.

关键词:

扩散模型是一个扩散模型.多领域的指导指南.文本到图像的生成.

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