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Easy diffusion是一个易于训练和修改的基于diffusion的模型,它是我在通读diffusion系列论文和相应代码的基础上开发的一个文本生成模型。我做这个项目的原因有两个:第一,公司的业务有一个垂直中文领域的文本生成需求,所以需要训练一个适用于这个领域的文本生成模型;第二,目前开源的diffusion模型代码已经非常完善,但是对于像我这样想要自定义修改的用户来说,修改代码会比较麻烦,因为那些代码基本上都已经在config文件中定义好了模型架构和训练流程。因此,我总结和设计了Easy diffusion,它的代码完全由基础的dataset、dataloader、模型架构和diffusion步骤组成。本项目的代码更多的是给读者理解如何从头开始构建一个diffusion模型
这个代码是phase by transform的python实现, notebook可以直接下到本地运行,进行缺陷检测。 由于缺陷检测使用深度学习基本上只要收集足够的数据就可以通过构建模型,来获得不错的效果,这里着重介绍一种无监督的方法来实现缺陷检测,从而避免深度学习模型的数据收集和模型训练,还有对算力的需求的这些缺点。
ServiceWorkerDemo
该代码仓库主要是进行视频生成的研究和分析,目前代码并没有上传,正在试验中,目前里面记录了一些我调研和阅读论文的一些总结,以及经过学习和总结之后,自己的一些理解和实验想法,后续如果取得理想的成果,会将代码开源出来。
爬虫的实践
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