Xin Dong

Results 12 comments of Xin Dong

> https://github.com/guyuchao/IPCGANs-Pytorch/blob/df3e28815eadb99ba9a74af6d58b9de56506a00b/model/IPCGANs.py#L153 > > https://github.com/guyuchao/IPCGANs-Pytorch/blob/df3e28815eadb99ba9a74af6d58b9de56506a00b/model/IPCGANs.py#L173 > > https://github.com/guyuchao/IPCGANs-Pytorch/blob/df3e28815eadb99ba9a74af6d58b9de56506a00b/model/IPCGANs.py#L166 > > https://github.com/guyuchao/IPCGANs-Pytorch/blob/df3e28815eadb99ba9a74af6d58b9de56506a00b/model/IPCGANs.py#L175 same problem

> I noticed that for creating train_age_group_x.txt and test_age_group_x.txt, you just use the same list train_age_groupx (not use test_age_groupx). Is it fine? Same problem

> > I noticed that for creating train_age_group_x.txt and test_age_group_x.txt, you just use the same list train_age_groupx (not use test_age_groupx). Is it fine? > > Same problem update The code...

Hope the code and weight release.

> ![image](https://user-images.githubusercontent.com/40386392/252306073-c01866ff-00ef-4a4f-9e2e-d059dd76a444.png) 跑起来,点击图片之后就一直处在加载,也没有报错 请问你解决这个问题了吗?我也想问这个问题。

> 麻烦提供一下命令行的截图 您好 这是命令行的截图 ![image](https://github.com/OpenGVLab/DragGAN/assets/20262070/a58b03fa-c8df-42d1-ba85-5db9417d5c0d) 我在服务器运行起来之后,GPU显示正常占用。我使用端口映射将服务器的7860端口映射到了本地电脑的7860 本地访问7860端口正常: ![image](https://github.com/OpenGVLab/DragGAN/assets/20262070/8b343f5d-1e42-498f-80f1-335a49aa6a69) 但在点击图片后就卡住了 ![image](https://github.com/OpenGVLab/DragGAN/assets/20262070/350d7499-e700-4d8b-9f83-bfeb4b9327d7) 命令行也没有打印错误信息 ![image](https://github.com/OpenGVLab/DragGAN/assets/20262070/4e62cf83-b70d-49e3-95ad-9036b37fc110)

问题解决啦 我这边遇到的问题,原因是gradio加载google font时卡住,因为服务器没有外网 我按照这个博客分享的方法解决了问题 https://blog.csdn.net/FL1623863129/article/details/131012978

> Hello, can you tell me how to upload and drag my image? Many thank you.

> Hi, thanks for your interesting, this project can drag own image now Hello, author. I want to know how to upload and drag my image? I read the code,...

Hi author, could you kindly provide information on the scheduled release date for the model weight with temporal modeling.