Yi Ding

Thank you for getting back to me. I'm on a multi-project schedule and I do not have access to that project recently. I'll take a look the next time I...

So I got to look at the snapshot. 1. `ModuleZero` is indeed a vendored framework of `ModulesOne`'s default sub-spec. The `Podspec` has definition: ``` s.subspec 'Payment' do |pay| pay.ios.vendored_frameworks =...

This also seems to affect Asset Catalog somehow. I have a Podspec that defines: ```ruby s.resources = "UIKitExtensions/**/*.{xib,storyboard,xcassets}" ``` and regular `pod install` succeeds but `pod binary --prebuild` fails with...

Unchecking "Use Custom Theme" in *Preferences > Appearance* seem to solve any element overlapping/collision issue in Beeftext windows. **Drawback**: This setting will return Beeftext to the most bare-bone look and...

There is `"archive-format"` key which controls which fields are stored in the database record. I use it to combine several fields to make a record unique for some other extractor....

Yes. Apple has been releasing new Macs exclusively with Apple Silicon chips for quite some time. Sooner or later x86 will be obsolete. A universal binary combines artifacts for both...

I went over the issue in your edit. I think the issue focuses more on building universal binaries using Rust/Cargo only. My script only uses Rust/Cargo to build single arch...

This seems to be an issue with dependencies introduced in *CocoaPods 1.10*. When the project references a Pod with its own Asset Catalog, and that library is **NOT** named *Assets.xcassets*...

这个问题是因为 `realesrgan-ncnn-vulkan` 尝试在工作路径 (`$PWD`, `%CD%`) 读取预训练模型，而通常做法应该在标准路径（平台标准软件安装路径、用户数据路径，或兜底到可执行文件所在路径）读取应用程序安装随附的数据。 ----- 临时解决方案是运行 `realesrgan-ncnn-vulkan` 时同时提供模型完整路径，使用 `-m` 参数： ```bash /usr/local/RealESRGAN/realesrgan-ncnn-vulkan -m /usr/local/RealESRGAN/models -i ... -o ... ``` 模型完整路径默认应该在 `realesrgan-ncnn-vulkan` 所在文件夹拼接上 `/models`。如果你把 `realesrgan-ncnn-vulkan` 放在了 `$PATH`，UNIX 下使用 `which`、Windows...

Vulkan 已经支持 Apple Silicon，RealESRGAN 的 macOS 版本也是通用二进制，在 M1 上可以原生运行并且似乎还能用到图像运算加速（不知道如何验证这一点）。 M1 有专门的神经网络处理器 (Neural Engine) 和底层框架 MLCompute，用来进行机器学习理论上比用 GPU 性能更好，但除了 TensorFlow 似乎其他通用机器学习框架（比如 PyTorch）都不支持。