pix2pix-基于Unity实现的实时pix2pix图像到图像转换-AIGC-附项目源码-优质项目实战.zip

Pix2Pix for Unity ================= This is an attempt to run [pix2pix] (image-to-image translation with deep neural network) in real time with [Unity]. It contains its own implementation of an inference engine, so it doesn't require installation of other neural network frameworks. Sketch Pad demo ---------------   **Sketch Pad** is a demonstration that resembles the famous [edges2cats] demo but in real time. You can download a pre-built binary from the [Releases] page. System requirements ------------------- - Unity 2018.1 - Compute shader capability (DX11, Metal, Vulkan, etc.) Although it's implemented in a platform agnostic fashion, many parts of it are optimized for NVIDIA GPU architectures. To run the Sketch Pad demo flawlessly, it's highly recomended to use a Windows system with GeForce GTX 1070 or greater. How to use a trained model -------------------------- This repository doesn't contain any trained model to save the bandwidth and storage quota. To run the example project on Unity Editor, download the pre-trained [edges2cats model] and copy it into `Assets/StreamingAssets`. [edges2cats model]: https://github.com/affinelayer/pix2pix-tensorflow-models/blob/master/edges2cats_AtoB.pict This implementation only supports the `.pict` weight data format which is used in Christopher Hesse's [interactive demo]. You can pick one of the [pre-trained models] or train your own model with using [pix2pix-tensorflow]. To export weight data from a checkpoint, please see the description in the [export-checkpoint.py] script. [interactive demo]: https://affinelayer.com/pixsrv/ [pre-trained models]: https://github.com/affinelayer/pix2pix-tensorflow-models [pix2pix-tensorflow]: https://github.com/affinelayer/pix2pix-tensorflow [export-checkpoint.py]: https://github.com/affinelayer/pix2pix-tensorflow/tree/master/server