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Pix2Pix

(★ 1,041)

Real-time pix2pix implementation with Unity

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최종 버전 다운로드 (.zip)

- - Compute.meta
  - Generator.cs
  - Generator.cs.meta
  - GpuBackend.cs
  - GpuBackend.cs.meta
  - Image.cs
  - Image.cs.meta
  - Math.cs
  - Math.cs.meta
  - PostProcessing.meta
  - Tensor.cs
  - Tensor.cs.meta
  - WeightReader.cs
  - WeightReader.cs.meta
- - edges2cats_AtoB.pict.meta
- - PostProcessing.meta
  - SketchPad.meta
- Pix2Pix.meta
- PostProcessing.unity
- PostProcessing.unity.meta
- SketchPad.unity
- SketchPad.unity.meta
- StreamingAssets.meta
- Test.meta
- manifest.json
- AudioManager.asset
- ClusterInputManager.asset
- DynamicsManager.asset
- EditorBuildSettings.asset
- EditorSettings.asset
- GraphicsSettings.asset
- InputManager.asset
- NavMeshAreas.asset
- NetworkManager.asset
- Physics2DSettings.asset
- PresetManager.asset
- ProjectSettings.asset
- ProjectVersion.txt
- QualitySettings.asset
- TagManager.asset
- TimeManager.asset
- UnityConnectSettings.asset
.gitattributes
.gitignore
README.md

// repository documentation

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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.

[pix2pix]: https://github.com/phillipi/pix2pix
[Unity]: https://unity3d.com

Sketch Pad demo
---------------

![screenshot](https://i.imgur.com/aXYYjes.gif)
![screenshot](https://i.imgur.com/Tb0nYqU.gif)

**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.

[Demo video](https://vimeo.com/287778343)

[edges2cats]: https://affinelayer.com/pixsrv/
[Releases]: https://github.com/keijiro/Pix2Pix/releases

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