Repository: keijiro/UnityMLStableDiffusion
Branch: main
Commit: b581493c3055
Files: 84
Total size: 118.3 KB

Directory structure:
gitextract_p1sl6ai2/

├── .gitattributes
├── .github/
│   └── workflows/
│       └── main.yml
├── .gitignore
├── Assets/
│   ├── GeneratorSettings.asset
│   ├── GeneratorSettings.asset.meta
│   ├── Pexels/
│   │   ├── pexels-anna-galimova-10416679.jpg.meta
│   │   └── pexels-moose-photos-1036627.jpg.meta
│   ├── Pexels.meta
│   ├── Scripts/
│   │   ├── GeneratorSettings.cs
│   │   ├── GeneratorSettings.cs.meta
│   │   ├── SourceSelector.cs
│   │   ├── SourceSelector.cs.meta
│   │   ├── Tester.cs
│   │   └── Tester.cs.meta
│   ├── Scripts.meta
│   ├── StreamingAssets/
│   │   └── StableDiffusion.meta
│   ├── StreamingAssets.meta
│   ├── Tester.unity
│   ├── Tester.unity.meta
│   ├── UI/
│   │   ├── Control.uxml
│   │   ├── Control.uxml.meta
│   │   ├── FlatStyles.uss
│   │   ├── FlatStyles.uss.meta
│   │   ├── Main.uxml
│   │   ├── Main.uxml.meta
│   │   ├── Panel.asset
│   │   ├── Panel.asset.meta
│   │   ├── Theme.tss
│   │   └── Theme.tss.meta
│   └── UI.meta
├── LICENSE
├── Packages/
│   ├── jp.keijiro.ml-stable-diffusion/
│   │   ├── LICENSE
│   │   ├── LICENSE.meta
│   │   ├── README.md
│   │   ├── README.md.meta
│   │   ├── Runtime/
│   │   │   ├── MLStableDiffusion.asmdef
│   │   │   ├── MLStableDiffusion.asmdef.meta
│   │   │   ├── Pipeline.cs
│   │   │   ├── Pipeline.cs.meta
│   │   │   ├── Plugin.cs
│   │   │   ├── Plugin.cs.meta
│   │   │   ├── Preprocess.compute
│   │   │   ├── Preprocess.compute.meta
│   │   │   ├── ResourceInfo.cs
│   │   │   ├── ResourceInfo.cs.meta
│   │   │   ├── iOS/
│   │   │   │   ├── StableDiffusionPlugin.framework/
│   │   │   │   │   ├── Info.plist
│   │   │   │   │   └── StableDiffusionPlugin
│   │   │   │   └── StableDiffusionPlugin.framework.meta
│   │   │   ├── iOS.meta
│   │   │   ├── macOS/
│   │   │   │   └── libStableDiffusionPlugin.dylib.meta
│   │   │   └── macOS.meta
│   │   ├── Runtime.meta
│   │   ├── package.json
│   │   └── package.json.meta
│   ├── manifest.json
│   └── packages-lock.json
├── Plugin/
│   ├── Package.resolved
│   ├── Package.swift
│   ├── Sources/
│   │   └── StableDiffusionPlugin/
│   │       └── StableDiffusionPlugin.swift
│   └── build.sh
├── ProjectSettings/
│   ├── AudioManager.asset
│   ├── ClusterInputManager.asset
│   ├── DynamicsManager.asset
│   ├── EditorBuildSettings.asset
│   ├── EditorSettings.asset
│   ├── GraphicsSettings.asset
│   ├── InputManager.asset
│   ├── MemorySettings.asset
│   ├── MultiplayerManager.asset
│   ├── NavMeshAreas.asset
│   ├── PackageManagerSettings.asset
│   ├── Physics2DSettings.asset
│   ├── PresetManager.asset
│   ├── ProjectSettings.asset
│   ├── ProjectVersion.txt
│   ├── QualitySettings.asset
│   ├── SceneTemplateSettings.json
│   ├── TagManager.asset
│   ├── TimeManager.asset
│   ├── UnityConnectSettings.asset
│   ├── VFXManager.asset
│   ├── VersionControlSettings.asset
│   └── XRSettings.asset
└── README.md

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitattributes
================================================
* -text

*.cs        text eol=lf diff=csharp
*.shader    text eol=lf
*.cginc     text eol=lf
*.hlsl      text eol=lf
*.compute   text eol=lf

*.meta      text eol=lf


================================================
FILE: .github/workflows/main.yml
================================================
name: UPM on npsjs.com
on:
  release:
    types: [created]
jobs:
  publish:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - uses: actions/setup-node@v2
      with:
        registry-url: 'https://registry.npmjs.org'
    - run: npm publish
      working-directory: Packages/jp.keijiro.ml-stable-diffusion
      env:
        NODE_AUTH_TOKEN: ${{secrets.NPM_TOKEN}}


================================================
FILE: .gitignore
================================================
Thumbs.db
Desktop.ini
.DS_Store
*.swp

/Library
/Logs
/Recordings
/Temp
/UserSettings

/Plugin/.build
/Plugin/.xcodebuild

/Assets/StreamingAssets/StableDiffusion


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================================================
FILE: Assets/Scripts/GeneratorSettings.cs
================================================
using UnityEngine;

[CreateAssetMenu]
public sealed class GeneratorSettings : ScriptableObject
{
    public string prompt;
    public int stepCount;
    public int seed;
    public float guidance;
    public float strength;
}


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================================================
FILE: Assets/Scripts/SourceSelector.cs
================================================
using Klak.TestTools;
using System.Collections.Generic;
using System.Linq;
using UnityEngine;
using UnityEngine.UIElements;
using Unity.Properties;

public sealed class SourceSelector : MonoBehaviour
{
    #region Editable properties

    [field:SerializeField]
    public Texture2D[] ImageList = null;

    #endregion

    #region Accessors for UI Toolkit

    [CreateProperty]
    public List<string> ImageNameList
      => ImageList.Select(x => x.name).ToList();

    [CreateProperty]
    public List<string> WebcamNameList
      => WebCamTexture.devices.Select(x => x.name).ToList();

    void OnChangeTab(Tab prev, Tab next)
    {
        ImageDropdown.value = null;
        WebcamDropdown.value = null;
    }

    void OnSelectImage(ChangeEvent<string> evt)
    {
        if (string.IsNullOrEmpty(evt.newValue)) return;
        Target.SourceType = ImageSourceType.Texture;
        Target.SourceTexture = ImageList.First(x => x.name == evt.newValue);
        ImagePreview.style.backgroundImage
          = Background.FromRenderTexture(Target.AsRenderTexture);
    }

    void OnSelectWebcam(ChangeEvent<string> evt)
    {
        if (string.IsNullOrEmpty(evt.newValue)) return;
        Target.SourceType = ImageSourceType.Webcam;
        Target.SourceName = evt.newValue;
        WebcamPreview.style.backgroundImage
          = Background.FromRenderTexture(Target.AsRenderTexture);
    }

    #endregion

    #region Private helper properties

    ImageSource Target
      => GetComponent<ImageSource>();

    VisualElement UIRoot
      => GetComponent<UIDocument>().rootVisualElement;

    TabView SourceTabs
      => UIRoot.Q<TabView>("image-source-tabs");

    DropdownField ImageDropdown
      => UIRoot.Q<DropdownField>("image-selector");

    VisualElement ImagePreview
      => UIRoot.Q<VisualElement>("image-preview");

    DropdownField WebcamDropdown
      => UIRoot.Q<DropdownField>("webcam-selector");

    VisualElement WebcamPreview
      => UIRoot.Q<VisualElement>("webcam-preview");

    #endregion

    #region MonoBehaviour implementation

    void Start()
    {
        SourceTabs.activeTabChanged += OnChangeTab;

        ImageDropdown.dataSource = this;
        ImageDropdown.RegisterValueChangedCallback(OnSelectImage);

        WebcamDropdown.dataSource = this;
        WebcamDropdown.RegisterValueChangedCallback(OnSelectWebcam);
    }

    #endregion
}


================================================
FILE: Assets/Scripts/SourceSelector.cs.meta
================================================
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================================================
FILE: Assets/Scripts/Tester.cs
================================================
using Unity.Properties;
using UnityEngine.UIElements;
using UnityEngine;
using Stopwatch = System.Diagnostics.Stopwatch;
using ImageSource = Klak.TestTools.ImageSource;
using ComputeUnits = MLStableDiffusion.ComputeUnits;
using Scheduler = MLStableDiffusion.Scheduler;

public sealed class Tester : MonoBehaviour
{
    #region Properties for UI binding

    [CreateProperty]
    public string LogText { get; set; }

    #endregion

    #region Editor-only properties

    [SerializeField]
    GeneratorSettings _settings = null;

    [SerializeField]
    string _resourceDir = "StableDiffusion";

    [SerializeField]
    Vector2Int _modelSize = new Vector2Int(512, 512);

    [SerializeField]
    ComputeUnits _computeUnits = ComputeUnits.CpuAndNE;

    [SerializeField]
    Scheduler _scheduler = Scheduler.Dpmpp;

    #endregion

    #region Project asset references

    [SerializeField, HideInInspector] ComputeShader _preprocessShader = null;

    #endregion

    #region UI helpers

    ImageSource ImageSource => GetComponent<ImageSource>();

    VisualElement UIRoot => GetComponent<UIDocument>().rootVisualElement;
    VisualElement UIControlPanel => UIRoot.Q("control-panel");
    VisualElement UIImageSourceTabs => UIRoot.Q("image-source-tabs");
    VisualElement UIPreview => UIRoot.Q("preview");
    VisualElement UIGenerateByTextButton => UIRoot.Q("text-generate-button");
    VisualElement UIGenerateByImageButton => UIRoot.Q("image-generate-button");
    VisualElement UIGenerateByWebcamButton => UIRoot.Q("webcam-generate-button");
    VisualElement UILogText => UIRoot.Q("log-label");

    async void OnClickGenerateByTextButton(ClickEvent e)
    {
        UIImageSourceTabs.SetEnabled(false);
        await RunPipelineAsync(null);
        UIImageSourceTabs.SetEnabled(true);
    }

    async void OnClickGenerateByImageButton(ClickEvent e)
    {
        UIImageSourceTabs.SetEnabled(false);
        await RunPipelineAsync(ImageSource.AsTexture);
        UIImageSourceTabs.SetEnabled(true);
    }

    async void OnClickGenerateByWebcamButton(ClickEvent e)
    {
        UIImageSourceTabs.SetEnabled(false);
        await RunPipelineAsync(ImageSource.AsTexture);
        UIImageSourceTabs.SetEnabled(true);
    }

    #endregion

    #region Stable Diffusion pipeline objects

    string ResourcePath
      => Application.streamingAssetsPath + "/" + _resourceDir;

    MLStableDiffusion.ResourceInfo ResourceInfo
      => MLStableDiffusion.ResourceInfo.FixedSizeModel
           (ResourcePath, _modelSize.x, _modelSize.y);

    MLStableDiffusion.Pipeline _pipeline;
    (RenderTexture rt, Texture2D tex2d) _generated;
    Awaitable _task;

    #endregion

    #region Async operations

    async Awaitable SetUpPipelineAsync()
    {
        LogText = "Loading resources...\n" +
          "(This takes a few minites for the first time.)";
        UIImageSourceTabs.SetEnabled(false);

        _pipeline = new MLStableDiffusion.Pipeline(_preprocessShader);
        await _pipeline.InitializeAsync(ResourceInfo, _computeUnits);

        LogText = "";
        UIImageSourceTabs.SetEnabled(true);
    }

    async Awaitable RunPipelineAsync(Texture sourceImage)
    {
        LogText = "Generating...";;

        _pipeline.Prompt = _settings.prompt;
        _pipeline.Strength = _settings.strength;
        _pipeline.Scheduler = _scheduler;
        _pipeline.StepCount = _settings.stepCount;
        _pipeline.Seed = _settings.seed;
        _pipeline.GuidanceScale = _settings.guidance;

        var time = new Stopwatch();
        time.Start();
        await _pipeline.RunAsync(sourceImage, _generated.rt, destroyCancellationToken);
        time.Stop();

        Graphics.CopyTexture(_generated.rt, _generated.tex2d);

        LogText = $"Generation time: {time.Elapsed.TotalSeconds:f2} sec";
    }

    #endregion

    #region MonoBehaviour implementation

    void Start()
    {
        UIControlPanel.dataSource = _settings;
        UILogText.dataSource = this;

        UIGenerateByTextButton.RegisterCallback<ClickEvent>(OnClickGenerateByTextButton);
        UIGenerateByImageButton.RegisterCallback<ClickEvent>(OnClickGenerateByImageButton);
        UIGenerateByWebcamButton.RegisterCallback<ClickEvent>(OnClickGenerateByWebcamButton);

        var (w, h) = (_modelSize.x, _modelSize.y);
        _generated.rt = new RenderTexture(w, h, 0, RenderTextureFormat.BGRA32);
        _generated.tex2d = new Texture2D(w, h, TextureFormat.BGRA32, false);
        UIPreview.style.backgroundImage = new StyleBackground(_generated.tex2d);

        _task = SetUpPipelineAsync();
    }

    #endregion
}


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================================================
FILE: LICENSE
================================================
# Stable Diffusion

MIT License

Copyright (c) 2022 Stability AI

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

---

# ml-stable-diffusion

Copyright (C) 2022 Apple Inc. All Rights Reserved.

IMPORTANT:  This Apple software is supplied to you by Apple
Inc. ("Apple") in consideration of your agreement to the following
terms, and your use, installation, modification or redistribution of
this Apple software constitutes acceptance of these terms.  If you do
not agree with these terms, please do not use, install, modify or
redistribute this Apple software.

In consideration of your agreement to abide by the following terms, and
subject to these terms, Apple grants you a personal, non-exclusive
license, under Apple's copyrights in this original Apple software (the
"Apple Software"), to use, reproduce, modify and redistribute the Apple
Software, with or without modifications, in source and/or binary forms;
provided that if you redistribute the Apple Software in its entirety and
without modifications, you must retain this notice and the following
text and disclaimers in all such redistributions of the Apple Software.
Neither the name, trademarks, service marks or logos of Apple Inc. may
be used to endorse or promote products derived from the Apple Software
without specific prior written permission from Apple.  Except as
expressly stated in this notice, no other rights or licenses, express or
implied, are granted by Apple herein, including but not limited to any
patent rights that may be infringed by your derivative works or by other
works in which the Apple Software may be incorporated.

The Apple Software is provided by Apple on an "AS IS" basis.  APPLE
MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION
THE IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY AND FITNESS
FOR A PARTICULAR PURPOSE, REGARDING THE APPLE SOFTWARE OR ITS USE AND
OPERATION ALONE OR IN COMBINATION WITH YOUR PRODUCTS.

IN NO EVENT SHALL APPLE BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL
OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) ARISING IN ANY WAY OUT OF THE USE, REPRODUCTION,
MODIFICATION AND/OR DISTRIBUTION OF THE APPLE SOFTWARE, HOWEVER CAUSED
AND WHETHER UNDER THEORY OF CONTRACT, TORT (INCLUDING NEGLIGENCE),
STRICT LIABILITY OR OTHERWISE, EVEN IF APPLE HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.


================================================
FILE: Packages/jp.keijiro.ml-stable-diffusion/LICENSE
================================================
# Stable Diffusion

MIT License

Copyright (c) 2022 Stability AI

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

---

# ml-stable-diffusion

Copyright (C) 2022 Apple Inc. All Rights Reserved.

IMPORTANT:  This Apple software is supplied to you by Apple
Inc. ("Apple") in consideration of your agreement to the following
terms, and your use, installation, modification or redistribution of
this Apple software constitutes acceptance of these terms.  If you do
not agree with these terms, please do not use, install, modify or
redistribute this Apple software.

In consideration of your agreement to abide by the following terms, and
subject to these terms, Apple grants you a personal, non-exclusive
license, under Apple's copyrights in this original Apple software (the
"Apple Software"), to use, reproduce, modify and redistribute the Apple
Software, with or without modifications, in source and/or binary forms;
provided that if you redistribute the Apple Software in its entirety and
without modifications, you must retain this notice and the following
text and disclaimers in all such redistributions of the Apple Software.
Neither the name, trademarks, service marks or logos of Apple Inc. may
be used to endorse or promote products derived from the Apple Software
without specific prior written permission from Apple.  Except as
expressly stated in this notice, no other rights or licenses, express or
implied, are granted by Apple herein, including but not limited to any
patent rights that may be infringed by your derivative works or by other
works in which the Apple Software may be incorporated.

The Apple Software is provided by Apple on an "AS IS" basis.  APPLE
MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION
THE IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY AND FITNESS
FOR A PARTICULAR PURPOSE, REGARDING THE APPLE SOFTWARE OR ITS USE AND
OPERATION ALONE OR IN COMBINATION WITH YOUR PRODUCTS.

IN NO EVENT SHALL APPLE BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL
OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) ARISING IN ANY WAY OUT OF THE USE, REPRODUCTION,
MODIFICATION AND/OR DISTRIBUTION OF THE APPLE SOFTWARE, HOWEVER CAUSED
AND WHETHER UNDER THEORY OF CONTRACT, TORT (INCLUDING NEGLIGENCE),
STRICT LIABILITY OR OTHERWISE, EVEN IF APPLE HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.


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================================================
FILE: Packages/jp.keijiro.ml-stable-diffusion/README.md
================================================
Unity Core ML Stable Diffusion Plugin
=====================================

![gif](https://user-images.githubusercontent.com/343936/228759539-a35a37f2-77d6-4a10-8392-d875b968fea6.gif)

Stable Diffusion plugin for Unity, based on [Apple's Core ML port]. You can run
the model on-editor and at-runtime without needing any extra components.

[Apple's Core ML port]: https://github.com/apple/ml-stable-diffusion

System Requirements
-------------------

- Unity 2023.1 or later
- Apple Silicon Mac (editor/runtime support) with macOS 13.1 or later
- iPad Pro with Apple silicon (runtime support) with iOS 16.2 or later

Although the plugin supports iOS, it requires huge amount of memory to run the
model, so it only supports memory-rich iPad models.

How To Try
----------

Before running the sample project, you must put the model files in the
`Assets/StreamingAssets` directory.

- Clone or download the [pre-converted Stable Diffusion 2 model repository].
- Copy the `split_einsum/compiled` directory into `Assets/StreamingAssets`.
- Rename the directory to `StableDiffusion`.

[pre-converted Stable Diffusion 2 model repository]:
  https://huggingface.co/apple/coreml-stable-diffusion-2-base

It takes a long time (a few minutes) for the first run. After this
initialization step, it only takes a few tens of seconds to generate an image.

Performance Considerations
--------------------------

You can change which processing unit it uses by switching the "Compute Units"
property in the Tester component.

It depends on the device model to choose the best option:

- M1/M2 Mac and iOS: GPUs in those devices are not powerful enough, so you
  should select "CPU and NE" (Neural Engine) or "All".
- M1/M2 Pro/Max Mac: GPUs in those devices have enough processing power
  compared to NE, so "CPU and GPU" can be a better option.

When using "CPU and GPU" mode, you must use the "original" model instead of
the "split_einsum" model. Please overwrite the `StreamingAssets/StableDiffusion`
directory with the `original/compiled` directory.

LCM (SD-Turbo) Support
----------------------

You can use [SD-Turbo] or other LCMs (latent consistency models) with setting
`Pipeline.Scheduler` to `Lcm`. You might also have to change `StepCount` to 1\~4
and `GuidanceScale` to 1\~2. Please refer to the model description to know the
correct settings.

You can download the [pre-converted SD-Turbo model] from my Hugging Face
repository.

[SD-Turbo]: https://huggingface.co/stabilityai/sd-turbo
[pre-converted SD-Turbo model]:
  https://huggingface.co/keijiro-tk/coreml-sd-turbo

Sample Projects
---------------

![gif](https://user-images.githubusercontent.com/343936/228760795-9e712684-2ee6-4e63-9241-06d8aa125a17.gif)

- [Flipbook3](https://github.com/keijiro/Flipbook3): Running the image-to-image
  pipeline with a real-time 3D scene.


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================================================
FILE: Packages/jp.keijiro.ml-stable-diffusion/Runtime/Pipeline.cs
================================================
using Unity.Collections;
using UnityEngine.Rendering;
using UnityEngine;
using CancellationToken = System.Threading.CancellationToken;

namespace MLStableDiffusion {

public sealed class Pipeline : System.IDisposable
{
    #region Public properties

    public int Width { get; private set; }
    public int Height { get; private set; }
    public string Prompt { get; set; }
    public float Strength { get; set; }
    public Scheduler Scheduler { get; set; }
    public int StepCount { get; set; }
    public int Seed { get; set; }
    public float GuidanceScale { get; set; }

    #endregion

    #region Private members

    Plugin _plugin;
    ComputeShader _preprocess;
    (GraphicsBuffer reorder, NativeArray<byte> source, Texture2D output) _buffer;

    // Async readback from a GPU backed texture into a NativeArray
    async Awaitable ReadbackSourceAsync(Texture source)
    {
        // Is gamma decompression needed?
        var isLinear = QualitySettings.activeColorSpace == ColorSpace.Linear;

        // Reordering compute shader invocation
        _preprocess.SetTexture(0, "Input", source);
        _preprocess.SetBuffer(0, "Output", _buffer.reorder);
        _preprocess.SetInts("Size", Width, Height);
        _preprocess.SetBool("IsLinear", isLinear);
        _preprocess.Dispatch(0, Width / 4 / 8, Height / 8, 1);

        // Async readback
        await AsyncGPUReadback.
           RequestIntoNativeArrayAsync(ref _buffer.source, _buffer.reorder);
    }

    #endregion

    #region Public members

    public Pipeline(ComputeShader preprocess)
      => _preprocess = preprocess;

    public void Dispose()
    {
        _buffer.reorder?.Release();
        _buffer.reorder = null;

        if (_buffer.source.IsCreated) _buffer.source.Dispose();

        if (_buffer.output != null) Object.Destroy(_buffer.output);
        _buffer.output = null;

        _plugin?.Dispose();
        _plugin = null;
    }

    public async Awaitable InitializeAsync(ResourceInfo res, ComputeUnits units)
    {
        // Model information
        Width = res.ModelWidth;
        Height = res.ModelHeight;

        // Buffer allocation
        _buffer.reorder = new GraphicsBuffer(GraphicsBuffer.Target.Structured,
                                             Width * Height * 3 / 4, 4);
        _buffer.source = new NativeArray<byte>(Width * Height * 3, Allocator.Persistent,
                                               NativeArrayOptions.UninitializedMemory);
        _buffer.output = new Texture2D(Width, Height, TextureFormat.RGB24, false);

        // Pipeline initialization on the background thread
        await Awaitable.BackgroundThreadAsync();
        _plugin = Plugin.Create(res.ModelPath, units);
        await Awaitable.MainThreadAsync();
    }

    public async Awaitable RunAsync
      (Texture source, RenderTexture dest, CancellationToken cancel)
    {
        // Pipeline configuration
        _plugin.SetConfig(Prompt, Scheduler, StepCount, Seed, GuidanceScale);

        // Source texture async readback
        if (source) await ReadbackSourceAsync(source);

        // Run the pipeline on the background thread.
        await Awaitable.BackgroundThreadAsync();

        if (source)
            _plugin.RunGeneratorFromImage(_buffer.source.AsReadOnlySpan(), Width, Height, Strength);
        else
            _plugin.RunGenerator();

        await Awaitable.MainThreadAsync();
        cancel.ThrowIfCancellationRequested();

        // Load into a temporary texture and flip it into the destination.
        _buffer.output.LoadRawTextureData(_plugin.ImageBufferPointer, Width * Height * 3);
        _buffer.output.Apply();
        Graphics.Blit(_buffer.output, dest, new Vector2(1, -1), new Vector2(0, 1));
    }

    #endregion
}

} // namespace MLStableDiffusion


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FILE: Packages/jp.keijiro.ml-stable-diffusion/Runtime/Plugin.cs
================================================
using System.Runtime.InteropServices;
using IntPtr = System.IntPtr;
using Microsoft.Win32.SafeHandles;
using System;

namespace MLStableDiffusion {

public enum ComputeUnits { Cpu, CpuAndGpu, All, CpuAndNE }
public enum Scheduler { Pndm, Dpmpp, Lcm }

public class Plugin : SafeHandleZeroOrMinusOneIsInvalid
{
    #region SafeHandle implementation

    Plugin() : base(true) {}

    protected override bool ReleaseHandle()
    {
        _Destroy(handle);
        return true;
    }

    #endregion

    #region Public methods

    public static Plugin Create(string resourcePath, ComputeUnits units)
      => _Create(resourcePath, (int)units);

    public void SetConfig
      (string prompt, Scheduler scheduler, int steps, int seed, float guidance)
      => _SetConfig(this, prompt, (int)scheduler, steps, seed, guidance);

    public void RunGenerator()
      => _Generate(this);

    public unsafe void RunGeneratorFromImage
      (ReadOnlySpan<byte> image, int width, int height, float strength)
    {
        fixed (byte* ptr = image)
          _GenerateFromImage(this, (IntPtr)ptr, width, height, strength);
    }

    public IntPtr ImageBufferPointer
      => _GetImage(this);

    #endregion

    #region Unmanaged interface

#if UNITY_IOS && !UNITY_EDITOR
    const string DllName = "__Internal";
#else
    const string DllName = "StableDiffusionPlugin";
#endif

    [DllImport(DllName, EntryPoint = "SDCreate")]
    static extern Plugin _Create(string resourcePath, int computeUnits);

    [DllImport(DllName, EntryPoint = "SDSetConfig")]
    static extern void _SetConfig
      (Plugin self, string prompt,
       int scheduler, int steps, int seed, float guidance);

    [DllImport(DllName, EntryPoint = "SDGenerate")]
    static extern void _Generate(Plugin self);

    [DllImport(DllName, EntryPoint = "SDGenerateFromImage")]
    static extern void _GenerateFromImage
      (Plugin self, IntPtr image, int width, int height, float strength);

    [DllImport(DllName, EntryPoint = "SDGetImage")]
    static extern IntPtr _GetImage(Plugin self);

    [DllImport(DllName, EntryPoint = "SDDestroy")]
    static extern void _Destroy(IntPtr self);

    #endregion
}

} // namespace MLStableDiffusion


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================================================
FILE: Packages/jp.keijiro.ml-stable-diffusion/Runtime/Preprocess.compute
================================================
#pragma kernel Preprocess

// Input
sampler2D Input;
uint2 Size;
bool IsLinear;

// Output
RWStructuredBuffer<uint> Output;

// Color space conversion between sRGB and linear space.
// http://chilliant.blogspot.com/2012/08/srgb-approximations-for-hlsl.html
float3 Preprocess_LinearToSRGB(float3 c)
{
    return max(1.055 * pow(saturate(c), 0.416666667) - 0.055, 0.0);
}

uint Float4ToByte4AsUInt(float4 v)
{
    uint4 i = v * 255;
    return i.x + (i.y << 8) + (i.z << 16) + (i.w << 24);
}

float3 SampleInput(uint x, uint y)
{
    // UV (vertically flipped)
    float2 uv = float2(0.5 + x, Size.y - 0.5 - y) / Size;

    // UV gradients
    float2 duv_dx = float2(1.0 / Size.x, 0);
    float2 duv_dy = float2(0, -1.0 / Size.y);

    // Texture sample
    float3 rgb = tex2Dgrad(Input, uv, duv_dx, duv_dy).rgb;
    if (IsLinear) rgb = Preprocess_LinearToSRGB(rgb);

    return rgb;
}

[numthreads(8, 8, 1)]
void Preprocess(uint2 id : SV_DispatchThreadID)
{
    uint x = id.x * 4;
    uint y = id.y;

    float3 s1 = SampleInput(x + 0, y);
    float3 s2 = SampleInput(x + 1, y);
    float3 s3 = SampleInput(x + 2, y);
    float3 s4 = SampleInput(x + 3, y);

    uint addr = (y * Size.x / 4 + id.x) * 3;
    Output[addr + 0] = Float4ToByte4AsUInt(float4(s1.xyz, s2.x));
    Output[addr + 1] = Float4ToByte4AsUInt(float4(s2.yz, s3.xy));
    Output[addr + 2] = Float4ToByte4AsUInt(float4(s3.z, s4.xyz));
}


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FILE: Packages/jp.keijiro.ml-stable-diffusion/Runtime/ResourceInfo.cs
================================================
namespace MLStableDiffusion {

public readonly struct ResourceInfo
{
    public readonly string ModelPath;
    public readonly int ModelWidth;
    public readonly int ModelHeight;

    ResourceInfo(string path, int width, int height)
    {
        ModelPath = path;
        ModelWidth = width;
        ModelHeight = height;
    }

    public static implicit operator ResourceInfo(string path)
      => new ResourceInfo(path, 512, 512);

    public static ResourceInfo FixedSizeModel(string path, int width, int height)
      => new ResourceInfo(path, width, height);
}

} // namespace MLStableDiffusion


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================================================
FILE: Packages/jp.keijiro.ml-stable-diffusion/package.json
================================================
{
    "author": "Keijiro Takahashi",
    "dependencies": {},
    "description": "Stable Diffusion plugin based on Apple's Core ML port",
    "displayName": "Core ML Stable Diffusion",
    "keywords": [ "unity" ],
    "license": "SEE LICENSE in LICENSE",
    "name": "jp.keijiro.ml-stable-diffusion",
    "unity": "6000.0",
    "unityRelease": "0f1",
    "version": "1.5.0"
}


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FILE: Packages/manifest.json
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FILE: Packages/packages-lock.json
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}


================================================
FILE: Plugin/Package.resolved
================================================
{
  "pins" : [
    {
      "identity" : "ml-stable-diffusion",
      "kind" : "remoteSourceControl",
      "location" : "https://github.com/keijiro/ml-stable-diffusion.git",
      "state" : {
        "revision" : "996a58170bb2bb22e0105eb687842c4d1b9231a5",
        "version" : "1.2.0"
      }
    },
    {
      "identity" : "swift-argument-parser",
      "kind" : "remoteSourceControl",
      "location" : "https://github.com/apple/swift-argument-parser.git",
      "state" : {
        "revision" : "fddd1c00396eed152c45a46bea9f47b98e59301d",
        "version" : "1.2.0"
      }
    }
  ],
  "version" : 2
}


================================================
FILE: Plugin/Package.swift
================================================
// swift-tools-version: 5.7

import PackageDescription

let package = Package(
    name: "StableDiffusionPlugin",
    platforms: [
        .macOS("13.1"),
        .iOS("16.2")
    ],
    products: [
        .library(
            name: "StableDiffusionPlugin",
            type: .dynamic,
            targets: ["StableDiffusionPlugin"]),
    ],
    dependencies: [
        .package(url: "https://github.com/keijiro/ml-stable-diffusion.git", from: "1.2.0")
    ],
    targets: [
        .target(
            name: "StableDiffusionPlugin",
            dependencies: [
                .product(name: "StableDiffusion", package: "ml-stable-diffusion")
            ])
    ]
)


================================================
FILE: Plugin/Sources/StableDiffusionPlugin/StableDiffusionPlugin.swift
================================================
import CoreML
import Accelerate
import StableDiffusion

final class Plugin {

    var pipeline: StableDiffusionPipeline

    var config: StableDiffusionPipeline.Configuration

    var generated: CGImage?

    // Create a pipeline and load resources into it
    init(resourcePath: String, computeUnits: MLComputeUnits) throws {
        // Compute unit selection (all)
        let mlConfig = MLModelConfiguration()
        mlConfig.computeUnits = computeUnits

        // Pipeline initialization
        let resourceURL = URL(filePath: resourcePath)
        pipeline = try StableDiffusionPipeline(
            resourcesAt: resourceURL,
            controlNet: [],
            configuration: mlConfig,
            disableSafety: true,
            reduceMemory: false)
        try pipeline.loadResources()

        // Initial configuration
        config = StableDiffusionPipeline.Configuration(prompt: "")
    }

    // Run the pipeline and generate an image
    func runGenerator() throws {
        let images = try pipeline.generateImages(configuration: config)
        generated = images[0]!
    }
}

@_cdecl("SDCreate")
public func SDCreate(resourcePath: OpaquePointer, units: CInt) -> OpaquePointer! {
    let resourcePath = String(cString: UnsafePointer<CChar>(resourcePath))
    let units = MLComputeUnits(rawValue: Int(units))!
    if let plugin = try? Plugin(resourcePath: resourcePath, computeUnits: units) {
        return OpaquePointer(Unmanaged.passRetained(plugin).toOpaque())
    }
    return nil;
}

@_cdecl("SDSetConfig")
public func SDSetConfig(
    _ plugin: OpaquePointer,
    prompt: OpaquePointer,
    scheduler: CInt,
    stepCount: CInt,
    seed: CInt,
    guidanceScale: CFloat
) {
    let plugin = Unmanaged<Plugin>.fromOpaque(UnsafeRawPointer(plugin)).takeUnretainedValue()
    plugin.config.prompt = String(cString: UnsafePointer<CChar>(prompt))
    plugin.config.schedulerType = StableDiffusionScheduler(rawValue: Int(scheduler))!
    plugin.config.stepCount = Int(stepCount)
    plugin.config.seed = UInt32(seed)
    plugin.config.guidanceScale = guidanceScale
}

@_cdecl("SDGenerate")
public func SDGenerate(_ plugin: OpaquePointer) {
    let plugin = Unmanaged<Plugin>.fromOpaque(UnsafeRawPointer(plugin)).takeUnretainedValue()
    try? plugin.runGenerator()
}

@_cdecl("SDGenerateFromImage")
public func SDGenerateFromImage(
    _ plugin: OpaquePointer,
    image: OpaquePointer,
    width: CInt,
    height: CInt,
    strength: CFloat
) {
    let plugin = Unmanaged<Plugin>.fromOpaque(UnsafeRawPointer(plugin)).takeUnretainedValue()
    let pointer = UnsafeMutableRawPointer(image)

    // Raw pointer to CGImage object
    let buffer = vImage.PixelBuffer<vImage.Interleaved8x3>(
        data: pointer,
        width: Int(width),
        height: Int(height),
        byteCountPerRow: Int(width) * 3)
    let format = vImage_CGImageFormat(
        bitsPerComponent: 8,
        bitsPerPixel: 3 * 8,
        colorSpace: CGColorSpaceCreateDeviceRGB(),
        bitmapInfo: CGBitmapInfo(rawValue: CGImageAlphaInfo.none.rawValue))!
    let cgImage = buffer.makeCGImage(cgImageFormat: format)!

    // img2img run
    plugin.config.startingImage = cgImage
    plugin.config.strength = strength
    try? plugin.runGenerator()
}

@_cdecl("SDGetImage")
public func SDGetImage(_ plugin: OpaquePointer) -> OpaquePointer! {
    let plugin = Unmanaged<Plugin>.fromOpaque(UnsafeRawPointer(plugin)).takeUnretainedValue()
    return OpaquePointer(CFDataGetBytePtr(plugin.generated!.dataProvider!.data))
}

@_cdecl("SDDestroy")
public func SDDestroy(_ plugin: OpaquePointer) {
    _ = Unmanaged<Plugin>.fromOpaque(UnsafeRawPointer(plugin)).takeRetainedValue()
}


================================================
FILE: Plugin/build.sh
================================================
#!/bin/sh -e -x

MODE="Release"
MODE_LOW=`echo $MODE | tr '[:upper:]' '[:lower:]'`

DEST='../Packages/jp.keijiro.ml-stable-diffusion/Runtime'

# Build directory removal
#rm -rf .build
#rm -rf .xcodebuild

# macOS binary build using SPM
swift build -c $MODE_LOW --arch arm64 --arch x86_64

# iOS binary build using Xcode
xcodebuild build -scheme StableDiffusionPlugin -configuration $MODE -sdk iphoneos \
 -destination generic/platform=iOS -derivedDataPath .xcodebuild ENABLE_BITCODE=YES

# Binary copy to the destination
cp -f .build/apple/Products/$MODE/libStableDiffusionPlugin.dylib $DEST/macOS
cp -rf .xcodebuild/Build/Products/$MODE-iphoneos/PackageFrameworks/StableDiffusionPlugin.framework $DEST/iOS


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================================================
FILE: README.md
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Unity Core ML Stable Diffusion Plugin
=====================================

![gif](https://user-images.githubusercontent.com/343936/228759539-a35a37f2-77d6-4a10-8392-d875b968fea6.gif)

Stable Diffusion plugin for Unity, based on [Apple's Core ML port]. You can run
the model on-editor and at-runtime without needing any extra components.

[Apple's Core ML port]: https://github.com/apple/ml-stable-diffusion

System Requirements
-------------------

- Unity 2023.1 or later
- Apple Silicon Mac (editor/runtime support) with macOS 13.1 or later
- iPad Pro with Apple silicon (runtime support) with iOS 16.2 or later

Although the plugin supports iOS, it requires huge amount of memory to run the
model, so it only supports memory-rich iPad models.

How To Try
----------

Before running the sample project, you must put the model files in the
`Assets/StreamingAssets` directory.

- Clone or download the [pre-converted Stable Diffusion 2 model repository].
- Copy the `split_einsum/compiled` directory into `Assets/StreamingAssets`.
- Rename the directory to `StableDiffusion`.

[pre-converted Stable Diffusion 2 model repository]:
  https://huggingface.co/apple/coreml-stable-diffusion-2-base

It takes a long time (a few minutes) for the first run. After this
initialization step, it only takes a few tens of seconds to generate an image.

Performance Considerations
--------------------------

You can change which processing unit it uses by switching the "Compute Units"
property in the Tester component.

It depends on the device model to choose the best option:

- M1/M2 Mac and iOS: GPUs in those devices are not powerful enough, so you
  should select "CPU and NE" (Neural Engine) or "All".
- M1/M2 Pro/Max Mac: GPUs in those devices have enough processing power
  compared to NE, so "CPU and GPU" can be a better option.

When using "CPU and GPU" mode, you must use the "original" model instead of
the "split_einsum" model. Please overwrite the `StreamingAssets/StableDiffusion`
directory with the `original/compiled` directory.

LCM (SD-Turbo) Support
----------------------

You can use [SD-Turbo] or other LCMs (latent consistency models) with setting
`Pipeline.Scheduler` to `Lcm`. You might also have to change `StepCount` to 1\~4
and `GuidanceScale` to 1\~2. Please refer to the model description to know the
correct settings.

You can download the [pre-converted SD-Turbo model] from my Hugging Face
repository.

[SD-Turbo]: https://huggingface.co/stabilityai/sd-turbo
[pre-converted SD-Turbo model]:
  https://huggingface.co/keijiro-tk/coreml-sd-turbo

Sample Projects
---------------

![gif](https://user-images.githubusercontent.com/343936/228760795-9e712684-2ee6-4e63-9241-06d8aa125a17.gif)

- [Flipbook3](https://github.com/keijiro/Flipbook3): Running the image-to-image
  pipeline with a real-time 3D scene.