Stable Diffusion Library for .NET
Get Started
$ dotnet add package OnnxStack.StableDiffusionReadme
OnnxStack.StableDiffusion - Onnx Stable Diffusion Services for .NET Applications
OnnxStack.StableDiffusion is a library that provides higher-level Stable Diffusion services for use in .NET applications. It offers extensive support for features such as dependency injection, .NET configuration implementations, ASP.NET Core integration, and IHostedService support.
Getting Started
OnnxStack.StableDiffusion can be found via the nuget package manager, download and install it.
PM> Install-Package OnnxStack.StableDiffusion
Microsoft.ML.OnnxRuntime
Depending on the devices you have and the platform you are running on, you will want to install the Microsoft.ML.OnnxRuntime package that best suits your needs.
CPU-GPU via Microsoft Drirect ML
PM> Install-Package Microsoft.ML.OnnxRuntime.DirectML
GPU support for both NVIDIA and AMD?
PM> Install-Package Microsoft.ML.OnnxRuntime.Gpu
.NET Core Registration
You can easily integrate OnnxStack.StableDiffusion into your application services layer. This registration process sets up the necessary services and loads the appsettings.json configuration.
Example: Registering OnnxStack.StableDiffusion
builder.Services.AddOnnxStackStableDiffusion();
.NET Console Application Example
Required Nuget Packages for example
Microsoft.Extensions.Hosting
Microsoft.Extensions.Logging
using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Hosting;
using OnnxStack.StableDiffusion.Common;
using OnnxStack.StableDiffusion.Config;
internal class Program
{
static async Task Main(string[] _)
{
var builder = Host.CreateApplicationBuilder();
builder.Logging.ClearProviders();
builder.Services.AddLogging((loggingBuilder) => loggingBuilder.SetMinimumLevel(LogLevel.Error));
// Add OnnxStack Stable Diffusion
builder.Services.AddOnnxStackStableDiffusion();
// Add AppService
builder.Services.AddHostedService<AppService>();
// Start
await builder.Build().RunAsync();
}
}
internal class AppService : IHostedService
{
private readonly string _outputDirectory;
private readonly IStableDiffusionService _stableDiffusionService;
public AppService(IStableDiffusionService stableDiffusionService)
{
_stableDiffusionService = stableDiffusionService;
_outputDirectory = Path.Combine(Directory.GetCurrentDirectory(), "Images");
}
public async Task StartAsync(CancellationToken cancellationToken)
{
Directory.CreateDirectory(_outputDirectory);
while (true)
{
System.Console.WriteLine("Please type a prompt and press ENTER");
var prompt = System.Console.ReadLine();
System.Console.WriteLine("Please type a negative prompt and press ENTER (optional)");
var negativePrompt = System.Console.ReadLine();
System.Console.WriteLine("Please enter image filepath for Img2Img and press ENTER (optional)");
var inputImageFile = System.Console.ReadLine();
var promptOptions = new PromptOptions
{
Prompt = prompt,
NegativePrompt = negativePrompt,
SchedulerType = SchedulerType.LMSScheduler,
InputImage = new InputImage
{
ImagePath = inputImageFile
}
};
var schedulerOptions = new SchedulerOptions
{
Seed = Random.Shared.Next(),
GuidanceScale = 7.5f,
InferenceSteps = 30,
Height = 512,
Width = 512,
Strength = 0.6f // Img2Img
};
System.Console.WriteLine("Generating Image...");
var outputFilename = Path.Combine(_outputDirectory, $"{schedulerOptions.Seed}_{promptOptions.SchedulerType}.png");
var result = await _stableDiffusionService.GenerateAsImageAsync(prompt, options);
if (result is not null)
{
await result.SaveAsPngAsync(outputFilename);
System.Console.WriteLine($"Image Created, FilePath: {outputFilename}");
}
}
}
public Task StopAsync(CancellationToken cancellationToken)
{
return Task.CompletedTask;
}
}