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saddam213saddam213
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Merge pull request #107 from saddam213/API
Major: API Refactor
2 parents 5d74009 + 9c1aa87 commit aa87704

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‎Assets/Samples/OpenPose.png‎

25.9 KB
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‎OnnxStack.Console/AppService.cs‎

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@@ -12,6 +12,7 @@ public AppService(IServiceProvider serviceProvider, IEnumerable<Type> exampleRun
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_exampleRunners = exampleRunnerTypes
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.Select(serviceProvider.GetService)
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.Cast<IExampleRunner>()
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.OrderBy(x => x.Index)
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.ToList();
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}
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‎OnnxStack.Console/Examples/ControlNetExample.cs‎

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using OnnxStack.Core.Image;
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using OnnxStack.StableDiffusion.Common;
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using OnnxStack.StableDiffusion.Config;
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using OnnxStack.StableDiffusion.Enums;
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using OnnxStack.StableDiffusion.Models;
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using OnnxStack.StableDiffusion.Pipelines;
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using SixLabors.ImageSharp;
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namespace OnnxStack.Console.Runner
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{
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public sealed class ControlNetExample : IExampleRunner
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{
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private readonly string _outputDirectory;
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private readonly StableDiffusionConfig _configuration;
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public ControlNetExample(StableDiffusionConfig configuration)
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{
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_configuration = configuration;
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_outputDirectory = Path.Combine(Directory.GetCurrentDirectory(), "Examples", nameof(ControlNetExample));
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Directory.CreateDirectory(_outputDirectory);
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}
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public int Index => 11;
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public string Name => "ControlNet Example";
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public string Description => "ControlNet Example";
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/// <summary>
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/// ControlNet Example
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/// </summary>
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public async Task RunAsync()
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{
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// Load Control Image
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var controlImage = await InputImage.FromFileAsync("D:\\Repositories\\OnnxStack\\Assets\\Samples\\OpenPose.png");
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// Create ControlNet
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var controlNet = ControlNetModel.Create("D:\\Repositories\\controlnet_onnx\\controlnet\\openpose.onnx");
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// Create Pipeline
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var pipeline = StableDiffusionPipeline.CreatePipeline("D:\\Repositories\\stable_diffusion_onnx", ModelType.ControlNet);
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// Prompt
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var promptOptions = new PromptOptions
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{
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Prompt = "Stormtrooper",
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DiffuserType = DiffuserType.ControlNet,
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InputContolImage = controlImage
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};
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// Run pipeline
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var result = await pipeline.RunAsync(promptOptions, controlNet: controlNet, progressCallback: OutputHelpers.ProgressCallback);
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// Create Image from Tensor result
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var image = result.ToImage();
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// Save Image File
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var outputFilename = Path.Combine(_outputDirectory, $"Output.png");
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await image.SaveAsPngAsync(outputFilename);
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//Unload
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await controlNet.UnloadAsync();
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await pipeline.UnloadAsync();
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}
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}
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}

‎OnnxStack.Console/Examples/ControlNetFeatureExample.cs‎

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using OnnxStack.Core.Image;
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using OnnxStack.FeatureExtractor.Pipelines;
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using OnnxStack.StableDiffusion.Config;
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using OnnxStack.StableDiffusion.Enums;
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using OnnxStack.StableDiffusion.Models;
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using OnnxStack.StableDiffusion.Pipelines;
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using SixLabors.ImageSharp;
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namespace OnnxStack.Console.Runner
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{
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public sealed class ControlNetFeatureExample : IExampleRunner
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{
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private readonly string _outputDirectory;
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private readonly StableDiffusionConfig _configuration;
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public ControlNetFeatureExample(StableDiffusionConfig configuration)
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{
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_configuration = configuration;
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_outputDirectory = Path.Combine(Directory.GetCurrentDirectory(), "Examples", nameof(ControlNetFeatureExample));
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Directory.CreateDirectory(_outputDirectory);
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}
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public int Index => 12;
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public string Name => "ControlNet + Feature Extraction Example";
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public string Description => "ControlNet StableDiffusion with input image Depth feature extraction";
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/// <summary>
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/// ControlNet Example
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/// </summary>
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public async Task RunAsync()
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{
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// Load Control Image
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var inputImage = await InputImage.FromFileAsync("D:\\Repositories\\OnnxStack\\Assets\\Samples\\Img2Img_Start.bmp");
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// Create Annotation pipeline
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var annotationPipeline = AnnotationPipeline.CreatePipeline("D:\\Repositories\\controlnet_onnx\\annotators");
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// Create Depth Image
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var controlImage = await annotationPipeline.DepthImage(inputImage);
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// Save Depth Image (Debug Only)
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await controlImage.Image.SaveAsPngAsync(Path.Combine(_outputDirectory, $"Depth.png"));
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// Create ControlNet
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var controlNet = ControlNetModel.Create("D:\\Repositories\\controlnet_onnx\\controlnet\\depth.onnx");
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// Create Pipeline
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var pipeline = StableDiffusionPipeline.CreatePipeline("D:\\Repositories\\stable_diffusion_onnx", ModelType.ControlNet);
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// Prompt
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var promptOptions = new PromptOptions
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{
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Prompt = "steampunk dog",
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DiffuserType = DiffuserType.ControlNet,
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InputContolImage = controlImage
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};
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// Run pipeline
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var result = await pipeline.RunAsync(promptOptions, controlNet: controlNet, progressCallback: OutputHelpers.ProgressCallback);
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// Create Image from Tensor result
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var image = result.ToImage();
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// Save Image File
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var outputFilename = Path.Combine(_outputDirectory, $"Output.png");
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await image.SaveAsPngAsync(outputFilename);
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//Unload
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await annotationPipeline.UnloadAsync();
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await controlNet.UnloadAsync();
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await pipeline.UnloadAsync();
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}
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}
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}

‎OnnxStack.Console/Examples/StableDebug.cs‎

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@@ -1,7 +1,6 @@
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using OnnxStack.StableDiffusion;
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using OnnxStack.StableDiffusion.Common;
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using OnnxStack.Core.Image;
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using OnnxStack.StableDiffusion.Config;
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using OnnxStack.StableDiffusion.Enums;
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using OnnxStack.StableDiffusion.Pipelines;
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using SixLabors.ImageSharp;
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using System.Diagnostics;
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@@ -11,15 +10,15 @@ public sealed class StableDebug : IExampleRunner
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{
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private readonly string _outputDirectory;
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private readonly StableDiffusionConfig _configuration;
14-
private readonly IStableDiffusionService _stableDiffusionService;
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16-
public StableDebug(StableDiffusionConfig configuration, IStableDiffusionService stableDiffusionService)
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public StableDebug(StableDiffusionConfig configuration)
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{
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_configuration = configuration;
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_stableDiffusionService = stableDiffusionService;
2017
_outputDirectory = Path.Combine(Directory.GetCurrentDirectory(), "Examples", nameof(StableDebug));
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}
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public int Index => 0;
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public string Name => "Stable Diffusion Debug";
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public string Description => "Stable Diffusion Debugger";
@@ -35,59 +34,58 @@ public async Task RunAsync()
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var negativePrompt = "painting, drawing, sketches, monochrome, grayscale, illustration, anime, cartoon, graphic, text, crayon, graphite, abstract, easynegative, low quality, normal quality, worst quality, lowres, close up, cropped, out of frame, jpeg artifacts, duplicate, morbid, mutilated, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, glitch, deformed, mutated, cross-eyed, ugly, dehydrated, bad anatomy, bad proportions, gross proportions, cloned face, disfigured, malformed limbs, missing arms, missing legs fused fingers, too many fingers,extra fingers, extra limbs,, extra arms, extra legs,disfigured,";
3635
while (true)
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{
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var developmentSeed = 624461087;
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var promptOptions = new PromptOptions
3939
{
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Prompt = prompt,
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NegativePrompt = negativePrompt,
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};
4343

44-
var schedulerOptions = new SchedulerOptions
44+
// Loop though the appsettings.json model sets
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foreach (var modelSet in _configuration.ModelSets)
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{
46-
SchedulerType = SchedulerType.LMS,
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Seed = 624461087,
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GuidanceScale = 8,
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InferenceSteps = 22,
50-
Strength = 0.6f
51-
};
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OutputHelpers.WriteConsole($"Loading Model `{modelSet.Name}`...", ConsoleColor.Cyan);
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53-
foreach (var model in _configuration.ModelSets)
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{
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OutputHelpers.WriteConsole($"Loading Model `{model.Name}`...", ConsoleColor.Green);
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await _stableDiffusionService.LoadModelAsync(model);
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// Create Pipeline
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var pipeline = PipelineBase.CreatePipeline(modelSet);
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58-
schedulerOptions.Width = model.SampleSize;
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schedulerOptions.Height = model.SampleSize;
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// Preload Models (optional)
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await pipeline.LoadAsync();
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61-
foreach (var schedulerType in model.PipelineType.GetSchedulerTypes())
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// Loop though schedulers
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foreach (var scheduler in pipeline.SupportedSchedulers)
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{
63-
schedulerOptions.SchedulerType = schedulerType;
64-
OutputHelpers.WriteConsole($"Generating {schedulerType} Image...", ConsoleColor.Green);
65-
await GenerateImage(model, promptOptions, schedulerOptions);
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// Create SchedulerOptions based on pipeline defaults
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var schedulerOptions = pipeline.DefaultSchedulerOptions with
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{
61+
Seed = developmentSeed,
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SchedulerType = scheduler
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};
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var timestamp = Stopwatch.GetTimestamp();
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OutputHelpers.WriteConsole($"Generating {scheduler} Image...", ConsoleColor.Green);
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68+
// Run pipeline
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var result = await pipeline.RunAsync(promptOptions, schedulerOptions, progressCallback: OutputHelpers.ProgressCallback);
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// Create Image from Tensor result
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var image = result.ToImage();
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// Save Image File
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var outputFilename = Path.Combine(_outputDirectory, $"{modelSet.Name}_{schedulerOptions.SchedulerType}.png");
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await image.SaveAsPngAsync(outputFilename);
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OutputHelpers.WriteConsole($"{schedulerOptions.SchedulerType} Image Created: {Path.GetFileName(outputFilename)}", ConsoleColor.Green);
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OutputHelpers.WriteConsole($"Elapsed: {Stopwatch.GetElapsedTime(timestamp)}ms", ConsoleColor.Yellow);
6680
}
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68-
OutputHelpers.WriteConsole($"Unloading Model `{model.Name}`...", ConsoleColor.Green);
69-
await _stableDiffusionService.UnloadModelAsync(model);
82+
OutputHelpers.WriteConsole($"Unloading Model `{modelSet.Name}`...", ConsoleColor.Cyan);
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84+
// Unload pipeline
85+
await pipeline.UnloadAsync();
7086
}
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break;
7288
}
7389
}
74-
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76-
private async Task<bool> GenerateImage(StableDiffusionModelSet model, PromptOptions prompt, SchedulerOptions options)
77-
{
78-
var timestamp = Stopwatch.GetTimestamp();
79-
var outputFilename = Path.Combine(_outputDirectory, $"{model.Name}_{options.Seed}_{options.SchedulerType}.png");
80-
var result = await _stableDiffusionService.GenerateAsImageAsync(new ModelOptions(model), prompt, options);
81-
if (result is not null)
82-
{
83-
await result.SaveAsPngAsync(outputFilename);
84-
OutputHelpers.WriteConsole($"{options.SchedulerType} Image Created: {Path.GetFileName(outputFilename)}", ConsoleColor.Green);
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OutputHelpers.WriteConsole($"Elapsed: {Stopwatch.GetElapsedTime(timestamp)}ms", ConsoleColor.Yellow);
86-
return true;
87-
}
88-
89-
OutputHelpers.WriteConsole($"Failed to create image", ConsoleColor.Red);
90-
return false;
91-
}
9290
}
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}

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