Update README

Author: saddam213

OnnxStack.Core/README.md

@@ -1,9 +1,18 @@
 # OnnxStack.Core - Onnx Services for .NET Applications
 
-OnnxStack.Core is a library that provides higher-level 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.
+OnnxStack.Core is a library that provides higher-level ONNX 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.
+
+You can configure a model set for runtime, offloading individual models to different devices to make better use of resources or run on lower-end hardware. The first use-case is StableDiffusion; however, it will be expanded, and other model sets, such as object detection and classification, will be added.
 
 ## Getting Started
 
+
+OnnxStack.Core can be found via the nuget package manager, download and install it.
+```
+PM> Install-Package OnnxStack.Core
+```
+
+
 ### .NET Core Registration
 
 You can easily integrate `OnnxStack.Core` into your application services layer. This registration process sets up the necessary services and loads the `appsettings.json` configuration.
@@ -13,6 +22,69 @@ Example: Registering OnnxStack
 builder.Services.AddOnnxStack();
 ```
 
+## Configuration example
+The `appsettings.json` is the easiest option for configuring model sets. Below is an example of `Stable Diffusion 1.5`. The example adds the necessary paths to each model file required for Stable Diffusion, as well as any model-specific configurations. Each model can be assigned to its own device, which is handy if you have only a small GPU. This way, you can offload only what you need. There are limitations depending on the version of the `Microsoft.ML.OnnxRuntime` package you are using, but in most cases, you can split the load between CPU and GPU.
+
+```json
+{
+	"Logging": {
+		"LogLevel": {
+			"Default": "Information",
+			"Microsoft.AspNetCore": "Warning"
+		}
+	},
+	"AllowedHosts": "*",
+
+	"OnnxStackConfig": {
+		"Name": "StableDiffusion 1.5",
+		"PadTokenId": 49407,
+		"BlankTokenId": 49407,
+		"InputTokenLimit": 512,
+		"TokenizerLimit": 77,
+		"EmbeddingsLength": 768,
+		"ScaleFactor": 0.18215,
+		"ModelConfigurations": [
+			{
+				"Type": "Unet",
+				"DeviceId": 0,
+				"ExecutionProvider": "DirectML",
+				"OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\unet\\model.onnx"
+			},
+			{
+				"Type": "Tokenizer",
+				"DeviceId": 0,
+				"ExecutionProvider": "Cpu",
+				"OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\cliptokenizer.onnx"
+			},
+			{
+				"Type": "TextEncoder",
+				"DeviceId": 0,
+				"ExecutionProvider": "Cpu",
+				"OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\text_encoder\\model.onnx"
+			},
+			{
+				"Type": "VaeEncoder",
+				"DeviceId": 0,
+				"ExecutionProvider": "Cpu",
+				"OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\vae_encoder\\model.onnx"
+			},
+			{
+				"Type": "VaeDecoder",
+				"DeviceId": 0,
+				"ExecutionProvider": "Cpu",
+				"OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\vae_decoder\\model.onnx"
+			},
+			{
+				"Type": "SafetyChecker",
+				"IsDisabled": true,
+				"DeviceId": 0,
+				"ExecutionProvider": "Cpu",
+				"OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\safety_checker\\model.onnx"
+			}
+		]
+	}
+}
+```
 
 
 
@@ -21,12 +93,19 @@ builder.Services.AddOnnxStack();
 // Create Configuration
 var onnxStackConfig = new OnnxStackConfig
 {
-	IsSafetyModelEnabled = false,
-	ExecutionProviderTarget = ExecutionProvider.DirectML,
-	OnnxUnetPath = "stable-diffusion-v1-5\\unet\\model.onnx",
-	OnnxVaeDecoderPath = "stable-diffusion-v1-5\\vae_decoder\\model.onnx",
-	OnnxTextEncoderPath = "stable-diffusion-v1-5\\text_encoder\\model.onnx",
-	OnnxSafetyModelPath = "stable-diffusion-v1-5\\safety_checker\\model.onnx"
+	Name = "OnnxStack",
+	TokenizerLimit = 77,
+	ModelConfigurations = new List<OnnxModelSessionConfig>
+	{
+		new OnnxModelSessionConfig
+		{
+				DeviceId = 0,
+				ExecutionProvider = ExecutionProvider.DirectML,
+
+				Type = OnnxModelType.Tokenizer,
+				OnnxModelPath = "clip_tokenizer.onnx",
+		}
+	}
 };
 
 // Create Service
@@ -38,14 +117,14 @@ var text = "Text To Tokenize";
 var inputTensor = new DenseTensor<string>(new string[] { text }, new int[] { 1 });
 var inputString = new List<NamedOnnxValue>
 {
-    NamedOnnxValue.CreateFromTensor("string_input", inputTensor)
+	NamedOnnxValue.CreateFromTensor("string_input", inputTensor)
 };
 
 // Create an InferenceSession from the Onnx clip tokenizer.
 // Run session and send the input data in to get inference output. 
 using (var tokens = onnxModelService.RunInference(OnnxModelType.Tokenizer, inputString))
 {
-    var resultTensor = tokens.FirstElementAs<Tensor<long>>();
+	var resultTensor = tokens.ToArray();
 }
 
 ```

OnnxStack.StableDiffusion/README.md

@@ -4,6 +4,26 @@
 
 ## 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.
@@ -18,11 +38,10 @@ builder.Services.AddOnnxStackStableDiffusion();
 
 ## .NET Console Application Example
 
-Required Nuget Packages
+Required Nuget Packages for example
 ```nuget
 Microsoft.Extensions.Hosting
 Microsoft.Extensions.Logging
-Microsoft.ML.OnnxRuntime.DirectML
 ```
 
 ```csharp
@@ -81,7 +100,10 @@ internal class AppService : IHostedService
                Prompt = prompt,
                NegativePrompt = negativePrompt,
                SchedulerType = SchedulerType.LMSScheduler,
-               InputImage = inputImageFile
+               InputImage = new InputImage
+               {
+                  ImagePath = inputImageFile
+               }
             };
 
             var schedulerOptions = new SchedulerOptions
@@ -96,9 +118,10 @@ internal class AppService : IHostedService
 
             System.Console.WriteLine("Generating Image...");
             var outputFilename = Path.Combine(_outputDirectory, $"{schedulerOptions.Seed}_{promptOptions.SchedulerType}.png");
-            var result = await _stableDiffusionService.TextToImageFile(promptOptions, schedulerOptions, outputFilename);
+            var result = await _stableDiffusionService.GenerateAsImageAsync(prompt, options);
             if (result is not null)
-            {
+            { 
+               await result.SaveAsPngAsync(outputFilename);
                System.Console.WriteLine($"Image Created, FilePath: {outputFilename}");
             }
       }