diff --git a/Docs/AppSettings.md b/Docs/AppSettings.md
new file mode 100644
index 00000000..e69de29b
diff --git a/OnnxStack.StableDiffusion/README.md b/OnnxStack.StableDiffusion/README.md
index 60b4f8da..eca1d169 100644
--- a/OnnxStack.StableDiffusion/README.md
+++ b/OnnxStack.StableDiffusion/README.md
@@ -2,226 +2,109 @@
`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
-```
+### **Prompt**
+
+Stable Diffusion models take a text prompt and create an image that represents the text.
+
+*Example:*
+`
+High-fashion photography in an abandoned industrial warehouse, with dramatic lighting and edgy outfits, detailed clothing, intricate clothing, seductive pose, action pose, motion, beautiful digital artwork, atmospheric, warm sunlight, photography, neo noir, bokeh, beautiful dramatic lighting, shallow depth of field, photorealism, volumetric lighting, Ultra HD, raytracing, studio quality, octane render
+`
+
+### **Negative Prompt**
+
+A negative prompt can be provided to guide the inference to exclude in calculations
+
+*Example:*
+
+`
+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,
+`
+
+### **Schedulers**
+
+Many different scheduler algorithms can be used for this computation, each having its pro- and cons.
+So far `OnnxStack.StableDiffusion` as included `LMS Discrete`, `Euler Ancestral`, `DDPM`, `DDIM`, and `KDPM2` options with more in the works.
+
+*Example:*
+| LMS Scheduler | Euler Ancestral Scheduler | DDPM Scheduler |
+| :--- | :--- | :--- |
+
| 
|
|
+
+ Seed: 624461087 GuidanceScale: 8 NumInferenceSteps: 22
-### 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.
+### **Text To Image**
+Text To Image Stable Diffusion is a powerful machine learning technique that allows you to generate high-quality images from textual descriptions. It combines the capabilities of text understanding and image synthesis to convert natural language descriptions into visually coherent and meaningful images
+
+| Input Text | Output Image | Diffusion Steps |
+| :--- | :--- | :--- |
+
| 
|
|
+
+### **Image To Image**
+Image To Image Stable Diffusion is an advanced image processing and generation method that excels in transforming one image into another while preserving the visual quality and structure of the original content. Using stable diffusion, this technique can perform a wide range of image-to-image tasks, such as style transfer, super-resolution, colorization, and more
+
+| Input Image | Output Image | Diffusion Steps |
+| :--- | :--- | :--- |
+
| 
|
|
-### CPU-GPU via Microsoft Drirect ML
```
-PM> Install-Package Microsoft.ML.OnnxRuntime.DirectML
+ Prompt: Dog wearing storm trooper helmet, head shot
```
-### GPU support for both NVIDIA and AMD?
+### **Image Inpainting**
+Image inpainting is an image modification/restoration technique that intelligently fills in missing or damaged portions of an image while maintaining visual consistency. It's used for tasks like photo restoration and object removal, creating seamless and convincing results.
+
+In the below example we use a simple mask image + prompt to add a rider to the horse
+The black part of the mask will be used buy the process to generate new content, in this case the rider
+
+| Input Image | Mask Image | Masked Image | Result
+| :--- | :--- | :--- | :--- |
+
| 
|
|
|
+
```
-PM> Install-Package Microsoft.ML.OnnxRuntime.Gpu
+ Prompt: Rider on horse
```
+## **Realtime Stable Diffusion**
+Realtime stable diffusion is a process where the results are constantly rendered as you are working with the image or changing the settings, This can be fantastic if you are creating new artworks or editing existing images.
-### .NET Core Registration
+Performance will depend on hardware and models selected, but for `Latent Consistency Models` you can get up to 4fps with a 3090 :)
-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.
+### Text To Image
+https://user-images.githubusercontent.com/4353684/285347887-99db7f37-cff4-48b6-805b-3ca55e8f0c3a.mp4
-Example: Registering OnnxStack.StableDiffusion
-```csharp
-builder.Services.AddOnnxStackStableDiffusion();
-```
+### Image To Image
+https://user-images.githubusercontent.com/4353684/285348410-c19a2111-6745-4f01-8400-d137d40180fe.mp4
+### Image Inpaint
+https://user-images.githubusercontent.com/4353684/285347894-9d044d7d-7c22-4379-8187-9cf7b9cac89c.mp4
+### Paint To Image
+https://user-images.githubusercontent.com/4353684/285347896-8da6709b-fea6-4cd4-ba65-ec692401f475.mp4
+https://user-images.githubusercontent.com/4353684/285547207-3a7ea067-fcbf-47f0-9372-fafa94d301f7.mp4
-## .NET Console Application Example
-Required Nuget Packages for example
-```nuget
-Microsoft.Extensions.Hosting
-Microsoft.Extensions.Logging
-```
-```csharp
-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();
-
- // 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();
-
-
- // Example only, full config depends on model
- // appsettings.json is recommended for ease of use
- var modelOptions = new ModelOptions
- {
- Name = "Stable Diffusion 1.5",
- ExecutionProvider = ExecutionProvider.DirectML,
- ModelConfigurations = new List
- {
- new OnnxModelSessionConfig
- {
- Type = OnnxModelType.Unet,
- OnnxModelPath = "model path"
- }
- }
- };
-
- var promptOptions = new PromptOptions
- {
- Prompt = prompt,
- NegativePrompt = negativePrompt,
- DiffuserType = DiffuserType.TextToImage,
-
- // Input for ImageToImage
- // InputImage = new InputImage(File.ReadAllBytesAsync("image to image filename"))
- };
-
- var schedulerOptions = new SchedulerOptions
- {
- Seed = Random.Shared.Next(),
- GuidanceScale = 7.5f,
- InferenceSteps = 30,
- Height = 512,
- Width = 512,
- SchedulerType = SchedulerType.LMS,
- };
-
-
- // Generate Image Example
- var outputFilename = Path.Combine(_outputDirectory, $"{schedulerOptions.Seed}_{schedulerOptions.SchedulerType}.png");
- var result = await _stableDiffusionService.GenerateAsImageAsync(modelOptions, promptOptions, schedulerOptions);
- if (result is not null)
- {
- // Save image to disk
- await result.SaveAsPngAsync(outputFilename);
- }
-
-
-
-
- // Generate Batch Example
- var batchOptions = new BatchOptions
- {
- BatchType = BatchOptionType.Seed,
- ValueTo = 20
- };
-
- await foreach (var batchResult in _stableDiffusionService.GenerateBatchAsImageAsync(modelOptions, promptOptions, schedulerOptions, batchOptions))
- {
- // Save image to disk
- await batchResult.SaveAsPngAsync(outputFilename);
- }
-
-
- }
- }
-
- public Task StopAsync(CancellationToken cancellationToken)
- {
- return Task.CompletedTask;
- }
-}
-```
+## ONNX Model Download
+You will need an ONNX compatible model to use, Hugging Face is a great place to download the Stable Diffusion models
+
+- [OpenJourney V4](https://huggingface.co/TheyCallMeHex/OpenJourney-V4-ONNX)
+- [DreamLike PhotoReal 2.0](https://huggingface.co/TheyCallMeHex/DreamLike-PhotoReal-2.0-ONNX)
+- [CyberPunk Anime Diffusion](https://huggingface.co/TheyCallMeHex/Cyberpunk-Anime-Diffusion-ONNX)
+- [InkPunk Diffusion](https://huggingface.co/TheyCallMeHex/Inkpunk-Diffusion-ONNX)
+- [Mo-Di Diffusion](https://huggingface.co/TheyCallMeHex/Mo-Di-Diffusion-ONNX)
+- [epiCRealism](https://huggingface.co/TheyCallMeHex/epiCRealism-ONNX)
+- [Comic Diffusion](https://huggingface.co/TheyCallMeHex/Comic-Diffusion-ONNX)
+- [Redshift Diffusion](https://huggingface.co/TheyCallMeHex/Redshift-Diffusion-ONNX)
+
+
+## Resources
+- [Hugging Face Stable Diffusion Blog](https://huggingface.co/blog/stable_diffusion)
+- [ONNX Runtime tutorial for Stable Diffusion in C#](https://onnxruntime.ai/docs/tutorials/csharp/stable-diffusion-csharp.html)
-## Configuration
-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"
- }
- },
-
- "OnnxStackConfig": {
- "Name": "StableDiffusion 1.5",
- "IsEnabled": true,
- "PadTokenId": 49407,
- "BlankTokenId": 49407,
- "TokenizerLimit": 77,
- "EmbeddingsLength": 768,
- "ScaleFactor": 0.18215,
- "PipelineType": "StableDiffusion",
- "Diffusers": [
- "TextToImage",
- "ImageToImage",
- "ImageInpaintLegacy"
- ],
- "DeviceId": 0,
- "InterOpNumThreads": 0,
- "IntraOpNumThreads": 0,
- "ExecutionMode": "ORT_SEQUENTIAL",
- "ExecutionProvider": "DirectML",
- "ModelConfigurations": [
- {
- "Type": "Tokenizer",
- "OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\cliptokenizer.onnx"
- },
- {
- "Type": "Unet",
- "OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\unet\\model.onnx"
- },
- {
- "Type": "TextEncoder",
- "OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\text_encoder\\model.onnx"
- },
- {
- "Type": "VaeEncoder",
- "OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\vae_encoder\\model.onnx"
- },
- {
- "Type": "VaeDecoder",
- "OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\vae_decoder\\model.onnx"
- }
- ]
- }
-}
-```
\ No newline at end of file
+## Reference
+This work is based on the original C# implementation of Stable Diffusion by Cassie Breviu here: [Stable Diffusion with C# and ONNX Runtime](https://github.com/cassiebreviu/stablediffusion).
\ No newline at end of file
diff --git a/OnnxStack.StableDiffusion/README_Example.md b/OnnxStack.StableDiffusion/README_Example.md
new file mode 100644
index 00000000..60b4f8da
--- /dev/null
+++ b/OnnxStack.StableDiffusion/README_Example.md
@@ -0,0 +1,227 @@
+# 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
+```csharp
+builder.Services.AddOnnxStackStableDiffusion();
+```
+
+
+
+
+## .NET Console Application Example
+
+Required Nuget Packages for example
+```nuget
+Microsoft.Extensions.Hosting
+Microsoft.Extensions.Logging
+```
+
+```csharp
+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();
+
+ // 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();
+
+
+ // Example only, full config depends on model
+ // appsettings.json is recommended for ease of use
+ var modelOptions = new ModelOptions
+ {
+ Name = "Stable Diffusion 1.5",
+ ExecutionProvider = ExecutionProvider.DirectML,
+ ModelConfigurations = new List
+ {
+ new OnnxModelSessionConfig
+ {
+ Type = OnnxModelType.Unet,
+ OnnxModelPath = "model path"
+ }
+ }
+ };
+
+ var promptOptions = new PromptOptions
+ {
+ Prompt = prompt,
+ NegativePrompt = negativePrompt,
+ DiffuserType = DiffuserType.TextToImage,
+
+ // Input for ImageToImage
+ // InputImage = new InputImage(File.ReadAllBytesAsync("image to image filename"))
+ };
+
+ var schedulerOptions = new SchedulerOptions
+ {
+ Seed = Random.Shared.Next(),
+ GuidanceScale = 7.5f,
+ InferenceSteps = 30,
+ Height = 512,
+ Width = 512,
+ SchedulerType = SchedulerType.LMS,
+ };
+
+
+ // Generate Image Example
+ var outputFilename = Path.Combine(_outputDirectory, $"{schedulerOptions.Seed}_{schedulerOptions.SchedulerType}.png");
+ var result = await _stableDiffusionService.GenerateAsImageAsync(modelOptions, promptOptions, schedulerOptions);
+ if (result is not null)
+ {
+ // Save image to disk
+ await result.SaveAsPngAsync(outputFilename);
+ }
+
+
+
+
+ // Generate Batch Example
+ var batchOptions = new BatchOptions
+ {
+ BatchType = BatchOptionType.Seed,
+ ValueTo = 20
+ };
+
+ await foreach (var batchResult in _stableDiffusionService.GenerateBatchAsImageAsync(modelOptions, promptOptions, schedulerOptions, batchOptions))
+ {
+ // Save image to disk
+ await batchResult.SaveAsPngAsync(outputFilename);
+ }
+
+
+ }
+ }
+
+ public Task StopAsync(CancellationToken cancellationToken)
+ {
+ return Task.CompletedTask;
+ }
+}
+```
+
+
+## Configuration
+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"
+ }
+ },
+
+ "OnnxStackConfig": {
+ "Name": "StableDiffusion 1.5",
+ "IsEnabled": true,
+ "PadTokenId": 49407,
+ "BlankTokenId": 49407,
+ "TokenizerLimit": 77,
+ "EmbeddingsLength": 768,
+ "ScaleFactor": 0.18215,
+ "PipelineType": "StableDiffusion",
+ "Diffusers": [
+ "TextToImage",
+ "ImageToImage",
+ "ImageInpaintLegacy"
+ ],
+ "DeviceId": 0,
+ "InterOpNumThreads": 0,
+ "IntraOpNumThreads": 0,
+ "ExecutionMode": "ORT_SEQUENTIAL",
+ "ExecutionProvider": "DirectML",
+ "ModelConfigurations": [
+ {
+ "Type": "Tokenizer",
+ "OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\cliptokenizer.onnx"
+ },
+ {
+ "Type": "Unet",
+ "OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\unet\\model.onnx"
+ },
+ {
+ "Type": "TextEncoder",
+ "OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\text_encoder\\model.onnx"
+ },
+ {
+ "Type": "VaeEncoder",
+ "OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\vae_encoder\\model.onnx"
+ },
+ {
+ "Type": "VaeDecoder",
+ "OnnxModelPath": "D:\\Repositories\\stable-diffusion-v1-5\\vae_decoder\\model.onnx"
+ }
+ ]
+ }
+}
+```
\ No newline at end of file
diff --git a/README.md b/README.md
index 0465c802..9d1c8a9a 100644
--- a/README.md
+++ b/README.md
@@ -3,127 +3,79 @@
-
-[](https://www.nuget.org/packages/OnnxStack.Core)
-
-[](https://www.nuget.org/packages/OnnxStack.StableDiffusion)
-
-[](https://github.com/saddam213/OnnxStack/releases)
-
-
-
[](https://discord.gg/YmUpFaaFsf)
[](https://www.youtube.com/@OnnxStack)
-
-
-
-
-
-### Welcome to OnnxStack!
-OnnxStack transforms machine learning in .NET, Seamlessly integrating with `ONNX Runtime` and `Microsoft ML`, this library empowers you to build, deploy, and execute machine learning models entirely within the .NET ecosystem. Bid farewell to Python dependencies and embrace a new era of intelligent applications tailored for .NET
-Welcome to Python-free AI integration with OnnxStack!
-
-
-## **Latest Release Download: [OnnxStack UI v0.12.0](https://github.com/saddam213/OnnxStack/releases/download/v0.12.0/WindowsUI.v0.12.0.zip)**
-
### **[Discord](https://discord.gg/YxbQfbegdS)** | **[Youtube](https://www.youtube.com/@OnnxStack)**
+__________________________
-# Projects
-
-## **[OnnxStack.StableDiffusion](OnnxStack.StableDiffusion/README.md)**
-Inference Stable Diffusion with C# and ONNX Runtime
-
-### **Prompt**
-
-Stable Diffusion models take a text prompt and create an image that represents the text.
-
-*Example:*
-`
-High-fashion photography in an abandoned industrial warehouse, with dramatic lighting and edgy outfits, detailed clothing, intricate clothing, seductive pose, action pose, motion, beautiful digital artwork, atmospheric, warm sunlight, photography, neo noir, bokeh, beautiful dramatic lighting, shallow depth of field, photorealism, volumetric lighting, Ultra HD, raytracing, studio quality, octane render
-`
-
-### **Negative Prompt**
-
-A negative prompt can be provided to guide the inference to exclude in calculations
-*Example:*
-`
-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,
-`
-### **Schedulers**
+# Welcome to OnnxStack!
+OnnxStack transforms machine learning in .NET, Seamlessly integrating with `ONNX Runtime` and `Microsoft ML`, this library empowers you to build, deploy, and execute machine learning models entirely within the .NET ecosystem. Bid farewell to Python dependencies and embrace a new era of intelligent applications tailored for .NET
-Many different scheduler algorithms can be used for this computation, each having its pro- and cons.
-So far `OnnxStack.StableDiffusion` as included `LMS Discrete`, `Euler Ancestral`, `DDPM`, `DDIM`, and `KDPM2` options with more in the works.
+### Welcome to Python-free AI integration with OnnxStack!
-*Example:*
-| LMS Scheduler | Euler Ancestral Scheduler | DDPM Scheduler |
-| :--- | :--- | :--- |
-
| 
|
|
- Seed: 624461087 GuidanceScale: 8 NumInferenceSteps: 22
-### **Text To Image**
-Text To Image Stable Diffusion is a powerful machine learning technique that allows you to generate high-quality images from textual descriptions. It combines the capabilities of text understanding and image synthesis to convert natural language descriptions into visually coherent and meaningful images
-| Input Text | Output Image | Diffusion Steps |
-| :--- | :--- | :--- |
-
| 
|
|
+# Projects
-### **Image To Image**
-Image To Image Stable Diffusion is an advanced image processing and generation method that excels in transforming one image into another while preserving the visual quality and structure of the original content. Using stable diffusion, this technique can perform a wide range of image-to-image tasks, such as style transfer, super-resolution, colorization, and more
+## **[OnnxStack.Core](OnnxStack.Core/README.md)**
+**Model Inference with C# and ONNX Runtime**
-| Input Image | Output Image | Diffusion Steps |
-| :--- | :--- | :--- |
-
| 
|
|
+[](https://www.nuget.org/packages/OnnxStack.Core)
+
-```
- Prompt: Dog wearing storm trooper helmet, head shot
-```
+`OnnxStack.Core` is a .NET library designed to facilitate seamless interaction with the `OnnxRuntime` C# API. This project simplifies the creation and disposal of `OrtValues` and offers straightforward services for loading and running inferences on a variety of models. With a focus on improving developer efficiency, the library abstracts complexities, allowing for smoother integration of `OnnxRuntime` into .NET applications.
-### **Image Inpainting**
-Image inpainting is an image modification/restoration technique that intelligently fills in missing or damaged portions of an image while maintaining visual consistency. It's used for tasks like photo restoration and object removal, creating seamless and convincing results.
+More information and examples can be found in the `OnnxStack.Core` project **[README](OnnxStack.Core/README.md)**
-In the below example we use a simple mask image + prompt to add a rider to the horse
-The black part of the mask will be used buy the process to generate new content, in this case the rider
+__________________________
-| Input Image | Mask Image | Masked Image | Result
-| :--- | :--- | :--- | :--- |
-
| 
|
|
|
+__________________________
-```
- Prompt: Rider on horse
-```
+## **[OnnxStack.StableDiffusion](OnnxStack.StableDiffusion/README.md)**
+**Stable Diffusion Inference with C# and ONNX Runtime**
-## **Realtime Stable Diffusion**
-Realtime stable diffusion is a process where the results are constantly rendered as you are working with the image or changing the settings, This can be fantastic if you are creating new artworks or editing existing images.
+[](https://www.nuget.org/packages/OnnxStack.StableDiffusion)
+
-Performance will depend on hardware and models selected, but for `Latent Consistency Models` you can get up to 4fps with a 3090 :)
+`OnnxStack.StableDiffusion` is a .NET library for latent diffusion in C#, Leveraging `OnnxStack.Core`, this library seamlessly integrates many StableDiffusion capabilities, including:
+* Text to Image
+* Image to Image
+* Image Inpaint
-### Text To Image
-https://user-images.githubusercontent.com/4353684/285347887-99db7f37-cff4-48b6-805b-3ca55e8f0c3a.mp4
+`OnnxStack.StableDiffusion` provides compatibility with a diverse set of models, including
+* StableDiffusion 1.5
+* StableDiffusion Inpaint
+* SDXL
+* SDXL Inpaint
+* SDXL-Turbo
+* LatentConsistency
+* LatentConsistency XL
+* Instaflow
-### Image To Image
-https://user-images.githubusercontent.com/4353684/285348410-c19a2111-6745-4f01-8400-d137d40180fe.mp4
-### Image Inpaint
-https://user-images.githubusercontent.com/4353684/285347894-9d044d7d-7c22-4379-8187-9cf7b9cac89c.mp4
+More information can be found in the `OnnxStack.StableDiffusion` project **[README](OnnxStack.StableDiffusion/README.md)**
-### Paint To Image
-https://user-images.githubusercontent.com/4353684/285347896-8da6709b-fea6-4cd4-ba65-ec692401f475.mp4
+Code and development examples for `OnnxStack.StableDiffusion` can be founds here **[Dev Examples](OnnxStack.StableDiffusion/README_Example.md)**
-https://user-images.githubusercontent.com/4353684/285547207-3a7ea067-fcbf-47f0-9372-fafa94d301f7.mp4
+__________________________
+__________________________
+## **[OnnxStack.ImageUpscaler]()**
+**Image upscaler with C# and ONNX Runtime**
+[](https://www.nuget.org/packages/OnnxStack.ImageUpscaler)
+
+`OnnxStack.ImageUpscaler` is a library designed to elevate image quality through superior upscaling techniques. Leveraging `OnnxStack.Core`, this library provides seamless integration for enhancing image resolution and supports a variety of upscaling models, allowing developers to improve image clarity and quality. Whether you are working on image processing, content creation, or any application requiring enhanced visuals, the ImageUpscale project delivers efficient and high-quality upscaling solutions.
-More information and Examples can be found in the `OnnxStack.StableDiffusion` project **[README](OnnxStack.StableDiffusion/README.md)**
+More information and examples can be found in the `OnnxStack.ImageUpscaler` project **[README](OnnxStack.ImageUpscaler/README.md)**
__________________________
-
-
__________________________
## **[OnnxStack.ImageRecognition]()**
**Image recognition with ResNet50v2 and ONNX Runtime**
@@ -145,61 +97,23 @@ Enable robust object detection in your applications using RCNN (Region-based Con
__________________________
-# Getting Started
-
-
-## ONNX Model Download
-You will need an ONNX compatible model to use, Hugging Face is a great place to download the Stable Diffusion models
-
-Download the [ONNX Stable Diffusion models from Hugging Face](https://huggingface.co/models?sort=downloads&search=Stable+Diffusion).
-
-- [Stable Diffusion Models v1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4/tree/onnx)
-- [Stable Diffusion Models v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/onnx)
-
-
-Once you have selected a model version repo, click `Files and Versions`, then select the `ONNX` branch. If there isn't an ONNX model branch available, use the `main` branch and convert it to ONNX. See the [ONNX conversion tutorial for PyTorch](https://learn.microsoft.com/windows/ai/windows-ml/tutorials/pytorch-convert-model) for more information.
-
-Clone the model repo:
-```text
-git lfs install
-git clone https://huggingface.co/runwayml/stable-diffusion-v1-5 -b onnx
-```
-
-
-## Stable Diffusion Models We've Converted To ONNX And Tested
-The following are othe stable diffusion models we've converted to ONNX and tested with OnnxStack.
-
-- [OpenJourney V4](https://huggingface.co/TheyCallMeHex/OpenJourney-V4-ONNX)
-- [DreamLike PhotoReal 2.0](https://huggingface.co/TheyCallMeHex/DreamLike-PhotoReal-2.0-ONNX)
-- [CyberPunk Anime Diffusion](https://huggingface.co/TheyCallMeHex/Cyberpunk-Anime-Diffusion-ONNX)
-- [InkPunk Diffusion](https://huggingface.co/TheyCallMeHex/Inkpunk-Diffusion-ONNX)
-- [Mo-Di Diffusion](https://huggingface.co/TheyCallMeHex/Mo-Di-Diffusion-ONNX)
-- [epiCRealism](https://huggingface.co/TheyCallMeHex/epiCRealism-ONNX)
-- [Comic Diffusion](https://huggingface.co/TheyCallMeHex/Comic-Diffusion-ONNX)
-- [Redshift Diffusion](https://huggingface.co/TheyCallMeHex/Redshift-Diffusion-ONNX)
-
-
-## Hardware Requirements
-You can choose between `Cpu` and `DirectML`(GPU) for inference,
-Other `Microsoft.ML.OnnxRuntime.*` executors like `Cuda` may work but are untested
-
-`Cpu` > 12GB RAM
-
-`DirectML` > 10GB VRAM
-
## Contribution
We welcome contributions to OnnxStack! If you have any ideas, bug reports, or improvements, feel free to open an issue or submit a pull request.
+* Join our Discord: **[OnnxStack Discord](https://discord.gg/uQzQgxMYWy)**
+* Chat to us here: **[Project Discussion Board](https://github.com/saddam213/OnnxStack/discussions)**
+
__________________________
-## Resources
+## ONNX Runtime Resources
- [ONNX Runtime C# API Doc](https://onnxruntime.ai/docs/api/csharp/api)
- [Get Started with C# in ONNX Runtime](https://onnxruntime.ai/docs/get-started/with-csharp.html)
-- [Hugging Face Stable Diffusion Blog](https://huggingface.co/blog/stable_diffusion)
-- [ONNX Runtime tutorial for Stable Diffusion in C#](https://onnxruntime.ai/docs/tutorials/csharp/stable-diffusion-csharp.html)
## Reference
+Special thanks to the creators of the fantastic repositories below; all were instrumental in the creation of OnnxStack.
-This work is based on the original C# implementation of Stable Diffusion by Cassie Breviu here: [Stable Diffusion with C# and ONNX Runtime](https://github.com/cassiebreviu/stablediffusion).
\ No newline at end of file
+* [Stable Diffusion with C# and ONNX Runtime](https://github.com/cassiebreviu/stablediffusion) by Cassie Breviu (@cassiebreviu)
+* [Diffusers](https://github.com/huggingface/diffusers) by Huggingface (@huggingface)
+* [Onnx-Web](https://github.com/ssube/onnx-web) by Sean Sube (@ssube)
\ No newline at end of file