Inpaint Legacy prototype

Author: saddam213

OnnxStack.StableDiffusion/Diffusers/InpaintDiffuser.cs

@@ -126,7 +126,7 @@ private DenseTensor<float> PrepareMask(PromptOptions promptOptions, SchedulerOpt
                         for (int y = 0; y < height; y++)
                         {
                             var pixelSpan = img.GetRowSpan(y);
-                            var value = pixelSpan[x].PackedValue / 255.0f;
+                            var value = pixelSpan[x].A / 255.0f;
                             if (value < 0.5f)
                                 value = 0f;
                             else if (value >= 0.5f)

OnnxStack.StableDiffusion/Diffusers/InpaintLegacyDiffuser.cs

@@ -8,7 +8,6 @@
 using OnnxStack.StableDiffusion.Helpers;
 using SixLabors.ImageSharp;
 using SixLabors.ImageSharp.Processing;
-using SixLabors.ImageSharp.Processing.Processors.Transforms;
 using System;
 using System.Collections.Generic;
 using System.Linq;
@@ -91,8 +90,6 @@ public override async Task<DenseTensor<float>> DiffuseAsync(PromptOptions prompt
 
                         // Apply mask and combine 
                         latents = ApplyMaskedLatents(steplatents, initLatentsProper, maskImage);
-
-                        ImageHelpers.TensorToImageDebug(latents, $@"D:\Debug\Latent{step}.png");
                     }
 
                     progress?.Invoke(++step, timesteps.Count);
@@ -167,9 +164,7 @@ private DenseTensor<float> PrepareMask(PromptOptions promptOptions, SchedulerOpt
                         {
                             var pixelSpan = img.GetRowSpan(y);
                             var value = (float)pixelSpan[x].A / 255.0f;
-
-                            //TODO: mask = 1 - mask  # repaint white, keep black
-                            maskTensor[0, 0, y, x] = 0f;
+                            maskTensor[0, 0, y, x] = 1f - value;
                             maskTensor[0, 1, y, x] = 0f; // Needed for shape only
                             maskTensor[0, 2, y, x] = 0f; // Needed for shape only
                             maskTensor[0, 3, y, x] = 0f; // Needed for shape only
@@ -203,7 +198,7 @@ private DenseTensor<float> ApplyMaskedLatents(DenseTensor<float> latents, DenseT
                             float latentsValue = latents[batch, channel, height, width];
                             float initLatentsProperValue = initLatentsProper[batch, channel, height, width];
 
-                            //TODO: Apply the logic to compute the result based on the mask
+                            //Apply the logic to compute the result based on the mask
                             float newValue = (initLatentsProperValue * maskValue) + (latentsValue * (1f - maskValue));
                             result[batch, channel, height, width] = newValue;
                         }

OnnxStack.StableDiffusion/Helpers/ImageHelpers.cs

@@ -173,7 +173,7 @@ public static void TensorToImageDebug2(DenseTensor<float> imageTensor, string fi
                 {
                     for (var x = 0; x < width; x++)
                     {
-                        result[x, y] = new L8(CalculateByte(imageTensor, 0, y, x));
+                        result[x, y] = new L8((byte)(imageTensor[0, 0, y, x] * 255.0f));
                     }
                 }
                 result.SaveAsPng(filename);

OnnxStack.WebUI/Pages/StableDiffusion/ImageInpaint.cshtml.cs

@@ -3,6 +3,7 @@
 using Models;
 using OnnxStack.Core;
 using OnnxStack.StableDiffusion.Config;
+using OnnxStack.StableDiffusion.Enums;
 using Services;
 
 namespace OnnxStack.WebUI.Pages.StableDiffusion
@@ -38,12 +39,12 @@ public async Task OnGet(string img = null, int width = 0, int height = 0)
 
             Prompt = new PromptOptions
             {
-              //  Prompt = "A photo of a cat",
+               SchedulerType = SchedulerType.DDPM
             };
 
             Options = new SchedulerOptions
             {
-              //  InferenceSteps = 5,
+               Strength = 1.0f,
             };
         }
     }