Fixed issue where batched inpainting (batch size > 1) wouldn't work because of mismatched tensor sizes. The 'already_decoded' decoded case should also be handled correctly (tested indirectly).

modules/processing.py

@@ -883,20 +883,27 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if getattr(samples_ddim, 'already_decoded', False):
                 x_samples_ddim = samples_ddim
                 # todo: generate adaptive masks based on pixel differences.
-                # if p.masks_for_overlay is used, it will already be populated with masks
+                if getattr(p, "image_mask", None) is not None and getattr(p, "soft_inpainting", None) is not None:
+                    si.apply_masks(soft_inpainting=p.soft_inpainting,
+                                   nmask=p.nmask,
+                                   overlay_images=p.overlay_images,
+                                   masks_for_overlay=p.masks_for_overlay,
+                                   width=p.width,
+                                   height=p.height,
+                                   paste_to=p.paste_to)
             else:
                 if opts.sd_vae_decode_method != 'Full':
                     p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
 
                 # Generate the mask(s) based on similarity between the original and denoised latent vectors
                 if getattr(p, "image_mask", None) is not None and getattr(p, "soft_inpainting", None) is not None:
-                    si.generate_adaptive_masks(latent_orig=p.init_latent,
+                    si.apply_adaptive_masks(latent_orig=p.init_latent,
-                                               latent_processed=samples_ddim,
+                                            latent_processed=samples_ddim,
-                                               overlay_images=p.overlay_images,
+                                            overlay_images=p.overlay_images,
-                                               masks_for_overlay=p.masks_for_overlay,
+                                            masks_for_overlay=p.masks_for_overlay,
-                                               width=p.width,
+                                            width=p.width,
-                                               height=p.height,
+                                            height=p.height,
-                                               paste_to=p.paste_to)
+                                            paste_to=p.paste_to)
 
                 x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
 

modules/soft_inpainting.py

@@ -25,26 +25,32 @@ def latent_blend(soft_inpainting, a, b, t):
 
     # NOTE: We use inplace operations wherever possible.
 
-    one_minus_t = 1 - t
+    # [4][w][h] to [1][4][w][h]
+    t2 = t.unsqueeze(0)
+    # [4][w][h] to [1][1][w][h] - the [4] seem redundant.
+    t3 = t[0].unsqueeze(0).unsqueeze(0)
+
+    one_minus_t2 = 1 - t2
+    one_minus_t3 = 1 - t3
 
     # Linearly interpolate the image vectors.
-    a_scaled = a * one_minus_t
+    a_scaled = a * one_minus_t2
-    b_scaled = b * t
+    b_scaled = b * t2
     image_interp = a_scaled
     image_interp.add_(b_scaled)
     result_type = image_interp.dtype
-    del a_scaled, b_scaled
+    del a_scaled, b_scaled, t2, one_minus_t2
 
     # Calculate the magnitude of the interpolated vectors. (We will remove this magnitude.)
     # 64-bit operations are used here to allow large exponents.
-    current_magnitude = torch.norm(image_interp, p=2, dim=1).to(torch.float64).add_(0.00001)
+    current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(torch.float64).add_(0.00001)
 
     # Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1).
-    a_magnitude = torch.norm(a, p=2, dim=1).to(torch.float64).pow_(soft_inpainting.inpaint_detail_preservation) * one_minus_t
+    a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(torch.float64).pow_(soft_inpainting.inpaint_detail_preservation) * one_minus_t3
-    b_magnitude = torch.norm(b, p=2, dim=1).to(torch.float64).pow_(soft_inpainting.inpaint_detail_preservation) * t
+    b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(torch.float64).pow_(soft_inpainting.inpaint_detail_preservation) * t3
     desired_magnitude = a_magnitude
     desired_magnitude.add_(b_magnitude).pow_(1 / soft_inpainting.inpaint_detail_preservation)
-    del a_magnitude, b_magnitude, one_minus_t
+    del a_magnitude, b_magnitude, t3, one_minus_t3
 
     # Change the linearly interpolated image vectors' magnitudes to the value we want.
     # This is the last 64-bit operation.
@@ -78,10 +84,11 @@ def get_modified_nmask(soft_inpainting, nmask, sigma):
     NOTE: "mask" is not used
     """
     import torch
-    return torch.pow(nmask, (sigma ** soft_inpainting.mask_blend_power) * soft_inpainting.mask_blend_scale)
+    # todo: Why is sigma 2D? Both values are the same.
+    return torch.pow(nmask, (sigma[0] ** soft_inpainting.mask_blend_power) * soft_inpainting.mask_blend_scale)
 
 
-def generate_adaptive_masks(
+def apply_adaptive_masks(
         latent_orig,
         latent_processed,
         overlay_images,
@@ -142,6 +149,45 @@ def generate_adaptive_masks(
 
         overlay_images[i] = image_masked.convert('RGBA')
 
+def apply_masks(
+        soft_inpainting,
+        nmask,
+        overlay_images,
+        masks_for_overlay,
+        width, height,
+        paste_to):
+    import torch
+    import numpy as np
+    import modules.processing as proc
+    import modules.images as images
+    from PIL import Image, ImageOps, ImageFilter
+
+    converted_mask = nmask[0].float()
+    converted_mask = torch.clamp(converted_mask, min=0, max=1).pow_(soft_inpainting.mask_blend_scale / 2)
+    converted_mask = 255. * converted_mask
+    converted_mask = converted_mask.cpu().numpy().astype(np.uint8)
+    converted_mask = Image.fromarray(converted_mask)
+    converted_mask = images.resize_image(2, converted_mask, width, height)
+    converted_mask = proc.create_binary_mask(converted_mask, round=False)
+
+    # Remove aliasing artifacts using a gaussian blur.
+    converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4))
+
+    # Expand the mask to fit the whole image if needed.
+    if paste_to is not None:
+        converted_mask = proc.uncrop(converted_mask,
+                                     (width, height),
+                                     paste_to)
+
+    for i, overlay_image in enumerate(overlay_images):
+        masks_for_overlay[i] = converted_mask
+
+        image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height))
+        image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"),
+                           mask=ImageOps.invert(converted_mask.convert('L')))
+
+        overlay_images[i] = image_masked.convert('RGBA')
+
 
 # ------------------- Constants -------------------