fix(canvas): use corrected mask for pasteback

invokeai/app/invocations/latent.py

@@ -181,6 +181,16 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
         )
 
 
+@invocation_output("gradient_mask_output")
+class GradientMaskOutput(BaseInvocationOutput):
+    """Outputs a denoise mask and an image representing the total gradient of the mask."""
+
+    denoise_mask: DenoiseMaskField = OutputField(description="Mask for denoise model run")
+    expanded_mask_area: ImageField = OutputField(
+        description="Image representing the total gradient area of the mask. For paste-back purposes."
+    )
+
+
 @invocation(
     "create_gradient_mask",
     title="Create Gradient Mask",
@@ -201,38 +211,42 @@ class CreateGradientMaskInvocation(BaseInvocation):
     )
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> DenoiseMaskOutput:
+    def invoke(self, context: InvocationContext) -> GradientMaskOutput:
         mask_image = context.images.get_pil(self.mask.image_name, mode="L")
-        if self.coherence_mode == "Box Blur":
-            blur_mask = mask_image.filter(ImageFilter.BoxBlur(self.edge_radius))
-        else:  # Gaussian Blur OR Staged
-            # Gaussian Blur uses standard deviation. 1/2 radius is a good approximation
-            blur_mask = mask_image.filter(ImageFilter.GaussianBlur(self.edge_radius / 2))
+        if self.edge_radius > 0:
+            if self.coherence_mode == "Box Blur":
+                blur_mask = mask_image.filter(ImageFilter.BoxBlur(self.edge_radius))
+            else:  # Gaussian Blur OR Staged
+                # Gaussian Blur uses standard deviation. 1/2 radius is a good approximation
+                blur_mask = mask_image.filter(ImageFilter.GaussianBlur(self.edge_radius / 2))
 
-        mask_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
-        blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(blur_mask, normalize=False)
+            blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(blur_mask, normalize=False)
 
-        # redistribute blur so that the edges are 0 and blur out to 1
-        blur_tensor = (blur_tensor - 0.5) * 2
+            # redistribute blur so that the original edges are 0 and blur outwards to 1
+            blur_tensor = (blur_tensor - 0.5) * 2
 
-        threshold = 1 - self.minimum_denoise
+            threshold = 1 - self.minimum_denoise
+
+            if self.coherence_mode == "Staged":
+                # wherever the blur_tensor is less than fully masked, convert it to threshold
+                blur_tensor = torch.where((blur_tensor < 1) & (blur_tensor > 0), threshold, blur_tensor)
+            else:
+                # wherever the blur_tensor is above threshold but less than 1, drop it to threshold
+                blur_tensor = torch.where((blur_tensor > threshold) & (blur_tensor < 1), threshold, blur_tensor)
 
-        if self.coherence_mode == "Staged":
-            # wherever the blur_tensor is masked to any degree, convert it to threshold
-            blur_tensor = torch.where((blur_tensor < 1), threshold, blur_tensor)
         else:
-            # wherever the blur_tensor is above threshold but less than 1, drop it to threshold
-            blur_tensor = torch.where((blur_tensor > threshold) & (blur_tensor < 1), threshold, blur_tensor)
-
-        # multiply original mask to force actually masked regions to 0
-        blur_tensor = mask_tensor * blur_tensor
+            blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
 
         mask_name = context.tensors.save(tensor=blur_tensor.unsqueeze(1))
 
-        return DenoiseMaskOutput.build(
-            mask_name=mask_name,
-            masked_latents_name=None,
-            gradient=True,
+        # compute a [0, 1] mask from the blur_tensor
+        expanded_mask = torch.where((blur_tensor < 1), 0, 1)
+        expanded_mask_image = Image.fromarray((expanded_mask.squeeze(0).numpy() * 255).astype(np.uint8), mode="L")
+        expanded_image_dto = context.images.save(expanded_mask_image)
+
+        return GradientMaskOutput(
+            denoise_mask=DenoiseMaskField(mask_name=mask_name, masked_latents_name=None, gradient=True),
+            expanded_mask_area=ImageField(image_name=expanded_image_dto.image_name),
         )
 
 

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasInpaintGraph.ts

@@ -344,8 +344,8 @@ export const buildCanvasInpaintGraph = (
       },
       {
         source: {
-          node_id: MASK_RESIZE_UP,
-          field: 'image',
+          node_id: INPAINT_CREATE_MASK,
+          field: 'expanded_mask_area',
         },
         destination: {
           node_id: MASK_RESIZE_DOWN,

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasOutpaintGraph.ts

@@ -439,8 +439,8 @@ export const buildCanvasOutpaintGraph = (
       },
       {
         source: {
-          node_id: MASK_RESIZE_UP,
-          field: 'image',
+          node_id: INPAINT_CREATE_MASK,
+          field: 'expanded_mask_area',
         },
         destination: {
           node_id: MASK_RESIZE_DOWN,

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasSDXLInpaintGraph.ts

@@ -355,8 +355,8 @@ export const buildCanvasSDXLInpaintGraph = (
       },
       {
         source: {
-          node_id: MASK_RESIZE_UP,
-          field: 'image',
+          node_id: INPAINT_CREATE_MASK,
+          field: 'expanded_mask_area',
         },
         destination: {
           node_id: MASK_RESIZE_DOWN,

invokeai/frontend/web/src/features/nodes/util/graph/buildCanvasSDXLOutpaintGraph.ts

@@ -448,8 +448,8 @@ export const buildCanvasSDXLOutpaintGraph = (
       },
       {
         source: {
-          node_id: MASK_RESIZE_UP,
-          field: 'image',
+          node_id: INPAINT_CREATE_MASK,
+          field: 'expanded_mask_area',
         },
         destination: {
           node_id: MASK_RESIZE_DOWN,