Add a mask to the ConditioningField primitive type.

invokeai/app/invocations/conditioning.py

@@ -0,0 +1,41 @@
+import numpy as np
+import torch
+from PIL.Image import Image
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
+from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField
+
+
+@invocation(
+    "add_conditioning_mask",
+    title="Add Conditioning Mask",
+    tags=["conditioning"],
+    category="conditioning",
+    version="1.0.0",
+)
+class AddConditioningMaskInvocation(BaseInvocation):
+    """Add a mask to an existing conditioning tensor."""
+
+    conditioning: ConditioningField = InputField(description="The conditioning tensor to add a mask to.")
+    image: ImageField = InputField(
+        description="A mask image to add to the conditioning tensor. Only the first channel of the image is used. "
+        "Pixels <128 are excluded from the mask, pixels >=128 are included in the mask."
+    )
+
+    @staticmethod
+    def convert_image_to_mask(image: Image) -> torch.Tensor:
+        """Convert a PIL image to a uint8 mask tensor."""
+        np_image = np.array(image)
+        torch_image = torch.from_numpy(np_image[0, :, :])
+        mask = torch_image >= 128
+        return mask.to(dtype=torch.uint8)
+
+    def invoke(self, context: InvocationContext) -> ConditioningOutput:
+        image = context.services.images.get_pil_image(self.image.image_name)
+        mask = self.convert_image_to_mask(image)
+
+        mask_name = f"{context.graph_execution_state_id}__{self.id}_conditioning_mask"
+        context.services.latents.save(mask_name, mask)
+
+        self.conditioning.mask_name = mask_name
+        return ConditioningOutput(conditioning=self.conditioning)

invokeai/app/invocations/primitives.py

@@ -428,6 +428,11 @@ class ConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 
     conditioning_name: str = Field(description="The name of conditioning tensor")
+    mask_name: Optional[str] = Field(
+        default=None,
+        description="The mask associated with this conditioning tensor. Excluded regions should be set to 0, included "
+        "regions should be set to 1.",
+    )
 
 
 @invocation_output("conditioning_output")