Return a MaskOutput from SegmentAnythingModelInvocation. And add a MaskTensorToImageInvocation.

invokeai/app/invocations/mask.py

@@ -1,9 +1,10 @@
 import numpy as np
 import torch
+from PIL import Image
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, InvocationContext, invocation
-from invokeai.app.invocations.fields import ImageField, InputField, TensorField, WithMetadata
+from invokeai.app.invocations.fields import ImageField, InputField, TensorField, WithBoard, WithMetadata
-from invokeai.app.invocations.primitives import MaskOutput
+from invokeai.app.invocations.primitives import ImageOutput, MaskOutput
 
 
 @invocation(
@@ -118,3 +119,28 @@ class ImageMaskToTensorInvocation(BaseInvocation, WithMetadata):
             height=mask.shape[1],
             width=mask.shape[2],
         )
+
+
+@invocation(
+    "tensor_mask_to_image",
+    title="Tensor Mask to Image",
+    tags=["mask"],
+    category="mask",
+    version="1.0.0",
+)
+class MaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Convert a mask tensor to an image."""
+
+    mask: TensorField = InputField(description="The mask tensor to convert.")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        mask = context.tensors.load(self.mask.tensor_name)
+        # Ensure that the mask is binary.
+        if mask.dtype != torch.bool:
+            mask = mask > 0.5
+        mask_np = mask.float().cpu().detach().numpy() * 255
+        mask_np = mask_np.astype(np.uint8)
+
+        mask_pil = Image.fromarray(mask_np, mode="L")
+        image_dto = context.images.save(image=mask_pil)
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/segment_anything_model.py

@@ -2,7 +2,6 @@ from pathlib import Path
 from typing import Literal
 
 import numpy as np
-import numpy.typing as npt
 import torch
 from PIL import Image
 from transformers import AutoModelForMaskGeneration, AutoProcessor
@@ -10,8 +9,8 @@ from transformers.models.sam import SamModel
 from transformers.models.sam.processing_sam import SamProcessor
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.fields import BoundingBoxField, ImageField, InputField
+from invokeai.app.invocations.fields import BoundingBoxField, ImageField, InputField, TensorField
-from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.invocations.primitives import MaskOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.image_util.segment_anything.mask_refinement import mask_to_polygon, polygon_to_mask
 from invokeai.backend.image_util.segment_anything.segment_anything_model import SegmentAnythingModel
@@ -46,24 +45,22 @@ class SegmentAnythingModelInvocation(BaseInvocation):
     )
 
     @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ImageOutput:
+    def invoke(self, context: InvocationContext) -> MaskOutput:
         # The models expect a 3-channel RGB image.
         image_pil = context.images.get_pil(self.image.image_name, mode="RGB")
 
         if len(self.bounding_boxes) == 0:
-            combined_mask = np.zeros(image_pil.size[::-1], dtype=np.uint8)
+            combined_mask = torch.zeros(image_pil.size[::-1], dtype=torch.bool)
         else:
             masks = self._segment(context=context, image=image_pil)
             masks = self._filter_masks(masks=masks, bounding_boxes=self.bounding_boxes)
-            # masks contains binary values of 0 or 1, so we merge them via max-reduce.
-            combined_mask = np.maximum.reduce(masks)
 
-        # Map [0, 1] to [0, 255].
+            # masks contains bool values, so we merge them via max-reduce.
-        mask_np = combined_mask * 255
+            combined_mask, _ = torch.stack(masks).max(dim=0)
-        mask_pil = Image.fromarray(mask_np)
 
-        image_dto = context.images.save(image=mask_pil)
+        mask_tensor_name = context.tensors.save(combined_mask)
-        return ImageOutput.build(image_dto)
+        height, width = combined_mask.shape
+        return MaskOutput(mask=TensorField(tensor_name=mask_tensor_name), width=width, height=height)
 
     @staticmethod
     def _load_sam_model(model_path: Path):
@@ -84,7 +81,7 @@ class SegmentAnythingModelInvocation(BaseInvocation):
         self,
         context: InvocationContext,
         image: Image.Image,
-    ) -> list[npt.NDArray[np.uint8]]:
+    ) -> list[torch.Tensor]:
         """Use Segment Anything (SAM) to generate masks given an image + a set of bounding boxes."""
         # Convert the bounding boxes to the SAM input format.
         sam_bounding_boxes = [[bb.x_min, bb.y_min, bb.x_max, bb.y_max] for bb in self.bounding_boxes]
@@ -97,22 +94,23 @@ class SegmentAnythingModelInvocation(BaseInvocation):
             assert isinstance(sam_pipeline, SegmentAnythingModel)
             masks = sam_pipeline.segment(image=image, bounding_boxes=sam_bounding_boxes)
 
-        masks = self._to_numpy_masks(masks)
+        masks = self._process_masks(masks)
         if self.apply_polygon_refinement:
             masks = self._apply_polygon_refinement(masks)
 
         return masks
 
-    def _to_numpy_masks(self, masks: torch.Tensor) -> list[npt.NDArray[np.uint8]]:
+    def _process_masks(self, masks: torch.Tensor) -> list[torch.Tensor]:
-        """Convert the tensor output from the Segment Anything model to a list of numpy masks."""
+        """Convert the tensor output from the Segment Anything model from a tensor of shape
-        eps = 0.0001
+        [num_masks, channels, height, width] to a list of tensors of shape [height, width].
+        """
+        assert masks.dtype == torch.bool
         # [num_masks, channels, height, width] -> [num_masks, height, width]
-        masks = masks.permute(0, 2, 3, 1).float().mean(dim=-1)
+        masks, _ = masks.max(dim=1)
-        masks = masks > eps
+        # Split the first dimension into a list of masks.
-        np_masks = masks.cpu().numpy().astype(np.uint8)
+        return list(masks.cpu().unbind(dim=0))
-        return list(np_masks)
 
-    def _apply_polygon_refinement(self, masks: list[npt.NDArray[np.uint8]]) -> list[npt.NDArray[np.uint8]]:
+    def _apply_polygon_refinement(self, masks: list[torch.Tensor]) -> list[torch.Tensor]:
         """Apply polygon refinement to the masks.
 
         Convert each mask to a polygon, then back to a mask. This has the following effect:
@@ -121,18 +119,23 @@ class SegmentAnythingModelInvocation(BaseInvocation):
             - Removes small mask pieces.
             - Removes holes from the mask.
         """
-        for idx, mask in enumerate(masks):
+        # Convert tensor masks to np masks.
+        np_masks = [mask.cpu().numpy().astype(np.uint8) for mask in masks]
+
+        # Apply polygon refinement.
+        for idx, mask in enumerate(np_masks):
             shape = mask.shape
             assert len(shape) == 2  # Assert length to satisfy type checker.
             polygon = mask_to_polygon(mask)
             mask = polygon_to_mask(polygon, shape)
-            masks[idx] = mask
+            np_masks[idx] = mask
+
+        # Convert np masks back to tensor masks.
+        masks = [torch.tensor(mask, dtype=torch.bool) for mask in np_masks]
 
         return masks
 
-    def _filter_masks(
+    def _filter_masks(self, masks: list[torch.Tensor], bounding_boxes: list[BoundingBoxField]) -> list[torch.Tensor]:
-        self, masks: list[npt.NDArray[np.uint8]], bounding_boxes: list[BoundingBoxField]
-    ) -> list[npt.NDArray[np.uint8]]:
         """Filter the detected masks based on the specified mask filter."""
         assert len(masks) == len(bounding_boxes)
 
@@ -140,7 +143,7 @@ class SegmentAnythingModelInvocation(BaseInvocation):
             return masks
         elif self.mask_filter == "largest":
             # Find the largest mask.
-            return [max(masks, key=lambda x: x.sum())]
+            return [max(masks, key=lambda x: float(x.sum()))]
         elif self.mask_filter == "highest_box_score":
             # Find the index of the bounding box with the highest score.
             # Note that we fallback to -1.0 if the score is None. This is mainly to satisfy the type checker. In most