initial: Implement PBR Maps node

A node to generate normal, roughness and displacement maps from a single image. Based on: https://github.com/joeyballentine/Material-Map-Generator

invokeai/app/invocations/pbr_maps.py

@@ -0,0 +1,57 @@
+import pathlib
+from typing import Literal
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from invokeai.app.invocations.fields import ImageField, InputField, OutputField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.image_util.pbr_maps.architecture.pbr_rrdb_net import PBR_RRDB_Net
+from invokeai.backend.image_util.pbr_maps.pbr_maps import NORMAL_MAP_MODEL, OTHER_MAP_MODEL, PBRMapsGenerator
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation_output("pbr_maps-output")
+class PBRMapsOutput(BaseInvocationOutput):
+    normal_map: ImageField = OutputField(default=None, description="The generated normal map")
+    roughness_map: ImageField = OutputField(default=None, description="The generated roughness map")
+    displacement_map: ImageField = OutputField(default=None, description="The generated displacement map")
+
+
+@invocation("pbr_maps", title="PBR Maps", tags=["image", "material"], category="image", version="1.0.0")
+class PBRMapsInvocation(BaseInvocation):
+    """Generate Normal, Displacement and Roughness Map from a given image"""
+
+    image: ImageField = InputField(default=None, description="Input image")
+    tile_size: int = InputField(default=512, description="Tile size")
+    border_mode: Literal["none", "seamless", "mirror", "replicate"] = InputField(
+        default="none", description="Border mode to apply to eliminate any artifacts or seams"
+    )
+
+    def invoke(self, context: InvocationContext) -> PBRMapsOutput:
+        image_pil = context.images.get_pil(self.image.image_name, mode="RGB")
+
+        def loader(model_path: pathlib.Path):
+            return PBRMapsGenerator.load_model(model_path, TorchDevice.choose_torch_device())
+
+        with (
+            context.models.load_remote_model(NORMAL_MAP_MODEL, loader) as normal_map_model,
+            context.models.load_remote_model(OTHER_MAP_MODEL, loader) as other_map_model,
+        ):
+            assert isinstance(normal_map_model, PBR_RRDB_Net)
+            assert isinstance(other_map_model, PBR_RRDB_Net)
+            pbr_pipeline = PBRMapsGenerator(normal_map_model, other_map_model, TorchDevice.choose_torch_device())
+            normal_map, roughness_map, displacement_map = pbr_pipeline.generate_maps(
+                image_pil, self.tile_size, self.border_mode
+            )
+
+            normal_map = context.images.save(normal_map)
+            normal_map_field = ImageField(image_name=normal_map.image_name)
+
+            roughness_map = context.images.save(roughness_map)
+            roughness_map_field = ImageField(image_name=roughness_map.image_name)
+
+            displacement_map = context.images.save(displacement_map)
+            displacement_map_map_field = ImageField(image_name=displacement_map.image_name)
+
+        return PBRMapsOutput(
+            normal_map=normal_map_field, roughness_map=roughness_map_field, displacement_map=displacement_map_map_field
+        )