feat(backend): lift managed model loading out of lama class

invokeai/app/invocations/infill.py

@@ -133,9 +133,11 @@ class LaMaInfillInvocation(InfillImageProcessorInvocation):
     """Infills transparent areas of an image using the LaMa model"""
 
     def infill(self, image: Image.Image, context: InvocationContext):
-        # Note that this accesses a protected attribute to get to the model manager service.
+        with context.models.load_ckpt_from_url(
-        # Is there a better way?
+            source="https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
-        lama = LaMA(context._services.model_manager)
+            loader=LaMA.load_jit_model,
+        ) as model:
+            lama = LaMA(model)
             return lama(image)
 
 

invokeai/backend/image_util/infill_methods/lama.py

@@ -1,13 +1,12 @@
-from typing import TYPE_CHECKING, Any
+from pathlib import Path
+from typing import Any
 
 import numpy as np
 import torch
 from PIL import Image
 
 import invokeai.backend.util.logging as logger
-
+from invokeai.backend.model_manager.config import AnyModel
-if TYPE_CHECKING:
-    from invokeai.app.services.model_manager import ModelManagerServiceBase
 
 
 def norm_img(np_img):
@@ -18,24 +17,11 @@ def norm_img(np_img):
     return np_img
 
 
-def load_jit_model(url_or_path, device) -> torch.nn.Module:
-    model_path = url_or_path
-    logger.info(f"Loading model from: {model_path}")
-    model: torch.nn.Module = torch.jit.load(model_path, map_location="cpu").to(device)  # type: ignore
-    model.eval()
-    return model
-
-
 class LaMA:
-    def __init__(self, model_manager: "ModelManagerServiceBase"):
+    def __init__(self, model: AnyModel):
-        self._model_manager = model_manager
+        self._model = model
 
     def __call__(self, input_image: Image.Image, *args: Any, **kwds: Any) -> Any:
-        loaded_model = self._model_manager.load_ckpt_from_url(
-            source="https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
-            loader=lambda path: load_jit_model(path, "cpu"),
-        )
-
         image = np.asarray(input_image.convert("RGB"))
         image = norm_img(image)
 
@@ -45,16 +31,23 @@ class LaMA:
         mask = norm_img(mask)
         mask = (mask > 0) * 1
 
-        with loaded_model as model:
+        device = next(self._model.buffers()).device
-            device = next(model.buffers()).device
         image = torch.from_numpy(image).unsqueeze(0).to(device)
         mask = torch.from_numpy(mask).unsqueeze(0).to(device)
 
         with torch.inference_mode():
-                infilled_image = model(image, mask)
+            infilled_image = self._model(image, mask)
 
         infilled_image = infilled_image[0].permute(1, 2, 0).detach().cpu().numpy()
         infilled_image = np.clip(infilled_image * 255, 0, 255).astype("uint8")
         infilled_image = Image.fromarray(infilled_image)
 
         return infilled_image
+
+    @staticmethod
+    def load_jit_model(url_or_path: str | Path, device: torch.device | str = "cpu") -> torch.nn.Module:
+        model_path = url_or_path
+        logger.info(f"Loading model from: {model_path}")
+        model: torch.nn.Module = torch.jit.load(model_path, map_location="cpu").to(device)  # type: ignore
+        model.eval()
+        return model