feat(nodes): emit model loading events

- remove dependency on having access to a `node` during emits, would need a bit of additional args passed through the system and I don't think its necessary at this point. this also allowed us to drop an extraneous fetching/parsing of the session from db.
- provide the invocation context to all `get_model()` calls, so the events are able to be emitted
- test all model loading events in the app and confirm socket events are received

invokeai/app/invocations/latent.py

@@ -76,7 +76,7 @@ def get_scheduler(
         scheduler_name, SCHEDULER_MAP['ddim']
     )
     orig_scheduler_info = context.services.model_manager.get_model(
-        **scheduler_info.dict()
+        **scheduler_info.dict(), context=context,
     )
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
@@ -262,6 +262,7 @@ class TextToLatentsInvocation(BaseInvocation):
                         model_name=control_info.control_model.model_name,
                         model_type=ModelType.ControlNet,
                         base_model=control_info.control_model.base_model,
+                        context=context,
                     )
                 )
 
@@ -313,14 +314,14 @@ class TextToLatentsInvocation(BaseInvocation):
         def _lora_loader():
             for lora in self.unet.loras:
                 lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"})
+                    **lora.dict(exclude={"weight"}), context=context,
                 )
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
         unet_info = context.services.model_manager.get_model(
-            **self.unet.unet.dict()
+            **self.unet.unet.dict(), context=context,
         )
         with ExitStack() as exit_stack,\
                 ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
@@ -403,14 +404,14 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
         def _lora_loader():
             for lora in self.unet.loras:
                 lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"})
+                    **lora.dict(exclude={"weight"}), context=context,
                 )
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
         unet_info = context.services.model_manager.get_model(
-            **self.unet.unet.dict()
+            **self.unet.unet.dict(), context=context,
         )
         with ExitStack() as exit_stack,\
                 ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
@@ -491,7 +492,7 @@ class LatentsToImageInvocation(BaseInvocation):
         latents = context.services.latents.get(self.latents.latents_name)
 
         vae_info = context.services.model_manager.get_model(
-            **self.vae.vae.dict(),
+            **self.vae.vae.dict(), context=context,
         )
 
         with vae_info as vae:
@@ -636,7 +637,7 @@ class ImageToLatentsInvocation(BaseInvocation):
 
         #vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
         vae_info = context.services.model_manager.get_model(
-            **self.vae.vae.dict(),
+            **self.vae.vae.dict(), context=context,
         )
 
         image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))

invokeai/app/services/events.py

@@ -105,8 +105,6 @@ class EventServiceBase:
     def emit_model_load_started (
             self,
             graph_execution_state_id: str,
-            node: dict,
-            source_node_id: str,
             model_name: str,
             base_model: BaseModelType,
             model_type: ModelType,
@@ -117,8 +115,6 @@ class EventServiceBase:
             event_name="model_load_started",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,
@@ -129,8 +125,6 @@ class EventServiceBase:
     def emit_model_load_completed(
             self,
             graph_execution_state_id: str,
-            node: dict,
-            source_node_id: str,
             model_name: str,
             base_model: BaseModelType,
             model_type: ModelType,
@@ -142,12 +136,12 @@ class EventServiceBase:
             event_name="model_load_completed",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,
                 submodel=submodel,
-                model_info=model_info,
+                hash=model_info.hash,
+                location=model_info.location,
+                precision=str(model_info.precision),
             ),
         )

invokeai/app/services/model_manager_service.py

@@ -338,7 +338,6 @@ class ModelManagerService(ModelManagerServiceBase):
         base_model: BaseModelType,
         model_type: ModelType,
         submodel: Optional[SubModelType] = None,
-        node: Optional[BaseInvocation] = None,
         context: Optional[InvocationContext] = None,
     ) -> ModelInfo:
         """
@@ -346,11 +345,9 @@ class ModelManagerService(ModelManagerServiceBase):
         part (such as the vae) of a diffusers mode.
         """
 
-        # if we are called from within a node, then we get to emit
+        # we can emit model loading events if we are executing with access to the invocation context
-        # load start and complete events
+        if context:
-        if node and context:
             self._emit_load_event(
-                node=node,
                 context=context,
                 model_name=model_name,
                 base_model=base_model,
@@ -365,9 +362,8 @@ class ModelManagerService(ModelManagerServiceBase):
             submodel,
         )
 
-        if node and context:
+        if context:
             self._emit_load_event(
-                node=node,
                 context=context,
                 model_name=model_name,
                 base_model=base_model,
@@ -509,23 +505,19 @@ class ModelManagerService(ModelManagerServiceBase):
 
     def _emit_load_event(
         self,
-        node,
         context,
         model_name: str,
         base_model: BaseModelType,
         model_type: ModelType,
-        submodel: SubModelType,
+        submodel: Optional[SubModelType] = None,
         model_info: Optional[ModelInfo] = None,
     ):
         if context.services.queue.is_canceled(context.graph_execution_state_id):
             raise CanceledException()
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+
-        source_node_id = graph_execution_state.prepared_source_mapping[node.id]
         if model_info:
             context.services.events.emit_model_load_completed(
                 graph_execution_state_id=context.graph_execution_state_id,
-                node=node.dict(),
-                source_node_id=source_node_id,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,
@@ -535,8 +527,6 @@ class ModelManagerService(ModelManagerServiceBase):
         else:
             context.services.events.emit_model_load_started(
                 graph_execution_state_id=context.graph_execution_state_id,
-                node=node.dict(),
-                source_node_id=source_node_id,
                 model_name=model_name,
                 base_model=base_model,
                 model_type=model_type,