hook tiled_decode up to configuration

invokeai/app/invocations/latent.py

@@ -25,7 +25,7 @@ from ..services.model_manager_service import ModelManagerService
 from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
                              InvocationConfig, InvocationContext)
 from .compel import ConditioningField
-from .image import ImageField, ImageOutput
+from .image import ImageCategory, ImageField, ImageOutput
 from .model import ModelInfo, UNetField, VaeField
 
 
@@ -390,7 +390,7 @@ class LatentsToImageInvocation(BaseInvocation):
         )
 
         with vae_info as vae:
-            if self.tiled:
+            if self.tiled or context.services.configuration.tiled_decode:
                 vae.enable_tiling()
             else:
                 vae.disable_tiling()
@@ -408,15 +408,6 @@ class LatentsToImageInvocation(BaseInvocation):
 
                 image = VaeImageProcessor.numpy_to_pil(np_image)[0]
 
-        image_type = ImageType.RESULT
-        image_name = context.services.images.create_name(
-            context.graph_execution_state_id, self.id
-        )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
         torch.cuda.empty_cache()
 
         image_dto = context.services.images.create(

invokeai/app/services/config.py

@@ -352,7 +352,7 @@ setting environment variables INVOKEAI_<setting>.
     precision           : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='float16',description='Floating point precision', category='Memory/Performance')
     sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
     xformers_enabled    : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
-
+    tiled_decode        : bool = Field(default=False, description"Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Memory/Performance')
 
     root                : Path = Field(default=_find_root(), description='InvokeAI runtime root directory', category='Paths')
     autoconvert_dir     : Path = Field(default=None, description='Path to a directory of ckpt files to be converted into diffusers and imported on startup.', category='Paths')

invokeai/app/services/model_manager_service.py

@@ -549,16 +549,7 @@ class ModelManagerService(ModelManagerServiceBase):
             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 context:
-            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,
-                model_type=model_type,
-                submodel=submodel,
-            )
-        else:
+        if model_info:
             context.services.events.emit_model_load_completed(
                 graph_execution_state_id=context.graph_execution_state_id,
                 node=node.dict(),
@@ -568,6 +559,16 @@ class ModelManagerService(ModelManagerServiceBase):
                 submodel=submodel,
                 model_info=model_info
             )
+        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,
+                model_type=model_type,
+                submodel=submodel,
+            )
+
 
     @property
     def logger(self):