Run typegen, bump version

This should further insulate canvas from excessive GCs.

invokeai/app/api/routers/model_manager.py

@@ -614,8 +614,8 @@ async def convert_model(
     The return value is the model configuration for the converted model.
     """
     model_manager = ApiDependencies.invoker.services.model_manager
+    loader = model_manager.load
     logger = ApiDependencies.invoker.services.logger
-    loader = ApiDependencies.invoker.services.model_manager.load
     store = ApiDependencies.invoker.services.model_manager.store
     installer = ApiDependencies.invoker.services.model_manager.install
 
@@ -630,7 +630,13 @@ async def convert_model(
         raise HTTPException(400, f"The model with key {key} is not a main checkpoint model.")
 
     # loading the model will convert it into a cached diffusers file
-    model_manager.load.load_model(model_config, submodel_type=SubModelType.Scheduler)
+    try:
+        cc_size = loader.convert_cache.max_size
+        if cc_size == 0:  # temporary set the convert cache to a positive number so that cached model is written
+            loader._convert_cache.max_size = 1.0
+        loader.load_model(model_config, submodel_type=SubModelType.Scheduler)
+    finally:
+        loader._convert_cache.max_size = cc_size
 
     # Get the path of the converted model from the loader
     cache_path = loader.convert_cache.cache_path(key)

invokeai/app/invocations/compel.py

@@ -9,8 +9,7 @@ from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
 from invokeai.app.invocations.primitives import ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.ti_utils import generate_ti_list
-from invokeai.backend.lora_model_patcher import LoraModelPatcher
-from invokeai.backend.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
@@ -81,8 +80,7 @@ class CompelInvocation(BaseInvocation):
             ),
             text_encoder_info as text_encoder,
             # Apply the LoRA after text_encoder has been moved to its target device for faster patching.
-            # ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()),
-            LoraModelPatcher.apply_lora_to_text_encoder(text_encoder, _lora_loader(), "text_encoder"),
+            ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()),
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
             ModelPatcher.apply_clip_skip(text_encoder_model, self.clip.skipped_layers),
         ):
@@ -183,8 +181,7 @@ class SDXLPromptInvocationBase:
             ),
             text_encoder_info as text_encoder,
             # Apply the LoRA after text_encoder has been moved to its target device for faster patching.
-            # ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
-            LoraModelPatcher.apply_lora_to_text_encoder(text_encoder, _lora_loader(), lora_prefix),
+            ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
             ModelPatcher.apply_clip_skip(text_encoder_model, clip_field.skipped_layers),
         ):
@@ -262,15 +259,15 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
         c1, c1_pooled, ec1 = self.run_clip_compel(
-            context, self.clip, self.prompt, False, "text_encoder", zero_on_empty=True
+            context, self.clip, self.prompt, False, "lora_te1_", zero_on_empty=True
         )
         if self.style.strip() == "":
             c2, c2_pooled, ec2 = self.run_clip_compel(
-                context, self.clip2, self.prompt, True, "text_encoder_2", zero_on_empty=True
+                context, self.clip2, self.prompt, True, "lora_te2_", zero_on_empty=True
             )
         else:
             c2, c2_pooled, ec2 = self.run_clip_compel(
-                context, self.clip2, self.style, True, "text_encoder_2", zero_on_empty=True
+                context, self.clip2, self.style, True, "lora_te2_", zero_on_empty=True
             )
 
         original_size = (self.original_height, self.original_width)

invokeai/app/invocations/custom_nodes/init.py

@@ -3,6 +3,7 @@ Invoke-managed custom node loader. See README.md for more information.
 """
 
 import sys
+import traceback
 from importlib.util import module_from_spec, spec_from_file_location
 from pathlib import Path
 
@@ -41,11 +42,15 @@ for d in Path(__file__).parent.iterdir():
 
     logger.info(f"Loading node pack {module_name}")
 
-    module = module_from_spec(spec)
-    sys.modules[spec.name] = module
-    spec.loader.exec_module(module)
+    try:
+        module = module_from_spec(spec)
+        sys.modules[spec.name] = module
+        spec.loader.exec_module(module)
 
-    loaded_count += 1
+        loaded_count += 1
+    except Exception:
+        full_error = traceback.format_exc()
+        logger.error(f"Failed to load node pack {module_name}:\n{full_error}")
 
     del init, module_name
 

invokeai/app/invocations/latent.py

@@ -52,8 +52,7 @@ from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
-from invokeai.backend.lora_model_patcher import LoraModelPatcher
-from invokeai.backend.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import BaseModelType, LoadedModel
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
@@ -740,8 +739,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 set_seamless(unet_info.model, self.unet.seamless_axes),  # FIXME
                 unet_info as unet,
                 # Apply the LoRA after unet has been moved to its target device for faster patching.
-                # ModelPatcher.apply_lora_unet(unet, _lora_loader()),
-                LoraModelPatcher.apply_lora_to_unet(unet, _lora_loader()),
+                ModelPatcher.apply_lora_unet(unet, _lora_loader()),
             ):
                 assert isinstance(unet, UNet2DConditionModel)
                 latents = latents.to(device=unet.device, dtype=unet.dtype)

invokeai/app/services/config/config_default.py

@@ -373,13 +373,16 @@ def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
             if k == "conf_path":
                 parsed_config_dict["legacy_models_yaml_path"] = v
             if k == "legacy_conf_dir":
-                # The old default for this was "configs/stable-diffusion". If if the incoming config has that as the value, we won't set it.
-                # Else if the path ends in "stable-diffusion", we assume the parent is the new correct path.
-                # Else we do not attempt to migrate this setting
-                if v != "configs/stable-diffusion":
-                    parsed_config_dict["legacy_conf_dir"] = v
+                # The old default for this was "configs/stable-diffusion" ("configs\stable-diffusion" on Windows).
+                if v == "configs/stable-diffusion" or v == "configs\\stable-diffusion":
+                    # If if the incoming config has the default value, skip
+                    continue
                 elif Path(v).name == "stable-diffusion":
+                    # Else if the path ends in "stable-diffusion", we assume the parent is the new correct path.
                     parsed_config_dict["legacy_conf_dir"] = str(Path(v).parent)
+                else:
+                    # Else we do not attempt to migrate this setting
+                    parsed_config_dict["legacy_conf_dir"] = v
             elif k in InvokeAIAppConfig.model_fields:
                 # skip unknown fields
                 parsed_config_dict[k] = v

invokeai/app/services/model_install/model_install_default.py

@@ -348,8 +348,13 @@ class ModelInstallService(ModelInstallServiceBase):
                     config: dict[str, Any] = {}
                     config["name"] = model_name
                     config["description"] = stanza.get("description")
-                    config["config_path"] = stanza.get("config")
-
+                    legacy_config_path = stanza.get("config")
+                    if legacy_config_path:
+                        # In v3, these paths were relative to the root. Migrate them to be relative to the legacy_conf_dir.
+                        legacy_config_path: Path = self._app_config.root_path / legacy_config_path
+                        if legacy_config_path.is_relative_to(self._app_config.legacy_conf_path):
+                            legacy_config_path = legacy_config_path.relative_to(self._app_config.legacy_conf_path)
+                        config["config_path"] = str(legacy_config_path)
                     try:
                         id = self.register_path(model_path=model_path, config=config)
                         self._logger.info(f"Migrated {model_name} with id {id}")
@@ -368,11 +373,13 @@ class ModelInstallService(ModelInstallServiceBase):
     def delete(self, key: str) -> None:  # noqa D102
         """Unregister the model. Delete its files only if they are within our models directory."""
         model = self.record_store.get_model(key)
-        models_dir = self.app_config.models_path
-        model_path = models_dir / Path(model.path)  # handle legacy relative model paths
-        if model_path.is_relative_to(models_dir):
+        model_path = self.app_config.models_path / model.path
+
+        if model_path.is_relative_to(self.app_config.models_path):
+            # If the models is in the Invoke-managed models dir, we delete it
             self.unconditionally_delete(key)
         else:
+            # Else we only unregister it, leaving the file in place
             self.unregister(key)
 
     def unconditionally_delete(self, key: str) -> None:  # noqa D102
@@ -500,9 +507,9 @@ class ModelInstallService(ModelInstallServiceBase):
     def _scan_for_missing_models(self) -> list[AnyModelConfig]:
         """Scan the models directory for missing models and return a list of them."""
         missing_models: list[AnyModelConfig] = []
-        for x in self.record_store.all_models():
-            if not Path(x.path).resolve().exists():
-                missing_models.append(x)
+        for model_config in self.record_store.all_models():
+            if not (self.app_config.models_path / model_config.path).resolve().exists():
+                missing_models.append(model_config)
         return missing_models
 
     def _register_orphaned_models(self) -> None:
@@ -512,7 +519,9 @@ class ModelInstallService(ModelInstallServiceBase):
         only situations in which we may have orphaned models in the models directory.
         """
 
-        installed_model_paths = {Path(x.path).resolve() for x in self.record_store.all_models()}
+        installed_model_paths = {
+            (self._app_config.models_path / x.path).resolve() for x in self.record_store.all_models()
+        }
 
         # The bool returned by this callback determines if the model is added to the list of models found by the search
         def on_model_found(model_path: Path) -> bool:
@@ -548,10 +557,11 @@ class ModelInstallService(ModelInstallServiceBase):
         May raise an UnknownModelException.
         """
         model = self.record_store.get_model(key)
-        old_path = Path(model.path).resolve()
-        models_dir = self.app_config.models_path.resolve()
+        models_dir = self.app_config.models_path
+        old_path = self.app_config.models_path / model.path
 
         if not old_path.is_relative_to(models_dir):
+            # The model is not in the models directory - we don't need to move it.
             return model
 
         new_path = (models_dir / model.base.value / model.type.value / model.name).with_suffix(old_path.suffix)
@@ -561,7 +571,7 @@ class ModelInstallService(ModelInstallServiceBase):
 
         self._logger.info(f"Moving {model.name} to {new_path}.")
         new_path = self._move_model(old_path, new_path)
-        model.path = new_path.as_posix()
+        model.path = new_path.relative_to(models_dir).as_posix()
         self.record_store.update_model(key, ModelRecordChanges(path=model.path))
         return model
 
@@ -600,12 +610,19 @@ class ModelInstallService(ModelInstallServiceBase):
 
         model_path = model_path.resolve()
 
+        # Models in the Invoke-managed models dir should use relative paths.
+        if model_path.is_relative_to(self.app_config.models_path):
+            model_path = model_path.relative_to(self.app_config.models_path)
+
         info.path = model_path.as_posix()
 
-        # Checkpoints have a config file needed for conversion - resolve this to an absolute path
         if isinstance(info, CheckpointConfigBase):
-            legacy_conf = (self.app_config.legacy_conf_path / info.config_path).resolve()
-            info.config_path = legacy_conf.as_posix()
+            # Checkpoints have a config file needed for conversion. Same handling as the model weights - if it's in the
+            # invoke-managed legacy config dir, we use a relative path.
+            legacy_config_path = self.app_config.legacy_conf_path / info.config_path
+            if legacy_config_path.is_relative_to(self.app_config.legacy_conf_path):
+                legacy_config_path = legacy_config_path.relative_to(self.app_config.legacy_conf_path)
+            info.config_path = legacy_config_path.as_posix()
         self.record_store.add_model(info)
         return info.key
 

invokeai/app/services/session_processor/session_processor_default.py

@@ -70,8 +70,18 @@ class DefaultSessionProcessor(SessionProcessorBase):
     async def _on_queue_event(self, event: FastAPIEvent) -> None:
         event_name = event[1]["event"]
 
-        if event_name == "session_canceled" or event_name == "queue_cleared":
-            # These both mean we should cancel the current session.
+        if (
+            event_name == "session_canceled"
+            and self._queue_item
+            and self._queue_item.item_id == event[1]["data"]["queue_item_id"]
+        ):
+            self._cancel_event.set()
+            self._poll_now()
+        elif (
+            event_name == "queue_cleared"
+            and self._queue_item
+            and self._queue_item.queue_id == event[1]["data"]["queue_id"]
+        ):
             self._cancel_event.set()
             self._poll_now()
         elif event_name == "batch_enqueued":
@@ -111,141 +121,146 @@ class DefaultSessionProcessor(SessionProcessorBase):
                 poll_now_event.clear()
                 # Middle processor try block; any unhandled exception is a non-fatal processor error
                 try:
+                    # If we are paused, wait for resume event
+                    resume_event.wait()
+
                     # Get the next session to process
                     self._queue_item = self._invoker.services.session_queue.dequeue()
-                    if self._queue_item is not None and resume_event.is_set():
-                        self._invoker.services.logger.debug(f"Executing queue item {self._queue_item.item_id}")
-                        cancel_event.clear()
 
-                        # If profiling is enabled, start the profiler
-                        if self._profiler is not None:
-                            self._profiler.start(profile_id=self._queue_item.session_id)
+                    if self._queue_item is None:
+                        # The queue was empty, wait for next polling interval or event to try again
+                        self._invoker.services.logger.debug("Waiting for next polling interval or event")
+                        poll_now_event.wait(self._polling_interval)
+                        continue
 
-                        # Prepare invocations and take the first
-                        self._invocation = self._queue_item.session.next()
+                    self._invoker.services.logger.debug(f"Executing queue item {self._queue_item.item_id}")
+                    cancel_event.clear()
 
-                        # Loop over invocations until the session is complete or canceled
-                        while self._invocation is not None and not cancel_event.is_set():
-                            # get the source node id to provide to clients (the prepared node id is not as useful)
-                            source_invocation_id = self._queue_item.session.prepared_source_mapping[self._invocation.id]
+                    # If profiling is enabled, start the profiler
+                    if self._profiler is not None:
+                        self._profiler.start(profile_id=self._queue_item.session_id)
 
-                            # Send starting event
-                            self._invoker.services.events.emit_invocation_started(
-                                queue_batch_id=self._queue_item.batch_id,
-                                queue_item_id=self._queue_item.item_id,
-                                queue_id=self._queue_item.queue_id,
-                                graph_execution_state_id=self._queue_item.session_id,
-                                node=self._invocation.model_dump(),
-                                source_node_id=source_invocation_id,
-                            )
+                    # Prepare invocations and take the first
+                    self._invocation = self._queue_item.session.next()
 
-                            # Innermost processor try block; any unhandled exception is an invocation error & will fail the graph
-                            try:
-                                with self._invoker.services.performance_statistics.collect_stats(
-                                    self._invocation, self._queue_item.session.id
-                                ):
-                                    # Build invocation context (the node-facing API)
-                                    data = InvocationContextData(
-                                        invocation=self._invocation,
-                                        source_invocation_id=source_invocation_id,
-                                        queue_item=self._queue_item,
-                                    )
-                                    context = build_invocation_context(
-                                        data=data,
-                                        services=self._invoker.services,
-                                        cancel_event=self._cancel_event,
-                                    )
+                    # Loop over invocations until the session is complete or canceled
+                    while self._invocation is not None and not cancel_event.is_set():
+                        # get the source node id to provide to clients (the prepared node id is not as useful)
+                        source_invocation_id = self._queue_item.session.prepared_source_mapping[self._invocation.id]
 
-                                    # Invoke the node
-                                    outputs = self._invocation.invoke_internal(
-                                        context=context, services=self._invoker.services
-                                    )
+                        # Send starting event
+                        self._invoker.services.events.emit_invocation_started(
+                            queue_batch_id=self._queue_item.batch_id,
+                            queue_item_id=self._queue_item.item_id,
+                            queue_id=self._queue_item.queue_id,
+                            graph_execution_state_id=self._queue_item.session_id,
+                            node=self._invocation.model_dump(),
+                            source_node_id=source_invocation_id,
+                        )
 
-                                    # Save outputs and history
-                                    self._queue_item.session.complete(self._invocation.id, outputs)
-
-                                    # Send complete event
-                                    self._invoker.services.events.emit_invocation_complete(
-                                        queue_batch_id=self._queue_item.batch_id,
-                                        queue_item_id=self._queue_item.item_id,
-                                        queue_id=self._queue_item.queue_id,
-                                        graph_execution_state_id=self._queue_item.session.id,
-                                        node=self._invocation.model_dump(),
-                                        source_node_id=source_invocation_id,
-                                        result=outputs.model_dump(),
-                                    )
-
-                            except KeyboardInterrupt:
-                                # TODO(MM2): Create an event for this
-                                pass
-
-                            except CanceledException:
-                                # When the user cancels the graph, we first set the cancel event. The event is checked
-                                # between invocations, in this loop. Some invocations are long-running, and we need to
-                                # be able to cancel them mid-execution.
-                                #
-                                # For example, denoising is a long-running invocation with many steps. A step callback
-                                # is executed after each step. This step callback checks if the canceled event is set,
-                                # then raises a CanceledException to stop execution immediately.
-                                #
-                                # When we get a CanceledException, we don't need to do anything - just pass and let the
-                                # loop go to its next iteration, and the cancel event will be handled correctly.
-                                pass
-
-                            except Exception as e:
-                                error = traceback.format_exc()
-
-                                # Save error
-                                self._queue_item.session.set_node_error(self._invocation.id, error)
-                                self._invoker.services.logger.error(
-                                    f"Error while invoking session {self._queue_item.session_id}, invocation {self._invocation.id} ({self._invocation.get_type()}):\n{e}"
+                        # Innermost processor try block; any unhandled exception is an invocation error & will fail the graph
+                        try:
+                            with self._invoker.services.performance_statistics.collect_stats(
+                                self._invocation, self._queue_item.session.id
+                            ):
+                                # Build invocation context (the node-facing API)
+                                data = InvocationContextData(
+                                    invocation=self._invocation,
+                                    source_invocation_id=source_invocation_id,
+                                    queue_item=self._queue_item,
+                                )
+                                context = build_invocation_context(
+                                    data=data,
+                                    services=self._invoker.services,
+                                    cancel_event=self._cancel_event,
                                 )
-                                self._invoker.services.logger.error(error)
 
-                                # Send error event
-                                self._invoker.services.events.emit_invocation_error(
-                                    queue_batch_id=self._queue_item.session_id,
+                                # Invoke the node
+                                outputs = self._invocation.invoke_internal(
+                                    context=context, services=self._invoker.services
+                                )
+
+                                # Save outputs and history
+                                self._queue_item.session.complete(self._invocation.id, outputs)
+
+                                # Send complete event
+                                self._invoker.services.events.emit_invocation_complete(
+                                    queue_batch_id=self._queue_item.batch_id,
                                     queue_item_id=self._queue_item.item_id,
                                     queue_id=self._queue_item.queue_id,
                                     graph_execution_state_id=self._queue_item.session.id,
                                     node=self._invocation.model_dump(),
                                     source_node_id=source_invocation_id,
-                                    error_type=e.__class__.__name__,
-                                    error=error,
+                                    result=outputs.model_dump(),
                                 )
-                                pass
 
-                            # The session is complete if the all invocations are complete or there was an error
-                            if self._queue_item.session.is_complete() or cancel_event.is_set():
-                                # Send complete event
-                                self._invoker.services.events.emit_graph_execution_complete(
-                                    queue_batch_id=self._queue_item.batch_id,
-                                    queue_item_id=self._queue_item.item_id,
-                                    queue_id=self._queue_item.queue_id,
-                                    graph_execution_state_id=self._queue_item.session.id,
+                        except KeyboardInterrupt:
+                            # TODO(MM2): Create an event for this
+                            pass
+
+                        except CanceledException:
+                            # When the user cancels the graph, we first set the cancel event. The event is checked
+                            # between invocations, in this loop. Some invocations are long-running, and we need to
+                            # be able to cancel them mid-execution.
+                            #
+                            # For example, denoising is a long-running invocation with many steps. A step callback
+                            # is executed after each step. This step callback checks if the canceled event is set,
+                            # then raises a CanceledException to stop execution immediately.
+                            #
+                            # When we get a CanceledException, we don't need to do anything - just pass and let the
+                            # loop go to its next iteration, and the cancel event will be handled correctly.
+                            pass
+
+                        except Exception as e:
+                            error = traceback.format_exc()
+
+                            # Save error
+                            self._queue_item.session.set_node_error(self._invocation.id, error)
+                            self._invoker.services.logger.error(
+                                f"Error while invoking session {self._queue_item.session_id}, invocation {self._invocation.id} ({self._invocation.get_type()}):\n{e}"
+                            )
+                            self._invoker.services.logger.error(error)
+
+                            # Send error event
+                            self._invoker.services.events.emit_invocation_error(
+                                queue_batch_id=self._queue_item.session_id,
+                                queue_item_id=self._queue_item.item_id,
+                                queue_id=self._queue_item.queue_id,
+                                graph_execution_state_id=self._queue_item.session.id,
+                                node=self._invocation.model_dump(),
+                                source_node_id=source_invocation_id,
+                                error_type=e.__class__.__name__,
+                                error=error,
+                            )
+                            pass
+
+                        # The session is complete if the all invocations are complete or there was an error
+                        if self._queue_item.session.is_complete() or cancel_event.is_set():
+                            # Send complete event
+                            self._invoker.services.events.emit_graph_execution_complete(
+                                queue_batch_id=self._queue_item.batch_id,
+                                queue_item_id=self._queue_item.item_id,
+                                queue_id=self._queue_item.queue_id,
+                                graph_execution_state_id=self._queue_item.session.id,
+                            )
+                            # If we are profiling, stop the profiler and dump the profile & stats
+                            if self._profiler:
+                                profile_path = self._profiler.stop()
+                                stats_path = profile_path.with_suffix(".json")
+                                self._invoker.services.performance_statistics.dump_stats(
+                                    graph_execution_state_id=self._queue_item.session.id, output_path=stats_path
                                 )
-                                # If we are profiling, stop the profiler and dump the profile & stats
-                                if self._profiler:
-                                    profile_path = self._profiler.stop()
-                                    stats_path = profile_path.with_suffix(".json")
-                                    self._invoker.services.performance_statistics.dump_stats(
-                                        graph_execution_state_id=self._queue_item.session.id, output_path=stats_path
-                                    )
-                                # We'll get a GESStatsNotFoundError if we try to log stats for an untracked graph, but in the processor
-                                # we don't care about that - suppress the error.
-                                with suppress(GESStatsNotFoundError):
-                                    self._invoker.services.performance_statistics.log_stats(self._queue_item.session.id)
-                                    self._invoker.services.performance_statistics.reset_stats()
+                            # We'll get a GESStatsNotFoundError if we try to log stats for an untracked graph, but in the processor
+                            # we don't care about that - suppress the error.
+                            with suppress(GESStatsNotFoundError):
+                                self._invoker.services.performance_statistics.log_stats(self._queue_item.session.id)
+                                self._invoker.services.performance_statistics.reset_stats()
 
-                                # Set the invocation to None to prepare for the next session
-                                self._invocation = None
-                            else:
-                                # Prepare the next invocation
-                                self._invocation = self._queue_item.session.next()
-
-                        # The session is complete, immediately poll for next session
-                        self._queue_item = None
-                        poll_now_event.set()
+                            # Set the invocation to None to prepare for the next session
+                            self._invocation = None
+                        else:
+                            # Prepare the next invocation
+                            self._invocation = self._queue_item.session.next()
                     else:
                         # The queue was empty, wait for next polling interval or event to try again
                         self._invoker.services.logger.debug("Waiting for next polling interval or event")

invokeai/app/services/shared/sqlite/sqlite_util.py

@@ -10,6 +10,8 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_4 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_5 import build_migration_5
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_6 import build_migration_6
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_7 import build_migration_7
+from invokeai.app.services.shared.sqlite_migrator.migrations.migration_8 import build_migration_8
+from invokeai.app.services.shared.sqlite_migrator.migrations.migration_9 import build_migration_9
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -37,6 +39,8 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_5())
     migrator.register_migration(build_migration_6())
     migrator.register_migration(build_migration_7())
+    migrator.register_migration(build_migration_8(app_config=config))
+    migrator.register_migration(build_migration_9())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_7.py

@@ -11,7 +11,7 @@ class Migration7Callback:
     def _drop_old_models_tables(self, cursor: sqlite3.Cursor) -> None:
         """Drops the old model_records, model_metadata, model_tags and tags tables."""
 
-        tables = ["model_records", "model_metadata", "model_tags", "tags"]
+        tables = ["model_config", "model_metadata", "model_tags", "tags"]
 
         for table in tables:
             cursor.execute(f"DROP TABLE IF EXISTS {table};")

invokeai/app/services/shared/sqlite_migrator/migrations/migration_8.py

@@ -0,0 +1,91 @@
+import sqlite3
+from pathlib import Path
+
+from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
+
+
+class Migration8Callback:
+    def __init__(self, app_config: InvokeAIAppConfig) -> None:
+        self._app_config = app_config
+
+    def __call__(self, cursor: sqlite3.Cursor) -> None:
+        self._drop_model_config_table(cursor)
+        self._migrate_abs_models_to_rel(cursor)
+
+    def _drop_model_config_table(self, cursor: sqlite3.Cursor) -> None:
+        """Drops the old model_config table. This was missed in a previous migration."""
+
+        cursor.execute("DROP TABLE IF EXISTS model_config;")
+
+    def _migrate_abs_models_to_rel(self, cursor: sqlite3.Cursor) -> None:
+        """Check all model paths & legacy config paths to determine if they are inside Invoke-managed directories. If
+        they are, update the paths to be relative to the managed directories.
+
+        This migration is a no-op for normal users (their paths will already be relative), but is necessary for users
+        who have been testing the RCs with their live databases. The paths were made absolute in the initial RC, but this
+        change was reverted. To smooth over the revert for our tests, we can migrate the paths back to relative.
+        """
+
+        models_path = self._app_config.models_path
+        legacy_conf_path = self._app_config.legacy_conf_path
+        legacy_conf_dir = self._app_config.legacy_conf_dir
+
+        stmt = """---sql
+        SELECT
+            id,
+            path,
+            json_extract(config, '$.config_path') as config_path
+        FROM models;
+        """
+
+        all_models = cursor.execute(stmt).fetchall()
+
+        for model_id, model_path, model_config_path in all_models:
+            # If the model path is inside the models directory, update it to be relative to the models directory.
+            if Path(model_path).is_relative_to(models_path):
+                new_path = Path(model_path).relative_to(models_path)
+                cursor.execute(
+                    """--sql
+                    UPDATE models
+                    SET config = json_set(config, '$.path', ?)
+                    WHERE id = ?;
+                    """,
+                    (str(new_path), model_id),
+                )
+            # If the model has a legacy config path and it is inside the legacy conf directory, update it to be
+            # relative to the legacy conf directory. This also fixes up cases in which the config path was
+            # incorrectly relativized to the root directory. It will now be relativized to the legacy conf directory.
+            if model_config_path:
+                if Path(model_config_path).is_relative_to(legacy_conf_path):
+                    new_config_path = Path(model_config_path).relative_to(legacy_conf_path)
+                elif Path(model_config_path).is_relative_to(legacy_conf_dir):
+                    new_config_path = Path(*Path(model_config_path).parts[1:])
+                else:
+                    new_config_path = None
+                if new_config_path:
+                    cursor.execute(
+                        """--sql
+                        UPDATE models
+                        SET config = json_set(config, '$.config_path', ?)
+                        WHERE id = ?;
+                        """,
+                        (str(new_config_path), model_id),
+                    )
+
+
+def build_migration_8(app_config: InvokeAIAppConfig) -> Migration:
+    """
+    Build the migration from database version 7 to 8.
+
+    This migration does the following:
+    - Removes the `model_config` table.
+    - Migrates absolute model & legacy config paths to be relative to the models directory.
+    """
+    migration_8 = Migration(
+        from_version=7,
+        to_version=8,
+        callback=Migration8Callback(app_config),
+    )
+
+    return migration_8

invokeai/app/services/shared/sqlite_migrator/migrations/migration_9.py

@@ -0,0 +1,29 @@
+import sqlite3
+
+from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
+
+
+class Migration9Callback:
+    def __call__(self, cursor: sqlite3.Cursor) -> None:
+        self._empty_session_queue(cursor)
+
+    def _empty_session_queue(self, cursor: sqlite3.Cursor) -> None:
+        """Empties the session queue. This is done to prevent any lingering session queue items from causing pydantic errors due to changed schemas."""
+
+        cursor.execute("DELETE FROM session_queue;")
+
+
+def build_migration_9() -> Migration:
+    """
+    Build the migration from database version 8 to 9.
+
+    This migration does the following:
+    - Empties the session queue. This is done to prevent any lingering session queue items from causing pydantic errors due to changed schemas.
+    """
+    migration_9 = Migration(
+        from_version=8,
+        to_version=9,
+        callback=Migration9Callback(),
+    )
+
+    return migration_9

invokeai/app/services/shared/sqlite_migrator/sqlite_migrator_impl.py

@@ -1,4 +1,6 @@
 import sqlite3
+from contextlib import closing
+from datetime import datetime
 from pathlib import Path
 from typing import Optional
 
@@ -32,6 +34,7 @@ class SqliteMigrator:
         self._db = db
         self._logger = db.logger
         self._migration_set = MigrationSet()
+        self._backup_path: Optional[Path] = None
 
     def register_migration(self, migration: Migration) -> None:
         """Registers a migration."""
@@ -55,6 +58,18 @@ class SqliteMigrator:
                 return False
 
             self._logger.info("Database update needed")
+
+            # Make a backup of the db if it needs to be updated and is a file db
+            if self._db.db_path is not None:
+                timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
+                self._backup_path = self._db.db_path.parent / f"{self._db.db_path.stem}_backup_{timestamp}.db"
+                self._logger.info(f"Backing up database to {str(self._backup_path)}")
+                # Use SQLite to do the backup
+                with closing(sqlite3.connect(self._backup_path)) as backup_conn:
+                    self._db.conn.backup(backup_conn)
+            else:
+                self._logger.info("Using in-memory database, no backup needed")
+
             next_migration = self._migration_set.get(from_version=self._get_current_version(cursor))
             while next_migration is not None:
                 self._run_migration(next_migration)

invokeai/backend/ip_adapter/ip_adapter.py

@@ -9,6 +9,7 @@ from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
 from invokeai.backend.ip_adapter.ip_attention_weights import IPAttentionWeights
 
+from ..raw_model import RawModel
 from .resampler import Resampler
 
 
@@ -91,7 +92,7 @@ class MLPProjModel(torch.nn.Module):
         return clip_extra_context_tokens
 
 
-class IPAdapter(torch.nn.Module):
+class IPAdapter(RawModel):
     """IP-Adapter: https://arxiv.org/pdf/2308.06721.pdf"""
 
     def __init__(

invokeai/backend/lora.py

@@ -11,6 +11,8 @@ from typing_extensions import Self
 
 from invokeai.backend.model_manager import BaseModelType
 
+from .raw_model import RawModel
+
 
 class LoRALayerBase:
     # rank: Optional[int]
@@ -366,7 +368,7 @@ class IA3Layer(LoRALayerBase):
 AnyLoRALayer = Union[LoRALayer, LoHALayer, LoKRLayer, FullLayer, IA3Layer]
 
 
-class LoRAModelRaw(torch.nn.Module):
+class LoRAModelRaw(RawModel):  # (torch.nn.Module):
     _name: str
     layers: Dict[str, AnyLoRALayer]
 

invokeai/backend/lora_model_patcher.py

@@ -1,65 +0,0 @@
-from contextlib import contextmanager
-from typing import Iterator, Tuple, Union
-
-from diffusers.loaders.lora import LoraLoaderMixin
-from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
-from diffusers.utils.peft_utils import recurse_remove_peft_layers
-from transformers import CLIPTextModel
-
-from invokeai.backend.lora_model_raw import LoRAModelRaw
-
-
-class LoraModelPatcher:
-    @classmethod
-    def unload_lora_from_model(cls, m: Union[UNet2DConditionModel, CLIPTextModel]):
-        """Unload all LoRA models from a UNet or Text Encoder.
-        This implementation is base on LoraLoaderMixin.unload_lora_weights().
-        """
-        recurse_remove_peft_layers(m)
-        if hasattr(m, "peft_config"):
-            del m.peft_config  # type: ignore
-        if hasattr(m, "_hf_peft_config_loaded"):
-            m._hf_peft_config_loaded = None  # type: ignore
-
-    @classmethod
-    @contextmanager
-    def apply_lora_to_unet(cls, unet: UNet2DConditionModel, loras: Iterator[Tuple[LoRAModelRaw, float]]):
-        try:
-            # TODO(ryand): Test speed of low_cpu_mem_usage=True.
-            for lora, lora_weight in loras:
-                LoraLoaderMixin.load_lora_into_unet(
-                    state_dict=lora.state_dict,
-                    network_alphas=lora.network_alphas,
-                    unet=unet,
-                    low_cpu_mem_usage=True,
-                    adapter_name=lora.name,
-                    _pipeline=None,
-                )
-            yield
-        finally:
-            cls.unload_lora_from_model(unet)
-
-    @classmethod
-    @contextmanager
-    def apply_lora_to_text_encoder(
-        cls, text_encoder: CLIPTextModel, loras: Iterator[Tuple[LoRAModelRaw, float]], prefix: str
-    ):
-        assert prefix in ["text_encoder", "text_encoder_2"]
-        try:
-            for lora, lora_weight in loras:
-                # Filter the state_dict to only include the keys that start with the prefix.
-                text_encoder_state_dict = {
-                    key: value for key, value in lora.state_dict.items() if key.startswith(prefix + ".")
-                }
-                if len(text_encoder_state_dict) > 0:
-                    LoraLoaderMixin.load_lora_into_text_encoder(
-                        state_dict=text_encoder_state_dict,
-                        network_alphas=lora.network_alphas,
-                        text_encoder=text_encoder,
-                        low_cpu_mem_usage=True,
-                        adapter_name=lora.name,
-                        _pipeline=None,
-                    )
-            yield
-        finally:
-            cls.unload_lora_from_model(text_encoder)

invokeai/backend/lora_model_raw.py

@@ -1,66 +0,0 @@
-from pathlib import Path
-from typing import Optional, Union
-
-import torch
-from diffusers.loaders.lora import LoraLoaderMixin
-from typing_extensions import Self
-
-
-class LoRAModelRaw:
-    def __init__(
-        self,
-        name: str,
-        state_dict: dict[str, torch.Tensor],
-        network_alphas: Optional[dict[str, float]],
-    ):
-        self._name = name
-        self.state_dict = state_dict
-        self.network_alphas = network_alphas
-
-    @property
-    def name(self) -> str:
-        return self._name
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        for key, layer in self.state_dict.items():
-            self.state_dict[key] = layer.to(device=device, dtype=dtype)
-
-    def calc_size(self) -> int:
-        """Calculate the size of the model in bytes."""
-        model_size = 0
-        for layer in self.state_dict.values():
-            model_size += layer.numel() * layer.element_size()
-        return model_size
-
-    @classmethod
-    def from_checkpoint(
-        cls, file_path: Union[str, Path], device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None
-    ) -> Self:
-        """This function is based on diffusers LoraLoaderMixin.load_lora_weights()."""
-
-        file_path = Path(file_path)
-        if file_path.is_dir():
-            raise NotImplementedError("LoRA models from directories are not yet supported.")
-
-        dir_path = file_path.parent
-        file_name = file_path.name
-
-        state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(
-            pretrained_model_name_or_path_or_dict=str(file_path), local_files_only=True, weight_name=str(file_name)
-        )
-
-        is_correct_format = all("lora" in key for key in state_dict.keys())
-        if not is_correct_format:
-            raise ValueError("Invalid LoRA checkpoint.")
-
-        model = cls(
-            # TODO(ryand): Handle both files and directories here?
-            name=Path(file_path).stem,
-            state_dict=state_dict,
-            network_alphas=network_alphas,
-        )
-
-        device = device or torch.device("cpu")
-        dtype = dtype or torch.float32
-        model.to(device=device, dtype=dtype)
-        return model

invokeai/backend/model_manager/__init__.py

@@ -33,42 +33,3 @@ __all__ = [
     "SchedulerPredictionType",
     "SubModelType",
 ]
-
-########## to help populate the openapi_schema with format enums for each config ###########
-# This code is no longer necessary?
-# leave it here just in case
-#
-# import inspect
-# from enum import Enum
-# from typing import Any, Iterable, Dict, get_args, Set
-# def _expand(something: Any) -> Iterable[type]:
-#     if isinstance(something, type):
-#         yield something
-#     else:
-#         for x in get_args(something):
-#             for y in _expand(x):
-#                 yield y
-
-# def _find_format(cls: type) -> Iterable[Enum]:
-#     if hasattr(inspect, "get_annotations"):
-#         fields = inspect.get_annotations(cls)
-#     else:
-#         fields = cls.__annotations__
-#     if "format" in fields:
-#         for x in get_args(fields["format"]):
-#             yield x
-#     for parent_class in cls.__bases__:
-#         for x in _find_format(parent_class):
-#             yield x
-#     return None
-
-# def get_model_config_formats() -> Dict[str, Set[Enum]]:
-#     result: Dict[str, Set[Enum]] = {}
-#     for model_config in _expand(AnyModelConfig):
-#         for field in _find_format(model_config):
-#             if field is None:
-#                 continue
-#             if not result.get(model_config.__qualname__):
-#                 result[model_config.__qualname__] = set()
-#             result[model_config.__qualname__].add(field)
-#     return result

invokeai/backend/model_manager/config.py

@@ -31,13 +31,12 @@ from typing_extensions import Annotated, Any, Dict
 
 from invokeai.app.invocations.constants import SCHEDULER_NAME_VALUES
 from invokeai.app.util.misc import uuid_string
-from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.lora_model_raw import LoRAModelRaw
-from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
-from invokeai.backend.textual_inversion import TextualInversionModelRaw
+
+from ..raw_model import RawModel
 
 # ModelMixin is the base class for all diffusers and transformers models
-AnyModel = Union[ModelMixin, torch.nn.Module, IPAdapter, LoRAModelRaw, TextualInversionModelRaw, IAIOnnxRuntimeModel]
+# RawModel is the InvokeAI wrapper class for ip_adapters, loras, textual_inversion and onnx runtime
+AnyModel = Union[ModelMixin, RawModel, torch.nn.Module]
 
 
 class InvalidModelConfigException(Exception):

invokeai/backend/model_manager/convert_ckpt_to_diffusers.py

@@ -3,10 +3,10 @@
 """Conversion script for the Stable Diffusion checkpoints."""
 
 from pathlib import Path
-from typing import Dict
+from typing import Optional
 
 import torch
-from diffusers import AutoencoderKL
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
 from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
     convert_ldm_vae_checkpoint,
     create_vae_diffusers_config,
@@ -15,11 +15,14 @@ from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
 )
 from omegaconf import DictConfig
 
+from . import AnyModel
+
 
 def convert_ldm_vae_to_diffusers(
-    checkpoint: Dict[str, torch.Tensor],
+    checkpoint: torch.Tensor | dict[str, torch.Tensor],
     vae_config: DictConfig,
     image_size: int,
+    dump_path: Optional[Path] = None,
     precision: torch.dtype = torch.float16,
 ) -> AutoencoderKL:
     """Convert a checkpoint-style VAE into a Diffusers VAE"""
@@ -28,16 +31,21 @@ def convert_ldm_vae_to_diffusers(
 
     vae = AutoencoderKL(**vae_config)
     vae.load_state_dict(converted_vae_checkpoint)
-    return vae.to(precision)
+    vae.to(precision)
+
+    if dump_path:
+        vae.save_pretrained(dump_path, safe_serialization=True)
+
+    return vae
 
 
 def convert_ckpt_to_diffusers(
     checkpoint_path: str | Path,
-    dump_path: str | Path,
+    dump_path: Optional[str | Path] = None,
     precision: torch.dtype = torch.float16,
     use_safetensors: bool = True,
     **kwargs,
-):
+) -> AnyModel:
     """
     Takes all the arguments of download_from_original_stable_diffusion_ckpt(),
     and in addition a path-like object indicating the location of the desired diffusers
@@ -47,18 +55,20 @@ def convert_ckpt_to_diffusers(
     pipe = pipe.to(precision)
 
     # TO DO: save correct repo variant
-    pipe.save_pretrained(
-        dump_path,
-        safe_serialization=use_safetensors,
-    )
+    if dump_path:
+        pipe.save_pretrained(
+            dump_path,
+            safe_serialization=use_safetensors,
+        )
+    return pipe
 
 
 def convert_controlnet_to_diffusers(
     checkpoint_path: Path,
-    dump_path: Path,
+    dump_path: Optional[Path] = None,
     precision: torch.dtype = torch.float16,
     **kwargs,
-):
+) -> AnyModel:
     """
     Takes all the arguments of download_controlnet_from_original_ckpt(),
     and in addition a path-like object indicating the location of the desired diffusers
@@ -68,4 +78,6 @@ def convert_controlnet_to_diffusers(
     pipe = pipe.to(precision)
 
     # TO DO: save correct repo variant
-    pipe.save_pretrained(dump_path, safe_serialization=True)
+    if dump_path:
+        pipe.save_pretrained(dump_path, safe_serialization=True)
+    return pipe

invokeai/backend/model_manager/load/convert_cache/convert_cache_default.py

@@ -19,11 +19,20 @@ class ModelConvertCache(ModelConvertCacheBase):
         self._cache_path = cache_path
         self._max_size = max_size
 
+        # adjust cache size at startup in case it has been changed
+        if self._cache_path.exists():
+            self.make_room(0.0)
+
     @property
     def max_size(self) -> float:
         """Return the maximum size of this cache directory (GB)."""
         return self._max_size
 
+    @max_size.setter
+    def max_size(self, value: float) -> None:
+        """Set the maximum size of this cache directory (GB)."""
+        self._max_size = value
+
     def cache_path(self, key: str) -> Path:
         """Return the path for a model with the indicated key."""
         return self._cache_path / key

invokeai/backend/model_manager/load/load_base.py

@@ -83,3 +83,15 @@ class ModelLoaderBase(ABC):
     ) -> int:
         """Return size in bytes of the model, calculated before loading."""
         pass
+
+    @property
+    @abstractmethod
+    def convert_cache(self) -> ModelConvertCacheBase:
+        """Return the convert cache associated with this loader."""
+        pass
+
+    @property
+    @abstractmethod
+    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+        """Return the ram cache associated with this loader."""
+        pass

invokeai/backend/model_manager/load/load_default.py

@@ -3,14 +3,13 @@
 
 from logging import Logger
 from pathlib import Path
-from typing import Optional, Tuple
+from typing import Optional
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.model_manager import (
     AnyModel,
     AnyModelConfig,
     InvalidModelConfigException,
-    ModelRepoVariant,
     SubModelType,
 )
 from invokeai.backend.model_manager.config import DiffusersConfigBase, ModelType
@@ -54,51 +53,43 @@ class ModelLoader(ModelLoaderBase):
         if model_config.type is ModelType.Main and not submodel_type:
             raise InvalidModelConfigException("submodel_type is required when loading a main model")
 
-        model_path, model_config, submodel_type = self._get_model_path(model_config, submodel_type)
+        model_path = self._get_model_path(model_config)
 
         if not model_path.exists():
             raise InvalidModelConfigException(f"Files for model '{model_config.name}' not found at {model_path}")
 
-        model_path = self._convert_if_needed(model_config, model_path, submodel_type)
-        locker = self._load_if_needed(model_config, model_path, submodel_type)
+        with skip_torch_weight_init():
+            locker = self._convert_and_load(model_config, model_path, submodel_type)
         return LoadedModel(config=model_config, _locker=locker)
 
-    def _get_model_path(
-        self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None
-    ) -> Tuple[Path, AnyModelConfig, Optional[SubModelType]]:
+    @property
+    def convert_cache(self) -> ModelConvertCacheBase:
+        """Return the convert cache associated with this loader."""
+        return self._convert_cache
+
+    @property
+    def ram_cache(self) -> ModelCacheBase[AnyModel]:
+        """Return the ram cache associated with this loader."""
+        return self._ram_cache
+
+    def _get_model_path(self, config: AnyModelConfig) -> Path:
         model_base = self._app_config.models_path
-        result = (model_base / config.path).resolve(), config, submodel_type
-        return result
+        return (model_base / config.path).resolve()
 
-    def _convert_if_needed(
-        self, config: AnyModelConfig, model_path: Path, submodel_type: Optional[SubModelType] = None
-    ) -> Path:
-        cache_path: Path = self._convert_cache.cache_path(config.key)
-
-        if not self._needs_conversion(config, model_path, cache_path):
-            return cache_path if cache_path.exists() else model_path
-
-        self._convert_cache.make_room(self.get_size_fs(config, model_path, submodel_type))
-        return self._convert_model(config, model_path, cache_path)
-
-    def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
-        return False
-
-    def _load_if_needed(
+    def _convert_and_load(
         self, config: AnyModelConfig, model_path: Path, submodel_type: Optional[SubModelType] = None
     ) -> ModelLockerBase:
-        # TO DO: This is not thread safe!
         try:
             return self._ram_cache.get(config.key, submodel_type)
         except IndexError:
             pass
 
-        model_variant = getattr(config, "repo_variant", None)
-        self._ram_cache.make_room(self.get_size_fs(config, model_path, submodel_type))
-
-        # This is where the model is actually loaded!
-        with skip_torch_weight_init():
-            loaded_model = self._load_model(model_path, model_variant=model_variant, submodel_type=submodel_type)
+        cache_path: Path = self._convert_cache.cache_path(config.key)
+        if self._needs_conversion(config, model_path, cache_path):
+            loaded_model = self._do_convert(config, model_path, cache_path, submodel_type)
+        else:
+            config.path = str(cache_path) if cache_path.exists() else str(self._get_model_path(config))
+            loaded_model = self._load_model(config, submodel_type)
 
         self._ram_cache.put(
             config.key,
@@ -123,15 +114,34 @@ class ModelLoader(ModelLoaderBase):
             variant=config.repo_variant if isinstance(config, DiffusersConfigBase) else None,
         )
 
+    def _do_convert(
+        self, config: AnyModelConfig, model_path: Path, cache_path: Path, submodel_type: Optional[SubModelType] = None
+    ) -> AnyModel:
+        self.convert_cache.make_room(calc_model_size_by_fs(model_path))
+        pipeline = self._convert_model(config, model_path, cache_path if self.convert_cache.max_size > 0 else None)
+        if submodel_type:
+            # Proactively load the various submodels into the RAM cache so that we don't have to re-convert
+            # the entire pipeline every time a new submodel is needed.
+            for subtype in SubModelType:
+                if subtype == submodel_type:
+                    continue
+                if submodel := getattr(pipeline, subtype.value, None):
+                    self._ram_cache.put(
+                        config.key, submodel_type=subtype, model=submodel, size=calc_model_size_by_data(submodel)
+                    )
+        return getattr(pipeline, submodel_type.value) if submodel_type else pipeline
+
+    def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
+        return False
+
     # This needs to be implemented in subclasses that handle checkpoints
-    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
+    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel:
         raise NotImplementedError
 
     # This needs to be implemented in the subclass
     def _load_model(
         self,
-        model_path: Path,
-        model_variant: Optional[ModelRepoVariant] = None,
+        config: AnyModelConfig,
         submodel_type: Optional[SubModelType] = None,
     ) -> AnyModel:
         raise NotImplementedError

invokeai/backend/model_manager/load/model_cache/model_cache_default.py

@@ -122,6 +122,11 @@ class ModelCache(ModelCacheBase[AnyModel]):
         """Return the cap on cache size."""
         return self._max_cache_size
 
+    @max_cache_size.setter
+    def max_cache_size(self, value: float) -> None:
+        """Set the cap on cache size."""
+        self._max_cache_size = value
+
     @property
     def stats(self) -> Optional[CacheStats]:
         """Return collected CacheStats object."""
@@ -157,8 +162,9 @@ class ModelCache(ModelCacheBase[AnyModel]):
     ) -> None:
         """Store model under key and optional submodel_type."""
         key = self._make_cache_key(key, submodel_type)
-        assert key not in self._cached_models
-
+        if key in self._cached_models:
+            return
+        self.make_room(size)
         cache_record = CacheRecord(key, model, size)
         self._cached_models[key] = cache_record
         self._cache_stack.append(key)
@@ -405,6 +411,8 @@ class ModelCache(ModelCacheBase[AnyModel]):
             #
             # Keep in mind that gc is only responsible for handling reference cycles. Most objects should be cleaned up
             # immediately when their reference count hits 0.
+            if self.stats:
+                self.stats.cleared = models_cleared
             gc.collect()
 
         torch.cuda.empty_cache()

invokeai/backend/model_manager/load/model_loaders/controlnet.py

@@ -2,8 +2,10 @@
 """Class for ControlNet model loading in InvokeAI."""
 
 from pathlib import Path
+from typing import Optional
 
 from invokeai.backend.model_manager import (
+    AnyModel,
     AnyModelConfig,
     BaseModelType,
     ModelFormat,
@@ -33,7 +35,7 @@ class ControlNetLoader(GenericDiffusersLoader):
         else:
             return True
 
-    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
+    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel:
         assert isinstance(config, CheckpointConfigBase)
         image_size = (
             512
@@ -44,8 +46,8 @@ class ControlNetLoader(GenericDiffusersLoader):
         )
 
         self._logger.info(f"Converting {model_path} to diffusers format")
-        with open(self._app_config.root_path / config.config_path, "r") as config_stream:
-            convert_controlnet_to_diffusers(
+        with open(self._app_config.legacy_conf_path / config.config_path, "r") as config_stream:
+            result = convert_controlnet_to_diffusers(
                 model_path,
                 output_path,
                 original_config_file=config_stream,
@@ -53,4 +55,4 @@ class ControlNetLoader(GenericDiffusersLoader):
                 precision=self._torch_dtype,
                 from_safetensors=model_path.suffix == ".safetensors",
             )
-        return output_path
+        return result

invokeai/backend/model_manager/load/model_loaders/generic_diffusers.py

@@ -10,13 +10,14 @@ from diffusers.models.modeling_utils import ModelMixin
 
 from invokeai.backend.model_manager import (
     AnyModel,
+    AnyModelConfig,
     BaseModelType,
     InvalidModelConfigException,
     ModelFormat,
-    ModelRepoVariant,
     ModelType,
     SubModelType,
 )
+from invokeai.backend.model_manager.config import DiffusersConfigBase
 
 from .. import ModelLoader, ModelLoaderRegistry
 
@@ -28,14 +29,15 @@ class GenericDiffusersLoader(ModelLoader):
 
     def _load_model(
         self,
-        model_path: Path,
-        model_variant: Optional[ModelRepoVariant] = None,
+        config: AnyModelConfig,
         submodel_type: Optional[SubModelType] = None,
     ) -> AnyModel:
+        model_path = Path(config.path)
         model_class = self.get_hf_load_class(model_path)
         if submodel_type is not None:
             raise Exception(f"There are no submodels in models of type {model_class}")
-        variant = model_variant.value if model_variant else None
+        repo_variant = config.repo_variant if isinstance(config, DiffusersConfigBase) else None
+        variant = repo_variant.value if repo_variant else None
         try:
             result: AnyModel = model_class.from_pretrained(model_path, torch_dtype=self._torch_dtype, variant=variant)
         except OSError as e:

invokeai/backend/model_manager/load/model_loaders/ip_adapter.py

@@ -9,13 +9,14 @@ import torch
 from invokeai.backend.ip_adapter.ip_adapter import build_ip_adapter
 from invokeai.backend.model_manager import (
     AnyModel,
+    AnyModelConfig,
     BaseModelType,
     ModelFormat,
-    ModelRepoVariant,
     ModelType,
     SubModelType,
 )
 from invokeai.backend.model_manager.load import ModelLoader, ModelLoaderRegistry
+from invokeai.backend.raw_model import RawModel
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.IPAdapter, format=ModelFormat.InvokeAI)
@@ -24,13 +25,13 @@ class IPAdapterInvokeAILoader(ModelLoader):
 
     def _load_model(
         self,
-        model_path: Path,
-        model_variant: Optional[ModelRepoVariant] = None,
+        config: AnyModelConfig,
         submodel_type: Optional[SubModelType] = None,
     ) -> AnyModel:
         if submodel_type is not None:
             raise ValueError("There are no submodels in an IP-Adapter model.")
-        model = build_ip_adapter(
+        model_path = Path(config.path)
+        model: RawModel = build_ip_adapter(
             ip_adapter_ckpt_path=str(model_path / "ip_adapter.bin"),
             device=torch.device("cpu"),
             dtype=self._torch_dtype,

invokeai/backend/model_manager/load/model_loaders/lora.py

@@ -3,16 +3,15 @@
 
 from logging import Logger
 from pathlib import Path
-from typing import Optional, Tuple
+from typing import Optional
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import (
     AnyModel,
     AnyModelConfig,
     BaseModelType,
     ModelFormat,
-    ModelRepoVariant,
     ModelType,
     SubModelType,
 )
@@ -41,26 +40,24 @@ class LoRALoader(ModelLoader):
 
     def _load_model(
         self,
-        model_path: Path,
-        model_variant: Optional[ModelRepoVariant] = None,
+        config: AnyModelConfig,
         submodel_type: Optional[SubModelType] = None,
     ) -> AnyModel:
         if submodel_type is not None:
             raise ValueError("There are no submodels in a LoRA model.")
+        model_path = Path(config.path)
         assert self._model_base is not None
         model = LoRAModelRaw.from_checkpoint(
             file_path=model_path,
             dtype=self._torch_dtype,
+            base_model=self._model_base,
         )
         return model
 
     # override
-    def _get_model_path(
-        self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None
-    ) -> Tuple[Path, AnyModelConfig, Optional[SubModelType]]:
-        self._model_base = (
-            config.base
-        )  # cheating a little - we remember this variable for using in the subsequent call to _load_model()
+    def _get_model_path(self, config: AnyModelConfig) -> Path:
+        # cheating a little - we remember this variable for using in the subsequent call to _load_model()
+        self._model_base = config.base
 
         model_base_path = self._app_config.models_path
         model_path = model_base_path / config.path
@@ -72,5 +69,4 @@ class LoRALoader(ModelLoader):
                     model_path = path
                     break
 
-        result = model_path.resolve(), config, submodel_type
-        return result
+        return model_path.resolve()

invokeai/backend/model_manager/load/model_loaders/onnx.py

@@ -7,9 +7,9 @@ from typing import Optional
 
 from invokeai.backend.model_manager import (
     AnyModel,
+    AnyModelConfig,
     BaseModelType,
     ModelFormat,
-    ModelRepoVariant,
     ModelType,
     SubModelType,
 )
@@ -25,18 +25,19 @@ class OnnyxDiffusersModel(GenericDiffusersLoader):
 
     def _load_model(
         self,
-        model_path: Path,
-        model_variant: Optional[ModelRepoVariant] = None,
+        config: AnyModelConfig,
         submodel_type: Optional[SubModelType] = None,
     ) -> AnyModel:
         if not submodel_type is not None:
             raise Exception("A submodel type must be provided when loading onnx pipelines.")
+        model_path = Path(config.path)
         load_class = self.get_hf_load_class(model_path, submodel_type)
-        variant = model_variant.value if model_variant else None
+        repo_variant = getattr(config, "repo_variant", None)
+        variant = repo_variant.value if repo_variant else None
         model_path = model_path / submodel_type.value
         result: AnyModel = load_class.from_pretrained(
             model_path,
             torch_dtype=self._torch_dtype,
             variant=variant,
-        )  # type: ignore
+        )
         return result

invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py

@@ -9,12 +9,16 @@ from invokeai.backend.model_manager import (
     AnyModelConfig,
     BaseModelType,
     ModelFormat,
-    ModelRepoVariant,
     ModelType,
     SchedulerPredictionType,
     SubModelType,
 )
-from invokeai.backend.model_manager.config import CheckpointConfigBase, MainCheckpointConfig, ModelVariantType
+from invokeai.backend.model_manager.config import (
+    CheckpointConfigBase,
+    DiffusersConfigBase,
+    MainCheckpointConfig,
+    ModelVariantType,
+)
 from invokeai.backend.model_manager.convert_ckpt_to_diffusers import convert_ckpt_to_diffusers
 
 from .. import ModelLoaderRegistry
@@ -41,14 +45,15 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
 
     def _load_model(
         self,
-        model_path: Path,
-        model_variant: Optional[ModelRepoVariant] = None,
+        config: AnyModelConfig,
         submodel_type: Optional[SubModelType] = None,
     ) -> AnyModel:
         if not submodel_type is not None:
             raise Exception("A submodel type must be provided when loading main pipelines.")
+        model_path = Path(config.path)
         load_class = self.get_hf_load_class(model_path, submodel_type)
-        variant = model_variant.value if model_variant else None
+        repo_variant = config.repo_variant if isinstance(config, DiffusersConfigBase) else None
+        variant = repo_variant.value if repo_variant else None
         model_path = model_path / submodel_type.value
         try:
             result: AnyModel = load_class.from_pretrained(
@@ -78,7 +83,7 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
         else:
             return True
 
-    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
+    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel:
         assert isinstance(config, MainCheckpointConfig)
         base = config.base
 
@@ -94,11 +99,11 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
 
         self._logger.info(f"Converting {model_path} to diffusers format")
 
-        convert_ckpt_to_diffusers(
+        loaded_model = convert_ckpt_to_diffusers(
             model_path,
             output_path,
             model_type=self.model_base_to_model_type[base],
-            original_config_file=self._app_config.root_path / config.config_path,
+            original_config_file=self._app_config.legacy_conf_path / config.config_path,
             extract_ema=True,
             from_safetensors=model_path.suffix == ".safetensors",
             precision=self._torch_dtype,
@@ -108,4 +113,4 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
             load_safety_checker=False,
             num_in_channels=VARIANT_TO_IN_CHANNEL_MAP[config.variant],
         )
-        return output_path
+        return loaded_model

invokeai/backend/model_manager/load/model_loaders/textual_inversion.py

@@ -2,14 +2,13 @@
 """Class for TI model loading in InvokeAI."""
 
 from pathlib import Path
-from typing import Optional, Tuple
+from typing import Optional
 
 from invokeai.backend.model_manager import (
     AnyModel,
     AnyModelConfig,
     BaseModelType,
     ModelFormat,
-    ModelRepoVariant,
     ModelType,
     SubModelType,
 )
@@ -27,22 +26,19 @@ class TextualInversionLoader(ModelLoader):
 
     def _load_model(
         self,
-        model_path: Path,
-        model_variant: Optional[ModelRepoVariant] = None,
+        config: AnyModelConfig,
         submodel_type: Optional[SubModelType] = None,
     ) -> AnyModel:
         if submodel_type is not None:
             raise ValueError("There are no submodels in a TI model.")
         model = TextualInversionModelRaw.from_checkpoint(
-            file_path=model_path,
+            file_path=config.path,
             dtype=self._torch_dtype,
         )
         return model
 
     # override
-    def _get_model_path(
-        self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None
-    ) -> Tuple[Path, AnyModelConfig, Optional[SubModelType]]:
+    def _get_model_path(self, config: AnyModelConfig) -> Path:
         model_path = self._app_config.models_path / config.path
 
         if config.format == ModelFormat.EmbeddingFolder:
@@ -53,4 +49,4 @@ class TextualInversionLoader(ModelLoader):
         if not path.exists():
             raise OSError(f"The embedding file at {path} was not found")
 
-        return path, config, submodel_type
+        return path

invokeai/backend/model_manager/load/model_loaders/vae.py

@@ -2,6 +2,7 @@
 """Class for VAE model loading in InvokeAI."""
 
 from pathlib import Path
+from typing import Optional
 
 import torch
 from omegaconf import DictConfig, OmegaConf
@@ -13,7 +14,7 @@ from invokeai.backend.model_manager import (
     ModelFormat,
     ModelType,
 )
-from invokeai.backend.model_manager.config import CheckpointConfigBase
+from invokeai.backend.model_manager.config import AnyModel, CheckpointConfigBase
 from invokeai.backend.model_manager.convert_ckpt_to_diffusers import convert_ldm_vae_to_diffusers
 
 from .. import ModelLoaderRegistry
@@ -38,13 +39,13 @@ class VAELoader(GenericDiffusersLoader):
         else:
             return True
 
-    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
+    def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel:
         # TODO(MM2): check whether sdxl VAE models convert.
         if config.base not in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}:
             raise Exception(f"VAE conversion not supported for model type: {config.base}")
         else:
             assert isinstance(config, CheckpointConfigBase)
-            config_file = self._app_config.root_path / config.config_path
+            config_file = self._app_config.legacy_conf_path / config.config_path
 
         if model_path.suffix == ".safetensors":
             checkpoint = safetensors_load_file(model_path, device="cpu")
@@ -63,6 +64,6 @@ class VAELoader(GenericDiffusersLoader):
             vae_config=ckpt_config,
             image_size=512,
             precision=self._torch_dtype,
+            dump_path=output_path,
         )
-        vae_model.save_pretrained(output_path, safe_serialization=True)
-        return output_path
+        return vae_model

invokeai/backend/model_patcher.py

@@ -17,7 +17,7 @@ from invokeai.backend.model_manager import AnyModel
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
 
-from .lora_model_raw import LoRAModelRaw
+from .lora import LoRAModelRaw
 from .textual_inversion import TextualInversionManager, TextualInversionModelRaw
 
 """

invokeai/backend/onnx/onnx_runtime.py

@@ -6,16 +6,17 @@ from typing import Any, List, Optional, Tuple, Union
 
 import numpy as np
 import onnx
-import torch
 from onnx import numpy_helper
 from onnxruntime import InferenceSession, SessionOptions, get_available_providers
 
+from ..raw_model import RawModel
+
 ONNX_WEIGHTS_NAME = "model.onnx"
 
 
 # NOTE FROM LS: This was copied from Stalker's original implementation.
 # I have not yet gone through and fixed all the type hints
-class IAIOnnxRuntimeModel(torch.nn.Module):
+class IAIOnnxRuntimeModel(RawModel):
     class _tensor_access:
         def __init__(self, model):  # type: ignore
             self.model = model

invokeai/backend/raw_model.py

@@ -0,0 +1,15 @@
+"""Base class for 'Raw' models.
+
+The RawModel class is the base class of LoRAModelRaw and TextualInversionModelRaw,
+and is used for type checking of calls to the model patcher. Its main purpose
+is to avoid a circular import issues when lora.py tries to import BaseModelType
+from invokeai.backend.model_manager.config, and the latter tries to import LoRAModelRaw
+from lora.py.
+
+The term 'raw' was introduced to describe a wrapper around a torch.nn.Module
+that adds additional methods and attributes.
+"""
+
+
+class RawModel:
+    """Base class for 'Raw' model wrappers."""

invokeai/backend/stable_diffusion/seamless.py

@@ -28,6 +28,10 @@ def _conv_forward_asymmetric(self, input, weight, bias):
 
 @contextmanager
 def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL, AutoencoderTiny], seamless_axes: List[str]):
+    if not seamless_axes:
+        yield
+        return
+
     # Callable: (input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor
     to_restore: list[tuple[nn.Conv2d | nn.ConvTranspose2d, Callable]] = []
     try:

invokeai/backend/textual_inversion.py

@@ -9,8 +9,10 @@ from safetensors.torch import load_file
 from transformers import CLIPTokenizer
 from typing_extensions import Self
 
+from .raw_model import RawModel
 
-class TextualInversionModelRaw(torch.nn.Module):
+
+class TextualInversionModelRaw(RawModel):
     embedding: torch.Tensor  # [n, 768]|[n, 1280]
     embedding_2: Optional[torch.Tensor] = None  # [n, 768]|[n, 1280]   - for SDXL models
 

invokeai/backend/util/db_maintenance.py

@@ -31,6 +31,9 @@ class ConfigMapper:
     YAML_FILENAME = "invokeai.yaml"
     DATABASE_FILENAME = "invokeai.db"
 
+    DEFAULT_OUTDIR = "outputs"
+    DEFAULT_DB_DIR = "databases"
+
     database_path = None
     database_backup_dir = None
     outputs_path = None
@@ -50,12 +53,18 @@ class ConfigMapper:
     def __load_from_root_config(self, invoke_root):
         """Validate a yaml path exists, confirm the user wants to use it and load config."""
         yaml_path = os.path.join(invoke_root, self.YAML_FILENAME)
+        if not os.path.exists(yaml_path):
+            print(f"Unable to find invokeai.yaml at {yaml_path}!")
+            return False
         if os.path.exists(yaml_path):
             db_dir, outdir = self.__load_paths_from_yaml_file(yaml_path)
 
-            if db_dir is None or outdir is None:
-                print("The invokeai.yaml file was found but is missing the db_dir and/or outdir setting!")
-                return False
+            if db_dir is None:
+                db_dir = self.DEFAULT_DB_DIR
+                print(f"The invokeai.yaml file was found but is missing the db_dir setting! Defaulting to {db_dir}")
+            if outdir is None:
+                outdir = self.DEFAULT_OUTDIR
+                print(f"The invokeai.yaml file was found but is missing the outdir setting! Defaulting to {outdir}")
 
             if os.path.isabs(db_dir):
                 self.database_path = os.path.join(db_dir, self.DATABASE_FILENAME)

invokeai/frontend/web/package.json

@@ -94,6 +94,7 @@
     "reactflow": "^11.10.4",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.1.0",
+    "rfdc": "^1.3.1",
     "roarr": "^7.21.1",
     "serialize-error": "^11.0.3",
     "socket.io-client": "^4.7.5",

invokeai/frontend/web/pnpm-lock.yaml

@@ -137,6 +137,9 @@ dependencies:
   redux-remember:
     specifier: ^5.1.0
     version: 5.1.0(redux@5.0.1)
+  rfdc:
+    specifier: ^1.3.1
+    version: 1.3.1
   roarr:
     specifier: ^7.21.1
     version: 7.21.1
@@ -12128,6 +12131,10 @@ packages:
     resolution: {integrity: sha512-/x8uIPdTafBqakK0TmPNJzgkLP+3H+yxpUJhCQHsLBg1rYEVNR2D8BRYNWQhVBjyOd7oo1dZRVzIkwMY2oqfYQ==}
     dev: true
 
+  /rfdc@1.3.1:
+    resolution: {integrity: sha512-r5a3l5HzYlIC68TpmYKlxWjmOP6wiPJ1vWv2HeLhNsRZMrCkxeqxiHlQ21oXmQ4F3SiryXBHhAD7JZqvOJjFmg==}
+    dev: false
+
   /rimraf@2.6.3:
     resolution: {integrity: sha512-mwqeW5XsA2qAejG46gYdENaxXjx9onRNCfn7L0duuP4hCuTIi/QO7PDK07KJfp1d+izWPrzEJDcSqBa0OZQriA==}
     hasBin: true

invokeai/frontend/web/public/locales/de.json

@@ -4,7 +4,7 @@
         "reportBugLabel": "Fehler melden",
         "settingsLabel": "Einstellungen",
         "img2img": "Bild zu Bild",
-        "nodes": "Knoten Editor",
+        "nodes": "Arbeitsabläufe",
         "upload": "Hochladen",
         "load": "Laden",
         "statusDisconnected": "Getrennt",
@@ -74,7 +74,8 @@
         "updated": "Aktualisiert",
         "copy": "Kopieren",
         "aboutHeading": "Nutzen Sie Ihre kreative Energie",
-        "toResolve": "Lösen"
+        "toResolve": "Lösen",
+        "add": "Hinzufügen"
     },
     "gallery": {
         "galleryImageSize": "Bildgröße",
@@ -104,11 +105,16 @@
         "dropToUpload": "$t(gallery.drop) zum hochladen",
         "dropOrUpload": "$t(gallery.drop) oder hochladen",
         "drop": "Ablegen",
-        "problemDeletingImages": "Problem beim Löschen der Bilder"
+        "problemDeletingImages": "Problem beim Löschen der Bilder",
+        "bulkDownloadRequested": "Download vorbereiten",
+        "bulkDownloadRequestedDesc": "Dein Download wird vorbereitet. Dies kann ein paar Momente dauern.",
+        "bulkDownloadRequestFailed": "Problem beim Download vorbereiten",
+        "bulkDownloadFailed": "Download fehlgeschlagen",
+        "alwaysShowImageSizeBadge": "Zeige immer Bilder Größe Abzeichen"
     },
     "hotkeys": {
         "keyboardShortcuts": "Tastenkürzel",
-        "appHotkeys": "App-Tastenkombinationen",
+        "appHotkeys": "App",
         "generalHotkeys": "Allgemein",
         "galleryHotkeys": "Galerie",
         "unifiedCanvasHotkeys": "Leinwand",
@@ -757,7 +763,9 @@
         "scheduler": "Planer",
         "noRecallParameters": "Es wurden keine Parameter zum Abrufen gefunden",
         "recallParameters": "Parameter wiederherstellen",
-        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)"
+        "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
+        "allPrompts": "Alle Prompts",
+        "imageDimensions": "Bilder Auslösungen"
     },
     "popovers": {
         "noiseUseCPU": {
@@ -1068,5 +1076,10 @@
     },
     "dynamicPrompts": {
         "showDynamicPrompts": "Dynamische Prompts anzeigen"
+    },
+    "prompt": {
+        "noMatchingTriggers": "Keine passenden Auslöser",
+        "addPromptTrigger": "Auslöse Text hinzufügen",
+        "compatibleEmbeddings": "Kompatible Einbettungen"
     }
 }

invokeai/frontend/web/public/locales/it.json

@@ -73,7 +73,8 @@
         "ai": "ia",
         "file": "File",
         "toResolve": "Da risolvere",
-        "add": "Aggiungi"
+        "add": "Aggiungi",
+        "loglevel": "Livello di log"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -934,7 +935,9 @@
         "base": "Base",
         "lineart": "Linea",
         "controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
-        "mediapipeFace": "Mediapipe Volto"
+        "mediapipeFace": "Mediapipe Volto",
+        "ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
+        "t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))"
     },
     "queue": {
         "queueFront": "Aggiungi all'inizio della coda",
@@ -1490,7 +1493,8 @@
             "title": "Generazione"
         },
         "advanced": {
-            "title": "Avanzate"
+            "title": "Avanzate",
+            "options": "Opzioni $t(accordions.advanced.title)"
         },
         "image": {
             "title": "Immagine"

invokeai/frontend/web/public/locales/ru.json

@@ -75,7 +75,8 @@
         "copy": "Копировать",
         "localSystem": "Локальная система",
         "aboutDesc": "Используя Invoke для работы? Проверьте это:",
-        "add": "Добавить"
+        "add": "Добавить",
+        "loglevel": "Уровень логов"
     },
     "gallery": {
         "galleryImageSize": "Размер изображений",
@@ -1505,7 +1506,8 @@
             "title": "Генерация"
         },
         "advanced": {
-            "title": "Расширенные"
+            "title": "Расширенные",
+            "options": "Опции $t(accordions.advanced.title)"
         },
         "image": {
             "title": "Изображение"

invokeai/frontend/web/src/app/store/middleware/devtools/actionSanitizer.ts

@@ -1,7 +1,7 @@
 import type { UnknownAction } from '@reduxjs/toolkit';
+import { deepClone } from 'common/util/deepClone';
 import { isAnyGraphBuilt } from 'features/nodes/store/actions';
 import { nodeTemplatesBuilt } from 'features/nodes/store/nodesSlice';
-import { cloneDeep } from 'lodash-es';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 import type { Graph } from 'services/api/types';
 import { socketGeneratorProgress } from 'services/events/actions';
@@ -33,7 +33,7 @@ export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
   }
 
   if (socketGeneratorProgress.match(action)) {
-    const sanitized = cloneDeep(action);
+    const sanitized = deepClone(action);
     if (sanitized.payload.data.progress_image) {
       sanitized.payload.data.progress_image.dataURL = '<Progress image omitted>';
     }

invokeai/frontend/web/src/common/components/OverlayScrollbars/constants.ts

@@ -1,4 +1,5 @@
-import { cloneDeep, merge } from 'lodash-es';
+import { deepClone } from 'common/util/deepClone';
+import { merge } from 'lodash-es';
 import { ClickScrollPlugin, OverlayScrollbars } from 'overlayscrollbars';
 import type { UseOverlayScrollbarsParams } from 'overlayscrollbars-react';
 
@@ -22,7 +23,7 @@ export const getOverlayScrollbarsParams = (
   overflowX: 'hidden' | 'scroll' = 'hidden',
   overflowY: 'hidden' | 'scroll' = 'scroll'
 ) => {
-  const params = cloneDeep(overlayScrollbarsParams);
+  const params = deepClone(overlayScrollbarsParams);
   merge(params, { options: { overflow: { y: overflowY, x: overflowX } } });
   return params;
 };

invokeai/frontend/web/src/common/util/deepClone.ts

@@ -0,0 +1,15 @@
+import rfdc from 'rfdc';
+const _rfdc = rfdc();
+
+/**
+ * Deep-clones an object using Really Fast Deep Clone.
+ * This is the fastest deep clone library on Chrome, but not the fastest on FF. Still, it's much faster than lodash
+ * and structuredClone, so it's the best all-around choice.
+ *
+ * Simple Benchmark: https://www.measurethat.net/Benchmarks/Show/30358/0/lodash-clonedeep-vs-jsonparsejsonstringify-vs-recursive
+ * Repo: https://github.com/davidmarkclements/rfdc
+ *
+ * @param obj The object to deep-clone
+ * @returns The cloned object
+ */
+export const deepClone = <T>(obj: T): T => _rfdc(obj);

invokeai/frontend/web/src/features/canvas/store/canvasSlice.ts

@@ -1,6 +1,7 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
+import { deepClone } from 'common/util/deepClone';
 import { roundDownToMultiple, roundToMultiple } from 'common/util/roundDownToMultiple';
 import calculateCoordinates from 'features/canvas/util/calculateCoordinates';
 import calculateScale from 'features/canvas/util/calculateScale';
@@ -13,7 +14,7 @@ import { modelChanged } from 'features/parameters/store/generationSlice';
 import type { PayloadActionWithOptimalDimension } from 'features/parameters/store/types';
 import { getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';
 import type { IRect, Vector2d } from 'konva/lib/types';
-import { clamp, cloneDeep } from 'lodash-es';
+import { clamp } from 'lodash-es';
 import type { RgbaColor } from 'react-colorful';
 import { queueApi } from 'services/api/endpoints/queue';
 import type { ImageDTO } from 'services/api/types';
@@ -36,7 +37,7 @@ import { CANVAS_GRID_SIZE_FINE } from './constants';
 /**
  * The maximum history length to keep in the past/future layer states.
  */
-const MAX_HISTORY = 128;
+const MAX_HISTORY = 100;
 
 const initialLayerState: CanvasLayerState = {
   objects: [],
@@ -121,7 +122,7 @@ export const canvasSlice = createSlice({
       state.brushSize = action.payload;
     },
     clearMask: (state) => {
-      state.pastLayerStates.push(cloneDeep(state.layerState));
+      pushToPrevLayerStates(state);
       state.layerState.objects = state.layerState.objects.filter((obj) => !isCanvasMaskLine(obj));
       state.futureLayerStates = [];
       state.shouldPreserveMaskedArea = false;
@@ -163,10 +164,10 @@ export const canvasSlice = createSlice({
         state.boundingBoxDimensions = newBoundingBoxDimensions;
         state.boundingBoxCoordinates = newBoundingBoxCoordinates;
 
-        state.pastLayerStates.push(cloneDeep(state.layerState));
+        pushToPrevLayerStates(state);
 
         state.layerState = {
-          ...cloneDeep(initialLayerState),
+          ...deepClone(initialLayerState),
           objects: [
             {
               kind: 'image',
@@ -261,11 +262,7 @@ export const canvasSlice = createSlice({
         return;
       }
 
-      state.pastLayerStates.push(cloneDeep(state.layerState));
-
-      if (state.pastLayerStates.length > MAX_HISTORY) {
-        state.pastLayerStates.shift();
-      }
+      pushToPrevLayerStates(state);
 
       state.layerState.stagingArea.images.push({
         kind: 'image',
@@ -279,13 +276,9 @@ export const canvasSlice = createSlice({
       state.futureLayerStates = [];
     },
     discardStagedImages: (state) => {
-      state.pastLayerStates.push(cloneDeep(state.layerState));
+      pushToPrevLayerStates(state);
 
-      if (state.pastLayerStates.length > MAX_HISTORY) {
-        state.pastLayerStates.shift();
-      }
-
-      state.layerState.stagingArea = cloneDeep(cloneDeep(initialLayerState)).stagingArea;
+      state.layerState.stagingArea = deepClone(initialLayerState.stagingArea);
 
       state.futureLayerStates = [];
       state.shouldShowStagingOutline = true;
@@ -294,11 +287,7 @@ export const canvasSlice = createSlice({
     },
     discardStagedImage: (state) => {
       const { images, selectedImageIndex } = state.layerState.stagingArea;
-      state.pastLayerStates.push(cloneDeep(state.layerState));
-
-      if (state.pastLayerStates.length > MAX_HISTORY) {
-        state.pastLayerStates.shift();
-      }
+      pushToPrevLayerStates(state);
 
       if (!images.length) {
         return;
@@ -320,11 +309,7 @@ export const canvasSlice = createSlice({
     addFillRect: (state) => {
       const { boundingBoxCoordinates, boundingBoxDimensions, brushColor } = state;
 
-      state.pastLayerStates.push(cloneDeep(state.layerState));
-
-      if (state.pastLayerStates.length > MAX_HISTORY) {
-        state.pastLayerStates.shift();
-      }
+      pushToPrevLayerStates(state);
 
       state.layerState.objects.push({
         kind: 'fillRect',
@@ -339,11 +324,7 @@ export const canvasSlice = createSlice({
     addEraseRect: (state) => {
       const { boundingBoxCoordinates, boundingBoxDimensions } = state;
 
-      state.pastLayerStates.push(cloneDeep(state.layerState));
-
-      if (state.pastLayerStates.length > MAX_HISTORY) {
-        state.pastLayerStates.shift();
-      }
+      pushToPrevLayerStates(state);
 
       state.layerState.objects.push({
         kind: 'eraseRect',
@@ -367,11 +348,7 @@ export const canvasSlice = createSlice({
       // set & then spread this to only conditionally add the "color" key
       const newColor = layer === 'base' && tool === 'brush' ? { color: brushColor } : {};
 
-      state.pastLayerStates.push(cloneDeep(state.layerState));
-
-      if (state.pastLayerStates.length > MAX_HISTORY) {
-        state.pastLayerStates.shift();
-      }
+      pushToPrevLayerStates(state);
 
       const newLine: CanvasMaskLine | CanvasBaseLine = {
         kind: 'line',
@@ -409,11 +386,7 @@ export const canvasSlice = createSlice({
         return;
       }
 
-      state.futureLayerStates.unshift(cloneDeep(state.layerState));
-
-      if (state.futureLayerStates.length > MAX_HISTORY) {
-        state.futureLayerStates.pop();
-      }
+      pushToFutureLayerStates(state);
 
       state.layerState = targetState;
     },
@@ -424,11 +397,7 @@ export const canvasSlice = createSlice({
         return;
       }
 
-      state.pastLayerStates.push(cloneDeep(state.layerState));
-
-      if (state.pastLayerStates.length > MAX_HISTORY) {
-        state.pastLayerStates.shift();
-      }
+      pushToPrevLayerStates(state);
 
       state.layerState = targetState;
     },
@@ -445,8 +414,8 @@ export const canvasSlice = createSlice({
       state.shouldShowIntermediates = action.payload;
     },
     resetCanvas: (state) => {
-      state.pastLayerStates.push(cloneDeep(state.layerState));
-      state.layerState = cloneDeep(initialLayerState);
+      pushToPrevLayerStates(state);
+      state.layerState = deepClone(initialLayerState);
       state.futureLayerStates = [];
       state.batchIds = [];
       state.boundingBoxCoordinates = {
@@ -540,11 +509,7 @@ export const canvasSlice = createSlice({
 
       const { images, selectedImageIndex } = state.layerState.stagingArea;
 
-      state.pastLayerStates.push(cloneDeep(state.layerState));
-
-      if (state.pastLayerStates.length > MAX_HISTORY) {
-        state.pastLayerStates.shift();
-      }
+      pushToPrevLayerStates(state);
 
       const imageToCommit = images[selectedImageIndex];
 
@@ -553,7 +518,7 @@ export const canvasSlice = createSlice({
           ...imageToCommit,
         });
       }
-      state.layerState.stagingArea = cloneDeep(initialLayerState).stagingArea;
+      state.layerState.stagingArea = deepClone(initialLayerState.stagingArea);
 
       state.futureLayerStates = [];
       state.shouldShowStagingOutline = true;
@@ -623,7 +588,7 @@ export const canvasSlice = createSlice({
       };
     },
     setMergedCanvas: (state, action: PayloadAction<CanvasImage>) => {
-      state.pastLayerStates.push(cloneDeep(state.layerState));
+      pushToPrevLayerStates(state);
 
       state.futureLayerStates = [];
 
@@ -743,3 +708,17 @@ export const canvasPersistConfig: PersistConfig<CanvasState> = {
   migrate: migrateCanvasState,
   persistDenylist: [],
 };
+
+const pushToPrevLayerStates = (state: CanvasState) => {
+  state.pastLayerStates.push(deepClone(state.layerState));
+  if (state.pastLayerStates.length > MAX_HISTORY) {
+    state.pastLayerStates = state.pastLayerStates.slice(-MAX_HISTORY);
+  }
+};
+
+const pushToFutureLayerStates = (state: CanvasState) => {
+  state.futureLayerStates.unshift(deepClone(state.layerState));
+  if (state.futureLayerStates.length > MAX_HISTORY) {
+    state.futureLayerStates = state.futureLayerStates.slice(0, MAX_HISTORY);
+  }
+};

invokeai/frontend/web/src/features/controlAdapters/store/controlAdaptersSlice.ts

@@ -2,10 +2,11 @@ import type { PayloadAction, Update } from '@reduxjs/toolkit';
 import { createEntityAdapter, createSlice, isAnyOf } from '@reduxjs/toolkit';
 import { getSelectorsOptions } from 'app/store/createMemoizedSelector';
 import type { PersistConfig, RootState } from 'app/store/store';
+import { deepClone } from 'common/util/deepClone';
 import { buildControlAdapter } from 'features/controlAdapters/util/buildControlAdapter';
 import { buildControlAdapterProcessor } from 'features/controlAdapters/util/buildControlAdapterProcessor';
 import { zModelIdentifierField } from 'features/nodes/types/common';
-import { cloneDeep, merge, uniq } from 'lodash-es';
+import { merge, uniq } from 'lodash-es';
 import type { ControlNetModelConfig, IPAdapterModelConfig, T2IAdapterModelConfig } from 'services/api/types';
 import { socketInvocationError } from 'services/events/actions';
 import { v4 as uuidv4 } from 'uuid';
@@ -114,7 +115,7 @@ export const controlAdaptersSlice = createSlice({
         if (!controlAdapter) {
           return;
         }
-        const newControlAdapter = merge(cloneDeep(controlAdapter), {
+        const newControlAdapter = merge(deepClone(controlAdapter), {
           id: newId,
           isEnabled: true,
         });
@@ -270,7 +271,7 @@ export const controlAdaptersSlice = createSlice({
         return;
       }
 
-      const processorNode = merge(cloneDeep(cn.processorNode), params);
+      const processorNode = merge(deepClone(cn.processorNode), params);
 
       caAdapter.updateOne(state, {
         id,
@@ -293,7 +294,7 @@ export const controlAdaptersSlice = createSlice({
         return;
       }
 
-      const processorNode = cloneDeep(
+      const processorNode = deepClone(
         CONTROLNET_PROCESSORS[processorType].buildDefaults(cn.model?.base)
       ) as RequiredControlAdapterProcessorNode;
 
@@ -333,7 +334,7 @@ export const controlAdaptersSlice = createSlice({
       caAdapter.updateOne(state, update);
     },
     controlAdaptersReset: () => {
-      return cloneDeep(initialControlAdaptersState);
+      return deepClone(initialControlAdaptersState);
     },
     pendingControlImagesCleared: (state) => {
       state.pendingControlImages = [];
@@ -406,7 +407,7 @@ const migrateControlAdaptersState = (state: any): any => {
     state._version = 1;
   }
   if (state._version === 1) {
-    state = cloneDeep(initialControlAdaptersState);
+    state = deepClone(initialControlAdaptersState);
   }
   return state;
 };

invokeai/frontend/web/src/features/controlAdapters/util/buildControlAdapter.ts

@@ -1,3 +1,4 @@
+import { deepClone } from 'common/util/deepClone';
 import { CONTROLNET_PROCESSORS } from 'features/controlAdapters/store/constants';
 import type {
   ControlAdapterConfig,
@@ -7,7 +8,7 @@ import type {
   RequiredCannyImageProcessorInvocation,
   T2IAdapterConfig,
 } from 'features/controlAdapters/store/types';
-import { cloneDeep, merge } from 'lodash-es';
+import { merge } from 'lodash-es';
 
 export const initialControlNet: Omit<ControlNetConfig, 'id'> = {
   type: 'controlnet',
@@ -57,11 +58,11 @@ export const buildControlAdapter = (
 ): ControlAdapterConfig => {
   switch (type) {
     case 'controlnet':
-      return merge(cloneDeep(initialControlNet), { id, ...overrides });
+      return merge(deepClone(initialControlNet), { id, ...overrides });
     case 't2i_adapter':
-      return merge(cloneDeep(initialT2IAdapter), { id, ...overrides });
+      return merge(deepClone(initialT2IAdapter), { id, ...overrides });
     case 'ip_adapter':
-      return merge(cloneDeep(initialIPAdapter), { id, ...overrides });
+      return merge(deepClone(initialIPAdapter), { id, ...overrides });
     default:
       throw new Error(`Unknown control adapter type: ${type}`);
   }

invokeai/frontend/web/src/features/lora/store/loraSlice.ts

@@ -1,9 +1,9 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
+import { deepClone } from 'common/util/deepClone';
 import { zModelIdentifierField } from 'features/nodes/types/common';
 import type { ParameterLoRAModel } from 'features/parameters/types/parameterSchemas';
-import { cloneDeep } from 'lodash-es';
 import type { LoRAModelConfig } from 'services/api/types';
 
 export type LoRA = {
@@ -58,7 +58,7 @@ export const loraSlice = createSlice({
       }
       lora.isEnabled = isEnabled;
     },
-    lorasReset: () => cloneDeep(initialLoraState),
+    lorasReset: () => deepClone(initialLoraState),
   },
 });
 
@@ -74,7 +74,7 @@ const migrateLoRAState = (state: any): any => {
   }
   if (state._version === 1) {
     // Model type has changed, so we need to reset the state - too risky to migrate
-    state = cloneDeep(initialLoraState);
+    state = deepClone(initialLoraState);
   }
   return state;
 };

invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts

@@ -1,6 +1,7 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice, isAnyOf } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
+import { deepClone } from 'common/util/deepClone';
 import { workflowLoaded } from 'features/nodes/store/actions';
 import { SHARED_NODE_PROPERTIES } from 'features/nodes/types/constants';
 import type {
@@ -44,7 +45,7 @@ import {
 } from 'features/nodes/types/field';
 import type { AnyNode, InvocationTemplate, NodeExecutionState } from 'features/nodes/types/invocation';
 import { isInvocationNode, isNotesNode, zNodeStatus } from 'features/nodes/types/invocation';
-import { cloneDeep, forEach } from 'lodash-es';
+import { forEach } from 'lodash-es';
 import type {
   Connection,
   Edge,
@@ -571,8 +572,23 @@ export const nodesSlice = createSlice({
       );
     },
     selectionCopied: (state) => {
-      state.nodesToCopy = state.nodes.filter((n) => n.selected).map(cloneDeep);
-      state.edgesToCopy = state.edges.filter((e) => e.selected).map(cloneDeep);
+      const nodesToCopy: AnyNode[] = [];
+      const edgesToCopy: Edge[] = [];
+
+      for (const node of state.nodes) {
+        if (node.selected) {
+          nodesToCopy.push(deepClone(node));
+        }
+      }
+
+      for (const edge of state.edges) {
+        if (edge.selected) {
+          edgesToCopy.push(deepClone(edge));
+        }
+      }
+
+      state.nodesToCopy = nodesToCopy;
+      state.edgesToCopy = edgesToCopy;
 
       if (state.nodesToCopy.length > 0) {
         const averagePosition = { x: 0, y: 0 };
@@ -594,11 +610,21 @@ export const nodesSlice = createSlice({
     },
     selectionPasted: (state, action: PayloadAction<{ cursorPosition?: XYPosition }>) => {
       const { cursorPosition } = action.payload;
-      const newNodes = state.nodesToCopy.map(cloneDeep);
+      const newNodes: AnyNode[] = [];
+
+      for (const node of state.nodesToCopy) {
+        newNodes.push(deepClone(node));
+      }
+
       const oldNodeIds = newNodes.map((n) => n.data.id);
-      const newEdges = state.edgesToCopy
-        .filter((e) => oldNodeIds.includes(e.source) && oldNodeIds.includes(e.target))
-        .map(cloneDeep);
+
+      const newEdges: Edge[] = [];
+
+      for (const edge of state.edgesToCopy) {
+        if (oldNodeIds.includes(edge.source) && oldNodeIds.includes(edge.target)) {
+          newEdges.push(deepClone(edge));
+        }
+      }
 
       newEdges.forEach((e) => (e.selected = true));
 

invokeai/frontend/web/src/features/nodes/store/workflowSlice.ts

@@ -1,6 +1,7 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
+import { deepClone } from 'common/util/deepClone';
 import { workflowLoaded } from 'features/nodes/store/actions';
 import { isAnyNodeOrEdgeMutation, nodeEditorReset, nodesChanged, nodesDeleted } from 'features/nodes/store/nodesSlice';
 import type {
@@ -11,7 +12,7 @@ import type {
 import type { FieldIdentifier } from 'features/nodes/types/field';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 import type { WorkflowCategory, WorkflowV3 } from 'features/nodes/types/workflow';
-import { cloneDeep, isEqual, omit, uniqBy } from 'lodash-es';
+import { isEqual, omit, uniqBy } from 'lodash-es';
 
 const blankWorkflow: Omit<WorkflowV3, 'nodes' | 'edges'> = {
   name: '',
@@ -131,8 +132,8 @@ export const workflowSlice = createSlice({
       });
 
       return {
-        ...cloneDeep(initialWorkflowState),
-        ...cloneDeep(workflowExtra),
+        ...deepClone(initialWorkflowState),
+        ...deepClone(workflowExtra),
         originalExposedFieldValues,
         mode: state.mode,
       };
@@ -144,7 +145,7 @@ export const workflowSlice = createSlice({
       });
     });
 
-    builder.addCase(nodeEditorReset, () => cloneDeep(initialWorkflowState));
+    builder.addCase(nodeEditorReset, () => deepClone(initialWorkflowState));
 
     builder.addCase(nodesChanged, (state, action) => {
       // Not all changes to nodes should result in the workflow being marked touched

invokeai/frontend/web/src/features/nodes/util/node/nodeUpdate.ts

@@ -1,8 +1,9 @@
+import { deepClone } from 'common/util/deepClone';
 import { satisfies } from 'compare-versions';
 import { NodeUpdateError } from 'features/nodes/types/error';
 import type { InvocationNode, InvocationTemplate } from 'features/nodes/types/invocation';
 import { zParsedSemver } from 'features/nodes/types/semver';
-import { cloneDeep, defaultsDeep, keys, pick } from 'lodash-es';
+import { defaultsDeep, keys, pick } from 'lodash-es';
 
 import { buildInvocationNode } from './buildInvocationNode';
 
@@ -50,7 +51,7 @@ export const updateNode = (node: InvocationNode, template: InvocationTemplate):
   // The updateability of a node, via semver comparison, relies on the this kind of recursive merge
   // being valid. We rely on the template's major version to be majorly incremented if this kind of
   // merge would result in an invalid node.
-  const clone = cloneDeep(node);
+  const clone = deepClone(node);
   clone.data.version = template.version;
   defaultsDeep(clone, defaults); // mutates!
 

invokeai/frontend/web/src/features/nodes/util/workflow/buildWorkflow.ts

@@ -1,11 +1,12 @@
 import { logger } from 'app/logging/logger';
+import { deepClone } from 'common/util/deepClone';
 import { parseify } from 'common/util/serialize';
 import type { NodesState, WorkflowsState } from 'features/nodes/store/types';
 import { isInvocationNode, isNotesNode } from 'features/nodes/types/invocation';
 import type { WorkflowV3 } from 'features/nodes/types/workflow';
 import { zWorkflowV3 } from 'features/nodes/types/workflow';
 import i18n from 'i18n';
-import { cloneDeep, pick } from 'lodash-es';
+import { pick } from 'lodash-es';
 import { fromZodError } from 'zod-validation-error';
 
 export type BuildWorkflowArg = {
@@ -30,7 +31,7 @@ const workflowKeys = [
 type BuildWorkflowFunction = (arg: BuildWorkflowArg) => WorkflowV3;
 
 export const buildWorkflowFast: BuildWorkflowFunction = ({ nodes, edges, workflow }: BuildWorkflowArg): WorkflowV3 => {
-  const clonedWorkflow = pick(cloneDeep(workflow), workflowKeys);
+  const clonedWorkflow = pick(deepClone(workflow), workflowKeys);
 
   const newWorkflow: WorkflowV3 = {
     ...clonedWorkflow,
@@ -43,14 +44,14 @@ export const buildWorkflowFast: BuildWorkflowFunction = ({ nodes, edges, workflo
       newWorkflow.nodes.push({
         id: node.id,
         type: node.type,
-        data: cloneDeep(node.data),
+        data: deepClone(node.data),
         position: { ...node.position },
       });
     } else if (isNotesNode(node) && node.type) {
       newWorkflow.nodes.push({
         id: node.id,
         type: node.type,
-        data: cloneDeep(node.data),
+        data: deepClone(node.data),
         position: { ...node.position },
       });
     }

invokeai/frontend/web/src/features/nodes/util/workflow/migrations.ts

@@ -1,4 +1,5 @@
 import { $store } from 'app/store/nanostores/store';
+import { deepClone } from 'common/util/deepClone';
 import { WorkflowMigrationError, WorkflowVersionError } from 'features/nodes/types/error';
 import type { FieldType } from 'features/nodes/types/field';
 import type { InvocationNodeData } from 'features/nodes/types/invocation';
@@ -11,7 +12,7 @@ import { zWorkflowV2 } from 'features/nodes/types/v2/workflow';
 import type { WorkflowV3 } from 'features/nodes/types/workflow';
 import { zWorkflowV3 } from 'features/nodes/types/workflow';
 import { t } from 'i18next';
-import { cloneDeep, forEach } from 'lodash-es';
+import { forEach } from 'lodash-es';
 import { z } from 'zod';
 
 /**
@@ -89,7 +90,7 @@ export const parseAndMigrateWorkflow = (data: unknown): WorkflowV3 => {
     throw new WorkflowVersionError(t('nodes.unableToGetWorkflowVersion'));
   }
 
-  let workflow = cloneDeep(data) as WorkflowV1 | WorkflowV2 | WorkflowV3;
+  let workflow = deepClone(data) as WorkflowV1 | WorkflowV2 | WorkflowV3;
 
   if (workflow.meta.version === '1.0.0') {
     const v1 = zWorkflowV1.parse(workflow);

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "4.0.0rc6"
+__version__ = "4.0.1"

pyproject.toml

@@ -44,7 +44,6 @@ dependencies = [
   "onnx==1.15.0",
   "onnxruntime==1.16.3",
   "opencv-python==4.9.0.80",
-  "peft==0.9.0",
   "pytorch-lightning==2.1.3",
   "safetensors==0.4.2",
   "timm==0.6.13",               # needed to override timm latest in controlnet_aux, see  https://github.com/isl-org/ZoeDepth/issues/26
@@ -74,7 +73,7 @@ dependencies = [
   "easing-functions",
   "einops",
   "facexlib",
-  "matplotlib",                            # needed for plotting of Penner easing functions
+  "matplotlib",       # needed for plotting of Penner easing functions
   "npyscreen",
   "omegaconf",
   "picklescan",

tests/app/services/model_install/test_model_install.py

@@ -43,8 +43,7 @@ def test_registration_meta(mm2_installer: ModelInstallServiceBase, embedding_fil
     assert model_record is not None
     assert model_record.name == "test_embedding"
     assert model_record.type == ModelType.TextualInversion
-    assert model_record.path.endswith(embedding_file.as_posix())
-    assert Path(model_record.path).is_absolute()
+    assert Path(model_record.path) == embedding_file
     assert Path(model_record.path).exists()
     assert model_record.base == BaseModelType("sd-1")
     assert model_record.description is not None
@@ -77,8 +76,7 @@ def test_install(
     key = mm2_installer.install_path(embedding_file)
     model_record = store.get_model(key)
     assert model_record.path.endswith("sd-1/embedding/test_embedding.safetensors")
-    assert Path(model_record.path).is_absolute()
-    assert Path(model_record.path).exists()
+    assert (mm2_app_config.models_path / model_record.path).exists()
     assert model_record.source == embedding_file.as_posix()
 
 
@@ -147,10 +145,7 @@ def test_background_install(
     model_record = mm2_installer.record_store.get_model(key)
     assert model_record is not None
     assert model_record.path.endswith(destination)
-    assert Path(model_record.path).is_absolute()
-    assert Path(model_record.path).exists()
-    assert model_record.key != "<NOKEY>"
-    assert Path(model_record.path).exists()
+    assert (mm2_app_config.models_path / model_record.path).exists()
 
     # see if metadata was properly passed through
     assert model_record.description == description
@@ -172,7 +167,7 @@ def test_not_inplace_install(
     assert job is not None
     assert job.config_out is not None
     assert Path(job.config_out.path) != embedding_file
-    assert Path(job.config_out.path).exists()
+    assert (mm2_app_config.models_path / job.config_out.path).exists()
 
 
 def test_inplace_install(
@@ -184,16 +179,21 @@ def test_inplace_install(
     assert job is not None
     assert job.config_out is not None
     assert Path(job.config_out.path) == embedding_file
+    assert Path(job.config_out.path).exists()
 
 
-def test_delete_install(mm2_installer: ModelInstallServiceBase, embedding_file: Path) -> None:
+def test_delete_install(
+    mm2_installer: ModelInstallServiceBase, embedding_file: Path, mm2_app_config: InvokeAIAppConfig
+) -> None:
     store = mm2_installer.record_store
     key = mm2_installer.install_path(embedding_file)
     model_record = store.get_model(key)
-    assert Path(model_record.path).exists()
+    assert (mm2_app_config.models_path / model_record.path).exists()
     assert embedding_file.exists()  # original should still be there after installation
     mm2_installer.delete(key)
-    assert not Path(model_record.path).exists()  # after deletion, installed copy should not exist
+    assert not (
+        mm2_app_config.models_path / model_record.path
+    ).exists()  # after deletion, installed copy should not exist
     assert embedding_file.exists()  # but original should still be there
     with pytest.raises(UnknownModelException):
         store.get_model(key)
@@ -232,7 +232,7 @@ def test_simple_download(mm2_installer: ModelInstallServiceBase, mm2_app_config:
 
     key = job.config_out.key
     model_record = store.get_model(key)
-    assert Path(model_record.path).exists()
+    assert (mm2_app_config.models_path / model_record.path).exists()
 
     assert len(bus.events) == 4
     event_names = [x.event_name for x in bus.events]
@@ -261,7 +261,7 @@ def test_huggingface_download(mm2_installer: ModelInstallServiceBase, mm2_app_co
 
     key = job.config_out.key
     model_record = store.get_model(key)
-    assert Path(model_record.path).exists()
+    assert (mm2_app_config.models_path / model_record.path).exists()
     assert model_record.type == ModelType.Main
     assert model_record.format == ModelFormat.Diffusers
 

tests/backend/model_manager/test_lora.py

@@ -5,7 +5,7 @@
 import pytest
 import torch
 
-from invokeai.backend.lora_model_raw import LoRALayer, LoRAModelRaw
+from invokeai.backend.lora import LoRALayer, LoRAModelRaw
 from invokeai.backend.model_patcher import ModelPatcher
 
 

tests/test_config.py

@@ -98,6 +98,32 @@ def test_migrate_v3_config_from_file(tmp_path: Path, patch_rootdir: None):
     assert not hasattr(config, "esrgan")
 
 
+@pytest.mark.parametrize(
+    "legacy_conf_dir,expected_value,expected_is_set",
+    [
+        # not set, expected value is the default value
+        ("configs/stable-diffusion", Path("configs"), False),
+        # not set, expected value is the default value
+        ("configs\\stable-diffusion", Path("configs"), False),
+        # set, best-effort resolution of the path
+        ("partial_custom_path/stable-diffusion", Path("partial_custom_path"), True),
+        # set, exact path
+        ("full/custom/path", Path("full/custom/path"), True),
+    ],
+)
+def test_migrate_v3_legacy_conf_dir_defaults(
+    tmp_path: Path, patch_rootdir: None, legacy_conf_dir: str, expected_value: Path, expected_is_set: bool
+):
+    """Test reading configuration from a file."""
+    config_content = f"InvokeAI:\n    Paths:\n        legacy_conf_dir: {legacy_conf_dir}"
+    temp_config_file = tmp_path / "temp_invokeai.yaml"
+    temp_config_file.write_text(config_content)
+
+    config = load_and_migrate_config(temp_config_file)
+    assert config.legacy_conf_dir == expected_value
+    assert ("legacy_conf_dir" in config.model_fields_set) is expected_is_set
+
+
 def test_migrate_v3_backup(tmp_path: Path, patch_rootdir: None):
     """Test the backup of the config file."""
     temp_config_file = tmp_path / "temp_invokeai.yaml"

tests/test_sqlite_migrator.py

@@ -250,6 +250,32 @@ def test_migrator_runs_all_migrations_file(logger: Logger) -> None:
         db.conn.close()
 
 
+def test_migrator_backs_up_db(logger: Logger) -> None:
+    with TemporaryDirectory() as tempdir:
+        original_db_path = Path(tempdir) / "invokeai.db"
+        db = SqliteDatabase(db_path=original_db_path, logger=logger, verbose=False)
+        # Write some data to the db to test for successful backup
+        temp_cursor = db.conn.cursor()
+        temp_cursor.execute("CREATE TABLE test (id INTEGER PRIMARY KEY);")
+        db.conn.commit()
+        # Set up the migrator
+        migrator = SqliteMigrator(db=db)
+        migrations = [Migration(from_version=i, to_version=i + 1, callback=create_migrate(i)) for i in range(0, 3)]
+        for migration in migrations:
+            migrator.register_migration(migration)
+        migrator.run_migrations()
+        # Must manually close else we get an error on Windows
+        db.conn.close()
+        assert original_db_path.exists()
+        # We should have a backup file when we migrated a file db
+        assert migrator._backup_path
+        # Check that the test table exists as a proxy for successful backup
+        with closing(sqlite3.connect(migrator._backup_path)) as backup_db_conn:
+            backup_db_cursor = backup_db_conn.cursor()
+            backup_db_cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='test';")
+            assert backup_db_cursor.fetchone() is not None
+
+
 def test_migrator_makes_no_changes_on_failed_migration(
     migrator: SqliteMigrator, migration_no_op: Migration, failing_migrate_callback: MigrateCallback
 ) -> None: