ruff fixes and restore default map location of object serializer load

- temporarily disable vram cache

docs/contributing/MODEL_MANAGER.md

@@ -1328,7 +1328,7 @@ from invokeai.app.services.model_load import ModelLoadService, ModelLoaderRegist
 
 config = InvokeAIAppConfig.get_config()
 ram_cache = ModelCache(
- max_cache_size=config.ram_cache_size, max_vram_cache_size=config.vram_cache_size, logger=logger
+ max_cache_size=config.ram_cache_size, logger=logger
 )
 convert_cache = ModelConvertCache(
  cache_path=config.models_convert_cache_path, max_size=config.convert_cache_size

invokeai/app/invocations/compel.py

@@ -103,6 +103,7 @@ class CompelInvocation(BaseInvocation):
                 textual_inversion_manager=ti_manager,
                 dtype_for_device_getter=TorchDevice.choose_torch_dtype,
                 truncate_long_prompts=False,
+                device=TorchDevice.choose_torch_device(),
             )
 
             conjunction = Compel.parse_prompt_string(self.prompt)
@@ -117,6 +118,7 @@ class CompelInvocation(BaseInvocation):
         conditioning_data = ConditioningFieldData(conditionings=[BasicConditioningInfo(embeds=c)])
 
         conditioning_name = context.conditioning.save(conditioning_data)
+
         return ConditioningOutput(
             conditioning=ConditioningField(
                 conditioning_name=conditioning_name,
@@ -203,6 +205,7 @@ class SDXLPromptInvocationBase:
                 truncate_long_prompts=False,  # TODO:
                 returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,  # TODO: clip skip
                 requires_pooled=get_pooled,
+                device=TorchDevice.choose_torch_device(),
             )
 
             conjunction = Compel.parse_prompt_string(prompt)
@@ -313,7 +316,6 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
                 )
             ]
         )
-
         conditioning_name = context.conditioning.save(conditioning_data)
 
         return ConditioningOutput(

invokeai/app/invocations/denoise_latents.py

@@ -1,4 +1,5 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
+import copy
 import inspect
 from contextlib import ExitStack
 from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
@@ -193,9 +194,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
         text_embeddings: Union[list[BasicConditioningInfo], list[SDXLConditioningInfo]] = []
         text_embeddings_masks: list[Optional[torch.Tensor]] = []
         for cond in cond_list:
-            cond_data = context.conditioning.load(cond.conditioning_name)
+            cond_data = copy.deepcopy(context.conditioning.load(cond.conditioning_name))
             text_embeddings.append(cond_data.conditionings[0].to(device=device, dtype=dtype))
-
             mask = cond.mask
             if mask is not None:
                 mask = context.tensors.load(mask.tensor_name)
@@ -226,6 +226,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         # Add a batch dimension to the mask, because torchvision expects shape (batch, channels, h, w).
         mask = mask.unsqueeze(0)  # Shape: (1, h, w) -> (1, 1, h, w)
         resized_mask = tf(mask)
+        assert isinstance(resized_mask, torch.Tensor)
         return resized_mask
 
     def _concat_regional_text_embeddings(

invokeai/app/services/config/config_default.py

@@ -26,13 +26,13 @@ LEGACY_INIT_FILE = Path("invokeai.init")
 DEFAULT_RAM_CACHE = 10.0
 DEFAULT_VRAM_CACHE = 0.25
 DEFAULT_CONVERT_CACHE = 20.0
-DEVICE = Literal["auto", "cpu", "cuda", "cuda:1", "mps"]
-PRECISION = Literal["auto", "float16", "bfloat16", "float32"]
+DEVICE = Literal["auto", "cpu", "cuda:0", "cuda:1", "cuda:2", "cuda:3", "cuda:4", "cuda:5", "cuda:6", "cuda:7", "mps"]
+PRECISION = Literal["auto", "float16", "bfloat16", "float32", "autocast"]
 ATTENTION_TYPE = Literal["auto", "normal", "xformers", "sliced", "torch-sdp"]
 ATTENTION_SLICE_SIZE = Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8]
 LOG_FORMAT = Literal["plain", "color", "syslog", "legacy"]
 LOG_LEVEL = Literal["debug", "info", "warning", "error", "critical"]
-CONFIG_SCHEMA_VERSION = "4.0.1"
+CONFIG_SCHEMA_VERSION = "4.0.2"
 
 
 def get_default_ram_cache_size() -> float:
@@ -105,14 +105,16 @@ class InvokeAIAppConfig(BaseSettings):
         convert_cache: Maximum size of on-disk converted models cache (GB).
         lazy_offload: Keep models in VRAM until their space is needed.
         log_memory_usage: If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.
-        device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
-        precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`
+        device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda:0`, `cuda:1`, `cuda:2`, `cuda:3`, `cuda:4`, `cuda:5`, `cuda:6`, `cuda:7`, `mps`
+        devices: List of execution devices; will override default device selected.
+        precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`, `autocast`
         sequential_guidance: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
         attention_type: Attention type.<br>Valid values: `auto`, `normal`, `xformers`, `sliced`, `torch-sdp`
         attention_slice_size: Slice size, valid when attention_type=="sliced".<br>Valid values: `auto`, `balanced`, `max`, `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`
         force_tiled_decode: Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).
         pil_compress_level: The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.
         max_queue_size: Maximum number of items in the session queue.
+        max_threads: Maximum number of session queue execution threads. Autocalculated from number of GPUs if not set.
         clear_queue_on_startup: Empties session queue on startup.
         allow_nodes: List of nodes to allow. Omit to allow all.
         deny_nodes: List of nodes to deny. Omit to deny none.
@@ -178,6 +180,7 @@ class InvokeAIAppConfig(BaseSettings):
 
     # DEVICE
     device:                      DEVICE = Field(default="auto",             description="Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.")
+    devices:      Optional[list[DEVICE]] = Field(default=None,              description="List of execution devices; will override default device selected.")
     precision:                PRECISION = Field(default="auto",             description="Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.")
 
     # GENERATION
@@ -187,6 +190,7 @@ class InvokeAIAppConfig(BaseSettings):
     force_tiled_decode:            bool = Field(default=False,              description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).")
     pil_compress_level:             int = Field(default=1,                  description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.")
     max_queue_size:                 int = Field(default=10000, gt=0,        description="Maximum number of items in the session queue.")
+    max_threads:          Optional[int] = Field(default=None,               description="Maximum number of session queue execution threads. Autocalculated from number of GPUs if not set.")
     clear_queue_on_startup:        bool = Field(default=False,              description="Empties session queue on startup.")
 
     # NODES
@@ -376,9 +380,6 @@ def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
             # `max_cache_size` was renamed to `ram` some time in v3, but both names were used
             if k == "max_cache_size" and "ram" not in category_dict:
                 parsed_config_dict["ram"] = v
-            # `max_vram_cache_size` was renamed to `vram` some time in v3, but both names were used
-            if k == "max_vram_cache_size" and "vram" not in category_dict:
-                parsed_config_dict["vram"] = v
             # autocast was removed in v4.0.1
             if k == "precision" and v == "autocast":
                 parsed_config_dict["precision"] = "auto"
@@ -426,6 +427,27 @@ def migrate_v4_0_0_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig
     return config
 
 
+def migrate_v4_0_1_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
+    """Migrate v4.0.1 config dictionary to a current config object.
+
+    A few new multi-GPU options were added in 4.0.2, and this simply
+    updates the schema label.
+
+    Args:
+        config_dict: A dictionary of settings from a v4.0.1 config file.
+
+    Returns:
+        An instance of `InvokeAIAppConfig` with the migrated settings.
+    """
+    parsed_config_dict: dict[str, Any] = {}
+    for k, _ in config_dict.items():
+        if k == "schema_version":
+            parsed_config_dict[k] = CONFIG_SCHEMA_VERSION
+    config = DefaultInvokeAIAppConfig.model_validate(parsed_config_dict)
+    return config
+
+
+# TO DO: replace this with a formal registration and migration system
 def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
     """Load and migrate a config file to the latest version.
 
@@ -457,6 +479,10 @@ def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
         loaded_config_dict = migrate_v4_0_0_config_dict(loaded_config_dict)
         loaded_config_dict.write_file(config_path)
 
+    elif loaded_config_dict["schema_version"] == "4.0.1":
+        loaded_config_dict = migrate_v4_0_1_config_dict(loaded_config_dict)
+        loaded_config_dict.write_file(config_path)
+
     # Attempt to load as a v4 config file
     try:
         # Meta is not included in the model fields, so we need to validate it separately

invokeai/app/services/invocation_services.py

@@ -53,11 +53,11 @@ class InvocationServices:
         model_images: "ModelImageFileStorageBase",
         model_manager: "ModelManagerServiceBase",
         download_queue: "DownloadQueueServiceBase",
-        performance_statistics: "InvocationStatsServiceBase",
         session_queue: "SessionQueueBase",
         session_processor: "SessionProcessorBase",
         invocation_cache: "InvocationCacheBase",
         names: "NameServiceBase",
+        performance_statistics: "InvocationStatsServiceBase",
         urls: "UrlServiceBase",
         workflow_records: "WorkflowRecordsStorageBase",
         tensors: "ObjectSerializerBase[torch.Tensor]",
@@ -77,11 +77,11 @@ class InvocationServices:
         self.model_images = model_images
         self.model_manager = model_manager
         self.download_queue = download_queue
-        self.performance_statistics = performance_statistics
         self.session_queue = session_queue
         self.session_processor = session_processor
         self.invocation_cache = invocation_cache
         self.names = names
+        self.performance_statistics = performance_statistics
         self.urls = urls
         self.workflow_records = workflow_records
         self.tensors = tensors

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -74,9 +74,9 @@ class InvocationStatsService(InvocationStatsServiceBase):
             )
             self._stats[graph_execution_state_id].add_node_execution_stats(node_stats)
 
-    def reset_stats(self):
-        self._stats = {}
-        self._cache_stats = {}
+    def reset_stats(self, graph_execution_state_id: str):
+        self._stats.pop(graph_execution_state_id)
+        self._cache_stats.pop(graph_execution_state_id)
 
     def get_stats(self, graph_execution_state_id: str) -> InvocationStatsSummary:
         graph_stats_summary = self._get_graph_summary(graph_execution_state_id)

invokeai/app/services/model_install/model_install_default.py

@@ -284,9 +284,14 @@ class ModelInstallService(ModelInstallServiceBase):
         unfinished_jobs = [x for x in self._install_jobs if not x.in_terminal_state]
         self._install_jobs = unfinished_jobs
 
-    def _migrate_yaml(self) -> None:
+    def _migrate_yaml(self, rename_yaml: Optional[bool] = True, overwrite_db: Optional[bool] = False) -> None:
         db_models = self.record_store.all_models()
 
+        if overwrite_db:
+            for model in db_models:
+                self.record_store.del_model(model.key)
+            db_models = self.record_store.all_models()
+
         legacy_models_yaml_path = (
             self._app_config.legacy_models_yaml_path or self._app_config.root_path / "configs" / "models.yaml"
         )
@@ -336,7 +341,8 @@ class ModelInstallService(ModelInstallServiceBase):
                         self._logger.warning(f"Model at {model_path} could not be migrated: {e}")
 
             # Rename `models.yaml` to `models.yaml.bak` to prevent re-migration
-            legacy_models_yaml_path.rename(legacy_models_yaml_path.with_suffix(".yaml.bak"))
+            if rename_yaml:
+                legacy_models_yaml_path.rename(legacy_models_yaml_path.with_suffix(".yaml.bak"))
 
         # Unset the path - we are done with it either way
         self._app_config.legacy_models_yaml_path = None

invokeai/app/services/model_load/model_load_base.py

@@ -33,6 +33,11 @@ class ModelLoadServiceBase(ABC):
     def convert_cache(self) -> ModelConvertCacheBase:
         """Return the checkpoint convert cache used by this loader."""
 
+    @property
+    @abstractmethod
+    def gpu_count(self) -> int:
+        """Return the number of GPUs we are configured to use."""
+
     @abstractmethod
     def load_model_from_path(
         self, model_path: Path, loader: Optional[Callable[[Path], AnyModel]] = None

invokeai/app/services/model_load/model_load_default.py

@@ -46,6 +46,7 @@ class ModelLoadService(ModelLoadServiceBase):
         self._registry = registry
 
     def start(self, invoker: Invoker) -> None:
+        """Start the service."""
         self._invoker = invoker
 
     @property
@@ -53,6 +54,11 @@ class ModelLoadService(ModelLoadServiceBase):
         """Return the RAM cache used by this loader."""
         return self._ram_cache
 
+    @property
+    def gpu_count(self) -> int:
+        """Return the number of GPUs available for our uses."""
+        return len(self._ram_cache.execution_devices)
+
     @property
     def convert_cache(self) -> ModelConvertCacheBase:
         """Return the checkpoint convert cache used by this loader."""

invokeai/app/services/model_manager/model_manager_base.py

@@ -1,6 +1,7 @@
 # Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
 
 from abc import ABC, abstractmethod
+from typing import Optional, Set
 
 import torch
 from typing_extensions import Self
@@ -31,7 +32,7 @@ class ModelManagerServiceBase(ABC):
         model_record_service: ModelRecordServiceBase,
         download_queue: DownloadQueueServiceBase,
         events: EventServiceBase,
-        execution_device: torch.device,
+        execution_devices: Optional[Set[torch.device]] = None,
     ) -> Self:
         """
         Construct the model manager service instance.

invokeai/app/services/model_manager/model_manager_default.py

@@ -1,14 +1,10 @@
 # Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
 """Implementation of ModelManagerServiceBase."""
 
-from typing import Optional
-
-import torch
 from typing_extensions import Self
 
 from invokeai.app.services.invoker import Invoker
 from invokeai.backend.model_manager.load import ModelCache, ModelConvertCache, ModelLoaderRegistry
-from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
 from ..config import InvokeAIAppConfig
@@ -69,7 +65,6 @@ class ModelManagerService(ModelManagerServiceBase):
         model_record_service: ModelRecordServiceBase,
         download_queue: DownloadQueueServiceBase,
         events: EventServiceBase,
-        execution_device: Optional[torch.device] = None,
     ) -> Self:
         """
         Construct the model manager service instance.
@@ -82,9 +77,7 @@ class ModelManagerService(ModelManagerServiceBase):
         ram_cache = ModelCache(
             max_cache_size=app_config.ram,
             max_vram_cache_size=app_config.vram,
-            lazy_offloading=app_config.lazy_offload,
             logger=logger,
-            execution_device=execution_device or TorchDevice.choose_torch_device(),
         )
         convert_cache = ModelConvertCache(cache_path=app_config.convert_cache_path, max_size=app_config.convert_cache)
         loader = ModelLoadService(

invokeai/app/services/object_serializer/object_serializer_disk.py

@@ -1,5 +1,6 @@
 import shutil
 import tempfile
+import threading
 import typing
 from pathlib import Path
 from typing import TYPE_CHECKING, Optional, TypeVar
@@ -9,6 +10,7 @@ import torch
 from invokeai.app.services.object_serializer.object_serializer_base import ObjectSerializerBase
 from invokeai.app.services.object_serializer.object_serializer_common import ObjectNotFoundError
 from invokeai.app.util.misc import uuid_string
+from invokeai.backend.util.devices import TorchDevice
 
 if TYPE_CHECKING:
     from invokeai.app.services.invoker import Invoker
@@ -70,7 +72,10 @@ class ObjectSerializerDisk(ObjectSerializerBase[T]):
         return self._output_dir / name
 
     def _new_name(self) -> str:
-        return f"{self._obj_class_name}_{uuid_string()}"
+        tid = threading.current_thread().ident
+        # Add tid to the object name because uuid4 not thread-safe on windows
+        # See https://stackoverflow.com/questions/2759644/python-multiprocessing-doesnt-play-nicely-with-uuid-uuid4
+        return f"{self._obj_class_name}_{tid}-{uuid_string()}"
 
     def _tempdir_cleanup(self) -> None:
         """Calls `cleanup` on the temporary directory, if it exists."""

invokeai/app/services/session_processor/session_processor_default.py

@@ -1,8 +1,9 @@
 import traceback
 from contextlib import suppress
-from threading import BoundedSemaphore, Thread
+from queue import Queue
+from threading import BoundedSemaphore, Lock, Thread
 from threading import Event as ThreadEvent
-from typing import Optional
+from typing import Optional, Set
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput
 from invokeai.app.services.events.events_common import (
@@ -26,6 +27,7 @@ from invokeai.app.services.session_queue.session_queue_common import SessionQueu
 from invokeai.app.services.shared.graph import NodeInputError
 from invokeai.app.services.shared.invocation_context import InvocationContextData, build_invocation_context
 from invokeai.app.util.profiler import Profiler
+from invokeai.backend.util.devices import TorchDevice
 
 from ..invoker import Invoker
 from .session_processor_base import InvocationServices, SessionProcessorBase, SessionRunnerBase
@@ -57,8 +59,11 @@ class DefaultSessionRunner(SessionRunnerBase):
         self._on_after_run_node_callbacks = on_after_run_node_callbacks or []
         self._on_node_error_callbacks = on_node_error_callbacks or []
         self._on_after_run_session_callbacks = on_after_run_session_callbacks or []
+        self._process_lock = Lock()
 
-    def start(self, services: InvocationServices, cancel_event: ThreadEvent, profiler: Optional[Profiler] = None):
+    def start(
+        self, services: InvocationServices, cancel_event: ThreadEvent, profiler: Optional[Profiler] = None
+    ) -> None:
         self._services = services
         self._cancel_event = cancel_event
         self._profiler = profiler
@@ -76,7 +81,8 @@ class DefaultSessionRunner(SessionRunnerBase):
         # Loop over invocations until the session is complete or canceled
         while True:
             try:
-                invocation = queue_item.session.next()
+                with self._process_lock:
+                    invocation = queue_item.session.next()
             # Anything other than a `NodeInputError` is handled as a processor error
             except NodeInputError as e:
                 error_type = e.__class__.__name__
@@ -108,7 +114,7 @@ class DefaultSessionRunner(SessionRunnerBase):
 
         self._on_after_run_session(queue_item=queue_item)
 
-    def run_node(self, invocation: BaseInvocation, queue_item: SessionQueueItem):
+    def run_node(self, invocation: BaseInvocation, queue_item: SessionQueueItem) -> None:
         try:
             # Any unhandled exception in this scope is an invocation error & will fail the graph
             with self._services.performance_statistics.collect_stats(invocation, queue_item.session_id):
@@ -210,7 +216,7 @@ class DefaultSessionRunner(SessionRunnerBase):
             # we don't care about that - suppress the error.
             with suppress(GESStatsNotFoundError):
                 self._services.performance_statistics.log_stats(queue_item.session.id)
-                self._services.performance_statistics.reset_stats()
+                self._services.performance_statistics.reset_stats(queue_item.session.id)
 
             for callback in self._on_after_run_session_callbacks:
                 callback(queue_item=queue_item)
@@ -324,7 +330,7 @@ class DefaultSessionProcessor(SessionProcessorBase):
 
     def start(self, invoker: Invoker) -> None:
         self._invoker: Invoker = invoker
-        self._queue_item: Optional[SessionQueueItem] = None
+        self._active_queue_items: Set[SessionQueueItem] = set()
         self._invocation: Optional[BaseInvocation] = None
 
         self._resume_event = ThreadEvent()
@@ -350,7 +356,14 @@ class DefaultSessionProcessor(SessionProcessorBase):
             else None
         )
 
+        self._worker_thread_count = self._invoker.services.configuration.max_threads or len(
+            TorchDevice.execution_devices()
+        )
+
+        self._session_worker_queue: Queue[SessionQueueItem] = Queue()
+
         self.session_runner.start(services=invoker.services, cancel_event=self._cancel_event, profiler=self._profiler)
+        # Session processor - singlethreaded
         self._thread = Thread(
             name="session_processor",
             target=self._process,
@@ -363,6 +376,16 @@ class DefaultSessionProcessor(SessionProcessorBase):
         )
         self._thread.start()
 
+        # Session processor workers - multithreaded
+        self._invoker.services.logger.debug(f"Starting {self._worker_thread_count} session processing threads.")
+        for _i in range(0, self._worker_thread_count):
+            worker = Thread(
+                name="session_worker",
+                target=self._process_next_session,
+                daemon=True,
+            )
+            worker.start()
+
     def stop(self, *args, **kwargs) -> None:
         self._stop_event.set()
 
@@ -370,7 +393,7 @@ class DefaultSessionProcessor(SessionProcessorBase):
         self._poll_now_event.set()
 
     async def _on_queue_cleared(self, event: FastAPIEvent[QueueClearedEvent]) -> None:
-        if self._queue_item and self._queue_item.queue_id == event[1].queue_id:
+        if any(item.queue_id == event[1].queue_id for item in self._active_queue_items):
             self._cancel_event.set()
             self._poll_now()
 
@@ -378,7 +401,7 @@ class DefaultSessionProcessor(SessionProcessorBase):
         self._poll_now()
 
     async def _on_queue_item_status_changed(self, event: FastAPIEvent[QueueItemStatusChangedEvent]) -> None:
-        if self._queue_item and event[1].status in ["completed", "failed", "canceled"]:
+        if self._active_queue_items and event[1].status in ["completed", "failed", "canceled"]:
             # When the queue item is canceled via HTTP, the queue item status is set to `"canceled"` and this event is
             # emitted. We need to respond to this event and stop graph execution. This is done by setting the cancel
             # event, which the session runner checks between invocations. If set, the session runner loop is broken.
@@ -403,7 +426,7 @@ class DefaultSessionProcessor(SessionProcessorBase):
     def get_status(self) -> SessionProcessorStatus:
         return SessionProcessorStatus(
             is_started=self._resume_event.is_set(),
-            is_processing=self._queue_item is not None,
+            is_processing=len(self._active_queue_items) > 0,
         )
 
     def _process(
@@ -428,30 +451,22 @@ class DefaultSessionProcessor(SessionProcessorBase):
                     resume_event.wait()
 
                     # Get the next session to process
-                    self._queue_item = self._invoker.services.session_queue.dequeue()
+                    queue_item = self._invoker.services.session_queue.dequeue()
 
-                    if self._queue_item is None:
+                    if 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
 
-                    self._invoker.services.logger.debug(f"Executing queue item {self._queue_item.item_id}")
+                    self._session_worker_queue.put(queue_item)
+                    self._invoker.services.logger.debug(f"Scheduling queue item {queue_item.item_id} to run")
                     cancel_event.clear()
 
                     # Run the graph
-                    self.session_runner.run(queue_item=self._queue_item)
+                    # self.session_runner.run(queue_item=self._queue_item)
 
-                except Exception as e:
-                    error_type = e.__class__.__name__
-                    error_message = str(e)
-                    error_traceback = traceback.format_exc()
-                    self._on_non_fatal_processor_error(
-                        queue_item=self._queue_item,
-                        error_type=error_type,
-                        error_message=error_message,
-                        error_traceback=error_traceback,
-                    )
+                except Exception:
                     # Wait for next polling interval or event to try again
                     poll_now_event.wait(self._polling_interval)
                     continue
@@ -466,9 +481,25 @@ class DefaultSessionProcessor(SessionProcessorBase):
         finally:
             stop_event.clear()
             poll_now_event.clear()
-            self._queue_item = None
             self._thread_semaphore.release()
 
+    def _process_next_session(self) -> None:
+        while True:
+            self._resume_event.wait()
+            queue_item = self._session_worker_queue.get()
+            if queue_item.status == "canceled":
+                continue
+            try:
+                self._active_queue_items.add(queue_item)
+                # reserve a GPU for this session - may block
+                with self._invoker.services.model_manager.load.ram_cache.reserve_execution_device():
+                    # Run the session on the reserved GPU
+                    self.session_runner.run(queue_item=queue_item)
+            except Exception:
+                continue
+            finally:
+                self._active_queue_items.remove(queue_item)
+
     def _on_non_fatal_processor_error(
         self,
         queue_item: Optional[SessionQueueItem],

invokeai/app/services/session_queue/session_queue_common.py

@@ -236,6 +236,9 @@ class SessionQueueItemWithoutGraph(BaseModel):
         }
     )
 
+    def __hash__(self) -> int:
+        return self.item_id
+
 
 class SessionQueueItemDTO(SessionQueueItemWithoutGraph):
     pass

invokeai/app/services/shared/graph.py

@@ -652,7 +652,7 @@ class Graph(BaseModel):
         output_fields = [get_input_field(self.get_node(e.node_id), e.field) for e in outputs]
 
         # Input type must be a list
-        if get_origin(input_field) != list:
+        if get_origin(input_field) is not list:
             return False
 
         # Validate that all outputs match the input type

invokeai/app/services/shared/invocation_context.py

@@ -2,6 +2,7 @@ from dataclasses import dataclass
 from pathlib import Path
 from typing import TYPE_CHECKING, Callable, Optional, Union
 
+import torch
 from PIL.Image import Image
 from pydantic.networks import AnyHttpUrl
 from torch import Tensor
@@ -26,11 +27,13 @@ from invokeai.backend.model_manager.config import (
 from invokeai.backend.model_manager.load.load_base import LoadedModel, LoadedModelWithoutConfig
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
+from invokeai.backend.util.devices import TorchDevice
 
 if TYPE_CHECKING:
     from invokeai.app.invocations.baseinvocation import BaseInvocation
     from invokeai.app.invocations.model import ModelIdentifierField
     from invokeai.app.services.session_queue.session_queue_common import SessionQueueItem
+    from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
 
 """
 The InvocationContext provides access to various services and data about the current invocation.
@@ -323,7 +326,6 @@ class ConditioningInterface(InvocationContextInterface):
         Returns:
             The loaded conditioning data.
         """
-
         return self._services.conditioning.load(name)
 
 
@@ -557,6 +559,28 @@ class UtilInterface(InvocationContextInterface):
             is_canceled=self.is_canceled,
         )
 
+    def torch_device(self) -> torch.device:
+        """
+        Return a torch device to use in the current invocation.
+
+        Returns:
+            A torch.device not currently in use by the system.
+        """
+        ram_cache: "ModelCacheBase[AnyModel]" = self._services.model_manager.load.ram_cache
+        return ram_cache.get_execution_device()
+
+    def torch_dtype(self, device: Optional[torch.device] = None) -> torch.dtype:
+        """
+        Return a precision type to use with the current invocation and torch device.
+
+        Args:
+            device: Optional device.
+
+        Returns:
+            A torch.dtype suited for the current device.
+        """
+        return TorchDevice.choose_torch_dtype(device)
+
 
 class InvocationContext:
     """Provides access to various services and data for the current invocation.

invokeai/backend/model_manager/config.py

@@ -25,6 +25,7 @@ from enum import Enum
 from typing import Literal, Optional, Type, TypeAlias, Union
 
 import torch
+from diffusers.configuration_utils import ConfigMixin
 from diffusers.models.modeling_utils import ModelMixin
 from pydantic import BaseModel, ConfigDict, Discriminator, Field, Tag, TypeAdapter
 from typing_extensions import Annotated, Any, Dict
@@ -37,7 +38,7 @@ from ..raw_model import RawModel
 
 # ModelMixin is the base class for all diffusers and transformers models
 # RawModel is the InvokeAI wrapper class for ip_adapters, loras, textual_inversion and onnx runtime
-AnyModel = Union[ModelMixin, RawModel, torch.nn.Module, Dict[str, torch.Tensor]]
+AnyModel = Union[ConfigMixin, ModelMixin, RawModel, torch.nn.Module, Dict[str, torch.Tensor]]
 
 
 class InvalidModelConfigException(Exception):
@@ -177,6 +178,7 @@ class ModelConfigBase(BaseModel):
 
     @staticmethod
     def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
+        """Extend the pydantic schema from a json."""
         schema["required"].extend(["key", "type", "format"])
 
     model_config = ConfigDict(validate_assignment=True, json_schema_extra=json_schema_extra)
@@ -443,7 +445,7 @@ class ModelConfigFactory(object):
             model = dest_class.model_validate(model_data)
         else:
             # mypy doesn't typecheck TypeAdapters well?
-            model = AnyModelConfigValidator.validate_python(model_data)  # type: ignore
+            model = AnyModelConfigValidator.validate_python(model_data)
         assert model is not None
         if key:
             model.key = key

invokeai/backend/model_manager/load/load_base.py

@@ -65,8 +65,7 @@ class LoadedModelWithoutConfig:
 
     def __enter__(self) -> AnyModel:
         """Context entry."""
-        self._locker.lock()
-        return self.model
+        return self._locker.lock()
 
     def __exit__(self, *args: Any, **kwargs: Any) -> None:
         """Context exit."""

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

@@ -8,9 +8,10 @@ model will be cleared and (re)loaded from disk when next needed.
 """
 
 from abc import ABC, abstractmethod
+from contextlib import contextmanager
 from dataclasses import dataclass, field
 from logging import Logger
-from typing import Dict, Generic, Optional, TypeVar
+from typing import Dict, Generator, Generic, Optional, Set, TypeVar
 
 import torch
 
@@ -51,44 +52,13 @@ class CacheRecord(Generic[T]):
     Elements of the cache:
 
     key: Unique key for each model, same as used in the models database.
-    model: Model in memory.
-    state_dict: A read-only copy of the model's state dict in RAM. It will be
-                used as a template for creating a copy in the VRAM.
+    model: Read-only copy of the model *without weights* residing in the "meta device"
     size: Size of the model
-    loaded: True if the model's state dict is currently in VRAM
-
-    Before a model is executed, the state_dict template is copied into VRAM,
-    and then injected into the model. When the model is finished, the VRAM
-    copy of the state dict is deleted, and the RAM version is reinjected
-    into the model.
-
-    The state_dict should be treated as a read-only attribute. Do not attempt
-    to patch or otherwise modify it. Instead, patch the copy of the state_dict
-    after it is loaded into the execution device (e.g. CUDA) using the `LoadedModel`
-    context manager call `model_on_device()`.
     """
 
     key: str
-    model: T
-    device: torch.device
-    state_dict: Optional[Dict[str, torch.Tensor]]
     size: int
-    loaded: bool = False
-    _locks: int = 0
-
-    def lock(self) -> None:
-        """Lock this record."""
-        self._locks += 1
-
-    def unlock(self) -> None:
-        """Unlock this record."""
-        self._locks -= 1
-        assert self._locks >= 0
-
-    @property
-    def locked(self) -> bool:
-        """Return true if record is locked."""
-        return self._locks > 0
+    model: T
 
 
 @dataclass
@@ -115,14 +85,27 @@ class ModelCacheBase(ABC, Generic[T]):
 
     @property
     @abstractmethod
-    def execution_device(self) -> torch.device:
-        """Return the exection device (e.g. "cuda" for VRAM)."""
+    def execution_devices(self) -> Set[torch.device]:
+        """Return the set of available execution devices."""
         pass
 
-    @property
+    @contextmanager
     @abstractmethod
-    def lazy_offloading(self) -> bool:
-        """Return true if the cache is configured to lazily offload models in VRAM."""
+    def reserve_execution_device(self, timeout: int = 0) -> Generator[torch.device, None, None]:
+        """Reserve an execution device (GPU) under the current thread id."""
+        pass
+
+    @abstractmethod
+    def get_execution_device(self) -> torch.device:
+        """
+        Return an execution device that has been reserved for current thread.
+
+        Note that reservations are done using the current thread's TID.
+        It might be better to do this using the session ID, but that involves
+        too many detailed changes to model manager calls.
+
+        May generate a ValueError if no GPU has been reserved.
+        """
         pass
 
     @property
@@ -131,16 +114,6 @@ class ModelCacheBase(ABC, Generic[T]):
         """Return true if the cache is configured to lazily offload models in VRAM."""
         pass
 
-    @abstractmethod
-    def offload_unlocked_models(self, size_required: int) -> None:
-        """Offload from VRAM any models not actively in use."""
-        pass
-
-    @abstractmethod
-    def move_model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> None:
-        """Move model into the indicated device."""
-        pass
-
     @property
     @abstractmethod
     def stats(self) -> Optional[CacheStats]:
@@ -202,6 +175,11 @@ class ModelCacheBase(ABC, Generic[T]):
         """Return true if the model identified by key and submodel_type is in the cache."""
         pass
 
+    @abstractmethod
+    def model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> AnyModel:
+        """Move a copy of the model into the indicated device and return it."""
+        pass
+
     @abstractmethod
     def cache_size(self) -> int:
         """Get the total size of the models currently cached."""

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

@@ -18,17 +18,19 @@ context. Use like this:
 
 """
 
+import copy
 import gc
-import math
-import time
-from contextlib import suppress
+import sys
+import threading
+from contextlib import contextmanager, suppress
 from logging import Logger
-from typing import Dict, List, Optional
+from threading import BoundedSemaphore
+from typing import Dict, Generator, List, Optional, Set
 
 import torch
 
 from invokeai.backend.model_manager import AnyModel, SubModelType
-from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot, get_pretty_snapshot_diff
+from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
@@ -39,9 +41,7 @@ from .model_locker import ModelLocker
 # Maximum size of the cache, in gigs
 # Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously
 DEFAULT_MAX_CACHE_SIZE = 6.0
-
-# amount of GPU memory to hold in reserve for use by generations (GB)
-DEFAULT_MAX_VRAM_CACHE_SIZE = 2.75
+DEFAULT_MAX_VRAM_CACHE_SIZE = 0.25
 
 # actual size of a gig
 GIG = 1073741824
@@ -57,12 +57,8 @@ class ModelCache(ModelCacheBase[AnyModel]):
         self,
         max_cache_size: float = DEFAULT_MAX_CACHE_SIZE,
         max_vram_cache_size: float = DEFAULT_MAX_VRAM_CACHE_SIZE,
-        execution_device: torch.device = torch.device("cuda"),
         storage_device: torch.device = torch.device("cpu"),
         precision: torch.dtype = torch.float16,
-        sequential_offload: bool = False,
-        lazy_offloading: bool = True,
-        sha_chunksize: int = 16777216,
         log_memory_usage: bool = False,
         logger: Optional[Logger] = None,
     ):
@@ -70,23 +66,19 @@ class ModelCache(ModelCacheBase[AnyModel]):
         Initialize the model RAM cache.
 
         :param max_cache_size: Maximum size of the RAM cache [6.0 GB]
-        :param execution_device: Torch device to load active model into [torch.device('cuda')]
         :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
         :param precision: Precision for loaded models [torch.float16]
-        :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
         :param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially
         :param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
             operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
             snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's
             behaviour.
         """
-        # allow lazy offloading only when vram cache enabled
-        self._lazy_offloading = lazy_offloading and max_vram_cache_size > 0
         self._precision: torch.dtype = precision
         self._max_cache_size: float = max_cache_size
         self._max_vram_cache_size: float = max_vram_cache_size
-        self._execution_device: torch.device = execution_device
         self._storage_device: torch.device = storage_device
+        self._ram_lock = threading.Lock()
         self._logger = logger or InvokeAILogger.get_logger(self.__class__.__name__)
         self._log_memory_usage = log_memory_usage
         self._stats: Optional[CacheStats] = None
@@ -94,25 +86,87 @@ class ModelCache(ModelCacheBase[AnyModel]):
         self._cached_models: Dict[str, CacheRecord[AnyModel]] = {}
         self._cache_stack: List[str] = []
 
+        # device to thread id
+        self._device_lock = threading.Lock()
+        self._execution_devices: Dict[torch.device, int] = {x: 0 for x in TorchDevice.execution_devices()}
+        self._free_execution_device = BoundedSemaphore(len(self._execution_devices))
+
+        self.logger.info(
+            f"Using rendering device(s): {', '.join(sorted([str(x) for x in self._execution_devices.keys()]))}"
+        )
+
     @property
     def logger(self) -> Logger:
         """Return the logger used by the cache."""
         return self._logger
 
-    @property
-    def lazy_offloading(self) -> bool:
-        """Return true if the cache is configured to lazily offload models in VRAM."""
-        return self._lazy_offloading
-
     @property
     def storage_device(self) -> torch.device:
         """Return the storage device (e.g. "CPU" for RAM)."""
         return self._storage_device
 
     @property
-    def execution_device(self) -> torch.device:
-        """Return the exection device (e.g. "cuda" for VRAM)."""
-        return self._execution_device
+    def execution_devices(self) -> Set[torch.device]:
+        """Return the set of available execution devices."""
+        devices = self._execution_devices.keys()
+        return set(devices)
+
+    def get_execution_device(self) -> torch.device:
+        """
+        Return an execution device that has been reserved for current thread.
+
+        Note that reservations are done using the current thread's TID.
+        It would be better to do this using the session ID, but that involves
+        too many detailed changes to model manager calls.
+
+        May generate a ValueError if no GPU has been reserved.
+        """
+        current_thread = threading.current_thread().ident
+        assert current_thread is not None
+        assigned = [x for x, tid in self._execution_devices.items() if current_thread == tid]
+        if not assigned:
+            raise ValueError(f"No GPU has been reserved for the use of thread {current_thread}")
+        return assigned[0]
+
+    @contextmanager
+    def reserve_execution_device(self, timeout: Optional[int] = None) -> Generator[torch.device, None, None]:
+        """Reserve an execution device (e.g. GPU) for exclusive use by a generation thread.
+
+        Note that the reservation is done using the current thread's TID.
+        It would be better to do this using the session ID, but that involves
+        too many detailed changes to model manager calls.
+        """
+        device = None
+        with self._device_lock:
+            current_thread = threading.current_thread().ident
+            assert current_thread is not None
+
+            # look for a device that has already been assigned to this thread
+            assigned = [x for x, tid in self._execution_devices.items() if current_thread == tid]
+            if assigned:
+                device = assigned[0]
+
+        # no device already assigned. Get one.
+        if device is None:
+            self._free_execution_device.acquire(timeout=timeout)
+            with self._device_lock:
+                free_device = [x for x, tid in self._execution_devices.items() if tid == 0]
+                self._execution_devices[free_device[0]] = current_thread
+                device = free_device[0]
+
+        # we are outside the lock region now
+        self.logger.info(f"{current_thread} Reserved torch device {device}")
+
+        # Tell TorchDevice to use this object to get the torch device.
+        TorchDevice.set_model_cache(self)
+        try:
+            yield device
+        finally:
+            with self._device_lock:
+                self.logger.info(f"{current_thread} Released torch device {device}")
+                self._execution_devices[device] = 0
+                self._free_execution_device.release()
+                torch.cuda.empty_cache()
 
     @property
     def max_cache_size(self) -> float:
@@ -157,16 +211,16 @@ class ModelCache(ModelCacheBase[AnyModel]):
         submodel_type: Optional[SubModelType] = None,
     ) -> None:
         """Store model under key and optional submodel_type."""
-        key = self._make_cache_key(key, submodel_type)
-        if key in self._cached_models:
-            return
-        size = calc_model_size_by_data(model)
-        self.make_room(size)
+        with self._ram_lock:
+            key = self._make_cache_key(key, submodel_type)
+            if key in self._cached_models:
+                return
+            size = calc_model_size_by_data(model)
+            self.make_room(size)
 
-        state_dict = model.state_dict() if isinstance(model, torch.nn.Module) else None
-        cache_record = CacheRecord(key=key, model=model, device=self.storage_device, state_dict=state_dict, size=size)
-        self._cached_models[key] = cache_record
-        self._cache_stack.append(key)
+            cache_record = CacheRecord(key=key, model=model, size=size)
+            self._cached_models[key] = cache_record
+            self._cache_stack.append(key)
 
     def get(
         self,
@@ -184,36 +238,37 @@ class ModelCache(ModelCacheBase[AnyModel]):
 
         This may raise an IndexError if the model is not in the cache.
         """
-        key = self._make_cache_key(key, submodel_type)
-        if key in self._cached_models:
-            if self.stats:
-                self.stats.hits += 1
-        else:
-            if self.stats:
-                self.stats.misses += 1
-            raise IndexError(f"The model with key {key} is not in the cache.")
+        with self._ram_lock:
+            key = self._make_cache_key(key, submodel_type)
+            if key in self._cached_models:
+                if self.stats:
+                    self.stats.hits += 1
+            else:
+                if self.stats:
+                    self.stats.misses += 1
+                raise IndexError(f"The model with key {key} is not in the cache.")
 
-        cache_entry = self._cached_models[key]
+            cache_entry = self._cached_models[key]
 
-        # more stats
-        if self.stats:
-            stats_name = stats_name or key
-            self.stats.cache_size = int(self._max_cache_size * GIG)
-            self.stats.high_watermark = max(self.stats.high_watermark, self.cache_size())
-            self.stats.in_cache = len(self._cached_models)
-            self.stats.loaded_model_sizes[stats_name] = max(
-                self.stats.loaded_model_sizes.get(stats_name, 0), cache_entry.size
+            # more stats
+            if self.stats:
+                stats_name = stats_name or key
+                self.stats.cache_size = int(self._max_cache_size * GIG)
+                self.stats.high_watermark = max(self.stats.high_watermark, self.cache_size())
+                self.stats.in_cache = len(self._cached_models)
+                self.stats.loaded_model_sizes[stats_name] = max(
+                    self.stats.loaded_model_sizes.get(stats_name, 0), cache_entry.size
+                )
+
+            # this moves the entry to the top (right end) of the stack
+            with suppress(Exception):
+                self._cache_stack.remove(key)
+            self._cache_stack.append(key)
+            return ModelLocker(
+                cache=self,
+                cache_entry=cache_entry,
             )
 
-        # this moves the entry to the top (right end) of the stack
-        with suppress(Exception):
-            self._cache_stack.remove(key)
-        self._cache_stack.append(key)
-        return ModelLocker(
-            cache=self,
-            cache_entry=cache_entry,
-        )
-
     def _capture_memory_snapshot(self) -> Optional[MemorySnapshot]:
         if self._log_memory_usage:
             return MemorySnapshot.capture()
@@ -225,127 +280,34 @@ class ModelCache(ModelCacheBase[AnyModel]):
         else:
             return model_key
 
-    def offload_unlocked_models(self, size_required: int) -> None:
-        """Move any unused models from VRAM."""
-        reserved = self._max_vram_cache_size * GIG
-        vram_in_use = torch.cuda.memory_allocated() + size_required
-        self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM needed for models; max allowed={(reserved/GIG):.2f}GB")
-        for _, cache_entry in sorted(self._cached_models.items(), key=lambda x: x[1].size):
-            if vram_in_use <= reserved:
-                break
-            if not cache_entry.loaded:
-                continue
-            if not cache_entry.locked:
-                self.move_model_to_device(cache_entry, self.storage_device)
-                cache_entry.loaded = False
-                vram_in_use = torch.cuda.memory_allocated() + size_required
-                self.logger.debug(
-                    f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GIG):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GIG):.2f}GB"
-                )
-
-        TorchDevice.empty_cache()
-
-    def move_model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> None:
-        """Move model into the indicated device.
+    def model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> AnyModel:
+        """Move a copy of the model into the indicated device and return it.
 
         :param cache_entry: The CacheRecord for the model
         :param target_device: The torch.device to move the model into
 
         May raise a torch.cuda.OutOfMemoryError
         """
-        self.logger.debug(f"Called to move {cache_entry.key} to {target_device}")
-        source_device = cache_entry.device
+        with self._ram_lock:
+            self.logger.debug(f"Called to move {cache_entry.key} ({type(cache_entry.model)=}) to {target_device}")
 
-        # Note: We compare device types only so that 'cuda' == 'cuda:0'.
-        # This would need to be revised to support multi-GPU.
-        if torch.device(source_device).type == torch.device(target_device).type:
-            return
-
-        # Some models don't have a `to` method, in which case they run in RAM/CPU.
-        if not hasattr(cache_entry.model, "to"):
-            return
-
-        # This roundabout method for moving the model around is done to avoid
-        # the cost of moving the model from RAM to VRAM and then back from VRAM to RAM.
-        # When moving to VRAM, we copy (not move) each element of the state dict from
-        # RAM to a new state dict in VRAM, and then inject it into the model.
-        # This operation is slightly faster than running `to()` on the whole model.
-        #
-        # When the model needs to be removed from VRAM we simply delete the copy
-        # of the state dict in VRAM, and reinject the state dict that is cached
-        # in RAM into the model. So this operation is very fast.
-        start_model_to_time = time.time()
-        snapshot_before = self._capture_memory_snapshot()
-
-        try:
-            if cache_entry.state_dict is not None:
-                assert hasattr(cache_entry.model, "load_state_dict")
-                if target_device == self.storage_device:
-                    cache_entry.model.load_state_dict(cache_entry.state_dict, assign=True)
-                else:
-                    new_dict: Dict[str, torch.Tensor] = {}
-                    for k, v in cache_entry.state_dict.items():
-                        new_dict[k] = v.to(torch.device(target_device), copy=True, non_blocking=True)
-                    cache_entry.model.load_state_dict(new_dict, assign=True)
-            cache_entry.model.to(target_device, non_blocking=True)
-            cache_entry.device = target_device
-        except Exception as e:  # blow away cache entry
-            self._delete_cache_entry(cache_entry)
-            raise e
-
-        snapshot_after = self._capture_memory_snapshot()
-        end_model_to_time = time.time()
-        self.logger.debug(
-            f"Moved model '{cache_entry.key}' from {source_device} to"
-            f" {target_device} in {(end_model_to_time-start_model_to_time):.2f}s."
-            f"Estimated model size: {(cache_entry.size/GIG):.3f} GB."
-            f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
-        )
-
-        if (
-            snapshot_before is not None
-            and snapshot_after is not None
-            and snapshot_before.vram is not None
-            and snapshot_after.vram is not None
-        ):
-            vram_change = abs(snapshot_before.vram - snapshot_after.vram)
-
-            # If the estimated model size does not match the change in VRAM, log a warning.
-            if not math.isclose(
-                vram_change,
-                cache_entry.size,
-                rel_tol=0.1,
-                abs_tol=10 * MB,
-            ):
-                self.logger.debug(
-                    f"Moving model '{cache_entry.key}' from {source_device} to"
-                    f" {target_device} caused an unexpected change in VRAM usage. The model's"
-                    " estimated size may be incorrect. Estimated model size:"
-                    f" {(cache_entry.size/GIG):.3f} GB.\n"
-                    f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
-                )
+            # Some models don't have a state dictionary, in which case the
+            # stored model will still reside in CPU
+            if hasattr(cache_entry.model, "to"):
+                model_in_gpu = copy.deepcopy(cache_entry.model)
+                assert hasattr(model_in_gpu, "to")
+                model_in_gpu.to(target_device)
+                return model_in_gpu
+            else:
+                return cache_entry.model  # what happens in CPU stays in CPU
 
     def print_cuda_stats(self) -> None:
         """Log CUDA diagnostics."""
         vram = "%4.2fG" % (torch.cuda.memory_allocated() / GIG)
         ram = "%4.2fG" % (self.cache_size() / GIG)
 
-        in_ram_models = 0
-        in_vram_models = 0
-        locked_in_vram_models = 0
-        for cache_record in self._cached_models.values():
-            if hasattr(cache_record.model, "device"):
-                if cache_record.model.device == self.storage_device:
-                    in_ram_models += 1
-                else:
-                    in_vram_models += 1
-                if cache_record.locked:
-                    locked_in_vram_models += 1
-
-                self.logger.debug(
-                    f"Current VRAM/RAM usage: {vram}/{ram}; models_in_ram/models_in_vram(locked) ="
-                    f" {in_ram_models}/{in_vram_models}({locked_in_vram_models})"
-                )
+        in_ram_models = len(self._cached_models)
+        self.logger.debug(f"Current VRAM/RAM usage for {in_ram_models} models: {vram}/{ram}")
 
     def make_room(self, size: int) -> None:
         """Make enough room in the cache to accommodate a new model of indicated size."""
@@ -368,12 +330,14 @@ class ModelCache(ModelCacheBase[AnyModel]):
         while current_size + bytes_needed > maximum_size and pos < len(self._cache_stack):
             model_key = self._cache_stack[pos]
             cache_entry = self._cached_models[model_key]
-            device = cache_entry.model.device if hasattr(cache_entry.model, "device") else None
-            self.logger.debug(
-                f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded}"
-            )
 
-            if not cache_entry.locked:
+            refs = sys.getrefcount(cache_entry.model)
+
+            # Expected refs:
+            # 1 from cache_entry
+            # 1 from getrefcount function
+            # 1 from onnx runtime object
+            if refs <= (3 if "onnx" in model_key else 2):
                 self.logger.debug(
                     f"Removing {model_key} from RAM cache to free at least {(size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
                 )
@@ -400,10 +364,26 @@ class ModelCache(ModelCacheBase[AnyModel]):
             if self.stats:
                 self.stats.cleared = models_cleared
             gc.collect()
-
         TorchDevice.empty_cache()
         self.logger.debug(f"After making room: cached_models={len(self._cached_models)}")
 
+    def _check_free_vram(self, target_device: torch.device, needed_size: int) -> None:
+        if target_device.type != "cuda":
+            return
+        vram_device = (  # mem_get_info() needs an indexed device
+            target_device if target_device.index is not None else torch.device(str(target_device), index=0)
+        )
+        free_mem, _ = torch.cuda.mem_get_info(torch.device(vram_device))
+        if needed_size > free_mem:
+            raise torch.cuda.OutOfMemoryError
+
     def _delete_cache_entry(self, cache_entry: CacheRecord[AnyModel]) -> None:
-        self._cache_stack.remove(cache_entry.key)
-        del self._cached_models[cache_entry.key]
+        try:
+            self._cache_stack.remove(cache_entry.key)
+            del self._cached_models[cache_entry.key]
+        except ValueError:
+            pass
+
+    @staticmethod
+    def _device_name(device: torch.device) -> str:
+        return f"{device.type}:{device.index}"

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

@@ -10,6 +10,8 @@ from invokeai.backend.model_manager import AnyModel
 
 from .model_cache_base import CacheRecord, ModelCacheBase, ModelLockerBase
 
+MAX_GPU_WAIT = 600  # wait up to 10 minutes for a GPU to become free
+
 
 class ModelLocker(ModelLockerBase):
     """Internal class that mediates movement in and out of GPU."""
@@ -29,33 +31,29 @@ class ModelLocker(ModelLockerBase):
         """Return the model without moving it around."""
         return self._cache_entry.model
 
-    def get_state_dict(self) -> Optional[Dict[str, torch.Tensor]]:
-        """Return the state dict (if any) for the cached model."""
-        return self._cache_entry.state_dict
-
     def lock(self) -> AnyModel:
         """Move the model into the execution device (GPU) and lock it."""
-        self._cache_entry.lock()
         try:
-            if self._cache.lazy_offloading:
-                self._cache.offload_unlocked_models(self._cache_entry.size)
-            self._cache.move_model_to_device(self._cache_entry, self._cache.execution_device)
-            self._cache_entry.loaded = True
-            self._cache.logger.debug(f"Locking {self._cache_entry.key} in {self._cache.execution_device}")
+            device = self._cache.get_execution_device()
+            model_on_device = self._cache.model_to_device(self._cache_entry, device)
+            self._cache.logger.debug(f"Moved {self._cache_entry.key} to {device}")
             self._cache.print_cuda_stats()
         except torch.cuda.OutOfMemoryError:
             self._cache.logger.warning("Insufficient GPU memory to load model. Aborting")
-            self._cache_entry.unlock()
             raise
         except Exception:
-            self._cache_entry.unlock()
             raise
 
-        return self.model
+        return model_on_device
 
+    # It is no longer necessary to move the model out of VRAM
+    # because it will be removed when it goes out of scope
+    # in the caller's context
     def unlock(self) -> None:
         """Call upon exit from context."""
-        self._cache_entry.unlock()
-        if not self._cache.lazy_offloading:
-            self._cache.offload_unlocked_models(0)
-            self._cache.print_cuda_stats()
+        self._cache.print_cuda_stats()
+
+    # This is no longer in use in MGPU.
+    def get_state_dict(self) -> Optional[Dict[str, torch.Tensor]]:
+        """Return the state dict (if any) for the cached model."""
+        return None

invokeai/backend/model_patcher.py

@@ -4,6 +4,7 @@
 from __future__ import annotations
 
 import pickle
+import threading
 from contextlib import contextmanager
 from typing import Any, Dict, Generator, Iterator, List, Optional, Tuple, Union
 
@@ -34,6 +35,8 @@ with LoRAHelper.apply_lora_unet(unet, loras):
 
 # TODO: rename smth like ModelPatcher and add TI method?
 class ModelPatcher:
+    _thread_lock = threading.Lock()
+
     @staticmethod
     def _resolve_lora_key(model: torch.nn.Module, lora_key: str, prefix: str) -> Tuple[str, torch.nn.Module]:
         assert "." not in lora_key
@@ -106,7 +109,7 @@ class ModelPatcher:
         """
         original_weights = {}
         try:
-            with torch.no_grad():
+            with torch.no_grad(), cls._thread_lock:
                 for lora, lora_weight in loras:
                     # assert lora.device.type == "cpu"
                     for layer_key, layer in lora.layers.items():
@@ -129,9 +132,7 @@ class ModelPatcher:
                         dtype = module.weight.dtype
 
                         if module_key not in original_weights:
-                            if model_state_dict is not None:  # we were provided with the CPU copy of the state dict
-                                original_weights[module_key] = model_state_dict[module_key + ".weight"]
-                            else:
+                            if model_state_dict is None:  # no CPU copy of the state dict was provided
                                 original_weights[module_key] = module.weight.detach().to(device="cpu", copy=True)
 
                         layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -32,8 +32,11 @@ class SDXLConditioningInfo(BasicConditioningInfo):
 
     def to(self, device, dtype=None):
         self.pooled_embeds = self.pooled_embeds.to(device=device, dtype=dtype)
+        assert self.pooled_embeds.device == device
         self.add_time_ids = self.add_time_ids.to(device=device, dtype=dtype)
-        return super().to(device=device, dtype=dtype)
+        result = super().to(device=device, dtype=dtype)
+        assert self.embeds.device == device
+        return result
 
 
 @dataclass

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import math
+import threading
 from typing import Any, Callable, Optional, Union
 
 import torch
@@ -293,24 +294,31 @@ class InvokeAIDiffuserComponent:
             cross_attention_kwargs["regional_ip_data"] = regional_ip_data
 
         added_cond_kwargs = None
-        if conditioning_data.is_sdxl():
-            added_cond_kwargs = {
-                "text_embeds": torch.cat(
-                    [
-                        # TODO: how to pad? just by zeros? or even truncate?
-                        conditioning_data.uncond_text.pooled_embeds,
-                        conditioning_data.cond_text.pooled_embeds,
-                    ],
-                    dim=0,
-                ),
-                "time_ids": torch.cat(
-                    [
-                        conditioning_data.uncond_text.add_time_ids,
-                        conditioning_data.cond_text.add_time_ids,
-                    ],
-                    dim=0,
-                ),
-            }
+        try:
+            if conditioning_data.is_sdxl():
+                # tid = threading.current_thread().ident
+                # print(f'DEBUG {tid} {conditioning_data.uncond_text.pooled_embeds.device=} {conditioning_data.cond_text.pooled_embeds.device=}', flush=True),
+                added_cond_kwargs = {
+                    "text_embeds": torch.cat(
+                        [
+                            # TODO: how to pad? just by zeros? or even truncate?
+                            conditioning_data.uncond_text.pooled_embeds,
+                            conditioning_data.cond_text.pooled_embeds,
+                        ],
+                        dim=0,
+                    ),
+                    "time_ids": torch.cat(
+                        [
+                            conditioning_data.uncond_text.add_time_ids,
+                            conditioning_data.cond_text.add_time_ids,
+                        ],
+                        dim=0,
+                    ),
+                }
+        except Exception as e:
+            tid = threading.current_thread().ident
+            print(f"DEBUG: {tid} {str(e)}")
+            raise e
 
         if conditioning_data.cond_regions is not None or conditioning_data.uncond_regions is not None:
             # TODO(ryand): We currently initialize RegionalPromptData for every denoising step. The text conditionings

invokeai/backend/util/devices.py

@@ -1,10 +1,16 @@
-from typing import Dict, Literal, Optional, Union
+"""Torch Device class provides torch device selection services."""
+
+from typing import TYPE_CHECKING, Dict, Literal, Optional, Set, Union
 
 import torch
 from deprecated import deprecated
 
 from invokeai.app.services.config.config_default import get_config
 
+if TYPE_CHECKING:
+    from invokeai.backend.model_manager.config import AnyModel
+    from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
+
 # legacy APIs
 TorchPrecisionNames = Literal["float32", "float16", "bfloat16"]
 CPU_DEVICE = torch.device("cpu")
@@ -42,9 +48,23 @@ PRECISION_TO_NAME: Dict[torch.dtype, TorchPrecisionNames] = {v: k for k, v in NA
 class TorchDevice:
     """Abstraction layer for torch devices."""
 
+    _model_cache: Optional["ModelCacheBase[AnyModel]"] = None
+
+    @classmethod
+    def set_model_cache(cls, cache: "ModelCacheBase[AnyModel]"):
+        """Set the current model cache."""
+        cls._model_cache = cache
+
     @classmethod
     def choose_torch_device(cls) -> torch.device:
         """Return the torch.device to use for accelerated inference."""
+        if cls._model_cache:
+            return cls._model_cache.get_execution_device()
+        else:
+            return cls._choose_device()
+
+    @classmethod
+    def _choose_device(cls) -> torch.device:
         app_config = get_config()
         if app_config.device != "auto":
             device = torch.device(app_config.device)
@@ -56,11 +76,19 @@ class TorchDevice:
             device = CPU_DEVICE
         return cls.normalize(device)
 
+    @classmethod
+    def execution_devices(cls) -> Set[torch.device]:
+        """Return a list of torch.devices that can be used for accelerated inference."""
+        app_config = get_config()
+        if app_config.devices is None:
+            return cls._lookup_execution_devices()
+        return {torch.device(x) for x in app_config.devices}
+
     @classmethod
     def choose_torch_dtype(cls, device: Optional[torch.device] = None) -> torch.dtype:
         """Return the precision to use for accelerated inference."""
-        device = device or cls.choose_torch_device()
         config = get_config()
+        device = device or cls._choose_device()
         if device.type == "cuda" and torch.cuda.is_available():
             device_name = torch.cuda.get_device_name(device)
             if "GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name:
@@ -108,3 +136,13 @@ class TorchDevice:
     @classmethod
     def _to_dtype(cls, precision_name: TorchPrecisionNames) -> torch.dtype:
         return NAME_TO_PRECISION[precision_name]
+
+    @classmethod
+    def _lookup_execution_devices(cls) -> Set[torch.device]:
+        if torch.cuda.is_available():
+            devices = {torch.device(f"cuda:{x}") for x in range(0, torch.cuda.device_count())}
+        elif torch.backends.mps.is_available():
+            devices = {torch.device("mps")}
+        else:
+            devices = {torch.device("cpu")}
+        return devices

scripts/populate_model_db_from_yaml.py

@@ -0,0 +1,54 @@
+#!/bin/env python
+
+from argparse import ArgumentParser, Namespace
+from pathlib import Path
+
+from invokeai.app.services.config import InvokeAIAppConfig, get_config
+from invokeai.app.services.download import DownloadQueueService
+from invokeai.app.services.model_install import ModelInstallService
+from invokeai.app.services.model_records import ModelRecordServiceSQL
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+from invokeai.backend.util.logging import InvokeAILogger
+
+
+def get_args() -> Namespace:
+    parser = ArgumentParser(description="Update models database from yaml file")
+    parser.add_argument("--root", type=Path, required=False, default=None)
+    parser.add_argument("--yaml_file", type=Path, required=False, default=None)
+    return parser.parse_args()
+
+
+def populate_config() -> InvokeAIAppConfig:
+    args = get_args()
+    config = get_config()
+    if args.root:
+        config._root = args.root
+    if args.yaml_file:
+        config.legacy_models_yaml_path = args.yaml_file
+    else:
+        config.legacy_models_yaml_path = config.root_path / "configs/models.yaml"
+    return config
+
+
+def initialize_installer(config: InvokeAIAppConfig) -> ModelInstallService:
+    logger = InvokeAILogger.get_logger(config=config)
+    db = SqliteDatabase(config.db_path, logger)
+    record_store = ModelRecordServiceSQL(db)
+    queue = DownloadQueueService()
+    queue.start()
+    installer = ModelInstallService(app_config=config, record_store=record_store, download_queue=queue)
+    return installer
+
+
+def main() -> None:
+    config = populate_config()
+    installer = initialize_installer(config)
+    installer._migrate_yaml(rename_yaml=False, overwrite_db=True)
+    print("\n<INSTALLED MODELS>")
+    print("\t".join(["key", "name", "type", "path"]))
+    for model in installer.record_store.all_models():
+        print("\t".join([model.key, model.name, model.type, (config.models_path / model.path).as_posix()]))
+
+
+if __name__ == "__main__":
+    main()

tests/backend/model_manager/model_loading/test_model_load.py

@@ -14,13 +14,14 @@ def test_loading(mm2_model_manager: ModelManagerServiceBase, embedding_file: Pat
     matches = store.search_by_attr(model_name="test_embedding")
     assert len(matches) == 0
     key = mm2_model_manager.install.register_path(embedding_file)
-    loaded_model = mm2_model_manager.load.load_model(store.get_model(key))
-    assert loaded_model is not None
-    assert loaded_model.config.key == key
-    with loaded_model as model:
-        assert isinstance(model, TextualInversionModelRaw)
+    with mm2_model_manager.load.ram_cache.reserve_execution_device():
+        loaded_model = mm2_model_manager.load.load_model(store.get_model(key))
+        assert loaded_model is not None
+        assert loaded_model.config.key == key
+        with loaded_model as model:
+            assert isinstance(model, TextualInversionModelRaw)
 
-    config = mm2_model_manager.store.get_model(key)
-    loaded_model_2 = mm2_model_manager.load.load_model(config)
+        config = mm2_model_manager.store.get_model(key)
+        loaded_model_2 = mm2_model_manager.load.load_model(config)
 
-    assert loaded_model.config.key == loaded_model_2.config.key
+        assert loaded_model.config.key == loaded_model_2.config.key

tests/backend/model_manager/model_manager_fixtures.py

@@ -89,11 +89,10 @@ def mm2_download_queue(mm2_session: Session) -> DownloadQueueServiceBase:
 
 
 @pytest.fixture
-def mm2_loader(mm2_app_config: InvokeAIAppConfig, mm2_record_store: ModelRecordServiceBase) -> ModelLoadServiceBase:
+def mm2_loader(mm2_app_config: InvokeAIAppConfig) -> ModelLoadServiceBase:
     ram_cache = ModelCache(
         logger=InvokeAILogger.get_logger(),
         max_cache_size=mm2_app_config.ram,
-        max_vram_cache_size=mm2_app_config.vram,
     )
     convert_cache = ModelConvertCache(mm2_app_config.convert_cache_path)
     return ModelLoadService(

tests/backend/util/test_devices.py

@@ -8,7 +8,9 @@ import pytest
 import torch
 
 from invokeai.app.services.config import get_config
+from invokeai.backend.model_manager.load import ModelCache
 from invokeai.backend.util.devices import TorchDevice, choose_precision, choose_torch_device, torch_dtype
+from tests.backend.model_manager.model_manager_fixtures import *  # noqa F403
 
 devices = ["cpu", "cuda:0", "cuda:1", "mps"]
 device_types_cpu = [("cpu", torch.float32), ("cuda:0", torch.float32), ("mps", torch.float32)]
@@ -20,6 +22,7 @@ device_types_mps = [("cpu", torch.float32), ("cuda:0", torch.float32), ("mps", t
 def test_device_choice(device_name):
     config = get_config()
     config.device = device_name
+    TorchDevice.set_model_cache(None)  # disable dynamic selection of GPU device
     torch_device = TorchDevice.choose_torch_device()
     assert torch_device == torch.device(device_name)
 
@@ -130,3 +133,32 @@ def test_legacy_precision_name():
         assert "float16" == choose_precision(torch.device("cuda"))
         assert "float16" == choose_precision(torch.device("mps"))
         assert "float32" == choose_precision(torch.device("cpu"))
+
+
+def test_multi_device_support_1():
+    config = get_config()
+    config.devices = ["cuda:0", "cuda:1"]
+    assert TorchDevice.execution_devices() == {torch.device("cuda:0"), torch.device("cuda:1")}
+
+
+def test_multi_device_support_2():
+    config = get_config()
+    config.devices = None
+    with (
+        patch("torch.cuda.device_count", return_value=3),
+        patch("torch.cuda.is_available", return_value=True),
+    ):
+        assert TorchDevice.execution_devices() == {
+            torch.device("cuda:0"),
+            torch.device("cuda:1"),
+            torch.device("cuda:2"),
+        }
+
+
+def test_multi_device_support_3():
+    config = get_config()
+    config.devices = ["cuda:0", "cuda:1"]
+    cache = ModelCache()
+    with cache.reserve_execution_device() as gpu:
+        assert gpu in [torch.device(x) for x in config.devices]
+        assert TorchDevice.choose_torch_device() == gpu

tests/conftest.py

@@ -17,7 +17,6 @@ from invokeai.app.services.config.config_default import InvokeAIAppConfig
 from invokeai.app.services.images.images_default import ImageService
 from invokeai.app.services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
 from invokeai.app.services.invocation_services import InvocationServices
-from invokeai.app.services.invocation_stats.invocation_stats_default import InvocationStatsService
 from invokeai.app.services.invoker import Invoker
 from invokeai.backend.util.logging import InvokeAILogger
 from tests.backend.model_manager.model_manager_fixtures import *  # noqa: F403
@@ -49,13 +48,13 @@ def mock_services() -> InvocationServices:
         model_manager=None,  # type: ignore
         download_queue=None,  # type: ignore
         names=None,  # type: ignore
-        performance_statistics=InvocationStatsService(),
         session_processor=None,  # type: ignore
         session_queue=None,  # type: ignore
         urls=None,  # type: ignore
         workflow_records=None,  # type: ignore
         tensors=None,  # type: ignore
         conditioning=None,  # type: ignore
+        performance_statistics=None,  # type: ignore
     )
 
 

tests/test_config.py

@@ -92,7 +92,6 @@ def test_migrate_v3_config_from_file(tmp_path: Path, patch_rootdir: None):
     assert config.host == "192.168.1.1"
     assert config.port == 8080
     assert config.ram == 100
-    assert config.vram == 50
     assert config.legacy_models_yaml_path == Path("/custom/models.yaml")
     # This should be stripped out
     assert not hasattr(config, "esrgan")