from __future__ import annotations import asyncio import copy import gc import json import logging import threading import uuid from asyncio import get_event_loop from dataclasses import dataclass from multiprocessing import RLock from typing import Optional, Generator from opentelemetry import context, propagate from opentelemetry.context import Context, attach, detach from opentelemetry.trace import Status, StatusCode from .async_progress_iterable import _ProgressHandler, QueuePromptWithProgress from ..cmd.main_pre import tracer from .client_types import V1QueuePromptResponse from ..api.components.schema.prompt import PromptDict from ..cli_args_types import Configuration from ..cmd.folder_paths import init_default_paths # pylint: disable=import-error from ..component_model.executor_types import ExecutorToClientProgress from ..component_model.make_mutable import make_mutable from ..distributed.executors import ContextVarExecutor from ..distributed.process_pool_executor import ProcessPoolExecutor from ..distributed.server_stub import ServerStub from ..execution_context import current_execution_context _prompt_executor = threading.local() logger = logging.getLogger(__name__) def _execute_prompt( prompt: dict, prompt_id: str, client_id: str, span_context: dict, progress_handler: ExecutorToClientProgress | None, configuration: Configuration | None, partial_execution_targets: Optional[list[str]] = None) -> dict: configuration = copy.deepcopy(configuration) if configuration is not None else None execution_context = current_execution_context() if len(execution_context.folder_names_and_paths) == 0 or configuration is not None: init_default_paths(execution_context.folder_names_and_paths, configuration, replace_existing=True) span_context: Context = propagate.extract(span_context) token = attach(span_context) try: # there is never an event loop running on a thread or process pool thread here # this also guarantees nodes will be able to successfully call await return asyncio.run(__execute_prompt(prompt, prompt_id, client_id, span_context, progress_handler, configuration, partial_execution_targets)) finally: detach(token) async def __execute_prompt( prompt: dict, prompt_id: str, client_id: str, span_context: Context, progress_handler: ExecutorToClientProgress | None, configuration: Configuration | None, partial_execution_targets: list[str] | None) -> dict: from .. import options from ..cmd.execution import PromptExecutor progress_handler = progress_handler or ServerStub() prompt_executor: PromptExecutor = None try: prompt_executor: PromptExecutor = _prompt_executor.executor except (LookupError, AttributeError): if configuration is None: options.enable_args_parsing() else: from ..cmd.main_pre import args args.clear() args.update(configuration) with tracer.start_as_current_span("Initialize Prompt Executor", context=span_context): # todo: deal with new caching features prompt_executor = PromptExecutor(progress_handler) prompt_executor.raise_exceptions = True _prompt_executor.executor = prompt_executor with tracer.start_as_current_span("Execute Prompt", context=span_context) as span: try: prompt_mut = make_mutable(prompt) from ..cmd.execution import validate_prompt validation_tuple = await validate_prompt(prompt_id, prompt_mut, partial_execution_targets) if not validation_tuple.valid: if validation_tuple.node_errors is not None and len(validation_tuple.node_errors) > 0: validation_error_dict = validation_tuple.node_errors elif validation_tuple.error is not None: validation_error_dict = validation_tuple.error else: validation_error_dict = {"message": "Unknown", "details": ""} raise ValueError(json.dumps(validation_error_dict)) if client_id is None: prompt_executor.server = ServerStub() else: prompt_executor.server = progress_handler await prompt_executor.execute_async(prompt_mut, prompt_id, {"client_id": client_id}, execute_outputs=validation_tuple.good_output_node_ids) return prompt_executor.outputs_ui except Exception as exc_info: span.set_status(Status(StatusCode.ERROR)) span.record_exception(exc_info) raise exc_info def _cleanup(): from ..cmd.execution import PromptExecutor try: prompt_executor: PromptExecutor = _prompt_executor.executor # this should clear all references to output tensors and make it easier to collect back the memory prompt_executor.reset() except (LookupError, AttributeError): pass from .. import model_management model_management.unload_all_models() gc.collect() try: model_management.soft_empty_cache() except: pass class Comfy: """ This manages a single-threaded executor to run long-running or blocking workflows asynchronously without blocking the asyncio event loop. It initializes a PromptExecutor in a dedicated thread for executing prompts and handling server-stub communications. Example usage: Asynchronous (non-blocking) usage with async-await: ``` # Write a workflow, or enable Dev Mode in the UI settings, then Save (API Format) to get the workflow in your # workspace. prompt_dict = { "1": {"class_type": "KSamplerAdvanced", ...} ... } # Validate your workflow (the prompt) from comfy.api.components.schema.prompt import Prompt prompt = Prompt.validate(prompt_dict) # Then use the client to run your workflow. This will start, then stop, a local ComfyUI workflow executor. # It does not connect to a remote server. async def main(): async with EmbeddedComfyClient() as client: outputs = await client.queue_prompt(prompt) print(outputs) print("Now that we've exited the with statement, all your VRAM has been cleared from ComfyUI") if __name__ == "__main__" asyncio.run(main()) ``` In order to use this in blocking methods, learn more about asyncio online. """ def __init__(self, configuration: Optional[Configuration] = None, progress_handler: Optional[ExecutorToClientProgress] = None, max_workers: int = 1, executor: ProcessPoolExecutor | ContextVarExecutor = None): self._progress_handler = progress_handler or ServerStub() self._executor = executor or ContextVarExecutor(max_workers=max_workers) self._configuration = configuration self._is_running = False self._task_count_lock = RLock() self._task_count = 0 @property def is_running(self) -> bool: return self._is_running @property def task_count(self) -> int: return self._task_count def __enter__(self): self._is_running = True return self def __exit__(self, *args): get_event_loop().run_in_executor(self._executor, _cleanup) self._executor.shutdown(wait=True) self._is_running = False async def __aenter__(self): self._is_running = True return self async def __aexit__(self, *args): while self.task_count > 0: await asyncio.sleep(0.1) await get_event_loop().run_in_executor(self._executor, _cleanup) self._executor.shutdown(wait=True) self._is_running = False async def queue_prompt_api(self, prompt: PromptDict | str | dict, progress_handler: Optional[ExecutorToClientProgress] = None) -> V1QueuePromptResponse: """ Queues a prompt for execution, returning the output when it is complete. :param prompt: a PromptDict, string or dictionary containing a so-called Workflow API prompt :return: a response of URLs for Save-related nodes and the node outputs """ if isinstance(prompt, str): prompt = json.loads(prompt) if isinstance(prompt, dict): from ..api.components.schema.prompt import Prompt prompt = Prompt.validate(prompt) outputs = await self.queue_prompt(prompt, progress_handler=progress_handler) return V1QueuePromptResponse(urls=[], outputs=outputs) def queue_with_progress(self, prompt: PromptDict | str | dict) -> QueuePromptWithProgress: """ Queues a prompt with progress notifications. >>> from comfy.client.embedded_comfy_client import Comfy >>> from comfy.client.client_types import ProgressNotification >>> async with Comfy() as comfy: >>> task = comfy.queue_with_progress({ ... }) >>> # Raises an exception while iterating >>> notification: ProgressNotification >>> async for notification in task.progress(): >>> print(notification.data) >>> # If you get this far, no errors occurred. >>> result = await task.get() :param prompt: :return: """ handler = QueuePromptWithProgress() task = asyncio.create_task(self.queue_prompt_api(prompt, progress_handler=handler.progress_handler)) task.add_done_callback(handler.complete) return handler @tracer.start_as_current_span("Queue Prompt") async def queue_prompt(self, prompt: PromptDict | dict, prompt_id: Optional[str] = None, client_id: Optional[str] = None, partial_execution_targets: Optional[list[str]] = None, progress_handler: Optional[ExecutorToClientProgress] = None) -> dict: if isinstance(self._executor, ProcessPoolExecutor) and progress_handler is not None: logger.debug(f"a progress_handler={progress_handler} was passed, it must be pickleable to support ProcessPoolExecutor") progress_handler = progress_handler or self._progress_handler with self._task_count_lock: self._task_count += 1 prompt_id = prompt_id or str(uuid.uuid4()) client_id = client_id or self._progress_handler.client_id or None span_context = context.get_current() carrier = {} propagate.inject(carrier, span_context) try: return await get_event_loop().run_in_executor( self._executor, _execute_prompt, make_mutable(prompt), prompt_id, client_id, carrier, # todo: a proxy object or something more sophisticated will have to be done here to restore progress notifications for ProcessPoolExecutors None if isinstance(self._executor, ProcessPoolExecutor) else progress_handler, self._configuration, partial_execution_targets, ) finally: with self._task_count_lock: self._task_count -= 1 EmbeddedComfyClient = Comfy