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ComfyUI/main.py

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import comfy.options
comfy.options.enable_args_parsing()
import os
import importlib.util
import folder_paths
import time
from comfy.cli_args import args
from app.logger import setup_logger
import itertools
import utils.extra_config
import logging
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import sys
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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from comfy_execution.progress import get_progress_state
from comfy_execution.utils import get_executing_context
from comfy_api import feature_flags
if __name__ == "__main__":
#NOTE: These do not do anything on core ComfyUI, they are for custom nodes.
os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
os.environ['DO_NOT_TRACK'] = '1'
setup_logger(log_level=args.verbose, use_stdout=args.log_stdout)
def apply_custom_paths():
# extra model paths
extra_model_paths_config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "extra_model_paths.yaml")
if os.path.isfile(extra_model_paths_config_path):
utils.extra_config.load_extra_path_config(extra_model_paths_config_path)
if args.extra_model_paths_config:
for config_path in itertools.chain(*args.extra_model_paths_config):
utils.extra_config.load_extra_path_config(config_path)
# --output-directory, --input-directory, --user-directory
if args.output_directory:
output_dir = os.path.abspath(args.output_directory)
logging.info(f"Setting output directory to: {output_dir}")
folder_paths.set_output_directory(output_dir)
# These are the default folders that checkpoints, clip and vae models will be saved to when using CheckpointSave, etc.. nodes
folder_paths.add_model_folder_path("checkpoints", os.path.join(folder_paths.get_output_directory(), "checkpoints"))
folder_paths.add_model_folder_path("clip", os.path.join(folder_paths.get_output_directory(), "clip"))
folder_paths.add_model_folder_path("vae", os.path.join(folder_paths.get_output_directory(), "vae"))
folder_paths.add_model_folder_path("diffusion_models",
os.path.join(folder_paths.get_output_directory(), "diffusion_models"))
folder_paths.add_model_folder_path("loras", os.path.join(folder_paths.get_output_directory(), "loras"))
if args.input_directory:
input_dir = os.path.abspath(args.input_directory)
logging.info(f"Setting input directory to: {input_dir}")
folder_paths.set_input_directory(input_dir)
if args.user_directory:
user_dir = os.path.abspath(args.user_directory)
logging.info(f"Setting user directory to: {user_dir}")
folder_paths.set_user_directory(user_dir)
def execute_prestartup_script():
if args.disable_all_custom_nodes and len(args.whitelist_custom_nodes) == 0:
return
def execute_script(script_path):
module_name = os.path.splitext(script_path)[0]
try:
spec = importlib.util.spec_from_file_location(module_name, script_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return True
except Exception as e:
logging.error(f"Failed to execute startup-script: {script_path} / {e}")
return False
node_paths = folder_paths.get_folder_paths("custom_nodes")
for custom_node_path in node_paths:
possible_modules = os.listdir(custom_node_path)
node_prestartup_times = []
for possible_module in possible_modules:
module_path = os.path.join(custom_node_path, possible_module)
if os.path.isfile(module_path) or module_path.endswith(".disabled") or module_path == "__pycache__":
continue
script_path = os.path.join(module_path, "prestartup_script.py")
if os.path.exists(script_path):
if args.disable_all_custom_nodes and possible_module not in args.whitelist_custom_nodes:
logging.info(f"Prestartup Skipping {possible_module} due to disable_all_custom_nodes and whitelist_custom_nodes")
continue
time_before = time.perf_counter()
success = execute_script(script_path)
node_prestartup_times.append((time.perf_counter() - time_before, module_path, success))
if len(node_prestartup_times) > 0:
logging.info("\nPrestartup times for custom nodes:")
for n in sorted(node_prestartup_times):
if n[2]:
import_message = ""
else:
import_message = " (PRESTARTUP FAILED)"
logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1]))
logging.info("")
apply_custom_paths()
execute_prestartup_script()
# Main code
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import asyncio
import shutil
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import threading
import gc
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if os.name == "nt":
logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
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if __name__ == "__main__":
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if args.default_device is not None:
default_dev = args.default_device
devices = list(range(32))
devices.remove(default_dev)
devices.insert(0, default_dev)
devices = ','.join(map(str, devices))
os.environ['CUDA_VISIBLE_DEVICES'] = str(devices)
os.environ['HIP_VISIBLE_DEVICES'] = str(devices)
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if args.cuda_device is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device)
os.environ['HIP_VISIBLE_DEVICES'] = str(args.cuda_device)
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logging.info("Set cuda device to: {}".format(args.cuda_device))
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if args.oneapi_device_selector is not None:
os.environ['ONEAPI_DEVICE_SELECTOR'] = args.oneapi_device_selector
logging.info("Set oneapi device selector to: {}".format(args.oneapi_device_selector))
if args.deterministic:
if 'CUBLAS_WORKSPACE_CONFIG' not in os.environ:
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ":4096:8"
import cuda_malloc
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if 'torch' in sys.modules:
logging.warning("WARNING: Potential Error in code: Torch already imported, torch should never be imported before this point.")
import comfy.utils
import execution
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import server
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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from protocol import BinaryEventTypes
import nodes
import comfy.model_management
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import comfyui_version
import app.logger
import hook_breaker_ac10a0
def cuda_malloc_warning():
device = comfy.model_management.get_torch_device()
device_name = comfy.model_management.get_torch_device_name(device)
cuda_malloc_warning = False
if "cudaMallocAsync" in device_name:
for b in cuda_malloc.blacklist:
if b in device_name:
cuda_malloc_warning = True
if cuda_malloc_warning:
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logging.warning("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n")
def prompt_worker(q, server_instance):
current_time: float = 0.0
cache_type = execution.CacheType.CLASSIC
if args.cache_lru > 0:
cache_type = execution.CacheType.LRU
elif args.cache_none:
cache_type = execution.CacheType.DEPENDENCY_AWARE
e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_size=args.cache_lru)
last_gc_collect = 0
need_gc = False
gc_collect_interval = 10.0
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while True:
timeout = 1000.0
if need_gc:
timeout = max(gc_collect_interval - (current_time - last_gc_collect), 0.0)
queue_item = q.get(timeout=timeout)
if queue_item is not None:
item, item_id = queue_item
execution_start_time = time.perf_counter()
prompt_id = item[1]
server_instance.last_prompt_id = prompt_id
e.execute(item[2], prompt_id, item[3], item[4])
need_gc = True
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q.task_done(item_id,
Execution Model Inversion (#2666) * Execution Model Inversion This PR inverts the execution model -- from recursively calling nodes to using a topological sort of the nodes. This change allows for modification of the node graph during execution. This allows for two major advantages: 1. The implementation of lazy evaluation in nodes. For example, if a "Mix Images" node has a mix factor of exactly 0.0, the second image input doesn't even need to be evaluated (and visa-versa if the mix factor is 1.0). 2. Dynamic expansion of nodes. This allows for the creation of dynamic "node groups". Specifically, custom nodes can return subgraphs that replace the original node in the graph. This is an incredibly powerful concept. Using this functionality, it was easy to implement: a. Components (a.k.a. node groups) b. Flow control (i.e. while loops) via tail recursion c. All-in-one nodes that replicate the WebUI functionality d. and more All of those were able to be implemented entirely via custom nodes, so those features are *not* a part of this PR. (There are some front-end changes that should occur before that functionality is made widely available, particularly around variant sockets.) The custom nodes associated with this PR can be found at: https://github.com/BadCafeCode/execution-inversion-demo-comfyui Note that some of them require that variant socket types ("*") be enabled. * Allow `input_info` to be of type `None` * Handle errors (like OOM) more gracefully * Add a command-line argument to enable variants This allows the use of nodes that have sockets of type '*' without applying a patch to the code. * Fix an overly aggressive assertion. This could happen when attempting to evaluate `IS_CHANGED` for a node during the creation of the cache (in order to create the cache key). * Fix Pyright warnings * Add execution model unit tests * Fix issue with unused literals Behavior should now match the master branch with regard to undeclared inputs. Undeclared inputs that are socket connections will be used while undeclared inputs that are literals will be ignored. * Make custom VALIDATE_INPUTS skip normal validation Additionally, if `VALIDATE_INPUTS` takes an argument named `input_types`, that variable will be a dictionary of the socket type of all incoming connections. If that argument exists, normal socket type validation will not occur. This removes the last hurdle for enabling variant types entirely from custom nodes, so I've removed that command-line option. I've added appropriate unit tests for these changes. * Fix example in unit test This wouldn't have caused any issues in the unit test, but it would have bugged the UI if someone copy+pasted it into their own node pack. * Use fstrings instead of '%' formatting syntax * Use custom exception types. * Display an error for dependency cycles Previously, dependency cycles that were created during node expansion would cause the application to quit (due to an uncaught exception). Now, we'll throw a proper error to the UI. We also make an attempt to 'blame' the most relevant node in the UI. * Add docs on when ExecutionBlocker should be used * Remove unused functionality * Rename ExecutionResult.SLEEPING to PENDING * Remove superfluous function parameter * Pass None for uneval inputs instead of default This applies to `VALIDATE_INPUTS`, `check_lazy_status`, and lazy values in evaluation functions. * Add a test for mixed node expansion This test ensures that a node that returns a combination of expanded subgraphs and literal values functions correctly. * Raise exception for bad get_node calls. * Minor refactor of IsChangedCache.get * Refactor `map_node_over_list` function * Fix ui output for duplicated nodes * Add documentation on `check_lazy_status` * Add file for execution model unit tests * Clean up Javascript code as per review * Improve documentation Converted some comments to docstrings as per review * Add a new unit test for mixed lazy results This test validates that when an output list is fed to a lazy node, the node will properly evaluate previous nodes that are needed by any inputs to the lazy node. No code in the execution model has been changed. The test already passes. * Allow kwargs in VALIDATE_INPUTS functions When kwargs are used, validation is skipped for all inputs as if they had been mentioned explicitly. * List cached nodes in `execution_cached` message This was previously just bugged in this PR.
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e.history_result,
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status=execution.PromptQueue.ExecutionStatus(
status_str='success' if e.success else 'error',
completed=e.success,
messages=e.status_messages))
if server_instance.client_id is not None:
server_instance.send_sync("executing", {"node": None, "prompt_id": prompt_id}, server_instance.client_id)
current_time = time.perf_counter()
execution_time = current_time - execution_start_time
# Log Time in a more readable way after 10 minutes
if execution_time > 600:
execution_time = time.strftime("%H:%M:%S", time.gmtime(execution_time))
logging.info(f"Prompt executed in {execution_time}")
else:
logging.info("Prompt executed in {:.2f} seconds".format(execution_time))
flags = q.get_flags()
free_memory = flags.get("free_memory", False)
if flags.get("unload_models", free_memory):
comfy.model_management.unload_all_models()
need_gc = True
last_gc_collect = 0
if free_memory:
e.reset()
need_gc = True
last_gc_collect = 0
if need_gc:
current_time = time.perf_counter()
if (current_time - last_gc_collect) > gc_collect_interval:
gc.collect()
comfy.model_management.soft_empty_cache()
last_gc_collect = current_time
need_gc = False
hook_breaker_ac10a0.restore_functions()
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async def run(server_instance, address='', port=8188, verbose=True, call_on_start=None):
addresses = []
for addr in address.split(","):
addresses.append((addr, port))
await asyncio.gather(
server_instance.start_multi_address(addresses, call_on_start, verbose), server_instance.publish_loop()
)
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def hijack_progress(server_instance):
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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def hook(value, total, preview_image, prompt_id=None, node_id=None):
executing_context = get_executing_context()
if prompt_id is None and executing_context is not None:
prompt_id = executing_context.prompt_id
if node_id is None and executing_context is not None:
node_id = executing_context.node_id
comfy.model_management.throw_exception_if_processing_interrupted()
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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if prompt_id is None:
prompt_id = server_instance.last_prompt_id
if node_id is None:
node_id = server_instance.last_node_id
progress = {"value": value, "max": total, "prompt_id": prompt_id, "node": node_id}
get_progress_state().update_progress(node_id, value, total, preview_image)
server_instance.send_sync("progress", progress, server_instance.client_id)
if preview_image is not None:
Support for async node functions (#8830) * Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
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# Only send old method if client doesn't support preview metadata
if not feature_flags.supports_feature(
server_instance.sockets_metadata,
server_instance.client_id,
"supports_preview_metadata",
):
server_instance.send_sync(
BinaryEventTypes.UNENCODED_PREVIEW_IMAGE,
preview_image,
server_instance.client_id,
)
comfy.utils.set_progress_bar_global_hook(hook)
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def cleanup_temp():
temp_dir = folder_paths.get_temp_directory()
if os.path.exists(temp_dir):
shutil.rmtree(temp_dir, ignore_errors=True)
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def setup_database():
try:
from app.database.db import init_db, dependencies_available
if dependencies_available():
init_db()
except Exception as e:
logging.error(f"Failed to initialize database. Please ensure you have installed the latest requirements. If the error persists, please report this as in future the database will be required: {e}")
def start_comfyui(asyncio_loop=None):
"""
Starts the ComfyUI server using the provided asyncio event loop or creates a new one.
Returns the event loop, server instance, and a function to start the server asynchronously.
"""
if args.temp_directory:
temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp")
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logging.info(f"Setting temp directory to: {temp_dir}")
folder_paths.set_temp_directory(temp_dir)
cleanup_temp()
if args.windows_standalone_build:
try:
import new_updater
new_updater.update_windows_updater()
except:
pass
if not asyncio_loop:
asyncio_loop = asyncio.new_event_loop()
asyncio.set_event_loop(asyncio_loop)
prompt_server = server.PromptServer(asyncio_loop)
hook_breaker_ac10a0.save_functions()
asyncio_loop.run_until_complete(nodes.init_extra_nodes(
init_custom_nodes=(not args.disable_all_custom_nodes) or len(args.whitelist_custom_nodes) > 0,
init_api_nodes=not args.disable_api_nodes
))
hook_breaker_ac10a0.restore_functions()
cuda_malloc_warning()
setup_database()
prompt_server.add_routes()
hijack_progress(prompt_server)
threading.Thread(target=prompt_worker, daemon=True, args=(prompt_server.prompt_queue, prompt_server,)).start()
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if args.quick_test_for_ci:
exit(0)
os.makedirs(folder_paths.get_temp_directory(), exist_ok=True)
call_on_start = None
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if args.auto_launch:
def startup_server(scheme, address, port):
import webbrowser
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if os.name == 'nt' and address == '0.0.0.0':
address = '127.0.0.1'
if ':' in address:
address = "[{}]".format(address)
webbrowser.open(f"{scheme}://{address}:{port}")
call_on_start = startup_server
async def start_all():
await prompt_server.setup()
await run(prompt_server, address=args.listen, port=args.port, verbose=not args.dont_print_server, call_on_start=call_on_start)
# Returning these so that other code can integrate with the ComfyUI loop and server
return asyncio_loop, prompt_server, start_all
if __name__ == "__main__":
# Running directly, just start ComfyUI.
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logging.info("Python version: {}".format(sys.version))
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logging.info("ComfyUI version: {}".format(comfyui_version.__version__))
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if sys.version_info.major == 3 and sys.version_info.minor < 10:
logging.warning("WARNING: You are using a python version older than 3.10, please upgrade to a newer one. 3.12 and above is recommended.")
event_loop, _, start_all_func = start_comfyui()
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try:
x = start_all_func()
app.logger.print_startup_warnings()
event_loop.run_until_complete(x)
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except KeyboardInterrupt:
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logging.info("\nStopped server")
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cleanup_temp()