Merge branch 'master' into worksplit-multigpu

alembic.ini

@@ -1,84 +0,0 @@
-# A generic, single database configuration.
-
-[alembic]
-# path to migration scripts
-# Use forward slashes (/) also on windows to provide an os agnostic path
-script_location = alembic_db
-
-# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
-# Uncomment the line below if you want the files to be prepended with date and time
-# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file
-# for all available tokens
-# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
-
-# sys.path path, will be prepended to sys.path if present.
-# defaults to the current working directory.
-prepend_sys_path = .
-
-# timezone to use when rendering the date within the migration file
-# as well as the filename.
-# If specified, requires the python>=3.9 or backports.zoneinfo library and tzdata library.
-# Any required deps can installed by adding `alembic[tz]` to the pip requirements
-# string value is passed to ZoneInfo()
-# leave blank for localtime
-# timezone =
-
-# max length of characters to apply to the "slug" field
-# truncate_slug_length = 40
-
-# set to 'true' to run the environment during
-# the 'revision' command, regardless of autogenerate
-# revision_environment = false
-
-# set to 'true' to allow .pyc and .pyo files without
-# a source .py file to be detected as revisions in the
-# versions/ directory
-# sourceless = false
-
-# version location specification; This defaults
-# to alembic_db/versions.  When using multiple version
-# directories, initial revisions must be specified with --version-path.
-# The path separator used here should be the separator specified by "version_path_separator" below.
-# version_locations = %(here)s/bar:%(here)s/bat:alembic_db/versions
-
-# version path separator; As mentioned above, this is the character used to split
-# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep.
-# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas.
-# Valid values for version_path_separator are:
-#
-# version_path_separator = :
-# version_path_separator = ;
-# version_path_separator = space
-# version_path_separator = newline
-#
-# Use os.pathsep. Default configuration used for new projects.
-version_path_separator = os
-
-# set to 'true' to search source files recursively
-# in each "version_locations" directory
-# new in Alembic version 1.10
-# recursive_version_locations = false
-
-# the output encoding used when revision files
-# are written from script.py.mako
-# output_encoding = utf-8
-
-sqlalchemy.url = sqlite:///user/comfyui.db
-
-
-[post_write_hooks]
-# post_write_hooks defines scripts or Python functions that are run
-# on newly generated revision scripts.  See the documentation for further
-# detail and examples
-
-# format using "black" - use the console_scripts runner, against the "black" entrypoint
-# hooks = black
-# black.type = console_scripts
-# black.entrypoint = black
-# black.options = -l 79 REVISION_SCRIPT_FILENAME
-
-# lint with attempts to fix using "ruff" - use the exec runner, execute a binary
-# hooks = ruff
-# ruff.type = exec
-# ruff.executable = %(here)s/.venv/bin/ruff
-# ruff.options = check --fix REVISION_SCRIPT_FILENAME

alembic_db/README.md

@@ -1,4 +0,0 @@
-## Generate new revision
-
-1. Update models in `/app/database/models.py`
-2. Run `alembic revision --autogenerate -m "{your message}"`

alembic_db/env.py

@@ -1,69 +0,0 @@
-from sqlalchemy import engine_from_config
-from sqlalchemy import pool
-
-from alembic import context
-
-# this is the Alembic Config object, which provides
-# access to the values within the .ini file in use.
-config = context.config
-
-
-from app.database.models import Base
-target_metadata = Base.metadata
-
-# other values from the config, defined by the needs of env.py,
-# can be acquired:
-# my_important_option = config.get_main_option("my_important_option")
-# ... etc.
-
-
-def run_migrations_offline() -> None:
-    """Run migrations in 'offline' mode.
-
-    This configures the context with just a URL
-    and not an Engine, though an Engine is acceptable
-    here as well.  By skipping the Engine creation
-    we don't even need a DBAPI to be available.
-
-    Calls to context.execute() here emit the given string to the
-    script output.
-
-    """
-    url = config.get_main_option("sqlalchemy.url")
-    context.configure(
-        url=url,
-        target_metadata=target_metadata,
-        literal_binds=True,
-        dialect_opts={"paramstyle": "named"},
-    )
-
-    with context.begin_transaction():
-        context.run_migrations()
-
-
-def run_migrations_online() -> None:
-    """Run migrations in 'online' mode.
-
-    In this scenario we need to create an Engine
-    and associate a connection with the context.
-
-    """
-    connectable = engine_from_config(
-        config.get_section(config.config_ini_section, {}),
-        prefix="sqlalchemy.",
-        poolclass=pool.NullPool,
-    )
-
-    with connectable.connect() as connection:
-        context.configure(
-            connection=connection, target_metadata=target_metadata
-        )
-
-        with context.begin_transaction():
-            context.run_migrations()
-
-
-if context.is_offline_mode():
-    run_migrations_offline()
-else:
-    run_migrations_online()

alembic_db/script.py.mako

@@ -1,28 +0,0 @@
-"""${message}
-
-Revision ID: ${up_revision}
-Revises: ${down_revision | comma,n}
-Create Date: ${create_date}
-
-"""
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-${imports if imports else ""}
-
-# revision identifiers, used by Alembic.
-revision: str = ${repr(up_revision)}
-down_revision: Union[str, None] = ${repr(down_revision)}
-branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
-depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
-
-
-def upgrade() -> None:
-    """Upgrade schema."""
-    ${upgrades if upgrades else "pass"}
-
-
-def downgrade() -> None:
-    """Downgrade schema."""
-    ${downgrades if downgrades else "pass"}

alembic_db/versions/e9c714da8d57_init.py

@@ -1,40 +0,0 @@
-"""init
-
-Revision ID: e9c714da8d57
-Revises:
-Create Date: 2025-05-30 20:14:33.772039
-
-"""
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision: str = 'e9c714da8d57'
-down_revision: Union[str, None] = None
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    """Upgrade schema."""
-    op.create_table('model',
-    sa.Column('type', sa.Text(), nullable=False),
-    sa.Column('path', sa.Text(), nullable=False),
-    sa.Column('file_name', sa.Text(), nullable=True),
-    sa.Column('file_size', sa.Integer(), nullable=True),
-    sa.Column('hash', sa.Text(), nullable=True),
-    sa.Column('hash_algorithm', sa.Text(), nullable=True),
-    sa.Column('source_url', sa.Text(), nullable=True),
-    sa.Column('date_added', sa.DateTime(), server_default=sa.text('(CURRENT_TIMESTAMP)'), nullable=True),
-    sa.PrimaryKeyConstraint('type', 'path')
-    )
-
-
-def downgrade() -> None:
-    """Downgrade schema."""
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_table('model')
-    # ### end Alembic commands ###

app/database/db.py

@@ -1,112 +0,0 @@
-import logging
-import os
-import shutil
-from app.logger import log_startup_warning
-from utils.install_util import get_missing_requirements_message
-from comfy.cli_args import args
-
-_DB_AVAILABLE = False
-Session = None
-
-
-try:
-    from alembic import command
-    from alembic.config import Config
-    from alembic.runtime.migration import MigrationContext
-    from alembic.script import ScriptDirectory
-    from sqlalchemy import create_engine
-    from sqlalchemy.orm import sessionmaker
-
-    _DB_AVAILABLE = True
-except ImportError as e:
-    log_startup_warning(
-        f"""
-------------------------------------------------------------------------
-Error importing dependencies: {e}
-
-{get_missing_requirements_message()}
-
-This error is happening because ComfyUI now uses a local sqlite database.
-------------------------------------------------------------------------
-""".strip()
-    )
-
-
-def dependencies_available():
-    """
-    Temporary function to check if the dependencies are available
-    """
-    return _DB_AVAILABLE
-
-
-def can_create_session():
-    """
-    Temporary function to check if the database is available to create a session
-    During initial release there may be environmental issues (or missing dependencies) that prevent the database from being created
-    """
-    return dependencies_available() and Session is not None
-
-
-def get_alembic_config():
-    root_path = os.path.join(os.path.dirname(__file__), "../..")
-    config_path = os.path.abspath(os.path.join(root_path, "alembic.ini"))
-    scripts_path = os.path.abspath(os.path.join(root_path, "alembic_db"))
-
-    config = Config(config_path)
-    config.set_main_option("script_location", scripts_path)
-    config.set_main_option("sqlalchemy.url", args.database_url)
-
-    return config
-
-
-def get_db_path():
-    url = args.database_url
-    if url.startswith("sqlite:///"):
-        return url.split("///")[1]
-    else:
-        raise ValueError(f"Unsupported database URL '{url}'.")
-
-
-def init_db():
-    db_url = args.database_url
-    logging.debug(f"Database URL: {db_url}")
-    db_path = get_db_path()
-    db_exists = os.path.exists(db_path)
-
-    config = get_alembic_config()
-
-    # Check if we need to upgrade
-    engine = create_engine(db_url)
-    conn = engine.connect()
-
-    context = MigrationContext.configure(conn)
-    current_rev = context.get_current_revision()
-
-    script = ScriptDirectory.from_config(config)
-    target_rev = script.get_current_head()
-
-    if current_rev != target_rev:
-        # Backup the database pre upgrade
-        backup_path = db_path + ".bkp"
-        if db_exists:
-            shutil.copy(db_path, backup_path)
-        else:
-            backup_path = None
-
-        try:
-            command.upgrade(config, target_rev)
-            logging.info(f"Database upgraded from {current_rev} to {target_rev}")
-        except Exception as e:
-            if backup_path:
-                # Restore the database from backup if upgrade fails
-                shutil.copy(backup_path, db_path)
-                os.remove(backup_path)
-            logging.error(f"Error upgrading database: {e}")
-            raise e
-
-    global Session
-    Session = sessionmaker(bind=engine)
-
-
-def create_session():
-    return Session()

app/database/models.py

@@ -1,59 +0,0 @@
-from sqlalchemy import (
-    Column,
-    Integer,
-    Text,
-    DateTime,
-)
-from sqlalchemy.orm import declarative_base
-from sqlalchemy.sql import func
-
-Base = declarative_base()
-
-
-def to_dict(obj):
-    fields = obj.__table__.columns.keys()
-    return {
-        field: (val.to_dict() if hasattr(val, "to_dict") else val)
-        for field in fields
-        if (val := getattr(obj, field))
-    }
-
-
-class Model(Base):
-    """
-    sqlalchemy model representing a model file in the system.
-
-    This class defines the database schema for storing information about model files,
-    including their type, path, hash, and when they were added to the system.
-
-    Attributes:
-        type (Text): The type of the model, this is the name of the folder in the models folder (primary key)
-        path (Text): The file path of the model relative to the type folder (primary key)
-        file_name (Text): The name of the model file
-        file_size (Integer): The size of the model file in bytes
-        hash (Text): A hash of the model file
-        hash_algorithm (Text): The algorithm used to generate the hash
-        source_url (Text): The URL of the model file
-        date_added (DateTime): Timestamp of when the model was added to the system
-    """
-
-    __tablename__ = "model"
-
-    type = Column(Text, primary_key=True)
-    path = Column(Text, primary_key=True)
-    file_name = Column(Text)
-    file_size = Column(Integer)
-    hash = Column(Text)
-    hash_algorithm = Column(Text)
-    source_url = Column(Text)
-    date_added = Column(DateTime, server_default=func.now())
-
-    def to_dict(self):
-        """
-        Convert the model instance to a dictionary representation.
-
-        Returns:
-            dict: A dictionary containing the attributes of the model
-        """
-        dict = to_dict(self)
-        return dict

app/frontend_management.py

@@ -16,15 +16,26 @@ from importlib.metadata import version
 import requests
 from typing_extensions import NotRequired
 
-from utils.install_util import get_missing_requirements_message, requirements_path
 from comfy.cli_args import DEFAULT_VERSION_STRING
 import app.logger
 
+# The path to the requirements.txt file
+req_path = Path(__file__).parents[1] / "requirements.txt"
+
+
 def frontend_install_warning_message():
+    """The warning message to display when the frontend version is not up to date."""
+
+    extra = ""
+    if sys.flags.no_user_site:
+        extra = "-s "
     return f"""
-{get_missing_requirements_message()}
+Please install the updated requirements.txt file by running:
+{sys.executable} {extra}-m pip install -r {req_path}
 
 This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.
+
+If you are on the portable package you can run: update\\update_comfyui.bat to solve this problem
 """.strip()
 
 
@@ -37,7 +48,7 @@ def check_frontend_version():
     try:
         frontend_version_str = version("comfyui-frontend-package")
         frontend_version = parse_version(frontend_version_str)
-        with open(requirements_path, "r", encoding="utf-8") as f:
+        with open(req_path, "r", encoding="utf-8") as f:
             required_frontend = parse_version(f.readline().split("=")[-1])
         if frontend_version < required_frontend:
             app.logger.log_startup_warning(
@@ -151,30 +162,10 @@ def download_release_asset_zip(release: Release, destination_path: str) -> None:
 
 
 class FrontendManager:
-    """
-    A class to manage ComfyUI frontend versions and installations.
-
-    This class handles the initialization and management of different frontend versions,
-    including the default frontend from the pip package and custom frontend versions
-    from GitHub repositories.
-
-    Attributes:
-        CUSTOM_FRONTENDS_ROOT (str): The root directory where custom frontend versions are stored.
-    """
-
     CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions")
 
     @classmethod
     def default_frontend_path(cls) -> str:
-        """
-        Get the path to the default frontend installation from the pip package.
-
-        Returns:
-            str: The path to the default frontend static files.
-
-        Raises:
-            SystemExit: If the comfyui-frontend-package is not installed.
-        """
         try:
             import comfyui_frontend_package
 
@@ -195,15 +186,6 @@ comfyui-frontend-package is not installed.
 
     @classmethod
     def templates_path(cls) -> str:
-        """
-        Get the path to the workflow templates.
-
-        Returns:
-            str: The path to the workflow templates directory.
-
-        Raises:
-            SystemExit: If the comfyui-workflow-templates package is not installed.
-        """
         try:
             import comfyui_workflow_templates
 
@@ -239,16 +221,11 @@ comfyui-workflow-templates is not installed.
     @classmethod
     def parse_version_string(cls, value: str) -> tuple[str, str, str]:
         """
-        Parse a version string into its components.
-
-        The version string should be in the format: 'owner/repo@version'
-        where version can be either a semantic version (v1.2.3) or 'latest'.
-
         Args:
             value (str): The version string to parse.
 
         Returns:
-            tuple[str, str, str]: A tuple containing (owner, repo, version).
+            tuple[str, str]: A tuple containing provider name and version.
 
         Raises:
             argparse.ArgumentTypeError: If the version string is invalid.
@@ -265,22 +242,18 @@ comfyui-workflow-templates is not installed.
         cls, version_string: str, provider: Optional[FrontEndProvider] = None
     ) -> str:
         """
-        Initialize a frontend version without error handling.
-
-        This method attempts to initialize a specific frontend version, either from
-        the default pip package or from a custom GitHub repository. It will download
-        and extract the frontend files if necessary.
+        Initializes the frontend for the specified version.
 
         Args:
-            version_string (str): The version string specifying which frontend to use.
-            provider (FrontEndProvider, optional): The provider to use for custom frontends.
+            version_string (str): The version string.
+            provider (FrontEndProvider, optional): The provider to use. Defaults to None.
 
         Returns:
             str: The path to the initialized frontend.
 
         Raises:
-            Exception: If there is an error during initialization (e.g., network timeout,
-                      invalid URL, or missing assets).
+            Exception: If there is an error during the initialization process.
+            main error source might be request timeout or invalid URL.
         """
         if version_string == DEFAULT_VERSION_STRING:
             check_frontend_version()
@@ -332,17 +305,13 @@ comfyui-workflow-templates is not installed.
     @classmethod
     def init_frontend(cls, version_string: str) -> str:
         """
-        Initialize a frontend version with error handling.
-
-        This is the main method to initialize a frontend version. It wraps init_frontend_unsafe
-        with error handling, falling back to the default frontend if initialization fails.
+        Initializes the frontend with the specified version string.
 
         Args:
-            version_string (str): The version string specifying which frontend to use.
+            version_string (str): The version string to initialize the frontend with.
 
         Returns:
-            str: The path to the initialized frontend. If initialization fails,
-                 returns the path to the default frontend.
+            str: The path of the initialized frontend.
         """
         try:
             return cls.init_frontend_unsafe(version_string)

app/model_processor.py

@@ -1,331 +0,0 @@
-import os
-import logging
-import time
-
-import requests
-from tqdm import tqdm
-from folder_paths import get_relative_path, get_full_path
-from app.database.db import create_session, dependencies_available, can_create_session
-import blake3
-import comfy.utils
-
-
-if dependencies_available():
-    from app.database.models import Model
-
-
-class ModelProcessor:
-    def _validate_path(self, model_path):
-        try:
-            if not self._file_exists(model_path):
-                logging.error(f"Model file not found: {model_path}")
-                return None
-
-            result = get_relative_path(model_path)
-            if not result:
-                logging.error(
-                    f"Model file not in a recognized model directory: {model_path}"
-                )
-                return None
-
-            return result
-        except Exception as e:
-            logging.error(f"Error validating model path {model_path}: {str(e)}")
-            return None
-
-    def _file_exists(self, path):
-        """Check if a file exists."""
-        return os.path.exists(path)
-
-    def _get_file_size(self, path):
-        """Get file size."""
-        return os.path.getsize(path)
-
-    def _get_hasher(self):
-        return blake3.blake3()
-
-    def _hash_file(self, model_path):
-        try:
-            hasher = self._get_hasher()
-            with open(model_path, "rb", buffering=0) as f:
-                b = bytearray(128 * 1024)
-                mv = memoryview(b)
-                while n := f.readinto(mv):
-                    hasher.update(mv[:n])
-            return hasher.hexdigest()
-        except Exception as e:
-            logging.error(f"Error hashing file {model_path}: {str(e)}")
-            return None
-
-    def _get_existing_model(self, session, model_type, model_relative_path):
-        return (
-            session.query(Model)
-            .filter(Model.type == model_type)
-            .filter(Model.path == model_relative_path)
-            .first()
-        )
-
-    def _ensure_source_url(self, session, model, source_url):
-        if model.source_url is None:
-            model.source_url = source_url
-            session.commit()
-
-    def _update_database(
-        self,
-        session,
-        model_type,
-        model_path,
-        model_relative_path,
-        model_hash,
-        model,
-        source_url,
-    ):
-        try:
-            if not model:
-                model = self._get_existing_model(
-                    session, model_type, model_relative_path
-                )
-
-            if not model:
-                model = Model(
-                    path=model_relative_path,
-                    type=model_type,
-                    file_name=os.path.basename(model_path),
-                )
-                session.add(model)
-
-            model.file_size = self._get_file_size(model_path)
-            model.hash = model_hash
-            if model_hash:
-                model.hash_algorithm = "blake3"
-            model.source_url = source_url
-
-            session.commit()
-            return model
-        except Exception as e:
-            logging.error(
-                f"Error updating database for {model_relative_path}: {str(e)}"
-            )
-
-    def process_file(self, model_path, source_url=None, model_hash=None):
-        """
-        Process a model file and update the database with metadata.
-        If the file already exists and matches the database, it will not be processed again.
-        Returns the model object or if an error occurs, returns None.
-        """
-        try:
-            if not can_create_session():
-                return
-
-            result = self._validate_path(model_path)
-            if not result:
-                return
-            model_type, model_relative_path = result
-
-            with create_session() as session:
-                session.expire_on_commit = False
-
-                existing_model = self._get_existing_model(
-                    session, model_type, model_relative_path
-                )
-                if (
-                    existing_model
-                    and existing_model.hash
-                    and existing_model.file_size == self._get_file_size(model_path)
-                ):
-                    # File exists with hash and same size, no need to process
-                    self._ensure_source_url(session, existing_model, source_url)
-                    return existing_model
-
-                if model_hash:
-                    model_hash = model_hash.lower()
-                    logging.info(f"Using provided hash: {model_hash}")
-                else:
-                    start_time = time.time()
-                    logging.info(f"Hashing model {model_relative_path}")
-                    model_hash = self._hash_file(model_path)
-                    if not model_hash:
-                        return
-                    logging.info(
-                        f"Model hash: {model_hash} (duration: {time.time() - start_time} seconds)"
-                    )
-
-                return self._update_database(
-                    session,
-                    model_type,
-                    model_path,
-                    model_relative_path,
-                    model_hash,
-                    existing_model,
-                    source_url,
-                )
-        except Exception as e:
-            logging.error(f"Error processing model file {model_path}: {str(e)}")
-            return None
-
-    def retrieve_model_by_hash(self, model_hash, model_type=None, session=None):
-        """
-        Retrieve a model file from the database by hash and optionally by model type.
-        Returns the model object or None if the model doesnt exist or an error occurs.
-        """
-        try:
-            if not can_create_session():
-                return
-
-            dispose_session = False
-
-            if session is None:
-                session = create_session()
-                dispose_session = True
-
-            model = session.query(Model).filter(Model.hash == model_hash)
-            if model_type is not None:
-                model = model.filter(Model.type == model_type)
-            return model.first()
-        except Exception as e:
-            logging.error(f"Error retrieving model by hash {model_hash}: {str(e)}")
-            return None
-        finally:
-            if dispose_session:
-                session.close()
-
-    def retrieve_hash(self, model_path, model_type=None):
-        """
-        Retrieve the hash of a model file from the database.
-        Returns the hash or None if the model doesnt exist or an error occurs.
-        """
-        try:
-            if not can_create_session():
-                return
-
-            if model_type is not None:
-                result = self._validate_path(model_path)
-                if not result:
-                    return None
-            model_type, model_relative_path = result
-
-            with create_session() as session:
-                model = self._get_existing_model(
-                    session, model_type, model_relative_path
-                )
-                if model and model.hash:
-                    return model.hash
-                return None
-        except Exception as e:
-            logging.error(f"Error retrieving hash for {model_path}: {str(e)}")
-            return None
-
-    def _validate_file_extension(self, file_name):
-        """Validate that the file extension is supported."""
-        extension = os.path.splitext(file_name)[1]
-        if extension not in (".safetensors", ".sft", ".txt", ".csv", ".json", ".yaml"):
-            raise ValueError(f"Unsupported unsafe file for download: {file_name}")
-
-    def _check_existing_file(self, model_type, file_name, expected_hash):
-        """Check if file exists and has correct hash."""
-        destination_path = get_full_path(model_type, file_name, allow_missing=True)
-        if self._file_exists(destination_path):
-            model = self.process_file(destination_path)
-            if model and (expected_hash is None or model.hash == expected_hash):
-                logging.debug(
-                    f"File {destination_path} already exists in the database and has the correct hash or no hash was provided."
-                )
-                return destination_path
-            else:
-                raise ValueError(
-                    f"File {destination_path} exists with hash {model.hash if model else 'unknown'} but expected {expected_hash}. Please delete the file and try again."
-                )
-        return None
-
-    def _check_existing_file_by_hash(self, hash, type, url):
-        """Check if a file with the given hash exists in the database and on disk."""
-        hash = hash.lower()
-        with create_session() as session:
-            model = self.retrieve_model_by_hash(hash, type, session)
-            if model:
-                existing_path = get_full_path(type, model.path)
-                if existing_path:
-                    logging.debug(
-                        f"File {model.path} already exists in the database at {existing_path}"
-                    )
-                    self._ensure_source_url(session, model, url)
-                    return existing_path
-                else:
-                    logging.debug(
-                        f"File {model.path} exists in the database but not on disk"
-                    )
-        return None
-
-    def _download_file(self, url, destination_path, hasher):
-        """Download a file and update the hasher with its contents."""
-        response = requests.get(url, stream=True)
-        logging.info(f"Downloading {url} to {destination_path}")
-
-        with open(destination_path, "wb") as f:
-            total_size = int(response.headers.get("content-length", 0))
-            if total_size > 0:
-                pbar = comfy.utils.ProgressBar(total_size)
-            else:
-                pbar = None
-            with tqdm(total=total_size, unit="B", unit_scale=True) as progress_bar:
-                for chunk in response.iter_content(chunk_size=128 * 1024):
-                    if chunk:
-                        f.write(chunk)
-                        hasher.update(chunk)
-                        progress_bar.update(len(chunk))
-                        if pbar:
-                            pbar.update(len(chunk))
-
-    def _verify_downloaded_hash(self, calculated_hash, expected_hash, destination_path):
-        """Verify that the downloaded file has the expected hash."""
-        if expected_hash is not None and calculated_hash != expected_hash:
-            self._remove_file(destination_path)
-            raise ValueError(
-                f"Downloaded file hash {calculated_hash} does not match expected hash {expected_hash}"
-            )
-
-    def _remove_file(self, file_path):
-        """Remove a file from disk."""
-        os.remove(file_path)
-
-    def ensure_downloaded(self, type, url, desired_file_name, hash=None):
-        """
-        Ensure a model file is downloaded and has the correct hash.
-        Returns the path to the downloaded file.
-        """
-        logging.debug(
-            f"Ensuring {type} file is downloaded. URL='{url}' Destination='{desired_file_name}' Hash='{hash}'"
-        )
-
-        # Validate file extension
-        self._validate_file_extension(desired_file_name)
-
-        # Check if file exists with correct hash
-        if hash:
-            existing_path = self._check_existing_file_by_hash(hash, type, url)
-            if existing_path:
-                return existing_path
-
-        # Check if file exists locally
-        destination_path = get_full_path(type, desired_file_name, allow_missing=True)
-        existing_path = self._check_existing_file(type, desired_file_name, hash)
-        if existing_path:
-            return existing_path
-
-        # Download the file
-        hasher = self._get_hasher()
-        self._download_file(url, destination_path, hasher)
-
-        # Verify hash
-        calculated_hash = hasher.hexdigest()
-        self._verify_downloaded_hash(calculated_hash, hash, destination_path)
-
-        # Update database
-        self.process_file(destination_path, url, calculated_hash)
-
-        # TODO: Notify frontend to reload models
-
-        return destination_path
-
-
-model_processor = ModelProcessor()

comfy/cli_args.py

@@ -49,7 +49,7 @@ parser.add_argument("--temp-directory", type=str, default=None, help="Set the Co
 parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-directory.")
 parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
 parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
-parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
+parser.add_argument("--cuda-device", type=str, default=None, metavar="DEVICE_ID", help="Set the ids of cuda devices this instance will use.")
 cm_group = parser.add_mutually_exclusive_group()
 cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
 cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.")
@@ -203,12 +203,6 @@ parser.add_argument(
     help="Set the base URL for the ComfyUI API.  (default: https://api.comfy.org)",
 )
 
-database_default_path = os.path.abspath(
-    os.path.join(os.path.dirname(__file__), "..", "user", "comfyui.db")
-)
-parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
-parser.add_argument("--disable-model-processing", action="store_true", help="Disable model file processing, e.g. computing hashes and extracting metadata.")
-
 if comfy.options.args_parsing:
     args = parser.parse_args()
 else:

comfy/controlnet.py

@@ -15,13 +15,14 @@
     You should have received a copy of the GNU General Public License
     along with this program.  If not, see <https://www.gnu.org/licenses/>.
 """
-
+from __future__ import annotations
 
 import torch
 from enum import Enum
 import math
 import os
 import logging
+import copy
 import comfy.utils
 import comfy.model_management
 import comfy.model_detection
@@ -36,7 +37,7 @@ import comfy.cldm.mmdit
 import comfy.ldm.hydit.controlnet
 import comfy.ldm.flux.controlnet
 import comfy.cldm.dit_embedder
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Union
 if TYPE_CHECKING:
     from comfy.hooks import HookGroup
 
@@ -63,6 +64,18 @@ class StrengthType(Enum):
     CONSTANT = 1
     LINEAR_UP = 2
 
+class ControlIsolation:
+    '''Temporarily set a ControlBase object's previous_controlnet to None to prevent cascading calls.'''
+    def __init__(self, control: ControlBase):
+        self.control = control
+        self.orig_previous_controlnet = control.previous_controlnet
+
+    def __enter__(self):
+        self.control.previous_controlnet = None
+
+    def __exit__(self, *args):
+        self.control.previous_controlnet = self.orig_previous_controlnet
+
 class ControlBase:
     def __init__(self):
         self.cond_hint_original = None
@@ -76,7 +89,7 @@ class ControlBase:
         self.compression_ratio = 8
         self.upscale_algorithm = 'nearest-exact'
         self.extra_args = {}
-        self.previous_controlnet = None
+        self.previous_controlnet: Union[ControlBase, None] = None
         self.extra_conds = []
         self.strength_type = StrengthType.CONSTANT
         self.concat_mask = False
@@ -84,6 +97,7 @@ class ControlBase:
         self.extra_concat = None
         self.extra_hooks: HookGroup = None
         self.preprocess_image = lambda a: a
+        self.multigpu_clones: dict[torch.device, ControlBase] = {}
 
     def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]):
         self.cond_hint_original = cond_hint
@@ -110,17 +124,38 @@ class ControlBase:
     def cleanup(self):
         if self.previous_controlnet is not None:
             self.previous_controlnet.cleanup()
-
+        for device_cnet in self.multigpu_clones.values():
+            with ControlIsolation(device_cnet):
+                device_cnet.cleanup()
         self.cond_hint = None
         self.extra_concat = None
         self.timestep_range = None
 
     def get_models(self):
         out = []
+        for device_cnet in self.multigpu_clones.values():
+            out += device_cnet.get_models_only_self()
         if self.previous_controlnet is not None:
             out += self.previous_controlnet.get_models()
         return out
 
+    def get_models_only_self(self):
+        'Calls get_models, but temporarily sets previous_controlnet to None.'
+        with ControlIsolation(self):
+            return self.get_models()
+
+    def get_instance_for_device(self, device):
+        'Returns instance of this Control object intended for selected device.'
+        return self.multigpu_clones.get(device, self)
+
+    def deepclone_multigpu(self, load_device, autoregister=False):
+        '''
+        Create deep clone of Control object where model(s) is set to other devices.
+
+        When autoregister is set to True, the deep clone is also added to multigpu_clones dict.
+        '''
+        raise NotImplementedError("Classes inheriting from ControlBase should define their own deepclone_multigpu funtion.")
+
     def get_extra_hooks(self):
         out = []
         if self.extra_hooks is not None:
@@ -129,7 +164,7 @@ class ControlBase:
             out += self.previous_controlnet.get_extra_hooks()
         return out
 
-    def copy_to(self, c):
+    def copy_to(self, c: ControlBase):
         c.cond_hint_original = self.cond_hint_original
         c.strength = self.strength
         c.timestep_percent_range = self.timestep_percent_range
@@ -280,6 +315,14 @@ class ControlNet(ControlBase):
         self.copy_to(c)
         return c
 
+    def deepclone_multigpu(self, load_device, autoregister=False):
+        c = self.copy()
+        c.control_model = copy.deepcopy(c.control_model)
+        c.control_model_wrapped = comfy.model_patcher.ModelPatcher(c.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
+        if autoregister:
+            self.multigpu_clones[load_device] = c
+        return c
+
     def get_models(self):
         out = super().get_models()
         out.append(self.control_model_wrapped)
@@ -805,6 +848,14 @@ class T2IAdapter(ControlBase):
         self.copy_to(c)
         return c
 
+    def deepclone_multigpu(self, load_device, autoregister=False):
+        c = self.copy()
+        c.t2i_model = copy.deepcopy(c.t2i_model)
+        c.device = load_device
+        if autoregister:
+            self.multigpu_clones[load_device] = c
+        return c
+
 def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options
     compression_ratio = 8
     upscale_algorithm = 'nearest-exact'

comfy/model_base.py

@@ -102,13 +102,6 @@ def model_sampling(model_config, model_type):
     return ModelSampling(model_config)
 
 
-def convert_tensor(extra, dtype):
-    if hasattr(extra, "dtype"):
-        if extra.dtype != torch.int and extra.dtype != torch.long:
-            extra = extra.to(dtype)
-    return extra
-
-
 class BaseModel(torch.nn.Module):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel):
         super().__init__()
@@ -172,13 +165,13 @@ class BaseModel(torch.nn.Module):
         extra_conds = {}
         for o in kwargs:
             extra = kwargs[o]
-
             if hasattr(extra, "dtype"):
-                extra = convert_tensor(extra, dtype)
-            elif isinstance(extra, list):
+                if extra.dtype != torch.int and extra.dtype != torch.long:
+                    extra = extra.to(dtype)
+            if isinstance(extra, list):
                 ex = []
                 for ext in extra:
-                    ex.append(convert_tensor(ext, dtype))
+                    ex.append(ext.to(dtype))
                 extra = ex
             extra_conds[o] = extra
 

comfy/model_management.py

@@ -15,6 +15,7 @@
     You should have received a copy of the GNU General Public License
     along with this program.  If not, see <https://www.gnu.org/licenses/>.
 """
+from __future__ import annotations
 
 import psutil
 import logging
@@ -26,6 +27,10 @@ import platform
 import weakref
 import gc
 
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from comfy.model_patcher import ModelPatcher
+
 class VRAMState(Enum):
     DISABLED = 0    #No vram present: no need to move models to vram
     NO_VRAM = 1     #Very low vram: enable all the options to save vram
@@ -171,6 +176,25 @@ def get_torch_device():
         else:
             return torch.device(torch.cuda.current_device())
 
+def get_all_torch_devices(exclude_current=False):
+    global cpu_state
+    devices = []
+    if cpu_state == CPUState.GPU:
+        if is_nvidia():
+            for i in range(torch.cuda.device_count()):
+                devices.append(torch.device(i))
+        elif is_intel_xpu():
+            for i in range(torch.xpu.device_count()):
+                devices.append(torch.device(i))
+        elif is_ascend_npu():
+            for i in range(torch.npu.device_count()):
+                devices.append(torch.device(i))
+    else:
+        devices.append(get_torch_device())
+    if exclude_current:
+        devices.remove(get_torch_device())
+    return devices
+
 def get_total_memory(dev=None, torch_total_too=False):
     global directml_enabled
     if dev is None:
@@ -387,9 +411,13 @@ try:
     logging.info("Device: {}".format(get_torch_device_name(get_torch_device())))
 except:
     logging.warning("Could not pick default device.")
+try:
+    for device in get_all_torch_devices(exclude_current=True):
+        logging.info("Device: {}".format(get_torch_device_name(device)))
+except:
+    pass
 
-
-current_loaded_models = []
+current_loaded_models: list[LoadedModel] = []
 
 def module_size(module):
     module_mem = 0
@@ -400,7 +428,7 @@ def module_size(module):
     return module_mem
 
 class LoadedModel:
-    def __init__(self, model):
+    def __init__(self, model: ModelPatcher):
         self._set_model(model)
         self.device = model.load_device
         self.real_model = None
@@ -408,7 +436,7 @@ class LoadedModel:
         self.model_finalizer = None
         self._patcher_finalizer = None
 
-    def _set_model(self, model):
+    def _set_model(self, model: ModelPatcher):
         self._model = weakref.ref(model)
         if model.parent is not None:
             self._parent_model = weakref.ref(model.parent)
@@ -1300,8 +1328,34 @@ def soft_empty_cache(force=False):
         torch.cuda.ipc_collect()
 
 def unload_all_models():
-    free_memory(1e30, get_torch_device())
+    for device in get_all_torch_devices():
+        free_memory(1e30, device)
 
+def unload_model_and_clones(model: ModelPatcher, unload_additional_models=True, all_devices=False):
+    'Unload only model and its clones - primarily for multigpu cloning purposes.'
+    initial_keep_loaded: list[LoadedModel] = current_loaded_models.copy()
+    additional_models = []
+    if unload_additional_models:
+        additional_models = model.get_nested_additional_models()
+    keep_loaded = []
+    for loaded_model in initial_keep_loaded:
+        if loaded_model.model is not None:
+            if model.clone_base_uuid == loaded_model.model.clone_base_uuid:
+                continue
+            # check additional models if they are a match
+            skip = False
+            for add_model in additional_models:
+                if add_model.clone_base_uuid == loaded_model.model.clone_base_uuid:
+                    skip = True
+                    break
+            if skip:
+                continue
+        keep_loaded.append(loaded_model)
+    if not all_devices:
+        free_memory(1e30, get_torch_device(), keep_loaded)
+    else:
+        for device in get_all_torch_devices():
+            free_memory(1e30, device, keep_loaded)
 
 #TODO: might be cleaner to put this somewhere else
 import threading

comfy/model_patcher.py

@@ -84,12 +84,15 @@ def set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_
 def create_model_options_clone(orig_model_options: dict):
     return comfy.patcher_extension.copy_nested_dicts(orig_model_options)
 
-def create_hook_patches_clone(orig_hook_patches):
+def create_hook_patches_clone(orig_hook_patches, copy_tuples=False):
     new_hook_patches = {}
     for hook_ref in orig_hook_patches:
         new_hook_patches[hook_ref] = {}
         for k in orig_hook_patches[hook_ref]:
             new_hook_patches[hook_ref][k] = orig_hook_patches[hook_ref][k][:]
+            if copy_tuples:
+                for i in range(len(new_hook_patches[hook_ref][k])):
+                    new_hook_patches[hook_ref][k][i] = tuple(new_hook_patches[hook_ref][k][i])
     return new_hook_patches
 
 def wipe_lowvram_weight(m):
@@ -240,6 +243,9 @@ class ModelPatcher:
         self.is_clip = False
         self.hook_mode = comfy.hooks.EnumHookMode.MaxSpeed
 
+        self.is_multigpu_base_clone = False
+        self.clone_base_uuid = uuid.uuid4()
+
         if not hasattr(self.model, 'model_loaded_weight_memory'):
             self.model.model_loaded_weight_memory = 0
 
@@ -318,16 +324,90 @@ class ModelPatcher:
         n.is_clip = self.is_clip
         n.hook_mode = self.hook_mode
 
+        n.is_multigpu_base_clone = self.is_multigpu_base_clone
+        n.clone_base_uuid = self.clone_base_uuid
+
         for callback in self.get_all_callbacks(CallbacksMP.ON_CLONE):
             callback(self, n)
         return n
 
+    def deepclone_multigpu(self, new_load_device=None, models_cache: dict[uuid.UUID,ModelPatcher]=None):
+        logging.info(f"Creating deepclone of {self.model.__class__.__name__} for {new_load_device if new_load_device else self.load_device}.")
+        comfy.model_management.unload_model_and_clones(self)
+        n = self.clone()
+        # set load device, if present
+        if new_load_device is not None:
+            n.load_device = new_load_device
+        # unlike for normal clone, backup dicts that shared same ref should not;
+        # otherwise, patchers that have deep copies of base models will erroneously influence each other.
+        n.backup = copy.deepcopy(n.backup)
+        n.object_patches_backup = copy.deepcopy(n.object_patches_backup)
+        n.hook_backup = copy.deepcopy(n.hook_backup)
+        n.model = copy.deepcopy(n.model)
+        # multigpu clone should not have multigpu additional_models entry
+        n.remove_additional_models("multigpu")
+        # multigpu_clone all stored additional_models; make sure circular references are properly handled
+        if models_cache is None:
+            models_cache = {}
+        for key, model_list in n.additional_models.items():
+            for i in range(len(model_list)):
+                add_model = n.additional_models[key][i]
+                if add_model.clone_base_uuid not in models_cache:
+                    models_cache[add_model.clone_base_uuid] = add_model.deepclone_multigpu(new_load_device=new_load_device, models_cache=models_cache)
+                n.additional_models[key][i] = models_cache[add_model.clone_base_uuid]
+        for callback in self.get_all_callbacks(CallbacksMP.ON_DEEPCLONE_MULTIGPU):
+            callback(self, n)
+        return n
+
+    def match_multigpu_clones(self):
+        multigpu_models = self.get_additional_models_with_key("multigpu")
+        if len(multigpu_models) > 0:
+            new_multigpu_models = []
+            for mm in multigpu_models:
+                # clone main model, but bring over relevant props from existing multigpu clone
+                n = self.clone()
+                n.load_device = mm.load_device
+                n.backup = mm.backup
+                n.object_patches_backup = mm.object_patches_backup
+                n.hook_backup = mm.hook_backup
+                n.model = mm.model
+                n.is_multigpu_base_clone = mm.is_multigpu_base_clone
+                n.remove_additional_models("multigpu")
+                orig_additional_models: dict[str, list[ModelPatcher]] = comfy.patcher_extension.copy_nested_dicts(n.additional_models)
+                n.additional_models = comfy.patcher_extension.copy_nested_dicts(mm.additional_models)
+                # figure out which additional models are not present in multigpu clone
+                models_cache = {}
+                for mm_add_model in mm.get_additional_models():
+                    models_cache[mm_add_model.clone_base_uuid] = mm_add_model
+                remove_models_uuids = set(list(models_cache.keys()))
+                for key, model_list in orig_additional_models.items():
+                    for orig_add_model in model_list:
+                        if orig_add_model.clone_base_uuid not in models_cache:
+                            models_cache[orig_add_model.clone_base_uuid] = orig_add_model.deepclone_multigpu(new_load_device=n.load_device, models_cache=models_cache)
+                            existing_list = n.get_additional_models_with_key(key)
+                            existing_list.append(models_cache[orig_add_model.clone_base_uuid])
+                            n.set_additional_models(key, existing_list)
+                        if orig_add_model.clone_base_uuid in remove_models_uuids:
+                            remove_models_uuids.remove(orig_add_model.clone_base_uuid)
+                # remove duplicate additional models
+                for key, model_list in n.additional_models.items():
+                    new_model_list = [x for x in model_list if x.clone_base_uuid not in remove_models_uuids]
+                    n.set_additional_models(key, new_model_list)
+                for callback in self.get_all_callbacks(CallbacksMP.ON_MATCH_MULTIGPU_CLONES):
+                    callback(self, n)
+                new_multigpu_models.append(n)
+            self.set_additional_models("multigpu", new_multigpu_models)
+
     def is_clone(self, other):
         if hasattr(other, 'model') and self.model is other.model:
             return True
         return False
 
-    def clone_has_same_weights(self, clone: 'ModelPatcher'):
+    def clone_has_same_weights(self, clone: ModelPatcher, allow_multigpu=False):
+        if allow_multigpu:
+            if self.clone_base_uuid != clone.clone_base_uuid:
+                return False
+        else:
             if not self.is_clone(clone):
                 return False
 
@@ -929,7 +1009,7 @@ class ModelPatcher:
         return self.additional_models.get(key, [])
 
     def get_additional_models(self):
-        all_models = []
+        all_models: list[ModelPatcher] = []
         for models in self.additional_models.values():
             all_models.extend(models)
         return all_models
@@ -983,9 +1063,13 @@ class ModelPatcher:
         for callback in self.get_all_callbacks(CallbacksMP.ON_PRE_RUN):
             callback(self)
 
-    def prepare_state(self, timestep):
+    def prepare_state(self, timestep, model_options, ignore_multigpu=False):
         for callback in self.get_all_callbacks(CallbacksMP.ON_PREPARE_STATE):
-            callback(self, timestep)
+            callback(self, timestep, model_options, ignore_multigpu)
+        if not ignore_multigpu and "multigpu_clones" in model_options:
+            for p in model_options["multigpu_clones"].values():
+                p: ModelPatcher
+                p.prepare_state(timestep, model_options, ignore_multigpu=True)
 
     def restore_hook_patches(self):
         if self.hook_patches_backup is not None:
@@ -998,12 +1082,18 @@ class ModelPatcher:
     def prepare_hook_patches_current_keyframe(self, t: torch.Tensor, hook_group: comfy.hooks.HookGroup, model_options: dict[str]):
         curr_t = t[0]
         reset_current_hooks = False
+        multigpu_kf_changed_cache = None
         transformer_options = model_options.get("transformer_options", {})
         for hook in hook_group.hooks:
             changed = hook.hook_keyframe.prepare_current_keyframe(curr_t=curr_t, transformer_options=transformer_options)
             # if keyframe changed, remove any cached HookGroups that contain hook with the same hook_ref;
             # this will cause the weights to be recalculated when sampling
             if changed:
+                # cache changed for multigpu usage
+                if "multigpu_clones" in model_options:
+                    if multigpu_kf_changed_cache is None:
+                        multigpu_kf_changed_cache = []
+                    multigpu_kf_changed_cache.append(hook)
                 # reset current_hooks if contains hook that changed
                 if self.current_hooks is not None:
                     for current_hook in self.current_hooks.hooks:
@@ -1015,6 +1105,28 @@ class ModelPatcher:
                         self.cached_hook_patches.pop(cached_group)
         if reset_current_hooks:
             self.patch_hooks(None)
+        if "multigpu_clones" in model_options:
+            for p in model_options["multigpu_clones"].values():
+                p: ModelPatcher
+                p._handle_changed_hook_keyframes(multigpu_kf_changed_cache)
+
+    def _handle_changed_hook_keyframes(self, kf_changed_cache: list[comfy.hooks.Hook]):
+        'Used to handle multigpu behavior inside prepare_hook_patches_current_keyframe.'
+        if kf_changed_cache is None:
+            return
+        reset_current_hooks = False
+        # reset current_hooks if contains hook that changed
+        for hook in kf_changed_cache:
+            if self.current_hooks is not None:
+                for current_hook in self.current_hooks.hooks:
+                    if current_hook == hook:
+                        reset_current_hooks = True
+                        break
+            for cached_group in list(self.cached_hook_patches.keys()):
+                if cached_group.contains(hook):
+                    self.cached_hook_patches.pop(cached_group)
+        if reset_current_hooks:
+            self.patch_hooks(None)
 
     def register_all_hook_patches(self, hooks: comfy.hooks.HookGroup, target_dict: dict[str], model_options: dict=None,
                                   registered: comfy.hooks.HookGroup = None):

comfy/multigpu.py

@@ -0,0 +1,167 @@
+from __future__ import annotations
+import torch
+import logging
+
+from collections import namedtuple
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from comfy.model_patcher import ModelPatcher
+import comfy.utils
+import comfy.patcher_extension
+import comfy.model_management
+
+
+class GPUOptions:
+    def __init__(self, device_index: int, relative_speed: float):
+        self.device_index = device_index
+        self.relative_speed = relative_speed
+
+    def clone(self):
+        return GPUOptions(self.device_index, self.relative_speed)
+
+    def create_dict(self):
+        return {
+            "relative_speed": self.relative_speed
+        }
+
+class GPUOptionsGroup:
+    def __init__(self):
+        self.options: dict[int, GPUOptions] = {}
+
+    def add(self, info: GPUOptions):
+        self.options[info.device_index] = info
+
+    def clone(self):
+        c = GPUOptionsGroup()
+        for opt in self.options.values():
+            c.add(opt)
+        return c
+
+    def register(self, model: ModelPatcher):
+        opts_dict = {}
+        # get devices that are valid for this model
+        devices: list[torch.device] = [model.load_device]
+        for extra_model in model.get_additional_models_with_key("multigpu"):
+            extra_model: ModelPatcher
+            devices.append(extra_model.load_device)
+        # create dictionary with actual device mapped to its GPUOptions
+        device_opts_list: list[GPUOptions] = []
+        for device in devices:
+            device_opts = self.options.get(device.index, GPUOptions(device_index=device.index, relative_speed=1.0))
+            opts_dict[device] = device_opts.create_dict()
+            device_opts_list.append(device_opts)
+        # make relative_speed relative to 1.0
+        min_speed = min([x.relative_speed for x in device_opts_list])
+        for value in opts_dict.values():
+            value['relative_speed'] /= min_speed
+        model.model_options['multigpu_options'] = opts_dict
+
+
+def create_multigpu_deepclones(model: ModelPatcher, max_gpus: int, gpu_options: GPUOptionsGroup=None, reuse_loaded=False):
+    'Prepare ModelPatcher to contain deepclones of its BaseModel and related properties.'
+    model = model.clone()
+    # check if multigpu is already prepared - get the load devices from them if possible to exclude
+    skip_devices = set()
+    multigpu_models = model.get_additional_models_with_key("multigpu")
+    if len(multigpu_models) > 0:
+        for mm in multigpu_models:
+            skip_devices.add(mm.load_device)
+    skip_devices = list(skip_devices)
+
+    full_extra_devices = comfy.model_management.get_all_torch_devices(exclude_current=True)
+    limit_extra_devices = full_extra_devices[:max_gpus-1]
+    extra_devices = limit_extra_devices.copy()
+    # exclude skipped devices
+    for skip in skip_devices:
+        if skip in extra_devices:
+            extra_devices.remove(skip)
+    # create new deepclones
+    if len(extra_devices) > 0:
+        for device in extra_devices:
+            device_patcher = None
+            if reuse_loaded:
+                # check if there are any ModelPatchers currently loaded that could be referenced here after a clone
+                loaded_models: list[ModelPatcher] = comfy.model_management.loaded_models()
+                for lm in loaded_models:
+                    if lm.model is not None and lm.clone_base_uuid == model.clone_base_uuid and lm.load_device == device:
+                        device_patcher = lm.clone()
+                        logging.info(f"Reusing loaded deepclone of {device_patcher.model.__class__.__name__} for {device}")
+                        break
+            if device_patcher is None:
+                device_patcher = model.deepclone_multigpu(new_load_device=device)
+                device_patcher.is_multigpu_base_clone = True
+            multigpu_models = model.get_additional_models_with_key("multigpu")
+            multigpu_models.append(device_patcher)
+            model.set_additional_models("multigpu", multigpu_models)
+        model.match_multigpu_clones()
+        if gpu_options is None:
+            gpu_options = GPUOptionsGroup()
+        gpu_options.register(model)
+    else:
+        logging.info("No extra torch devices need initialization, skipping initializing MultiGPU Work Units.")
+    # TODO: only keep model clones that don't go 'past' the intended max_gpu count
+    # multigpu_models = model.get_additional_models_with_key("multigpu")
+    # new_multigpu_models = []
+    # for m in multigpu_models:
+    #     if m.load_device in limit_extra_devices:
+    #         new_multigpu_models.append(m)
+    # model.set_additional_models("multigpu", new_multigpu_models)
+    # persist skip_devices for use in sampling code
+    # if len(skip_devices) > 0 or "multigpu_skip_devices" in model.model_options:
+    #     model.model_options["multigpu_skip_devices"] = skip_devices
+    return model
+
+
+LoadBalance = namedtuple('LoadBalance', ['work_per_device', 'idle_time'])
+def load_balance_devices(model_options: dict[str], total_work: int, return_idle_time=False, work_normalized: int=None):
+    'Optimize work assigned to different devices, accounting for their relative speeds and splittable work.'
+    opts_dict = model_options['multigpu_options']
+    devices = list(model_options['multigpu_clones'].keys())
+    speed_per_device = []
+    work_per_device = []
+    # get sum of each device's relative_speed
+    total_speed = 0.0
+    for opts in opts_dict.values():
+        total_speed += opts['relative_speed']
+    # get relative work for each device;
+    # obtained by w = (W*r)/R
+    for device in devices:
+        relative_speed = opts_dict[device]['relative_speed']
+        relative_work = (total_work*relative_speed) / total_speed
+        speed_per_device.append(relative_speed)
+        work_per_device.append(relative_work)
+    # relative work must be expressed in whole numbers, but likely is a decimal;
+    # perform rounding while maintaining total sum equal to total work (sum of relative works)
+    work_per_device = round_preserved(work_per_device)
+    dict_work_per_device = {}
+    for device, relative_work in zip(devices, work_per_device):
+        dict_work_per_device[device] = relative_work
+    if not return_idle_time:
+        return LoadBalance(dict_work_per_device, None)
+    # divide relative work by relative speed to get estimated completion time of said work by each device;
+    # time here is relative and does not correspond to real-world units
+    completion_time = [w/r for w,r in zip(work_per_device, speed_per_device)]
+    # calculate relative time spent by the devices waiting on each other after their work is completed
+    idle_time = abs(min(completion_time) - max(completion_time))
+    # if need to compare work idle time, need to normalize to a common total work
+    if work_normalized:
+        idle_time *= (work_normalized/total_work)
+
+    return LoadBalance(dict_work_per_device, idle_time)
+
+def round_preserved(values: list[float]):
+    'Round all values in a list, preserving the combined sum of values.'
+    # get floor of values; casting to int does it too
+    floored = [int(x) for x in values]
+    total_floored = sum(floored)
+    # get remainder to distribute
+    remainder = round(sum(values)) - total_floored
+    # pair values with fractional portions
+    fractional = [(i, x-floored[i]) for i, x in enumerate(values)]
+    # sort by fractional part in descending order
+    fractional.sort(key=lambda x: x[1], reverse=True)
+    # distribute the remainder
+    for i in range(remainder):
+        index = fractional[i][0]
+        floored[index] += 1
+    return floored

comfy/patcher_extension.py

@@ -3,6 +3,8 @@ from typing import Callable
 
 class CallbacksMP:
     ON_CLONE = "on_clone"
+    ON_DEEPCLONE_MULTIGPU = "on_deepclone_multigpu"
+    ON_MATCH_MULTIGPU_CLONES = "on_match_multigpu_clones"
     ON_LOAD = "on_load_after"
     ON_DETACH = "on_detach_after"
     ON_CLEANUP = "on_cleanup"

comfy/sampler_helpers.py

@@ -1,9 +1,11 @@
 from __future__ import annotations
+import torch
 import uuid
 import math
 import collections
 import comfy.model_management
 import comfy.conds
+import comfy.model_patcher
 import comfy.utils
 import comfy.hooks
 import comfy.patcher_extension
@@ -106,6 +108,47 @@ def cleanup_additional_models(models):
         if hasattr(m, 'cleanup'):
             m.cleanup()
 
+def preprocess_multigpu_conds(conds: dict[str, list[dict[str]]], model: ModelPatcher, model_options: dict[str]):
+    '''If multigpu acceleration required, creates deepclones of ControlNets and GLIGEN per device.'''
+    multigpu_models: list[ModelPatcher] = model.get_additional_models_with_key("multigpu")
+    if len(multigpu_models) == 0:
+        return
+    extra_devices = [x.load_device for x in multigpu_models]
+    # handle controlnets
+    controlnets: set[ControlBase] = set()
+    for k in conds:
+        for kk in conds[k]:
+            if 'control' in kk:
+                controlnets.add(kk['control'])
+    if len(controlnets) > 0:
+        # first, unload all controlnet clones
+        for cnet in list(controlnets):
+            cnet_models = cnet.get_models()
+            for cm in cnet_models:
+                comfy.model_management.unload_model_and_clones(cm, unload_additional_models=True)
+
+        # next, make sure each controlnet has a deepclone for all relevant devices
+        for cnet in controlnets:
+            curr_cnet = cnet
+            while curr_cnet is not None:
+                for device in extra_devices:
+                    if device not in curr_cnet.multigpu_clones:
+                        curr_cnet.deepclone_multigpu(device, autoregister=True)
+                curr_cnet = curr_cnet.previous_controlnet
+        # since all device clones are now present, recreate the linked list for cloned cnets per device
+        for cnet in controlnets:
+            curr_cnet = cnet
+            while curr_cnet is not None:
+                prev_cnet = curr_cnet.previous_controlnet
+                for device in extra_devices:
+                    device_cnet = curr_cnet.get_instance_for_device(device)
+                    prev_device_cnet = None
+                    if prev_cnet is not None:
+                        prev_device_cnet = prev_cnet.get_instance_for_device(device)
+                    device_cnet.set_previous_controlnet(prev_device_cnet)
+                curr_cnet = prev_cnet
+    # potentially handle gligen - since not widely used, ignored for now
+
 def estimate_memory(model, noise_shape, conds):
     cond_shapes = collections.defaultdict(list)
     cond_shapes_min = {}
@@ -130,7 +173,8 @@ def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None
     return executor.execute(model, noise_shape, conds, model_options=model_options)
 
 def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None):
-    real_model: BaseModel = None
+    model.match_multigpu_clones()
+    preprocess_multigpu_conds(conds, model, model_options)
     models, inference_memory = get_additional_models(conds, model.model_dtype())
     models += get_additional_models_from_model_options(model_options)
     models += model.get_nested_additional_models()  # TODO: does this require inference_memory update?
@@ -149,7 +193,7 @@ def cleanup_models(conds, models):
 
     cleanup_additional_models(set(control_cleanup))
 
-def prepare_model_patcher(model: 'ModelPatcher', conds, model_options: dict):
+def prepare_model_patcher(model: ModelPatcher, conds, model_options: dict):
     '''
     Registers hooks from conds.
     '''
@@ -182,3 +226,18 @@ def prepare_model_patcher(model: 'ModelPatcher', conds, model_options: dict):
         comfy.patcher_extension.merge_nested_dicts(to_load_options.setdefault(wc_name, {}), model_options["transformer_options"][wc_name],
                                                     copy_dict1=False)
     return to_load_options
+
+def prepare_model_patcher_multigpu_clones(model_patcher: ModelPatcher, loaded_models: list[ModelPatcher], model_options: dict):
+    '''
+    In case multigpu acceleration is enabled, prep ModelPatchers for each device.
+    '''
+    multigpu_patchers: list[ModelPatcher] = [x for x in loaded_models if x.is_multigpu_base_clone]
+    if len(multigpu_patchers) > 0:
+        multigpu_dict: dict[torch.device, ModelPatcher] = {}
+        multigpu_dict[model_patcher.load_device] = model_patcher
+        for x in multigpu_patchers:
+            x.hook_patches = comfy.model_patcher.create_hook_patches_clone(model_patcher.hook_patches, copy_tuples=True)
+            x.hook_mode = model_patcher.hook_mode # match main model's hook_mode
+            multigpu_dict[x.load_device] = x
+        model_options["multigpu_clones"] = multigpu_dict
+    return multigpu_patchers

comfy/samplers.py

@@ -1,4 +1,6 @@
 from __future__ import annotations
+
+import comfy.model_management
 from .k_diffusion import sampling as k_diffusion_sampling
 from .extra_samplers import uni_pc
 from typing import TYPE_CHECKING, Callable, NamedTuple
@@ -18,6 +20,7 @@ import comfy.patcher_extension
 import comfy.hooks
 import scipy.stats
 import numpy
+import threading
 
 
 def add_area_dims(area, num_dims):
@@ -140,7 +143,7 @@ def can_concat_cond(c1, c2):
 
     return cond_equal_size(c1.conditioning, c2.conditioning)
 
-def cond_cat(c_list):
+def cond_cat(c_list, device=None):
     temp = {}
     for x in c_list:
         for k in x:
@@ -152,6 +155,8 @@ def cond_cat(c_list):
     for k in temp:
         conds = temp[k]
         out[k] = conds[0].concat(conds[1:])
+        if device is not None and hasattr(out[k], 'to'):
+            out[k] = out[k].to(device)
 
     return out
 
@@ -205,7 +210,9 @@ def calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Ten
     )
     return executor.execute(model, conds, x_in, timestep, model_options)
 
-def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options):
+def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
+    if 'multigpu_clones' in model_options:
+        return _calc_cond_batch_multigpu(model, conds, x_in, timestep, model_options)
     out_conds = []
     out_counts = []
     # separate conds by matching hooks
@@ -237,7 +244,7 @@ def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Te
     if has_default_conds:
         finalize_default_conds(model, hooked_to_run, default_conds, x_in, timestep, model_options)
 
-    model.current_patcher.prepare_state(timestep)
+    model.current_patcher.prepare_state(timestep, model_options)
 
     # run every hooked_to_run separately
     for hooks, to_run in hooked_to_run.items():
@@ -345,6 +352,190 @@ def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Te
 
     return out_conds
 
+def _calc_cond_batch_multigpu(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
+    out_conds = []
+    out_counts = []
+    # separate conds by matching hooks
+    hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]] = {}
+    default_conds = []
+    has_default_conds = False
+
+    output_device = x_in.device
+
+    for i in range(len(conds)):
+        out_conds.append(torch.zeros_like(x_in))
+        out_counts.append(torch.ones_like(x_in) * 1e-37)
+
+        cond = conds[i]
+        default_c = []
+        if cond is not None:
+            for x in cond:
+                if 'default' in x:
+                    default_c.append(x)
+                    has_default_conds = True
+                    continue
+                p = get_area_and_mult(x, x_in, timestep)
+                if p is None:
+                    continue
+                if p.hooks is not None:
+                    model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options)
+                hooked_to_run.setdefault(p.hooks, list())
+                hooked_to_run[p.hooks] += [(p, i)]
+        default_conds.append(default_c)
+
+    if has_default_conds:
+        finalize_default_conds(model, hooked_to_run, default_conds, x_in, timestep, model_options)
+
+    model.current_patcher.prepare_state(timestep, model_options)
+
+    devices = [dev_m for dev_m in model_options['multigpu_clones'].keys()]
+    device_batched_hooked_to_run: dict[torch.device, list[tuple[comfy.hooks.HookGroup, tuple]]] = {}
+
+    total_conds = 0
+    for to_run in hooked_to_run.values():
+        total_conds += len(to_run)
+    conds_per_device = max(1, math.ceil(total_conds//len(devices)))
+    index_device = 0
+    current_device = devices[index_device]
+    # run every hooked_to_run separately
+    for hooks, to_run in hooked_to_run.items():
+        while len(to_run) > 0:
+            current_device = devices[index_device % len(devices)]
+            batched_to_run = device_batched_hooked_to_run.setdefault(current_device, [])
+            # keep track of conds currently scheduled onto this device
+            batched_to_run_length = 0
+            for btr in batched_to_run:
+                batched_to_run_length += len(btr[1])
+
+            first = to_run[0]
+            first_shape = first[0][0].shape
+            to_batch_temp = []
+            # make sure not over conds_per_device limit when creating temp batch
+            for x in range(len(to_run)):
+                if can_concat_cond(to_run[x][0], first[0]) and len(to_batch_temp) < (conds_per_device - batched_to_run_length):
+                    to_batch_temp += [x]
+
+            to_batch_temp.reverse()
+            to_batch = to_batch_temp[:1]
+
+            free_memory = model_management.get_free_memory(current_device)
+            for i in range(1, len(to_batch_temp) + 1):
+                batch_amount = to_batch_temp[:len(to_batch_temp)//i]
+                input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
+                if model.memory_required(input_shape) * 1.5 < free_memory:
+                    to_batch = batch_amount
+                    break
+            conds_to_batch = []
+            for x in to_batch:
+                conds_to_batch.append(to_run.pop(x))
+            batched_to_run_length += len(conds_to_batch)
+
+            batched_to_run.append((hooks, conds_to_batch))
+            if batched_to_run_length >= conds_per_device:
+                index_device += 1
+
+    thread_result = collections.namedtuple('thread_result', ['output', 'mult', 'area', 'batch_chunks', 'cond_or_uncond'])
+    def _handle_batch(device: torch.device, batch_tuple: tuple[comfy.hooks.HookGroup, tuple], results: list[thread_result]):
+        model_current: BaseModel = model_options["multigpu_clones"][device].model
+        # run every hooked_to_run separately
+        with torch.no_grad():
+            for hooks, to_batch in batch_tuple:
+                input_x = []
+                mult = []
+                c = []
+                cond_or_uncond = []
+                uuids = []
+                area = []
+                control: ControlBase = None
+                patches = None
+                for x in to_batch:
+                    o = x
+                    p = o[0]
+                    input_x.append(p.input_x)
+                    mult.append(p.mult)
+                    c.append(p.conditioning)
+                    area.append(p.area)
+                    cond_or_uncond.append(o[1])
+                    uuids.append(p.uuid)
+                    control = p.control
+                    patches = p.patches
+
+                batch_chunks = len(cond_or_uncond)
+                input_x = torch.cat(input_x).to(device)
+                c = cond_cat(c, device=device)
+                timestep_ = torch.cat([timestep.to(device)] * batch_chunks)
+
+                transformer_options = model_current.current_patcher.apply_hooks(hooks=hooks)
+                if 'transformer_options' in model_options:
+                    transformer_options = comfy.patcher_extension.merge_nested_dicts(transformer_options,
+                                                                                    model_options['transformer_options'],
+                                                                                    copy_dict1=False)
+
+                if patches is not None:
+                    # TODO: replace with merge_nested_dicts function
+                    if "patches" in transformer_options:
+                        cur_patches = transformer_options["patches"].copy()
+                        for p in patches:
+                            if p in cur_patches:
+                                cur_patches[p] = cur_patches[p] + patches[p]
+                            else:
+                                cur_patches[p] = patches[p]
+                        transformer_options["patches"] = cur_patches
+                    else:
+                        transformer_options["patches"] = patches
+
+                transformer_options["cond_or_uncond"] = cond_or_uncond[:]
+                transformer_options["uuids"] = uuids[:]
+                transformer_options["sigmas"] = timestep
+                transformer_options["sample_sigmas"] = transformer_options["sample_sigmas"].to(device)
+                transformer_options["multigpu_thread_device"] = device
+
+                cast_transformer_options(transformer_options, device=device)
+                c['transformer_options'] = transformer_options
+
+                if control is not None:
+                    device_control = control.get_instance_for_device(device)
+                    c['control'] = device_control.get_control(input_x, timestep_, c, len(cond_or_uncond), transformer_options)
+
+                if 'model_function_wrapper' in model_options:
+                    output = model_options['model_function_wrapper'](model_current.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).to(output_device).chunk(batch_chunks)
+                else:
+                    output = model_current.apply_model(input_x, timestep_, **c).to(output_device).chunk(batch_chunks)
+                results.append(thread_result(output, mult, area, batch_chunks, cond_or_uncond))
+
+
+    results: list[thread_result] = []
+    threads: list[threading.Thread] = []
+    for device, batch_tuple in device_batched_hooked_to_run.items():
+        new_thread = threading.Thread(target=_handle_batch, args=(device, batch_tuple, results))
+        threads.append(new_thread)
+        new_thread.start()
+
+    for thread in threads:
+        thread.join()
+
+    for output, mult, area, batch_chunks, cond_or_uncond in results:
+        for o in range(batch_chunks):
+            cond_index = cond_or_uncond[o]
+            a = area[o]
+            if a is None:
+                out_conds[cond_index] += output[o] * mult[o]
+                out_counts[cond_index] += mult[o]
+            else:
+                out_c = out_conds[cond_index]
+                out_cts = out_counts[cond_index]
+                dims = len(a) // 2
+                for i in range(dims):
+                    out_c = out_c.narrow(i + 2, a[i + dims], a[i])
+                    out_cts = out_cts.narrow(i + 2, a[i + dims], a[i])
+                out_c += output[o] * mult[o]
+                out_cts += mult[o]
+
+    for i in range(len(out_conds)):
+        out_conds[i] /= out_counts[i]
+
+    return out_conds
+
 def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove
     logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.")
     return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options))
@@ -642,6 +833,8 @@ def pre_run_control(model, conds):
         percent_to_timestep_function = lambda a: s.percent_to_sigma(a)
         if 'control' in x:
             x['control'].pre_run(model, percent_to_timestep_function)
+            for device_cnet in x['control'].multigpu_clones.values():
+                device_cnet.pre_run(model, percent_to_timestep_function)
 
 def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
     cond_cnets = []
@@ -884,7 +1077,9 @@ def cast_to_load_options(model_options: dict[str], device=None, dtype=None):
     to_load_options = model_options.get("to_load_options", None)
     if to_load_options is None:
         return
+    cast_transformer_options(to_load_options, device, dtype)
 
+def cast_transformer_options(transformer_options: dict[str], device=None, dtype=None):
     casts = []
     if device is not None:
         casts.append(device)
@@ -893,18 +1088,17 @@ def cast_to_load_options(model_options: dict[str], device=None, dtype=None):
     # if nothing to apply, do nothing
     if len(casts) == 0:
         return
-
     # try to call .to on patches
-    if "patches" in to_load_options:
-        patches = to_load_options["patches"]
+    if "patches" in transformer_options:
+        patches = transformer_options["patches"]
         for name in patches:
             patch_list = patches[name]
             for i in range(len(patch_list)):
                 if hasattr(patch_list[i], "to"):
                     for cast in casts:
                         patch_list[i] = patch_list[i].to(cast)
-    if "patches_replace" in to_load_options:
-        patches = to_load_options["patches_replace"]
+    if "patches_replace" in transformer_options:
+        patches = transformer_options["patches_replace"]
         for name in patches:
             patch_list = patches[name]
             for k in patch_list:
@@ -914,8 +1108,8 @@ def cast_to_load_options(model_options: dict[str], device=None, dtype=None):
     # try to call .to on any wrappers/callbacks
     wrappers_and_callbacks = ["wrappers", "callbacks"]
     for wc_name in wrappers_and_callbacks:
-        if wc_name in to_load_options:
-            wc: dict[str, list] = to_load_options[wc_name]
+        if wc_name in transformer_options:
+            wc: dict[str, list] = transformer_options[wc_name]
             for wc_dict in wc.values():
                 for wc_list in wc_dict.values():
                     for i in range(len(wc_list)):
@@ -923,7 +1117,6 @@ def cast_to_load_options(model_options: dict[str], device=None, dtype=None):
                             for cast in casts:
                                 wc_list[i] = wc_list[i].to(cast)
 
-
 class CFGGuider:
     def __init__(self, model_patcher: ModelPatcher):
         self.model_patcher = model_patcher
@@ -969,6 +1162,8 @@ class CFGGuider:
         self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
         device = self.model_patcher.load_device
 
+        multigpu_patchers = comfy.sampler_helpers.prepare_model_patcher_multigpu_clones(self.model_patcher, self.loaded_models, self.model_options)
+
         if denoise_mask is not None:
             denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device)
 
@@ -979,9 +1174,13 @@ class CFGGuider:
 
         try:
             self.model_patcher.pre_run()
+            for multigpu_patcher in multigpu_patchers:
+                multigpu_patcher.pre_run()
             output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
         finally:
             self.model_patcher.cleanup()
+            for multigpu_patcher in multigpu_patchers:
+                multigpu_patcher.cleanup()
 
         comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models)
         del self.inner_model

comfy/utils.py

@@ -49,16 +49,10 @@ if hasattr(torch.serialization, "add_safe_globals"):  # TODO: this was added in
 else:
     logging.info("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended.")
 
-def is_html_file(file_path):
-    with open(file_path, "rb") as f:
-        content = f.read(100)
-        return b"<!DOCTYPE html>" in content or b"<html" in content
-
 def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
     if device is None:
         device = torch.device("cpu")
     metadata = None
-
     if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"):
         try:
             with safetensors.safe_open(ckpt, framework="pt", device=device.type) as f:
@@ -68,8 +62,6 @@ def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
                 if return_metadata:
                     metadata = f.metadata()
         except Exception as e:
-            if is_html_file(ckpt):
-                raise ValueError("{}\n\nFile path: {}\n\nThe requested file is an HTML document not a safetensors file. Please re-download the file, not the web page.".format(e, ckpt))
             if len(e.args) > 0:
                 message = e.args[0]
                 if "HeaderTooLarge" in message:
@@ -96,13 +88,6 @@ def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
                     sd = pl_sd
             else:
                 sd = pl_sd
-
-    try:
-        from app.model_processor import model_processor
-        model_processor.process_file(ckpt)
-    except Exception as e:
-        logging.error(f"Error processing file {ckpt}: {e}")
-
     return (sd, metadata) if return_metadata else sd
 
 def save_torch_file(sd, ckpt, metadata=None):

comfy_extras/nodes_multigpu.py

@@ -0,0 +1,86 @@
+from __future__ import annotations
+from inspect import cleandoc
+
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+    from comfy.model_patcher import ModelPatcher
+import comfy.multigpu
+
+
+class MultiGPUWorkUnitsNode:
+    """
+    Prepares model to have sampling accelerated via splitting work units.
+
+    Should be placed after nodes that modify the model object itself, such as compile or attention-switch nodes.
+
+    Other than those exceptions, this node can be placed in any order.
+    """
+
+    NodeId = "MultiGPU_WorkUnits"
+    NodeName = "MultiGPU Work Units"
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "max_gpus" : ("INT", {"default": 8, "min": 1, "step": 1}),
+            },
+            "optional": {
+                "gpu_options": ("GPU_OPTIONS",)
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "init_multigpu"
+    CATEGORY = "advanced/multigpu"
+    DESCRIPTION = cleandoc(__doc__)
+
+    def init_multigpu(self, model: ModelPatcher, max_gpus: int, gpu_options: comfy.multigpu.GPUOptionsGroup=None):
+        model = comfy.multigpu.create_multigpu_deepclones(model, max_gpus, gpu_options, reuse_loaded=True)
+        return (model,)
+
+class MultiGPUOptionsNode:
+    """
+    Select the relative speed of GPUs in the special case they have significantly different performance from one another.
+    """
+
+    NodeId = "MultiGPU_Options"
+    NodeName = "MultiGPU Options"
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "device_index": ("INT", {"default": 0, "min": 0, "max": 64}),
+                "relative_speed": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.01})
+            },
+            "optional": {
+                "gpu_options": ("GPU_OPTIONS",)
+            }
+        }
+
+    RETURN_TYPES = ("GPU_OPTIONS",)
+    FUNCTION = "create_gpu_options"
+    CATEGORY = "advanced/multigpu"
+    DESCRIPTION = cleandoc(__doc__)
+
+    def create_gpu_options(self, device_index: int, relative_speed: float, gpu_options: comfy.multigpu.GPUOptionsGroup=None):
+        if not gpu_options:
+            gpu_options = comfy.multigpu.GPUOptionsGroup()
+        gpu_options.clone()
+
+        opt = comfy.multigpu.GPUOptions(device_index=device_index, relative_speed=relative_speed)
+        gpu_options.add(opt)
+
+        return (gpu_options,)
+
+
+node_list = [
+    MultiGPUWorkUnitsNode,
+    MultiGPUOptionsNode
+]
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+for node in node_list:
+    NODE_CLASS_MAPPINGS[node.NodeId] = node
+    NODE_DISPLAY_NAME_MAPPINGS[node.NodeId] = node.NodeName

folder_paths.py

@@ -275,7 +275,7 @@ def filter_files_extensions(files: Collection[str], extensions: Collection[str])
 
 
 
-def get_full_path(folder_name: str, filename: str, allow_missing: bool = False) -> str | None:
+def get_full_path(folder_name: str, filename: str) -> str | None:
     global folder_names_and_paths
     folder_name = map_legacy(folder_name)
     if folder_name not in folder_names_and_paths:
@@ -288,8 +288,6 @@ def get_full_path(folder_name: str, filename: str, allow_missing: bool = False)
             return full_path
         elif os.path.islink(full_path):
             logging.warning("WARNING path {} exists but doesn't link anywhere, skipping.".format(full_path))
-        elif allow_missing:
-            return full_path
 
     return None
 
@@ -301,27 +299,6 @@ def get_full_path_or_raise(folder_name: str, filename: str) -> str:
     return full_path
 
 
-def get_relative_path(full_path: str) -> tuple[str, str] | None:
-    """Convert a full path back to a type-relative path.
-
-    Args:
-        full_path: The full path to the file
-
-    Returns:
-        tuple[str, str] | None: A tuple of (model_type, relative_path) if found, None otherwise
-    """
-    global folder_names_and_paths
-    full_path = os.path.normpath(full_path)
-
-    for model_type, (paths, _) in folder_names_and_paths.items():
-        for base_path in paths:
-            base_path = os.path.normpath(base_path)
-            if full_path.startswith(base_path):
-                relative_path = os.path.relpath(full_path, base_path)
-                return model_type, relative_path
-
-    return None
-
 def get_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float]:
     folder_name = map_legacy(folder_name)
     global folder_names_and_paths

main.py

@@ -147,6 +147,7 @@ def cuda_malloc_warning():
         if cuda_malloc_warning:
             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
@@ -236,13 +237,6 @@ def cleanup_temp():
     if os.path.exists(temp_dir):
         shutil.rmtree(temp_dir, ignore_errors=True)
 
-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):
     """
@@ -272,7 +266,6 @@ def start_comfyui(asyncio_loop=None):
     hook_breaker_ac10a0.restore_functions()
 
     cuda_malloc_warning()
-    setup_database()
 
     prompt_server.add_routes()
     hijack_progress(prompt_server)

nodes.py

@@ -2241,6 +2241,7 @@ def init_builtin_extra_nodes():
         "nodes_mahiro.py",
         "nodes_lt.py",
         "nodes_hooks.py",
+        "nodes_multigpu.py",
         "nodes_load_3d.py",
         "nodes_cosmos.py",
         "nodes_video.py",

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.21.3
-comfyui-workflow-templates==0.1.25
+comfyui-workflow-templates==0.1.23
 comfyui-embedded-docs==0.2.0
 torch
 torchsde
@@ -18,9 +18,6 @@ Pillow
 scipy
 tqdm
 psutil
-alembic
-SQLAlchemy
-blake3
 
 #non essential dependencies:
 kornia>=0.7.1

tests-unit/app_test/model_processor_test.py

@@ -1,253 +0,0 @@
-import pytest
-from unittest.mock import patch, MagicMock
-from sqlalchemy import create_engine
-from sqlalchemy.orm import sessionmaker
-from app.model_processor import ModelProcessor
-from app.database.models import Model, Base
-import os
-
-# Test data constants
-TEST_MODEL_TYPE = "checkpoints"
-TEST_URL = "http://example.com/model.safetensors"
-TEST_FILE_NAME = "model.safetensors"
-TEST_EXPECTED_HASH = "abc123"
-TEST_DESTINATION_PATH = "/path/to/model.safetensors"
-
-
-def create_test_model(session, file_name, model_type, hash_value, file_size=1000, source_url=None):
-    """Helper to create a test model in the database."""
-    model = Model(path=file_name, type=model_type, hash=hash_value, file_size=file_size, source_url=source_url)
-    session.add(model)
-    session.commit()
-    return model
-
-
-def setup_mock_hash_calculation(model_processor, hash_value):
-    """Helper to setup hash calculation mocks."""
-    mock_hash = MagicMock()
-    mock_hash.hexdigest.return_value = hash_value
-    return patch.object(model_processor, "_get_hasher", return_value=mock_hash)
-
-
-def verify_model_in_db(session, file_name, expected_hash=None, expected_type=None):
-    """Helper to verify model exists in database with correct attributes."""
-    db_model = session.query(Model).filter_by(path=file_name).first()
-    assert db_model is not None
-    if expected_hash:
-        assert db_model.hash == expected_hash
-    if expected_type:
-        assert db_model.type == expected_type
-    return db_model
-
-
-@pytest.fixture
-def db_engine():
-    # Configure in-memory database
-    engine = create_engine("sqlite:///:memory:")
-    Base.metadata.create_all(engine)
-    yield engine
-    Base.metadata.drop_all(engine)
-
-
-@pytest.fixture
-def db_session(db_engine):
-    Session = sessionmaker(bind=db_engine)
-    session = Session()
-    yield session
-    session.close()
-
-
-@pytest.fixture
-def mock_get_relative_path():
-    with patch("app.model_processor.get_relative_path") as mock:
-        mock.side_effect = lambda path: (TEST_MODEL_TYPE, os.path.basename(path))
-        yield mock
-
-
-@pytest.fixture
-def mock_get_full_path():
-    with patch("app.model_processor.get_full_path") as mock:
-        mock.return_value = TEST_DESTINATION_PATH
-        yield mock
-
-
-@pytest.fixture
-def model_processor(db_session, mock_get_relative_path, mock_get_full_path):
-    with patch("app.model_processor.create_session", return_value=db_session):
-        with patch("app.model_processor.can_create_session", return_value=True):
-            processor = ModelProcessor()
-            # Setup test state
-            processor.removed_files = []
-            processor.downloaded_files = []
-            processor.file_exists = {}
-
-            def mock_download_file(url, destination_path, hasher):
-                processor.downloaded_files.append((url, destination_path))
-                processor.file_exists[destination_path] = True
-                # Simulate writing some data to the file
-                test_data = b"test data"
-                hasher.update(test_data)
-
-            def mock_remove_file(file_path):
-                processor.removed_files.append(file_path)
-                if file_path in processor.file_exists:
-                    del processor.file_exists[file_path]
-
-            # Setup common patches
-            file_exists_patch = patch.object(
-                processor,
-                "_file_exists",
-                side_effect=lambda path: processor.file_exists.get(path, False),
-            )
-            file_size_patch = patch.object(
-                processor,
-                "_get_file_size",
-                side_effect=lambda path: (
-                    1000 if processor.file_exists.get(path, False) else 0
-                ),
-            )
-            download_file_patch = patch.object(
-                processor, "_download_file", side_effect=mock_download_file
-            )
-            remove_file_patch = patch.object(
-                processor, "_remove_file", side_effect=mock_remove_file
-            )
-
-            with (
-                file_exists_patch,
-                file_size_patch,
-                download_file_patch,
-                remove_file_patch,
-            ):
-                yield processor
-
-
-def test_ensure_downloaded_invalid_extension(model_processor):
-    # Ensure that an unsupported file extension raises an error to prevent unsafe file downloads
-    with pytest.raises(ValueError, match="Unsupported unsafe file for download"):
-        model_processor.ensure_downloaded(TEST_MODEL_TYPE, TEST_URL, "model.exe")
-
-
-def test_ensure_downloaded_existing_file_with_hash(model_processor, db_session):
-    # Ensure that a file with the same hash but from a different source is not downloaded again
-    SOURCE_URL = "https://example.com/other.sft"
-    create_test_model(db_session, TEST_FILE_NAME, TEST_MODEL_TYPE, TEST_EXPECTED_HASH, source_url=SOURCE_URL)
-    model_processor.file_exists[TEST_DESTINATION_PATH] = True
-
-    result = model_processor.ensure_downloaded(
-        TEST_MODEL_TYPE, TEST_URL, TEST_FILE_NAME, TEST_EXPECTED_HASH
-    )
-
-    assert result == TEST_DESTINATION_PATH
-    model = verify_model_in_db(db_session, TEST_FILE_NAME, TEST_EXPECTED_HASH, TEST_MODEL_TYPE)
-    assert model.source_url == SOURCE_URL # Ensure the source URL is not overwritten
-
-
-def test_ensure_downloaded_existing_file_hash_mismatch(model_processor, db_session):
-    # Ensure that a file with a different hash raises an error
-    create_test_model(db_session, TEST_FILE_NAME, TEST_MODEL_TYPE, "different_hash")
-    model_processor.file_exists[TEST_DESTINATION_PATH] = True
-
-    with pytest.raises(ValueError, match="File .* exists with hash .* but expected .*"):
-        model_processor.ensure_downloaded(
-            TEST_MODEL_TYPE, TEST_URL, TEST_FILE_NAME, TEST_EXPECTED_HASH
-        )
-
-
-def test_ensure_downloaded_new_file(model_processor, db_session):
-    # Ensure that a new file is downloaded
-    model_processor.file_exists[TEST_DESTINATION_PATH] = False
-
-    with setup_mock_hash_calculation(model_processor, TEST_EXPECTED_HASH):
-        result = model_processor.ensure_downloaded(
-            TEST_MODEL_TYPE, TEST_URL, TEST_FILE_NAME, TEST_EXPECTED_HASH
-        )
-
-    assert result == TEST_DESTINATION_PATH
-    assert len(model_processor.downloaded_files) == 1
-    assert model_processor.downloaded_files[0] == (TEST_URL, TEST_DESTINATION_PATH)
-    assert model_processor.file_exists[TEST_DESTINATION_PATH]
-    verify_model_in_db(db_session, TEST_FILE_NAME, TEST_EXPECTED_HASH, TEST_MODEL_TYPE)
-
-
-def test_ensure_downloaded_hash_mismatch(model_processor, db_session):
-    # Ensure that download that results in a different hash raises an error
-    model_processor.file_exists[TEST_DESTINATION_PATH] = False
-
-    with setup_mock_hash_calculation(model_processor, "different_hash"):
-        with pytest.raises(
-            ValueError,
-            match="Downloaded file hash .* does not match expected hash .*",
-        ):
-            model_processor.ensure_downloaded(
-                TEST_MODEL_TYPE,
-                TEST_URL,
-                TEST_FILE_NAME,
-                TEST_EXPECTED_HASH,
-            )
-
-    assert len(model_processor.removed_files) == 1
-    assert model_processor.removed_files[0] == TEST_DESTINATION_PATH
-    assert TEST_DESTINATION_PATH not in model_processor.file_exists
-    assert db_session.query(Model).filter_by(path=TEST_FILE_NAME).first() is None
-
-
-def test_process_file_without_hash(model_processor, db_session):
-    # Test processing file without provided hash
-    model_processor.file_exists[TEST_DESTINATION_PATH] = True
-
-    with patch.object(model_processor, "_hash_file", return_value=TEST_EXPECTED_HASH):
-        result = model_processor.process_file(TEST_DESTINATION_PATH)
-        assert result is not None
-        assert result.hash == TEST_EXPECTED_HASH
-
-
-def test_retrieve_model_by_hash(model_processor, db_session):
-    # Test retrieving model by hash
-    create_test_model(db_session, TEST_FILE_NAME, TEST_MODEL_TYPE, TEST_EXPECTED_HASH)
-    result = model_processor.retrieve_model_by_hash(TEST_EXPECTED_HASH)
-    assert result is not None
-    assert result.hash == TEST_EXPECTED_HASH
-
-
-def test_retrieve_model_by_hash_and_type(model_processor, db_session):
-    # Test retrieving model by hash and type
-    create_test_model(db_session, TEST_FILE_NAME, TEST_MODEL_TYPE, TEST_EXPECTED_HASH)
-    result = model_processor.retrieve_model_by_hash(TEST_EXPECTED_HASH, TEST_MODEL_TYPE)
-    assert result is not None
-    assert result.hash == TEST_EXPECTED_HASH
-    assert result.type == TEST_MODEL_TYPE
-
-
-def test_retrieve_hash(model_processor, db_session):
-    # Test retrieving hash for existing model
-    create_test_model(db_session, TEST_FILE_NAME, TEST_MODEL_TYPE, TEST_EXPECTED_HASH)
-    with patch.object(
-        model_processor,
-        "_validate_path",
-        return_value=(TEST_MODEL_TYPE, TEST_FILE_NAME),
-    ):
-        result = model_processor.retrieve_hash(TEST_DESTINATION_PATH, TEST_MODEL_TYPE)
-        assert result == TEST_EXPECTED_HASH
-
-
-def test_validate_file_extension_valid_extensions(model_processor):
-    # Test all valid file extensions
-    valid_extensions = [".safetensors", ".sft", ".txt", ".csv", ".json", ".yaml"]
-    for ext in valid_extensions:
-        model_processor._validate_file_extension(f"test{ext}")  # Should not raise
-
-
-def test_process_file_existing_without_source_url(model_processor, db_session):
-    # Test processing an existing file that needs its source URL updated
-    model_processor.file_exists[TEST_DESTINATION_PATH] = True
-
-    create_test_model(db_session, TEST_FILE_NAME, TEST_MODEL_TYPE, TEST_EXPECTED_HASH)
-    result = model_processor.process_file(TEST_DESTINATION_PATH, source_url=TEST_URL)
-
-    assert result is not None
-    assert result.hash == TEST_EXPECTED_HASH
-    assert result.source_url == TEST_URL
-
-    db_model = db_session.query(Model).filter_by(path=TEST_FILE_NAME).first()
-    assert db_model.source_url == TEST_URL

utils/install_util.py

@@ -1,19 +0,0 @@
-from pathlib import Path
-import sys
-
-# The path to the requirements.txt file
-requirements_path = Path(__file__).parents[1] / "requirements.txt"
-
-
-def get_missing_requirements_message():
-    """The warning message to display when a package is missing."""
-
-    extra = ""
-    if sys.flags.no_user_site:
-        extra = "-s "
-    return f"""
-Please install the updated requirements.txt file by running:
-{sys.executable} {extra}-m pip install -r {requirements_path}
-
-If you are on the portable package you can run: update\\update_comfyui.bat to solve this problem.
-""".strip()