Allow running with non working

- use sqlalchemy + alembic + sqlite for db
- extract model data and previews
- endpoints for db interactions
- add tests

.github/workflows/stable-release.yml

@@ -12,7 +12,7 @@ on:
         description: 'CUDA version'
         required: true
         type: string
-        default: "126"
+        default: "124"
       python_minor:
         description: 'Python minor version'
         required: true

.github/workflows/test-build.yml

@@ -18,7 +18,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}

.github/workflows/test-unit.yml

@@ -18,7 +18,7 @@ jobs:
     - name: Set up Python      
       uses: actions/setup-python@v4
       with:
-        python-version: '3.12'
+        python-version: '3.10'
     - name: Install requirements
       run: |
         python -m pip install --upgrade pip

.github/workflows/windows_release_dependencies.yml

@@ -17,7 +17,7 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "126"
+        default: "124"
 
       python_minor:
         description: 'python minor version'

.github/workflows/windows_release_package.yml

@@ -7,7 +7,7 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "126"
+        default: "124"
 
       python_minor:
         description: 'python minor version'

CODEOWNERS

@@ -15,7 +15,6 @@
 # Python web server
 /api_server/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
 /app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
-/utils/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
 
 # Frontend assets
 /web/ @huchenlei @webfiltered @pythongosssss @yoland68 @robinjhuang

README.md

@@ -47,13 +47,11 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
    - [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/)
    - [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/)
    - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
-   - [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/)
 - Video Models
    - [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)
    - [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
    - [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
    - [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
-   - [Nvidia Cosmos](https://comfyanonymous.github.io/ComfyUI_examples/cosmos/)
 - [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
@@ -131,8 +129,6 @@ Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you
 
 If you have trouble extracting it, right click the file -> properties -> unblock
 
-If you have a 50 series Blackwell card like a 5090 or 5080 see [this discussion thread](https://github.com/comfyanonymous/ComfyUI/discussions/6643)
-
 #### How do I share models between another UI and ComfyUI?
 
 See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.
@@ -143,7 +139,7 @@ To run it on services like paperspace, kaggle or colab you can use my [Jupyter N
 
 ## Manual Install (Windows, Linux)
 
-python 3.13 is supported but using 3.12 is recommended because some custom nodes and their dependencies might not support it yet.
+Note that some dependencies do not yet support python 3.13 so using 3.12 is recommended.
 
 Git clone this repo.
 
@@ -155,11 +151,11 @@ Put your VAE in: models/vae
 ### AMD GPUs (Linux only)
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2.4```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2```
 
-This is the command to install the nightly with ROCm 6.3 which might have some performance improvements:
+This is the command to install the nightly with ROCm 6.2 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.3```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.2.4```
 
 ### Intel GPUs (Windows and Linux)
 
@@ -189,7 +185,7 @@ Additional discussion and help can be found [here](https://github.com/comfyanony
 
 Nvidia users should install stable pytorch using this command:
 
-```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu126```
+```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu124```
 
 This is the command to install pytorch nightly instead which might have performance improvements:
 

alembic.ini

@@ -0,0 +1,119 @@
+# 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
+
+# Logging configuration
+[loggers]
+keys = root,sqlalchemy,alembic
+
+[handlers]
+keys = console
+
+[formatters]
+keys = generic
+
+[logger_root]
+level = WARNING
+handlers = console
+qualname =
+
+[logger_sqlalchemy]
+level = WARNING
+handlers =
+qualname = sqlalchemy.engine
+
+[logger_alembic]
+level = INFO
+handlers =
+qualname = alembic
+
+[handler_console]
+class = StreamHandler
+args = (sys.stderr,)
+level = NOTSET
+formatter = generic
+
+[formatter_generic]
+format = %(levelname)-5.5s [%(name)s] %(message)s
+datefmt = %H:%M:%S

alembic_db/README.md

@@ -0,0 +1,3 @@
+## Generate new revision
+1. Update models in `/app/database/models.py`
+2. Run `alembic revision --autogenerate -m "{your message}"`

alembic_db/env.py

@@ -0,0 +1,75 @@
+from logging.config import fileConfig
+
+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
+
+# Interpret the config file for Python logging.
+# This line sets up loggers basically.
+if config.config_file_name is not None:
+    fileConfig(config.config_file_name)
+
+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

@@ -0,0 +1,28 @@
+"""${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/2fb22c4fff36_init.py

@@ -0,0 +1,58 @@
+"""init
+
+Revision ID: 2fb22c4fff36
+Revises: 
+Create Date: 2025-03-27 19:00:47.686079
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '2fb22c4fff36'
+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."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('model',
+    sa.Column('type', sa.Text(), nullable=False),
+    sa.Column('path', sa.Text(), nullable=False),
+    sa.Column('title', sa.Text(), nullable=True),
+    sa.Column('description', sa.Text(), nullable=True),
+    sa.Column('architecture', sa.Text(), nullable=True),
+    sa.Column('hash', 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')
+    )
+    op.create_table('tag',
+    sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
+    sa.Column('name', sa.Text(), nullable=False),
+    sa.PrimaryKeyConstraint('id'),
+    sa.UniqueConstraint('name')
+    )
+    op.create_table('model_tag',
+    sa.Column('model_type', sa.Text(), nullable=False),
+    sa.Column('model_path', sa.Text(), nullable=False),
+    sa.Column('tag_id', sa.Integer(), nullable=False),
+    sa.ForeignKeyConstraint(['model_type', 'model_path'], ['model.type', 'model.path'], ondelete='CASCADE'),
+    sa.ForeignKeyConstraint(['tag_id'], ['tag.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('model_type', 'model_path', 'tag_id')
+    )
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_table('model_tag')
+    op.drop_table('tag')
+    op.drop_table('model')
+    # ### end Alembic commands ###

app/custom_node_manager.py

@@ -4,93 +4,12 @@ import os
 import folder_paths
 import glob
 from aiohttp import web
-import json
-import logging
-from functools import lru_cache
-
-from utils.json_util import merge_json_recursive
-
-
-# Extra locale files to load into main.json
-EXTRA_LOCALE_FILES = [
-    "nodeDefs.json",
-    "commands.json",
-    "settings.json",
-]
-
-
-def safe_load_json_file(file_path: str) -> dict:
-    if not os.path.exists(file_path):
-        return {}
-
-    try:
-        with open(file_path, "r", encoding="utf-8") as f:
-            return json.load(f)
-    except json.JSONDecodeError:
-        logging.error(f"Error loading {file_path}")
-        return {}
-
 
 class CustomNodeManager:
-    @lru_cache(maxsize=1)
-    def build_translations(self):
-        """Load all custom nodes translations during initialization. Translations are
-        expected to be loaded from `locales/` folder.
-
-        The folder structure is expected to be the following:
-        - custom_nodes/
-            - custom_node_1/
-                - locales/
-                    - en/
-                        - main.json
-                        - commands.json
-                        - settings.json
-
-        returned translations are expected to be in the following format:
-        {
-            "en": {
-                "nodeDefs": {...},
-                "commands": {...},
-                "settings": {...},
-                ...{other main.json keys}
-            }
-        }
     """
-
-        translations = {}
-
-        for folder in folder_paths.get_folder_paths("custom_nodes"):
-            # Sort glob results for deterministic ordering
-            for custom_node_dir in sorted(glob.glob(os.path.join(folder, "*/"))):
-                locales_dir = os.path.join(custom_node_dir, "locales")
-                if not os.path.exists(locales_dir):
-                    continue
-
-                for lang_dir in glob.glob(os.path.join(locales_dir, "*/")):
-                    lang_code = os.path.basename(os.path.dirname(lang_dir))
-
-                    if lang_code not in translations:
-                        translations[lang_code] = {}
-
-                    # Load main.json
-                    main_file = os.path.join(lang_dir, "main.json")
-                    node_translations = safe_load_json_file(main_file)
-
-                    # Load extra locale files
-                    for extra_file in EXTRA_LOCALE_FILES:
-                        extra_file_path = os.path.join(lang_dir, extra_file)
-                        key = extra_file.split(".")[0]
-                        json_data = safe_load_json_file(extra_file_path)
-                        if json_data:
-                            node_translations[key] = json_data
-
-                    if node_translations:
-                        translations[lang_code] = merge_json_recursive(
-                            translations[lang_code], node_translations
-                        )
-
-        return translations
-
+    Placeholder to refactor the custom node management features from ComfyUI-Manager.
+    Currently it only contains the custom workflow templates feature.
+    """
     def add_routes(self, routes, webapp, loadedModules):
 
         @routes.get("/workflow_templates")
@@ -99,36 +18,17 @@ class CustomNodeManager:
             files = [
                 file
                 for folder in folder_paths.get_folder_paths("custom_nodes")
-                for file in glob.glob(
-                    os.path.join(folder, "*/example_workflows/*.json")
-                )
+                for file in glob.glob(os.path.join(folder, '*/example_workflows/*.json'))
             ]
-            workflow_templates_dict = (
-                {}
-            )  # custom_nodes folder name -> example workflow names
+            workflow_templates_dict = {} # custom_nodes folder name -> example workflow names
             for file in files:
-                custom_nodes_name = os.path.basename(
-                    os.path.dirname(os.path.dirname(file))
-                )
+                custom_nodes_name = os.path.basename(os.path.dirname(os.path.dirname(file)))
                 workflow_name = os.path.splitext(os.path.basename(file))[0]
-                workflow_templates_dict.setdefault(custom_nodes_name, []).append(
-                    workflow_name
-                )
+                workflow_templates_dict.setdefault(custom_nodes_name, []).append(workflow_name)
             return web.json_response(workflow_templates_dict)
 
         # Serve workflow templates from custom nodes.
         for module_name, module_dir in loadedModules:
-            workflows_dir = os.path.join(module_dir, "example_workflows")
+            workflows_dir = os.path.join(module_dir, 'example_workflows')
             if os.path.exists(workflows_dir):
-                webapp.add_routes(
-                    [
-                        web.static(
-                            "/api/workflow_templates/" + module_name, workflows_dir
-                        )
-                    ]
-                )
-
-        @routes.get("/i18n")
-        async def get_i18n(request):
-            """Returns translations from all custom nodes' locales folders."""
-            return web.json_response(self.build_translations())
+                webapp.add_routes([web.static('/api/workflow_templates/' + module_name, workflows_dir)])

app/database/db.py

@@ -0,0 +1,118 @@
+import logging
+import os
+import shutil
+import sys
+from app.database.models import Tag
+from comfy.cli_args import args
+
+try:
+    import alembic
+    import sqlalchemy
+except ImportError as e:
+    req_path = os.path.abspath(
+        os.path.join(os.path.dirname(__file__), "../..", "requirements.txt")
+    )
+    logging.error(
+        f"\n\n********** ERROR ***********\n\nRequirements are not installed ({e}). Please install the requirements.txt file by running:\n{sys.executable} -s -m pip install -r {req_path}\n\nIf you are on the portable package you can run: update\\update_comfyui.bat to solve this problem\n********** ERROR **********\n"
+    )
+    exit(-1)
+
+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
+
+Session = 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}")
+
+    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
+        db_path = get_db_path()
+        backup_path = db_path + ".bkp"
+        if os.path.exists(db_path):
+            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)
+
+    if not current_rev:
+        # Init db, populate models
+        from app.model_processor import model_processor
+
+        session = create_session()
+        model_processor.populate_models(session)
+
+        # populate tags
+        tags = (
+            "character",
+            "style",
+            "concept",
+            "clothing",
+            "pose",
+            "background",
+            "vehicle",
+            "object",
+            "animal",
+            "action",
+        )
+        for tag in tags:
+            session.add(Tag(name=tag))
+
+        session.commit()
+
+def can_create_session():
+    return Session is not None
+
+def create_session():
+    return Session()

app/database/models.py

@@ -0,0 +1,76 @@
+from sqlalchemy import (
+    Column,
+    Integer,
+    Text,
+    DateTime,
+    Table,
+    ForeignKeyConstraint,
+)
+from sqlalchemy.orm import relationship, 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))
+    }
+
+
+ModelTag = Table(
+    "model_tag",
+    Base.metadata,
+    Column(
+        "model_type",
+        Text,
+        primary_key=True,
+    ),
+    Column(
+        "model_path",
+        Text,
+        primary_key=True,
+    ),
+    Column("tag_id", Integer, primary_key=True),
+    ForeignKeyConstraint(
+        ["model_type", "model_path"], ["model.type", "model.path"], ondelete="CASCADE"
+    ),
+    ForeignKeyConstraint(["tag_id"], ["tag.id"], ondelete="CASCADE"),
+)
+
+
+class Model(Base):
+    __tablename__ = "model"
+
+    type = Column(Text, primary_key=True)
+    path = Column(Text, primary_key=True)
+    title = Column(Text)
+    description = Column(Text)
+    architecture = Column(Text)
+    hash = Column(Text)
+    source_url = Column(Text)
+    date_added = Column(DateTime, server_default=func.now())
+
+    # Relationship with tags
+    tags = relationship("Tag", secondary=ModelTag, back_populates="models")
+
+    def to_dict(self):
+        dict = to_dict(self)
+        dict["tags"] = [tag.to_dict() for tag in self.tags]
+        return dict
+
+
+class Tag(Base):
+    __tablename__ = "tag"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    name = Column(Text, nullable=False, unique=True)
+
+    # Relationship with models
+    models = relationship("Model", secondary=ModelTag, back_populates="tags")
+
+    def to_dict(self):
+        return to_dict(self)

app/model_manager.py

@@ -1,19 +1,30 @@
 from __future__ import annotations
 
 import os
-import base64
-import json
 import time
 import logging
+from app.database.db import create_session
 import folder_paths
-import glob
-import comfy.utils
 from aiohttp import web
 from PIL import Image
 from io import BytesIO
-from folder_paths import map_legacy, filter_files_extensions, filter_files_content_types
+from folder_paths import map_legacy, filter_files_extensions, get_full_path
+from app.database.models import Tag, Model
+from app.model_processor import get_model_previews, model_processor
+from utils.web import dumps
+from sqlalchemy.orm import joinedload
+import sqlalchemy.exc
 
 
+def bad_request(message: str):
+    return web.json_response({"error": message}, status=400)
+
+def missing_field(field: str):
+    return bad_request(f"{field} is required")
+
+def not_found(message: str):
+    return web.json_response({"error": message + " not found"}, status=404)
+
 class ModelFileManager:
     def __init__(self) -> None:
         self.cache: dict[str, tuple[list[dict], dict[str, float], float]] = {}
@@ -62,7 +73,7 @@ class ModelFileManager:
             folder = folders[0][path_index]
             full_filename = os.path.join(folder, filename)
 
-            previews = self.get_model_previews(full_filename)
+            previews = get_model_previews(full_filename)
             default_preview = previews[0] if len(previews) > 0 else None
             if default_preview is None or (isinstance(default_preview, str) and not os.path.isfile(default_preview)):
                 return web.Response(status=404)
@@ -76,6 +87,183 @@ class ModelFileManager:
             except:
                 return web.Response(status=404)
 
+        @routes.get("/v2/models")
+        async def get_models(request):
+            with create_session() as session:
+                model_path = request.query.get("path", None)
+                model_type = request.query.get("type", None)
+                query = session.query(Model).options(joinedload(Model.tags))
+                if model_path:
+                    query = query.filter(Model.path == model_path)
+                if model_type:
+                    query = query.filter(Model.type == model_type)
+                models = query.all()
+                if model_path and model_type:
+                    if len(models) == 0:
+                        return not_found("Model")
+                    return web.json_response(models[0].to_dict(), dumps=dumps)
+                
+                return web.json_response([model.to_dict() for model in models], dumps=dumps)
+
+        @routes.post("/v2/models")
+        async def add_model(request):
+            with create_session() as session:
+                data = await request.json()
+                model_type = data.get("type", None)
+                model_path = data.get("path", None)
+
+                if not model_type:
+                    return missing_field("type")
+                if not model_path:
+                    return missing_field("path")
+
+                tags = data.pop("tags", [])
+                fields = Model.metadata.tables["model"].columns.keys()
+
+                # Validate keys are valid model fields
+                for key in data.keys():
+                    if key not in fields:
+                        return bad_request(f"Invalid field: {key}")
+
+                # Validate file exists
+                if not get_full_path(model_type, model_path):
+                    return not_found(f"File '{model_type}/{model_path}'")
+
+                model = Model()
+                for field in fields:
+                    if field in data:
+                        setattr(model, field, data[field])
+
+                model.tags = session.query(Tag).filter(Tag.id.in_(tags)).all()
+                for tag in tags:
+                    if tag not in [t.id for t in model.tags]:
+                        return not_found(f"Tag '{tag}'")
+
+                try:
+                    session.add(model)
+                    session.commit()
+                except sqlalchemy.exc.IntegrityError as e:
+                    session.rollback()
+                    return bad_request(e.orig.args[0])
+
+                model_processor.run()
+
+                return web.json_response(model.to_dict(), dumps=dumps)
+            
+        @routes.delete("/v2/models")
+        async def delete_model(request):
+            with create_session() as session:
+                model_path = request.query.get("path", None)
+                model_type = request.query.get("type", None)
+                if not model_path:
+                    return missing_field("path")
+                if not model_type:
+                    return missing_field("type")
+                
+                full_path = get_full_path(model_type, model_path)
+                if full_path:
+                    return bad_request("Model file exists, please delete the file before deleting the model record.")
+
+                model = session.query(Model).filter(Model.path == model_path, Model.type == model_type).first()
+                if not model:
+                    return not_found("Model")
+                session.delete(model)
+                session.commit()
+                return web.Response(status=204)
+
+        @routes.get("/v2/tags")
+        async def get_tags(request):
+            with create_session() as session:
+                tags = session.query(Tag).all()
+                return web.json_response(
+                    [{"id": tag.id, "name": tag.name} for tag in tags]
+                )
+
+        @routes.post("/v2/tags")
+        async def create_tag(request):
+            with create_session() as session:
+                data = await request.json()
+                name = data.get("name", None)
+                if not name:
+                    return missing_field("name")
+                tag = Tag(name=name)
+                session.add(tag)
+                session.commit()
+                return web.json_response({"id": tag.id, "name": tag.name})
+            
+        @routes.delete("/v2/tags")
+        async def delete_tag(request):
+            with create_session() as session:
+                tag_id = request.query.get("id", None)
+                if not tag_id:
+                    return missing_field("id")
+                tag = session.query(Tag).filter(Tag.id == tag_id).first()
+                if not tag:
+                    return not_found("Tag")
+                session.delete(tag)
+                session.commit()
+                return web.Response(status=204)
+
+        @routes.post("/v2/models/tags")
+        async def add_model_tag(request):
+            with create_session() as session:
+                data = await request.json()
+                tag_id = data.get("tag", None)
+                model_path = data.get("path", None)
+                model_type = data.get("type", None)
+
+                if tag_id is None:
+                    return missing_field("tag")
+                if model_path is None:
+                    return missing_field("path")
+                if model_type is None:
+                    return missing_field("type")
+
+                try:
+                    tag_id = int(tag_id)
+                except ValueError:
+                    return bad_request("Invalid tag id")
+
+                tag = session.query(Tag).filter(Tag.id == tag_id).first()
+                model = session.query(Model).filter(Model.path == model_path, Model.type == model_type).first()
+                if not model:
+                    return not_found("Model")
+                model.tags.append(tag)
+                session.commit()
+                return web.json_response(model.to_dict(), dumps=dumps)
+
+        @routes.delete("/v2/models/tags")
+        async def delete_model_tag(request):
+            with create_session() as session:
+                tag_id = request.query.get("tag", None)
+                model_path = request.query.get("path", None)
+                model_type = request.query.get("type", None)
+
+                if tag_id is None:
+                    return missing_field("tag")
+                if model_path is None:
+                    return missing_field("path")
+                if model_type is None:
+                    return missing_field("type")
+                
+                try:
+                    tag_id = int(tag_id)
+                except ValueError:
+                    return bad_request("Invalid tag id")
+
+                model = session.query(Model).filter(Model.path == model_path, Model.type == model_type).first()
+                if not model:
+                    return not_found("Model")
+                model.tags = [tag for tag in model.tags if tag.id != tag_id]
+                session.commit()
+                return web.Response(status=204)
+        
+            
+
+        @routes.get("/v2/models/missing")
+        async def get_missing_models(request):
+            return web.json_response(model_processor.missing_models)
+
     def get_model_file_list(self, folder_name: str):
         folder_name = map_legacy(folder_name)
         folders = folder_paths.folder_names_and_paths[folder_name]
@@ -146,39 +334,5 @@ class ModelFileManager:
 
         return [{"name": f, "pathIndex": pathIndex} for f in result], dirs, time.perf_counter()
 
-    def get_model_previews(self, filepath: str) -> list[str | BytesIO]:
-        dirname = os.path.dirname(filepath)
-
-        if not os.path.exists(dirname):
-            return []
-
-        basename = os.path.splitext(filepath)[0]
-        match_files = glob.glob(f"{basename}.*", recursive=False)
-        image_files = filter_files_content_types(match_files, "image")
-        safetensors_file = next(filter(lambda x: x.endswith(".safetensors"), match_files), None)
-        safetensors_metadata = {}
-
-        result: list[str | BytesIO] = []
-
-        for filename in image_files:
-            _basename = os.path.splitext(filename)[0]
-            if _basename == basename:
-                result.append(filename)
-            if _basename == f"{basename}.preview":
-                result.append(filename)
-
-        if safetensors_file:
-            safetensors_filepath = os.path.join(dirname, safetensors_file)
-            header = comfy.utils.safetensors_header(safetensors_filepath, max_size=8*1024*1024)
-            if header:
-                safetensors_metadata = json.loads(header)
-        safetensors_images = safetensors_metadata.get("__metadata__", {}).get("ssmd_cover_images", None)
-        if safetensors_images:
-            safetensors_images = json.loads(safetensors_images)
-            for image in safetensors_images:
-                result.append(BytesIO(base64.b64decode(image)))
-
-        return result
-
     def __exit__(self, exc_type, exc_value, traceback):
         self.clear_cache()

app/model_processor.py

@@ -0,0 +1,263 @@
+import base64
+from datetime import datetime
+import glob
+import hashlib
+from io import BytesIO
+import json
+import logging
+import os
+import threading
+import time
+import comfy.utils
+from app.database.models import Model
+from app.database.db import create_session
+from comfy.cli_args import args
+from folder_paths import (
+    filter_files_content_types,
+    get_full_path,
+    folder_names_and_paths,
+    get_filename_list,
+)
+from PIL import Image
+from urllib import request
+
+
+def get_model_previews(
+    filepath: str, check_metadata: bool = True
+) -> list[str | BytesIO]:
+    dirname = os.path.dirname(filepath)
+
+    if not os.path.exists(dirname):
+        return []
+
+    basename = os.path.splitext(filepath)[0]
+    match_files = glob.glob(f"{basename}.*", recursive=False)
+    image_files = filter_files_content_types(match_files, "image")
+
+    result: list[str | BytesIO] = []
+
+    for filename in image_files:
+        _basename = os.path.splitext(filename)[0]
+        if _basename == basename:
+            result.append(filename)
+        if _basename == f"{basename}.preview":
+            result.append(filename)
+
+    if not check_metadata:
+        return result
+
+    safetensors_file = next(
+        filter(lambda x: x.endswith(".safetensors"), match_files), None
+    )
+    safetensors_metadata = {}
+
+    if safetensors_file:
+        safetensors_filepath = os.path.join(dirname, safetensors_file)
+        header = comfy.utils.safetensors_header(
+            safetensors_filepath, max_size=8 * 1024 * 1024
+        )
+        if header:
+            safetensors_metadata = json.loads(header)
+    safetensors_images = safetensors_metadata.get("__metadata__", {}).get(
+        "ssmd_cover_images", None
+    )
+    if safetensors_images:
+        safetensors_images = json.loads(safetensors_images)
+        for image in safetensors_images:
+            result.append(BytesIO(base64.b64decode(image)))
+
+    return result
+
+
+class ModelProcessor:
+    def __init__(self):
+        self._thread = None
+        self._lock = threading.Lock()
+        self._run = False
+        self.missing_models = []
+
+    def run(self):
+        if args.disable_model_processing:
+            return
+
+        if self._thread is None:
+            # Lock to prevent multiple threads from starting
+            with self._lock:
+                self._run = True
+                if self._thread is None:
+                    self._thread = threading.Thread(target=self._process_models)
+                    self._thread.daemon = True
+                    self._thread.start()
+
+    def populate_models(self, session):
+        # Ensure database state matches filesystem
+
+        existing_models = session.query(Model).all()
+
+        for folder_name in folder_names_and_paths.keys():
+            if folder_name == "custom_nodes" or folder_name == "configs":
+                continue
+            seen = set()
+            files = get_filename_list(folder_name)
+
+            for file in files:
+                if file in seen:
+                    logging.warning(f"Skipping duplicate named model: {file}")
+                    continue
+                seen.add(file)
+
+                existing_model = None
+                for model in existing_models:
+                    if model.path == file and model.type == folder_name:
+                        existing_model = model
+                        break
+
+                if existing_model:
+                    # Model already exists in db, remove from list and skip
+                    existing_models.remove(existing_model)
+                    continue
+
+                file_path = get_full_path(folder_name, file)
+
+                model = Model(
+                    path=file,
+                    type=folder_name,
+                    date_added=datetime.fromtimestamp(os.path.getctime(file_path)),
+                )
+                session.add(model)
+
+        for model in existing_models:
+            if not get_full_path(model.type, model.path):
+                logging.warning(f"Model {model.path} not found")
+                self.missing_models.append({"type": model.type, "path": model.path})
+
+        session.commit()
+
+    def _get_models(self, session):
+        models = session.query(Model).filter(Model.hash == None).all()
+        return models
+
+    def _process_file(self, model_path):
+        is_safetensors = model_path.endswith(".safetensors")
+        metadata = {}
+        h = hashlib.sha256()
+
+        with open(model_path, "rb", buffering=0) as f:
+            if is_safetensors:
+                # Read header length (8 bytes)
+                header_size_bytes = f.read(8)
+                header_len = int.from_bytes(header_size_bytes, "little")
+                h.update(header_size_bytes)
+
+                # Read header
+                header_bytes = f.read(header_len)
+                h.update(header_bytes)
+                try:
+                    metadata = json.loads(header_bytes)
+                except json.JSONDecodeError:
+                    pass
+
+            # Read rest of file
+            b = bytearray(128 * 1024)
+            mv = memoryview(b)
+            while n := f.readinto(mv):
+                h.update(mv[:n])
+
+        return h.hexdigest(), metadata
+
+    def _populate_info(self, model, metadata):
+        model.title = metadata.get("modelspec.title", None)
+        model.description = metadata.get("modelspec.description", None)
+        model.architecture = metadata.get("modelspec.architecture", None)
+
+    def _extract_image(self, model_path, metadata):
+        # check if image already exists
+        if len(get_model_previews(model_path, check_metadata=False)) > 0:
+            return
+
+        image_path = os.path.splitext(model_path)[0] + ".webp"
+        if os.path.exists(image_path):
+            return
+
+        cover_images = metadata.get("ssmd_cover_images", None)
+        image = None
+        if cover_images:
+            try:
+                cover_images = json.loads(cover_images)
+                if len(cover_images) > 0:
+                    image_data = cover_images[0]
+                    image = Image.open(BytesIO(base64.b64decode(image_data)))
+            except Exception as e:
+                logging.warning(
+                    f"Error extracting cover image for model {model_path}: {e}"
+                )
+
+        if not image:
+            thumbnail = metadata.get("modelspec.thumbnail", None)
+            if thumbnail:
+                try:
+                    response = request.urlopen(thumbnail)
+                    image = Image.open(response)
+                except Exception as e:
+                    logging.warning(
+                        f"Error extracting thumbnail for model {model_path}: {e}"
+                    )
+
+        if image:
+            image.thumbnail((512, 512))
+            image.save(image_path)
+            image.close()
+
+    def _process_models(self):
+        with create_session() as session:
+            checked = set()
+            self.populate_models(session)
+
+            while self._run:
+                self._run = False
+
+                models = self._get_models(session)
+
+                if len(models) == 0:
+                    break
+
+                for model in models:
+                    # prevent looping on the same model if it crashes
+                    if model.path in checked:
+                        continue
+
+                    checked.add(model.path)
+
+                    try:
+                        time.sleep(0)
+                        now = time.time()
+                        model_path = get_full_path(model.type, model.path)
+
+                        if not model_path:
+                            logging.warning(f"Model {model.path} not found")
+                            self.missing_models.append(model.path)
+                            continue
+
+                        logging.debug(f"Processing model {model_path}")
+                        hash, header = self._process_file(model_path)
+                        logging.debug(
+                            f"Processed model {model_path} in {time.time() - now} seconds"
+                        )
+                        model.hash = hash
+
+                        if header:
+                            metadata = header.get("__metadata__", None)
+
+                            if metadata:
+                                self._populate_info(model, metadata)
+                                self._extract_image(model_path, metadata)
+
+                        session.commit()
+                    except Exception as e:
+                        logging.error(f"Error processing model {model.path}: {e}")
+
+        with self._lock:
+            self._thread = None
+
+
+model_processor = ModelProcessor()

comfy/cli_args.py

@@ -43,11 +43,10 @@ parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certific
 parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.")
 parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.")
 
-parser.add_argument("--base-directory", type=str, default=None, help="Set the ComfyUI base directory for models, custom_nodes, input, output, temp, and user directories.")
 parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.")
-parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory. Overrides --base-directory.")
-parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory). Overrides --base-directory.")
-parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-directory.")
+parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory.")
+parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory).")
+parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input 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.")
@@ -177,9 +176,13 @@ parser.add_argument(
     help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.",
 )
 
-parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path. Overrides --base-directory.")
+parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path.")
 
-parser.add_argument("--disable-compres-response-body", action="store_true", help="Disable compressing response body.")
+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()

comfy/clip_model.py

@@ -102,9 +102,9 @@ class CLIPTextModel_(torch.nn.Module):
         mask = None
         if attention_mask is not None:
             mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
-            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
+            mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
 
-        causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(-torch.finfo(x.dtype).max).triu_(1)
+        causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
         if mask is not None:
             mask += causal_mask
         else:

comfy/conds.py

@@ -3,6 +3,9 @@ import math
 import comfy.utils
 
 
+def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
+    return abs(a*b) // math.gcd(a, b)
+
 class CONDRegular:
     def __init__(self, cond):
         self.cond = cond
@@ -43,7 +46,7 @@ class CONDCrossAttn(CONDRegular):
             if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
                 return False
 
-            mult_min = math.lcm(s1[1], s2[1])
+            mult_min = lcm(s1[1], s2[1])
             diff = mult_min // min(s1[1], s2[1])
             if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
                 return False
@@ -54,7 +57,7 @@ class CONDCrossAttn(CONDRegular):
         crossattn_max_len = self.cond.shape[1]
         for x in others:
             c = x.cond
-            crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1])
+            crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
             conds.append(c)
 
         out = []

comfy/diffusers_convert.py

@@ -4,6 +4,105 @@ import logging
 
 # conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
 
+# =================#
+# UNet Conversion #
+# =================#
+
+unet_conversion_map = [
+    # (stable-diffusion, HF Diffusers)
+    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
+    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
+    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
+    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
+    ("input_blocks.0.0.weight", "conv_in.weight"),
+    ("input_blocks.0.0.bias", "conv_in.bias"),
+    ("out.0.weight", "conv_norm_out.weight"),
+    ("out.0.bias", "conv_norm_out.bias"),
+    ("out.2.weight", "conv_out.weight"),
+    ("out.2.bias", "conv_out.bias"),
+]
+
+unet_conversion_map_resnet = [
+    # (stable-diffusion, HF Diffusers)
+    ("in_layers.0", "norm1"),
+    ("in_layers.2", "conv1"),
+    ("out_layers.0", "norm2"),
+    ("out_layers.3", "conv2"),
+    ("emb_layers.1", "time_emb_proj"),
+    ("skip_connection", "conv_shortcut"),
+]
+
+unet_conversion_map_layer = []
+# hardcoded number of downblocks and resnets/attentions...
+# would need smarter logic for other networks.
+for i in range(4):
+    # loop over downblocks/upblocks
+
+    for j in range(2):
+        # loop over resnets/attentions for downblocks
+        hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
+        sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0."
+        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
+
+        if i < 3:
+            # no attention layers in down_blocks.3
+            hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
+            sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1."
+            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
+
+    for j in range(3):
+        # loop over resnets/attentions for upblocks
+        hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
+        sd_up_res_prefix = f"output_blocks.{3 * i + j}.0."
+        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
+
+        if i > 0:
+            # no attention layers in up_blocks.0
+            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
+            sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1."
+            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
+
+    if i < 3:
+        # no downsample in down_blocks.3
+        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
+        sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op."
+        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
+
+        # no upsample in up_blocks.3
+        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
+        sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}."
+        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
+
+hf_mid_atn_prefix = "mid_block.attentions.0."
+sd_mid_atn_prefix = "middle_block.1."
+unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
+
+for j in range(2):
+    hf_mid_res_prefix = f"mid_block.resnets.{j}."
+    sd_mid_res_prefix = f"middle_block.{2 * j}."
+    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
+
+
+def convert_unet_state_dict(unet_state_dict):
+    # buyer beware: this is a *brittle* function,
+    # and correct output requires that all of these pieces interact in
+    # the exact order in which I have arranged them.
+    mapping = {k: k for k in unet_state_dict.keys()}
+    for sd_name, hf_name in unet_conversion_map:
+        mapping[hf_name] = sd_name
+    for k, v in mapping.items():
+        if "resnets" in k:
+            for sd_part, hf_part in unet_conversion_map_resnet:
+                v = v.replace(hf_part, sd_part)
+            mapping[k] = v
+    for k, v in mapping.items():
+        for sd_part, hf_part in unet_conversion_map_layer:
+            v = v.replace(hf_part, sd_part)
+        mapping[k] = v
+    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
+    return new_state_dict
+
+
 # ================#
 # VAE Conversion #
 # ================#
@@ -114,7 +213,6 @@ textenc_pattern = re.compile("|".join(protected.keys()))
 # Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
 code2idx = {"q": 0, "k": 1, "v": 2}
 
-
 # This function exists because at the time of writing torch.cat can't do fp8 with cuda
 def cat_tensors(tensors):
     x = 0
@@ -131,7 +229,6 @@ def cat_tensors(tensors):
 
     return out
 
-
 def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
     new_state_dict = {}
     capture_qkv_weight = {}
@@ -187,3 +284,5 @@ def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
 
 def convert_text_enc_state_dict(text_enc_dict):
     return text_enc_dict
+
+

comfy/extra_samplers/uni_pc.py

@@ -661,7 +661,7 @@ class UniPC:
 
             if x_t is None:
                 if use_predictor:
-                    pred_res = torch.tensordot(D1s, rhos_p, dims=([1], [0]))  # torch.einsum('k,bkchw->bchw', rhos_p, D1s)
+                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
                 else:
                     pred_res = 0
                 x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res
@@ -669,7 +669,7 @@ class UniPC:
             if use_corrector:
                 model_t = self.model_fn(x_t, t)
                 if D1s is not None:
-                    corr_res = torch.tensordot(D1s, rhos_c[:-1], dims=([1], [0]))  # torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
+                    corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
                 else:
                     corr_res = 0
                 D1_t = (model_t - model_prev_0)

comfy/k_diffusion/sampling.py

@@ -40,7 +40,7 @@ def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'):
 def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
     """Constructs a continuous VP noise schedule."""
     t = torch.linspace(1, eps_s, n, device=device)
-    sigmas = torch.sqrt(torch.special.expm1(beta_d * t ** 2 / 2 + beta_min * t))
+    sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1)
     return append_zero(sigmas)
 
 
@@ -1336,26 +1336,3 @@ def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disab
 @torch.no_grad()
 def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1., noise_sampler=None):
     return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_churn=s_churn, s_tmin=s_tmin, s_tmax=s_tmax, s_noise=s_noise, noise_sampler=noise_sampler, cfg_pp=True)
-
-@torch.no_grad()
-def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):
-    """Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK"""
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    old_d = None
-
-    for i in trange(len(sigmas) - 1, disable=disable):
-        denoised = model(x, sigmas[i] * s_in, **extra_args)
-        d = to_d(x, sigmas[i], denoised)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        dt = sigmas[i + 1] - sigmas[i]
-        if i == 0:
-            # Euler method
-            x = x + d * dt
-        else:
-            # Gradient estimation
-            d_bar = ge_gamma * d + (1 - ge_gamma) * old_d
-            x = x + d_bar * dt
-        old_d = d
-    return x

comfy/ldm/cosmos/blocks.py

@@ -168,19 +168,15 @@ class Attention(nn.Module):
         k = self.to_k[1](k)
         v = self.to_v[1](v)
         if self.is_selfattn and rope_emb is not None:  # only apply to self-attention!
-            # apply_rotary_pos_emb inlined
-            q_shape = q.shape
-            q = q.reshape(*q.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2)
-            q = rope_emb[..., 0] * q[..., 0] + rope_emb[..., 1] * q[..., 1]
-            q = q.movedim(-1, -2).reshape(*q_shape).to(x.dtype)
-
-            # apply_rotary_pos_emb inlined
-            k_shape = k.shape
-            k = k.reshape(*k.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2)
-            k = rope_emb[..., 0] * k[..., 0] + rope_emb[..., 1] * k[..., 1]
-            k = k.movedim(-1, -2).reshape(*k_shape).to(x.dtype)
+            q = apply_rotary_pos_emb(q, rope_emb)
+            k = apply_rotary_pos_emb(k, rope_emb)
         return q, k, v
 
+    def cal_attn(self, q, k, v, mask=None):
+        out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True)
+        out = rearrange(out, " b n s c -> s b (n c)")
+        return self.to_out(out)
+
     def forward(
         self,
         x,
@@ -195,10 +191,7 @@ class Attention(nn.Module):
             context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None
         """
         q, k, v = self.cal_qkv(x, context, mask, rope_emb=rope_emb, **kwargs)
-        out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True)
-        del q, k, v
-        out = rearrange(out, " b n s c -> s b (n c)")
-        return self.to_out(out)
+        return self.cal_attn(q, k, v, mask)
 
 
 class FeedForward(nn.Module):
@@ -795,7 +788,10 @@ class GeneralDITTransformerBlock(nn.Module):
         crossattn_mask: Optional[torch.Tensor] = None,
         rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
         adaln_lora_B_3D: Optional[torch.Tensor] = None,
+        extra_per_block_pos_emb: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
+        if extra_per_block_pos_emb is not None:
+            x = x + extra_per_block_pos_emb
         for block in self.blocks:
             x = block(
                 x,

comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py

@@ -30,8 +30,6 @@ import torch.nn as nn
 import torch.nn.functional as F
 import logging
 
-from comfy.ldm.modules.diffusionmodules.model import vae_attention
-
 from .patching import (
     Patcher,
     Patcher3D,
@@ -402,8 +400,6 @@ class CausalAttnBlock(nn.Module):
             in_channels, in_channels, kernel_size=1, stride=1, padding=0
         )
 
-        self.optimized_attention = vae_attention()
-
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         h_ = x
         h_ = self.norm(h_)
@@ -417,7 +413,18 @@ class CausalAttnBlock(nn.Module):
         v, batch_size = time2batch(v)
 
         b, c, h, w = q.shape
-        h_ = self.optimized_attention(q, k, v)
+        q = q.reshape(b, c, h * w)
+        q = q.permute(0, 2, 1)
+        k = k.reshape(b, c, h * w)
+        w_ = torch.bmm(q, k)
+        w_ = w_ * (int(c) ** (-0.5))
+        w_ = F.softmax(w_, dim=2)
+
+        # attend to values
+        v = v.reshape(b, c, h * w)
+        w_ = w_.permute(0, 2, 1)
+        h_ = torch.bmm(v, w_)
+        h_ = h_.reshape(b, c, h, w)
 
         h_ = batch2time(h_, batch_size)
         h_ = self.proj_out(h_)
@@ -864,16 +871,18 @@ class EncoderFactorized(nn.Module):
         x = self.patcher3d(x)
 
         # downsampling
-        h = self.conv_in(x)
+        hs = [self.conv_in(x)]
         for i_level in range(self.num_resolutions):
             for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](h)
+                h = self.down[i_level].block[i_block](hs[-1])
                 if len(self.down[i_level].attn) > 0:
                     h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
             if i_level != self.num_resolutions - 1:
-                h = self.down[i_level].downsample(h)
+                hs.append(self.down[i_level].downsample(hs[-1]))
 
         # middle
+        h = hs[-1]
         h = self.mid.block_1(h)
         h = self.mid.attn_1(h)
         h = self.mid.block_2(h)

comfy/ldm/cosmos/cosmos_tokenizer/patching.py

@@ -281,76 +281,54 @@ class UnPatcher3D(UnPatcher):
         hh = hh.to(dtype=dtype)
 
         xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh = torch.chunk(x, 8, dim=1)
-        del x
 
         # Height height transposed convolutions.
         xll = F.conv_transpose3d(
             xlll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
         )
-        del xlll
-
         xll += F.conv_transpose3d(
             xllh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
         )
-        del xllh
 
         xlh = F.conv_transpose3d(
             xlhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
         )
-        del xlhl
-
         xlh += F.conv_transpose3d(
             xlhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
         )
-        del xlhh
 
         xhl = F.conv_transpose3d(
             xhll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
         )
-        del xhll
-
         xhl += F.conv_transpose3d(
             xhlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
         )
-        del xhlh
 
         xhh = F.conv_transpose3d(
             xhhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
         )
-        del xhhl
-
         xhh += F.conv_transpose3d(
             xhhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
         )
-        del xhhh
 
         # Handles width transposed convolutions.
         xl = F.conv_transpose3d(
             xll, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
         )
-        del xll
-
         xl += F.conv_transpose3d(
             xlh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
         )
-        del xlh
-
         xh = F.conv_transpose3d(
             xhl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
         )
-        del xhl
-
         xh += F.conv_transpose3d(
             xhh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
         )
-        del xhh
 
         # Handles time axis transposed convolutions.
         x = F.conv_transpose3d(
             xl, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)
         )
-        del xl
-
         x += F.conv_transpose3d(
             xh, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)
         )

comfy/ldm/cosmos/model.py

@@ -168,7 +168,7 @@ class GeneralDIT(nn.Module):
             operations=operations,
         )
 
-        self.build_pos_embed(device=device, dtype=dtype)
+        self.build_pos_embed(device=device)
         self.block_x_format = block_x_format
         self.use_adaln_lora = use_adaln_lora
         self.adaln_lora_dim = adaln_lora_dim
@@ -210,7 +210,7 @@ class GeneralDIT(nn.Module):
             operations=operations,
         )
 
-    def build_pos_embed(self, device=None, dtype=None):
+    def build_pos_embed(self, device=None):
         if self.pos_emb_cls == "rope3d":
             cls_type = VideoRopePosition3DEmb
         else:
@@ -242,7 +242,6 @@ class GeneralDIT(nn.Module):
             kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio
             kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio
             kwargs["device"] = device
-            kwargs["dtype"] = dtype
             self.extra_pos_embedder = LearnablePosEmbAxis(
                 **kwargs,
             )
@@ -293,7 +292,7 @@ class GeneralDIT(nn.Module):
         x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W)
 
         if self.extra_per_block_abs_pos_emb:
-            extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype)
+            extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device)
         else:
             extra_pos_emb = None
 
@@ -477,8 +476,6 @@ class GeneralDIT(nn.Module):
             inputs["original_shape"],
         )
         extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = inputs["extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D"].to(x.dtype)
-        del inputs
-
         if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:
             assert (
                 x.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape
@@ -489,8 +486,6 @@ class GeneralDIT(nn.Module):
                 self.blocks["block0"].x_format == block.x_format
             ), f"First block has x_format {self.blocks[0].x_format}, got {block.x_format}"
 
-            if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:
-                x += extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D
             x = block(
                 x,
                 affline_emb_B_D,
@@ -498,6 +493,7 @@ class GeneralDIT(nn.Module):
                 crossattn_mask,
                 rope_emb_L_1_1_D=rope_emb_L_1_1_D,
                 adaln_lora_B_3D=adaln_lora_B_3D,
+                extra_per_block_pos_emb=extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
             )
 
         x_B_T_H_W_D = rearrange(x, "T H W B D -> B T H W D")

comfy/ldm/cosmos/position_embedding.py

@@ -41,12 +41,12 @@ def normalize(x: torch.Tensor, dim: Optional[List[int]] = None, eps: float = 0)
 
 
 class VideoPositionEmb(nn.Module):
-    def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor:
+    def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor], device=None) -> torch.Tensor:
         """
         It delegates the embedding generation to generate_embeddings function.
         """
         B_T_H_W_C = x_B_T_H_W_C.shape
-        embeddings = self.generate_embeddings(B_T_H_W_C, fps=fps, device=device, dtype=dtype)
+        embeddings = self.generate_embeddings(B_T_H_W_C, fps=fps, device=device)
 
         return embeddings
 
@@ -104,7 +104,6 @@ class VideoRopePosition3DEmb(VideoPositionEmb):
         w_ntk_factor: Optional[float] = None,
         t_ntk_factor: Optional[float] = None,
         device=None,
-        dtype=None,
     ):
         """
         Generate embeddings for the given input size.
@@ -174,7 +173,6 @@ class LearnablePosEmbAxis(VideoPositionEmb):
         len_w: int,
         len_t: int,
         device=None,
-        dtype=None,
         **kwargs,
     ):
         """
@@ -186,16 +184,17 @@ class LearnablePosEmbAxis(VideoPositionEmb):
         self.interpolation = interpolation
         assert self.interpolation in ["crop"], f"Unknown interpolation method {self.interpolation}"
 
-        self.pos_emb_h = nn.Parameter(torch.empty(len_h, model_channels, device=device, dtype=dtype))
-        self.pos_emb_w = nn.Parameter(torch.empty(len_w, model_channels, device=device, dtype=dtype))
-        self.pos_emb_t = nn.Parameter(torch.empty(len_t, model_channels, device=device, dtype=dtype))
+        self.pos_emb_h = nn.Parameter(torch.empty(len_h, model_channels, device=device))
+        self.pos_emb_w = nn.Parameter(torch.empty(len_w, model_channels, device=device))
+        self.pos_emb_t = nn.Parameter(torch.empty(len_t, model_channels, device=device))
 
-    def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor:
+
+    def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None) -> torch.Tensor:
         B, T, H, W, _ = B_T_H_W_C
         if self.interpolation == "crop":
-            emb_h_H = self.pos_emb_h[:H].to(device=device, dtype=dtype)
-            emb_w_W = self.pos_emb_w[:W].to(device=device, dtype=dtype)
-            emb_t_T = self.pos_emb_t[:T].to(device=device, dtype=dtype)
+            emb_h_H = self.pos_emb_h[:H].to(device=device)
+            emb_w_W = self.pos_emb_w[:W].to(device=device)
+            emb_t_T = self.pos_emb_t[:T].to(device=device)
             emb = (
                 repeat(emb_t_T, "t d-> b t h w d", b=B, h=H, w=W)
                 + repeat(emb_h_H, "h d-> b t h w d", b=B, t=T, w=W)

comfy/ldm/cosmos/vae.py

@@ -18,7 +18,6 @@ import logging
 import torch
 from torch import nn
 from enum import Enum
-import math
 
 from .cosmos_tokenizer.layers3d import (
     EncoderFactorized,
@@ -90,8 +89,8 @@ class CausalContinuousVideoTokenizer(nn.Module):
         self.distribution = IdentityDistribution()  # ContinuousFormulation[formulation_name].value()
 
         num_parameters = sum(param.numel() for param in self.parameters())
-        logging.debug(f"model={self.name}, num_parameters={num_parameters:,}")
-        logging.debug(
+        logging.info(f"model={self.name}, num_parameters={num_parameters:,}")
+        logging.info(
             f"z_channels={z_channels}, latent_channels={self.latent_channels}."
         )
 
@@ -106,23 +105,17 @@ class CausalContinuousVideoTokenizer(nn.Module):
         z, posteriors = self.distribution(moments)
         latent_ch = z.shape[1]
         latent_t = z.shape[2]
-        in_dtype = z.dtype
-        mean = self.latent_mean.view(latent_ch, -1)
-        std = self.latent_std.view(latent_ch, -1)
-
-        mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
-        std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
+        dtype = z.dtype
+        mean = self.latent_mean.view(latent_ch, -1)[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=dtype, device=z.device)
+        std = self.latent_std.view(latent_ch, -1)[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=dtype, device=z.device)
         return ((z - mean) / std) * self.sigma_data
 
     def decode(self, z):
         in_dtype = z.dtype
         latent_ch = z.shape[1]
         latent_t = z.shape[2]
-        mean = self.latent_mean.view(latent_ch, -1)
-        std = self.latent_std.view(latent_ch, -1)
-
-        mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
-        std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
+        mean = self.latent_mean.view(latent_ch, -1)[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
+        std = self.latent_std.view(latent_ch, -1)[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
 
         z = z / self.sigma_data
         z = z * std + mean

comfy/ldm/flux/layers.py

@@ -230,7 +230,8 @@ class SingleStreamBlock(nn.Module):
 
     def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None) -> Tensor:
         mod, _ = self.modulation(vec)
-        qkv, mlp = torch.split(self.linear1((1 + mod.scale) * self.pre_norm(x) + mod.shift), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
+        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
 
         q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         q, k = self.norm(q, k, v)

comfy/ldm/flux/math.py

@@ -5,15 +5,8 @@ from torch import Tensor
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
 
-
 def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor:
-    q_shape = q.shape
-    k_shape = k.shape
-
-    q = q.float().reshape(*q.shape[:-1], -1, 1, 2)
-    k = k.float().reshape(*k.shape[:-1], -1, 1, 2)
-    q = (pe[..., 0] * q[..., 0] + pe[..., 1] * q[..., 1]).reshape(*q_shape).type_as(v)
-    k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v)
+    q, k = apply_rope(q, k, pe)
 
     heads = q.shape[1]
     x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask)

comfy/ldm/flux/model.py

@@ -109,7 +109,8 @@ class Flux(nn.Module):
         img = self.img_in(img)
         vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype))
         if self.params.guidance_embed:
-            if guidance is not None:
+            if guidance is None:
+                raise ValueError("Didn't get guidance strength for guidance distilled model.")
             vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
         vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
@@ -185,7 +186,7 @@ class Flux(nn.Module):
         img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
         return img
 
-    def forward(self, x, timestep, context, y, guidance=None, control=None, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, y, guidance, control=None, transformer_options={}, **kwargs):
         bs, c, h, w = x.shape
         patch_size = self.patch_size
         x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))

comfy/ldm/hunyuan_video/model.py

@@ -240,7 +240,8 @@ class HunyuanVideo(nn.Module):
         vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
 
         if self.params.guidance_embed:
-            if guidance is not None:
+            if guidance is None:
+                raise ValueError("Didn't get guidance strength for guidance distilled model.")
             vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
         if txt_mask is not None and not torch.is_floating_point(txt_mask):
@@ -313,7 +314,7 @@ class HunyuanVideo(nn.Module):
         img = img.reshape(initial_shape)
         return img
 
-    def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, control=None, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, y, guidance, attention_mask=None, control=None, transformer_options={}, **kwargs):
         bs, c, t, h, w = x.shape
         patch_size = self.patch_size
         t_len = ((t + (patch_size[0] // 2)) // patch_size[0])

comfy/ldm/lumina/model.py

@@ -1,674 +0,0 @@
-# Code from: https://github.com/Alpha-VLLM/Lumina-Image-2.0/blob/main/models/model.py
-from __future__ import annotations
-
-from typing import List, Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, RMSNorm
-from comfy.ldm.modules.attention import optimized_attention_masked
-from comfy.ldm.flux.layers import EmbedND
-
-
-def modulate(x, scale):
-    return x * (1 + scale.unsqueeze(1))
-
-#############################################################################
-#                               Core NextDiT Model                              #
-#############################################################################
-
-
-class JointAttention(nn.Module):
-    """Multi-head attention module."""
-
-    def __init__(
-        self,
-        dim: int,
-        n_heads: int,
-        n_kv_heads: Optional[int],
-        qk_norm: bool,
-        operation_settings={},
-    ):
-        """
-        Initialize the Attention module.
-
-        Args:
-            dim (int): Number of input dimensions.
-            n_heads (int): Number of heads.
-            n_kv_heads (Optional[int]): Number of kv heads, if using GQA.
-
-        """
-        super().__init__()
-        self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads
-        self.n_local_heads = n_heads
-        self.n_local_kv_heads = self.n_kv_heads
-        self.n_rep = self.n_local_heads // self.n_local_kv_heads
-        self.head_dim = dim // n_heads
-
-        self.qkv = operation_settings.get("operations").Linear(
-            dim,
-            (n_heads + self.n_kv_heads + self.n_kv_heads) * self.head_dim,
-            bias=False,
-            device=operation_settings.get("device"),
-            dtype=operation_settings.get("dtype"),
-        )
-        self.out = operation_settings.get("operations").Linear(
-            n_heads * self.head_dim,
-            dim,
-            bias=False,
-            device=operation_settings.get("device"),
-            dtype=operation_settings.get("dtype"),
-        )
-
-        if qk_norm:
-            self.q_norm = RMSNorm(self.head_dim, elementwise_affine=True, **operation_settings)
-            self.k_norm = RMSNorm(self.head_dim, elementwise_affine=True, **operation_settings)
-        else:
-            self.q_norm = self.k_norm = nn.Identity()
-
-    @staticmethod
-    def apply_rotary_emb(
-        x_in: torch.Tensor,
-        freqs_cis: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-        Apply rotary embeddings to input tensors using the given frequency
-        tensor.
-
-        This function applies rotary embeddings to the given query 'xq' and
-        key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The
-        input tensors are reshaped as complex numbers, and the frequency tensor
-        is reshaped for broadcasting compatibility. The resulting tensors
-        contain rotary embeddings and are returned as real tensors.
-
-        Args:
-            x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings.
-            freqs_cis (torch.Tensor): Precomputed frequency tensor for complex
-                exponentials.
-
-        Returns:
-            Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor
-                and key tensor with rotary embeddings.
-        """
-
-        t_ = x_in.reshape(*x_in.shape[:-1], -1, 1, 2)
-        t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1]
-        return t_out.reshape(*x_in.shape)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        x_mask: torch.Tensor,
-        freqs_cis: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-
-        Args:
-            x:
-            x_mask:
-            freqs_cis:
-
-        Returns:
-
-        """
-        bsz, seqlen, _ = x.shape
-
-        xq, xk, xv = torch.split(
-            self.qkv(x),
-            [
-                self.n_local_heads * self.head_dim,
-                self.n_local_kv_heads * self.head_dim,
-                self.n_local_kv_heads * self.head_dim,
-            ],
-            dim=-1,
-        )
-        xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
-        xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
-        xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
-
-        xq = self.q_norm(xq)
-        xk = self.k_norm(xk)
-
-        xq = JointAttention.apply_rotary_emb(xq, freqs_cis=freqs_cis)
-        xk = JointAttention.apply_rotary_emb(xk, freqs_cis=freqs_cis)
-
-        n_rep = self.n_local_heads // self.n_local_kv_heads
-        if n_rep >= 1:
-            xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
-            xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
-        output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True)
-
-        return self.out(output)
-
-
-class FeedForward(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        hidden_dim: int,
-        multiple_of: int,
-        ffn_dim_multiplier: Optional[float],
-        operation_settings={},
-    ):
-        """
-        Initialize the FeedForward module.
-
-        Args:
-            dim (int): Input dimension.
-            hidden_dim (int): Hidden dimension of the feedforward layer.
-            multiple_of (int): Value to ensure hidden dimension is a multiple
-                of this value.
-            ffn_dim_multiplier (float, optional): Custom multiplier for hidden
-                dimension. Defaults to None.
-
-        """
-        super().__init__()
-        # custom dim factor multiplier
-        if ffn_dim_multiplier is not None:
-            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
-        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
-
-        self.w1 = operation_settings.get("operations").Linear(
-            dim,
-            hidden_dim,
-            bias=False,
-            device=operation_settings.get("device"),
-            dtype=operation_settings.get("dtype"),
-        )
-        self.w2 = operation_settings.get("operations").Linear(
-            hidden_dim,
-            dim,
-            bias=False,
-            device=operation_settings.get("device"),
-            dtype=operation_settings.get("dtype"),
-        )
-        self.w3 = operation_settings.get("operations").Linear(
-            dim,
-            hidden_dim,
-            bias=False,
-            device=operation_settings.get("device"),
-            dtype=operation_settings.get("dtype"),
-        )
-
-    # @torch.compile
-    def _forward_silu_gating(self, x1, x3):
-        return F.silu(x1) * x3
-
-    def forward(self, x):
-        return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
-
-
-class JointTransformerBlock(nn.Module):
-    def __init__(
-        self,
-        layer_id: int,
-        dim: int,
-        n_heads: int,
-        n_kv_heads: int,
-        multiple_of: int,
-        ffn_dim_multiplier: float,
-        norm_eps: float,
-        qk_norm: bool,
-        modulation=True,
-        operation_settings={},
-    ) -> None:
-        """
-        Initialize a TransformerBlock.
-
-        Args:
-            layer_id (int): Identifier for the layer.
-            dim (int): Embedding dimension of the input features.
-            n_heads (int): Number of attention heads.
-            n_kv_heads (Optional[int]): Number of attention heads in key and
-                value features (if using GQA), or set to None for the same as
-                query.
-            multiple_of (int):
-            ffn_dim_multiplier (float):
-            norm_eps (float):
-
-        """
-        super().__init__()
-        self.dim = dim
-        self.head_dim = dim // n_heads
-        self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, operation_settings=operation_settings)
-        self.feed_forward = FeedForward(
-            dim=dim,
-            hidden_dim=4 * dim,
-            multiple_of=multiple_of,
-            ffn_dim_multiplier=ffn_dim_multiplier,
-            operation_settings=operation_settings,
-        )
-        self.layer_id = layer_id
-        self.attention_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
-        self.ffn_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
-
-        self.attention_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
-        self.ffn_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
-
-        self.modulation = modulation
-        if modulation:
-            self.adaLN_modulation = nn.Sequential(
-                nn.SiLU(),
-                operation_settings.get("operations").Linear(
-                    min(dim, 1024),
-                    4 * dim,
-                    bias=True,
-                    device=operation_settings.get("device"),
-                    dtype=operation_settings.get("dtype"),
-                ),
-            )
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        x_mask: torch.Tensor,
-        freqs_cis: torch.Tensor,
-        adaln_input: Optional[torch.Tensor]=None,
-    ):
-        """
-        Perform a forward pass through the TransformerBlock.
-
-        Args:
-            x (torch.Tensor): Input tensor.
-            freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies.
-
-        Returns:
-            torch.Tensor: Output tensor after applying attention and
-                feedforward layers.
-
-        """
-        if self.modulation:
-            assert adaln_input is not None
-            scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1)
-
-            x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2(
-                self.attention(
-                    modulate(self.attention_norm1(x), scale_msa),
-                    x_mask,
-                    freqs_cis,
-                )
-            )
-            x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
-                self.feed_forward(
-                    modulate(self.ffn_norm1(x), scale_mlp),
-                )
-            )
-        else:
-            assert adaln_input is None
-            x = x + self.attention_norm2(
-                self.attention(
-                    self.attention_norm1(x),
-                    x_mask,
-                    freqs_cis,
-                )
-            )
-            x = x + self.ffn_norm2(
-                self.feed_forward(
-                    self.ffn_norm1(x),
-                )
-            )
-        return x
-
-
-class FinalLayer(nn.Module):
-    """
-    The final layer of NextDiT.
-    """
-
-    def __init__(self, hidden_size, patch_size, out_channels, operation_settings={}):
-        super().__init__()
-        self.norm_final = operation_settings.get("operations").LayerNorm(
-            hidden_size,
-            elementwise_affine=False,
-            eps=1e-6,
-            device=operation_settings.get("device"),
-            dtype=operation_settings.get("dtype"),
-        )
-        self.linear = operation_settings.get("operations").Linear(
-            hidden_size,
-            patch_size * patch_size * out_channels,
-            bias=True,
-            device=operation_settings.get("device"),
-            dtype=operation_settings.get("dtype"),
-        )
-
-        self.adaLN_modulation = nn.Sequential(
-            nn.SiLU(),
-            operation_settings.get("operations").Linear(
-                min(hidden_size, 1024),
-                hidden_size,
-                bias=True,
-                device=operation_settings.get("device"),
-                dtype=operation_settings.get("dtype"),
-            ),
-        )
-
-    def forward(self, x, c):
-        scale = self.adaLN_modulation(c)
-        x = modulate(self.norm_final(x), scale)
-        x = self.linear(x)
-        return x
-
-
-class RopeEmbedder:
-    def __init__(
-        self, theta: float = 10000.0, axes_dims: List[int] = (16, 56, 56), axes_lens: List[int] = (1, 512, 512)
-    ):
-        super().__init__()
-        self.theta = theta
-        self.axes_dims = axes_dims
-        self.axes_lens = axes_lens
-        self.freqs_cis = NextDiT.precompute_freqs_cis(self.axes_dims, self.axes_lens, theta=self.theta)
-
-    def __call__(self, ids: torch.Tensor):
-        self.freqs_cis = [freqs_cis.to(ids.device) for freqs_cis in self.freqs_cis]
-        result = []
-        for i in range(len(self.axes_dims)):
-            index = ids[:, :, i:i+1].repeat(1, 1, self.freqs_cis[i].shape[-1]).to(torch.int64)
-            result.append(torch.gather(self.freqs_cis[i].unsqueeze(0).repeat(index.shape[0], 1, 1), dim=1, index=index))
-        return torch.cat(result, dim=-1)
-
-
-class NextDiT(nn.Module):
-    """
-    Diffusion model with a Transformer backbone.
-    """
-
-    def __init__(
-        self,
-        patch_size: int = 2,
-        in_channels: int = 4,
-        dim: int = 4096,
-        n_layers: int = 32,
-        n_refiner_layers: int = 2,
-        n_heads: int = 32,
-        n_kv_heads: Optional[int] = None,
-        multiple_of: int = 256,
-        ffn_dim_multiplier: Optional[float] = None,
-        norm_eps: float = 1e-5,
-        qk_norm: bool = False,
-        cap_feat_dim: int = 5120,
-        axes_dims: List[int] = (16, 56, 56),
-        axes_lens: List[int] = (1, 512, 512),
-        image_model=None,
-        device=None,
-        dtype=None,
-        operations=None,
-    ) -> None:
-        super().__init__()
-        self.dtype = dtype
-        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
-        self.in_channels = in_channels
-        self.out_channels = in_channels
-        self.patch_size = patch_size
-
-        self.x_embedder = operation_settings.get("operations").Linear(
-            in_features=patch_size * patch_size * in_channels,
-            out_features=dim,
-            bias=True,
-            device=operation_settings.get("device"),
-            dtype=operation_settings.get("dtype"),
-        )
-
-        self.noise_refiner = nn.ModuleList(
-            [
-                JointTransformerBlock(
-                    layer_id,
-                    dim,
-                    n_heads,
-                    n_kv_heads,
-                    multiple_of,
-                    ffn_dim_multiplier,
-                    norm_eps,
-                    qk_norm,
-                    modulation=True,
-                    operation_settings=operation_settings,
-                )
-                for layer_id in range(n_refiner_layers)
-            ]
-        )
-        self.context_refiner = nn.ModuleList(
-            [
-                JointTransformerBlock(
-                    layer_id,
-                    dim,
-                    n_heads,
-                    n_kv_heads,
-                    multiple_of,
-                    ffn_dim_multiplier,
-                    norm_eps,
-                    qk_norm,
-                    modulation=False,
-                    operation_settings=operation_settings,
-                )
-                for layer_id in range(n_refiner_layers)
-            ]
-        )
-
-        self.t_embedder = TimestepEmbedder(min(dim, 1024), **operation_settings)
-        self.cap_embedder = nn.Sequential(
-            RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, **operation_settings),
-            operation_settings.get("operations").Linear(
-                cap_feat_dim,
-                dim,
-                bias=True,
-                device=operation_settings.get("device"),
-                dtype=operation_settings.get("dtype"),
-            ),
-        )
-
-        self.layers = nn.ModuleList(
-            [
-                JointTransformerBlock(
-                    layer_id,
-                    dim,
-                    n_heads,
-                    n_kv_heads,
-                    multiple_of,
-                    ffn_dim_multiplier,
-                    norm_eps,
-                    qk_norm,
-                    operation_settings=operation_settings,
-                )
-                for layer_id in range(n_layers)
-            ]
-        )
-        self.norm_final = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
-        self.final_layer = FinalLayer(dim, patch_size, self.out_channels, operation_settings=operation_settings)
-
-        assert (dim // n_heads) == sum(axes_dims)
-        self.axes_dims = axes_dims
-        self.axes_lens = axes_lens
-        # self.rope_embedder = RopeEmbedder(axes_dims=axes_dims, axes_lens=axes_lens)
-        self.rope_embedder = EmbedND(dim=dim // n_heads, theta=10000.0, axes_dim=axes_dims)
-        self.dim = dim
-        self.n_heads = n_heads
-
-    def unpatchify(
-        self, x: torch.Tensor, img_size: List[Tuple[int, int]], cap_size: List[int], return_tensor=False
-    ) -> List[torch.Tensor]:
-        """
-        x: (N, T, patch_size**2 * C)
-        imgs: (N, H, W, C)
-        """
-        pH = pW = self.patch_size
-        imgs = []
-        for i in range(x.size(0)):
-            H, W = img_size[i]
-            begin = cap_size[i]
-            end = begin + (H // pH) * (W // pW)
-            imgs.append(
-                x[i][begin:end]
-                .view(H // pH, W // pW, pH, pW, self.out_channels)
-                .permute(4, 0, 2, 1, 3)
-                .flatten(3, 4)
-                .flatten(1, 2)
-            )
-
-        if return_tensor:
-            imgs = torch.stack(imgs, dim=0)
-        return imgs
-
-    def patchify_and_embed(
-        self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens
-    ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]:
-        bsz = len(x)
-        pH = pW = self.patch_size
-        device = x[0].device
-        dtype = x[0].dtype
-
-        if cap_mask is not None:
-            l_effective_cap_len = cap_mask.sum(dim=1).tolist()
-        else:
-            l_effective_cap_len = [num_tokens] * bsz
-
-        if cap_mask is not None and not torch.is_floating_point(cap_mask):
-            cap_mask = (cap_mask - 1).to(dtype) * torch.finfo(dtype).max
-
-        img_sizes = [(img.size(1), img.size(2)) for img in x]
-        l_effective_img_len = [(H // pH) * (W // pW) for (H, W) in img_sizes]
-
-        max_seq_len = max(
-            (cap_len+img_len for cap_len, img_len in zip(l_effective_cap_len, l_effective_img_len))
-        )
-        max_cap_len = max(l_effective_cap_len)
-        max_img_len = max(l_effective_img_len)
-
-        position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.int32, device=device)
-
-        for i in range(bsz):
-            cap_len = l_effective_cap_len[i]
-            img_len = l_effective_img_len[i]
-            H, W = img_sizes[i]
-            H_tokens, W_tokens = H // pH, W // pW
-            assert H_tokens * W_tokens == img_len
-
-            position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.int32, device=device)
-            position_ids[i, cap_len:cap_len+img_len, 0] = cap_len
-            row_ids = torch.arange(H_tokens, dtype=torch.int32, device=device).view(-1, 1).repeat(1, W_tokens).flatten()
-            col_ids = torch.arange(W_tokens, dtype=torch.int32, device=device).view(1, -1).repeat(H_tokens, 1).flatten()
-            position_ids[i, cap_len:cap_len+img_len, 1] = row_ids
-            position_ids[i, cap_len:cap_len+img_len, 2] = col_ids
-
-        freqs_cis = self.rope_embedder(position_ids).movedim(1, 2).to(dtype)
-
-        # build freqs_cis for cap and image individually
-        cap_freqs_cis_shape = list(freqs_cis.shape)
-        # cap_freqs_cis_shape[1] = max_cap_len
-        cap_freqs_cis_shape[1] = cap_feats.shape[1]
-        cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
-
-        img_freqs_cis_shape = list(freqs_cis.shape)
-        img_freqs_cis_shape[1] = max_img_len
-        img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
-
-        for i in range(bsz):
-            cap_len = l_effective_cap_len[i]
-            img_len = l_effective_img_len[i]
-            cap_freqs_cis[i, :cap_len] = freqs_cis[i, :cap_len]
-            img_freqs_cis[i, :img_len] = freqs_cis[i, cap_len:cap_len+img_len]
-
-        # refine context
-        for layer in self.context_refiner:
-            cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis)
-
-        # refine image
-        flat_x = []
-        for i in range(bsz):
-            img = x[i]
-            C, H, W = img.size()
-            img = img.view(C, H // pH, pH, W // pW, pW).permute(1, 3, 2, 4, 0).flatten(2).flatten(0, 1)
-            flat_x.append(img)
-        x = flat_x
-        padded_img_embed = torch.zeros(bsz, max_img_len, x[0].shape[-1], device=device, dtype=x[0].dtype)
-        padded_img_mask = torch.zeros(bsz, max_img_len, dtype=dtype, device=device)
-        for i in range(bsz):
-            padded_img_embed[i, :l_effective_img_len[i]] = x[i]
-            padded_img_mask[i, l_effective_img_len[i]:] = -torch.finfo(dtype).max
-
-        padded_img_embed = self.x_embedder(padded_img_embed)
-        padded_img_mask = padded_img_mask.unsqueeze(1)
-        for layer in self.noise_refiner:
-            padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t)
-
-        if cap_mask is not None:
-            mask = torch.zeros(bsz, max_seq_len, dtype=dtype, device=device)
-            mask[:, :max_cap_len] = cap_mask[:, :max_cap_len]
-        else:
-            mask = None
-
-        padded_full_embed = torch.zeros(bsz, max_seq_len, self.dim, device=device, dtype=x[0].dtype)
-        for i in range(bsz):
-            cap_len = l_effective_cap_len[i]
-            img_len = l_effective_img_len[i]
-
-            padded_full_embed[i, :cap_len] = cap_feats[i, :cap_len]
-            padded_full_embed[i, cap_len:cap_len+img_len] = padded_img_embed[i, :img_len]
-
-        return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis
-
-
-    # def forward(self, x, t, cap_feats, cap_mask):
-    def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
-        t = 1.0 - timesteps
-        cap_feats = context
-        cap_mask = attention_mask
-        """
-        Forward pass of NextDiT.
-        t: (N,) tensor of diffusion timesteps
-        y: (N,) tensor of text tokens/features
-        """
-
-        t = self.t_embedder(t, dtype=x.dtype)  # (N, D)
-        adaln_input = t
-
-        cap_feats = self.cap_embedder(cap_feats)  # (N, L, D)  # todo check if able to batchify w.o. redundant compute
-
-        x_is_tensor = isinstance(x, torch.Tensor)
-        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens)
-        freqs_cis = freqs_cis.to(x.device)
-
-        for layer in self.layers:
-            x = layer(x, mask, freqs_cis, adaln_input)
-
-        x = self.final_layer(x, adaln_input)
-        x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)
-
-        return -x
-
-    @staticmethod
-    def precompute_freqs_cis(
-        dim: List[int],
-        end: List[int],
-        theta: float = 10000.0,
-    ):
-        """
-        Precompute the frequency tensor for complex exponentials (cis) with
-        given dimensions.
-
-        This function calculates a frequency tensor with complex exponentials
-        using the given dimension 'dim' and the end index 'end'. The 'theta'
-        parameter scales the frequencies. The returned tensor contains complex
-        values in complex64 data type.
-
-        Args:
-            dim (list): Dimension of the frequency tensor.
-            end (list): End index for precomputing frequencies.
-            theta (float, optional): Scaling factor for frequency computation.
-                Defaults to 10000.0.
-
-        Returns:
-            torch.Tensor: Precomputed frequency tensor with complex
-                exponentials.
-        """
-        freqs_cis = []
-        for i, (d, e) in enumerate(zip(dim, end)):
-            freqs = 1.0 / (theta ** (torch.arange(0, d, 2, dtype=torch.float64, device="cpu") / d))
-            timestep = torch.arange(e, device=freqs.device, dtype=torch.float64)
-            freqs = torch.outer(timestep, freqs).float()
-            freqs_cis_i = torch.polar(torch.ones_like(freqs), freqs).to(torch.complex64)  # complex64
-            freqs_cis.append(freqs_cis_i)
-
-        return freqs_cis

comfy/ldm/modules/attention.py

@@ -1,6 +1,4 @@
 import math
-import sys
-
 import torch
 import torch.nn.functional as F
 from torch import nn, einsum
@@ -18,11 +16,7 @@ if model_management.xformers_enabled():
     import xformers.ops
 
 if model_management.sage_attention_enabled():
-    try:
     from sageattention import sageattn
-    except ModuleNotFoundError:
-        logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
-        exit(-1)
 
 from comfy.cli_args import args
 import comfy.ops

comfy/ldm/modules/diffusionmodules/mmdit.py

@@ -321,7 +321,7 @@ class SelfAttention(nn.Module):
 
 class RMSNorm(torch.nn.Module):
     def __init__(
-        self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None, **kwargs
+        self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None
     ):
         """
         Initialize the RMSNorm normalization layer.

comfy/ldm/modules/diffusionmodules/model.py

@@ -293,17 +293,6 @@ def pytorch_attention(q, k, v):
     return out
 
 
-def vae_attention():
-    if model_management.xformers_enabled_vae():
-        logging.info("Using xformers attention in VAE")
-        return xformers_attention
-    elif model_management.pytorch_attention_enabled():
-        logging.info("Using pytorch attention in VAE")
-        return pytorch_attention
-    else:
-        logging.info("Using split attention in VAE")
-        return normal_attention
-
 class AttnBlock(nn.Module):
     def __init__(self, in_channels, conv_op=ops.Conv2d):
         super().__init__()
@@ -331,7 +320,15 @@ class AttnBlock(nn.Module):
                                         stride=1,
                                         padding=0)
 
-        self.optimized_attention = vae_attention()
+        if model_management.xformers_enabled_vae():
+            logging.info("Using xformers attention in VAE")
+            self.optimized_attention = xformers_attention
+        elif model_management.pytorch_attention_enabled():
+            logging.info("Using pytorch attention in VAE")
+            self.optimized_attention = pytorch_attention
+        else:
+            logging.info("Using split attention in VAE")
+            self.optimized_attention = normal_attention
 
     def forward(self, x):
         h_ = x
@@ -702,6 +699,9 @@ class Decoder(nn.Module):
                                         padding=1)
 
     def forward(self, z, **kwargs):
+        #assert z.shape[1:] == self.z_shape[1:]
+        self.last_z_shape = z.shape
+
         # timestep embedding
         temb = None
 

comfy/lora.py

@@ -307,6 +307,7 @@ def model_lora_keys_unet(model, key_map={}):
             if k.endswith(".weight"):
                 key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
                 key_map["lora_unet_{}".format(key_lora)] = k
+                key_map["lora_prior_unet_{}".format(key_lora)] = k #cascade lora: TODO put lora key prefix in the model config
                 key_map["{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names
             else:
                 key_map["{}".format(k)] = k #generic lora format for not .weight without any weird key names
@@ -326,13 +327,6 @@ def model_lora_keys_unet(model, key_map={}):
                     diffusers_lora_key = diffusers_lora_key[:-2]
                 key_map[diffusers_lora_key] = unet_key
 
-    if isinstance(model, comfy.model_base.StableCascade_C):
-        for k in sdk:
-            if k.startswith("diffusion_model."):
-                if k.endswith(".weight"):
-                    key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
-                    key_map["lora_prior_unet_{}".format(key_lora)] = k
-
     if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3
         diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
         for k in diffusers_keys:

comfy/model_base.py

@@ -34,7 +34,6 @@ import comfy.ldm.flux.model
 import comfy.ldm.lightricks.model
 import comfy.ldm.hunyuan_video.model
 import comfy.ldm.cosmos.model
-import comfy.ldm.lumina.model
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -149,9 +148,7 @@ class BaseModel(torch.nn.Module):
 
         xc = xc.to(dtype)
         t = self.model_sampling.timestep(t).float()
-        if context is not None:
         context = context.to(dtype)
-
         extra_conds = {}
         for o in kwargs:
             extra = kwargs[o]
@@ -552,10 +549,6 @@ class SD_X4Upscaler(BaseModel):
 
         out['c_concat'] = comfy.conds.CONDNoiseShape(image)
         out['y'] = comfy.conds.CONDRegular(noise_level)
-
-        cross_attn = kwargs.get("cross_attn", None)
-        if cross_attn is not None:
-            out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
         return out
 
 class IP2P:
@@ -813,10 +806,7 @@ class Flux(BaseModel):
             (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size))
             attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok))
             out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
-
-        guidance = kwargs.get("guidance", 3.5)
-        if guidance is not None:
-            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
+        out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
         return out
 
 class GenmoMochi(BaseModel):
@@ -873,10 +863,7 @@ class HunyuanVideo(BaseModel):
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
-
-        guidance = kwargs.get("guidance", 6.0)
-        if guidance is not None:
-            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
+        out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 6.0)]))
         return out
 
 class CosmosVideo(BaseModel):
@@ -905,19 +892,3 @@ class CosmosVideo(BaseModel):
             latent_image = latent_image + noise
         latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image)
         return latent_image * ((sigma ** 2 + self.model_sampling.sigma_data ** 2) ** 0.5)
-
-class Lumina2(BaseModel):
-    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiT)
-
-    def extra_conds(self, **kwargs):
-        out = super().extra_conds(**kwargs)
-        attention_mask = kwargs.get("attention_mask", None)
-        if attention_mask is not None:
-            if torch.numel(attention_mask) != attention_mask.sum():
-                out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
-            out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item()))
-        cross_attn = kwargs.get("cross_attn", None)
-        if cross_attn is not None:
-            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
-        return out

comfy/model_detection.py

@@ -239,7 +239,7 @@ def detect_unet_config(state_dict, key_prefix):
             dit_config["micro_condition"] = False
         return dit_config
 
-    if '{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix) in state_dict_keys:  # Cosmos
+    if '{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix) in state_dict_keys:
         dit_config = {}
         dit_config["image_model"] = "cosmos"
         dit_config["max_img_h"] = 240
@@ -284,21 +284,6 @@ def detect_unet_config(state_dict, key_prefix):
             dit_config["extra_per_block_abs_pos_emb_type"] = "learnable"
         return dit_config
 
-    if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys:  # Lumina 2
-        dit_config = {}
-        dit_config["image_model"] = "lumina2"
-        dit_config["patch_size"] = 2
-        dit_config["in_channels"] = 16
-        dit_config["dim"] = 2304
-        dit_config["cap_feat_dim"] = 2304
-        dit_config["n_layers"] = 26
-        dit_config["n_heads"] = 24
-        dit_config["n_kv_heads"] = 8
-        dit_config["qk_norm"] = True
-        dit_config["axes_dims"] = [32, 32, 32]
-        dit_config["axes_lens"] = [300, 512, 512]
-        return dit_config
-
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 

comfy/model_management.py

@@ -218,7 +218,7 @@ def is_amd():
 
 MIN_WEIGHT_MEMORY_RATIO = 0.4
 if is_nvidia():
-    MIN_WEIGHT_MEMORY_RATIO = 0.1
+    MIN_WEIGHT_MEMORY_RATIO = 0.2
 
 ENABLE_PYTORCH_ATTENTION = False
 if args.use_pytorch_cross_attention:
@@ -535,11 +535,14 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
             vram_set_state = vram_state
         lowvram_model_memory = 0
         if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM) and not force_full_load:
+            model_size = loaded_model.model_memory_required(torch_dev)
             loaded_memory = loaded_model.model_loaded_memory()
             current_free_mem = get_free_memory(torch_dev) + loaded_memory
 
             lowvram_model_memory = max(64 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory()))
             lowvram_model_memory = max(0.1, lowvram_model_memory - loaded_memory)
+            if model_size <= lowvram_model_memory: #only switch to lowvram if really necessary
+                lowvram_model_memory = 0
 
         if vram_set_state == VRAMState.NO_VRAM:
             lowvram_model_memory = 0.1

comfy/model_sampling.py

@@ -31,7 +31,6 @@ class EPS:
         return model_input - model_output * sigma
 
     def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
         if max_denoise:
             noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
         else:
@@ -62,11 +61,9 @@ class CONST:
         return model_input - model_output * sigma
 
     def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
         return sigma * noise + (1.0 - sigma) * latent_image
 
     def inverse_noise_scaling(self, sigma, latent):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (latent.ndim - 1))
         return latent / (1.0 - sigma)
 
 class ModelSamplingDiscrete(torch.nn.Module):

comfy/sampler_helpers.py

@@ -58,6 +58,7 @@ def convert_cond(cond):
         temp = c[1].copy()
         model_conds = temp.get("model_conds", {})
         if c[0] is not None:
+            model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) #TODO: remove
             temp["cross_attn"] = c[0]
         temp["model_conds"] = model_conds
         temp["uuid"] = uuid.uuid4()

comfy/samplers.py

@@ -12,6 +12,7 @@ import collections
 from comfy import model_management
 import math
 import logging
+import comfy.samplers
 import comfy.sampler_helpers
 import comfy.model_patcher
 import comfy.patcher_extension
@@ -177,7 +178,7 @@ def finalize_default_conds(model: 'BaseModel', hooked_to_run: dict[comfy.hooks.H
         cond = default_conds[i]
         for x in cond:
             # do get_area_and_mult to get all the expected values
-            p = get_area_and_mult(x, x_in, timestep)
+            p = comfy.samplers.get_area_and_mult(x, x_in, timestep)
             if p is None:
                 continue
             # replace p's mult with calculated mult
@@ -214,7 +215,7 @@ def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Te
                     default_c.append(x)
                     has_default_conds = True
                     continue
-                p = get_area_and_mult(x, x_in, timestep)
+                p = comfy.samplers.get_area_and_mult(x, x_in, timestep)
                 if p is None:
                     continue
                 if p.hooks is not None:
@@ -686,7 +687,7 @@ class Sampler:
 KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
                   "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
-                  "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "gradient_estimation"]
+                  "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

comfy/sd.py

@@ -36,7 +36,6 @@ import comfy.text_encoders.genmo
 import comfy.text_encoders.lt
 import comfy.text_encoders.hunyuan_video
 import comfy.text_encoders.cosmos
-import comfy.text_encoders.lumina2
 
 import comfy.model_patcher
 import comfy.lora
@@ -389,8 +388,8 @@ class VAE:
                 ddconfig = {'z_channels': 16, 'latent_channels': self.latent_channels, 'z_factor': 1, 'resolution': 1024, 'in_channels': 3, 'out_channels': 3, 'channels': 128, 'channels_mult': [2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [32], 'dropout': 0.0, 'patch_size': 4, 'num_groups': 1, 'temporal_compression': 8, 'spacial_compression': 8}
                 self.first_stage_model = comfy.ldm.cosmos.vae.CausalContinuousVideoTokenizer(**ddconfig)
                 #TODO: these values are a bit off because this is not a standard VAE
-                self.memory_used_decode = lambda shape, dtype: (50 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
-                self.memory_used_encode = lambda shape, dtype: (50 * (round((shape[2] + 7) / 8) * 8) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (220 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (500 * max(shape[2], 2) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                 self.working_dtypes = [torch.bfloat16, torch.float32]
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
@@ -658,7 +657,6 @@ class CLIPType(Enum):
     HUNYUAN_VIDEO = 9
     PIXART = 10
     COSMOS = 11
-    LUMINA2 = 12
 
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
@@ -677,7 +675,6 @@ class TEModel(Enum):
     T5_BASE = 6
     LLAMA3_8 = 7
     T5_XXL_OLD = 8
-    GEMMA_2_2B = 9
 
 def detect_te_model(sd):
     if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
@@ -696,8 +693,6 @@ def detect_te_model(sd):
         return TEModel.T5_XXL_OLD
     if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd:
         return TEModel.T5_BASE
-    if 'model.layers.0.post_feedforward_layernorm.weight' in sd:
-        return TEModel.GEMMA_2_2B
     if "model.layers.0.post_attention_layernorm.weight" in sd:
         return TEModel.LLAMA3_8
     return None
@@ -735,7 +730,6 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             if "text_projection" in clip_data[i]:
                 clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node
 
-    tokenizer_data = {}
     clip_target = EmptyClass()
     clip_target.params = {}
     if len(clip_data) == 1:
@@ -775,10 +769,6 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         elif te_model == TEModel.T5_BASE:
             clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model
             clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer
-        elif te_model == TEModel.GEMMA_2_2B:
-            clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data))
-            clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer
-            tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
         else:
             if clip_type == CLIPType.SD3:
                 clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False)
@@ -808,6 +798,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
 
     parameters = 0
+    tokenizer_data = {}
     for c in clip_data:
         parameters += comfy.utils.calculate_parameters(c)
         tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)

comfy/sd1_clip.py

@@ -421,10 +421,10 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
     return embed_out
 
 class SDTokenizer:
-    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, tokenizer_data={}, tokenizer_args={}):
+    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, tokenizer_data={}):
         if tokenizer_path is None:
             tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
-        self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args)
+        self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path)
         self.max_length = max_length
         self.min_length = min_length
         self.end_token = None
@@ -585,14 +585,9 @@ class SDTokenizer:
         return {}
 
 class SD1Tokenizer:
-    def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer, name=None):
-        if name is not None:
-            self.clip_name = name
-            self.clip = "{}".format(self.clip_name)
-        else:
+    def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer):
         self.clip_name = clip_name
         self.clip = "clip_{}".format(self.clip_name)
-
         tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer)
         setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data))
 
@@ -605,7 +600,7 @@ class SD1Tokenizer:
         return getattr(self, self.clip).untokenize(token_weight_pair)
 
     def state_dict(self):
-        return getattr(self, self.clip).state_dict()
+        return {}
 
 class SD1CheckpointClipModel(SDClipModel):
     def __init__(self, device="cpu", dtype=None, model_options={}):

comfy/supported_models.py

@@ -15,7 +15,6 @@ import comfy.text_encoders.genmo
 import comfy.text_encoders.lt
 import comfy.text_encoders.hunyuan_video
 import comfy.text_encoders.cosmos
-import comfy.text_encoders.lumina2
 
 from . import supported_models_base
 from . import latent_formats
@@ -789,7 +788,7 @@ class HunyuanVideo(supported_models_base.BASE):
     unet_extra_config = {}
     latent_format = latent_formats.HunyuanVideo
 
-    memory_usage_factor = 1.8 #TODO
+    memory_usage_factor = 2.0 #TODO
 
     supported_inference_dtypes = [torch.bfloat16, torch.float32]
 
@@ -840,7 +839,7 @@ class CosmosT2V(supported_models_base.BASE):
     unet_extra_config = {}
     latent_format = latent_formats.Cosmos1CV8x8x8
 
-    memory_usage_factor = 1.6 #TODO
+    memory_usage_factor = 2.4 #TODO
 
     supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] #TODO
 
@@ -866,35 +865,6 @@ class CosmosI2V(CosmosT2V):
         out = model_base.CosmosVideo(self, image_to_video=True, device=device)
         return out
 
-class Lumina2(supported_models_base.BASE):
-    unet_config = {
-        "image_model": "lumina2",
-    }
-
-    sampling_settings = {
-        "multiplier": 1.0,
-        "shift": 6.0,
-    }
-
-    memory_usage_factor = 1.2
-
-    unet_extra_config = {}
-    latent_format = latent_formats.Flux
-
-    supported_inference_dtypes = [torch.bfloat16, torch.float32]
-
-    vae_key_prefix = ["vae."]
-    text_encoder_key_prefix = ["text_encoders."]
-
-    def get_model(self, state_dict, prefix="", device=None):
-        out = model_base.Lumina2(self, device=device)
-        return out
-
-    def clip_target(self, state_dict={}):
-        pref = self.text_encoder_key_prefix[0]
-        hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}gemma2_2b.transformer.".format(pref))
-        return supported_models_base.ClipTarget(comfy.text_encoders.lumina2.LuminaTokenizer, comfy.text_encoders.lumina2.te(**hunyuan_detect))
-
-models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2]
+models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo, CosmosT2V, CosmosI2V]
 
 models += [SVD_img2vid]

comfy/text_encoders/bert.py

@@ -118,7 +118,7 @@ class BertModel_(torch.nn.Module):
         mask = None
         if attention_mask is not None:
             mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
-            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
+            mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
 
         x, i = self.encoder(x, mask, intermediate_output)
         return x, i

comfy/text_encoders/llama.py

@@ -1,5 +1,6 @@
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 from dataclasses import dataclass
 from typing import Optional, Any
 
@@ -20,41 +21,15 @@ class Llama2Config:
     max_position_embeddings: int = 8192
     rms_norm_eps: float = 1e-5
     rope_theta: float = 500000.0
-    transformer_type: str = "llama"
-    head_dim = 128
-    rms_norm_add = False
-    mlp_activation = "silu"
-
-@dataclass
-class Gemma2_2B_Config:
-    vocab_size: int = 256000
-    hidden_size: int = 2304
-    intermediate_size: int = 9216
-    num_hidden_layers: int = 26
-    num_attention_heads: int = 8
-    num_key_value_heads: int = 4
-    max_position_embeddings: int = 8192
-    rms_norm_eps: float = 1e-6
-    rope_theta: float = 10000.0
-    transformer_type: str = "gemma2"
-    head_dim = 256
-    rms_norm_add = True
-    mlp_activation = "gelu_pytorch_tanh"
 
 class RMSNorm(nn.Module):
-    def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None):
+    def __init__(self, dim: int, eps: float = 1e-5, device=None, dtype=None):
         super().__init__()
         self.eps = eps
         self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
-        self.add = add
 
     def forward(self, x: torch.Tensor):
-        w = self.weight
-        if self.add:
-            w = w + 1.0
-
-        return comfy.ldm.common_dit.rms_norm(x, w, self.eps)
-
+        return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps)
 
 
 def rotate_half(x):
@@ -93,15 +68,13 @@ class Attention(nn.Module):
         self.num_heads = config.num_attention_heads
         self.num_kv_heads = config.num_key_value_heads
         self.hidden_size = config.hidden_size
-
-        self.head_dim = config.head_dim
-        self.inner_size = self.num_heads * self.head_dim
+        self.head_dim = self.hidden_size // self.num_heads
 
         ops = ops or nn
-        self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=False, device=device, dtype=dtype)
+        self.q_proj = ops.Linear(config.hidden_size, config.hidden_size, bias=False, device=device, dtype=dtype)
         self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False, device=device, dtype=dtype)
         self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False, device=device, dtype=dtype)
-        self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype)
+        self.o_proj = ops.Linear(config.hidden_size, config.hidden_size, bias=False, device=device, dtype=dtype)
 
     def forward(
         self,
@@ -111,6 +84,7 @@ class Attention(nn.Module):
         optimized_attention=None,
     ):
         batch_size, seq_length, _ = hidden_states.shape
+
         xq = self.q_proj(hidden_states)
         xk = self.k_proj(hidden_states)
         xv = self.v_proj(hidden_states)
@@ -134,13 +108,9 @@ class MLP(nn.Module):
         self.gate_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype)
         self.up_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype)
         self.down_proj = ops.Linear(config.intermediate_size, config.hidden_size, bias=False, device=device, dtype=dtype)
-        if config.mlp_activation == "silu":
-            self.activation = torch.nn.functional.silu
-        elif config.mlp_activation == "gelu_pytorch_tanh":
-            self.activation = lambda a: torch.nn.functional.gelu(a, approximate="tanh")
 
     def forward(self, x):
-        return self.down_proj(self.activation(self.gate_proj(x)) * self.up_proj(x))
+        return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
 
 class TransformerBlock(nn.Module):
     def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
@@ -176,45 +146,6 @@ class TransformerBlock(nn.Module):
 
         return x
 
-class TransformerBlockGemma2(nn.Module):
-    def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
-        super().__init__()
-        self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops)
-        self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
-        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
-        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
-        self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
-        self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        freqs_cis: Optional[torch.Tensor] = None,
-        optimized_attention=None,
-    ):
-        # Self Attention
-        residual = x
-        x = self.input_layernorm(x)
-        x = self.self_attn(
-            hidden_states=x,
-            attention_mask=attention_mask,
-            freqs_cis=freqs_cis,
-            optimized_attention=optimized_attention,
-        )
-
-        x = self.post_attention_layernorm(x)
-        x = residual + x
-
-        # MLP
-        residual = x
-        x = self.pre_feedforward_layernorm(x)
-        x = self.mlp(x)
-        x = self.post_feedforward_layernorm(x)
-        x = residual + x
-
-        return x
-
 class Llama2_(nn.Module):
     def __init__(self, config, device=None, dtype=None, ops=None):
         super().__init__()
@@ -227,27 +158,17 @@ class Llama2_(nn.Module):
             device=device,
             dtype=dtype
         )
-        if self.config.transformer_type == "gemma2":
-            transformer = TransformerBlockGemma2
-            self.normalize_in = True
-        else:
-            transformer = TransformerBlock
-            self.normalize_in = False
-
         self.layers = nn.ModuleList([
-            transformer(config, device=device, dtype=dtype, ops=ops)
+            TransformerBlock(config, device=device, dtype=dtype, ops=ops)
             for _ in range(config.num_hidden_layers)
         ])
-        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype)
         # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
 
     def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None):
         x = self.embed_tokens(x, out_dtype=dtype)
 
-        if self.normalize_in:
-            x *= self.config.hidden_size ** 0.5
-
-        freqs_cis = precompute_freqs_cis(self.config.head_dim,
+        freqs_cis = precompute_freqs_cis(self.config.hidden_size // self.config.num_attention_heads,
                                          x.shape[1],
                                          self.config.rope_theta,
                                          device=x.device)
@@ -285,18 +206,8 @@ class Llama2_(nn.Module):
 
         return x, intermediate
 
-class BaseLlama:
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
 
-    def set_input_embeddings(self, embeddings):
-        self.model.embed_tokens = embeddings
-
-    def forward(self, input_ids, *args, **kwargs):
-        return self.model(input_ids, *args, **kwargs)
-
-
-class Llama2(BaseLlama, torch.nn.Module):
+class Llama2(torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()
         config = Llama2Config(**config_dict)
@@ -305,12 +216,11 @@ class Llama2(BaseLlama, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
 
-class Gemma2_2B(BaseLlama, torch.nn.Module):
-    def __init__(self, config_dict, dtype, device, operations):
-        super().__init__()
-        config = Gemma2_2B_Config(**config_dict)
-        self.num_layers = config.num_hidden_layers
+    def set_input_embeddings(self, embeddings):
+        self.model.embed_tokens = embeddings
 
-        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
-        self.dtype = dtype
+    def forward(self, input_ids, *args, **kwargs):
+        return self.model(input_ids, *args, **kwargs)

comfy/text_encoders/lumina2.py

@@ -1,44 +0,0 @@
-from comfy import sd1_clip
-from .spiece_tokenizer import SPieceTokenizer
-import comfy.text_encoders.llama
-
-
-class Gemma2BTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, embedding_directory=None, tokenizer_data={}):
-        tokenizer = tokenizer_data.get("spiece_model", None)
-        super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False})
-
-    def state_dict(self):
-        return {"spiece_model": self.tokenizer.serialize_model()}
-
-
-class LuminaTokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None, tokenizer_data={}):
-        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma2_2b", tokenizer=Gemma2BTokenizer)
-
-
-class Gemma2_2BModel(sd1_clip.SDClipModel):
-    def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
-        llama_scaled_fp8 = model_options.get("llama_scaled_fp8", None)
-        if llama_scaled_fp8 is not None:
-            model_options = model_options.copy()
-            model_options["scaled_fp8"] = llama_scaled_fp8
-
-        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma2_2B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
-
-
-class LuminaModel(sd1_clip.SD1ClipModel):
-    def __init__(self, device="cpu", dtype=None, model_options={}):
-        super().__init__(device=device, dtype=dtype, name="gemma2_2b", clip_model=Gemma2_2BModel, model_options=model_options)
-
-
-def te(dtype_llama=None, llama_scaled_fp8=None):
-    class LuminaTEModel_(LuminaModel):
-        def __init__(self, device="cpu", dtype=None, model_options={}):
-            if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options:
-                model_options = model_options.copy()
-                model_options["llama_scaled_fp8"] = llama_scaled_fp8
-                if dtype_llama is not None:
-                    dtype = dtype_llama
-            super().__init__(device=device, dtype=dtype, model_options=model_options)
-    return LuminaTEModel_

comfy/text_encoders/spiece_tokenizer.py

@@ -1,21 +1,21 @@
 import torch
 
 class SPieceTokenizer:
-    @staticmethod
-    def from_pretrained(path, **kwargs):
-        return SPieceTokenizer(path, **kwargs)
+    add_eos = True
 
-    def __init__(self, tokenizer_path, add_bos=False, add_eos=True):
-        self.add_bos = add_bos
-        self.add_eos = add_eos
+    @staticmethod
+    def from_pretrained(path):
+        return SPieceTokenizer(path)
+
+    def __init__(self, tokenizer_path):
         import sentencepiece
         if torch.is_tensor(tokenizer_path):
             tokenizer_path = tokenizer_path.numpy().tobytes()
 
         if isinstance(tokenizer_path, bytes):
-            self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos)
+            self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_eos=self.add_eos)
         else:
-            self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos)
+            self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_eos=self.add_eos)
 
     def get_vocab(self):
         out = {}

comfy/text_encoders/t5.py

@@ -203,7 +203,7 @@ class T5Stack(torch.nn.Module):
         mask = None
         if attention_mask is not None:
             mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
-            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
+            mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
 
         intermediate = None
         optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True)

comfy/utils.py

@@ -43,23 +43,13 @@ if hasattr(torch.serialization, "add_safe_globals"):  # TODO: this was added in
     torch.serialization.add_safe_globals([ModelCheckpoint, scalar, dtype, Float64DType, encode])
     ALWAYS_SAFE_LOAD = True
     logging.info("Checkpoint files will always be loaded safely.")
-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 load_torch_file(ckpt, safe_load=False, device=None):
     if device is None:
         device = torch.device("cpu")
     if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"):
-        try:
         sd = safetensors.torch.load_file(ckpt, device=device.type)
-        except Exception as e:
-            if len(e.args) > 0:
-                message = e.args[0]
-                if "HeaderTooLarge" in message:
-                    raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt or invalid. Make sure this is actually a safetensors file and not a ckpt or pt or other filetype.".format(message, ckpt))
-                if "MetadataIncompleteBuffer" in message:
-                    raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is incomplete. Check the file size and make sure you have copied/downloaded it correctly.".format(message, ckpt))
-            raise e
     else:
         if safe_load or ALWAYS_SAFE_LOAD:
             pl_sd = torch.load(ckpt, map_location=device, weights_only=True)

comfy_extras/nodes_cosmos.py

@@ -71,8 +71,8 @@ class CosmosImageToVideoLatent:
             mask[:, :, -latent_temp.shape[-3]:] *= 0.0
 
         out_latent = {}
-        out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
-        out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
+        out_latent["samples"] = latent
+        out_latent["noise_mask"] = mask
         return (out_latent,)
 
 

comfy_extras/nodes_flux.py

@@ -38,26 +38,7 @@ class FluxGuidance:
         return (c, )
 
 
-class FluxDisableGuidance:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "conditioning": ("CONDITIONING", ),
-            }}
-
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "append"
-
-    CATEGORY = "advanced/conditioning/flux"
-    DESCRIPTION = "This node completely disables the guidance embed on Flux and Flux like models"
-
-    def append(self, conditioning):
-        c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
-        return (c, )
-
-
 NODE_CLASS_MAPPINGS = {
     "CLIPTextEncodeFlux": CLIPTextEncodeFlux,
     "FluxGuidance": FluxGuidance,
-    "FluxDisableGuidance": FluxDisableGuidance,
 }

comfy_extras/nodes_latent.py

@@ -2,14 +2,10 @@ import comfy.utils
 import comfy_extras.nodes_post_processing
 import torch
 
-
-def reshape_latent_to(target_shape, latent, repeat_batch=True):
+def reshape_latent_to(target_shape, latent):
     if latent.shape[1:] != target_shape[1:]:
-        latent = comfy.utils.common_upscale(latent, target_shape[-1], target_shape[-2], "bilinear", "center")
-    if repeat_batch:
+        latent = comfy.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center")
     return comfy.utils.repeat_to_batch_size(latent, target_shape[0])
-    else:
-        return latent
 
 
 class LatentAdd:
@@ -120,7 +116,8 @@ class LatentBatch:
         s1 = samples1["samples"]
         s2 = samples2["samples"]
 
-        s2 = reshape_latent_to(s1.shape, s2, repeat_batch=False)
+        if s1.shape[1:] != s2.shape[1:]:
+            s2 = comfy.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center")
         s = torch.cat((s1, s2), dim=0)
         samples_out["samples"] = s
         samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])

comfy_extras/nodes_load_3d.py

@@ -19,7 +19,11 @@ class Load3D():
             "image": ("LOAD_3D", {}),
             "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
+            "show_grid": ([True, False],),
+            "camera_type": (["perspective", "orthographic"],),
+            "view": (["front", "right", "top", "isometric"],),
             "material": (["original", "normal", "wireframe", "depth"],),
+            "bg_color": ("STRING", {"default": "#000000", "multiline": False}),
             "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}),
             "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
             "fov": ("INT", {"default": 75, "min": 10, "max": 150, "step": 1}),
@@ -65,9 +69,14 @@ class Load3DAnimation():
             "image": ("LOAD_3D_ANIMATION", {}),
             "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
+            "show_grid": ([True, False],),
+            "camera_type": (["perspective", "orthographic"],),
+            "view": (["front", "right", "top", "isometric"],),
             "material": (["original", "normal", "wireframe", "depth"],),
+            "bg_color": ("STRING", {"default": "#000000", "multiline": False}),
             "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}),
             "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
+            "animation_speed": (["0.1", "0.5", "1", "1.5", "2"], {"default": "1"}),
             "fov": ("INT", {"default": 75, "min": 10, "max": 150, "step": 1}),
         }}
 
@@ -100,29 +109,11 @@ class Preview3D():
     def INPUT_TYPES(s):
         return {"required": {
             "model_file": ("STRING", {"default": "", "multiline": False}),
+            "show_grid": ([True, False],),
+            "camera_type": (["perspective", "orthographic"],),
+            "view": (["front", "right", "top", "isometric"],),
             "material": (["original", "normal", "wireframe", "depth"],),
-            "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}),
-            "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
-            "fov": ("INT", {"default": 75, "min": 10, "max": 150, "step": 1}),
-        }}
-
-    OUTPUT_NODE = True
-    RETURN_TYPES = ()
-
-    CATEGORY = "3d"
-
-    FUNCTION = "process"
-    EXPERIMENTAL = True
-
-    def process(self, model_file, **kwargs):
-        return {"ui": {"model_file": [model_file]}, "result": ()}
-
-class Preview3DAnimation():
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "model_file": ("STRING", {"default": "", "multiline": False}),
-            "material": (["original", "normal", "wireframe", "depth"],),
+            "bg_color": ("STRING", {"default": "#000000", "multiline": False}),
             "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}),
             "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
             "fov": ("INT", {"default": 75, "min": 10, "max": 150, "step": 1}),
@@ -142,13 +133,11 @@ class Preview3DAnimation():
 NODE_CLASS_MAPPINGS = {
     "Load3D": Load3D,
     "Load3DAnimation": Load3DAnimation,
-    "Preview3D": Preview3D,
-    "Preview3DAnimation": Preview3DAnimation
+    "Preview3D": Preview3D
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "Load3D": "Load 3D",
     "Load3DAnimation": "Load 3D - Animation",
-    "Preview3D": "Preview 3D",
-    "Preview3DAnimation": "Preview 3D - Animation"
+    "Preview3D": "Preview 3D"
 }

comfy_extras/nodes_model_merging_model_specific.py

@@ -196,54 +196,6 @@ class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
         return {"required": arg_dict}
 
-class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
-
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
-
-        arg_dict["pos_embedder."] = argument
-        arg_dict["extra_pos_embedder."] = argument
-        arg_dict["x_embedder."] = argument
-        arg_dict["t_embedder."] = argument
-        arg_dict["affline_norm."] = argument
-
-
-        for i in range(28):
-            arg_dict["blocks.block{}.".format(i)] = argument
-
-        arg_dict["final_layer."] = argument
-
-        return {"required": arg_dict}
-
-class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
-    CATEGORY = "advanced/model_merging/model_specific"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        arg_dict = { "model1": ("MODEL",),
-                              "model2": ("MODEL",)}
-
-        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
-
-        arg_dict["pos_embedder."] = argument
-        arg_dict["extra_pos_embedder."] = argument
-        arg_dict["x_embedder."] = argument
-        arg_dict["t_embedder."] = argument
-        arg_dict["affline_norm."] = argument
-
-
-        for i in range(36):
-            arg_dict["blocks.block{}.".format(i)] = argument
-
-        arg_dict["final_layer."] = argument
-
-        return {"required": arg_dict}
-
 NODE_CLASS_MAPPINGS = {
     "ModelMergeSD1": ModelMergeSD1,
     "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
@@ -254,6 +206,4 @@ NODE_CLASS_MAPPINGS = {
     "ModelMergeSD35_Large": ModelMergeSD35_Large,
     "ModelMergeMochiPreview": ModelMergeMochiPreview,
     "ModelMergeLTXV": ModelMergeLTXV,
-    "ModelMergeCosmos7B": ModelMergeCosmos7B,
-    "ModelMergeCosmos14B": ModelMergeCosmos14B,
 }

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.14"
+__version__ = "0.3.10"

folder_paths.py

@@ -7,18 +7,11 @@ import logging
 from typing import Literal
 from collections.abc import Collection
 
-from comfy.cli_args import args
-
-supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
+supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
 
 folder_names_and_paths: dict[str, tuple[list[str], set[str]]] = {}
 
-# --base-directory - Resets all default paths configured in folder_paths with a new base path
-if args.base_directory:
-    base_path = os.path.abspath(args.base_directory)
-else:
 base_path = os.path.dirname(os.path.realpath(__file__))
-
 models_dir = os.path.join(base_path, "models")
 folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions)
 folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"])
@@ -46,10 +39,10 @@ folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")]
 
 folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""})
 
-output_directory = os.path.join(base_path, "output")
-temp_directory = os.path.join(base_path, "temp")
-input_directory = os.path.join(base_path, "input")
-user_directory = os.path.join(base_path, "user")
+output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")
+temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
+input_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
+user_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "user")
 
 filename_list_cache: dict[str, tuple[list[str], dict[str, float], float]] = {}
 

latent_preview.py

@@ -12,10 +12,7 @@ MAX_PREVIEW_RESOLUTION = args.preview_size
 def preview_to_image(latent_image):
         latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1)  # change scale from -1..1 to 0..1
                             .mul(0xFF)  # to 0..255
-                            )
-        if comfy.model_management.directml_enabled:
-                latents_ubyte = latents_ubyte.to(dtype=torch.uint8)
-        latents_ubyte = latents_ubyte.to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
+                            ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
 
         return Image.fromarray(latents_ubyte.numpy())
 

main.py

@@ -138,8 +138,8 @@ import server
 from server import BinaryEventTypes
 import nodes
 import comfy.model_management
-import comfyui_version
-
+from app.database.db import can_create_session, init_db
+from app.model_processor import model_processor
 
 def cuda_malloc_warning():
     device = comfy.model_management.get_torch_device()
@@ -264,6 +264,11 @@ def start_comfyui(asyncio_loop=None):
 
     cuda_malloc_warning()
 
+    try:
+        init_db()   
+    except Exception as e:
+        logging.error(f"Failed to initialize database. Please report this error as in future the database will be required: {e}")
+        
     prompt_server.add_routes()
     hijack_progress(prompt_server)
 
@@ -272,6 +277,10 @@ def start_comfyui(asyncio_loop=None):
     if args.quick_test_for_ci:
         exit(0)
     
+    # Scan for changed model files and update db
+    if can_create_session():
+        model_processor.run()
+
     os.makedirs(folder_paths.get_temp_directory(), exist_ok=True)
     call_on_start = None
     if args.auto_launch:
@@ -294,7 +303,6 @@ def start_comfyui(asyncio_loop=None):
 
 if __name__ == "__main__":
     # Running directly, just start ComfyUI.
-    logging.info("ComfyUI version: {}".format(comfyui_version.__version__))
     event_loop, _, start_all_func = start_comfyui()
     try:
         event_loop.run_until_complete(start_all_func())

nodes.py

@@ -63,8 +63,6 @@ class CLIPTextEncode(ComfyNodeABC):
     DESCRIPTION = "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images."
 
     def encode(self, clip, text):
-        if clip is None:
-            raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")
         tokens = clip.tokenize(text)
         return (clip.encode_from_tokens_scheduled(tokens), )
 
@@ -914,7 +912,7 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
-                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos"], ),
                               },
                 "optional": {
                               "device": (["default", "cpu"], {"advanced": True}),
@@ -939,10 +937,6 @@ class CLIPLoader:
             clip_type = comfy.sd.CLIPType.LTXV
         elif type == "pixart":
             clip_type = comfy.sd.CLIPType.PIXART
-        elif type == "cosmos":
-            clip_type = comfy.sd.CLIPType.COSMOS
-        elif type == "lumina2":
-            clip_type = comfy.sd.CLIPType.LUMINA2
         else:
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.14"
+version = "0.3.10"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -2,7 +2,6 @@ torch
 torchsde
 torchvision
 torchaudio
-numpy>=1.25.0
 einops
 transformers>=4.28.1
 tokenizers>=0.13.3
@@ -14,6 +13,8 @@ Pillow
 scipy
 tqdm
 psutil
+alembic
+SQLAlchemy
 
 #non essential dependencies:
 kornia>=0.7.1

server.py

@@ -52,22 +52,6 @@ async def cache_control(request: web.Request, handler):
         response.headers.setdefault('Cache-Control', 'no-cache')
     return response
 
-
-@web.middleware
-async def compress_body(request: web.Request, handler):
-    accept_encoding = request.headers.get("Accept-Encoding", "")
-    response: web.Response = await handler(request)
-    if args.disable_compres_response_body:
-        return response
-    if not isinstance(response, web.Response):
-        return response
-    if response.content_type not in ["application/json", "text/plain"]:
-        return response
-    if response.body and "gzip" in accept_encoding:
-        response.enable_compression()
-    return response
-
-
 def create_cors_middleware(allowed_origin: str):
     @web.middleware
     async def cors_middleware(request: web.Request, handler):
@@ -165,7 +149,7 @@ class PromptServer():
         self.client_session:Optional[aiohttp.ClientSession] = None
         self.number = 0
 
-        middlewares = [cache_control, compress_body]
+        middlewares = [cache_control]
         if args.enable_cors_header:
             middlewares.append(create_cors_middleware(args.enable_cors_header))
         else:
@@ -345,9 +329,6 @@ class PromptServer():
                 original_ref = json.loads(post.get("original_ref"))
                 filename, output_dir = folder_paths.annotated_filepath(original_ref['filename'])
 
-                if not filename:
-                    return web.Response(status=400)
-
                 # validation for security: prevent accessing arbitrary path
                 if filename[0] == '/' or '..' in filename:
                     return web.Response(status=400)
@@ -389,9 +370,6 @@ class PromptServer():
                 filename = request.rel_url.query["filename"]
                 filename,output_dir = folder_paths.annotated_filepath(filename)
 
-                if not filename:
-                    return web.Response(status=400)
-
                 # validation for security: prevent accessing arbitrary path
                 if filename[0] == '/' or '..' in filename:
                     return web.Response(status=400)

tests-unit/app_test/custom_node_manager_test.py

@@ -2,146 +2,39 @@ import pytest
 from aiohttp import web
 from unittest.mock import patch
 from app.custom_node_manager import CustomNodeManager
-import json
 
 pytestmark = (
     pytest.mark.asyncio
 )  # This applies the asyncio mark to all test functions in the module
 
-
 @pytest.fixture
 def custom_node_manager():
     return CustomNodeManager()
 
-
 @pytest.fixture
 def app(custom_node_manager):
     app = web.Application()
     routes = web.RouteTableDef()
-    custom_node_manager.add_routes(
-        routes, app, [("ComfyUI-TestExtension1", "ComfyUI-TestExtension1")]
-    )
+    custom_node_manager.add_routes(routes, app, [("ComfyUI-TestExtension1", "ComfyUI-TestExtension1")])
     app.add_routes(routes)
     return app
 
-
 async def test_get_workflow_templates(aiohttp_client, app, tmp_path):
     client = await aiohttp_client(app)
     # Setup temporary custom nodes file structure with 1 workflow file
     custom_nodes_dir = tmp_path / "custom_nodes"
-    example_workflows_dir = (
-        custom_nodes_dir / "ComfyUI-TestExtension1" / "example_workflows"
-    )
+    example_workflows_dir = custom_nodes_dir / "ComfyUI-TestExtension1" / "example_workflows"
     example_workflows_dir.mkdir(parents=True)
     template_file = example_workflows_dir / "workflow1.json"
-    template_file.write_text("")
+    template_file.write_text('')
 
-    with patch(
-        "folder_paths.folder_names_and_paths",
-        {"custom_nodes": ([str(custom_nodes_dir)], None)},
-    ):
-        response = await client.get("/workflow_templates")
+    with patch('folder_paths.folder_names_and_paths', {
+        'custom_nodes': ([str(custom_nodes_dir)], None)
+    }):
+        response = await client.get('/workflow_templates')
         assert response.status == 200
         workflows_dict = await response.json()
         assert isinstance(workflows_dict, dict)
         assert "ComfyUI-TestExtension1" in workflows_dict
         assert isinstance(workflows_dict["ComfyUI-TestExtension1"], list)
         assert workflows_dict["ComfyUI-TestExtension1"][0] == "workflow1"
-
-
-async def test_build_translations_empty_when_no_locales(custom_node_manager, tmp_path):
-    custom_nodes_dir = tmp_path / "custom_nodes"
-    custom_nodes_dir.mkdir(parents=True)
-
-    with patch("folder_paths.get_folder_paths", return_value=[str(custom_nodes_dir)]):
-        translations = custom_node_manager.build_translations()
-        assert translations == {}
-
-
-async def test_build_translations_loads_all_files(custom_node_manager, tmp_path):
-    # Setup test directory structure
-    custom_nodes_dir = tmp_path / "custom_nodes" / "test-extension"
-    locales_dir = custom_nodes_dir / "locales" / "en"
-    locales_dir.mkdir(parents=True)
-
-    # Create test translation files
-    main_content = {"title": "Test Extension"}
-    (locales_dir / "main.json").write_text(json.dumps(main_content))
-
-    node_defs = {"node1": "Node 1"}
-    (locales_dir / "nodeDefs.json").write_text(json.dumps(node_defs))
-
-    commands = {"cmd1": "Command 1"}
-    (locales_dir / "commands.json").write_text(json.dumps(commands))
-
-    settings = {"setting1": "Setting 1"}
-    (locales_dir / "settings.json").write_text(json.dumps(settings))
-
-    with patch(
-        "folder_paths.get_folder_paths", return_value=[tmp_path / "custom_nodes"]
-    ):
-        translations = custom_node_manager.build_translations()
-
-        assert translations == {
-            "en": {
-                "title": "Test Extension",
-                "nodeDefs": {"node1": "Node 1"},
-                "commands": {"cmd1": "Command 1"},
-                "settings": {"setting1": "Setting 1"},
-            }
-        }
-
-
-async def test_build_translations_handles_invalid_json(custom_node_manager, tmp_path):
-    # Setup test directory structure
-    custom_nodes_dir = tmp_path / "custom_nodes" / "test-extension"
-    locales_dir = custom_nodes_dir / "locales" / "en"
-    locales_dir.mkdir(parents=True)
-
-    # Create valid main.json
-    main_content = {"title": "Test Extension"}
-    (locales_dir / "main.json").write_text(json.dumps(main_content))
-
-    # Create invalid JSON file
-    (locales_dir / "nodeDefs.json").write_text("invalid json{")
-
-    with patch(
-        "folder_paths.get_folder_paths", return_value=[tmp_path / "custom_nodes"]
-    ):
-        translations = custom_node_manager.build_translations()
-
-        assert translations == {
-            "en": {
-                "title": "Test Extension",
-            }
-        }
-
-
-async def test_build_translations_merges_multiple_extensions(
-    custom_node_manager, tmp_path
-):
-    # Setup test directory structure for two extensions
-    custom_nodes_dir = tmp_path / "custom_nodes"
-    ext1_dir = custom_nodes_dir / "extension1" / "locales" / "en"
-    ext2_dir = custom_nodes_dir / "extension2" / "locales" / "en"
-    ext1_dir.mkdir(parents=True)
-    ext2_dir.mkdir(parents=True)
-
-    # Create translation files for extension 1
-    ext1_main = {"title": "Extension 1", "shared": "Original"}
-    (ext1_dir / "main.json").write_text(json.dumps(ext1_main))
-
-    # Create translation files for extension 2
-    ext2_main = {"description": "Extension 2", "shared": "Override"}
-    (ext2_dir / "main.json").write_text(json.dumps(ext2_main))
-
-    with patch("folder_paths.get_folder_paths", return_value=[str(custom_nodes_dir)]):
-        translations = custom_node_manager.build_translations()
-
-        assert translations == {
-            "en": {
-                "title": "Extension 1",
-                "description": "Extension 2",
-                "shared": "Override",  # Second extension should override first
-            }
-        }

tests-unit/app_test/model_manager_test.py

@@ -7,11 +7,33 @@ from PIL import Image
 from aiohttp import web
 from unittest.mock import patch
 from app.model_manager import ModelFileManager
+from app.database.models import Base, Model, Tag
+from comfy.cli_args import args
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
 
 pytestmark = (
     pytest.mark.asyncio
 )  # This applies the asyncio mark to all test functions in the module
 
+@pytest.fixture
+def session():
+    # Configure in-memory database
+    args.database_url = "sqlite:///:memory:"
+    
+    # Create engine and session factory
+    engine = create_engine(args.database_url)
+    Session = sessionmaker(bind=engine)
+    
+    # Create all tables
+    Base.metadata.create_all(engine)
+    
+    # Patch Session factory
+    with patch('app.database.db.Session', Session):
+        yield Session()
+            
+    Base.metadata.drop_all(engine)
+
 @pytest.fixture
 def model_manager():
     return ModelFileManager()
@@ -60,3 +82,287 @@ async def test_get_model_preview_safetensors(aiohttp_client, app, tmp_path):
 
         # Clean up
         img.close()
+
+async def test_get_models(aiohttp_client, app, session):
+    tag = Tag(name='test_tag')
+    model = Model(
+        type='checkpoints',
+        path='model1.safetensors',
+        title='Test Model'
+    )
+    model.tags.append(tag)
+    session.add(tag)
+    session.add(model)
+    session.commit()
+
+    client = await aiohttp_client(app)
+    resp = await client.get('/v2/models')
+    assert resp.status == 200
+    data = await resp.json()
+    assert len(data) == 1
+    assert data[0]['path'] == 'model1.safetensors'
+    assert len(data[0]['tags']) == 1
+    assert data[0]['tags'][0]['name'] == 'test_tag'
+
+async def test_add_model(aiohttp_client, app, session):
+    tag = Tag(name='test_tag')
+    session.add(tag)
+    session.commit()
+    tag_id = tag.id
+
+    with patch('app.model_manager.model_processor') as mock_processor:
+        with patch('app.model_manager.get_full_path', return_value='/checkpoints/model1.safetensors'):
+            client = await aiohttp_client(app)
+            resp = await client.post('/v2/models', json={
+                'type': 'checkpoints',
+                'path': 'model1.safetensors',
+                'title': 'Test Model',
+                'tags': [tag_id]
+            })
+            
+            assert resp.status == 200
+            data = await resp.json()
+            assert data['path'] == 'model1.safetensors'
+            assert len(data['tags']) == 1
+            assert data['tags'][0]['name'] == 'test_tag'
+
+            # Ensure that models are re-processed after adding
+            mock_processor.run.assert_called_once()
+
+async def test_delete_model(aiohttp_client, app, session):
+    model = Model(
+        type='checkpoints',
+        path='model1.safetensors',
+        title='Test Model'
+    )
+    session.add(model)
+    session.commit()
+    
+    with patch('app.model_manager.get_full_path', return_value=None):
+        client = await aiohttp_client(app)
+        resp = await client.delete('/v2/models?type=checkpoints&path=model1.safetensors')
+        assert resp.status == 204
+        
+        # Verify model was deleted
+        model = session.query(Model).first()
+        assert model is None
+
+async def test_delete_model_file_exists(aiohttp_client, app, session):
+    model = Model(
+        type='checkpoints',
+        path='model1.safetensors',
+        title='Test Model'
+    )
+    session.add(model)
+    session.commit()
+    
+    with patch('app.model_manager.get_full_path', return_value='/checkpoints/model1.safetensors'):
+        client = await aiohttp_client(app)
+        resp = await client.delete('/v2/models?type=checkpoints&path=model1.safetensors')
+        assert resp.status == 400
+        
+        data = await resp.json()
+        assert "file exists" in data["error"].lower()
+        
+        # Verify model was not deleted
+        model = session.query(Model).first()
+        assert model is not None
+        assert model.path == 'model1.safetensors'
+
+async def test_get_tags(aiohttp_client, app, session):
+    tags = [Tag(name='tag1'), Tag(name='tag2')]
+    for tag in tags:
+        session.add(tag)
+    session.commit()
+
+    client = await aiohttp_client(app)
+    resp = await client.get('/v2/tags')
+    assert resp.status == 200
+    data = await resp.json()
+    assert len(data) == 2
+    assert {t['name'] for t in data} == {'tag1', 'tag2'}
+
+async def test_create_tag(aiohttp_client, app, session):
+    client = await aiohttp_client(app)
+    resp = await client.post('/v2/tags', json={'name': 'new_tag'})
+    assert resp.status == 200
+    data = await resp.json()
+    assert data['name'] == 'new_tag'
+    
+    # Verify tag was created
+    tag = session.query(Tag).first()
+    assert tag.name == 'new_tag'
+
+async def test_delete_tag(aiohttp_client, app, session):
+    tag = Tag(name='test_tag')
+    session.add(tag)
+    session.commit()
+    tag_id = tag.id
+
+    client = await aiohttp_client(app)
+    resp = await client.delete(f'/v2/tags?id={tag_id}')
+    assert resp.status == 204
+    
+    # Verify tag was deleted
+    tag = session.query(Tag).first()
+    assert tag is None
+
+async def test_add_model_tag(aiohttp_client, app, session):
+    tag = Tag(name='test_tag')
+    model = Model(
+        type='checkpoints',
+        path='model1.safetensors',
+        title='Test Model'
+    )
+    session.add(tag)
+    session.add(model)
+    session.commit()
+    tag_id = tag.id
+
+    client = await aiohttp_client(app)
+    resp = await client.post('/v2/models/tags', json={
+        'tag': tag_id,
+        'type': 'checkpoints',
+        'path': 'model1.safetensors'
+    })
+    assert resp.status == 200
+    data = await resp.json()
+    assert len(data['tags']) == 1
+    assert data['tags'][0]['name'] == 'test_tag'
+
+async def test_delete_model_tag(aiohttp_client, app, session):
+    tag = Tag(name='test_tag')
+    model = Model(
+        type='checkpoints',
+        path='model1.safetensors',
+        title='Test Model'
+    )
+    model.tags.append(tag)
+    session.add(tag)
+    session.add(model)
+    session.commit()
+    tag_id = tag.id
+
+    client = await aiohttp_client(app)
+    resp = await client.delete(f'/v2/models/tags?tag={tag_id}&type=checkpoints&path=model1.safetensors')
+    assert resp.status == 204
+    
+    # Verify tag was removed
+    model = session.query(Model).first()
+    assert len(model.tags) == 0
+
+async def test_add_model_duplicate(aiohttp_client, app, session):
+    model = Model(
+        type='checkpoints',
+        path='model1.safetensors',
+        title='Test Model'
+    )
+    session.add(model)
+    session.commit()
+    
+    with patch('app.model_manager.get_full_path', return_value='/checkpoints/model1.safetensors'):
+        client = await aiohttp_client(app)
+        resp = await client.post('/v2/models', json={
+            'type': 'checkpoints',
+            'path': 'model1.safetensors',
+            'title': 'Duplicate Model'
+        })
+        assert resp.status == 400
+
+async def test_add_model_missing_fields(aiohttp_client, app, session):
+    client = await aiohttp_client(app)
+    resp = await client.post('/v2/models', json={})
+    assert resp.status == 400
+
+async def test_add_tag_missing_name(aiohttp_client, app, session):
+    client = await aiohttp_client(app)
+    resp = await client.post('/v2/tags', json={})
+    assert resp.status == 400
+
+async def test_delete_model_not_found(aiohttp_client, app, session):
+    client = await aiohttp_client(app)
+    resp = await client.delete('/v2/models?type=checkpoints&path=nonexistent.safetensors')
+    assert resp.status == 404
+
+async def test_delete_tag_not_found(aiohttp_client, app, session):
+    client = await aiohttp_client(app)
+    resp = await client.delete('/v2/tags?id=999')
+    assert resp.status == 404
+
+async def test_add_model_missing_path(aiohttp_client, app, session):
+    client = await aiohttp_client(app)
+    resp = await client.post('/v2/models', json={
+        'type': 'checkpoints',
+        'title': 'Test Model'
+    })
+    assert resp.status == 400
+    data = await resp.json()
+    assert "path" in data["error"].lower()
+
+async def test_add_model_invalid_field(aiohttp_client, app, session):
+    client = await aiohttp_client(app)
+    resp = await client.post('/v2/models', json={
+        'type': 'checkpoints',
+        'path': 'model1.safetensors',
+        'invalid_field': 'some value'
+    })
+    assert resp.status == 400
+    data = await resp.json()
+    assert "invalid field" in data["error"].lower()
+
+async def test_add_model_nonexistent_file(aiohttp_client, app, session):
+    with patch('app.model_manager.get_full_path', return_value=None):
+        client = await aiohttp_client(app)
+        resp = await client.post('/v2/models', json={
+            'type': 'checkpoints',
+            'path': 'nonexistent.safetensors'
+        })
+        assert resp.status == 404
+        data = await resp.json()
+        assert "file" in data["error"].lower()
+
+async def test_add_model_invalid_tag(aiohttp_client, app, session):
+    with patch('app.model_manager.get_full_path', return_value='/checkpoints/model1.safetensors'):
+        client = await aiohttp_client(app)
+        resp = await client.post('/v2/models', json={
+            'type': 'checkpoints',
+            'path': 'model1.safetensors',
+            'tags': [999]  # Non-existent tag ID
+        })
+        assert resp.status == 404
+        data = await resp.json()
+        assert "tag" in data["error"].lower()
+
+async def test_add_tag_to_nonexistent_model(aiohttp_client, app, session):
+    # Create a tag but no model
+    tag = Tag(name='test_tag')
+    session.add(tag)
+    session.commit()
+    tag_id = tag.id
+
+    client = await aiohttp_client(app)
+    resp = await client.post('/v2/models/tags', json={
+        'tag': tag_id,
+        'type': 'checkpoints',
+        'path': 'nonexistent.safetensors'
+    })
+    assert resp.status == 404
+    data = await resp.json()
+    assert "model" in data["error"].lower()
+
+async def test_delete_model_tag_invalid_tag_id(aiohttp_client, app, session):
+    # Create a model first
+    model = Model(
+        type='checkpoints',
+        path='model1.safetensors',
+        title='Test Model'
+    )
+    session.add(model)
+    session.commit()
+
+    client = await aiohttp_client(app)
+    resp = await client.delete('/v2/models/tags?tag=not_a_number&type=checkpoint&path=model1.safetensors')
+    assert resp.status == 400
+    data = await resp.json()
+    assert "invalid tag id" in data["error"].lower()
+

tests-unit/comfy_test/folder_path_test.py

@@ -1,23 +1,19 @@
 ### 🗻 This file is created through the spirit of Mount Fuji at its peak
 # TODO(yoland): clean up this after I get back down
-import sys
 import pytest
 import os
 import tempfile
 from unittest.mock import patch
-from importlib import reload
 
 import folder_paths
-import comfy.cli_args
-from comfy.options import enable_args_parsing
-enable_args_parsing()
-
 
 @pytest.fixture()
 def clear_folder_paths():
-    # Reload the module after each test to ensure isolation
+    # Clear the global dictionary before each test to ensure isolation
+    original = folder_paths.folder_names_and_paths.copy()
+    folder_paths.folder_names_and_paths.clear()
     yield
-    reload(folder_paths)
+    folder_paths.folder_names_and_paths = original
 
 @pytest.fixture
 def temp_dir():
@@ -25,21 +21,7 @@ def temp_dir():
         yield tmpdirname
 
 
-@pytest.fixture
-def set_base_dir():
-    def _set_base_dir(base_dir):
-        # Mock CLI args
-        with patch.object(sys, 'argv', ["main.py", "--base-directory", base_dir]):
-            reload(comfy.cli_args)
-            reload(folder_paths)
-    yield _set_base_dir
-    # Reload the modules after each test to ensure isolation
-    with patch.object(sys, 'argv', ["main.py"]):
-        reload(comfy.cli_args)
-        reload(folder_paths)
-
-
-def test_get_directory_by_type(clear_folder_paths):
+def test_get_directory_by_type():
     test_dir = "/test/dir"
     folder_paths.set_output_directory(test_dir)
     assert folder_paths.get_directory_by_type("output") == test_dir
@@ -114,49 +96,3 @@ def test_get_save_image_path(temp_dir):
         assert counter == 1
         assert subfolder == ""
         assert filename_prefix == "test"
-
-
-def test_base_path_changes(set_base_dir):
-    test_dir = os.path.abspath("/test/dir")
-    set_base_dir(test_dir)
-
-    assert folder_paths.base_path == test_dir
-    assert folder_paths.models_dir == os.path.join(test_dir, "models")
-    assert folder_paths.input_directory == os.path.join(test_dir, "input")
-    assert folder_paths.output_directory == os.path.join(test_dir, "output")
-    assert folder_paths.temp_directory == os.path.join(test_dir, "temp")
-    assert folder_paths.user_directory == os.path.join(test_dir, "user")
-
-    assert os.path.join(test_dir, "custom_nodes") in folder_paths.get_folder_paths("custom_nodes")
-
-    for name in ["checkpoints", "loras", "vae", "configs", "embeddings", "controlnet", "classifiers"]:
-        assert folder_paths.get_folder_paths(name)[0] == os.path.join(test_dir, "models", name)
-
-
-def test_base_path_change_clears_old(set_base_dir):
-    test_dir = os.path.abspath("/test/dir")
-    set_base_dir(test_dir)
-
-    assert len(folder_paths.get_folder_paths("custom_nodes")) == 1
-
-    single_model_paths = [
-        "checkpoints",
-        "loras",
-        "vae",
-        "configs",
-        "clip_vision",
-        "style_models",
-        "diffusers",
-        "vae_approx",
-        "gligen",
-        "upscale_models",
-        "embeddings",
-        "hypernetworks",
-        "photomaker",
-        "classifiers",
-    ]
-    for name in single_model_paths:
-        assert len(folder_paths.get_folder_paths(name)) == 1
-
-    for name in ["controlnet", "diffusion_models", "text_encoders"]:
-        assert len(folder_paths.get_folder_paths(name)) == 2

tests-unit/utils/json_util_test.py

@@ -1,71 +0,0 @@
-from utils.json_util import merge_json_recursive
-
-
-def test_merge_simple_dicts():
-    base = {"a": 1, "b": 2}
-    update = {"b": 3, "c": 4}
-    expected = {"a": 1, "b": 3, "c": 4}
-    assert merge_json_recursive(base, update) == expected
-
-
-def test_merge_nested_dicts():
-    base = {"a": {"x": 1, "y": 2}, "b": 3}
-    update = {"a": {"y": 4, "z": 5}}
-    expected = {"a": {"x": 1, "y": 4, "z": 5}, "b": 3}
-    assert merge_json_recursive(base, update) == expected
-
-
-def test_merge_lists():
-    base = {"a": [1, 2], "b": 3}
-    update = {"a": [3, 4]}
-    expected = {"a": [1, 2, 3, 4], "b": 3}
-    assert merge_json_recursive(base, update) == expected
-
-
-def test_merge_nested_lists():
-    base = {"a": {"x": [1, 2]}}
-    update = {"a": {"x": [3, 4]}}
-    expected = {"a": {"x": [1, 2, 3, 4]}}
-    assert merge_json_recursive(base, update) == expected
-
-
-def test_merge_mixed_types():
-    base = {"a": [1, 2], "b": {"x": 1}}
-    update = {"a": [3], "b": {"y": 2}}
-    expected = {"a": [1, 2, 3], "b": {"x": 1, "y": 2}}
-    assert merge_json_recursive(base, update) == expected
-
-
-def test_merge_overwrite_non_dict():
-    base = {"a": 1}
-    update = {"a": {"x": 2}}
-    expected = {"a": {"x": 2}}
-    assert merge_json_recursive(base, update) == expected
-
-
-def test_merge_empty_dicts():
-    base = {}
-    update = {"a": 1}
-    expected = {"a": 1}
-    assert merge_json_recursive(base, update) == expected
-
-
-def test_merge_none_values():
-    base = {"a": None}
-    update = {"a": {"x": 1}}
-    expected = {"a": {"x": 1}}
-    assert merge_json_recursive(base, update) == expected
-
-
-def test_merge_different_types():
-    base = {"a": [1, 2]}
-    update = {"a": "string"}
-    expected = {"a": "string"}
-    assert merge_json_recursive(base, update) == expected
-
-
-def test_merge_complex_nested():
-    base = {"a": [1, 2], "b": {"x": [3, 4], "y": {"p": 1}}}
-    update = {"a": [5], "b": {"x": [6], "y": {"q": 2}}}
-    expected = {"a": [1, 2, 5], "b": {"x": [3, 4, 6], "y": {"p": 1, "q": 2}}}
-    assert merge_json_recursive(base, update) == expected

utils/json_util.py

@@ -1,26 +0,0 @@
-def merge_json_recursive(base, update):
-    """Recursively merge two JSON-like objects.
-    - Dictionaries are merged recursively
-    - Lists are concatenated
-    - Other types are overwritten by the update value
-
-    Args:
-        base: Base JSON-like object
-        update: Update JSON-like object to merge into base
-
-    Returns:
-        Merged JSON-like object
-    """
-    if not isinstance(base, dict) or not isinstance(update, dict):
-        if isinstance(base, list) and isinstance(update, list):
-            return base + update
-        return update
-
-    merged = base.copy()
-    for key, value in update.items():
-        if key in merged:
-            merged[key] = merge_json_recursive(merged[key], value)
-        else:
-            merged[key] = value
-
-    return merged

utils/web.py

@@ -0,0 +1,12 @@
+import json
+from datetime import datetime
+
+
+class DateTimeEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, datetime):
+            return obj.isoformat()
+        return super().default(obj)
+
+
+dumps = DateTimeEncoder().encode

web/assets/BaseViewTemplate-BNGF4K22.js

@@ -0,0 +1,23 @@
+import { d as defineComponent, o as openBlock, f as createElementBlock, J as renderSlot, T as normalizeClass } from "./index-DjNHn37O.js";
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "BaseViewTemplate",
+  props: {
+    dark: { type: Boolean, default: false }
+  },
+  setup(__props) {
+    const props = __props;
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", {
+        class: normalizeClass(["font-sans w-screen h-screen flex items-center justify-center pointer-events-auto overflow-auto", [
+          props.dark ? "text-neutral-300 bg-neutral-900 dark-theme" : "text-neutral-900 bg-neutral-300"
+        ]])
+      }, [
+        renderSlot(_ctx.$slots, "default")
+      ], 2);
+    };
+  }
+});
+export {
+  _sfc_main as _
+};
+//# sourceMappingURL=BaseViewTemplate-BNGF4K22.js.map

web/assets/BaseViewTemplate-v6omkdXg.js

@@ -1,51 +0,0 @@
-import { d as defineComponent, U as ref, p as onMounted, b4 as isElectron, W as nextTick, b5 as electronAPI, o as openBlock, f as createElementBlock, i as withDirectives, v as vShow, j as unref, b6 as isNativeWindow, m as createBaseVNode, A as renderSlot, ai as normalizeClass } from "./index-4Hb32CNk.js";
-const _hoisted_1 = { class: "flex-grow w-full flex items-center justify-center overflow-auto" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "BaseViewTemplate",
-  props: {
-    dark: { type: Boolean, default: false }
-  },
-  setup(__props) {
-    const props = __props;
-    const darkTheme = {
-      color: "rgba(0, 0, 0, 0)",
-      symbolColor: "#d4d4d4"
-    };
-    const lightTheme = {
-      color: "rgba(0, 0, 0, 0)",
-      symbolColor: "#171717"
-    };
-    const topMenuRef = ref(null);
-    onMounted(async () => {
-      if (isElectron()) {
-        await nextTick();
-        electronAPI().changeTheme({
-          ...props.dark ? darkTheme : lightTheme,
-          height: topMenuRef.value.getBoundingClientRect().height
-        });
-      }
-    });
-    return (_ctx, _cache) => {
-      return openBlock(), createElementBlock("div", {
-        class: normalizeClass(["font-sans w-screen h-screen flex flex-col pointer-events-auto", [
-          props.dark ? "text-neutral-300 bg-neutral-900 dark-theme" : "text-neutral-900 bg-neutral-300"
-        ]])
-      }, [
-        withDirectives(createBaseVNode("div", {
-          ref_key: "topMenuRef",
-          ref: topMenuRef,
-          class: "app-drag w-full h-[var(--comfy-topbar-height)]"
-        }, null, 512), [
-          [vShow, unref(isNativeWindow)()]
-        ]),
-        createBaseVNode("div", _hoisted_1, [
-          renderSlot(_ctx.$slots, "default")
-        ])
-      ], 2);
-    };
-  }
-});
-export {
-  _sfc_main as _
-};
-//# sourceMappingURL=BaseViewTemplate-v6omkdXg.js.map

web/assets/DesktopStartView-coDnSXEF.js

@@ -1,22 +0,0 @@
-import { d as defineComponent, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, k as createVNode, j as unref, bz as script } from "./index-4Hb32CNk.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-v6omkdXg.js";
-const _hoisted_1 = { class: "max-w-screen-sm w-screen p-8" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "DesktopStartView",
-  setup(__props) {
-    return (_ctx, _cache) => {
-      return openBlock(), createBlock(_sfc_main$1, { dark: "" }, {
-        default: withCtx(() => [
-          createBaseVNode("div", _hoisted_1, [
-            createVNode(unref(script), { mode: "indeterminate" })
-          ])
-        ]),
-        _: 1
-      });
-    };
-  }
-});
-export {
-  _sfc_main as default
-};
-//# sourceMappingURL=DesktopStartView-coDnSXEF.js.map

web/assets/DownloadGitView-DeC7MBzG.js

@@ -1,7 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, k as createVNode, j as unref, l as script, be as useRouter } from "./index-4Hb32CNk.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-v6omkdXg.js";
+import { d as defineComponent, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, l as script, bW as useRouter } from "./index-DjNHn37O.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
 const _hoisted_1 = { class: "max-w-screen-sm flex flex-col gap-8 p-8 bg-[url('/assets/images/Git-Logo-White.svg')] bg-no-repeat bg-right-top bg-origin-padding" };
 const _hoisted_2 = { class: "mt-24 text-4xl font-bold text-red-500" };
 const _hoisted_3 = { class: "space-y-4" };
@@ -55,4 +55,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=DownloadGitView-3STu4yxt.js.map
+//# sourceMappingURL=DownloadGitView-DeC7MBzG.js.map

web/assets/ExtensionPanel-D4Phn0Zr.js

@@ -1,8 +1,9 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, U as ref, dl as FilterMatchMode, dr as useExtensionStore, a as useSettingStore, p as onMounted, c as computed, o as openBlock, y as createBlock, z as withCtx, k as createVNode, dm as SearchBox, j as unref, bj as script, m as createBaseVNode, f as createElementBlock, D as renderList, E as toDisplayString, a7 as createTextVNode, F as Fragment, l as script$1, B as createCommentVNode, a4 as script$3, ax as script$4, bn as script$5, dn as _sfc_main$1 } from "./index-4Hb32CNk.js";
-import { g as script$2, h as script$6 } from "./index-nJubvliG.js";
-import "./index-D6zf5KAf.js";
+import { d as defineComponent, ab as ref, cn as FilterMatchMode, cs as useExtensionStore, a as useSettingStore, m as onMounted, c as computed, o as openBlock, k as createBlock, M as withCtx, N as createVNode, co as SearchBox, j as unref, bZ as script, H as createBaseVNode, f as createElementBlock, E as renderList, X as toDisplayString, aE as createTextVNode, F as Fragment, l as script$1, I as createCommentVNode, aI as script$3, bO as script$4, c4 as script$5, cp as _sfc_main$1 } from "./index-DjNHn37O.js";
+import { s as script$2, a as script$6 } from "./index-B5F0uxTQ.js";
+import "./index-B-aVupP5.js";
+import "./index-5HFeZax4.js";
 const _hoisted_1 = { class: "flex justify-end" };
 const _sfc_main = /* @__PURE__ */ defineComponent({
   __name: "ExtensionPanel",
@@ -179,4 +180,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=ExtensionPanel-GE0aOkbr.js.map
+//# sourceMappingURL=ExtensionPanel-D4Phn0Zr.js.map

web/assets/GraphView-CIRWBKTm.css

@@ -1,10 +1,8 @@
 
-.comfy-menu-hamburger[data-v-7ed57d1a] {
+.comfy-menu-hamburger[data-v-5661bed0] {
   pointer-events: auto;
   position: fixed;
   z-index: 9999;
-    display: flex;
-    flex-direction: row
 }
 
 [data-v-e50caa15] .p-splitter-gutter {
@@ -41,14 +39,14 @@
   z-index: 999;
 }
 
-.p-buttongroup-vertical[data-v-cb8f9a1a] {
+.p-buttongroup-vertical[data-v-cf40dd39] {
   display: flex;
   flex-direction: column;
   border-radius: var(--p-button-border-radius);
   overflow: hidden;
   border: 1px solid var(--p-panel-border-color);
 }
-.p-buttongroup-vertical .p-button[data-v-cb8f9a1a] {
+.p-buttongroup-vertical .p-button[data-v-cf40dd39] {
   margin: 0;
   border-radius: 0;
 }
@@ -84,7 +82,7 @@
   font-size: inherit;
 }
 
-[data-v-fd0a74bd] .highlight {
+[data-v-5741c9ae] .highlight {
   background-color: var(--p-primary-color);
   color: var(--p-primary-contrast-color);
   font-weight: bold;
@@ -133,7 +131,16 @@
   border-right: 4px solid var(--p-button-text-primary-color);
 }
 
-.side-tool-bar-container[data-v-33cac83a] {
+:root {
+  --sidebar-width: 64px;
+  --sidebar-icon-size: 1.5rem;
+}
+:root .small-sidebar {
+  --sidebar-width: 40px;
+  --sidebar-icon-size: 1rem;
+}
+
+.side-tool-bar-container[data-v-37d8d7b4] {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -146,91 +153,18 @@
   background-color: var(--comfy-menu-secondary-bg);
   color: var(--fg-color);
   box-shadow: var(--bar-shadow);
-
-  --sidebar-width: 4rem;
-  --sidebar-icon-size: 1.5rem;
 }
-.side-tool-bar-container.small-sidebar[data-v-33cac83a] {
-  --sidebar-width: 2.5rem;
-  --sidebar-icon-size: 1rem;
-}
-.side-tool-bar-end[data-v-33cac83a] {
+.side-tool-bar-end[data-v-37d8d7b4] {
   align-self: flex-end;
   margin-top: auto;
 }
 
-.status-indicator[data-v-8d011a31] {
-  position: absolute;
-  font-weight: 700;
-  font-size: 1.5rem;
-  top: 50%;
-  left: 50%;
-  transform: translate(-50%, -50%)
-}
-
-[data-v-54fadc45] .p-togglebutton {
-  position: relative;
-  flex-shrink: 0;
-  border-radius: 0px;
-  border-width: 0px;
-  border-right-width: 1px;
-  border-style: solid;
-  background-color: transparent;
-  padding: 0px;
-  border-right-color: var(--border-color)
-}
-[data-v-54fadc45] .p-togglebutton::before {
-  display: none
-}
-[data-v-54fadc45] .p-togglebutton:first-child {
-  border-left-width: 1px;
-  border-style: solid;
-  border-left-color: var(--border-color)
-}
-[data-v-54fadc45] .p-togglebutton:not(:first-child) {
-  border-left-width: 0px
-}
-[data-v-54fadc45] .p-togglebutton.p-togglebutton-checked {
-  height: 100%;
-  border-bottom-width: 1px;
-  border-style: solid;
-  border-bottom-color: var(--p-button-text-primary-color)
-}
-[data-v-54fadc45] .p-togglebutton:not(.p-togglebutton-checked) {
-  opacity: 0.75
-}
-[data-v-54fadc45] .p-togglebutton-checked .close-button,[data-v-54fadc45] .p-togglebutton:hover .close-button {
-  visibility: visible
-}
-[data-v-54fadc45] .p-togglebutton:hover .status-indicator {
-  display: none
-}
-[data-v-54fadc45] .p-togglebutton .close-button {
-  visibility: hidden
-}
-[data-v-54fadc45] .p-scrollpanel-content {
-  height: 100%
-}
-
-/* Scrollbar half opacity to avoid blocking the active tab bottom border */
-[data-v-54fadc45] .p-scrollpanel:hover .p-scrollpanel-bar,[data-v-54fadc45] .p-scrollpanel:active .p-scrollpanel-bar {
-  opacity: 0.5
-}
-[data-v-54fadc45] .p-selectbutton {
-  height: 100%;
-  border-radius: 0px
-}
-
-[data-v-38831d8e] .workflow-tabs {
-  background-color: var(--comfy-menu-bg);
-}
-
-[data-v-26957f1f] .p-inputtext {
+[data-v-b9328350] .p-inputtext {
   border-top-left-radius: 0;
   border-bottom-left-radius: 0;
 }
 
-.comfyui-queue-button[data-v-91a628af] .p-splitbutton-dropdown {
+.comfyui-queue-button[data-v-7f4f551b] .p-splitbutton-dropdown {
   border-top-right-radius: 0;
   border-bottom-right-radius: 0;
 }
@@ -261,23 +195,55 @@
   display: none;
 }
 
-.top-menubar[data-v-56df69d2] .p-menubar-item-link svg {
+.top-menubar[data-v-6fecd137] .p-menubar-item-link svg {
   display: none;
 }
-[data-v-56df69d2] .p-menubar-submenu.dropdown-direction-up {
+[data-v-6fecd137] .p-menubar-submenu.dropdown-direction-up {
   top: auto;
   bottom: 100%;
   flex-direction: column-reverse;
 }
-.keybinding-tag[data-v-56df69d2] {
+.keybinding-tag[data-v-6fecd137] {
   background: var(--p-content-hover-background);
   border-color: var(--p-content-border-color);
   border-style: solid;
 }
 
-.comfyui-menu[data-v-929e7543] {
+.status-indicator[data-v-8d011a31] {
+  position: absolute;
+  font-weight: 700;
+  font-size: 1.5rem;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%)
+}
+
+[data-v-d485c044] .p-togglebutton::before {
+  display: none
+}
+[data-v-d485c044] .p-togglebutton {
+  position: relative;
+  flex-shrink: 0;
+  border-radius: 0px;
+  background-color: transparent;
+  padding: 0px
+}
+[data-v-d485c044] .p-togglebutton.p-togglebutton-checked {
+  border-bottom-width: 2px;
+  border-bottom-color: var(--p-button-text-primary-color)
+}
+[data-v-d485c044] .p-togglebutton-checked .close-button,[data-v-d485c044] .p-togglebutton:hover .close-button {
+  visibility: visible
+}
+[data-v-d485c044] .p-togglebutton:hover .status-indicator {
+  display: none
+}
+[data-v-d485c044] .p-togglebutton .close-button {
+  visibility: hidden
+}
+
+.comfyui-menu[data-v-878b63b8] {
   width: 100vw;
-  height: var(--comfy-topbar-height);
   background: var(--comfy-menu-bg);
   color: var(--fg-color);
   box-shadow: var(--bar-shadow);
@@ -287,17 +253,18 @@
   z-index: 1000;
   order: 0;
   grid-column: 1/-1;
+  max-height: 90vh;
 }
-.comfyui-menu.dropzone[data-v-929e7543] {
+.comfyui-menu.dropzone[data-v-878b63b8] {
   background: var(--p-highlight-background);
 }
-.comfyui-menu.dropzone-active[data-v-929e7543] {
+.comfyui-menu.dropzone-active[data-v-878b63b8] {
   background: var(--p-highlight-background-focus);
 }
-[data-v-929e7543] .p-menubar-item-label {
+[data-v-878b63b8] .p-menubar-item-label {
   line-height: revert;
 }
-.comfyui-logo[data-v-929e7543] {
+.comfyui-logo[data-v-878b63b8] {
   font-size: 1.2em;
   -webkit-user-select: none;
      -moz-user-select: none;

web/assets/InstallView-CwQdoH-C.css

@@ -1,20 +1,18 @@
 
-.p-tag[data-v-79125ff6] {
+:root {
   --p-tag-gap: 0.5rem;
 }
 .hover-brighten {
-&[data-v-79125ff6] {
   transition-property: color, background-color, border-color, text-decoration-color, fill, stroke;
   transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1);
   transition-duration: 150ms;
   transition-property: filter, box-shadow;
-  }
-&[data-v-79125ff6]:hover {
+&:hover {
     filter: brightness(107%) contrast(105%);
     box-shadow: 0 0 0.25rem #ffffff79;
 }
 }
-.p-accordioncontent-content[data-v-79125ff6] {
+.p-accordioncontent-content {
   border-radius: 0.5rem;
   --tw-bg-opacity: 1;
   background-color: rgb(23 23 23 / var(--tw-bg-opacity));
@@ -23,14 +21,14 @@
   transition-duration: 150ms;
 }
 div.selected {
-.gpu-button[data-v-79125ff6]:not(.selected) {
+.gpu-button:not(.selected) {
     opacity: 0.5;
 }
-.gpu-button[data-v-79125ff6]:not(.selected):hover {
+.gpu-button:not(.selected):hover {
     opacity: 1;
 }
 }
-.gpu-button[data-v-79125ff6] {
+.gpu-button {
   margin: 0px;
   display: flex;
   width: 50%;
@@ -45,37 +43,37 @@ div.selected {
   transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1);
   transition-duration: 150ms;
 }
-.gpu-button[data-v-79125ff6]:hover {
+.gpu-button:hover {
   --tw-bg-opacity: 0.75;
 }
 .gpu-button {
-&.selected[data-v-79125ff6] {
+&.selected {
     --tw-bg-opacity: 1;
     background-color: rgb(64 64 64 / var(--tw-bg-opacity));
 }
-&.selected[data-v-79125ff6] {
+&.selected {
     --tw-bg-opacity: 0.5;
 }
-&.selected[data-v-79125ff6] {
+&.selected {
     opacity: 1;
 }
-&.selected[data-v-79125ff6]:hover {
+&.selected:hover {
     --tw-bg-opacity: 0.6;
 }
 }
-.disabled[data-v-79125ff6] {
+.disabled {
   pointer-events: none;
   opacity: 0.4;
 }
-.p-card-header[data-v-79125ff6] {
+.p-card-header {
   flex-grow: 1;
   text-align: center;
 }
-.p-card-body[data-v-79125ff6] {
+.p-card-body {
   padding-top: 0px;
   text-align: center;
 }
 
-[data-v-cd6731d2] .p-steppanel {
+[data-v-de33872d] .p-steppanel {
     background-color: transparent
 }

web/assets/InstallView-DTDlVr0Z.js

@@ -1,945 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, U as ref, bm as useModel, o as openBlock, f as createElementBlock, m as createBaseVNode, E as toDisplayString, k as createVNode, j as unref, bn as script, bh as script$1, ar as withModifiers, z as withCtx, ab as script$2, K as useI18n, c as computed, ai as normalizeClass, B as createCommentVNode, a4 as script$3, a7 as createTextVNode, b5 as electronAPI, _ as _export_sfc, p as onMounted, r as resolveDirective, bg as script$4, i as withDirectives, bo as script$5, bp as script$6, l as script$7, y as createBlock, bj as script$8, bq as MigrationItems, w as watchEffect, F as Fragment, D as renderList, br as script$9, bs as mergeModels, bt as ValidationState, Y as normalizeI18nKey, O as watch, bu as checkMirrorReachable, bv as _sfc_main$7, bw as mergeValidationStates, bc as t, a$ as script$a, bx as CUDA_TORCH_URL, by as NIGHTLY_CPU_TORCH_URL, be as useRouter, ag as toRaw } from "./index-4Hb32CNk.js";
-import { s as script$b, a as script$c, b as script$d, c as script$e, d as script$f } from "./index-hkkV7N7e.js";
-import { P as PYTHON_MIRROR, a as PYPI_MIRROR } from "./uvMirrors-B-HKMf6X.js";
-import { _ as _sfc_main$8 } from "./BaseViewTemplate-v6omkdXg.js";
-const _hoisted_1$5 = { class: "flex flex-col gap-6 w-[600px]" };
-const _hoisted_2$5 = { class: "flex flex-col gap-4" };
-const _hoisted_3$5 = { class: "text-2xl font-semibold text-neutral-100" };
-const _hoisted_4$5 = { class: "text-neutral-400 my-0" };
-const _hoisted_5$3 = { class: "flex flex-col bg-neutral-800 p-4 rounded-lg" };
-const _hoisted_6$3 = { class: "flex items-center gap-4" };
-const _hoisted_7$3 = { class: "flex-1" };
-const _hoisted_8$3 = { class: "text-lg font-medium text-neutral-100" };
-const _hoisted_9$3 = { class: "text-sm text-neutral-400 mt-1" };
-const _hoisted_10$3 = { class: "flex items-center gap-4" };
-const _hoisted_11$3 = { class: "flex-1" };
-const _hoisted_12$3 = { class: "text-lg font-medium text-neutral-100" };
-const _hoisted_13$1 = { class: "text-sm text-neutral-400 mt-1" };
-const _hoisted_14$1 = { class: "text-neutral-300" };
-const _hoisted_15 = { class: "font-medium mb-2" };
-const _hoisted_16 = { class: "list-disc pl-6 space-y-1" };
-const _hoisted_17 = { class: "font-medium mt-4 mb-2" };
-const _hoisted_18 = { class: "list-disc pl-6 space-y-1" };
-const _hoisted_19 = { class: "mt-4" };
-const _hoisted_20 = {
-  href: "https://comfy.org/privacy",
-  target: "_blank",
-  class: "text-blue-400 hover:text-blue-300 underline"
-};
-const _sfc_main$6 = /* @__PURE__ */ defineComponent({
-  __name: "DesktopSettingsConfiguration",
-  props: {
-    "autoUpdate": { type: Boolean, ...{ required: true } },
-    "autoUpdateModifiers": {},
-    "allowMetrics": { type: Boolean, ...{ required: true } },
-    "allowMetricsModifiers": {}
-  },
-  emits: ["update:autoUpdate", "update:allowMetrics"],
-  setup(__props) {
-    const showDialog = ref(false);
-    const autoUpdate = useModel(__props, "autoUpdate");
-    const allowMetrics = useModel(__props, "allowMetrics");
-    const showMetricsInfo = /* @__PURE__ */ __name(() => {
-      showDialog.value = true;
-    }, "showMetricsInfo");
-    return (_ctx, _cache) => {
-      return openBlock(), createElementBlock("div", _hoisted_1$5, [
-        createBaseVNode("div", _hoisted_2$5, [
-          createBaseVNode("h2", _hoisted_3$5, toDisplayString(_ctx.$t("install.desktopAppSettings")), 1),
-          createBaseVNode("p", _hoisted_4$5, toDisplayString(_ctx.$t("install.desktopAppSettingsDescription")), 1)
-        ]),
-        createBaseVNode("div", _hoisted_5$3, [
-          createBaseVNode("div", _hoisted_6$3, [
-            createBaseVNode("div", _hoisted_7$3, [
-              createBaseVNode("h3", _hoisted_8$3, toDisplayString(_ctx.$t("install.settings.autoUpdate")), 1),
-              createBaseVNode("p", _hoisted_9$3, toDisplayString(_ctx.$t("install.settings.autoUpdateDescription")), 1)
-            ]),
-            createVNode(unref(script), {
-              modelValue: autoUpdate.value,
-              "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => autoUpdate.value = $event)
-            }, null, 8, ["modelValue"])
-          ]),
-          createVNode(unref(script$1)),
-          createBaseVNode("div", _hoisted_10$3, [
-            createBaseVNode("div", _hoisted_11$3, [
-              createBaseVNode("h3", _hoisted_12$3, toDisplayString(_ctx.$t("install.settings.allowMetrics")), 1),
-              createBaseVNode("p", _hoisted_13$1, toDisplayString(_ctx.$t("install.settings.allowMetricsDescription")), 1),
-              createBaseVNode("a", {
-                href: "#",
-                class: "text-sm text-blue-400 hover:text-blue-300 mt-1 inline-block",
-                onClick: withModifiers(showMetricsInfo, ["prevent"])
-              }, toDisplayString(_ctx.$t("install.settings.learnMoreAboutData")), 1)
-            ]),
-            createVNode(unref(script), {
-              modelValue: allowMetrics.value,
-              "onUpdate:modelValue": _cache[1] || (_cache[1] = ($event) => allowMetrics.value = $event)
-            }, null, 8, ["modelValue"])
-          ])
-        ]),
-        createVNode(unref(script$2), {
-          visible: showDialog.value,
-          "onUpdate:visible": _cache[2] || (_cache[2] = ($event) => showDialog.value = $event),
-          modal: "",
-          header: _ctx.$t("install.settings.dataCollectionDialog.title")
-        }, {
-          default: withCtx(() => [
-            createBaseVNode("div", _hoisted_14$1, [
-              createBaseVNode("h4", _hoisted_15, toDisplayString(_ctx.$t("install.settings.dataCollectionDialog.whatWeCollect")), 1),
-              createBaseVNode("ul", _hoisted_16, [
-                createBaseVNode("li", null, toDisplayString(_ctx.$t("install.settings.dataCollectionDialog.collect.errorReports")), 1),
-                createBaseVNode("li", null, toDisplayString(_ctx.$t("install.settings.dataCollectionDialog.collect.systemInfo")), 1),
-                createBaseVNode("li", null, toDisplayString(_ctx.$t(
-                  "install.settings.dataCollectionDialog.collect.userJourneyEvents"
-                )), 1)
-              ]),
-              createBaseVNode("h4", _hoisted_17, toDisplayString(_ctx.$t("install.settings.dataCollectionDialog.whatWeDoNotCollect")), 1),
-              createBaseVNode("ul", _hoisted_18, [
-                createBaseVNode("li", null, toDisplayString(_ctx.$t(
-                  "install.settings.dataCollectionDialog.doNotCollect.personalInformation"
-                )), 1),
-                createBaseVNode("li", null, toDisplayString(_ctx.$t(
-                  "install.settings.dataCollectionDialog.doNotCollect.workflowContents"
-                )), 1),
-                createBaseVNode("li", null, toDisplayString(_ctx.$t(
-                  "install.settings.dataCollectionDialog.doNotCollect.fileSystemInformation"
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-        case "mps":
-          return {
-            settingId,
-            mirror: NIGHTLY_CPU_TORCH_URL,
-            fallbackMirror: NIGHTLY_CPU_TORCH_URL
-          };
-        case "nvidia":
-          return {
-            settingId,
-            mirror: CUDA_TORCH_URL,
-            fallbackMirror: CUDA_TORCH_URL
-          };
-        case "cpu":
-        default:
-          return {
-            settingId,
-            mirror: PYPI_MIRROR.mirror,
-            fallbackMirror: PYPI_MIRROR.fallbackMirror
-          };
-      }
-    }, "getTorchMirrorItem");
-    const mirrors = computed(() => [
-      [PYTHON_MIRROR, pythonMirror],
-      [PYPI_MIRROR, pypiMirror],
-      [getTorchMirrorItem(__props.device), torchMirror]
-    ]);
-    const validationStates = ref(
-      mirrors.value.map(() => ValidationState.IDLE)
-    );
-    const validationState = computed(() => {
-      return mergeValidationStates(validationStates.value);
-    });
-    const validationStateTooltip = computed(() => {
-      switch (validationState.value) {
-        case ValidationState.INVALID:
-          return t("install.settings.mirrorsUnreachable");
-        case ValidationState.VALID:
-          return t("install.settings.mirrorsReachable");
-        default:
-          return t("install.settings.checkingMirrors");
-      }
-    });
-    return (_ctx, _cache) => {
-      const _directive_tooltip = resolveDirective("tooltip");
-      return openBlock(), createBlock(unref(script$a), {
-        header: _ctx.$t("install.settings.mirrorSettings"),
-        toggleable: "",
-        collapsed: !showMirrorInputs.value,
-        "pt:root": "bg-neutral-800 border-none w-[600px]"
-      }, {
-        icons: withCtx(() => [
-          withDirectives(createBaseVNode("i", {
-            class: normalizeClass({
-              "pi pi-spin pi-spinner text-neutral-400": validationState.value === unref(ValidationState).LOADING,
-              "pi pi-check text-green-500": validationState.value === unref(ValidationState).VALID,
-              "pi pi-times text-red-500": validationState.value === unref(ValidationState).INVALID
-            })
-          }, null, 2), [
-            [_directive_tooltip, validationStateTooltip.value]
-          ])
-        ]),
-        default: withCtx(() => [
-          (openBlock(true), createElementBlock(Fragment, null, renderList(mirrors.value, ([item, modelValue], index) => {
-            return openBlock(), createElementBlock(Fragment, {
-              key: item.settingId + item.mirror
-            }, [
-              index > 0 ? (openBlock(), createBlock(unref(script$1), { key: 0 })) : createCommentVNode("", true),
-              createVNode(_sfc_main$2, {
-                item,
-                modelValue: modelValue.value,
-                "onUpdate:modelValue": /* @__PURE__ */ __name(($event) => modelValue.value = $event, "onUpdate:modelValue"),
-                onStateChange: /* @__PURE__ */ __name(($event) => validationStates.value[index] = $event, "onStateChange")
-              }, null, 8, ["item", "modelValue", "onUpdate:modelValue", "onStateChange"])
-            ], 64);
-          }), 128))
-        ]),
-        _: 1
-      }, 8, ["header", "collapsed"]);
-    };
-  }
-});
-const _hoisted_1 = { class: "flex pt-6 justify-end" };
-const _hoisted_2 = { class: "flex pt-6 justify-between" };
-const _hoisted_3 = { class: "flex pt-6 justify-between" };
-const _hoisted_4 = { class: "flex mt-6 justify-between" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "InstallView",
-  setup(__props) {
-    const device = ref(null);
-    const installPath = ref("");
-    const pathError = ref("");
-    const migrationSourcePath = ref("");
-    const migrationItemIds = ref([]);
-    const autoUpdate = ref(true);
-    const allowMetrics = ref(true);
-    const pythonMirror = ref("");
-    const pypiMirror = ref("");
-    const torchMirror = ref("");
-    const highestStep = ref(0);
-    const handleStepChange = /* @__PURE__ */ __name((value) => {
-      setHighestStep(value);
-      electronAPI().Events.trackEvent("install_stepper_change", {
-        step: value
-      });
-    }, "handleStepChange");
-    const setHighestStep = /* @__PURE__ */ __name((value) => {
-      const int = typeof value === "number" ? value : parseInt(value, 10);
-      if (!isNaN(int) && int > highestStep.value) highestStep.value = int;
-    }, "setHighestStep");
-    const hasError = computed(() => pathError.value !== "");
-    const noGpu = computed(() => typeof device.value !== "string");
-    const electron = electronAPI();
-    const router = useRouter();
-    const install = /* @__PURE__ */ __name(() => {
-      const options = {
-        installPath: installPath.value,
-        autoUpdate: autoUpdate.value,
-        allowMetrics: allowMetrics.value,
-        migrationSourcePath: migrationSourcePath.value,
-        migrationItemIds: toRaw(migrationItemIds.value),
-        pythonMirror: pythonMirror.value,
-        pypiMirror: pypiMirror.value,
-        torchMirror: torchMirror.value,
-        device: device.value
-      };
-      electron.installComfyUI(options);
-      const nextPage = options.device === "unsupported" ? "/manual-configuration" : "/server-start";
-      router.push(nextPage);
-    }, "install");
-    onMounted(async () => {
-      if (!electron) return;
-      const detectedGpu = await electron.Config.getDetectedGpu();
-      if (detectedGpu === "mps" || detectedGpu === "nvidia") {
-        device.value = detectedGpu;
-      }
-      electronAPI().Events.trackEvent("install_stepper_change", {
-        step: "0",
-        gpu: detectedGpu
-      });
-    });
-    return (_ctx, _cache) => {
-      return openBlock(), createBlock(_sfc_main$8, { dark: "" }, {
-        default: withCtx(() => [
-          createVNode(unref(script$f), {
-            class: "h-full p-8 2xl:p-16",
-            value: "0",
-            "onUpdate:value": handleStepChange
-          }, {
-            default: withCtx(() => [
-              createVNode(unref(script$b), { class: "select-none" }, {
-                default: withCtx(() => [
-                  createVNode(unref(script$c), { value: "0" }, {
-                    default: withCtx(() => [
-                      createTextVNode(toDisplayString(_ctx.$t("install.gpu")), 1)
-                    ]),
-                    _: 1
-                  }),
-                  createVNode(unref(script$c), {
-                    value: "1",
-                    disabled: noGpu.value
-                  }, {
-                    default: withCtx(() => [
-                      createTextVNode(toDisplayString(_ctx.$t("install.installLocation")), 1)
-                    ]),
-                    _: 1
-                  }, 8, ["disabled"]),
-                  createVNode(unref(script$c), {
-                    value: "2",
-                    disabled: noGpu.value || hasError.value || highestStep.value < 1
-                  }, {
-                    default: withCtx(() => [
-                      createTextVNode(toDisplayString(_ctx.$t("install.migration")), 1)
-                    ]),
-                    _: 1
-                  }, 8, ["disabled"]),
-                  createVNode(unref(script$c), {
-                    value: "3",
-                    disabled: noGpu.value || hasError.value || highestStep.value < 2
-                  }, {
-                    default: withCtx(() => [
-                      createTextVNode(toDisplayString(_ctx.$t("install.desktopSettings")), 1)
-                    ]),
-                    _: 1
-                  }, 8, ["disabled"])
-                ]),
-                _: 1
-              }),
-              createVNode(unref(script$d), null, {
-                default: withCtx(() => [
-                  createVNode(unref(script$e), { value: "0" }, {
-                    default: withCtx(({ activateCallback }) => [
-                      createVNode(GpuPicker, {
-                        device: device.value,
-                        "onUpdate:device": _cache[0] || (_cache[0] = ($event) => device.value = $event)
-                      }, null, 8, ["device"]),
-                      createBaseVNode("div", _hoisted_1, [
-                        createVNode(unref(script$7), {
-                          label: _ctx.$t("g.next"),
-                          icon: "pi pi-arrow-right",
-                          iconPos: "right",
-                          onClick: /* @__PURE__ */ __name(($event) => activateCallback("1"), "onClick"),
-                          disabled: typeof device.value !== "string"
-                        }, null, 8, ["label", "onClick", "disabled"])
-                      ])
-                    ]),
-                    _: 1
-                  }),
-                  createVNode(unref(script$e), { value: "1" }, {
-                    default: withCtx(({ activateCallback }) => [
-                      createVNode(_sfc_main$4, {
-                        installPath: installPath.value,
-                        "onUpdate:installPath": _cache[1] || (_cache[1] = ($event) => installPath.value = $event),
-                        pathError: pathError.value,
-                        "onUpdate:pathError": _cache[2] || (_cache[2] = ($event) => pathError.value = $event)
-                      }, null, 8, ["installPath", "pathError"]),
-                      createBaseVNode("div", _hoisted_2, [
-                        createVNode(unref(script$7), {
-                          label: _ctx.$t("g.back"),
-                          severity: "secondary",
-                          icon: "pi pi-arrow-left",
-                          onClick: /* @__PURE__ */ __name(($event) => activateCallback("0"), "onClick")
-                        }, null, 8, ["label", "onClick"]),
-                        createVNode(unref(script$7), {
-                          label: _ctx.$t("g.next"),
-                          icon: "pi pi-arrow-right",
-                          iconPos: "right",
-                          onClick: /* @__PURE__ */ __name(($event) => activateCallback("2"), "onClick"),
-                          disabled: pathError.value !== ""
-                        }, null, 8, ["label", "onClick", "disabled"])
-                      ])
-                    ]),
-                    _: 1
-                  }),
-                  createVNode(unref(script$e), { value: "2" }, {
-                    default: withCtx(({ activateCallback }) => [
-                      createVNode(_sfc_main$3, {
-                        sourcePath: migrationSourcePath.value,
-                        "onUpdate:sourcePath": _cache[3] || (_cache[3] = ($event) => migrationSourcePath.value = $event),
-                        migrationItemIds: migrationItemIds.value,
-                        "onUpdate:migrationItemIds": _cache[4] || (_cache[4] = ($event) => migrationItemIds.value = $event)
-                      }, null, 8, ["sourcePath", "migrationItemIds"]),
-                      createBaseVNode("div", _hoisted_3, [
-                        createVNode(unref(script$7), {
-                          label: _ctx.$t("g.back"),
-                          severity: "secondary",
-                          icon: "pi pi-arrow-left",
-                          onClick: /* @__PURE__ */ __name(($event) => activateCallback("1"), "onClick")
-                        }, null, 8, ["label", "onClick"]),
-                        createVNode(unref(script$7), {
-                          label: _ctx.$t("g.next"),
-                          icon: "pi pi-arrow-right",
-                          iconPos: "right",
-                          onClick: /* @__PURE__ */ __name(($event) => activateCallback("3"), "onClick")
-                        }, null, 8, ["label", "onClick"])
-                      ])
-                    ]),
-                    _: 1
-                  }),
-                  createVNode(unref(script$e), { value: "3" }, {
-                    default: withCtx(({ activateCallback }) => [
-                      createVNode(_sfc_main$6, {
-                        autoUpdate: autoUpdate.value,
-                        "onUpdate:autoUpdate": _cache[5] || (_cache[5] = ($event) => autoUpdate.value = $event),
-                        allowMetrics: allowMetrics.value,
-                        "onUpdate:allowMetrics": _cache[6] || (_cache[6] = ($event) => allowMetrics.value = $event)
-                      }, null, 8, ["autoUpdate", "allowMetrics"]),
-                      createVNode(_sfc_main$1, {
-                        device: device.value,
-                        pythonMirror: pythonMirror.value,
-                        "onUpdate:pythonMirror": _cache[7] || (_cache[7] = ($event) => pythonMirror.value = $event),
-                        pypiMirror: pypiMirror.value,
-                        "onUpdate:pypiMirror": _cache[8] || (_cache[8] = ($event) => pypiMirror.value = $event),
-                        torchMirror: torchMirror.value,
-                        "onUpdate:torchMirror": _cache[9] || (_cache[9] = ($event) => torchMirror.value = $event),
-                        class: "mt-6"
-                      }, null, 8, ["device", "pythonMirror", "pypiMirror", "torchMirror"]),
-                      createBaseVNode("div", _hoisted_4, [
-                        createVNode(unref(script$7), {
-                          label: _ctx.$t("g.back"),
-                          severity: "secondary",
-                          icon: "pi pi-arrow-left",
-                          onClick: /* @__PURE__ */ __name(($event) => activateCallback("2"), "onClick")
-                        }, null, 8, ["label", "onClick"]),
-                        createVNode(unref(script$7), {
-                          label: _ctx.$t("g.install"),
-                          icon: "pi pi-check",
-                          iconPos: "right",
-                          disabled: hasError.value,
-                          onClick: _cache[10] || (_cache[10] = ($event) => install())
-                        }, null, 8, ["label", "disabled"])
-                      ])
-                    ]),
-                    _: 1
-                  })
-                ]),
-                _: 1
-              })
-            ]),
-            _: 1
-          })
-        ]),
-        _: 1
-      });
-    };
-  }
-});
-const InstallView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-cd6731d2"]]);
-export {
-  InstallView as default
-};
-//# sourceMappingURL=InstallView-DTDlVr0Z.js.map

web/assets/KeybindingPanel-Dc3C4lG1.js

@@ -1,9 +1,10 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, c as computed, o as openBlock, f as createElementBlock, F as Fragment, D as renderList, k as createVNode, z as withCtx, a7 as createTextVNode, E as toDisplayString, j as unref, a4 as script, B as createCommentVNode, U as ref, dl as FilterMatchMode, an as useKeybindingStore, L as useCommandStore, K as useI18n, Y as normalizeI18nKey, w as watchEffect, aR as useToast, r as resolveDirective, y as createBlock, dm as SearchBox, m as createBaseVNode, l as script$2, bg as script$4, ar as withModifiers, bj as script$5, ab as script$6, i as withDirectives, dn as _sfc_main$2, dp as KeyComboImpl, dq as KeybindingImpl, _ as _export_sfc } from "./index-4Hb32CNk.js";
-import { g as script$1, h as script$3 } from "./index-nJubvliG.js";
-import { u as useKeybindingService } from "./keybindingService-BTNdTpfl.js";
-import "./index-D6zf5KAf.js";
+import { d as defineComponent, c as computed, o as openBlock, f as createElementBlock, F as Fragment, E as renderList, N as createVNode, M as withCtx, aE as createTextVNode, X as toDisplayString, j as unref, aI as script, I as createCommentVNode, ab as ref, cn as FilterMatchMode, a$ as useKeybindingStore, a2 as useCommandStore, a1 as useI18n, af as normalizeI18nKey, w as watchEffect, bs as useToast, r as resolveDirective, k as createBlock, co as SearchBox, H as createBaseVNode, l as script$2, av as script$4, bM as withModifiers, bZ as script$5, aP as script$6, i as withDirectives, cp as _sfc_main$2, aL as pushScopeId, aM as popScopeId, cq as KeyComboImpl, cr as KeybindingImpl, _ as _export_sfc } from "./index-DjNHn37O.js";
+import { s as script$1, a as script$3 } from "./index-B5F0uxTQ.js";
+import { u as useKeybindingService } from "./keybindingService-Bx7YdkXn.js";
+import "./index-B-aVupP5.js";
+import "./index-5HFeZax4.js";
 const _hoisted_1$1 = {
   key: 0,
   class: "px-2"
@@ -36,6 +37,7 @@ const _sfc_main$1 = /* @__PURE__ */ defineComponent({
     };
   }
 });
+const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-2554ab36"), n = n(), popScopeId(), n), "_withScopeId");
 const _hoisted_1 = { class: "actions invisible flex flex-row" };
 const _hoisted_2 = ["title"];
 const _hoisted_3 = { key: 1 };
@@ -246,7 +248,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
                   severity: "error"
                 }, {
                   default: withCtx(() => [
-                    _cache[3] || (_cache[3] = createTextVNode(" Keybinding already exists on ")),
+                    createTextVNode(" Keybinding already exists on "),
                     createVNode(unref(script), {
                       severity: "secondary",
                       value: existingKeybindingOnCombo.value.commandId
@@ -279,4 +281,4 @@ const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "d
 export {
   KeybindingPanel as default
 };
-//# sourceMappingURL=KeybindingPanel-C0Nt6GXU.js.map
+//# sourceMappingURL=KeybindingPanel-Dc3C4lG1.js.map

web/assets/MaintenanceView-Bj5_Vr6o.css

@@ -1,87 +0,0 @@
-
-.task-card-ok[data-v-c3bd7658] {
-
-  position: absolute;
-
-  right: -1rem;
-
-  bottom: -1rem;
-
-  grid-column: 1 / -1;
-
-  grid-row: 1 / -1;
-
-  --tw-text-opacity: 1;
-
-  color: rgb(150 206 76 / var(--tw-text-opacity));
-
-  opacity: 1;
-
-  transition-property: opacity;
-
-  transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1);
-
-  transition-duration: 150ms;
-
-  font-size: 4rem;
-  text-shadow: 0.25rem 0 0.5rem black;
-  z-index: 10;
-}
-.p-card {
-&[data-v-c3bd7658] {
-
-  transition-property: opacity;
-
-  transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1);
-
-  transition-duration: 150ms;
-
-  --p-card-background: var(--p-button-secondary-background);
-  opacity: 0.9;
-  }
-&.opacity-65[data-v-c3bd7658] {
-    opacity: 0.4;
-}
-&[data-v-c3bd7658]:hover {
-    opacity: 1;
-}
-}
-[data-v-c3bd7658] .p-card-header {
-  z-index: 0;
-}
-[data-v-c3bd7658] .p-card-body {
-  z-index: 1;
-  flex-grow: 1;
-  justify-content: space-between;
-}
-.task-div {
-> i[data-v-c3bd7658] {
-    pointer-events: none;
-}
-&:hover > i[data-v-c3bd7658] {
-    opacity: 0.2;
-}
-}
-
-[data-v-74b78f7d] .p-tag {
-  --p-tag-gap: 0.375rem;
-}
-.backspan[data-v-74b78f7d]::before {
-  position: absolute;
-  margin: 0px;
-  color: var(--p-text-muted-color);
-  font-family: 'primeicons';
-  top: -2rem;
-  right: -2rem;
-  speak: none;
-  font-style: normal;
-  font-weight: normal;
-  font-variant: normal;
-  text-transform: none;
-  line-height: 1;
-  display: inline-block;
-  -webkit-font-smoothing: antialiased;
-  opacity: 0.02;
-  font-size: min(14rem, 90vw);
-  z-index: 0;
-}

web/assets/ManualConfigurationView-B6ecEClB.css

@@ -1,7 +1,7 @@
 
-.p-tag[data-v-dc169863] {
+:root {
   --p-tag-gap: 0.5rem;
 }
-.comfy-installer[data-v-dc169863] {
+.comfy-installer {
   margin-top: max(1rem, max(0px, calc((100vh - 42rem) * 0.5)));
 }

web/assets/ManualConfigurationView-Bi_qHE-n.js

@@ -1,7 +1,9 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, K as useI18n, U as ref, p as onMounted, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, k as createVNode, j as unref, a4 as script, a$ as script$1, l as script$2, b5 as electronAPI, _ as _export_sfc } from "./index-4Hb32CNk.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-v6omkdXg.js";
+import { d as defineComponent, a1 as useI18n, ab as ref, m as onMounted, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, aI as script, l as script$2, c0 as electronAPI } from "./index-DjNHn37O.js";
+import { s as script$1 } from "./index-jXPKy3pP.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
+import "./index-5HFeZax4.js";
 const _hoisted_1 = { class: "comfy-installer grow flex flex-col gap-4 text-neutral-300 max-w-110" };
 const _hoisted_2 = { class: "text-2xl font-semibold text-neutral-100" };
 const _hoisted_3 = { class: "m-1 text-neutral-300" };
@@ -67,8 +69,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     };
   }
 });
-const ManualConfigurationView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-dc169863"]]);
 export {
-  ManualConfigurationView as default
+  _sfc_main as default
 };
-//# sourceMappingURL=ManualConfigurationView-DueOvLuK.js.map
+//# sourceMappingURL=ManualConfigurationView-Bi_qHE-n.js.map

web/assets/MetricsConsentView-DTQYUF4Z.js

@@ -1,86 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-v6omkdXg.js";
-import { d as defineComponent, aR as useToast, K as useI18n, U as ref, be as useRouter, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, a7 as createTextVNode, k as createVNode, j as unref, bn as script, l as script$1, b5 as electronAPI } from "./index-4Hb32CNk.js";
-const _hoisted_1 = { class: "h-full p-8 2xl:p-16 flex flex-col items-center justify-center" };
-const _hoisted_2 = { class: "bg-neutral-800 rounded-lg shadow-lg p-6 w-full max-w-[600px] flex flex-col gap-6" };
-const _hoisted_3 = { class: "text-3xl font-semibold text-neutral-100" };
-const _hoisted_4 = { class: "text-neutral-400" };
-const _hoisted_5 = { class: "text-neutral-400" };
-const _hoisted_6 = {
-  href: "https://comfy.org/privacy",
-  target: "_blank",
-  class: "text-blue-400 hover:text-blue-300 underline"
-};
-const _hoisted_7 = { class: "flex items-center gap-4" };
-const _hoisted_8 = {
-  id: "metricsDescription",
-  class: "text-neutral-100"
-};
-const _hoisted_9 = { class: "flex pt-6 justify-end" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "MetricsConsentView",
-  setup(__props) {
-    const toast = useToast();
-    const { t } = useI18n();
-    const allowMetrics = ref(true);
-    const router = useRouter();
-    const isUpdating = ref(false);
-    const updateConsent = /* @__PURE__ */ __name(async () => {
-      isUpdating.value = true;
-      try {
-        await electronAPI().setMetricsConsent(allowMetrics.value);
-      } catch (error) {
-        toast.add({
-          severity: "error",
-          summary: t("install.errorUpdatingConsent"),
-          detail: t("install.errorUpdatingConsentDetail"),
-          life: 3e3
-        });
-      } finally {
-        isUpdating.value = false;
-      }
-      router.push("/");
-    }, "updateConsent");
-    return (_ctx, _cache) => {
-      const _component_BaseViewTemplate = _sfc_main$1;
-      return openBlock(), createBlock(_component_BaseViewTemplate, { dark: "" }, {
-        default: withCtx(() => [
-          createBaseVNode("div", _hoisted_1, [
-            createBaseVNode("div", _hoisted_2, [
-              createBaseVNode("h2", _hoisted_3, toDisplayString(_ctx.$t("install.helpImprove")), 1),
-              createBaseVNode("p", _hoisted_4, toDisplayString(_ctx.$t("install.updateConsent")), 1),
-              createBaseVNode("p", _hoisted_5, [
-                createTextVNode(toDisplayString(_ctx.$t("install.moreInfo")) + " ", 1),
-                createBaseVNode("a", _hoisted_6, toDisplayString(_ctx.$t("install.privacyPolicy")), 1),
-                _cache[1] || (_cache[1] = createTextVNode(". "))
-              ]),
-              createBaseVNode("div", _hoisted_7, [
-                createVNode(unref(script), {
-                  modelValue: allowMetrics.value,
-                  "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => allowMetrics.value = $event),
-                  "aria-describedby": "metricsDescription"
-                }, null, 8, ["modelValue"]),
-                createBaseVNode("span", _hoisted_8, toDisplayString(allowMetrics.value ? _ctx.$t("install.metricsEnabled") : _ctx.$t("install.metricsDisabled")), 1)
-              ]),
-              createBaseVNode("div", _hoisted_9, [
-                createVNode(unref(script$1), {
-                  label: _ctx.$t("g.ok"),
-                  icon: "pi pi-check",
-                  loading: isUpdating.value,
-                  iconPos: "right",
-                  onClick: updateConsent
-                }, null, 8, ["label", "loading"])
-              ])
-            ])
-          ])
-        ]),
-        _: 1
-      });
-    };
-  }
-});
-export {
-  _sfc_main as default
-};
-//# sourceMappingURL=MetricsConsentView-DTQYUF4Z.js.map

web/assets/NotSupportedView-Drz3x2d-.js

@@ -1,16 +1,21 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, be as useRouter, r as resolveDirective, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, k as createVNode, j as unref, l as script, i as withDirectives, _ as _export_sfc } from "./index-4Hb32CNk.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-v6omkdXg.js";
+import { d as defineComponent, bW as useRouter, r as resolveDirective, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, l as script, i as withDirectives } from "./index-DjNHn37O.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
 const _imports_0 = "" + new URL("images/sad_girl.png", import.meta.url).href;
 const _hoisted_1 = { class: "sad-container" };
-const _hoisted_2 = { class: "no-drag sad-text flex items-center" };
-const _hoisted_3 = { class: "flex flex-col gap-8 p-8 min-w-110" };
-const _hoisted_4 = { class: "text-4xl font-bold text-red-500" };
-const _hoisted_5 = { class: "space-y-4" };
-const _hoisted_6 = { class: "text-xl" };
-const _hoisted_7 = { class: "list-disc list-inside space-y-1 text-neutral-800" };
-const _hoisted_8 = { class: "flex gap-4" };
+const _hoisted_2 = /* @__PURE__ */ createBaseVNode("img", {
+  class: "sad-girl",
+  src: _imports_0,
+  alt: "Sad girl illustration"
+}, null, -1);
+const _hoisted_3 = { class: "no-drag sad-text flex items-center" };
+const _hoisted_4 = { class: "flex flex-col gap-8 p-8 min-w-110" };
+const _hoisted_5 = { class: "text-4xl font-bold text-red-500" };
+const _hoisted_6 = { class: "space-y-4" };
+const _hoisted_7 = { class: "text-xl" };
+const _hoisted_8 = { class: "list-disc list-inside space-y-1 text-neutral-800" };
+const _hoisted_9 = { class: "flex gap-4" };
 const _sfc_main = /* @__PURE__ */ defineComponent({
   __name: "NotSupportedView",
   setup(__props) {
@@ -32,22 +37,18 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
       return openBlock(), createBlock(_sfc_main$1, null, {
         default: withCtx(() => [
           createBaseVNode("div", _hoisted_1, [
-            _cache[0] || (_cache[0] = createBaseVNode("img", {
-              class: "sad-girl",
-              src: _imports_0,
-              alt: "Sad girl illustration"
-            }, null, -1)),
-            createBaseVNode("div", _hoisted_2, [
+            _hoisted_2,
             createBaseVNode("div", _hoisted_3, [
-                createBaseVNode("h1", _hoisted_4, toDisplayString(_ctx.$t("notSupported.title")), 1),
-                createBaseVNode("div", _hoisted_5, [
-                  createBaseVNode("p", _hoisted_6, toDisplayString(_ctx.$t("notSupported.message")), 1),
-                  createBaseVNode("ul", _hoisted_7, [
+              createBaseVNode("div", _hoisted_4, [
+                createBaseVNode("h1", _hoisted_5, toDisplayString(_ctx.$t("notSupported.title")), 1),
+                createBaseVNode("div", _hoisted_6, [
+                  createBaseVNode("p", _hoisted_7, toDisplayString(_ctx.$t("notSupported.message")), 1),
+                  createBaseVNode("ul", _hoisted_8, [
                     createBaseVNode("li", null, toDisplayString(_ctx.$t("notSupported.supportedDevices.macos")), 1),
                     createBaseVNode("li", null, toDisplayString(_ctx.$t("notSupported.supportedDevices.windows")), 1)
                   ])
                 ]),
-                createBaseVNode("div", _hoisted_8, [
+                createBaseVNode("div", _hoisted_9, [
                   createVNode(unref(script), {
                     label: _ctx.$t("notSupported.learnMore"),
                     icon: "pi pi-github",
@@ -79,8 +80,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     };
   }
 });
-const NotSupportedView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-ebb20958"]]);
 export {
-  NotSupportedView as default
+  _sfc_main as default
 };
-//# sourceMappingURL=NotSupportedView-PDDrAb9U.js.map
+//# sourceMappingURL=NotSupportedView-Drz3x2d-.js.map

web/assets/NotSupportedView-bFzHmqNj.css

@@ -1,19 +1,17 @@
 
 .sad-container {
-&[data-v-ebb20958] {
   display: grid;
   align-items: center;
   justify-content: space-evenly;
   grid-template-columns: 25rem 1fr;
-}
-&[data-v-ebb20958] > * {
+& > * {
     grid-row: 1;
 }
 }
-.sad-text[data-v-ebb20958] {
+.sad-text {
   grid-column: 1/3;
 }
-.sad-girl[data-v-ebb20958] {
+.sad-girl {
   grid-column: 2/3;
   width: min(75vw, 100vh);
 }

web/assets/ServerConfigPanel-Be4StJmv.js

@@ -1,23 +1,25 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { o as openBlock, f as createElementBlock, m as createBaseVNode, H as markRaw, d as defineComponent, a as useSettingStore, ae as storeToRefs, O as watch, dy as useCopyToClipboard, K as useI18n, y as createBlock, z as withCtx, j as unref, bj as script, E as toDisplayString, D as renderList, F as Fragment, k as createVNode, l as script$1, B as createCommentVNode, bh as script$2, dz as FormItem, dn as _sfc_main$1, b5 as electronAPI } from "./index-4Hb32CNk.js";
-import { u as useServerConfigStore } from "./serverConfigStore-BYbZcbWj.js";
+import { H as createBaseVNode, o as openBlock, f as createElementBlock, Z as markRaw, d as defineComponent, a as useSettingStore, aS as storeToRefs, a5 as watch, cO as useCopyToClipboard, a1 as useI18n, k as createBlock, M as withCtx, j as unref, bZ as script, X as toDisplayString, E as renderList, F as Fragment, N as createVNode, l as script$1, I as createCommentVNode, bQ as script$2, cP as FormItem, cp as _sfc_main$1, c0 as electronAPI } from "./index-DjNHn37O.js";
+import { u as useServerConfigStore } from "./serverConfigStore-CvyKFVuP.js";
 const _hoisted_1$1 = {
   viewBox: "0 0 24 24",
   width: "1.2em",
   height: "1.2em"
 };
-function render(_ctx, _cache) {
-  return openBlock(), createElementBlock("svg", _hoisted_1$1, _cache[0] || (_cache[0] = [
-    createBaseVNode("path", {
+const _hoisted_2$1 = /* @__PURE__ */ createBaseVNode("path", {
   fill: "none",
   stroke: "currentColor",
   "stroke-linecap": "round",
   "stroke-linejoin": "round",
   "stroke-width": "2",
   d: "m4 17l6-6l-6-6m8 14h8"
-    }, null, -1)
-  ]));
+}, null, -1);
+const _hoisted_3$1 = [
+  _hoisted_2$1
+];
+function render(_ctx, _cache) {
+  return openBlock(), createElementBlock("svg", _hoisted_1$1, [..._hoisted_3$1]);
 }
 __name(render, "render");
 const __unplugin_components_0 = markRaw({ name: "lucide-terminal", render });
@@ -153,4 +155,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=ServerConfigPanel-DnGhsuUV.js.map
+//# sourceMappingURL=ServerConfigPanel-Be4StJmv.js.map

web/assets/ServerStartView-CIDTUh4x.js

@@ -0,0 +1,98 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, a1 as useI18n, ab as ref, b_ as ProgressStatus, m as onMounted, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, aE as createTextVNode, X as toDisplayString, j as unref, f as createElementBlock, I as createCommentVNode, N as createVNode, l as script, i as withDirectives, v as vShow, b$ as BaseTerminal, aL as pushScopeId, aM as popScopeId, c0 as electronAPI, _ as _export_sfc } from "./index-DjNHn37O.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
+const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-42c1131d"), n = n(), popScopeId(), n), "_withScopeId");
+const _hoisted_1 = { class: "text-2xl font-bold" };
+const _hoisted_2 = { key: 0 };
+const _hoisted_3 = {
+  key: 0,
+  class: "flex flex-col items-center gap-4"
+};
+const _hoisted_4 = { class: "flex items-center my-4 gap-2" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "ServerStartView",
+  setup(__props) {
+    const electron = electronAPI();
+    const { t } = useI18n();
+    const status = ref(ProgressStatus.INITIAL_STATE);
+    const electronVersion = ref("");
+    let xterm;
+    const terminalVisible = ref(true);
+    const updateProgress = /* @__PURE__ */ __name(({ status: newStatus }) => {
+      status.value = newStatus;
+      if (newStatus === ProgressStatus.ERROR) terminalVisible.value = false;
+      else xterm?.clear();
+    }, "updateProgress");
+    const terminalCreated = /* @__PURE__ */ __name(({ terminal, useAutoSize }, root) => {
+      xterm = terminal;
+      useAutoSize(root, true, true);
+      electron.onLogMessage((message) => {
+        terminal.write(message);
+      });
+      terminal.options.cursorBlink = false;
+      terminal.options.disableStdin = true;
+      terminal.options.cursorInactiveStyle = "block";
+    }, "terminalCreated");
+    const reinstall = /* @__PURE__ */ __name(() => electron.reinstall(), "reinstall");
+    const reportIssue = /* @__PURE__ */ __name(() => {
+      window.open("https://forum.comfy.org/c/v1-feedback/", "_blank");
+    }, "reportIssue");
+    const openLogs = /* @__PURE__ */ __name(() => electron.openLogsFolder(), "openLogs");
+    onMounted(async () => {
+      electron.sendReady();
+      electron.onProgressUpdate(updateProgress);
+      electronVersion.value = await electron.getElectronVersion();
+    });
+    return (_ctx, _cache) => {
+      return openBlock(), createBlock(_sfc_main$1, {
+        dark: "",
+        class: "flex-col"
+      }, {
+        default: withCtx(() => [
+          createBaseVNode("h2", _hoisted_1, [
+            createTextVNode(toDisplayString(unref(t)(`serverStart.process.${status.value}`)) + " ", 1),
+            status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("span", _hoisted_2, " v" + toDisplayString(electronVersion.value), 1)) : createCommentVNode("", true)
+          ]),
+          status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("div", _hoisted_3, [
+            createBaseVNode("div", _hoisted_4, [
+              createVNode(unref(script), {
+                icon: "pi pi-flag",
+                severity: "secondary",
+                label: unref(t)("serverStart.reportIssue"),
+                onClick: reportIssue
+              }, null, 8, ["label"]),
+              createVNode(unref(script), {
+                icon: "pi pi-file",
+                severity: "secondary",
+                label: unref(t)("serverStart.openLogs"),
+                onClick: openLogs
+              }, null, 8, ["label"]),
+              createVNode(unref(script), {
+                icon: "pi pi-refresh",
+                label: unref(t)("serverStart.reinstall"),
+                onClick: reinstall
+              }, null, 8, ["label"])
+            ]),
+            !terminalVisible.value ? (openBlock(), createBlock(unref(script), {
+              key: 0,
+              icon: "pi pi-search",
+              severity: "secondary",
+              label: unref(t)("serverStart.showTerminal"),
+              onClick: _cache[0] || (_cache[0] = ($event) => terminalVisible.value = true)
+            }, null, 8, ["label"])) : createCommentVNode("", true)
+          ])) : createCommentVNode("", true),
+          withDirectives(createVNode(BaseTerminal, { onCreated: terminalCreated }, null, 512), [
+            [vShow, terminalVisible.value]
+          ])
+        ]),
+        _: 1
+      });
+    };
+  }
+});
+const ServerStartView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-42c1131d"]]);
+export {
+  ServerStartView as default
+};
+//# sourceMappingURL=ServerStartView-CIDTUh4x.js.map

web/assets/ServerStartView-CnyN4Ib6.css

@@ -1,5 +1,5 @@
 
-[data-v-4140d62b] .xterm-helper-textarea {
+[data-v-42c1131d] .xterm-helper-textarea {
   /* Hide this as it moves all over when uv is running */
   display: none;
 }

web/assets/ServerStartView-yzYZ8gms.js

@@ -1,100 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, K as useI18n, U as ref, bk as ProgressStatus, p as onMounted, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, a7 as createTextVNode, E as toDisplayString, j as unref, f as createElementBlock, B as createCommentVNode, k as createVNode, l as script, i as withDirectives, v as vShow, bl as BaseTerminal, b5 as electronAPI, _ as _export_sfc } from "./index-4Hb32CNk.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-v6omkdXg.js";
-const _hoisted_1 = { class: "flex flex-col w-full h-full items-center" };
-const _hoisted_2 = { class: "text-2xl font-bold" };
-const _hoisted_3 = { key: 0 };
-const _hoisted_4 = {
-  key: 0,
-  class: "flex flex-col items-center gap-4"
-};
-const _hoisted_5 = { class: "flex items-center my-4 gap-2" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "ServerStartView",
-  setup(__props) {
-    const electron = electronAPI();
-    const { t } = useI18n();
-    const status = ref(ProgressStatus.INITIAL_STATE);
-    const electronVersion = ref("");
-    let xterm;
-    const terminalVisible = ref(true);
-    const updateProgress = /* @__PURE__ */ __name(({ status: newStatus }) => {
-      status.value = newStatus;
-      if (newStatus === ProgressStatus.ERROR) terminalVisible.value = false;
-      else xterm?.clear();
-    }, "updateProgress");
-    const terminalCreated = /* @__PURE__ */ __name(({ terminal, useAutoSize }, root) => {
-      xterm = terminal;
-      useAutoSize({ root, autoRows: true, autoCols: true });
-      electron.onLogMessage((message) => {
-        terminal.write(message);
-      });
-      terminal.options.cursorBlink = false;
-      terminal.options.disableStdin = true;
-      terminal.options.cursorInactiveStyle = "block";
-    }, "terminalCreated");
-    const reinstall = /* @__PURE__ */ __name(() => electron.reinstall(), "reinstall");
-    const reportIssue = /* @__PURE__ */ __name(() => {
-      window.open("https://forum.comfy.org/c/v1-feedback/", "_blank");
-    }, "reportIssue");
-    const openLogs = /* @__PURE__ */ __name(() => electron.openLogsFolder(), "openLogs");
-    onMounted(async () => {
-      electron.sendReady();
-      electron.onProgressUpdate(updateProgress);
-      electronVersion.value = await electron.getElectronVersion();
-    });
-    return (_ctx, _cache) => {
-      return openBlock(), createBlock(_sfc_main$1, {
-        dark: "",
-        class: "flex-col"
-      }, {
-        default: withCtx(() => [
-          createBaseVNode("div", _hoisted_1, [
-            createBaseVNode("h2", _hoisted_2, [
-              createTextVNode(toDisplayString(unref(t)(`serverStart.process.${status.value}`)) + " ", 1),
-              status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("span", _hoisted_3, " v" + toDisplayString(electronVersion.value), 1)) : createCommentVNode("", true)
-            ]),
-            status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("div", _hoisted_4, [
-              createBaseVNode("div", _hoisted_5, [
-                createVNode(unref(script), {
-                  icon: "pi pi-flag",
-                  severity: "secondary",
-                  label: unref(t)("serverStart.reportIssue"),
-                  onClick: reportIssue
-                }, null, 8, ["label"]),
-                createVNode(unref(script), {
-                  icon: "pi pi-file",
-                  severity: "secondary",
-                  label: unref(t)("serverStart.openLogs"),
-                  onClick: openLogs
-                }, null, 8, ["label"]),
-                createVNode(unref(script), {
-                  icon: "pi pi-refresh",
-                  label: unref(t)("serverStart.reinstall"),
-                  onClick: reinstall
-                }, null, 8, ["label"])
-              ]),
-              !terminalVisible.value ? (openBlock(), createBlock(unref(script), {
-                key: 0,
-                icon: "pi pi-search",
-                severity: "secondary",
-                label: unref(t)("serverStart.showTerminal"),
-                onClick: _cache[0] || (_cache[0] = ($event) => terminalVisible.value = true)
-              }, null, 8, ["label"])) : createCommentVNode("", true)
-            ])) : createCommentVNode("", true),
-            withDirectives(createVNode(BaseTerminal, { onCreated: terminalCreated }, null, 512), [
-              [vShow, terminalVisible.value]
-            ])
-          ])
-        ]),
-        _: 1
-      });
-    };
-  }
-});
-const ServerStartView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-4140d62b"]]);
-export {
-  ServerStartView as default
-};
-//# sourceMappingURL=ServerStartView-yzYZ8gms.js.map

web/assets/UserSelectView-B3jYchWu.js

@@ -1,17 +1,18 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, aj as useUserStore, be as useRouter, U as ref, c as computed, p as onMounted, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, k as createVNode, bf as withKeys, j as unref, bg as script, bh as script$1, bi as script$2, bj as script$3, a7 as createTextVNode, B as createCommentVNode, l as script$4 } from "./index-4Hb32CNk.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-v6omkdXg.js";
+import { d as defineComponent, aX as useUserStore, bW as useRouter, ab as ref, c as computed, m as onMounted, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, bX as withKeys, j as unref, av as script, bQ as script$1, bY as script$2, bZ as script$3, aE as createTextVNode, I as createCommentVNode, l as script$4 } from "./index-DjNHn37O.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
 const _hoisted_1 = {
   id: "comfy-user-selection",
   class: "min-w-84 relative rounded-lg bg-[var(--comfy-menu-bg)] p-5 px-10 shadow-lg"
 };
-const _hoisted_2 = { class: "flex w-full flex-col items-center" };
-const _hoisted_3 = { class: "flex w-full flex-col gap-2" };
-const _hoisted_4 = { for: "new-user-input" };
-const _hoisted_5 = { class: "flex w-full flex-col gap-2" };
-const _hoisted_6 = { for: "existing-user-select" };
-const _hoisted_7 = { class: "mt-5" };
+const _hoisted_2 = /* @__PURE__ */ createBaseVNode("h1", { class: "my-2.5 mb-7 font-normal" }, "ComfyUI", -1);
+const _hoisted_3 = { class: "flex w-full flex-col items-center" };
+const _hoisted_4 = { class: "flex w-full flex-col gap-2" };
+const _hoisted_5 = { for: "new-user-input" };
+const _hoisted_6 = { class: "flex w-full flex-col gap-2" };
+const _hoisted_7 = { for: "existing-user-select" };
+const _hoisted_8 = { class: "mt-5" };
 const _sfc_main = /* @__PURE__ */ defineComponent({
   __name: "UserSelectView",
   setup(__props) {
@@ -46,10 +47,10 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
       return openBlock(), createBlock(_sfc_main$1, { dark: "" }, {
         default: withCtx(() => [
           createBaseVNode("main", _hoisted_1, [
-            _cache[2] || (_cache[2] = createBaseVNode("h1", { class: "my-2.5 mb-7 font-normal" }, "ComfyUI", -1)),
-            createBaseVNode("div", _hoisted_2, [
+            _hoisted_2,
             createBaseVNode("div", _hoisted_3, [
-                createBaseVNode("label", _hoisted_4, toDisplayString(_ctx.$t("userSelect.newUser")) + ":", 1),
+              createBaseVNode("div", _hoisted_4, [
+                createBaseVNode("label", _hoisted_5, toDisplayString(_ctx.$t("userSelect.newUser")) + ":", 1),
                 createVNode(unref(script), {
                   id: "new-user-input",
                   modelValue: newUsername.value,
@@ -59,8 +60,8 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
                 }, null, 8, ["modelValue", "placeholder"])
               ]),
               createVNode(unref(script$1)),
-              createBaseVNode("div", _hoisted_5, [
-                createBaseVNode("label", _hoisted_6, toDisplayString(_ctx.$t("userSelect.existingUser")) + ":", 1),
+              createBaseVNode("div", _hoisted_6, [
+                createBaseVNode("label", _hoisted_7, toDisplayString(_ctx.$t("userSelect.existingUser")) + ":", 1),
                 createVNode(unref(script$2), {
                   modelValue: selectedUser.value,
                   "onUpdate:modelValue": _cache[1] || (_cache[1] = ($event) => selectedUser.value = $event),
@@ -81,7 +82,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
                   _: 1
                 })) : createCommentVNode("", true)
               ]),
-              createBaseVNode("footer", _hoisted_7, [
+              createBaseVNode("footer", _hoisted_8, [
                 createVNode(unref(script$4), {
                   label: _ctx.$t("userSelect.next"),
                   onClick: login
@@ -98,4 +99,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=UserSelectView-DeJDnrF0.js.map
+//# sourceMappingURL=UserSelectView-B3jYchWu.js.map

web/assets/WelcomeView-N0ZXLjdi.js

@@ -1,7 +1,8 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, be as useRouter, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, k as createVNode, j as unref, l as script, _ as _export_sfc } from "./index-4Hb32CNk.js";
-import { _ as _sfc_main$1 } from "./BaseViewTemplate-v6omkdXg.js";
+import { d as defineComponent, bW as useRouter, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, l as script, aL as pushScopeId, aM as popScopeId, _ as _export_sfc } from "./index-DjNHn37O.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
+const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-7dfaf74c"), n = n(), popScopeId(), n), "_withScopeId");
 const _hoisted_1 = { class: "flex flex-col items-center justify-center gap-8 p-8" };
 const _hoisted_2 = { class: "animated-gradient-text text-glow select-none" };
 const _sfc_main = /* @__PURE__ */ defineComponent({
@@ -36,4 +37,4 @@ const WelcomeView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-
 export {
   WelcomeView as default
 };
-//# sourceMappingURL=WelcomeView-DkwLdayn.js.map
+//# sourceMappingURL=WelcomeView-N0ZXLjdi.js.map

web/assets/index-5HFeZax4.js

@@ -0,0 +1,27 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { ct as script$1, H as createBaseVNode, o as openBlock, f as createElementBlock, D as mergeProps } from "./index-DjNHn37O.js";
+var script = {
+  name: "PlusIcon",
+  "extends": script$1
+};
+var _hoisted_1 = /* @__PURE__ */ createBaseVNode("path", {
+  d: "M7.67742 6.32258V0.677419C7.67742 0.497757 7.60605 0.325452 7.47901 0.198411C7.35197 0.0713707 7.17966 0 7 0C6.82034 0 6.64803 0.0713707 6.52099 0.198411C6.39395 0.325452 6.32258 0.497757 6.32258 0.677419V6.32258H0.677419C0.497757 6.32258 0.325452 6.39395 0.198411 6.52099C0.0713707 6.64803 0 6.82034 0 7C0 7.17966 0.0713707 7.35197 0.198411 7.47901C0.325452 7.60605 0.497757 7.67742 0.677419 7.67742H6.32258V13.3226C6.32492 13.5015 6.39704 13.6725 6.52358 13.799C6.65012 13.9255 6.82106 13.9977 7 14C7.17966 14 7.35197 13.9286 7.47901 13.8016C7.60605 13.6745 7.67742 13.5022 7.67742 13.3226V7.67742H13.3226C13.5022 7.67742 13.6745 7.60605 13.8016 7.47901C13.9286 7.35197 14 7.17966 14 7C13.9977 6.82106 13.9255 6.65012 13.799 6.52358C13.6725 6.39704 13.5015 6.32492 13.3226 6.32258H7.67742Z",
+  fill: "currentColor"
+}, null, -1);
+var _hoisted_2 = [_hoisted_1];
+function render(_ctx, _cache, $props, $setup, $data, $options) {
+  return openBlock(), createElementBlock("svg", mergeProps({
+    width: "14",
+    height: "14",
+    viewBox: "0 0 14 14",
+    fill: "none",
+    xmlns: "http://www.w3.org/2000/svg"
+  }, _ctx.pti()), _hoisted_2, 16);
+}
+__name(render, "render");
+script.render = render;
+export {
+  script as s
+};
+//# sourceMappingURL=index-5HFeZax4.js.map

web/assets/index-B-aVupP5.js

@@ -0,0 +1,29 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { ct as script$1, H as createBaseVNode, o as openBlock, f as createElementBlock, D as mergeProps } from "./index-DjNHn37O.js";
+var script = {
+  name: "BarsIcon",
+  "extends": script$1
+};
+var _hoisted_1 = /* @__PURE__ */ createBaseVNode("path", {
+  "fill-rule": "evenodd",
+  "clip-rule": "evenodd",
+  d: "M13.3226 3.6129H0.677419C0.497757 3.6129 0.325452 3.54152 0.198411 3.41448C0.0713707 3.28744 0 3.11514 0 2.93548C0 2.75581 0.0713707 2.58351 0.198411 2.45647C0.325452 2.32943 0.497757 2.25806 0.677419 2.25806H13.3226C13.5022 2.25806 13.6745 2.32943 13.8016 2.45647C13.9286 2.58351 14 2.75581 14 2.93548C14 3.11514 13.9286 3.28744 13.8016 3.41448C13.6745 3.54152 13.5022 3.6129 13.3226 3.6129ZM13.3226 7.67741H0.677419C0.497757 7.67741 0.325452 7.60604 0.198411 7.479C0.0713707 7.35196 0 7.17965 0 6.99999C0 6.82033 0.0713707 6.64802 0.198411 6.52098C0.325452 6.39394 0.497757 6.32257 0.677419 6.32257H13.3226C13.5022 6.32257 13.6745 6.39394 13.8016 6.52098C13.9286 6.64802 14 6.82033 14 6.99999C14 7.17965 13.9286 7.35196 13.8016 7.479C13.6745 7.60604 13.5022 7.67741 13.3226 7.67741ZM0.677419 11.7419H13.3226C13.5022 11.7419 13.6745 11.6706 13.8016 11.5435C13.9286 11.4165 14 11.2442 14 11.0645C14 10.8848 13.9286 10.7125 13.8016 10.5855C13.6745 10.4585 13.5022 10.3871 13.3226 10.3871H0.677419C0.497757 10.3871 0.325452 10.4585 0.198411 10.5855C0.0713707 10.7125 0 10.8848 0 11.0645C0 11.2442 0.0713707 11.4165 0.198411 11.5435C0.325452 11.6706 0.497757 11.7419 0.677419 11.7419Z",
+  fill: "currentColor"
+}, null, -1);
+var _hoisted_2 = [_hoisted_1];
+function render(_ctx, _cache, $props, $setup, $data, $options) {
+  return openBlock(), createElementBlock("svg", mergeProps({
+    width: "14",
+    height: "14",
+    viewBox: "0 0 14 14",
+    fill: "none",
+    xmlns: "http://www.w3.org/2000/svg"
+  }, _ctx.pti()), _hoisted_2, 16);
+}
+__name(render, "render");
+script.render = render;
+export {
+  script as s
+};
+//# sourceMappingURL=index-B-aVupP5.js.map