Remove useless function.

Currently only applies to PyTorch nightly releases. (>=20250208)

.github/workflows/stable-release.yml

@@ -12,7 +12,7 @@ on:
         description: 'CUDA version'
         required: true
         type: string
-        default: "124"
+        default: "126"
       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: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
@@ -28,4 +28,4 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install -r requirements.txt
+          pip install -r requirements.txt

.github/workflows/test-unit.yml

@@ -18,7 +18,7 @@ jobs:
     - name: Set up Python      
       uses: actions/setup-python@v4
       with:
-        python-version: '3.10'
+        python-version: '3.12'
     - 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: "124"
+        default: "126"
 
       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: "124"
+        default: "126"
 
       python_minor:
         description: 'python minor version'

CODEOWNERS

@@ -15,6 +15,7 @@
 # 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

@@ -47,11 +47,13 @@ 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.
@@ -129,6 +131,8 @@ 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.
@@ -139,7 +143,7 @@ To run it on services like paperspace, kaggle or colab you can use my [Jupyter N
 
 ## Manual Install (Windows, Linux)
 
-Note that some dependencies do not yet support python 3.13 so using 3.12 is recommended.
+python 3.13 is supported but using 3.12 is recommended because some custom nodes and their dependencies might not support it yet.
 
 Git clone this repo.
 
@@ -151,11 +155,11 @@ 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```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2.4```
 
-This is the command to install the nightly with ROCm 6.2 which might have some performance improvements:
+This is the command to install the nightly with ROCm 6.3 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.2.4```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.3```
 
 ### Intel GPUs (Windows and Linux)
 
@@ -185,7 +189,7 @@ 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/cu124```
+```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu126```
 
 This is the command to install pytorch nightly instead which might have performance improvements:
 

alembic.ini

@@ -1,119 +0,0 @@
-# A generic, single database configuration.
-
-[alembic]
-# path to migration scripts
-# Use forward slashes (/) also on windows to provide an os agnostic path
-script_location = alembic_db
-
-# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
-# Uncomment the line below if you want the files to be prepended with date and time
-# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file
-# for all available tokens
-# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
-
-# sys.path path, will be prepended to sys.path if present.
-# defaults to the current working directory.
-prepend_sys_path = .
-
-# timezone to use when rendering the date within the migration file
-# as well as the filename.
-# If specified, requires the python>=3.9 or backports.zoneinfo library and tzdata library.
-# Any required deps can installed by adding `alembic[tz]` to the pip requirements
-# string value is passed to ZoneInfo()
-# leave blank for localtime
-# timezone =
-
-# max length of characters to apply to the "slug" field
-# truncate_slug_length = 40
-
-# set to 'true' to run the environment during
-# the 'revision' command, regardless of autogenerate
-# revision_environment = false
-
-# set to 'true' to allow .pyc and .pyo files without
-# a source .py file to be detected as revisions in the
-# versions/ directory
-# sourceless = false
-
-# version location specification; This defaults
-# to alembic_db/versions.  When using multiple version
-# directories, initial revisions must be specified with --version-path.
-# The path separator used here should be the separator specified by "version_path_separator" below.
-# version_locations = %(here)s/bar:%(here)s/bat:alembic_db/versions
-
-# version path separator; As mentioned above, this is the character used to split
-# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep.
-# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas.
-# Valid values for version_path_separator are:
-#
-# version_path_separator = :
-# version_path_separator = ;
-# version_path_separator = space
-# version_path_separator = newline
-#
-# Use os.pathsep. Default configuration used for new projects.
-version_path_separator = os
-
-# set to 'true' to search source files recursively
-# in each "version_locations" directory
-# new in Alembic version 1.10
-# recursive_version_locations = false
-
-# the output encoding used when revision files
-# are written from script.py.mako
-# output_encoding = utf-8
-
-sqlalchemy.url = sqlite:///user/comfyui.db
-
-
-[post_write_hooks]
-# post_write_hooks defines scripts or Python functions that are run
-# on newly generated revision scripts.  See the documentation for further
-# detail and examples
-
-# format using "black" - use the console_scripts runner, against the "black" entrypoint
-# hooks = black
-# black.type = console_scripts
-# black.entrypoint = black
-# black.options = -l 79 REVISION_SCRIPT_FILENAME
-
-# lint with attempts to fix using "ruff" - use the exec runner, execute a binary
-# hooks = ruff
-# ruff.type = exec
-# ruff.executable = %(here)s/.venv/bin/ruff
-# ruff.options = check --fix REVISION_SCRIPT_FILENAME
-
-# 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

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

alembic_db/env.py

@@ -1,75 +0,0 @@
-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

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

alembic_db/versions/2fb22c4fff36_init.py

@@ -1,58 +0,0 @@
-"""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 ###

api_server/routes/internal/internal_routes.py

@@ -1,7 +1,6 @@
 from aiohttp import web
 from typing import Optional
-from folder_paths import models_dir, user_directory, output_directory, folder_names_and_paths
-from api_server.services.file_service import FileService
+from folder_paths import folder_names_and_paths
 from api_server.services.terminal_service import TerminalService
 import app.logger
 
@@ -15,26 +14,10 @@ class InternalRoutes:
     def __init__(self, prompt_server):
         self.routes: web.RouteTableDef = web.RouteTableDef()
         self._app: Optional[web.Application] = None
-        self.file_service = FileService({
-            "models": models_dir,
-            "user": user_directory,
-            "output": output_directory
-        })
         self.prompt_server = prompt_server
         self.terminal_service = TerminalService(prompt_server)
 
     def setup_routes(self):
-        @self.routes.get('/files')
-        async def list_files(request):
-            directory_key = request.query.get('directory', '')
-            try:
-                file_list = self.file_service.list_files(directory_key)
-                return web.json_response({"files": file_list})
-            except ValueError as e:
-                return web.json_response({"error": str(e)}, status=400)
-            except Exception as e:
-                return web.json_response({"error": str(e)}, status=500)
-
         @self.routes.get('/logs')
         async def get_logs(request):
             return web.json_response("".join([(l["t"] + " - " + l["m"]) for l in app.logger.get_logs()]))

api_server/services/file_service.py

@@ -1,13 +0,0 @@
-from typing import Dict, List, Optional
-from api_server.utils.file_operations import FileSystemOperations, FileSystemItem
-
-class FileService:
-    def __init__(self, allowed_directories: Dict[str, str], file_system_ops: Optional[FileSystemOperations] = None):
-        self.allowed_directories: Dict[str, str] = allowed_directories
-        self.file_system_ops: FileSystemOperations = file_system_ops or FileSystemOperations()
-
-    def list_files(self, directory_key: str) -> List[FileSystemItem]:
-        if directory_key not in self.allowed_directories:
-            raise ValueError("Invalid directory key")
-        directory_path: str = self.allowed_directories[directory_key]
-        return self.file_system_ops.walk_directory(directory_path)

app/custom_node_manager.py

@@ -4,12 +4,93 @@ 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:
-    """
-    Placeholder to refactor the custom node management features from ComfyUI-Manager.
-    Currently it only contains the custom workflow templates feature.
-    """
+    @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
+
     def add_routes(self, routes, webapp, loadedModules):
 
         @routes.get("/workflow_templates")
@@ -18,17 +99,36 @@ 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)])
+                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())

app/database/db.py

@@ -1,118 +0,0 @@
-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

@@ -1,76 +0,0 @@
-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

@@ -1,30 +1,19 @@
 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, 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
+from folder_paths import map_legacy, filter_files_extensions, filter_files_content_types
 
 
-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]] = {}
@@ -73,7 +62,7 @@ class ModelFileManager:
             folder = folders[0][path_index]
             full_filename = os.path.join(folder, filename)
 
-            previews = get_model_previews(full_filename)
+            previews = self.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)
@@ -87,183 +76,6 @@ 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]
@@ -334,5 +146,39 @@ 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

@@ -1,263 +0,0 @@
-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

@@ -43,10 +43,11 @@ 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.")
-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("--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("--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.")
@@ -176,13 +177,9 @@ 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.")
+parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path. Overrides --base-directory.")
 
-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.")
+parser.add_argument("--enable-compress-response-body", action="store_true", help="Enable compressing response body.")
 
 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), float("-inf"))
+            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
 
-        causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
+        causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(-torch.finfo(x.dtype).max).triu_(1)
         if mask is not None:
             mask += causal_mask
         else:

comfy/conds.py

@@ -3,9 +3,6 @@ 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
@@ -46,7 +43,7 @@ class CONDCrossAttn(CONDRegular):
             if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
                 return False
 
-            mult_min = lcm(s1[1], s2[1])
+            mult_min = math.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
@@ -57,7 +54,7 @@ class CONDCrossAttn(CONDRegular):
         crossattn_max_len = self.cond.shape[1]
         for x in others:
             c = x.cond
-            crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
+            crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1])
             conds.append(c)
 
         out = []

comfy/diffusers_convert.py

@@ -4,105 +4,6 @@ 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 #
 # ================#
@@ -213,6 +114,7 @@ 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
@@ -229,6 +131,7 @@ def cat_tensors(tensors):
 
     return out
 
+
 def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
     new_state_dict = {}
     capture_qkv_weight = {}
@@ -284,5 +187,3 @@ 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.einsum('k,bkchw->bchw', rhos_p, D1s)
+                    pred_res = torch.tensordot(D1s, rhos_p, dims=([1], [0]))  # 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.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
+                    corr_res = torch.tensordot(D1s, rhos_c[:-1], dims=([1], [0]))  # 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.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1)
+    sigmas = torch.sqrt(torch.special.expm1(beta_d * t ** 2 / 2 + beta_min * t))
     return append_zero(sigmas)
 
 
@@ -1267,7 +1267,7 @@ def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, dis
     return x
 
 @torch.no_grad()
-def res_multistep(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, cfg_pp=False):
+def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1., cfg_pp=False):
     extra_args = {} if extra_args is None else extra_args
     seed = extra_args.get("seed", None)
     noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
@@ -1289,50 +1289,80 @@ def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None
         extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
 
     for i in trange(len(sigmas) - 1, disable=disable):
-        if s_churn > 0:
-            gamma = min(s_churn / (len(sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.0
-            sigma_hat = sigmas[i] * (gamma + 1)
-        else:
-            gamma = 0
-            sigma_hat = sigmas[i]
-
-        if gamma > 0:
-            eps = torch.randn_like(x) * s_noise
-            x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
-        denoised = model(x, sigma_hat * s_in, **extra_args)
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
         if callback is not None:
-            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised})
-        if sigmas[i + 1] == 0 or old_denoised is None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
+        if sigma_down == 0 or old_denoised is None:
             # Euler method
             if cfg_pp:
-                d = to_d(x, sigma_hat, uncond_denoised)
-                x = denoised + d * sigmas[i + 1]
+                d = to_d(x, sigmas[i], uncond_denoised)
+                x = denoised + d * sigma_down
             else:
-                d = to_d(x, sigma_hat, denoised)
-                dt = sigmas[i + 1] - sigma_hat
+                d = to_d(x, sigmas[i], denoised)
+                dt = sigma_down - sigmas[i]
                 x = x + d * dt
         else:
             # Second order multistep method in https://arxiv.org/pdf/2308.02157
-            t, t_next, t_prev = t_fn(sigmas[i]), t_fn(sigmas[i + 1]), t_fn(sigmas[i - 1])
+            t, t_next, t_prev = t_fn(sigmas[i]), t_fn(sigma_down), t_fn(sigmas[i - 1])
             h = t_next - t
             c2 = (t_prev - t) / h
 
             phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h)
-            b1 = torch.nan_to_num(phi1_val - 1.0 / c2 * phi2_val, nan=0.0)
-            b2 = torch.nan_to_num(1.0 / c2 * phi2_val, nan=0.0)
+            b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0)
+            b2 = torch.nan_to_num(phi2_val / c2, nan=0.0)
 
             if cfg_pp:
                 x = x + (denoised - uncond_denoised)
+                x = sigma_fn(h) * x + h * (b1 * uncond_denoised + b2 * old_denoised)
+            else:
+                x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised)
 
-            x = (sigma_fn(t_next) / sigma_fn(t)) * x + h * (b1 * denoised + b2 * old_denoised)
+        # Noise addition
+        if sigmas[i + 1] > 0:
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
 
-        old_denoised = denoised
+        if cfg_pp:
+            old_denoised = uncond_denoised
+        else:
+            old_denoised = denoised
     return x
 
 @torch.no_grad()
-def sample_res_multistep(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=False)
+def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None):
+    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=False)
 
 @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)
+def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None):
+    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=True)
+
+@torch.no_grad()
+def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=False)
+
+@torch.no_grad()
+def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, 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

@@ -168,14 +168,18 @@ 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!
-            q = apply_rotary_pos_emb(q, rope_emb)
-            k = apply_rotary_pos_emb(k, rope_emb)
-        return q, k, v
+            # 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)
 
-    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)
+            # 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)
+        return q, k, v
 
     def forward(
         self,
@@ -191,7 +195,10 @@ 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)
-        return self.cal_attn(q, k, v, mask)
+        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)
 
 
 class FeedForward(nn.Module):
@@ -788,10 +795,7 @@ 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

@@ -30,6 +30,8 @@ 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,
@@ -400,6 +402,8 @@ 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_)
@@ -413,18 +417,7 @@ class CausalAttnBlock(nn.Module):
         v, batch_size = time2batch(v)
 
         b, c, h, w = q.shape
-        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_ = self.optimized_attention(q, k, v)
 
         h_ = batch2time(h_, batch_size)
         h_ = self.proj_out(h_)
@@ -871,18 +864,16 @@ class EncoderFactorized(nn.Module):
         x = self.patcher3d(x)
 
         # downsampling
-        hs = [self.conv_in(x)]
+        h = 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](hs[-1])
+                h = self.down[i_level].block[i_block](h)
                 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:
-                hs.append(self.down[i_level].downsample(hs[-1]))
+                h = self.down[i_level].downsample(h)
 
         # 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

@@ -281,54 +281,76 @@ 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)
+        self.build_pos_embed(device=device, dtype=dtype)
         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):
+    def build_pos_embed(self, device=None, dtype=None):
         if self.pos_emb_cls == "rope3d":
             cls_type = VideoRopePosition3DEmb
         else:
@@ -242,6 +242,7 @@ 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,
             )
@@ -292,7 +293,7 @@ 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)
+            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)
         else:
             extra_pos_emb = None
 
@@ -476,6 +477,8 @@ 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
@@ -486,6 +489,8 @@ 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,
@@ -493,7 +498,6 @@ 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) -> torch.Tensor:
+    def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor], device=None, dtype=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)
+        embeddings = self.generate_embeddings(B_T_H_W_C, fps=fps, device=device, dtype=dtype)
 
         return embeddings
 
@@ -104,6 +104,7 @@ 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.
@@ -173,6 +174,7 @@ class LearnablePosEmbAxis(VideoPositionEmb):
         len_w: int,
         len_t: int,
         device=None,
+        dtype=None,
         **kwargs,
     ):
         """
@@ -184,17 +186,16 @@ 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))
-        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))
+        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))
 
-
-    def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None) -> torch.Tensor:
+    def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None, dtype=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)
-            emb_w_W = self.pos_emb_w[:W].to(device=device)
-            emb_t_T = self.pos_emb_t[:T].to(device=device)
+            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 = (
                 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

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

comfy/ldm/flux/layers.py

@@ -230,8 +230,7 @@ class SingleStreamBlock(nn.Module):
 
     def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None) -> Tensor:
         mod, _ = self.modulation(vec)
-        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)
+        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)
 
         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

@@ -5,8 +5,15 @@ 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, k = apply_rope(q, k, pe)
+    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)
 
     heads = q.shape[1]
     x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask)

comfy/ldm/flux/model.py

@@ -109,9 +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 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 guidance is not None:
+                vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
         vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
         txt = self.txt_in(txt)
@@ -186,7 +185,7 @@ 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, control=None, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, y, guidance=None, 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

@@ -240,9 +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 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 guidance is not None:
+                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):
             txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max
@@ -314,7 +313,7 @@ class HunyuanVideo(nn.Module):
         img = img.reshape(initial_shape)
         return img
 
-    def forward(self, x, timestep, context, y, guidance, attention_mask=None, control=None, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, y, guidance=None, 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

@@ -0,0 +1,619 @@
+# 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 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 = 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
+

comfy/ldm/modules/attention.py

@@ -1,4 +1,6 @@
 import math
+import sys
+
 import torch
 import torch.nn.functional as F
 from torch import nn, einsum
@@ -16,7 +18,11 @@ if model_management.xformers_enabled():
     import xformers.ops
 
 if model_management.sage_attention_enabled():
-    from sageattention import sageattn
+    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
+        self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None, **kwargs
     ):
         """
         Initialize the RMSNorm normalization layer.

comfy/ldm/modules/diffusionmodules/model.py

@@ -293,6 +293,17 @@ 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__()
@@ -320,15 +331,7 @@ class AttnBlock(nn.Module):
                                         stride=1,
                                         padding=0)
 
-        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
+        self.optimized_attention = vae_attention()
 
     def forward(self, x):
         h_ = x
@@ -699,9 +702,6 @@ 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

@@ -307,7 +307,6 @@ 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
@@ -327,6 +326,13 @@ 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

@@ -34,6 +34,7 @@ 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
@@ -148,7 +149,9 @@ class BaseModel(torch.nn.Module):
 
         xc = xc.to(dtype)
         t = self.model_sampling.timestep(t).float()
-        context = context.to(dtype)
+        if context is not None:
+            context = context.to(dtype)
+
         extra_conds = {}
         for o in kwargs:
             extra = kwargs[o]
@@ -163,9 +166,6 @@ class BaseModel(torch.nn.Module):
     def get_dtype(self):
         return self.diffusion_model.dtype
 
-    def is_adm(self):
-        return self.adm_channels > 0
-
     def encode_adm(self, **kwargs):
         return None
 
@@ -549,6 +549,10 @@ 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:
@@ -806,7 +810,10 @@ 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)
-        out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
+
+        guidance = kwargs.get("guidance", 3.5)
+        if guidance is not None:
+            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
         return out
 
 class GenmoMochi(BaseModel):
@@ -863,7 +870,10 @@ class HunyuanVideo(BaseModel):
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
-        out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 6.0)]))
+
+        guidance = kwargs.get("guidance", 6.0)
+        if guidance is not None:
+            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
         return out
 
 class CosmosVideo(BaseModel):
@@ -892,3 +902,19 @@ 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:
+    if '{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix) in state_dict_keys:  # Cosmos
         dit_config = {}
         dit_config["image_model"] = "cosmos"
         dit_config["max_img_h"] = 240
@@ -284,6 +284,21 @@ 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 = 0.2
+    MIN_WEIGHT_MEMORY_RATIO = 0.1
 
 ENABLE_PYTORCH_ATTENTION = False
 if args.use_pytorch_cross_attention:
@@ -241,6 +241,12 @@ if ENABLE_PYTORCH_ATTENTION:
     torch.backends.cuda.enable_flash_sdp(True)
     torch.backends.cuda.enable_mem_efficient_sdp(True)
 
+try:
+    if is_nvidia() and args.fast:
+        torch.backends.cuda.matmul.allow_fp16_accumulation = True
+except:
+    pass
+
 try:
     if int(torch_version[0]) == 2 and int(torch_version[2]) >= 5:
         torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)
@@ -535,14 +541,11 @@ 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

@@ -31,6 +31,7 @@ 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:
@@ -61,9 +62,11 @@ 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

@@ -58,7 +58,6 @@ 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

@@ -12,7 +12,6 @@ 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
@@ -178,7 +177,7 @@ 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 = comfy.samplers.get_area_and_mult(x, x_in, timestep)
+            p = get_area_and_mult(x, x_in, timestep)
             if p is None:
                 continue
             # replace p's mult with calculated mult
@@ -215,7 +214,7 @@ def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Te
                     default_c.append(x)
                     has_default_conds = True
                     continue
-                p = comfy.samplers.get_area_and_mult(x, x_in, timestep)
+                p = get_area_and_mult(x, x_in, timestep)
                 if p is None:
                     continue
                 if p.hooks is not None:
@@ -687,7 +686,8 @@ 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"]
+                  "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp",
+                  "gradient_estimation"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

comfy/sd.py

@@ -36,6 +36,7 @@ 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
@@ -388,8 +389,8 @@ 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: (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.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.working_dtypes = [torch.bfloat16, torch.float32]
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
@@ -657,6 +658,7 @@ 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={}):
@@ -675,6 +677,7 @@ 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:
@@ -693,6 +696,8 @@ 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
@@ -730,6 +735,7 @@ 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:
@@ -769,6 +775,10 @@ 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)
@@ -798,7 +808,6 @@ 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={}):
+    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={}):
         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)
+        self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args)
         self.max_length = max_length
         self.min_length = min_length
         self.end_token = None
@@ -585,9 +585,14 @@ class SDTokenizer:
         return {}
 
 class SD1Tokenizer:
-    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)
+    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:
+            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))
 
@@ -600,7 +605,7 @@ class SD1Tokenizer:
         return getattr(self, self.clip).untokenize(token_weight_pair)
 
     def state_dict(self):
-        return {}
+        return getattr(self, self.clip).state_dict()
 
 class SD1CheckpointClipModel(SDClipModel):
     def __init__(self, device="cpu", dtype=None, model_options={}):

comfy/supported_models.py

@@ -15,6 +15,7 @@ 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
@@ -788,7 +789,7 @@ class HunyuanVideo(supported_models_base.BASE):
     unet_extra_config = {}
     latent_format = latent_formats.HunyuanVideo
 
-    memory_usage_factor = 2.0 #TODO
+    memory_usage_factor = 1.8 #TODO
 
     supported_inference_dtypes = [torch.bfloat16, torch.float32]
 
@@ -839,7 +840,7 @@ class CosmosT2V(supported_models_base.BASE):
     unet_extra_config = {}
     latent_format = latent_formats.Cosmos1CV8x8x8
 
-    memory_usage_factor = 2.4 #TODO
+    memory_usage_factor = 1.6 #TODO
 
     supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] #TODO
 
@@ -865,6 +866,35 @@ class CosmosI2V(CosmosT2V):
         out = model_base.CosmosVideo(self, image_to_video=True, device=device)
         return out
 
-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]
+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 += [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), float("-inf"))
+            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
 
         x, i = self.encoder(x, mask, intermediate_output)
         return x, i

comfy/text_encoders/llama.py

@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 from dataclasses import dataclass
 from typing import Optional, Any
 
@@ -21,15 +20,41 @@ 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, device=None, dtype=None):
+    def __init__(self, dim: int, eps: float = 1e-5, add=False, 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):
-        return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps)
+        w = self.weight
+        if self.add:
+            w = w + 1.0
+
+        return comfy.ldm.common_dit.rms_norm(x, w, self.eps)
+
 
 
 def rotate_half(x):
@@ -68,13 +93,15 @@ 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 = self.hidden_size // self.num_heads
+
+        self.head_dim = config.head_dim
+        self.inner_size = self.num_heads * self.head_dim
 
         ops = ops or nn
-        self.q_proj = ops.Linear(config.hidden_size, config.hidden_size, bias=False, device=device, dtype=dtype)
+        self.q_proj = ops.Linear(config.hidden_size, self.inner_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(config.hidden_size, config.hidden_size, bias=False, device=device, dtype=dtype)
+        self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype)
 
     def forward(
         self,
@@ -84,7 +111,6 @@ 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)
@@ -108,9 +134,13 @@ 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(F.silu(self.gate_proj(x)) * self.up_proj(x))
+        return self.down_proj(self.activation(self.gate_proj(x)) * self.up_proj(x))
 
 class TransformerBlock(nn.Module):
     def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
@@ -146,6 +176,45 @@ 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__()
@@ -158,17 +227,27 @@ 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([
-            TransformerBlock(config, device=device, dtype=dtype, ops=ops)
+            transformer(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, device=device, dtype=dtype)
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, 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)
 
-        freqs_cis = precompute_freqs_cis(self.config.hidden_size // self.config.num_attention_heads,
+        if self.normalize_in:
+            x *= self.config.hidden_size ** 0.5
+
+        freqs_cis = precompute_freqs_cis(self.config.head_dim,
                                          x.shape[1],
                                          self.config.rope_theta,
                                          device=x.device)
@@ -206,16 +285,7 @@ class Llama2_(nn.Module):
 
         return x, intermediate
 
-
-class Llama2(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device, operations):
-        super().__init__()
-        config = Llama2Config(**config_dict)
-        self.num_layers = config.num_hidden_layers
-
-        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
-        self.dtype = dtype
-
+class BaseLlama:
     def get_input_embeddings(self):
         return self.model.embed_tokens
 
@@ -224,3 +294,23 @@ class Llama2(torch.nn.Module):
 
     def forward(self, input_ids, *args, **kwargs):
         return self.model(input_ids, *args, **kwargs)
+
+
+class Llama2(BaseLlama, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Llama2Config(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
+
+
+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
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype

comfy/text_encoders/lumina2.py

@@ -0,0 +1,44 @@
+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:
-    add_eos = True
-
     @staticmethod
-    def from_pretrained(path):
-        return SPieceTokenizer(path)
+    def from_pretrained(path, **kwargs):
+        return SPieceTokenizer(path, **kwargs)
 
-    def __init__(self, tokenizer_path):
+    def __init__(self, tokenizer_path, add_bos=False, add_eos=True):
+        self.add_bos = add_bos
+        self.add_eos = add_eos
         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_eos=self.add_eos)
+            self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos)
         else:
-            self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_eos=self.add_eos)
+            self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_bos=self.add_bos, 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), float("-inf"))
+            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
 
         intermediate = None
         optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True)

comfy/utils.py

@@ -43,13 +43,23 @@ 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"):
-        sd = safetensors.torch.load_file(ckpt, device=device.type)
+        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 corrupt/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
-        out_latent["noise_mask"] = mask
+        out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
+        out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
         return (out_latent,)
 
 

comfy_extras/nodes_flux.py

@@ -38,7 +38,26 @@ 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

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

@@ -19,11 +19,7 @@ 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}),
@@ -69,14 +65,9 @@ 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}),
         }}
 
@@ -109,11 +100,29 @@ 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"],),
-            "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}),
+        }}
+
+    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"],),
             "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}),
@@ -133,11 +142,13 @@ class Preview3D():
 NODE_CLASS_MAPPINGS = {
     "Load3D": Load3D,
     "Load3DAnimation": Load3DAnimation,
-    "Preview3D": Preview3D
+    "Preview3D": Preview3D,
+    "Preview3DAnimation": Preview3DAnimation
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "Load3D": "Load 3D",
     "Load3DAnimation": "Load 3D - Animation",
-    "Preview3D": "Preview 3D"
+    "Preview3D": "Preview 3D",
+    "Preview3DAnimation": "Preview 3D - Animation"
 }

comfy_extras/nodes_model_merging_model_specific.py

@@ -196,6 +196,54 @@ 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
@@ -206,4 +254,6 @@ 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__ = "0.3.10"
+__version__ = "0.3.14"

folder_paths.py

@@ -7,11 +7,18 @@ import logging
 from typing import Literal
 from collections.abc import Collection
 
-supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
+from comfy.cli_args import args
+
+supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
 
 folder_names_and_paths: dict[str, tuple[list[str], set[str]]] = {}
 
-base_path = os.path.dirname(os.path.realpath(__file__))
+# --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"])
@@ -39,10 +46,10 @@ 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(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")
+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")
 
 filename_list_cache: dict[str, tuple[list[str], dict[str, float], float]] = {}
 

latent_preview.py

@@ -12,7 +12,10 @@ 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
-                            ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
+                            )
+        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))
 
         return Image.fromarray(latents_ubyte.numpy())
 

main.py

@@ -138,8 +138,8 @@ import server
 from server import BinaryEventTypes
 import nodes
 import comfy.model_management
-from app.database.db import can_create_session, init_db
-from app.model_processor import model_processor
+import comfyui_version
+
 
 def cuda_malloc_warning():
     device = comfy.model_management.get_torch_device()
@@ -264,11 +264,6 @@ 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)
 
@@ -276,10 +271,6 @@ 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
@@ -303,6 +294,7 @@ 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

@@ -63,6 +63,8 @@ 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), )
 
@@ -912,7 +914,7 @@ 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"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2"], ),
                               },
                 "optional": {
                               "device": (["default", "cpu"], {"advanced": True}),
@@ -922,7 +924,7 @@ class CLIPLoader:
 
     CATEGORY = "advanced/loaders"
 
-    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 / clip-g / clip-l\nstable_audio: t5\nmochi: t5\ncosmos: old t5 xxl"
+    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 / clip-g / clip-l\nstable_audio: t5\nmochi: t5\ncosmos: old t5 xxl\nlumina2: gemma 2 2B"
 
     def load_clip(self, clip_name, type="stable_diffusion", device="default"):
         if type == "stable_cascade":
@@ -937,6 +939,10 @@ 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
 
@@ -1058,10 +1064,11 @@ class StyleModelApply:
         for t in conditioning:
             (txt, keys) = t
             keys = keys.copy()
-            if strength_type == "attn_bias" and strength != 1.0:
+            # even if the strength is 1.0 (i.e, no change), if there's already a mask, we have to add to it
+            if "attention_mask" in keys or (strength_type == "attn_bias" and strength != 1.0):
                 # math.log raises an error if the argument is zero
                 # torch.log returns -inf, which is what we want
-                attn_bias = torch.log(torch.Tensor([strength]))
+                attn_bias = torch.log(torch.Tensor([strength if strength_type == "attn_bias" else 1.0]))
                 # get the size of the mask image
                 mask_ref_size = keys.get("attention_mask_img_shape", (1, 1))
                 n_ref = mask_ref_size[0] * mask_ref_size[1]

pyproject.toml

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

requirements.txt

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

server.py

@@ -52,6 +52,20 @@ 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 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):
@@ -150,6 +164,9 @@ class PromptServer():
         self.number = 0
 
         middlewares = [cache_control]
+        if args.enable_compress_response_body:
+            middlewares.append(compress_body)
+
         if args.enable_cors_header:
             middlewares.append(create_cors_middleware(args.enable_cors_header))
         else:
@@ -329,6 +346,9 @@ 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)
@@ -370,6 +390,9 @@ 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

@@ -2,39 +2,146 @@ 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

@@ -7,33 +7,11 @@ 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()
@@ -82,287 +60,3 @@ 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

@@ -1,19 +1,23 @@
 ### 🗻 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():
-    # 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()
+    # Reload the module after each test to ensure isolation
     yield
-    folder_paths.folder_names_and_paths = original
+    reload(folder_paths)
 
 @pytest.fixture
 def temp_dir():
@@ -21,7 +25,21 @@ def temp_dir():
         yield tmpdirname
 
 
-def test_get_directory_by_type():
+@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):
     test_dir = "/test/dir"
     folder_paths.set_output_directory(test_dir)
     assert folder_paths.get_directory_by_type("output") == test_dir
@@ -96,3 +114,49 @@ 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/server/routes/internal_routes_test.py

@@ -1,115 +0,0 @@
-import pytest
-from aiohttp import web
-from unittest.mock import MagicMock, patch
-from api_server.routes.internal.internal_routes import InternalRoutes
-from api_server.services.file_service import FileService
-from folder_paths import models_dir, user_directory, output_directory
-
-
-@pytest.fixture
-def internal_routes():
-    return InternalRoutes(None)
-
-@pytest.fixture
-def aiohttp_client_factory(aiohttp_client, internal_routes):
-    async def _get_client():
-        app = internal_routes.get_app()
-        return await aiohttp_client(app)
-    return _get_client
-
-@pytest.mark.asyncio
-async def test_list_files_valid_directory(aiohttp_client_factory, internal_routes):
-    mock_file_list = [
-        {"name": "file1.txt", "path": "file1.txt", "type": "file", "size": 100},
-        {"name": "dir1", "path": "dir1", "type": "directory"}
-    ]
-    internal_routes.file_service.list_files = MagicMock(return_value=mock_file_list)
-    client = await aiohttp_client_factory()
-    resp = await client.get('/files?directory=models')
-    assert resp.status == 200
-    data = await resp.json()
-    assert 'files' in data
-    assert len(data['files']) == 2
-    assert data['files'] == mock_file_list
-
-    # Check other valid directories
-    resp = await client.get('/files?directory=user')
-    assert resp.status == 200
-    resp = await client.get('/files?directory=output')
-    assert resp.status == 200
-
-@pytest.mark.asyncio
-async def test_list_files_invalid_directory(aiohttp_client_factory, internal_routes):
-    internal_routes.file_service.list_files = MagicMock(side_effect=ValueError("Invalid directory key"))
-    client = await aiohttp_client_factory()
-    resp = await client.get('/files?directory=invalid')
-    assert resp.status == 400
-    data = await resp.json()
-    assert 'error' in data
-    assert data['error'] == "Invalid directory key"
-
-@pytest.mark.asyncio
-async def test_list_files_exception(aiohttp_client_factory, internal_routes):
-    internal_routes.file_service.list_files = MagicMock(side_effect=Exception("Unexpected error"))
-    client = await aiohttp_client_factory()
-    resp = await client.get('/files?directory=models')
-    assert resp.status == 500
-    data = await resp.json()
-    assert 'error' in data
-    assert data['error'] == "Unexpected error"
-
-@pytest.mark.asyncio
-async def test_list_files_no_directory_param(aiohttp_client_factory, internal_routes):
-    mock_file_list = []
-    internal_routes.file_service.list_files = MagicMock(return_value=mock_file_list)
-    client = await aiohttp_client_factory()
-    resp = await client.get('/files')
-    assert resp.status == 200
-    data = await resp.json()
-    assert 'files' in data
-    assert len(data['files']) == 0
-
-def test_setup_routes(internal_routes):
-    internal_routes.setup_routes()
-    routes = internal_routes.routes
-    assert any(route.method == 'GET' and str(route.path) == '/files' for route in routes)
-
-def test_get_app(internal_routes):
-    app = internal_routes.get_app()
-    assert isinstance(app, web.Application)
-    assert internal_routes._app is not None
-
-def test_get_app_reuse(internal_routes):
-    app1 = internal_routes.get_app()
-    app2 = internal_routes.get_app()
-    assert app1 is app2
-
-@pytest.mark.asyncio
-async def test_routes_added_to_app(aiohttp_client_factory, internal_routes):
-    client = await aiohttp_client_factory()
-    try:
-        resp = await client.get('/files')
-        print(f"Response received: status {resp.status}")  # noqa: T201
-    except Exception as e:
-        print(f"Exception occurred during GET request: {e}")  # noqa: T201
-        raise
-
-    assert resp.status != 404, "Route /files does not exist"
-
-@pytest.mark.asyncio
-async def test_file_service_initialization():
-    with patch('api_server.routes.internal.internal_routes.FileService') as MockFileService:
-        # Create a mock instance
-        mock_file_service_instance = MagicMock(spec=FileService)
-        MockFileService.return_value = mock_file_service_instance
-        internal_routes = InternalRoutes(None)
-
-        # Check if FileService was initialized with the correct parameters
-        MockFileService.assert_called_once_with({
-            "models": models_dir,
-            "user": user_directory,
-            "output": output_directory
-        })
-
-        # Verify that the file_service attribute of InternalRoutes is set
-        assert internal_routes.file_service == mock_file_service_instance

tests-unit/server/services/file_service_test.py

@@ -1,54 +0,0 @@
-import pytest
-from unittest.mock import MagicMock
-from api_server.services.file_service import FileService
-
-@pytest.fixture
-def mock_file_system_ops():
-    return MagicMock()
-
-@pytest.fixture
-def file_service(mock_file_system_ops):
-    allowed_directories = {
-        "models": "/path/to/models",
-        "user": "/path/to/user",
-        "output": "/path/to/output"
-    }
-    return FileService(allowed_directories, file_system_ops=mock_file_system_ops)
-
-def test_list_files_valid_directory(file_service, mock_file_system_ops):
-    mock_file_system_ops.walk_directory.return_value = [
-        {"name": "file1.txt", "path": "file1.txt", "type": "file", "size": 100},
-        {"name": "dir1", "path": "dir1", "type": "directory"}
-    ]
-
-    result = file_service.list_files("models")
-
-    assert len(result) == 2
-    assert result[0]["name"] == "file1.txt"
-    assert result[1]["name"] == "dir1"
-    mock_file_system_ops.walk_directory.assert_called_once_with("/path/to/models")
-
-def test_list_files_invalid_directory(file_service):
-    # Does not support walking directories outside of the allowed directories
-    with pytest.raises(ValueError, match="Invalid directory key"):
-        file_service.list_files("invalid_key")
-
-def test_list_files_empty_directory(file_service, mock_file_system_ops):
-    mock_file_system_ops.walk_directory.return_value = []
-
-    result = file_service.list_files("models")
-
-    assert len(result) == 0
-    mock_file_system_ops.walk_directory.assert_called_once_with("/path/to/models")
-
-@pytest.mark.parametrize("directory_key", ["models", "user", "output"])
-def test_list_files_all_allowed_directories(file_service, mock_file_system_ops, directory_key):
-    mock_file_system_ops.walk_directory.return_value = [
-        {"name": f"file_{directory_key}.txt", "path": f"file_{directory_key}.txt", "type": "file", "size": 100}
-    ]
-
-    result = file_service.list_files(directory_key)
-
-    assert len(result) == 1
-    assert result[0]["name"] == f"file_{directory_key}.txt"
-    mock_file_system_ops.walk_directory.assert_called_once_with(f"/path/to/{directory_key}")

tests-unit/utils/json_util_test.py

@@ -0,0 +1,71 @@
+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

@@ -0,0 +1,26 @@
+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

@@ -1,12 +0,0 @@
-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

@@ -1,23 +0,0 @@
-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-Cz111_1A.js

@@ -0,0 +1,51 @@
+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-DqqhYDnY.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-Cz111_1A.js.map

web/assets/DesktopStartView-FKlxS2Lt.js

@@ -0,0 +1,22 @@
+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-DqqhYDnY.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-Cz111_1A.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-FKlxS2Lt.js.map

web/assets/DownloadGitView-DVXUne-M.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, 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";
+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-DqqhYDnY.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-Cz111_1A.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-DeC7MBzG.js.map
+//# sourceMappingURL=DownloadGitView-DVXUne-M.js.map

web/assets/ExtensionPanel-iPOrhDVM.js

@@ -1,9 +1,8 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-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";
+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-DqqhYDnY.js";
+import { g as script$2, h as script$6 } from "./index-BapOFhAR.js";
+import "./index-DXE47DZl.js";
 const _hoisted_1 = { class: "flex justify-end" };
 const _sfc_main = /* @__PURE__ */ defineComponent({
   __name: "ExtensionPanel",
@@ -180,4 +179,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=ExtensionPanel-D4Phn0Zr.js.map
+//# sourceMappingURL=ExtensionPanel-iPOrhDVM.js.map

web/assets/GraphView-CVCdiww1.css

@@ -1,8 +1,10 @@
 
-.comfy-menu-hamburger[data-v-5661bed0] {
-  pointer-events: auto;
-  position: fixed;
-  z-index: 9999;
+.comfy-menu-hamburger[data-v-7ed57d1a] {
+    pointer-events: auto;
+    position: fixed;
+    z-index: 9999;
+    display: flex;
+    flex-direction: row
 }
 
 [data-v-e50caa15] .p-splitter-gutter {
@@ -39,14 +41,14 @@
   z-index: 999;
 }
 
-.p-buttongroup-vertical[data-v-cf40dd39] {
+.p-buttongroup-vertical[data-v-cb8f9a1a] {
   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-cf40dd39] {
+.p-buttongroup-vertical .p-button[data-v-cb8f9a1a] {
   margin: 0;
   border-radius: 0;
 }
@@ -82,7 +84,7 @@
   font-size: inherit;
 }
 
-[data-v-5741c9ae] .highlight {
+[data-v-fd0a74bd] .highlight {
   background-color: var(--p-primary-color);
   color: var(--p-primary-contrast-color);
   font-weight: bold;
@@ -131,16 +133,7 @@
   border-right: 4px solid var(--p-button-text-primary-color);
 }
 
-: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] {
+.side-tool-bar-container[data-v-33cac83a] {
   display: flex;
   flex-direction: column;
   align-items: center;
@@ -153,18 +146,91 @@
   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-end[data-v-37d8d7b4] {
+.side-tool-bar-container.small-sidebar[data-v-33cac83a] {
+  --sidebar-width: 2.5rem;
+  --sidebar-icon-size: 1rem;
+}
+.side-tool-bar-end[data-v-33cac83a] {
   align-self: flex-end;
   margin-top: auto;
 }
 
-[data-v-b9328350] .p-inputtext {
+.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 {
   border-top-left-radius: 0;
   border-bottom-left-radius: 0;
 }
 
-.comfyui-queue-button[data-v-7f4f551b] .p-splitbutton-dropdown {
+.comfyui-queue-button[data-v-91a628af] .p-splitbutton-dropdown {
   border-top-right-radius: 0;
   border-bottom-right-radius: 0;
 }
@@ -195,55 +261,23 @@
   display: none;
 }
 
-.top-menubar[data-v-6fecd137] .p-menubar-item-link svg {
+.top-menubar[data-v-56df69d2] .p-menubar-item-link svg {
   display: none;
 }
-[data-v-6fecd137] .p-menubar-submenu.dropdown-direction-up {
+[data-v-56df69d2] .p-menubar-submenu.dropdown-direction-up {
   top: auto;
   bottom: 100%;
   flex-direction: column-reverse;
 }
-.keybinding-tag[data-v-6fecd137] {
+.keybinding-tag[data-v-56df69d2] {
   background: var(--p-content-hover-background);
   border-color: var(--p-content-border-color);
   border-style: solid;
 }
 
-.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] {
+.comfyui-menu[data-v-929e7543] {
   width: 100vw;
+  height: var(--comfy-topbar-height);
   background: var(--comfy-menu-bg);
   color: var(--fg-color);
   box-shadow: var(--bar-shadow);
@@ -253,18 +287,17 @@
   z-index: 1000;
   order: 0;
   grid-column: 1/-1;
-  max-height: 90vh;
 }
-.comfyui-menu.dropzone[data-v-878b63b8] {
+.comfyui-menu.dropzone[data-v-929e7543] {
   background: var(--p-highlight-background);
 }
-.comfyui-menu.dropzone-active[data-v-878b63b8] {
+.comfyui-menu.dropzone-active[data-v-929e7543] {
   background: var(--p-highlight-background-focus);
 }
-[data-v-878b63b8] .p-menubar-item-label {
+[data-v-929e7543] .p-menubar-item-label {
   line-height: revert;
 }
-.comfyui-logo[data-v-878b63b8] {
+.comfyui-logo[data-v-929e7543] {
   font-size: 1.2em;
   -webkit-user-select: none;
      -moz-user-select: none;

web/assets/InstallView-CVZcZZXJ.js

@@ -0,0 +1,945 @@
+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$a, bx as CUDA_TORCH_URL, by as NIGHTLY_CPU_TORCH_URL, be as useRouter, ag as toRaw } from "./index-DqqhYDnY.js";
+import { s as script$b, a as script$c, b as script$d, c as script$e, d as script$f } from "./index-BNlqgrYT.js";
+import { P as PYTHON_MIRROR, a as PYPI_MIRROR } from "./uvMirrors-B-HKMf6X.js";
+import { _ as _sfc_main$8 } from "./BaseViewTemplate-Cz111_1A.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.$t(
+                  "install.settings.dataCollectionDialog.doNotCollect.fileSystemInformation"
+                )), 1),
+                createBaseVNode("li", null, toDisplayString(_ctx.$t(
+                  "install.settings.dataCollectionDialog.doNotCollect.customNodeConfigurations"
+                )), 1)
+              ]),
+              createBaseVNode("div", _hoisted_19, [
+                createBaseVNode("a", _hoisted_20, toDisplayString(_ctx.$t("install.settings.dataCollectionDialog.viewFullPolicy")), 1)
+              ])
+            ])
+          ]),
+          _: 1
+        }, 8, ["visible", "header"])
+      ]);
+    };
+  }
+});
+const _imports_0 = "" + new URL("images/nvidia-logo.svg", import.meta.url).href;
+const _imports_1 = "" + new URL("images/apple-mps-logo.png", import.meta.url).href;
+const _imports_2 = "" + new URL("images/manual-configuration.svg", import.meta.url).href;
+const _hoisted_1$4 = { class: "flex flex-col gap-6 w-[600px] h-[30rem] select-none" };
+const _hoisted_2$4 = { class: "grow flex flex-col gap-4 text-neutral-300" };
+const _hoisted_3$4 = { class: "text-2xl font-semibold text-neutral-100" };
+const _hoisted_4$4 = { class: "m-1 text-neutral-400" };
+const _hoisted_5$2 = {
+  key: 0,
+  class: "m-1"
+};
+const _hoisted_6$2 = {
+  key: 1,
+  class: "m-1"
+};
+const _hoisted_7$2 = {
+  key: 2,
+  class: "text-neutral-300"
+};
+const _hoisted_8$2 = { class: "m-1" };
+const _hoisted_9$2 = { key: 3 };
+const _hoisted_10$2 = { class: "m-1" };
+const _hoisted_11$2 = { class: "m-1" };
+const _hoisted_12$2 = {
+  for: "cpu-mode",
+  class: "select-none"
+};
+const _sfc_main$5 = /* @__PURE__ */ defineComponent({
+  __name: "GpuPicker",
+  props: {
+    "device": {
+      required: true
+    },
+    "deviceModifiers": {}
+  },
+  emits: ["update:device"],
+  setup(__props) {
+    const { t: t2 } = useI18n();
+    const cpuMode = computed({
+      get: /* @__PURE__ */ __name(() => selected.value === "cpu", "get"),
+      set: /* @__PURE__ */ __name((value) => {
+        selected.value = value ? "cpu" : null;
+      }, "set")
+    });
+    const selected = useModel(__props, "device");
+    const electron = electronAPI();
+    const platform = electron.getPlatform();
+    const pickGpu = /* @__PURE__ */ __name((value) => {
+      const newValue = selected.value === value ? null : value;
+      selected.value = newValue;
+    }, "pickGpu");
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", _hoisted_1$4, [
+        createBaseVNode("div", _hoisted_2$4, [
+          createBaseVNode("h2", _hoisted_3$4, toDisplayString(_ctx.$t("install.gpuSelection.selectGpu")), 1),
+          createBaseVNode("p", _hoisted_4$4, toDisplayString(_ctx.$t("install.gpuSelection.selectGpuDescription")) + ": ", 1),
+          createBaseVNode("div", {
+            class: normalizeClass(["flex gap-2 text-center transition-opacity", { selected: selected.value }])
+          }, [
+            unref(platform) !== "darwin" ? (openBlock(), createElementBlock("div", {
+              key: 0,
+              class: normalizeClass(["gpu-button", { selected: selected.value === "nvidia" }]),
+              role: "button",
+              onClick: _cache[0] || (_cache[0] = ($event) => pickGpu("nvidia"))
+            }, _cache[4] || (_cache[4] = [
+              createBaseVNode("img", {
+                class: "m-12",
+                alt: "NVIDIA logo",
+                width: "196",
+                height: "32",
+                src: _imports_0
+              }, null, -1)
+            ]), 2)) : createCommentVNode("", true),
+            unref(platform) === "darwin" ? (openBlock(), createElementBlock("div", {
+              key: 1,
+              class: normalizeClass(["gpu-button", { selected: selected.value === "mps" }]),
+              role: "button",
+              onClick: _cache[1] || (_cache[1] = ($event) => pickGpu("mps"))
+            }, _cache[5] || (_cache[5] = [
+              createBaseVNode("img", {
+                class: "rounded-lg hover-brighten",
+                alt: "Apple Metal Performance Shaders Logo",
+                width: "292",
+                ratio: "",
+                src: _imports_1
+              }, null, -1)
+            ]), 2)) : createCommentVNode("", true),
+            createBaseVNode("div", {
+              class: normalizeClass(["gpu-button", { selected: selected.value === "unsupported" }]),
+              role: "button",
+              onClick: _cache[2] || (_cache[2] = ($event) => pickGpu("unsupported"))
+            }, _cache[6] || (_cache[6] = [
+              createBaseVNode("img", {
+                class: "m-12",
+                alt: "Manual configuration",
+                width: "196",
+                src: _imports_2
+              }, null, -1)
+            ]), 2)
+          ], 2),
+          selected.value === "nvidia" ? (openBlock(), createElementBlock("p", _hoisted_5$2, [
+            createVNode(unref(script$3), {
+              icon: "pi pi-check",
+              severity: "success",
+              value: "CUDA"
+            }),
+            createTextVNode(" " + toDisplayString(_ctx.$t("install.gpuSelection.nvidiaDescription")), 1)
+          ])) : createCommentVNode("", true),
+          selected.value === "mps" ? (openBlock(), createElementBlock("p", _hoisted_6$2, [
+            createVNode(unref(script$3), {
+              icon: "pi pi-check",
+              severity: "success",
+              value: "MPS"
+            }),
+            createTextVNode(" " + toDisplayString(_ctx.$t("install.gpuSelection.mpsDescription")), 1)
+          ])) : createCommentVNode("", true),
+          selected.value === "unsupported" ? (openBlock(), createElementBlock("div", _hoisted_7$2, [
+            createBaseVNode("p", _hoisted_8$2, [
+              createVNode(unref(script$3), {
+                icon: "pi pi-exclamation-triangle",
+                severity: "warn",
+                value: unref(t2)("icon.exclamation-triangle")
+              }, null, 8, ["value"]),
+              createTextVNode(" " + toDisplayString(_ctx.$t("install.gpuSelection.customSkipsPython")), 1)
+            ]),
+            createBaseVNode("ul", null, [
+              createBaseVNode("li", null, [
+                createBaseVNode("strong", null, toDisplayString(_ctx.$t("install.gpuSelection.customComfyNeedsPython")), 1)
+              ]),
+              createBaseVNode("li", null, toDisplayString(_ctx.$t("install.gpuSelection.customManualVenv")), 1),
+              createBaseVNode("li", null, toDisplayString(_ctx.$t("install.gpuSelection.customInstallRequirements")), 1),
+              createBaseVNode("li", null, toDisplayString(_ctx.$t("install.gpuSelection.customMayNotWork")), 1)
+            ])
+          ])) : createCommentVNode("", true),
+          selected.value === "cpu" ? (openBlock(), createElementBlock("div", _hoisted_9$2, [
+            createBaseVNode("p", _hoisted_10$2, [
+              createVNode(unref(script$3), {
+                icon: "pi pi-exclamation-triangle",
+                severity: "warn",
+                value: unref(t2)("icon.exclamation-triangle")
+              }, null, 8, ["value"]),
+              createTextVNode(" " + toDisplayString(_ctx.$t("install.gpuSelection.cpuModeDescription")), 1)
+            ]),
+            createBaseVNode("p", _hoisted_11$2, toDisplayString(_ctx.$t("install.gpuSelection.cpuModeDescription2")), 1)
+          ])) : createCommentVNode("", true)
+        ]),
+        createBaseVNode("div", {
+          class: normalizeClass(["transition-opacity flex gap-3 h-0", {
+            "opacity-40": selected.value && selected.value !== "cpu"
+          }])
+        }, [
+          createVNode(unref(script), {
+            modelValue: cpuMode.value,
+            "onUpdate:modelValue": _cache[3] || (_cache[3] = ($event) => cpuMode.value = $event),
+            inputId: "cpu-mode",
+            class: "-translate-y-40"
+          }, null, 8, ["modelValue"]),
+          createBaseVNode("label", _hoisted_12$2, toDisplayString(_ctx.$t("install.gpuSelection.enableCpuMode")), 1)
+        ], 2)
+      ]);
+    };
+  }
+});
+const GpuPicker = /* @__PURE__ */ _export_sfc(_sfc_main$5, [["__scopeId", "data-v-79125ff6"]]);
+const _hoisted_1$3 = { class: "flex flex-col gap-6 w-[600px]" };
+const _hoisted_2$3 = { class: "flex flex-col gap-4" };
+const _hoisted_3$3 = { class: "text-2xl font-semibold text-neutral-100" };
+const _hoisted_4$3 = { class: "text-neutral-400 my-0" };
+const _hoisted_5$1 = { class: "flex gap-2" };
+const _hoisted_6$1 = { class: "bg-neutral-800 p-4 rounded-lg" };
+const _hoisted_7$1 = { class: "text-lg font-medium mt-0 mb-3 text-neutral-100" };
+const _hoisted_8$1 = { class: "flex flex-col gap-2" };
+const _hoisted_9$1 = { class: "flex items-center gap-2" };
+const _hoisted_10$1 = { class: "text-neutral-200" };
+const _hoisted_11$1 = { class: "pi pi-info-circle" };
+const _hoisted_12$1 = { class: "flex items-center gap-2" };
+const _hoisted_13 = { class: "text-neutral-200" };
+const _hoisted_14 = { class: "pi pi-info-circle" };
+const _sfc_main$4 = /* @__PURE__ */ defineComponent({
+  __name: "InstallLocationPicker",
+  props: {
+    "installPath": { required: true },
+    "installPathModifiers": {},
+    "pathError": { required: true },
+    "pathErrorModifiers": {}
+  },
+  emits: ["update:installPath", "update:pathError"],
+  setup(__props) {
+    const { t: t2 } = useI18n();
+    const installPath = useModel(__props, "installPath");
+    const pathError = useModel(__props, "pathError");
+    const pathExists = ref(false);
+    const appData = ref("");
+    const appPath = ref("");
+    const electron = electronAPI();
+    onMounted(async () => {
+      const paths = await electron.getSystemPaths();
+      appData.value = paths.appData;
+      appPath.value = paths.appPath;
+      installPath.value = paths.defaultInstallPath;
+      await validatePath(paths.defaultInstallPath);
+    });
+    const validatePath = /* @__PURE__ */ __name(async (path) => {
+      try {
+        pathError.value = "";
+        pathExists.value = false;
+        const validation = await electron.validateInstallPath(path);
+        if (!validation.isValid) {
+          const errors = [];
+          if (validation.cannotWrite) errors.push(t2("install.cannotWrite"));
+          if (validation.freeSpace < validation.requiredSpace) {
+            const requiredGB = validation.requiredSpace / 1024 / 1024 / 1024;
+            errors.push(`${t2("install.insufficientFreeSpace")}: ${requiredGB} GB`);
+          }
+          if (validation.parentMissing) errors.push(t2("install.parentMissing"));
+          if (validation.error)
+            errors.push(`${t2("install.unhandledError")}: ${validation.error}`);
+          pathError.value = errors.join("\n");
+        }
+        if (validation.exists) pathExists.value = true;
+      } catch (error) {
+        pathError.value = t2("install.pathValidationFailed");
+      }
+    }, "validatePath");
+    const browsePath = /* @__PURE__ */ __name(async () => {
+      try {
+        const result = await electron.showDirectoryPicker();
+        if (result) {
+          installPath.value = result;
+          await validatePath(result);
+        }
+      } catch (error) {
+        pathError.value = t2("install.failedToSelectDirectory");
+      }
+    }, "browsePath");
+    return (_ctx, _cache) => {
+      const _directive_tooltip = resolveDirective("tooltip");
+      return openBlock(), createElementBlock("div", _hoisted_1$3, [
+        createBaseVNode("div", _hoisted_2$3, [
+          createBaseVNode("h2", _hoisted_3$3, toDisplayString(_ctx.$t("install.chooseInstallationLocation")), 1),
+          createBaseVNode("p", _hoisted_4$3, toDisplayString(_ctx.$t("install.installLocationDescription")), 1),
+          createBaseVNode("div", _hoisted_5$1, [
+            createVNode(unref(script$6), { class: "flex-1" }, {
+              default: withCtx(() => [
+                createVNode(unref(script$4), {
+                  modelValue: installPath.value,
+                  "onUpdate:modelValue": [
+                    _cache[0] || (_cache[0] = ($event) => installPath.value = $event),
+                    validatePath
+                  ],
+                  class: normalizeClass(["w-full", { "p-invalid": pathError.value }])
+                }, null, 8, ["modelValue", "class"]),
+                withDirectives(createVNode(unref(script$5), { class: "pi pi-info-circle" }, null, 512), [
+                  [_directive_tooltip, _ctx.$t("install.installLocationTooltip")]
+                ])
+              ]),
+              _: 1
+            }),
+            createVNode(unref(script$7), {
+              icon: "pi pi-folder",
+              onClick: browsePath,
+              class: "w-12"
+            })
+          ]),
+          pathError.value ? (openBlock(), createBlock(unref(script$8), {
+            key: 0,
+            severity: "error",
+            class: "whitespace-pre-line"
+          }, {
+            default: withCtx(() => [
+              createTextVNode(toDisplayString(pathError.value), 1)
+            ]),
+            _: 1
+          })) : createCommentVNode("", true),
+          pathExists.value ? (openBlock(), createBlock(unref(script$8), {
+            key: 1,
+            severity: "warn"
+          }, {
+            default: withCtx(() => [
+              createTextVNode(toDisplayString(_ctx.$t("install.pathExists")), 1)
+            ]),
+            _: 1
+          })) : createCommentVNode("", true)
+        ]),
+        createBaseVNode("div", _hoisted_6$1, [
+          createBaseVNode("h3", _hoisted_7$1, toDisplayString(_ctx.$t("install.systemLocations")), 1),
+          createBaseVNode("div", _hoisted_8$1, [
+            createBaseVNode("div", _hoisted_9$1, [
+              _cache[1] || (_cache[1] = createBaseVNode("i", { class: "pi pi-folder text-neutral-400" }, null, -1)),
+              _cache[2] || (_cache[2] = createBaseVNode("span", { class: "text-neutral-400" }, "App Data:", -1)),
+              createBaseVNode("span", _hoisted_10$1, toDisplayString(appData.value), 1),
+              withDirectives(createBaseVNode("span", _hoisted_11$1, null, 512), [
+                [_directive_tooltip, _ctx.$t("install.appDataLocationTooltip")]
+              ])
+            ]),
+            createBaseVNode("div", _hoisted_12$1, [
+              _cache[3] || (_cache[3] = createBaseVNode("i", { class: "pi pi-desktop text-neutral-400" }, null, -1)),
+              _cache[4] || (_cache[4] = createBaseVNode("span", { class: "text-neutral-400" }, "App Path:", -1)),
+              createBaseVNode("span", _hoisted_13, toDisplayString(appPath.value), 1),
+              withDirectives(createBaseVNode("span", _hoisted_14, null, 512), [
+                [_directive_tooltip, _ctx.$t("install.appPathLocationTooltip")]
+              ])
+            ])
+          ])
+        ])
+      ]);
+    };
+  }
+});
+const _hoisted_1$2 = { class: "flex flex-col gap-6 w-[600px]" };
+const _hoisted_2$2 = { class: "flex flex-col gap-4" };
+const _hoisted_3$2 = { class: "text-2xl font-semibold text-neutral-100" };
+const _hoisted_4$2 = { class: "text-neutral-400 my-0" };
+const _hoisted_5 = { class: "flex gap-2" };
+const _hoisted_6 = {
+  key: 0,
+  class: "flex flex-col gap-4 bg-neutral-800 p-4 rounded-lg"
+};
+const _hoisted_7 = { class: "text-lg mt-0 font-medium text-neutral-100" };
+const _hoisted_8 = { class: "flex flex-col gap-3" };
+const _hoisted_9 = ["onClick"];
+const _hoisted_10 = ["for"];
+const _hoisted_11 = { class: "text-sm text-neutral-400 my-1" };
+const _hoisted_12 = {
+  key: 1,
+  class: "text-neutral-400 italic"
+};
+const _sfc_main$3 = /* @__PURE__ */ defineComponent({
+  __name: "MigrationPicker",
+  props: {
+    "sourcePath": { required: false },
+    "sourcePathModifiers": {},
+    "migrationItemIds": {
+      required: false
+    },
+    "migrationItemIdsModifiers": {}
+  },
+  emits: ["update:sourcePath", "update:migrationItemIds"],
+  setup(__props) {
+    const { t: t2 } = useI18n();
+    const electron = electronAPI();
+    const sourcePath = useModel(__props, "sourcePath");
+    const migrationItemIds = useModel(__props, "migrationItemIds");
+    const migrationItems = ref(
+      MigrationItems.map((item) => ({
+        ...item,
+        selected: true
+      }))
+    );
+    const pathError = ref("");
+    const isValidSource = computed(
+      () => sourcePath.value !== "" && pathError.value === ""
+    );
+    const validateSource = /* @__PURE__ */ __name(async (sourcePath2) => {
+      if (!sourcePath2) {
+        pathError.value = "";
+        return;
+      }
+      try {
+        pathError.value = "";
+        const validation = await electron.validateComfyUISource(sourcePath2);
+        if (!validation.isValid) pathError.value = validation.error;
+      } catch (error) {
+        console.error(error);
+        pathError.value = t2("install.pathValidationFailed");
+      }
+    }, "validateSource");
+    const browsePath = /* @__PURE__ */ __name(async () => {
+      try {
+        const result = await electron.showDirectoryPicker();
+        if (result) {
+          sourcePath.value = result;
+          await validateSource(result);
+        }
+      } catch (error) {
+        console.error(error);
+        pathError.value = t2("install.failedToSelectDirectory");
+      }
+    }, "browsePath");
+    watchEffect(() => {
+      migrationItemIds.value = migrationItems.value.filter((item) => item.selected).map((item) => item.id);
+    });
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", _hoisted_1$2, [
+        createBaseVNode("div", _hoisted_2$2, [
+          createBaseVNode("h2", _hoisted_3$2, toDisplayString(_ctx.$t("install.migrateFromExistingInstallation")), 1),
+          createBaseVNode("p", _hoisted_4$2, toDisplayString(_ctx.$t("install.migrationSourcePathDescription")), 1),
+          createBaseVNode("div", _hoisted_5, [
+            createVNode(unref(script$4), {
+              modelValue: sourcePath.value,
+              "onUpdate:modelValue": [
+                _cache[0] || (_cache[0] = ($event) => sourcePath.value = $event),
+                validateSource
+              ],
+              placeholder: "Select existing ComfyUI installation (optional)",
+              class: normalizeClass(["flex-1", { "p-invalid": pathError.value }])
+            }, null, 8, ["modelValue", "class"]),
+            createVNode(unref(script$7), {
+              icon: "pi pi-folder",
+              onClick: browsePath,
+              class: "w-12"
+            })
+          ]),
+          pathError.value ? (openBlock(), createBlock(unref(script$8), {
+            key: 0,
+            severity: "error"
+          }, {
+            default: withCtx(() => [
+              createTextVNode(toDisplayString(pathError.value), 1)
+            ]),
+            _: 1
+          })) : createCommentVNode("", true)
+        ]),
+        isValidSource.value ? (openBlock(), createElementBlock("div", _hoisted_6, [
+          createBaseVNode("h3", _hoisted_7, toDisplayString(_ctx.$t("install.selectItemsToMigrate")), 1),
+          createBaseVNode("div", _hoisted_8, [
+            (openBlock(true), createElementBlock(Fragment, null, renderList(migrationItems.value, (item) => {
+              return openBlock(), createElementBlock("div", {
+                key: item.id,
+                class: "flex items-center gap-3 p-2 hover:bg-neutral-700 rounded",
+                onClick: /* @__PURE__ */ __name(($event) => item.selected = !item.selected, "onClick")
+              }, [
+                createVNode(unref(script$9), {
+                  modelValue: item.selected,
+                  "onUpdate:modelValue": /* @__PURE__ */ __name(($event) => item.selected = $event, "onUpdate:modelValue"),
+                  inputId: item.id,
+                  binary: true,
+                  onClick: _cache[1] || (_cache[1] = withModifiers(() => {
+                  }, ["stop"]))
+                }, null, 8, ["modelValue", "onUpdate:modelValue", "inputId"]),
+                createBaseVNode("div", null, [
+                  createBaseVNode("label", {
+                    for: item.id,
+                    class: "text-neutral-200 font-medium"
+                  }, toDisplayString(item.label), 9, _hoisted_10),
+                  createBaseVNode("p", _hoisted_11, toDisplayString(item.description), 1)
+                ])
+              ], 8, _hoisted_9);
+            }), 128))
+          ])
+        ])) : (openBlock(), createElementBlock("div", _hoisted_12, toDisplayString(_ctx.$t("install.migrationOptional")), 1))
+      ]);
+    };
+  }
+});
+const _hoisted_1$1 = { class: "flex flex-col items-center gap-4" };
+const _hoisted_2$1 = { class: "w-full" };
+const _hoisted_3$1 = { class: "text-lg font-medium text-neutral-100" };
+const _hoisted_4$1 = { class: "text-sm text-neutral-400 mt-1" };
+const _sfc_main$2 = /* @__PURE__ */ defineComponent({
+  __name: "MirrorItem",
+  props: /* @__PURE__ */ mergeModels({
+    item: {}
+  }, {
+    "modelValue": { required: true },
+    "modelModifiers": {}
+  }),
+  emits: /* @__PURE__ */ mergeModels(["state-change"], ["update:modelValue"]),
+  setup(__props, { emit: __emit }) {
+    const emit = __emit;
+    const modelValue = useModel(__props, "modelValue");
+    const validationState = ref(ValidationState.IDLE);
+    const normalizedSettingId = computed(() => {
+      return normalizeI18nKey(__props.item.settingId);
+    });
+    onMounted(() => {
+      modelValue.value = __props.item.mirror;
+    });
+    watch(validationState, (newState) => {
+      emit("state-change", newState);
+      if (newState === ValidationState.INVALID && modelValue.value === __props.item.mirror) {
+        modelValue.value = __props.item.fallbackMirror;
+      }
+    });
+    return (_ctx, _cache) => {
+      const _component_UrlInput = _sfc_main$7;
+      return openBlock(), createElementBlock("div", _hoisted_1$1, [
+        createBaseVNode("div", _hoisted_2$1, [
+          createBaseVNode("h3", _hoisted_3$1, toDisplayString(_ctx.$t(`settings.${normalizedSettingId.value}.name`)), 1),
+          createBaseVNode("p", _hoisted_4$1, toDisplayString(_ctx.$t(`settings.${normalizedSettingId.value}.tooltip`)), 1)
+        ]),
+        createVNode(_component_UrlInput, {
+          modelValue: modelValue.value,
+          "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => modelValue.value = $event),
+          "validate-url-fn": /* @__PURE__ */ __name((mirror) => unref(checkMirrorReachable)(mirror + (_ctx.item.validationPathSuffix ?? "")), "validate-url-fn"),
+          onStateChange: _cache[1] || (_cache[1] = ($event) => validationState.value = $event)
+        }, null, 8, ["modelValue", "validate-url-fn"])
+      ]);
+    };
+  }
+});
+const _sfc_main$1 = /* @__PURE__ */ defineComponent({
+  __name: "MirrorsConfiguration",
+  props: /* @__PURE__ */ mergeModels({
+    device: {}
+  }, {
+    "pythonMirror": { required: true },
+    "pythonMirrorModifiers": {},
+    "pypiMirror": { required: true },
+    "pypiMirrorModifiers": {},
+    "torchMirror": { required: true },
+    "torchMirrorModifiers": {}
+  }),
+  emits: ["update:pythonMirror", "update:pypiMirror", "update:torchMirror"],
+  setup(__props) {
+    const showMirrorInputs = ref(false);
+    const pythonMirror = useModel(__props, "pythonMirror");
+    const pypiMirror = useModel(__props, "pypiMirror");
+    const torchMirror = useModel(__props, "torchMirror");
+    const getTorchMirrorItem = /* @__PURE__ */ __name((device) => {
+      const settingId = "Comfy-Desktop.UV.TorchInstallMirror";
+      switch (device) {
+        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-CVZcZZXJ.js.map

web/assets/InstallView-DbJ2cGfL.css

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

web/assets/KeybindingPanel-CeHhC2F4.js

@@ -1,10 +1,9 @@
 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, 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";
+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-DqqhYDnY.js";
+import { g as script$1, h as script$3 } from "./index-BapOFhAR.js";
+import { u as useKeybindingService } from "./keybindingService-DEgCutrm.js";
+import "./index-DXE47DZl.js";
 const _hoisted_1$1 = {
   key: 0,
   class: "px-2"
@@ -37,7 +36,6 @@ 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 };
@@ -248,7 +246,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
                   severity: "error"
                 }, {
                   default: withCtx(() => [
-                    createTextVNode(" Keybinding already exists on "),
+                    _cache[3] || (_cache[3] = createTextVNode(" Keybinding already exists on ")),
                     createVNode(unref(script), {
                       severity: "secondary",
                       value: existingKeybindingOnCombo.value.commandId
@@ -281,4 +279,4 @@ const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "d
 export {
   KeybindingPanel as default
 };
-//# sourceMappingURL=KeybindingPanel-Dc3C4lG1.js.map
+//# sourceMappingURL=KeybindingPanel-CeHhC2F4.js.map

web/assets/MaintenanceView-Bj5_Vr6o.css

@@ -0,0 +1,87 @@
+
+.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-CsirlNfV.css

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

web/assets/ManualConfigurationView-Cz0_f_T-.js

@@ -1,9 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-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$1 } from "./BaseViewTemplate-BNGF4K22.js";
-import "./index-5HFeZax4.js";
+import { d as defineComponent, K as useI18n, U as ref, p as onMounted, o as openBlock, y as createBlock, z as withCtx, m as createBaseVNode, E as toDisplayString, k as createVNode, j as unref, a4 as script, a$ as script$1, l as script$2, b5 as electronAPI, _ as _export_sfc } from "./index-DqqhYDnY.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-Cz111_1A.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" };
@@ -69,7 +67,8 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     };
   }
 });
+const ManualConfigurationView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-dc169863"]]);
 export {
-  _sfc_main as default
+  ManualConfigurationView as default
 };
-//# sourceMappingURL=ManualConfigurationView-Bi_qHE-n.js.map
+//# sourceMappingURL=ManualConfigurationView-Cz0_f_T-.js.map

web/assets/MetricsConsentView-B5NlgqrS.js

@@ -0,0 +1,86 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-Cz111_1A.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-DqqhYDnY.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-B5NlgqrS.js.map

web/assets/NotSupportedView-BUpntA4x.js

@@ -1,21 +1,16 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-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";
+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-DqqhYDnY.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-Cz111_1A.js";
 const _imports_0 = "" + new URL("images/sad_girl.png", import.meta.url).href;
 const _hoisted_1 = { class: "sad-container" };
-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 _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 _sfc_main = /* @__PURE__ */ defineComponent({
   __name: "NotSupportedView",
   setup(__props) {
@@ -37,18 +32,22 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
       return openBlock(), createBlock(_sfc_main$1, null, {
         default: withCtx(() => [
           createBaseVNode("div", _hoisted_1, [
-            _hoisted_2,
-            createBaseVNode("div", _hoisted_3, [
-              createBaseVNode("div", _hoisted_4, [
-                createBaseVNode("h1", _hoisted_5, toDisplayString(_ctx.$t("notSupported.title")), 1),
-                createBaseVNode("div", _hoisted_6, [
-                  createBaseVNode("p", _hoisted_7, toDisplayString(_ctx.$t("notSupported.message")), 1),
-                  createBaseVNode("ul", _hoisted_8, [
+            _cache[0] || (_cache[0] = createBaseVNode("img", {
+              class: "sad-girl",
+              src: _imports_0,
+              alt: "Sad girl illustration"
+            }, null, -1)),
+            createBaseVNode("div", _hoisted_2, [
+              createBaseVNode("div", _hoisted_3, [
+                createBaseVNode("h1", _hoisted_4, toDisplayString(_ctx.$t("notSupported.title")), 1),
+                createBaseVNode("div", _hoisted_5, [
+                  createBaseVNode("p", _hoisted_6, toDisplayString(_ctx.$t("notSupported.message")), 1),
+                  createBaseVNode("ul", _hoisted_7, [
                     createBaseVNode("li", null, toDisplayString(_ctx.$t("notSupported.supportedDevices.macos")), 1),
                     createBaseVNode("li", null, toDisplayString(_ctx.$t("notSupported.supportedDevices.windows")), 1)
                   ])
                 ]),
-                createBaseVNode("div", _hoisted_9, [
+                createBaseVNode("div", _hoisted_8, [
                   createVNode(unref(script), {
                     label: _ctx.$t("notSupported.learnMore"),
                     icon: "pi pi-github",
@@ -80,7 +79,8 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     };
   }
 });
+const NotSupportedView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-ebb20958"]]);
 export {
-  _sfc_main as default
+  NotSupportedView as default
 };
-//# sourceMappingURL=NotSupportedView-Drz3x2d-.js.map
+//# sourceMappingURL=NotSupportedView-BUpntA4x.js.map

web/assets/NotSupportedView-RFx6eCkN.css

@@ -1,17 +1,19 @@
 
 .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 {
+.sad-text[data-v-ebb20958] {
   grid-column: 1/3;
 }
-.sad-girl {
+.sad-girl[data-v-ebb20958] {
   grid-column: 2/3;
   width: min(75vw, 100vh);
 }

web/assets/ServerConfigPanel-B1lI5M9c.js

@@ -1,25 +1,23 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-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";
+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-DqqhYDnY.js";
+import { u as useServerConfigStore } from "./serverConfigStore-Kb5DJVFt.js";
 const _hoisted_1$1 = {
   viewBox: "0 0 24 24",
   width: "1.2em",
   height: "1.2em"
 };
-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);
-const _hoisted_3$1 = [
-  _hoisted_2$1
-];
 function render(_ctx, _cache) {
-  return openBlock(), createElementBlock("svg", _hoisted_1$1, [..._hoisted_3$1]);
+  return openBlock(), createElementBlock("svg", _hoisted_1$1, _cache[0] || (_cache[0] = [
+    createBaseVNode("path", {
+      fill: "none",
+      stroke: "currentColor",
+      "stroke-linecap": "round",
+      "stroke-linejoin": "round",
+      "stroke-width": "2",
+      d: "m4 17l6-6l-6-6m8 14h8"
+    }, null, -1)
+  ]));
 }
 __name(render, "render");
 const __unplugin_components_0 = markRaw({ name: "lucide-terminal", render });
@@ -155,4 +153,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=ServerConfigPanel-Be4StJmv.js.map
+//# sourceMappingURL=ServerConfigPanel-B1lI5M9c.js.map

web/assets/ServerStartView-BpH4TXPO.js

@@ -0,0 +1,100 @@
+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-DqqhYDnY.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-Cz111_1A.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-BpH4TXPO.js.map

web/assets/ServerStartView-CIDTUh4x.js

@@ -1,98 +0,0 @@
-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-CJiwVDQY.css

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

web/assets/UserSelectView-wxa07xPk.js

@@ -1,18 +1,17 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-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";
+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-DqqhYDnY.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-Cz111_1A.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 = /* @__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 _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 _sfc_main = /* @__PURE__ */ defineComponent({
   __name: "UserSelectView",
   setup(__props) {
@@ -47,10 +46,10 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
       return openBlock(), createBlock(_sfc_main$1, { dark: "" }, {
         default: withCtx(() => [
           createBaseVNode("main", _hoisted_1, [
-            _hoisted_2,
-            createBaseVNode("div", _hoisted_3, [
-              createBaseVNode("div", _hoisted_4, [
-                createBaseVNode("label", _hoisted_5, toDisplayString(_ctx.$t("userSelect.newUser")) + ":", 1),
+            _cache[2] || (_cache[2] = createBaseVNode("h1", { class: "my-2.5 mb-7 font-normal" }, "ComfyUI", -1)),
+            createBaseVNode("div", _hoisted_2, [
+              createBaseVNode("div", _hoisted_3, [
+                createBaseVNode("label", _hoisted_4, toDisplayString(_ctx.$t("userSelect.newUser")) + ":", 1),
                 createVNode(unref(script), {
                   id: "new-user-input",
                   modelValue: newUsername.value,
@@ -60,8 +59,8 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
                 }, null, 8, ["modelValue", "placeholder"])
               ]),
               createVNode(unref(script$1)),
-              createBaseVNode("div", _hoisted_6, [
-                createBaseVNode("label", _hoisted_7, toDisplayString(_ctx.$t("userSelect.existingUser")) + ":", 1),
+              createBaseVNode("div", _hoisted_5, [
+                createBaseVNode("label", _hoisted_6, toDisplayString(_ctx.$t("userSelect.existingUser")) + ":", 1),
                 createVNode(unref(script$2), {
                   modelValue: selectedUser.value,
                   "onUpdate:modelValue": _cache[1] || (_cache[1] = ($event) => selectedUser.value = $event),
@@ -82,7 +81,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
                   _: 1
                 })) : createCommentVNode("", true)
               ]),
-              createBaseVNode("footer", _hoisted_8, [
+              createBaseVNode("footer", _hoisted_7, [
                 createVNode(unref(script$4), {
                   label: _ctx.$t("userSelect.next"),
                   onClick: login
@@ -99,4 +98,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=UserSelectView-B3jYchWu.js.map
+//# sourceMappingURL=UserSelectView-wxa07xPk.js.map

web/assets/WelcomeView-BrXELNIm.js

@@ -1,8 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-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");
+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-DqqhYDnY.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-Cz111_1A.js";
 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({
@@ -37,4 +36,4 @@ const WelcomeView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-
 export {
   WelcomeView as default
 };
-//# sourceMappingURL=WelcomeView-N0ZXLjdi.js.map
+//# sourceMappingURL=WelcomeView-BrXELNIm.js.map