Upload folder using huggingface_hub

.gitignore

@@ -1,26 +0,0 @@
-__pycache__/
-*.py[cod]
-/output/
-/input/
-!/input/example.png
-/models/
-/temp/
-/custom_nodes/
-!custom_nodes/example_node.py.example
-extra_model_paths.yaml
-/.vs
-.vscode/
-.idea/
-venv/
-.venv/
-/web/extensions/*
-!/web/extensions/logging.js.example
-!/web/extensions/core/
-/tests-ui/data/object_info.json
-/user/
-*.log
-web_custom_versions/
-.DS_Store
-openapi.yaml
-filtered-openapi.yaml
-uv.lock

CODEOWNERS

@@ -1,24 +0,0 @@
-# Admins
-* @comfyanonymous
-
-# Note: Github teams syntax cannot be used here as the repo is not owned by Comfy-Org.
-# Inlined the team members for now.
-
-# Maintainers
-*.md @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/tests/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/tests-unit/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/notebooks/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/script_examples/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/.github/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/requirements.txt @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-/pyproject.toml @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne
-
-# Python web server
-/api_server/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
-/app/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
-/utils/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne
-
-# Node developers
-/comfy_extras/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne
-/comfy/comfy_types/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne

CONTRIBUTING.md

@@ -1,41 +0,0 @@
-# Contributing to ComfyUI
-
-Welcome, and thank you for your interest in contributing to ComfyUI!
-
-There are several ways in which you can contribute, beyond writing code. The goal of this document is to provide a high-level overview of how you can get involved.
-
-## Asking Questions
-
-Have a question? Instead of opening an issue, please ask on [Discord](https://comfy.org/discord) or [Matrix](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) channels. Our team and the community will help you.
-
-## Providing Feedback
-
-Your comments and feedback are welcome, and the development team is available via a handful of different channels.
-
-See the `#bug-report`, `#feature-request` and `#feedback` channels on Discord.
-
-## Reporting Issues
-
-Have you identified a reproducible problem in ComfyUI? Do you have a feature request? We want to hear about it! Here's how you can report your issue as effectively as possible.
-
-
-### Look For an Existing Issue
-
-Before you create a new issue, please do a search in [open issues](https://github.com/comfyanonymous/ComfyUI/issues) to see if the issue or feature request has already been filed.
-
-If you find your issue already exists, make relevant comments and add your [reaction](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments). Use a reaction in place of a "+1" comment:
-
-* 👍 - upvote
-* 👎 - downvote
-
-If you cannot find an existing issue that describes your bug or feature, create a new issue. We have an issue template in place to organize new issues.
-
-
-### Creating Pull Requests
-
-* Please refer to the article on [creating pull requests](https://github.com/comfyanonymous/ComfyUI/wiki/How-to-Contribute-Code) and contributing to this project.
-
-
-## Thank You
-
-Your contributions to open source, large or small, make great projects like this possible. Thank you for taking the time to contribute.

LICENSE

@@ -1,674 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
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-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<https://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,5 +1,5 @@
 ---
-title: Animated T2I with LoRAs
+title: ImageGen - NoobAI
 emoji: 🖼
 colorFrom: purple
 colorTo: red

alembic.ini

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

alembic_db/README.md

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

alembic_db/env.py

@@ -1,64 +0,0 @@
-from sqlalchemy import engine_from_config
-from sqlalchemy import pool
-
-from alembic import context
-
-# this is the Alembic Config object, which provides
-# access to the values within the .ini file in use.
-config = context.config
-
-
-from app.database.models import Base
-target_metadata = Base.metadata
-
-# other values from the config, defined by the needs of env.py,
-# can be acquired:
-# my_important_option = config.get_main_option("my_important_option")
-# ... etc.
-
-
-def run_migrations_offline() -> None:
-    """Run migrations in 'offline' mode.
-    This configures the context with just a URL
-    and not an Engine, though an Engine is acceptable
-    here as well.  By skipping the Engine creation
-    we don't even need a DBAPI to be available.
-    Calls to context.execute() here emit the given string to the
-    script output.
-    """
-    url = config.get_main_option("sqlalchemy.url")
-    context.configure(
-        url=url,
-        target_metadata=target_metadata,
-        literal_binds=True,
-        dialect_opts={"paramstyle": "named"},
-    )
-
-    with context.begin_transaction():
-        context.run_migrations()
-
-
-def run_migrations_online() -> None:
-    """Run migrations in 'online' mode.
-    In this scenario we need to create an Engine
-    and associate a connection with the context.
-    """
-    connectable = engine_from_config(
-        config.get_section(config.config_ini_section, {}),
-        prefix="sqlalchemy.",
-        poolclass=pool.NullPool,
-    )
-
-    with connectable.connect() as connection:
-        context.configure(
-            connection=connection, target_metadata=target_metadata
-        )
-
-        with context.begin_transaction():
-            context.run_migrations()
-
-
-if context.is_offline_mode():
-    run_migrations_offline()
-else:
-    run_migrations_online()

alembic_db/script.py.mako

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

api_server/routes/internal/README.md

@@ -1,3 +0,0 @@
-# ComfyUI Internal Routes
-
-All routes under the `/internal` path are designated for **internal use by ComfyUI only**. These routes are not intended for use by external applications may change at any time without notice.

api_server/routes/internal/internal_routes.py

@@ -1,73 +0,0 @@
-from aiohttp import web
-from typing import Optional
-from folder_paths import folder_names_and_paths, get_directory_by_type
-from api_server.services.terminal_service import TerminalService
-import app.logger
-import os
-
-class InternalRoutes:
-    '''
-    The top level web router for internal routes: /internal/*
-    The endpoints here should NOT be depended upon. It is for ComfyUI frontend use only.
-    Check README.md for more information.
-    '''
-
-    def __init__(self, prompt_server):
-        self.routes: web.RouteTableDef = web.RouteTableDef()
-        self._app: Optional[web.Application] = None
-        self.prompt_server = prompt_server
-        self.terminal_service = TerminalService(prompt_server)
-
-    def setup_routes(self):
-        @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()]))
-
-        @self.routes.get('/logs/raw')
-        async def get_raw_logs(request):
-            self.terminal_service.update_size()
-            return web.json_response({
-                "entries": list(app.logger.get_logs()),
-                "size": {"cols": self.terminal_service.cols, "rows": self.terminal_service.rows}
-            })
-
-        @self.routes.patch('/logs/subscribe')
-        async def subscribe_logs(request):
-            json_data = await request.json()
-            client_id = json_data["clientId"]
-            enabled = json_data["enabled"]
-            if enabled:
-                self.terminal_service.subscribe(client_id)
-            else:
-                self.terminal_service.unsubscribe(client_id)
-
-            return web.Response(status=200)
-
-
-        @self.routes.get('/folder_paths')
-        async def get_folder_paths(request):
-            response = {}
-            for key in folder_names_and_paths:
-                response[key] = folder_names_and_paths[key][0]
-            return web.json_response(response)
-
-        @self.routes.get('/files/{directory_type}')
-        async def get_files(request: web.Request) -> web.Response:
-            directory_type = request.match_info['directory_type']
-            if directory_type not in ("output", "input", "temp"):
-                return web.json_response({"error": "Invalid directory type"}, status=400)
-
-            directory = get_directory_by_type(directory_type)
-            sorted_files = sorted(
-                (entry for entry in os.scandir(directory) if entry.is_file()),
-                key=lambda entry: -entry.stat().st_mtime
-            )
-            return web.json_response([entry.name for entry in sorted_files], status=200)
-
-
-    def get_app(self):
-        if self._app is None:
-            self._app = web.Application()
-            self.setup_routes()
-            self._app.add_routes(self.routes)
-        return self._app

api_server/services/terminal_service.py

@@ -1,60 +0,0 @@
-from app.logger import on_flush
-import os
-import shutil
-
-
-class TerminalService:
-    def __init__(self, server):
-        self.server = server
-        self.cols = None
-        self.rows = None
-        self.subscriptions = set()
-        on_flush(self.send_messages)
-
-    def get_terminal_size(self):
-        try:
-            size = os.get_terminal_size()
-            return (size.columns, size.lines)
-        except OSError:
-            try:
-                size = shutil.get_terminal_size()
-                return (size.columns, size.lines)
-            except OSError:
-                return (80, 24)  # fallback to 80x24
-
-    def update_size(self):
-        columns, lines = self.get_terminal_size()
-        changed = False
-
-        if columns != self.cols:
-            self.cols = columns
-            changed = True
-
-        if lines != self.rows:
-            self.rows = lines
-            changed = True
-
-        if changed:
-            return {"cols": self.cols, "rows": self.rows}
-
-        return None
-
-    def subscribe(self, client_id):
-        self.subscriptions.add(client_id)
-
-    def unsubscribe(self, client_id):
-        self.subscriptions.discard(client_id)
-
-    def send_messages(self, entries):
-        if not len(entries) or not len(self.subscriptions):
-            return
-
-        new_size = self.update_size()
-
-        for client_id in self.subscriptions.copy(): # prevent: Set changed size during iteration
-            if client_id not in self.server.sockets:
-                # Automatically unsub if the socket has disconnected
-                self.unsubscribe(client_id)
-                continue
-
-            self.server.send_sync("logs", {"entries": entries, "size": new_size}, client_id)

api_server/utils/file_operations.py

@@ -1,42 +0,0 @@
-import os
-from typing import List, Union, TypedDict, Literal
-from typing_extensions import TypeGuard
-class FileInfo(TypedDict):
-    name: str
-    path: str
-    type: Literal["file"]
-    size: int
-
-class DirectoryInfo(TypedDict):
-    name: str
-    path: str
-    type: Literal["directory"]
-
-FileSystemItem = Union[FileInfo, DirectoryInfo]
-
-def is_file_info(item: FileSystemItem) -> TypeGuard[FileInfo]:
-    return item["type"] == "file"
-
-class FileSystemOperations:
-    @staticmethod
-    def walk_directory(directory: str) -> List[FileSystemItem]:
-        file_list: List[FileSystemItem] = []
-        for root, dirs, files in os.walk(directory):
-            for name in files:
-                file_path = os.path.join(root, name)
-                relative_path = os.path.relpath(file_path, directory)
-                file_list.append({
-                    "name": name,
-                    "path": relative_path,
-                    "type": "file",
-                    "size": os.path.getsize(file_path)
-                })
-            for name in dirs:
-                dir_path = os.path.join(root, name)
-                relative_path = os.path.relpath(dir_path, directory)
-                file_list.append({
-                    "name": name,
-                    "path": relative_path,
-                    "type": "directory"
-                })
-        return file_list

app.py

@@ -1,553 +1,127 @@
+import spaces
 import os
-import random
 import sys
-from typing import Sequence, Mapping, Any, Union
-import torch
-import gradio as gr
-from PIL import Image
-from huggingface_hub import hf_hub_download
-import spaces
-from comfy import model_management # We need to import this early
-import gc
 import requests
-import re
-import hashlib
-import shutil
-
-# --- Startup Dummy Function ---
-@spaces.GPU(duration=60)
-def dummy_gpu_for_startup():
-    print("Dummy function for startup check executed. This is normal.")
-    return "Startup check passed."
-
-# --- ComfyUI Backend Setup ---
-def find_path(name: str, path: str = None) -> str:
-    if path is None: path = os.getcwd()
-    if name in os.listdir(path): return os.path.join(path, name)
-    parent_directory = os.path.dirname(path)
-    if parent_directory == path: return None
-    return find_path(name, parent_directory)
-
-def add_comfyui_directory_to_sys_path() -> None:
-    comfyui_path = find_path("ComfyUI")
-    if comfyui_path and os.path.isdir(comfyui_path):
-        sys.path.append(comfyui_path)
-        print(f"'{comfyui_path}' added to sys.path")
-
-def add_extra_model_paths() -> None:
-    try: from main import load_extra_path_config
-    except ImportError: from utils.extra_config import load_extra_path_config
-    extra_model_paths = find_path("extra_model_paths.yaml")
-    if extra_model_paths: load_extra_path_config(extra_model_paths)
-    else: print("Could not find extra_model_paths.yaml")
-
-add_comfyui_directory_to_sys_path()
-add_extra_model_paths()
-
-# Monkey-patch for Sage Attention
-print("Attempting to monkey-patch ComfyUI for Sage Attention...")
-try:
-    model_management.sage_attention_enabled = lambda: True
-    model_management.pytorch_attention_enabled = lambda: False
-    print("Successfully monkey-patched model_management for Sage Attention.")
-except Exception as e:
-    print(f"An error occurred during monkey-patching: {e}")
-
-# --- Constants & Configuration ---
-CHECKPOINT_DIR = "models/checkpoints"
-LORA_DIR = "models/loras"
-os.makedirs(CHECKPOINT_DIR, exist_ok=True)
-os.makedirs(LORA_DIR, exist_ok=True)
-
-# --- Model Definitions with Hashes ---
-# Format: {Display Name: (Repo ID, Filename, Type, Hash)}
-MODEL_MAP_ILLUSTRIOUS = {
-    "Laxhar/noobai-XL-Vpred-1.0": ("Laxhar/noobai-XL-Vpred-1.0", "NoobAI-XL-Vpred-v1.0.safetensors", "SDXL", "ea349eeae8"),
-    "Laxhar/noobai-XL-1.1": ("Laxhar/noobai-XL-1.1", "NoobAI-XL-v1.1.safetensors", "SDXL", "6681e8e4b1"),
-    "WAI0731/wai-nsfw-illustrious-sdxl-v140": ("Ine007/waiNSFWIllustrious_v140", "waiNSFWIllustrious_v140.safetensors", "SDXL", "bdb59bac77"),
-    "Ikena/hassaku-xl-illustrious-v30": ("misri/hassakuXLIllustrious_v30", "hassakuXLIllustrious_v30.safetensors", "SDXL", "b4fb5f829a"),
-    "bluepen5805/noob_v_pencil-XL": ("bluepen5805/noob_v_pencil-XL", "noob_v_pencil-XL-v3.0.0.safetensors", "SDXL", "90b7911a78"),
-    "RedRayz/hikari_noob_v-pred_1.2.2": ("RedRayz/hikari_noob_v-pred_1.2.2", "Hikari_Noob_v-pred_1.2.2.safetensors", "SDXL", "874170688a"),
-}
-MODEL_MAP_ANIMAGINE = {
-    "cagliostrolab/animagine-xl-4.0": ("cagliostrolab/animagine-xl-4.0", "animagine-xl-4.0.safetensors", "SDXL", "6327eca98b"),
-    "cagliostrolab/animagine-xl-3.1": ("cagliostrolab/animagine-xl-3.1", "animagine-xl-3.1.safetensors", "SDXL", "e3c47aedb0"),
-}
-MODEL_MAP_PONY = {
-    "PurpleSmartAI/Pony_Diffusion_V6_XL": ("LyliaEngine/Pony_Diffusion_V6_XL", "ponyDiffusionV6XL_v6StartWithThisOne.safetensors", "SDXL", "67ab2fd8ec"),
-}
-MODEL_MAP_SD15 = {
-    "Yuno779/anything-v3": ("ckpt/anything-v3.0", "Anything-V3.0-pruned.safetensors", "SD1.5", "ddd565f806"),
-}
-
-# --- Combined Maps for Global Lookup ---
-ALL_MODEL_MAP = {**MODEL_MAP_ILLUSTRIOUS, **MODEL_MAP_ANIMAGINE, **MODEL_MAP_PONY, **MODEL_MAP_SD15}
-MODEL_TYPE_MAP = {k: v[2] for k, v in ALL_MODEL_MAP.items()}
-DISPLAY_NAME_TO_HASH_MAP = {k: v[3] for k, v in ALL_MODEL_MAP.items()}
-HASH_TO_DISPLAY_NAME_MAP = {v[3]: k for k, v in ALL_MODEL_MAP.items()}
-
-# --- UI Defaults ---
-DEFAULT_NEGATIVE_PROMPT = "monochrome, (low quality, worst quality:1.2), 3d, watermark, signature, ugly, poorly drawn,"
-MAX_LORAS = 5
-LORA_SOURCE_CHOICES = ["Civitai", "TensorArt", "Custom URL", "File"]
+import site
 
-def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
-    try: return obj[index]
-    except (KeyError, IndexError):
-        try: return obj["result"][index]
-        except (KeyError, IndexError): return None
+APP_DIR = os.path.dirname(os.path.abspath(__file__))
+if APP_DIR not in sys.path:
+    sys.path.insert(0, APP_DIR)
+    print(f"✅ Added project root '{APP_DIR}' to sys.path.")
 
-def import_custom_nodes() -> None:
-    import asyncio, execution, server
-    from nodes import init_extra_nodes
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-    server_instance = server.PromptServer(loop)
-    execution.PromptQueue(server_instance)
-    loop.run_until_complete(init_extra_nodes())
+SAGE_PATCH_APPLIED = False
 
-# --- Import ComfyUI Nodes & Get Choices ---
-from nodes import CheckpointLoaderSimple, EmptyLatentImage, KSampler, VAEDecode, SaveImage, NODE_CLASS_MAPPINGS
-import_custom_nodes()
-CLIPTextEncodeSDXL = NODE_CLASS_MAPPINGS['CLIPTextEncodeSDXL']
-CLIPTextEncode = NODE_CLASS_MAPPINGS['CLIPTextEncode']
-LoraLoader = NODE_CLASS_MAPPINGS['LoraLoader']
-CLIPSetLastLayer = NODE_CLASS_MAPPINGS['CLIPSetLastLayer']
-try:
-    SAMPLER_CHOICES = KSampler.INPUT_TYPES()["required"]["sampler_name"][0]
-    SCHEDULER_CHOICES = KSampler.INPUT_TYPES()["required"]["scheduler"][0]
-except Exception:
-    SAMPLER_CHOICES = ['euler', 'dpmpp_2m_sde_gpu']
-    SCHEDULER_CHOICES = ['normal', 'karras']
+def apply_sage_attention_patch():
+    global SAGE_PATCH_APPLIED
+    if SAGE_PATCH_APPLIED:
+        return "SageAttention patch already applied."
 
-# --- Instantiate Node Objects ---
-checkpointloadersimple = CheckpointLoaderSimple(); cliptextencodesdxl = CLIPTextEncodeSDXL()
-cliptextencode_sd15 = CLIPTextEncode(); emptylatentimage = EmptyLatentImage()
-ksampler = KSampler(); vaedecode = VAEDecode(); saveimage = SaveImage(); loraloader = LoraLoader()
-clipsetlastlayer = CLIPSetLastLayer()
-
-# --- LoRA & File Utils ---
-def get_civitai_file_info(version_id):
-    api_url = f"https://civitai.com/api/v1/model-versions/{version_id}"
-    try:
-        response = requests.get(api_url, timeout=10); response.raise_for_status(); data = response.json()
-        for file_data in data.get('files', []):
-            if file_data.get('type') == 'Model' and file_data['name'].endswith('.safetensors'): return file_data
-        if data.get('files'): return data['files'][0]
-    except Exception: return None
-
-def get_tensorart_file_info(model_id):
-    api_url = f"https://tensor.art/api/v1/models/{model_id}"
     try:
-        response = requests.get(api_url, timeout=10); response.raise_for_status(); data = response.json()
-        model_versions = data.get('modelVersions', [])
-        if not model_versions: return None
-        for file_data in model_versions[0].get('files', []):
-            if file_data['name'].endswith('.safetensors'): return file_data
-        return model_versions[0]['files'][0] if model_versions[0].get('files') else None
-    except Exception: return None
-
-def download_file(url, save_path, api_key=None, progress=None, desc=""):
-    if os.path.exists(save_path): return f"File already exists: {os.path.basename(save_path)}"
-    headers = {'Authorization': f'Bearer {api_key}'} if api_key and api_key.strip() else {}
-    try:
-        if progress: progress(0, desc=desc)
-        response = requests.get(url, stream=True, headers=headers, timeout=15); response.raise_for_status()
-        total_size = int(response.headers.get('content-length', 0))
-        with open(save_path, "wb") as f:
-            downloaded = 0
-            for chunk in response.iter_content(chunk_size=8192):
-                f.write(chunk)
-                if progress and total_size > 0: downloaded += len(chunk); progress(downloaded / total_size, desc=desc)
-        return f"Successfully downloaded: {os.path.basename(save_path)}"
+        from comfy import model_management
+        import sageattention
+        
+        print("--- [Runtime Patch] sageattention package found. Applying patch... ---")
+        model_management.sage_attention_enabled = lambda: True
+        model_management.pytorch_attention_enabled = lambda: False
+        
+        SAGE_PATCH_APPLIED = True
+        return "✅ Successfully enabled SageAttention."
+    except ImportError:
+        SAGE_PATCH_APPLIED = False
+        msg = "--- [Runtime Patch] ⚠️ sageattention package not found. Continuing with default attention. ---"
+        print(msg)
+        return msg
     except Exception as e:
-        if os.path.exists(save_path): os.remove(save_path)
-        return f"Download failed for {os.path.basename(save_path)}: {e}"
+        SAGE_PATCH_APPLIED = False
+        msg = f"--- [Runtime Patch] ❌ An error occurred while applying SageAttention patch: {e} ---"
+        print(msg)
+        return msg
 
-def get_lora_path(source, id_or_url, civitai_key, tensorart_key, progress):
-    if not id_or_url or not id_or_url.strip(): return None, "No ID/URL provided."
-    if source == "Civitai":
-        version_id = id_or_url.strip(); local_path = os.path.join(LORA_DIR, f"civitai_{version_id}.safetensors"); file_info, api_key_to_use = get_civitai_file_info(version_id), civitai_key; source_name = f"Civitai ID {version_id}"
-    elif source == "TensorArt":
-        model_id = id_or_url.strip(); local_path = os.path.join(LORA_DIR, f"tensorart_{model_id}.safetensors"); file_info, api_key_to_use = get_tensorart_file_info(model_id), tensorart_key; source_name = f"TensorArt ID {model_id}"
-    elif source == "Custom URL":
-        url = id_or_url.strip(); url_hash = hashlib.md5(url.encode()).hexdigest(); local_path = os.path.join(LORA_DIR, f"custom_{url_hash}.safetensors"); file_info, api_key_to_use = {'downloadUrl': url}, None; source_name = f"URL {url[:30]}..."
-    else: return None, "Invalid source."
-    if os.path.exists(local_path): return local_path, "File already exists."
-    if not file_info or not file_info.get('downloadUrl'): return None, f"Could not get download link for {source_name}."
-    status = download_file(file_info['downloadUrl'], local_path, api_key_to_use, progress=progress, desc=f"Downloading {source_name}")
-    return (local_path, status) if "Successfully" in status else (None, status)
+@spaces.GPU
+def dummy_gpu_for_startup():
+    print("--- [GPU Startup] Dummy function for startup check initiated. ---")
+    # SageAttention patch temporarily disabled for faster iteration.
+    # patch_result = apply_sage_attention_patch()
+    # print(f"--- [GPU Startup] {patch_result} ---")
+    print("--- [GPU Startup] Startup check passed. ---")
+    return "Startup check passed."
 
-def pre_download_loras(civitai_api_key, tensorart_api_key, *lora_data, progress=gr.Progress(track_tqdm=True)):
-    sources, ids, _, files = lora_data[0::4], lora_data[1::4], lora_data[2::4], lora_data[3::4]
-    active_loras = [(s, i) for s, i, f in zip(sources, ids, files) if s in ["Civitai", "TensorArt", "Custom URL"] and i and i.strip() and f is None]
-    if not active_loras: return "No remote LoRAs specified for pre-downloading."
-    log = [f"* {s} ID {i}: {get_lora_path(s, i, civitai_api_key, tensorart_api_key, progress)[1]}" for s, i in active_loras]
-    return "\n".join(log)
+def main():
+    from utils.app_utils import print_welcome_message
+    # from scripts import build_sage_attention # SageAttention disabled
 
-# --- Model Management & Core Logic ---
-current_loaded_model_name = None; loaded_checkpoint_tuple = None
-def load_model(model_display_name: str, progress=gr.Progress()):
-    global current_loaded_model_name, loaded_checkpoint_tuple
-    if model_display_name == current_loaded_model_name and loaded_checkpoint_tuple: return loaded_checkpoint_tuple
-    if loaded_checkpoint_tuple: model_management.unload_all_models(); loaded_checkpoint_tuple = None; gc.collect(); torch.cuda.empty_cache()
-    
-    repo_id, filename, _, _ = ALL_MODEL_MAP[model_display_name]
-    local_file_path = os.path.join(CHECKPOINT_DIR, filename)
+    print_welcome_message()
     
-    if not os.path.exists(local_file_path):
-        progress(0, desc=f"Downloading model: {model_display_name}")
-        hf_hub_download(repo_id=repo_id, filename=filename, local_dir=CHECKPOINT_DIR, local_dir_use_symlinks=False)
-    
-    progress(0.5, desc=f"Loading '{filename}'")
-    MODEL_TUPLE = checkpointloadersimple.load_checkpoint(ckpt_name=filename)
-    model_management.load_models_gpu([get_value_at_index(MODEL_TUPLE, 0)])
-    current_loaded_model_name = model_display_name; loaded_checkpoint_tuple = MODEL_TUPLE
-    progress(1.0, desc="Model loaded"); return loaded_checkpoint_tuple
-
-def _generate_image_logic(model_display_name: str, positive_prompt: str, negative_prompt: str,
-                   seed: int, batch_size: int, width: int, height: int, guidance_scale: float, num_inference_steps: int,
-                   sampler_name: str, scheduler: str, civitai_api_key: str, tensorart_api_key: str, *lora_data,
-                   progress=gr.Progress(track_tqdm=True)):
-    output_images = []
-    is_sd15 = MODEL_TYPE_MAP.get(model_display_name) == "SD1.5"
-    clip_skip = 1
-    if is_sd15 and len(lora_data) > MAX_LORAS * 4:
-        clip_skip = int(lora_data[-1])
-        lora_data = lora_data[:-1]
-
-    with torch.inference_mode():
-        model_tuple = load_model(model_display_name, progress)
-        model, clip, vae = (get_value_at_index(model_tuple, i) for i in range(3))
-        
-        if is_sd15:
-            clip = get_value_at_index(clipsetlastlayer.set_last_layer(clip=clip, stop_at_clip_layer=-clip_skip), 0)
-        
-        active_loras_for_meta = []
-        sources, ids, scales, files = lora_data[0::4], lora_data[1::4], lora_data[2::4], lora_data[3::4]
-        for i, (source, lora_id, scale, custom_file) in enumerate(zip(sources, ids, scales, files)):
-            if scale > 0:
-                lora_filename = None
-                if custom_file:
-                    lora_filename = os.path.basename(custom_file.name)
-                    shutil.copy(custom_file.name, LORA_DIR)
-                elif lora_id and lora_id.strip():
-                    local_path, _ = get_lora_path(source, lora_id, civitai_api_key, tensorart_api_key, progress)
-                    if local_path: lora_filename = os.path.basename(local_path)
-                
-                if lora_filename:
-                    lora_tuple = loraloader.load_lora(model=model, clip=clip, lora_name=lora_filename, strength_model=scale, strength_clip=scale)
-                    model, clip = get_value_at_index(lora_tuple, 0), get_value_at_index(lora_tuple, 1)
-                    active_loras_for_meta.append(f"{source} {lora_id}:{scale}")
-        
-        loras_string = f"LoRAs: [{', '.join(active_loras_for_meta)}]" if active_loras_for_meta else ""
-
-        if is_sd15:
-            pos_cond = cliptextencode_sd15.encode(text=positive_prompt, clip=clip)
-            neg_cond = cliptextencode_sd15.encode(text=negative_prompt, clip=clip)
-        else:
-            pos_cond = cliptextencodesdxl.encode(width=width, height=height, text_g=positive_prompt, text_l=positive_prompt, clip=clip, target_width=width, target_height=height, crop_w=0, crop_h=0)
-            neg_cond = cliptextencodesdxl.encode(width=width, height=height, text_g=negative_prompt, text_l=negative_prompt, clip=clip, target_width=width, target_height=height, crop_w=0, crop_h=0)
-
-        start_seed = seed if seed != -1 else random.randint(0, 2**64 - 1)
-        
-        latent = emptylatentimage.generate(width=width, height=height, batch_size=batch_size)
-        
-        sampled = ksampler.sample(
-            seed=start_seed, 
-            steps=num_inference_steps, 
-            cfg=guidance_scale, 
-            sampler_name=sampler_name, 
-            scheduler=scheduler, 
-            denoise=1.0, 
-            model=model, 
-            positive=get_value_at_index(pos_cond, 0), 
-            negative=get_value_at_index(neg_cond, 0), 
-            latent_image=get_value_at_index(latent, 0)
-        )
-        
-        decoded_images_tensor = get_value_at_index(vaedecode.decode(samples=get_value_at_index(sampled, 0), vae=vae), 0)
+    # SageAttention build is temporarily disabled for faster iteration.
+    # print("--- [Setup] Attempting to build and install SageAttention... ---")
+    # try:
+    #     build_sage_attention.install_sage_attention()
+    #     print("--- [Setup] ✅ SageAttention installation process finished. ---")
+    # except Exception as e:
+    #     print(f"--- [Setup] ❌ SageAttention installation failed: {e}. Continuing with default attention. ---")
 
-        for i in range(decoded_images_tensor.shape[0]):
-            img_tensor = decoded_images_tensor[i]
-            pil_image = Image.fromarray((img_tensor.cpu().numpy() * 255.0).astype("uint8"))
-            
-            current_seed = start_seed + i
-            
-            model_hash = DISPLAY_NAME_TO_HASH_MAP.get(model_display_name, "N/A")
-            params_string = f"{positive_prompt}\nNegative prompt: {negative_prompt}\n"
-            params_string += f"Steps: {num_inference_steps}, Sampler: {sampler_name}, Scheduler: {scheduler}, CFG scale: {guidance_scale}, Seed: {current_seed}, Size: {width}x{height}, Base Model: {model_display_name}, Model hash: {model_hash}"
-            if is_sd15: params_string += f", Clip skip: {clip_skip}"
-            params_string += f", {loras_string}"
-            pil_image.info = {'parameters': params_string.strip()}
-            
-            output_images.append(pil_image)
-            
-    return output_images
 
-def generate_image_wrapper(*args, **kwargs):
-    logic_args_list = list(args[:11])
-    zero_gpu_duration = args[11]
-    logic_args_list.extend(args[12:])
-    duration = 60
+    print("--- [Setup] Reloading site-packages to detect newly installed packages... ---")
     try:
-        if zero_gpu_duration and int(zero_gpu_duration) > 0:
-            duration = int(zero_gpu_duration)
-    except (ValueError, TypeError):
-        pass
-    return spaces.GPU(duration=duration)(_generate_image_logic)(*logic_args_list, **kwargs)
-
-
-# --- PNG Info & UI Logic ---
-def _parse_parameters(params_text):
-    data = {}; lines = params_text.strip().split('\n'); data['prompt'] = lines[0]
-    data['negative_prompt'] = lines[1].replace("Negative prompt:", "").strip() if len(lines) > 1 and lines[1].startswith("Negative prompt:") else ""
-    params_line = '\n'.join(lines[2:])
-    def find_param(key, default, cast_type=str):
-        match = re.search(fr"\b{key}: ([^,]+?)(,|$|\n)", params_line)
-        return cast_type(match.group(1).strip()) if match else default
-    data['steps'] = find_param("Steps", 28, int); data['sampler'] = find_param("Sampler", SAMPLER_CHOICES[0], str)
-    data['scheduler'] = find_param("Scheduler", SCHEDULER_CHOICES[0], str); data['cfg_scale'] = find_param("CFG scale", 7.5, float)
-    data['seed'] = find_param("Seed", -1, int); data['clip_skip'] = find_param("Clip skip", 1, int)
-    data['base_model'] = find_param("Base Model", list(ALL_MODEL_MAP.keys())[0], str); data['model_hash'] = find_param("Model hash", None, str)
-    size_match = re.search(r"Size: (\d+)x(\d+)", params_line)
-    data['width'], data['height'] = (int(size_match.group(1)), int(size_match.group(2))) if size_match else (1024, 1024)
-    return data
-
-def get_png_info(image):
-    if not image or not (params := image.info.get('parameters')): return "", "", "No metadata found in the image."
-    parsed_data = _parse_parameters(params)
-    other_params_text = "\n".join([p.strip() for p in '\n'.join(params.strip().split('\n')[2:]).split(',')])
-    return parsed_data.get('prompt', ''), parsed_data.get('negative_prompt', ''), other_params_text
-
-def apply_data_to_ui(data, target_tab):
-    final_sampler = data.get('sampler') if data.get('sampler') in SAMPLER_CHOICES else SAMPLER_CHOICES[0]
-    default_scheduler = 'normal' if 'normal' in SCHEDULER_CHOICES else SCHEDULER_CHOICES[0]
-    final_scheduler = data.get('scheduler') if data.get('scheduler') in SCHEDULER_CHOICES else default_scheduler
-
-    updates = {}
-    base_model_name = data.get('base_model')
-
-    if target_tab == "Illustrious":
-        if base_model_name in MODEL_MAP_ILLUSTRIOUS:
-            updates.update({base_model_name_input_illustrious: base_model_name})
-        updates.update({prompt_illustrious: data['prompt'], negative_prompt_illustrious: data['negative_prompt'], seed_illustrious: data['seed'], width_illustrious: data['width'], height_illustrious: data['height'], guidance_scale_illustrious: data['cfg_scale'], num_inference_steps_illustrious: data['steps'], sampler_illustrious: final_sampler, schedule_type_illustrious: final_scheduler, model_tabs: gr.Tabs(selected=0)})
-    elif target_tab == "Animagine":
-        if base_model_name in MODEL_MAP_ANIMAGINE:
-            updates.update({base_model_name_input_animagine: base_model_name})
-        updates.update({prompt_animagine: data['prompt'], negative_prompt_animagine: data['negative_prompt'], seed_animagine: data['seed'], width_animagine: data['width'], height_animagine: data['height'], guidance_scale_animagine: data['cfg_scale'], num_inference_steps_animagine: data['steps'], sampler_animagine: final_sampler, schedule_type_animagine: final_scheduler, model_tabs: gr.Tabs(selected=1)})
-    elif target_tab == "Pony":
-        if base_model_name in MODEL_MAP_PONY:
-            updates.update({base_model_name_input_pony: base_model_name})
-        updates.update({prompt_pony: data['prompt'], negative_prompt_pony: data['negative_prompt'], seed_pony: data['seed'], width_pony: data['width'], height_pony: data['height'], guidance_scale_pony: data['cfg_scale'], num_inference_steps_pony: data['steps'], sampler_pony: final_sampler, schedule_type_pony: final_scheduler, model_tabs: gr.Tabs(selected=2)})
-    elif target_tab == "SD1.5":
-        if base_model_name in MODEL_MAP_SD15:
-            updates.update({base_model_name_input_sd15: base_model_name})
-        updates.update({prompt_sd15: data['prompt'], negative_prompt_sd15: data['negative_prompt'], seed_sd15: data['seed'], width_sd15: data['width'], height_sd15: data['height'], guidance_scale_sd15: data['cfg_scale'], num_inference_steps_sd15: data['steps'], sampler_sd15: final_sampler, schedule_type_sd15: final_scheduler, clip_skip_sd15: data.get('clip_skip', 1), model_tabs: gr.Tabs(selected=3)})
+        site.main()
+        print("--- [Setup] ✅ Site-packages reloaded. ---")
+    except Exception as e:
+        print(f"--- [Setup] ⚠️  Warning: Could not fully reload site-packages: {e} ---")
+
+    from comfy_integration import setup as setup_comfyui
+    from utils.app_utils import (
+        build_preprocessor_model_map,
+        build_preprocessor_parameter_map,
+        load_ipadapter_presets
+    )
+    from core import shared_state
+    from core.settings import ALL_MODEL_MAP
+
+    def check_all_model_urls_on_startup():
+        print("--- [Setup] Checking all model URL validity (one-time check) ---")
+        for display_name, model_info in ALL_MODEL_MAP.items():
+            repo_id, filename, _, _ = model_info
+            if not repo_id: continue
+            url = f"https://huggingface.co/{repo_id}/resolve/main/{filename}"
+            try:
+                response = requests.head(url, timeout=5, allow_redirects=True)
+                if response.status_code >= 400:
+                    print(f"❌ Invalid URL for '{display_name}': {url} (Status: {response.status_code})")
+                    shared_state.INVALID_MODEL_URLS[display_name] = True
+            except requests.RequestException as e:
+                print(f"❌ URL check failed for '{display_name}': {e}")
+                shared_state.INVALID_MODEL_URLS[display_name] = True
+        print("--- [Setup] ✅ Finished checking model URLs. ---")
+
+    print("--- Starting Application Setup ---")
     
-    updates[tabs] = gr.Tabs(selected=0)
-    return updates
-
-def send_info_to_tab(image, target_tab):
-    if not image or not image.info.get('parameters', ''): return {comp: gr.update() for comp in all_ui_components}
-    data = _parse_parameters(image.info['parameters'])
-    return apply_data_to_ui(data, target_tab)
-
-def send_info_by_hash(image):
-    if not image or not image.info.get('parameters', ''): return {comp: gr.update() for comp in all_ui_components}
-    data = _parse_parameters(image.info['parameters'])
-    model_hash = data.get('model_hash')
-    display_name = HASH_TO_DISPLAY_NAME_MAP.get(model_hash)
-
-    if not display_name:
-        raise gr.Error("Model hash not found in this app's model list. The original model name from the PNG will be used if it exists in the target tab.")
-
-    if display_name in MODEL_MAP_ILLUSTRIOUS: target_tab = "Illustrious"
-    elif display_name in MODEL_MAP_ANIMAGINE: target_tab = "Animagine"
-    elif display_name in MODEL_MAP_PONY: target_tab = "Pony"
-    elif display_name in MODEL_MAP_SD15: target_tab = "SD1.5"
-    else:
-        raise gr.Error("Cannot determine the correct tab for this model.")
-
-    data['base_model'] = display_name
-    return apply_data_to_ui(data, target_tab)
-
-# --- UI Generation Functions ---
-def create_lora_settings_ui():
-    with gr.Accordion("LoRA Settings", open=False):
-        gr.Markdown("⚠️ **Responsible Use Notice:** Please avoid excessive, rapid, or automated (scripted) use of the pre-download LoRA feature. Overt misuse may lead to service disruption. Thank you for your cooperation.")
-        gr.Markdown("For LoRAs that require login to download, you may need to enter the corresponding API Key.")
-        with gr.Row():
-            civitai_api_key = gr.Textbox(label="Civitai API Key", placeholder="Enter your Civitai API Key", type="password", scale=1)
-            tensorart_api_key = gr.Textbox(label="TensorArt API Key", placeholder="Enter your TensorArt API Key", type="password", scale=1)
-        gr.Markdown("---")
-        gr.Markdown("For each LoRA, choose a source, provide an ID/URL, or upload a file.")
-        gr.Markdown("""
-        <div style='background-color: #282828; color: #a0aec0; padding: 10px; border-radius: 5px; margin-top: 10px; margin-bottom: 15px;'>
-            <b>Input Examples:</b>
-            <ul>
-                <li><b>Civitai:</b> Enter the <b>Model Version ID</b>, not the Model ID. Example: <code>133755</code> (Found in the URL, e.g., <code>civitai.com/models/122136?modelVersionId=<b>133755</b></code>)</li>
-                <li><b>TensorArt:</b> Enter the <b>Model ID</b>. Example: <code>706684852832599558</code> (Found in the URL, e.g., <code>tensor.art/models/<b>706684852832599558</b></code>)</li>
-                <li><b>Custom URL:</b> Provide a direct download link to a <code>.safetensors</code> file. Example: <code>https://huggingface.co/path/to/your/lora.safetensors</code></li>
-                <li><b>File:</b> Use the "Upload" button. The source will be set automatically.</li>
-            </ul>
-        </div>
-        """)
-        gr.Markdown("""
-        <div style='background-color: #282828; color: #a0aec0; padding: 10px; border-radius: 5px; margin-bottom: 15px;'>
-            <b>Notice:</b>
-            <ul style='margin-bottom: 0;'>
-                <li>With Gradio, the page may become unresponsive until a file is fully uploaded. Please be patient and wait for the process to complete.</li>
-            </ul>
-        </div>
-        """)
-        lora_rows, sources, ids, scales, uploads = [], [], [], [], []
-        for i in range(MAX_LORAS):
-            with gr.Row(visible=(i == 0)) as row:
-                source = gr.Dropdown(label=f"LoRA {i+1} Source", choices=LORA_SOURCE_CHOICES, value="Civitai", scale=1)
-                lora_id = gr.Textbox(label="ID / URL / File", placeholder="e.g.: 133755", scale=2)
-                scale = gr.Slider(label="Weight", minimum=0.0, maximum=2.0, step=0.05, value=0.0, scale=2)
-                upload = gr.UploadButton("Upload", file_types=[".safetensors"], scale=1)
-                lora_rows.append(row); sources.append(source); ids.append(lora_id); scales.append(scale); uploads.append(upload)
-                upload.upload(fn=lambda f: (os.path.basename(f.name), "File") if f else (gr.update(), gr.update()), inputs=[upload], outputs=[lora_id, source])
-        with gr.Row(): add_button = gr.Button("✚ Add LoRA"); delete_button = gr.Button("➖ Delete LoRA", visible=False)
-        count_state = gr.State(value=1)
-        all_components = [item for sublist in zip(sources, ids, scales, uploads) for item in sublist]
-    return (civitai_api_key, tensorart_api_key, lora_rows, sources, ids, scales, uploads, add_button, delete_button, count_state, all_components)
+    setup_comfyui.initialize_comfyui()
 
-def download_all_models_on_startup():
-    """Downloads all base models listed in ALL_MODEL_MAP when the app starts."""
-    print("--- Starting pre-download of all base models ---")
-    for model_display_name, model_info in ALL_MODEL_MAP.items():
-        repo_id, filename, _, _ = model_info
-        local_file_path = os.path.join(CHECKPOINT_DIR, filename)
+    check_all_model_urls_on_startup()
+    
+    print("--- Building ControlNet preprocessor maps ---")
+    from core.generation_logic import build_reverse_map
+    build_reverse_map()
+    build_preprocessor_model_map()
+    build_preprocessor_parameter_map()
+    print("--- ✅ ControlNet preprocessor setup complete. ---")
 
-        if os.path.exists(local_file_path):
-            print(f"✅ Model '{filename}' already exists. Skipping download.")
-            continue
+    print("--- Loading IPAdapter presets ---")
+    load_ipadapter_presets()
+    print("--- ✅ IPAdapter setup complete. ---")
 
-        try:
-            print(f"Downloading: {model_display_name} ({filename})...")
-            hf_hub_download(
-                repo_id=repo_id,
-                filename=filename,
-                local_dir=CHECKPOINT_DIR,
-                local_dir_use_symlinks=False
-            )
-            print(f"✅ Successfully downloaded {filename}.")
-        except Exception as e:
-            print(f"❌ Failed to download {filename} from {repo_id}: {e}")
-    print("--- Finished pre-downloading all base models ---")
 
-# --- Execute model download on startup ---
-download_all_models_on_startup()
+    print("--- Environment configured. Proceeding with module imports. ---")
+    from ui.layout import build_ui
+    from ui.events import attach_event_handlers
 
-# --- Gradio UI ---
-with gr.Blocks(css="#col-container {margin: 0 auto; max-width: 1024px;}") as demo:
-    gr.Markdown("# Animated T2I with LoRAs")
-    with gr.Tabs(elem_id="tabs_container") as tabs:
-        with gr.TabItem("txt2img", id=0):
-            with gr.Tabs() as model_tabs:
-                for tab_name, model_map, defaults in [
-                    ("Illustrious", MODEL_MAP_ILLUSTRIOUS, {'w': 1024, 'h': 1024, 'cs_vis': False, 'cs_val': 1}),
-                    ("Animagine", MODEL_MAP_ANIMAGINE, {'w': 1024, 'h': 1024, 'cs_vis': False, 'cs_val': 1}),
-                    ("Pony", MODEL_MAP_PONY, {'w': 1024, 'h': 1024, 'cs_vis': False, 'cs_val': 1}),
-                    ("SD1.5", MODEL_MAP_SD15, {'w': 512, 'h': 768, 'cs_vis': True, 'cs_val': 1})
-                ]:
-                    with gr.TabItem(tab_name):
-                        gr.Markdown("💡 **Tip:** Pre-downloading LoRAs before 'Run' can maximize ZeroGPU time.")
-                        with gr.Column():
-                            with gr.Row():
-                                base_model = gr.Dropdown(label="Base Model", choices=list(model_map.keys()), value=list(model_map.keys())[0], scale=3)
-                                with gr.Column(scale=1): predownload_lora = gr.Button("Pre-download LoRAs"); run = gr.Button("Run", variant="primary")
-                            predownload_status = gr.Markdown("")
-                            prompt = gr.Text(label="Prompt", lines=3, placeholder="Enter your prompt")
-                            neg_prompt = gr.Text(label="Negative prompt", lines=3, value=DEFAULT_NEGATIVE_PROMPT)
-                            with gr.Row():
-                                with gr.Column(scale=2):
-                                    with gr.Row(): width = gr.Slider(label="Width", minimum=256, maximum=2048, step=64, value=defaults['w']); height = gr.Slider(label="Height", minimum=256, maximum=2048, step=64, value=defaults['h'])
-                                    with gr.Row(): 
-                                        sampler = gr.Dropdown(label="Sampling method", choices=SAMPLER_CHOICES, value=SAMPLER_CHOICES[0])
-                                        default_scheduler = 'normal' if 'normal' in SCHEDULER_CHOICES else SCHEDULER_CHOICES[0]
-                                        scheduler = gr.Dropdown(label="Scheduler", choices=SCHEDULER_CHOICES, value=default_scheduler)
-                                    with gr.Row(): cfg = gr.Slider(label="CFG Scale", minimum=0.0, maximum=20.0, step=0.1, value=7.5); steps = gr.Slider(label="Sampling steps", minimum=1, maximum=50, step=1, value=28)
-                                with gr.Column(scale=1): result = gr.Gallery(label="Result", show_label=False, columns=2, object_fit="contain", height="auto")
-                            with gr.Row():
-                                seed = gr.Number(label="Seed (-1 for random)", value=-1, precision=0)
-                                batch_size = gr.Slider(label="Batch size", minimum=1, maximum=8, step=1, value=1)
-                                clip_skip = gr.Slider(label="Clip Skip", minimum=1, maximum=2, step=1, value=defaults['cs_val'], visible=defaults['cs_vis'])
-                                zero_gpu = gr.Number(label="ZeroGPU Duration (s)", value=None, placeholder="Default: 60s", info="Optional: Leave empty for default (60s), max to 120")
-                            lora_settings = create_lora_settings_ui()
-                            
-                            # Assign specific variables for event handlers
-                            if tab_name == "Illustrious":
-                                base_model_name_input_illustrious, prompt_illustrious, negative_prompt_illustrious, seed_illustrious, batch_size_illustrious, width_illustrious, height_illustrious, guidance_scale_illustrious, num_inference_steps_illustrious, sampler_illustrious, schedule_type_illustrious, zero_gpu_duration_illustrious, result_illustrious = base_model, prompt, neg_prompt, seed, batch_size, width, height, cfg, steps, sampler, scheduler, zero_gpu, result
-                                civitai_api_key_illustrious, tensorart_api_key_illustrious, lora_rows_illustrious, _, lora_id_inputs_illustrious, lora_scale_inputs_illustrious, _, add_lora_button_illustrious, delete_lora_button_illustrious, lora_count_state_illustrious, all_lora_components_flat_illustrious = lora_settings
-                                predownload_lora_button_illustrious, run_button_illustrious, predownload_status_illustrious = predownload_lora, run, predownload_status
-                            elif tab_name == "Animagine":
-                                base_model_name_input_animagine, prompt_animagine, negative_prompt_animagine, seed_animagine, batch_size_animagine, width_animagine, height_animagine, guidance_scale_animagine, num_inference_steps_animagine, sampler_animagine, schedule_type_animagine, zero_gpu_duration_animagine, result_animagine = base_model, prompt, neg_prompt, seed, batch_size, width, height, cfg, steps, sampler, scheduler, zero_gpu, result
-                                civitai_api_key_animagine, tensorart_api_key_animagine, lora_rows_animagine, _, lora_id_inputs_animagine, lora_scale_inputs_animagine, _, add_lora_button_animagine, delete_lora_button_animagine, lora_count_state_animagine, all_lora_components_flat_animagine = lora_settings
-                                predownload_lora_button_animagine, run_button_animagine, predownload_status_animagine = predownload_lora, run, predownload_status
-                            elif tab_name == "Pony":
-                                base_model_name_input_pony, prompt_pony, negative_prompt_pony, seed_pony, batch_size_pony, width_pony, height_pony, guidance_scale_pony, num_inference_steps_pony, sampler_pony, schedule_type_pony, zero_gpu_duration_pony, result_pony = base_model, prompt, neg_prompt, seed, batch_size, width, height, cfg, steps, sampler, scheduler, zero_gpu, result
-                                civitai_api_key_pony, tensorart_api_key_pony, lora_rows_pony, _, lora_id_inputs_pony, lora_scale_inputs_pony, _, add_lora_button_pony, delete_lora_button_pony, lora_count_state_pony, all_lora_components_flat_pony = lora_settings
-                                predownload_lora_button_pony, run_button_pony, predownload_status_pony = predownload_lora, run, predownload_status
-                            elif tab_name == "SD1.5":
-                                base_model_name_input_sd15, prompt_sd15, negative_prompt_sd15, seed_sd15, batch_size_sd15, width_sd15, height_sd15, guidance_scale_sd15, num_inference_steps_sd15, sampler_sd15, schedule_type_sd15, clip_skip_sd15, zero_gpu_duration_sd15, result_sd15 = base_model, prompt, neg_prompt, seed, batch_size, width, height, cfg, steps, sampler, scheduler, clip_skip, zero_gpu, result
-                                civitai_api_key_sd15, tensorart_api_key_sd15, lora_rows_sd15, _, lora_id_inputs_sd15, lora_scale_inputs_sd15, _, add_lora_button_sd15, delete_lora_button_sd15, lora_count_state_sd15, all_lora_components_flat_sd15 = lora_settings
-                                predownload_lora_button_sd15, run_button_sd15, predownload_status_sd15 = predownload_lora, run, predownload_status
-        with gr.TabItem("PNG Info", id=1):
-            with gr.Column():
-                info_image_input = gr.Image(type="pil", label="Upload Image", height=512)
-                with gr.Row():
-                    info_get_button = gr.Button("Get Info")
-                    send_by_hash_button = gr.Button("Send to txt2img by Model Hash", variant="primary")
-                with gr.Row():
-                    send_to_illustrious_button = gr.Button("Send to Illustrious")
-                    send_to_animagine_button = gr.Button("Send to Animagine")
-                    send_to_pony_button = gr.Button("Send to Pony")
-                    send_to_sd15_button = gr.Button("Send to SD1.5")
-                gr.Markdown("### Positive Prompt"); info_prompt_output = gr.Textbox(lines=3, interactive=False, show_label=False)
-                gr.Markdown("### Negative Prompt"); info_neg_prompt_output = gr.Textbox(lines=3, interactive=False, show_label=False)
-                gr.Markdown("### Other Parameters"); info_params_output = gr.Textbox(lines=5, interactive=False, show_label=False)
-    gr.Markdown("<div style='text-align: center; margin-top: 20px;'>Made by <a href='https://civitai.com/user/RioShiina'>RioShiina</a> with ❤️</div>")
-    
-    # --- Event Handlers ---
-    def create_lora_event_handlers(lora_rows, count_state, add_button, del_button, lora_ids, lora_scales):
-        def add_lora_row(c): return {count_state: c+1, lora_rows[c]: gr.update(visible=True), del_button: gr.update(visible=True), add_button: gr.update(visible=c+1 < MAX_LORAS)}
-        def del_lora_row(c): c-=1; return {count_state: c, lora_rows[c]: gr.update(visible=False), lora_ids[c]: "", lora_scales[c]: 0.0, add_button: gr.update(visible=True), del_button: gr.update(visible=c > 1)}
-        add_button.click(add_lora_row, [count_state], [count_state, add_button, del_button, *lora_rows])
-        del_button.click(del_lora_row, [count_state], [count_state, add_button, del_button, *lora_rows, *lora_ids, *lora_scales])
+    print(f"✅ Working directory is stable: {os.getcwd()}")
 
-    create_lora_event_handlers(lora_rows_illustrious, lora_count_state_illustrious, add_lora_button_illustrious, delete_lora_button_illustrious, lora_id_inputs_illustrious, lora_scale_inputs_illustrious)
-    predownload_lora_button_illustrious.click(lambda: "⏳ Downloading...", None, [predownload_status_illustrious]).then(pre_download_loras, [civitai_api_key_illustrious, tensorart_api_key_illustrious, *all_lora_components_flat_illustrious], [predownload_status_illustrious])
-    run_button_illustrious.click(generate_image_wrapper, [base_model_name_input_illustrious, prompt_illustrious, negative_prompt_illustrious, seed_illustrious, batch_size_illustrious, width_illustrious, height_illustrious, guidance_scale_illustrious, num_inference_steps_illustrious, sampler_illustrious, schedule_type_illustrious, zero_gpu_duration_illustrious, civitai_api_key_illustrious, tensorart_api_key_illustrious, *all_lora_components_flat_illustrious], [result_illustrious])
-    
-    create_lora_event_handlers(lora_rows_animagine, lora_count_state_animagine, add_lora_button_animagine, delete_lora_button_animagine, lora_id_inputs_animagine, lora_scale_inputs_animagine)
-    predownload_lora_button_animagine.click(lambda: "⏳ Downloading...", None, [predownload_status_animagine]).then(pre_download_loras, [civitai_api_key_animagine, tensorart_api_key_animagine, *all_lora_components_flat_animagine], [predownload_status_animagine])
-    run_button_animagine.click(generate_image_wrapper, [base_model_name_input_animagine, prompt_animagine, negative_prompt_animagine, seed_animagine, batch_size_animagine, width_animagine, height_animagine, guidance_scale_animagine, num_inference_steps_animagine, sampler_animagine, schedule_type_animagine, zero_gpu_duration_animagine, civitai_api_key_animagine, tensorart_api_key_animagine, *all_lora_components_flat_animagine], [result_animagine])
+    demo = build_ui(attach_event_handlers)
+        
+    print("--- Launching Gradio Interface ---")
+    demo.queue().launch(server_name="0.0.0.0", server_port=7860)
 
-    create_lora_event_handlers(lora_rows_pony, lora_count_state_pony, add_lora_button_pony, delete_lora_button_pony, lora_id_inputs_pony, lora_scale_inputs_pony)
-    predownload_lora_button_pony.click(lambda: "⏳ Downloading...", None, [predownload_status_pony]).then(pre_download_loras, [civitai_api_key_pony, tensorart_api_key_pony, *all_lora_components_flat_pony], [predownload_status_pony])
-    run_button_pony.click(generate_image_wrapper, [base_model_name_input_pony, prompt_pony, negative_prompt_pony, seed_pony, batch_size_pony, width_pony, height_pony, guidance_scale_pony, num_inference_steps_pony, sampler_pony, schedule_type_pony, zero_gpu_duration_pony, civitai_api_key_pony, tensorart_api_key_pony, *all_lora_components_flat_pony], [result_pony])
-    
-    create_lora_event_handlers(lora_rows_sd15, lora_count_state_sd15, add_lora_button_sd15, delete_lora_button_sd15, lora_id_inputs_sd15, lora_scale_inputs_sd15)
-    predownload_lora_button_sd15.click(lambda: "⏳ Downloading...", None, [predownload_status_sd15]).then(pre_download_loras, [civitai_api_key_sd15, tensorart_api_key_sd15, *all_lora_components_flat_sd15], [predownload_status_sd15])
-    run_button_sd15.click(generate_image_wrapper, [base_model_name_input_sd15, prompt_sd15, negative_prompt_sd15, seed_sd15, batch_size_sd15, width_sd15, height_sd15, guidance_scale_sd15, num_inference_steps_sd15, sampler_sd15, schedule_type_sd15, zero_gpu_duration_sd15, civitai_api_key_sd15, tensorart_api_key_sd15, *all_lora_components_flat_sd15, clip_skip_sd15], [result_sd15])
-    
-    info_get_button.click(get_png_info, [info_image_input], [info_prompt_output, info_neg_prompt_output, info_params_output])
-    all_ui_components = [
-        base_model_name_input_illustrious, prompt_illustrious, negative_prompt_illustrious, seed_illustrious, width_illustrious, height_illustrious, guidance_scale_illustrious, num_inference_steps_illustrious, sampler_illustrious, schedule_type_illustrious,
-        base_model_name_input_animagine, prompt_animagine, negative_prompt_animagine, seed_animagine, width_animagine, height_animagine, guidance_scale_animagine, num_inference_steps_animagine, sampler_animagine, schedule_type_animagine,
-        base_model_name_input_pony, prompt_pony, negative_prompt_pony, seed_pony, width_pony, height_pony, guidance_scale_pony, num_inference_steps_pony, sampler_pony, schedule_type_pony,
-        base_model_name_input_sd15, prompt_sd15, negative_prompt_sd15, seed_sd15, width_sd15, height_sd15, guidance_scale_sd15, num_inference_steps_sd15, sampler_sd15, schedule_type_sd15, clip_skip_sd15,
-        tabs, model_tabs
-    ]
-    send_to_illustrious_button.click(lambda img: send_info_to_tab(img, "Illustrious"), [info_image_input], all_ui_components)
-    send_to_animagine_button.click(lambda img: send_info_to_tab(img, "Animagine"), [info_image_input], all_ui_components)
-    send_to_pony_button.click(lambda img: send_info_to_tab(img, "Pony"), [info_image_input], all_ui_components)
-    send_to_sd15_button.click(lambda img: send_info_to_tab(img, "SD1.5"), [info_image_input], all_ui_components)
-    send_by_hash_button.click(send_info_by_hash, [info_image_input], all_ui_components)
 
 if __name__ == "__main__":
-    demo.queue().launch()
+    main()

app/app_settings.py

@@ -1,65 +0,0 @@
-import os
-import json
-from aiohttp import web
-import logging
-
-
-class AppSettings():
-    def __init__(self, user_manager):
-        self.user_manager = user_manager
-
-    def get_settings(self, request):
-        try:
-            file = self.user_manager.get_request_user_filepath(
-                request,
-                "comfy.settings.json"
-            )
-        except KeyError as e:
-            logging.error("User settings not found.")
-            raise web.HTTPUnauthorized() from e
-        if os.path.isfile(file):
-            try:
-                with open(file) as f:
-                    return json.load(f)
-            except:
-                logging.error(f"The user settings file is corrupted: {file}")
-                return {}
-        else:
-            return {}
-
-    def save_settings(self, request, settings):
-        file = self.user_manager.get_request_user_filepath(
-            request, "comfy.settings.json")
-        with open(file, "w") as f:
-            f.write(json.dumps(settings, indent=4))
-
-    def add_routes(self, routes):
-        @routes.get("/settings")
-        async def get_settings(request):
-            return web.json_response(self.get_settings(request))
-
-        @routes.get("/settings/{id}")
-        async def get_setting(request):
-            value = None
-            settings = self.get_settings(request)
-            setting_id = request.match_info.get("id", None)
-            if setting_id and setting_id in settings:
-                value = settings[setting_id]
-            return web.json_response(value)
-
-        @routes.post("/settings")
-        async def post_settings(request):
-            settings = self.get_settings(request)
-            new_settings = await request.json()
-            self.save_settings(request, {**settings, **new_settings})
-            return web.Response(status=200)
-
-        @routes.post("/settings/{id}")
-        async def post_setting(request):
-            setting_id = request.match_info.get("id", None)
-            if not setting_id:
-                return web.Response(status=400)
-            settings = self.get_settings(request)
-            settings[setting_id] = await request.json()
-            self.save_settings(request, settings)
-            return web.Response(status=200)

app/custom_node_manager.py

@@ -1,145 +0,0 @@
-from __future__ import annotations
-
-import os
-import folder_paths
-import glob
-from aiohttp import web
-import json
-import logging
-from functools import lru_cache
-
-from utils.json_util import merge_json_recursive
-
-
-# Extra locale files to load into main.json
-EXTRA_LOCALE_FILES = [
-    "nodeDefs.json",
-    "commands.json",
-    "settings.json",
-]
-
-
-def safe_load_json_file(file_path: str) -> dict:
-    if not os.path.exists(file_path):
-        return {}
-
-    try:
-        with open(file_path, "r", encoding="utf-8") as f:
-            return json.load(f)
-    except json.JSONDecodeError:
-        logging.error(f"Error loading {file_path}")
-        return {}
-
-
-class CustomNodeManager:
-    @lru_cache(maxsize=1)
-    def build_translations(self):
-        """Load all custom nodes translations during initialization. Translations are
-        expected to be loaded from `locales/` folder.
-
-        The folder structure is expected to be the following:
-        - custom_nodes/
-            - custom_node_1/
-                - locales/
-                    - en/
-                        - main.json
-                        - commands.json
-                        - settings.json
-
-        returned translations are expected to be in the following format:
-        {
-            "en": {
-                "nodeDefs": {...},
-                "commands": {...},
-                "settings": {...},
-                ...{other main.json keys}
-            }
-        }
-        """
-
-        translations = {}
-
-        for folder in folder_paths.get_folder_paths("custom_nodes"):
-            # Sort glob results for deterministic ordering
-            for custom_node_dir in sorted(glob.glob(os.path.join(folder, "*/"))):
-                locales_dir = os.path.join(custom_node_dir, "locales")
-                if not os.path.exists(locales_dir):
-                    continue
-
-                for lang_dir in glob.glob(os.path.join(locales_dir, "*/")):
-                    lang_code = os.path.basename(os.path.dirname(lang_dir))
-
-                    if lang_code not in translations:
-                        translations[lang_code] = {}
-
-                    # Load main.json
-                    main_file = os.path.join(lang_dir, "main.json")
-                    node_translations = safe_load_json_file(main_file)
-
-                    # Load extra locale files
-                    for extra_file in EXTRA_LOCALE_FILES:
-                        extra_file_path = os.path.join(lang_dir, extra_file)
-                        key = extra_file.split(".")[0]
-                        json_data = safe_load_json_file(extra_file_path)
-                        if json_data:
-                            node_translations[key] = json_data
-
-                    if node_translations:
-                        translations[lang_code] = merge_json_recursive(
-                            translations[lang_code], node_translations
-                        )
-
-        return translations
-
-    def add_routes(self, routes, webapp, loadedModules):
-
-        example_workflow_folder_names = ["example_workflows", "example", "examples", "workflow", "workflows"]
-
-        @routes.get("/workflow_templates")
-        async def get_workflow_templates(request):
-            """Returns a web response that contains the map of custom_nodes names and their associated workflow templates. The ones without templates are omitted."""
-
-            files = []
-
-            for folder in folder_paths.get_folder_paths("custom_nodes"):
-                for folder_name in example_workflow_folder_names:
-                    pattern = os.path.join(folder, f"*/{folder_name}/*.json")
-                    matched_files = glob.glob(pattern)
-                    files.extend(matched_files)
-
-            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))
-                )
-                workflow_name = os.path.splitext(os.path.basename(file))[0]
-                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:
-            for folder_name in example_workflow_folder_names:
-                workflows_dir = os.path.join(module_dir, folder_name)
-
-                if os.path.exists(workflows_dir):
-                    if folder_name != "example_workflows":
-                        logging.debug(
-                            "Found example workflow folder '%s' for custom node '%s', consider renaming it to 'example_workflows'",
-                            folder_name, module_name)
-
-                    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

@@ -1,112 +0,0 @@
-import logging
-import os
-import shutil
-from app.logger import log_startup_warning
-from utils.install_util import get_missing_requirements_message
-from comfy.cli_args import args
-
-_DB_AVAILABLE = False
-Session = None
-
-
-try:
-    from alembic import command
-    from alembic.config import Config
-    from alembic.runtime.migration import MigrationContext
-    from alembic.script import ScriptDirectory
-    from sqlalchemy import create_engine
-    from sqlalchemy.orm import sessionmaker
-
-    _DB_AVAILABLE = True
-except ImportError as e:
-    log_startup_warning(
-        f"""
-------------------------------------------------------------------------
-Error importing dependencies: {e}
-{get_missing_requirements_message()}
-This error is happening because ComfyUI now uses a local sqlite database.
-------------------------------------------------------------------------
-""".strip()
-    )
-
-
-def dependencies_available():
-    """
-    Temporary function to check if the dependencies are available
-    """
-    return _DB_AVAILABLE
-
-
-def can_create_session():
-    """
-    Temporary function to check if the database is available to create a session
-    During initial release there may be environmental issues (or missing dependencies) that prevent the database from being created
-    """
-    return dependencies_available() and Session is not None
-
-
-def get_alembic_config():
-    root_path = os.path.join(os.path.dirname(__file__), "../..")
-    config_path = os.path.abspath(os.path.join(root_path, "alembic.ini"))
-    scripts_path = os.path.abspath(os.path.join(root_path, "alembic_db"))
-
-    config = Config(config_path)
-    config.set_main_option("script_location", scripts_path)
-    config.set_main_option("sqlalchemy.url", args.database_url)
-
-    return config
-
-
-def get_db_path():
-    url = args.database_url
-    if url.startswith("sqlite:///"):
-        return url.split("///")[1]
-    else:
-        raise ValueError(f"Unsupported database URL '{url}'.")
-
-
-def init_db():
-    db_url = args.database_url
-    logging.debug(f"Database URL: {db_url}")
-    db_path = get_db_path()
-    db_exists = os.path.exists(db_path)
-
-    config = get_alembic_config()
-
-    # Check if we need to upgrade
-    engine = create_engine(db_url)
-    conn = engine.connect()
-
-    context = MigrationContext.configure(conn)
-    current_rev = context.get_current_revision()
-
-    script = ScriptDirectory.from_config(config)
-    target_rev = script.get_current_head()
-
-    if target_rev is None:
-        logging.warning("No target revision found.")
-    elif current_rev != target_rev:
-        # Backup the database pre upgrade
-        backup_path = db_path + ".bkp"
-        if db_exists:
-            shutil.copy(db_path, backup_path)
-        else:
-            backup_path = None
-
-        try:
-            command.upgrade(config, target_rev)
-            logging.info(f"Database upgraded from {current_rev} to {target_rev}")
-        except Exception as e:
-            if backup_path:
-                # Restore the database from backup if upgrade fails
-                shutil.copy(backup_path, db_path)
-                os.remove(backup_path)
-            logging.exception("Error upgrading database: ")
-            raise e
-
-    global Session
-    Session = sessionmaker(bind=engine)
-
-
-def create_session():
-    return Session()

app/database/models.py

@@ -1,14 +0,0 @@
-from sqlalchemy.orm import declarative_base
-
-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))
-    }
-
-# TODO: Define models here

app/frontend_management.py

@@ -1,361 +0,0 @@
-from __future__ import annotations
-import argparse
-import logging
-import os
-import re
-import sys
-import tempfile
-import zipfile
-import importlib
-from dataclasses import dataclass
-from functools import cached_property
-from pathlib import Path
-from typing import TypedDict, Optional
-from importlib.metadata import version
-
-import requests
-from typing_extensions import NotRequired
-
-from utils.install_util import get_missing_requirements_message, requirements_path
-
-from comfy.cli_args import DEFAULT_VERSION_STRING
-import app.logger
-
-
-def frontend_install_warning_message():
-    return f"""
-{get_missing_requirements_message()}
-
-This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.
-""".strip()
-
-def parse_version(version: str) -> tuple[int, int, int]:
-        return tuple(map(int, version.split(".")))
-
-def is_valid_version(version: str) -> bool:
-    """Validate if a string is a valid semantic version (X.Y.Z format)."""
-    pattern = r"^(\d+)\.(\d+)\.(\d+)$"
-    return bool(re.match(pattern, version))
-
-def get_installed_frontend_version():
-    """Get the currently installed frontend package version."""
-    frontend_version_str = version("comfyui-frontend-package")
-    return frontend_version_str
-
-def get_required_frontend_version():
-    """Get the required frontend version from requirements.txt."""
-    try:
-        with open(requirements_path, "r", encoding="utf-8") as f:
-            for line in f:
-                line = line.strip()
-                if line.startswith("comfyui-frontend-package=="):
-                    version_str = line.split("==")[-1]
-                    if not is_valid_version(version_str):
-                        logging.error(f"Invalid version format in requirements.txt: {version_str}")
-                        return None
-                    return version_str
-            logging.error("comfyui-frontend-package not found in requirements.txt")
-            return None
-    except FileNotFoundError:
-        logging.error("requirements.txt not found. Cannot determine required frontend version.")
-        return None
-    except Exception as e:
-        logging.error(f"Error reading requirements.txt: {e}")
-        return None
-
-def check_frontend_version():
-    """Check if the frontend version is up to date."""
-
-    try:
-        frontend_version_str = get_installed_frontend_version()
-        frontend_version = parse_version(frontend_version_str)
-        required_frontend_str = get_required_frontend_version()
-        required_frontend = parse_version(required_frontend_str)
-        if frontend_version < required_frontend:
-            app.logger.log_startup_warning(
-                f"""
-________________________________________________________________________
-WARNING WARNING WARNING WARNING WARNING
-
-Installed frontend version {".".join(map(str, frontend_version))} is lower than the recommended version {".".join(map(str, required_frontend))}.
-
-{frontend_install_warning_message()}
-________________________________________________________________________
-""".strip()
-            )
-        else:
-            logging.info("ComfyUI frontend version: {}".format(frontend_version_str))
-    except Exception as e:
-        logging.error(f"Failed to check frontend version: {e}")
-
-
-REQUEST_TIMEOUT = 10  # seconds
-
-
-class Asset(TypedDict):
-    url: str
-
-
-class Release(TypedDict):
-    id: int
-    tag_name: str
-    name: str
-    prerelease: bool
-    created_at: str
-    published_at: str
-    body: str
-    assets: NotRequired[list[Asset]]
-
-
-@dataclass
-class FrontEndProvider:
-    owner: str
-    repo: str
-
-    @property
-    def folder_name(self) -> str:
-        return f"{self.owner}_{self.repo}"
-
-    @property
-    def release_url(self) -> str:
-        return f"https://api.github.com/repos/{self.owner}/{self.repo}/releases"
-
-    @cached_property
-    def all_releases(self) -> list[Release]:
-        releases = []
-        api_url = self.release_url
-        while api_url:
-            response = requests.get(api_url, timeout=REQUEST_TIMEOUT)
-            response.raise_for_status()  # Raises an HTTPError if the response was an error
-            releases.extend(response.json())
-            # GitHub uses the Link header to provide pagination links. Check if it exists and update api_url accordingly.
-            if "next" in response.links:
-                api_url = response.links["next"]["url"]
-            else:
-                api_url = None
-        return releases
-
-    @cached_property
-    def latest_release(self) -> Release:
-        latest_release_url = f"{self.release_url}/latest"
-        response = requests.get(latest_release_url, timeout=REQUEST_TIMEOUT)
-        response.raise_for_status()  # Raises an HTTPError if the response was an error
-        return response.json()
-
-    @cached_property
-    def latest_prerelease(self) -> Release:
-        """Get the latest pre-release version - even if it's older than the latest release"""
-        release = [release for release in self.all_releases if release["prerelease"]]
-
-        if not release:
-            raise ValueError("No pre-releases found")
-
-        # GitHub returns releases in reverse chronological order, so first is latest
-        return release[0]
-
-    def get_release(self, version: str) -> Release:
-        if version == "latest":
-            return self.latest_release
-        elif version == "prerelease":
-            return self.latest_prerelease
-        else:
-            for release in self.all_releases:
-                if release["tag_name"] in [version, f"v{version}"]:
-                    return release
-            raise ValueError(f"Version {version} not found in releases")
-
-
-def download_release_asset_zip(release: Release, destination_path: str) -> None:
-    """Download dist.zip from github release."""
-    asset_url = None
-    for asset in release.get("assets", []):
-        if asset["name"] == "dist.zip":
-            asset_url = asset["url"]
-            break
-
-    if not asset_url:
-        raise ValueError("dist.zip not found in the release assets")
-
-    # Use a temporary file to download the zip content
-    with tempfile.TemporaryFile() as tmp_file:
-        headers = {"Accept": "application/octet-stream"}
-        response = requests.get(
-            asset_url, headers=headers, allow_redirects=True, timeout=REQUEST_TIMEOUT
-        )
-        response.raise_for_status()  # Ensure we got a successful response
-
-        # Write the content to the temporary file
-        tmp_file.write(response.content)
-
-        # Go back to the beginning of the temporary file
-        tmp_file.seek(0)
-
-        # Extract the zip file content to the destination path
-        with zipfile.ZipFile(tmp_file, "r") as zip_ref:
-            zip_ref.extractall(destination_path)
-
-
-class FrontendManager:
-    CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions")
-
-    @classmethod
-    def get_required_frontend_version(cls) -> str:
-        """Get the required frontend package version."""
-        return get_required_frontend_version()
-
-    @classmethod
-    def default_frontend_path(cls) -> str:
-        try:
-            import comfyui_frontend_package
-
-            return str(importlib.resources.files(comfyui_frontend_package) / "static")
-        except ImportError:
-            logging.error(
-                f"""
-********** ERROR ***********
-
-comfyui-frontend-package is not installed.
-
-{frontend_install_warning_message()}
-
-********** ERROR ***********
-""".strip()
-            )
-            sys.exit(-1)
-
-    @classmethod
-    def templates_path(cls) -> str:
-        try:
-            import comfyui_workflow_templates
-
-            return str(
-                importlib.resources.files(comfyui_workflow_templates) / "templates"
-            )
-        except ImportError:
-            logging.error(
-                f"""
-********** ERROR ***********
-
-comfyui-workflow-templates is not installed.
-
-{frontend_install_warning_message()}
-
-********** ERROR ***********
-""".strip()
-            )
-
-    @classmethod
-    def embedded_docs_path(cls) -> str:
-        """Get the path to embedded documentation"""
-        try:
-            import comfyui_embedded_docs
-
-            return str(
-                importlib.resources.files(comfyui_embedded_docs) / "docs"
-            )
-        except ImportError:
-            logging.info("comfyui-embedded-docs package not found")
-            return None
-
-    @classmethod
-    def parse_version_string(cls, value: str) -> tuple[str, str, str]:
-        """
-        Args:
-            value (str): The version string to parse.
-
-        Returns:
-            tuple[str, str]: A tuple containing provider name and version.
-
-        Raises:
-            argparse.ArgumentTypeError: If the version string is invalid.
-        """
-        VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+[-._a-zA-Z0-9]*|latest|prerelease)$"
-        match_result = re.match(VERSION_PATTERN, value)
-        if match_result is None:
-            raise argparse.ArgumentTypeError(f"Invalid version string: {value}")
-
-        return match_result.group(1), match_result.group(2), match_result.group(3)
-
-    @classmethod
-    def init_frontend_unsafe(
-        cls, version_string: str, provider: Optional[FrontEndProvider] = None
-    ) -> str:
-        """
-        Initializes the frontend for the specified version.
-
-        Args:
-            version_string (str): The version string.
-            provider (FrontEndProvider, optional): The provider to use. Defaults to None.
-
-        Returns:
-            str: The path to the initialized frontend.
-
-        Raises:
-            Exception: If there is an error during the initialization process.
-            main error source might be request timeout or invalid URL.
-        """
-        if version_string == DEFAULT_VERSION_STRING:
-            check_frontend_version()
-            return cls.default_frontend_path()
-
-        repo_owner, repo_name, version = cls.parse_version_string(version_string)
-
-        if version.startswith("v"):
-            expected_path = str(
-                Path(cls.CUSTOM_FRONTENDS_ROOT)
-                / f"{repo_owner}_{repo_name}"
-                / version.lstrip("v")
-            )
-            if os.path.exists(expected_path):
-                logging.info(
-                    f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}"
-                )
-                return expected_path
-
-        logging.info(
-            f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub..."
-        )
-
-        provider = provider or FrontEndProvider(repo_owner, repo_name)
-        release = provider.get_release(version)
-
-        semantic_version = release["tag_name"].lstrip("v")
-        web_root = str(
-            Path(cls.CUSTOM_FRONTENDS_ROOT) / provider.folder_name / semantic_version
-        )
-        if not os.path.exists(web_root):
-            try:
-                os.makedirs(web_root, exist_ok=True)
-                logging.info(
-                    "Downloading frontend(%s) version(%s) to (%s)",
-                    provider.folder_name,
-                    semantic_version,
-                    web_root,
-                )
-                logging.debug(release)
-                download_release_asset_zip(release, destination_path=web_root)
-            finally:
-                # Clean up the directory if it is empty, i.e. the download failed
-                if not os.listdir(web_root):
-                    os.rmdir(web_root)
-
-        return web_root
-
-    @classmethod
-    def init_frontend(cls, version_string: str) -> str:
-        """
-        Initializes the frontend with the specified version string.
-
-        Args:
-            version_string (str): The version string to initialize the frontend with.
-
-        Returns:
-            str: The path of the initialized frontend.
-        """
-        try:
-            return cls.init_frontend_unsafe(version_string)
-        except Exception as e:
-            logging.error("Failed to initialize frontend: %s", e)
-            logging.info("Falling back to the default frontend.")
-            check_frontend_version()
-            return cls.default_frontend_path()

app/logger.py

@@ -1,98 +0,0 @@
-from collections import deque
-from datetime import datetime
-import io
-import logging
-import sys
-import threading
-
-logs = None
-stdout_interceptor = None
-stderr_interceptor = None
-
-
-class LogInterceptor(io.TextIOWrapper):
-    def __init__(self, stream,  *args, **kwargs):
-        buffer = stream.buffer
-        encoding = stream.encoding
-        super().__init__(buffer, *args, **kwargs, encoding=encoding, line_buffering=stream.line_buffering)
-        self._lock = threading.Lock()
-        self._flush_callbacks = []
-        self._logs_since_flush = []
-
-    def write(self, data):
-        entry = {"t": datetime.now().isoformat(), "m": data}
-        with self._lock:
-            self._logs_since_flush.append(entry)
-
-            # Simple handling for cr to overwrite the last output if it isnt a full line
-            # else logs just get full of progress messages
-            if isinstance(data, str) and data.startswith("\r") and not logs[-1]["m"].endswith("\n"):
-                logs.pop()
-            logs.append(entry)
-        super().write(data)
-
-    def flush(self):
-        super().flush()
-        for cb in self._flush_callbacks:
-            cb(self._logs_since_flush)
-            self._logs_since_flush = []
-
-    def on_flush(self, callback):
-        self._flush_callbacks.append(callback)
-
-
-def get_logs():
-    return logs
-
-
-def on_flush(callback):
-    if stdout_interceptor is not None:
-        stdout_interceptor.on_flush(callback)
-    if stderr_interceptor is not None:
-        stderr_interceptor.on_flush(callback)
-
-def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool = False):
-    global logs
-    if logs:
-        return
-
-    # Override output streams and log to buffer
-    logs = deque(maxlen=capacity)
-
-    global stdout_interceptor
-    global stderr_interceptor
-    stdout_interceptor = sys.stdout = LogInterceptor(sys.stdout)
-    stderr_interceptor = sys.stderr = LogInterceptor(sys.stderr)
-
-    # Setup default global logger
-    logger = logging.getLogger()
-    logger.setLevel(log_level)
-
-    stream_handler = logging.StreamHandler()
-    stream_handler.setFormatter(logging.Formatter("%(message)s"))
-
-    if use_stdout:
-        # Only errors and critical to stderr
-        stream_handler.addFilter(lambda record: not record.levelno < logging.ERROR)
-
-        # Lesser to stdout
-        stdout_handler = logging.StreamHandler(sys.stdout)
-        stdout_handler.setFormatter(logging.Formatter("%(message)s"))
-        stdout_handler.addFilter(lambda record: record.levelno < logging.ERROR)
-        logger.addHandler(stdout_handler)
-
-    logger.addHandler(stream_handler)
-
-
-STARTUP_WARNINGS = []
-
-
-def log_startup_warning(msg):
-    logging.warning(msg)
-    STARTUP_WARNINGS.append(msg)
-
-
-def print_startup_warnings():
-    for s in STARTUP_WARNINGS:
-        logging.warning(s)
-    STARTUP_WARNINGS.clear()

app/model_manager.py

@@ -1,195 +0,0 @@
-from __future__ import annotations
-
-import os
-import base64
-import json
-import time
-import logging
-import folder_paths
-import glob
-import comfy.utils
-from aiohttp import web
-from PIL import Image
-from io import BytesIO
-from folder_paths import map_legacy, filter_files_extensions, filter_files_content_types
-
-
-class ModelFileManager:
-    def __init__(self) -> None:
-        self.cache: dict[str, tuple[list[dict], dict[str, float], float]] = {}
-
-    def get_cache(self, key: str, default=None) -> tuple[list[dict], dict[str, float], float] | None:
-        return self.cache.get(key, default)
-
-    def set_cache(self, key: str, value: tuple[list[dict], dict[str, float], float]):
-        self.cache[key] = value
-
-    def clear_cache(self):
-        self.cache.clear()
-
-    def add_routes(self, routes):
-        # NOTE: This is an experiment to replace `/models`
-        @routes.get("/experiment/models")
-        async def get_model_folders(request):
-            model_types = list(folder_paths.folder_names_and_paths.keys())
-            folder_black_list = ["configs", "custom_nodes"]
-            output_folders: list[dict] = []
-            for folder in model_types:
-                if folder in folder_black_list:
-                    continue
-                output_folders.append({"name": folder, "folders": folder_paths.get_folder_paths(folder)})
-            return web.json_response(output_folders)
-
-        # NOTE: This is an experiment to replace `/models/{folder}`
-        @routes.get("/experiment/models/{folder}")
-        async def get_all_models(request):
-            folder = request.match_info.get("folder", None)
-            if not folder in folder_paths.folder_names_and_paths:
-                return web.Response(status=404)
-            files = self.get_model_file_list(folder)
-            return web.json_response(files)
-
-        @routes.get("/experiment/models/preview/{folder}/{path_index}/{filename:.*}")
-        async def get_model_preview(request):
-            folder_name = request.match_info.get("folder", None)
-            path_index = int(request.match_info.get("path_index", None))
-            filename = request.match_info.get("filename", None)
-
-            if not folder_name in folder_paths.folder_names_and_paths:
-                return web.Response(status=404)
-
-            folders = folder_paths.folder_names_and_paths[folder_name]
-            folder = folders[0][path_index]
-            full_filename = os.path.join(folder, 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)
-
-            try:
-                with Image.open(default_preview) as img:
-                    img_bytes = BytesIO()
-                    img.save(img_bytes, format="WEBP")
-                    img_bytes.seek(0)
-                    return web.Response(body=img_bytes.getvalue(), content_type="image/webp")
-            except:
-                return web.Response(status=404)
-
-    def get_model_file_list(self, folder_name: str):
-        folder_name = map_legacy(folder_name)
-        folders = folder_paths.folder_names_and_paths[folder_name]
-        output_list: list[dict] = []
-
-        for index, folder in enumerate(folders[0]):
-            if not os.path.isdir(folder):
-                continue
-            out = self.cache_model_file_list_(folder)
-            if out is None:
-                out = self.recursive_search_models_(folder, index)
-                self.set_cache(folder, out)
-            output_list.extend(out[0])
-
-        return output_list
-
-    def cache_model_file_list_(self, folder: str):
-        model_file_list_cache = self.get_cache(folder)
-
-        if model_file_list_cache is None:
-            return None
-        if not os.path.isdir(folder):
-            return None
-        if os.path.getmtime(folder) != model_file_list_cache[1]:
-            return None
-        for x in model_file_list_cache[1]:
-            time_modified = model_file_list_cache[1][x]
-            folder = x
-            if os.path.getmtime(folder) != time_modified:
-                return None
-
-        return model_file_list_cache
-
-    def recursive_search_models_(self, directory: str, pathIndex: int) -> tuple[list[str], dict[str, float], float]:
-        if not os.path.isdir(directory):
-            return [], {}, time.perf_counter()
-
-        excluded_dir_names = [".git"]
-        # TODO use settings
-        include_hidden_files = False
-
-        result: list[str] = []
-        dirs: dict[str, float] = {}
-
-        for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
-            subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
-            if not include_hidden_files:
-                subdirs[:] = [d for d in subdirs if not d.startswith(".")]
-                filenames = [f for f in filenames if not f.startswith(".")]
-
-            filenames = filter_files_extensions(filenames, folder_paths.supported_pt_extensions)
-
-            for file_name in filenames:
-                try:
-                    full_path = os.path.join(dirpath, file_name)
-                    relative_path = os.path.relpath(full_path, directory)
-
-                    # Get file metadata
-                    file_info = {
-                        "name": relative_path,
-                        "pathIndex": pathIndex,
-                        "modified": os.path.getmtime(full_path),  # Add modification time
-                        "created": os.path.getctime(full_path),   # Add creation time
-                        "size": os.path.getsize(full_path)        # Add file size
-                    }
-                    result.append(file_info)
-
-                except Exception as e:
-                    logging.warning(f"Warning: Unable to access {file_name}. Error: {e}. Skipping this file.")
-                    continue
-
-            for d in subdirs:
-                path: str = os.path.join(dirpath, d)
-                try:
-                    dirs[path] = os.path.getmtime(path)
-                except FileNotFoundError:
-                    logging.warning(f"Warning: Unable to access {path}. Skipping this path.")
-                    continue
-
-        return 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/user_manager.py

@@ -1,438 +0,0 @@
-from __future__ import annotations
-import json
-import os
-import re
-import uuid
-import glob
-import shutil
-import logging
-from aiohttp import web
-from urllib import parse
-from comfy.cli_args import args
-import folder_paths
-from .app_settings import AppSettings
-from typing import TypedDict
-
-default_user = "default"
-
-
-class FileInfo(TypedDict):
-    path: str
-    size: int
-    modified: int
-    created: int
-
-
-def get_file_info(path: str, relative_to: str) -> FileInfo:
-    return {
-        "path": os.path.relpath(path, relative_to).replace(os.sep, '/'),
-        "size": os.path.getsize(path),
-        "modified": os.path.getmtime(path),
-        "created": os.path.getctime(path)
-    }
-
-
-class UserManager():
-    def __init__(self):
-        user_directory = folder_paths.get_user_directory()
-
-        self.settings = AppSettings(self)
-        if not os.path.exists(user_directory):
-            os.makedirs(user_directory, exist_ok=True)
-            if not args.multi_user:
-                logging.warning("****** User settings have been changed to be stored on the server instead of browser storage. ******")
-                logging.warning("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******")
-
-        if args.multi_user:
-            if os.path.isfile(self.get_users_file()):
-                with open(self.get_users_file()) as f:
-                    self.users = json.load(f)
-            else:
-                self.users = {}
-        else:
-            self.users = {"default": "default"}
-
-    def get_users_file(self):
-        return os.path.join(folder_paths.get_user_directory(), "users.json")
-
-    def get_request_user_id(self, request):
-        user = "default"
-        if args.multi_user and "comfy-user" in request.headers:
-            user = request.headers["comfy-user"]
-
-        if user not in self.users:
-            raise KeyError("Unknown user: " + user)
-
-        return user
-
-    def get_request_user_filepath(self, request, file, type="userdata", create_dir=True):
-        user_directory = folder_paths.get_user_directory()
-
-        if type == "userdata":
-            root_dir = user_directory
-        else:
-            raise KeyError("Unknown filepath type:" + type)
-
-        user = self.get_request_user_id(request)
-        path = user_root = os.path.abspath(os.path.join(root_dir, user))
-
-        # prevent leaving /{type}
-        if os.path.commonpath((root_dir, user_root)) != root_dir:
-            return None
-
-        if file is not None:
-            # Check if filename is url encoded
-            if "%" in file:
-                file = parse.unquote(file)
-
-            # prevent leaving /{type}/{user}
-            path = os.path.abspath(os.path.join(user_root, file))
-            if os.path.commonpath((user_root, path)) != user_root:
-                return None
-
-        parent = os.path.split(path)[0]
-
-        if create_dir and not os.path.exists(parent):
-            os.makedirs(parent, exist_ok=True)
-
-        return path
-
-    def add_user(self, name):
-        name = name.strip()
-        if not name:
-            raise ValueError("username not provided")
-        user_id = re.sub("[^a-zA-Z0-9-_]+", '-', name)
-        user_id = user_id + "_" + str(uuid.uuid4())
-
-        self.users[user_id] = name
-
-        with open(self.get_users_file(), "w") as f:
-            json.dump(self.users, f)
-
-        return user_id
-
-    def add_routes(self, routes):
-        self.settings.add_routes(routes)
-
-        @routes.get("/users")
-        async def get_users(request):
-            if args.multi_user:
-                return web.json_response({"storage": "server", "users": self.users})
-            else:
-                user_dir = self.get_request_user_filepath(request, None, create_dir=False)
-                return web.json_response({
-                    "storage": "server",
-                    "migrated": os.path.exists(user_dir)
-                })
-
-        @routes.post("/users")
-        async def post_users(request):
-            body = await request.json()
-            username = body["username"]
-            if username in self.users.values():
-                return web.json_response({"error": "Duplicate username."}, status=400)
-
-            user_id = self.add_user(username)
-            return web.json_response(user_id)
-
-        @routes.get("/userdata")
-        async def listuserdata(request):
-            """
-            List user data files in a specified directory.
-
-            This endpoint allows listing files in a user's data directory, with options for recursion,
-            full file information, and path splitting.
-
-            Query Parameters:
-            - dir (required): The directory to list files from.
-            - recurse (optional): If "true", recursively list files in subdirectories.
-            - full_info (optional): If "true", return detailed file information (path, size, modified time).
-            - split (optional): If "true", split file paths into components (only applies when full_info is false).
-
-            Returns:
-            - 400: If 'dir' parameter is missing.
-            - 403: If the requested path is not allowed.
-            - 404: If the requested directory does not exist.
-            - 200: JSON response with the list of files or file information.
-
-            The response format depends on the query parameters:
-            - Default: List of relative file paths.
-            - full_info=true: List of dictionaries with file details.
-            - split=true (and full_info=false): List of lists, each containing path components.
-            """
-            directory = request.rel_url.query.get('dir', '')
-            if not directory:
-                return web.Response(status=400, text="Directory not provided")
-
-            path = self.get_request_user_filepath(request, directory)
-            if not path:
-                return web.Response(status=403, text="Invalid directory")
-
-            if not os.path.exists(path):
-                return web.Response(status=404, text="Directory not found")
-
-            recurse = request.rel_url.query.get('recurse', '').lower() == "true"
-            full_info = request.rel_url.query.get('full_info', '').lower() == "true"
-            split_path = request.rel_url.query.get('split', '').lower() == "true"
-
-            # Use different patterns based on whether we're recursing or not
-            if recurse:
-                pattern = os.path.join(glob.escape(path), '**', '*')
-            else:
-                pattern = os.path.join(glob.escape(path), '*')
-
-            def process_full_path(full_path: str) -> FileInfo | str | list[str]:
-                if full_info:
-                    return get_file_info(full_path, path)
-
-                rel_path = os.path.relpath(full_path, path).replace(os.sep, '/')
-                if split_path:
-                    return [rel_path] + rel_path.split('/')
-
-                return rel_path
-
-            results = [
-                process_full_path(full_path)
-                for full_path in glob.glob(pattern, recursive=recurse)
-                if os.path.isfile(full_path)
-            ]
-
-            return web.json_response(results)
-
-        @routes.get("/v2/userdata")
-        async def list_userdata_v2(request):
-            """
-            List files and directories in a user's data directory.
-
-            This endpoint provides a structured listing of contents within a specified
-            subdirectory of the user's data storage.
-
-            Query Parameters:
-            - path (optional): The relative path within the user's data directory
-                               to list. Defaults to the root ('').
-
-            Returns:
-            - 400: If the requested path is invalid, outside the user's data directory, or is not a directory.
-            - 404: If the requested path does not exist.
-            - 403: If the user is invalid.
-            - 500: If there is an error reading the directory contents.
-            - 200: JSON response containing a list of file and directory objects.
-                   Each object includes:
-                   - name: The name of the file or directory.
-                   - type: 'file' or 'directory'.
-                   - path: The relative path from the user's data root.
-                   - size (for files): The size in bytes.
-                   - modified (for files): The last modified timestamp (Unix epoch).
-            """
-            requested_rel_path = request.rel_url.query.get('path', '')
-
-            # URL-decode the path parameter
-            try:
-                requested_rel_path = parse.unquote(requested_rel_path)
-            except Exception as e:
-                logging.warning(f"Failed to decode path parameter: {requested_rel_path}, Error: {e}")
-                return web.Response(status=400, text="Invalid characters in path parameter")
-
-
-            # Check user validity and get the absolute path for the requested directory
-            try:
-                 base_user_path = self.get_request_user_filepath(request, None, create_dir=False)
-
-                 if requested_rel_path:
-                     target_abs_path = self.get_request_user_filepath(request, requested_rel_path, create_dir=False)
-                 else:
-                     target_abs_path = base_user_path
-
-            except KeyError as e:
-                 # Invalid user detected by get_request_user_id inside get_request_user_filepath
-                 logging.warning(f"Access denied for user: {e}")
-                 return web.Response(status=403, text="Invalid user specified in request")
-
-
-            if not target_abs_path:
-                 # Path traversal or other issue detected by get_request_user_filepath
-                 return web.Response(status=400, text="Invalid path requested")
-
-            # Handle cases where the user directory or target path doesn't exist
-            if not os.path.exists(target_abs_path):
-                # Check if it's the base user directory that's missing (new user case)
-                if target_abs_path == base_user_path:
-                    # It's okay if the base user directory doesn't exist yet, return empty list
-                     return web.json_response([])
-                else:
-                    # A specific subdirectory was requested but doesn't exist
-                     return web.Response(status=404, text="Requested path not found")
-
-            if not os.path.isdir(target_abs_path):
-                 return web.Response(status=400, text="Requested path is not a directory")
-
-            results = []
-            try:
-                for root, dirs, files in os.walk(target_abs_path, topdown=True):
-                    # Process directories
-                    for dir_name in dirs:
-                        dir_path = os.path.join(root, dir_name)
-                        rel_path = os.path.relpath(dir_path, base_user_path).replace(os.sep, '/')
-                        results.append({
-                            "name": dir_name,
-                            "path": rel_path,
-                            "type": "directory"
-                        })
-
-                    # Process files
-                    for file_name in files:
-                        file_path = os.path.join(root, file_name)
-                        rel_path = os.path.relpath(file_path, base_user_path).replace(os.sep, '/')
-                        entry_info = {
-                            "name": file_name,
-                            "path": rel_path,
-                            "type": "file"
-                        }
-                        try:
-                            stats = os.stat(file_path) # Use os.stat for potentially better performance with os.walk
-                            entry_info["size"] = stats.st_size
-                            entry_info["modified"] = stats.st_mtime
-                        except OSError as stat_error:
-                            logging.warning(f"Could not stat file {file_path}: {stat_error}")
-                            pass # Include file with available info
-                        results.append(entry_info)
-            except OSError as e:
-                logging.error(f"Error listing directory {target_abs_path}: {e}")
-                return web.Response(status=500, text="Error reading directory contents")
-
-            # Sort results alphabetically, directories first then files
-            results.sort(key=lambda x: (x['type'] != 'directory', x['name'].lower()))
-
-            return web.json_response(results)
-
-        def get_user_data_path(request, check_exists = False, param = "file"):
-            file = request.match_info.get(param, None)
-            if not file:
-                return web.Response(status=400)
-
-            path = self.get_request_user_filepath(request, file)
-            if not path:
-                return web.Response(status=403)
-
-            if check_exists and not os.path.exists(path):
-                return web.Response(status=404)
-
-            return path
-
-        @routes.get("/userdata/{file}")
-        async def getuserdata(request):
-            path = get_user_data_path(request, check_exists=True)
-            if not isinstance(path, str):
-                return path
-
-            return web.FileResponse(path)
-
-        @routes.post("/userdata/{file}")
-        async def post_userdata(request):
-            """
-            Upload or update a user data file.
-
-            This endpoint handles file uploads to a user's data directory, with options for
-            controlling overwrite behavior and response format.
-
-            Query Parameters:
-            - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true".
-            - full_info (optional): If "true", returns detailed file information (path, size, modified time).
-                                  If "false", returns only the relative file path.
-
-            Path Parameters:
-            - file: The target file path (URL encoded if necessary).
-
-            Returns:
-            - 400: If 'file' parameter is missing.
-            - 403: If the requested path is not allowed.
-            - 409: If overwrite=false and the file already exists.
-            - 200: JSON response with either:
-                  - Full file information (if full_info=true)
-                  - Relative file path (if full_info=false)
-
-            The request body should contain the raw file content to be written.
-            """
-            path = get_user_data_path(request)
-            if not isinstance(path, str):
-                return path
-
-            overwrite = request.query.get("overwrite", 'true') != "false"
-            full_info = request.query.get('full_info', 'false').lower() == "true"
-
-            if not overwrite and os.path.exists(path):
-                return web.Response(status=409, text="File already exists")
-
-            body = await request.read()
-
-            with open(path, "wb") as f:
-                f.write(body)
-
-            user_path = self.get_request_user_filepath(request, None)
-            if full_info:
-                resp = get_file_info(path, user_path)
-            else:
-                resp = os.path.relpath(path, user_path)
-
-            return web.json_response(resp)
-
-        @routes.delete("/userdata/{file}")
-        async def delete_userdata(request):
-            path = get_user_data_path(request, check_exists=True)
-            if not isinstance(path, str):
-                return path
-
-            os.remove(path)
-
-            return web.Response(status=204)
-
-        @routes.post("/userdata/{file}/move/{dest}")
-        async def move_userdata(request):
-            """
-            Move or rename a user data file.
-
-            This endpoint handles moving or renaming files within a user's data directory, with options for
-            controlling overwrite behavior and response format.
-
-            Path Parameters:
-            - file: The source file path (URL encoded if necessary)
-            - dest: The destination file path (URL encoded if necessary)
-
-            Query Parameters:
-            - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true".
-            - full_info (optional): If "true", returns detailed file information (path, size, modified time).
-                                  If "false", returns only the relative file path.
-
-            Returns:
-            - 400: If either 'file' or 'dest' parameter is missing
-            - 403: If either requested path is not allowed
-            - 404: If the source file does not exist
-            - 409: If overwrite=false and the destination file already exists
-            - 200: JSON response with either:
-                  - Full file information (if full_info=true)
-                  - Relative file path (if full_info=false)
-            """
-            source = get_user_data_path(request, check_exists=True)
-            if not isinstance(source, str):
-                return source
-
-            dest = get_user_data_path(request, check_exists=False, param="dest")
-            if not isinstance(source, str):
-                return dest
-
-            overwrite = request.query.get("overwrite", 'true') != "false"
-            full_info = request.query.get('full_info', 'false').lower() == "true"
-
-            if not overwrite and os.path.exists(dest):
-                return web.Response(status=409, text="File already exists")
-
-            logging.info(f"moving '{source}' -> '{dest}'")
-            shutil.move(source, dest)
-
-            user_path = self.get_request_user_filepath(request, None)
-            if full_info:
-                resp = get_file_info(dest, user_path)
-            else:
-                resp = os.path.relpath(dest, user_path)
-
-            return web.json_response(resp)

chain_injectors/conditioning_injector.py

@@ -0,0 +1,81 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    
+    target_node_id = None
+    target_input_name = None
+
+    if ksampler_name in assembler.node_map:
+        ksampler_id = assembler.node_map[ksampler_name]
+        if 'positive' in assembler.workflow[ksampler_id]['inputs']:
+            target_node_id = ksampler_id
+            target_input_name = 'positive'
+            print(f"Conditioning injector targeting KSampler node '{ksampler_name}'.")
+    else:
+        print(f"Warning: KSampler node '{ksampler_name}' for Conditioning chain not found. Skipping.")
+        return
+    
+    if not target_node_id:
+        print("Warning: Conditioning chain could not find a valid injection point (KSampler may be missing 'positive' input). Skipping.")
+        return
+
+    clip_source_str = chain_definition.get('clip_source')
+    if not clip_source_str:
+        print("Warning: 'clip_source' definition missing in the recipe for the Conditioning chain. Skipping.")
+        return
+    clip_node_name, clip_idx_str = clip_source_str.split(':')
+    if clip_node_name not in assembler.node_map:
+        print(f"Warning: CLIP source node '{clip_node_name}' for Conditioning chain not found. Skipping.")
+        return
+    clip_connection = [assembler.node_map[clip_node_name], int(clip_idx_str)]
+    
+    original_positive_connection = assembler.workflow[target_node_id]['inputs'][target_input_name]
+    
+    area_conditioning_outputs = []
+    
+    for item_data in chain_items:
+        prompt = item_data.get('prompt', '')
+        if not prompt or not prompt.strip():
+            continue
+
+        text_encode_id = assembler._get_unique_id()
+        text_encode_node = assembler._get_node_template("CLIPTextEncode")
+        text_encode_node['inputs']['text'] = prompt
+        text_encode_node['inputs']['clip'] = clip_connection
+        assembler.workflow[text_encode_id] = text_encode_node
+
+        set_area_id = assembler._get_unique_id()
+        set_area_node = assembler._get_node_template("ConditioningSetArea")
+        set_area_node['inputs']['width'] = item_data.get('width', 1024)
+        set_area_node['inputs']['height'] = item_data.get('height', 1024)
+        set_area_node['inputs']['x'] = item_data.get('x', 0)
+        set_area_node['inputs']['y'] = item_data.get('y', 0)
+        set_area_node['inputs']['strength'] = item_data.get('strength', 1.0)
+        set_area_node['inputs']['conditioning'] = [text_encode_id, 0]
+        assembler.workflow[set_area_id] = set_area_node
+        
+        area_conditioning_outputs.append([set_area_id, 0])
+
+    if not area_conditioning_outputs:
+        return
+
+    current_combined_conditioning = area_conditioning_outputs[0]
+    if len(area_conditioning_outputs) > 1:
+        for i in range(1, len(area_conditioning_outputs)):
+            combine_id = assembler._get_unique_id()
+            combine_node = assembler._get_node_template("ConditioningCombine")
+            combine_node['inputs']['conditioning_1'] = current_combined_conditioning
+            combine_node['inputs']['conditioning_2'] = area_conditioning_outputs[i]
+            assembler.workflow[combine_id] = combine_node
+            current_combined_conditioning = [combine_id, 0]
+
+    final_combine_id = assembler._get_unique_id()
+    final_combine_node = assembler._get_node_template("ConditioningCombine")
+    final_combine_node['inputs']['conditioning_1'] = original_positive_connection
+    final_combine_node['inputs']['conditioning_2'] = current_combined_conditioning
+    assembler.workflow[final_combine_id] = final_combine_node
+
+    assembler.workflow[target_node_id]['inputs'][target_input_name] = [final_combine_id, 0]
+    print(f"Conditioning injector applied. Redirected '{target_input_name}' input with {len(area_conditioning_outputs)} regional prompts.")

chain_injectors/controlnet_injector.py

@@ -0,0 +1,49 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    ksampler_name = chain_definition.get('ksampler_node', 'ksampler')
+    if ksampler_name not in assembler.node_map:
+        print(f"Warning: Target node '{ksampler_name}' for ControlNet chain not found. Skipping chain injection.")
+        return
+        
+    ksampler_id = assembler.node_map[ksampler_name]
+
+    if 'positive' not in assembler.workflow[ksampler_id]['inputs'] or \
+       'negative' not in assembler.workflow[ksampler_id]['inputs']:
+        print(f"Warning: KSampler node '{ksampler_name}' is missing 'positive' or 'negative' inputs. Skipping ControlNet chain.")
+        return
+        
+    current_positive_connection = assembler.workflow[ksampler_id]['inputs']['positive']
+    current_negative_connection = assembler.workflow[ksampler_id]['inputs']['negative']
+    
+    for item_data in chain_items:
+        cn_loader_id = assembler._get_unique_id()
+        cn_loader_node = assembler._get_node_template("ControlNetLoader")
+        cn_loader_node['inputs']['control_net_name'] = item_data['control_net_name']
+        assembler.workflow[cn_loader_id] = cn_loader_node
+
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+
+        apply_cn_id = assembler._get_unique_id()
+        apply_cn_node = assembler._get_node_template(chain_definition['template'])
+        
+        apply_cn_node['inputs']['strength'] = item_data['strength']
+        
+        apply_cn_node['inputs']['positive'] = current_positive_connection
+        apply_cn_node['inputs']['negative'] = current_negative_connection
+        apply_cn_node['inputs']['control_net'] = [cn_loader_id, 0]
+        apply_cn_node['inputs']['image'] = [image_loader_id, 0]
+        
+        assembler.workflow[apply_cn_id] = apply_cn_node
+
+        current_positive_connection = [apply_cn_id, 0]
+        current_negative_connection = [apply_cn_id, 1]
+
+    assembler.workflow[ksampler_id]['inputs']['positive'] = current_positive_connection
+    assembler.workflow[ksampler_id]['inputs']['negative'] = current_negative_connection
+    
+    print(f"ControlNet injector applied. KSampler inputs redirected through {len(chain_items)} ControlNet nodes.")

chain_injectors/ipadapter_injector.py

@@ -0,0 +1,106 @@
+def inject(assembler, chain_definition, chain_items):
+    if not chain_items:
+        return
+
+    final_settings = {}
+    if chain_items and isinstance(chain_items[-1], dict) and chain_items[-1].get('is_final_settings'):
+        final_settings = chain_items.pop()
+
+    if not chain_items:
+        return
+
+    end_node_name = chain_definition.get('end')
+    if not end_node_name or end_node_name not in assembler.node_map:
+        print(f"Warning: Target node '{end_node_name}' for IPAdapter chain not found. Skipping chain injection.")
+        return
+        
+    end_node_id = assembler.node_map[end_node_name]
+    
+    if 'model' not in assembler.workflow[end_node_id]['inputs']:
+        print(f"Warning: Target node '{end_node_name}' is missing 'model' input. Skipping IPAdapter chain.")
+        return
+    
+    current_model_connection = assembler.workflow[end_node_id]['inputs']['model']
+    
+    model_type = final_settings.get('model_type', 'sdxl')
+    megapixels = 1.05 if model_type == 'sdxl' else 0.39
+
+    pos_embed_outputs = []
+    neg_embed_outputs = []
+
+    for i, item_data in enumerate(chain_items):
+        loader_type = 'FaceID' if 'FACEID' in item_data.get('preset', '') else 'Unified'
+        
+        loader_template_name = "IPAdapterUnifiedLoader"
+        if loader_type == 'FaceID':
+            loader_template_name = "IPAdapterUnifiedLoaderFaceID"
+
+        image_loader_id = assembler._get_unique_id()
+        image_loader_node = assembler._get_node_template("LoadImage")
+        image_loader_node['inputs']['image'] = item_data['image']
+        assembler.workflow[image_loader_id] = image_loader_node
+        
+        image_scaler_id = assembler._get_unique_id()
+        image_scaler_node = assembler._get_node_template("ImageScaleToTotalPixels")
+        image_scaler_node['inputs']['image'] = [image_loader_id, 0]
+        image_scaler_node['inputs']['megapixels'] = megapixels
+        image_scaler_node['inputs']['upscale_method'] = "lanczos"
+        assembler.workflow[image_scaler_id] = image_scaler_node
+
+        ipadapter_loader_id = assembler._get_unique_id()
+        ipadapter_loader_node = assembler._get_node_template(loader_template_name)
+        ipadapter_loader_node['inputs']['model'] = current_model_connection
+        ipadapter_loader_node['inputs']['preset'] = item_data['preset']
+        if loader_type == 'FaceID':
+             ipadapter_loader_node['inputs']['lora_strength'] = item_data.get('lora_strength', 0.6)
+        assembler.workflow[ipadapter_loader_id] = ipadapter_loader_node
+        
+        encoder_id = assembler._get_unique_id()
+        encoder_node = assembler._get_node_template("IPAdapterEncoder")
+        encoder_node['inputs']['weight'] = item_data['weight']
+        encoder_node['inputs']['ipadapter'] = [ipadapter_loader_id, 1]
+        encoder_node['inputs']['image'] = [image_scaler_id, 0]
+        assembler.workflow[encoder_id] = encoder_node
+        
+        pos_embed_outputs.append([encoder_id, 0])
+        neg_embed_outputs.append([encoder_id, 1])
+
+    pos_combiner_id = assembler._get_unique_id()
+    pos_combiner_node = assembler._get_node_template("IPAdapterCombineEmbeds")
+    pos_combiner_node['inputs']['method'] = final_settings.get('final_combine_method', 'concat')
+    for i, conn in enumerate(pos_embed_outputs):
+        pos_combiner_node['inputs'][f'embed{i+1}'] = conn
+    assembler.workflow[pos_combiner_id] = pos_combiner_node
+
+    neg_combiner_id = assembler._get_unique_id()
+    neg_combiner_node = assembler._get_node_template("IPAdapterCombineEmbeds")
+    neg_combiner_node['inputs']['method'] = final_settings.get('final_combine_method', 'concat')
+    for i, conn in enumerate(neg_embed_outputs):
+        neg_combiner_node['inputs'][f'embed{i+1}'] = conn
+    assembler.workflow[neg_combiner_id] = neg_combiner_node
+    
+    final_loader_type = 'FaceID' if 'FACEID' in final_settings.get('final_preset', '') else 'Unified'
+    final_loader_template_name = "IPAdapterUnifiedLoader"
+    if final_loader_type == 'FaceID':
+        final_loader_template_name = "IPAdapterUnifiedLoaderFaceID"
+
+    final_loader_id = assembler._get_unique_id()
+    final_loader_node = assembler._get_node_template(final_loader_template_name)
+    final_loader_node['inputs']['model'] = current_model_connection
+    final_loader_node['inputs']['preset'] = final_settings.get('final_preset', 'STANDARD (medium strength)')
+    if final_loader_type == 'FaceID':
+        final_loader_node['inputs']['lora_strength'] = final_settings.get('final_lora_strength', 0.6)
+    assembler.workflow[final_loader_id] = final_loader_node
+
+    apply_embeds_id = assembler._get_unique_id()
+    apply_embeds_node = assembler._get_node_template("IPAdapterEmbeds")
+    apply_embeds_node['inputs']['weight'] = final_settings.get('final_weight', 1.0)
+    apply_embeds_node['inputs']['embeds_scaling'] = final_settings.get('final_embeds_scaling', 'V only')
+    apply_embeds_node['inputs']['model'] = [final_loader_id, 0]
+    apply_embeds_node['inputs']['ipadapter'] = [final_loader_id, 1]
+    apply_embeds_node['inputs']['pos_embed'] = [pos_combiner_id, 0]
+    apply_embeds_node['inputs']['neg_embed'] = [neg_combiner_id, 0]
+    assembler.workflow[apply_embeds_id] = apply_embeds_node
+
+    assembler.workflow[end_node_id]['inputs']['model'] = [apply_embeds_id, 0]
+    print(f"IPAdapter injector applied. Redirected '{end_node_name}' model input through {len(chain_items)} reference images.")

comfy/checkpoint_pickle.py

@@ -1,13 +0,0 @@
-import pickle
-
-load = pickle.load
-
-class Empty:
-    pass
-
-class Unpickler(pickle.Unpickler):
-    def find_class(self, module, name):
-        #TODO: safe unpickle
-        if module.startswith("pytorch_lightning"):
-            return Empty
-        return super().find_class(module, name)

comfy/cldm/cldm.py

@@ -1,433 +0,0 @@
-#taken from: https://github.com/lllyasviel/ControlNet
-#and modified
-
-import torch
-import torch.nn as nn
-
-from ..ldm.modules.diffusionmodules.util import (
-    timestep_embedding,
-)
-
-from ..ldm.modules.attention import SpatialTransformer
-from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
-from ..ldm.util import exists
-from .control_types import UNION_CONTROLNET_TYPES
-from collections import OrderedDict
-import comfy.ops
-from comfy.ldm.modules.attention import optimized_attention
-
-class OptimizedAttention(nn.Module):
-    def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.heads = nhead
-        self.c = c
-
-        self.in_proj = operations.Linear(c, c * 3, bias=True, dtype=dtype, device=device)
-        self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
-
-    def forward(self, x):
-        x = self.in_proj(x)
-        q, k, v = x.split(self.c, dim=2)
-        out = optimized_attention(q, k, v, self.heads)
-        return self.out_proj(out)
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-class ResBlockUnionControlnet(nn.Module):
-    def __init__(self, dim, nhead, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.attn = OptimizedAttention(dim, nhead, dtype=dtype, device=device, operations=operations)
-        self.ln_1 = operations.LayerNorm(dim, dtype=dtype, device=device)
-        self.mlp = nn.Sequential(
-            OrderedDict([("c_fc", operations.Linear(dim, dim * 4, dtype=dtype, device=device)), ("gelu", QuickGELU()),
-                         ("c_proj", operations.Linear(dim * 4, dim, dtype=dtype, device=device))]))
-        self.ln_2 = operations.LayerNorm(dim, dtype=dtype, device=device)
-
-    def attention(self, x: torch.Tensor):
-        return self.attn(x)
-
-    def forward(self, x: torch.Tensor):
-        x = x + self.attention(self.ln_1(x))
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-class ControlledUnetModel(UNetModel):
-    #implemented in the ldm unet
-    pass
-
-class ControlNet(nn.Module):
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        hint_channels,
-        num_res_blocks,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        use_checkpoint=False,
-        dtype=torch.float32,
-        num_heads=-1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        use_spatial_transformer=False,    # custom transformer support
-        transformer_depth=1,              # custom transformer support
-        context_dim=None,                 # custom transformer support
-        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
-        legacy=True,
-        disable_self_attentions=None,
-        num_attention_blocks=None,
-        disable_middle_self_attn=False,
-        use_linear_in_transformer=False,
-        adm_in_channels=None,
-        transformer_depth_middle=None,
-        transformer_depth_output=None,
-        attn_precision=None,
-        union_controlnet_num_control_type=None,
-        device=None,
-        operations=comfy.ops.disable_weight_init,
-        **kwargs,
-    ):
-        super().__init__()
-        assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
-        if use_spatial_transformer:
-            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
-
-        if context_dim is not None:
-            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
-            # from omegaconf.listconfig import ListConfig
-            # if type(context_dim) == ListConfig:
-            #     context_dim = list(context_dim)
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        if num_heads == -1:
-            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
-
-        if num_head_channels == -1:
-            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
-
-        self.dims = dims
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-
-        if isinstance(num_res_blocks, int):
-            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
-        else:
-            if len(num_res_blocks) != len(channel_mult):
-                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
-                                 "as a list/tuple (per-level) with the same length as channel_mult")
-            self.num_res_blocks = num_res_blocks
-
-        if disable_self_attentions is not None:
-            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
-            assert len(disable_self_attentions) == len(channel_mult)
-        if num_attention_blocks is not None:
-            assert len(num_attention_blocks) == len(self.num_res_blocks)
-            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
-
-        transformer_depth = transformer_depth[:]
-
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.dtype = dtype
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-        self.predict_codebook_ids = n_embed is not None
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
-            nn.SiLU(),
-            operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
-        )
-
-        if self.num_classes is not None:
-            if isinstance(self.num_classes, int):
-                self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-            elif self.num_classes == "continuous":
-                self.label_emb = nn.Linear(1, time_embed_dim)
-            elif self.num_classes == "sequential":
-                assert adm_in_channels is not None
-                self.label_emb = nn.Sequential(
-                    nn.Sequential(
-                        operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
-                        nn.SiLU(),
-                        operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
-                    )
-                )
-            else:
-                raise ValueError()
-
-        self.input_blocks = nn.ModuleList(
-            [
-                TimestepEmbedSequential(
-                    operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
-                )
-            ]
-        )
-        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)])
-
-        self.input_hint_block = TimestepEmbedSequential(
-                    operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device),
-                    nn.SiLU(),
-                    operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device),
-                    nn.SiLU(),
-                    operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device),
-                    nn.SiLU(),
-                    operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device),
-                    nn.SiLU(),
-                    operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device),
-                    nn.SiLU(),
-                    operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device),
-                    nn.SiLU(),
-                    operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device),
-                    nn.SiLU(),
-                    operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device)
-        )
-
-        self._feature_size = model_channels
-        input_block_chans = [model_channels]
-        ch = model_channels
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for nr in range(self.num_res_blocks[level]):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=mult * model_channels,
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                        dtype=self.dtype,
-                        device=device,
-                        operations=operations,
-                    )
-                ]
-                ch = mult * model_channels
-                num_transformers = transformer_depth.pop(0)
-                if num_transformers > 0:
-                    if num_head_channels == -1:
-                        dim_head = ch // num_heads
-                    else:
-                        num_heads = ch // num_head_channels
-                        dim_head = num_head_channels
-                    if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-                    if exists(disable_self_attentions):
-                        disabled_sa = disable_self_attentions[level]
-                    else:
-                        disabled_sa = False
-
-                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
-                        layers.append(
-                            SpatialTransformer(
-                                ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,
-                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
-                            )
-                        )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                            dtype=self.dtype,
-                            device=device,
-                            operations=operations
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device))
-                ds *= 2
-                self._feature_size += ch
-
-        if num_head_channels == -1:
-            dim_head = ch // num_heads
-        else:
-            num_heads = ch // num_head_channels
-            dim_head = num_head_channels
-        if legacy:
-            #num_heads = 1
-            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
-        mid_block = [
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-                dtype=self.dtype,
-                device=device,
-                operations=operations
-            )]
-        if transformer_depth_middle >= 0:
-            mid_block += [SpatialTransformer(  # always uses a self-attn
-                            ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
-                            disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
-                            use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
-                        ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-                dtype=self.dtype,
-                device=device,
-                operations=operations
-            )]
-        self.middle_block = TimestepEmbedSequential(*mid_block)
-        self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)
-        self._feature_size += ch
-
-        if union_controlnet_num_control_type is not None:
-            self.num_control_type = union_controlnet_num_control_type
-            num_trans_channel = 320
-            num_trans_head = 8
-            num_trans_layer = 1
-            num_proj_channel = 320
-            # task_scale_factor = num_trans_channel ** 0.5
-            self.task_embedding = nn.Parameter(torch.empty(self.num_control_type, num_trans_channel, dtype=self.dtype, device=device))
-
-            self.transformer_layes = nn.Sequential(*[ResBlockUnionControlnet(num_trans_channel, num_trans_head, dtype=self.dtype, device=device, operations=operations) for _ in range(num_trans_layer)])
-            self.spatial_ch_projs = operations.Linear(num_trans_channel, num_proj_channel, dtype=self.dtype, device=device)
-            #-----------------------------------------------------------------------------------------------------
-
-            control_add_embed_dim = 256
-            class ControlAddEmbedding(nn.Module):
-                def __init__(self, in_dim, out_dim, num_control_type, dtype=None, device=None, operations=None):
-                    super().__init__()
-                    self.num_control_type = num_control_type
-                    self.in_dim = in_dim
-                    self.linear_1 = operations.Linear(in_dim * num_control_type, out_dim, dtype=dtype, device=device)
-                    self.linear_2 = operations.Linear(out_dim, out_dim, dtype=dtype, device=device)
-                def forward(self, control_type, dtype, device):
-                    c_type = torch.zeros((self.num_control_type,), device=device)
-                    c_type[control_type] = 1.0
-                    c_type = timestep_embedding(c_type.flatten(), self.in_dim, repeat_only=False).to(dtype).reshape((-1, self.num_control_type * self.in_dim))
-                    return self.linear_2(torch.nn.functional.silu(self.linear_1(c_type)))
-
-            self.control_add_embedding = ControlAddEmbedding(control_add_embed_dim, time_embed_dim, self.num_control_type, dtype=self.dtype, device=device, operations=operations)
-        else:
-            self.task_embedding = None
-            self.control_add_embedding = None
-
-    def union_controlnet_merge(self, hint, control_type, emb, context):
-        # Equivalent to: https://github.com/xinsir6/ControlNetPlus/tree/main
-        inputs = []
-        condition_list = []
-
-        for idx in range(min(1, len(control_type))):
-            controlnet_cond = self.input_hint_block(hint[idx], emb, context)
-            feat_seq = torch.mean(controlnet_cond, dim=(2, 3))
-            if idx < len(control_type):
-                feat_seq += self.task_embedding[control_type[idx]].to(dtype=feat_seq.dtype, device=feat_seq.device)
-
-            inputs.append(feat_seq.unsqueeze(1))
-            condition_list.append(controlnet_cond)
-
-        x = torch.cat(inputs, dim=1)
-        x = self.transformer_layes(x)
-        controlnet_cond_fuser = None
-        for idx in range(len(control_type)):
-            alpha = self.spatial_ch_projs(x[:, idx])
-            alpha = alpha.unsqueeze(-1).unsqueeze(-1)
-            o = condition_list[idx] + alpha
-            if controlnet_cond_fuser is None:
-                controlnet_cond_fuser = o
-            else:
-                controlnet_cond_fuser += o
-        return controlnet_cond_fuser
-
-    def make_zero_conv(self, channels, operations=None, dtype=None, device=None):
-        return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device))
-
-    def forward(self, x, hint, timesteps, context, y=None, **kwargs):
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
-        emb = self.time_embed(t_emb)
-
-        guided_hint = None
-        if self.control_add_embedding is not None: #Union Controlnet
-            control_type = kwargs.get("control_type", [])
-
-            if any([c >= self.num_control_type for c in control_type]):
-                max_type = max(control_type)
-                max_type_name = {
-                    v: k for k, v in UNION_CONTROLNET_TYPES.items()
-                }[max_type]
-                raise ValueError(
-                    f"Control type {max_type_name}({max_type}) is out of range for the number of control types" +
-                    f"({self.num_control_type}) supported.\n" +
-                    "Please consider using the ProMax ControlNet Union model.\n" +
-                    "https://huggingface.co/xinsir/controlnet-union-sdxl-1.0/tree/main"
-                )
-
-            emb += self.control_add_embedding(control_type, emb.dtype, emb.device)
-            if len(control_type) > 0:
-                if len(hint.shape) < 5:
-                    hint = hint.unsqueeze(dim=0)
-                guided_hint = self.union_controlnet_merge(hint, control_type, emb, context)
-
-        if guided_hint is None:
-            guided_hint = self.input_hint_block(hint, emb, context)
-
-        out_output = []
-        out_middle = []
-
-        if self.num_classes is not None:
-            assert y.shape[0] == x.shape[0]
-            emb = emb + self.label_emb(y)
-
-        h = x
-        for module, zero_conv in zip(self.input_blocks, self.zero_convs):
-            if guided_hint is not None:
-                h = module(h, emb, context)
-                h += guided_hint
-                guided_hint = None
-            else:
-                h = module(h, emb, context)
-            out_output.append(zero_conv(h, emb, context))
-
-        h = self.middle_block(h, emb, context)
-        out_middle.append(self.middle_block_out(h, emb, context))
-
-        return {"middle": out_middle, "output": out_output}
-

comfy/cldm/control_types.py

@@ -1,10 +0,0 @@
-UNION_CONTROLNET_TYPES = {
-    "openpose": 0,
-    "depth": 1,
-    "hed/pidi/scribble/ted": 2,
-    "canny/lineart/anime_lineart/mlsd": 3,
-    "normal": 4,
-    "segment": 5,
-    "tile": 6,
-    "repaint": 7,
-}

comfy/cldm/dit_embedder.py

@@ -1,120 +0,0 @@
-import math
-from typing import List, Optional, Tuple
-
-import torch
-import torch.nn as nn
-from torch import Tensor
-
-from comfy.ldm.modules.diffusionmodules.mmdit import DismantledBlock, PatchEmbed, VectorEmbedder, TimestepEmbedder, get_2d_sincos_pos_embed_torch
-
-
-class ControlNetEmbedder(nn.Module):
-
-    def __init__(
-        self,
-        img_size: int,
-        patch_size: int,
-        in_chans: int,
-        attention_head_dim: int,
-        num_attention_heads: int,
-        adm_in_channels: int,
-        num_layers: int,
-        main_model_double: int,
-        double_y_emb: bool,
-        device: torch.device,
-        dtype: torch.dtype,
-        pos_embed_max_size: Optional[int] = None,
-        operations = None,
-    ):
-        super().__init__()
-        self.main_model_double = main_model_double
-        self.dtype = dtype
-        self.hidden_size = num_attention_heads * attention_head_dim
-        self.patch_size = patch_size
-        self.x_embedder = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=in_chans,
-            embed_dim=self.hidden_size,
-            strict_img_size=pos_embed_max_size is None,
-            device=device,
-            dtype=dtype,
-            operations=operations,
-        )
-
-        self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations)
-
-        self.double_y_emb = double_y_emb
-        if self.double_y_emb:
-            self.orig_y_embedder = VectorEmbedder(
-                adm_in_channels, self.hidden_size, dtype, device, operations=operations
-            )
-            self.y_embedder = VectorEmbedder(
-                self.hidden_size, self.hidden_size, dtype, device, operations=operations
-            )
-        else:
-            self.y_embedder = VectorEmbedder(
-                adm_in_channels, self.hidden_size, dtype, device, operations=operations
-            )
-
-        self.transformer_blocks = nn.ModuleList(
-            DismantledBlock(
-                hidden_size=self.hidden_size, num_heads=num_attention_heads, qkv_bias=True,
-                dtype=dtype, device=device, operations=operations
-            )
-            for _ in range(num_layers)
-        )
-
-        # self.use_y_embedder = pooled_projection_dim != self.time_text_embed.text_embedder.linear_1.in_features
-        # TODO double check this logic when 8b
-        self.use_y_embedder = True
-
-        self.controlnet_blocks = nn.ModuleList([])
-        for _ in range(len(self.transformer_blocks)):
-            controlnet_block = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
-            self.controlnet_blocks.append(controlnet_block)
-
-        self.pos_embed_input = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=in_chans,
-            embed_dim=self.hidden_size,
-            strict_img_size=False,
-            device=device,
-            dtype=dtype,
-            operations=operations,
-        )
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        timesteps: torch.Tensor,
-        y: Optional[torch.Tensor] = None,
-        context: Optional[torch.Tensor] = None,
-        hint = None,
-    ) -> Tuple[Tensor, List[Tensor]]:
-        x_shape = list(x.shape)
-        x = self.x_embedder(x)
-        if not self.double_y_emb:
-            h = (x_shape[-2] + 1) // self.patch_size
-            w = (x_shape[-1] + 1) // self.patch_size
-            x += get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=x.device)
-        c = self.t_embedder(timesteps, dtype=x.dtype)
-        if y is not None and self.y_embedder is not None:
-            if self.double_y_emb:
-                y = self.orig_y_embedder(y)
-            y = self.y_embedder(y)
-            c = c + y
-
-        x = x + self.pos_embed_input(hint)
-
-        block_out = ()
-
-        repeat = math.ceil(self.main_model_double / len(self.transformer_blocks))
-        for i in range(len(self.transformer_blocks)):
-            out = self.transformer_blocks[i](x, c)
-            if not self.double_y_emb:
-                x = out
-            block_out += (self.controlnet_blocks[i](out),) * repeat
-
-        return {"output": block_out}

comfy/cldm/mmdit.py

@@ -1,81 +0,0 @@
-import torch
-from typing import Optional
-import comfy.ldm.modules.diffusionmodules.mmdit
-
-class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
-    def __init__(
-        self,
-        num_blocks = None,
-        control_latent_channels = None,
-        dtype = None,
-        device = None,
-        operations = None,
-        **kwargs,
-    ):
-        super().__init__(dtype=dtype, device=device, operations=operations, final_layer=False, num_blocks=num_blocks, **kwargs)
-        # controlnet_blocks
-        self.controlnet_blocks = torch.nn.ModuleList([])
-        for _ in range(len(self.joint_blocks)):
-            self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype))
-
-        if control_latent_channels is None:
-            control_latent_channels = self.in_channels
-
-        self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(
-            None,
-            self.patch_size,
-            control_latent_channels,
-            self.hidden_size,
-            bias=True,
-            strict_img_size=False,
-            dtype=dtype,
-            device=device,
-            operations=operations
-        )
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        timesteps: torch.Tensor,
-        y: Optional[torch.Tensor] = None,
-        context: Optional[torch.Tensor] = None,
-        hint = None,
-    ) -> torch.Tensor:
-
-        #weird sd3 controlnet specific stuff
-        y = torch.zeros_like(y)
-
-        if self.context_processor is not None:
-            context = self.context_processor(context)
-
-        hw = x.shape[-2:]
-        x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device)
-        x += self.pos_embed_input(hint)
-
-        c = self.t_embedder(timesteps, dtype=x.dtype)
-        if y is not None and self.y_embedder is not None:
-            y = self.y_embedder(y)
-            c = c + y
-
-        if context is not None:
-            context = self.context_embedder(context)
-
-        output = []
-
-        blocks = len(self.joint_blocks)
-        for i in range(blocks):
-            context, x = self.joint_blocks[i](
-                context,
-                x,
-                c=c,
-                use_checkpoint=self.use_checkpoint,
-            )
-
-            out = self.controlnet_blocks[i](x)
-            count = self.depth // blocks
-            if i == blocks - 1:
-                count -= 1
-            for j in range(count):
-                output.append(out)
-
-        return {"output": output}

comfy/cli_args.py

@@ -1,237 +0,0 @@
-import argparse
-import enum
-import os
-import comfy.options
-
-
-class EnumAction(argparse.Action):
-    """
-    Argparse action for handling Enums
-    """
-    def __init__(self, **kwargs):
-        # Pop off the type value
-        enum_type = kwargs.pop("type", None)
-
-        # Ensure an Enum subclass is provided
-        if enum_type is None:
-            raise ValueError("type must be assigned an Enum when using EnumAction")
-        if not issubclass(enum_type, enum.Enum):
-            raise TypeError("type must be an Enum when using EnumAction")
-
-        # Generate choices from the Enum
-        choices = tuple(e.value for e in enum_type)
-        kwargs.setdefault("choices", choices)
-        kwargs.setdefault("metavar", f"[{','.join(list(choices))}]")
-
-        super(EnumAction, self).__init__(**kwargs)
-
-        self._enum = enum_type
-
-    def __call__(self, parser, namespace, values, option_string=None):
-        # Convert value back into an Enum
-        value = self._enum(values)
-        setattr(namespace, self.dest, value)
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0,::", help="Specify the IP address to listen on (default: 127.0.0.1). You can give a list of ip addresses by separating them with a comma like: 127.2.2.2,127.3.3.3 If --listen is provided without an argument, it defaults to 0.0.0.0,:: (listens on all ipv4 and ipv6)")
-parser.add_argument("--port", type=int, default=8188, help="Set the listen port.")
-parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function")
-parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function")
-parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.")
-parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.")
-
-parser.add_argument("--base-directory", type=str, default=None, help="Set the ComfyUI base directory for models, custom_nodes, input, output, temp, and user directories.")
-parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.")
-parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory. Overrides --base-directory.")
-parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory). Overrides --base-directory.")
-parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-directory.")
-parser.add_argument("--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. All other devices will not be visible.")
-parser.add_argument("--default-device", type=int, default=None, metavar="DEFAULT_DEVICE_ID", help="Set the id of the default device, all other devices will stay visible.")
-cm_group = parser.add_mutually_exclusive_group()
-cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
-cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.")
-
-
-fp_group = parser.add_mutually_exclusive_group()
-fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
-fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.")
-
-fpunet_group = parser.add_mutually_exclusive_group()
-fpunet_group.add_argument("--fp32-unet", action="store_true", help="Run the diffusion model in fp32.")
-fpunet_group.add_argument("--fp64-unet", action="store_true", help="Run the diffusion model in fp64.")
-fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the diffusion model in bf16.")
-fpunet_group.add_argument("--fp16-unet", action="store_true", help="Run the diffusion model in fp16")
-fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Store unet weights in fp8_e4m3fn.")
-fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Store unet weights in fp8_e5m2.")
-fpunet_group.add_argument("--fp8_e8m0fnu-unet", action="store_true", help="Store unet weights in fp8_e8m0fnu.")
-
-fpvae_group = parser.add_mutually_exclusive_group()
-fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.")
-fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.")
-fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16.")
-
-parser.add_argument("--cpu-vae", action="store_true", help="Run the VAE on the CPU.")
-
-fpte_group = parser.add_mutually_exclusive_group()
-fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Store text encoder weights in fp8 (e4m3fn variant).")
-fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store text encoder weights in fp8 (e5m2 variant).")
-fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.")
-fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.")
-fpte_group.add_argument("--bf16-text-enc", action="store_true", help="Store text encoder weights in bf16.")
-
-parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.")
-
-parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
-
-parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
-parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.")
-parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
-
-class LatentPreviewMethod(enum.Enum):
-    NoPreviews = "none"
-    Auto = "auto"
-    Latent2RGB = "latent2rgb"
-    TAESD = "taesd"
-
-parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
-
-parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")
-
-cache_group = parser.add_mutually_exclusive_group()
-cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
-cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
-cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.")
-
-attn_group = parser.add_mutually_exclusive_group()
-attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
-attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.")
-attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.")
-attn_group.add_argument("--use-sage-attention", action="store_true", help="Use sage attention.")
-attn_group.add_argument("--use-flash-attention", action="store_true", help="Use FlashAttention.")
-
-parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.")
-
-upcast = parser.add_mutually_exclusive_group()
-upcast.add_argument("--force-upcast-attention", action="store_true", help="Force enable attention upcasting, please report if it fixes black images.")
-upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.")
-
-
-vram_group = parser.add_mutually_exclusive_group()
-vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
-vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
-vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.")
-vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.")
-vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
-vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
-
-parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.")
-
-parser.add_argument("--async-offload", action="store_true", help="Use async weight offloading.")
-
-parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help="Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.")
-
-parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.")
-parser.add_argument("--deterministic", action="store_true", help="Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.")
-
-class PerformanceFeature(enum.Enum):
-    Fp16Accumulation = "fp16_accumulation"
-    Fp8MatrixMultiplication = "fp8_matrix_mult"
-    CublasOps = "cublas_ops"
-
-parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult cublas_ops")
-
-parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.")
-parser.add_argument("--disable-mmap", action="store_true", help="Don't use mmap when loading safetensors.")
-
-parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
-parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")
-parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).")
-
-parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.")
-parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.")
-parser.add_argument("--whitelist-custom-nodes", type=str, nargs='+', default=[], help="Specify custom node folders to load even when --disable-all-custom-nodes is enabled.")
-parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes.")
-
-parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
-
-parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level')
-parser.add_argument("--log-stdout", action="store_true", help="Send normal process output to stdout instead of stderr (default).")
-
-# The default built-in provider hosted under web/
-DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest"
-
-parser.add_argument(
-    "--front-end-version",
-    type=str,
-    default=DEFAULT_VERSION_STRING,
-    help="""
-    Specifies the version of the frontend to be used. This command needs internet connectivity to query and
-    download available frontend implementations from GitHub releases.
-
-    The version string should be in the format of:
-    [repoOwner]/[repoName]@[version]
-    where version is one of: "latest" or a valid version number (e.g. "1.0.0")
-    """,
-)
-
-def is_valid_directory(path: str) -> str:
-    """Validate if the given path is a directory, and check permissions."""
-    if not os.path.exists(path):
-        raise argparse.ArgumentTypeError(f"The path '{path}' does not exist.")
-    if not os.path.isdir(path):
-        raise argparse.ArgumentTypeError(f"'{path}' is not a directory.")
-    if not os.access(path, os.R_OK):
-        raise argparse.ArgumentTypeError(f"You do not have read permissions for '{path}'.")
-    return path
-
-parser.add_argument(
-    "--front-end-root",
-    type=is_valid_directory,
-    default=None,
-    help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.",
-)
-
-parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path. Overrides --base-directory.")
-
-parser.add_argument("--enable-compress-response-body", action="store_true", help="Enable compressing response body.")
-
-parser.add_argument(
-    "--comfy-api-base",
-    type=str,
-    default="https://api.comfy.org",
-    help="Set the base URL for the ComfyUI API.  (default: https://api.comfy.org)",
-)
-
-database_default_path = os.path.abspath(
-    os.path.join(os.path.dirname(__file__), "..", "user", "comfyui.db")
-)
-parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
-
-if comfy.options.args_parsing:
-    args = parser.parse_args()
-else:
-    args = parser.parse_args([])
-
-if args.windows_standalone_build:
-    args.auto_launch = True
-
-if args.disable_auto_launch:
-    args.auto_launch = False
-
-if args.force_fp16:
-    args.fp16_unet = True
-
-
-# '--fast' is not provided, use an empty set
-if args.fast is None:
-    args.fast = set()
-# '--fast' is provided with an empty list, enable all optimizations
-elif args.fast == []:
-    args.fast = set(PerformanceFeature)
-# '--fast' is provided with a list of performance features, use that list
-else:
-    args.fast = set(args.fast)

comfy/clip_config_bigg.json

@@ -1,23 +0,0 @@
-{
-  "architectures": [
-    "CLIPTextModel"
-  ],
-  "attention_dropout": 0.0,
-  "bos_token_id": 0,
-  "dropout": 0.0,
-  "eos_token_id": 49407,
-  "hidden_act": "gelu",
-  "hidden_size": 1280,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 5120,
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 77,
-  "model_type": "clip_text_model",
-  "num_attention_heads": 20,
-  "num_hidden_layers": 32,
-  "pad_token_id": 1,
-  "projection_dim": 1280,
-  "torch_dtype": "float32",
-  "vocab_size": 49408
-}

comfy/clip_model.py

@@ -1,244 +0,0 @@
-import torch
-from comfy.ldm.modules.attention import optimized_attention_for_device
-import comfy.ops
-
-class CLIPAttention(torch.nn.Module):
-    def __init__(self, embed_dim, heads, dtype, device, operations):
-        super().__init__()
-
-        self.heads = heads
-        self.q_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
-        self.k_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
-        self.v_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
-
-        self.out_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
-
-    def forward(self, x, mask=None, optimized_attention=None):
-        q = self.q_proj(x)
-        k = self.k_proj(x)
-        v = self.v_proj(x)
-
-        out = optimized_attention(q, k, v, self.heads, mask)
-        return self.out_proj(out)
-
-ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a),
-               "gelu": torch.nn.functional.gelu,
-               "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),
-}
-
-class CLIPMLP(torch.nn.Module):
-    def __init__(self, embed_dim, intermediate_size, activation, dtype, device, operations):
-        super().__init__()
-        self.fc1 = operations.Linear(embed_dim, intermediate_size, bias=True, dtype=dtype, device=device)
-        self.activation = ACTIVATIONS[activation]
-        self.fc2 = operations.Linear(intermediate_size, embed_dim, bias=True, dtype=dtype, device=device)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.activation(x)
-        x = self.fc2(x)
-        return x
-
-class CLIPLayer(torch.nn.Module):
-    def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations):
-        super().__init__()
-        self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
-        self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations)
-        self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
-        self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device, operations)
-
-    def forward(self, x, mask=None, optimized_attention=None):
-        x += self.self_attn(self.layer_norm1(x), mask, optimized_attention)
-        x += self.mlp(self.layer_norm2(x))
-        return x
-
-
-class CLIPEncoder(torch.nn.Module):
-    def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations):
-        super().__init__()
-        self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)])
-
-    def forward(self, x, mask=None, intermediate_output=None):
-        optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
-
-        if intermediate_output is not None:
-            if intermediate_output < 0:
-                intermediate_output = len(self.layers) + intermediate_output
-
-        intermediate = None
-        for i, l in enumerate(self.layers):
-            x = l(x, mask, optimized_attention)
-            if i == intermediate_output:
-                intermediate = x.clone()
-        return x, intermediate
-
-class CLIPEmbeddings(torch.nn.Module):
-    def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None, operations=None):
-        super().__init__()
-        self.token_embedding = operations.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
-        self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
-
-    def forward(self, input_tokens, dtype=torch.float32):
-        return self.token_embedding(input_tokens, out_dtype=dtype) + comfy.ops.cast_to(self.position_embedding.weight, dtype=dtype, device=input_tokens.device)
-
-
-class CLIPTextModel_(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device, operations):
-        num_layers = config_dict["num_hidden_layers"]
-        embed_dim = config_dict["hidden_size"]
-        heads = config_dict["num_attention_heads"]
-        intermediate_size = config_dict["intermediate_size"]
-        intermediate_activation = config_dict["hidden_act"]
-        num_positions = config_dict["max_position_embeddings"]
-        self.eos_token_id = config_dict["eos_token_id"]
-
-        super().__init__()
-        self.embeddings = CLIPEmbeddings(embed_dim, num_positions=num_positions, dtype=dtype, device=device, operations=operations)
-        self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
-        self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
-
-    def forward(self, input_tokens=None, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32):
-        if embeds is not None:
-            x = embeds + comfy.ops.cast_to(self.embeddings.position_embedding.weight, dtype=dtype, device=embeds.device)
-        else:
-            x = self.embeddings(input_tokens, dtype=dtype)
-
-        mask = None
-        if attention_mask is not None:
-            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
-            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
-
-        causal_mask = torch.full((x.shape[1], x.shape[1]), -torch.finfo(x.dtype).max, dtype=x.dtype, device=x.device).triu_(1)
-
-        if mask is not None:
-            mask += causal_mask
-        else:
-            mask = causal_mask
-
-        x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output)
-        x = self.final_layer_norm(x)
-        if i is not None and final_layer_norm_intermediate:
-            i = self.final_layer_norm(i)
-
-        if num_tokens is not None:
-            pooled_output = x[list(range(x.shape[0])), list(map(lambda a: a - 1, num_tokens))]
-        else:
-            pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),]
-        return x, i, pooled_output
-
-class CLIPTextModel(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device, operations):
-        super().__init__()
-        self.num_layers = config_dict["num_hidden_layers"]
-        self.text_model = CLIPTextModel_(config_dict, dtype, device, operations)
-        embed_dim = config_dict["hidden_size"]
-        self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
-        self.dtype = dtype
-
-    def get_input_embeddings(self):
-        return self.text_model.embeddings.token_embedding
-
-    def set_input_embeddings(self, embeddings):
-        self.text_model.embeddings.token_embedding = embeddings
-
-    def forward(self, *args, **kwargs):
-        x = self.text_model(*args, **kwargs)
-        out = self.text_projection(x[2])
-        return (x[0], x[1], out, x[2])
-
-
-class CLIPVisionEmbeddings(torch.nn.Module):
-    def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type="", dtype=None, device=None, operations=None):
-        super().__init__()
-
-        num_patches = (image_size // patch_size) ** 2
-        if model_type == "siglip_vision_model":
-            self.class_embedding = None
-            patch_bias = True
-        else:
-            num_patches = num_patches + 1
-            self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device))
-            patch_bias = False
-
-        self.patch_embedding = operations.Conv2d(
-            in_channels=num_channels,
-            out_channels=embed_dim,
-            kernel_size=patch_size,
-            stride=patch_size,
-            bias=patch_bias,
-            dtype=dtype,
-            device=device
-        )
-
-        self.position_embedding = operations.Embedding(num_patches, embed_dim, dtype=dtype, device=device)
-
-    def forward(self, pixel_values):
-        embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2)
-        if self.class_embedding is not None:
-            embeds = torch.cat([comfy.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1)
-        return embeds + comfy.ops.cast_to_input(self.position_embedding.weight, embeds)
-
-
-class CLIPVision(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device, operations):
-        super().__init__()
-        num_layers = config_dict["num_hidden_layers"]
-        embed_dim = config_dict["hidden_size"]
-        heads = config_dict["num_attention_heads"]
-        intermediate_size = config_dict["intermediate_size"]
-        intermediate_activation = config_dict["hidden_act"]
-        model_type = config_dict["model_type"]
-
-        self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, dtype=dtype, device=device, operations=operations)
-        if model_type == "siglip_vision_model":
-            self.pre_layrnorm = lambda a: a
-            self.output_layernorm = True
-        else:
-            self.pre_layrnorm = operations.LayerNorm(embed_dim)
-            self.output_layernorm = False
-        self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
-        self.post_layernorm = operations.LayerNorm(embed_dim)
-
-    def forward(self, pixel_values, attention_mask=None, intermediate_output=None):
-        x = self.embeddings(pixel_values)
-        x = self.pre_layrnorm(x)
-        #TODO: attention_mask?
-        x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output)
-        if self.output_layernorm:
-            x = self.post_layernorm(x)
-            pooled_output = x
-        else:
-            pooled_output = self.post_layernorm(x[:, 0, :])
-        return x, i, pooled_output
-
-class LlavaProjector(torch.nn.Module):
-    def __init__(self, in_dim, out_dim, dtype, device, operations):
-        super().__init__()
-        self.linear_1 = operations.Linear(in_dim, out_dim, bias=True, device=device, dtype=dtype)
-        self.linear_2 = operations.Linear(out_dim, out_dim, bias=True, device=device, dtype=dtype)
-
-    def forward(self, x):
-        return self.linear_2(torch.nn.functional.gelu(self.linear_1(x[:, 1:])))
-
-class CLIPVisionModelProjection(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device, operations):
-        super().__init__()
-        self.vision_model = CLIPVision(config_dict, dtype, device, operations)
-        if "projection_dim" in config_dict:
-            self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False)
-        else:
-            self.visual_projection = lambda a: a
-
-        if "llava3" == config_dict.get("projector_type", None):
-            self.multi_modal_projector = LlavaProjector(config_dict["hidden_size"], 4096, dtype, device, operations)
-        else:
-            self.multi_modal_projector = None
-
-    def forward(self, *args, **kwargs):
-        x = self.vision_model(*args, **kwargs)
-        out = self.visual_projection(x[2])
-        projected = None
-        if self.multi_modal_projector is not None:
-            projected = self.multi_modal_projector(x[1])
-
-        return (x[0], x[1], out, projected)

comfy/clip_vision.py

@@ -1,148 +0,0 @@
-from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace
-import os
-import torch
-import json
-import logging
-
-import comfy.ops
-import comfy.model_patcher
-import comfy.model_management
-import comfy.utils
-import comfy.clip_model
-import comfy.image_encoders.dino2
-
-class Output:
-    def __getitem__(self, key):
-        return getattr(self, key)
-    def __setitem__(self, key, item):
-        setattr(self, key, item)
-
-def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
-    image = image[:, :, :, :3] if image.shape[3] > 3 else image
-    mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
-    std = torch.tensor(std, device=image.device, dtype=image.dtype)
-    image = image.movedim(-1, 1)
-    if not (image.shape[2] == size and image.shape[3] == size):
-        if crop:
-            scale = (size / min(image.shape[2], image.shape[3]))
-            scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
-        else:
-            scale_size = (size, size)
-
-        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
-        h = (image.shape[2] - size)//2
-        w = (image.shape[3] - size)//2
-        image = image[:,:,h:h+size,w:w+size]
-    image = torch.clip((255. * image), 0, 255).round() / 255.0
-    return (image - mean.view([3,1,1])) / std.view([3,1,1])
-
-IMAGE_ENCODERS = {
-    "clip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
-    "siglip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
-    "dinov2": comfy.image_encoders.dino2.Dinov2Model,
-}
-
-class ClipVisionModel():
-    def __init__(self, json_config):
-        with open(json_config) as f:
-            config = json.load(f)
-
-        self.image_size = config.get("image_size", 224)
-        self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073])
-        self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711])
-        model_class = IMAGE_ENCODERS.get(config.get("model_type", "clip_vision_model"))
-        self.load_device = comfy.model_management.text_encoder_device()
-        offload_device = comfy.model_management.text_encoder_offload_device()
-        self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
-        self.model = model_class(config, self.dtype, offload_device, comfy.ops.manual_cast)
-        self.model.eval()
-
-        self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
-
-    def load_sd(self, sd):
-        return self.model.load_state_dict(sd, strict=False)
-
-    def get_sd(self):
-        return self.model.state_dict()
-
-    def encode_image(self, image, crop=True):
-        comfy.model_management.load_model_gpu(self.patcher)
-        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
-        out = self.model(pixel_values=pixel_values, intermediate_output=-2)
-
-        outputs = Output()
-        outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device())
-        outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device())
-        outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device())
-        outputs["mm_projected"] = out[3]
-        return outputs
-
-def convert_to_transformers(sd, prefix):
-    sd_k = sd.keys()
-    if "{}transformer.resblocks.0.attn.in_proj_weight".format(prefix) in sd_k:
-        keys_to_replace = {
-            "{}class_embedding".format(prefix): "vision_model.embeddings.class_embedding",
-            "{}conv1.weight".format(prefix): "vision_model.embeddings.patch_embedding.weight",
-            "{}positional_embedding".format(prefix): "vision_model.embeddings.position_embedding.weight",
-            "{}ln_post.bias".format(prefix): "vision_model.post_layernorm.bias",
-            "{}ln_post.weight".format(prefix): "vision_model.post_layernorm.weight",
-            "{}ln_pre.bias".format(prefix): "vision_model.pre_layrnorm.bias",
-            "{}ln_pre.weight".format(prefix): "vision_model.pre_layrnorm.weight",
-        }
-
-        for x in keys_to_replace:
-            if x in sd_k:
-                sd[keys_to_replace[x]] = sd.pop(x)
-
-        if "{}proj".format(prefix) in sd_k:
-            sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1)
-
-        sd = transformers_convert(sd, prefix, "vision_model.", 48)
-    else:
-        replace_prefix = {prefix: ""}
-        sd = state_dict_prefix_replace(sd, replace_prefix)
-    return sd
-
-def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
-    if convert_keys:
-        sd = convert_to_transformers(sd, prefix)
-    if "vision_model.encoder.layers.47.layer_norm1.weight" in sd:
-        json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json")
-    elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
-        json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json")
-    elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd:
-        embed_shape = sd["vision_model.embeddings.position_embedding.weight"].shape[0]
-        if sd["vision_model.encoder.layers.0.layer_norm1.weight"].shape[0] == 1152:
-            if embed_shape == 729:
-                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json")
-            elif embed_shape == 1024:
-                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_512.json")
-        elif embed_shape == 577:
-            if "multi_modal_projector.linear_1.bias" in sd:
-                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336_llava.json")
-            else:
-                json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json")
-        else:
-            json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
-    elif "embeddings.patch_embeddings.projection.weight" in sd:
-        json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_giant.json")
-    else:
-        return None
-
-    clip = ClipVisionModel(json_config)
-    m, u = clip.load_sd(sd)
-    if len(m) > 0:
-        logging.warning("missing clip vision: {}".format(m))
-    u = set(u)
-    keys = list(sd.keys())
-    for k in keys:
-        if k not in u:
-            sd.pop(k)
-    return clip
-
-def load(ckpt_path):
-    sd = load_torch_file(ckpt_path)
-    if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd:
-        return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True)
-    else:
-        return load_clipvision_from_sd(sd)

comfy/clip_vision_config_g.json

@@ -1,18 +0,0 @@
-{
-  "attention_dropout": 0.0,
-  "dropout": 0.0,
-  "hidden_act": "gelu",
-  "hidden_size": 1664,
-  "image_size": 224,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 8192,
-  "layer_norm_eps": 1e-05,
-  "model_type": "clip_vision_model",
-  "num_attention_heads": 16,
-  "num_channels": 3,
-  "num_hidden_layers": 48,
-  "patch_size": 14,
-  "projection_dim": 1280,
-  "torch_dtype": "float32"
-}

comfy/clip_vision_config_h.json

@@ -1,18 +0,0 @@
-{
-  "attention_dropout": 0.0,
-  "dropout": 0.0,
-  "hidden_act": "gelu",
-  "hidden_size": 1280,
-  "image_size": 224,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 5120,
-  "layer_norm_eps": 1e-05,
-  "model_type": "clip_vision_model",
-  "num_attention_heads": 16,
-  "num_channels": 3,
-  "num_hidden_layers": 32,
-  "patch_size": 14,
-  "projection_dim": 1024,
-  "torch_dtype": "float32"
-}

comfy/clip_vision_config_vitl.json

@@ -1,18 +0,0 @@
-{
-  "attention_dropout": 0.0,
-  "dropout": 0.0,
-  "hidden_act": "quick_gelu",
-  "hidden_size": 1024,
-  "image_size": 224,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "layer_norm_eps": 1e-05,
-  "model_type": "clip_vision_model",
-  "num_attention_heads": 16,
-  "num_channels": 3,
-  "num_hidden_layers": 24,
-  "patch_size": 14,
-  "projection_dim": 768,
-  "torch_dtype": "float32"
-}

comfy/clip_vision_config_vitl_336.json

@@ -1,18 +0,0 @@
-{
-  "attention_dropout": 0.0,
-  "dropout": 0.0,
-  "hidden_act": "quick_gelu",
-  "hidden_size": 1024,
-  "image_size": 336,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "layer_norm_eps": 1e-5,
-  "model_type": "clip_vision_model",
-  "num_attention_heads": 16,
-  "num_channels": 3,
-  "num_hidden_layers": 24,
-  "patch_size": 14,
-  "projection_dim": 768,
-  "torch_dtype": "float32"
-}

comfy/clip_vision_config_vitl_336_llava.json

@@ -1,19 +0,0 @@
-{
-  "attention_dropout": 0.0,
-  "dropout": 0.0,
-  "hidden_act": "quick_gelu",
-  "hidden_size": 1024,
-  "image_size": 336,
-  "initializer_factor": 1.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "layer_norm_eps": 1e-5,
-  "model_type": "clip_vision_model",
-  "num_attention_heads": 16,
-  "num_channels": 3,
-  "num_hidden_layers": 24,
-  "patch_size": 14,
-  "projection_dim": 768,
-  "projector_type": "llava3",
-  "torch_dtype": "float32"
-}

comfy/clip_vision_siglip_384.json

@@ -1,13 +0,0 @@
-{
-  "num_channels": 3,
-  "hidden_act": "gelu_pytorch_tanh",
-  "hidden_size": 1152,
-  "image_size": 384,
-  "intermediate_size": 4304,
-  "model_type": "siglip_vision_model",
-  "num_attention_heads": 16,
-  "num_hidden_layers": 27,
-  "patch_size": 14,
-  "image_mean": [0.5, 0.5, 0.5],
-  "image_std": [0.5, 0.5, 0.5]
-}

comfy/clip_vision_siglip_512.json

@@ -1,13 +0,0 @@
-{
-  "num_channels": 3,
-  "hidden_act": "gelu_pytorch_tanh",
-  "hidden_size": 1152,
-  "image_size": 512,
-  "intermediate_size": 4304,
-  "model_type": "siglip_vision_model",
-  "num_attention_heads": 16,
-  "num_hidden_layers": 27,
-  "patch_size": 16,
-  "image_mean": [0.5, 0.5, 0.5],
-  "image_std": [0.5, 0.5, 0.5]
-}

comfy/comfy_types/README.md

@@ -1,43 +0,0 @@
-# Comfy Typing
-## Type hinting for ComfyUI Node development
-
-This module provides type hinting and concrete convenience types for node developers.
-If cloned to the custom_nodes directory of ComfyUI, types can be imported using:
-
-```python
-from comfy.comfy_types import IO, ComfyNodeABC, CheckLazyMixin
-
-class ExampleNode(ComfyNodeABC):
-    @classmethod
-    def INPUT_TYPES(s) -> InputTypeDict:
-        return {"required": {}}
-```
-
-Full example is in [examples/example_nodes.py](examples/example_nodes.py).
-
-# Types
-A few primary types are documented below.  More complete information is available via the docstrings on each type.
-
-## `IO`
-
-A string enum of built-in and a few custom data types.  Includes the following special types and their requisite plumbing:
-
-- `ANY`: `"*"`
-- `NUMBER`: `"FLOAT,INT"`
-- `PRIMITIVE`: `"STRING,FLOAT,INT,BOOLEAN"`
-
-## `ComfyNodeABC`
-
-An abstract base class for nodes, offering type-hinting / autocomplete, and somewhat-alright docstrings.
-
-### Type hinting for `INPUT_TYPES`
-
-![INPUT_TYPES auto-completion in Visual Studio Code](examples/input_types.png)
-
-### `INPUT_TYPES` return dict
-
-![INPUT_TYPES return value type hinting in Visual Studio Code](examples/required_hint.png)
-
-### Options for individual inputs
-
-![INPUT_TYPES return value option auto-completion in Visual Studio Code](examples/input_options.png)

comfy/comfy_types/__init__.py

@@ -1,46 +0,0 @@
-import torch
-from typing import Callable, Protocol, TypedDict, Optional, List
-from .node_typing import IO, InputTypeDict, ComfyNodeABC, CheckLazyMixin, FileLocator
-
-
-class UnetApplyFunction(Protocol):
-    """Function signature protocol on comfy.model_base.BaseModel.apply_model"""
-
-    def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torch.Tensor:
-        pass
-
-
-class UnetApplyConds(TypedDict):
-    """Optional conditions for unet apply function."""
-
-    c_concat: Optional[torch.Tensor]
-    c_crossattn: Optional[torch.Tensor]
-    control: Optional[torch.Tensor]
-    transformer_options: Optional[dict]
-
-
-class UnetParams(TypedDict):
-    # Tensor of shape [B, C, H, W]
-    input: torch.Tensor
-    # Tensor of shape [B]
-    timestep: torch.Tensor
-    c: UnetApplyConds
-    # List of [0, 1], [0], [1], ...
-    # 0 means conditional, 1 means conditional unconditional
-    cond_or_uncond: List[int]
-
-
-UnetWrapperFunction = Callable[[UnetApplyFunction, UnetParams], torch.Tensor]
-
-
-__all__ = [
-    "UnetWrapperFunction",
-    UnetApplyConds.__name__,
-    UnetParams.__name__,
-    UnetApplyFunction.__name__,
-    IO.__name__,
-    InputTypeDict.__name__,
-    ComfyNodeABC.__name__,
-    CheckLazyMixin.__name__,
-    FileLocator.__name__,
-]

comfy/comfy_types/examples/example_nodes.py

@@ -1,28 +0,0 @@
-from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
-from inspect import cleandoc
-
-
-class ExampleNode(ComfyNodeABC):
-    """An example node that just adds 1 to an input integer.
-
-    * Requires a modern IDE to provide any benefit (detail: an IDE configured with analysis paths etc).
-    * This node is intended as an example for developers only.
-    """
-
-    DESCRIPTION = cleandoc(__doc__)
-    CATEGORY = "examples"
-
-    @classmethod
-    def INPUT_TYPES(s) -> InputTypeDict:
-        return {
-            "required": {
-                "input_int": (IO.INT, {"defaultInput": True}),
-            }
-        }
-
-    RETURN_TYPES = (IO.INT,)
-    RETURN_NAMES = ("input_plus_one",)
-    FUNCTION = "execute"
-
-    def execute(self, input_int: int):
-        return (input_int + 1,)

comfy/comfy_types/node_typing.py

@@ -1,350 +0,0 @@
-"""Comfy-specific type hinting"""
-
-from __future__ import annotations
-from typing import Literal, TypedDict, Optional
-from typing_extensions import NotRequired
-from abc import ABC, abstractmethod
-from enum import Enum
-
-
-class StrEnum(str, Enum):
-    """Base class for string enums. Python's StrEnum is not available until 3.11."""
-
-    def __str__(self) -> str:
-        return self.value
-
-
-class IO(StrEnum):
-    """Node input/output data types.
-
-    Includes functionality for ``"*"`` (`ANY`) and ``"MULTI,TYPES"``.
-    """
-
-    STRING = "STRING"
-    IMAGE = "IMAGE"
-    MASK = "MASK"
-    LATENT = "LATENT"
-    BOOLEAN = "BOOLEAN"
-    INT = "INT"
-    FLOAT = "FLOAT"
-    COMBO = "COMBO"
-    CONDITIONING = "CONDITIONING"
-    SAMPLER = "SAMPLER"
-    SIGMAS = "SIGMAS"
-    GUIDER = "GUIDER"
-    NOISE = "NOISE"
-    CLIP = "CLIP"
-    CONTROL_NET = "CONTROL_NET"
-    VAE = "VAE"
-    MODEL = "MODEL"
-    LORA_MODEL = "LORA_MODEL"
-    LOSS_MAP = "LOSS_MAP"
-    CLIP_VISION = "CLIP_VISION"
-    CLIP_VISION_OUTPUT = "CLIP_VISION_OUTPUT"
-    STYLE_MODEL = "STYLE_MODEL"
-    GLIGEN = "GLIGEN"
-    UPSCALE_MODEL = "UPSCALE_MODEL"
-    AUDIO = "AUDIO"
-    WEBCAM = "WEBCAM"
-    POINT = "POINT"
-    FACE_ANALYSIS = "FACE_ANALYSIS"
-    BBOX = "BBOX"
-    SEGS = "SEGS"
-    VIDEO = "VIDEO"
-
-    ANY = "*"
-    """Always matches any type, but at a price.
-
-    Causes some functionality issues (e.g. reroutes, link types), and should be avoided whenever possible.
-    """
-    NUMBER = "FLOAT,INT"
-    """A float or an int - could be either"""
-    PRIMITIVE = "STRING,FLOAT,INT,BOOLEAN"
-    """Could be any of: string, float, int, or bool"""
-
-    def __ne__(self, value: object) -> bool:
-        if self == "*" or value == "*":
-            return False
-        if not isinstance(value, str):
-            return True
-        a = frozenset(self.split(","))
-        b = frozenset(value.split(","))
-        return not (b.issubset(a) or a.issubset(b))
-
-
-class RemoteInputOptions(TypedDict):
-    route: str
-    """The route to the remote source."""
-    refresh_button: bool
-    """Specifies whether to show a refresh button in the UI below the widget."""
-    control_after_refresh: Literal["first", "last"]
-    """Specifies the control after the refresh button is clicked. If "first", the first item will be automatically selected, and so on."""
-    timeout: int
-    """The maximum amount of time to wait for a response from the remote source in milliseconds."""
-    max_retries: int
-    """The maximum number of retries before aborting the request."""
-    refresh: int
-    """The TTL of the remote input's value in milliseconds. Specifies the interval at which the remote input's value is refreshed."""
-
-
-class MultiSelectOptions(TypedDict):
-    placeholder: NotRequired[str]
-    """The placeholder text to display in the multi-select widget when no items are selected."""
-    chip: NotRequired[bool]
-    """Specifies whether to use chips instead of comma separated values for the multi-select widget."""
-
-
-class InputTypeOptions(TypedDict):
-    """Provides type hinting for the return type of the INPUT_TYPES node function.
-
-    Due to IDE limitations with unions, for now all options are available for all types (e.g. `label_on` is hinted even when the type is not `IO.BOOLEAN`).
-
-    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/datatypes
-    """
-
-    default: NotRequired[bool | str | float | int | list | tuple]
-    """The default value of the widget"""
-    defaultInput: NotRequired[bool]
-    """@deprecated in v1.16 frontend. v1.16 frontend allows input socket and widget to co-exist.
-    - defaultInput on required inputs should be dropped.
-    - defaultInput on optional inputs should be replaced with forceInput.
-    Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3364
-    """
-    forceInput: NotRequired[bool]
-    """Forces the input to be an input slot rather than a widget even a widget is available for the input type."""
-    lazy: NotRequired[bool]
-    """Declares that this input uses lazy evaluation"""
-    rawLink: NotRequired[bool]
-    """When a link exists, rather than receiving the evaluated value, you will receive the link (i.e. `["nodeId", <outputIndex>]`). Designed for node expansion."""
-    tooltip: NotRequired[str]
-    """Tooltip for the input (or widget), shown on pointer hover"""
-    socketless: NotRequired[bool]
-    """All inputs (including widgets) have an input socket to connect links. When ``true``, if there is a widget for this input, no socket will be created.
-    Available from frontend v1.17.5
-    Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3548
-    """
-    widgetType: NotRequired[str]
-    """Specifies a type to be used for widget initialization if different from the input type.
-    Available from frontend v1.18.0
-    https://github.com/Comfy-Org/ComfyUI_frontend/pull/3550"""
-    # class InputTypeNumber(InputTypeOptions):
-    # default: float | int
-    min: NotRequired[float]
-    """The minimum value of a number (``FLOAT`` | ``INT``)"""
-    max: NotRequired[float]
-    """The maximum value of a number (``FLOAT`` | ``INT``)"""
-    step: NotRequired[float]
-    """The amount to increment or decrement a widget by when stepping up/down (``FLOAT`` | ``INT``)"""
-    round: NotRequired[float]
-    """Floats are rounded by this value (``FLOAT``)"""
-    # class InputTypeBoolean(InputTypeOptions):
-    # default: bool
-    label_on: NotRequired[str]
-    """The label to use in the UI when the bool is True (``BOOLEAN``)"""
-    label_off: NotRequired[str]
-    """The label to use in the UI when the bool is False (``BOOLEAN``)"""
-    # class InputTypeString(InputTypeOptions):
-    # default: str
-    multiline: NotRequired[bool]
-    """Use a multiline text box (``STRING``)"""
-    placeholder: NotRequired[str]
-    """Placeholder text to display in the UI when empty (``STRING``)"""
-    # Deprecated:
-    # defaultVal: str
-    dynamicPrompts: NotRequired[bool]
-    """Causes the front-end to evaluate dynamic prompts (``STRING``)"""
-    # class InputTypeCombo(InputTypeOptions):
-    image_upload: NotRequired[bool]
-    """Specifies whether the input should have an image upload button and image preview attached to it. Requires that the input's name is `image`."""
-    image_folder: NotRequired[Literal["input", "output", "temp"]]
-    """Specifies which folder to get preview images from if the input has the ``image_upload`` flag.
-    """
-    remote: NotRequired[RemoteInputOptions]
-    """Specifies the configuration for a remote input.
-    Available after ComfyUI frontend v1.9.7
-    https://github.com/Comfy-Org/ComfyUI_frontend/pull/2422"""
-    control_after_generate: NotRequired[bool]
-    """Specifies whether a control widget should be added to the input, adding options to automatically change the value after each prompt is queued. Currently only used for INT and COMBO types."""
-    options: NotRequired[list[str | int | float]]
-    """COMBO type only. Specifies the selectable options for the combo widget.
-    Prefer:
-    ["COMBO", {"options": ["Option 1", "Option 2", "Option 3"]}]
-    Over:
-    [["Option 1", "Option 2", "Option 3"]]
-    """
-    multi_select: NotRequired[MultiSelectOptions]
-    """COMBO type only. Specifies the configuration for a multi-select widget.
-    Available after ComfyUI frontend v1.13.4
-    https://github.com/Comfy-Org/ComfyUI_frontend/pull/2987"""
-
-
-class HiddenInputTypeDict(TypedDict):
-    """Provides type hinting for the hidden entry of node INPUT_TYPES."""
-
-    node_id: NotRequired[Literal["UNIQUE_ID"]]
-    """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages)."""
-    unique_id: NotRequired[Literal["UNIQUE_ID"]]
-    """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages)."""
-    prompt: NotRequired[Literal["PROMPT"]]
-    """PROMPT is the complete prompt sent by the client to the server. See the prompt object for a full description."""
-    extra_pnginfo: NotRequired[Literal["EXTRA_PNGINFO"]]
-    """EXTRA_PNGINFO is a dictionary that will be copied into the metadata of any .png files saved. Custom nodes can store additional information in this dictionary for saving (or as a way to communicate with a downstream node)."""
-    dynprompt: NotRequired[Literal["DYNPROMPT"]]
-    """DYNPROMPT is an instance of comfy_execution.graph.DynamicPrompt. It differs from PROMPT in that it may mutate during the course of execution in response to Node Expansion."""
-
-
-class InputTypeDict(TypedDict):
-    """Provides type hinting for node INPUT_TYPES.
-
-    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs
-    """
-
-    required: NotRequired[dict[str, tuple[IO, InputTypeOptions]]]
-    """Describes all inputs that must be connected for the node to execute."""
-    optional: NotRequired[dict[str, tuple[IO, InputTypeOptions]]]
-    """Describes inputs which do not need to be connected."""
-    hidden: NotRequired[HiddenInputTypeDict]
-    """Offers advanced functionality and server-client communication.
-
-    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs
-    """
-
-
-class ComfyNodeABC(ABC):
-    """Abstract base class for Comfy nodes.  Includes the names and expected types of attributes.
-
-    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview
-    """
-
-    DESCRIPTION: str
-    """Node description, shown as a tooltip when hovering over the node.
-
-    Usage::
-
-        # Explicitly define the description
-        DESCRIPTION = "Example description here."
-
-        # Use the docstring of the node class.
-        DESCRIPTION = cleandoc(__doc__)
-    """
-    CATEGORY: str
-    """The category of the node, as per the "Add Node" menu.
-
-    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#category
-    """
-    EXPERIMENTAL: bool
-    """Flags a node as experimental, informing users that it may change or not work as expected."""
-    DEPRECATED: bool
-    """Flags a node as deprecated, indicating to users that they should find alternatives to this node."""
-    API_NODE: Optional[bool]
-    """Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview."""
-
-    @classmethod
-    @abstractmethod
-    def INPUT_TYPES(s) -> InputTypeDict:
-        """Defines node inputs.
-
-        * Must include the ``required`` key, which describes all inputs that must be connected for the node to execute.
-        * The ``optional`` key can be added to describe inputs which do not need to be connected.
-        * The ``hidden`` key offers some advanced functionality.  More info at: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs
-
-        Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#input-types
-        """
-        return {"required": {}}
-
-    OUTPUT_NODE: bool
-    """Flags this node as an output node, causing any inputs it requires to be executed.
-
-    If a node is not connected to any output nodes, that node will not be executed.  Usage::
-
-        OUTPUT_NODE = True
-
-    From the docs:
-
-    By default, a node is not considered an output. Set ``OUTPUT_NODE = True`` to specify that it is.
-
-    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#output-node
-    """
-    INPUT_IS_LIST: bool
-    """A flag indicating if this node implements the additional code necessary to deal with OUTPUT_IS_LIST nodes.
-
-    All inputs of ``type`` will become ``list[type]``, regardless of how many items are passed in.  This also affects ``check_lazy_status``.
-
-    From the docs:
-
-    A node can also override the default input behaviour and receive the whole list in a single call. This is done by setting a class attribute `INPUT_IS_LIST` to ``True``.
-
-    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing
-    """
-    OUTPUT_IS_LIST: tuple[bool, ...]
-    """A tuple indicating which node outputs are lists, but will be connected to nodes that expect individual items.
-
-    Connected nodes that do not implement `INPUT_IS_LIST` will be executed once for every item in the list.
-
-    A ``tuple[bool]``, where the items match those in `RETURN_TYPES`::
-
-        RETURN_TYPES = (IO.INT, IO.INT, IO.STRING)
-        OUTPUT_IS_LIST = (True, True, False) # The string output will be handled normally
-
-    From the docs:
-
-    In order to tell Comfy that the list being returned should not be wrapped, but treated as a series of data for sequential processing,
-    the node should provide a class attribute `OUTPUT_IS_LIST`, which is a ``tuple[bool]``, of the same length as `RETURN_TYPES`,
-    specifying which outputs which should be so treated.
-
-    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing
-    """
-
-    RETURN_TYPES: tuple[IO, ...]
-    """A tuple representing the outputs of this node.
-
-    Usage::
-
-        RETURN_TYPES = (IO.INT, "INT", "CUSTOM_TYPE")
-
-    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-types
-    """
-    RETURN_NAMES: tuple[str, ...]
-    """The output slot names for each item in `RETURN_TYPES`, e.g. ``RETURN_NAMES = ("count", "filter_string")``
-
-    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-names
-    """
-    OUTPUT_TOOLTIPS: tuple[str, ...]
-    """A tuple of strings to use as tooltips for node outputs, one for each item in `RETURN_TYPES`."""
-    FUNCTION: str
-    """The name of the function to execute as a literal string, e.g. `FUNCTION = "execute"`
-
-    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#function
-    """
-
-
-class CheckLazyMixin:
-    """Provides a basic check_lazy_status implementation and type hinting for nodes that use lazy inputs."""
-
-    def check_lazy_status(self, **kwargs) -> list[str]:
-        """Returns a list of input names that should be evaluated.
-
-        This basic mixin impl. requires all inputs.
-
-        :kwargs: All node inputs will be included here.  If the input is ``None``, it should be assumed that it has not yet been evaluated.  \
-            When using ``INPUT_IS_LIST = True``, unevaluated will instead be ``(None,)``.
-
-        Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name).
-        Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params).
-
-        Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status
-        """
-
-        need = [name for name in kwargs if kwargs[name] is None]
-        return need
-
-
-class FileLocator(TypedDict):
-    """Provides type hinting for the file location"""
-
-    filename: str
-    """The filename of the file."""
-    subfolder: str
-    """The subfolder of the file."""
-    type: Literal["input", "output", "temp"]
-    """The root folder of the file."""

comfy/conds.py

@@ -1,137 +0,0 @@
-import torch
-import math
-import comfy.utils
-import logging
-
-
-class CONDRegular:
-    def __init__(self, cond):
-        self.cond = cond
-
-    def _copy_with(self, cond):
-        return self.__class__(cond)
-
-    def process_cond(self, batch_size, **kwargs):
-        return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size))
-
-    def can_concat(self, other):
-        if self.cond.shape != other.cond.shape:
-            return False
-        if self.cond.device != other.cond.device:
-            logging.warning("WARNING: conds not on same device, skipping concat.")
-            return False
-        return True
-
-    def concat(self, others):
-        conds = [self.cond]
-        for x in others:
-            conds.append(x.cond)
-        return torch.cat(conds)
-
-    def size(self):
-        return list(self.cond.size())
-
-
-class CONDNoiseShape(CONDRegular):
-    def process_cond(self, batch_size, area, **kwargs):
-        data = self.cond
-        if area is not None:
-            dims = len(area) // 2
-            for i in range(dims):
-                data = data.narrow(i + 2, area[i + dims], area[i])
-
-        return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size))
-
-
-class CONDCrossAttn(CONDRegular):
-    def can_concat(self, other):
-        s1 = self.cond.shape
-        s2 = other.cond.shape
-        if s1 != s2:
-            if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
-                return False
-
-            mult_min = math.lcm(s1[1], s2[1])
-            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
-        if self.cond.device != other.cond.device:
-            logging.warning("WARNING: conds not on same device: skipping concat.")
-            return False
-        return True
-
-    def concat(self, others):
-        conds = [self.cond]
-        crossattn_max_len = self.cond.shape[1]
-        for x in others:
-            c = x.cond
-            crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1])
-            conds.append(c)
-
-        out = []
-        for c in conds:
-            if c.shape[1] < crossattn_max_len:
-                c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result
-            out.append(c)
-        return torch.cat(out)
-
-
-class CONDConstant(CONDRegular):
-    def __init__(self, cond):
-        self.cond = cond
-
-    def process_cond(self, batch_size, **kwargs):
-        return self._copy_with(self.cond)
-
-    def can_concat(self, other):
-        if self.cond != other.cond:
-            return False
-        return True
-
-    def concat(self, others):
-        return self.cond
-
-    def size(self):
-        return [1]
-
-
-class CONDList(CONDRegular):
-    def __init__(self, cond):
-        self.cond = cond
-
-    def process_cond(self, batch_size, **kwargs):
-        out = []
-        for c in self.cond:
-            out.append(comfy.utils.repeat_to_batch_size(c, batch_size))
-
-        return self._copy_with(out)
-
-    def can_concat(self, other):
-        if len(self.cond) != len(other.cond):
-            return False
-        for i in range(len(self.cond)):
-            if self.cond[i].shape != other.cond[i].shape:
-                return False
-
-        return True
-
-    def concat(self, others):
-        out = []
-        for i in range(len(self.cond)):
-            o = [self.cond[i]]
-            for x in others:
-                o.append(x.cond[i])
-            out.append(torch.cat(o))
-
-        return out
-
-    def size(self):  # hackish implementation to make the mem estimation work
-        o = 0
-        c = 1
-        for c in self.cond:
-            size = c.size()
-            o += math.prod(size)
-            if len(size) > 1:
-                c = size[1]
-
-        return [1, c, o // c]