Dariusz L
1a4156f00d
When a model search returns no results for a specific base_model, retry the same query with base_model_context=None so the provider can match any model. Fallback matches are marked with any_model_match and base_model_fallback on the result payload. The UI reports 'Found (Any Model)' when a result came from this fallback path, and the cancel/progress button styling is tightened to 16x16px with border-radius.
2478 lines
122 KiB
Python
2478 lines
122 KiB
Python
"""
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@author: Model Resolver Team
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@title: ComfyUI Model Resolver
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@nickname: Model Resolver
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@version: 1.1.0
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@description: Extension for resolving missing models and downloading from HuggingFace/CivitAI
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"""
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import asyncio
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from .core.log_system.log_funcs import (
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create_module_logger,
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log_debug,
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log_info,
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log_error,
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log_exception,
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log_warn,
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)
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from .core.log_system.config import LOG_LEVEL as BACKEND_DEFAULT_LOG_LEVEL
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from .core.log_system.logger import LogLevel, logger as backend_log_controller
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# Web directory for JavaScript interface
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WEB_DIRECTORY = "./web"
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# Empty NODE_CLASS_MAPPINGS - we don't provide custom nodes, only web extension
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# This prevents ComfyUI from showing "IMPORT FAILED" message
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NODE_CLASS_MAPPINGS = {}
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__all__ = ["WEB_DIRECTORY"]
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class ModelResolverExtension:
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"""Main extension class for Model Resolver."""
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def __init__(self):
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self.routes_setup = False
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self.logger = create_module_logger(__name__)
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self.analysis_progress = {}
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self.search_result_timestamps = {}
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def initialize(self):
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"""Initialize the extension and set up API routes."""
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try:
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self.setup_routes()
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self.logger.info("Model Resolver: Extension initialized successfully")
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except Exception as e:
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self.logger.error(
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f"Model Resolver: Extension initialization failed: {e}", exc_info=True
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)
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def setup_routes(self):
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"""Register API routes for the Model Resolver extension."""
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if self.routes_setup:
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return # Already set up
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try:
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from aiohttp import web
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# Try to get routes from PromptServer
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try:
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from server import PromptServer
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if (
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not hasattr(PromptServer, "instance")
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or PromptServer.instance is None
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):
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self.logger.debug("Model Resolver: PromptServer not available yet")
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return False
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routes = PromptServer.instance.routes
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except (ImportError, AttributeError) as e:
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self.logger.debug(f"Model Resolver: Could not access PromptServer: {e}")
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return False
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# Import resolver modules
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try:
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from .core.resolver import (
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analyze_and_find_matches,
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apply_resolution,
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search_local_matches,
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)
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from .core.path_templates import infer_download_path_templates
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from .core.scanner import get_model_files, invalidate_model_files_cache
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from .core.settings import (
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bool_setting as resolver_bool_setting,
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get_default_root_for_category,
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load_settings as load_resolver_settings,
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resolve_download_subfolder,
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save_settings as save_resolver_settings,
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)
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except ImportError as e:
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self.logger.error(f"Model Resolver: Could not import core modules: {e}")
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return False
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# Import download modules
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try:
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from .core.downloader import (
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start_background_download,
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get_progress,
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get_all_progress,
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cancel_download,
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get_download_directory,
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normalize_download_category,
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)
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from .core.sources.popular import (
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get_popular_model_url,
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search_popular_models,
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reload_databases as reload_popular_databases,
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)
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from .core.sources.model_list import (
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search_model_list,
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search_model_list_multiple,
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get_model_list_update_status,
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update_model_list_from_remote,
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reload_model_list,
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)
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from .core.sources.huggingface import (
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search_huggingface_for_file,
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get_author_fallback_index_status,
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refresh_author_fallback_index,
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check_huggingface_token,
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check_brave_search_api_key,
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clear_search_cache as clear_huggingface_search_cache,
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)
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from .core.sources.civitai import (
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search_civitai_for_file,
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search_civitai,
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get_civitai_download_url,
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get_civitai_model_details,
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resolve_urn,
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check_civitai_api_key,
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check_civitai_session_token,
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clear_search_cache as clear_civitai_search_cache,
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)
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from .core.sources.civarchive import (
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CivArchiveSearchError,
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is_civarchive_available,
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search_civarchive_for_file,
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resolve_civarchive_model_version,
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get_civarchive_model_details,
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clear_search_cache as clear_civarchive_search_cache,
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)
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from .core.sources.lora_manager_archive import (
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is_lora_manager_archive_available,
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search_lora_manager_archive_for_file,
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clear_search_cache as clear_lora_manager_archive_search_cache,
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)
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download_available = True
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except ImportError as e:
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self.logger.warning(
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f"Model Resolver: Download features not available: {e}"
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)
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download_available = False
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# ==================== BASE MODELS CONFIG ROUTE ====================
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@routes.get("/model_resolver/base-models")
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async def get_base_models(request):
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"""Return the base models config from metadata/base-models.json.
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Returns {base_models: [{name, aliases}]} so the frontend
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dropdown can populate correctly via baseModels.base_models.
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"""
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try:
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from .core.sources.popular import get_base_models_config
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data = get_base_models_config()
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return web.json_response(data)
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except Exception as e:
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self.logger.error(
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f"Model Resolver base-models error: {e}", exc_info=True
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)
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return web.json_response({"error": str(e)}, status=500)
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@routes.get("/model_resolver/base-models/status")
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async def get_base_models_status_route(request):
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"""Get local and optional remote base models status."""
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try:
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check_remote = request.query.get("check_remote") == "1"
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from .core.sources.popular import get_base_models_status
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status = await asyncio.to_thread(get_base_models_status, check_remote)
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return web.json_response(status)
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except Exception as e:
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self.logger.error(
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f"Model Resolver base-models status error: {e}", exc_info=True
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)
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return web.json_response({"error": str(e)}, status=500)
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@routes.post("/model_resolver/base-models/update")
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async def update_base_models_route(request):
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"""Update base models list from CivitAI."""
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try:
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from .core.sources.popular import update_base_models_from_remote
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status = await asyncio.to_thread(update_base_models_from_remote)
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return web.json_response(status)
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except Exception as e:
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self.logger.error(
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f"Model Resolver base-models update error: {e}", exc_info=True
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)
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return web.json_response({"error": str(e)}, status=500)
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# ==================== ANALYZE ROUTES ====================
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@routes.post("/model_resolver/analyze")
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async def analyze_workflow(request):
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"""Analyze workflow and return missing models with matches."""
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try:
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data = await request.json()
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workflow_json = data.get("workflow")
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analysis_id = str(data.get("analysis_id") or "").strip()
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force_rescan = data.get("force_rescan", False)
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force_rescan = (
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force_rescan
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if isinstance(force_rescan, bool)
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else str(force_rescan).lower() in {"1", "true", "yes"}
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)
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if not workflow_json:
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return web.json_response(
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{"error": "Workflow JSON is required"}, status=400
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)
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if analysis_id:
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self.analysis_progress[analysis_id] = {
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"status": "starting",
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"stage": "starting",
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"message": "Starting analysis...",
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"current": 0,
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"total": 0,
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}
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def update_analysis_progress(payload):
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if not analysis_id:
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return
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self.analysis_progress[analysis_id] = {
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**self.analysis_progress.get(analysis_id, {}),
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**payload,
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"status": "running"
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if payload.get("stage") != "completed"
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else "completed",
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}
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# Analyze and find matches
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result = await asyncio.to_thread(
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analyze_and_find_matches,
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workflow_json,
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0.0,
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10,
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update_analysis_progress if analysis_id else None,
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force_rescan=force_rescan,
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)
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# Filter out LoraManager lorAs that already exist locally (exists=True)
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# These should not appear in missing models at all
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missing_models = result.get("missing_models", [])
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filtered_missing = []
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for missing in missing_models:
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is_lora = missing.get("is_lora_v2")
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exists = missing.get("exists")
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name = missing.get("name") or missing.get("original_path", "")
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self.logger.debug(
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f"Filtering: {name} is_lora_v2={is_lora} exists={exists}"
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)
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# Skip LoraManager lorAs that already exist locally
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if is_lora and exists:
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self.logger.info(
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f"Filtered out LoraManager lora: {name}"
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)
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continue
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filtered_missing.append(missing)
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result["missing_models"] = filtered_missing
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result["total_missing"] = len(filtered_missing)
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# If download available, check for download sources only from LOCAL sources
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# (workflow_url, popular, model-list.json) - skip automatic online search
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# Online search is now only triggered on-demand via search button
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if download_available:
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for missing in result.get("missing_models", []):
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# Check if there's a 100% local match
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matches = missing.get("matches", [])
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has_perfect_match = any(
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m.get("confidence", 0) == 100 for m in matches
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)
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if not has_perfect_match:
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filename = (
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missing.get("original_path", "")
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.split("/")[-1]
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.split("\\")[-1]
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)
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# 0. Check workflow URL first (highest priority - directly from workflow)
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workflow_url = missing.get("workflow_url", "")
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if workflow_url:
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# Determine source from URL
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if "huggingface.co" in workflow_url:
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source = "huggingface"
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elif "civitai.com" in workflow_url:
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source = "civitai"
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else:
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source = "workflow"
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# Try to get file size with HEAD request (non-blocking, timeout quickly)
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file_size = None
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try:
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import requests
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head_response = requests.head(
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workflow_url,
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allow_redirects=True,
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timeout=5,
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)
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if head_response.status_code == 200:
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file_size = int(
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head_response.headers.get(
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"content-length", 0
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)
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)
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except Exception:
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pass # Size unknown is fine
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missing["download_source"] = {
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"source": source,
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"url": workflow_url,
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"model_url": missing.get(
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"workflow_model_url", workflow_url
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),
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"filename": filename,
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"directory": missing.get(
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"workflow_directory", ""
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)
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or missing.get("category", "checkpoints"),
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"match_type": "exact",
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"url_source": "workflow",
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"size": file_size,
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}
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continue
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# 1. Check popular models (always exact match)
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popular_info = get_popular_model_url(filename)
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if popular_info:
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popular_model_list_result = search_model_list(
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filename, exact_only=True
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)
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missing["download_source"] = {
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"source": "popular",
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"url": popular_info.get("url"),
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"filename": filename,
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"type": popular_info.get("type"),
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"directory": popular_info.get("directory"),
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"size": (
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popular_model_list_result.get("size")
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if popular_model_list_result
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else None
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)
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or popular_info.get("size"),
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"match_type": "exact",
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}
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continue
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# 2. Check model list (ComfyUI Manager database)
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# Use exact_only=True to avoid confusing fuzzy matches for downloads
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model_list_result = search_model_list(
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filename, exact_only=True
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)
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if model_list_result:
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missing["download_source"] = {
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"source": "model_list",
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"url": model_list_result.get("url"),
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"filename": model_list_result.get("filename"),
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"name": model_list_result.get("name"),
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"type": model_list_result.get("type"),
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"directory": model_list_result.get("directory"),
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"size": model_list_result.get("size"),
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"match_type": model_list_result.get(
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"match_type"
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),
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"confidence": model_list_result.get(
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"confidence"
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),
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}
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continue
|
|
|
|
# NOTE: Search for online sources (HuggingFace, CivitAI) is
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# now done on-demand via /model_resolver/search endpoint
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# when user clicks "Search Online" button, not automatically
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|
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if analysis_id:
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self.analysis_progress[analysis_id] = {
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**self.analysis_progress.get(analysis_id, {}),
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"status": "completed",
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"stage": "completed",
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"message": "Analysis complete",
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"current": result.get("total_missing", 0),
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"total": result.get("total_missing", 0),
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}
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return web.json_response(result)
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except Exception as e:
|
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if "analysis_id" in locals() and analysis_id:
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self.analysis_progress[analysis_id] = {
|
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"status": "error",
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"stage": "error",
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"message": str(e),
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"current": 0,
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"total": 0,
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}
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self.logger.error(f"Model Resolver analyze error: {e}", exc_info=True)
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return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.get("/model_resolver/analyze-progress/{analysis_id}")
|
|
async def get_analyze_progress(request):
|
|
"""Get workflow analysis progress."""
|
|
analysis_id = request.match_info.get("analysis_id", "").strip()
|
|
if not analysis_id:
|
|
return web.json_response(
|
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{"error": "Analysis ID is required"}, status=400
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|
)
|
|
|
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progress = self.analysis_progress.get(analysis_id)
|
|
if not progress:
|
|
return web.json_response(
|
|
{
|
|
"status": "unknown",
|
|
"stage": "unknown",
|
|
"message": "No analysis progress available",
|
|
"current": 0,
|
|
"total": 0,
|
|
}
|
|
)
|
|
|
|
return web.json_response(progress)
|
|
|
|
@routes.post("/model_resolver/resolve")
|
|
async def resolve_models(request):
|
|
"""Apply model resolution and return updated workflow."""
|
|
try:
|
|
data = await request.json()
|
|
workflow_json = data.get("workflow")
|
|
resolutions = data.get("resolutions", [])
|
|
|
|
if not workflow_json:
|
|
return web.json_response(
|
|
{"error": "Workflow JSON is required"}, status=400
|
|
)
|
|
|
|
if not resolutions:
|
|
return web.json_response(
|
|
{"error": "Resolutions array is required"}, status=400
|
|
)
|
|
|
|
# Apply resolutions
|
|
updated_workflow = apply_resolution(workflow_json, resolutions)
|
|
|
|
return web.json_response(
|
|
{"workflow": updated_workflow, "success": True}
|
|
)
|
|
except Exception as e:
|
|
self.logger.error(f"Model Resolver resolve error: {e}", exc_info=True)
|
|
return web.json_response(
|
|
{"error": str(e), "success": False}, status=500
|
|
)
|
|
|
|
@routes.post("/model_resolver/local-matches")
|
|
async def local_matches(request):
|
|
"""Search local model files by filename/path."""
|
|
try:
|
|
data = await request.json()
|
|
filename = data.get("filename", "")
|
|
category = data.get("category", "")
|
|
force_rescan = data.get("force_rescan", False)
|
|
force_rescan = (
|
|
force_rescan
|
|
if isinstance(force_rescan, bool)
|
|
else str(force_rescan).lower() == "true"
|
|
)
|
|
|
|
if not filename:
|
|
return web.json_response(
|
|
{"error": "filename is required"}, status=400
|
|
)
|
|
|
|
matches = search_local_matches(
|
|
filename,
|
|
category=category or None,
|
|
similarity_threshold=0.0,
|
|
max_matches_per_model=10,
|
|
force_rescan=force_rescan,
|
|
)
|
|
return web.json_response({"matches": matches})
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver local-matches error: {e}", exc_info=True
|
|
)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.post("/model_resolver/open-containing-folder")
|
|
async def open_containing_folder(request):
|
|
"""Open Explorer at the folder containing the selected model."""
|
|
try:
|
|
import os
|
|
import subprocess
|
|
|
|
data = await request.json()
|
|
target_path = data.get("path", "")
|
|
|
|
if not target_path:
|
|
return web.json_response(
|
|
{"error": "path is required"}, status=400
|
|
)
|
|
|
|
normalized_path = os.path.normpath(target_path)
|
|
if not os.path.exists(normalized_path):
|
|
return web.json_response(
|
|
{"error": "path does not exist"}, status=404
|
|
)
|
|
|
|
if os.path.isfile(normalized_path):
|
|
absolute_path = os.path.abspath(normalized_path)
|
|
subprocess.Popen(
|
|
["explorer.exe", "/select,", absolute_path],
|
|
shell=False,
|
|
)
|
|
else:
|
|
os.startfile(normalized_path)
|
|
|
|
return web.json_response({"success": True})
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver open-containing-folder error: {e}",
|
|
exc_info=True,
|
|
)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.get("/model_resolver/models")
|
|
async def get_models(request):
|
|
"""Get list of all available models."""
|
|
try:
|
|
force_rescan = str(
|
|
request.query.get("force")
|
|
or request.query.get("force_rescan")
|
|
or ""
|
|
).lower() in {"1", "true", "yes"}
|
|
models = get_model_files(force_rescan=force_rescan)
|
|
return web.json_response(models)
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver get_models error: {e}", exc_info=True
|
|
)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.post("/model_resolver/loaded")
|
|
async def get_loaded_models(request):
|
|
"""Get all currently loaded models in the workflow."""
|
|
try:
|
|
data = await request.json()
|
|
workflow_json = data.get("workflow")
|
|
|
|
if not workflow_json:
|
|
return web.json_response(
|
|
{"error": "Workflow JSON is required"}, status=400
|
|
)
|
|
|
|
# Import workflow analyzer to extract models
|
|
from .core.workflow_analyzer import (
|
|
analyze_workflow_models,
|
|
try_resolve_model_path,
|
|
is_model_filename,
|
|
URN_REGEX,
|
|
URN_TYPE_MAP,
|
|
)
|
|
|
|
# Get available models for existence checking
|
|
available_models = get_model_files()
|
|
available_paths = {m.get("path") for m in available_models}
|
|
# Create lookup for full paths by filename (with and without extension)
|
|
path_by_filename = {}
|
|
for m in available_models:
|
|
rel_path = m.get("relative_path", "")
|
|
if rel_path:
|
|
filename = rel_path.split("/")[-1].split("\\")[-1]
|
|
path_by_filename[filename] = m.get("path")
|
|
# Also add without extension for matching (simple approach)
|
|
if "." in filename:
|
|
filename_no_ext = filename.rsplit(".", 1)[0]
|
|
if filename_no_ext not in path_by_filename:
|
|
path_by_filename[filename_no_ext] = m.get("path")
|
|
# Add the full relative path as key too
|
|
path_by_filename[rel_path] = m.get("path")
|
|
|
|
# Also use folder_paths.get_full_path() to get paths
|
|
import folder_paths
|
|
|
|
for cat in [
|
|
"loras",
|
|
"checkpoints",
|
|
"vae",
|
|
"controlnet",
|
|
"upscale_models",
|
|
]:
|
|
try:
|
|
filenames = folder_paths.get_filename_list(cat)
|
|
for fn in filenames:
|
|
full_path = folder_paths.get_full_path(cat, fn)
|
|
if (
|
|
full_path
|
|
and full_path not in path_by_filename.values()
|
|
):
|
|
path_by_filename[fn] = full_path
|
|
fn_no_ext = (
|
|
fn.rsplit(".", 1)[0] if "." in fn else fn
|
|
)
|
|
if fn_no_ext not in path_by_filename:
|
|
path_by_filename[fn_no_ext] = full_path
|
|
except Exception:
|
|
pass
|
|
|
|
# Analyze workflow to get all model references
|
|
all_model_refs = analyze_workflow_models(
|
|
workflow_json, available_models=available_models
|
|
)
|
|
|
|
# Also extract from node.properties.models
|
|
nodes = list(workflow_json.get("nodes", []))
|
|
definitions = workflow_json.get("definitions", {})
|
|
subgraphs = definitions.get("subgraphs", [])
|
|
for subgraph in subgraphs:
|
|
nodes.extend(subgraph.get("nodes", []))
|
|
|
|
# Collect all loaded models with their values
|
|
loaded_models = []
|
|
|
|
# Process each model reference from analyze_workflow_models
|
|
for ref in all_model_refs:
|
|
original_path = ref.get("original_path", "")
|
|
node_id = ref.get("node_id")
|
|
widget_index = ref.get("widget_index")
|
|
node_type = ref.get("node_type", "")
|
|
category = ref.get("category", "unknown")
|
|
|
|
# Determine model name and strength
|
|
model_name = original_path.split("/")[-1].split("\\")[-1]
|
|
strength = None
|
|
|
|
# For standard LoraLoader nodes, strength is in next widget_value
|
|
if node_type in ["LoraLoader", "LoraLoaderModelOnly"]:
|
|
# Find the node in workflow to get strength value
|
|
for node in nodes:
|
|
if str(node.get("id")) == str(node_id):
|
|
widgets_values = node.get("widgets_values", [])
|
|
if len(widgets_values) > widget_index + 1:
|
|
try:
|
|
strength = float(
|
|
widgets_values[widget_index + 1]
|
|
)
|
|
except (ValueError, TypeError):
|
|
strength = 1.0
|
|
break
|
|
|
|
if ref.get("strength") is not None:
|
|
strength = ref.get("strength")
|
|
|
|
# For text-based lora loaders (LoraLoaderV2, LoraManager), get strength from ref
|
|
if ref.get("is_lora_v2"):
|
|
strength = ref.get("strength")
|
|
model_name = ref.get("name", model_name)
|
|
|
|
# Check if model exists locally
|
|
exists = ref.get("exists", False)
|
|
|
|
# If URN, resolve to display name
|
|
if ref.get("is_urn"):
|
|
urn = ref.get("urn", {})
|
|
# Use model name from URN as display name
|
|
model_name = (
|
|
f"urn:{urn.get('type', 'model')}:{urn.get('model_id')}"
|
|
)
|
|
category = urn.get("type", category)
|
|
if category in URN_TYPE_MAP:
|
|
category = URN_TYPE_MAP[category]
|
|
|
|
loaded_models.append(
|
|
{
|
|
"name": model_name,
|
|
"category": category,
|
|
"node_id": node_id,
|
|
"widget_index": widget_index,
|
|
"node_type": node_type,
|
|
"exists": exists,
|
|
"strength": strength,
|
|
"original_path": original_path,
|
|
"is_urn": ref.get("is_urn", False),
|
|
"is_lora_v2": ref.get("is_lora_v2", False),
|
|
"active": ref.get("active"),
|
|
"connected": ref.get("connected", True),
|
|
"resolved_path": (
|
|
path_by_filename.get(model_name)
|
|
or path_by_filename.get(original_path)
|
|
),
|
|
}
|
|
)
|
|
|
|
# Also check node.properties.models for embedded models
|
|
for node in nodes:
|
|
node_type = node.get("type", "")
|
|
properties = node.get("properties", {})
|
|
models_list = properties.get("models", [])
|
|
|
|
for model_info in models_list:
|
|
if isinstance(model_info, dict):
|
|
name = model_info.get("name", "")
|
|
url = model_info.get("url", "")
|
|
directory = model_info.get("directory", "")
|
|
|
|
if name:
|
|
# Check if this model is already in loaded_models
|
|
existing = next(
|
|
(
|
|
m
|
|
for m in loaded_models
|
|
if m.get("original_path") == name
|
|
),
|
|
None,
|
|
)
|
|
if not existing:
|
|
loaded_models.append(
|
|
{
|
|
"name": name.split("/")[-1].split("\\")[
|
|
-1
|
|
],
|
|
"category": directory or "checkpoints",
|
|
"node_id": node.get("id"),
|
|
"widget_index": None,
|
|
"node_type": node_type,
|
|
"exists": True, # Embedded models are loaded
|
|
"strength": None,
|
|
"original_path": name,
|
|
"is_urn": False,
|
|
}
|
|
)
|
|
|
|
return web.json_response(
|
|
{"loaded_models": loaded_models, "total": len(loaded_models)}
|
|
)
|
|
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver get_loaded_models error: {e}", exc_info=True
|
|
)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
# ==================== CIVITAI SEARCH ROUTE ====================
|
|
|
|
@routes.post("/model_resolver/civitai-search")
|
|
async def civitai_search(request):
|
|
"""Search CivitAI for a model using file hash."""
|
|
try:
|
|
data = await request.json()
|
|
filename = data.get("filename", "")
|
|
category = data.get("category", "")
|
|
resolved_path = data.get("resolved_path", "")
|
|
|
|
if not filename:
|
|
return web.json_response(
|
|
{"error": "Filename is required"}, status=400
|
|
)
|
|
|
|
# Clean filename for display
|
|
import os as _os
|
|
|
|
clean_name = _os.path.splitext(filename)[0]
|
|
|
|
# Get the file path to hash
|
|
file_path = resolved_path if resolved_path else None
|
|
|
|
if not file_path and category:
|
|
# Try to find the file in the model directories using folder_paths
|
|
try:
|
|
import folder_paths
|
|
|
|
# Map category to folder_paths type
|
|
category_map = {
|
|
"loras": "loras",
|
|
"checkpoints": "checkpoints",
|
|
"vae": "vae",
|
|
"controlnet": "controlnet",
|
|
"clip": "text_encoders",
|
|
"clips": "text_encoders",
|
|
"text_encoder": "text_encoders",
|
|
"text_encoders": "text_encoders",
|
|
"diffusion_model": "diffusion_models",
|
|
"diffusion_models": "diffusion_models",
|
|
"unet": "diffusion_models",
|
|
"upscale_models": "upscale_models",
|
|
"upscale_model": "upscale_models",
|
|
"latent_upscale_model": "latent_upscale_models",
|
|
"latent_upscale_models": "latent_upscale_models",
|
|
"style_model": "style_models",
|
|
"style_models": "style_models",
|
|
"gligen": "gligen",
|
|
"diffusers": "diffusers",
|
|
"vae_approx": "vae_approx",
|
|
"sam": "sams",
|
|
"sam_model": "sams",
|
|
"sam_models": "sams",
|
|
"sams": "sams",
|
|
"ultralytics": "ultralytics",
|
|
"ultralytics_bbox": "ultralytics",
|
|
"ultralytics_segm": "ultralytics",
|
|
"yolo": "ultralytics",
|
|
"audio_encoder": "audio_encoders",
|
|
"audio_encoders": "audio_encoders",
|
|
"background_removal": "background_removal",
|
|
"background_removal_model": "background_removal",
|
|
"frame_interpolation": "frame_interpolation",
|
|
"frame_interpolation_model": "frame_interpolation",
|
|
"geometry_estimation": "geometry_estimation",
|
|
"geometry_estimation_model": "geometry_estimation",
|
|
"detection": "detection",
|
|
"model_patch": "model_patches",
|
|
"model_patches": "model_patches",
|
|
"photomaker": "photomaker",
|
|
"optical_flow": "optical_flow",
|
|
"optical_flow_model": "optical_flow",
|
|
}
|
|
folder_type = category_map.get(
|
|
category.lower(), category.lower()
|
|
)
|
|
file_path = folder_paths.get_full_path(
|
|
folder_type, filename
|
|
)
|
|
except Exception:
|
|
pass
|
|
|
|
# If not found, try scanner
|
|
if not file_path:
|
|
try:
|
|
from .core.scanner import get_model_files
|
|
|
|
available_models = get_model_files()
|
|
for m in available_models:
|
|
if (
|
|
m.get("relative_path", "").endswith(filename)
|
|
or m.get("filename", "") == filename
|
|
):
|
|
file_path = m.get("path")
|
|
break
|
|
except Exception:
|
|
pass
|
|
|
|
# Search CivitAI for the model using hash
|
|
if download_available and file_path and _os.path.exists(file_path):
|
|
try:
|
|
from .core.sources.civitai import (
|
|
get_model_info_for_file,
|
|
)
|
|
|
|
result = get_model_info_for_file(file_path)
|
|
if result and result.get("url"):
|
|
return web.json_response(
|
|
{
|
|
"filename": filename,
|
|
"url": result.get("url"),
|
|
"version_url": result.get("version_url"),
|
|
"model_id": result.get("model_id"),
|
|
"model_name": result.get(
|
|
"model_name", clean_name
|
|
),
|
|
"model_type": result.get("model_type", ""),
|
|
"version_id": result.get("version_id"),
|
|
"version_name": result.get("version_name", ""),
|
|
"sha256": result.get("sha256"),
|
|
"base_model": result.get("base_model"),
|
|
"tags": result.get("tags", []),
|
|
"trained_words": result.get(
|
|
"trained_words", []
|
|
),
|
|
"images": result.get("images", []),
|
|
"clip_skip": result.get("clip_skip"),
|
|
"description": result.get("description", ""),
|
|
"model_description": result.get(
|
|
"model_description", ""
|
|
),
|
|
}
|
|
)
|
|
except Exception as e:
|
|
self.logger.warning(f"CivitAI search error: {e}")
|
|
|
|
# No result found - try fallback to filename search
|
|
if download_available:
|
|
try:
|
|
from .core.sources.civitai import (
|
|
search_civitai_for_file,
|
|
)
|
|
|
|
result = search_civitai_for_file(
|
|
filename, model_type=category
|
|
)
|
|
if result and result.get("url"):
|
|
return web.json_response(
|
|
{
|
|
"url": result["url"],
|
|
"model_name": result.get("name", clean_name),
|
|
"version_id": result.get("version_id"),
|
|
}
|
|
)
|
|
except Exception as e:
|
|
self.logger.warning(f"CivitAI fallback search error: {e}")
|
|
|
|
# No result found
|
|
return web.json_response({"url": None})
|
|
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver civitai-search error: {e}", exc_info=True
|
|
)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.post("/model_resolver/model-details")
|
|
async def model_details(request):
|
|
"""Return normalized full model details for sources that expose model pages."""
|
|
try:
|
|
data = await request.json()
|
|
source = str(data.get("source", "")).strip().lower()
|
|
model_id = data.get("model_id")
|
|
version_id = data.get("version_id")
|
|
civitai_key = data.get("civitai_key", "")
|
|
|
|
if not download_available:
|
|
return web.json_response(
|
|
{"error": "Download providers are not available"}, status=503
|
|
)
|
|
|
|
try:
|
|
model_id = (
|
|
int(model_id)
|
|
if model_id is not None and str(model_id).strip()
|
|
else None
|
|
)
|
|
except (TypeError, ValueError):
|
|
model_id = None
|
|
|
|
try:
|
|
version_id = (
|
|
int(version_id)
|
|
if version_id is not None and str(version_id).strip()
|
|
else None
|
|
)
|
|
except (TypeError, ValueError):
|
|
version_id = None
|
|
|
|
if source == "lora_manager_archive":
|
|
source = "civitai"
|
|
|
|
if source not in {"civitai", "civarchive"}:
|
|
return web.json_response(
|
|
{"error": "Unsupported model details source"}, status=400
|
|
)
|
|
if not model_id:
|
|
return web.json_response(
|
|
{"error": "model_id is required"}, status=400
|
|
)
|
|
|
|
if source == "civitai":
|
|
details = await asyncio.to_thread(
|
|
get_civitai_model_details,
|
|
model_id,
|
|
version_id,
|
|
civitai_key or None,
|
|
)
|
|
else:
|
|
details = await asyncio.to_thread(
|
|
get_civarchive_model_details,
|
|
model_id,
|
|
version_id,
|
|
)
|
|
|
|
if not details:
|
|
return web.json_response(
|
|
{"error": "Model details not found"}, status=404
|
|
)
|
|
|
|
return web.json_response(details)
|
|
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver model-details error: {e}", exc_info=True
|
|
)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
# ==================== DOWNLOAD ROUTES ====================
|
|
|
|
if download_available:
|
|
|
|
@routes.post("/model_resolver/search")
|
|
async def search_sources(request):
|
|
"""Search for model download sources."""
|
|
try:
|
|
def format_log_value(value):
|
|
if value is None or value == "":
|
|
return None
|
|
if isinstance(value, bool):
|
|
return "yes" if value else "no"
|
|
if isinstance(value, (list, tuple, set)):
|
|
return ",".join(str(item) for item in value)
|
|
|
|
text = str(value)
|
|
if any(char.isspace() for char in text):
|
|
return '"' + text.replace("\\", "\\\\").replace('"', '\\"') + '"'
|
|
return text
|
|
|
|
def format_log_size(value):
|
|
if value is None or value == "":
|
|
return None
|
|
if isinstance(value, str):
|
|
return value
|
|
try:
|
|
size = float(value)
|
|
except (TypeError, ValueError):
|
|
return str(value)
|
|
|
|
units = ("B", "KB", "MB", "GB", "TB")
|
|
unit_index = 0
|
|
while size >= 1024 and unit_index < len(units) - 1:
|
|
size /= 1024
|
|
unit_index += 1
|
|
return f"{size:.1f}{units[unit_index]}"
|
|
|
|
def format_log_fields(**fields):
|
|
parts = []
|
|
for key, value in fields.items():
|
|
formatted = format_log_value(value)
|
|
if formatted is not None:
|
|
parts.append(f"{key}={formatted}")
|
|
return " ".join(parts)
|
|
|
|
def normalize_result_extra(extra):
|
|
if not extra:
|
|
return {}
|
|
normalized = dict(extra)
|
|
model_id = normalized.pop("model_id", None)
|
|
version_id = normalized.pop("version_id", None)
|
|
if model_id or version_id:
|
|
normalized["ids"] = (
|
|
f"{model_id}@{version_id}"
|
|
if model_id and version_id
|
|
else model_id or version_id
|
|
)
|
|
if "files_count" in normalized:
|
|
normalized["files"] = normalized.pop("files_count")
|
|
return normalized
|
|
|
|
def format_result_details(result, extra=None):
|
|
if isinstance(result, list):
|
|
return format_log_fields(count=len(result))
|
|
if not isinstance(result, dict):
|
|
fields = {"result": "none" if result is None else result}
|
|
fields.update(normalize_result_extra(extra))
|
|
return format_log_fields(**fields)
|
|
|
|
model_id = result.get("model_id")
|
|
version_id = result.get("version_id")
|
|
ids = (
|
|
f"{model_id}@{version_id}"
|
|
if model_id and version_id
|
|
else model_id or version_id
|
|
)
|
|
fields = {
|
|
"name": result.get("name"),
|
|
"file": result.get("filename") or result.get("path"),
|
|
"match": result.get("match_type"),
|
|
"repo": result.get("repo_id") or result.get("repo"),
|
|
"ids": ids,
|
|
"size": format_log_size(result.get("size")),
|
|
"files": result.get("files_count"),
|
|
}
|
|
fields.update(normalize_result_extra(extra))
|
|
return format_log_fields(**fields)
|
|
|
|
def log_search_result(source_name, result, extra=None):
|
|
details = format_result_details(result, extra)
|
|
if result and (
|
|
not isinstance(result, list) or len(result) > 0
|
|
):
|
|
log_info(f"Search [{source_name}] found {details}")
|
|
else:
|
|
log_info(f"Search [{source_name}] miss {details}")
|
|
|
|
data = await request.json()
|
|
filename = data.get("filename", "")
|
|
category = data.get("category", "")
|
|
base_model_context = data.get("base_model_context", "")
|
|
civitai_candidate_limit_raw = data.get(
|
|
"civitai_candidate_limit", 5
|
|
)
|
|
try:
|
|
civitai_candidate_limit = int(
|
|
civitai_candidate_limit_raw
|
|
)
|
|
except (TypeError, ValueError):
|
|
civitai_candidate_limit = 5
|
|
civitai_candidate_limit = max(
|
|
1, min(civitai_candidate_limit, 20)
|
|
)
|
|
civarchive_candidate_limit_raw = data.get(
|
|
"civarchive_candidate_limit", 10
|
|
)
|
|
try:
|
|
civarchive_candidate_limit = int(
|
|
civarchive_candidate_limit_raw
|
|
)
|
|
except (TypeError, ValueError):
|
|
civarchive_candidate_limit = 10
|
|
civarchive_candidate_limit = max(
|
|
1, min(civarchive_candidate_limit, 30)
|
|
)
|
|
# Handle both boolean and string forms
|
|
is_urn_raw = data.get("is_urn", False)
|
|
civitai_session_token = data.get("civitai_session_token", "")
|
|
hf_token = data.get("hf_token", "")
|
|
brave_search_api_key = data.get("brave_search_api_key", "")
|
|
civitai_use_trpc_search = data.get(
|
|
"civitai_use_trpc_search", True
|
|
)
|
|
civitai_use_html_fallback = data.get(
|
|
"civitai_use_html_fallback", True
|
|
)
|
|
hf_use_api_search = data.get("hf_use_api_search", True)
|
|
hf_use_comfy_org_fallback = data.get(
|
|
"hf_use_comfy_org_fallback", True
|
|
)
|
|
hf_use_brave_fallback = data.get(
|
|
"hf_use_brave_fallback", True
|
|
)
|
|
is_urn = (
|
|
is_urn_raw
|
|
if isinstance(is_urn_raw, bool)
|
|
else (str(is_urn_raw).lower() == "true")
|
|
)
|
|
hf_use_api_search = (
|
|
hf_use_api_search
|
|
if isinstance(hf_use_api_search, bool)
|
|
else str(hf_use_api_search).lower() == "true"
|
|
)
|
|
civitai_use_trpc_search = (
|
|
civitai_use_trpc_search
|
|
if isinstance(civitai_use_trpc_search, bool)
|
|
else str(civitai_use_trpc_search).lower() == "true"
|
|
)
|
|
civitai_use_html_fallback = (
|
|
civitai_use_html_fallback
|
|
if isinstance(civitai_use_html_fallback, bool)
|
|
else str(civitai_use_html_fallback).lower() == "true"
|
|
)
|
|
hf_use_comfy_org_fallback = (
|
|
hf_use_comfy_org_fallback
|
|
if isinstance(hf_use_comfy_org_fallback, bool)
|
|
else str(hf_use_comfy_org_fallback).lower() == "true"
|
|
)
|
|
hf_use_brave_fallback = (
|
|
hf_use_brave_fallback
|
|
if isinstance(hf_use_brave_fallback, bool)
|
|
else str(hf_use_brave_fallback).lower() == "true"
|
|
)
|
|
|
|
# For URN-only requests, model_id and version_id are required instead of filename
|
|
model_id = data.get("model_id")
|
|
version_id = data.get("version_id")
|
|
if not filename and not (is_urn and model_id and version_id):
|
|
return web.json_response(
|
|
{
|
|
"error": "Filename is required for non-URN, or model_id+version_id for URN"
|
|
},
|
|
status=400,
|
|
)
|
|
|
|
raw_sources = data.get("sources", ["all"])
|
|
if isinstance(raw_sources, str):
|
|
raw_sources = [raw_sources]
|
|
elif not isinstance(raw_sources, list):
|
|
raw_sources = ["all"]
|
|
|
|
normalized_sources = {
|
|
str(source).strip().lower()
|
|
for source in raw_sources
|
|
if str(source).strip()
|
|
}
|
|
if not normalized_sources:
|
|
normalized_sources = {"all"}
|
|
|
|
if "all" in normalized_sources:
|
|
normalized_sources = {
|
|
"local",
|
|
"huggingface",
|
|
"civitai",
|
|
"civarchive",
|
|
"lora_manager_archive",
|
|
}
|
|
|
|
search_local = "local" in normalized_sources
|
|
search_huggingface_source = "huggingface" in normalized_sources
|
|
search_civitai_source = "civitai" in normalized_sources
|
|
search_civarchive_source = "civarchive" in normalized_sources
|
|
search_lora_manager_archive_source = (
|
|
"lora_manager_archive" in normalized_sources
|
|
)
|
|
force_search = data.get("force_search", False)
|
|
force_search = (
|
|
force_search
|
|
if isinstance(force_search, bool)
|
|
else str(force_search).lower() == "true"
|
|
)
|
|
|
|
if force_search:
|
|
if search_local:
|
|
reload_popular_databases()
|
|
reload_model_list()
|
|
if search_huggingface_source:
|
|
clear_huggingface_search_cache()
|
|
if search_civitai_source:
|
|
clear_civitai_search_cache()
|
|
if search_civarchive_source:
|
|
clear_civarchive_search_cache()
|
|
if search_lora_manager_archive_source:
|
|
clear_lora_manager_archive_search_cache()
|
|
log_debug(
|
|
"Force search enabled: cleared cache "
|
|
+ format_log_fields(sources=sorted(normalized_sources))
|
|
)
|
|
|
|
log_info(
|
|
f"Search [{','.join(sorted(normalized_sources))}] request "
|
|
+ format_log_fields(
|
|
file=filename,
|
|
cat=category,
|
|
urn=is_urn,
|
|
ids=(
|
|
f"{data.get('model_id')}@{data.get('version_id')}"
|
|
if data.get("model_id") and data.get("version_id")
|
|
else data.get("model_id")
|
|
or data.get("version_id")
|
|
),
|
|
base=base_model_context,
|
|
force=force_search,
|
|
)
|
|
)
|
|
|
|
results = {
|
|
"popular": None,
|
|
"model_list": None,
|
|
"huggingface": None,
|
|
"civitai": None,
|
|
"civarchive": None,
|
|
"lora_manager_archive": None,
|
|
"found": False,
|
|
"searched_sources": sorted(normalized_sources),
|
|
"source_errors": {},
|
|
}
|
|
|
|
def current_search_timestamp():
|
|
from datetime import datetime, timezone
|
|
|
|
return (
|
|
datetime.now(timezone.utc)
|
|
.replace(microsecond=0)
|
|
.isoformat()
|
|
)
|
|
|
|
def get_search_result_signature(source_key, result):
|
|
if isinstance(result, list):
|
|
return "|".join(
|
|
get_search_result_signature(source_key, item)
|
|
for item in result
|
|
)
|
|
if not isinstance(result, dict):
|
|
return ""
|
|
|
|
parts = [
|
|
source_key,
|
|
result.get("download_url")
|
|
or result.get("url")
|
|
or result.get("model_url")
|
|
or "",
|
|
result.get("filename") or result.get("path") or "",
|
|
result.get("repo_id") or result.get("repo") or "",
|
|
result.get("model_id") or "",
|
|
result.get("version_id") or "",
|
|
result.get("name") or "",
|
|
]
|
|
return "::".join(str(part).strip() for part in parts)
|
|
|
|
def stamp_search_result(source_key, result):
|
|
if isinstance(result, list):
|
|
return [
|
|
stamp_search_result(source_key, item)
|
|
for item in result
|
|
]
|
|
if not isinstance(result, dict):
|
|
return result
|
|
|
|
signature = get_search_result_signature(source_key, result)
|
|
if not signature:
|
|
return result
|
|
|
|
timestamp = (
|
|
result.get("searched_at")
|
|
or result.get("searchedAt")
|
|
or (
|
|
None
|
|
if force_search
|
|
else self.search_result_timestamps.get(signature)
|
|
)
|
|
)
|
|
if not timestamp:
|
|
timestamp = current_search_timestamp()
|
|
if force_search:
|
|
self.search_result_timestamps[signature] = timestamp
|
|
else:
|
|
self.search_result_timestamps.setdefault(
|
|
signature, timestamp
|
|
)
|
|
result["searched_at"] = timestamp
|
|
return result
|
|
|
|
def stamp_search_results(payload):
|
|
for source_key in (
|
|
"popular",
|
|
"model_list",
|
|
"huggingface",
|
|
"civitai",
|
|
"civarchive",
|
|
"lora_manager_archive",
|
|
):
|
|
if payload.get(source_key):
|
|
payload[source_key] = stamp_search_result(
|
|
source_key, payload[source_key]
|
|
)
|
|
return payload
|
|
|
|
def mark_any_model_fallback(result):
|
|
if isinstance(result, list):
|
|
return [
|
|
mark_any_model_fallback(item)
|
|
for item in result
|
|
]
|
|
if not isinstance(result, dict):
|
|
return result
|
|
|
|
marked = dict(result)
|
|
marked["any_model_match"] = True
|
|
marked["base_model_fallback"] = True
|
|
marked["requested_base_model"] = base_model_context
|
|
return marked
|
|
|
|
def search_local_sources():
|
|
source_results = {"popular": None, "model_list": None}
|
|
source_found = False
|
|
|
|
log_info(
|
|
"Search [local] start "
|
|
+ format_log_fields(file=filename, cat=category)
|
|
)
|
|
popular_info = get_popular_model_url(filename)
|
|
log_search_result("popular", popular_info)
|
|
model_list_result = search_model_list(filename)
|
|
log_search_result(
|
|
"model_list",
|
|
model_list_result,
|
|
{
|
|
"confidence": model_list_result.get("confidence")
|
|
if model_list_result
|
|
else None
|
|
},
|
|
)
|
|
if popular_info:
|
|
popular_result = {
|
|
"source": "popular",
|
|
"filename": filename,
|
|
**popular_info,
|
|
}
|
|
if (
|
|
model_list_result
|
|
and model_list_result.get("filename", "").lower()
|
|
== filename.lower()
|
|
and model_list_result.get("size")
|
|
):
|
|
popular_result["size"] = model_list_result.get(
|
|
"size"
|
|
)
|
|
source_results["popular"] = popular_result
|
|
source_found = True
|
|
|
|
if model_list_result:
|
|
confidence = model_list_result.get("confidence", 0)
|
|
if is_urn and confidence >= 70:
|
|
source_results["model_list"] = model_list_result
|
|
source_found = True
|
|
elif not is_urn:
|
|
source_results["model_list"] = model_list_result
|
|
source_found = True
|
|
|
|
return source_results, source_found
|
|
|
|
def search_huggingface_source_task():
|
|
log_info(
|
|
"Search [huggingface] start "
|
|
+ format_log_fields(file=filename)
|
|
)
|
|
hf_result = search_huggingface_for_file(
|
|
filename,
|
|
token=hf_token or None,
|
|
brave_api_key=brave_search_api_key or None,
|
|
use_api_search=hf_use_api_search,
|
|
use_comfy_org_fallback=hf_use_comfy_org_fallback,
|
|
use_brave_fallback=hf_use_brave_fallback,
|
|
force_refresh=force_search,
|
|
)
|
|
log_search_result("huggingface", hf_result)
|
|
return {"huggingface": hf_result}, bool(hf_result)
|
|
|
|
def search_civitai_source_task():
|
|
source_results = {"civitai": None}
|
|
source_found = False
|
|
|
|
log_info(
|
|
"Search [civitai] start "
|
|
+ format_log_fields(
|
|
file=filename,
|
|
cat=category,
|
|
urn=is_urn,
|
|
)
|
|
)
|
|
# For URNs, use direct model_id/version_id to get download URL
|
|
if is_urn:
|
|
# Get model_id and version_id from request data
|
|
model_id = data.get("model_id")
|
|
version_id = data.get("version_id")
|
|
|
|
if model_id and version_id:
|
|
# Use resolve_urn to get model info (cached)
|
|
model_info = resolve_urn(model_id, version_id)
|
|
if model_info:
|
|
primary_file = None
|
|
for file_info in model_info.get("files", []):
|
|
if (
|
|
file_info.get("name")
|
|
== model_info.get("expected_filename")
|
|
):
|
|
primary_file = file_info
|
|
break
|
|
if primary_file is None:
|
|
primary_file = (
|
|
model_info.get("files") or [{}]
|
|
)[0]
|
|
|
|
download_url = get_civitai_download_url(
|
|
version_id
|
|
)
|
|
source_results["civitai"] = {
|
|
"source": "civitai",
|
|
"name": model_info.get("model_name"),
|
|
"version_name": model_info.get(
|
|
"version_name"
|
|
),
|
|
"filename": model_info.get(
|
|
"expected_filename"
|
|
),
|
|
"type": category,
|
|
"download_url": download_url,
|
|
"url": f"https://civitai.com/models/{model_id}?modelVersionId={version_id}",
|
|
"model_id": model_id,
|
|
"version_id": version_id,
|
|
"size": primary_file.get("size"),
|
|
"base_model": model_info.get("base_model"),
|
|
"tags": model_info.get("tags", []),
|
|
}
|
|
log_search_result(
|
|
"civitai/urn",
|
|
source_results["civitai"],
|
|
{
|
|
"files_count": len(
|
|
model_info.get("files", [])
|
|
)
|
|
},
|
|
)
|
|
source_found = True
|
|
else:
|
|
log_search_result(
|
|
"civitai/urn",
|
|
None,
|
|
{
|
|
"model_id": model_id,
|
|
"version_id": version_id,
|
|
},
|
|
)
|
|
elif category:
|
|
# Fallback to search if no IDs
|
|
log_info(
|
|
"Search [civitai] URN ids missing; falling back"
|
|
)
|
|
civitai_results = search_civitai(
|
|
filename,
|
|
model_type=category,
|
|
)
|
|
log_search_result(
|
|
"civitai/fallback",
|
|
civitai_results[0] if civitai_results else None,
|
|
{
|
|
"results_count": len(civitai_results),
|
|
},
|
|
)
|
|
if civitai_results:
|
|
first_result = civitai_results[0]
|
|
source_results["civitai"] = {
|
|
"source": "civitai",
|
|
"name": first_result.get("name"),
|
|
"filename": first_result.get("filename"),
|
|
"type": first_result.get("type"),
|
|
"download_url": first_result.get(
|
|
"download_url"
|
|
),
|
|
"url": first_result.get("url"),
|
|
"size": first_result.get("size"),
|
|
"base_model": first_result.get("base_model"),
|
|
"tags": first_result.get("tags", []),
|
|
}
|
|
source_found = True
|
|
else:
|
|
civitai_result = search_civitai_for_file(
|
|
filename,
|
|
model_type=category,
|
|
base_model_context=base_model_context or None,
|
|
session_token=civitai_session_token or None,
|
|
candidate_limit=civitai_candidate_limit,
|
|
use_trpc_search=civitai_use_trpc_search,
|
|
use_html_fallback=civitai_use_html_fallback,
|
|
)
|
|
log_search_result("civitai", civitai_result)
|
|
if not civitai_result and base_model_context:
|
|
log_info(
|
|
"Search [civitai] retry any model "
|
|
+ format_log_fields(
|
|
file=filename,
|
|
cat=category,
|
|
base=base_model_context,
|
|
)
|
|
)
|
|
civitai_result = search_civitai_for_file(
|
|
filename,
|
|
model_type=category,
|
|
base_model_context=None,
|
|
session_token=civitai_session_token or None,
|
|
candidate_limit=civitai_candidate_limit,
|
|
use_trpc_search=civitai_use_trpc_search,
|
|
use_html_fallback=civitai_use_html_fallback,
|
|
)
|
|
log_search_result(
|
|
"civitai/any_model",
|
|
civitai_result,
|
|
)
|
|
if civitai_result:
|
|
civitai_result = mark_any_model_fallback(
|
|
civitai_result
|
|
)
|
|
if civitai_result:
|
|
source_results["civitai"] = civitai_result
|
|
source_found = True
|
|
|
|
return source_results, source_found
|
|
|
|
def search_civarchive_source_task():
|
|
source_results = {"civarchive": None}
|
|
source_found = False
|
|
|
|
log_info(
|
|
"Search [civarchive] start "
|
|
+ format_log_fields(
|
|
file=filename,
|
|
cat=category,
|
|
urn=is_urn,
|
|
)
|
|
)
|
|
try:
|
|
if is_urn:
|
|
model_id = data.get("model_id")
|
|
version_id = data.get("version_id")
|
|
if model_id and version_id:
|
|
civarchive_result = resolve_civarchive_model_version(
|
|
model_id,
|
|
version_id,
|
|
query=filename,
|
|
)
|
|
log_search_result(
|
|
"civarchive/urn",
|
|
civarchive_result,
|
|
{
|
|
"model_id": model_id,
|
|
"version_id": version_id,
|
|
},
|
|
)
|
|
if civarchive_result:
|
|
source_results["civarchive"] = civarchive_result
|
|
source_found = True
|
|
else:
|
|
log_search_result(
|
|
"civarchive/urn",
|
|
None,
|
|
{
|
|
"model_id": model_id,
|
|
"version_id": version_id,
|
|
},
|
|
)
|
|
else:
|
|
civarchive_result = search_civarchive_for_file(
|
|
filename,
|
|
model_type=category,
|
|
base_model_context=base_model_context or None,
|
|
limit=civarchive_candidate_limit,
|
|
)
|
|
log_search_result("civarchive", civarchive_result)
|
|
if not civarchive_result and base_model_context:
|
|
log_info(
|
|
"Search [civarchive] retry any model "
|
|
+ format_log_fields(
|
|
file=filename,
|
|
cat=category,
|
|
base=base_model_context,
|
|
)
|
|
)
|
|
civarchive_result = search_civarchive_for_file(
|
|
filename,
|
|
model_type=category,
|
|
base_model_context=None,
|
|
limit=civarchive_candidate_limit,
|
|
)
|
|
log_search_result(
|
|
"civarchive/any_model",
|
|
civarchive_result,
|
|
)
|
|
if civarchive_result:
|
|
civarchive_result = mark_any_model_fallback(
|
|
civarchive_result
|
|
)
|
|
if civarchive_result:
|
|
source_results["civarchive"] = civarchive_result
|
|
source_found = True
|
|
except CivArchiveSearchError as e:
|
|
error_message = f"CivArchive search failed: {e}"
|
|
log_warn(error_message)
|
|
source_results["source_errors"] = {
|
|
"civarchive": error_message
|
|
}
|
|
|
|
return source_results, source_found
|
|
|
|
def search_lora_manager_archive_source_task():
|
|
log_info(
|
|
"Search [lora_manager_archive] start "
|
|
+ format_log_fields(file=filename, cat=category)
|
|
)
|
|
lora_manager_archive_result = (
|
|
search_lora_manager_archive_for_file(
|
|
filename,
|
|
model_type=category,
|
|
base_model_context=base_model_context or None,
|
|
)
|
|
)
|
|
log_search_result(
|
|
"lora_manager_archive",
|
|
lora_manager_archive_result,
|
|
)
|
|
if not lora_manager_archive_result and base_model_context:
|
|
log_info(
|
|
"Search [lora_manager_archive] retry any model "
|
|
+ format_log_fields(
|
|
file=filename,
|
|
cat=category,
|
|
base=base_model_context,
|
|
)
|
|
)
|
|
lora_manager_archive_result = (
|
|
search_lora_manager_archive_for_file(
|
|
filename,
|
|
model_type=category,
|
|
base_model_context=None,
|
|
)
|
|
)
|
|
log_search_result(
|
|
"lora_manager_archive/any_model",
|
|
lora_manager_archive_result,
|
|
)
|
|
if lora_manager_archive_result:
|
|
lora_manager_archive_result = (
|
|
mark_any_model_fallback(
|
|
lora_manager_archive_result
|
|
)
|
|
)
|
|
return (
|
|
{
|
|
"lora_manager_archive": lora_manager_archive_result
|
|
},
|
|
bool(lora_manager_archive_result),
|
|
)
|
|
|
|
search_tasks = []
|
|
if search_local:
|
|
search_tasks.append(
|
|
asyncio.to_thread(search_local_sources)
|
|
)
|
|
if search_huggingface_source:
|
|
search_tasks.append(
|
|
asyncio.to_thread(search_huggingface_source_task)
|
|
)
|
|
if search_civitai_source:
|
|
search_tasks.append(
|
|
asyncio.to_thread(search_civitai_source_task)
|
|
)
|
|
if search_civarchive_source and (
|
|
filename or (is_urn and model_id and version_id)
|
|
):
|
|
search_tasks.append(
|
|
asyncio.to_thread(search_civarchive_source_task)
|
|
)
|
|
if search_lora_manager_archive_source and filename:
|
|
search_tasks.append(
|
|
asyncio.to_thread(
|
|
search_lora_manager_archive_source_task
|
|
)
|
|
)
|
|
|
|
if len(search_tasks) > 1:
|
|
log_debug(
|
|
"Search sources async "
|
|
+ format_log_fields(count=len(search_tasks))
|
|
)
|
|
|
|
for source_results, source_found in await asyncio.gather(
|
|
*search_tasks
|
|
):
|
|
for source_key, source_result in source_results.items():
|
|
if source_key == "source_errors":
|
|
results["source_errors"].update(source_result or {})
|
|
continue
|
|
if source_result:
|
|
results[source_key] = source_result
|
|
if source_found:
|
|
results["found"] = True
|
|
|
|
log_info(
|
|
f"Search [{','.join(results['searched_sources'])}] done "
|
|
+ format_log_fields(
|
|
found=results["found"],
|
|
)
|
|
)
|
|
stamp_search_results(results)
|
|
return web.json_response(results)
|
|
|
|
except Exception as e:
|
|
log_exception(f"Model Resolver search error: {e}")
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.post("/model_resolver/clear-search-cache")
|
|
async def clear_search_cache_route(request):
|
|
"""Clear backend search caches after token/settings changes."""
|
|
try:
|
|
clear_huggingface_search_cache()
|
|
clear_civitai_search_cache()
|
|
clear_civarchive_search_cache()
|
|
clear_lora_manager_archive_search_cache()
|
|
reload_popular_databases()
|
|
reload_model_list()
|
|
invalidate_model_files_cache()
|
|
self.search_result_timestamps.clear()
|
|
log_info("Cleared backend search caches")
|
|
return web.json_response({"success": True, "cleared": "all"})
|
|
except Exception as e:
|
|
log_exception(f"Clear search cache error: {e}")
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.post("/model_resolver/civitai/session-token/check")
|
|
async def civitai_session_token_check_route(request):
|
|
"""Check whether a CivitAI browser session token is valid."""
|
|
try:
|
|
data = await request.json()
|
|
token = data.get("civitai_session_token", "")
|
|
result = await asyncio.to_thread(
|
|
check_civitai_session_token, token
|
|
)
|
|
return web.json_response(result)
|
|
except Exception as e:
|
|
log_exception(f"CivitAI session token check error: {e}")
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.post("/model_resolver/civitai/api-key/check")
|
|
async def civitai_api_key_check_route(request):
|
|
"""Check whether a CivitAI API key is valid."""
|
|
try:
|
|
data = await request.json()
|
|
api_key = data.get("civitai_key", "")
|
|
result = await asyncio.to_thread(
|
|
check_civitai_api_key, api_key
|
|
)
|
|
return web.json_response(result)
|
|
except Exception as e:
|
|
log_exception(f"CivitAI API key check error: {e}")
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.post("/model_resolver/huggingface/token/check")
|
|
async def huggingface_token_check_route(request):
|
|
"""Check whether a HuggingFace token is valid."""
|
|
try:
|
|
data = await request.json()
|
|
token = data.get("hf_token", "")
|
|
result = await asyncio.to_thread(check_huggingface_token, token)
|
|
return web.json_response(result)
|
|
except Exception as e:
|
|
log_exception(f"HuggingFace token check error: {e}")
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.post("/model_resolver/brave/api-key/check")
|
|
async def brave_api_key_check_route(request):
|
|
"""Check whether a Brave Search API key is valid."""
|
|
try:
|
|
data = await request.json()
|
|
api_key = data.get("brave_search_api_key", "")
|
|
result = await asyncio.to_thread(
|
|
check_brave_search_api_key, api_key
|
|
)
|
|
return web.json_response(result)
|
|
except Exception as e:
|
|
log_exception(f"Brave Search API key check error: {e}")
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.get("/model_resolver/huggingface/author-index/status")
|
|
async def huggingface_author_index_status_route(request):
|
|
"""Return local HuggingFace author fallback index status."""
|
|
try:
|
|
return web.json_response(get_author_fallback_index_status())
|
|
except Exception as e:
|
|
log_exception(f"HuggingFace author index status error: {e}")
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.post("/model_resolver/huggingface/author-index/refresh")
|
|
async def huggingface_author_index_refresh_route(request):
|
|
"""Refresh HuggingFace author fallback index."""
|
|
try:
|
|
data = await request.json()
|
|
hf_token = data.get("hf_token", "")
|
|
result = await asyncio.to_thread(
|
|
refresh_author_fallback_index, hf_token or None
|
|
)
|
|
clear_huggingface_search_cache()
|
|
return web.json_response(result)
|
|
except Exception as e:
|
|
log_exception(f"HuggingFace author index refresh error: {e}")
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.get("/model_resolver/model-list/status")
|
|
async def model_list_status_route(request):
|
|
"""Return local model-list status and optionally compare with GitHub."""
|
|
try:
|
|
check_remote = (
|
|
str(request.query.get("check_remote", "")).lower()
|
|
in {"1", "true", "yes"}
|
|
)
|
|
return web.json_response(
|
|
get_model_list_update_status(check_remote=check_remote)
|
|
)
|
|
except Exception as e:
|
|
log_exception(f"Model list status error: {e}")
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.post("/model_resolver/model-list/update")
|
|
async def model_list_update_route(request):
|
|
"""Download latest ComfyUI-Manager model-list.json."""
|
|
try:
|
|
result = await asyncio.to_thread(update_model_list_from_remote)
|
|
clear_huggingface_search_cache()
|
|
clear_civitai_search_cache()
|
|
clear_civarchive_search_cache()
|
|
clear_lora_manager_archive_search_cache()
|
|
self.search_result_timestamps.clear()
|
|
return web.json_response(result)
|
|
except Exception as e:
|
|
log_exception(f"Model list update error: {e}")
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.post("/model_resolver/download")
|
|
async def download_model(request):
|
|
"""Start downloading a model."""
|
|
try:
|
|
data = await request.json()
|
|
url = data.get("url", "")
|
|
filename = data.get("filename", "")
|
|
category = data.get("category", "checkpoints")
|
|
category = normalize_download_category(category)
|
|
subfolder = data.get("subfolder", "")
|
|
base_directory = data.get("base_directory", "")
|
|
path_metadata = data.get("path_metadata", {})
|
|
if not isinstance(path_metadata, dict):
|
|
path_metadata = {}
|
|
download_metadata = data.get("download_metadata") or data.get(
|
|
"metadata", {}
|
|
)
|
|
if not isinstance(download_metadata, dict):
|
|
download_metadata = {}
|
|
download_metadata = dict(download_metadata)
|
|
settings = load_resolver_settings()
|
|
if not base_directory:
|
|
base_directory = get_default_root_for_category(category, settings)
|
|
subfolder = resolve_download_subfolder(
|
|
category,
|
|
subfolder,
|
|
path_metadata,
|
|
settings,
|
|
)
|
|
|
|
if not url:
|
|
return web.json_response(
|
|
{"error": "URL is required"}, status=400
|
|
)
|
|
|
|
if not filename:
|
|
# Extract filename from URL
|
|
from urllib.parse import urlparse, unquote
|
|
|
|
parsed = urlparse(url)
|
|
filename = unquote(parsed.path.split("/")[-1])
|
|
|
|
if not filename:
|
|
return web.json_response(
|
|
{"error": "Could not determine filename"}, status=400
|
|
)
|
|
|
|
# Build headers if needed
|
|
headers = {}
|
|
if "huggingface.co" in url:
|
|
hf_token = data.get("hf_token", "")
|
|
if hf_token:
|
|
headers["Authorization"] = f"Bearer {hf_token}"
|
|
elif "civitai.com" in url:
|
|
civitai_key = data.get("civitai_key", "")
|
|
if civitai_key and "token=" not in url:
|
|
url += (
|
|
f"{'&' if '?' in url else '?'}token={civitai_key}"
|
|
)
|
|
|
|
def _first_metadata_value(*values):
|
|
for value in values:
|
|
if value is None:
|
|
continue
|
|
if isinstance(value, str) and not value.strip():
|
|
continue
|
|
return value
|
|
return ""
|
|
|
|
def _metadata_int(value):
|
|
try:
|
|
return int(value)
|
|
except (TypeError, ValueError):
|
|
return None
|
|
|
|
inferred_source = ""
|
|
if "civitai.com" in url:
|
|
inferred_source = "civitai"
|
|
elif "huggingface.co" in url:
|
|
inferred_source = "huggingface"
|
|
|
|
download_metadata.setdefault("filename", filename)
|
|
download_metadata.setdefault("category", category)
|
|
download_metadata.setdefault("download_url", url)
|
|
download_metadata.setdefault("source_url", url)
|
|
download_metadata.setdefault("path_metadata", path_metadata)
|
|
download_metadata.setdefault(
|
|
"source",
|
|
_first_metadata_value(
|
|
download_metadata.get("details_source"),
|
|
path_metadata.get("source"),
|
|
inferred_source,
|
|
),
|
|
)
|
|
|
|
model_id = _metadata_int(
|
|
_first_metadata_value(
|
|
download_metadata.get("model_id"),
|
|
download_metadata.get("modelId"),
|
|
path_metadata.get("model_id"),
|
|
)
|
|
)
|
|
version_id = _metadata_int(
|
|
_first_metadata_value(
|
|
download_metadata.get("version_id"),
|
|
download_metadata.get("versionId"),
|
|
path_metadata.get("version_id"),
|
|
)
|
|
)
|
|
source_name = str(
|
|
_first_metadata_value(
|
|
download_metadata.get("details_source"),
|
|
download_metadata.get("source"),
|
|
)
|
|
).lower()
|
|
try:
|
|
if (
|
|
source_name == "civitai"
|
|
and model_id
|
|
and not download_metadata.get("civitai_details")
|
|
):
|
|
details = await asyncio.to_thread(
|
|
get_civitai_model_details,
|
|
model_id,
|
|
version_id,
|
|
data.get("civitai_key", ""),
|
|
)
|
|
if details:
|
|
download_metadata["civitai_details"] = details
|
|
elif (
|
|
source_name == "civarchive"
|
|
and model_id
|
|
and not download_metadata.get("civitai_details")
|
|
):
|
|
details = await asyncio.to_thread(
|
|
get_civarchive_model_details,
|
|
model_id,
|
|
version_id,
|
|
)
|
|
if details:
|
|
download_metadata["civitai_details"] = details
|
|
except Exception as metadata_error:
|
|
self.logger.warning(
|
|
f"Model metadata lookup failed: {metadata_error}"
|
|
)
|
|
|
|
target_directory = ""
|
|
target_path = ""
|
|
try:
|
|
import os as _download_os
|
|
|
|
target_directory = (
|
|
get_download_directory(category, base_directory) or ""
|
|
)
|
|
if target_directory and subfolder:
|
|
target_directory = _download_os.path.join(
|
|
target_directory, subfolder
|
|
)
|
|
if target_directory:
|
|
target_path = _download_os.path.join(
|
|
target_directory, filename
|
|
)
|
|
except Exception:
|
|
target_directory = ""
|
|
target_path = ""
|
|
|
|
# Start background download
|
|
download_id = start_background_download(
|
|
url=url,
|
|
filename=filename,
|
|
category=category,
|
|
headers=headers if headers else None,
|
|
subfolder=subfolder,
|
|
base_directory=base_directory,
|
|
metadata=download_metadata,
|
|
)
|
|
|
|
return web.json_response(
|
|
{
|
|
"success": True,
|
|
"download_id": download_id,
|
|
"filename": filename,
|
|
"category": category,
|
|
"path": target_path,
|
|
"directory": target_directory,
|
|
}
|
|
)
|
|
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver download error: {e}", exc_info=True
|
|
)
|
|
return web.json_response(
|
|
{"error": str(e), "success": False}, status=500
|
|
)
|
|
|
|
@routes.get("/model_resolver/progress/{download_id}")
|
|
async def get_download_progress(request):
|
|
"""Get progress for a specific download."""
|
|
try:
|
|
download_id = request.match_info["download_id"]
|
|
progress = get_progress(download_id)
|
|
|
|
if progress:
|
|
return web.json_response(progress)
|
|
else:
|
|
return web.json_response(
|
|
{"error": "Download not found"}, status=404
|
|
)
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver progress error: {e}", exc_info=True
|
|
)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.get("/model_resolver/progress")
|
|
async def get_all_downloads_progress(request):
|
|
"""Get progress for all downloads."""
|
|
try:
|
|
progress = get_all_progress()
|
|
return web.json_response(progress)
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver progress error: {e}", exc_info=True
|
|
)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.post("/model_resolver/cancel/{download_id}")
|
|
async def cancel_download_route(request):
|
|
"""Cancel a download in progress."""
|
|
try:
|
|
download_id = request.match_info["download_id"]
|
|
cancel_download(download_id)
|
|
return web.json_response({"success": True})
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver cancel error: {e}", exc_info=True
|
|
)
|
|
return web.json_response(
|
|
{"error": str(e), "success": False}, status=500
|
|
)
|
|
|
|
@routes.get("/model_resolver/directories")
|
|
async def get_directories(request):
|
|
"""Get available model directories."""
|
|
try:
|
|
import folder_paths
|
|
|
|
preferred_categories = [
|
|
"checkpoints",
|
|
"loras",
|
|
"vae",
|
|
"controlnet",
|
|
"clip",
|
|
"clip_vision",
|
|
"embeddings",
|
|
"upscale_models",
|
|
"diffusion_models",
|
|
"text_encoders",
|
|
"ipadapter",
|
|
"sams",
|
|
"ultralytics",
|
|
]
|
|
skip_categories = {"custom_nodes", "configs"}
|
|
categories = []
|
|
for cat in [
|
|
*preferred_categories,
|
|
*folder_paths.folder_names_and_paths.keys(),
|
|
]:
|
|
normalized_cat = normalize_download_category(cat)
|
|
if (
|
|
not normalized_cat
|
|
or normalized_cat in skip_categories
|
|
or normalized_cat in categories
|
|
):
|
|
continue
|
|
categories.append(normalized_cat)
|
|
|
|
directories = {}
|
|
for cat in categories:
|
|
path = get_download_directory(cat)
|
|
if path:
|
|
directories[cat] = path
|
|
|
|
return web.json_response(directories)
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver directories error: {e}", exc_info=True
|
|
)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.get("/model_resolver/root-directories")
|
|
async def get_root_directories(request):
|
|
"""Get configured ComfyUI root directories for path settings."""
|
|
try:
|
|
import os
|
|
import folder_paths
|
|
|
|
categories = [
|
|
"loras",
|
|
"checkpoints",
|
|
"diffusion_models",
|
|
"embeddings",
|
|
"text_encoders",
|
|
"vae",
|
|
"upscale_models",
|
|
]
|
|
roots = {}
|
|
for cat in categories:
|
|
folder_key = normalize_download_category(cat)
|
|
candidate_keys = [folder_key]
|
|
if folder_key == "diffusion_models":
|
|
candidate_keys.append("unet")
|
|
elif folder_key == "text_encoders":
|
|
candidate_keys.append("clip")
|
|
paths = []
|
|
for candidate_key in candidate_keys:
|
|
paths.extend(folder_paths.get_folder_paths(candidate_key) or [])
|
|
normalized_paths = []
|
|
seen = set()
|
|
for path in paths:
|
|
if not path:
|
|
continue
|
|
path_abs = os.path.abspath(path)
|
|
path_key = os.path.normcase(path_abs)
|
|
if path_key in seen:
|
|
continue
|
|
seen.add(path_key)
|
|
normalized_paths.append(path_abs)
|
|
roots[folder_key] = normalized_paths
|
|
|
|
return web.json_response(roots)
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver root directories error: {e}", exc_info=True
|
|
)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.get("/model_resolver/path-template-suggestions")
|
|
async def get_path_template_suggestions(request):
|
|
"""Infer path template presets from existing local model folders."""
|
|
try:
|
|
from .core.sources.popular import get_base_models_config
|
|
|
|
force_rescan = request.query.get("force") == "1"
|
|
models = await asyncio.to_thread(get_model_files, force_rescan)
|
|
base_models_config = get_base_models_config()
|
|
suggestions = await asyncio.to_thread(
|
|
infer_download_path_templates,
|
|
models,
|
|
base_models_config,
|
|
)
|
|
return web.json_response(suggestions)
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver path template suggestions error: {e}",
|
|
exc_info=True,
|
|
)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.get("/model_resolver/capabilities")
|
|
async def get_capabilities(request):
|
|
"""Get optional source capabilities available in this install."""
|
|
try:
|
|
return web.json_response(
|
|
{
|
|
"sources": {
|
|
"civarchive": is_civarchive_available(),
|
|
"lora_manager_archive": is_lora_manager_archive_available()
|
|
}
|
|
}
|
|
)
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver capabilities error: {e}", exc_info=True
|
|
)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.get("/model_resolver/subfolders/{category}")
|
|
async def get_subfolders(request):
|
|
"""Get known subfolders for a category using ComfyUI folder_paths."""
|
|
try:
|
|
import os
|
|
import folder_paths
|
|
|
|
raw_category = (request.match_info.get("category") or "").strip()
|
|
category = normalize_download_category(raw_category)
|
|
|
|
if not category or category == "unknown":
|
|
return web.json_response([])
|
|
|
|
known_categories = set(folder_paths.folder_names_and_paths.keys())
|
|
folder_keys = [category]
|
|
if category == "diffusion_models":
|
|
folder_keys.append("unet")
|
|
elif category == "text_encoders":
|
|
folder_keys.append("clip")
|
|
available_folder_keys = [
|
|
folder_key for folder_key in folder_keys if folder_key in known_categories
|
|
]
|
|
if not available_folder_keys:
|
|
self.logger.debug(
|
|
f"Model Resolver: skipping subfolder lookup for unknown category '{raw_category}' -> '{category}'"
|
|
)
|
|
return web.json_response([])
|
|
|
|
subfolders = {}
|
|
|
|
def add_subfolder(rel_path, base_dir=""):
|
|
rel_path = os.path.normpath(str(rel_path or "")).replace(
|
|
os.sep, "\\"
|
|
)
|
|
if not rel_path or rel_path == ".":
|
|
return
|
|
base_dir = os.path.abspath(base_dir) if base_dir else ""
|
|
key = (rel_path.lower(), os.path.normcase(base_dir))
|
|
base_label = (
|
|
os.path.basename(os.path.normpath(base_dir))
|
|
if base_dir
|
|
else ""
|
|
)
|
|
subfolders[key] = {
|
|
"value": rel_path,
|
|
"label": rel_path,
|
|
"base_label": base_label,
|
|
"base_directory": base_dir,
|
|
}
|
|
|
|
base_dirs = []
|
|
seen_base_dirs = set()
|
|
for folder_key in available_folder_keys:
|
|
for base_dir in folder_paths.get_folder_paths(folder_key) or []:
|
|
if not base_dir or not os.path.isdir(base_dir):
|
|
continue
|
|
base_abs = os.path.abspath(base_dir)
|
|
base_key = os.path.normcase(base_abs)
|
|
if base_key in seen_base_dirs:
|
|
continue
|
|
seen_base_dirs.add(base_key)
|
|
base_dirs.append(base_abs)
|
|
|
|
def find_base_dir(full_path):
|
|
if not full_path:
|
|
return ""
|
|
full_path = os.path.abspath(full_path)
|
|
for base_dir in base_dirs:
|
|
base_abs = os.path.abspath(base_dir)
|
|
try:
|
|
if (
|
|
os.path.commonpath([full_path, base_abs])
|
|
== base_abs
|
|
):
|
|
return base_abs
|
|
except Exception:
|
|
continue
|
|
return ""
|
|
|
|
for folder_key in available_folder_keys:
|
|
filenames = folder_paths.get_filename_list(folder_key) or []
|
|
for rel_path in filenames:
|
|
if not isinstance(rel_path, str):
|
|
continue
|
|
base_dir = ""
|
|
try:
|
|
base_dir = find_base_dir(
|
|
folder_paths.get_full_path(folder_key, rel_path)
|
|
)
|
|
except Exception:
|
|
base_dir = ""
|
|
parts = [p for p in rel_path.replace("/", "\\").split("\\") if p]
|
|
if len(parts) <= 1:
|
|
continue
|
|
current = ""
|
|
for part in parts[:-1]:
|
|
current = f"{current}\\{part}" if current else part
|
|
add_subfolder(current, base_dir)
|
|
|
|
for base_dir in base_dirs:
|
|
for root, dirs, _files in os.walk(base_dir):
|
|
rel_root = os.path.relpath(root, base_dir)
|
|
for dirname in dirs:
|
|
rel_path = (
|
|
dirname
|
|
if rel_root in ("", ".")
|
|
else os.path.join(rel_root, dirname)
|
|
)
|
|
add_subfolder(rel_path, base_dir)
|
|
|
|
value_counts = {}
|
|
for item in subfolders.values():
|
|
value_key = item.get("value", "").lower()
|
|
value_counts[value_key] = value_counts.get(value_key, 0) + 1
|
|
|
|
response_items = []
|
|
for item in subfolders.values():
|
|
value = item.get("value", "")
|
|
base_label = item.get("base_label", "")
|
|
label = (
|
|
f"{value} ({base_label})"
|
|
if base_label and value_counts.get(value.lower(), 0) > 1
|
|
else value
|
|
)
|
|
response_items.append(
|
|
{
|
|
"value": value,
|
|
"label": label,
|
|
"base_directory": item.get("base_directory", ""),
|
|
}
|
|
)
|
|
|
|
return web.json_response(
|
|
sorted(
|
|
response_items,
|
|
key=lambda item: (
|
|
item.get("value", "").lower(),
|
|
item.get("base_directory", "").lower(),
|
|
),
|
|
)
|
|
)
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver subfolders error: {e}", exc_info=True
|
|
)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
# ==================== SETTINGS (server-side persistence) ====================
|
|
|
|
def _log_level_setting(value, default: str = BACKEND_DEFAULT_LOG_LEVEL) -> LogLevel:
|
|
normalized = str(value or default or "INFO").strip().upper()
|
|
if hasattr(LogLevel, normalized):
|
|
return getattr(LogLevel, normalized)
|
|
fallback = str(default or "INFO").strip().upper()
|
|
return getattr(LogLevel, fallback, LogLevel.INFO)
|
|
|
|
def _apply_backend_logging_settings(settings: dict) -> None:
|
|
enabled = resolver_bool_setting(settings.get("backend_logs_enabled"), True)
|
|
level = _log_level_setting(settings.get("backend_log_level"))
|
|
backend_log_controller.set_enabled(enabled)
|
|
backend_log_controller.set_global_level(level)
|
|
|
|
_apply_backend_logging_settings(load_resolver_settings())
|
|
|
|
@routes.get("/model_resolver/settings")
|
|
async def get_settings_route(request):
|
|
"""Return persisted settings (API keys, preferences)."""
|
|
try:
|
|
data = await asyncio.to_thread(load_resolver_settings)
|
|
return web.json_response(data)
|
|
except Exception as e:
|
|
self.logger.error(f"Model Resolver settings GET error: {e}", exc_info=True)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
@routes.post("/model_resolver/settings")
|
|
async def save_settings_route(request):
|
|
"""Persist settings (API keys, preferences) to disk."""
|
|
try:
|
|
payload = await request.json()
|
|
if not isinstance(payload, dict):
|
|
return web.json_response({"error": "Expected JSON object"}, status=400)
|
|
settings = await asyncio.to_thread(save_resolver_settings, payload)
|
|
_apply_backend_logging_settings(settings)
|
|
return web.json_response({"success": True})
|
|
except Exception as e:
|
|
self.logger.error(f"Model Resolver settings POST error: {e}", exc_info=True)
|
|
return web.json_response({"error": str(e)}, status=500)
|
|
|
|
self.routes_setup = True
|
|
self.logger.info("Model Resolver: API routes registered successfully")
|
|
return True
|
|
|
|
except ImportError as e:
|
|
self.logger.warning(
|
|
f"Model Resolver: Could not register routes (missing dependency): {e}"
|
|
)
|
|
return False
|
|
except Exception as e:
|
|
self.logger.error(
|
|
f"Model Resolver: Error setting up routes: {e}", exc_info=True
|
|
)
|
|
return False
|
|
|
|
|
|
# Initialize the extension
|
|
try:
|
|
extension = ModelResolverExtension()
|
|
extension.initialize()
|
|
except Exception as e:
|
|
log_error(
|
|
f"ComfyUI Model Resolver extension initialization failed: {e}", exc_info=True
|
|
)
|