mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-09-21 03:01:27 -03:00
Will Miao
5adfa3be36
Standalone users previously had to hand-edit settings.json to configure primary folder_paths. Add a standalone-only Model Paths section to the settings modal: - Backend exposes standalone_mode, folder_paths (with template placeholder values filtered out) and a data-driven folder_path_schema derived from OTHER_MODEL_FOLDER_SUBTYPES via GET /api/lm/settings - The new section renders multi-path editors per model type from the schema, with inline enable_other_models / sub-type controls so other model types are configured without leaving the tab - Persistent restart-required cues after a save: nav dot, inline notice and a global banner (unique id per change so dismissals don't mute future reminders) - The missing-model-paths startup banner and the Other Models no-paths empty state now deep-link into the new section instead of pointing at settings.json
428 lines
13 KiB
Python
428 lines
13 KiB
Python
from typing import Any, Dict, List
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NSFW_LEVELS = {
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"PG": 1,
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"PG13": 2,
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"R": 4,
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"X": 8,
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"XXX": 16,
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"Blocked": 32, # Probably not actually visible through the API without being logged in on model owner account?
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}
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# Node type constants
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NODE_TYPES = {
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"Lora Loader (LoraManager)": 1,
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"Lora Stacker (LoraManager)": 2,
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"WanVideo Lora Select (LoraManager)": 3,
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"Create Hook LoRA (LoraManager)": 4,
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}
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# Default ComfyUI node color when bgcolor is null
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DEFAULT_NODE_COLOR = "#353535"
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# preview extensions
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PREVIEW_EXTENSIONS = [
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".webp",
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".preview.webp",
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".preview.png",
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".preview.jpeg",
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".preview.jpg",
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".preview.mp4",
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".png",
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".jpeg",
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".jpg",
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".mp4",
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".gif",
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".webm",
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".avif",
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".jxl",
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]
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# Card preview image width
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CARD_PREVIEW_WIDTH = 480
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# Width for optimized example images
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EXAMPLE_IMAGE_WIDTH = 832
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# Supported media extensions for example downloads
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SUPPORTED_MEDIA_EXTENSIONS = {
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"images": [".jpg", ".jpeg", ".png", ".webp", ".gif", ".avif", ".jxl"],
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"videos": [".mp4", ".webm"],
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}
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# Model weight file extensions recognised by scanners.
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# This is the union of all scanner extensions (lora, checkpoint, embedding).
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MODEL_FILE_EXTENSIONS = {
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".safetensors",
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".ckpt",
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".pt",
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".pt2",
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".bin",
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".pth",
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".pkl",
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".sft",
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".gguf",
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}
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# CivitAI ModelFile.type values eligible as the main download file.
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# Mirrors CivitAI's getPrimaryFile() (model-helpers.ts): weight types are
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# preferred, but any file CivitAI marks `primary` is accepted — newer types
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# like 'Enhancement LoRA' (Anima/AIR image-editing LoRAs) are valid primary
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# files despite not being in the traditional weights allowlist.
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MODEL_WEIGHT_FILE_TYPES = (
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"Model",
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"Pruned Model",
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"Negative",
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"UNet",
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"Diffusion Model",
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"Enhancement LoRA",
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)
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# Valid sub-types for each scanner type
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VALID_LORA_SUB_TYPES = ["lora", "locon", "dora"]
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VALID_CHECKPOINT_SUB_TYPES = ["checkpoint", "diffusion_model"]
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VALID_EMBEDDING_SUB_TYPES = ["embedding"]
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# folder_paths key -> sub_type; single source of truth for extensibility.
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# Adding support for a new ComfyUI folder category is a one-line change here.
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OTHER_MODEL_FOLDER_SUBTYPES = {
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"vae": "vae",
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"upscale_models": "upscaler",
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"text_encoders": "text_encoder",
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"clip": "text_encoder", # legacy ComfyUI key
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"clip_vision": "clip_vision",
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"controlnet": "controlnet",
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}
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VALID_OTHER_SUB_TYPES = ["vae", "upscaler", "text_encoder", "clip_vision", "controlnet"]
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# Sub-types managed when the (opt-in) Other Models feature is switched on.
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# The feature itself defaults to off (``enable_other_models`` = False), so
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# nothing here is scanned until the user enables it.
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#
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# The default set is deliberately limited to the dependency-style assets every
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# pipeline needs and where "which one am I actually using" is the real problem:
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# VAE, upscalers and text encoders. ``clip_vision`` and ``controlnet`` are
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# workflow-driven instead (IPAdapter/SVD, per-workflow ControlNet variants) and
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# ControlNet libraries routinely run to dozens of files, so both stay opt-in
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# and are treated symmetrically.
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DEFAULT_ENABLED_OTHER_SUB_TYPES: List[str] = [
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"vae",
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"upscaler",
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"text_encoder",
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]
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def other_sub_type_folder_keys() -> Dict[str, List[str]]:
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"""Invert OTHER_MODEL_FOLDER_SUBTYPES into sub_type -> folder_paths keys.
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``text_encoder`` maps to two folder keys (``text_encoders`` and the legacy
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``clip``), so every consumer that resolves a sub_type back to folders must
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merge both.
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"""
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mapping: Dict[str, List[str]] = {}
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for folder_key, sub_type in OTHER_MODEL_FOLDER_SUBTYPES.items():
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mapping.setdefault(sub_type, []).append(folder_key)
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return mapping
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# Precomputed inverse of OTHER_MODEL_FOLDER_SUBTYPES, keeping the table order.
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OTHER_SUB_TYPE_FOLDER_KEYS: Dict[str, List[str]] = other_sub_type_folder_keys()
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# Core folder_paths keys every LoRA Manager installation understands.
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CORE_FOLDER_PATH_KEYS: List[str] = ["loras", "checkpoints", "unet", "embeddings"]
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def folder_path_schema() -> List[Dict[str, Any]]:
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"""Ordered schema describing the editable folder_paths keys.
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Drives the standalone-only Model Paths settings UI: the frontend renders
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one multi-path editor per entry and resolves labels via the
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``settings.modelPaths.folderKeys.<key>`` i18n keys, so adding a new model
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category is a constants + locale change only. ``sub_type`` lets the UI
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hide editors for other-model categories the user has not enabled.
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"""
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schema: List[Dict[str, Any]] = [
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{"key": key, "category": "core", "sub_type": None}
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for key in CORE_FOLDER_PATH_KEYS
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]
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schema.extend(
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{"key": folder_key, "category": "other", "sub_type": sub_type}
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for folder_key, sub_type in OTHER_MODEL_FOLDER_SUBTYPES.items()
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)
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return schema
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def normalize_other_sub_types(value: Any) -> List[str]:
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"""Normalize a stored/requested enabled-sub_type list.
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Unknown values and duplicates are dropped; the result follows the
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canonical VALID_OTHER_SUB_TYPES order so the stored setting and the UI
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stay stable. Non-list input falls back to the defaults.
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"""
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if isinstance(value, str):
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candidates: Any = [value]
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elif isinstance(value, (list, tuple, set)):
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candidates = value
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else:
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return list(DEFAULT_ENABLED_OTHER_SUB_TYPES)
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allowed = {item for item in candidates if isinstance(item, str)}
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return [sub_type for sub_type in VALID_OTHER_SUB_TYPES if sub_type in allowed]
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# CivitAI model.type values accepted by the "other" page's fetch-metadata
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# validation (lowercased). CLIP/CLIPVision are retired upstream but still
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# appear on grandfathered models.
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VALID_OTHER_CIVITAI_TYPES = {
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"vae",
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"upscaler",
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"textencoder",
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"clip",
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"clipvision",
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"controlnet",
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"other",
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}
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# CivitAI model.type -> internal sub_type for the "other" model page.
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CIVITAI_TYPE_TO_OTHER_SUB_TYPE = {
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"vae": "vae",
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"upscaler": "upscaler",
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"textencoder": "text_encoder",
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"clip": "text_encoder",
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"clipvision": "clip_vision",
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"controlnet": "controlnet",
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}
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# CivitAI ModelFile.type values -> internal sub_type for the "other" model
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# page. Used for download routing only, and strictly as an explicit user file
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# pick or a fallback when model.type maps to nothing — checkpoint models
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# routinely bundle VAE/Text Encoder component files, so file types must never
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# override a mapped model.type.
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CIVITAI_FILE_TYPE_TO_OTHER_SUB_TYPE = {
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"VAE": "vae",
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"Upscaler": "upscaler",
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"Text Encoder": "text_encoder",
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"Vision Encoder": "clip_vision",
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"CLIPVision": "clip_vision",
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"ControlNet": "controlnet",
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}
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# Backward compatibility alias
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VALID_LORA_TYPES = VALID_LORA_SUB_TYPES
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# Supported Civitai model types for user model queries (case-insensitive)
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CIVITAI_USER_MODEL_TYPES = [
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*VALID_LORA_TYPES,
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"textualinversion",
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"checkpoint",
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*sorted(VALID_OTHER_CIVITAI_TYPES),
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]
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# Default chunk size in megabytes used for hashing large files.
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DEFAULT_HASH_CHUNK_SIZE_MB = 4
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# Upper bound for a safetensors header block (bytes). Real headers are at most
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# a few MB (tensor name/shape lists); the cap prevents a crafted file with an
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# absurd 64-bit header length from forcing a multi-GB allocation during scan.
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MAX_SAFETENSORS_HEADER_BYTES = 64 * 1024 * 1024
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# SHA256 of an empty byte string. Some (re-packaging) training tools write a
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# truncated form of this placeholder into safetensors metadata (as
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# ``modelspec.hash_sha256`` / ``sshs_model_hash``), and hashing an empty or
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# unreadable file produces it directly. It must never be treated as a valid
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# hash: several broken models share it, CivitAI's by-hash index can contain
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# such polluted entries, and matching it falsely attributes recipes.
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EMPTY_HASH_SHA256 = "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
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INVALID_AUTOV3_EMPTY_HASH = EMPTY_HASH_SHA256[:12]
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INVALID_AUTOV2_EMPTY_HASH = EMPTY_HASH_SHA256[:10]
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def is_empty_placeholder_hash(value: Any) -> bool:
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"""True for a 10/12/64-hex-char spelling of the empty-hash placeholder.
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These are the AutoV2, AutoV3 and full-SHA256 forms of the placeholder;
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such values identify no real model and must never be resolved against
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local files or CivitAI.
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"""
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if not isinstance(value, str):
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return False
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v = value.strip().lower()
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if len(v) not in (10, 12, 64):
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return False
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return v == EMPTY_HASH_SHA256[: len(v)]
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# Auto-organize settings
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AUTO_ORGANIZE_BATCH_SIZE = (
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50 # Process models in batches to avoid overwhelming the system
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)
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# Civitai model tags in priority order for subfolder organization
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CIVITAI_MODEL_TAGS = [
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"character",
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"concept",
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"clothing",
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"realistic",
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"anime",
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"toon",
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"furry",
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"style",
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"poses",
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"background",
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"tool",
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"vehicle",
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"buildings",
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"objects",
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"assets",
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"animal",
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"action",
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]
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# Default priority tag configuration strings for each model type
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DEFAULT_PRIORITY_TAG_CONFIG = {
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"lora": ", ".join(CIVITAI_MODEL_TAGS),
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"checkpoint": ", ".join(CIVITAI_MODEL_TAGS),
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"embedding": ", ".join(CIVITAI_MODEL_TAGS),
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}
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# Default download path template for each model type. "other" defaults to a
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# flat layout (empty template) on purpose: other-model downloads are already
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# separated by sub_type roots (default_other_roots), and priority_tags has no
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# "other" entry, so {first_tag} would resolve to an arbitrary CivitAI tag and
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# scatter files into unstable folders. Users can still opt in to a template by
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# writing "other" into download_path_templates in settings.json.
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DEFAULT_DOWNLOAD_PATH_TEMPLATES: Dict[str, str] = {
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"lora": "{base_model}/{first_tag}",
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"checkpoint": "{base_model}/{first_tag}",
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"embedding": "{base_model}/{first_tag}",
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"other": "",
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}
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# baseModel values from CivitAI that should be treated as diffusion models (unet)
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# These model types are incorrectly labeled as "checkpoint" by CivitAI but are actually diffusion models
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DIFFUSION_MODEL_BASE_MODELS = frozenset(
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[
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"Anima",
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# Flux series — DiT architecture, loaded via UNETLoader in ComfyUI
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"Flux.1 D",
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"Flux.1 S",
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"Flux.1 Krea",
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"Flux.1 Kontext",
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"Flux.2 D",
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"Flux.2 Klein 9B",
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"Flux.2 Klein 9B-base",
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"Flux.2 Klein 4B",
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"Flux.2 Klein 4B-base",
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# Non-UNet / DiT image diffusion models
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"AuraFlow",
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"Chroma",
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"HiDream",
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"Hunyuan 1",
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"Kolors",
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"Lumina",
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"PixArt a",
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"PixArt E",
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# Video diffusion models
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"CogVideoX",
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"Hunyuan Video",
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"LTXV",
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"LTXV2",
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"LTXV 2.3",
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"Mochi",
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"SVD",
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"Wan Video",
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"Wan Video 1.3B t2v",
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"Wan Video 14B t2v",
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"Wan Video 14B i2v 480p",
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"Wan Video 14B i2v 720p",
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"Wan Video 2.2 TI2V-5B",
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"Wan Video 2.2 I2V-A14B",
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"Wan Video 2.2 T2V-A14B",
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"Wan Video 2.5 T2V",
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"Wan Video 2.5 I2V",
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# Other diffusion models
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"Ernie",
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"Ernie Turbo",
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"Nucleus",
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"Qwen",
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"ZImageBase",
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"ZImageTurbo",
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# Krea 2 — loaded via UNETLoader in ComfyUI
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"Krea 2",
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]
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)
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# Supported baseModel values for download exclusion settings.
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# Keep this aligned with static/js/utils/constants.js, excluding the generic "Other" value.
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SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS = frozenset(
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[
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"SD 1.4",
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"SD 1.5",
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"SD 1.5 LCM",
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"SD 1.5 Hyper",
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"SD 2.0",
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"SD 2.1",
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"SD 3",
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"SD 3.5",
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"SD 3.5 Medium",
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"SD 3.5 Large",
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"SD 3.5 Large Turbo",
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"SDXL 1.0",
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"SDXL Lightning",
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"SDXL Hyper",
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"Flux.1 D",
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"Flux.1 S",
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"Flux.1 Krea",
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"Flux.1 Kontext",
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"Flux.2 D",
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"Flux.2 Klein 9B",
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"Flux.2 Klein 9B-base",
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"Flux.2 Klein 4B",
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"Flux.2 Klein 4B-base",
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"AuraFlow",
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"Chroma",
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"PixArt a",
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"PixArt E",
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"Hunyuan 1",
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"Lumina",
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"Kolors",
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"NoobAI",
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"Illustrious",
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"Pony",
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"Pony V7",
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"HiDream",
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"Qwen",
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"ZImageTurbo",
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"ZImageBase",
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"SVD",
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"LTXV",
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"LTXV2",
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"LTXV 2.3",
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"CogVideoX",
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"Mochi",
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"Wan Video",
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"Wan Video 1.3B t2v",
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"Wan Video 14B t2v",
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"Wan Video 14B i2v 480p",
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"Wan Video 14B i2v 720p",
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"Wan Video 2.2 TI2V-5B",
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"Wan Video 2.2 T2V-A14B",
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"Wan Video 2.2 I2V-A14B",
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"Wan Video 2.5 T2V",
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"Wan Video 2.5 I2V",
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"Hunyuan Video",
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"Anima",
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"ACE Audio",
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"Boogu",
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"Ernie",
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"Ernie Turbo",
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"Grok",
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"HappyHorse",
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"HiDream-O1",
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"Ideogram 4.0",
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"Krea 2",
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"Lens",
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"MAI",
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"Nucleus",
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"Qwen 2",
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"Upscaler",
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"Wan Image 2.7",
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"Wan Video 2.7",
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]
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)
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