Merge 09c250184d into 1dc3da6314

Add most basic Asset support for models (#11315 )
* Brought over minimal elements from PR 10045 to reproduce seed_assets and register_assets_system without adding anything to the DB or server routes yet, for now making everything sync (can introduce async once everything is cleaned up and brought over) * Added db script to insert assets stuff, cleaned up some code; assets (models) now get added/rescanned * Added support for 5 http endpoints for assets * Replaced Optional with | None in schemas_in.py and schemas_out.py * Remove two routes that will not be relevant yet in this PR: HEAD /api/assets/hash/<hash> and PUT /api/assets/<id>/preview * Remove some functions the two deleted endpoints were using * Don't show assets scan message upon calling /object_info endpoint * removed unsued import to satisfy ruff * Simplified hashing function tpye hint and _hash_file_obj * Satisfied ruff
2026-01-26 06:10:15 +08:00 · 2026-01-09 11:23:30 +08:00 · 2026-01-08 22:21:51 -05:00 · 2026-01-08 22:16:15 -05:00 · 2026-01-08 22:15:50 -05:00 · 2026-01-08 22:14:06 -05:00
37 changed files with 2321 additions and 124 deletions
--- a/.github/workflows/stable-release.yml
+++ b/.github/workflows/stable-release.yml
@ -117,7 +117,7 @@ jobs:
          ./python.exe get-pip.py
          ./python.exe -s -m pip install ../${{ inputs.cache_tag }}_python_deps/*

-          grep comfyui ../ComfyUI/requirements.txt > ./requirements_comfyui.txt
+          grep comfy ../ComfyUI/requirements.txt > ./requirements_comfyui.txt
          ./python.exe -s -m pip install -r requirements_comfyui.txt
          rm requirements_comfyui.txt

--- a/alembic_db/versions/0001_assets.py
+++ b/alembic_db/versions/0001_assets.py
@ -0,0 +1,174 @@
+"""
+Initial assets schema
+Revision ID: 0001_assets
+Revises: None
+Create Date: 2025-12-10 00:00:00
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+revision = "0001_assets"
+down_revision = None
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # ASSETS: content identity
+    op.create_table(
+        "assets",
+        sa.Column("id", sa.String(length=36), primary_key=True),
+        sa.Column("hash", sa.String(length=256), nullable=True),
+        sa.Column("size_bytes", sa.BigInteger(), nullable=False, server_default="0"),
+        sa.Column("mime_type", sa.String(length=255), nullable=True),
+        sa.Column("created_at", sa.DateTime(timezone=False), nullable=False),
+        sa.CheckConstraint("size_bytes >= 0", name="ck_assets_size_nonneg"),
+    )
+    op.create_index("uq_assets_hash", "assets", ["hash"], unique=True)
+    op.create_index("ix_assets_mime_type", "assets", ["mime_type"])
+
+    # ASSETS_INFO: user-visible references
+    op.create_table(
+        "assets_info",
+        sa.Column("id", sa.String(length=36), primary_key=True),
+        sa.Column("owner_id", sa.String(length=128), nullable=False, server_default=""),
+        sa.Column("name", sa.String(length=512), nullable=False),
+        sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="RESTRICT"), nullable=False),
+        sa.Column("preview_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="SET NULL"), nullable=True),
+        sa.Column("user_metadata", sa.JSON(), nullable=True),
+        sa.Column("created_at", sa.DateTime(timezone=False), nullable=False),
+        sa.Column("updated_at", sa.DateTime(timezone=False), nullable=False),
+        sa.Column("last_access_time", sa.DateTime(timezone=False), nullable=False),
+        sa.UniqueConstraint("asset_id", "owner_id", "name", name="uq_assets_info_asset_owner_name"),
+    )
+    op.create_index("ix_assets_info_owner_id", "assets_info", ["owner_id"])
+    op.create_index("ix_assets_info_asset_id", "assets_info", ["asset_id"])
+    op.create_index("ix_assets_info_name", "assets_info", ["name"])
+    op.create_index("ix_assets_info_created_at", "assets_info", ["created_at"])
+    op.create_index("ix_assets_info_last_access_time", "assets_info", ["last_access_time"])
+    op.create_index("ix_assets_info_owner_name", "assets_info", ["owner_id", "name"])
+
+    # TAGS: normalized tag vocabulary
+    op.create_table(
+        "tags",
+        sa.Column("name", sa.String(length=512), primary_key=True),
+        sa.Column("tag_type", sa.String(length=32), nullable=False, server_default="user"),
+        sa.CheckConstraint("name = lower(name)", name="ck_tags_lowercase"),
+    )
+    op.create_index("ix_tags_tag_type", "tags", ["tag_type"])
+
+    # ASSET_INFO_TAGS: many-to-many for tags on AssetInfo
+    op.create_table(
+        "asset_info_tags",
+        sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False),
+        sa.Column("tag_name", sa.String(length=512), sa.ForeignKey("tags.name", ondelete="RESTRICT"), nullable=False),
+        sa.Column("origin", sa.String(length=32), nullable=False, server_default="manual"),
+        sa.Column("added_at", sa.DateTime(timezone=False), nullable=False),
+        sa.PrimaryKeyConstraint("asset_info_id", "tag_name", name="pk_asset_info_tags"),
+    )
+    op.create_index("ix_asset_info_tags_tag_name", "asset_info_tags", ["tag_name"])
+    op.create_index("ix_asset_info_tags_asset_info_id", "asset_info_tags", ["asset_info_id"])
+
+    # ASSET_CACHE_STATE: N:1 local cache rows per Asset
+    op.create_table(
+        "asset_cache_state",
+        sa.Column("id", sa.Integer(), primary_key=True, autoincrement=True),
+        sa.Column("asset_id", sa.String(length=36), sa.ForeignKey("assets.id", ondelete="CASCADE"), nullable=False),
+        sa.Column("file_path", sa.Text(), nullable=False),  # absolute local path to cached file
+        sa.Column("mtime_ns", sa.BigInteger(), nullable=True),
+        sa.Column("needs_verify", sa.Boolean(), nullable=False, server_default=sa.text("false")),
+        sa.CheckConstraint("(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_acs_mtime_nonneg"),
+        sa.UniqueConstraint("file_path", name="uq_asset_cache_state_file_path"),
+    )
+    op.create_index("ix_asset_cache_state_file_path", "asset_cache_state", ["file_path"])
+    op.create_index("ix_asset_cache_state_asset_id", "asset_cache_state", ["asset_id"])
+
+    # ASSET_INFO_META: typed KV projection of user_metadata for filtering/sorting
+    op.create_table(
+        "asset_info_meta",
+        sa.Column("asset_info_id", sa.String(length=36), sa.ForeignKey("assets_info.id", ondelete="CASCADE"), nullable=False),
+        sa.Column("key", sa.String(length=256), nullable=False),
+        sa.Column("ordinal", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("val_str", sa.String(length=2048), nullable=True),
+        sa.Column("val_num", sa.Numeric(38, 10), nullable=True),
+        sa.Column("val_bool", sa.Boolean(), nullable=True),
+        sa.Column("val_json", sa.JSON(), nullable=True),
+        sa.PrimaryKeyConstraint("asset_info_id", "key", "ordinal", name="pk_asset_info_meta"),
+    )
+    op.create_index("ix_asset_info_meta_key", "asset_info_meta", ["key"])
+    op.create_index("ix_asset_info_meta_key_val_str", "asset_info_meta", ["key", "val_str"])
+    op.create_index("ix_asset_info_meta_key_val_num", "asset_info_meta", ["key", "val_num"])
+    op.create_index("ix_asset_info_meta_key_val_bool", "asset_info_meta", ["key", "val_bool"])
+
+    # Tags vocabulary
+    tags_table = sa.table(
+        "tags",
+        sa.column("name", sa.String(length=512)),
+        sa.column("tag_type", sa.String()),
+    )
+    op.bulk_insert(
+        tags_table,
+        [
+            {"name": "models", "tag_type": "system"},
+            {"name": "input", "tag_type": "system"},
+            {"name": "output", "tag_type": "system"},
+
+            {"name": "configs", "tag_type": "system"},
+            {"name": "checkpoints", "tag_type": "system"},
+            {"name": "loras", "tag_type": "system"},
+            {"name": "vae", "tag_type": "system"},
+            {"name": "text_encoders", "tag_type": "system"},
+            {"name": "diffusion_models", "tag_type": "system"},
+            {"name": "clip_vision", "tag_type": "system"},
+            {"name": "style_models", "tag_type": "system"},
+            {"name": "embeddings", "tag_type": "system"},
+            {"name": "diffusers", "tag_type": "system"},
+            {"name": "vae_approx", "tag_type": "system"},
+            {"name": "controlnet", "tag_type": "system"},
+            {"name": "gligen", "tag_type": "system"},
+            {"name": "upscale_models", "tag_type": "system"},
+            {"name": "hypernetworks", "tag_type": "system"},
+            {"name": "photomaker", "tag_type": "system"},
+            {"name": "classifiers", "tag_type": "system"},
+
+            {"name": "encoder", "tag_type": "system"},
+            {"name": "decoder", "tag_type": "system"},
+
+            {"name": "missing", "tag_type": "system"},
+            {"name": "rescan", "tag_type": "system"},
+        ],
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("ix_asset_info_meta_key_val_bool", table_name="asset_info_meta")
+    op.drop_index("ix_asset_info_meta_key_val_num", table_name="asset_info_meta")
+    op.drop_index("ix_asset_info_meta_key_val_str", table_name="asset_info_meta")
+    op.drop_index("ix_asset_info_meta_key", table_name="asset_info_meta")
+    op.drop_table("asset_info_meta")
+
+    op.drop_index("ix_asset_cache_state_asset_id", table_name="asset_cache_state")
+    op.drop_index("ix_asset_cache_state_file_path", table_name="asset_cache_state")
+    op.drop_constraint("uq_asset_cache_state_file_path", table_name="asset_cache_state")
+    op.drop_table("asset_cache_state")
+
+    op.drop_index("ix_asset_info_tags_asset_info_id", table_name="asset_info_tags")
+    op.drop_index("ix_asset_info_tags_tag_name", table_name="asset_info_tags")
+    op.drop_table("asset_info_tags")
+
+    op.drop_index("ix_tags_tag_type", table_name="tags")
+    op.drop_table("tags")
+
+    op.drop_constraint("uq_assets_info_asset_owner_name", table_name="assets_info")
+    op.drop_index("ix_assets_info_owner_name", table_name="assets_info")
+    op.drop_index("ix_assets_info_last_access_time", table_name="assets_info")
+    op.drop_index("ix_assets_info_created_at", table_name="assets_info")
+    op.drop_index("ix_assets_info_name", table_name="assets_info")
+    op.drop_index("ix_assets_info_asset_id", table_name="assets_info")
+    op.drop_index("ix_assets_info_owner_id", table_name="assets_info")
+    op.drop_table("assets_info")
+
+    op.drop_index("uq_assets_hash", table_name="assets")
+    op.drop_index("ix_assets_mime_type", table_name="assets")
+    op.drop_table("assets")
--- a/app/assets/api/routes.py
+++ b/app/assets/api/routes.py
@ -0,0 +1,102 @@
+import logging
+import uuid
+from aiohttp import web
+
+from pydantic import ValidationError
+
+import app.assets.manager as manager
+from app import user_manager
+from app.assets.api import schemas_in
+from app.assets.helpers import get_query_dict
+
+ROUTES = web.RouteTableDef()
+USER_MANAGER: user_manager.UserManager | None = None
+
+# UUID regex (canonical hyphenated form, case-insensitive)
+UUID_RE = r"[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}"
+
+def register_assets_system(app: web.Application, user_manager_instance: user_manager.UserManager) -> None:
+    global USER_MANAGER
+    USER_MANAGER = user_manager_instance
+    app.add_routes(ROUTES)
+
+def _error_response(status: int, code: str, message: str, details: dict | None = None) -> web.Response:
+    return web.json_response({"error": {"code": code, "message": message, "details": details or {}}}, status=status)
+
+
+def _validation_error_response(code: str, ve: ValidationError) -> web.Response:
+    return _error_response(400, code, "Validation failed.", {"errors": ve.json()})
+
+
+@ROUTES.get("/api/assets")
+async def list_assets(request: web.Request) -> web.Response:
+    """
+    GET request to list assets.
+    """
+    query_dict = get_query_dict(request)
+    try:
+        q = schemas_in.ListAssetsQuery.model_validate(query_dict)
+    except ValidationError as ve:
+        return _validation_error_response("INVALID_QUERY", ve)
+
+    payload = manager.list_assets(
+        include_tags=q.include_tags,
+        exclude_tags=q.exclude_tags,
+        name_contains=q.name_contains,
+        metadata_filter=q.metadata_filter,
+        limit=q.limit,
+        offset=q.offset,
+        sort=q.sort,
+        order=q.order,
+        owner_id=USER_MANAGER.get_request_user_id(request),
+    )
+    return web.json_response(payload.model_dump(mode="json"))
+
+
+@ROUTES.get(f"/api/assets/{{id:{UUID_RE}}}")
+async def get_asset(request: web.Request) -> web.Response:
+    """
+    GET request to get an asset's info as JSON.
+    """
+    asset_info_id = str(uuid.UUID(request.match_info["id"]))
+    try:
+        result = manager.get_asset(
+            asset_info_id=asset_info_id,
+            owner_id=USER_MANAGER.get_request_user_id(request),
+        )
+    except ValueError as e:
+        return _error_response(404, "ASSET_NOT_FOUND", str(e), {"id": asset_info_id})
+    except Exception:
+        logging.exception(
+            "get_asset failed for asset_info_id=%s, owner_id=%s",
+            asset_info_id,
+            USER_MANAGER.get_request_user_id(request),
+        )
+        return _error_response(500, "INTERNAL", "Unexpected server error.")
+    return web.json_response(result.model_dump(mode="json"), status=200)
+
+
+@ROUTES.get("/api/tags")
+async def get_tags(request: web.Request) -> web.Response:
+    """
+    GET request to list all tags based on query parameters.
+    """
+    query_map = dict(request.rel_url.query)
+
+    try:
+        query = schemas_in.TagsListQuery.model_validate(query_map)
+    except ValidationError as e:
+        return web.json_response(
+            {"error": {"code": "INVALID_QUERY", "message": "Invalid query parameters", "details": e.errors()}},
+            status=400,
+        )
+
+    result = manager.list_tags(
+        prefix=query.prefix,
+        limit=query.limit,
+        offset=query.offset,
+        order=query.order,
+        include_zero=query.include_zero,
+        owner_id=USER_MANAGER.get_request_user_id(request),
+    )
+    return web.json_response(result.model_dump(mode="json"))
--- a/app/assets/api/schemas_in.py
+++ b/app/assets/api/schemas_in.py
@ -0,0 +1,94 @@
+import json
+import uuid
+from typing import Any, Literal
+
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    conint,
+    field_validator,
+)
+
+
+class ListAssetsQuery(BaseModel):
+    include_tags: list[str] = Field(default_factory=list)
+    exclude_tags: list[str] = Field(default_factory=list)
+    name_contains: str | None = None
+
+    # Accept either a JSON string (query param) or a dict
+    metadata_filter: dict[str, Any] | None = None
+
+    limit: conint(ge=1, le=500) = 20
+    offset: conint(ge=0) = 0
+
+    sort: Literal["name", "created_at", "updated_at", "size", "last_access_time"] = "created_at"
+    order: Literal["asc", "desc"] = "desc"
+
+    @field_validator("include_tags", "exclude_tags", mode="before")
+    @classmethod
+    def _split_csv_tags(cls, v):
+        # Accept "a,b,c" or ["a","b"] (we are liberal in what we accept)
+        if v is None:
+            return []
+        if isinstance(v, str):
+            return [t.strip() for t in v.split(",") if t.strip()]
+        if isinstance(v, list):
+            out: list[str] = []
+            for item in v:
+                if isinstance(item, str):
+                    out.extend([t.strip() for t in item.split(",") if t.strip()])
+            return out
+        return v
+
+    @field_validator("metadata_filter", mode="before")
+    @classmethod
+    def _parse_metadata_json(cls, v):
+        if v is None or isinstance(v, dict):
+            return v
+        if isinstance(v, str) and v.strip():
+            try:
+                parsed = json.loads(v)
+            except Exception as e:
+                raise ValueError(f"metadata_filter must be JSON: {e}") from e
+            if not isinstance(parsed, dict):
+                raise ValueError("metadata_filter must be a JSON object")
+            return parsed
+        return None
+
+
+class TagsListQuery(BaseModel):
+    model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
+
+    prefix: str | None = Field(None, min_length=1, max_length=256)
+    limit: int = Field(100, ge=1, le=1000)
+    offset: int = Field(0, ge=0, le=10_000_000)
+    order: Literal["count_desc", "name_asc"] = "count_desc"
+    include_zero: bool = True
+
+    @field_validator("prefix")
+    @classmethod
+    def normalize_prefix(cls, v: str | None) -> str | None:
+        if v is None:
+            return v
+        v = v.strip()
+        return v.lower() or None
+
+
+class SetPreviewBody(BaseModel):
+    """Set or clear the preview for an AssetInfo. Provide an Asset.id or null."""
+    preview_id: str | None = None
+
+    @field_validator("preview_id", mode="before")
+    @classmethod
+    def _norm_uuid(cls, v):
+        if v is None:
+            return None
+        s = str(v).strip()
+        if not s:
+            return None
+        try:
+            uuid.UUID(s)
+        except Exception:
+            raise ValueError("preview_id must be a UUID")
+        return s
--- a/app/assets/api/schemas_out.py
+++ b/app/assets/api/schemas_out.py
@ -0,0 +1,60 @@
+from datetime import datetime
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict, Field, field_serializer
+
+
+class AssetSummary(BaseModel):
+    id: str
+    name: str
+    asset_hash: str | None = None
+    size: int | None = None
+    mime_type: str | None = None
+    tags: list[str] = Field(default_factory=list)
+    preview_url: str | None = None
+    created_at: datetime | None = None
+    updated_at: datetime | None = None
+    last_access_time: datetime | None = None
+
+    model_config = ConfigDict(from_attributes=True)
+
+    @field_serializer("created_at", "updated_at", "last_access_time")
+    def _ser_dt(self, v: datetime | None, _info):
+        return v.isoformat() if v else None
+
+
+class AssetsList(BaseModel):
+    assets: list[AssetSummary]
+    total: int
+    has_more: bool
+
+
+class AssetDetail(BaseModel):
+    id: str
+    name: str
+    asset_hash: str | None = None
+    size: int | None = None
+    mime_type: str | None = None
+    tags: list[str] = Field(default_factory=list)
+    user_metadata: dict[str, Any] = Field(default_factory=dict)
+    preview_id: str | None = None
+    created_at: datetime | None = None
+    last_access_time: datetime | None = None
+
+    model_config = ConfigDict(from_attributes=True)
+
+    @field_serializer("created_at", "last_access_time")
+    def _ser_dt(self, v: datetime | None, _info):
+        return v.isoformat() if v else None
+
+
+class TagUsage(BaseModel):
+    name: str
+    count: int
+    type: str
+
+
+class TagsList(BaseModel):
+    tags: list[TagUsage] = Field(default_factory=list)
+    total: int
+    has_more: bool
--- a/app/assets/database/bulk_ops.py
+++ b/app/assets/database/bulk_ops.py
@ -0,0 +1,188 @@
+import os
+import uuid
+import sqlalchemy
+from typing import Iterable
+from sqlalchemy.orm import Session
+from sqlalchemy.dialects import sqlite
+
+from app.assets.helpers import utcnow
+from app.assets.database.models import Asset, AssetCacheState, AssetInfo, AssetInfoTag, AssetInfoMeta
+
+MAX_BIND_PARAMS = 800
+
+def _chunk_rows(rows: list[dict], cols_per_row: int, max_bind_params: int) -> Iterable[list[dict]]:
+    if not rows:
+        return []
+    rows_per_stmt = max(1, max_bind_params // max(1, cols_per_row))
+    for i in range(0, len(rows), rows_per_stmt):
+        yield rows[i:i + rows_per_stmt]
+
+def _iter_chunks(seq, n: int):
+    for i in range(0, len(seq), n):
+        yield seq[i:i + n]
+
+def _rows_per_stmt(cols: int) -> int:
+    return max(1, MAX_BIND_PARAMS // max(1, cols))
+
+
+def seed_from_paths_batch(
+    session: Session,
+    *,
+    specs: list[dict],
+    owner_id: str = "",
+) -> dict:
+    """Each spec is a dict with keys:
+      - abs_path: str
+      - size_bytes: int
+      - mtime_ns: int
+      - info_name: str
+      - tags: list[str]
+      - fname: Optional[str]
+    """
+    if not specs:
+        return {"inserted_infos": 0, "won_states": 0, "lost_states": 0}
+
+    now = utcnow()
+    asset_rows: list[dict] = []
+    state_rows: list[dict] = []
+    path_to_asset: dict[str, str] = {}
+    asset_to_info: dict[str, dict] = {}  # asset_id -> prepared info row
+    path_list: list[str] = []
+
+    for sp in specs:
+        ap = os.path.abspath(sp["abs_path"])
+        aid = str(uuid.uuid4())
+        iid = str(uuid.uuid4())
+        path_list.append(ap)
+        path_to_asset[ap] = aid
+
+        asset_rows.append(
+            {
+                "id": aid,
+                "hash": None,
+                "size_bytes": sp["size_bytes"],
+                "mime_type": None,
+                "created_at": now,
+            }
+        )
+        state_rows.append(
+            {
+                "asset_id": aid,
+                "file_path": ap,
+                "mtime_ns": sp["mtime_ns"],
+            }
+        )
+        asset_to_info[aid] = {
+            "id": iid,
+            "owner_id": owner_id,
+            "name": sp["info_name"],
+            "asset_id": aid,
+            "preview_id": None,
+            "user_metadata": {"filename": sp["fname"]} if sp["fname"] else None,
+            "created_at": now,
+            "updated_at": now,
+            "last_access_time": now,
+            "_tags": sp["tags"],
+            "_filename": sp["fname"],
+        }
+
+    # insert all seed Assets (hash=NULL)
+    ins_asset = sqlite.insert(Asset)
+    for chunk in _iter_chunks(asset_rows, _rows_per_stmt(5)):
+        session.execute(ins_asset, chunk)
+
+    # try to claim AssetCacheState (file_path)
+    winners_by_path: set[str] = set()
+    ins_state = (
+        sqlite.insert(AssetCacheState)
+        .on_conflict_do_nothing(index_elements=[AssetCacheState.file_path])
+        .returning(AssetCacheState.file_path)
+    )
+    for chunk in _iter_chunks(state_rows, _rows_per_stmt(3)):
+        winners_by_path.update((session.execute(ins_state, chunk)).scalars().all())
+
+    all_paths_set = set(path_list)
+    losers_by_path = all_paths_set - winners_by_path
+    lost_assets = [path_to_asset[p] for p in losers_by_path]
+    if lost_assets:  # losers get their Asset removed
+        for id_chunk in _iter_chunks(lost_assets, MAX_BIND_PARAMS):
+            session.execute(sqlalchemy.delete(Asset).where(Asset.id.in_(id_chunk)))
+
+    if not winners_by_path:
+        return {"inserted_infos": 0, "won_states": 0, "lost_states": len(losers_by_path)}
+
+    # insert AssetInfo only for winners
+    winner_info_rows = [asset_to_info[path_to_asset[p]] for p in winners_by_path]
+    ins_info = (
+        sqlite.insert(AssetInfo)
+        .on_conflict_do_nothing(index_elements=[AssetInfo.asset_id, AssetInfo.owner_id, AssetInfo.name])
+        .returning(AssetInfo.id)
+    )
+
+    inserted_info_ids: set[str] = set()
+    for chunk in _iter_chunks(winner_info_rows, _rows_per_stmt(9)):
+        inserted_info_ids.update((session.execute(ins_info, chunk)).scalars().all())
+
+    # build and insert tag + meta rows for the AssetInfo
+    tag_rows: list[dict] = []
+    meta_rows: list[dict] = []
+    if inserted_info_ids:
+        for row in winner_info_rows:
+            iid = row["id"]
+            if iid not in inserted_info_ids:
+                continue
+            for t in row["_tags"]:
+                tag_rows.append({
+                    "asset_info_id": iid,
+                    "tag_name": t,
+                    "origin": "automatic",
+                    "added_at": now,
+                })
+            if row["_filename"]:
+                meta_rows.append(
+                    {
+                        "asset_info_id": iid,
+                        "key": "filename",
+                        "ordinal": 0,
+                        "val_str": row["_filename"],
+                        "val_num": None,
+                        "val_bool": None,
+                        "val_json": None,
+                    }
+                )
+
+    bulk_insert_tags_and_meta(session, tag_rows=tag_rows, meta_rows=meta_rows, max_bind_params=MAX_BIND_PARAMS)
+    return {
+        "inserted_infos": len(inserted_info_ids),
+        "won_states": len(winners_by_path),
+        "lost_states": len(losers_by_path),
+    }
+
+
+def bulk_insert_tags_and_meta(
+    session: Session,
+    *,
+    tag_rows: list[dict],
+    meta_rows: list[dict],
+    max_bind_params: int,
+) -> None:
+    """Batch insert into asset_info_tags and asset_info_meta with ON CONFLICT DO NOTHING.
+    - tag_rows keys: asset_info_id, tag_name, origin, added_at
+    - meta_rows keys: asset_info_id, key, ordinal, val_str, val_num, val_bool, val_json
+    """
+    if tag_rows:
+        ins_links = (
+            sqlite.insert(AssetInfoTag)
+            .on_conflict_do_nothing(index_elements=[AssetInfoTag.asset_info_id, AssetInfoTag.tag_name])
+        )
+        for chunk in _chunk_rows(tag_rows, cols_per_row=4, max_bind_params=max_bind_params):
+            session.execute(ins_links, chunk)
+    if meta_rows:
+        ins_meta = (
+            sqlite.insert(AssetInfoMeta)
+            .on_conflict_do_nothing(
+                index_elements=[AssetInfoMeta.asset_info_id, AssetInfoMeta.key, AssetInfoMeta.ordinal]
+            )
+        )
+        for chunk in _chunk_rows(meta_rows, cols_per_row=7, max_bind_params=max_bind_params):
+            session.execute(ins_meta, chunk)
--- a/app/assets/database/models.py
+++ b/app/assets/database/models.py
@ -0,0 +1,233 @@
+from __future__ import annotations
+
+import uuid
+from datetime import datetime
+
+from typing import Any
+from sqlalchemy import (
+    JSON,
+    BigInteger,
+    Boolean,
+    CheckConstraint,
+    DateTime,
+    ForeignKey,
+    Index,
+    Integer,
+    Numeric,
+    String,
+    Text,
+    UniqueConstraint,
+)
+from sqlalchemy.orm import Mapped, foreign, mapped_column, relationship
+
+from app.assets.helpers import utcnow
+from app.database.models import to_dict, Base
+
+
+class Asset(Base):
+    __tablename__ = "assets"
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
+    hash: Mapped[str | None] = mapped_column(String(256), nullable=True)
+    size_bytes: Mapped[int] = mapped_column(BigInteger, nullable=False, default=0)
+    mime_type: Mapped[str | None] = mapped_column(String(255))
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=False), nullable=False, default=utcnow
+    )
+
+    infos: Mapped[list[AssetInfo]] = relationship(
+        "AssetInfo",
+        back_populates="asset",
+        primaryjoin=lambda: Asset.id == foreign(AssetInfo.asset_id),
+        foreign_keys=lambda: [AssetInfo.asset_id],
+        cascade="all,delete-orphan",
+        passive_deletes=True,
+    )
+
+    preview_of: Mapped[list[AssetInfo]] = relationship(
+        "AssetInfo",
+        back_populates="preview_asset",
+        primaryjoin=lambda: Asset.id == foreign(AssetInfo.preview_id),
+        foreign_keys=lambda: [AssetInfo.preview_id],
+        viewonly=True,
+    )
+
+    cache_states: Mapped[list[AssetCacheState]] = relationship(
+        back_populates="asset",
+        cascade="all, delete-orphan",
+        passive_deletes=True,
+    )
+
+    __table_args__ = (
+        Index("uq_assets_hash", "hash", unique=True),
+        Index("ix_assets_mime_type", "mime_type"),
+        CheckConstraint("size_bytes >= 0", name="ck_assets_size_nonneg"),
+    )
+
+    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
+        return to_dict(self, include_none=include_none)
+
+    def __repr__(self) -> str:
+        return f"<Asset id={self.id} hash={(self.hash or '')[:12]}>"
+
+
+class AssetCacheState(Base):
+    __tablename__ = "asset_cache_state"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    asset_id: Mapped[str] = mapped_column(String(36), ForeignKey("assets.id", ondelete="CASCADE"), nullable=False)
+    file_path: Mapped[str] = mapped_column(Text, nullable=False)
+    mtime_ns: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
+    needs_verify: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+
+    asset: Mapped[Asset] = relationship(back_populates="cache_states")
+
+    __table_args__ = (
+        Index("ix_asset_cache_state_file_path", "file_path"),
+        Index("ix_asset_cache_state_asset_id", "asset_id"),
+        CheckConstraint("(mtime_ns IS NULL) OR (mtime_ns >= 0)", name="ck_acs_mtime_nonneg"),
+        UniqueConstraint("file_path", name="uq_asset_cache_state_file_path"),
+    )
+
+    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
+        return to_dict(self, include_none=include_none)
+
+    def __repr__(self) -> str:
+        return f"<AssetCacheState id={self.id} asset_id={self.asset_id} path={self.file_path!r}>"
+
+
+class AssetInfo(Base):
+    __tablename__ = "assets_info"
+
+    id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
+    owner_id: Mapped[str] = mapped_column(String(128), nullable=False, default="")
+    name: Mapped[str] = mapped_column(String(512), nullable=False)
+    asset_id: Mapped[str] = mapped_column(String(36), ForeignKey("assets.id", ondelete="RESTRICT"), nullable=False)
+    preview_id: Mapped[str | None] = mapped_column(String(36), ForeignKey("assets.id", ondelete="SET NULL"))
+    user_metadata: Mapped[dict[str, Any] | None] = mapped_column(JSON(none_as_null=True))
+    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
+    updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
+    last_access_time: Mapped[datetime] = mapped_column(DateTime(timezone=False), nullable=False, default=utcnow)
+
+    asset: Mapped[Asset] = relationship(
+        "Asset",
+        back_populates="infos",
+        foreign_keys=[asset_id],
+        lazy="selectin",
+    )
+    preview_asset: Mapped[Asset | None] = relationship(
+        "Asset",
+        back_populates="preview_of",
+        foreign_keys=[preview_id],
+    )
+
+    metadata_entries: Mapped[list[AssetInfoMeta]] = relationship(
+        back_populates="asset_info",
+        cascade="all,delete-orphan",
+        passive_deletes=True,
+    )
+
+    tag_links: Mapped[list[AssetInfoTag]] = relationship(
+        back_populates="asset_info",
+        cascade="all,delete-orphan",
+        passive_deletes=True,
+        overlaps="tags,asset_infos",
+    )
+
+    tags: Mapped[list[Tag]] = relationship(
+        secondary="asset_info_tags",
+        back_populates="asset_infos",
+        lazy="selectin",
+        viewonly=True,
+        overlaps="tag_links,asset_info_links,asset_infos,tag",
+    )
+
+    __table_args__ = (
+        UniqueConstraint("asset_id", "owner_id", "name", name="uq_assets_info_asset_owner_name"),
+        Index("ix_assets_info_owner_name", "owner_id", "name"),
+        Index("ix_assets_info_owner_id", "owner_id"),
+        Index("ix_assets_info_asset_id", "asset_id"),
+        Index("ix_assets_info_name", "name"),
+        Index("ix_assets_info_created_at", "created_at"),
+        Index("ix_assets_info_last_access_time", "last_access_time"),
+    )
+
+    def to_dict(self, include_none: bool = False) -> dict[str, Any]:
+        data = to_dict(self, include_none=include_none)
+        data["tags"] = [t.name for t in self.tags]
+        return data
+
+    def __repr__(self) -> str:
+        return f"<AssetInfo id={self.id} name={self.name!r} asset_id={self.asset_id}>"
+
+
+class AssetInfoMeta(Base):
+    __tablename__ = "asset_info_meta"
+
+    asset_info_id: Mapped[str] = mapped_column(
+        String(36), ForeignKey("assets_info.id", ondelete="CASCADE"), primary_key=True
+    )
+    key: Mapped[str] = mapped_column(String(256), primary_key=True)
+    ordinal: Mapped[int] = mapped_column(Integer, primary_key=True, default=0)
+
+    val_str: Mapped[str | None] = mapped_column(String(2048), nullable=True)
+    val_num: Mapped[float | None] = mapped_column(Numeric(38, 10), nullable=True)
+    val_bool: Mapped[bool | None] = mapped_column(Boolean, nullable=True)
+    val_json: Mapped[Any | None] = mapped_column(JSON(none_as_null=True), nullable=True)
+
+    asset_info: Mapped[AssetInfo] = relationship(back_populates="metadata_entries")
+
+    __table_args__ = (
+        Index("ix_asset_info_meta_key", "key"),
+        Index("ix_asset_info_meta_key_val_str", "key", "val_str"),
+        Index("ix_asset_info_meta_key_val_num", "key", "val_num"),
+        Index("ix_asset_info_meta_key_val_bool", "key", "val_bool"),
+    )
+
+
+class AssetInfoTag(Base):
+    __tablename__ = "asset_info_tags"
+
+    asset_info_id: Mapped[str] = mapped_column(
+        String(36), ForeignKey("assets_info.id", ondelete="CASCADE"), primary_key=True
+    )
+    tag_name: Mapped[str] = mapped_column(
+        String(512), ForeignKey("tags.name", ondelete="RESTRICT"), primary_key=True
+    )
+    origin: Mapped[str] = mapped_column(String(32), nullable=False, default="manual")
+    added_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=False), nullable=False, default=utcnow
+    )
+
+    asset_info: Mapped[AssetInfo] = relationship(back_populates="tag_links")
+    tag: Mapped[Tag] = relationship(back_populates="asset_info_links")
+
+    __table_args__ = (
+        Index("ix_asset_info_tags_tag_name", "tag_name"),
+        Index("ix_asset_info_tags_asset_info_id", "asset_info_id"),
+    )
+
+
+class Tag(Base):
+    __tablename__ = "tags"
+
+    name: Mapped[str] = mapped_column(String(512), primary_key=True)
+    tag_type: Mapped[str] = mapped_column(String(32), nullable=False, default="user")
+
+    asset_info_links: Mapped[list[AssetInfoTag]] = relationship(
+        back_populates="tag",
+        overlaps="asset_infos,tags",
+    )
+    asset_infos: Mapped[list[AssetInfo]] = relationship(
+        secondary="asset_info_tags",
+        back_populates="tags",
+        viewonly=True,
+        overlaps="asset_info_links,tag_links,tags,asset_info",
+    )
+
+    __table_args__ = (
+        Index("ix_tags_tag_type", "tag_type"),
+    )
+
+    def __repr__(self) -> str:
+        return f"<Tag {self.name}>"
--- a/app/assets/database/queries.py
+++ b/app/assets/database/queries.py
@ -0,0 +1,267 @@
+import sqlalchemy as sa
+from collections import defaultdict
+from sqlalchemy import select, exists, func
+from sqlalchemy.orm import Session, contains_eager, noload
+from app.assets.database.models import Asset, AssetInfo, AssetInfoMeta, AssetInfoTag, Tag
+from app.assets.helpers import escape_like_prefix, normalize_tags
+from typing import Sequence
+
+
+def visible_owner_clause(owner_id: str) -> sa.sql.ClauseElement:
+    """Build owner visibility predicate for reads. Owner-less rows are visible to everyone."""
+    owner_id = (owner_id or "").strip()
+    if owner_id == "":
+        return AssetInfo.owner_id == ""
+    return AssetInfo.owner_id.in_(["", owner_id])
+
+
+def apply_tag_filters(
+    stmt: sa.sql.Select,
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+) -> sa.sql.Select:
+    """include_tags: every tag must be present; exclude_tags: none may be present."""
+    include_tags = normalize_tags(include_tags)
+    exclude_tags = normalize_tags(exclude_tags)
+
+    if include_tags:
+        for tag_name in include_tags:
+            stmt = stmt.where(
+                exists().where(
+                    (AssetInfoTag.asset_info_id == AssetInfo.id)
+                    & (AssetInfoTag.tag_name == tag_name)
+                )
+            )
+
+    if exclude_tags:
+        stmt = stmt.where(
+            ~exists().where(
+                (AssetInfoTag.asset_info_id == AssetInfo.id)
+                & (AssetInfoTag.tag_name.in_(exclude_tags))
+            )
+        )
+    return stmt
+
+def apply_metadata_filter(
+    stmt: sa.sql.Select,
+    metadata_filter: dict | None = None,
+) -> sa.sql.Select:
+    """Apply filters using asset_info_meta projection table."""
+    if not metadata_filter:
+        return stmt
+
+    def _exists_for_pred(key: str, *preds) -> sa.sql.ClauseElement:
+        return sa.exists().where(
+            AssetInfoMeta.asset_info_id == AssetInfo.id,
+            AssetInfoMeta.key == key,
+            *preds,
+        )
+
+    def _exists_clause_for_value(key: str, value) -> sa.sql.ClauseElement:
+        if value is None:
+            no_row_for_key = sa.not_(
+                sa.exists().where(
+                    AssetInfoMeta.asset_info_id == AssetInfo.id,
+                    AssetInfoMeta.key == key,
+                )
+            )
+            null_row = _exists_for_pred(
+                key,
+                AssetInfoMeta.val_json.is_(None),
+                AssetInfoMeta.val_str.is_(None),
+                AssetInfoMeta.val_num.is_(None),
+                AssetInfoMeta.val_bool.is_(None),
+            )
+            return sa.or_(no_row_for_key, null_row)
+
+        if isinstance(value, bool):
+            return _exists_for_pred(key, AssetInfoMeta.val_bool == bool(value))
+        if isinstance(value, (int, float)):
+            from decimal import Decimal
+            num = value if isinstance(value, Decimal) else Decimal(str(value))
+            return _exists_for_pred(key, AssetInfoMeta.val_num == num)
+        if isinstance(value, str):
+            return _exists_for_pred(key, AssetInfoMeta.val_str == value)
+        return _exists_for_pred(key, AssetInfoMeta.val_json == value)
+
+    for k, v in metadata_filter.items():
+        if isinstance(v, list):
+            ors = [_exists_clause_for_value(k, elem) for elem in v]
+            if ors:
+                stmt = stmt.where(sa.or_(*ors))
+        else:
+            stmt = stmt.where(_exists_clause_for_value(k, v))
+    return stmt
+
+
+def asset_exists_by_hash(session: Session, asset_hash: str) -> bool:
+    """
+    Check if an asset with a given hash exists in database.
+    """
+    row = (
+        session.execute(
+            select(sa.literal(True)).select_from(Asset).where(Asset.hash == asset_hash).limit(1)
+        )
+    ).first()
+    return row is not None
+
+def get_asset_info_by_id(session: Session, asset_info_id: str) -> AssetInfo | None:
+    return session.get(AssetInfo, asset_info_id)
+
+def list_asset_infos_page(
+    session: Session,
+    owner_id: str = "",
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+    name_contains: str | None = None,
+    metadata_filter: dict | None = None,
+    limit: int = 20,
+    offset: int = 0,
+    sort: str = "created_at",
+    order: str = "desc",
+) -> tuple[list[AssetInfo], dict[str, list[str]], int]:
+    base = (
+        select(AssetInfo)
+        .join(Asset, Asset.id == AssetInfo.asset_id)
+        .options(contains_eager(AssetInfo.asset), noload(AssetInfo.tags))
+        .where(visible_owner_clause(owner_id))
+    )
+
+    if name_contains:
+        escaped, esc = escape_like_prefix(name_contains)
+        base = base.where(AssetInfo.name.ilike(f"%{escaped}%", escape=esc))
+
+    base = apply_tag_filters(base, include_tags, exclude_tags)
+    base = apply_metadata_filter(base, metadata_filter)
+
+    sort = (sort or "created_at").lower()
+    order = (order or "desc").lower()
+    sort_map = {
+        "name": AssetInfo.name,
+        "created_at": AssetInfo.created_at,
+        "updated_at": AssetInfo.updated_at,
+        "last_access_time": AssetInfo.last_access_time,
+        "size": Asset.size_bytes,
+    }
+    sort_col = sort_map.get(sort, AssetInfo.created_at)
+    sort_exp = sort_col.desc() if order == "desc" else sort_col.asc()
+
+    base = base.order_by(sort_exp).limit(limit).offset(offset)
+
+    count_stmt = (
+        select(sa.func.count())
+        .select_from(AssetInfo)
+        .join(Asset, Asset.id == AssetInfo.asset_id)
+        .where(visible_owner_clause(owner_id))
+    )
+    if name_contains:
+        escaped, esc = escape_like_prefix(name_contains)
+        count_stmt = count_stmt.where(AssetInfo.name.ilike(f"%{escaped}%", escape=esc))
+    count_stmt = apply_tag_filters(count_stmt, include_tags, exclude_tags)
+    count_stmt = apply_metadata_filter(count_stmt, metadata_filter)
+
+    total = int((session.execute(count_stmt)).scalar_one() or 0)
+
+    infos = (session.execute(base)).unique().scalars().all()
+
+    id_list: list[str] = [i.id for i in infos]
+    tag_map: dict[str, list[str]] = defaultdict(list)
+    if id_list:
+        rows = session.execute(
+            select(AssetInfoTag.asset_info_id, Tag.name)
+            .join(Tag, Tag.name == AssetInfoTag.tag_name)
+            .where(AssetInfoTag.asset_info_id.in_(id_list))
+        )
+        for aid, tag_name in rows.all():
+            tag_map[aid].append(tag_name)
+
+    return infos, tag_map, total
+
+def fetch_asset_info_asset_and_tags(
+    session: Session,
+    asset_info_id: str,
+    owner_id: str = "",
+) -> tuple[AssetInfo, Asset, list[str]] | None:
+    stmt = (
+        select(AssetInfo, Asset, Tag.name)
+        .join(Asset, Asset.id == AssetInfo.asset_id)
+        .join(AssetInfoTag, AssetInfoTag.asset_info_id == AssetInfo.id, isouter=True)
+        .join(Tag, Tag.name == AssetInfoTag.tag_name, isouter=True)
+        .where(
+            AssetInfo.id == asset_info_id,
+            visible_owner_clause(owner_id),
+        )
+        .options(noload(AssetInfo.tags))
+        .order_by(Tag.name.asc())
+    )
+
+    rows = (session.execute(stmt)).all()
+    if not rows:
+        return None
+
+    first_info, first_asset, _ = rows[0]
+    tags: list[str] = []
+    seen: set[str] = set()
+    for _info, _asset, tag_name in rows:
+        if tag_name and tag_name not in seen:
+            seen.add(tag_name)
+            tags.append(tag_name)
+    return first_info, first_asset, tags
+
+def list_tags_with_usage(
+    session: Session,
+    prefix: str | None = None,
+    limit: int = 100,
+    offset: int = 0,
+    include_zero: bool = True,
+    order: str = "count_desc",
+    owner_id: str = "",
+) -> tuple[list[tuple[str, str, int]], int]:
+    counts_sq = (
+        select(
+            AssetInfoTag.tag_name.label("tag_name"),
+            func.count(AssetInfoTag.asset_info_id).label("cnt"),
+        )
+        .select_from(AssetInfoTag)
+        .join(AssetInfo, AssetInfo.id == AssetInfoTag.asset_info_id)
+        .where(visible_owner_clause(owner_id))
+        .group_by(AssetInfoTag.tag_name)
+        .subquery()
+    )
+
+    q = (
+        select(
+            Tag.name,
+            Tag.tag_type,
+            func.coalesce(counts_sq.c.cnt, 0).label("count"),
+        )
+        .select_from(Tag)
+        .join(counts_sq, counts_sq.c.tag_name == Tag.name, isouter=True)
+    )
+
+    if prefix:
+        escaped, esc = escape_like_prefix(prefix.strip().lower())
+        q = q.where(Tag.name.like(escaped + "%", escape=esc))
+
+    if not include_zero:
+        q = q.where(func.coalesce(counts_sq.c.cnt, 0) > 0)
+
+    if order == "name_asc":
+        q = q.order_by(Tag.name.asc())
+    else:
+        q = q.order_by(func.coalesce(counts_sq.c.cnt, 0).desc(), Tag.name.asc())
+
+    total_q = select(func.count()).select_from(Tag)
+    if prefix:
+        escaped, esc = escape_like_prefix(prefix.strip().lower())
+        total_q = total_q.where(Tag.name.like(escaped + "%", escape=esc))
+    if not include_zero:
+        total_q = total_q.where(
+            Tag.name.in_(select(AssetInfoTag.tag_name).group_by(AssetInfoTag.tag_name))
+        )
+
+    rows = (session.execute(q.limit(limit).offset(offset))).all()
+    total = (session.execute(total_q)).scalar_one()
+
+    rows_norm = [(name, ttype, int(count or 0)) for (name, ttype, count) in rows]
+    return rows_norm, int(total or 0)
--- a/app/assets/database/tags.py
+++ b/app/assets/database/tags.py
@ -0,0 +1,62 @@
+from typing import Iterable
+
+import sqlalchemy
+from sqlalchemy.orm import Session
+from sqlalchemy.dialects import sqlite
+
+from app.assets.helpers import normalize_tags, utcnow
+from app.assets.database.models import Tag, AssetInfoTag, AssetInfo
+
+
+def ensure_tags_exist(session: Session, names: Iterable[str], tag_type: str = "user") -> None:
+    wanted = normalize_tags(list(names))
+    if not wanted:
+        return
+    rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))]
+    ins = (
+            sqlite.insert(Tag)
+            .values(rows)
+            .on_conflict_do_nothing(index_elements=[Tag.name])
+        )
+    return session.execute(ins)
+
+def add_missing_tag_for_asset_id(
+    session: Session,
+    *,
+    asset_id: str,
+    origin: str = "automatic",
+) -> None:
+    select_rows = (
+        sqlalchemy.select(
+            AssetInfo.id.label("asset_info_id"),
+            sqlalchemy.literal("missing").label("tag_name"),
+            sqlalchemy.literal(origin).label("origin"),
+            sqlalchemy.literal(utcnow()).label("added_at"),
+        )
+        .where(AssetInfo.asset_id == asset_id)
+        .where(
+            sqlalchemy.not_(
+                sqlalchemy.exists().where((AssetInfoTag.asset_info_id == AssetInfo.id) & (AssetInfoTag.tag_name == "missing"))
+            )
+        )
+    )
+    session.execute(
+        sqlite.insert(AssetInfoTag)
+        .from_select(
+            ["asset_info_id", "tag_name", "origin", "added_at"],
+            select_rows,
+        )
+        .on_conflict_do_nothing(index_elements=[AssetInfoTag.asset_info_id, AssetInfoTag.tag_name])
+    )
+
+def remove_missing_tag_for_asset_id(
+    session: Session,
+    *,
+    asset_id: str,
+) -> None:
+    session.execute(
+        sqlalchemy.delete(AssetInfoTag).where(
+            AssetInfoTag.asset_info_id.in_(sqlalchemy.select(AssetInfo.id).where(AssetInfo.asset_id == asset_id)),
+            AssetInfoTag.tag_name == "missing",
+        )
+    )
--- a/app/assets/hashing.py
+++ b/app/assets/hashing.py
@ -0,0 +1,75 @@
+from blake3 import blake3
+from typing import IO
+import os
+import asyncio
+
+
+DEFAULT_CHUNK = 8 * 1024 *1024 # 8MB
+
+# NOTE: this allows hashing different representations of a file-like object
+def blake3_hash(
+    fp: str | IO[bytes],
+    chunk_size: int = DEFAULT_CHUNK,
+) -> str:
+    """
+    Returns a BLAKE3 hex digest for ``fp``, which may be:
+      - a filename (str/bytes) or PathLike
+      - an open binary file object
+    If ``fp`` is a file object, it must be opened in **binary** mode and support
+    ``read``, ``seek``, and ``tell``. The function will seek to the start before
+    reading and will attempt to restore the original position afterward.
+    """
+    # duck typing to check if input is a file-like object
+    if hasattr(fp, "read"):
+        return _hash_file_obj(fp, chunk_size)
+
+    with open(os.fspath(fp), "rb") as f:
+        return _hash_file_obj(f, chunk_size)
+
+
+async def blake3_hash_async(
+    fp: str | IO[bytes],
+    chunk_size: int = DEFAULT_CHUNK,
+) -> str:
+    """Async wrapper for ``blake3_hash_sync``.
+    Uses a worker thread so the event loop remains responsive.
+    """
+    # If it is a path, open inside the worker thread to keep I/O off the loop.
+    if hasattr(fp, "read"):
+        return await asyncio.to_thread(blake3_hash, fp, chunk_size)
+
+    def _worker() -> str:
+        with open(os.fspath(fp), "rb") as f:
+            return _hash_file_obj(f, chunk_size)
+
+    return await asyncio.to_thread(_worker)
+
+
+def _hash_file_obj(file_obj: IO, chunk_size: int = DEFAULT_CHUNK) -> str:
+    """
+    Hash an already-open binary file object by streaming in chunks.
+    - Seeks to the beginning before reading (if supported).
+    - Restores the original position afterward (if tell/seek are supported).
+    """
+    if chunk_size <= 0:
+        chunk_size = DEFAULT_CHUNK
+
+    # in case file object is already open and not at the beginning, track so can be restored after hashing
+    orig_pos = file_obj.tell()
+
+    try:
+        # seek to the beginning before reading
+        if orig_pos != 0:
+            file_obj.seek(0)
+
+        h = blake3()
+        while True:
+            chunk = file_obj.read(chunk_size)
+            if not chunk:
+                break
+            h.update(chunk)
+        return h.hexdigest()
+    finally:
+        # restore original position in file object, if needed
+        if orig_pos != 0:
+            file_obj.seek(orig_pos)
--- a/app/assets/helpers.py
+++ b/app/assets/helpers.py
@ -0,0 +1,216 @@
+import contextlib
+import os
+from aiohttp import web
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Literal, Any
+
+import folder_paths
+
+
+RootType = Literal["models", "input", "output"]
+ALLOWED_ROOTS: tuple[RootType, ...] = ("models", "input", "output")
+
+def get_query_dict(request: web.Request) -> dict[str, Any]:
+    """
+    Gets a dictionary of query parameters from the request.
+
+    'request.query' is a MultiMapping[str], needs to be converted to a dictionary to be validated by Pydantic.
+    """
+    query_dict = {
+        key: request.query.getall(key) if len(request.query.getall(key)) > 1 else request.query.get(key)
+        for key in request.query.keys()
+    }
+    return query_dict
+
+def list_tree(base_dir: str) -> list[str]:
+    out: list[str] = []
+    base_abs = os.path.abspath(base_dir)
+    if not os.path.isdir(base_abs):
+        return out
+    for dirpath, _subdirs, filenames in os.walk(base_abs, topdown=True, followlinks=False):
+        for name in filenames:
+            out.append(os.path.abspath(os.path.join(dirpath, name)))
+    return out
+
+def prefixes_for_root(root: RootType) -> list[str]:
+    if root == "models":
+        bases: list[str] = []
+        for _bucket, paths in get_comfy_models_folders():
+            bases.extend(paths)
+        return [os.path.abspath(p) for p in bases]
+    if root == "input":
+        return [os.path.abspath(folder_paths.get_input_directory())]
+    if root == "output":
+        return [os.path.abspath(folder_paths.get_output_directory())]
+    return []
+
+def escape_like_prefix(s: str, escape: str = "!") -> tuple[str, str]:
+    """Escapes %, _ and the escape char itself in a LIKE prefix.
+    Returns (escaped_prefix, escape_char). Caller should append '%' and pass escape=escape_char to .like().
+    """
+    s = s.replace(escape, escape + escape)  # escape the escape char first
+    s = s.replace("%", escape + "%").replace("_", escape + "_")  # escape LIKE wildcards
+    return s, escape
+
+def fast_asset_file_check(
+    *,
+    mtime_db: int | None,
+    size_db: int | None,
+    stat_result: os.stat_result,
+) -> bool:
+    if mtime_db is None:
+        return False
+    actual_mtime_ns = getattr(stat_result, "st_mtime_ns", int(stat_result.st_mtime * 1_000_000_000))
+    if int(mtime_db) != int(actual_mtime_ns):
+        return False
+    sz = int(size_db or 0)
+    if sz > 0:
+        return int(stat_result.st_size) == sz
+    return True
+
+def utcnow() -> datetime:
+    """Naive UTC timestamp (no tzinfo). We always treat DB datetimes as UTC."""
+    return datetime.now(timezone.utc).replace(tzinfo=None)
+
+def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
+    """Build a list of (folder_name, base_paths[]) categories that are configured for model locations.
+
+    We trust `folder_paths.folder_names_and_paths` and include a category if
+    *any* of its base paths lies under the Comfy `models_dir`.
+    """
+    targets: list[tuple[str, list[str]]] = []
+    models_root = os.path.abspath(folder_paths.models_dir)
+    for name, (paths, _exts) in folder_paths.folder_names_and_paths.items():
+        if any(os.path.abspath(p).startswith(models_root + os.sep) for p in paths):
+            targets.append((name, paths))
+    return targets
+
+def compute_relative_filename(file_path: str) -> str | None:
+    """
+    Return the model's path relative to the last well-known folder (the model category),
+    using forward slashes, eg:
+      /.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors"
+      /.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors"
+
+    For non-model paths, returns None.
+    NOTE: this is a temporary helper, used only for initializing metadata["filename"] field.
+    """
+    try:
+        root_category, rel_path = get_relative_to_root_category_path_of_asset(file_path)
+    except ValueError:
+        return None
+
+    p = Path(rel_path)
+    parts = [seg for seg in p.parts if seg not in (".", "..", p.anchor)]
+    if not parts:
+        return None
+
+    if root_category == "models":
+        # parts[0] is the category ("checkpoints", "vae", etc) – drop it
+        inside = parts[1:] if len(parts) > 1 else [parts[0]]
+        return "/".join(inside)
+    return "/".join(parts)  # input/output: keep all parts
+
+
+def get_relative_to_root_category_path_of_asset(file_path: str) -> tuple[Literal["input", "output", "models"], str]:
+    """Given an absolute or relative file path, determine which root category the path belongs to:
+      - 'input' if the file resides under `folder_paths.get_input_directory()`
+      - 'output' if the file resides under `folder_paths.get_output_directory()`
+      - 'models' if the file resides under any base path of categories returned by `get_comfy_models_folders()`
+
+    Returns:
+        (root_category, relative_path_inside_that_root)
+        For 'models', the relative path is prefixed with the category name:
+            e.g. ('models', 'vae/test/sub/ae.safetensors')
+
+    Raises:
+        ValueError: if the path does not belong to input, output, or configured model bases.
+    """
+    fp_abs = os.path.abspath(file_path)
+
+    def _is_within(child: str, parent: str) -> bool:
+        try:
+            return os.path.commonpath([child, parent]) == parent
+        except Exception:
+            return False
+
+    def _rel(child: str, parent: str) -> str:
+        return os.path.relpath(os.path.join(os.sep, os.path.relpath(child, parent)), os.sep)
+
+    # 1) input
+    input_base = os.path.abspath(folder_paths.get_input_directory())
+    if _is_within(fp_abs, input_base):
+        return "input", _rel(fp_abs, input_base)
+
+    # 2) output
+    output_base = os.path.abspath(folder_paths.get_output_directory())
+    if _is_within(fp_abs, output_base):
+        return "output", _rel(fp_abs, output_base)
+
+    # 3) models (check deepest matching base to avoid ambiguity)
+    best: tuple[int, str, str] | None = None  # (base_len, bucket, rel_inside_bucket)
+    for bucket, bases in get_comfy_models_folders():
+        for b in bases:
+            base_abs = os.path.abspath(b)
+            if not _is_within(fp_abs, base_abs):
+                continue
+            cand = (len(base_abs), bucket, _rel(fp_abs, base_abs))
+            if best is None or cand[0] > best[0]:
+                best = cand
+
+    if best is not None:
+        _, bucket, rel_inside = best
+        combined = os.path.join(bucket, rel_inside)
+        return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep)
+
+    raise ValueError(f"Path is not within input, output, or configured model bases: {file_path}")
+
+def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]:
+    """Return a tuple (name, tags) derived from a filesystem path.
+
+    Semantics:
+      - Root category is determined by `get_relative_to_root_category_path_of_asset`.
+      - The returned `name` is the base filename with extension from the relative path.
+      - The returned `tags` are:
+            [root_category] + parent folders of the relative path (in order)
+        For 'models', this means:
+            file '/.../ModelsDir/vae/test_tag/ae.safetensors'
+            -> root_category='models', some_path='vae/test_tag/ae.safetensors'
+            -> name='ae.safetensors', tags=['models', 'vae', 'test_tag']
+
+    Raises:
+        ValueError: if the path does not belong to input, output, or configured model bases.
+    """
+    root_category, some_path = get_relative_to_root_category_path_of_asset(file_path)
+    p = Path(some_path)
+    parent_parts = [part for part in p.parent.parts if part not in (".", "..", p.anchor)]
+    return p.name, list(dict.fromkeys(normalize_tags([root_category, *parent_parts])))
+
+def normalize_tags(tags: list[str] | None) -> list[str]:
+    """
+    Normalize a list of tags by:
+      - Stripping whitespace and converting to lowercase.
+      - Removing duplicates.
+    """
+    return [t.strip().lower() for t in (tags or []) if (t or "").strip()]
+
+def collect_models_files() -> list[str]:
+    out: list[str] = []
+    for folder_name, bases in get_comfy_models_folders():
+        rel_files = folder_paths.get_filename_list(folder_name) or []
+        for rel_path in rel_files:
+            abs_path = folder_paths.get_full_path(folder_name, rel_path)
+            if not abs_path:
+                continue
+            abs_path = os.path.abspath(abs_path)
+            allowed = False
+            for b in bases:
+                base_abs = os.path.abspath(b)
+                with contextlib.suppress(Exception):
+                    if os.path.commonpath([abs_path, base_abs]) == base_abs:
+                        allowed = True
+                        break
+            if allowed:
+                out.append(abs_path)
+    return out
--- a/app/assets/manager.py
+++ b/app/assets/manager.py
@ -0,0 +1,123 @@
+from typing import Sequence
+
+from app.database.db import create_session
+from app.assets.api import schemas_out
+from app.assets.database.queries import (
+    asset_exists_by_hash,
+    fetch_asset_info_asset_and_tags,
+    list_asset_infos_page,
+    list_tags_with_usage,
+)
+
+
+def _safe_sort_field(requested: str | None) -> str:
+    if not requested:
+        return "created_at"
+    v = requested.lower()
+    if v in {"name", "created_at", "updated_at", "size", "last_access_time"}:
+        return v
+    return "created_at"
+
+
+def asset_exists(asset_hash: str) -> bool:
+    with create_session() as session:
+        return asset_exists_by_hash(session, asset_hash=asset_hash)
+
+def list_assets(
+    include_tags: Sequence[str] | None = None,
+    exclude_tags: Sequence[str] | None = None,
+    name_contains: str | None = None,
+    metadata_filter: dict | None = None,
+    limit: int = 20,
+    offset: int = 0,
+    sort: str = "created_at",
+    order: str = "desc",
+    owner_id: str = "",
+) -> schemas_out.AssetsList:
+    sort = _safe_sort_field(sort)
+    order = "desc" if (order or "desc").lower() not in {"asc", "desc"} else order.lower()
+
+    with create_session() as session:
+        infos, tag_map, total = list_asset_infos_page(
+            session,
+            owner_id=owner_id,
+            include_tags=include_tags,
+            exclude_tags=exclude_tags,
+            name_contains=name_contains,
+            metadata_filter=metadata_filter,
+            limit=limit,
+            offset=offset,
+            sort=sort,
+            order=order,
+        )
+
+    summaries: list[schemas_out.AssetSummary] = []
+    for info in infos:
+        asset = info.asset
+        tags = tag_map.get(info.id, [])
+        summaries.append(
+            schemas_out.AssetSummary(
+                id=info.id,
+                name=info.name,
+                asset_hash=asset.hash if asset else None,
+                size=int(asset.size_bytes) if asset else None,
+                mime_type=asset.mime_type if asset else None,
+                tags=tags,
+                preview_url=f"/api/assets/{info.id}/content",
+                created_at=info.created_at,
+                updated_at=info.updated_at,
+                last_access_time=info.last_access_time,
+            )
+        )
+
+    return schemas_out.AssetsList(
+        assets=summaries,
+        total=total,
+        has_more=(offset + len(summaries)) < total,
+    )
+
+def get_asset(asset_info_id: str, owner_id: str = "") -> schemas_out.AssetDetail:
+    with create_session() as session:
+        res = fetch_asset_info_asset_and_tags(session, asset_info_id=asset_info_id, owner_id=owner_id)
+        if not res:
+            raise ValueError(f"AssetInfo {asset_info_id} not found")
+        info, asset, tag_names = res
+        preview_id = info.preview_id
+
+    return schemas_out.AssetDetail(
+        id=info.id,
+        name=info.name,
+        asset_hash=asset.hash if asset else None,
+        size=int(asset.size_bytes) if asset and asset.size_bytes is not None else None,
+        mime_type=asset.mime_type if asset else None,
+        tags=tag_names,
+        user_metadata=info.user_metadata or {},
+        preview_id=preview_id,
+        created_at=info.created_at,
+        last_access_time=info.last_access_time,
+    )
+
+def list_tags(
+    prefix: str | None = None,
+    limit: int = 100,
+    offset: int = 0,
+    order: str = "count_desc",
+    include_zero: bool = True,
+    owner_id: str = "",
+) -> schemas_out.TagsList:
+    limit = max(1, min(1000, limit))
+    offset = max(0, offset)
+
+    with create_session() as session:
+        rows, total = list_tags_with_usage(
+            session,
+            prefix=prefix,
+            limit=limit,
+            offset=offset,
+            include_zero=include_zero,
+            order=order,
+            owner_id=owner_id,
+        )
+
+    tags = [schemas_out.TagUsage(name=name, count=count, type=tag_type) for (name, tag_type, count) in rows]
+    return schemas_out.TagsList(tags=tags, total=total, has_more=(offset + len(tags)) < total)
--- a/app/assets/scanner.py
+++ b/app/assets/scanner.py
@ -0,0 +1,229 @@
+import contextlib
+import time
+import logging
+import os
+import sqlalchemy
+
+import folder_paths
+from app.database.db import create_session, dependencies_available
+from app.assets.helpers import (
+    collect_models_files, compute_relative_filename, fast_asset_file_check, get_name_and_tags_from_asset_path,
+    list_tree,prefixes_for_root, escape_like_prefix,
+    RootType
+)
+from app.assets.database.tags import add_missing_tag_for_asset_id, ensure_tags_exist, remove_missing_tag_for_asset_id
+from app.assets.database.bulk_ops import seed_from_paths_batch
+from app.assets.database.models import Asset, AssetCacheState, AssetInfo
+
+
+def seed_assets(roots: tuple[RootType, ...], enable_logging: bool = False) -> None:
+    """
+    Scan the given roots and seed the assets into the database.
+    """
+    if not dependencies_available():
+        if enable_logging:
+            logging.warning("Database dependencies not available, skipping assets scan")
+        return
+    t_start = time.perf_counter()
+    created = 0
+    skipped_existing = 0
+    paths: list[str] = []
+    try:
+        existing_paths: set[str] = set()
+        for r in roots:
+            try:
+                survivors: set[str] = _fast_db_consistency_pass(r, collect_existing_paths=True, update_missing_tags=True)
+                if survivors:
+                    existing_paths.update(survivors)
+            except Exception as e:
+                logging.exception("fast DB scan failed for %s: %s", r, e)
+
+        if "models" in roots:
+            paths.extend(collect_models_files())
+        if "input" in roots:
+            paths.extend(list_tree(folder_paths.get_input_directory()))
+        if "output" in roots:
+            paths.extend(list_tree(folder_paths.get_output_directory()))
+
+        specs: list[dict] = []
+        tag_pool: set[str] = set()
+        for p in paths:
+            abs_p = os.path.abspath(p)
+            if abs_p in existing_paths:
+                skipped_existing += 1
+                continue
+            try:
+                stat_p = os.stat(abs_p, follow_symlinks=False)
+            except OSError:
+                continue
+            # skip empty files
+            if not stat_p.st_size:
+                continue
+            name, tags = get_name_and_tags_from_asset_path(abs_p)
+            specs.append(
+                {
+                    "abs_path": abs_p,
+                    "size_bytes": stat_p.st_size,
+                    "mtime_ns": getattr(stat_p, "st_mtime_ns", int(stat_p.st_mtime * 1_000_000_000)),
+                    "info_name": name,
+                    "tags": tags,
+                    "fname": compute_relative_filename(abs_p),
+                }
+            )
+            for t in tags:
+                tag_pool.add(t)
+        # if no file specs, nothing to do
+        if not specs:
+            return
+        with create_session() as sess:
+            if tag_pool:
+                ensure_tags_exist(sess, tag_pool, tag_type="user")
+
+            result = seed_from_paths_batch(sess, specs=specs, owner_id="")
+            created += result["inserted_infos"]
+            sess.commit()
+    finally:
+        if enable_logging:
+            logging.info(
+                "Assets scan(roots=%s) completed in %.3fs (created=%d, skipped_existing=%d, total_seen=%d)",
+                roots,
+                time.perf_counter() - t_start,
+                created,
+                skipped_existing,
+                len(paths),
+            )
+
+
+def _fast_db_consistency_pass(
+    root: RootType,
+    *,
+    collect_existing_paths: bool = False,
+    update_missing_tags: bool = False,
+) -> set[str] | None:
+    """Fast DB+FS pass for a root:
+      - Toggle needs_verify per state using fast check
+      - For hashed assets with at least one fast-ok state in this root: delete stale missing states
+      - For seed assets with all states missing: delete Asset and its AssetInfos
+      - Optionally add/remove 'missing' tags based on fast-ok in this root
+      - Optionally return surviving absolute paths
+    """
+    prefixes = prefixes_for_root(root)
+    if not prefixes:
+        return set() if collect_existing_paths else None
+
+    conds = []
+    for p in prefixes:
+        base = os.path.abspath(p)
+        if not base.endswith(os.sep):
+            base += os.sep
+        escaped, esc = escape_like_prefix(base)
+        conds.append(AssetCacheState.file_path.like(escaped + "%", escape=esc))
+
+    with create_session() as sess:
+        rows = (
+            sess.execute(
+                sqlalchemy.select(
+                    AssetCacheState.id,
+                    AssetCacheState.file_path,
+                    AssetCacheState.mtime_ns,
+                    AssetCacheState.needs_verify,
+                    AssetCacheState.asset_id,
+                    Asset.hash,
+                    Asset.size_bytes,
+                )
+                .join(Asset, Asset.id == AssetCacheState.asset_id)
+                .where(sqlalchemy.or_(*conds))
+                .order_by(AssetCacheState.asset_id.asc(), AssetCacheState.id.asc())
+            )
+        ).all()
+
+        by_asset: dict[str, dict] = {}
+        for sid, fp, mtime_db, needs_verify, aid, a_hash, a_size in rows:
+            acc = by_asset.get(aid)
+            if acc is None:
+                acc = {"hash": a_hash, "size_db": int(a_size or 0), "states": []}
+                by_asset[aid] = acc
+
+            fast_ok = False
+            try:
+                exists = True
+                fast_ok = fast_asset_file_check(
+                    mtime_db=mtime_db,
+                    size_db=acc["size_db"],
+                    stat_result=os.stat(fp, follow_symlinks=True),
+                )
+            except FileNotFoundError:
+                exists = False
+            except OSError:
+                exists = False
+
+            acc["states"].append({
+                "sid": sid,
+                "fp": fp,
+                "exists": exists,
+                "fast_ok": fast_ok,
+                "needs_verify": bool(needs_verify),
+            })
+
+        to_set_verify: list[int] = []
+        to_clear_verify: list[int] = []
+        stale_state_ids: list[int] = []
+        survivors: set[str] = set()
+
+        for aid, acc in by_asset.items():
+            a_hash = acc["hash"]
+            states = acc["states"]
+            any_fast_ok = any(s["fast_ok"] for s in states)
+            all_missing = all(not s["exists"] for s in states)
+
+            for s in states:
+                if not s["exists"]:
+                    continue
+                if s["fast_ok"] and s["needs_verify"]:
+                    to_clear_verify.append(s["sid"])
+                if not s["fast_ok"] and not s["needs_verify"]:
+                    to_set_verify.append(s["sid"])
+
+            if a_hash is None:
+                if states and all_missing:  # remove seed Asset completely, if no valid AssetCache exists
+                    sess.execute(sqlalchemy.delete(AssetInfo).where(AssetInfo.asset_id == aid))
+                    asset = sess.get(Asset, aid)
+                    if asset:
+                        sess.delete(asset)
+                else:
+                    for s in states:
+                        if s["exists"]:
+                            survivors.add(os.path.abspath(s["fp"]))
+                continue
+
+            if any_fast_ok:  # if Asset has at least one valid AssetCache record, remove any invalid AssetCache records
+                for s in states:
+                    if not s["exists"]:
+                        stale_state_ids.append(s["sid"])
+                if update_missing_tags:
+                    with contextlib.suppress(Exception):
+                        remove_missing_tag_for_asset_id(sess, asset_id=aid)
+            elif update_missing_tags:
+                with contextlib.suppress(Exception):
+                    add_missing_tag_for_asset_id(sess, asset_id=aid, origin="automatic")
+
+            for s in states:
+                if s["exists"]:
+                    survivors.add(os.path.abspath(s["fp"]))
+
+        if stale_state_ids:
+            sess.execute(sqlalchemy.delete(AssetCacheState).where(AssetCacheState.id.in_(stale_state_ids)))
+        if to_set_verify:
+            sess.execute(
+                sqlalchemy.update(AssetCacheState)
+                .where(AssetCacheState.id.in_(to_set_verify))
+                .values(needs_verify=True)
+            )
+        if to_clear_verify:
+            sess.execute(
+                sqlalchemy.update(AssetCacheState)
+                .where(AssetCacheState.id.in_(to_clear_verify))
+                .values(needs_verify=False)
+            )
+        sess.commit()
+        return survivors if collect_existing_paths else None
--- a/app/database/models.py
+++ b/app/database/models.py
@ -1,14 +1,21 @@
-from sqlalchemy.orm import declarative_base
+from typing import Any
+from datetime import datetime
+from sqlalchemy.orm import DeclarativeBase

-Base = declarative_base()
+class Base(DeclarativeBase):
+    pass

-
-def to_dict(obj):
+def to_dict(obj: Any, include_none: bool = False) -> dict[str, Any]:
    fields = obj.__table__.columns.keys()
-    return {
-        field: (val.to_dict() if hasattr(val, "to_dict") else val)
-        for field in fields
-        if (val := getattr(obj, field))
-    }
+    out: dict[str, Any] = {}
+    for field in fields:
+        val = getattr(obj, field)
+        if val is None and not include_none:
+            continue
+        if isinstance(val, datetime):
+            out[field] = val.isoformat()
+        else:
+            out[field] = val
+    return out

 # TODO: Define models here
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@ -231,6 +231,7 @@ database_default_path = os.path.abspath(
    os.path.join(os.path.dirname(__file__), "..", "user", "comfyui.db")
 )
 parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
+parser.add_argument("--disable-assets-autoscan", action="store_true", help="Disable asset scanning on startup for database synchronization.")

 if comfy.options.args_parsing:
    args = parser.parse_args()
--- a/comfy/latent_formats.py
+++ b/comfy/latent_formats.py
@ -408,7 +408,9 @@ class LTXV(LatentFormat):
        self.latent_rgb_factors_bias = [-0.0571, -0.1657, -0.2512]

 class LTXAV(LTXV):
-    pass
+    def __init__(self):
+        self.latent_rgb_factors = None
+        self.latent_rgb_factors_bias = None

 class HunyuanVideo(LatentFormat):
    latent_channels = 16
--- a/comfy/ldm/flux/math.py
+++ b/comfy/ldm/flux/math.py
@ -4,6 +4,7 @@ from torch import Tensor

 from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
+import logging


 def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor:
@ -13,7 +14,6 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transforme
    x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options)
    return x

-
 def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
    assert dim % 2 == 0
    if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled():
@ -28,13 +28,20 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
    out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
    return out.to(dtype=torch.float32, device=pos.device)

-def apply_rope1(x: Tensor, freqs_cis: Tensor):
-    x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)

-    x_out = freqs_cis[..., 0] * x_[..., 0]
-    x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])
+try:
+    import comfy.quant_ops
+    apply_rope = comfy.quant_ops.ck.apply_rope
+    apply_rope1 = comfy.quant_ops.ck.apply_rope1
+except:
+    logging.warning("No comfy kitchen, using old apply_rope functions.")
+    def apply_rope1(x: Tensor, freqs_cis: Tensor):
+        x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)

-    return x_out.reshape(*x.shape).type_as(x)
+        x_out = freqs_cis[..., 0] * x_[..., 0]
+        x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])

-def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
-    return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)
+        return x_out.reshape(*x.shape).type_as(x)
+
+    def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
+        return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)
--- a/comfy/ldm/hunyuan_video/upsampler.py
+++ b/comfy/ldm/hunyuan_video/upsampler.py
@ -3,8 +3,8 @@ import torch.nn as nn
 import torch.nn.functional as F
 from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, VideoConv3d
 from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm
-import model_management
-import model_patcher
+import comfy.model_management
+import comfy.model_patcher

 class SRResidualCausalBlock3D(nn.Module):
    def __init__(self, channels: int):
@ -103,13 +103,13 @@ UPSAMPLERS = {

 class HunyuanVideo15SRModel():
    def __init__(self, model_type, config):
-        self.load_device = model_management.vae_device()
-        offload_device = model_management.vae_offload_device()
-        self.dtype = model_management.vae_dtype(self.load_device)
+        self.load_device = comfy.model_management.vae_device()
+        offload_device = comfy.model_management.vae_offload_device()
+        self.dtype = comfy.model_management.vae_dtype(self.load_device)
        self.model_class = UPSAMPLERS.get(model_type)
        self.model = self.model_class(**config).eval()

-        self.patcher = model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
+        self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)

    def load_sd(self, sd):
        return self.model.load_state_dict(sd, strict=True)
@ -118,5 +118,5 @@ class HunyuanVideo15SRModel():
        return self.model.state_dict()

    def resample_latent(self, latent):
-        model_management.load_model_gpu(self.patcher)
+        comfy.model_management.load_model_gpu(self.patcher)
        return self.model(latent.to(self.load_device))
--- a/comfy/ldm/lightricks/embeddings_connector.py
+++ b/comfy/ldm/lightricks/embeddings_connector.py
@ -276,7 +276,7 @@ class Embeddings1DConnector(nn.Module):
                max(1024, hidden_states.shape[1]) / self.num_learnable_registers
            )
            learnable_registers = torch.tile(
-                self.learnable_registers, (num_registers_duplications, 1)
+                self.learnable_registers.to(hidden_states), (num_registers_duplications, 1)
            )

            hidden_states = torch.cat((hidden_states, learnable_registers[hidden_states.shape[1]:].unsqueeze(0).repeat(hidden_states.shape[0], 1, 1)), dim=1)
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@ -22,7 +22,6 @@ from enum import Enum
 from comfy.cli_args import args, PerformanceFeature
 import torch
 import sys
-import importlib
 import platform
 import weakref
 import gc
@ -349,10 +348,22 @@ try:
        except:
            rocm_version = (6, -1)

+        def aotriton_supported(gpu_arch):
+            path = torch.__path__[0]
+            path = os.path.join(os.path.join(path, "lib"), "aotriton.images")
+            gfx = set(map(lambda a: a[4:], filter(lambda a: a.startswith("amd-gfx"), os.listdir(path))))
+            if gpu_arch in gfx:
+                return True
+            if "{}x".format(gpu_arch[:-1]) in gfx:
+                return True
+            if "{}xx".format(gpu_arch[:-2]) in gfx:
+                return True
+            return False
+
        logging.info("AMD arch: {}".format(arch))
        logging.info("ROCm version: {}".format(rocm_version))
        if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
-            if importlib.util.find_spec('triton') is not None:  # AMD efficient attention implementation depends on triton. TODO: better way of detecting if it's compiled in or not.
+            if aotriton_supported(arch):  # AMD efficient attention implementation depends on aotriton.
                if torch_version_numeric >= (2, 7):  # works on 2.6 but doesn't actually seem to improve much
                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
                        ENABLE_PYTORCH_ATTENTION = True
@ -1504,6 +1515,16 @@ def supports_fp8_compute(device=None):

    return True

+def supports_nvfp4_compute(device=None):
+    if not is_nvidia():
+        return False
+
+    props = torch.cuda.get_device_properties(device)
+    if props.major < 10:
+        return False
+
+    return True
+
 def extended_fp16_support():
    # TODO: check why some models work with fp16 on newer torch versions but not on older
    if torch_version_numeric < (2, 7):
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@ -718,6 +718,7 @@ class ModelPatcher:
                            continue

                cast_weight = self.force_cast_weights
+                m.comfy_force_cast_weights = self.force_cast_weights
                if lowvram_weight:
                    if hasattr(m, "comfy_cast_weights"):
                        m.weight_function = []
@ -790,11 +791,12 @@ class ModelPatcher:
                for param in params:
                    self.pin_weight_to_device("{}.{}".format(n, param))

+            usable_stat = "{:.2f} MB usable,".format(lowvram_model_memory / (1024 * 1024)) if lowvram_model_memory < 1e32 else ""
            if lowvram_counter > 0:
-                logging.info("loaded partially; {:.2f} MB usable, {:.2f} MB loaded, {:.2f} MB offloaded, {:.2f} MB buffer reserved, lowvram patches: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), offload_buffer / (1024 * 1024), patch_counter))
+                logging.info("loaded partially; {} {:.2f} MB loaded, {:.2f} MB offloaded, {:.2f} MB buffer reserved, lowvram patches: {}".format(usable_stat, mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), offload_buffer / (1024 * 1024), patch_counter))
                self.model.model_lowvram = True
            else:
-                logging.info("loaded completely; {:.2f} MB usable, {:.2f} MB loaded, full load: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load))
+                logging.info("loaded completely; {} {:.2f} MB loaded, full load: {}".format(usable_stat, mem_counter / (1024 * 1024), full_load))
                self.model.model_lowvram = False
                if full_load:
                    self.model.to(device_to)
--- a/comfy/ops.py
+++ b/comfy/ops.py
@ -427,12 +427,12 @@ def fp8_linear(self, input):
    input = torch.clamp(input, min=-448, max=448, out=input)
    input_fp8 = input.to(dtype).contiguous()
    layout_params_input = TensorCoreFP8Layout.Params(scale=scale_input, orig_dtype=input_dtype, orig_shape=tuple(input_fp8.shape))
-    quantized_input = QuantizedTensor(input_fp8, TensorCoreFP8Layout, layout_params_input)
+    quantized_input = QuantizedTensor(input_fp8, "TensorCoreFP8Layout", layout_params_input)

    # Wrap weight in QuantizedTensor - this enables unified dispatch
    # Call F.linear - __torch_dispatch__ routes to fp8_linear handler in quant_ops.py!
    layout_params_weight = TensorCoreFP8Layout.Params(scale=scale_weight, orig_dtype=input_dtype, orig_shape=tuple(w.shape))
-    quantized_weight = QuantizedTensor(w, TensorCoreFP8Layout, layout_params_weight)
+    quantized_weight = QuantizedTensor(w, "TensorCoreFP8Layout", layout_params_weight)
    o = torch.nn.functional.linear(quantized_input, quantized_weight, bias)

    uncast_bias_weight(self, w, bias, offload_stream)
@ -493,11 +493,12 @@ from .quant_ops import (
 )


-def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False):
+def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False, disabled=[]):
    class MixedPrecisionOps(manual_cast):
        _quant_config = quant_config
        _compute_dtype = compute_dtype
        _full_precision_mm = full_precision_mm
+        _disabled = disabled

        class Linear(torch.nn.Module, CastWeightBiasOp):
            def __init__(
@ -522,6 +523,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec

                self.tensor_class = None
                self._full_precision_mm = MixedPrecisionOps._full_precision_mm
+                self._full_precision_mm_config = False

            def reset_parameters(self):
                return None
@ -556,8 +558,12 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                    self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False)
                else:
                    self.quant_format = layer_conf.get("format", None)
+                    self._full_precision_mm_config = layer_conf.get("full_precision_matrix_mult", False)
                    if not self._full_precision_mm:
-                        self._full_precision_mm = layer_conf.get("full_precision_matrix_mult", False)
+                        self._full_precision_mm = self._full_precision_mm_config
+
+                    if self.quant_format in MixedPrecisionOps._disabled:
+                        self._full_precision_mm = True

                    if self.quant_format is None:
                        raise ValueError(f"Unknown quantization format for layer {layer_name}")
@ -630,7 +636,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                        sd["{}weight_scale".format(prefix)] = self.weight._params.block_scale

                    quant_conf = {"format": self.quant_format}
-                    if self._full_precision_mm:
+                    if self._full_precision_mm_config:
                        quant_conf["full_precision_matrix_mult"] = True
                    sd["{}comfy_quant".format(prefix)] = torch.tensor(list(json.dumps(quant_conf).encode('utf-8')), dtype=torch.uint8)
                return sd
@ -648,29 +654,29 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                run_every_op()

                input_shape = input.shape
-                tensor_3d = input.ndim == 3
-
-                if self._full_precision_mm or self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
-                    return self.forward_comfy_cast_weights(input, *args, **kwargs)
+                reshaped_3d = False

                if (getattr(self, 'layout_type', None) is not None and
-                    not isinstance(input, QuantizedTensor)):
+                    not isinstance(input, QuantizedTensor) and not self._full_precision_mm and
+                    not getattr(self, 'comfy_force_cast_weights', False) and
+                    len(self.weight_function) == 0 and len(self.bias_function) == 0):

                    # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others)
-                    if tensor_3d:
-                        input = input.reshape(-1, input_shape[2])
+                    input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input

-                    if input.ndim != 2:
-                        # Fall back to comfy_cast_weights for non-2D tensors
-                        return self.forward_comfy_cast_weights(input.reshape(input_shape), *args, **kwargs)
+                    # Fall back to non-quantized for non-2D tensors
+                    if input_reshaped.ndim == 2:
+                        reshaped_3d = input.ndim == 3
+                        # dtype is now implicit in the layout class
+                        scale = getattr(self, 'input_scale', None)
+                        if scale is not None:
+                            scale = comfy.model_management.cast_to_device(scale, input.device, None)
+                        input = QuantizedTensor.from_float(input_reshaped, self.layout_type, scale=scale)

-                    # dtype is now implicit in the layout class
-                    input = QuantizedTensor.from_float(input, self.layout_type, scale=getattr(self, 'input_scale', None))
-
-                output = self._forward(input, self.weight, self.bias)
+                output = self.forward_comfy_cast_weights(input)

                # Reshape output back to 3D if input was 3D
-                if tensor_3d:
+                if reshaped_3d:
                    output = output.reshape((input_shape[0], input_shape[1], self.weight.shape[0]))

                return output
@ -711,10 +717,17 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec

 def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, model_config=None):
    fp8_compute = comfy.model_management.supports_fp8_compute(load_device) # TODO: if we support more ops this needs to be more granular
+    nvfp4_compute = comfy.model_management.supports_nvfp4_compute(load_device)

    if model_config and hasattr(model_config, 'quant_config') and model_config.quant_config:
        logging.info("Using mixed precision operations")
-        return mixed_precision_ops(model_config.quant_config, compute_dtype, full_precision_mm=not fp8_compute)
+        disabled = set()
+        if not nvfp4_compute:
+            disabled.add("nvfp4")
+        if not fp8_compute:
+            disabled.add("float8_e4m3fn")
+            disabled.add("float8_e5m2")
+        return mixed_precision_ops(model_config.quant_config, compute_dtype, disabled=disabled)

    if (
        fp8_compute and
--- a/comfy/quant_ops.py
+++ b/comfy/quant_ops.py
@ -13,6 +13,14 @@ try:
        get_layout_class,
    )
    _CK_AVAILABLE = True
+    if torch.version.cuda is None:
+        ck.registry.disable("cuda")
+    else:
+        cuda_version = tuple(map(int, str(torch.version.cuda).split('.')))
+        if cuda_version < (13,):
+            ck.registry.disable("cuda")
+            logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.")
+
    ck.registry.disable("triton")
    for k, v in ck.list_backends().items():
        logging.info(f"Found comfy_kitchen backend {k}: {v}")
--- a/comfy/sd.py
+++ b/comfy/sd.py
@ -218,7 +218,7 @@ class CLIP:
            if unprojected:
                self.cond_stage_model.set_clip_options({"projected_pooled": False})

-            self.load_model()
+            self.load_model(tokens)
            self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
            all_hooks.reset()
            self.patcher.patch_hooks(None)
@ -266,7 +266,7 @@ class CLIP:
        if return_pooled == "unprojected":
            self.cond_stage_model.set_clip_options({"projected_pooled": False})

-        self.load_model()
+        self.load_model(tokens)
        self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
        o = self.cond_stage_model.encode_token_weights(tokens)
        cond, pooled = o[:2]
@ -299,8 +299,11 @@ class CLIP:
            sd_clip[k] = sd_tokenizer[k]
        return sd_clip

-    def load_model(self):
-        model_management.load_model_gpu(self.patcher)
+    def load_model(self, tokens={}):
+        memory_used = 0
+        if hasattr(self.cond_stage_model, "memory_estimation_function"):
+            memory_used = self.cond_stage_model.memory_estimation_function(tokens, device=self.patcher.load_device)
+        model_management.load_models_gpu([self.patcher], memory_required=memory_used)
        return self.patcher

    def get_key_patches(self):
@ -476,8 +479,8 @@ class VAE:
                self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE(version=version, config=vae_config)
                self.latent_channels = 128
                self.latent_dim = 3
-                self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
-                self.memory_used_encode = lambda shape, dtype: (70 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (1200 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (80 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 32, 32)
                self.upscale_index_formula = (8, 32, 32)
                self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 32, 32)
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@ -845,7 +845,7 @@ class LTXAV(LTXV):

    def __init__(self, unet_config):
        super().__init__(unet_config)
-        self.memory_usage_factor = 0.055  # TODO
+        self.memory_usage_factor = 0.061  # TODO

    def get_model(self, state_dict, prefix="", device=None):
        out = model_base.LTXAV(self, device=device)
--- a/comfy/text_encoders/lt.py
+++ b/comfy/text_encoders/lt.py
@ -36,10 +36,10 @@ class LTXAVGemmaTokenizer(sd1_clip.SD1Tokenizer):

 class Gemma3_12BModel(sd1_clip.SDClipModel):
    def __init__(self, device="cpu", layer="all", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
-        llama_scaled_fp8 = model_options.get("gemma_scaled_fp8", None)
-        if llama_scaled_fp8 is not None:
+        llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
+        if llama_quantization_metadata is not None:
            model_options = model_options.copy()
-            model_options["scaled_fp8"] = llama_scaled_fp8
+            model_options["quantization_metadata"] = llama_quantization_metadata

        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_12B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)

@ -86,20 +86,25 @@ class LTXAVTEModel(torch.nn.Module):
        )

    def set_clip_options(self, options):
+        self.execution_device = options.get("execution_device", self.execution_device)
        self.gemma3_12b.set_clip_options(options)

    def reset_clip_options(self):
        self.gemma3_12b.reset_clip_options()
+        self.execution_device = None

    def encode_token_weights(self, token_weight_pairs):
        token_weight_pairs = token_weight_pairs["gemma3_12b"]

        out, pooled, extra = self.gemma3_12b.encode_token_weights(token_weight_pairs)
        out_device = out.device
-        out = out.movedim(1, -1).to(self.text_embedding_projection.weight.device)
+        if comfy.model_management.should_use_bf16(self.execution_device):
+            out = out.to(device=self.execution_device, dtype=torch.bfloat16)
+        out = out.movedim(1, -1).to(self.execution_device)
        out = 8.0 * (out - out.mean(dim=(1, 2), keepdim=True)) / (out.amax(dim=(1, 2), keepdim=True) - out.amin(dim=(1, 2), keepdim=True) + 1e-6)
        out = out.reshape((out.shape[0], out.shape[1], -1))
        out = self.text_embedding_projection(out)
+        out = out.float()
        out_vid = self.video_embeddings_connector(out)[0]
        out_audio = self.audio_embeddings_connector(out)[0]
        out = torch.concat((out_vid, out_audio), dim=-1)
@ -116,13 +121,21 @@ class LTXAVTEModel(torch.nn.Module):

            return self.load_state_dict(sdo, strict=False)

+    def memory_estimation_function(self, token_weight_pairs, device=None):
+        constant = 6.0
+        if comfy.model_management.should_use_bf16(device):
+            constant /= 2.0

-def ltxav_te(dtype_llama=None, llama_scaled_fp8=None):
+        token_weight_pairs = token_weight_pairs.get("gemma3_12b", [])
+        num_tokens = sum(map(lambda a: len(a), token_weight_pairs))
+        return num_tokens * constant * 1024 * 1024
+
+def ltxav_te(dtype_llama=None, llama_quantization_metadata=None):
    class LTXAVTEModel_(LTXAVTEModel):
        def __init__(self, device="cpu", dtype=None, model_options={}):
-            if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options:
+            if llama_quantization_metadata is not None:
                model_options = model_options.copy()
-                model_options["llama_scaled_fp8"] = llama_scaled_fp8
+                model_options["llama_quantization_metadata"] = llama_quantization_metadata
            if dtype_llama is not None:
                dtype = dtype_llama
            super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)
--- a/comfy_api_nodes/nodes_wan.py
+++ b/comfy_api_nodes/nodes_wan.py
@ -13,7 +13,9 @@ from comfy_api_nodes.util import (
    poll_op,
    sync_op,
    tensor_to_base64_string,
+    upload_video_to_comfyapi,
    validate_audio_duration,
+    validate_video_duration,
 )


@ -41,6 +43,12 @@ class Image2VideoInputField(BaseModel):
    audio_url: str | None = Field(None)


+class Reference2VideoInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: str | None = Field(None)
+    reference_video_urls: list[str] = Field(...)
+
+
 class Txt2ImageParametersField(BaseModel):
    size: str = Field(...)
    n: int = Field(1, description="Number of images to generate.")  # we support only value=1
@ -76,6 +84,14 @@ class Image2VideoParametersField(BaseModel):
    shot_type: str = Field("single")


+class Reference2VideoParametersField(BaseModel):
+    size: str = Field(...)
+    duration: int = Field(5, ge=5, le=15)
+    shot_type: str = Field("single")
+    seed: int = Field(..., ge=0, le=2147483647)
+    watermark: bool = Field(False)
+
+
 class Text2ImageTaskCreationRequest(BaseModel):
    model: str = Field(...)
    input: Text2ImageInputField = Field(...)
@ -100,6 +116,12 @@ class Image2VideoTaskCreationRequest(BaseModel):
    parameters: Image2VideoParametersField = Field(...)


+class Reference2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Reference2VideoInputField = Field(...)
+    parameters: Reference2VideoParametersField = Field(...)
+
+
 class TaskCreationOutputField(BaseModel):
    task_id: str = Field(...)
    task_status: str = Field(...)
@ -721,6 +743,143 @@ class WanImageToVideoApi(IO.ComfyNode):
        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))


+class WanReferenceVideoApi(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="WanReferenceVideoApi",
+            display_name="Wan Reference to Video",
+            category="api node/video/Wan",
+            description="Use the character and voice from input videos, combined with a prompt, "
+            "to generate a new video that maintains character consistency.",
+            inputs=[
+                IO.Combo.Input("model", options=["wan2.6-r2v"]),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt describing the elements and visual features. Supports English and Chinese. "
+                    "Use identifiers such as `character1` and `character2` to refer to the reference characters.",
+                ),
+                IO.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Negative prompt describing what to avoid.",
+                ),
+                IO.Autogrow.Input(
+                    "reference_videos",
+                    template=IO.Autogrow.TemplateNames(
+                        IO.Video.Input("reference_video"),
+                        names=["character1", "character2", "character3"],
+                        min=1,
+                    ),
+                ),
+                IO.Combo.Input(
+                    "size",
+                    options=[
+                        "720p: 1:1 (960x960)",
+                        "720p: 16:9 (1280x720)",
+                        "720p: 9:16 (720x1280)",
+                        "720p: 4:3 (1088x832)",
+                        "720p: 3:4 (832x1088)",
+                        "1080p: 1:1 (1440x1440)",
+                        "1080p: 16:9 (1920x1080)",
+                        "1080p: 9:16 (1080x1920)",
+                        "1080p: 4:3 (1632x1248)",
+                        "1080p: 3:4 (1248x1632)",
+                    ],
+                ),
+                IO.Int.Input(
+                    "duration",
+                    default=5,
+                    min=5,
+                    max=10,
+                    step=5,
+                    display_mode=IO.NumberDisplay.slider,
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+                IO.Combo.Input(
+                    "shot_type",
+                    options=["single", "multi"],
+                    tooltip="Specifies the shot type for the generated video, that is, whether the video is a "
+                    "single continuous shot or multiple shots with cuts.",
+                ),
+                IO.Boolean.Input(
+                    "watermark",
+                    default=False,
+                    tooltip="Whether to add an AI-generated watermark to the result.",
+                ),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        negative_prompt: str,
+        reference_videos: IO.Autogrow.Type,
+        size: str,
+        duration: int,
+        seed: int,
+        shot_type: str,
+        watermark: bool,
+    ):
+        reference_video_urls = []
+        for i in reference_videos:
+            validate_video_duration(reference_videos[i], min_duration=2, max_duration=30)
+        for i in reference_videos:
+            reference_video_urls.append(await upload_video_to_comfyapi(cls, reference_videos[i]))
+        width, height = RES_IN_PARENS.search(size).groups()
+        initial_response = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", method="POST"),
+            response_model=TaskCreationResponse,
+            data=Reference2VideoTaskCreationRequest(
+                model=model,
+                input=Reference2VideoInputField(
+                    prompt=prompt, negative_prompt=negative_prompt, reference_video_urls=reference_video_urls
+                ),
+                parameters=Reference2VideoParametersField(
+                    size=f"{width}*{height}",
+                    duration=duration,
+                    shot_type=shot_type,
+                    watermark=watermark,
+                    seed=seed,
+                ),
+            ),
+        )
+        if not initial_response.output:
+            raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}")
+        response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"),
+            response_model=VideoTaskStatusResponse,
+            status_extractor=lambda x: x.output.task_status,
+            poll_interval=6,
+            max_poll_attempts=280,
+        )
+        return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
 class WanApiExtension(ComfyExtension):
    @override
    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@ -729,6 +888,7 @@ class WanApiExtension(ComfyExtension):
            WanImageToImageApi,
            WanTextToVideoApi,
            WanImageToVideoApi,
+            WanReferenceVideoApi,
        ]


--- a/comfy_api_nodes/util/upload_helpers.py
+++ b/comfy_api_nodes/util/upload_helpers.py
@ -119,7 +119,7 @@ async def upload_video_to_comfyapi(
            raise ValueError(f"Could not verify video duration from source: {e}") from e

    upload_mime_type = f"video/{container.value.lower()}"
-    filename = f"uploaded_video.{container.value.lower()}"
+    filename = f"{uuid.uuid4()}.{container.value.lower()}"

    # Convert VideoInput to BytesIO using specified container/codec
    video_bytes_io = BytesIO()
--- a/comfy_execution/graph.py
+++ b/comfy_execution/graph.py
@ -1,6 +1,12 @@
-from __future__ import annotations
-from typing import Type, Literal
+# graph.py — grouped/batched scheduler on top of the updated ExecutionList
+# Implements model-class batching to reduce device/context swaps while preserving
+# the new execution_cache behavior added upstream.

+
+from __future__ import annotations
+from typing import Type, Literal, Optional
+
+import os
 import nodes
 import asyncio
 import inspect
@ -10,15 +16,19 @@ from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputType
 # NOTE: ExecutionBlocker code got moved to graph_utils.py to prevent torch being imported too soon during unit tests
 ExecutionBlocker = ExecutionBlocker

+
 class DependencyCycleError(Exception):
    pass

+
 class NodeInputError(Exception):
    pass

+
 class NodeNotFoundError(Exception):
    pass

+
 class DynamicPrompt:
    def __init__(self, original_prompt):
        # The original prompt provided by the user
@ -62,6 +72,7 @@ class DynamicPrompt:
    def get_original_prompt(self):
        return self.original_prompt

+
 def get_input_info(
    class_def: Type[ComfyNodeABC],
    input_name: str,
@ -104,12 +115,13 @@ def get_input_info(
    #     input_type = IO.Combo.io_type
    return input_type, input_category, extra_info

+
 class TopologicalSort:
    def __init__(self, dynprompt):
        self.dynprompt = dynprompt
        self.pendingNodes = {}
-        self.blockCount = {} # Number of nodes this node is directly blocked by
-        self.blocking = {} # Which nodes are blocked by this node
+        self.blockCount = {}  # Number of nodes this node is directly blocked by
+        self.blocking = {}    # Which nodes are blocked by this node
        self.externalBlocks = 0
        self.unblockedEvent = asyncio.Event()

@ -170,6 +182,7 @@ class TopologicalSort:
        assert node_id in self.blockCount, "Can't add external block to a node that isn't pending"
        self.externalBlocks += 1
        self.blockCount[node_id] += 1
+
        def unblock():
            self.externalBlocks -= 1
            self.blockCount[node_id] -= 1
@ -191,18 +204,31 @@ class TopologicalSort:
    def is_empty(self):
        return len(self.pendingNodes) == 0

+
 class ExecutionList(TopologicalSort):
    """
-    ExecutionList implements a topological dissolve of the graph. After a node is staged for execution,
-    it can still be returned to the graph after having further dependencies added.
+    ExecutionList implements a topological dissolve of the graph with batching.
+    After a node is staged for execution, it can still be returned to the graph
+    after having further dependencies added.
+
+    Batching: we favor running nodes of the same class_type back-to-back
+    to reduce device/context thrash (e.g., model swaps). Within a batch we still
+    apply UX-friendly priorities (output/async early, VAEDecode→preview, etc.).
    """
+
    def __init__(self, dynprompt, output_cache):
        super().__init__(dynprompt)
        self.output_cache = output_cache
-        self.staged_node_id = None
+        self.staged_node_id: Optional[str] = None
+
+        # Upstream execution cache (kept intact)
        self.execution_cache = {}
        self.execution_cache_listeners = {}

+        # Batching state
+        self._current_group_class: Optional[str] = None
+
+    # ----------------------------- cache ---------------------------------
    def is_cached(self, node_id):
        return self.output_cache.get(node_id) is not None

@ -220,7 +246,7 @@ class ExecutionList(TopologicalSort):
        value = self.execution_cache[to_node_id].get(from_node_id)
        if value is None:
            return None
-        #Write back to the main cache on touch.
+        # Write back to the main cache on touch.
        self.output_cache.set(from_node_id, value)
        return value

@ -234,16 +260,93 @@ class ExecutionList(TopologicalSort):
        super().add_strong_link(from_node_id, from_socket, to_node_id)
        self.cache_link(from_node_id, to_node_id)

+    # --------------------------- group utils ------------------------------
+    def _pick_largest_group(self, node_list):
+        """Return the class_type with the most representatives in node_list.
+        Ties are resolved deterministically by class name."""
+        counts = {}
+        for nid in node_list:
+            ctype = self.dynprompt.get_node(nid)["class_type"]
+            counts[ctype] = counts.get(ctype, 0) + 1
+        # max by (count, class_name) for deterministic tie-break
+        return max(counts.items(), key=lambda kv: (kv[1], kv[0]))[0]
+
+    def _filter_by_group(self, node_list, group_cls):
+        """Keep only nodes that belong to the given class."""
+        return [nid for nid in node_list if self.dynprompt.get_node(nid)["class_type"] == group_cls]
+
+    # ------------------------- node classification ------------------------
+    def _is_output(self, node_id):
+        class_type = self.dynprompt.get_node(node_id)["class_type"]
+        class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+        return getattr(class_def, 'OUTPUT_NODE', False) is True
+
+    def _is_async(self, node_id):
+        class_type = self.dynprompt.get_node(node_id)["class_type"]
+        class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+        return inspect.iscoroutinefunction(getattr(class_def, class_def.FUNCTION))
+
+    # ------------------------- UX within a batch --------------------------
+    def _pick_in_batch_with_ux(self, candidates):
+        """
+        Original UX heuristics, but applied *within* the current batch.
+        """
+        # 1) Output nodes ASAP
+        for nid in candidates:
+            if self._is_output(nid):
+                return nid
+        # 1b) Async nodes early to overlap
+        for nid in candidates:
+            if self._is_async(nid):
+                return nid
+        # 2) decoder-before-preview pattern (within the batch)
+        for nid in candidates:
+            for blocked in self.blocking[nid]:
+                if self._is_output(blocked):
+                    return nid
+        # 3) VAELoader -> VAEDecode -> preview (within the batch)
+        for nid in candidates:
+            for blocked in self.blocking[nid]:
+                for blocked2 in self.blocking[blocked]:
+                    if self._is_output(blocked2):
+                        return nid
+        # 4) Otherwise, first candidate
+        return candidates[0]
+
+    # ------------------------- batch-aware picking ------------------------
+    def ux_friendly_pick_node(self, available):
+        """
+        Choose which ready node to execute next, honoring the current batch.
+        When the current batch runs dry, switch to the largest ready group.
+        """
+
+        # Ensure current batch is still present; otherwise pick a new largest group.
+        has_current = (
+            self._current_group_class is not None and
+            any(self.dynprompt.get_node(nid)["class_type"] == self._current_group_class for nid in available)
+        )
+        if not has_current:
+            new_group = self._pick_largest_group(available)
+            self._current_group_class = new_group
+
+        # Restrict to nodes of the current batch
+        candidates = self._filter_by_group(available, self._current_group_class)
+        return self._pick_in_batch_with_ux(candidates)
+
+    # --------------------------- staging / run ----------------------------
    async def stage_node_execution(self):
        assert self.staged_node_id is None
        if self.is_empty():
            return None, None, None
+
        available = self.get_ready_nodes()
+
+        # If nothing ready but there are external blockers, wait for unblocks.
        while len(available) == 0 and self.externalBlocks > 0:
-            # Wait for an external block to be released
            await self.unblockedEvent.wait()
            self.unblockedEvent.clear()
            available = self.get_ready_nodes()
+
        if len(available) == 0:
            cycled_nodes = self.get_nodes_in_cycle()
            # Because cycles composed entirely of static nodes are caught during initial validation,
@ -264,64 +367,30 @@ class ExecutionList(TopologicalSort):
            }
            return None, error_details, ex

+        # Batch-aware pick
        self.staged_node_id = self.ux_friendly_pick_node(available)
        return self.staged_node_id, None, None

-    def ux_friendly_pick_node(self, node_list):
-        # If an output node is available, do that first.
-        # Technically this has no effect on the overall length of execution, but it feels better as a user
-        # for a PreviewImage to display a result as soon as it can
-        # Some other heuristics could probably be used here to improve the UX further.
-        def is_output(node_id):
-            class_type = self.dynprompt.get_node(node_id)["class_type"]
-            class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-            if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True:
-                return True
-            return False
-
-        # If an available node is async, do that first.
-        # This will execute the asynchronous function earlier, reducing the overall time.
-        def is_async(node_id):
-            class_type = self.dynprompt.get_node(node_id)["class_type"]
-            class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-            return inspect.iscoroutinefunction(getattr(class_def, class_def.FUNCTION))
-
-        for node_id in node_list:
-            if is_output(node_id) or is_async(node_id):
-                return node_id
-
-        #This should handle the VAEDecode -> preview case
-        for node_id in node_list:
-            for blocked_node_id in self.blocking[node_id]:
-                if is_output(blocked_node_id):
-                    return node_id
-
-        #This should handle the VAELoader -> VAEDecode -> preview case
-        for node_id in node_list:
-            for blocked_node_id in self.blocking[node_id]:
-                for blocked_node_id1 in self.blocking[blocked_node_id]:
-                    if is_output(blocked_node_id1):
-                        return node_id
-
-        #TODO: this function should be improved
-        return node_list[0]
-
    def unstage_node_execution(self):
+        # If a node execution resolves to PENDING, return it to the pool
+        # but keep the current batch so we continue batching next time.
        assert self.staged_node_id is not None
        self.staged_node_id = None

    def complete_node_execution(self):
        node_id = self.staged_node_id
        self.pop_node(node_id)
+        # Maintain current batch; it will switch automatically when empty.
        self.execution_cache.pop(node_id, None)
        self.execution_cache_listeners.pop(node_id, None)
        self.staged_node_id = None

+    # ------------------------- cycle detection ----------------------------
    def get_nodes_in_cycle(self):
        # We'll dissolve the graph in reverse topological order to leave only the nodes in the cycle.
        # We're skipping some of the performance optimizations from the original TopologicalSort to keep
        # the code simple (and because having a cycle in the first place is a catastrophic error)
-        blocked_by = { node_id: {} for node_id in self.pendingNodes }
+        blocked_by = {node_id: {} for node_id in self.pendingNodes}
        for from_node_id in self.blocking:
            for to_node_id in self.blocking[from_node_id]:
                if True in self.blocking[from_node_id][to_node_id].values():
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@ -399,6 +399,58 @@ class SplitAudioChannels(IO.ComfyNode):

    separate = execute  # TODO: remove

+class JoinAudioChannels(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="JoinAudioChannels",
+            display_name="Join Audio Channels",
+            description="Joins left and right mono audio channels into a stereo audio.",
+            category="audio",
+            inputs=[
+                IO.Audio.Input("audio_left"),
+                IO.Audio.Input("audio_right"),
+            ],
+            outputs=[
+                IO.Audio.Output(display_name="audio"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, audio_left, audio_right) -> IO.NodeOutput:
+        waveform_left = audio_left["waveform"]
+        sample_rate_left = audio_left["sample_rate"]
+        waveform_right = audio_right["waveform"]
+        sample_rate_right = audio_right["sample_rate"]
+
+        if waveform_left.shape[1] != 1 or waveform_right.shape[1] != 1:
+            raise ValueError("AudioJoin: Both input audios must be mono.")
+
+        # Handle different sample rates by resampling to the higher rate
+        waveform_left, waveform_right, output_sample_rate = match_audio_sample_rates(
+            waveform_left, sample_rate_left, waveform_right, sample_rate_right
+        )
+
+        # Handle different lengths by trimming to the shorter length
+        length_left = waveform_left.shape[-1]
+        length_right = waveform_right.shape[-1]
+
+        if length_left != length_right:
+            min_length = min(length_left, length_right)
+            if length_left > min_length:
+                logging.info(f"JoinAudioChannels: Trimming left channel from {length_left} to {min_length} samples.")
+                waveform_left = waveform_left[..., :min_length]
+            if length_right > min_length:
+                logging.info(f"JoinAudioChannels: Trimming right channel from {length_right} to {min_length} samples.")
+                waveform_right = waveform_right[..., :min_length]
+
+        # Join the channels into stereo
+        left_channel = waveform_left[..., 0:1, :]
+        right_channel = waveform_right[..., 0:1, :]
+        stereo_waveform = torch.cat([left_channel, right_channel], dim=1)
+
+        return IO.NodeOutput({"waveform": stereo_waveform, "sample_rate": output_sample_rate})
+

 def match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2):
    if sample_rate_1 != sample_rate_2:
@ -616,6 +668,7 @@ class AudioExtension(ComfyExtension):
            RecordAudio,
            TrimAudioDuration,
            SplitAudioChannels,
+            JoinAudioChannels,
            AudioConcat,
            AudioMerge,
            AudioAdjustVolume,
--- a/comfy_extras/nodes_lt_audio.py
+++ b/comfy_extras/nodes_lt_audio.py
@ -185,6 +185,10 @@ class LTXAVTextEncoderLoader(io.ComfyNode):
                io.Combo.Input(
                    "ckpt_name",
                    options=folder_paths.get_filename_list("checkpoints"),
+                ),
+                io.Combo.Input(
+                    "device",
+                    options=["default", "cpu"],
                )
            ],
            outputs=[io.Clip.Output()],
@ -197,7 +201,11 @@ class LTXAVTextEncoderLoader(io.ComfyNode):
        clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", text_encoder)
        clip_path2 = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)

-        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
+        model_options = {}
+        if device == "cpu":
+            model_options["load_device"] = model_options["offload_device"] = torch.device("cpu")
+
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type, model_options=model_options)
        return io.NodeOutput(clip)


--- a/comfyui_version.py
+++ b/comfyui_version.py
@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.7.0"
+__version__ = "0.8.2"
--- a/main.py
+++ b/main.py
@ -7,6 +7,7 @@ import folder_paths
 import time
 from comfy.cli_args import args
 from app.logger import setup_logger
+from app.assets.scanner import seed_assets
 import itertools
 import utils.extra_config
 import logging
@ -324,6 +325,8 @@ def setup_database():
        from app.database.db import init_db, dependencies_available
        if dependencies_available():
            init_db()
+            if not args.disable_assets_autoscan:
+                seed_assets(["models"], enable_logging=True)
    except Exception as e:
        logging.error(f"Failed to initialize database. Please ensure you have installed the latest requirements. If the error persists, please report this as in future the database will be required: {e}")

--- a/manager_requirements.txt
+++ b/manager_requirements.txt
@ -1 +1 @@
-comfyui_manager==4.0.4
+comfyui_manager==4.0.5
--- a/pyproject.toml
+++ b/pyproject.toml
@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.7.0"
+version = "0.8.2"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"
--- a/requirements.txt
+++ b/requirements.txt
@ -1,5 +1,5 @@
-comfyui-frontend-package==1.35.9
-comfyui-workflow-templates==0.7.66
+comfyui-frontend-package==1.36.13
+comfyui-workflow-templates==0.7.69
 comfyui-embedded-docs==0.3.1
 torch
 torchsde
@ -21,7 +21,7 @@ psutil
 alembic
 SQLAlchemy
 av>=14.2.0
-comfy-kitchen>=0.2.1
+comfy-kitchen>=0.2.5

 #non essential dependencies:
 kornia>=0.7.1
--- a/server.py
+++ b/server.py
@ -33,6 +33,8 @@ import node_helpers
 from comfyui_version import __version__
 from app.frontend_management import FrontendManager, parse_version
 from comfy_api.internal import _ComfyNodeInternal
+from app.assets.scanner import seed_assets
+from app.assets.api.routes import register_assets_system

 from app.user_manager import UserManager
 from app.model_manager import ModelFileManager
@ -184,7 +186,7 @@ def create_block_external_middleware():
        else:
            response = await handler(request)

-        response.headers['Content-Security-Policy'] = "default-src 'self'; script-src 'self' 'unsafe-inline' 'unsafe-eval' blob:; style-src 'self' 'unsafe-inline'; img-src 'self' data: blob:; font-src 'self'; connect-src 'self'; frame-src 'self'; object-src 'self';"
+        response.headers['Content-Security-Policy'] = "default-src 'self'; script-src 'self' 'unsafe-inline' 'unsafe-eval' blob:; style-src 'self' 'unsafe-inline'; img-src 'self' data: blob:; font-src 'self'; connect-src 'self' data:; frame-src 'self'; object-src 'self';"
        return response

    return block_external_middleware
@ -235,6 +237,7 @@ class PromptServer():
            else args.front_end_root
        )
        logging.info(f"[Prompt Server] web root: {self.web_root}")
+        register_assets_system(self.app, self.user_manager)
        routes = web.RouteTableDef()
        self.routes = routes
        self.last_node_id = None
@ -683,6 +686,7 @@ class PromptServer():

        @routes.get("/object_info")
        async def get_object_info(request):
+            seed_assets(["models"])
            with folder_paths.cache_helper:
                out = {}
                for x in nodes.NODE_CLASS_MAPPINGS:
Author	SHA1	Message	Date
Miklos Nagy	9befd0e5da	Merge `09c250184d` into `1dc3da6314`	2026-01-09 11:23:30 +08:00
Jedrzej Kosinski	1dc3da6314	Add most basic Asset support for models (#11315 ) * Brought over minimal elements from PR 10045 to reproduce seed_assets and register_assets_system without adding anything to the DB or server routes yet, for now making everything sync (can introduce async once everything is cleaned up and brought over) * Added db script to insert assets stuff, cleaned up some code; assets (models) now get added/rescanned * Added support for 5 http endpoints for assets * Replaced Optional with \| None in schemas_in.py and schemas_out.py * Remove two routes that will not be relevant yet in this PR: HEAD /api/assets/hash/<hash> and PUT /api/assets/<id>/preview * Remove some functions the two deleted endpoints were using * Don't show assets scan message upon calling /object_info endpoint * removed unsued import to satisfy ruff * Simplified hashing function tpye hint and _hash_file_obj * Satisfied ruff	2026-01-08 22:21:51 -05:00
Comfy Org PR Bot	114fc73685	Bump comfyui-frontend-package to 1.36.13 (#11645 )	2026-01-08 22:16:15 -05:00
comfyanonymous	b48d6a83d4	Fix csp error in frontend when forcing offline. (#11749 )	2026-01-08 22:15:50 -05:00
Jukka Seppänen	027042db68	Add node: JoinAudioChannels (#11728 )	2026-01-08 22:14:06 -05:00
comfyanonymous	1a20656448	Fix import issue. (#11746 )	2026-01-08 17:23:59 -05:00
comfyanonymous	0f11869d55	Better detection if AMD torch compiled with efficient attention. (#11745 )	2026-01-08 17:16:58 -05:00
Dr.Lt.Data	5943fbf457	bump comfyui_manager version to the 4.0.5 (#11732 ) Some checks are pending Python Linting / Run Ruff (push) Waiting to run Details Python Linting / Run Pylint (push) Waiting to run Details Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.10, [self-hosted Linux], stable) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.11, [self-hosted Linux], stable) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.12, [self-hosted Linux], stable) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-unix-nightly (12.1, , linux, 3.11, [self-hosted Linux], nightly) (push) Waiting to run Details Execution Tests / test (macos-latest) (push) Waiting to run Details Execution Tests / test (ubuntu-latest) (push) Waiting to run Details Execution Tests / test (windows-latest) (push) Waiting to run Details Test server launches without errors / test (push) Waiting to run Details Unit Tests / test (macos-latest) (push) Waiting to run Details Unit Tests / test (ubuntu-latest) (push) Waiting to run Details Unit Tests / test (windows-2022) (push) Waiting to run Details	2026-01-08 08:15:42 -08:00
Yoland Yan	a60b7b86c5	Revert "Force sequential execution in CI test jobs (#11687 )" (#11725 ) Some checks are pending Python Linting / Run Ruff (push) Waiting to run Details Python Linting / Run Pylint (push) Waiting to run Details Execution Tests / test (macos-latest) (push) Waiting to run Details Execution Tests / test (ubuntu-latest) (push) Waiting to run Details Execution Tests / test (windows-latest) (push) Waiting to run Details Test server launches without errors / test (push) Waiting to run Details Unit Tests / test (macos-latest) (push) Waiting to run Details Unit Tests / test (ubuntu-latest) (push) Waiting to run Details Unit Tests / test (windows-2022) (push) Waiting to run Details This reverts commit `ce0000c4f2`.	2026-01-07 21:41:57 -08:00
comfyanonymous	2e9d51680a	ComfyUI version v0.8.2 Some checks are pending Python Linting / Run Ruff (push) Waiting to run Details Python Linting / Run Pylint (push) Waiting to run Details Build package / Build Test (3.10) (push) Waiting to run Details Build package / Build Test (3.11) (push) Waiting to run Details Build package / Build Test (3.12) (push) Waiting to run Details Build package / Build Test (3.13) (push) Waiting to run Details Build package / Build Test (3.14) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.10, [self-hosted Linux], stable) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.11, [self-hosted Linux], stable) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.12, [self-hosted Linux], stable) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-unix-nightly (12.1, , linux, 3.11, [self-hosted Linux], nightly) (push) Waiting to run Details Execution Tests / test (macos-latest) (push) Waiting to run Details Execution Tests / test (ubuntu-latest) (push) Waiting to run Details Execution Tests / test (windows-latest) (push) Waiting to run Details Test server launches without errors / test (push) Waiting to run Details Unit Tests / test (macos-latest) (push) Waiting to run Details Unit Tests / test (ubuntu-latest) (push) Waiting to run Details Unit Tests / test (windows-2022) (push) Waiting to run Details	2026-01-07 23:50:02 -05:00
comfyanonymous	50d6e1caf4	Tweak ltxv vae mem estimation. (#11722 )	2026-01-07 23:07:05 -05:00
comfyanonymous	ac12f77bed	ComfyUI version v0.8.1	2026-01-07 22:10:08 -05:00
ComfyUI Wiki	fcd9a236b0	Update template to 0.7.69 (#11719 )	2026-01-07 18:22:23 -08:00
comfyanonymous	21e8425087	Add warning for old pytorch. (#11718 )	2026-01-07 21:07:26 -05:00
rattus	b6c79a648a	ops: Fix offloading with FP8MM performance (#11697 ) This logic was checking comfy_cast_weights, and going straight to to the forward_comfy_cast_weights implementation without attempting to downscale input to fp8 in the event comfy_cast_weights is set. The main reason comfy_cast_weights would be set would be for async offload, which is not a good reason to nix FP8MM. So instead, and together the underlying exclusions for FP8MM which are: * having a weight_function (usually LowVramPatch) * force_cast_weights (compute dtype override) * the weight is not Quantized * the input is already quantized * the model or layer has MM explictily disabled. If you get past all of those exclusions, quantize the input tensor. Then hand the new input, quantized or not off to forward_comfy_cast_weights to handle it. If the weight is offloaded but input is quantized you will get an offloaded MM8.	2026-01-07 21:01:16 -05:00
comfyanonymous	25bc1b5b57	Add memory estimation function to ltxav text encoder. (#11716 )	2026-01-07 20:11:22 -05:00
comfyanonymous	3cd19e99c1	Increase ltxav mem estimation by a bit. (#11715 )	2026-01-07 20:04:56 -05:00
comfyanonymous	007b87e7ac	Bump required comfy-kitchen version. (#11714 )	2026-01-07 19:48:47 -05:00
comfyanonymous	34751fe9f9	Lower ltxv text encoder vram use. (#11713 )	2026-01-07 19:12:15 -05:00
Jukka Seppänen	1c705f7bfb	Add device selection for LTXAVTextEncoderLoader (#11700 )	2026-01-07 18:39:59 -05:00
rattus	48e5ea1dfd	model_patcher: Remove confusing load stat (#11710 ) If the loader passes 1e32 as the usable memory size, it means force the full load. This happens with CPU loads and a few other misc cases. Removing the confusing number and just leave the other details.	2026-01-07 18:39:20 -05:00
comfyanonymous	3cd7b32f1b	Support gemma 12B with quant weights. (#11696 ) Some checks are pending Python Linting / Run Ruff (push) Waiting to run Details Python Linting / Run Pylint (push) Waiting to run Details Build package / Build Test (3.10) (push) Waiting to run Details Build package / Build Test (3.11) (push) Waiting to run Details Build package / Build Test (3.12) (push) Waiting to run Details Build package / Build Test (3.13) (push) Waiting to run Details Build package / Build Test (3.14) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.10, [self-hosted Linux], stable) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.11, [self-hosted Linux], stable) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.12, [self-hosted Linux], stable) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-unix-nightly (12.1, , linux, 3.11, [self-hosted Linux], nightly) (push) Waiting to run Details Execution Tests / test (macos-latest) (push) Waiting to run Details Execution Tests / test (ubuntu-latest) (push) Waiting to run Details Execution Tests / test (windows-latest) (push) Waiting to run Details Test server launches without errors / test (push) Waiting to run Details Unit Tests / test (macos-latest) (push) Waiting to run Details Unit Tests / test (ubuntu-latest) (push) Waiting to run Details Unit Tests / test (windows-2022) (push) Waiting to run Details	2026-01-07 05:15:14 -05:00
comfyanonymous	c0c9720d77	Fix stable release workflow not pulling latest comfy kitchen. (#11695 )	2026-01-07 04:48:28 -05:00
comfyanonymous	fc0cb10bcb	ComfyUI v0.8.0	2026-01-07 04:07:31 -05:00
comfyanonymous	b7d7cc1d49	Fix fp8 fast issue. (#11688 )	2026-01-07 01:39:06 -05:00
Alexander Piskun	79e94544bd	feat(api-nodes): add WAN2.6 ReferenceToVideo (#11644 )	2026-01-06 22:04:50 -08:00
Yoland Yan	ce0000c4f2	Force sequential execution in CI test jobs (#11687 ) Added max-parallel setting to enforce sequential execution in test jobs.	2026-01-07 00:57:31 -05:00
comfyanonymous	c5cfb34c07	Update comfy-kitchen version to 0.2.3 (#11685 )	2026-01-06 23:51:45 -05:00
comfyanonymous	edee33f55e	Disable comfy kitchen cuda if pytorch cuda less than 13 (#11681 ) Some checks are pending Python Linting / Run Ruff (push) Waiting to run Details Python Linting / Run Pylint (push) Waiting to run Details Build package / Build Test (3.10) (push) Waiting to run Details Build package / Build Test (3.11) (push) Waiting to run Details Build package / Build Test (3.12) (push) Waiting to run Details Build package / Build Test (3.13) (push) Waiting to run Details Build package / Build Test (3.14) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.10, [self-hosted Linux], stable) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.11, [self-hosted Linux], stable) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.12, [self-hosted Linux], stable) (push) Waiting to run Details Full Comfy CI Workflow Runs / test-unix-nightly (12.1, , linux, 3.11, [self-hosted Linux], nightly) (push) Waiting to run Details Execution Tests / test (macos-latest) (push) Waiting to run Details Execution Tests / test (ubuntu-latest) (push) Waiting to run Details Execution Tests / test (windows-latest) (push) Waiting to run Details Test server launches without errors / test (push) Waiting to run Details Unit Tests / test (macos-latest) (push) Waiting to run Details Unit Tests / test (ubuntu-latest) (push) Waiting to run Details Unit Tests / test (windows-2022) (push) Waiting to run Details	2026-01-06 22:13:43 -05:00
comfyanonymous	2c03884f5f	Skip fp4 matrix mult on devices that don't support it. (#11677 )	2026-01-06 18:07:26 -05:00
comfyanonymous	6e9ee55cdd	Disable ltxav previews. (#11676 )	2026-01-06 17:41:27 -05:00
comfyanonymous	023cf13721	Fix lowvram issue with ltxv2 text encoder. (#11675 )	2026-01-06 17:33:03 -05:00
ComfyUI Wiki	c3566c0d76	chore: update workflow templates to v0.7.67 (#11667 )	2026-01-06 14:28:29 -08:00
comfyanonymous	c3c3e93c5b	Use rope functions from comfy kitchen. (#11674 )	2026-01-06 16:57:50 -05:00
Miklos Nagy	09c250184d	Update graph.py	2025-11-12 15:14:45 +01:00