Merge origin/master into expose-deploy-environment

README.md

@@ -462,16 +462,6 @@ To use the most up-to-date frontend version:
 
 This approach allows you to easily switch between the stable fortnightly release and the cutting-edge daily updates, or even specific versions for testing purposes.
 
-### Accessing the Legacy Frontend
-
-If you need to use the legacy frontend for any reason, you can access it using the following command line argument:
-
-```
---front-end-version Comfy-Org/ComfyUI_legacy_frontend@latest
-```
-
-This will use a snapshot of the legacy frontend preserved in the [ComfyUI Legacy Frontend repository](https://github.com/Comfy-Org/ComfyUI_legacy_frontend).
-
 # QA
 
 ### Which GPU should I buy for this?

alembic_db/versions/0004_drop_tag_type.py

@@ -0,0 +1,39 @@
+"""
+Drop the vestigial tags.tag_type column.
+
+tag_type was always "user" in practice — no code path ever set it to anything
+else (no system/seeded classification was ever wired up) and nothing queried it.
+The column, its index (ix_tags_tag_type), and the corresponding API field were
+dead weight, so they are removed.
+
+Revision ID: 0004_drop_tag_type
+Revises: 0003_add_metadata_job_id
+Create Date: 2026-06-03
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+revision = "0004_drop_tag_type"
+down_revision = "0003_add_metadata_job_id"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("tags") as batch_op:
+        batch_op.drop_index("ix_tags_tag_type")
+        batch_op.drop_column("tag_type")
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("tags") as batch_op:
+        batch_op.add_column(
+            sa.Column(
+                "tag_type",
+                sa.String(length=32),
+                nullable=False,
+                server_default="user",
+            )
+        )
+        batch_op.create_index("ix_tags_tag_type", ["tag_type"])

app/assets/api/routes.py

@@ -39,6 +39,7 @@ from app.assets.services import (
     update_asset_metadata,
     upload_from_temp_path,
 )
+from app.assets.services.cursor import InvalidCursorError
 from app.assets.services.tagging import list_tag_histogram
 
 ROUTES = web.RouteTableDef()
@@ -174,7 +175,7 @@ def _build_asset_response(result: schemas.AssetDetailResult | schemas.UploadResu
         user_metadata=result.ref.user_metadata or {},
         metadata=result.ref.system_metadata,
         job_id=result.ref.job_id,
-        prompt_id=result.ref.job_id,  # deprecated: mirrors job_id for cloud compat
+        prompt_id=result.ref.job_id,  # deprecated alias of job_id, kept for compatibility
         created_at=result.ref.created_at,
         updated_at=result.ref.updated_at,
         last_access_time=result.ref.last_access_time,
@@ -211,24 +212,37 @@ async def list_assets_route(request: web.Request) -> web.Response:
     order_candidate = (q.order or "desc").lower()
     order = order_candidate if order_candidate in {"asc", "desc"} else "desc"
 
-    result = list_assets_page(
-        owner_id=USER_MANAGER.get_request_user_id(request),
-        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=sort,
-        order=order,
-    )
+    try:
+        result = list_assets_page(
+            owner_id=USER_MANAGER.get_request_user_id(request),
+            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=sort,
+            order=order,
+            after=q.after,
+        )
+    except InvalidCursorError as e:
+        return _build_error_response(400, "INVALID_CURSOR", str(e))
 
     summaries = [_build_asset_response(item) for item in result.items]
 
+    # has_more semantics differ by mode:
+    #   - cursor mode: a non-empty next_cursor means there are more results.
+    #   - offset mode: derived from total - (offset + page size).
+    if q.after is not None:
+        has_more = result.next_cursor is not None
+    else:
+        has_more = (q.offset + len(summaries)) < result.total
+
     payload = schemas_out.AssetsList(
         assets=summaries,
         total=result.total,
-        has_more=(q.offset + len(summaries)) < result.total,
+        has_more=has_more,
+        next_cursor=result.next_cursor,
     )
     return web.json_response(payload.model_dump(mode="json", exclude_none=True))
 
@@ -519,18 +533,14 @@ async def update_asset_route(request: web.Request) -> web.Response:
 @_require_assets_feature_enabled
 async def delete_asset_route(request: web.Request) -> web.Response:
     reference_id = str(uuid.UUID(request.match_info["id"]))
-    delete_content_param = request.query.get("delete_content")
-    delete_content = (
-        False
-        if delete_content_param is None
-        else delete_content_param.lower() not in {"0", "false", "no"}
-    )
 
     try:
+        # Deleting an asset is a soft delete of the reference; the underlying
+        # content is preserved (it may be shared with other references).
         deleted = delete_asset_reference(
             reference_id=reference_id,
             owner_id=USER_MANAGER.get_request_user_id(request),
-            delete_content_if_orphan=delete_content,
+            delete_content_if_orphan=False,
         )
     except Exception:
         logging.exception(
@@ -575,8 +585,8 @@ async def get_tags(request: web.Request) -> web.Response:
     )
 
     tags = [
-        schemas_out.TagUsage(name=name, count=count, type=tag_type)
-        for (name, tag_type, count) in rows
+        schemas_out.TagUsage(name=name, count=count)
+        for (name, count) in rows
     ]
     payload = schemas_out.TagsList(
         tags=tags, total=total, has_more=(query.offset + len(tags)) < total

app/assets/api/schemas_in.py

@@ -59,6 +59,11 @@ class ListAssetsQuery(BaseModel):
 
     limit: conint(ge=1, le=500) = 20
     offset: conint(ge=0) = 0
+    # Opaque keyset cursor. When supplied, `offset` is ignored. Cursor pagination
+    # is supported for sort values `created_at`, `updated_at`, `name`, `size`.
+    # Supplying `after` together with `sort=last_access_time` returns
+    # 400 INVALID_CURSOR; that sort only supports offset/limit.
+    after: str | None = None
 
     sort: Literal["name", "created_at", "updated_at", "size", "last_access_time"] = (
         "created_at"

app/assets/api/schemas_out.py

@@ -41,12 +41,13 @@ class AssetsList(BaseModel):
     assets: list[Asset]
     total: int
     has_more: bool
+    # Opaque cursor for the next page. Omitted when there are no more results.
+    next_cursor: str | None = None
 
 
 class TagUsage(BaseModel):
     name: str
     count: int
-    type: str
 
 
 class TagsList(BaseModel):

app/assets/database/models.py

@@ -227,7 +227,6 @@ 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_reference_links: Mapped[list[AssetReferenceTag]] = relationship(
         back_populates="tag",
@@ -240,7 +239,5 @@ class Tag(Base):
         overlaps="asset_reference_links,tag_links,tags,asset_reference",
     )
 
-    __table_args__ = (Index("ix_tags_tag_type", "tag_type"),)
-
     def __repr__(self) -> str:
         return f"<Tag {self.name}>"

app/assets/database/queries/asset_reference.py

@@ -266,9 +266,18 @@ def list_references_page(
     metadata_filter: dict | None = None,
     sort: str | None = None,
     order: str | None = None,
+    after_cursor_value: object | None = None,
+    after_cursor_id: str | None = None,
 ) -> tuple[list[AssetReference], dict[str, list[str]], int]:
     """List references with pagination, filtering, and sorting.
 
+    When ``after_cursor_value``/``after_cursor_id`` are supplied the query uses
+    keyset pagination — ``offset`` is ignored and a WHERE clause selects rows
+    strictly after the given ``(sort_col, id)`` position in the active sort
+    direction. The cursor value must already be typed for the column
+    (datetime for time sorts, int for size, str for name); the caller decodes
+    the opaque cursor string and resolves to the typed value.
+
     Returns (references, tag_map, total_count).
     """
     base = (
@@ -297,9 +306,31 @@ def list_references_page(
         "size": Asset.size_bytes,
     }
     sort_col = sort_map.get(sort, AssetReference.created_at)
-    sort_exp = sort_col.desc() if order == "desc" else sort_col.asc()
+    descending = order == "desc"
 
-    base = base.order_by(sort_exp).limit(limit).offset(offset)
+    # Keyset WHERE: (sort_col, id) strictly less-than / greater-than the cursor.
+    # Equivalent to: sort_col <op> v  OR  (sort_col = v AND id <op> cursor_id).
+    if after_cursor_value is not None and after_cursor_id is not None:
+        if descending:
+            keyset = sa.or_(
+                sort_col < after_cursor_value,
+                sa.and_(sort_col == after_cursor_value, AssetReference.id < after_cursor_id),
+            )
+        else:
+            keyset = sa.or_(
+                sort_col > after_cursor_value,
+                sa.and_(sort_col == after_cursor_value, AssetReference.id > after_cursor_id),
+            )
+        base = base.where(keyset)
+
+    # Secondary ORDER BY id (matching the primary direction) gives the keyset
+    # comparison a deterministic tiebreaker on duplicate sort_col values.
+    id_exp = AssetReference.id.desc() if descending else AssetReference.id.asc()
+    sort_exp = sort_col.desc() if descending else sort_col.asc()
+
+    base = base.order_by(sort_exp, id_exp).limit(limit)
+    if after_cursor_id is None:
+        base = base.offset(offset)
 
     count_stmt = (
         select(sa.func.count())

app/assets/database/queries/tags.py

@@ -55,13 +55,11 @@ def validate_tags_exist(session: Session, tags: list[str]) -> None:
         raise ValueError(f"Unknown tags: {missing}")
 
 
-def ensure_tags_exist(
-    session: Session, names: Iterable[str], tag_type: str = "user"
-) -> None:
+def ensure_tags_exist(session: Session, names: Iterable[str]) -> None:
     wanted = normalize_tags(list(names))
     if not wanted:
         return
-    rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))]
+    rows = [{"name": n} for n in list(dict.fromkeys(wanted))]
     ins = (
         sqlite.insert(Tag)
         .values(rows)
@@ -97,7 +95,7 @@ def set_reference_tags(
     to_remove = [t for t in current if t not in desired]
 
     if to_add:
-        ensure_tags_exist(session, to_add, tag_type="user")
+        ensure_tags_exist(session, to_add)
         session.add_all(
             [
                 AssetReferenceTag(
@@ -142,7 +140,7 @@ def add_tags_to_reference(
         return AddTagsResult(added=[], already_present=[], total_tags=total)
 
     if create_if_missing:
-        ensure_tags_exist(session, norm, tag_type="user")
+        ensure_tags_exist(session, norm)
 
     current = set(get_reference_tags(session, reference_id))
 
@@ -289,7 +287,6 @@ def list_tags_with_usage(
     q = (
         select(
             Tag.name,
-            Tag.tag_type,
             func.coalesce(counts_sq.c.cnt, 0).label("count"),
         )
         .select_from(Tag)
@@ -331,7 +328,7 @@ def list_tags_with_usage(
     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]
+    rows_norm = [(name, int(count or 0)) for (name, count) in rows]
     return rows_norm, int(total or 0)
 
 

app/assets/scanner.py

@@ -355,7 +355,7 @@ def insert_asset_specs(specs: list[SeedAssetSpec], tag_pool: set[str]) -> int:
         return 0
     with create_session() as sess:
         if tag_pool:
-            ensure_tags_exist(sess, tag_pool, tag_type="user")
+            ensure_tags_exist(sess, tag_pool)
         result = batch_insert_seed_assets(sess, specs=specs, owner_id="")
         sess.commit()
         return result.inserted_refs

app/assets/services/asset_management.py

@@ -1,8 +1,19 @@
 import contextlib
 import mimetypes
 import os
+from datetime import timezone
 from typing import Sequence
 
+from app.assets.services.cursor import (
+    CursorPayload,
+    InvalidCursorError,
+    decode_cursor,
+    decode_cursor_int,
+    decode_cursor_time,
+    encode_cursor,
+    encode_cursor_from_time,
+)
+
 
 from app.assets.database.models import Asset
 from app.assets.database.queries import (
@@ -149,6 +160,16 @@ def delete_asset_reference(
     owner_id: str,
     delete_content_if_orphan: bool = True,
 ) -> bool:
+    """Delete an asset reference.
+
+    With ``delete_content_if_orphan=False`` (a soft delete), the reference is
+    hidden and the underlying content is preserved. With ``True``, the content
+    is also removed once it becomes orphaned.
+
+    Note: the public DELETE /api/assets/{id} endpoint always soft-deletes
+    (passes ``False``); the orphan-reclamation path is intentionally
+    internal-only, retained for a future GC/admin caller.
+    """
     with create_session() as session:
         if not delete_content_if_orphan:
             # Soft delete: mark the reference as deleted but keep everything
@@ -242,6 +263,11 @@ def get_asset_by_hash(asset_hash: str) -> AssetData | None:
         return extract_asset_data(asset)
 
 
+# Sort fields that support cursor pagination. `last_access_time` is not
+# in this list — it falls back to offset/limit.
+_CURSOR_SORT_FIELDS = ("created_at", "updated_at", "name", "size")
+
+
 def list_assets_page(
     owner_id: str = "",
     include_tags: Sequence[str] | None = None,
@@ -252,7 +278,39 @@ def list_assets_page(
     offset: int = 0,
     sort: str = "created_at",
     order: str = "desc",
+    after: str | None = None,
 ) -> ListAssetsResult:
+    """List assets with optional cursor pagination.
+
+    When ``after`` is supplied it overrides ``offset``. The cursor's sort field
+    must match ``sort`` and be in the cursor-supported allowlist; mismatches
+    raise InvalidCursorError so the handler can map to 400 INVALID_CURSOR.
+    """
+    cursor_value: object | None = None
+    cursor_id: str | None = None
+    # Mint next_cursor on every page where the sort is cursor-supported, not
+    # only when the request itself arrived with a cursor. Otherwise a first
+    # request (no `after`) returns next_cursor=None and the client can never
+    # enter cursor mode.
+    mint_cursor = sort in _CURSOR_SORT_FIELDS
+
+    if after is not None:
+        if sort not in _CURSOR_SORT_FIELDS:
+            raise InvalidCursorError(
+                f"cursor pagination is not supported for sort={sort!r}"
+            )
+        payload = decode_cursor(after, _CURSOR_SORT_FIELDS, expected_order=order)
+        if payload.sort_field != sort:
+            raise InvalidCursorError(
+                f"cursor sort field {payload.sort_field!r} does not match request sort {sort!r}"
+            )
+        cursor_value, cursor_id = _resolve_cursor_value(payload), payload.id
+
+    # Over-fetch by one row so we can distinguish "exactly `limit` rows total
+    # remaining" from "more rows past this page" without a second query. Drop
+    # the sentinel before returning.
+    fetch_limit = limit + 1 if mint_cursor else limit
+
     with create_session() as session:
         refs, tag_map, total = list_references_page(
             session,
@@ -261,12 +319,22 @@ def list_assets_page(
             exclude_tags=exclude_tags,
             name_contains=name_contains,
             metadata_filter=metadata_filter,
-            limit=limit,
+            limit=fetch_limit,
             offset=offset,
             sort=sort,
             order=order,
+            after_cursor_value=cursor_value,
+            after_cursor_id=cursor_id,
         )
 
+        next_cursor: str | None = None
+        if mint_cursor and len(refs) > limit:
+            # There's at least one more row past this page — mint a cursor from
+            # the last row of the page (i.e. index `limit - 1`, since we
+            # over-fetched), and drop the sentinel.
+            next_cursor = _encode_next_cursor(refs[limit - 1], sort, order)
+            refs = refs[:limit]
+
         items: list[AssetSummaryData] = []
         for ref in refs:
             items.append(
@@ -277,7 +345,39 @@ def list_assets_page(
                 )
             )
 
-        return ListAssetsResult(items=items, total=total)
+        return ListAssetsResult(items=items, total=total, next_cursor=next_cursor)
+
+
+def _resolve_cursor_value(payload: CursorPayload) -> object:
+    """Map a decoded cursor payload to a column-typed Python value."""
+    if payload.sort_field in ("created_at", "updated_at"):
+        # DB stores naive UTC; strip tzinfo so the comparison binds against a
+        # `TIMESTAMP WITHOUT TIME ZONE` column without an offset shift.
+        return decode_cursor_time(payload).replace(tzinfo=None)
+    if payload.sort_field == "size":
+        return decode_cursor_int(payload)
+    return payload.value  # name, str-typed
+
+
+def _encode_next_cursor(ref, sort: str, order: str) -> str | None:
+    """Mint a cursor pointing at *ref* for the given sort dimension.
+
+    Returns None when the boundary row carries a NULL sort value (e.g. an asset
+    record whose size_bytes hasn't been backfilled). Continuing pagination
+    across a NULL boundary is undefined under keyset ordering — better to
+    truncate cleanly here than to mint a cursor that mis-positions.
+    """
+    if sort == "name":
+        return encode_cursor("name", ref.name, ref.id, order=order)
+    if sort == "size":
+        if ref.asset is None or ref.asset.size_bytes is None:
+            return None
+        return encode_cursor("size", str(ref.asset.size_bytes), ref.id, order=order)
+    # created_at / updated_at — DB datetimes are naive UTC; attach tz before encoding.
+    value = ref.created_at if sort == "created_at" else ref.updated_at
+    if value is None:
+        return None
+    return encode_cursor_from_time(sort, value.replace(tzinfo=timezone.utc), ref.id, order=order)
 
 
 def resolve_hash_to_path(

app/assets/services/cursor.py

@@ -0,0 +1,213 @@
+"""Opaque keyset-pagination cursor for /api/assets.
+
+Payload JSON uses short keys to keep the encoded length small:
+
+    {"s": <sort_field>, "v": <value>, "id": <id>, "o": <order>}
+
+The `o` key binds the cursor to the sort direction it was minted under,
+so replaying a `desc` cursor against an `asc` request fails with
+``INVALID_CURSOR`` rather than silently walking the wrong direction.
+`o` is mandatory on every payload — a cursor without it is rejected as
+malformed.
+
+Encoding is base64url with no padding. Cursors are opaque tokens: the
+payload format is internal to this server, and clients must treat a
+cursor as a black box handed back via `next_cursor`. No byte-level
+compatibility with any other implementation is required.
+
+Time values are serialized as Unix microseconds (UTC) — microsecond
+precision is sufficient to round-trip the timestamps stored by the
+database without rounding rows in the same millisecond bucket.
+"""
+from __future__ import annotations
+
+import base64
+import json
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from typing import Iterable, Optional
+
+
+class InvalidCursorError(ValueError):
+    """Raised on a malformed, oversized, or unsupported-sort-field cursor.
+
+    Map to a 400 response with code ``INVALID_CURSOR`` at the handler.
+    """
+
+
+# Wire-format length caps. Cursors are user-controlled, so caps protect the
+# decode path from oversized allocations and downstream SQL predicates from
+# unbounded strings.
+#
+# MAX_CURSOR_VALUE_LENGTH is 512 to fit the `AssetReference.name` column max
+# (`String(512)`) — otherwise a long-named asset would mint a cursor the same
+# server then refuses on the next request.
+#
+# MAX_ENCODED_CURSOR_LENGTH is the decode-path guard, sized comfortably above
+# the largest cursor the per-field caps can produce. Worst case is value + id
+# at their caps with every character JSON-escaping to the six-byte `\uXXXX`
+# form (control characters), which is ~5.2 KB once base64url-encoded. At 8192
+# the encoder can never mint a cursor that exceeds it, so a freshly minted
+# cursor always decodes on the next request and there is no user-visible
+# "cursor too long" failure.
+MAX_ENCODED_CURSOR_LENGTH = 8192
+MAX_CURSOR_VALUE_LENGTH = 512
+MAX_CURSOR_ID_LENGTH = 128
+
+
+@dataclass(frozen=True)
+class CursorPayload:
+    sort_field: str
+    value: str
+    id: str
+    order: str
+
+
+_VALID_ORDERS = ("asc", "desc")
+
+
+def encode_cursor(sort_field: str, value: str, id: str, order: str = "desc") -> str:
+    """Encode a cursor payload as a base64url (no-padding) string.
+
+    `order` binds the cursor to the sort direction it was minted under so a
+    later request with a flipped `order` query parameter is rejected with
+    ``INVALID_CURSOR`` rather than silently walking the wrong direction.
+    """
+    if order not in _VALID_ORDERS:
+        raise InvalidCursorError(f"order must be one of {_VALID_ORDERS}, got {order!r}")
+    # Symmetric input validation: the encoder must reject anything the
+    # decoder rejects, or the same server will mint cursors it then 400s on
+    # the next request.
+    if not id:
+        raise InvalidCursorError("id must be non-empty")
+    if len(id) > MAX_CURSOR_ID_LENGTH:
+        raise InvalidCursorError("id exceeds maximum length")
+    if len(value) > MAX_CURSOR_VALUE_LENGTH:
+        raise InvalidCursorError("value exceeds maximum length")
+    payload = {"s": sort_field, "v": value, "id": id, "o": order}
+    raw = json.dumps(payload, separators=(",", ":"), ensure_ascii=False)
+    # No mint-time length guard is needed: the per-field caps above bound the
+    # encoded length well below MAX_ENCODED_CURSOR_LENGTH (see its definition),
+    # so the encoder can never produce a cursor the decode path would reject.
+    return base64.urlsafe_b64encode(raw.encode("utf-8")).rstrip(b"=").decode("ascii")
+
+
+def encode_cursor_from_time(sort_field: str, t: datetime, id: str, order: str = "desc") -> str:
+    """Encode a time-typed cursor at Unix microsecond precision.
+
+    Accepts an aware datetime (any timezone) and normalizes to UTC. Naive
+    datetimes are rejected so callers can't accidentally encode the local
+    wall-clock value of a UTC-stored timestamp.
+    """
+    if t.tzinfo is None:
+        raise ValueError("encode_cursor_from_time requires an aware datetime")
+    micros = _datetime_to_unix_micros(t.astimezone(timezone.utc))
+    return encode_cursor(sort_field, str(micros), id, order=order)
+
+
+def decode_cursor(
+    cursor: str,
+    allowed_sort_fields: Iterable[str],
+    expected_order: str | None = None,
+) -> CursorPayload:
+    """Parse an opaque cursor.
+
+    ``allowed_sort_fields`` is the endpoint's accepted sort-field list — a
+    cursor carrying a field outside this set is rejected so a cursor minted
+    for one column can't be replayed against another (e.g. a ``created_at``
+    timestamp string compared against a ``name`` column).
+
+    ``expected_order`` (``"asc"``/``"desc"``), when supplied, must match the
+    payload's ``o`` field. ``o`` is required on every payload; a cursor
+    missing it is rejected as malformed.
+
+    Passing no allowed fields rejects every cursor.
+    """
+    if len(cursor) > MAX_ENCODED_CURSOR_LENGTH:
+        raise InvalidCursorError("cursor exceeds maximum length")
+
+    try:
+        # urlsafe_b64decode requires correct padding; we strip on encode, so
+        # restore the trailing '=' pad here.
+        padding = "=" * (-len(cursor) % 4)
+        raw = base64.urlsafe_b64decode(cursor + padding)
+    except (ValueError, base64.binascii.Error) as e:
+        raise InvalidCursorError(f"encoding: {e}") from e
+
+    try:
+        decoded = json.loads(raw)
+    except (json.JSONDecodeError, UnicodeDecodeError) as e:
+        raise InvalidCursorError(f"payload: {e}") from e
+
+    if not isinstance(decoded, dict):
+        raise InvalidCursorError("payload: expected object")
+
+    sort_field = decoded.get("s")
+    value = decoded.get("v")
+    id = decoded.get("id")
+    order = decoded.get("o")
+
+    if not isinstance(sort_field, str) or not isinstance(value, str) or not isinstance(id, str):
+        raise InvalidCursorError("payload: missing or non-string s/v/id")
+
+    if id == "":
+        raise InvalidCursorError("missing id")
+    if len(id) > MAX_CURSOR_ID_LENGTH:
+        raise InvalidCursorError("id exceeds maximum length")
+    if len(value) > MAX_CURSOR_VALUE_LENGTH:
+        raise InvalidCursorError("value exceeds maximum length")
+
+    if sort_field not in allowed_sort_fields:
+        raise InvalidCursorError(f"unsupported sort field {sort_field!r}")
+
+    if not isinstance(order, str):
+        raise InvalidCursorError("missing or non-string o")
+    if order not in _VALID_ORDERS:
+        raise InvalidCursorError(f"unsupported order {order!r}")
+    if expected_order is not None and order != expected_order:
+        raise InvalidCursorError(
+            f"cursor order {order!r} does not match request order {expected_order!r}"
+        )
+
+    return CursorPayload(sort_field=sort_field, value=value, id=id, order=order)
+
+
+def decode_cursor_time(payload: Optional[CursorPayload]) -> datetime:
+    """Parse a time-typed cursor value as Unix microseconds, returning UTC."""
+    if payload is None:
+        raise InvalidCursorError("nil cursor payload")
+    try:
+        micros = int(payload.value)
+    except ValueError as e:
+        raise InvalidCursorError(f"value is not a valid timestamp: {e}") from e
+    try:
+        return _unix_micros_to_datetime(micros)
+    except (OverflowError, OSError, ValueError) as e:
+        # Crafted out-of-range microseconds (e.g. > datetime.MAX_YEAR) blow up
+        # in fromtimestamp / datetime construction. Map to 400, not 500.
+        raise InvalidCursorError(f"value is out of representable range: {e}") from e
+
+
+def decode_cursor_int(payload: Optional[CursorPayload]) -> int:
+    """Parse a cursor value as a base-10 integer."""
+    if payload is None:
+        raise InvalidCursorError("nil cursor payload")
+    try:
+        return int(payload.value)
+    except ValueError as e:
+        raise InvalidCursorError(f"value is not a valid integer: {e}") from e
+
+
+_EPOCH = datetime(1970, 1, 1, tzinfo=timezone.utc)
+
+
+def _datetime_to_unix_micros(t: datetime) -> int:
+    """Convert an aware UTC datetime to Unix microseconds (integer math)."""
+    delta = t - _EPOCH
+    return (delta.days * 86_400 + delta.seconds) * 1_000_000 + delta.microseconds
+
+
+def _unix_micros_to_datetime(micros: int) -> datetime:
+    """Convert Unix microseconds to a UTC datetime, preserving precision."""
+    seconds, micro_remainder = divmod(micros, 1_000_000)
+    return datetime.fromtimestamp(seconds, tz=timezone.utc).replace(microsecond=micro_remainder)

app/assets/services/schemas.py

@@ -56,7 +56,6 @@ class IngestResult:
 
 class TagUsage(NamedTuple):
     name: str
-    tag_type: str
     count: int
 
 
@@ -71,6 +70,7 @@ class AssetSummaryData:
 class ListAssetsResult:
     items: list[AssetSummaryData]
     total: int
+    next_cursor: str | None = None
 
 
 @dataclass(frozen=True)

app/assets/services/tagging.py

@@ -75,7 +75,7 @@ def list_tags(
             owner_id=owner_id,
         )
 
-    return [TagUsage(name, tag_type, count) for name, tag_type, count in rows], total
+    return [TagUsage(name, count) for name, count in rows], total
 
 
 def list_tag_histogram(

comfy/image_encoders/dino2.py

@@ -1,7 +1,13 @@
 import torch
+import torch.nn.functional as F
+
 from comfy.text_encoders.bert import BertAttention
 import comfy.model_management
 from comfy.ldm.modules.attention import optimized_attention_for_device
+from comfy.ldm.depth_anything_3.reference_view_selector import (
+    select_reference_view, reorder_by_reference, restore_original_order,
+    THRESH_FOR_REF_SELECTION,
+)
 
 
 class Dino2AttentionOutput(torch.nn.Module):
@@ -14,13 +20,41 @@ class Dino2AttentionOutput(torch.nn.Module):
 
 
 class Dino2AttentionBlock(torch.nn.Module):
-    def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations):
+    def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations,
+                 qk_norm=False):
         super().__init__()
+        self.heads = heads
+        self.head_dim = embed_dim // heads
         self.attention = BertAttention(embed_dim, heads, dtype, device, operations)
         self.output = Dino2AttentionOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations)
+        if qk_norm:
+            self.q_norm = operations.LayerNorm(self.head_dim, dtype=dtype, device=device)
+            self.k_norm = operations.LayerNorm(self.head_dim, dtype=dtype, device=device)
+        else:
+            self.q_norm = None
+            self.k_norm = None
 
-    def forward(self, x, mask, optimized_attention):
-        return self.output(self.attention(x, mask, optimized_attention))
+    def forward(self, x, mask, optimized_attention, pos=None, rope=None):
+        # Fast path used by the existing CLIP-vision DINOv2 (no DA3 extensions).
+        if self.q_norm is None and rope is None:
+            return self.output(self.attention(x, mask, optimized_attention))
+
+        # DA3 path: do QKV manually so we can apply per-head QK-norm and 2D RoPE.
+        attn = self.attention
+        B, N, C = x.shape
+        h = self.heads
+        d = self.head_dim
+        q = attn.query(x).view(B, N, h, d).transpose(1, 2)
+        k = attn.key(x).view(B, N, h, d).transpose(1, 2)
+        v = attn.value(x).view(B, N, h, d).transpose(1, 2)
+        if self.q_norm is not None:
+            q = self.q_norm(q)
+            k = self.k_norm(k)
+        if rope is not None and pos is not None:
+            q = rope(q, pos)
+            k = rope(k, pos)
+        out = optimized_attention(q, k, v, h, mask=mask, skip_reshape=True)
+        return self.output(out)
 
 
 class LayerScale(torch.nn.Module):
@@ -64,9 +98,11 @@ class SwiGLUFFN(torch.nn.Module):
 
 
 class Dino2Block(torch.nn.Module):
-    def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn):
+    def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn,
+                 qk_norm=False):
         super().__init__()
-        self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations)
+        self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations,
+                                             qk_norm=qk_norm)
         self.layer_scale1 = LayerScale(dim, dtype, device, operations)
         self.layer_scale2 = LayerScale(dim, dtype, device, operations)
         if use_swiglu_ffn:
@@ -76,19 +112,90 @@ class Dino2Block(torch.nn.Module):
         self.norm1 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
         self.norm2 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
 
-    def forward(self, x, optimized_attention):
-        x = x + self.layer_scale1(self.attention(self.norm1(x), None, optimized_attention))
+    def forward(self, x, optimized_attention, pos=None, rope=None, attn_mask=None):
+        x = x + self.layer_scale1(self.attention(self.norm1(x), attn_mask, optimized_attention,
+                                                 pos=pos, rope=rope))
         x = x + self.layer_scale2(self.mlp(self.norm2(x)))
         return x
 
 
-class Dino2Encoder(torch.nn.Module):
-    def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn):
+# -----------------------------------------------------------------------------
+# 2D Rotary position embedding (DA3 extension)
+# -----------------------------------------------------------------------------
+
+
+class _PositionGetter:
+    """Cache (h, w) -> flat (y, x) position grid used to feed ``rope``."""
+
+    def __init__(self):
+        self._cache: dict = {}
+
+    def __call__(self, batch_size: int, height: int, width: int, device) -> torch.Tensor:
+        key = (height, width, device)
+        if key not in self._cache:
+            y = torch.arange(height, device=device)
+            x = torch.arange(width, device=device)
+            self._cache[key] = torch.cartesian_prod(y, x)
+        cached = self._cache[key]
+        return cached.view(1, height * width, 2).expand(batch_size, -1, -1).clone()
+
+
+class RotaryPositionEmbedding2D(torch.nn.Module):
+    """2D RoPE used by DA3-Small/Base. No learnable parameters."""
+
+    def __init__(self, frequency: float = 100.0):
         super().__init__()
-        self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)
-                                          for _ in range(num_layers)])
+        self.base_frequency = frequency
+        self._freq_cache: dict = {}
+
+    def _components(self, dim: int, seq_len: int, device, dtype):
+        key = (dim, seq_len, device, dtype)
+        if key not in self._freq_cache:
+            exp = torch.arange(0, dim, 2, device=device).float() / dim
+            inv_freq = 1.0 / (self.base_frequency ** exp)
+            pos = torch.arange(seq_len, device=device, dtype=inv_freq.dtype)
+            ang = torch.einsum("i,j->ij", pos, inv_freq)
+            ang = ang.to(dtype)
+            ang = torch.cat((ang, ang), dim=-1)
+            self._freq_cache[key] = (ang.cos().to(dtype), ang.sin().to(dtype))
+        return self._freq_cache[key]
+
+    @staticmethod
+    def _rotate(x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1]
+        x1, x2 = x[..., : d // 2], x[..., d // 2:]
+        return torch.cat((-x2, x1), dim=-1)
+
+    def _apply_1d(self, tokens, positions, cos_c, sin_c):
+        cos = F.embedding(positions, cos_c)[:, None, :, :]
+        sin = F.embedding(positions, sin_c)[:, None, :, :]
+        return (tokens * cos) + (self._rotate(tokens) * sin)
+
+    def forward(self, tokens: torch.Tensor, positions: torch.Tensor) -> torch.Tensor:
+        feature_dim = tokens.size(-1) // 2
+        max_pos = int(positions.max()) + 1
+        cos_c, sin_c = self._components(feature_dim, max_pos, tokens.device, tokens.dtype)
+        v, h = tokens.chunk(2, dim=-1)
+        v = self._apply_1d(v, positions[..., 0], cos_c, sin_c)
+        h = self._apply_1d(h, positions[..., 1], cos_c, sin_c)
+        return torch.cat((v, h), dim=-1)
+
+
+class Dino2Encoder(torch.nn.Module):
+    def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn,
+                 qknorm_start: int = -1):
+        super().__init__()
+        self.layer = torch.nn.ModuleList([
+            Dino2Block(
+                dim, num_heads, layer_norm_eps, dtype, device, operations,
+                use_swiglu_ffn=use_swiglu_ffn,
+                qk_norm=(qknorm_start != -1 and i >= qknorm_start),
+            )
+            for i in range(num_layers)
+        ])
 
     def forward(self, x, intermediate_output=None):
+        # Backward-compat path used by ``ClipVisionModel`` (no DA3 extensions).
         optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
 
         if intermediate_output is not None:
@@ -122,16 +229,27 @@ class Dino2PatchEmbeddings(torch.nn.Module):
 
 
 class Dino2Embeddings(torch.nn.Module):
-    def __init__(self, dim, dtype, device, operations):
+    def __init__(self, dim, dtype, device, operations,
+                 patch_size: int = 14, image_size: int = 518,
+                 use_mask_token: bool = True,
+                 num_camera_tokens: int = 0):
         super().__init__()
-        patch_size = 14
-        image_size = 518
         self.patch_size = patch_size
+        self.image_size = image_size
 
         self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations)
         self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device))
         self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device)) # mask_token is a pre-training param, kept only so strict loading accepts the key.
-        self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device))
+        if use_mask_token:
+            self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device))
+        else:
+            self.mask_token = None
+        if num_camera_tokens > 0:
+            # DA3 stores (ref_token, src_token) pairs that get injected at the
+            # alt-attn boundary; see ``Dinov2Model._inject_camera_token``.
+            self.camera_token = torch.nn.Parameter(torch.empty(1, num_camera_tokens, dim, dtype=dtype, device=device))
+        else:
+            self.camera_token = None
 
     def interpolate_pos_encoding(self, x, h_pixels, w_pixels):
         pos_embed = comfy.model_management.cast_to_device(self.position_embeddings, x.device, torch.float32)
@@ -140,12 +258,22 @@ class Dino2Embeddings(torch.nn.Module):
         patch_pos = pos_embed[:, 1:]
         N = patch_pos.shape[1]
         M = int(N ** 0.5)
+        assert N == M * M, f"DINOv2 position grid must be square, got N={N} patches (sqrt={M})"
         h0 = h_pixels // self.patch_size
         w0 = w_pixels // self.patch_size
-        scale_factor = ((h0 + 0.1) / M, (w0 + 0.1) / M)  # +0.1 matches upstream DINOv2's FP-rounding workaround so the interpolate output size lands on (h0, w0).
+        # +0.1 matches upstream DINOv2's FP-rounding workaround so the interpolate output size lands on (h0, w0).
+        # scale_factor is (height_scale, width_scale) -- height MUST come first;
+        # swapping these only happens to work for square inputs and breaks
+        # non-square paths like DA3-Small / DA3-Base multi-view.
+        scale_factor = ((h0 + 0.1) / M, (w0 + 0.1) / M)
 
         patch_pos = patch_pos.reshape(1, M, M, -1).permute(0, 3, 1, 2)
         patch_pos = torch.nn.functional.interpolate(patch_pos, scale_factor=scale_factor, mode="bicubic", antialias=False)
+        assert (h0, w0) == patch_pos.shape[-2:], (
+            f"Interpolated pos-embed grid {tuple(patch_pos.shape[-2:])} does not match "
+            f"target patch grid ({h0}, {w0}) for input {h_pixels}x{w_pixels} (patch_size={self.patch_size}); "
+            f"check scale_factor axis order and +0.1 rounding workaround"
+        )
         patch_pos = patch_pos.permute(0, 2, 3, 1).flatten(1, 2)
         return torch.cat((class_pos, patch_pos), dim=1).to(x.dtype)
 
@@ -168,12 +296,51 @@ class Dinov2Model(torch.nn.Module):
         heads = config_dict["num_attention_heads"]
         layer_norm_eps = config_dict["layer_norm_eps"]
         use_swiglu_ffn = config_dict["use_swiglu_ffn"]
+        patch_size = config_dict.get("patch_size", 14)
+        image_size = config_dict.get("image_size", 518)
+        use_mask_token = config_dict.get("use_mask_token", True)
 
-        self.embeddings = Dino2Embeddings(dim, dtype, device, operations)
-        self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn)
+        # DA3 extensions (all default to disabled).
+        self.alt_start = config_dict.get("alt_start", -1)
+        self.qknorm_start = config_dict.get("qknorm_start", -1)
+        self.rope_start = config_dict.get("rope_start", -1)
+        self.cat_token = config_dict.get("cat_token", False)
+        rope_freq = config_dict.get("rope_freq", 100.0)
+
+        self.embed_dim = dim
+        self.patch_size = patch_size
+        self.num_register_tokens = 0
+        self.patch_start_idx = 1
+
+        if self.rope_start != -1 and rope_freq > 0:
+            self.rope = RotaryPositionEmbedding2D(frequency=rope_freq)
+            self._position_getter = _PositionGetter()
+        else:
+            self.rope = None
+            self._position_getter = None
+
+        # camera_token shape: (1, 2, dim) -> (ref_token, src_token).
+        num_cam_tokens = 2 if self.alt_start != -1 else 0
+
+        self.embeddings = Dino2Embeddings(
+            dim, dtype, device, operations,
+            patch_size=patch_size, image_size=image_size,
+            use_mask_token=use_mask_token, num_camera_tokens=num_cam_tokens,
+        )
+        self.encoder = Dino2Encoder(
+            dim, heads, layer_norm_eps, num_layers, dtype, device, operations,
+            use_swiglu_ffn=use_swiglu_ffn,
+            qknorm_start=self.qknorm_start,
+        )
         self.layernorm = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
 
     def forward(self, pixel_values, attention_mask=None, intermediate_output=None):
+        if self.alt_start != -1:
+            raise RuntimeError(
+                "Dinov2Model.forward() is the backward-compatible CLIP-vision path and does not "
+                "apply DA3 extensions (RoPE, alternating attention, camera-token injection). "
+                "Use get_intermediate_layers_da3() for Depth Anything 3 models."
+            )
         x = self.embeddings(pixel_values)
         x, i = self.encoder(x, intermediate_output=intermediate_output)
         x = self.layernorm(x)
@@ -181,6 +348,7 @@ class Dinov2Model(torch.nn.Module):
         return x, i, pooled_output, None
 
     def get_intermediate_layers(self, pixel_values, indices, apply_norm=True):
+        """Single-view multi-layer feature extraction."""
         x = self.embeddings(pixel_values)
         optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
         n_layers = len(self.encoder.layer)
@@ -197,3 +365,132 @@ class Dinov2Model(torch.nn.Module):
             if i >= max_idx:
                 break
         return [cache[i] for i in resolved]
+
+    # ------------------------------------------------------------------
+    # Depth Anything 3 forward
+    # ------------------------------------------------------------------
+    def _prepare_rope_positions(self, B, S, H, W, device):
+        if self.rope is None:
+            return None, None
+        ph, pw = H // self.patch_size, W // self.patch_size
+        pos = self._position_getter(B * S, ph, pw, device=device)
+        # Shift so the cls/cam token at position 0 is reserved for "no diff".
+        pos = pos + 1
+        cls_pos = torch.zeros(B * S, self.patch_start_idx, 2, device=device, dtype=pos.dtype)
+        # Per-view local: real grid positions for patches, 0 for cls token.
+        pos_local = torch.cat([cls_pos, pos], dim=1)
+        # Global (across views): same grid positions; cls token still at 0,
+        # but patches share the same positions in every view.
+        pos_global = torch.cat([cls_pos, torch.zeros_like(pos) + 1], dim=1)
+        return pos_local, pos_global
+
+    def _inject_camera_token(self, x: torch.Tensor, B: int, S: int, cam_token: "torch.Tensor | None") -> torch.Tensor:
+        # x: (B, S, N, C). Replace token at index 0 with the camera token.
+        if cam_token is not None:
+            inj = cam_token
+        else:
+            ct = comfy.model_management.cast_to_device(self.embeddings.camera_token, x.device, x.dtype)
+            ref_token = ct[:, :1].expand(B, -1, -1)
+            src_token = ct[:, 1:].expand(B, max(S - 1, 0), -1)
+            inj = torch.cat([ref_token, src_token], dim=1)
+        x = x.clone()
+        x[:, :, 0] = inj
+        return x
+
+    def get_intermediate_layers_da3(self, pixel_values, out_layers, cam_token=None, ref_view_strategy="saddle_balanced", export_feat_layers=None):
+        """Multi-view multi-layer feature extraction used by Depth Anything 3."""
+        if pixel_values.ndim == 4:
+            pixel_values = pixel_values.unsqueeze(1)
+        assert pixel_values.ndim == 5 and pixel_values.shape[2] == 3, \
+            f"expected (B,3,H,W) or (B,S,3,H,W); got {tuple(pixel_values.shape)}"
+        B, S, _, H, W = pixel_values.shape
+
+        # Patch + cls + (interpolated) pos embed for each view.
+        x = pixel_values.reshape(B * S, 3, H, W)
+        x = self.embeddings(x)                          # (B*S, 1+N, C)
+        x = x.reshape(B, S, x.shape[-2], x.shape[-1])    # (B, S, 1+N, C)
+
+        pos_local, pos_global = self._prepare_rope_positions(B, S, H, W, x.device)
+        # optimized_attention is only used by blocks without QK-norm/RoPE
+        # (vanilla DINOv2 path); enabling-aware blocks fall through to SDPA.
+        optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
+
+        out_set = set(out_layers)
+        export_set = set(export_feat_layers) if export_feat_layers else set()
+        outputs: list[torch.Tensor] = []
+        aux_outputs: list[torch.Tensor] = []
+        local_x = x
+        b_idx = None
+
+
+        for i, blk in enumerate(self.encoder.layer):
+            apply_rope = self.rope is not None and i >= self.rope_start
+            block_rope = self.rope if apply_rope else None
+            l_pos = pos_local if apply_rope else None
+            g_pos = pos_global if apply_rope else None
+
+            # Reference-view selection threshold: matches the upstream constant
+            # THRESH_FOR_REF_SELECTION = 3. Skipped when a user-supplied
+            # cam_token is provided (camera info already pins the geometry).
+            if (self.alt_start != -1 and i == self.alt_start - 1 and S >= THRESH_FOR_REF_SELECTION and cam_token is None):
+                b_idx = select_reference_view(x, strategy=ref_view_strategy)
+                x = reorder_by_reference(x, b_idx)
+                local_x = reorder_by_reference(local_x, b_idx)
+
+            if self.alt_start != -1 and i == self.alt_start:
+                x = self._inject_camera_token(x, B, S, cam_token)
+
+            if self.alt_start != -1 and i >= self.alt_start and (i % 2 == 1):
+                # Global attention across views: flatten S into the seq dim.
+                t = x.reshape(B, S * x.shape[-2], x.shape[-1])
+                p = g_pos.reshape(B, S * g_pos.shape[-2], g_pos.shape[-1]) if g_pos is not None else None
+                t = blk(t, optimized_attention=optimized_attention, pos=p, rope=block_rope)
+                x = t.reshape(B, S, x.shape[-2], x.shape[-1])
+            else:
+                # Per-view local attention.
+                t = x.reshape(B * S, x.shape[-2], x.shape[-1])
+                p = l_pos.reshape(B * S, l_pos.shape[-2], l_pos.shape[-1]) if l_pos is not None else None
+                t = blk(t, optimized_attention=optimized_attention, pos=p, rope=block_rope)
+                x = t.reshape(B, S, x.shape[-2], x.shape[-1])
+                local_x = x
+
+            if i in out_set:
+                if self.cat_token:
+                    out_x = torch.cat([local_x, x], dim=-1)
+                else:
+                    out_x = x
+                # Restore original view order on the way out so heads see views
+                # in the user's expected order.
+                if b_idx is not None and self.alt_start != -1:
+                    out_x = restore_original_order(out_x, b_idx)
+                outputs.append(out_x)
+
+            if i in export_set:
+                aux = x
+                if b_idx is not None and self.alt_start != -1:
+                    aux = restore_original_order(aux, b_idx)
+                aux_outputs.append(aux)
+
+        # Apply final norm. When cat_token is set, only the right half
+        # ("global" features) is normalised; the left half is left as-is to
+        # match the upstream DA3 head signature.
+        normed: list[torch.Tensor] = []
+        cls_tokens: list[torch.Tensor] = []
+        for out_x in outputs:
+            cls_tokens.append(out_x[:, :, 0])
+            if out_x.shape[-1] == self.embed_dim:
+                normed.append(self.layernorm(out_x))
+            elif out_x.shape[-1] == self.embed_dim * 2:
+                left = out_x[..., :self.embed_dim]
+                right = self.layernorm(out_x[..., self.embed_dim:])
+                normed.append(torch.cat([left, right], dim=-1))
+            else:
+                raise ValueError(f"Unexpected token width: {out_x.shape[-1]}")
+
+        # Drop cls/cam token from the patch sequence.
+        normed = [o[..., 1 + self.num_register_tokens:, :] for o in normed]
+
+        # Final layernorm + drop cls token from auxiliary features too.
+        aux_normed = [self.layernorm(o)[..., 1 + self.num_register_tokens:, :]
+                      for o in aux_outputs]
+        return list(zip(normed, cls_tokens)), aux_normed

comfy/ldm/colormap.py

@@ -0,0 +1,25 @@
+"""Colormap utilities for depth and geometry visualisation."""
+
+from __future__ import annotations
+
+import torch
+
+
+def turbo(x: torch.Tensor) -> torch.Tensor:
+    """Anton Mikhailov polynomial approximation of the Turbo colormap.
+
+    Args:
+        x: Float tensor with values in [0, 1].
+
+    Returns:
+        RGB tensor of the same shape as ``x`` with a trailing size-3 dimension.
+    """
+    x = x.clamp(0.0, 1.0)
+    x2 = x * x
+    x3 = x2 * x
+    x4 = x2 * x2
+    x5 = x4 * x
+    r = 0.13572138 + 4.61539260*x - 42.66032258*x2 + 132.13108234*x3 - 152.94239396*x4 + 59.28637943*x5
+    g = 0.09140261 + 2.19418839*x + 4.84296658*x2 - 14.18503333*x3 +   4.27729857*x4 +  2.82956604*x5
+    b = 0.10667330 + 12.64194608*x - 60.58204836*x2 + 110.36276771*x3 - 89.90310912*x4 + 27.34824973*x5
+    return torch.stack([r, g, b], dim=-1).clamp(0.0, 1.0)

comfy/ldm/depth_anything_3/camera.py

@@ -0,0 +1,177 @@
+"""Camera-token encoder and decoder for Depth Anything 3."""
+
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from comfy.ldm.modules.attention import optimized_attention_for_device
+from .transform import affine_inverse, extri_intri_to_pose_encoding
+
+
+# -----------------------------------------------------------------------
+# Building blocks (mirror depth_anything_3.model.utils.{attention,block})
+# -----------------------------------------------------------------------
+
+
+class _Mlp(nn.Module):
+    """Standard 2-layer MLP with GELU. Matches upstream ``utils.attention.Mlp``."""
+
+    def __init__(self, in_features, hidden_features=None, out_features=None, *, device=None, dtype=None, operations=None):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = operations.Linear(in_features, hidden_features, bias=True, device=device, dtype=dtype)
+        self.fc2 = operations.Linear(hidden_features, out_features, bias=True, device=device, dtype=dtype)
+
+    def forward(self, x):
+        return self.fc2(F.gelu(self.fc1(x)))
+
+
+class _LayerScale(nn.Module):
+    """Per-channel learnable scaling. Matches upstream LayerScale."""
+
+    def __init__(self, dim, *, device=None, dtype=None):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
+
+    def forward(self, x):
+        return x * self.gamma.to(dtype=x.dtype, device=x.device)
+
+
+class _Attention(nn.Module):
+    """ Self-attention with fused QKV projection. Mirrors upstream utils.attention.Attention;
+    Layout matches the HF safetensors (attn.qkv.{weight,bias} and attn.proj.{weight,bias})."""
+
+    def __init__(self, dim, num_heads, *, device=None, dtype=None, operations=None):
+        super().__init__()
+        assert dim % num_heads == 0
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.qkv = operations.Linear(dim, dim * 3, bias=True, device=device, dtype=dtype)
+        self.proj = operations.Linear(dim, dim, bias=True, device=device, dtype=dtype)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, C)
+        q, k, v = qkv.unbind(2)                      # each (B, N, C)
+        attn_fn = optimized_attention_for_device(x.device, small_input=True)
+        out = attn_fn(q, k, v, heads=self.num_heads)
+        return self.proj(out)
+
+
+class _Block(nn.Module):
+    """Pre-norm transformer block with LayerScale. Used by :class:CameraEnc. Layout follows upstream utils.block.Block."""
+
+    def __init__(self, dim, num_heads, mlp_ratio=4, init_values=0.01, *, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.norm1 = operations.LayerNorm(dim, device=device, dtype=dtype)
+        self.attn = _Attention(dim, num_heads, device=device, dtype=dtype, operations=operations)
+        self.ls1 = _LayerScale(dim, device=device, dtype=dtype) if init_values else nn.Identity()
+        self.norm2 = operations.LayerNorm(dim, device=device, dtype=dtype)
+        self.mlp = _Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), device=device, dtype=dtype, operations=operations)
+        self.ls2 = _LayerScale(dim, device=device, dtype=dtype) if init_values else nn.Identity()
+
+    def forward(self, x):
+        x = x + self.ls1(self.attn(self.norm1(x)))
+        x = x + self.ls2(self.mlp(self.norm2(x)))
+        return x
+
+
+class CameraEnc(nn.Module):
+    """Encode per-view (extrinsics, intrinsics) into a camera token.
+
+    Maps a 9-D pose-encoding vector through a small MLP up to the backbone's
+    ``embed_dim``, then runs ``trunk_depth`` transformer blocks. The output
+    has shape ``(B, S, embed_dim)`` and is injected at block ``alt_start``
+    of the DINOv2 backbone in place of the cls token.
+
+    Parameters mirror the upstream ``cam_enc.py`` so HF weights load directly.
+    """
+
+    def __init__(
+        self,
+        dim_out: int = 1024,
+        dim_in: int = 9,
+        trunk_depth: int = 4,
+        target_dim: int = 9,
+        num_heads: int = 16,
+        mlp_ratio: int = 4,
+        init_values: float = 0.01,
+        *,
+        device=None, dtype=None, operations=None,
+        **_kwargs,
+    ):
+        super().__init__()
+        self.target_dim = target_dim
+        self.trunk_depth = trunk_depth
+        self.trunk = nn.Sequential(*[
+            _Block(dim_out, num_heads=num_heads, mlp_ratio=mlp_ratio,
+                   init_values=init_values,
+                   device=device, dtype=dtype, operations=operations)
+            for _ in range(trunk_depth)
+        ])
+        self.token_norm = operations.LayerNorm(dim_out, device=device, dtype=dtype)
+        self.trunk_norm = operations.LayerNorm(dim_out, device=device, dtype=dtype)
+        self.pose_branch = _Mlp(
+            in_features=dim_in,
+            hidden_features=dim_out // 2,
+            out_features=dim_out,
+            device=device, dtype=dtype, operations=operations,
+        )
+
+    def forward(self, extrinsics: torch.Tensor, intrinsics: torch.Tensor,
+                image_size_hw) -> torch.Tensor:
+        """Encode camera parameters into ``(B, S, dim_out)`` tokens."""
+        c2ws = affine_inverse(extrinsics)
+        pose_encoding = extri_intri_to_pose_encoding(c2ws, intrinsics, image_size_hw)
+        tokens = self.pose_branch(pose_encoding.to(self.pose_branch.fc1.weight.dtype))
+        tokens = self.token_norm(tokens)
+        tokens = self.trunk(tokens)
+        tokens = self.trunk_norm(tokens)
+        return tokens
+
+
+class CameraDec(nn.Module):
+    """Decode the final cam token into a 9-D pose encoding.
+
+    Output layout: ``[T(3), quat_xyzw(4), fov_h, fov_w]``. The translation is
+    always predicted by the network; the quaternion and FoV can either be
+    predicted or supplied via ``camera_encoding`` (used at training time
+    when GT cameras are available -- not exercised at inference here).
+
+    Parameters mirror the upstream ``cam_dec.py`` so HF weights load directly.
+    """
+
+    def __init__(self, dim_in: int = 1536,
+                 *, device=None, dtype=None, operations=None, **_kwargs):
+        super().__init__()
+        d = dim_in
+        self.backbone = nn.Sequential(
+            operations.Linear(d, d, device=device, dtype=dtype),
+            nn.ReLU(),
+            operations.Linear(d, d, device=device, dtype=dtype),
+            nn.ReLU(),
+        )
+        self.fc_t = operations.Linear(d, 3, device=device, dtype=dtype)
+        self.fc_qvec = operations.Linear(d, 4, device=device, dtype=dtype)
+        self.fc_fov = nn.Sequential(
+            operations.Linear(d, 2, device=device, dtype=dtype),
+            nn.ReLU(),
+        )
+
+    def forward(self, feat: torch.Tensor,
+                camera_encoding: "torch.Tensor | None" = None) -> torch.Tensor:
+        """Decode ``(B, N, dim_in)`` cam tokens into ``(B, N, 9)`` pose enc."""
+        B, N = feat.shape[:2]
+        feat = feat.reshape(B * N, -1)
+        feat = self.backbone(feat)
+        out_t = self.fc_t(feat.float()).reshape(B, N, 3)
+        if camera_encoding is None:
+            out_qvec = self.fc_qvec(feat.float()).reshape(B, N, 4)
+            out_fov = self.fc_fov(feat.float()).reshape(B, N, 2)
+        else:
+            out_qvec = camera_encoding[..., 3:7]
+            out_fov = camera_encoding[..., -2:]
+        return torch.cat([out_t, out_qvec, out_fov], dim=-1)

comfy/ldm/depth_anything_3/dpt.py

@@ -0,0 +1,489 @@
+"""DPT / DualDPT heads for Depth Anything 3."""
+
+from __future__ import annotations
+
+from typing import List, Optional, Sequence, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Permute(nn.Module):
+    def __init__(self, dims: Tuple[int, ...]):
+        super().__init__()
+        self.dims = dims
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x.permute(*self.dims)
+
+
+def _custom_interpolate(
+    x: torch.Tensor,
+    size: Optional[Tuple[int, int]] = None,
+    scale_factor: Optional[float] = None,
+    mode: str = "bilinear",
+    align_corners: bool = True,
+) -> torch.Tensor:
+    if size is None:
+        assert scale_factor is not None
+        size = (int(x.shape[-2] * scale_factor), int(x.shape[-1] * scale_factor))
+    INT_MAX = 1610612736
+    total = size[0] * size[1] * x.shape[0] * x.shape[1]
+    if total > INT_MAX:
+        chunks = torch.chunk(x, chunks=(total // INT_MAX) + 1, dim=0)
+        outs = [F.interpolate(c, size=size, mode=mode, align_corners=align_corners) for c in chunks]
+        return torch.cat(outs, dim=0).contiguous()
+    return F.interpolate(x, size=size, mode=mode, align_corners=align_corners)
+
+
+def _create_uv_grid(width: int, height: int, aspect_ratio: float, dtype, device) -> torch.Tensor:
+    """Normalised UV grid spanning (-x_span, -y_span)..(x_span, y_span)."""
+    diag_factor = (aspect_ratio ** 2 + 1.0) ** 0.5
+    span_x = aspect_ratio / diag_factor
+    span_y = 1.0 / diag_factor
+    left_x = -span_x * (width - 1) / width
+    right_x = span_x * (width - 1) / width
+    top_y = -span_y * (height - 1) / height
+    bottom_y = span_y * (height - 1) / height
+    x_coords = torch.linspace(left_x, right_x, steps=width, dtype=dtype, device=device)
+    y_coords = torch.linspace(top_y, bottom_y, steps=height, dtype=dtype, device=device)
+    uu, vv = torch.meshgrid(x_coords, y_coords, indexing="xy")
+    return torch.stack((uu, vv), dim=-1)  # (H, W, 2)
+
+
+def _make_sincos_pos_embed(embed_dim: int, pos: torch.Tensor, omega_0: float = 100.0) -> torch.Tensor:
+    omega = torch.arange(embed_dim // 2, dtype=torch.float32, device=pos.device)
+    omega = 1.0 / omega_0 ** (omega / (embed_dim / 2.0))
+    pos = pos.reshape(-1)
+    out = torch.einsum("m,d->md", pos, omega)
+    return torch.cat([out.sin(), out.cos()], dim=1).float()
+
+
+def _position_grid_to_embed(pos_grid: torch.Tensor, embed_dim: int, omega_0: float = 100.0) -> torch.Tensor:
+    H, W, _ = pos_grid.shape
+    pos_flat = pos_grid.reshape(-1, 2)
+    emb_x = _make_sincos_pos_embed(embed_dim // 2, pos_flat[:, 0], omega_0=omega_0)
+    emb_y = _make_sincos_pos_embed(embed_dim // 2, pos_flat[:, 1], omega_0=omega_0)
+    emb = torch.cat([emb_x, emb_y], dim=-1)
+    return emb.view(H, W, embed_dim)
+
+
+def _add_pos_embed(x: torch.Tensor, W: int, H: int, ratio: float = 0.1) -> torch.Tensor:
+    """Stateless UV positional embedding added to a feature map (B, C, h, w)."""
+    pw, ph = x.shape[-1], x.shape[-2]
+    pe = _create_uv_grid(pw, ph, aspect_ratio=W / H, dtype=x.dtype, device=x.device)
+    pe = _position_grid_to_embed(pe, x.shape[1]) * ratio
+    pe = pe.permute(2, 0, 1)[None].expand(x.shape[0], -1, -1, -1).to(dtype=x.dtype)
+    return x + pe
+
+
+def _apply_activation(x: torch.Tensor, activation: str) -> torch.Tensor:
+    act = (activation or "linear").lower()
+    if act == "exp":
+        return torch.exp(x)
+    if act == "expp1":
+        return torch.exp(x) + 1
+    if act == "expm1":
+        return torch.expm1(x)
+    if act == "relu":
+        return torch.relu(x)
+    if act == "sigmoid":
+        return torch.sigmoid(x)
+    if act == "softplus":
+        return F.softplus(x)
+    if act == "tanh":
+        return torch.tanh(x)
+    return x
+
+
+# -----------------------------------------------------------------------------
+# Fusion building blocks
+# -----------------------------------------------------------------------------
+
+
+class ResidualConvUnit(nn.Module):
+    def __init__(self, features: int, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.conv1 = operations.Conv2d(features, features, 3, 1, 1, bias=True, device=device, dtype=dtype)
+        self.conv2 = operations.Conv2d(features, features, 3, 1, 1, bias=True, device=device, dtype=dtype)
+        self.activation = nn.ReLU(inplace=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        out = self.activation(x)
+        out = self.conv1(out)
+        out = self.activation(out)
+        out = self.conv2(out)
+        return out + x
+
+
+class FeatureFusionBlock(nn.Module):
+    def __init__(self, features: int, has_residual: bool = True, align_corners: bool = True, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.align_corners = align_corners
+        self.has_residual = has_residual
+        if has_residual:
+            self.resConfUnit1 = ResidualConvUnit(features, device=device, dtype=dtype, operations=operations)
+        else:
+            self.resConfUnit1 = None
+        self.resConfUnit2 = ResidualConvUnit(features, device=device, dtype=dtype, operations=operations)
+        self.out_conv = operations.Conv2d(features, features, 1, 1, 0, bias=True, device=device, dtype=dtype)
+
+    def forward(self, *xs: torch.Tensor, size: Optional[Tuple[int, int]] = None) -> torch.Tensor:
+        y = xs[0]
+        if self.has_residual and len(xs) > 1 and self.resConfUnit1 is not None:
+            y = y + self.resConfUnit1(xs[1])
+        y = self.resConfUnit2(y)
+        if size is None:
+            up_kwargs = {"scale_factor": 2.0}
+        else:
+            up_kwargs = {"size": size}
+        y = _custom_interpolate(y, **up_kwargs, mode="bilinear", align_corners=self.align_corners)
+        y = self.out_conv(y)
+        return y
+
+
+class _Scratch(nn.Module):
+    """Container that mirrors upstream ``scratch`` attribute layout."""
+
+
+def _make_scratch(in_shape: List[int], out_shape: int, device=None, dtype=None, operations=None) -> _Scratch:
+    scratch = _Scratch()
+    scratch.layer1_rn = operations.Conv2d(in_shape[0], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype)
+    scratch.layer2_rn = operations.Conv2d(in_shape[1], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype)
+    scratch.layer3_rn = operations.Conv2d(in_shape[2], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype)
+    scratch.layer4_rn = operations.Conv2d(in_shape[3], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype)
+    return scratch
+
+
+def _make_fusion_block(features: int, has_residual: bool = True, device=None, dtype=None, operations=None) -> FeatureFusionBlock:
+    return FeatureFusionBlock(features, has_residual=has_residual, align_corners=True, device=device, dtype=dtype, operations=operations)
+
+
+# -----------------------------------------------------------------------------
+# DPT (single head + optional sky head) -- used by DA3Mono/Metric
+# -----------------------------------------------------------------------------
+
+
+class DPT(nn.Module):
+    """Single-head DPT used by DA3Mono-Large and DA3Metric-Large."""
+
+    def __init__(
+        self,
+        dim_in: int,
+        patch_size: int = 14,
+        output_dim: int = 1,
+        activation: str = "exp",
+        conf_activation: str = "expp1",
+        features: int = 256,
+        out_channels: Sequence[int] = (256, 512, 1024, 1024),
+        pos_embed: bool = False,
+        down_ratio: int = 1,
+        head_name: str = "depth",
+        use_sky_head: bool = True,
+        sky_name: str = "sky",
+        sky_activation: str = "relu",
+        norm_type: str = "idt",
+        device=None, dtype=None, operations=None,
+    ):
+        super().__init__()
+        self.patch_size = patch_size
+        self.activation = activation
+        self.conf_activation = conf_activation
+        self.pos_embed = pos_embed
+        self.down_ratio = down_ratio
+        self.head_main = head_name
+        self.sky_name = sky_name
+        self.out_dim = output_dim
+        self.has_conf = output_dim > 1
+        self.use_sky_head = use_sky_head
+        self.sky_activation = sky_activation
+        self.intermediate_layer_idx: Tuple[int, int, int, int] = (0, 1, 2, 3)
+
+        if norm_type == "layer":
+            self.norm = operations.LayerNorm(dim_in, device=device, dtype=dtype)
+        else:
+            self.norm = nn.Identity()
+
+        out_channels = list(out_channels)
+        self.projects = nn.ModuleList([
+            operations.Conv2d(dim_in, oc, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype)
+            for oc in out_channels
+        ])
+        self.resize_layers = nn.ModuleList([
+            operations.ConvTranspose2d(out_channels[0], out_channels[0], kernel_size=4, stride=4, padding=0, device=device, dtype=dtype),
+            operations.ConvTranspose2d(out_channels[1], out_channels[1], kernel_size=2, stride=2, padding=0, device=device, dtype=dtype),
+            nn.Identity(),
+            operations.Conv2d(out_channels[3], out_channels[3], kernel_size=3, stride=2, padding=1, device=device, dtype=dtype),
+        ])
+
+        self.scratch = _make_scratch(out_channels, features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet1 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet2 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet3 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet4 = _make_fusion_block(features, has_residual=False, device=device, dtype=dtype, operations=operations)
+
+        head_features_1 = features
+        head_features_2 = 32
+        self.scratch.output_conv1 = operations.Conv2d(
+            head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1,
+            device=device, dtype=dtype,
+        )
+        self.scratch.output_conv2 = nn.Sequential(
+            operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype),
+            nn.ReLU(inplace=False),
+            operations.Conv2d(head_features_2, output_dim, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype),
+        )
+
+        if self.use_sky_head:
+            self.scratch.sky_output_conv2 = nn.Sequential(
+                operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype),
+                nn.ReLU(inplace=False),
+                operations.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype),
+            )
+
+    def forward(self, feats: List[torch.Tensor], H: int, W: int, patch_start_idx: int = 0, **_kwargs) -> dict:
+        # feats[i][0] is the patch-token tensor with shape (B, S, N_patch, C)
+        B, S, N, C = feats[0][0].shape
+        feats_flat = [feat[0].reshape(B * S, N, C) for feat in feats]
+
+        ph, pw = H // self.patch_size, W // self.patch_size
+        resized = []
+        for stage_idx, take_idx in enumerate(self.intermediate_layer_idx):
+            x = feats_flat[take_idx][:, patch_start_idx:]
+            x = self.norm(x)
+            x = x.permute(0, 2, 1).contiguous().reshape(B * S, C, ph, pw)
+            x = self.projects[stage_idx](x)
+            if self.pos_embed:
+                x = _add_pos_embed(x, W, H)
+            x = self.resize_layers[stage_idx](x)
+            resized.append(x)
+
+        l1_rn = self.scratch.layer1_rn(resized[0])
+        l2_rn = self.scratch.layer2_rn(resized[1])
+        l3_rn = self.scratch.layer3_rn(resized[2])
+        l4_rn = self.scratch.layer4_rn(resized[3])
+
+        out = self.scratch.refinenet4(l4_rn, size=l3_rn.shape[2:])
+        out = self.scratch.refinenet3(out, l3_rn, size=l2_rn.shape[2:])
+        out = self.scratch.refinenet2(out, l2_rn, size=l1_rn.shape[2:])
+        out = self.scratch.refinenet1(out, l1_rn)
+
+        h_out = int(ph * self.patch_size / self.down_ratio)
+        w_out = int(pw * self.patch_size / self.down_ratio)
+
+        fused = self.scratch.output_conv1(out)
+        fused = _custom_interpolate(fused, (h_out, w_out), mode="bilinear", align_corners=True)
+        if self.pos_embed:
+            fused = _add_pos_embed(fused, W, H)
+        feat = fused
+
+        main_logits = self.scratch.output_conv2(feat)
+        outs = {}
+        if self.has_conf:
+            fmap = main_logits.permute(0, 2, 3, 1)
+            pred = _apply_activation(fmap[..., :-1], self.activation)
+            conf = _apply_activation(fmap[..., -1], self.conf_activation)
+            outs[self.head_main] = pred.squeeze(-1).view(B, S, *pred.shape[1:-1])
+            outs[f"{self.head_main}_conf"] = conf.view(B, S, *conf.shape[1:])
+        else:
+            pred = _apply_activation(main_logits, self.activation)
+            outs[self.head_main] = pred.squeeze(1).view(B, S, *pred.shape[2:])
+
+        if self.use_sky_head:
+            sky_logits = self.scratch.sky_output_conv2(feat)
+            if self.sky_activation.lower() == "sigmoid":
+                sky = torch.sigmoid(sky_logits)
+            elif self.sky_activation.lower() == "relu":
+                sky = F.relu(sky_logits)
+            else:
+                sky = sky_logits
+            outs[self.sky_name] = sky.squeeze(1).view(B, S, *sky.shape[2:])
+
+        return outs
+
+
+# -----------------------------------------------------------------------------
+# DualDPT (depth + auxiliary "ray" head) -- used by DA3-Small / DA3-Base
+# -----------------------------------------------------------------------------
+
+
+class DualDPT(nn.Module):
+    """Two-head DPT used by DA3-Small / DA3-Base."""
+
+    def __init__(
+        self,
+        dim_in: int,
+        patch_size: int = 14,
+        output_dim: int = 2,
+        activation: str = "exp",
+        conf_activation: str = "expp1",
+        features: int = 256,
+        out_channels: Sequence[int] = (256, 512, 1024, 1024),
+        pos_embed: bool = True,
+        down_ratio: int = 1,
+        aux_pyramid_levels: int = 4,
+        aux_out1_conv_num: int = 5,
+        head_names: Tuple[str, str] = ("depth", "ray"),
+        device=None, dtype=None, operations=None,
+    ):
+        super().__init__()
+        self.patch_size = patch_size
+        self.activation = activation
+        self.conf_activation = conf_activation
+        self.pos_embed = pos_embed
+        self.down_ratio = down_ratio
+        self.aux_levels = aux_pyramid_levels
+        self.aux_out1_conv_num = aux_out1_conv_num
+        self.head_main, self.head_aux = head_names
+        self.intermediate_layer_idx: Tuple[int, int, int, int] = (0, 1, 2, 3)
+        # Toggle the auxiliary ray branch at runtime. Default off (mono path).
+        # DepthAnything3Net flips this on when running multi-view + ray-pose.
+        self.enable_aux: bool = False
+
+        self.norm = operations.LayerNorm(dim_in, device=device, dtype=dtype)
+        out_channels = list(out_channels)
+        self.projects = nn.ModuleList([
+            operations.Conv2d(dim_in, oc, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype)
+            for oc in out_channels
+        ])
+        self.resize_layers = nn.ModuleList([
+            operations.ConvTranspose2d(out_channels[0], out_channels[0], kernel_size=4, stride=4, padding=0, device=device, dtype=dtype),
+            operations.ConvTranspose2d(out_channels[1], out_channels[1], kernel_size=2, stride=2, padding=0, device=device, dtype=dtype),
+            nn.Identity(),
+            operations.Conv2d(out_channels[3], out_channels[3], kernel_size=3, stride=2, padding=1, device=device, dtype=dtype),
+        ])
+
+        self.scratch = _make_scratch(out_channels, features, device=device, dtype=dtype, operations=operations)
+        # Main fusion chain
+        self.scratch.refinenet1 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet2 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet3 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet4 = _make_fusion_block(features, has_residual=False, device=device, dtype=dtype, operations=operations)
+        # Auxiliary fusion chain (separate copies)
+        self.scratch.refinenet1_aux = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet2_aux = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet3_aux = _make_fusion_block(features, device=device, dtype=dtype, operations=operations)
+        self.scratch.refinenet4_aux = _make_fusion_block(features, has_residual=False, device=device, dtype=dtype, operations=operations)
+
+        head_features_1 = features
+        head_features_2 = 32
+
+        # Main head neck + final projection
+        self.scratch.output_conv1 = operations.Conv2d(
+            head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1,
+            device=device, dtype=dtype,
+        )
+        self.scratch.output_conv2 = nn.Sequential(
+            operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype),
+            nn.ReLU(inplace=False),
+            operations.Conv2d(head_features_2, output_dim, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype),
+        )
+
+        # Aux pre-head per level (multi-level pyramid)
+        self.scratch.output_conv1_aux = nn.ModuleList([
+            self._make_aux_out1_block(head_features_1, device=device, dtype=dtype, operations=operations)
+            for _ in range(self.aux_levels)
+        ])
+
+        # Aux final projection per level (includes LayerNorm permute path).
+        ln_seq = [Permute((0, 2, 3, 1)),
+                  operations.LayerNorm(head_features_2, device=device, dtype=dtype),
+                  Permute((0, 3, 1, 2))]
+        self.scratch.output_conv2_aux = nn.ModuleList([
+            nn.Sequential(
+                operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype),
+                *ln_seq,
+                nn.ReLU(inplace=False),
+                operations.Conv2d(head_features_2, 7, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype),
+            )
+            for _ in range(self.aux_levels)
+        ])
+
+    @staticmethod
+    def _make_aux_out1_block(in_ch: int, *, device=None, dtype=None, operations=None) -> nn.Sequential:
+        # aux_out1_conv_num=5 in all Apache-2.0 variants.
+        return nn.Sequential(
+            operations.Conv2d(in_ch, in_ch // 2, 3, 1, 1, device=device, dtype=dtype),
+            operations.Conv2d(in_ch // 2, in_ch, 3, 1, 1, device=device, dtype=dtype),
+            operations.Conv2d(in_ch, in_ch // 2, 3, 1, 1, device=device, dtype=dtype),
+            operations.Conv2d(in_ch // 2, in_ch, 3, 1, 1, device=device, dtype=dtype),
+            operations.Conv2d(in_ch, in_ch // 2, 3, 1, 1, device=device, dtype=dtype),
+        )
+
+    def forward(self, feats: List[torch.Tensor], H: int, W: int, patch_start_idx: int = 0, **_kwargs) -> dict:
+        B, S, N, C = feats[0][0].shape
+        feats_flat = [feat[0].reshape(B * S, N, C) for feat in feats]
+
+        ph, pw = H // self.patch_size, W // self.patch_size
+        resized = []
+        for stage_idx, take_idx in enumerate(self.intermediate_layer_idx):
+            x = feats_flat[take_idx][:, patch_start_idx:]
+            x = self.norm(x)
+            x = x.permute(0, 2, 1).contiguous().reshape(B * S, C, ph, pw)
+            x = self.projects[stage_idx](x)
+            if self.pos_embed:
+                x = _add_pos_embed(x, W, H)
+            x = self.resize_layers[stage_idx](x)
+            resized.append(x)
+
+        l1_rn = self.scratch.layer1_rn(resized[0])
+        l2_rn = self.scratch.layer2_rn(resized[1])
+        l3_rn = self.scratch.layer3_rn(resized[2])
+        l4_rn = self.scratch.layer4_rn(resized[3])
+
+        # Main pyramid (output_conv1 is applied inside the upstream `_fuse`,
+        # before interpolation -- replicate that order here).
+        m = self.scratch.refinenet4(l4_rn, size=l3_rn.shape[2:])
+        if self.enable_aux:
+            a4 = self.scratch.refinenet4_aux(l4_rn, size=l3_rn.shape[2:])
+            aux_pyr = [a4]
+        m = self.scratch.refinenet3(m, l3_rn, size=l2_rn.shape[2:])
+        if self.enable_aux:
+            aux_pyr.append(self.scratch.refinenet3_aux(aux_pyr[-1], l3_rn, size=l2_rn.shape[2:]))
+        m = self.scratch.refinenet2(m, l2_rn, size=l1_rn.shape[2:])
+        if self.enable_aux:
+            aux_pyr.append(self.scratch.refinenet2_aux(aux_pyr[-1], l2_rn, size=l1_rn.shape[2:]))
+        m = self.scratch.refinenet1(m, l1_rn)
+        if self.enable_aux:
+            aux_pyr.append(self.scratch.refinenet1_aux(aux_pyr[-1], l1_rn))
+        m = self.scratch.output_conv1(m)
+
+        h_out = int(ph * self.patch_size / self.down_ratio)
+        w_out = int(pw * self.patch_size / self.down_ratio)
+
+        m = _custom_interpolate(m, (h_out, w_out), mode="bilinear", align_corners=True)
+        if self.pos_embed:
+            m = _add_pos_embed(m, W, H)
+        main_logits = self.scratch.output_conv2(m)
+        fmap = main_logits.permute(0, 2, 3, 1)
+        depth_pred = _apply_activation(fmap[..., :-1], self.activation)
+        depth_conf = _apply_activation(fmap[..., -1], self.conf_activation)
+
+        outs = {
+            self.head_main: depth_pred.squeeze(-1).view(B, S, *depth_pred.shape[1:-1]),
+            f"{self.head_main}_conf": depth_conf.view(B, S, *depth_conf.shape[1:]),
+        }
+
+        if self.enable_aux:
+            # Auxiliary "ray" head (multi-level inside) -- only the last level
+            # is returned. Mirrors upstream ``DualDPT._fuse`` + ``_forward_impl``:
+            # each aux pyramid level goes through ``output_conv1_aux[i]``
+            # (5-layer conv stack that ends at ``features // 2`` channels),
+            # then the last level optionally gets a pos-embed and finally
+            # ``output_conv2_aux[-1]``.
+            aux_processed = [
+                self.scratch.output_conv1_aux[i](a) for i, a in enumerate(aux_pyr)
+            ]
+            last_aux = aux_processed[-1]
+            if self.pos_embed:
+                last_aux = _add_pos_embed(last_aux, W, H)
+            last_aux_logits = self.scratch.output_conv2_aux[-1](last_aux)
+            fmap_last = last_aux_logits.permute(0, 2, 3, 1)
+            # Channels: [ray(6), ray_conf(1)]; ray uses 'linear' activation.
+            aux_pred = fmap_last[..., :-1]
+            aux_conf = _apply_activation(fmap_last[..., -1], self.conf_activation)
+            outs[self.head_aux] = aux_pred.view(B, S, *aux_pred.shape[1:])
+            outs[f"{self.head_aux}_conf"] = aux_conf.view(B, S, *aux_conf.shape[1:])
+
+        return outs

comfy/ldm/depth_anything_3/model.py

@@ -0,0 +1,236 @@
+from __future__ import annotations
+
+from typing import Dict, Optional, Sequence
+
+import torch
+import torch.nn as nn
+
+from comfy.image_encoders.dino2 import Dinov2Model
+
+from .camera import CameraDec, CameraEnc
+from .dpt import DPT, DualDPT
+from .ray_pose import get_extrinsic_from_camray
+from .transform import affine_inverse, pose_encoding_to_extri_intri
+
+
+_HEAD_REGISTRY = {
+    "dpt": DPT,
+    "dualdpt": DualDPT,
+}
+
+
+# Backbone presets (mirror the upstream DINOv2 ViT variants).
+_BACKBONE_PRESETS = {
+    "vits": dict(hidden_size=384,  num_hidden_layers=12, num_attention_heads=6,  use_swiglu_ffn=False),
+    "vitb": dict(hidden_size=768,  num_hidden_layers=12, num_attention_heads=12, use_swiglu_ffn=False),
+    "vitl": dict(hidden_size=1024, num_hidden_layers=24, num_attention_heads=16, use_swiglu_ffn=False),
+    "vitg": dict(hidden_size=1536, num_hidden_layers=40, num_attention_heads=24, use_swiglu_ffn=True),
+}
+
+
+def _build_backbone_config(
+    backbone_name: str,
+    *,
+    alt_start: int,
+    qknorm_start: int,
+    rope_start: int,
+    cat_token: bool,
+) -> dict:
+    if backbone_name not in _BACKBONE_PRESETS:
+        raise ValueError(f"Unknown DINOv2 backbone variant: {backbone_name!r}")
+    cfg = dict(_BACKBONE_PRESETS[backbone_name])
+    cfg.update(dict(
+        layer_norm_eps=1e-6,
+        patch_size=14,
+        image_size=518,
+        # No mask_token in DA3 weights; omit param to avoid load warnings.
+        use_mask_token=False,
+        alt_start=alt_start,
+        qknorm_start=qknorm_start,
+        rope_start=rope_start,
+        cat_token=cat_token,
+        rope_freq=100.0,
+    ))
+    return cfg
+
+
+class DepthAnything3Net(nn.Module):
+
+    PATCH_SIZE = 14
+
+    def __init__(
+        self,
+        # --- Backbone ---
+        backbone_name: str = "vitl",
+        out_layers: Sequence[int] = (4, 11, 17, 23),
+        alt_start: int = -1,
+        qknorm_start: int = -1,
+        rope_start: int = -1,
+        cat_token: bool = False,
+        # --- Head ---
+        head_type: str = "dpt",  # dpt or dualdpt
+        head_dim_in: int = 1024,
+        head_output_dim: int = 1,  # 1 = depth only, 2 = depth+conf
+        head_features: int = 256,
+        head_out_channels: Sequence[int] = (256, 512, 1024, 1024),
+        head_use_sky_head: bool = True, # ignored by DualDPT
+        head_pos_embed: Optional[bool] = None,  # default: True for DualDPT, False for DPT
+        # --- Camera (multi-view) ---
+        has_cam_enc: bool = False,
+        has_cam_dec: bool = False,
+        cam_dim_out: Optional[int] = None,  # CameraEnc dim_out (defaults to embed_dim)
+        cam_dec_dim_in: Optional[int] = None,  # CameraDec dim_in  (defaults to 2*embed_dim with cat_token)
+        # ComfyUI plumbing
+        device=None, dtype=None, operations=None,
+        **_ignored,
+    ):
+        super().__init__()
+        head_cls = _HEAD_REGISTRY[head_type.lower()]
+        self.head_type = head_type.lower()
+        self.has_sky = (self.head_type == "dpt") and head_use_sky_head
+        self.has_conf = head_output_dim > 1
+        self.out_layers = list(out_layers)
+
+        backbone_cfg = _build_backbone_config(
+            backbone_name,
+            alt_start=alt_start,
+            qknorm_start=qknorm_start,
+            rope_start=rope_start,
+            cat_token=cat_token,
+        )
+        self.backbone = Dinov2Model(backbone_cfg, dtype, device, operations)
+
+        head_kwargs = dict(
+            dim_in=head_dim_in,
+            patch_size=self.PATCH_SIZE,
+            output_dim=head_output_dim,
+            features=head_features,
+            out_channels=tuple(head_out_channels),
+            device=device, dtype=dtype, operations=operations,
+        )
+        if self.head_type == "dpt":
+            head_kwargs.update(
+                use_sky_head=head_use_sky_head,
+                pos_embed=(False if head_pos_embed is None else head_pos_embed),
+            )
+        else:  # dualdpt
+            head_kwargs.update(
+                pos_embed=(True if head_pos_embed is None else head_pos_embed),
+            )
+        self.head = head_cls(**head_kwargs)
+
+        # Built only if checkpoint has weights; cam_enc output dim == embed_dim.
+        embed_dim = backbone_cfg["hidden_size"]
+        if has_cam_enc:
+            self.cam_enc = CameraEnc(
+                dim_out=cam_dim_out if cam_dim_out is not None else embed_dim,
+                num_heads=max(1, embed_dim // 64),
+                device=device, dtype=dtype, operations=operations,
+            )
+        else:
+            self.cam_enc = None
+        if has_cam_dec:
+            default_dim = embed_dim * (2 if cat_token else 1)
+            self.cam_dec = CameraDec(
+                dim_in=cam_dec_dim_in if cam_dec_dim_in is not None else default_dim,
+                device=device, dtype=dtype, operations=operations,
+            )
+        else:
+            self.cam_dec = None
+
+        self.dtype = dtype
+
+    def forward(
+        self,
+        image: torch.Tensor,
+        extrinsics: Optional[torch.Tensor] = None,
+        intrinsics: Optional[torch.Tensor] = None,
+        *,
+        use_ray_pose: bool = False,
+        ref_view_strategy: str = "saddle_balanced",
+        export_feat_layers: Optional[Sequence[int]] = None,
+        **_unused,
+    ) -> Dict[str, torch.Tensor]:
+        """Run depth and optionally pose prediction."""
+        if image.ndim == 4:
+            image = image.unsqueeze(1)  # (B, 1, 3, H, W)
+        assert image.ndim == 5 and image.shape[2] == 3, \
+            f"image must be (B,3,H,W) or (B,S,3,H,W); got {tuple(image.shape)}"
+
+        B, S, _, H, W = image.shape
+        assert H % self.PATCH_SIZE == 0 and W % self.PATCH_SIZE == 0, \
+            f"image H,W must be multiples of {self.PATCH_SIZE}; got {(H, W)}"
+
+        # Camera-token preparation (multi-view path).
+        cam_token = None
+        if extrinsics is not None and intrinsics is not None and self.cam_enc is not None:
+            cam_token = self.cam_enc(extrinsics, intrinsics, (H, W))
+
+        # Toggle aux ray output on/off depending on what the caller asked for.
+        if isinstance(self.head, DualDPT):
+            self.head.enable_aux = bool(use_ray_pose)
+
+        feats, aux_feats = self.backbone.get_intermediate_layers_da3(
+            image, self.out_layers, cam_token=cam_token,
+            ref_view_strategy=ref_view_strategy,
+            export_feat_layers=export_feat_layers,
+        )
+        head_out = self.head(feats, H=H, W=W, patch_start_idx=0)
+
+        # Pose prediction.
+        out: Dict[str, torch.Tensor] = {}
+        if use_ray_pose and "ray" in head_out and "ray_conf" in head_out:
+            ray = head_out["ray"]
+            ray_conf = head_out["ray_conf"]
+            extr_c2w, focal, pp = get_extrinsic_from_camray(
+                ray, ray_conf, ray.shape[-3], ray.shape[-2],
+            )
+            # Match the upstream output: w2c, drop the homogeneous row.
+            extr_w2c = affine_inverse(extr_c2w)[:, :, :3, :]
+            # Build pixel-space intrinsics from the normalised focal/pp output.
+            intr = torch.eye(3, device=ray.device, dtype=ray.dtype)
+            intr = intr[None, None].expand(extr_c2w.shape[0], extr_c2w.shape[1], 3, 3).clone()
+            intr[:, :, 0, 0] = focal[:, :, 0] / 2 * W
+            intr[:, :, 1, 1] = focal[:, :, 1] / 2 * H
+            intr[:, :, 0, 2] = pp[:, :, 0] * W * 0.5
+            intr[:, :, 1, 2] = pp[:, :, 1] * H * 0.5
+            out["extrinsics"] = extr_w2c
+            out["intrinsics"] = intr
+        elif self.cam_dec is not None and S > 1:
+            # Decode the cam-token of the final out_layer into a pose encoding.
+            cam_feat = feats[-1][1]  # (B, S, dim_in_to_cam_dec)
+            pose_enc = self.cam_dec(cam_feat)
+            c2w_3x4, intr = pose_encoding_to_extri_intri(pose_enc, (H, W))
+            # Match the upstream output convention: w2c (world->camera), 3x4.
+            c2w_4x4 = torch.cat([
+                c2w_3x4,
+                torch.tensor([0, 0, 0, 1], device=c2w_3x4.device, dtype=c2w_3x4.dtype)
+                    .view(1, 1, 1, 4).expand(B, S, 1, 4),
+            ], dim=-2)
+            out["extrinsics"] = affine_inverse(c2w_4x4)[:, :, :3, :]
+            out["intrinsics"] = intr
+
+        # Flatten the views axis for per-pixel outputs (depth/conf/sky) so the
+        # per-image consumer keeps its (B*S, H, W) interface.
+        for k, v in head_out.items():
+            if k in ("ray", "ray_conf"):
+                # Keep multi-view shape for downstream pose work.
+                out[k] = v
+            elif v.ndim >= 3 and v.shape[0] == B and v.shape[1] == S:
+                out[k] = v.reshape(B * S, *v.shape[2:])
+            else:
+                out[k] = v
+
+        if export_feat_layers:
+            out["aux_features"] = self._reshape_aux_features(aux_feats, H, W)
+        return out
+
+    def _reshape_aux_features(self, aux_feats, H: int, W: int):
+        """Reshape (B, S, N, C) aux features into (B, S, h_p, w_p, C)."""
+        ph, pw = H // self.PATCH_SIZE, W // self.PATCH_SIZE
+        out = []
+        for f in aux_feats:
+            B, S, N, C = f.shape
+            assert N == ph * pw, f"aux feature seq mismatch: {N} != {ph}*{pw}"
+            out.append(f.reshape(B, S, ph, pw, C))
+        return out

comfy/ldm/depth_anything_3/preprocess.py

@@ -0,0 +1,128 @@
+"""Input/output preprocessing helpers for Depth Anything 3."""
+
+from __future__ import annotations
+
+from typing import Tuple
+
+import torch
+
+import comfy.utils
+
+PATCH_SIZE = 14
+
+# ImageNet normalization constants used during DA3 training.
+_IMAGENET_MEAN = torch.tensor([0.485, 0.456, 0.406])
+_IMAGENET_STD = torch.tensor([0.229, 0.224, 0.225])
+
+
+def _round_to_patch(x: int, patch: int = PATCH_SIZE) -> int:
+    down = (x // patch) * patch
+    up = down + patch
+    return up if abs(up - x) <= abs(x - down) else down
+
+
+def compute_target_size(orig_h: int, orig_w: int, process_res: int, method: str = "upper_bound_resize") -> Tuple[int, int]:
+    """Compute (target_h, target_w) for a single image.
+    upper_bound_resize: scale longest side to process_res, then round each dim to nearest multiple of 14 (default upstream method).
+    lower_bound_resize: scale shortest side to process_res, then round."""
+
+    if method == "upper_bound_resize":
+        longest = max(orig_h, orig_w)
+        scale = process_res / float(longest)
+    elif method == "lower_bound_resize":
+        shortest = min(orig_h, orig_w)
+        scale = process_res / float(shortest)
+    else:
+        raise ValueError(f"Unsupported process_res_method: {method}")
+
+    new_w = max(1, _round_to_patch(int(round(orig_w * scale))))
+    new_h = max(1, _round_to_patch(int(round(orig_h * scale))))
+    return new_h, new_w
+
+
+def preprocess_image(image: torch.Tensor, process_res: int = 504, method: str = "upper_bound_resize") -> torch.Tensor:
+    assert image.ndim == 4 and image.shape[-1] == 3, f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}"
+    B, H, W, _ = image.shape
+    target_h, target_w = compute_target_size(H, W, process_res, method)
+
+    # (B, H, W, 3) -> (B, 3, H, W)
+    x = image.movedim(-1, 1).contiguous()
+    if (target_h, target_w) != (H, W):
+        # Upstream uses cv2 INTER_CUBIC (upscale) / INTER_AREA (downscale).
+        # Lanczos in ``common_upscale`` is anti-aliased and produces the
+        # closest pixel-wise match in a sweep across {bilinear, bicubic,
+        # area, lanczos, bislerp}. Used in both directions for simplicity.
+        x = comfy.utils.common_upscale(x.float(), target_w, target_h, "lanczos", "disabled",)
+    x = x.clamp(0.0, 1.0)
+
+    mean = _IMAGENET_MEAN.to(device=x.device, dtype=x.dtype).view(1, 3, 1, 1)
+    std = _IMAGENET_STD.to(device=x.device, dtype=x.dtype).view(1, 3, 1, 1)
+    x = (x - mean) / std
+    return x
+
+
+# -----------------------------------------------------------------------------
+# Output post-processing (sky-aware clipping for Mono/Metric variants)
+# -----------------------------------------------------------------------------
+
+
+def compute_non_sky_mask(sky_prediction: torch.Tensor, threshold: float = 0.3) -> torch.Tensor:
+    """Boolean mask: True for non-sky pixels (sky probability < threshold)."""
+    return sky_prediction < threshold
+
+
+def apply_sky_aware_clip(depth: torch.Tensor, sky: torch.Tensor, threshold: float = 0.3, quantile: float = 0.99) -> torch.Tensor:
+    """Clips sky regions to the 99th percentile of non-sky depth. Returns a new depth tensor."""
+    non_sky = compute_non_sky_mask(sky, threshold=threshold)
+    if non_sky.sum() <= 10 or (~non_sky).sum() <= 10:
+        return depth.clone()
+
+    non_sky_depth = depth[non_sky]
+    if non_sky_depth.numel() > 100_000:
+        idx = torch.randint(0, non_sky_depth.numel(), (100_000,), device=non_sky_depth.device)
+        sampled = non_sky_depth[idx]
+    else:
+        sampled = non_sky_depth
+
+    max_depth = torch.quantile(sampled, quantile)
+    out = depth.clone()
+    out[~non_sky] = max_depth
+    return out
+
+
+def normalize_depth_v2_style(depth: torch.Tensor, sky: torch.Tensor | None = None, low_quantile: float = 0.01, high_quantile: float = 0.99) -> torch.Tensor:
+    """V2-style normalization computes percentile bounds over non-sky pixels (when available), then maps depth into [0, 1] with near = white (1.0)."""
+    if sky is not None:
+        mask = compute_non_sky_mask(sky)
+        if mask.any():
+            valid = depth[mask]
+        else:
+            valid = depth.flatten()
+    else:
+        valid = depth.flatten()
+
+    if valid.numel() > 100_000:
+        idx = torch.randint(0, valid.numel(), (100_000,), device=valid.device)
+        sample = valid[idx]
+    else:
+        sample = valid
+
+    lo = torch.quantile(sample, low_quantile)
+    hi = torch.quantile(sample, high_quantile)
+    rng = (hi - lo).clamp(min=1e-6)
+    norm = ((depth - lo) / rng).clamp(0.0, 1.0)
+    # Nearer pixels are brighter (1.0)
+    norm = 1.0 - norm
+    if sky is not None:
+        # Sky pixels become black (far / unknown)
+        sky_mask = ~compute_non_sky_mask(sky)
+        norm = torch.where(sky_mask, torch.zeros_like(norm), norm)
+    return norm
+
+
+def normalize_depth_min_max(depth: torch.Tensor) -> torch.Tensor:
+    """Simple per-frame min/max normalization with near=1.0 convention."""
+    lo = depth.amin(dim=(-2, -1), keepdim=True)
+    hi = depth.amax(dim=(-2, -1), keepdim=True)
+    rng = (hi - lo).clamp(min=1e-6)
+    return 1.0 - ((depth - lo) / rng).clamp(0.0, 1.0)

comfy/ldm/depth_anything_3/ray_pose.py

@@ -0,0 +1,272 @@
+"""Ray-to-pose conversion for the multi-view path of Depth Anything 3."""
+
+from __future__ import annotations
+
+from typing import Optional, Tuple
+
+import torch
+
+
+# qr/svd use fp32: CUDA often has no fp16/bf16 kernels for these ops.
+
+
+def _ql_decomposition(A: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Decompose A = Q @ L with Q orthogonal and L lower-triangular.
+    Implemented in terms of QR by reversing the columns/rows; the standard
+    trick from the upstream reference. Inputs A are (3, 3)."""
+    P = torch.tensor([[0, 0, 1], [0, 1, 0], [1, 0, 0]], device=A.device, dtype=A.dtype)
+    A_tilde = A @ P
+    # CUDA QR is not implemented for fp16/bf16; upcast just for this call.
+    Q_tilde, R_tilde = torch.linalg.qr(A_tilde.float())
+    Q_tilde = Q_tilde.to(A.dtype)
+    R_tilde = R_tilde.to(A.dtype)
+    Q = Q_tilde @ P
+    L = P @ R_tilde @ P
+    d = torch.diag(L)
+    sign = torch.sign(d)
+    Q = Q * sign[None, :]  # scale columns of Q
+    L = L * sign[:, None]  # scale rows of L
+    return Q, L
+
+
+def _homogenize_points(points: torch.Tensor) -> torch.Tensor:
+    return torch.cat([points, torch.ones_like(points[..., :1])], dim=-1)
+
+
+# -----------------------------------------------------------------------------
+# Weighted-LSQ + RANSAC homography (batched)
+# -----------------------------------------------------------------------------
+
+
+def _find_homography_weighted_lsq(src_pts: torch.Tensor, dst_pts: torch.Tensor, confident_weight: torch.Tensor,) -> torch.Tensor:
+    """Solve a single H with weighted least-squares (DLT)."""
+    N = src_pts.shape[0]
+    if N < 4:
+        raise ValueError("At least 4 points are required to compute a homography.")
+    w = confident_weight.sqrt().unsqueeze(1)  # (N, 1)
+    x = src_pts[:, 0:1]
+    y = src_pts[:, 1:2]
+    u = dst_pts[:, 0:1]
+    v = dst_pts[:, 1:2]
+    zeros = torch.zeros_like(x)
+    A1 = torch.cat([-x * w, -y * w, -w, zeros, zeros, zeros, x * u * w, y * u * w, u * w], dim=1)
+    A2 = torch.cat([zeros, zeros, zeros, -x * w, -y * w, -w, x * v * w, y * v * w, v * w], dim=1)
+    A = torch.cat([A1, A2], dim=0)        # (2N, 9)
+    # CUDA SVD is not implemented for fp16/bf16; upcast just for this call.
+    _, _, Vh = torch.linalg.svd(A.float())
+    Vh = Vh.to(A.dtype)
+    H = Vh[-1].reshape(3, 3)
+    return H / H[-1, -1]
+
+
+def _find_homography_weighted_lsq_batched(src_pts_batch: torch.Tensor, dst_pts_batch: torch.Tensor, confident_weight_batch: torch.Tensor) -> torch.Tensor:
+    """Batched DLT solver. Inputs (B, K, 2) / (B, K); output (B, 3, 3)."""
+    B, K, _ = src_pts_batch.shape
+    w = confident_weight_batch.sqrt().unsqueeze(2)
+    x = src_pts_batch[:, :, 0:1]
+    y = src_pts_batch[:, :, 1:2]
+    u = dst_pts_batch[:, :, 0:1]
+    v = dst_pts_batch[:, :, 1:2]
+    zeros = torch.zeros_like(x)
+    A1 = torch.cat([-x * w, -y * w, -w, zeros, zeros, zeros, x * u * w, y * u * w, u * w], dim=2)
+    A2 = torch.cat([zeros, zeros, zeros, -x * w, -y * w, -w, x * v * w, y * v * w, v * w], dim=2)
+    A = torch.cat([A1, A2], dim=1)        # (B, 2K, 9)
+    # CUDA SVD is not implemented for fp16/bf16; upcast just for this call.
+    _, _, Vh = torch.linalg.svd(A.float())
+    Vh = Vh.to(A.dtype)
+    H = Vh[:, -1].reshape(B, 3, 3)
+    return H / H[:, 2:3, 2:3]
+
+
+def _ransac_find_homography_weighted_batched(
+    src_pts: torch.Tensor,                # (B, N, 2)
+    dst_pts: torch.Tensor,                # (B, N, 2)
+    confident_weight: torch.Tensor,       # (B, N)
+    n_sample: int,
+    n_iter: int = 100,
+    reproj_threshold: float = 3.0,
+    num_sample_for_ransac: int = 8,
+    max_inlier_num: int = 10000,
+    rand_sample_iters_idx: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    """Batched weighted-RANSAC homography estimator. Returns (B, 3, 3) homography matrices."""
+    B, N, _ = src_pts.shape
+    assert N >= 4
+    device = src_pts.device
+
+    sorted_idx = torch.argsort(confident_weight, descending=True, dim=1)
+    candidate_idx = sorted_idx[:, :n_sample]                  # (B, n_sample)
+
+    if rand_sample_iters_idx is None:
+        rand_sample_iters_idx = torch.stack(
+            [torch.randperm(n_sample, device=device)[:num_sample_for_ransac]
+             for _ in range(n_iter)],
+            dim=0,
+        )
+
+    rand_idx = candidate_idx[:, rand_sample_iters_idx]        # (B, n_iter, k)
+    b_idx = (
+        torch.arange(B, device=device)
+        .view(B, 1, 1)
+        .expand(B, n_iter, num_sample_for_ransac)
+    )
+    src_b = src_pts[b_idx, rand_idx]
+    dst_b = dst_pts[b_idx, rand_idx]
+    w_b = confident_weight[b_idx, rand_idx]
+
+    cB, cN = src_b.shape[:2]
+    H_batch = _find_homography_weighted_lsq_batched(
+        src_b.flatten(0, 1), dst_b.flatten(0, 1), w_b.flatten(0, 1),
+    ).unflatten(0, (cB, cN))                                  # (B, n_iter, 3, 3)
+
+    src_homo = torch.cat([src_pts, torch.ones(B, N, 1, device=device, dtype=src_pts.dtype)], dim=2)
+    proj = torch.bmm(
+        src_homo.unsqueeze(1).expand(B, n_iter, N, 3).reshape(-1, N, 3),
+        H_batch.reshape(-1, 3, 3).transpose(1, 2),
+    )                                                          # (B*n_iter, N, 3)
+    proj_xy = (proj[:, :, :2] / proj[:, :, 2:3]).reshape(B, n_iter, N, 2)
+    err = ((proj_xy - dst_pts.unsqueeze(1)) ** 2).sum(-1).sqrt()  # (B, n_iter, N)
+    inlier_mask = err < reproj_threshold
+    score = (inlier_mask * confident_weight.unsqueeze(1)).sum(dim=2)
+    best_idx = torch.argmax(score, dim=1)
+    best_inlier_mask = inlier_mask[torch.arange(B, device=device), best_idx]
+
+    # Refit with the inlier set (per-batch, since the inlier counts vary).
+    H_inlier_list = []
+    for b in range(B):
+        mask = best_inlier_mask[b]
+        in_src = src_pts[b][mask]
+        in_dst = dst_pts[b][mask]
+        in_w = confident_weight[b][mask]
+        if in_src.shape[0] < 4:
+            # Fall back to identity when RANSAC fails to find enough inliers.
+            H_inlier_list.append(torch.eye(3, device=device, dtype=src_pts.dtype))
+            continue
+        sorted_w = torch.argsort(in_w, descending=True)
+        if len(sorted_w) > max_inlier_num:
+            keep = max(int(len(sorted_w) * 0.95), max_inlier_num)
+            sorted_w = sorted_w[:keep][torch.randperm(keep, device=device)[:max_inlier_num]]
+        H_inlier_list.append(
+            _find_homography_weighted_lsq(in_src[sorted_w], in_dst[sorted_w], in_w[sorted_w])
+        )
+    return torch.stack(H_inlier_list, dim=0)
+
+
+# -----------------------------------------------------------------------------
+# Camera-ray utilities
+# -----------------------------------------------------------------------------
+
+
+def _unproject_identity(num_y: int, num_x: int, B: int, S: int, device, dtype) -> torch.Tensor:
+    """Camera-space unit rays for an identity intrinsic on a 2x2 image plane."""
+    dx = 1.0 / num_x
+    dy = 1.0 / num_y
+    # Centered camera-space coords directly (skip the K^-1 step since it's
+    # just a translation by -1 on x and y when K is identity-with-center=1).
+    y = torch.linspace(-(1 - dy), (1 - dy), num_y, device=device, dtype=dtype)
+    x = torch.linspace(-(1 - dx), (1 - dx), num_x, device=device, dtype=dtype)
+    yy, xx = torch.meshgrid(y, x, indexing="ij")
+    grid = torch.stack((xx, yy), dim=-1)            # (h, w, 2)
+    grid = grid.unsqueeze(0).unsqueeze(0).expand(B, S, num_y, num_x, 2)
+    return torch.cat([grid, torch.ones_like(grid[..., :1])], dim=-1)
+
+
+def _camray_to_caminfo(
+    camray: torch.Tensor,  # (B, S, h, w, 6)
+    confidence: Optional[torch.Tensor] = None,  # (B, S, h, w)
+    reproj_threshold: float = 0.2,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Convert per-pixel camera rays to per-view (R, T, focal, principal)."""
+    if confidence is None:
+        confidence = torch.ones_like(camray[..., 0])
+    B, S, h, w, _ = camray.shape
+    device = camray.device
+    dtype = camray.dtype
+
+    rays_target = camray[..., :3]                           # (B, S, h, w, 3)
+    rays_origin = _unproject_identity(h, w, B, S, device, dtype)
+
+    # Flatten (B*S, h*w, *) for the RANSAC routine.
+    rays_target = rays_target.flatten(0, 1).flatten(1, 2)
+    rays_origin = rays_origin.flatten(0, 1).flatten(1, 2)
+    weights = confidence.flatten(0, 1).flatten(1, 2).clone()
+
+    # Project to 2D in homogeneous form (the upstream calls this "perspective division").
+    z_thresh = 1e-4
+    mask = (rays_target[:, :, 2].abs() > z_thresh) & (rays_origin[:, :, 2].abs() > z_thresh)
+    weights = torch.where(mask, weights, torch.zeros_like(weights))
+    src = rays_origin.clone()
+    dst = rays_target.clone()
+    src[..., 0] = torch.where(mask, src[..., 0] / src[..., 2], src[..., 0])
+    src[..., 1] = torch.where(mask, src[..., 1] / src[..., 2], src[..., 1])
+    dst[..., 0] = torch.where(mask, dst[..., 0] / dst[..., 2], dst[..., 0])
+    dst[..., 1] = torch.where(mask, dst[..., 1] / dst[..., 2], dst[..., 1])
+    src = src[..., :2]
+    dst = dst[..., :2]
+
+    N = src.shape[1]
+    n_iter = 100
+    sample_ratio = 0.3
+    num_sample_for_ransac = 8
+    n_sample = max(num_sample_for_ransac, int(N * sample_ratio))
+    rand_idx = torch.stack(
+        [torch.randperm(n_sample, device=device)[:num_sample_for_ransac] for _ in range(n_iter)],
+        dim=0,
+    )
+
+    # Chunk along the view axis to keep peak memory predictable.
+    chunk = 2
+    A_list = []
+    for i in range(0, src.shape[0], chunk):
+        A = _ransac_find_homography_weighted_batched(
+            src[i:i + chunk], dst[i:i + chunk], weights[i:i + chunk],
+            n_sample=n_sample, n_iter=n_iter,
+            num_sample_for_ransac=num_sample_for_ransac,
+            reproj_threshold=reproj_threshold,
+            rand_sample_iters_idx=rand_idx,
+            max_inlier_num=8000,
+        )
+        # Flip sign on dets that come out < 0 (so that the QL produces a
+        # right-handed rotation). ``det`` lacks fp16/bf16 CUDA kernels, so
+        # do the comparison in fp32.
+        flip = torch.linalg.det(A.float()) < 0
+        A = torch.where(flip[:, None, None], -A, A)
+        A_list.append(A)
+    A = torch.cat(A_list, dim=0)                            # (B*S, 3, 3)
+
+    R_list, f_list, pp_list = [], [], []
+    for i in range(A.shape[0]):
+        R, L = _ql_decomposition(A[i])
+        L = L / L[2][2]
+        f_list.append(torch.stack((L[0][0], L[1][1])))
+        pp_list.append(torch.stack((L[2][0], L[2][1])))
+        R_list.append(R)
+    R = torch.stack(R_list).reshape(B, S, 3, 3)
+    focal = torch.stack(f_list).reshape(B, S, 2)
+    pp = torch.stack(pp_list).reshape(B, S, 2)
+
+    # Translation: confidence-weighted average of camray direction(s).
+    cf = confidence.flatten(0, 1).flatten(1, 2)
+    T = (camray.flatten(0, 1).flatten(1, 2)[..., 3:] * cf.unsqueeze(-1)).sum(dim=1)
+    T = T / cf.sum(dim=-1, keepdim=True)
+    T = T.reshape(B, S, 3)
+
+    # Match upstream output convention: focal -> 1/focal, pp + 1.
+    return R, T, 1.0 / focal, pp + 1.0
+
+
+def get_extrinsic_from_camray(
+    camray: torch.Tensor,  # (B, S, h, w, 6)
+    conf: torch.Tensor,  # (B, S, h, w, 1) or (B, S, h, w)
+    patch_size_y: int,
+    patch_size_x: int,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Wrap a 4x4 extrinsic + per-view focal + principal-point output."""
+    if conf.ndim == 5 and conf.shape[-1] == 1:
+        conf = conf.squeeze(-1)
+    R, T, focal, pp = _camray_to_caminfo(camray, confidence=conf)
+    extr = torch.cat([R, T.unsqueeze(-1)], dim=-1)           # (B, S, 3, 4)
+    homo_row = torch.tensor([0, 0, 0, 1], dtype=R.dtype, device=R.device)
+    homo_row = homo_row.view(1, 1, 1, 4).expand(R.shape[0], R.shape[1], 1, 4)
+    extr = torch.cat([extr, homo_row], dim=-2)               # (B, S, 4, 4)
+    return extr, focal, pp

comfy/ldm/depth_anything_3/reference_view_selector.py

@@ -0,0 +1,87 @@
+"""Reference-view selection for the multi-view path of Depth Anything 3."""
+
+from __future__ import annotations
+
+from typing import Literal
+
+import torch
+
+
+RefViewStrategy = Literal["first", "middle", "saddle_balanced", "saddle_sim_range"]
+
+
+# Per the upstream constants module: ``THRESH_FOR_REF_SELECTION = 3``.
+# Reference selection only runs when there are at least this many views.
+THRESH_FOR_REF_SELECTION: int = 3
+
+
+def select_reference_view(x: torch.Tensor, strategy: RefViewStrategy = "saddle_balanced") -> torch.Tensor:
+    """Pick a reference view index per batch element."""
+    B, S, _, _ = x.shape
+    if S <= 1:
+        return torch.zeros(B, dtype=torch.long, device=x.device)
+    if strategy == "first":
+        return torch.zeros(B, dtype=torch.long, device=x.device)
+    if strategy == "middle":
+        return torch.full((B,), S // 2, dtype=torch.long, device=x.device)
+
+    # Feature-based strategies: normalised cls/cam token per view.
+    img_class_feat = x[:, :, 0] / x[:, :, 0].norm(dim=-1, keepdim=True)  # (B,S,C)
+
+    if strategy == "saddle_balanced":
+        sim = torch.matmul(img_class_feat, img_class_feat.transpose(1, 2))  # (B,S,S)
+        sim_no_diag = sim - torch.eye(S, device=sim.device).unsqueeze(0)
+        sim_score = sim_no_diag.sum(dim=-1) / (S - 1)               # (B,S)
+        feat_norm = x[:, :, 0].norm(dim=-1)                          # (B,S)
+        feat_var = img_class_feat.var(dim=-1)                        # (B,S)
+
+        def _normalize(metric):
+            mn = metric.min(dim=1, keepdim=True).values
+            mx = metric.max(dim=1, keepdim=True).values
+            return (metric - mn) / (mx - mn + 1e-8)
+
+        sim_n, norm_n, var_n = _normalize(sim_score), _normalize(feat_norm), _normalize(feat_var)
+        balance = (sim_n - 0.5).abs() + (norm_n - 0.5).abs() + (var_n - 0.5).abs()
+        return balance.argmin(dim=1)
+
+    if strategy == "saddle_sim_range":
+        sim = torch.matmul(img_class_feat, img_class_feat.transpose(1, 2))
+        sim_no_diag = sim - torch.eye(S, device=sim.device).unsqueeze(0)
+        sim_max = sim_no_diag.max(dim=-1).values
+        sim_min = sim_no_diag.min(dim=-1).values
+        return (sim_max - sim_min).argmax(dim=1)
+
+    raise ValueError(
+        f"Unknown reference view selection strategy: {strategy!r}. "
+        f"Must be one of: 'first', 'middle', 'saddle_balanced', 'saddle_sim_range'"
+    )
+
+
+def reorder_by_reference(x: torch.Tensor, b_idx: torch.Tensor) -> torch.Tensor:
+    """Reorder x so the reference view is at position 0 in axis S."""
+    B, S = x.shape[0], x.shape[1]
+    if S <= 1:
+        return x
+    positions = torch.arange(S, device=x.device).unsqueeze(0).expand(B, -1)
+    b_idx_exp = b_idx.unsqueeze(1)
+    reorder = torch.where(
+        (positions > 0) & (positions <= b_idx_exp),
+        positions - 1,
+        positions,
+    )
+    reorder[:, 0] = b_idx
+    batch = torch.arange(B, device=x.device).unsqueeze(1)
+    return x[batch, reorder]
+
+
+def restore_original_order(x: torch.Tensor, b_idx: torch.Tensor) -> torch.Tensor:
+    """Inverse of reorder_by_reference."""
+    B, S = x.shape[0], x.shape[1]
+    if S <= 1:
+        return x
+    target_positions = torch.arange(S, device=x.device).unsqueeze(0).expand(B, -1)
+    b_idx_exp = b_idx.unsqueeze(1)
+    restore = torch.where(target_positions < b_idx_exp, target_positions + 1, target_positions)
+    restore = torch.scatter(restore, dim=1, index=b_idx_exp, src=torch.zeros_like(b_idx_exp))
+    batch = torch.arange(B, device=x.device).unsqueeze(1)
+    return x[batch, restore]

comfy/ldm/depth_anything_3/transform.py

@@ -0,0 +1,160 @@
+"""Geometry / camera transform helpers for Depth Anything 3."""
+
+from __future__ import annotations
+
+from typing import Tuple
+
+import torch
+import torch.nn.functional as F
+
+
+# -----------------------------------------------------------------------------
+# Affine 4x4 helpers
+# -----------------------------------------------------------------------------
+
+
+def as_homogeneous(ext: torch.Tensor) -> torch.Tensor:
+    """Promote (...,3,4) extrinsics to (...,4,4) homogeneous form. No-op when the input is already ``(...,4,4)``."""
+    if ext.shape[-2:] == (4, 4):
+        return ext
+    if ext.shape[-2:] == (3, 4):
+        ones = torch.zeros_like(ext[..., :1, :4])
+        ones[..., 0, 3] = 1.0
+        return torch.cat([ext, ones], dim=-2)
+    raise ValueError(f"Invalid affine shape: {ext.shape}")
+
+
+def affine_inverse(A: torch.Tensor) -> torch.Tensor:
+    """Inverse of an affine matrix ``[R|T; 0 0 0 1]``."""
+    R = A[..., :3, :3]
+    T = A[..., :3, 3:]
+    P = A[..., 3:, :]
+    return torch.cat([torch.cat([R.mT, -R.mT @ T], dim=-1), P], dim=-2)
+
+
+# -----------------------------------------------------------------------------
+# Quaternion <-> rotation matrix (xyzw / scalar-last)
+# -----------------------------------------------------------------------------
+
+
+def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor:
+    """sqrt(max(0, x)) with a zero subgradient where x == 0."""
+    ret = torch.zeros_like(x)
+    positive_mask = x > 0
+    if torch.is_grad_enabled():
+        ret[positive_mask] = torch.sqrt(x[positive_mask])
+    else:
+        ret = torch.where(positive_mask, torch.sqrt(x), ret)
+    return ret
+
+
+def standardize_quaternion(quaternions: torch.Tensor) -> torch.Tensor:
+    """Force the real part of a unit quaternion (xyzw) to be non-negative."""
+    return torch.where(quaternions[..., 3:4] < 0, -quaternions, quaternions)
+
+
+def quat_to_mat(quaternions: torch.Tensor) -> torch.Tensor:
+    """Convert quaternions (xyzw) to (...,3,3) rotation matrices."""
+    i, j, k, r = torch.unbind(quaternions, -1)
+    two_s = 2.0 / (quaternions * quaternions).sum(-1)
+    o = torch.stack(
+        (
+            1 - two_s * (j * j + k * k),
+            two_s * (i * j - k * r),
+            two_s * (i * k + j * r),
+            two_s * (i * j + k * r),
+            1 - two_s * (i * i + k * k),
+            two_s * (j * k - i * r),
+            two_s * (i * k - j * r),
+            two_s * (j * k + i * r),
+            1 - two_s * (i * i + j * j),
+        ),
+        -1,
+    )
+    return o.reshape(quaternions.shape[:-1] + (3, 3))
+
+
+def mat_to_quat(matrix: torch.Tensor) -> torch.Tensor:
+    """Convert (...,3,3) rotation matrices to quaternions (xyzw)."""
+    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
+        raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.")
+
+    batch_dim = matrix.shape[:-2]
+    m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind(
+        matrix.reshape(batch_dim + (9,)), dim=-1
+    )
+
+    q_abs = _sqrt_positive_part(
+        torch.stack(
+            [
+                1.0 + m00 + m11 + m22,
+                1.0 + m00 - m11 - m22,
+                1.0 - m00 + m11 - m22,
+                1.0 - m00 - m11 + m22,
+            ],
+            dim=-1,
+        )
+    )
+
+    quat_by_rijk = torch.stack(
+        [
+            torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1),
+            torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1),
+            torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1),
+            torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1),
+        ],
+        dim=-2,
+    )
+
+    flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device)
+    quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr))
+
+    out = quat_candidates[F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :].reshape(
+        batch_dim + (4,)
+    )
+    # Reorder rijk -> xyzw (i.e. ijkr).
+    out = out[..., [1, 2, 3, 0]]
+    return standardize_quaternion(out)
+
+
+# -----------------------------------------------------------------------------
+# Pose-encoding <-> extrinsics + intrinsics
+# -----------------------------------------------------------------------------
+
+
+def extri_intri_to_pose_encoding(extrinsics: torch.Tensor, intrinsics: torch.Tensor, image_size_hw: Tuple[int, int]) -> torch.Tensor:
+    """Pack (extr, intr, image_size) into the 9-D pose-encoding vector.
+    extrinsics: camera-to-world (c2w) (B,S,4,4) matrices,
+    intrinsics: pixel-space (B,S,3,3) matrices,
+    image_size_hw: is a (H, W) pair.
+    """
+    R = extrinsics[..., :3, :3]
+    T = extrinsics[..., :3, 3]
+    quat = mat_to_quat(R)
+    H, W = image_size_hw
+    fov_h = 2 * torch.atan((H / 2) / intrinsics[..., 1, 1])
+    fov_w = 2 * torch.atan((W / 2) / intrinsics[..., 0, 0])
+    return torch.cat([T, quat, fov_h[..., None], fov_w[..., None]], dim=-1).float()
+
+
+def pose_encoding_to_extri_intri(pose_encoding: torch.Tensor, image_size_hw: Tuple[int, int]) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Inverse of extri_intri_to_pose_encoding."""
+    T = pose_encoding[..., :3]
+    quat = pose_encoding[..., 3:7]
+    fov_h = pose_encoding[..., 7]
+    fov_w = pose_encoding[..., 8]
+    # Normalize to unit quaternion. CameraDec outputs raw values; a near-zero
+    # quaternion causes two_s = 2/norm² → inf in quat_to_mat → NaN extrinsics.
+    quat = quat / quat.norm(dim=-1, keepdim=True).clamp(min=1e-6)
+    R = quat_to_mat(quat)
+    extrinsics = torch.cat([R, T[..., None]], dim=-1)
+    H, W = image_size_hw
+    fy = (H / 2.0) / torch.clamp(torch.tan(fov_h / 2.0), 1e-6)
+    fx = (W / 2.0) / torch.clamp(torch.tan(fov_w / 2.0), 1e-6)
+    intrinsics = torch.zeros(pose_encoding.shape[:2] + (3, 3), device=pose_encoding.device, dtype=pose_encoding.dtype)
+    intrinsics[..., 0, 0] = fx
+    intrinsics[..., 1, 1] = fy
+    intrinsics[..., 0, 2] = W / 2
+    intrinsics[..., 1, 2] = H / 2
+    intrinsics[..., 2, 2] = 1.0
+    return extrinsics, intrinsics

comfy/ldm/wan/model.py

@@ -8,7 +8,7 @@ from einops import rearrange
 
 from comfy.ldm.modules.attention import optimized_attention
 from comfy.ldm.flux.layers import EmbedND
-from comfy.ldm.flux.math import apply_rope1
+from comfy.ldm.flux.math import apply_rope1, rope
 import comfy.ldm.common_dit
 import comfy.model_management
 import comfy.patcher_extension
@@ -570,6 +570,14 @@ class WanModel(torch.nn.Module):
                 full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2)
                 x = torch.concat((full_ref, x), dim=1)
 
+        # In-context reference (Bernini)
+        context_latents = kwargs.get("context_latents", None)
+        main_len = x.shape[1]
+        if context_latents is not None:
+            for lat in context_latents:
+                cl = self.patch_embedding(lat.float().to(x.device)).to(x.dtype).flatten(2).transpose(1, 2)
+                x = torch.cat([x, cl], dim=1)
+
         # context
         context = self.text_embedding(context)
 
@@ -599,6 +607,9 @@ class WanModel(torch.nn.Module):
         # head
         x = self.head(x, e)
 
+        if context_latents is not None:
+            x = x[:, :main_len]
+
         if full_ref is not None:
             x = x[:, full_ref.shape[1]:]
 
@@ -606,7 +617,7 @@ class WanModel(torch.nn.Module):
         x = self.unpatchify(x, grid_sizes)
         return x
 
-    def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}):
+    def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}, source_id=0):
         patch_size = self.patch_size
         t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
         h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
@@ -638,6 +649,13 @@ class WanModel(torch.nn.Module):
         img_ids = img_ids.reshape(1, -1, img_ids.shape[-1])
 
         freqs = self.rope_embedder(img_ids).movedim(1, 2)
+
+        # In-context reference: a non-zero source_id composes an extra rotation into the spatial rope
+        if source_id:
+            d = self.dim // self.num_heads
+            pos = torch.tensor([[float(source_id)]], device=freqs.device, dtype=torch.float32)
+            id_rot = rope(pos, d, self.rope_embedder.theta).reshape(1, 1, 1, d // 2, 2, 2).to(freqs.dtype)
+            freqs = torch.einsum('...ij,...jk->...ik', freqs, id_rot)
         return freqs
 
     def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs):
@@ -661,6 +679,15 @@ class WanModel(torch.nn.Module):
             t_len += 1
 
         freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options)
+
+        # In-context reference: one rope block per stream, each with it's own source_id (1, 2, ...) to distinguish from the target (id 0).
+        context_latents = kwargs.get("context_latents", None)
+        if context_latents is not None:
+            context_latents = [comfy.ldm.common_dit.pad_to_patch_size(lat, self.patch_size) for lat in context_latents]
+            for i, lat in enumerate(context_latents):
+                freqs = torch.cat([freqs, self.rope_encode(lat.shape[-3], lat.shape[-2], lat.shape[-1], device=x.device, dtype=x.dtype, transformer_options=transformer_options, source_id=i + 1)], dim=1)
+            kwargs = {**kwargs, "context_latents": context_latents}
+
         return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, **kwargs)[:, :, :t, :h, :w]
 
     def unpatchify(self, x, grid_sizes):

comfy/model_base.py

@@ -65,6 +65,7 @@ import comfy.ldm.ernie.model
 import comfy.ldm.sam3.detector
 import comfy.ldm.hidream_o1.model
 from comfy.ldm.hidream_o1.conditioning import build_extra_conds
+import comfy.ldm.depth_anything_3.model
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -1518,8 +1519,26 @@ class WAN21(BaseModel):
         if reference_latents is not None:
             out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1])[:, :, 0])
 
+        # In-context reference conditioning (Bernini)
+        context_latents = kwargs.get("context_latents", None)
+        if context_latents is not None:
+            out['context_latents'] = comfy.conds.CONDList([self.process_latent_in(l) for l in context_latents])
+
         return out
 
+    def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
+        # In-context cond slicing (Bernini)
+        if cond_key == "context_latents" and isinstance(getattr(cond_value, "cond", None), list):
+            dim = window.dim
+            out = []
+            for lat in cond_value.cond:
+                if lat.ndim > dim and lat.shape[dim] > 1 and lat.shape[dim] == x_in.shape[dim]:
+                    out.append(window.get_tensor(lat, device, dim=dim, retain_index_list=retain_index_list))
+                else:
+                    out.append(lat.to(device))
+            return cond_value._copy_with(out)
+        return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
+
 
 class WAN21_CausalAR(WAN21):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
@@ -1797,7 +1816,24 @@ class WAN21_SCAIL2(WAN21_SCAIL):
 
     def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
         if cond_key in ("sam_latents", "pose_latents"):
-            return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_offset=1)
+            # Return sliced view omitting retain_index_list
+            return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_offset=0)
+        if cond_key == "ref_mask_latents" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
+            # The ref mask is just a single frame padded with frames of zeros, so just grab the first frames for all windows
+            full_ref_mask = cond_value.cond
+            video_frame_count = x_in.shape[2]
+            if full_ref_mask.shape[2] != video_frame_count + 1:
+                return None
+            window_length = len(window.index_list)
+
+            # Account for the causal anchor frame if it exists
+            anchor_index = getattr(window, "causal_anchor_index", None)
+            if anchor_index is not None and anchor_index >= 0:
+                window_length += 1
+
+            window_ref_mask = full_ref_mask[:, :, :window_length + 1].to(device)
+            return cond_value._copy_with(window_ref_mask)
+
         return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
 
     def concat_cond(self, **kwargs):
@@ -2301,6 +2337,12 @@ class RT_DETR_v4(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.rt_detr.rtdetr_v4.RTv4)
 
+
+class DepthAnything3(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device,
+                         unet_model=comfy.ldm.depth_anything_3.model.DepthAnything3Net)
+
 class ErnieImage(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ernie.model.ErnieImageModel)

comfy/model_detection.py

@@ -862,6 +862,95 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["enc_h"] = state_dict['{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix)].shape[0]
         return dit_config
 
+    # Depth Anything 3 (repackaged to ComfyUI's native Dinov2Model layout via scripts/convert_da3.py)
+    if '{}backbone.embeddings.patch_embeddings.projection.weight'.format(key_prefix) in state_dict_keys:
+        dit_config = {}
+        dit_config["image_model"] = "DepthAnything3"
+
+        patch_w = state_dict['{}backbone.embeddings.patch_embeddings.projection.weight'.format(key_prefix)]
+        embed_dim = patch_w.shape[0]
+        depth = count_blocks(state_dict_keys, '{}backbone.encoder.layer.'.format(key_prefix) + '{}.')
+
+        # Backbone preset is determined by embed_dim (matches vits/vitb/vitl/vitg).
+        backbone_name = {384: "vits", 768: "vitb", 1024: "vitl", 1536: "vitg"}.get(embed_dim)
+        if backbone_name is None:
+            return None
+        dit_config["backbone_name"] = backbone_name
+
+        # Detect DA3 extensions on top of vanilla DINOv2.
+        has_camera_token = '{}backbone.embeddings.camera_token'.format(key_prefix) in state_dict_keys
+        # qk-norm shows up as `attention.q_norm.weight` on enabled blocks.
+        qknorm_indices = [
+            i for i in range(depth)
+            if '{}backbone.encoder.layer.{}.attention.q_norm.weight'.format(key_prefix, i) in state_dict_keys
+        ]
+        qknorm_start = qknorm_indices[0] if qknorm_indices else -1
+
+        # The DA3 main-series configs always set alt_start == qknorm_start == rope_start.
+        # cat_token=True is implied by the presence of camera_token.
+        if has_camera_token:
+            dit_config["alt_start"] = qknorm_start
+            dit_config["rope_start"] = qknorm_start
+            dit_config["qknorm_start"] = qknorm_start
+            dit_config["cat_token"] = True
+        else:
+            dit_config["alt_start"] = -1
+            dit_config["rope_start"] = -1
+            dit_config["qknorm_start"] = -1
+            dit_config["cat_token"] = False
+
+        # Detect head type and config.
+        has_aux = '{}head.scratch.refinenet1_aux.out_conv.weight'.format(key_prefix) in state_dict_keys
+        dit_config["head_dim_in"] = state_dict['{}head.projects.0.weight'.format(key_prefix)].shape[1]
+        dit_config["head_features"] = state_dict['{}head.scratch.refinenet1.out_conv.weight'.format(key_prefix)].shape[0]
+        dit_config["head_out_channels"] = [
+            state_dict['{}head.projects.{}.weight'.format(key_prefix, i)].shape[0]
+            for i in range(4)
+        ]
+        if has_aux:
+            # DualDPT: dim_in = 2 * embed_dim (because cat_token doubles token width).
+            dit_config["head_type"] = "dualdpt"
+            dit_config["head_output_dim"] = 2
+            dit_config["head_use_sky_head"] = False
+        else:
+            dit_config["head_type"] = "dpt"
+            dit_config["head_output_dim"] = state_dict[
+                '{}head.scratch.output_conv2.2.weight'.format(key_prefix)
+            ].shape[0]
+            dit_config["head_use_sky_head"] = (
+                '{}head.scratch.sky_output_conv2.0.weight'.format(key_prefix) in state_dict_keys
+            )
+
+        # out_layers: hard-coded per upstream YAML config (depth-aware default).
+        if depth >= 24:
+            # vitl: depths used vary between DA3-Large (DualDPT) and Mono/Metric (DPT).
+            if has_aux:
+                dit_config["out_layers"] = [11, 15, 19, 23]
+            else:
+                dit_config["out_layers"] = [4, 11, 17, 23]
+        else:
+            # vits/vitb: 12 blocks
+            dit_config["out_layers"] = [5, 7, 9, 11]
+
+        # Camera encoder/decoder presence (multi-view + pose path).
+        has_cam_enc = '{}cam_enc.token_norm.weight'.format(key_prefix) in state_dict_keys
+        has_cam_dec = '{}cam_dec.fc_t.weight'.format(key_prefix) in state_dict_keys
+        dit_config["has_cam_enc"] = has_cam_enc
+        dit_config["has_cam_dec"] = has_cam_dec
+        if has_cam_enc:
+            cam_enc_w = state_dict.get(
+                '{}cam_enc.pose_branch.fc2.weight'.format(key_prefix)
+            )
+            if cam_enc_w is not None:
+                dit_config["cam_dim_out"] = cam_enc_w.shape[0]
+        if has_cam_dec:
+            cam_dec_w = state_dict.get(
+                '{}cam_dec.fc_t.weight'.format(key_prefix)
+            )
+            if cam_dec_w is not None:
+                dit_config["cam_dec_dim_in"] = cam_dec_w.shape[1]
+        return dit_config
+
     if '{}layers.0.mlp.linear_fc2.weight'.format(key_prefix) in state_dict_keys: # Ernie Image
         dit_config = {}
         dit_config["image_model"] = "ernie"

comfy/model_management.py

@@ -534,8 +534,10 @@ try:
 except:
     pass
 
-if torch.cuda.is_available() and torch.backends.cudnn.is_available() and PerformanceFeature.AutoTune in args.fast:
-    torch.backends.cudnn.benchmark = True
+
+def set_cudnn_benchmark():
+    if torch.cuda.is_available() and torch.backends.cudnn.is_available():
+        torch.backends.cudnn.benchmark = PerformanceFeature.AutoTune in args.fast
 
 try:
     if torch_version_numeric >= (2, 5):
@@ -958,8 +960,6 @@ def loaded_models(only_currently_used=False):
 def cleanup_models_gc():
     do_gc = False
 
-    reset_cast_buffers()
-
     for i in range(len(current_loaded_models)):
         cur = current_loaded_models[i]
         if cur.is_dead():

comfy/model_patcher.py

@@ -379,10 +379,11 @@ class ModelPatcher:
     def get_clone_model_override(self):
         return self.model, (self.backup, self.backup_buffers, self.object_patches_backup, self.pinned)
 
-    def clone(self, disable_dynamic=False, model_override=None):
+    def clone(self, disable_dynamic=False, model_override=None, force_deepcopy=False):
         class_ = self.__class__
-        if self.is_dynamic() and disable_dynamic:
-            class_ = ModelPatcher
+        if self.is_dynamic() and disable_dynamic or force_deepcopy:
+            if self.is_dynamic() and disable_dynamic:
+                class_ = ModelPatcher
             if model_override is None:
                 if self.cached_patcher_init is None:
                     raise RuntimeError("Cannot create non-dynamic delegate: cached_patcher_init is not initialized.")

comfy/ops.py

@@ -299,21 +299,21 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
 
     non_blocking = comfy.model_management.device_supports_non_blocking(device)
 
-    if hasattr(s, "_v"):
+    if hasattr(s, "_v") and comfy.model_management.is_device_cpu(device):
 
         #vbar doesn't support CPU weights, but some custom nodes have weird paths
         #that might switch the layer to the CPU and expect it to work. We have to take
         #a clone conservatively as we are mmapped and some SFT files are packed misaligned
         #If you are a custom node author reading this, please move your layer to the GPU
         #or declare your ModelPatcher as CPU in the first place.
-        if comfy.model_management.is_device_cpu(device):
-            materialize_meta_param(s, ["weight", "bias"])
-            weight = s.weight.to(dtype=dtype, copy=True)
-            if isinstance(weight, QuantizedTensor):
-                weight = weight.dequantize()
-            bias = s.bias.to(dtype=bias_dtype, copy=True) if s.bias is not None else None
-            return format_return((weight, bias, (None, None, None)), offloadable)
+        materialize_meta_param(s, ["weight", "bias"])
+        weight = s.weight.to(dtype=dtype, copy=True)
+        if isinstance(weight, QuantizedTensor):
+            weight = weight.dequantize()
+        bias = s.bias.to(dtype=bias_dtype, copy=True) if s.bias is not None else None
+        return format_return((weight, bias, (None, None, None)), offloadable)
 
+    elif hasattr(s, "_v") and s.weight.device != device:
         prefetched = hasattr(s, "_prefetch")
         offload_stream = None
         offload_device = None

comfy/supported_models.py

@@ -2056,6 +2056,23 @@ class RT_DETR_v4(supported_models_base.BASE):
         return None
 
 
+class DepthAnything3(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "DepthAnything3",
+    }
+
+    # Mono path: no num_heads / num_head_channels needed.
+    unet_extra_config = {}
+
+    supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return model_base.DepthAnything3(self, device=device)
+
+    def clip_target(self, state_dict={}):
+        return None
+
+
 class ErnieImage(supported_models_base.BASE):
     unet_config = {
         "image_model": "ernie",
@@ -2298,4 +2315,5 @@ models = [
     CogVideoX_I2V,
     CogVideoX_T2V,
     SVD_img2vid,
+    DepthAnything3,
 ]

comfy_api_nodes/apis/__init__.py

@@ -1310,13 +1310,6 @@ class KlingTaskStatus(str, Enum):
     failed = 'failed'
 
 
-class KlingTextToVideoModelName(str, Enum):
-    kling_v1 = 'kling-v1'
-    kling_v1_6 = 'kling-v1-6'
-    kling_v2_1_master = 'kling-v2-1-master'
-    kling_v2_5_turbo = 'kling-v2-5-turbo'
-
-
 class KlingVideoGenAspectRatio(str, Enum):
     field_16_9 = '16:9'
     field_9_16 = '9:16'
@@ -5179,7 +5172,7 @@ class KlingText2VideoRequest(BaseModel):
     duration: Optional[KlingVideoGenDuration] = '5'
     external_task_id: Optional[str] = Field(None, description='Customized Task ID')
     mode: Optional[KlingVideoGenMode] = 'std'
-    model_name: Optional[KlingTextToVideoModelName] = 'kling-v1'
+    model_name: Optional[str] = 'kling-v1'
     negative_prompt: Optional[str] = Field(
         None, description='Negative text prompt', max_length=2500
     )

comfy_api_nodes/nodes_kling.py

@@ -436,7 +436,7 @@ async def execute_text2video(
             negative_prompt=negative_prompt if negative_prompt else None,
             duration=KlingVideoGenDuration(duration),
             mode=KlingVideoGenMode(model_mode),
-            model_name=KlingVideoGenModelName(model_name),
+            model_name=model_name,
             cfg_scale=cfg_scale,
             aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio),
             camera_control=camera_control,

comfy_api_nodes/nodes_openai.py

@@ -9,6 +9,7 @@ from PIL import Image
 from typing_extensions import override
 
 import folder_paths
+from comfy.utils import common_upscale
 from comfy_api.latest import IO, ComfyExtension, Input
 from comfy_api_nodes.apis.openai import (
     InputFileContent,
@@ -62,7 +63,8 @@ async def validate_and_cast_response(response, timeout: int = None) -> torch.Ten
         timeout: Request timeout in seconds. Defaults to None (no timeout).
 
     Returns:
-        A torch.Tensor representing the image (1, H, W, C).
+        A torch.Tensor of shape (N, H, W, C) with all returned images; images whose
+        dimensions differ from the first image's are resized to match it.
 
     Raises:
         ValueError: If the response is not valid.
@@ -89,6 +91,14 @@ async def validate_and_cast_response(response, timeout: int = None) -> torch.Ten
         arr = np.asarray(pil_img).astype(np.float32) / 255.0
         image_tensors.append(torch.from_numpy(arr))
 
+    # With size="auto" the API can return images whose dimensions differ by a few pixels within a single response
+    # resize them to the first image's dimensions so they can be stacked into one batch.
+    ref_h, ref_w = image_tensors[0].shape[:2]
+    for i, t in enumerate(image_tensors):
+        if t.shape[:2] != (ref_h, ref_w):
+            samples = t.unsqueeze(0).movedim(-1, 1)
+            samples = common_upscale(samples, ref_w, ref_h, "bilinear", "center")
+            image_tensors[i] = samples.movedim(1, -1).squeeze(0)
     return torch.stack(image_tensors, dim=0)
 
 

comfy_execution/asset_enrichment.py

@@ -0,0 +1,66 @@
+"""Enrich executed-node output entries with asset id."""
+import logging
+import os
+
+
+def enrich_output_with_assets(output_ui: dict) -> dict:
+    """Register file-type output entries as assets and inject their ``id``.
+
+    Runs at output-processing time, once per produced output, when
+    --enable-assets is set. Returns a new dict; entries without a resolvable
+    on-disk file path are left unchanged. Errors are caught per-entry so a
+    failure never blocks execution or the other entries.
+    """
+    from comfy.cli_args import args
+    if not args.enable_assets:
+        return output_ui
+
+    import folder_paths
+    from app.assets.services.ingest import register_file_in_place, DependencyMissingError
+
+    enriched = {}
+    for key, entries in output_ui.items():
+        if not isinstance(entries, list):
+            enriched[key] = entries
+            continue
+        new_entries = []
+        for entry in entries:
+            if not isinstance(entry, dict) or "filename" not in entry or "type" not in entry:
+                new_entries.append(entry)
+                continue
+            try:
+                base = folder_paths.get_directory_by_type(entry["type"])
+                if base is None:
+                    new_entries.append(entry)
+                    continue
+                base_abs = os.path.abspath(base)
+                abs_path = os.path.abspath(os.path.join(base_abs, entry.get("subfolder") or "", entry["filename"]))
+                try:
+                    if os.path.commonpath([base_abs, abs_path]) != base_abs:
+                        raise ValueError("escapes base")
+                except ValueError:
+                    logging.warning("Asset enrichment skipped (path escapes base): %s", entry.get("filename"))
+                    new_entries.append(entry)
+                    continue
+                if not os.path.isfile(abs_path):
+                    new_entries.append(entry)
+                    continue
+
+                # Register unconditionally: the file was just produced, and
+                # register_file_in_place re-hashes so an overwritten path can
+                # never carry a stale id.
+                result = register_file_in_place(
+                    abs_path=abs_path,
+                    name=entry["filename"],
+                    tags=[entry["type"]],
+                )
+
+                entry = dict(entry)
+                entry["id"] = result.ref.id
+            except DependencyMissingError:
+                logging.warning("Asset enrichment skipped (blake3 not available): %s", entry.get("filename"))
+            except Exception:
+                logging.warning("Failed to enrich output entry with asset id: %s", entry.get("filename"), exc_info=True)
+            new_entries.append(entry)
+        enriched[key] = new_entries
+    return enriched

comfy_execution/jobs.py

@@ -3,6 +3,7 @@ Job utilities for the /api/jobs endpoint.
 Provides normalization and helper functions for job status tracking.
 """
 
+import uuid
 from typing import Optional
 
 from comfy_api.internal import prune_dict
@@ -19,6 +20,25 @@ class JobStatus:
     ALL = [PENDING, IN_PROGRESS, COMPLETED, FAILED, CANCELLED]
 
 
+def validate_job_id(value) -> str:
+    """Validate a client-supplied job (prompt) id.
+
+    Job ids must be UUIDs in the canonical lowercase hyphenated form. The id
+    is stored and compared verbatim everywhere downstream — history keys,
+    websocket events, and /interrupt matching — so accepting another spelling
+    would silently rewrite the client's id and then miss every exact-match
+    lookup. Rejecting loudly beats that.
+
+    Returns the id unchanged. Raises ValueError when the value is not a
+    string in canonical UUID form.
+    """
+    if not isinstance(value, str):
+        raise ValueError(f"job id must be a string, got {type(value).__name__}")
+    if str(uuid.UUID(value)) != value:
+        raise ValueError("job id must be a UUID in canonical lowercase hyphenated form")
+    return value
+
+
 # Media types that can be previewed in the frontend
 PREVIEWABLE_MEDIA_TYPES = frozenset({'images', 'video', 'audio', '3d', 'text'})
 

comfy_extras/nodes_bernini.py

@@ -0,0 +1,115 @@
+import torch
+from typing_extensions import override
+
+import comfy.model_management
+import comfy.utils
+import node_helpers
+from comfy_api.latest import ComfyExtension, io
+
+
+def _resize_long_edge(image, max_size, stride=16):
+    """Resize (preserve aspect) so the long edge <= max_size, then snap each side to `stride`"""
+    h, w = image.shape[1], image.shape[2]
+    scale = min(max_size / max(h, w), 1.0)
+    nh = max(stride, round(h * scale / stride) * stride)
+    nw = max(stride, round(w * scale / stride) * stride)
+    return comfy.utils.common_upscale(image[:, :, :, :3].movedim(-1, 1), nw, nh, "area", "disabled").movedim(1, -1)
+
+
+class BerniniConditioning(io.ComfyNode):
+    """Bernini in-context conditioning for a Wan2.2-A14B model.
+
+    Attaches the VAE-encoded source video / reference images to the conditioning
+    source video first, then each reference image
+
+    The task is inferred from which inputs are connected:
+      (nothing)                  -> t2v (text-to-video)
+      source_video               -> v2v (video-to-video)
+      source_video + ref_images  -> rv2v (reference-guided video editing)
+      ref_images only            -> r2v (reference-to-video)
+      source_video + ref_video   -> ads2v (insert image/video into video)
+
+    source_video is the edit base / canvas (resized to width x height).
+    reference_video is moving content to composite in.
+    Streams are ordered source_video, reference_video, then reference_images -> source_id (1, 2, 3, ...).
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="BerniniConditioning",
+            display_name="Bernini Conditioning",
+            category="conditioning/video_models",
+            description="Conditioning node for Bernini in-context video/image conditioning. It can be used for the following tasks: t2v (text-to-video), v2v (video-to-video), rv2v (reference-guided video editing), r2v (reference-to-video), ads2v (insert image/video into video)."
+                         "Reference images injected as in-context tokens (r2v, rv2v) are encoded independently at their own native aspect ratio (long edge capped at ref_max_size)",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=832, min=16, max=8192, step=16),
+                io.Int.Input("height", default=480, min=16, max=8192, step=16),
+                io.Int.Input("length", default=81, min=1, max=8192, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Image.Input("source_video", optional=True, tooltip=(
+                    "Source video to edit or restyle (v2v, rv2v). Resized to width/height and trimmed to length.")),
+                io.Image.Input("reference_video", optional=True, tooltip=(
+                    "Video to insert into the source video (ads2v).")),
+                io.Autogrow.Input("reference_images", optional=True,
+                    template=io.Autogrow.TemplatePrefix(
+                        input=io.Image.Input("reference_image", tooltip=(
+                            "Reference image injected as an in-context token (r2v, rv2v).")),
+                        prefix="reference_image_", min=0, max=8)),
+                io.Int.Input("ref_max_size", default=848, min=16, max=8192, step=16, optional=True, tooltip=(
+                    "Max size for the long edge of reference_video and reference_images. Resized with preserved aspect ratio and snapped to 16px.")),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, vae, width, height, length, batch_size,
+                source_video=None, reference_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput:
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8],
+                             device=comfy.model_management.intermediate_device())
+
+        # source_video (1), reference_video (2), reference_images (3, 4, ...).
+        context = []
+        if source_video is not None:
+            vid = comfy.utils.common_upscale(source_video[:length, :, :, :3].movedim(-1, 1), width, height, "area", "center").movedim(1, -1)
+            context.append(vae.encode(vid[:, :, :, :3]))
+
+        if reference_video is not None:
+            ref_vid = _resize_long_edge(reference_video[:length], ref_max_size)  # moving content, native aspect
+            context.append(vae.encode(ref_vid[:, :, :, :3]))
+
+        # reference_images is an autogrow dict {reference_image_0: IMAGE, ...}; each slot is a
+        # separate stream at its own native aspect (a multi-image batch in one slot -> one stream per frame).
+        if reference_images:
+            for name in sorted(reference_images):
+                imgs = reference_images[name]
+                if imgs is None:
+                    continue
+                for i in range(imgs.shape[0]):
+                    img = _resize_long_edge(imgs[i:i + 1], ref_max_size)  # native aspect per ref
+                    context.append(vae.encode(img[:, :, :, :3]))
+
+        if context:
+            positive = node_helpers.conditioning_set_values(positive, {"context_latents": context})
+            negative = node_helpers.conditioning_set_values(negative, {"context_latents": context})
+
+        return io.NodeOutput(positive, negative, {"samples": latent})
+
+
+class BerniniExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            BerniniConditioning,
+        ]
+
+
+async def comfy_entrypoint() -> BerniniExtension:
+    return BerniniExtension()

comfy_extras/nodes_depth_anything_3.py

@@ -0,0 +1,681 @@
+"""ComfyUI nodes for Depth Anything 3.
+Model capability matrix:
+
+Variant               head_type  has_sky  has_conf  cam_dec
+DA3-Small             dualdpt    False    True      yes
+DA3-Base              dualdpt    False    True      yes
+DA3-Mono-Large        dpt        True     False     no
+DA3-Metric-Large      dpt        True     False     no  (raw output is metres)
+"""
+
+from __future__ import annotations
+
+import logging
+from typing_extensions import override
+
+import torch
+
+import comfy.model_management as mm
+import comfy.sd
+import folder_paths
+from comfy.ldm.colormap import turbo as _turbo
+from comfy.ldm.depth_anything_3 import preprocess as da3_preprocess
+from comfy_api.latest import ComfyExtension, Types, io
+from comfy.ldm.moge.geometry import triangulate_grid_mesh
+
+DA3ModelType = io.Custom("DA3_MODEL")
+DA3Geometry = io.Custom("DA3_GEOMETRY")
+DA3PointCloud = io.Custom("DA3_POINT_CLOUD")
+
+# DA3_GEOMETRY is a dict with these optional keys (absent when the upstream model didn't produce them):
+#
+# Per-frame tensors - B = batch size in mono mode; B = S (number of views) in multi-view mode.
+#   "depth":       torch.Tensor (B, H, W)         -- raw model depth (always present; matches MoGe convention)
+#   "image":       torch.Tensor (B, H, W, 3)      -- source image in [0, 1], CPU (always present)
+#   "mode":        str                            -- "mono" or "multiview" (always present)
+#   "sky":         torch.Tensor (B, H, W)         -- sky probability in [0, 1] (Mono/Metric variants only)
+#   "confidence":  torch.Tensor (B, H, W)         -- raw model confidence output (Small/Base variants only)
+#
+# Multi-view only - S = number of views; the leading 1 is the scene dimension from the model.
+#   "extrinsics":  torch.Tensor (1, S, 3, 4)      -- world-to-camera [R|t] matrices
+#   "intrinsics":  torch.Tensor (1, S, 3, 3)      -- pixel-space intrinsics
+#
+# DA3_POINT_CLOUD is a dict:
+#   "points":     torch.Tensor (N, 3)  -- 3-D coords in glTF convention (Y-up, Z-back)
+#   "colors":     torch.Tensor (N, 3)  -- RGB in [0, 1], or None
+#   "confidence": torch.Tensor (N,)    -- raw confidence per point, or None
+
+
+def _da3_unproject(depth: torch.Tensor, K: torch.Tensor) -> torch.Tensor:
+    """Pixel-space K⁻¹ unprojection: (H,W) depth → (H,W,3) point map in OpenCV space."""
+    H, W = depth.shape
+    u = torch.arange(W, dtype=torch.float32, device=depth.device)
+    v = torch.arange(H, dtype=torch.float32, device=depth.device)
+    u, v = torch.meshgrid(u, v, indexing='xy')             # both (H, W)
+    pix = torch.stack([u, v, torch.ones_like(u)], dim=-1)  # (H, W, 3)
+    rays = torch.einsum('ij,hwj->hwi', torch.linalg.inv(K.to(depth.device)), pix)
+    return rays * depth.unsqueeze(-1)                       # (H, W, 3)
+
+
+def _da3_default_K(H: int, W: int) -> torch.Tensor:
+    """Fallback ~60° FOV pinhole K for mono-mode DA3 (no intrinsics in geometry)."""
+    fx = fy = float(W) * 0.7
+    return torch.tensor([[fx, 0.0, (W - 1) / 2.0],
+                         [0.0, fy, (H - 1) / 2.0],
+                         [0.0, 0.0, 1.0]], dtype=torch.float32)
+
+
+def _da3_get_K(geometry: dict, b: int, H: int, W: int) -> torch.Tensor:
+    """Return pixel-space K for batch element b, falling back to a default estimate."""
+    if "intrinsics" in geometry:
+        # shape (1, S, 3, 3) - leading scene dimension from the multiview head
+        return geometry["intrinsics"][0, b].float()
+    logging.getLogger("comfy").warning(
+        "DA3_GEOMETRY has no intrinsics (mono-mode model). "
+        "Using a ~60° FOV estimate; 3-D reconstruction may be inaccurate."
+    )
+    return _da3_default_K(H, W)
+
+
+def _da3_get_extrinsic(geometry: dict, b: int) -> torch.Tensor | None:
+    """Return the world-to-camera extrinsic for batch element b, or None in mono mode.
+
+    The model outputs (1, S, 3, 4) [R|t] matrices; the fallback identity is (4, 4).
+    _da3_apply_extrinsic handles both shapes via [:3, :3] / [:3, 3] slicing.
+    """
+    if "extrinsics" not in geometry:
+        return None
+    return geometry["extrinsics"][0, b].float()
+
+
+def _da3_apply_extrinsic(points_cam: torch.Tensor, E: torch.Tensor) -> torch.Tensor:
+    """Transform (H,W,3) OpenCV camera-space points to world space."""
+    E = E.to(points_cam.device).float()
+    if not torch.isfinite(E).all():
+        logging.getLogger("comfy").warning(
+            "DA3 extrinsic matrix contains non-finite values (pose estimation may have failed). "
+            "Falling back to camera-space coordinates."
+        )
+        return points_cam
+    H, W, _ = points_cam.shape
+    R = E[:3, :3]           # (3, 3) rotation
+    t = E[:3, 3]            # (3,)   translation
+    R_inv = R.T             # rotation inverse = transpose for orthogonal R
+    t_inv = -(R_inv @ t)    # (3,)
+    pts = points_cam.reshape(-1, 3)                 # (N, 3)
+    pts_world = pts @ R_inv.T + t_inv               # (N, 3)
+    return pts_world.reshape(H, W, 3)
+
+
+def _normalize_confidence(conf: torch.Tensor) -> torch.Tensor:
+    """Map raw confidence to [0, 1] per image."""
+    B = conf.shape[0]
+    out = []
+    for i in range(B):
+        c = conf[i]
+        c_min, c_max = c.min(), c.max()
+        out.append((c - c_min) / (c_max - c_min) if c_max > c_min else torch.ones_like(c))
+    return torch.stack(out, dim=0)
+
+
+def _da3_build_mask(geometry: dict, b: int, H: int, W: int, confidence_threshold: float, use_sky_mask: bool) -> torch.Tensor:
+    """Build (H,W) bool keep-mask from sky probability and confidence."""
+    mask = torch.ones(H, W, dtype=torch.bool)
+    if use_sky_mask and "sky" in geometry:
+        mask = mask & (geometry["sky"][b] < 0.5)
+    if "confidence" in geometry and confidence_threshold > 0.0:
+        conf_norm = _normalize_confidence(geometry["confidence"][b:b + 1])[0]
+        mask = mask & (conf_norm >= confidence_threshold)
+    return mask
+
+
+class LoadDA3Model(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoadDA3Model",
+            display_name="Load Depth Anything 3",
+            category="model/loaders",
+            inputs=[
+                io.Combo.Input(
+                    "model_name",
+                    options=folder_paths.get_filename_list("geometry_estimation"),
+                ),
+                io.Combo.Input(
+                    "weight_dtype",
+                    options=["default", "fp16", "bf16", "fp32"],
+                    default="default",
+                ),
+            ],
+            outputs=[DA3ModelType.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model_name, weight_dtype) -> io.NodeOutput:
+        model_options = {}
+        if weight_dtype == "fp16":
+            model_options["dtype"] = torch.float16
+        elif weight_dtype == "bf16":
+            model_options["dtype"] = torch.bfloat16
+        elif weight_dtype == "fp32":
+            model_options["dtype"] = torch.float32
+
+        path = folder_paths.get_full_path_or_raise("geometry_estimation", model_name)
+        model = comfy.sd.load_diffusion_model(path, model_options=model_options)
+        return io.NodeOutput(model)
+
+
+def _run_da3(model_patcher, image: torch.Tensor, process_res: int, method: str = "upper_bound_resize"):
+    """Run DA3 on (B,H,W,3), returns depth/conf/sky at original resolution (or None)."""
+    assert image.ndim == 4 and image.shape[-1] == 3, f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}"
+
+    B, H, W, _ = image.shape
+    mm.load_model_gpu(model_patcher)
+    diffusion = model_patcher.model.diffusion_model
+    device = mm.get_torch_device()
+    dtype = diffusion.dtype if diffusion.dtype is not None else torch.float32
+
+    depths, confs, skies = [], [], []
+    for i in range(B):
+        single = image[i:i + 1].to(device)
+        x = da3_preprocess.preprocess_image(single, process_res=process_res, method=method)
+        x = x.to(dtype=dtype)
+        with torch.no_grad():
+            out = diffusion(x)
+
+        depth_lr = out["depth"]
+        depth_full = torch.nn.functional.interpolate(
+            depth_lr.unsqueeze(1).float(), size=(H, W),
+            mode="bilinear", align_corners=False,
+        ).squeeze(1).cpu()
+        depths.append(depth_full)
+
+        if "depth_conf" in out:
+            conf_full = torch.nn.functional.interpolate(
+                out["depth_conf"].unsqueeze(1).float(), size=(H, W),
+                mode="bilinear", align_corners=False,
+            ).squeeze(1).cpu()
+            confs.append(conf_full)
+        if "sky" in out:
+            sky_full = torch.nn.functional.interpolate(
+                out["sky"].unsqueeze(1).float(), size=(H, W),
+                mode="bilinear", align_corners=False,
+            ).squeeze(1).cpu()
+            skies.append(sky_full)
+
+    depth = torch.cat(depths, dim=0)
+    confidence = torch.cat(confs, dim=0) if confs else None
+    sky = torch.cat(skies, dim=0) if skies else None
+    return depth, confidence, sky
+
+
+class DA3Inference(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DA3Inference",
+            search_aliases=["depth", "geometry", "da3", "depth anything", "monocular", "pointmap", "sky", "3d", "metric depth", "disparity"],
+            display_name="Run Depth Anything 3",
+            category="image/geometry estimation",
+            description="Run Depth Anything 3 on an image. In multi-view mode each image is treated as a separate view of the same scene.",
+            inputs=[
+                DA3ModelType.Input("da3_model"),
+                io.Image.Input("image"),
+                io.Int.Input("resolution", default=504, min=140, max=2520, step=14,
+                    tooltip="Resolution the model runs at (longest side, multiple of 14).\n"
+                        "Lower = faster / less VRAM.\n"
+                        "Higher = more detail.\n"
+                        "Output is upsampled back to the original size."),
+                io.Combo.Input("resize_method", options=["upper_bound_resize", "lower_bound_resize"], default="upper_bound_resize",
+                    tooltip="upper_bound_resize: scale so the longest side = resolution (caps memory, default).\n"
+                        "lower_bound_resize: scale so the shortest side = resolution (preserves more detail on tall/wide images, uses more memory)."),
+                io.DynamicCombo.Input("mode", tooltip="mono: single view image (works with any model variant).\n"
+                    "multiview: all images processed together for geometric consistency + camera pose (for Small/Base models only).",
+                    options=[
+                        io.DynamicCombo.Option("mono", []),
+                        io.DynamicCombo.Option("multiview", [
+                        io.Combo.Input("ref_view_strategy", options=["saddle_balanced", "saddle_sim_range", "first", "middle"], default="saddle_balanced",
+                            tooltip="Which view acts as the geometric anchor.\n"
+                                "- saddle_balanced: the view most 'average' across all others (best general choice).\n"
+                                "- saddle_sim_range: the view most visually distinct from the others.\n"
+                                "- first / middle: fixed positional picks."),
+                        io.Combo.Input("pose_method", options=["cam_dec", "ray_pose"], default="cam_dec",
+                            tooltip="How the camera field-of-view is estimated (for Small/Base models only).\n"
+                                "- cam_dec: learned from image features.\n"
+                                "- ray_pose: derived geometrically from the model's 3D ray output.\n"
+                                "Affects perspective correctness of the 3D output. Try both if results look distorted."),
+                    ]),
+                ]),
+            ],
+            outputs=[
+                DA3Geometry.Output("da3_geometry", tooltip="Dictionary of non-normalized tensors.\n"
+                    "Always has the keys: depth, image, mode.\n"
+                    "Optional keys: sky (for Mono/Metric), confidence (for Small/Base), extrinsics + intrinsics (for multi-view)."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, da3_model, image, resolution, resize_method, mode) -> io.NodeOutput:
+        mode_val = mode["mode"]  # "mono" or "multiview"
+
+        if mode_val == "mono":
+            return cls._execute_mono(da3_model, image, resolution, resize_method)
+
+        # Capability checks for multi-view mode.
+        diffusion = da3_model.model.diffusion_model
+        pose_method = mode["pose_method"]
+        ref_view_strategy = mode["ref_view_strategy"]
+
+        has_cam_dec = diffusion.cam_dec is not None
+        has_dualdpt = diffusion.head_type == "dualdpt"
+
+        if not has_cam_dec and not has_dualdpt:
+            raise ValueError(
+                "multi-view mode requires Small or Base model. The loaded model "
+                f"(head_type='{diffusion.head_type}') does not support cross-view "
+                "attention or camera pose estimation. Switch mode to 'mono', or "
+                "load Small or Base model for mult-view."
+            )
+
+        if pose_method == "cam_dec" and not has_cam_dec:
+            raise ValueError(
+                "pose_method='cam_dec' requires a camera decoder, but the loaded "
+                f"model (head_type='{diffusion.head_type}') does not have one. "
+                "Use pose_method='ray_pose' instead."
+            )
+        if pose_method == "ray_pose" and not has_dualdpt:
+            raise ValueError(
+                "pose_method='ray_pose' requires a DualDPT head, but the loaded "
+                f"model has a '{diffusion.head_type}' head. "
+                "Use pose_method='cam_dec' instead."
+            )
+
+        return cls._execute_multiview(
+            da3_model, image, resolution, resize_method,
+            ref_view_strategy, pose_method,
+        )
+
+    @classmethod
+    def _execute_mono(cls, model, image, resolution, resize_method) -> io.NodeOutput:
+        depth, confidence, sky = _run_da3(model, image, resolution, method=resize_method)
+
+        geometry: dict = {
+            "depth": depth.contiguous(),
+            "image": image[..., :3].cpu(),
+            "mode": "mono",
+        }
+        if sky is not None:
+            geometry["sky"] = sky.contiguous()
+        if confidence is not None:
+            geometry["confidence"] = confidence.contiguous()
+        return io.NodeOutput(geometry)
+
+    @classmethod
+    def _execute_multiview(cls, model, image, resolution, resize_method, ref_view_strategy, pose_method) -> io.NodeOutput:
+        assert image.ndim == 4 and image.shape[-1] == 3, \
+            f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}"
+        S, H, W, _ = image.shape
+
+        mm.load_model_gpu(model)
+        diffusion = model.model.diffusion_model
+        device = mm.get_torch_device()
+        dtype = diffusion.dtype if diffusion.dtype is not None else torch.float32
+
+        # All views in a single forward pass: (1, S, 3, H', W').
+        x = image.to(device)
+        x = da3_preprocess.preprocess_image(x, process_res=resolution, method=resize_method)
+        x = x.to(dtype=dtype).unsqueeze(0)
+
+        use_ray_pose = (pose_method == "ray_pose")
+        with torch.no_grad():
+            out = diffusion(x, use_ray_pose=use_ray_pose, ref_view_strategy=ref_view_strategy)
+
+        depth = torch.nn.functional.interpolate(
+            out["depth"].float().unsqueeze(1), size=(H, W),
+            mode="bilinear", align_corners=False,
+        ).squeeze(1).cpu()
+
+        sky = None
+        if "sky" in out:
+            sky = torch.nn.functional.interpolate(
+                out["sky"].unsqueeze(1).float(), size=(H, W),
+                mode="bilinear", align_corners=False,
+            ).squeeze(1).cpu()
+
+        if "extrinsics" in out and "intrinsics" in out:
+            extrinsics = out["extrinsics"].float().cpu()
+            intrinsics = out["intrinsics"].float().cpu()
+        else:
+            extrinsics = torch.eye(4)[None, None].expand(1, S, 4, 4).clone()
+            intrinsics = torch.eye(3)[None, None].expand(1, S, 3, 3).clone()
+
+        geometry: dict = {
+            "depth": depth.contiguous(),
+            "image": image[..., :3].cpu(),
+            "mode": "multiview",
+            "extrinsics": extrinsics.contiguous(),
+            "intrinsics": intrinsics.contiguous(),
+        }
+        if sky is not None:
+            geometry["sky"] = sky.contiguous()
+        if "depth_conf" in out:
+            conf = torch.nn.functional.interpolate(
+                out["depth_conf"].unsqueeze(1).float(), size=(H, W),
+                mode="bilinear", align_corners=False,
+            ).squeeze(1).cpu()
+            geometry["confidence"] = conf.contiguous()
+        return io.NodeOutput(geometry)
+
+
+class DA3Render(io.ComfyNode):
+    """Render a visualization from a DA3_GEOMETRY packet."""
+
+    _DEPTH_RENDER_INPUTS = [
+        io.Combo.Input("normalization",
+            options=["v2_style", "min_max", "raw"],
+            default="v2_style",
+            tooltip="- v2_style: mean/std normalisation for perceptually balanced results (default).\n"
+                "- min_max: stretches the full depth range to [0, 1] for maximum contrast.\n"
+                "- raw: no scaling,preserves metric units for Metric model."),
+        io.Boolean.Input("apply_sky_clip", default=False,
+            tooltip="Clip sky-region depth to the 99th percentile of foreground depth before normalisation. "
+                "Requires a sky key in the da3_geometry input (for Mono/Metric models only)."),
+    ]
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DA3Render",
+            display_name="Render Depth Anything 3",
+            category="image/geometry estimation",
+            description="Render a depth map, confidence map, or sky mask from Depth Anything 3 geometry data.",
+            inputs=[
+                DA3Geometry.Input("da3_geometry"),
+                io.DynamicCombo.Input("output",
+                    tooltip="- depth: normalised greyscale depth image.\n"
+                        "- depth_colored: depth mapped through the Turbo colormap.\n"
+                        "- sky_mask: sky probability in [0, 1] (for Mono/Metric models only).\n"
+                        "- confidence: normalised depth confidence (for Small/Base models only).",
+                options=[
+                    io.DynamicCombo.Option("depth", cls._DEPTH_RENDER_INPUTS),
+                    io.DynamicCombo.Option("depth_colored", cls._DEPTH_RENDER_INPUTS),
+                    io.DynamicCombo.Option("sky_mask", [
+                        io.Boolean.Input("colored", default=False, tooltip="Apply the Turbo colormap to the sky mask."),
+                    ]),
+                    io.DynamicCombo.Option("confidence", [
+                        io.Boolean.Input("colored", default=False, tooltip="Apply the Turbo colormap to the confidence map."),
+                    ]),
+                ]),
+            ],
+            outputs=[io.Image.Output()],
+        )
+
+    @classmethod
+    def execute(cls, da3_geometry, output) -> io.NodeOutput:
+        output_val = output["output"]
+
+        if output_val in ("depth", "depth_colored"):
+            normalization = output["normalization"]
+            apply_sky_clip = output["apply_sky_clip"]
+            if apply_sky_clip and "sky" not in da3_geometry:
+                raise ValueError(
+                    "apply_sky_clip=True requires a sky tensor in the da3_geometry input, but none is present. "
+                    "Run with Mono/Metric models or set apply_sky_clip=False."
+                )
+            depth = da3_geometry["depth"]
+            sky = da3_geometry.get("sky")
+            if apply_sky_clip and sky is not None:
+                depth = torch.stack([
+                    da3_preprocess.apply_sky_aware_clip(depth[i], sky[i])
+                    for i in range(depth.shape[0])
+                ], dim=0)
+            grey = cls._depth_to_image(depth, sky, normalization)  # (B,H,W,3) greyscale
+            result = _turbo(grey[..., 0]) if output_val == "depth_colored" else grey
+
+        elif output_val == "sky_mask":
+            if "sky" not in da3_geometry:
+                raise ValueError("geometry has no sky output; run with Mono/Metric models.")
+            sky = da3_geometry["sky"]
+            if output["colored"]:
+                result = _turbo(sky)
+            else:
+                result = sky.unsqueeze(-1).expand(*sky.shape, 3).contiguous()
+
+        elif output_val == "confidence":
+            if "confidence" not in da3_geometry:
+                raise ValueError("da3_geometry has no confidence output; run with Small/Base models.")
+            conf = _normalize_confidence(da3_geometry["confidence"])
+            if output["colored"]:
+                result = _turbo(conf)
+            else:
+                result = conf.unsqueeze(-1).expand(*conf.shape, 3).contiguous()
+
+        else:
+            raise ValueError(f"Unknown output mode: {output_val}")
+
+        return io.NodeOutput(result.float())
+
+    @staticmethod
+    def _depth_to_image(depth: torch.Tensor, sky_for_norm: torch.Tensor | None, normalization: str) -> torch.Tensor:
+        """Normalise depth and pack as an (B,H,W,3) image tensor."""
+
+        N = depth.shape[0]
+        if normalization == "v2_style":
+            norm = torch.stack([
+                da3_preprocess.normalize_depth_v2_style(
+                    depth[i], sky_for_norm[i] if sky_for_norm is not None else None)
+                for i in range(N)
+            ], dim=0)
+        elif normalization == "min_max":
+            norm = da3_preprocess.normalize_depth_min_max(depth)
+        else:
+            norm = depth
+
+        out = norm.unsqueeze(-1).repeat(1, 1, 1, 3)
+        if normalization != "raw":
+            out = out.clamp(0.0, 1.0)
+        return out.contiguous()
+
+
+class DA3GeometryToMesh(io.ComfyNode):
+    """Convert a DA3_GEOMETRY packet into a Types.MESH by unprojecting depth and triangulating."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DA3GeometryToMesh",
+            search_aliases=["da3", "depth anything", "mesh", "geometry", "3d", "triangulate"],
+            display_name="Convert DA3 Geometry to Mesh",
+            category="image/geometry estimation",
+            description="Convert a depth map into a triangulated 3D mesh.",
+            inputs=[
+                DA3Geometry.Input("da3_geometry"),
+                io.Int.Input("batch_index", default=0, min=0, max=4096, tooltip="Which image of a batch to convert. Per-image vertex counts differ so batches cannot be stacked."),
+                io.Int.Input("decimation", default=1, min=1, max=8, tooltip="Vertex stride. 1 = full resolution, 2 = half, etc."),
+                io.Float.Input("discontinuity_threshold", default=0.04, min=0.0, max=1.0, step=0.01, tooltip="Drop triangles whose 3x3 depth span exceeds this fraction. 0 = off."),
+                io.Float.Input("confidence_threshold", default=0.1, min=0.0, max=1.0, step=0.01,
+                    tooltip="Exclude pixels whose per-image normalised confidence is below this value (0 = keep all, 1 = keep only the single most confident pixel). "
+                        "Used when the geometry has a confidence map (Small/Base models)."),
+                io.Boolean.Input("use_sky_mask", default=True, tooltip="Exclude sky-probability pixels (sky >= 0.5) from the mesh. Used when the geometry has a sky map (Mono/Metric models)."),
+                io.Boolean.Input("texture", default=True, tooltip="Use the source image as a base color texture."),
+            ],
+            outputs=[io.Mesh.Output()],
+        )
+
+    @classmethod
+    def execute(cls, da3_geometry, batch_index, decimation, discontinuity_threshold, confidence_threshold, use_sky_mask, texture) -> io.NodeOutput:
+        depth_all = da3_geometry["depth"]   # (B, H, W)
+        B = depth_all.shape[0]
+        if batch_index >= B:
+            raise ValueError(f"batch_index {batch_index} is out of range; DA3_GEOMETRY has batch size {B}.")
+
+        depth = depth_all[batch_index]      # (H, W)
+        H, W = depth.shape
+
+        # NaN/inf depth would propagate silently through unproject and produce an
+        # empty mesh; replace them with 0 here so those pixels are later excluded
+        # by the isfinite check inside triangulate_grid_mesh.
+        depth = depth.clone()
+        n_bad = (~torch.isfinite(depth)).sum().item()
+        if n_bad:
+            logging.getLogger("comfy").warning(
+                f"DA3GeometryToMesh: depth[{batch_index}] has {n_bad} non-finite pixels "
+                f"({100*n_bad/(H*W):.1f}%) - zeroed before unproject."
+            )
+        depth[~torch.isfinite(depth)] = 0.0
+        logging.getLogger("comfy").debug(
+            f"DA3GeometryToMesh: depth[{batch_index}] range "
+            f"[{depth.min():.4g}, {depth.max():.4g}], mean={depth.mean():.4g}"
+        )
+
+        K = _da3_get_K(da3_geometry, batch_index, H, W)
+        points = _da3_unproject(depth, K)   # (H, W, 3) in OpenCV camera space
+
+        # Apply world-to-camera inverse so multi-view frames share a common world frame.
+        E = _da3_get_extrinsic(da3_geometry, batch_index)
+        if E is not None:
+            points = _da3_apply_extrinsic(points, E)
+
+        # Mask invalid pixels by setting them to inf so triangulate_grid_mesh skips them.
+        mask = _da3_build_mask(da3_geometry, batch_index, H, W, confidence_threshold, use_sky_mask)
+        # Also exclude pixels where depth was invalid.
+        mask = mask & (depth_all[batch_index] > 0) & torch.isfinite(depth_all[batch_index])
+        points = points.clone()
+        points[~mask] = float('inf')
+
+        verts, faces, uvs = triangulate_grid_mesh(
+            points,
+            decimation=decimation,
+            discontinuity_threshold=discontinuity_threshold,
+            depth=depth,
+        )
+        if verts.shape[0] == 0 or faces.shape[0] == 0:
+            raise ValueError(
+                "DA3GeometryToMesh produced an empty mesh. "
+                "Try raising discontinuity_threshold, lowering confidence_threshold, "
+                "or disabling use_sky_mask."
+            )
+
+        # OpenCV (X right, Y down, Z forward) → glTF (X right, Y up, Z back).
+        # Same transform as MoGePointMapToMesh perspective branch.
+        verts = verts * torch.tensor([1.0, -1.0, -1.0], dtype=verts.dtype)
+        faces = faces[:, [0, 2, 1]].contiguous()
+
+        tex = da3_geometry["image"][batch_index:batch_index + 1] if texture else None
+        mesh = Types.MESH(
+            vertices=verts.unsqueeze(0),
+            faces=faces.unsqueeze(0),
+            uvs=uvs.unsqueeze(0),
+            texture=tex,
+        )
+        return io.NodeOutput(mesh)
+
+
+class DA3GeometryToPointCloud(io.ComfyNode):
+    """Unproject a DA3_GEOMETRY depth map into a filtered DA3_POINT_CLOUD."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DA3GeometryToPointCloud",
+            search_aliases=["da3", "depth anything", "point cloud", "pointcloud", "3d", "geometry"],
+            display_name="Convert DA3 Geometry to Point Cloud",
+            category="image/geometry estimation",
+            description="Convert a depth map into a 3D point cloud.",
+            inputs=[
+                DA3Geometry.Input("da3_geometry"),
+                io.Int.Input("batch_index", default=0, min=0, max=4096, tooltip="Which image of a batch to convert."),
+                io.Float.Input("confidence_threshold", default=0.1, min=0.0, max=1.0, step=0.01,
+                    tooltip="Exclude pixels whose per-image normalised confidence is below this value (0 = keep all). Used when the geometry has a confidence map (Small/Base models)."),
+                io.Boolean.Input("use_sky_mask", default=True,
+                    tooltip="Exclude sky-probability pixels (sky >= 0.5). Used when the geometry has a sky map (Mono/Metric models)."),
+                io.Int.Input("downsample", default=1, min=1, max=16,
+                    tooltip="Take every Nth pixel (1 = full resolution). Higher values give fewer points and faster processing."),
+            ],
+            # TODO: add a proper PointCloud output type
+            outputs=[DA3PointCloud.Output(display_name="point_cloud")],
+        )
+
+    @classmethod
+    def execute(cls, da3_geometry, batch_index, confidence_threshold, use_sky_mask, downsample) -> io.NodeOutput:
+        depth_all = da3_geometry["depth"]   # (B, H, W)
+        B = depth_all.shape[0]
+        if batch_index >= B:
+            raise ValueError(f"batch_index {batch_index} is out of range; DA3_GEOMETRY has batch size {B}.")
+
+        depth = depth_all[batch_index].clone()  # (H, W)
+        depth[~torch.isfinite(depth)] = 0.0
+        H, W = depth.shape
+
+        K = _da3_get_K(da3_geometry, batch_index, H, W)
+
+        if downsample > 1:
+            depth = depth[::downsample, ::downsample].contiguous()
+            # Scale intrinsics to the downsampled grid.
+            K = K.clone()
+            K[0, :] /= downsample
+            K[1, :] /= downsample
+
+        H_ds, W_ds = depth.shape
+        points = _da3_unproject(depth, K)   # (H_ds, W_ds, 3) in OpenCV camera space
+
+        # Apply world-to-camera inverse so multi-view frames share a common world frame.
+        E = _da3_get_extrinsic(da3_geometry, batch_index)
+        if E is not None:
+            points = _da3_apply_extrinsic(points, E)
+
+        # Rebuild mask at downsampled resolution.
+        mask = _da3_build_mask(da3_geometry, batch_index, H, W, confidence_threshold, use_sky_mask)
+        if downsample > 1:
+            mask = mask[::downsample, ::downsample]
+
+        mask = mask & torch.isfinite(depth)
+
+        # OpenCV → glTF: flip Y and Z.
+        points_gltf = points.clone()
+        points_gltf[..., 1] *= -1.0
+        points_gltf[..., 2] *= -1.0
+
+        pts_flat = points_gltf.reshape(-1, 3)[mask.reshape(-1)]
+
+        colors_flat = None
+        if "image" in da3_geometry:
+            img = da3_geometry["image"][batch_index]     # (H, W, 3)
+            if downsample > 1:
+                img = img[::downsample, ::downsample]
+            colors_flat = img.reshape(-1, 3)[mask.reshape(-1)]
+
+        conf_flat = None
+        if "confidence" in da3_geometry:
+            conf = da3_geometry["confidence"][batch_index]   # (H, W)
+            if downsample > 1:
+                conf = conf[::downsample, ::downsample]
+            conf_flat = conf.reshape(-1)[mask.reshape(-1)]
+
+        if pts_flat.shape[0] == 0:
+            raise ValueError(
+                "DA3GeometryToPointCloud produced zero points after filtering. "
+                "Try lowering confidence_threshold or disabling use_sky_mask."
+            )
+
+        return io.NodeOutput({
+            "points": pts_flat,
+            "colors": colors_flat,
+            "confidence": conf_flat,
+        })
+
+
+class DA3Extension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            LoadDA3Model,
+            DA3Inference,
+            DA3Render,
+            DA3GeometryToMesh,
+            # DA3GeometryToPointCloud,  # Keep this commented out for now until we have a proper PointCloud output type
+        ]
+
+
+async def comfy_entrypoint() -> DA3Extension:
+    return DA3Extension()

comfy_extras/nodes_moge.py

@@ -8,6 +8,7 @@ import folder_paths
 from comfy_api.latest import ComfyExtension, Types, io
 from typing_extensions import override
 
+from comfy.ldm.colormap import turbo as _turbo
 from comfy.ldm.moge.model import MoGeModel
 from comfy.ldm.moge.geometry import triangulate_grid_mesh
 from comfy.ldm.moge.panorama import get_panorama_cameras, split_panorama_image, merge_panorama_depth, spherical_uv_to_directions, _uv_grid
@@ -27,19 +28,6 @@ MoGeGeometry = io.Custom("MOGE_GEOMETRY")
 #   "image":      torch.Tensor (B, H, W, 3) in [0, 1], CPU (always present)
 
 
-def _turbo(x: torch.Tensor) -> torch.Tensor:
-    """Anton Mikhailov polynomial approximation of the turbo colormap."""
-    x = x.clamp(0.0, 1.0)
-    x2 = x * x
-    x3 = x2 * x
-    x4 = x2 * x2
-    x5 = x4 * x
-    r = 0.13572138 + 4.61539260*x - 42.66032258*x2 + 132.13108234*x3 - 152.94239396*x4 + 59.28637943*x5
-    g = 0.09140261 + 2.19418839*x + 4.84296658*x2 - 14.18503333*x3 + 4.27729857*x4 + 2.82956604*x5
-    b = 0.10667330 + 12.64194608*x - 60.58204836*x2 + 110.36276771*x3 - 89.90310912*x4 + 27.34824973*x5
-    return torch.stack([r, g, b], dim=-1).clamp(0.0, 1.0)
-
-
 def _normals_from_points(points: torch.Tensor) -> torch.Tensor:
     """Camera-space surface normals from a (B, H, W, 3) point map (v1 fallback)."""
     finite = torch.isfinite(points).all(dim=-1)

comfy_extras/nodes_scail.py

@@ -267,7 +267,8 @@ class SCAIL2ColoredMask(io.ComfyNode):
                 io.Combo.Input("sort_by", options=["none", "left_to_right", "area"], default="left_to_right",
                                tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). left_to_right = leftmost object (by first-frame centroid) gets the first color; area = biggest object (by first-frame mask area) gets the first color; none = keep SAM3's order."),
                 io.Boolean.Input("replacement_mode", default=False,
-                                 tooltip="False = mask_video has black bg (Animation Mode). True = white bg (Replacement Mode). Set the matching replacement_mode on WanSCAILToVideo. reference_image_mask is always black-bg regardless."),
+                                 tooltip="False = Animation Mode (pose_video_mask has black background, reference_image_mask has white background). "
+                                         "True = Replacement Mode (pose_video_mask has white background, reference_image_mask has black background)."),
             ],
             outputs=[
                 io.Image.Output("pose_video_mask"),
@@ -296,14 +297,17 @@ class SCAIL2ColoredMask(io.ComfyNode):
             return td
 
         drv = _prep(driving_track_data)
+        # Animation: driving=black, ref=white. Replacement: driving=white, ref=black.
         mask_video = _render_colored_masks(drv, "white" if replacement_mode else "black")
+        ref_bg = "black" if replacement_mode else "white"
 
         if ref_track_data is not None:
             ref = _prep(ref_track_data)
-            reference_image_mask = _render_colored_masks(ref, "black")
+            reference_image_mask = _render_colored_masks(ref, ref_bg)
         else:
             H, W = drv["orig_size"]
-            reference_image_mask = torch.zeros(1, H, W, 3, device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
+            fill_value = 1.0 if ref_bg == "white" else 0.0
+            reference_image_mask = torch.full((1, H, W, 3), fill_value, device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
 
         return io.NodeOutput(mask_video, reference_image_mask)
 

comfy_extras/nodes_train.py

@@ -15,6 +15,7 @@ import comfy.sampler_helpers
 import comfy.sd
 import comfy.utils
 import comfy.model_management
+from comfy.conds import CONDRegular, CONDList
 from comfy.cli_args import args, PerformanceFeature
 import comfy_extras.nodes_custom_sampler
 import folder_paths
@@ -120,6 +121,11 @@ def process_cond_list(d, prefix=""):
                 process_cond_list(v, f"{prefix}.{k}")
             elif isinstance(v, torch.Tensor):
                 d[k] = v.clone()
+            elif isinstance(v, CONDList):
+                v.cond = [t.detach() if isinstance(t, torch.Tensor) else t for t in v.cond]
+            elif isinstance(v, CONDRegular):
+                if isinstance(v.cond, torch.Tensor):
+                    v.cond = v.cond.detach()
             elif isinstance(v, (list, tuple)):
                 for index, item in enumerate(v):
                     process_cond_list(item, f"{prefix}.{k}.{index}")
@@ -1143,45 +1149,45 @@ class TrainLoraNode(io.ComfyNode):
         # Process conditioning
         positive = _process_conditioning(positive)
 
-        # Setup model and dtype
-        mp = model.clone()
-        use_grad_scaler = False
-        lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype)
-        if training_dtype != "none":
-            dtype = node_helpers.string_to_torch_dtype(training_dtype)
-            mp.set_model_compute_dtype(dtype)
-        else:
-            # Detect model's native dtype for autocast
-            model_dtype = mp.model.get_dtype()
-            if model_dtype == torch.float16:
-                dtype = torch.float16
-                # GradScaler only supports float16 gradients, not bfloat16.
-                # Only enable it when lora params will also be in float16.
-                if lora_dtype != torch.bfloat16:
-                    use_grad_scaler = True
-                # Warn about fp16 accumulation instability during training
-                if PerformanceFeature.Fp16Accumulation in args.fast:
-                    logging.warning(
-                        "WARNING: FP16 model detected with fp16_accumulation enabled. "
-                        "This combination can be numerically unstable during training and may cause NaN values. "
-                        "Suggested fixes: 1) Set training_dtype to 'bf16', or 2) Disable fp16_accumulation (remove from --fast flags)."
-                    )
-            else:
-                # For fp8, bf16, or other dtypes, use bf16 autocast
-                dtype = torch.bfloat16
-
-        # Prepare latents and compute counts
-        latents_dtype = dtype if dtype not in (None,) else torch.bfloat16
-        latents, num_images, multi_res = _prepare_latents_and_count(
-            latents, latents_dtype, bucket_mode
-        )
-
-        # Validate and expand conditioning
-        positive = _validate_and_expand_conditioning(positive, num_images, bucket_mode)
-
         with torch.inference_mode(False):
+            # Setup model and dtype
+            mp = model.clone(force_deepcopy=True)
+            use_grad_scaler = False
+            lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype)
+            if training_dtype != "none":
+                dtype = node_helpers.string_to_torch_dtype(training_dtype)
+                mp.set_model_compute_dtype(dtype)
+            else:
+                # Detect model's native dtype for autocast
+                model_dtype = mp.model.get_dtype()
+                if model_dtype == torch.float16:
+                    dtype = torch.float16
+                    # GradScaler only supports float16 gradients, not bfloat16.
+                    # Only enable it when lora params will also be in float16.
+                    if lora_dtype != torch.bfloat16:
+                        use_grad_scaler = True
+                    # Warn about fp16 accumulation instability during training
+                    if PerformanceFeature.Fp16Accumulation in args.fast:
+                        logging.warning(
+                            "WARNING: FP16 model detected with fp16_accumulation enabled. "
+                            "This combination can be numerically unstable during training and may cause NaN values. "
+                            "Suggested fixes: 1) Set training_dtype to 'bf16', or 2) Disable fp16_accumulation (remove from --fast flags)."
+                        )
+                else:
+                    # For fp8, bf16, or other dtypes, use bf16 autocast
+                    dtype = torch.bfloat16
+
+            # Prepare latents and compute counts
+            latents_dtype = dtype if dtype not in (None,) else torch.bfloat16
+            latents, num_images, multi_res = _prepare_latents_and_count(
+                latents, latents_dtype, bucket_mode
+            )
+
+            # Validate and expand conditioning
+            positive = _validate_and_expand_conditioning(positive, num_images, bucket_mode)
+
             # Setup models for training
-            mp.model.requires_grad_(False)
+            mp.model.requires_grad_(False).train()
 
             # Load existing LoRA weights if provided
             existing_weights, existing_steps = _load_existing_lora(existing_lora)

cuda_malloc.py

@@ -2,6 +2,7 @@ import os
 import importlib.util
 from comfy.cli_args import args, PerformanceFeature
 import subprocess
+import re
 
 #Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import.
 def get_gpu_names():
@@ -77,11 +78,24 @@ try:
 except:
     pass
 
+def get_raw_cuda_version(version_str):
+    match = re.search(r'\+cu(\d+)', version_str)
+    if match:
+        try:
+            return int(match.group(1))
+        except:
+            pass
+    return None
+
 if not args.cuda_malloc:
     try:
         if int(version[0]) >= 2 and "+cu" in version:  # enable by default for torch version 2.0 and up only on cuda torch
             if PerformanceFeature.AutoTune not in args.fast:  # Autotune has issues with cuda malloc
-                args.cuda_malloc = cuda_malloc_supported()
+                cuda_version = get_raw_cuda_version(version)
+                if cuda_version is not None and cuda_version >= 130:
+                    args.cuda_malloc = True
+                else:
+                    args.cuda_malloc = cuda_malloc_supported()
     except:
         pass
 

execution.py

@@ -40,6 +40,7 @@ from comfy_execution.graph_utils import GraphBuilder, is_link
 from comfy_execution.validation import validate_node_input
 from comfy_execution.progress import get_progress_state, reset_progress_state, add_progress_handler, WebUIProgressHandler
 from comfy_execution.utils import CurrentNodeContext
+from comfy_execution.asset_enrichment import enrich_output_with_assets
 from comfy_api.internal import _ComfyNodeInternal, _NodeOutputInternal, first_real_override, is_class, make_locked_method_func
 from comfy_api.latest import io, _io
 from comfy_execution.cache_provider import _has_cache_providers, _get_cache_providers, _logger as _cache_logger
@@ -418,6 +419,7 @@ def _is_intermediate_output(dynprompt, node_id):
     class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
     return getattr(class_def, 'HAS_INTERMEDIATE_OUTPUT', False)
 
+
 def _send_cached_ui(server, node_id, display_node_id, cached, prompt_id, ui_outputs):
     if server.client_id is None:
         return
@@ -552,6 +554,10 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                 asyncio.create_task(await_completion())
                 return (ExecutionResult.PENDING, None, None)
         if len(output_ui) > 0:
+            # Enrich at output-processing time (not in the send path) so assets
+            # are registered even when no client is connected, and the asset id
+            # flows into ui_outputs and the cache alongside the raw entries.
+            output_ui = enrich_output_with_assets(output_ui)
             ui_outputs[unique_id] = {
                 "meta": {
                     "node_id": unique_id,

main.py

@@ -490,6 +490,11 @@ def start_comfyui(asyncio_loop=None):
         init_custom_nodes=(not args.disable_all_custom_nodes) or len(args.whitelist_custom_nodes) > 0,
         init_api_nodes=not args.disable_api_nodes
     ))
+
+    # Re-apply Comfy's cuDNN benchmark policy after custom-node imports. Benchmark
+    # mode can request near-card-sized autotune workspaces, and some custom nodes set it at import time.
+    comfy.model_management.set_cudnn_benchmark()
+
     hook_breaker_ac10a0.restore_functions()
 
     cuda_malloc_warning()

nodes.py

@@ -2404,6 +2404,7 @@ async def init_builtin_extra_nodes():
         "nodes_video.py",
         "nodes_lumina2.py",
         "nodes_wan.py",
+        "nodes_bernini.py",
         "nodes_lotus.py",
         "nodes_hunyuan3d.py",
         "nodes_primitive.py",
@@ -2458,7 +2459,8 @@ async def init_builtin_extra_nodes():
         "nodes_moge.py",
         "nodes_mediapipe.py",
         "nodes_gaussian_splat.py",
-        "nodes_triposplat.py"
+        "nodes_triposplat.py",
+        "nodes_depth_anything_3.py",
     ]
 
     import_failed = []

openapi.yaml

@@ -896,6 +896,11 @@ components:
                     additionalProperties: true
                     description: The workflow graph to execute
                     type: object
+                prompt_id:
+                    description: Optional client-supplied job id. Must be a UUID in canonical lowercase hyphenated form; it is echoed back in the response. Omitted or null means the server generates one.
+                    format: uuid
+                    nullable: true
+                    type: string
                 workflow_id:
                     description: UUID identifying the cloud workflow entity to associate with this job
                     type: string
@@ -1063,7 +1068,7 @@ components:
                             description: ComfyUI version
                             type: string
                         deploy_environment:
-                            description: How this ComfyUI instance was installed (e.g. local-git, local-portable, local-desktop)
+                            description: How this ComfyUI instance is deployed (e.g. cloud, local-git, local-portable, local-desktop)
                             type: string
                         embedded_python:
                             description: Whether using embedded Python
@@ -1963,8 +1968,8 @@ paths:
                         schema:
                             properties:
                                 hash:
-                                    description: Hash of the existing asset. Supports Blake3 (blake3:) or SHA256 (sha256:) formats
-                                    pattern: ^(blake3|sha256):[a-f0-9]{64}$
+                                    description: 'Blake3 content hash of the existing asset (blake3: prefix)'
+                                    pattern: ^blake3:[a-f0-9]{64}$
                                     type: string
                                 mime_type:
                                     description: MIME type of the asset (e.g., "image/png", "video/mp4")

server.py

@@ -8,7 +8,7 @@ import time
 import nodes
 import folder_paths
 import execution
-from comfy_execution.jobs import JobStatus, get_job, get_all_jobs
+from comfy_execution.jobs import JobStatus, get_job, get_all_jobs, validate_job_id
 import uuid
 import urllib
 import json
@@ -944,7 +944,21 @@ class PromptServer():
 
             if "prompt" in json_data:
                 prompt = json_data["prompt"]
-                prompt_id = str(json_data.get("prompt_id", uuid.uuid4()))
+                client_prompt_id = json_data.get("prompt_id")
+                if client_prompt_id is None:
+                    # Absent or explicit null: the server mints the id.
+                    prompt_id = str(uuid.uuid4())
+                else:
+                    try:
+                        prompt_id = validate_job_id(client_prompt_id)
+                    except ValueError:
+                        error = {
+                            "type": "invalid_prompt_id",
+                            "message": "prompt_id must be a valid UUID",
+                            "details": "prompt_id must be a UUID string in canonical lowercase hyphenated form; omit it to let the server generate one",
+                            "extra_info": {}
+                        }
+                        return web.json_response({"error": error, "node_errors": {}}, status=400)
 
                 partial_execution_targets = None
                 if "partial_execution_targets" in json_data:

tests-unit/assets_test/conftest.py

@@ -6,6 +6,7 @@ import subprocess
 import sys
 import tempfile
 import time
+import uuid
 from pathlib import Path
 from typing import Callable, Iterator, Optional
 
@@ -188,9 +189,17 @@ def _post_multipart_asset(
 
 @pytest.fixture
 def make_asset_bytes() -> Callable[[str, int], bytes]:
+    # Salt content per test so it never collides with assets left over from
+    # earlier tests. Delete is now always a soft delete (content is preserved),
+    # so the suite can no longer rely on hard-deleting content for isolation.
+    # Deterministic within a test: the same (name, size) yields the same bytes.
+    salt = uuid.uuid4().bytes
+
     def _make(name: str, size: int = 8192) -> bytes:
         seed = sum(ord(c) for c in name) % 251
-        return bytes((i * 31 + seed) % 256 for i in range(size))
+        body = bytearray((i * 31 + seed) % 256 for i in range(size))
+        body[: len(salt)] = salt[:size]
+        return bytes(body)
     return _make
 
 
@@ -212,7 +221,7 @@ def asset_factory(http: requests.Session, api_base: str):
 
     for aid in created:
         with contextlib.suppress(Exception):
-            http.delete(f"{api_base}/api/assets/{aid}?delete_content=true", timeout=30)
+            http.delete(f"{api_base}/api/assets/{aid}", timeout=30)
 
 
 @pytest.fixture
@@ -227,7 +236,11 @@ def seeded_asset(request: pytest.FixtureRequest, http: requests.Session, api_bas
     if tags is None:
         tags = ["models", "checkpoints", "unit-tests", "alpha"]
     meta = {"purpose": "test", "epoch": 1, "flags": ["x", "y"], "nullable": None}
-    files = {"file": (name, b"A" * 4096, "application/octet-stream")}
+    # Unique content per test so the seed always creates a fresh asset (201).
+    # Delete is now always a soft delete, so content from a prior test survives
+    # and would otherwise dedup this upload into an existing asset (200).
+    content = uuid.uuid4().bytes + b"A" * (4096 - 16)
+    files = {"file": (name, content, "application/octet-stream")}
     form_data = {
         "tags": json.dumps(tags),
         "name": name,
@@ -260,4 +273,4 @@ def autoclean_unit_test_assets(http: requests.Session, api_base: str):
             break
         for aid in ids:
             with contextlib.suppress(Exception):
-                http.delete(f"{api_base}/api/assets/{aid}?delete_content=true", timeout=30)
+                http.delete(f"{api_base}/api/assets/{aid}", timeout=30)

tests-unit/assets_test/queries/test_asset_reference_keyset.py

@@ -0,0 +1,112 @@
+"""Keyset-pagination tiebreaker tests for list_references_page.
+
+When multiple rows share the same primary sort value (e.g. four assets
+created in the same microsecond), the secondary `ORDER BY id` is what keeps
+keyset pagination from losing or repeating rows. This file exercises that
+branch directly against an in-memory SQLite session — engineering identical
+timestamps via HTTP is unreliable enough that we work at the query layer.
+"""
+import uuid
+from datetime import datetime
+
+import pytest
+from sqlalchemy.orm import Session
+
+from app.assets.database.models import Asset, AssetReference
+from app.assets.database.queries.asset_reference import list_references_page
+
+
+def _make_ref(session: Session, created_at: datetime, name: str, owner: str = "") -> AssetReference:
+    asset = Asset(hash=f"blake3:{uuid.uuid4().hex}", size_bytes=1024)
+    session.add(asset)
+    session.flush()
+    ref = AssetReference(
+        id=str(uuid.uuid4()),
+        asset_id=asset.id,
+        owner_id=owner,
+        name=name,
+        file_path=f"/tmp/{name}",
+        created_at=created_at,
+        updated_at=created_at,
+        last_access_time=created_at,
+        is_missing=False,
+    )
+    session.add(ref)
+    return ref
+
+
+@pytest.mark.parametrize("order", ["desc", "asc"])
+def test_tiebreaker_walks_duplicate_sort_values(session: Session, order: str):
+    """Four rows with the SAME created_at must paginate cleanly under cursor
+    mode — no row dropped, no row repeated, despite the primary sort column
+    being non-discriminating.
+    """
+    shared_ts = datetime(2024, 5, 20, 12, 0, 0)  # naive UTC, like the DB stores
+    refs = [_make_ref(session, shared_ts, f"tie_{i}.png") for i in range(4)]
+    session.commit()
+
+    expected_ids = sorted([r.id for r in refs], reverse=(order == "desc"))
+
+    # Walk the cursor by hand: page size 2, take 3 pages (2 + 2 + 0).
+    seen: list[str] = []
+    after_value = None
+    after_id = None
+    for _ in range(4):  # generous loop bound; ought to be 2 iterations
+        page, _tag_map, _total = list_references_page(
+            session,
+            limit=2,
+            sort="created_at",
+            order=order,
+            after_cursor_value=after_value,
+            after_cursor_id=after_id,
+        )
+        if not page:
+            break
+        seen.extend(p.id for p in page)
+        # Use the last row's (created_at, id) as the next cursor input.
+        last = page[-1]
+        after_value, after_id = last.created_at, last.id
+        if len(page) < 2:
+            break
+
+    assert seen == expected_ids, (
+        f"keyset tiebreaker failed for order={order}: expected {expected_ids}, got {seen}"
+    )
+
+
+def test_tiebreaker_no_duplicates_under_mixed_collisions(session: Session):
+    """Some rows share a timestamp, some don't. The cursor must still walk
+    every row exactly once regardless of where ties sit relative to a
+    page boundary."""
+    t1 = datetime(2024, 5, 20, 12, 0, 0)
+    t2 = datetime(2024, 5, 20, 12, 0, 1)
+    layout = [t1, t1, t1, t2, t2]  # three rows at t1, two at t2
+    refs = [_make_ref(session, ts, f"mix_{i}.png") for i, ts in enumerate(layout)]
+    session.commit()
+
+    all_ids = {r.id for r in refs}
+    seen_set: set[str] = set()
+    seen_list: list[str] = []
+    after_value = None
+    after_id = None
+    for _ in range(6):
+        page, _, _ = list_references_page(
+            session,
+            limit=2,
+            sort="created_at",
+            order="desc",
+            after_cursor_value=after_value,
+            after_cursor_id=after_id,
+        )
+        if not page:
+            break
+        for p in page:
+            assert p.id not in seen_set, f"duplicate row {p.id} appeared in cursor walk"
+            seen_set.add(p.id)
+            seen_list.append(p.id)
+        last = page[-1]
+        after_value, after_id = last.created_at, last.id
+        if len(page) < 2:
+            break
+
+    assert seen_set == all_ids, f"missing rows: expected {all_ids}, got {seen_set}"

tests-unit/assets_test/queries/test_tags.py

@@ -40,15 +40,15 @@ def _make_reference(session: Session, asset: Asset, name: str = "test", owner_id
 
 class TestEnsureTagsExist:
     def test_creates_new_tags(self, session: Session):
-        ensure_tags_exist(session, ["alpha", "beta"], tag_type="user")
+        ensure_tags_exist(session, ["alpha", "beta"])
         session.commit()
 
         tags = session.query(Tag).all()
         assert {t.name for t in tags} == {"alpha", "beta"}
 
     def test_is_idempotent(self, session: Session):
-        ensure_tags_exist(session, ["alpha"], tag_type="user")
-        ensure_tags_exist(session, ["alpha"], tag_type="user")
+        ensure_tags_exist(session, ["alpha"])
+        ensure_tags_exist(session, ["alpha"])
         session.commit()
 
         assert session.query(Tag).count() == 1
@@ -65,13 +65,6 @@ class TestEnsureTagsExist:
         session.commit()
         assert session.query(Tag).count() == 0
 
-    def test_tag_type_is_set(self, session: Session):
-        ensure_tags_exist(session, ["system-tag"], tag_type="system")
-        session.commit()
-
-        tag = session.query(Tag).filter_by(name="system-tag").one()
-        assert tag.tag_type == "system"
-
 
 class TestGetReferenceTags:
     def test_returns_empty_for_no_tags(self, session: Session):
@@ -193,7 +186,7 @@ class TestMissingTagFunctions:
     def test_add_missing_tag_for_asset_id(self, session: Session):
         asset = _make_asset(session, "hash1")
         ref = _make_reference(session, asset)
-        ensure_tags_exist(session, ["missing"], tag_type="system")
+        ensure_tags_exist(session, ["missing"])
 
         add_missing_tag_for_asset_id(session, asset_id=asset.id)
         session.commit()
@@ -204,7 +197,7 @@ class TestMissingTagFunctions:
     def test_add_missing_tag_is_idempotent(self, session: Session):
         asset = _make_asset(session, "hash1")
         ref = _make_reference(session, asset)
-        ensure_tags_exist(session, ["missing"], tag_type="system")
+        ensure_tags_exist(session, ["missing"])
 
         add_missing_tag_for_asset_id(session, asset_id=asset.id)
         add_missing_tag_for_asset_id(session, asset_id=asset.id)
@@ -216,7 +209,7 @@ class TestMissingTagFunctions:
     def test_remove_missing_tag_for_asset_id(self, session: Session):
         asset = _make_asset(session, "hash1")
         ref = _make_reference(session, asset)
-        ensure_tags_exist(session, ["missing"], tag_type="system")
+        ensure_tags_exist(session, ["missing"])
         add_missing_tag_for_asset_id(session, asset_id=asset.id)
 
         remove_missing_tag_for_asset_id(session, asset_id=asset.id)
@@ -237,7 +230,7 @@ class TestListTagsWithUsage:
 
         rows, total = list_tags_with_usage(session)
 
-        tag_dict = {name: count for name, _, count in rows}
+        tag_dict = {name: count for name, count in rows}
         assert tag_dict["used"] == 1
         assert tag_dict["unused"] == 0
         assert total == 2
@@ -252,7 +245,7 @@ class TestListTagsWithUsage:
 
         rows, total = list_tags_with_usage(session, include_zero=False)
 
-        tag_names = {name for name, _, _ in rows}
+        tag_names = {name for name, _ in rows}
         assert "used" in tag_names
         assert "unused" not in tag_names
 
@@ -262,7 +255,7 @@ class TestListTagsWithUsage:
 
         rows, total = list_tags_with_usage(session, prefix="alph")
 
-        tag_names = {name for name, _, _ in rows}
+        tag_names = {name for name, _ in rows}
         assert tag_names == {"alpha", "alphabet"}
 
     def test_order_by_name(self, session: Session):
@@ -271,7 +264,7 @@ class TestListTagsWithUsage:
 
         rows, _ = list_tags_with_usage(session, order="name_asc")
 
-        names = [name for name, _, _ in rows]
+        names = [name for name, _ in rows]
         assert names == ["alpha", "middle", "zebra"]
 
     def test_owner_visibility(self, session: Session):
@@ -287,13 +280,13 @@ class TestListTagsWithUsage:
 
         # Empty owner sees only shared
         rows, _ = list_tags_with_usage(session, owner_id="", include_zero=False)
-        tag_dict = {name: count for name, _, count in rows}
+        tag_dict = {name: count for name, count in rows}
         assert tag_dict.get("shared-tag", 0) == 1
         assert tag_dict.get("owner-tag", 0) == 0
 
         # User1 sees both
         rows, _ = list_tags_with_usage(session, owner_id="user1", include_zero=False)
-        tag_dict = {name: count for name, _, count in rows}
+        tag_dict = {name: count for name, count in rows}
         assert tag_dict.get("shared-tag", 0) == 1
         assert tag_dict.get("owner-tag", 0) == 1
 

tests-unit/assets_test/services/test_cursor.py

@@ -0,0 +1,278 @@
+"""Tests for app.assets.services.cursor.
+
+Cursors are opaque tokens internal to this server — these tests cover
+round-tripping, validation, and length caps, not any particular wire
+byte layout.
+"""
+from __future__ import annotations
+
+import base64
+from datetime import datetime, timedelta, timezone
+
+import pytest
+
+from app.assets.services.cursor import (
+    MAX_CURSOR_ID_LENGTH,
+    MAX_CURSOR_VALUE_LENGTH,
+    MAX_ENCODED_CURSOR_LENGTH,
+    CursorPayload,
+    InvalidCursorError,
+    decode_cursor,
+    decode_cursor_int,
+    decode_cursor_time,
+    encode_cursor,
+    encode_cursor_from_time,
+)
+
+
+ALLOWED = ("created_at", "updated_at", "name", "size")
+
+
+class TestRoundTrip:
+    @pytest.mark.parametrize(
+        "sort_field, value, id",
+        [
+            ("created_at", "1716200000000000", "a1b2c3d4-e5f6-7a89-b0c1-d2e3f4a5b6c7"),
+            ("size", "1024", "asset-123"),
+            ("name", "my-asset.png", "asset-abc"),
+            ("name", "résumé.txt", "asset-uni"),
+            ("name", "foo<&>bar.png", "asset-html"),
+            ("name", 'quo"te\\back\nnewline.png', "asset-esc"),
+        ],
+    )
+    def test_encode_decode(self, sort_field, value, id):
+        encoded = encode_cursor(sort_field, value, id)
+        assert encoded != ""
+        payload = decode_cursor(encoded, ALLOWED)
+        assert payload.sort_field == sort_field
+        assert payload.value == value
+        assert payload.id == id
+
+
+class TestTimeCursor:
+    def test_microsecond_precision_preserved(self):
+        # Pick a time with non-zero microseconds — encoding at ms would lose the µs.
+        ts = datetime(2024, 5, 20, 12, 53, 20, 123456, tzinfo=timezone.utc)
+        encoded = encode_cursor_from_time("created_at", ts, "id-1")
+        payload = decode_cursor(encoded, ALLOWED)
+        # Value must be a microsecond integer string, not a millisecond one.
+        assert payload.value == "1716209600123456"
+        decoded = decode_cursor_time(payload)
+        assert decoded == ts
+
+    def test_decode_returns_utc(self):
+        payload = CursorPayload(sort_field="created_at", value="1716200000123456", id="id-1", order="desc")
+        decoded = decode_cursor_time(payload)
+        assert decoded.tzinfo == timezone.utc
+
+    def test_naive_datetime_rejected_on_encode(self):
+        naive = datetime(2024, 5, 20, 12, 0, 0)
+        with pytest.raises(ValueError):
+            encode_cursor_from_time("created_at", naive, "id-1")
+
+    def test_non_integer_value_rejected_on_decode(self):
+        with pytest.raises(InvalidCursorError):
+            decode_cursor_time(CursorPayload("created_at", "not-a-number", "id-1", "desc"))
+
+    def test_none_payload_rejected(self):
+        with pytest.raises(InvalidCursorError):
+            decode_cursor_time(None)
+
+    def test_non_utc_aware_normalized(self):
+        # Same instant, different timezone — must encode to the same micros.
+        utc_ts = datetime(2024, 5, 20, 12, 0, 0, tzinfo=timezone.utc)
+        offset_ts = utc_ts.astimezone(timezone(timedelta(hours=-5)))
+        assert encode_cursor_from_time("created_at", utc_ts, "x") == encode_cursor_from_time(
+            "created_at", offset_ts, "x"
+        )
+
+
+class TestIntCursor:
+    def test_decode_int(self):
+        assert decode_cursor_int(CursorPayload("size", "1024", "id-1", "desc")) == 1024
+
+    def test_decode_int_rejects_non_int(self):
+        with pytest.raises(InvalidCursorError):
+            decode_cursor_int(CursorPayload("size", "abc", "id-1", "desc"))
+
+    def test_decode_int_rejects_none(self):
+        with pytest.raises(InvalidCursorError):
+            decode_cursor_int(None)
+
+
+class TestInvalidInputs:
+    def test_oversized_cursor(self):
+        oversized = "a" * (MAX_ENCODED_CURSOR_LENGTH + 1)
+        with pytest.raises(InvalidCursorError, match="maximum length"):
+            decode_cursor(oversized, ALLOWED)
+
+    def test_not_base64(self):
+        with pytest.raises(InvalidCursorError):
+            decode_cursor("not base64!!!", ALLOWED)
+
+    def test_not_json(self):
+        encoded = base64.urlsafe_b64encode(b"definitely not json").rstrip(b"=").decode("ascii")
+        with pytest.raises(InvalidCursorError):
+            decode_cursor(encoded, ALLOWED)
+
+    def test_empty_id(self):
+        # Encoder rejects empty id symmetrically with the decoder, so build the
+        # payload manually to exercise the decoder's missing-id branch.
+        raw = b'{"s":"created_at","v":"1","id":"","o":"desc"}'
+        encoded = base64.urlsafe_b64encode(raw).rstrip(b"=").decode("ascii")
+        with pytest.raises(InvalidCursorError, match="missing id"):
+            decode_cursor(encoded, ALLOWED)
+
+    def test_oversized_id(self):
+        # Encoder enforces the cap symmetrically; hand-build to exercise decode.
+        big_id = "a" * (MAX_CURSOR_ID_LENGTH + 1)
+        raw = ('{"s":"created_at","v":"1","id":"' + big_id + '","o":"desc"}').encode("ascii")
+        encoded = base64.urlsafe_b64encode(raw).rstrip(b"=").decode("ascii")
+        with pytest.raises(InvalidCursorError, match="id exceeds maximum length"):
+            decode_cursor(encoded, ALLOWED)
+
+    def test_oversized_value(self):
+        # Encoder enforces the cap symmetrically; hand-build to exercise decode.
+        big_v = "v" * (MAX_CURSOR_VALUE_LENGTH + 1)
+        raw = ('{"s":"created_at","v":"' + big_v + '","id":"id-1","o":"desc"}').encode("ascii")
+        encoded = base64.urlsafe_b64encode(raw).rstrip(b"=").decode("ascii")
+        with pytest.raises(InvalidCursorError, match="value exceeds maximum length"):
+            decode_cursor(encoded, ALLOWED)
+
+    def test_unsupported_sort_field(self):
+        encoded = encode_cursor("execution_time", "1", "id-1")
+        with pytest.raises(InvalidCursorError, match="unsupported sort field"):
+            decode_cursor(encoded, ALLOWED)
+
+    def test_no_allowed_fields_rejects_everything(self):
+        encoded = encode_cursor("created_at", "1", "id-1")
+        with pytest.raises(InvalidCursorError):
+            decode_cursor(encoded, ())
+
+    def test_non_dict_payload_rejected(self):
+        encoded = base64.urlsafe_b64encode(b'["array","not","dict"]').rstrip(b"=").decode("ascii")
+        with pytest.raises(InvalidCursorError, match="expected object"):
+            decode_cursor(encoded, ALLOWED)
+
+
+class TestEncodeAtCapsFits:
+    def test_max_field_lengths_fit_wire_cap(self):
+        # Worst-case payload: value and id at their per-field caps, with a long
+        # sort field name. The encoded cursor must fit within MAX_ENCODED_CURSOR_LENGTH
+        # so the wire cap cannot reject a cursor the encoder mints at the per-field caps.
+        value = "v" * MAX_CURSOR_VALUE_LENGTH
+        id = "i" * MAX_CURSOR_ID_LENGTH
+        sort_field = "very_long_sort_field_name"
+
+        encoded = encode_cursor(sort_field, value, id)
+        assert len(encoded) <= MAX_ENCODED_CURSOR_LENGTH
+        payload = decode_cursor(encoded, (sort_field,))
+        assert payload.value == value
+        assert payload.id == id
+
+
+class TestDatetimeOverflow:
+    """Crafted cursors with extreme micros must map to InvalidCursorError,
+    not OverflowError/OSError leaking as 500.
+    """
+
+    @pytest.mark.parametrize(
+        "micros_str",
+        [
+            "999999999999999999999",   # 10^21 µs — past datetime.MAX_YEAR by ~14 orders
+            "-999999999999999999999",  # symmetric negative — pre-epoch overflow
+        ],
+    )
+    def test_out_of_range_micros_rejected(self, micros_str):
+        encoded = encode_cursor("created_at", micros_str, "asset-x")
+        payload = decode_cursor(encoded, ALLOWED)
+        with pytest.raises(InvalidCursorError):
+            decode_cursor_time(payload)
+
+
+class TestEncoderDecoderSymmetry:
+    """The encoder must never mint a cursor the decoder would reject, or the
+    same server would 400 on a cursor it just handed out. Per-field caps keep
+    the encoded length below the wire cap, so a freshly minted cursor always
+    round-trips.
+    """
+
+    def test_long_name_within_cap_round_trips(self):
+        """Assets allow names up to 512 chars (`String(512)`); the cursor
+        encoder must round-trip a value at that cap so a freshly minted
+        cursor never fails decode on the next request."""
+        long_name = "n" * MAX_CURSOR_VALUE_LENGTH
+        encoded = encode_cursor("name", long_name, "asset-x")
+        payload = decode_cursor(encoded, ALLOWED)
+        assert payload.value == long_name
+
+    def test_encoder_rejects_empty_id(self):
+        with pytest.raises(InvalidCursorError, match="id must be non-empty"):
+            encode_cursor("created_at", "1", "")
+
+    def test_encoder_rejects_oversized_id(self):
+        with pytest.raises(InvalidCursorError, match="id exceeds maximum length"):
+            encode_cursor("created_at", "1", "a" * (MAX_CURSOR_ID_LENGTH + 1))
+
+    def test_encoder_rejects_oversized_value(self):
+        with pytest.raises(InvalidCursorError, match="value exceeds maximum length"):
+            encode_cursor("name", "v" * (MAX_CURSOR_VALUE_LENGTH + 1), "id-1")
+
+    def test_multibyte_value_at_cap_round_trips(self):
+        """A value at the char-count cap made of multibyte characters
+        (e.g. 'é' = 2 UTF-8 bytes) stays under the wire cap, so it mints and
+        round-trips — the per-field caps, not a mint-time length check, are
+        what bound cursor size."""
+        value = "é" * MAX_CURSOR_VALUE_LENGTH
+        encoded = encode_cursor("name", value, "asset-multibyte")
+        assert len(encoded) <= MAX_ENCODED_CURSOR_LENGTH
+        payload = decode_cursor(encoded, ALLOWED)
+        assert payload.value == value
+
+    def test_escape_heavy_value_at_cap_round_trips(self):
+        """JSON escape expansion is the worst case: each control character
+        serializes to the six-byte `\\uXXXX` form. A value of 512 of them is
+        the largest a cursor can get, and it still fits the wire cap, mints,
+        and round-trips."""
+        value = "\x01" * MAX_CURSOR_VALUE_LENGTH
+        encoded = encode_cursor("name", value, "asset-escape")
+        assert len(encoded) <= MAX_ENCODED_CURSOR_LENGTH
+        payload = decode_cursor(encoded, ALLOWED)
+        assert payload.value == value
+
+
+class TestOrderBinding:
+    def test_order_baked_into_payload(self):
+        encoded = encode_cursor("created_at", "1", "id-1", order="asc")
+        payload = decode_cursor(encoded, ALLOWED)
+        assert payload.order == "asc"
+
+    def test_mismatched_order_rejected(self):
+        encoded = encode_cursor("created_at", "1", "id-1", order="desc")
+        with pytest.raises(InvalidCursorError, match="does not match request order"):
+            decode_cursor(encoded, ALLOWED, expected_order="asc")
+
+    def test_matching_order_accepted(self):
+        encoded = encode_cursor("created_at", "1", "id-1", order="desc")
+        payload = decode_cursor(encoded, ALLOWED, expected_order="desc")
+        assert payload.order == "desc"
+
+    def test_invalid_order_token_rejected_on_encode(self):
+        with pytest.raises(ValueError):
+            encode_cursor("created_at", "1", "id-1", order="sideways")
+
+    def test_invalid_order_token_rejected_on_decode(self):
+        # Hand-craft a payload with an illegal `o` value.
+        raw = b'{"s":"name","v":"x","id":"id-1","o":"sideways"}'
+        encoded = base64.urlsafe_b64encode(raw).rstrip(b"=").decode("ascii")
+        with pytest.raises(InvalidCursorError, match="unsupported order"):
+            decode_cursor(encoded, ALLOWED)
+
+    def test_cursor_without_order_rejected(self):
+        """`o` is mandatory. A cursor minted without it is rejected as
+        malformed rather than silently walking the keyset in whatever
+        direction the request happens to ask for."""
+        raw = b'{"s":"name","v":"x","id":"id-1"}'
+        encoded = base64.urlsafe_b64encode(raw).rstrip(b"=").decode("ascii")
+        with pytest.raises(InvalidCursorError, match="missing or non-string o"):
+            decode_cursor(encoded, ALLOWED, expected_order="desc")

tests-unit/assets_test/services/test_tagging.py

@@ -141,7 +141,7 @@ class TestListTags:
 
         rows, total = list_tags()
 
-        tag_dict = {name: count for name, _, count in rows}
+        tag_dict = {name: count for name, count in rows}
         assert tag_dict["used"] == 1
         assert tag_dict["unused"] == 0
         assert total == 2
@@ -155,7 +155,7 @@ class TestListTags:
 
         rows, total = list_tags(include_zero=False)
 
-        tag_names = {name for name, _, _ in rows}
+        tag_names = {name for name, _ in rows}
         assert "used" in tag_names
         assert "unused" not in tag_names
 
@@ -165,7 +165,7 @@ class TestListTags:
 
         rows, _ = list_tags(prefix="alph")
 
-        tag_names = {name for name, _, _ in rows}
+        tag_names = {name for name, _ in rows}
         assert tag_names == {"alpha", "alphabet"}
 
     def test_order_by_name(self, mock_create_session, session: Session):
@@ -174,7 +174,7 @@ class TestListTags:
 
         rows, _ = list_tags(order="name_asc")
 
-        names = [name for name, _, _ in rows]
+        names = [name for name, _ in rows]
         assert names == ["alpha", "middle", "zebra"]
 
     def test_pagination(self, mock_create_session, session: Session):
@@ -185,7 +185,7 @@ class TestListTags:
 
         assert total == 5
         assert len(rows) == 2
-        names = [name for name, _, _ in rows]
+        names = [name for name, _ in rows]
         assert names == ["b", "c"]
 
     def test_clamps_limit(self, mock_create_session, session: Session):

tests-unit/assets_test/test_crud.py

@@ -45,8 +45,8 @@ def test_get_and_delete_asset(http: requests.Session, api_base: str, seeded_asse
     assert "user_metadata" in detail
     assert "filename" in detail["user_metadata"]
 
-    # DELETE (hard delete to also remove underlying asset and file)
-    rd = http.delete(f"{api_base}/api/assets/{aid}?delete_content=true", timeout=120)
+    # Soft delete — the reference is hidden, content is preserved
+    rd = http.delete(f"{api_base}/api/assets/{aid}", timeout=120)
     assert rd.status_code == 204
 
     # GET again -> 404
@@ -60,7 +60,7 @@ def test_soft_delete_hides_from_get(http: requests.Session, api_base: str, seede
     aid = seeded_asset["id"]
     asset_hash = seeded_asset["asset_hash"]
 
-    # Soft-delete (default, no delete_content param)
+    # Soft delete — the reference is hidden, content is preserved
     rd = http.delete(f"{api_base}/api/assets/{aid}", timeout=120)
     assert rd.status_code == 204
 
@@ -81,11 +81,10 @@ def test_soft_delete_hides_from_get(http: requests.Session, api_base: str, seede
     ids = [a["id"] for a in rl.json().get("assets", [])]
     assert aid not in ids
 
-    # Clean up: hard-delete the soft-deleted reference and orphaned asset
-    http.delete(f"{api_base}/api/assets/{aid}?delete_content=true", timeout=120)
+    # The reference is already soft-deleted; content is preserved.
 
 
-def test_delete_upon_reference_count(
+def test_soft_delete_preserves_asset_identity_across_references(
     http: requests.Session, api_base: str, seeded_asset: dict
 ):
     # Create a second reference to the same asset via from-hash
@@ -119,16 +118,20 @@ def test_delete_upon_reference_count(
     rh2 = http.head(f"{api_base}/api/assets/hash/{src_hash}", timeout=120)
     assert rh2.status_code == 200  # asset identity preserved (soft delete)
 
-    # Re-associate via from-hash, then hard-delete -> orphan content removed
+    # Re-associate via from-hash: it must reuse the same preserved content
+    # (created_new False AND the same hash), proving the soft deletes did not
+    # destroy the underlying asset. Then soft-delete again -> still preserved.
     r3 = http.post(f"{api_base}/api/assets/from-hash", json=payload, timeout=120)
     assert r3.status_code == 201, r3.json()
+    assert r3.json()["created_new"] is False
+    assert r3.json()["asset_hash"] == src_hash  # reused the surviving content
     aid3 = r3.json()["id"]
 
-    rd3 = http.delete(f"{api_base}/api/assets/{aid3}?delete_content=true", timeout=120)
+    rd3 = http.delete(f"{api_base}/api/assets/{aid3}", timeout=120)
     assert rd3.status_code == 204
 
     rh3 = http.head(f"{api_base}/api/assets/hash/{src_hash}", timeout=120)
-    assert rh3.status_code == 404  # orphan content removed
+    assert rh3.status_code == 200  # content preserved (soft delete)
 
 
 def test_update_asset_fields(http: requests.Session, api_base: str, seeded_asset: dict):
@@ -249,7 +252,7 @@ def test_concurrent_delete_same_asset_info_single_204(
 
     # Hit the same endpoint N times in parallel.
     n_tests = 4
-    url = f"{api_base}/api/assets/{aid}?delete_content=false"
+    url = f"{api_base}/api/assets/{aid}"
 
     def _do_delete(delete_url):
         with requests.Session() as s:

tests-unit/assets_test/test_downloads.py

@@ -117,7 +117,7 @@ def test_download_missing_file_returns_404(
         assert body["error"]["code"] == "FILE_NOT_FOUND"
     finally:
         # We created asset without the "unit-tests" tag(see `autoclean_unit_test_assets`), we need to clear it manually.
-        dr = http.delete(f"{api_base}/api/assets/{aid}?delete_content=true", timeout=120)
+        dr = http.delete(f"{api_base}/api/assets/{aid}", timeout=120)
         dr.content
 
 

tests-unit/assets_test/test_list_cursor.py

@@ -0,0 +1,349 @@
+"""Integration tests for cursor-based pagination on GET /api/assets.
+
+These tests exercise the handler/service/query path end-to-end;
+cursor-encoding-level tests live in
+tests-unit/assets_test/services/test_cursor.py.
+"""
+import pytest
+import requests
+
+
+def _seed(asset_factory, make_asset_bytes, count: int, tag: str) -> list[str]:
+    names = [f"cursor_{i:02d}.safetensors" for i in range(count)]
+    for n in names:
+        asset_factory(
+            n,
+            ["models", "checkpoints", "unit-tests", tag],
+            {},
+            make_asset_bytes(n, size=2048),
+        )
+    return sorted(names)
+
+
+def test_cursor_pages_all_items_in_order(http: requests.Session, api_base: str, asset_factory, make_asset_bytes):
+    names = _seed(asset_factory, make_asset_bytes, count=5, tag="cursor-walk")
+
+    params = {
+        "include_tags": "unit-tests,cursor-walk",
+        "sort": "name",
+        "order": "asc",
+        "limit": "2",
+    }
+
+    seen: list[str] = []
+    after: str | None = None
+    pages = 0
+    while True:
+        page_params = dict(params)
+        if after is not None:
+            page_params["after"] = after
+        r = http.get(api_base + "/api/assets", params=page_params, timeout=120)
+        assert r.status_code == 200, r.text
+        body = r.json()
+        seen.extend(a["name"] for a in body["assets"])
+        pages += 1
+        after = body.get("next_cursor")
+        if after is None:
+            break
+        assert body["has_more"] is True
+        assert pages < 10, "guard against runaway cursor loop"
+
+    assert seen == names, f"expected {names}, got {seen}"
+    # Last page should have has_more False
+    assert body["has_more"] is False
+    assert "next_cursor" not in body
+
+
+def test_cursor_invalid_returns_400(http: requests.Session, api_base: str):
+    r = http.get(
+        api_base + "/api/assets",
+        params={"after": "not-a-real-cursor", "sort": "created_at"},
+        timeout=120,
+    )
+    assert r.status_code == 400, r.text
+    body = r.json()
+    assert body["error"]["code"] == "INVALID_CURSOR"
+
+
+def test_cursor_sort_mismatch_returns_400(http: requests.Session, api_base: str, asset_factory, make_asset_bytes):
+    _seed(asset_factory, make_asset_bytes, count=2, tag="cursor-mismatch")
+
+    # Take a real cursor minted for sort=name.
+    r = http.get(
+        api_base + "/api/assets",
+        params={
+            "include_tags": "unit-tests,cursor-mismatch",
+            "sort": "name",
+            "order": "asc",
+            "limit": "1",
+        },
+        timeout=120,
+    )
+    assert r.status_code == 200
+    cursor = r.json()["next_cursor"]
+    assert cursor is not None
+
+    # Replay against sort=created_at — should fail with INVALID_CURSOR.
+    r2 = http.get(
+        api_base + "/api/assets",
+        params={"after": cursor, "sort": "created_at"},
+        timeout=120,
+    )
+    assert r2.status_code == 400, r2.text
+    assert r2.json()["error"]["code"] == "INVALID_CURSOR"
+
+
+def test_cursor_wins_over_offset(http: requests.Session, api_base: str, asset_factory, make_asset_bytes):
+    names = _seed(asset_factory, make_asset_bytes, count=4, tag="cursor-vs-offset")
+
+    # Take a cursor that points past the first item.
+    r = http.get(
+        api_base + "/api/assets",
+        params={
+            "include_tags": "unit-tests,cursor-vs-offset",
+            "sort": "name",
+            "order": "asc",
+            "limit": "1",
+        },
+        timeout=120,
+    )
+    assert r.status_code == 200, r.text
+    cursor = r.json()["next_cursor"]
+    assert cursor is not None
+
+    # Pass both 'after' and a large offset. Cursor must win; offset is ignored.
+    r2 = http.get(
+        api_base + "/api/assets",
+        params={
+            "include_tags": "unit-tests,cursor-vs-offset",
+            "sort": "name",
+            "order": "asc",
+            "limit": "1",
+            "after": cursor,
+            "offset": "999",
+        },
+        timeout=120,
+    )
+    assert r2.status_code == 200
+    body = r2.json()
+    # Should land on the second name in sorted order — not skip ahead by 999.
+    assert [a["name"] for a in body["assets"]] == [names[1]]
+
+
+def test_next_cursor_absent_when_no_more_results(http: requests.Session, api_base: str, asset_factory, make_asset_bytes):
+    _seed(asset_factory, make_asset_bytes, count=2, tag="cursor-exhaust")
+
+    r = http.get(
+        api_base + "/api/assets",
+        params={
+            "include_tags": "unit-tests,cursor-exhaust",
+            "sort": "name",
+            "order": "asc",
+            "limit": "50",
+        },
+        timeout=120,
+    )
+    assert r.status_code == 200, r.text
+    body = r.json()
+    assert body["has_more"] is False
+    assert "next_cursor" not in body
+
+
+def test_cursor_pagination_first_page_mints_cursor(http: requests.Session, api_base: str, asset_factory, make_asset_bytes):
+    """First-page request (no `after`) must still return `next_cursor` when
+    more rows exist, or pagination is unreachable from a cold start.
+    """
+    _seed(asset_factory, make_asset_bytes, count=3, tag="cursor-first-page")
+    r = http.get(
+        api_base + "/api/assets",
+        params={"include_tags": "unit-tests,cursor-first-page", "sort": "name", "order": "asc", "limit": "2"},
+        timeout=120,
+    )
+    assert r.status_code == 200, r.text
+    body = r.json()
+    assert body["has_more"] is True
+    assert body.get("next_cursor"), "first page must mint a cursor when more rows exist"
+
+
+def test_cursor_no_spurious_cursor_when_page_size_equals_remainder(http: requests.Session, api_base: str, asset_factory, make_asset_bytes):
+    """When `total` is an exact multiple of `limit`, the final page must
+    NOT carry a next_cursor — there is nothing past it.
+    """
+    _seed(asset_factory, make_asset_bytes, count=4, tag="cursor-exact-multiple")
+    # Page 1
+    r = http.get(
+        api_base + "/api/assets",
+        params={"include_tags": "unit-tests,cursor-exact-multiple", "sort": "name", "order": "asc", "limit": "2"},
+        timeout=120,
+    )
+    assert r.status_code == 200, r.text
+    cursor = r.json()["next_cursor"]
+    assert cursor is not None
+    # Page 2 — should exhaust the set with no cursor for a phantom page 3
+    r2 = http.get(
+        api_base + "/api/assets",
+        params={"include_tags": "unit-tests,cursor-exact-multiple", "sort": "name", "order": "asc", "limit": "2", "after": cursor},
+        timeout=120,
+    )
+    assert r2.status_code == 200, r2.text
+    body = r2.json()
+    assert len(body["assets"]) == 2
+    assert body["has_more"] is False
+    assert "next_cursor" not in body
+
+
+@pytest.mark.parametrize("sort_field", ["created_at", "updated_at", "size"])
+def test_cursor_walks_for_non_name_sorts(sort_field, http: requests.Session, api_base: str, asset_factory, make_asset_bytes):
+    """Cursor pagination must work for every sort field the contract claims.
+
+    Without this, the `created_at` / `updated_at` (time-encoded micros) and
+    `size` (int-encoded) cursor paths go entirely unexercised end-to-end.
+    """
+    # Sizes increase strictly by index, so `size desc` has a deterministic
+    # expected order. Time-based sorts (created_at / updated_at) can tie when
+    # rows are inserted faster than the DB's timestamp resolution; for those
+    # we check coverage and no-duplicates and let the keyset tiebreaker do
+    # the rest, instead of sleeping between inserts and asserting an order
+    # that depends on clock granularity.
+    names = []
+    for i in range(4):
+        n = f"cursor_{sort_field}_{i:02d}.safetensors"
+        asset_factory(n, ["models", "checkpoints", "unit-tests", f"cursor-{sort_field}"], {}, make_asset_bytes(n, size=2048 + i))
+        names.append(n)
+
+    params = {
+        "include_tags": f"unit-tests,cursor-{sort_field}",
+        "sort": sort_field,
+        "order": "desc",
+        "limit": "2",
+    }
+    seen: list[str] = []
+    after: str | None = None
+    pages = 0
+    while True:
+        page_params = dict(params)
+        if after is not None:
+            page_params["after"] = after
+        r = http.get(api_base + "/api/assets", params=page_params, timeout=120)
+        assert r.status_code == 200, r.text
+        body = r.json()
+        seen.extend(a["name"] for a in body["assets"])
+        after = body.get("next_cursor")
+        pages += 1
+        if after is None:
+            break
+        assert pages < 10, "guard against runaway cursor loop"
+
+    # No duplicates: a faulty keyset boundary that returns the same row across
+    # two pages must fail this check.
+    assert len(seen) == len(set(seen)), (
+        f"cursor walk repeated rows for sort={sort_field}: {seen}"
+    )
+    # Full coverage: every seeded asset reached exactly once.
+    assert set(seen) == set(names), (
+        f"missing items for sort={sort_field}: expected {set(names)}, got {set(seen)}"
+    )
+    # Strict order check for the only field with a clock-independent ordering.
+    if sort_field == "size":
+        assert seen == list(reversed(names)), (
+            f"size cursor walked out of order: got {seen}, expected {list(reversed(names))}"
+        )
+
+
+def test_cursor_order_mismatch_returns_400(http: requests.Session, api_base: str, asset_factory, make_asset_bytes):
+    """A cursor minted under desc order replayed against asc must 400, not
+    silently walk the wrong direction."""
+    _seed(asset_factory, make_asset_bytes, count=3, tag="cursor-order-flip")
+
+    r = http.get(
+        api_base + "/api/assets",
+        params={
+            "include_tags": "unit-tests,cursor-order-flip",
+            "sort": "name",
+            "order": "desc",
+            "limit": "1",
+        },
+        timeout=120,
+    )
+    assert r.status_code == 200, r.text
+    cursor = r.json()["next_cursor"]
+    assert cursor is not None
+
+    # Replay with order flipped to asc — server must reject the cursor.
+    r2 = http.get(
+        api_base + "/api/assets",
+        params={
+            "include_tags": "unit-tests,cursor-order-flip",
+            "sort": "name",
+            "order": "asc",
+            "limit": "1",
+            "after": cursor,
+        },
+        timeout=120,
+    )
+    assert r2.status_code == 400, r2.text
+    assert r2.json()["error"]["code"] == "INVALID_CURSOR"
+
+
+def test_cursor_invalid_cursor_at_microsecond_boundary(http: requests.Session, api_base: str):
+    """A cursor carrying an out-of-range microsecond timestamp must map to
+    400 INVALID_CURSOR, not 500."""
+    import base64
+    import json
+    # 10^18 microseconds ≈ year 33658, well past datetime.MAX_YEAR.
+    # `o` and `order=` must be set; otherwise decode fails earlier on the
+    # missing-order branch and the µs-overflow path is never exercised.
+    payload = {"s": "created_at", "o": "desc", "v": "999999999999999999999", "id": "asset-x"}
+    raw = json.dumps(payload, separators=(",", ":")).encode("utf-8")
+    cursor = base64.urlsafe_b64encode(raw).rstrip(b"=").decode("ascii")
+    r = http.get(
+        api_base + "/api/assets",
+        params={"after": cursor, "sort": "created_at", "order": "desc"},
+        timeout=120,
+    )
+    assert r.status_code == 400, r.text
+    assert r.json()["error"]["code"] == "INVALID_CURSOR"
+
+
+def test_cursor_pagination_stable_after_delete(http: requests.Session, api_base: str, asset_factory, make_asset_bytes):
+    names = _seed(asset_factory, make_asset_bytes, count=4, tag="cursor-delete")
+
+    # Page 1.
+    r = http.get(
+        api_base + "/api/assets",
+        params={
+            "include_tags": "unit-tests,cursor-delete",
+            "sort": "name",
+            "order": "asc",
+            "limit": "2",
+        },
+        timeout=120,
+    )
+    assert r.status_code == 200
+    body = r.json()
+    page1_names = [a["name"] for a in body["assets"]]
+    cursor = body["next_cursor"]
+    assert cursor is not None
+    assert page1_names == names[:2]
+
+    # Delete an item from page 1 (already returned) — cursor should still
+    # locate the next page from where it was minted, not re-index.
+    target_id = body["assets"][0]["id"]
+    d = http.delete(api_base + f"/api/assets/{target_id}", timeout=120)
+    assert d.status_code in (200, 204), d.text
+
+    # Page 2 via cursor.
+    r2 = http.get(
+        api_base + "/api/assets",
+        params={
+            "include_tags": "unit-tests,cursor-delete",
+            "sort": "name",
+            "order": "asc",
+            "limit": "2",
+            "after": cursor,
+        },
+        timeout=120,
+    )
+    assert r2.status_code == 200, r2.text
+    body2 = r2.json()
+    assert [a["name"] for a in body2["assets"]] == names[2:]

tests-unit/assets_test/test_prompt_id_enforcement.py

@@ -0,0 +1,69 @@
+"""POST /prompt enforces canonical-UUID job ids at creation time.
+
+Lives in assets_test because it uses this suite's booted-server fixture. The
+invariant itself is pipeline-wide: a job id is stored and compared verbatim
+downstream — history keys, websocket correlation, and /interrupt matching —
+so a job minted with a non-canonical id would miss every exact-match lookup.
+
+The prompt bodies here are intentionally invalid workflows — prompt_id
+validation happens before workflow validation, so a rejected id returns
+``invalid_prompt_id`` while an accepted id falls through to the ordinary
+workflow-validation error (proving it cleared the id check).
+"""
+import requests
+
+
+def _post_prompt(http: requests.Session, api_base: str, body: dict) -> requests.Response:
+    return http.post(api_base + "/prompt", json=body, timeout=30)
+
+
+def _error_type(r: requests.Response) -> str:
+    return r.json()["error"]["type"]
+
+
+def test_non_uuid_prompt_id_rejected(http: requests.Session, api_base: str):
+    r = _post_prompt(http, api_base, {"prompt": {}, "prompt_id": "not-a-uuid"})
+    assert r.status_code == 400, r.text
+    assert _error_type(r) == "invalid_prompt_id"
+
+
+def test_non_string_prompt_id_rejected(http: requests.Session, api_base: str):
+    # Previously str()-coerced (123 became the job id "123"); must now be a 400,
+    # not a 500 from uuid.UUID choking on a non-string.
+    r = _post_prompt(http, api_base, {"prompt": {}, "prompt_id": 123})
+    assert r.status_code == 400, r.text
+    assert _error_type(r) == "invalid_prompt_id"
+
+
+def test_non_canonical_uuid_rejected(http: requests.Session, api_base: str):
+    # Parseable as a UUID, but not the canonical lowercase form: rejected
+    # loudly rather than silently rewritten (downstream lookups match the
+    # stored id exactly).
+    r = _post_prompt(
+        http,
+        api_base,
+        {"prompt": {}, "prompt_id": "AAAAAAAA-BBBB-4CCC-8DDD-EEEEEEEEEEEE"},
+    )
+    assert r.status_code == 400, r.text
+    assert _error_type(r) == "invalid_prompt_id"
+
+
+def test_canonical_uuid_accepted(http: requests.Session, api_base: str):
+    # The id clears validation; the empty workflow then fails ordinary prompt
+    # validation, proving the request got past the id check.
+    r = _post_prompt(
+        http,
+        api_base,
+        {"prompt": {}, "prompt_id": "aaaaaaaa-bbbb-4ccc-8ddd-eeeeeeeeeeee"},
+    )
+    assert r.status_code == 400, r.text
+    assert _error_type(r) != "invalid_prompt_id"
+
+
+def test_null_prompt_id_not_rejected(http: requests.Session, api_base: str):
+    # Explicit null means "server generates" and must not be rejected as an
+    # invalid id. (The minted id itself is not observable here because the
+    # workflow is invalid; unit tests cover validate_job_id directly.)
+    r = _post_prompt(http, api_base, {"prompt": {}, "prompt_id": None})
+    assert r.status_code == 400, r.text
+    assert _error_type(r) != "invalid_prompt_id"

tests-unit/assets_test/test_sync_references.py

@@ -95,7 +95,7 @@ def _make_asset(
 def _ensure_missing_tag(session: Session):
     """Ensure the 'missing' tag exists."""
     if not session.get(Tag, "missing"):
-        session.add(Tag(name="missing", tag_type="system"))
+        session.add(Tag(name="missing"))
         session.flush()
 
 

tests-unit/assets_test/test_tags_api.py

@@ -69,8 +69,8 @@ def test_tags_empty_usage(http: requests.Session, api_base: str, asset_factory,
     used_names = [t["name"] for t in body2["tags"]]
     assert custom_tag in used_names
 
-    # Hard-delete the asset so the tag usage drops to zero
-    rd = http.delete(f"{api_base}/api/assets/{_asset['id']}?delete_content=true", timeout=120)
+    # Delete the asset reference so the tag usage drops to zero
+    rd = http.delete(f"{api_base}/api/assets/{_asset['id']}", timeout=120)
     assert rd.status_code == 204
 
     # Now the custom tag must not be returned when include_zero=false

tests-unit/execution_test/test_enrich_output.py

@@ -0,0 +1,205 @@
+"""Tests for enrich_output_with_assets in comfy_execution/asset_enrichment.py."""
+import os
+import types
+import unittest
+from unittest.mock import MagicMock, patch
+
+
+def _make_args(enable_assets: bool):
+    a = types.SimpleNamespace()
+    a.enable_assets = enable_assets
+    return a
+
+
+def _make_register_result(ref_id="ref-id-2"):
+    result = MagicMock()
+    result.ref.id = ref_id
+    return result
+
+
+# Platform-appropriate absolute base. tempfile.gettempdir() returns C:\... on
+# Windows and /tmp on POSIX, so containment via commonpath behaves naturally.
+_DEFAULT_BASE = os.path.join(__import__("tempfile").gettempdir(), "asset-enrichment-test-base")
+
+
+def _mocked_modules(*, enable_assets=True, register_file_in_place=None, directory=_DEFAULT_BASE):
+    return {
+        "comfy.cli_args": MagicMock(args=_make_args(enable_assets)),
+        "folder_paths": MagicMock(get_directory_by_type=MagicMock(return_value=directory)),
+        "app.assets.services.ingest": MagicMock(
+            register_file_in_place=register_file_in_place or MagicMock(return_value=_make_register_result()),
+            DependencyMissingError=type("DependencyMissingError", (Exception,), {}),
+        ),
+    }
+
+
+def _call(output_ui, *, enable_assets=True, file_exists=True, register_result=None, directory=_DEFAULT_BASE):
+    register_mock = MagicMock(return_value=register_result or _make_register_result())
+    mocked = _mocked_modules(
+        enable_assets=enable_assets,
+        register_file_in_place=register_mock,
+        directory=directory,
+    )
+
+    # Only os.path.isfile is patched — abspath/join must run natively so the
+    # containment check sees real platform paths.
+    with patch.dict("sys.modules", mocked), \
+         patch("os.path.isfile", return_value=file_exists):
+        import importlib
+        import comfy_execution.asset_enrichment as mod
+        importlib.reload(mod)
+        return mod.enrich_output_with_assets(output_ui)
+
+
+class TestEnrichOutputWithAssets(unittest.TestCase):
+
+    def test_disabled_returns_unchanged(self):
+        output = {"images": [{"filename": "a.png", "subfolder": "", "type": "output"}]}
+        result = _call(output, enable_assets=False)
+        self.assertNotIn("id", result["images"][0])
+
+    def test_non_list_value_passed_through(self):
+        output = {"text": "hello"}
+        result = _call(output)
+        self.assertEqual(result["text"], "hello")
+
+    def test_entry_without_filename_unchanged(self):
+        output = {"latent": [{"subfolder": "", "type": "output"}]}
+        result = _call(output)
+        self.assertNotIn("id", result["latent"][0])
+
+    def test_entry_without_type_unchanged(self):
+        output = {"data": [{"filename": "a.png", "subfolder": ""}]}
+        result = _call(output)
+        self.assertNotIn("id", result["data"][0])
+
+    def test_file_not_on_disk_unchanged(self):
+        output = {"images": [{"filename": "missing.png", "subfolder": "", "type": "output"}]}
+        result = _call(output, file_exists=False)
+        self.assertNotIn("id", result["images"][0])
+
+    def test_unknown_type_returns_none_directory_unchanged(self):
+        output = {"images": [{"filename": "a.png", "subfolder": "", "type": "unknown"}]}
+        result = _call(output, directory=None)
+        self.assertNotIn("id", result["images"][0])
+
+    def test_register_injects_only_id(self):
+        reg = _make_register_result(ref_id="inline-ref")
+        output = {"images": [{"filename": "new.png", "subfolder": "", "type": "output"}]}
+        result = _call(output, register_result=reg)
+        img = result["images"][0]
+        self.assertEqual(img["id"], "inline-ref")
+        # Only id is injected — no asset_hash, name, preview_url, size
+        self.assertNotIn("asset_hash", img)
+        self.assertNotIn("name", img)
+        self.assertNotIn("preview_url", img)
+        self.assertNotIn("size", img)
+
+    def test_register_called_per_entry(self):
+        register_mock = MagicMock(return_value=_make_register_result())
+        mocked = _mocked_modules(register_file_in_place=register_mock)
+        output = {
+            "images": [
+                {"filename": "a.png", "subfolder": "", "type": "output"},
+                {"filename": "b.png", "subfolder": "", "type": "output"},
+            ]
+        }
+
+        with patch.dict("sys.modules", mocked), \
+             patch("os.path.isfile", return_value=True):
+            import importlib
+            import comfy_execution.asset_enrichment as mod
+            importlib.reload(mod)
+            mod.enrich_output_with_assets(output)
+
+        self.assertEqual(register_mock.call_count, 2)
+
+    def test_original_entry_not_mutated(self):
+        orig = {"filename": "a.png", "subfolder": "", "type": "output"}
+        output = {"images": [orig]}
+        _call(output)
+        self.assertNotIn("id", orig)
+
+    def test_enrichment_error_does_not_block_sibling_entries(self):
+        call_count = [0]
+        good_reg = _make_register_result(ref_id="good-ref")
+
+        def register_side_effect(abs_path, name, tags):
+            call_count[0] += 1
+            if call_count[0] == 1:
+                raise RuntimeError("boom")
+            return good_reg
+
+        mocked = _mocked_modules(register_file_in_place=register_side_effect)
+
+        output = {
+            "images": [
+                {"filename": "bad.png", "subfolder": "", "type": "output"},
+                {"filename": "good.png", "subfolder": "", "type": "output"},
+            ]
+        }
+
+        with patch.dict("sys.modules", mocked), \
+             patch("os.path.isfile", return_value=True):
+            import importlib
+            import comfy_execution.asset_enrichment as mod
+            importlib.reload(mod)
+            result = mod.enrich_output_with_assets(output)
+
+        imgs = result["images"]
+        self.assertNotIn("id", imgs[0])
+        self.assertEqual(imgs[1]["id"], "good-ref")
+
+    def test_multiple_output_keys_all_enriched(self):
+        output = {
+            "images": [{"filename": "a.png", "subfolder": "", "type": "output"}],
+            "videos": [{"filename": "b.mp4", "subfolder": "", "type": "output"}],
+        }
+        result = _call(output)
+        self.assertIn("id", result["images"][0])
+        self.assertIn("id", result["videos"][0])
+
+    def test_none_entry_in_list_unchanged(self):
+        output = {"images": [None, {"filename": "a.png", "subfolder": "", "type": "output"}]}
+        result = _call(output)
+        self.assertIsNone(result["images"][0])
+        self.assertIn("id", result["images"][1])
+
+    def test_path_traversal_subfolder_skipped(self):
+        register_mock = MagicMock(return_value=_make_register_result())
+        mocked = _mocked_modules(register_file_in_place=register_mock)
+
+        output = {"images": [{"filename": "passwd", "subfolder": "../../etc", "type": "output"}]}
+
+        # Do NOT patch os.path.abspath — real resolution is required for the containment check.
+        with patch.dict("sys.modules", mocked), \
+             patch("os.path.isfile", return_value=True):
+            import importlib
+            import comfy_execution.asset_enrichment as mod
+            importlib.reload(mod)
+            result = mod.enrich_output_with_assets(output)
+
+        self.assertNotIn("id", result["images"][0])
+        register_mock.assert_not_called()
+
+    def test_absolute_filename_skipped(self):
+        register_mock = MagicMock(return_value=_make_register_result())
+        mocked = _mocked_modules(register_file_in_place=register_mock)
+
+        # Absolute filename — os.path.join discards earlier components when a later one is absolute.
+        absolute_filename = os.path.abspath(os.sep + "etc" + os.sep + "passwd")
+        output = {"images": [{"filename": absolute_filename, "subfolder": "", "type": "output"}]}
+
+        with patch.dict("sys.modules", mocked), \
+             patch("os.path.isfile", return_value=True):
+            import importlib
+            import comfy_execution.asset_enrichment as mod
+            importlib.reload(mod)
+            result = mod.enrich_output_with_assets(output)
+
+        self.assertNotIn("id", result["images"][0])
+        register_mock.assert_not_called()
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/execution/test_jobs.py

@@ -1,5 +1,7 @@
 """Unit tests for comfy_execution/jobs.py"""
 
+import pytest
+
 from comfy_execution.jobs import (
     JobStatus,
     is_previewable,
@@ -10,9 +12,50 @@ from comfy_execution.jobs import (
     get_outputs_summary,
     apply_sorting,
     has_3d_extension,
+    validate_job_id,
 )
 
 
+class TestValidateJobId:
+    """validate_job_id guards job creation: POST /prompt rejects ids it raises on."""
+
+    def test_canonical_form_passes_through(self):
+        cid = "a1b2c3d4-e5f6-7a89-b0c1-d2e3f4a5b6c7"
+        assert validate_job_id(cid) == cid
+
+    @pytest.mark.parametrize(
+        "variant",
+        [
+            "A1B2C3D4-E5F6-7A89-B0C1-D2E3F4A5B6C7",          # uppercase
+            "{a1b2c3d4-e5f6-7a89-b0c1-d2e3f4a5b6c7}",        # braced
+            "urn:uuid:a1b2c3d4-e5f6-7a89-b0c1-d2e3f4a5b6c7",  # URN
+            "a1b2c3d4e5f67a89b0c1d2e3f4a5b6c7",              # bare hex
+            " a1b2c3d4-e5f6-7a89-b0c1-d2e3f4a5b6c7 ",        # padded
+        ],
+    )
+    def test_non_canonical_spellings_rejected(self, variant):
+        # uuid.UUID parses all of these, but accepting them would silently
+        # rewrite the client's id (history keys, websocket events, and
+        # /interrupt matching all match the stored form exactly).
+        with pytest.raises(ValueError):
+            validate_job_id(variant)
+
+    @pytest.mark.parametrize(
+        "bad",
+        ["", "not-a-uuid", "prompt-123", "a1b2c3d4-e5f6-7a89-b0c1", "None"],
+    )
+    def test_non_uuid_strings_rejected(self, bad):
+        with pytest.raises(ValueError):
+            validate_job_id(bad)
+
+    @pytest.mark.parametrize("bad", [123, 1.5, True, None, ["a"], {"id": "x"}])
+    def test_non_strings_rejected(self, bad):
+        # uuid.UUID raises AttributeError/TypeError on non-strings; the helper
+        # must normalize those to ValueError so callers need one except clause.
+        with pytest.raises(ValueError):
+            validate_job_id(bad)
+
+
 class TestJobStatus:
     """Test JobStatus constants."""