feat(Core): support partial graph execution

Co-authored-by: mattmillerai <7741082+mattmillerai@users.noreply.github.com>
Co-authored-by: Alexis Rolland <alexisrolland@hotmail.com>
Co-authored-by: Matt Miller <mattmiller@comfy.org>

.github/workflows/cla.yml

@@ -32,9 +32,11 @@ jobs:
           PR_NUMBER: ${{ github.event.pull_request.number || github.event.issue.number }}
           PR_AUTHOR: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
           BASE_ALLOWLIST: action@github.com,actions-user,ampagent,claude,comfy-pr-bot,GitHub Action,github-actions,github-actions[bot],Glary Bot,Glary-Bot,*[bot]
+        # For each commit emit the GitHub login when the author/committer email resolves to a GitHub account
+        # otherwise fall back to the raw git name.
         run: |
           others=$(gh api "repos/${{ github.repository }}/pulls/${PR_NUMBER}/commits" --paginate \
-            --jq '.[] | (.author.login // empty), (.committer.login // empty)' \
+            --jq '.[] | (.author.login // .commit.author.name // empty), (.committer.login // .commit.committer.name // empty)' \
             | sort -u | grep -vix "${PR_AUTHOR}" | paste -sd, -)
           if [ -n "$others" ]; then
             echo "allowlist=${BASE_ALLOWLIST},${others}" >> "$GITHUB_OUTPUT"
@@ -43,7 +45,7 @@ jobs:
           fi
 
       - name: CLA Assistant
-        # Run on PR events, on "recheck" comment, or when someone posts the exact signing phrase.
+        # Run on PR events, on "recheck" comment, or when someone posts the signing phrase.
         # IMPORTANT: this phrase must match `custom-pr-sign-comment` below.
         if: >
           github.event_name == 'pull_request_target' ||

alembic_db/versions/0005_allow_case_sensitive_tags.py

@@ -0,0 +1,107 @@
+"""
+Allow case-sensitive tag names.
+
+Revision ID: 0005_allow_case_sensitive_tags
+Revises: 0004_drop_tag_type
+Create Date: 2026-06-16
+"""
+
+import sqlalchemy as sa
+from alembic import op
+
+revision = "0005_allow_case_sensitive_tags"
+down_revision = "0004_drop_tag_type"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    bind = op.get_bind()
+    if bind.dialect.name == "sqlite":
+        # SQLite cannot ALTER/DROP CHECK constraints. Recreate the small tag
+        # vocabulary table without the lowercase constraint while preserving
+        # existing tag names.
+        op.execute("PRAGMA foreign_keys=OFF")
+        try:
+            op.execute(
+                "CREATE TABLE tags_new ("
+                "name VARCHAR(512) NOT NULL, "
+                "CONSTRAINT pk_tags PRIMARY KEY (name)"
+                ")"
+            )
+            op.execute("INSERT INTO tags_new(name) SELECT name FROM tags")
+            op.execute("DROP TABLE tags")
+            op.execute("ALTER TABLE tags_new RENAME TO tags")
+        finally:
+            op.execute("PRAGMA foreign_keys=ON")
+        return
+
+    op.drop_constraint("ck_tags_ck_tags_lowercase", "tags", type_="check")
+
+
+def downgrade() -> None:
+    # Existing mixed-case tags cannot satisfy the old constraint. Lowercase them
+    # before restoring it, merging duplicate vocabulary/link rows that collide.
+    bind = op.get_bind()
+
+    tag_names = [row[0] for row in bind.execute(sa.text("SELECT name FROM tags"))]
+    existing_names = set(tag_names)
+    lowercase_names = sorted({name.lower() for name in tag_names})
+    missing_lowercase_rows = [
+        {"name": name} for name in lowercase_names if name not in existing_names
+    ]
+    if missing_lowercase_rows:
+        bind.execute(sa.text("INSERT INTO tags(name) VALUES (:name)"), missing_lowercase_rows)
+
+    link_rows = bind.execute(
+        sa.text(
+            "SELECT asset_reference_id, tag_name, origin, added_at "
+            "FROM asset_reference_tags "
+            "ORDER BY asset_reference_id, tag_name"
+        )
+    ).mappings()
+    deduped_links = {}
+    for row in link_rows:
+        key = (row["asset_reference_id"], row["tag_name"].lower())
+        deduped_links.setdefault(
+            key,
+            {
+                "asset_reference_id": row["asset_reference_id"],
+                "tag_name": row["tag_name"].lower(),
+                "origin": row["origin"],
+                "added_at": row["added_at"],
+            },
+        )
+
+    op.execute("DELETE FROM asset_reference_tags")
+    if deduped_links:
+        bind.execute(
+            sa.text(
+                "INSERT INTO asset_reference_tags "
+                "(asset_reference_id, tag_name, origin, added_at) "
+                "VALUES (:asset_reference_id, :tag_name, :origin, :added_at)"
+            ),
+            list(deduped_links.values()),
+        )
+    op.execute("DELETE FROM tags WHERE name != lower(name)")
+
+    if bind.dialect.name == "sqlite":
+        op.execute("PRAGMA foreign_keys=OFF")
+        try:
+            op.execute(
+                "CREATE TABLE tags_new ("
+                "name VARCHAR(512) NOT NULL, "
+                "CONSTRAINT pk_tags PRIMARY KEY (name), "
+                "CONSTRAINT ck_tags_lowercase CHECK (name = lower(name))"
+                ")"
+            )
+            op.execute("INSERT INTO tags_new(name) SELECT name FROM tags")
+            op.execute("DROP TABLE tags")
+            op.execute("ALTER TABLE tags_new RENAME TO tags")
+        finally:
+            op.execute("PRAGMA foreign_keys=ON")
+        return
+
+    op.create_check_constraint(
+        "ck_tags_ck_tags_lowercase", "tags", "name = lower(name)"
+    )

alembic_db/versions/0006_add_loader_path.py

@@ -0,0 +1,30 @@
+"""
+Add loader_path column to asset_references.
+
+Stores the in-root loader path (path relative to the storage root with the
+top-level model category dropped) derived from file_path at scan/ingest time,
+so the assets API can return it without re-resolving against every registered
+model-folder base on every request.
+
+Revision ID: 0006_add_loader_path
+Revises: 0005_allow_case_sensitive_tags
+Create Date: 2026-07-02
+"""
+
+from alembic import op
+import sqlalchemy as sa
+
+revision = "0006_add_loader_path"
+down_revision = "0005_allow_case_sensitive_tags"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("asset_references") as batch_op:
+        batch_op.add_column(sa.Column("loader_path", sa.Text(), nullable=True))
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("asset_references") as batch_op:
+        batch_op.drop_column("loader_path")

app/assets/api/routes.py

@@ -40,6 +40,7 @@ from app.assets.services import (
     upload_from_temp_path,
 )
 from app.assets.services.cursor import InvalidCursorError
+from app.assets.services.path_utils import compute_display_name
 from app.assets.services.tagging import list_tag_histogram
 
 ROUTES = web.RouteTableDef()
@@ -161,11 +162,19 @@ def _build_asset_response(result: schemas.AssetDetailResult | schemas.UploadResu
             preview_url = None
     else:
         preview_url = _build_preview_url_from_view(result.tags, result.ref.user_metadata)
+    if result.ref.file_path:
+        display_name = compute_display_name(result.ref.file_path)
+        # In-root loader path (model category dropped): what model loaders consume.
+        loader_path = result.ref.loader_path
+    else:
+        display_name, loader_path = None, None
     asset_content_hash = result.asset.hash if result.asset else None
     return schemas_out.Asset(
         id=result.ref.id,
         name=result.ref.name,
         hash=asset_content_hash,
+        loader_path=loader_path,
+        display_name=display_name,
         asset_hash=asset_content_hash,
         size=int(result.asset.size_bytes) if result.asset else None,
         mime_type=result.asset.mime_type if result.asset else None,
@@ -419,17 +428,6 @@ async def upload_asset(request: web.Request) -> web.Response:
             400, "INVALID_BODY", f"Validation failed: {ve.json()}"
         )
 
-    if spec.tags and spec.tags[0] == "models":
-        if (
-            len(spec.tags) < 2
-            or spec.tags[1] not in folder_paths.folder_names_and_paths
-        ):
-            delete_temp_file_if_exists(parsed.tmp_path)
-            category = spec.tags[1] if len(spec.tags) >= 2 else ""
-            return _build_error_response(
-                400, "INVALID_BODY", f"unknown models category '{category}'"
-            )
-
     try:
         # Fast path: hash exists, create AssetReference without writing anything
         if spec.hash and parsed.provided_hash_exists is True:
@@ -473,7 +471,7 @@ async def upload_asset(request: web.Request) -> web.Response:
         return _build_error_response(400, e.code, str(e))
     except ValueError as e:
         delete_temp_file_if_exists(parsed.tmp_path)
-        return _build_error_response(400, "BAD_REQUEST", str(e))
+        return _build_error_response(400, "INVALID_BODY", str(e))
     except HashMismatchError as e:
         delete_temp_file_if_exists(parsed.tmp_path)
         return _build_error_response(400, "HASH_MISMATCH", str(e))

app/assets/api/schemas_in.py

@@ -140,7 +140,7 @@ class CreateFromHashBody(BaseModel):
         if v is None:
             return []
         if isinstance(v, list):
-            out = [str(t).strip().lower() for t in v if str(t).strip()]
+            out = [str(t).strip() for t in v if str(t).strip()]
             seen = set()
             dedup = []
             for t in out:
@@ -149,7 +149,7 @@ class CreateFromHashBody(BaseModel):
                     dedup.append(t)
             return dedup
         if isinstance(v, str):
-            return [t.strip().lower() for t in v.split(",") if t.strip()]
+            return list(dict.fromkeys(t.strip() for t in v.split(",") if t.strip()))
         return []
 
 
@@ -206,7 +206,7 @@ class TagsListQuery(BaseModel):
         if v is None:
             return v
         v = v.strip()
-        return v.lower() or None
+        return v or None
 
 
 class TagsAdd(BaseModel):
@@ -220,7 +220,7 @@ class TagsAdd(BaseModel):
         for t in v:
             if not isinstance(t, str):
                 raise TypeError("tags must be strings")
-            tnorm = t.strip().lower()
+            tnorm = t.strip()
             if tnorm:
                 out.append(tnorm)
         seen = set()
@@ -239,8 +239,8 @@ class TagsRemove(TagsAdd):
 class UploadAssetSpec(BaseModel):
     """Upload Asset operation.
 
-    - tags: optional list; if provided, first is root ('models'|'input'|'output');
-            if root == 'models', second must be a valid category
+    - tags: labels plus one destination role ('models'|'input'|'output') for new bytes;
+            if role == 'models', exactly one model_type:<folder_name> tag is required
     - name: display name
     - user_metadata: arbitrary JSON object (optional)
     - hash: optional canonical 'blake3:<hex>' for validation / fast-path
@@ -309,7 +309,7 @@ class UploadAssetSpec(BaseModel):
         norm = []
         seen = set()
         for t in items:
-            tnorm = str(t).strip().lower()
+            tnorm = str(t).strip()
             if tnorm and tnorm not in seen:
                 seen.add(tnorm)
                 norm.append(tnorm)
@@ -335,14 +335,4 @@ class UploadAssetSpec(BaseModel):
 
     @model_validator(mode="after")
     def _validate_order(self):
-        if not self.tags:
-            raise ValueError("at least one tag is required for uploads")
-        root = self.tags[0]
-        if root not in {"models", "input", "output"}:
-            raise ValueError("first tag must be one of: models, input, output")
-        if root == "models":
-            if len(self.tags) < 2:
-                raise ValueError(
-                    "models uploads require a category tag as the second tag"
-                )
         return self

app/assets/api/schemas_out.py

@@ -9,8 +9,20 @@ class Asset(BaseModel):
     ``id`` here is the AssetReference id, not the content-addressed Asset id."""
 
     id: str
-    name: str
+    name: str = Field(
+        ...,
+        deprecated=True,
+        description="Reference label, often caller-provided or derived from the filename. Deprecated for storage path/display semantics; use `loader_path` and `display_name` when present.",
+    )
     hash: str | None = None
+    loader_path: str | None = Field(
+        default=None,
+        description="The value a loader consumes to load this asset. `None` when no loader can resolve the file.",
+    )
+    display_name: str | None = Field(
+        default=None,
+        description="Human-facing label for the asset. Not unique.",
+    )
     asset_hash: str | None = None
     size: int | None = None
     mime_type: str | None = None

app/assets/api/upload.py

@@ -140,7 +140,6 @@ async def parse_multipart_upload(
             provided_mime_type = ((await field.text()) or "").strip() or None
         elif fname == "preview_id":
             provided_preview_id = ((await field.text()) or "").strip() or None
-
     if not file_present and not (provided_hash and provided_hash_exists):
         raise UploadError(
             400, "MISSING_FILE", "Form must include a 'file' part or a known 'hash'."

app/assets/database/models.py

@@ -76,6 +76,8 @@ class AssetReference(Base):
 
     # Cache state fields (from former AssetCacheState)
     file_path: Mapped[str | None] = mapped_column(Text, nullable=True)
+    # In-root loader path derived from file_path at scan/ingest time.
+    loader_path: Mapped[str | None] = mapped_column(Text, nullable=True)
     mtime_ns: Mapped[int | None] = mapped_column(BigInteger, nullable=True)
     needs_verify: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
     is_missing: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)

app/assets/database/queries/asset_reference.py

@@ -650,6 +650,7 @@ def upsert_reference(
     name: str,
     mtime_ns: int,
     owner_id: str = "",
+    loader_path: str | None = None,
 ) -> tuple[bool, bool]:
     """Upsert a reference by file_path. Returns (created, updated).
 
@@ -659,6 +660,7 @@ def upsert_reference(
     vals = {
         "asset_id": asset_id,
         "file_path": file_path,
+        "loader_path": loader_path,
         "name": name,
         "owner_id": owner_id,
         "mtime_ns": int(mtime_ns),
@@ -686,13 +688,14 @@ def upsert_reference(
                 AssetReference.asset_id != asset_id,
                 AssetReference.mtime_ns.is_(None),
                 AssetReference.mtime_ns != int(mtime_ns),
+                AssetReference.loader_path.is_distinct_from(loader_path),
                 AssetReference.is_missing == True,  # noqa: E712
                 AssetReference.deleted_at.isnot(None),
             )
         )
         .values(
-            asset_id=asset_id, mtime_ns=int(mtime_ns), is_missing=False,
-            deleted_at=None, updated_at=now,
+            asset_id=asset_id, mtime_ns=int(mtime_ns), loader_path=loader_path,
+            is_missing=False, deleted_at=None, updated_at=now,
         )
     )
     res2 = session.execute(upd)

app/assets/database/queries/tags.py

@@ -265,6 +265,8 @@ def list_tags_with_usage(
     order: str = "count_desc",
     owner_id: str = "",
 ) -> tuple[list[tuple[str, str, int]], int]:
+    prefix_filter = prefix.strip() if prefix else ""
+
     counts_sq = (
         select(
             AssetReferenceTag.tag_name.label("tag_name"),
@@ -293,9 +295,8 @@ def list_tags_with_usage(
         .join(counts_sq, counts_sq.c.tag_name == Tag.name, isouter=True)
     )
 
-    if prefix:
-        escaped, esc = escape_sql_like_string(prefix.strip().lower())
-        q = q.where(Tag.name.like(escaped + "%", escape=esc))
+    if prefix_filter:
+        q = q.where(func.substr(Tag.name, 1, len(prefix_filter)) == prefix_filter)
 
     if not include_zero:
         q = q.where(func.coalesce(counts_sq.c.cnt, 0) > 0)
@@ -306,9 +307,8 @@ def list_tags_with_usage(
         q = q.order_by(func.coalesce(counts_sq.c.cnt, 0).desc(), Tag.name.asc())
 
     total_q = select(func.count()).select_from(Tag)
-    if prefix:
-        escaped, esc = escape_sql_like_string(prefix.strip().lower())
-        total_q = total_q.where(Tag.name.like(escaped + "%", escape=esc))
+    if prefix_filter:
+        total_q = total_q.where(func.substr(Tag.name, 1, len(prefix_filter)) == prefix_filter)
     if not include_zero:
         visible_tags_sq = (
             select(AssetReferenceTag.tag_name)

app/assets/helpers.py

@@ -41,10 +41,10 @@ def get_utc_now() -> datetime:
 def normalize_tags(tags: list[str] | None) -> list[str]:
     """
     Normalize a list of tags by:
-      - Stripping whitespace and converting to lowercase.
-      - Removing duplicates.
+      - Stripping whitespace.
+      - Removing exact duplicates while preserving order and case.
     """
-    return list(dict.fromkeys(t.strip().lower() for t in (tags or []) if (t or "").strip()))
+    return list(dict.fromkeys(t.strip() for t in (tags or []) if (t or "").strip()))
 
 
 def validate_blake3_hash(s: str) -> str:

app/assets/scanner.py

@@ -36,7 +36,7 @@ from app.assets.services.hashing import HashCheckpoint, compute_blake3_hash
 from app.assets.services.image_dimensions import extract_image_dimensions
 from app.assets.services.metadata_extract import extract_file_metadata
 from app.assets.services.path_utils import (
-    compute_relative_filename,
+    compute_loader_path,
     get_comfy_models_folders,
     get_name_and_tags_from_asset_path,
 )
@@ -63,7 +63,7 @@ RootType = Literal["models", "input", "output"]
 def get_prefixes_for_root(root: RootType) -> list[str]:
     if root == "models":
         bases: list[str] = []
-        for _bucket, paths in get_comfy_models_folders():
+        for _bucket, paths, _exts in get_comfy_models_folders():
             bases.extend(paths)
         return [os.path.abspath(p) for p in bases]
     if root == "input":
@@ -81,7 +81,7 @@ def get_all_known_prefixes() -> list[str]:
 
 def collect_models_files() -> list[str]:
     out: list[str] = []
-    for folder_name, bases in get_comfy_models_folders():
+    for folder_name, bases, _exts in get_comfy_models_folders():
         rel_files = folder_paths.get_filename_list(folder_name) or []
         for rel_path in rel_files:
             if not all(is_visible(part) for part in Path(rel_path).parts):
@@ -308,7 +308,7 @@ def build_asset_specs(
         if not stat_p.st_size:
             continue
         name, tags = get_name_and_tags_from_asset_path(abs_p)
-        rel_fname = compute_relative_filename(abs_p)
+        rel_fname = compute_loader_path(abs_p)
 
         # Extract metadata (tier 1: filesystem, tier 2: safetensors header)
         metadata = None
@@ -430,7 +430,7 @@ def enrich_asset(
         return new_level
 
     initial_mtime_ns = get_mtime_ns(stat_p)
-    rel_fname = compute_relative_filename(file_path)
+    rel_fname = compute_loader_path(file_path)
     mime_type: str | None = None
     metadata = None
 

app/assets/services/asset_management.py

@@ -38,7 +38,7 @@ from app.assets.database.queries import (
     update_reference_updated_at,
 )
 from app.assets.helpers import select_best_live_path
-from app.assets.services.path_utils import compute_relative_filename
+from app.assets.services.path_utils import compute_loader_path
 from app.assets.services.schemas import (
     AssetData,
     AssetDetailResult,
@@ -91,7 +91,7 @@ def update_asset_metadata(
             update_reference_name(session, reference_id=reference_id, name=name)
             touched = True
 
-        computed_filename = compute_relative_filename(ref.file_path) if ref.file_path else None
+        computed_filename = compute_loader_path(ref.file_path) if ref.file_path else None
 
         new_meta: dict | None = None
         if user_metadata is not None:

app/assets/services/bulk_ingest.py

@@ -56,6 +56,7 @@ class ReferenceRow(TypedDict):
     id: str
     asset_id: str
     file_path: str
+    loader_path: str | None
     mtime_ns: int
     owner_id: str
     name: str
@@ -134,6 +135,14 @@ def batch_insert_seed_assets(
 
     for spec in specs:
         absolute_path = os.path.abspath(spec["abs_path"])
+        existing_asset_id = path_to_asset_id.get(absolute_path)
+        if existing_asset_id is not None:
+            existing_tags = asset_id_to_ref_data[existing_asset_id]["tags"]
+            asset_id_to_ref_data[existing_asset_id]["tags"] = list(
+                dict.fromkeys([*existing_tags, *spec["tags"]])
+            )
+            continue
+
         asset_id = str(uuid.uuid4())
         reference_id = str(uuid.uuid4())
         absolute_path_list.append(absolute_path)
@@ -164,6 +173,8 @@ def batch_insert_seed_assets(
                 "id": reference_id,
                 "asset_id": asset_id,
                 "file_path": absolute_path,
+                # spec["fname"] is compute_loader_path(abs_path) from build_asset_specs.
+                "loader_path": spec["fname"],
                 "mtime_ns": spec["mtime_ns"],
                 "owner_id": owner_id,
                 "name": spec["info_name"],

app/assets/services/ingest.py

@@ -33,8 +33,9 @@ from app.assets.services.bulk_ingest import batch_insert_seed_assets
 from app.assets.services.file_utils import get_size_and_mtime_ns
 from app.assets.services.image_dimensions import extract_image_dimensions
 from app.assets.services.path_utils import (
-    compute_relative_filename,
+    compute_loader_path,
     get_name_and_tags_from_asset_path,
+    get_path_derived_tags_from_path,
     resolve_destination_from_tags,
     validate_path_within_base,
 )
@@ -91,6 +92,7 @@ def _ingest_file_from_path(
             name=info_name or os.path.basename(locator),
             mtime_ns=mtime_ns,
             owner_id=owner_id,
+            loader_path=compute_loader_path(locator),
         )
 
         # Get the reference we just created/updated
@@ -101,17 +103,32 @@ def _ingest_file_from_path(
             if preview_id and ref.preview_id != preview_id:
                 ref.preview_id = preview_id
 
-            norm = normalize_tags(list(tags))
-            if norm:
+            try:
+                backend_tags = get_path_derived_tags_from_path(locator)
+            except ValueError:
+                backend_tags = []
+            caller_tags = normalize_tags(tags)
+            backend_tags = normalize_tags(backend_tags)
+            all_tags = normalize_tags([*caller_tags, *backend_tags])
+            if all_tags:
                 if require_existing_tags:
-                    validate_tags_exist(session, norm)
-                add_tags_to_reference(
-                    session,
-                    reference_id=reference_id,
-                    tags=norm,
-                    origin=tag_origin,
-                    create_if_missing=not require_existing_tags,
-                )
+                    validate_tags_exist(session, all_tags)
+                if backend_tags:
+                    add_tags_to_reference(
+                        session,
+                        reference_id=reference_id,
+                        tags=backend_tags,
+                        origin="automatic",
+                        create_if_missing=not require_existing_tags,
+                    )
+                if caller_tags:
+                    add_tags_to_reference(
+                        session,
+                        reference_id=reference_id,
+                        tags=caller_tags,
+                        origin=tag_origin,
+                        create_if_missing=not require_existing_tags,
+                    )
 
             _update_metadata_with_filename(
                 session,
@@ -228,7 +245,7 @@ def ingest_existing_file(
             "mtime_ns": mtime_ns,
             "info_name": name,
             "tags": tags,
-            "fname": os.path.basename(abs_path),
+            "fname": compute_loader_path(abs_path),
             "metadata": None,
             "hash": None,
             "mime_type": mime_type,
@@ -288,7 +305,7 @@ def _register_existing_asset(
             return result
 
         new_meta = dict(user_metadata)
-        computed_filename = compute_relative_filename(ref.file_path) if ref.file_path else None
+        computed_filename = compute_loader_path(ref.file_path) if ref.file_path else None
         if computed_filename:
             new_meta["filename"] = computed_filename
 
@@ -335,7 +352,7 @@ def _update_metadata_with_filename(
     current_metadata: dict | None,
     user_metadata: dict[str, Any],
 ) -> None:
-    computed_filename = compute_relative_filename(file_path) if file_path else None
+    computed_filename = compute_loader_path(file_path) if file_path else None
 
     current_meta = current_metadata or {}
     new_meta = dict(current_meta)
@@ -474,6 +491,10 @@ def upload_from_temp_path(
         existing = get_asset_by_hash(session, asset_hash=asset_hash)
 
     if existing is not None:
+        # Once content is already known, duplicate byte uploads are treated as
+        # reference-only creation. Request tags are labels only here: do not
+        # require upload destination tags, do not move bytes, and do not
+        # synthesize path-derived classification or uploaded provenance.
         with contextlib.suppress(Exception):
             if temp_path and os.path.exists(temp_path):
                 os.remove(temp_path)
@@ -535,7 +556,7 @@ def upload_from_temp_path(
         owner_id=owner_id,
         preview_id=preview_id,
         user_metadata=user_metadata or {},
-        tags=tags,
+        tags=[*(tags or []), "uploaded"],
         tag_origin="manual",
         require_existing_tags=False,
     )
@@ -569,15 +590,19 @@ def register_file_in_place(
 ) -> UploadResult:
     """Register an already-saved file in the asset database without moving it.
 
-    Tags are derived from the filesystem path (root category + subfolder names),
-    merged with any caller-provided tags, matching the behavior of the scanner.
+    This helper is used by upload paths that have already written bytes before
+    registering the file, so it records the same ``uploaded`` tag as the
+    multipart byte-upload path.
+
+    Tags are derived from trusted filesystem classification and merged with any
+    caller-provided tags, matching the behavior of the scanner.
     If the path is not under a known root, only the caller-provided tags are used.
     """
     try:
         _, path_tags = get_name_and_tags_from_asset_path(abs_path)
     except ValueError:
         path_tags = []
-    merged_tags = normalize_tags([*path_tags, *tags])
+    merged_tags = normalize_tags([*path_tags, *tags, "uploaded"])
 
     try:
         digest, _ = hashing.compute_blake3_hash(abs_path)

app/assets/services/path_utils.py

@@ -3,59 +3,66 @@ from pathlib import Path
 from typing import Literal
 
 import folder_paths
-from app.assets.helpers import normalize_tags
 
 
-_NON_MODEL_FOLDER_NAMES = frozenset({"custom_nodes"})
+_NON_MODEL_FOLDER_NAMES = frozenset({"configs", "custom_nodes"})
+_KNOWN_SUBFOLDER_TAGS = frozenset({"3d", "pasted", "painter", "threed", "webcam"})
 
 
-def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
-    """Build list of (folder_name, base_paths[]) for all model locations.
+def get_comfy_models_folders() -> list[tuple[str, list[str], set[str]]]:
+    """Build list of (folder_name, base_paths[], extensions) for all model locations.
 
     Includes every category registered in folder_names_and_paths,
     regardless of whether its paths are under the main models_dir,
-    but excludes non-model entries like custom_nodes.
+    but excludes non-model entries like configs and custom_nodes.
+
+    An empty extensions set means the category accepts any extension,
+    matching folder_paths.filter_files_extensions semantics.
     """
-    targets: list[tuple[str, list[str]]] = []
+    targets: list[tuple[str, list[str], set[str]]] = []
     for name, values in folder_paths.folder_names_and_paths.items():
         if name in _NON_MODEL_FOLDER_NAMES:
             continue
-        paths, _exts = values[0], values[1]
+        paths, exts = values[0], values[1]
         if paths:
-            targets.append((name, paths))
+            targets.append((name, paths, set(exts)))
     return targets
 
 
 def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[str]]:
-    """Validates and maps tags -> (base_dir, subdirs_for_fs)"""
-    if not tags:
-        raise ValueError("tags must not be empty")
-    root = tags[0].lower()
+    """Validates and maps upload routing tags -> (base_dir, subdirs_for_fs).
+
+    The request tags are only used to choose the write destination. Extra tags
+    remain labels; they do not become path components or trusted classification.
+    """
+    destination_roles = [t for t in tags if t in {"input", "models", "output"}]
+    if len(destination_roles) != 1:
+        raise ValueError("uploads require exactly one destination role: input, models, or output")
+
+    root = destination_roles[0]
     if root == "models":
-        if len(tags) < 2:
-            raise ValueError("at least two tags required for model asset")
+        model_type_tags = [t for t in tags if t.startswith("model_type:")]
+        if len(model_type_tags) != 1:
+            raise ValueError("models uploads require exactly one model_type:<folder_name> tag")
+        folder_name = model_type_tags[0].split(":", 1)[1]
+        if not folder_name:
+            raise ValueError("models uploads require exactly one model_type:<folder_name> tag")
+        model_folder_paths = {
+            name: paths for name, paths, _exts in get_comfy_models_folders()
+        }
         try:
-            bases = folder_paths.folder_names_and_paths[tags[1]][0]
+            bases = model_folder_paths[folder_name]
         except KeyError:
-            raise ValueError(f"unknown model category '{tags[1]}'")
+            raise ValueError(f"unknown model category '{folder_name}'")
         if not bases:
-            raise ValueError(f"no base path configured for category '{tags[1]}'")
+            raise ValueError(f"no base path configured for category '{folder_name}'")
         base_dir = os.path.abspath(bases[0])
-        raw_subdirs = tags[2:]
     elif root == "input":
         base_dir = os.path.abspath(folder_paths.get_input_directory())
-        raw_subdirs = tags[1:]
-    elif root == "output":
-        base_dir = os.path.abspath(folder_paths.get_output_directory())
-        raw_subdirs = tags[1:]
     else:
-        raise ValueError(f"unknown root tag '{tags[0]}'; expected 'models', 'input', or 'output'")
-    _sep_chars = frozenset(("/", "\\", os.sep))
-    for i in raw_subdirs:
-        if i in (".", "..") or _sep_chars & set(i):
-            raise ValueError("invalid path component in tags")
+        base_dir = os.path.abspath(folder_paths.get_output_directory())
 
-    return base_dir, raw_subdirs if raw_subdirs else []
+    return base_dir, []
 
 
 def validate_path_within_base(candidate: str, base: str) -> None:
@@ -65,14 +72,79 @@ def validate_path_within_base(candidate: str, base: str) -> None:
         raise ValueError("destination escapes base directory")
 
 
-def compute_relative_filename(file_path: str) -> str | None:
+def _compute_relative_path(child: str, parent: str) -> str:
+    rel = os.path.relpath(os.path.abspath(child), os.path.abspath(parent))
+    if rel == ".":
+        return ""
+    return rel.replace(os.sep, "/")
+
+
+def _is_relative_to(child: str, parent: str) -> bool:
+    return Path(os.path.abspath(child)).is_relative_to(os.path.abspath(parent))
+
+
+def compute_asset_response_paths(file_path: str) -> tuple[str, str | None] | None:
+    """Return (logical_path, display_name) for a file path.
+
+    ``logical_path`` is the internal namespaced storage locator (e.g.
+    ``models/checkpoints/foo/bar.safetensors``); ``display_name`` is the
+    human-facing label below that namespace, served on Asset responses. These
+    are storage locators, not model-loader namespaces. Registered model-folder
+    membership is represented by backend tags such as
+    ``model_type:<folder_name>``; these paths only use known storage roots.
     """
-    Return the model's path relative to the last well-known folder (the model category),
-    using forward slashes, eg:
+    fp_abs = os.path.abspath(file_path)
+    candidates: list[tuple[int, int, str, str]] = []
+
+    for order, (namespace, base) in enumerate(
+        (
+            ("input", folder_paths.get_input_directory()),
+            ("output", folder_paths.get_output_directory()),
+            ("temp", folder_paths.get_temp_directory()),
+            ("models", getattr(folder_paths, "models_dir", "")),
+        )
+    ):
+        if not base:
+            continue
+        base_abs = os.path.abspath(base)
+        if _is_relative_to(fp_abs, base_abs):
+            candidates.append((len(base_abs), -order, namespace, base_abs))
+
+    if not candidates:
+        return None
+
+    _base_len, _order, namespace, base = max(candidates)
+    rel = _compute_relative_path(fp_abs, base)
+    public_path = f"{namespace}/{rel}" if rel else namespace
+    return public_path, rel or None
+
+
+def compute_display_name(file_path: str) -> str | None:
+    """Return the asset's `display_name`, or None for unknown paths."""
+    result = compute_asset_response_paths(file_path)
+    return result[1] if result else None
+
+
+def compute_logical_path(file_path: str) -> str | None:
+    """Return the internal namespaced storage locator, or None for unknown paths."""
+    result = compute_asset_response_paths(file_path)
+    return result[0] if result else None
+
+
+def compute_loader_path(file_path: str) -> str | None:
+    """
+    Return the asset's in-root loader path: the path relative to the last
+    well-known folder (the model category), using forward slashes, eg:
       /.../models/checkpoints/flux/123/flux.safetensors -> "flux/123/flux.safetensors"
       /.../models/text_encoders/clip_g.safetensors -> "clip_g.safetensors"
 
-    For non-model paths, returns None.
+    This is the value model loaders consume (the model category is dropped). It
+    is persisted as ``AssetReference.loader_path`` and served as the public
+    Asset response `loader_path` field. The human-facing `display_name` comes
+    from compute_asset_response_paths().
+
+    For input/output/temp paths the full path relative to that root is returned.
+    For paths outside any known root, returns None.
     """
     try:
         root_category, rel_path = get_asset_category_and_relative_path(file_path)
@@ -116,9 +188,10 @@ def get_asset_category_and_relative_path(
     def _compute_relative(child: str, parent: str) -> str:
         # Normalize relative path, stripping any leading ".." components
         # by anchoring to root (os.sep) then computing relpath back from it.
-        return os.path.relpath(
+        rel = os.path.relpath(
             os.path.join(os.sep, os.path.relpath(child, parent)), os.sep
         )
+        return "" if rel == "." else rel.replace(os.sep, "/")
 
     # 1) input
     input_base = os.path.abspath(folder_paths.get_input_directory())
@@ -136,8 +209,14 @@ def get_asset_category_and_relative_path(
         return "temp", _compute_relative(fp_abs, temp_base)
 
     # 4) models (check deepest matching base to avoid ambiguity)
+    ext = os.path.splitext(fp_abs)[1].lower()
     best: tuple[int, str, str] | None = None  # (base_len, bucket, rel_inside_bucket)
-    for bucket, bases in get_comfy_models_folders():
+    for bucket, bases, extensions in get_comfy_models_folders():
+        # A bucket only lists files within its extension set (empty set
+        # accepts any extension), so a bucket that cannot load the file
+        # must not contribute a loader path.
+        if extensions and ext not in extensions:
+            continue
         for b in bases:
             base_abs = os.path.abspath(b)
             if not _check_is_within(fp_abs, base_abs):
@@ -149,25 +228,111 @@ def get_asset_category_and_relative_path(
     if best is not None:
         _, bucket, rel_inside = best
         combined = os.path.join(bucket, rel_inside)
-        return "models", os.path.relpath(os.path.join(os.sep, combined), os.sep)
+        normalized = os.path.relpath(os.path.join(os.sep, combined), os.sep)
+        return "models", normalized.replace(os.sep, "/")
 
     raise ValueError(
         f"Path is not within input, output, temp, or configured model bases: {file_path}"
     )
 
 
+def get_backend_system_tags_from_path(path: str) -> list[str]:
+    """Return trusted backend tags derived from current filesystem facts.
+
+    The returned tags are only the backend-generated system tags: ``models``,
+    ``model_type:<folder_name>``, ``input``, ``output``, and ``temp``. Model
+    type tags are based on registered folder names, not path components.
+
+    A ``model_type:<folder_name>`` tag is only emitted when the file's
+    extension is accepted by that folder's registered extension set, so
+    categories sharing a base directory tag only the files they can
+    actually load. Files under a model base whose extension matches no
+    category still get the ``models`` tag.
+    """
+    fp_abs = os.path.abspath(path)
+    fp_path = Path(fp_abs)
+    tags: list[str] = []
+
+    def _add(tag: str) -> None:
+        if tag not in tags:
+            tags.append(tag)
+
+    for role, base in (
+        ("input", folder_paths.get_input_directory()),
+        ("output", folder_paths.get_output_directory()),
+        ("temp", folder_paths.get_temp_directory()),
+    ):
+        if fp_path.is_relative_to(os.path.abspath(base)):
+            _add(role)
+
+    ext = os.path.splitext(fp_abs)[1].lower()
+    model_types: list[str] = []
+    under_models_base = False
+    for folder_name, bases, extensions in get_comfy_models_folders():
+        for base in bases:
+            if fp_path.is_relative_to(os.path.abspath(base)):
+                under_models_base = True
+                # Empty set accepts any extension, matching
+                # folder_paths.filter_files_extensions semantics.
+                if not extensions or ext in extensions:
+                    model_types.append(folder_name)
+                break
+
+    if under_models_base:
+        _add("models")
+    for folder_name in model_types:
+        _add(f"model_type:{folder_name}")
+
+    if not tags:
+        raise ValueError(
+            f"Path is not within input, output, temp, or configured model bases: {path}"
+        )
+    return tags
+
+
+def get_known_subfolder_tags(subfolder: str | None) -> list[str]:
+    """Return tags for known UI/input subfolder names."""
+    if subfolder in _KNOWN_SUBFOLDER_TAGS:
+        return [subfolder]
+    return []
+
+
+def get_known_input_subfolder_tags_from_path(path: str) -> list[str]:
+    """Return known input-layout tags for files in canonical input subfolders.
+
+    These are compatibility tags for current UI-origin input directories such as
+    ``pasted`` and ``webcam``. They are intentionally narrow: only files directly
+    inside a known top-level input directory receive the matching tag.
+    """
+    fp_abs = os.path.abspath(path)
+    input_base = os.path.abspath(folder_paths.get_input_directory())
+    if not Path(fp_abs).is_relative_to(input_base):
+        return []
+
+    rel = os.path.relpath(fp_abs, input_base)
+    parts = Path(rel).parts
+    if len(parts) == 2:
+        return get_known_subfolder_tags(parts[0])
+    return []
+
+
+def get_path_derived_tags_from_path(path: str) -> list[str]:
+    """Return all backend-derived tags for an asset path."""
+    tags = get_backend_system_tags_from_path(path)
+    for tag in get_known_input_subfolder_tags_from_path(path):
+        if tag not in tags:
+            tags.append(tag)
+    return tags
+
+
 def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list[str]]:
     """Return (name, tags) derived from a filesystem path.
 
     - name: base filename with extension
-    - tags: [root_category] + parent folder names in order
+    - tags: backend-derived tags from root/model classification and known input
+      subfolder layout conventions
 
     Raises:
         ValueError: path does not belong to any known root.
     """
-    root_category, some_path = get_asset_category_and_relative_path(file_path)
-    p = Path(some_path)
-    parent_parts = [
-        part for part in p.parent.parts if part not in (".", "..", p.anchor)
-    ]
-    return p.name, list(dict.fromkeys(normalize_tags([root_category, *parent_parts])))
+    return Path(file_path).name, get_path_derived_tags_from_path(file_path)

app/assets/services/schemas.py

@@ -25,6 +25,7 @@ class ReferenceData:
     preview_id: str | None
     created_at: datetime
     updated_at: datetime
+    loader_path: str | None = None
     system_metadata: dict[str, Any] | None = None
     job_id: str | None = None
     last_access_time: datetime | None = None
@@ -93,6 +94,7 @@ def extract_reference_data(ref: AssetReference) -> ReferenceData:
         id=ref.id,
         name=ref.name,
         file_path=ref.file_path,
+        loader_path=ref.loader_path,
         user_metadata=ref.user_metadata,
         preview_id=ref.preview_id,
         system_metadata=ref.system_metadata,

app/model_manager.py

@@ -35,7 +35,11 @@ class ModelFileManager:
             for folder in model_types:
                 if folder in folder_black_list:
                     continue
-                output_folders.append({"name": folder, "folders": folder_paths.get_folder_paths(folder)})
+                output_folders.append({
+                    "name": folder,
+                    "folders": folder_paths.get_folder_paths(folder),
+                    "extensions": sorted(folder_paths.folder_names_and_paths[folder][1]),
+                })
             return web.json_response(output_folders)
 
         # NOTE: This is an experiment to replace `/models/{folder}`

comfy/cli_args.py

@@ -92,6 +92,7 @@ parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE"
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
 parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
 parser.add_argument("--enable-triton-backend", action="store_true", help="ComfyUI will enable the use of Triton backend in comfy-kitchen. Is disabled at launch by default.")
+parser.add_argument("--disable-triton-backend", action="store_true", help="Force-disable the comfy-kitchen Triton backend, overriding the automatic ROCm/AMD default and --enable-triton-backend.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"

comfy/comfy_api_env.py

@@ -0,0 +1,46 @@
+"""Runtime config the frontend reads from /features to follow --comfy-api-base.
+
+For a non-prod comfy.org backend (staging or an ephemeral preview env), "/features" exposes the api and
+platform base so the frontend talks to it without a rebuild, plus the Firebase environment it should use.
+Prod bases are left alone and keep their build-time defaults.
+"""
+
+from typing import Any
+from urllib.parse import urlparse
+
+from comfy.cli_args import args
+
+_STAGING_API_HOST = "stagingapi.comfy.org"
+_TESTENV_HOST_SUFFIX = ".testenvs.comfy.org"
+_STAGING_PLATFORM_BASE_URL = "https://stagingplatform.comfy.org"
+
+
+def _is_staging_tier(host: str) -> bool:
+    return host == _STAGING_API_HOST or host.endswith(_TESTENV_HOST_SUFFIX)
+
+
+def normalize_comfy_api_base(url: str) -> str:
+    """Rewrite a testenv's friendly main host to its comfy-api '-registry' sibling."""
+    parsed = urlparse(url)
+    host = parsed.hostname or ""
+    if not host.endswith(_TESTENV_HOST_SUFFIX):
+        return url
+    label = host[: -len(_TESTENV_HOST_SUFFIX)]
+    if label.endswith("-registry"):
+        return url
+    return f"{parsed.scheme or 'https'}://{label}-registry{_TESTENV_HOST_SUFFIX}"
+
+
+def environment_overrides_for_base(base_url: str) -> dict[str, Any] | None:
+    """The /features overrides for a staging-tier base, or None for prod."""
+    if not _is_staging_tier(urlparse(base_url).hostname or ""):
+        return None
+    return {
+        "comfy_api_base_url": normalize_comfy_api_base(base_url).rstrip("/"),
+        "comfy_platform_base_url": _STAGING_PLATFORM_BASE_URL,
+        "firebase_env": "dev",
+    }
+
+
+def get_environment_overrides() -> dict[str, Any] | None:
+    return environment_overrides_for_base(getattr(args, "comfy_api_base", "") or "")

comfy/latent_formats.py

@@ -779,6 +779,10 @@ class ACEAudio(LatentFormat):
     latent_channels = 8
     latent_dimensions = 2
 
+class SeedVR2(LatentFormat):
+    latent_channels = 16
+    latent_dimensions = 3
+
 class ACEAudio15(LatentFormat):
     latent_channels = 64
     latent_dimensions = 1

comfy/ldm/hidream_o1/attention.py

@@ -15,24 +15,24 @@ def make_two_pass_attention(ar_len: int, transformer_options=None):
     The AR pass goes through SDPA directand bypasses wrappers, it is only ~1% of T at typical edit sizes.
     """
 
-    def two_pass_attention(q, k, v, heads, **kwargs):
+    def two_pass_attention(q, k, v, heads, enable_gqa=False, **kwargs):
         B, H, T, D = q.shape
 
         if T < k.shape[2]: # KV-cache hot path: Q is shorter than K/V (cached AR prefix is in K/V only), all fresh Q positions are in the gen region, single full-attention call
-            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
+            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options, enable_gqa=enable_gqa)
         elif ar_len >= T:
-            out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True)
+            out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True, enable_gqa=enable_gqa)
         elif ar_len <= 0:
-            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
+            out = optimized_attention(q, k, v, heads, mask=None, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options, enable_gqa=enable_gqa)
         else:
             out_ar = comfy.ops.scaled_dot_product_attention(
                 q[:, :, :ar_len], k[:, :, :ar_len], v[:, :, :ar_len],
-                attn_mask=None, dropout_p=0.0, is_causal=True,
+                attn_mask=None, dropout_p=0.0, is_causal=True, enable_gqa=enable_gqa,
             )
             out_gen = optimized_attention(
                 q[:, :, ar_len:], k, v, heads,
                 mask=None, skip_reshape=True, skip_output_reshape=True,
-                transformer_options=transformer_options,
+                transformer_options=transformer_options, enable_gqa=enable_gqa,
             )
             out = torch.cat([out_ar, out_gen], dim=2)
 

comfy/ldm/modules/attention.py

@@ -709,7 +709,7 @@ def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
     return out
 
 try:
-    @torch.library.custom_op("flash_attention::flash_attn", mutates_args=())
+    @torch.library.custom_op("comfy::flash_attn", mutates_args=())
     def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
                     dropout_p: float = 0.0, causal: bool = False, softmax_scale: float = -1.0) -> torch.Tensor:
         softmax_scale_arg = None if softmax_scale == -1.0 else softmax_scale

comfy/ldm/modules/diffusionmodules/model.py

@@ -22,7 +22,7 @@ def torch_cat_if_needed(xl, dim):
     else:
         return None
 
-def get_timestep_embedding(timesteps, embedding_dim):
+def get_timestep_embedding(timesteps, embedding_dim, flip_sin_to_cos=False, downscale_freq_shift=1):
     """
     This matches the implementation in Denoising Diffusion Probabilistic Models:
     From Fairseq.
@@ -33,11 +33,13 @@ def get_timestep_embedding(timesteps, embedding_dim):
     assert len(timesteps.shape) == 1
 
     half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - 1)
+    emb = math.log(10000) / (half_dim - downscale_freq_shift)
     emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
     emb = emb.to(device=timesteps.device)
     emb = timesteps.float()[:, None] * emb[None, :]
     emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
     if embedding_dim % 2 == 1:  # zero pad
         emb = torch.nn.functional.pad(emb, (0,1,0,0))
     return emb

comfy/ldm/pixeldit/model.py

@@ -197,6 +197,9 @@ class PixDiT_T2I(nn.Module):
         """Hook for subclasses to inject per-block state into the patch stream (e.g. PiD's LQ gate)."""
         return s
 
+    def _pre_pixel_blocks(self, s, **kwargs):
+        return s
+
     def _forward(self, x, timesteps, context=None, attention_mask=None, transformer_options={}, **kwargs):
         H_orig, W_orig = x.shape[2], x.shape[3]
         x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
@@ -226,6 +229,7 @@ class PixDiT_T2I(nn.Module):
             s, y_emb = blk(s, y_emb, condition, pos_img, pos_txt, None, transformer_options=transformer_options)
         s = F.silu(t_emb + s)
 
+        s = self._pre_pixel_blocks(s, **kwargs)
         s_cond = s.view(B * L, self.hidden_size)
         x_pixels = self.pixel_embedder(x, patch_size=self.patch_size)
         for blk in self.pixel_blocks:

comfy/ldm/pixeldit/pid.py

@@ -13,15 +13,15 @@ from .model import PixDiT_T2I
 from .modules import precompute_freqs_cis_2d
 
 
-class SigmaAwareGatePerTokenPerDim(nn.Module):
+class SigmaAwareGate(nn.Module):
     """gate = sigmoid(content_proj(cat[x, lq]) - exp(log_alpha) * sigma); out = x + gate * lq.
 
     Trained init gives ~0.88 gate at sigma=0, ~0.05 at sigma=1.
     """
 
-    def __init__(self, dim: int, dtype=None, device=None, operations=None):
+    def __init__(self, dim: int, per_token: bool = False, dtype=None, device=None, operations=None):
         super().__init__()
-        self.content_proj = operations.Linear(dim * 2, dim, dtype=dtype, device=device)
+        self.content_proj = operations.Linear(dim * 2, 1 if per_token else dim, dtype=dtype, device=device)
         self.log_alpha = nn.Parameter(torch.empty((), dtype=dtype, device=device))
 
     def forward(self, x: torch.Tensor, lq: torch.Tensor, sigma: torch.Tensor) -> torch.Tensor:
@@ -36,15 +36,15 @@ class SigmaAwareGatePerTokenPerDim(nn.Module):
 class ResBlock(nn.Module):
     """Pre-activation ResNet block: GN -> SiLU -> Conv -> GN -> SiLU -> Conv + skip."""
 
-    def __init__(self, channels: int, num_groups: int = 4, dtype=None, device=None, operations=None):
+    def __init__(self, channels: int, num_groups: int = 4, conv_padding_mode: str = "zeros", dtype=None, device=None, operations=None):
         super().__init__()
         self.block = nn.Sequential(
             operations.GroupNorm(num_groups, channels, dtype=dtype, device=device),
             nn.SiLU(),
-            operations.Conv2d(channels, channels, kernel_size=3, padding=1, dtype=dtype, device=device),
+            operations.Conv2d(channels, channels, kernel_size=3, padding=1, padding_mode=conv_padding_mode, dtype=dtype, device=device),
             operations.GroupNorm(num_groups, channels, dtype=dtype, device=device),
             nn.SiLU(),
-            operations.Conv2d(channels, channels, kernel_size=3, padding=1, dtype=dtype, device=device),
+            operations.Conv2d(channels, channels, kernel_size=3, padding=1, padding_mode=conv_padding_mode, dtype=dtype, device=device),
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -62,9 +62,13 @@ class LQProjection2D(nn.Module):
         patch_size: int = 16,
         sr_scale: int = 4,
         latent_spatial_down_factor: int = 8,
+        latent_unpatchify_factor: int = 1,
         num_res_blocks: int = 4,
         num_outputs: int = 7,
         interval: int = 2,
+        conv_padding_mode: str = "zeros",
+        gate_per_token: bool = False,
+        pit_output: bool = False,
         dtype=None, device=None, operations=None,
     ):
         super().__init__()
@@ -74,34 +78,38 @@ class LQProjection2D(nn.Module):
         self.patch_size = patch_size
         self.sr_scale = sr_scale
         self.latent_spatial_down_factor = latent_spatial_down_factor
+        self.latent_unpatchify_factor = latent_unpatchify_factor
         self.num_outputs = num_outputs
         self.interval = interval
 
-        z_to_patch_ratio = (sr_scale * latent_spatial_down_factor) / patch_size
+        effective_latent_channels = latent_channels // (latent_unpatchify_factor * latent_unpatchify_factor)
+        effective_spatial_down_factor = latent_spatial_down_factor // latent_unpatchify_factor
+        z_to_patch_ratio = (sr_scale * effective_spatial_down_factor) / patch_size
         self.z_to_patch_ratio = z_to_patch_ratio
         if z_to_patch_ratio >= 1:
             self.latent_fold_factor = 0
-            latent_proj_in_ch = latent_channels
+            latent_proj_in_ch = effective_latent_channels
         else:
             fold_factor = int(1 / z_to_patch_ratio)
             assert fold_factor * z_to_patch_ratio == 1.0
             self.latent_fold_factor = fold_factor
-            latent_proj_in_ch = latent_channels * fold_factor * fold_factor
+            latent_proj_in_ch = effective_latent_channels * fold_factor * fold_factor
 
         layers = [
-            operations.Conv2d(latent_proj_in_ch, hidden_dim, kernel_size=3, padding=1, dtype=dtype, device=device),
+            operations.Conv2d(latent_proj_in_ch, hidden_dim, kernel_size=3, padding=1, padding_mode=conv_padding_mode, dtype=dtype, device=device),
             nn.SiLU(),
-            operations.Conv2d(hidden_dim, hidden_dim, kernel_size=3, padding=1, dtype=dtype, device=device),
+            operations.Conv2d(hidden_dim, hidden_dim, kernel_size=3, padding=1, padding_mode=conv_padding_mode, dtype=dtype, device=device),
         ]
         for _ in range(num_res_blocks):
-            layers.append(ResBlock(hidden_dim, dtype=dtype, device=device, operations=operations))
+            layers.append(ResBlock(hidden_dim, conv_padding_mode=conv_padding_mode, dtype=dtype, device=device, operations=operations))
         self.latent_proj = nn.Sequential(*layers)
 
         self.output_heads = nn.ModuleList(
             [operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device) for _ in range(num_outputs)]
         )
+        self.pit_head = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device) if pit_output else None
         self.gate_modules = nn.ModuleList(
-            [SigmaAwareGatePerTokenPerDim(out_dim, dtype=dtype, device=device, operations=operations)
+            [SigmaAwareGate(out_dim, per_token=gate_per_token, dtype=dtype, device=device, operations=operations)
              for _ in range(num_outputs)]
         )
 
@@ -115,6 +123,11 @@ class LQProjection2D(nn.Module):
         return self.gate_modules[out_idx](x, lq_feature, sigma)
 
     def _align_latent_to_patch_grid(self, lq_latent: torch.Tensor, pH: int, pW: int) -> torch.Tensor:
+        f = self.latent_unpatchify_factor
+        if f > 1:
+            B, C, H, W = lq_latent.shape
+            lq_latent = lq_latent.reshape(B, C // (f * f), f, f, H, W)
+            lq_latent = lq_latent.permute(0, 1, 4, 2, 5, 3).reshape(B, C // (f * f), H * f, W * f)
         B, z_dim = lq_latent.shape[:2]
         if self.z_to_patch_ratio >= 1:
             if lq_latent.shape[2] != pH or lq_latent.shape[3] != pW:
@@ -134,7 +147,10 @@ class LQProjection2D(nn.Module):
         feat = self._align_latent_to_patch_grid(lq_latent, target_pH, target_pW)
         B, C, H, W = feat.shape
         tokens = feat.permute(0, 2, 3, 1).contiguous().view(B, H * W, C)
-        return [head(tokens) for head in self.output_heads]
+        outputs = [head(tokens) for head in self.output_heads]
+        if self.pit_head is not None:
+            outputs.append(self.pit_head(tokens))
+        return outputs
 
 
 class PidNet(PixDiT_T2I):
@@ -148,6 +164,10 @@ class PidNet(PixDiT_T2I):
         lq_interval: int = 2,
         sr_scale: int = 4,
         latent_spatial_down_factor: int = 8,
+        lq_latent_unpatchify_factor: int = 1,
+        lq_conv_padding_mode: str = "zeros",
+        lq_gate_per_token: bool = False,
+        pit_lq_inject: bool = False,
         rope_ref_h: int = 1024, # NTK ref resolution in PIXEL units: 1024px / patch=16 -> grid_ref=64.
         rope_ref_w: int = 1024,
         image_model=None,
@@ -165,6 +185,8 @@ class PidNet(PixDiT_T2I):
         for blk in self.pixel_blocks:
             blk._rope_fn = _pit_rope_fn
 
+        self.pit_lq_inject = pit_lq_inject
+
         num_lq_outputs = (self.patch_depth + lq_interval - 1) // lq_interval
         self.lq_proj = LQProjection2D(
             latent_channels=lq_latent_channels,
@@ -173,13 +195,20 @@ class PidNet(PixDiT_T2I):
             patch_size=self.patch_size,
             sr_scale=sr_scale,
             latent_spatial_down_factor=latent_spatial_down_factor,
+            latent_unpatchify_factor=lq_latent_unpatchify_factor,
             num_res_blocks=lq_num_res_blocks,
             num_outputs=num_lq_outputs,
             interval=lq_interval,
+            conv_padding_mode=lq_conv_padding_mode,
+            gate_per_token=lq_gate_per_token,
+            pit_output=pit_lq_inject,
             dtype=dtype,
             device=device,
             operations=operations,
         )
+        self.pit_lq_gate = SigmaAwareGate(
+            self.hidden_size, per_token=lq_gate_per_token, dtype=dtype, device=device, operations=operations
+        ) if pit_lq_inject else None
 
     def _fetch_patch_pos(self, height, width, device, dtype, **rope_opts):
         return precompute_freqs_cis_2d(
@@ -197,6 +226,11 @@ class PidNet(PixDiT_T2I):
             return s
         return self.lq_proj.gate(s, pid_lq_features[out_idx], pid_degrade_sigma, out_idx)
 
+    def _pre_pixel_blocks(self, s, pid_pit_lq_feature=None, pid_degrade_sigma=None, **kwargs):
+        if pid_pit_lq_feature is None:
+            return s
+        return self.pit_lq_gate(s, pid_pit_lq_feature, pid_degrade_sigma)
+
     def _forward(self, x, timesteps, context=None, attention_mask=None, transformer_options={}, lq_latent=None, degrade_sigma=None, **kwargs):
         if lq_latent is None:
             raise ValueError("PidNet requires lq_latent — attach via PiDConditioning")
@@ -216,12 +250,14 @@ class PidNet(PixDiT_T2I):
             degrade_sigma = degrade_sigma.expand(B).contiguous()
 
         lq_features = self.lq_proj(lq_latent=lq_latent.to(x), target_pH=Hs, target_pW=Ws)
+        pit_lq_feature = lq_features.pop() if self.pit_lq_inject else None
 
         return super()._forward(
             x, timesteps,
             context=context, attention_mask=attention_mask,
             transformer_options=transformer_options,
             pid_lq_features=lq_features,
+            pid_pit_lq_feature=pit_lq_feature,
             pid_degrade_sigma=degrade_sigma,
             **kwargs,
         )

comfy/ldm/seedvr/attention.py

@@ -0,0 +1,51 @@
+import torch
+
+from comfy.ldm.modules import attention as _attention
+
+
+def _var_attention_qkv(q, k, v, heads, skip_reshape):
+    if skip_reshape:
+        return q, k, v, q.shape[-1]
+    total_tokens, embed_dim = q.shape
+    head_dim = embed_dim // heads
+    return (
+        q.view(total_tokens, heads, head_dim),
+        k.view(k.shape[0], heads, head_dim),
+        v.view(v.shape[0], heads, head_dim),
+        head_dim,
+    )
+
+
+def _var_attention_output(out, heads, head_dim, skip_output_reshape):
+    if skip_output_reshape:
+        return out
+    return out.reshape(-1, heads * head_dim)
+
+
+def var_attention_optimized_split(q, k, v, heads, cu_seqlens_q, cu_seqlens_k, *args, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    q, k, v, head_dim = _var_attention_qkv(q, k, v, heads, skip_reshape)
+
+    q_split_indices = cu_seqlens_q[1:-1]
+    k_split_indices = cu_seqlens_k[1:-1]
+    if k.shape[0] != v.shape[0]:
+        raise ValueError("cu_seqlens_k does not match v token count")
+
+    q_splits = torch.tensor_split(q, q_split_indices, dim=0)
+    k_splits = torch.tensor_split(k, k_split_indices, dim=0)
+    v_splits = torch.tensor_split(v, k_split_indices, dim=0)
+    if len(q_splits) != len(k_splits) or len(q_splits) != len(v_splits):
+        raise ValueError("cu_seqlens_q and cu_seqlens_k must describe the same sequence count")
+
+    out = []
+    for q_i, k_i, v_i in zip(q_splits, k_splits, v_splits):
+        q_i = q_i.permute(1, 0, 2).unsqueeze(0)
+        k_i = k_i.permute(1, 0, 2).unsqueeze(0)
+        v_i = v_i.permute(1, 0, 2).unsqueeze(0)
+        out_i = _attention.optimized_attention(q_i, k_i, v_i, heads, skip_reshape=True, skip_output_reshape=True)
+        out.append(out_i.squeeze(0).permute(1, 0, 2))
+
+    out = torch.cat(out, dim=0)
+    return _var_attention_output(out, heads, head_dim, skip_output_reshape)
+
+
+optimized_var_attention = var_attention_optimized_split

comfy/ldm/seedvr/color_fix.py

@@ -0,0 +1,301 @@
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+
+from comfy.ldm.seedvr.constants import (
+    CIELAB_DELTA,
+    CIELAB_KAPPA,
+    D65_WHITE_X,
+    D65_WHITE_Z,
+    WAVELET_DECOMP_LEVELS,
+)
+
+
+def wavelet_blur(image: Tensor, radius):
+    max_safe_radius = max(1, min(image.shape[-2:]) // 8)
+    if radius > max_safe_radius:
+        radius = max_safe_radius
+
+    num_channels = image.shape[1]
+
+    kernel_vals = [
+        [0.0625, 0.125, 0.0625],
+        [0.125,  0.25,  0.125],
+        [0.0625, 0.125, 0.0625],
+    ]
+    kernel = torch.tensor(kernel_vals, dtype=image.dtype, device=image.device)
+    kernel = kernel[None, None].repeat(num_channels, 1, 1, 1)
+
+    image = F.pad(image, (radius, radius, radius, radius), mode='replicate')
+    output = F.conv2d(image, kernel, groups=num_channels, dilation=radius)
+
+    return output
+
+def wavelet_decomposition(image: Tensor, levels: int = WAVELET_DECOMP_LEVELS):
+    high_freq = torch.zeros_like(image)
+
+    for i in range(levels):
+        radius = 2 ** i
+        low_freq = wavelet_blur(image, radius)
+        high_freq.add_(image).sub_(low_freq)
+        image = low_freq
+
+    return high_freq, low_freq
+
+def wavelet_reconstruction(content_feat: Tensor, style_feat: Tensor) -> Tensor:
+
+    if content_feat.shape != style_feat.shape:
+        if len(content_feat.shape) >= 3:
+            style_feat = F.interpolate(
+                style_feat,
+                size=content_feat.shape[-2:],
+                mode='bilinear',
+                align_corners=False
+            )
+
+    content_high_freq, content_low_freq = wavelet_decomposition(content_feat)
+    del content_low_freq
+
+    style_high_freq, style_low_freq = wavelet_decomposition(style_feat)
+    del style_high_freq
+
+    if content_high_freq.shape != style_low_freq.shape:
+        style_low_freq = F.interpolate(
+            style_low_freq,
+            size=content_high_freq.shape[-2:],
+            mode='bilinear',
+            align_corners=False
+        )
+
+    content_high_freq.add_(style_low_freq)
+
+    return content_high_freq.clamp_(-1.0, 1.0)
+
+def _histogram_matching_channel(source: Tensor, reference: Tensor) -> Tensor:
+    original_shape = source.shape
+
+    source_flat = source.flatten()
+    reference_flat = reference.flatten()
+
+    source_sorted, source_indices = torch.sort(source_flat)
+    reference_sorted, _ = torch.sort(reference_flat)
+    del reference_flat
+
+    n_source = len(source_sorted)
+    n_reference = len(reference_sorted)
+
+    if n_source == n_reference:
+        matched_sorted = reference_sorted
+    else:
+        source_quantiles = torch.linspace(0, 1, n_source, device=source.device)
+        ref_indices = (source_quantiles * (n_reference - 1)).long()
+        ref_indices.clamp_(0, n_reference - 1)
+        matched_sorted = reference_sorted[ref_indices]
+        del source_quantiles, ref_indices, reference_sorted
+
+    del source_sorted, source_flat
+
+    inverse_indices = torch.argsort(source_indices)
+    del source_indices
+    matched_flat = matched_sorted[inverse_indices]
+    del matched_sorted, inverse_indices
+
+    return matched_flat.reshape(original_shape)
+
+def _lab_to_rgb_batch(lab: Tensor, matrix_inv: Tensor, epsilon: float, kappa: float) -> Tensor:
+    L, a, b = lab[:, 0], lab[:, 1], lab[:, 2]
+
+    fy = (L + 16.0) / 116.0
+    fx = a.div(500.0).add_(fy)
+    fz = fy - b / 200.0
+    del L, a, b
+
+    x = torch.where(
+        fx > epsilon,
+        torch.pow(fx, 3.0),
+        fx.mul(116.0).sub_(16.0).div_(kappa)
+    )
+    y = torch.where(
+        fy > epsilon,
+        torch.pow(fy, 3.0),
+        fy.mul(116.0).sub_(16.0).div_(kappa)
+    )
+    z = torch.where(
+        fz > epsilon,
+        torch.pow(fz, 3.0),
+        fz.mul(116.0).sub_(16.0).div_(kappa)
+    )
+    del fx, fy, fz
+
+    x.mul_(D65_WHITE_X)
+    z.mul_(D65_WHITE_Z)
+
+    xyz = torch.stack([x, y, z], dim=1)
+    del x, y, z
+
+    B, _, H, W = xyz.shape
+    xyz_flat = xyz.permute(0, 2, 3, 1).reshape(-1, 3)
+    del xyz
+
+    xyz_flat = xyz_flat.to(dtype=matrix_inv.dtype)
+    rgb_linear_flat = torch.matmul(xyz_flat, matrix_inv.T)
+    del xyz_flat
+
+    rgb_linear = rgb_linear_flat.reshape(B, H, W, 3).permute(0, 3, 1, 2)
+    del rgb_linear_flat
+
+    mask = rgb_linear > 0.0031308
+    rgb = torch.where(
+        mask,
+        torch.pow(torch.clamp(rgb_linear, min=0.0), 1.0 / 2.4).mul_(1.055).sub_(0.055),
+        rgb_linear * 12.92
+    )
+    del mask, rgb_linear
+
+    return torch.clamp(rgb, 0.0, 1.0)
+
+def _rgb_to_lab_batch(rgb: Tensor, matrix: Tensor, epsilon: float, kappa: float) -> Tensor:
+    mask = rgb > 0.04045
+    rgb_linear = torch.where(
+        mask,
+        torch.pow((rgb + 0.055) / 1.055, 2.4),
+        rgb / 12.92
+    )
+    del mask
+
+    B, _, H, W = rgb_linear.shape
+    rgb_flat = rgb_linear.permute(0, 2, 3, 1).reshape(-1, 3)
+    del rgb_linear
+
+    rgb_flat = rgb_flat.to(dtype=matrix.dtype)
+    xyz_flat = torch.matmul(rgb_flat, matrix.T)
+    del rgb_flat
+
+    xyz = xyz_flat.reshape(B, H, W, 3).permute(0, 3, 1, 2)
+    del xyz_flat
+
+    xyz[:, 0].div_(D65_WHITE_X)
+    xyz[:, 2].div_(D65_WHITE_Z)
+
+    epsilon_cubed = epsilon ** 3
+    mask = xyz > epsilon_cubed
+    f_xyz = torch.where(
+        mask,
+        torch.pow(xyz, 1.0 / 3.0),
+        xyz.mul(kappa).add_(16.0).div_(116.0)
+    )
+    del xyz, mask
+
+    L = f_xyz[:, 1].mul(116.0).sub_(16.0)
+    a = (f_xyz[:, 0] - f_xyz[:, 1]).mul_(500.0)
+    b = (f_xyz[:, 1] - f_xyz[:, 2]).mul_(200.0)
+    del f_xyz
+
+    return torch.stack([L, a, b], dim=1)
+
+def lab_color_transfer(
+    content_feat: Tensor,
+    style_feat: Tensor,
+    luminance_weight: float = 0.8
+) -> Tensor:
+    content_feat = wavelet_reconstruction(content_feat, style_feat)
+
+    if content_feat.shape != style_feat.shape:
+        style_feat = F.interpolate(
+            style_feat,
+            size=content_feat.shape[-2:],
+            mode='bilinear',
+            align_corners=False
+        )
+
+    device = content_feat.device
+    original_dtype = content_feat.dtype
+    content_feat = content_feat.float()
+    style_feat = style_feat.float()
+
+    rgb_to_xyz_matrix = torch.tensor([
+        [0.4124564, 0.3575761, 0.1804375],
+        [0.2126729, 0.7151522, 0.0721750],
+        [0.0193339, 0.1191920, 0.9503041]
+    ], dtype=torch.float32, device=device)
+
+    xyz_to_rgb_matrix = torch.tensor([
+        [ 3.2404542, -1.5371385, -0.4985314],
+        [-0.9692660,  1.8760108,  0.0415560],
+        [ 0.0556434, -0.2040259,  1.0572252]
+    ], dtype=torch.float32, device=device)
+
+    epsilon = CIELAB_DELTA
+    kappa = CIELAB_KAPPA
+
+    content_feat.add_(1.0).mul_(0.5).clamp_(0.0, 1.0)
+    style_feat.add_(1.0).mul_(0.5).clamp_(0.0, 1.0)
+
+    content_lab = _rgb_to_lab_batch(content_feat, rgb_to_xyz_matrix, epsilon, kappa)
+    del content_feat
+
+    style_lab = _rgb_to_lab_batch(style_feat, rgb_to_xyz_matrix, epsilon, kappa)
+    del style_feat, rgb_to_xyz_matrix
+
+    matched_a = _histogram_matching_channel(content_lab[:, 1], style_lab[:, 1])
+    matched_b = _histogram_matching_channel(content_lab[:, 2], style_lab[:, 2])
+
+    if luminance_weight < 1.0:
+        matched_L = _histogram_matching_channel(content_lab[:, 0], style_lab[:, 0])
+        result_L = content_lab[:, 0].mul(luminance_weight).add_(matched_L.mul(1.0 - luminance_weight))
+        del matched_L
+    else:
+        result_L = content_lab[:, 0]
+
+    del content_lab, style_lab
+
+    result_lab = torch.stack([result_L, matched_a, matched_b], dim=1)
+    del result_L, matched_a, matched_b
+
+    result_rgb = _lab_to_rgb_batch(result_lab, xyz_to_rgb_matrix, epsilon, kappa)
+    del result_lab, xyz_to_rgb_matrix
+
+    result = result_rgb.mul_(2.0).sub_(1.0)
+    del result_rgb
+
+    result = result.to(original_dtype)
+
+    return result
+
+
+def wavelet_color_transfer(content_feat: Tensor, style_feat: Tensor) -> Tensor:
+    return wavelet_reconstruction(content_feat, style_feat)
+
+
+def adain_color_transfer(content_feat: Tensor, style_feat: Tensor, eps: float = 1e-5) -> Tensor:
+    if content_feat.shape != style_feat.shape:
+        style_feat = F.interpolate(
+            style_feat,
+            size=content_feat.shape[-2:],
+            mode='bilinear',
+            align_corners=False,
+        )
+
+    original_dtype = content_feat.dtype
+    content_feat = content_feat.float()
+    style_feat = style_feat.float()
+
+    b, c = content_feat.shape[:2]
+    content_flat = content_feat.reshape(b, c, -1)
+    style_flat = style_feat.reshape(b, c, -1)
+
+    content_mean = content_flat.mean(dim=2).reshape(b, c, 1, 1)
+    content_std = (content_flat.var(dim=2, correction=0) + eps).sqrt().reshape(b, c, 1, 1)
+    style_mean = style_flat.mean(dim=2).reshape(b, c, 1, 1)
+    style_std = (style_flat.var(dim=2, correction=0) + eps).sqrt().reshape(b, c, 1, 1)
+    del content_flat, style_flat
+
+    normalized = (content_feat - content_mean) / content_std
+    del content_mean, content_std
+    result = normalized * style_std + style_mean
+    del normalized, style_mean, style_std
+
+    result = result.clamp_(-1.0, 1.0)
+    if result.dtype != original_dtype:
+        result = result.to(original_dtype)
+    return result

comfy/ldm/seedvr/constants.py

@@ -0,0 +1,48 @@
+"""SeedVR2 constants."""
+
+# Temporal chunk-size law: the sampler's activation wall is linear in
+# T_latent * pixel area (17-cell resolution sweep + T bisection, RTX 5090, 3b fp16):
+#   max_latent_frames = (free_GiB - RESERVED - K*SIGMA) / (GIB_PER_MPX_FRAME * megapixels)
+# RESERVED covers model staging plus fixed CUDA/torch overhead; SIGMA is the measured
+# run-to-run spread of the wall; K=4 trades ~10% smaller chunks for ~1e-5 OOM odds.
+SEEDVR2_CHUNK_GIB_PER_MPX_FRAME = 0.55
+SEEDVR2_CHUNK_RESERVED_GIB = 8.5
+SEEDVR2_CHUNK_SIGMA_GIB = 0.55
+SEEDVR2_CHUNK_SIGMA_K = 4
+
+SEEDVR2_7B_VID_DIM = 3072
+SEEDVR2_OOM_BACKOFF_DIVISOR = 2
+SEEDVR2_DTYPE_BYTES_FLOOR = 4
+SEEDVR2_7B_MLP_CHUNK = 8192
+SEEDVR2_ROPE_PARTIAL_CHUNK_TOKENS = 4096  # partial-RoPE application token-chunk.
+SEEDVR2_LATENT_CHANNELS = 16
+
+SEEDVR2_COLOR_MEM_HEADROOM = 0.75
+SEEDVR2_LAB_SCALE_MULTIPLIER = 13
+SEEDVR2_WAVELET_SCALE_MULTIPLIER = 10  # per-frame byte multiplier, wavelet path.
+SEEDVR2_ADAIN_SCALE_MULTIPLIER = 6
+
+BYTEDANCE_VAE_SCALING_FACTOR = 0.9152   # configs_3b/main.yaml:57.
+BYTEDANCE_VAE_SHIFTING_FACTOR = 0.0
+BYTEDANCE_VAE_CONV_MEM_GIB = 0.5
+BYTEDANCE_VAE_NORM_MEM_GIB = 0.5
+BYTEDANCE_LOGVAR_CLAMP_MIN = -30.0      # video_vae_v3/modules/types.py:28.
+BYTEDANCE_LOGVAR_CLAMP_MAX = 20.0       # video_vae_v3/modules/types.py:28.
+BYTEDANCE_GN_CHUNKS_FP16 = 4            # causal_inflation_lib.py:351 (GroupNorm chunk count, fp16).
+BYTEDANCE_GN_CHUNKS_FP32 = 2            # causal_inflation_lib.py:351 (GroupNorm chunk count, fp32).
+BYTEDANCE_BLOCK_OUT_CHANNELS = (128, 256, 512, 512)  # s8_c16_t4_inflation_sd3.yaml:7-11.
+BYTEDANCE_SLICING_SAMPLE_MIN = 4        # s8_c16_t4_inflation_sd3.yaml:22 (slicing_sample_min_size).
+BYTEDANCE_VAE_TEMPORAL_DOWNSAMPLE = 4   # infer.py:230 (temporal_downsample_factor); the 4n+1 factor.
+BYTEDANCE_VAE_SPATIAL_DOWNSAMPLE = 8    # infer.py:231 (spatial_downsample_factor).
+BYTEDANCE_720P_REF_AREA = 45 * 80       # dit_v2/window.py:32 (720p reference area for window scaling).
+BYTEDANCE_MAX_TEMPORAL_WINDOW = 30      # dit_v2/window.py:35 (max temporal window frames).
+BYTEDANCE_ROPE_MAX_FREQ = 256           # dit_v2/rope.py:31 (pixel-RoPE max frequency).
+BYTEDANCE_SINUSOIDAL_DIM = 256          # dit_3b/nadit.py:120 (timestep sinusoidal embed dim).
+
+ROPE_THETA = 10000   # RoPE base; Su et al., "RoFormer", arXiv:2104.09864.
+
+CIELAB_DELTA = 6.0 / 29.0          # CIE 15 (delta).
+CIELAB_KAPPA = (29.0 / 3.0) ** 3   # CIE 15 (kappa).
+D65_WHITE_X = 0.95047              # CIE D65 standard illuminant Xn (Yn = 1).
+D65_WHITE_Z = 1.08883              # CIE D65 standard illuminant Zn.
+WAVELET_DECOMP_LEVELS = 5          # wavelet color-fix decomposition depth (GIMP/Krita; StableSR).

comfy/model_base.py

@@ -55,6 +55,7 @@ import comfy.ldm.pixeldit.model
 import comfy.ldm.pixeldit.pid
 import comfy.ldm.ace.model
 import comfy.ldm.omnigen.omnigen2
+import comfy.ldm.seedvr.model
 import comfy.ldm.boogu.model
 import comfy.ldm.qwen_image.model
 import comfy.ldm.ideogram4.model
@@ -932,6 +933,17 @@ class HunyuanDiT(BaseModel):
         out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]]))
         return out
 
+class SeedVR2(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.seedvr.model.NaDiT)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        condition = kwargs.get("condition", None)
+        if condition is not None:
+            out["condition"] = comfy.conds.CONDRegular(condition)
+        return out
+
 class PixArt(BaseModel):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.pixart.pixartms.PixArtMS)

comfy/model_detection.py

@@ -470,15 +470,46 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
     # PiD (Pixel Diffusion Decoder). Must check BEFORE plain PixelDiT_T2I.
     _lq_w_key = '{}lq_proj.latent_proj.0.weight'.format(key_prefix)
     if _lq_w_key in state_dict_keys:
-        in_ch = int(state_dict[_lq_w_key].shape[1])
+        latent_proj_in_channels = int(state_dict[_lq_w_key].shape[1])
+        hidden_dim = int(state_dict[_lq_w_key].shape[0])
         _gate_prefix = '{}lq_proj.gate_modules.'.format(key_prefix)
         num_gates = len({k[len(_gate_prefix):].split('.')[0]
                          for k in state_dict_keys if k.startswith(_gate_prefix)})
+        pid_v1_5 = '{}lq_proj.pit_head.weight'.format(key_prefix) in state_dict_keys
         dit_config = {"image_model": "pid",
-                      "lq_latent_channels": in_ch,
-                      "latent_spatial_down_factor": 16 if in_ch >= 64 else 8}
+                      "lq_hidden_dim": hidden_dim}
         if num_gates > 0:
             dit_config["lq_interval"] = (14 + num_gates - 1) // num_gates
+        if pid_v1_5:
+            pid_v1_5_variants = {
+                16: {  # Flux and QwenImage
+                    "lq_latent_channels": 16,
+                    "latent_spatial_down_factor": 8,
+                    "lq_latent_unpatchify_factor": 1,
+                },
+                32: {  # Flux2 after 2x latent unpatchify
+                    "lq_latent_channels": 128,
+                    "latent_spatial_down_factor": 16,
+                    "lq_latent_unpatchify_factor": 2,
+                },
+            }
+            variant = pid_v1_5_variants.get(latent_proj_in_channels)
+            if variant is None:
+                raise ValueError(f"Unsupported PiD v1.5 latent projection with {latent_proj_in_channels} input channels")
+            gate_weight = state_dict['{}lq_proj.gate_modules.0.content_proj.weight'.format(key_prefix)]
+            dit_config.update(variant)
+            dit_config.update({
+                "lq_conv_padding_mode": "replicate",
+                "lq_gate_per_token": gate_weight.shape[0] == 1,
+                "pit_lq_inject": True,
+                "rope_ref_h": 2048,
+                "rope_ref_w": 2048,
+            })
+        else:
+            dit_config.update({
+                "lq_latent_channels": latent_proj_in_channels,
+                "latent_spatial_down_factor": 16 if latent_proj_in_channels >= 64 else 8,
+            })
         return dit_config
 
     if '{}core.pixel_embedder.proj.weight'.format(key_prefix) in state_dict_keys:  # PixelDiT T2I
@@ -598,6 +629,44 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
         return dit_config
 
+    seedvr2_7b_separate_key = "{}blocks.35.mlp.vid.proj_out.weight".format(key_prefix)
+    if seedvr2_7b_separate_key in state_dict_keys and state_dict[seedvr2_7b_separate_key].shape[0] == 3072: # seedvr2 7b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 3072
+        dit_config["heads"] = 24
+        dit_config["num_layers"] = 36
+        # This checkpoint uses separate vid/txt MMModule keys in every block.
+        dit_config["mm_layers"] = 36
+        dit_config["norm_eps"] = 1e-5
+        dit_config["rope_type"] = "rope3d"
+        dit_config["rope_dim"] = 64
+        dit_config["mlp_type"] = "normal"
+        return dit_config
+    if "{}blocks.35.mlp.all.proj_in_gate.weight".format(key_prefix) in state_dict_keys: # seedvr2 7b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 3072
+        dit_config["heads"] = 24
+        dit_config["num_layers"] = 36
+        # This checkpoint uses shared all.* MMModule keys after the initial blocks.
+        dit_config["mm_layers"] = 10
+        dit_config["norm_eps"] = 1e-5
+        dit_config["rope_type"] = "rope3d"
+        dit_config["rope_dim"] = 64
+        dit_config["mlp_type"] = "swiglu"
+        return dit_config
+    if "{}blocks.31.mlp.all.proj_in_gate.weight".format(key_prefix) in state_dict_keys: # seedvr2 3b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 2560
+        dit_config["heads"] = 20
+        dit_config["num_layers"] = 32
+        dit_config["norm_eps"] = 1.0e-05
+        dit_config["mlp_type"] = "swiglu"
+        dit_config["vid_out_norm"] = True
+        return dit_config
+
     if '{}head.modulation'.format(key_prefix) in state_dict_keys:  # Wan 2.1
         dit_config = {}
         dit_config["image_model"] = "wan2.1"
@@ -1119,9 +1188,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
     return unet_config
 
-def model_config_from_unet_config(unet_config, state_dict=None):
+
+def model_config_from_unet_config(unet_config, state_dict=None, unet_key_prefix=""):
     for model_config in comfy.supported_models.models:
-        if model_config.matches(unet_config, state_dict):
+        if model_config.matches(unet_config, state_dict, unet_key_prefix=unet_key_prefix):
             return model_config(unet_config)
 
     logging.error("no match {}".format(unet_config))
@@ -1131,7 +1201,7 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
     unet_config = detect_unet_config(state_dict, unet_key_prefix, metadata=metadata)
     if unet_config is None:
         return None
-    model_config = model_config_from_unet_config(unet_config, state_dict)
+    model_config = model_config_from_unet_config(unet_config, state_dict, unet_key_prefix)
     if model_config is None and use_base_if_no_match:
         model_config = comfy.supported_models_base.BASE(unet_config)
 

comfy/model_management.py

@@ -616,6 +616,8 @@ PIN_PRESSURE_HYSTERESIS = 256 * 1024 * 1024
 #Freeing registerables on pressure does imply a GPU sync, so go big on
 #the hysteresis so each expensive sync gives us back a good chunk.
 REGISTERABLE_PIN_HYSTERESIS = 2048 * 1024 * 1024
+WINDOWS_PIN_EVICTION_SWAP_PERCENT = 5.0
+WINDOWS_PIN_EVICTION_EMERGENCY_AVAILABLE = 512 * 1024 ** 2
 
 def module_size(module):
     module_mem = 0
@@ -642,6 +644,15 @@ def free_pins(size, evict_active=False):
             size -= freed
     return freed_total
 
+def should_free_pins_for_ram_pressure(shortfall):
+    if shortfall <= 0:
+        return False
+    if not WINDOWS:
+        return True
+    if psutil.virtual_memory().available < WINDOWS_PIN_EVICTION_EMERGENCY_AVAILABLE:
+        return True
+    return psutil.swap_memory().percent >= WINDOWS_PIN_EVICTION_SWAP_PERCENT
+
 def ensure_pin_budget(size, evict_active=False):
     if args.high_ram:
         return True

comfy/ops.py

@@ -1104,6 +1104,21 @@ def _load_quantized_module(module, super_load, state_dict, prefix, local_metadat
                 scales["convrot_groupsize"] = int(
                     layer_conf.get("convrot_groupsize", params_conf.get("convrot_groupsize", 256))
                 )
+        elif module.quant_format == "convrot_w4a4":
+            scale = pop_scale("weight_scale")
+            if scale is None:
+                raise ValueError(f"Missing ConvRot W4A4 weight scale for layer {layer_name}")
+            params_conf = layer_conf.get("params", {})
+            if not isinstance(params_conf, dict):
+                params_conf = {}
+            scales = {
+                "scale": scale,
+                "convrot_groupsize": int(
+                    layer_conf.get("convrot_groupsize", params_conf.get("convrot_groupsize", 256))
+                ),
+                "quant_group_size": 64,
+                "linear_dtype": layer_conf.get("linear_dtype", params_conf.get("linear_dtype", "int4")),
+            }
         else:
             raise ValueError(f"Unsupported quantization format: {module.quant_format}")
 
@@ -1150,6 +1165,11 @@ def _quantized_weight_state_dict(module, sd, prefix, extra_quant_conf=None, extr
         if module.quant_format == "int8_tensorwise" and getattr(params, "convrot", False):
             quant_conf["convrot"] = True
             quant_conf["convrot_groupsize"] = getattr(params, "convrot_groupsize", 256)
+        elif module.quant_format == "convrot_w4a4":
+            quant_conf["convrot_groupsize"] = getattr(params, "convrot_groupsize", 256)
+            linear_dtype = getattr(params, "linear_dtype", "int4")
+            if linear_dtype != "int4":
+                quant_conf["linear_dtype"] = linear_dtype
         if extra_quant_conf:
             quant_conf.update(extra_quant_conf)
         sd[f"{prefix}comfy_quant"] = torch.tensor(list(json.dumps(quant_conf).encode("utf-8")), dtype=torch.uint8)
@@ -1237,7 +1257,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 run_every_op()
 
                 input_shape = input.shape
-                reshaped_3d = False
+                reshaped_nd = False
                 #If cast needs to apply lora, it should be done in the compute dtype
                 compute_dtype = input.dtype
 
@@ -1274,12 +1294,12 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 # Inference path (unchanged)
                 if _use_quantized and quantize_input:
 
-                    # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others)
-                    input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input
+                    # Reshape >=3D tensors to 2D for quantization (needed for NVFP4 and others)
+                    input_reshaped = input.reshape(-1, input_shape[-1]) if input.ndim >= 3 else input
 
                     # Fall back to non-quantized for non-2D tensors
                     if input_reshaped.ndim == 2:
-                        reshaped_3d = input.ndim == 3
+                        reshaped_nd = input.ndim >= 3
                         # dtype is now implicit in the layout class
                         scale = getattr(self, 'input_scale', None)
                         if scale is not None:
@@ -1294,9 +1314,9 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                     weight_only_quant=weight_only_quant,
                 )
 
-                # Reshape output back to 3D if input was 3D
-                if reshaped_3d:
-                    output = output.reshape((input_shape[0], input_shape[1], self.weight.shape[0]))
+                # Reshape output back to original rank if input was >2D
+                if reshaped_nd:
+                    output = output.reshape((*input_shape[:-1], self.weight.shape[0]))
 
                 return output
 
@@ -1430,6 +1450,12 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 }
                 if hasattr(params, "block_scale"): # NVFP4
                     kwargs["block_scale"] = params.block_scale[i]
+                if hasattr(params, "quant_group_size"):
+                    kwargs["quant_group_size"] = params.quant_group_size
+                if hasattr(params, "convrot_groupsize"):
+                    kwargs["convrot_groupsize"] = params.convrot_groupsize
+                if hasattr(params, "linear_dtype"):
+                    kwargs["linear_dtype"] = params.linear_dtype
                 return QuantizedTensor(weight._qdata[i], weight._layout_cls, type(params)(**kwargs))
 
             def state_dict(self, *args, destination=None, prefix="", **kwargs):

comfy/quant_ops.py

@@ -3,6 +3,22 @@ import logging
 
 from comfy.cli_args import args
 
+
+def _rocm_kitchen_arch_supported():
+    """comfy-kitchen's INT8 Triton kernels compile tl.dot to matrix-core instructions.
+    RDNA3/3.5/4 (gfx11xx/gfx12xx) have WMMA and CDNA (gfx9xx) has MFMA; RDNA1/RDNA2
+    (gfx10xx) have neither, so the INT8 path hangs the GPU there. Gates the automatic
+    ROCm default so those cards stay on the eager fallback (an explicit
+    --enable-triton-backend still forces it on any arch)."""
+    try:
+        arch = torch.cuda.get_device_properties(torch.cuda.current_device()).gcnArchName.split(":")[0]
+    except Exception:
+        return False
+    if arch.startswith(("gfx11", "gfx12")):
+        return True
+    return arch in ("gfx908", "gfx90a", "gfx940", "gfx941", "gfx942", "gfx950")
+
+
 try:
     import comfy_kitchen as ck
     from comfy_kitchen.tensor import (
@@ -10,6 +26,7 @@ try:
         QuantizedLayout,
         TensorCoreFP8Layout as _CKFp8Layout,
         TensorCoreNVFP4Layout as _CKNvfp4Layout,
+        TensorCoreConvRotW4A4Layout as _CKTensorCoreConvRotW4A4Layout,
         TensorWiseINT8Layout as _CKTensorWiseINT8Layout,
         register_layout_op,
         register_layout_class,
@@ -24,10 +41,22 @@ try:
             ck.registry.disable("cuda")
             logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.")
 
-    if args.enable_triton_backend:
+    # On ROCm/AMD the CUDA backend is unavailable, so Triton is the only accelerated
+    # comfy-kitchen backend. Enable it by default there, but only on Triton >= 3.7 AND a
+    # matrix-core GPU (RDNA3+ WMMA gfx11xx/gfx12xx, CDNA MFMA gfx9xx). RDNA1/RDNA2
+    # (gfx10xx) have no WMMA -> the INT8 tl.dot path hangs the GPU, so they stay eager.
+    # older Triton lacks libdevice.rint on the HIP backend and hard-crashes the INT8 path.
+    if args.disable_triton_backend:
+        ck.registry.disable("triton")
+    elif args.enable_triton_backend: # or (torch.version.hip is not None and _rocm_kitchen_arch_supported()):
         try:
             import triton
-            logging.info("Found triton %s. Enabling comfy-kitchen triton backend.", triton.__version__)
+            triton_version = tuple(int(v) for v in triton.__version__.split(".")[:2])
+            if args.enable_triton_backend or triton_version >= (3, 7):
+                logging.info("Found triton %s. Enabling comfy-kitchen triton backend.", triton.__version__)
+            else:
+                logging.info("Triton %s is too old for the ROCm INT8 path (needs >= 3.7); comfy-kitchen triton backend disabled.", triton.__version__)
+                ck.registry.disable("triton")
         except ImportError as e:
             logging.error(f"Failed to import triton, Error: {e}, the comfy-kitchen triton backend will not be available.")
             ck.registry.disable("triton")
@@ -51,6 +80,9 @@ except ImportError as e:
     class _CKTensorWiseINT8Layout:
         pass
 
+    class _CKTensorCoreConvRotW4A4Layout:
+        pass
+
     def register_layout_class(name, cls):
         pass
 
@@ -179,6 +211,7 @@ class TensorCoreFP8E5M2Layout(_TensorCoreFP8LayoutBase):
 # Backward compatibility alias - default to E4M3
 TensorCoreFP8Layout = TensorCoreFP8E4M3Layout
 TensorWiseINT8Layout = _CKTensorWiseINT8Layout
+TensorCoreConvRotW4A4Layout = _CKTensorCoreConvRotW4A4Layout
 
 
 # ==============================================================================
@@ -190,6 +223,7 @@ register_layout_class("TensorCoreFP8E4M3Layout", TensorCoreFP8E4M3Layout)
 register_layout_class("TensorCoreFP8E5M2Layout", TensorCoreFP8E5M2Layout)
 register_layout_class("TensorCoreNVFP4Layout", TensorCoreNVFP4Layout)
 register_layout_class("TensorWiseINT8Layout", _CKTensorWiseINT8Layout)
+register_layout_class("TensorCoreConvRotW4A4Layout", _CKTensorCoreConvRotW4A4Layout)
 if _CK_MXFP8_AVAILABLE:
     register_layout_class("TensorCoreMXFP8Layout", TensorCoreMXFP8Layout)
 
@@ -227,6 +261,13 @@ QUANT_ALGOS["int8_tensorwise"] = {
     "quantize_input": False,
 }
 
+QUANT_ALGOS["convrot_w4a4"] = {
+    "storage_t": torch.int8,
+    "parameters": {"weight_scale"},
+    "comfy_tensor_layout": "TensorCoreConvRotW4A4Layout",
+    "quantize_input": False,
+}
+
 
 # ==============================================================================
 # Re-exports for backward compatibility
@@ -239,6 +280,7 @@ __all__ = [
     "TensorCoreFP8E4M3Layout",
     "TensorCoreFP8E5M2Layout",
     "TensorCoreNVFP4Layout",
+    "TensorCoreConvRotW4A4Layout",
     "TensorWiseINT8Layout",
     "QUANT_ALGOS",
     "register_layout_op",

comfy/sd.py

@@ -16,6 +16,7 @@ import comfy.ldm.cosmos.vae
 import comfy.ldm.wan.vae
 import comfy.ldm.wan.vae2_2
 import comfy.ldm.hunyuan3d.vae
+import comfy.ldm.seedvr.vae
 import comfy.ldm.triposplat.vae
 import comfy.ldm.ace.vae.music_dcae_pipeline
 import comfy.ldm.cogvideo.vae
@@ -473,7 +474,8 @@ class CLIP:
 
 class VAE:
     def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None):
-        if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
+        is_seedvr2_vae = "decoder.up_blocks.2.upsamplers.0.upscale_conv.weight" in sd
+        if not is_seedvr2_vae and 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
             sd = diffusers_convert.convert_vae_state_dict(sd)
 
         if model_management.is_amd():
@@ -500,6 +502,8 @@ class VAE:
         self.upscale_index_formula = None
         self.extra_1d_channel = None
         self.crop_input = True
+        self.handles_tiling = False
+        self.format_encoded = None
 
         self.audio_sample_rate = 44100
 
@@ -546,6 +550,22 @@ class VAE:
                 self.first_stage_model = StageC_coder()
                 self.downscale_ratio = 32
                 self.latent_channels = 16
+            elif "decoder.up_blocks.2.upsamplers.0.upscale_conv.weight" in sd: # seedvr2
+                self.first_stage_model = comfy.ldm.seedvr.vae.VideoAutoencoderKLWrapper()
+                self.latent_channels = comfy.ldm.seedvr.vae.SEEDVR2_LATENT_CHANNELS
+                self.latent_dim = 3
+                self.disable_offload = True
+                self.memory_used_decode = lambda shape, dtype: self.first_stage_model.comfy_memory_used_decode(shape)
+                self.memory_used_encode = lambda shape, dtype: (max(shape[2], 5) * shape[3] * shape[4] * 64) * model_management.dtype_size(dtype)
+                self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+                self.handles_tiling = True
+                self.format_encoded = self.first_stage_model.comfy_format_encoded
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
+                self.downscale_index_formula = (4, 8, 8)
+                self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
+                self.upscale_index_formula = (4, 8, 8)
+                self.process_input = lambda image: image * 2.0 - 1.0
+                self.crop_input = False
             elif "decoder.conv_in.weight" in sd:
                 if sd['decoder.conv_in.weight'].shape[1] == 64:
                     ddconfig = {"block_out_channels": [128, 256, 512, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 32, "downsample_match_channel": True, "upsample_match_channel": True}
@@ -1012,6 +1032,10 @@ class VAE:
         decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, index_formulas=self.upscale_index_formula, output_device=self.output_device))
 
+    def _decode_tiled_owned(self, samples, **kwargs):
+        out = self.first_stage_model.decode_tiled(samples.to(self.vae_dtype).to(self.device), **kwargs)
+        return self.process_output(out.to(device=self.output_device, dtype=self.vae_output_dtype(), copy=True))
+
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
         steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
         steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
@@ -1048,6 +1072,25 @@ class VAE:
         encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device)
 
+    def _encode_tiled_owned(self, pixel_samples, **kwargs):
+        x = self.process_input(pixel_samples).to(self.vae_dtype).to(self.device)
+        out = self.first_stage_model.encode_tiled(x, **kwargs)
+        return out.to(device=self.output_device, dtype=self.vae_output_dtype())
+
+    def _owned_tiled_args(self, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
+        args = {}
+        if tile_x is not None:
+            args["tile_x"] = tile_x
+        if tile_y is not None:
+            args["tile_y"] = tile_y
+        if overlap is not None:
+            args["overlap"] = overlap
+        if tile_t is not None:
+            args["tile_t"] = tile_t
+        if overlap_t is not None:
+            args["overlap_t"] = overlap_t
+        return args
+
     def decode(self, samples_in, vae_options={}):
         self.throw_exception_if_invalid()
         pixel_samples = None
@@ -1095,11 +1138,19 @@ class VAE:
                 if dims == 1 or self.extra_1d_channel is not None:
                     pixel_samples = self.decode_tiled_1d(samples_in)
                 elif dims == 2:
-                    pixel_samples = self.decode_tiled_(samples_in)
+                    if self.handles_tiling:
+                        tile = 256 // self.spacial_compression_decode()
+                        overlap = tile // 4
+                        pixel_samples = self._decode_tiled_owned(samples_in, tile_x=tile, tile_y=tile, overlap=overlap)
+                    else:
+                        pixel_samples = self.decode_tiled_(samples_in)
                 elif dims == 3:
                     tile = 256 // self.spacial_compression_decode()
                     overlap = tile // 4
-                    pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
+                    if self.handles_tiling:
+                        pixel_samples = self._decode_tiled_owned(samples_in, tile_x=tile, tile_y=tile, overlap=overlap)
+                    else:
+                        pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
 
         pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
         return pixel_samples
@@ -1118,7 +1169,9 @@ class VAE:
             args["overlap"] = overlap
 
         with model_management.cuda_device_context(self.device):
-            if dims == 1 or self.extra_1d_channel is not None:
+            if self.handles_tiling and dims in (2, 3):
+                output = self._decode_tiled_owned(samples, **self._owned_tiled_args(tile_x, tile_y, overlap, tile_t, overlap_t))
+            elif dims == 1 or self.extra_1d_channel is not None:
                 args.pop("tile_y")
                 output = self.decode_tiled_1d(samples, **args)
             elif dims == 2:
@@ -1179,12 +1232,17 @@ class VAE:
                 if self.latent_dim == 3:
                     tile = 256
                     overlap = tile // 4
-                    samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
+                    if self.handles_tiling:
+                        samples = self._encode_tiled_owned(pixel_samples, tile_x=tile, tile_y=tile, overlap=overlap)
+                    else:
+                        samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
                 elif self.latent_dim == 1 or self.extra_1d_channel is not None:
                     samples = self.encode_tiled_1d(pixel_samples)
                 else:
                     samples = self.encode_tiled_(pixel_samples)
 
+        if self.format_encoded is not None:
+            samples = self.format_encoded(samples)
         return samples
 
     def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
@@ -1192,7 +1250,7 @@ class VAE:
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
         dims = self.latent_dim
         pixel_samples = pixel_samples.movedim(-1, 1)
-        if dims == 3:
+        if dims == 3 and pixel_samples.ndim < 5:
             if not self.not_video:
                 pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
             else:
@@ -1216,21 +1274,27 @@ class VAE:
             elif dims == 2:
                 samples = self.encode_tiled_(pixel_samples, **args)
             elif dims == 3:
-                if tile_t is not None:
-                    tile_t_latent = max(2, self.downscale_ratio[0](tile_t))
+                if self.handles_tiling:
+                    samples = self._encode_tiled_owned(pixel_samples, **self._owned_tiled_args(tile_x, tile_y, overlap, tile_t, overlap_t))
                 else:
-                    tile_t_latent = 9999
-                args["tile_t"] = self.upscale_ratio[0](tile_t_latent)
+                    if tile_t is not None:
+                        tile_t_latent = max(2, self.downscale_ratio[0](tile_t))
+                    else:
+                        tile_t_latent = 9999
+                    args["tile_t"] = self.upscale_ratio[0](tile_t_latent)
 
-                if overlap_t is None:
-                    args["overlap"] = (1, overlap, overlap)
-                else:
-                    args["overlap"] = (self.upscale_ratio[0](max(1, min(tile_t_latent // 2, self.downscale_ratio[0](overlap_t)))), overlap, overlap)
-                maximum = pixel_samples.shape[2]
-                maximum = self.upscale_ratio[0](self.downscale_ratio[0](maximum))
+                    spatial_overlap = overlap if overlap is not None else 64
+                    if overlap_t is None:
+                        args["overlap"] = (1, spatial_overlap, spatial_overlap)
+                    else:
+                        args["overlap"] = (self.upscale_ratio[0](max(1, min(tile_t_latent // 2, self.downscale_ratio[0](overlap_t)))), spatial_overlap, spatial_overlap)
+                    maximum = pixel_samples.shape[2]
+                    maximum = self.upscale_ratio[0](self.downscale_ratio[0](maximum))
 
-                samples = self.encode_tiled_3d(pixel_samples[:,:,:maximum], **args)
+                    samples = self.encode_tiled_3d(pixel_samples[:,:,:maximum], **args)
 
+        if self.format_encoded is not None:
+            samples = self.format_encoded(samples)
         return samples
 
     def get_sd(self):
@@ -1898,7 +1962,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
         manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
     else:
         manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
-    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
+    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype, device=load_device)
 
     if model_config.clip_vision_prefix is not None:
         if output_clipvision:
@@ -2039,7 +2103,7 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None, disable
         manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
     else:
         manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
-    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
+    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype, device=load_device)
 
     if custom_operations is not None:
         model_config.custom_operations = custom_operations

comfy/supported_models.py

@@ -1685,6 +1685,40 @@ class Chroma(supported_models_base.BASE):
         t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect))
 
+class SeedVR2(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "seedvr2"
+    }
+    unet_extra_config = {}
+    required_keys = {
+        "{}positive_conditioning",
+        "{}negative_conditioning",
+    }
+    latent_format = comfy.latent_formats.SeedVR2
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+    sampling_settings = {
+        "shift": 1.0,
+    }
+
+    def set_inference_dtype(self, dtype, manual_cast_dtype, device=None):
+        if (
+            dtype == torch.float16
+            and manual_cast_dtype is None
+            and comfy.model_management.should_use_bf16(device)
+        ):
+            manual_cast_dtype = torch.bfloat16
+        super().set_inference_dtype(dtype, manual_cast_dtype, device=device)
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.SeedVR2(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        return None
+
 class ChromaRadiance(Chroma):
     unet_config = {
         "image_model": "chroma_radiance",
@@ -2348,6 +2382,7 @@ models = [
     HiDream,
     HiDreamO1,
     Chroma,
+    SeedVR2,
     ChromaRadiance,
     ACEStep,
     ACEStep15,

comfy/supported_models_base.py

@@ -54,13 +54,13 @@ class BASE:
     optimizations = {"fp8": False}
 
     @classmethod
-    def matches(s, unet_config, state_dict=None):
+    def matches(s, unet_config, state_dict=None, unet_key_prefix=""):
         for k in s.unet_config:
             if k not in unet_config or s.unet_config[k] != unet_config[k]:
                 return False
         if state_dict is not None:
             for k in s.required_keys:
-                if k not in state_dict:
+                if k.format(unet_key_prefix) not in state_dict:
                     return False
         return True
 
@@ -115,7 +115,7 @@ class BASE:
         replace_prefix = {"": self.vae_key_prefix[0]}
         return utils.state_dict_prefix_replace(state_dict, replace_prefix)
 
-    def set_inference_dtype(self, dtype, manual_cast_dtype):
+    def set_inference_dtype(self, dtype, manual_cast_dtype, device=None):
         self.unet_config['dtype'] = dtype
         self.manual_cast_dtype = manual_cast_dtype
 

comfy/text_encoders/gemma4.py

@@ -1088,7 +1088,7 @@ class Gemma4_Tokenizer():
             h, w = samples.shape[2], samples.shape[3]
             patch_size = 16
             pooling_k = 3
-            max_soft_tokens = 70 if is_video else 280  # video uses smaller token budget per frame
+            max_soft_tokens = kwargs.get("max_soft_tokens", 70 if is_video else 280)
             max_patches = max_soft_tokens * pooling_k * pooling_k
             target_px = max_patches * patch_size * patch_size
             factor = (target_px / (h * w)) ** 0.5

comfy/text_encoders/qwen3vl.py

@@ -90,6 +90,27 @@ class Qwen3VL(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):
                 deepstack = [torch.cat([deepstack[i], ds[i]], dim=0) for i in range(len(ds))]
         return position_ids, visual_pos_masks, deepstack
 
+    def forward(self, input_ids, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[], **kwargs):
+        position_ids = kwargs.pop("position_ids", None)
+        visual_pos_masks = kwargs.pop("visual_pos_masks", None)
+        deepstack_embeds = kwargs.pop("deepstack_embeds", None)
+        if embeds is not None and position_ids is None:
+            position_ids, visual_pos_masks, deepstack_embeds = self.build_image_inputs(embeds, embeds_info)
+        return self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            embeds=embeds,
+            num_tokens=num_tokens,
+            intermediate_output=intermediate_output,
+            final_layer_norm_intermediate=final_layer_norm_intermediate,
+            dtype=dtype,
+            position_ids=position_ids,
+            embeds_info=embeds_info,
+            visual_pos_masks=visual_pos_masks,
+            deepstack_embeds=deepstack_embeds,
+            **kwargs,
+        )
+
 
 def _make_qwen3vl_model(model_type):
     class Qwen3VL_(Qwen3VL):

comfy_api/feature_flags.py

@@ -100,6 +100,8 @@ def _parse_cli_feature_flags() -> dict[str, Any]:
 # Default server capabilities
 _CORE_FEATURE_FLAGS: dict[str, Any] = {
     "supports_preview_metadata": True,
+    "supports_node_failure_policy": True,
+    "supports_model_type_tags": True,
     "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes
     "extension": {"manager": {"supports_v4": True}},
     "node_replacements": True,

comfy_api_nodes/apis/bytedance.py

@@ -17,6 +17,10 @@ class Seedream4Options(BaseModel):
     max_images: int = Field(15)
 
 
+class Seedream5OptimizePromptOptions(BaseModel):
+    thinking: Literal["auto", "enabled", "disabled"] = Field(...)
+
+
 class Seedream4TaskCreationRequest(BaseModel):
     model: str = Field(...)
     prompt: str = Field(...)
@@ -28,6 +32,7 @@ class Seedream4TaskCreationRequest(BaseModel):
     sequential_image_generation_options: Seedream4Options | None = Field(Seedream4Options(max_images=15))
     watermark: bool = Field(False)
     output_format: str | None = None
+    optimize_prompt_options: Seedream5OptimizePromptOptions | None = None
 
 
 class ImageTaskCreationResponse(BaseModel):

comfy_api_nodes/apis/hunyuan3d.py

@@ -77,6 +77,7 @@ class To3DUVTaskRequest(BaseModel):
 
 class To3DPartTaskRequest(BaseModel):
     File: TaskFile3DInput = Field(...)
+    EnableStagedGeneration: bool | None = Field(None)
 
 
 class TextureEditImageInfo(BaseModel):

comfy_api_nodes/nodes_bytedance.py

@@ -34,6 +34,7 @@ from comfy_api_nodes.apis.bytedance import (
     SeedanceVirtualLibraryCreateAssetRequest,
     Seedream4Options,
     Seedream4TaskCreationRequest,
+    Seedream5OptimizePromptOptions,
     TaskAudioContent,
     TaskAudioContentUrl,
     TaskCreationResponse,
@@ -875,6 +876,17 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
                     tooltip='Whether to add an "AI generated" watermark to the image.',
                     advanced=True,
                 ),
+                IO.Boolean.Input(
+                    "thinking",
+                    default=True,
+                    tooltip=(
+                        "Enable the model's prompt-optimization reasoning ('thinking') for better adherence. "
+                        "Can substantially increase generation time — notably on Seedream 5.0 Pro. "
+                        "Can only be disabled for text-to-image (not when reference images are provided)."
+                    ),
+                    optional=True,
+                    advanced=True,
+                ),
             ],
             outputs=[
                 IO.Image.Output(),
@@ -920,6 +932,7 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
         model: dict,
         seed: int = 0,
         watermark: bool = False,
+        thinking: bool = True,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
         model_id = SEEDREAM_MODELS[model["model"]]
@@ -979,6 +992,10 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
             raise ValueError(
                 "The maximum number of generated images plus the number of reference images cannot exceed 15."
             )
+        if not thinking and n_input_images > 0:
+            raise ValueError(
+                "'thinking' can only be disabled for text-to-image; enable it when using reference images."
+            )
 
         reference_images_urls: list[str] = []
         if image_tensors:
@@ -992,6 +1009,9 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
                 wait_label="Uploading reference images",
             )
 
+        optimize_prompt_options = None
+        if n_input_images == 0:
+            optimize_prompt_options = Seedream5OptimizePromptOptions(thinking="enabled" if thinking else "disabled")
         response = await sync_op(
             cls,
             ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"),
@@ -1005,6 +1025,7 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
                 sequential_image_generation=None if is_pro else sequential_image_generation,
                 sequential_image_generation_options=None if is_pro else Seedream4Options(max_images=max_images),
                 watermark=watermark,
+                optimize_prompt_options=optimize_prompt_options,
             ),
         )
         if len(response.data) == 1:

comfy_api_nodes/nodes_gemini.py

@@ -1133,7 +1133,9 @@ class GeminiImage2(IO.ComfyNode):
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
         if model == "Nano Banana 2 (Gemini 3.1 Flash Image)":
-            model = "gemini-3.1-flash-image-preview"
+            model = "gemini-3.1-flash-image"
+        elif model == "gemini-3-pro-image-preview":
+            model = "gemini-3-pro-image"
 
         parts: list[GeminiPart] = [GeminiPart(text=prompt)]
         if images is not None:
@@ -1507,7 +1509,7 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
         validate_string(prompt, strip_whitespace=True, min_length=1)
         model_choice = model["model"]
         if model_choice == "Nano Banana 2 (Gemini 3.1 Flash Image)":
-            model_id = "gemini-3.1-flash-image-preview"
+            model_id = "gemini-3.1-flash-image"
         elif model_choice == "Nano Banana 2 Lite":
             model_id = "gemini-3.1-flash-lite-image"
         else:

comfy_api_nodes/nodes_hunyuan3d.py

@@ -642,6 +642,7 @@ class Tencent3DPartNode(IO.ComfyNode):
             response_model=To3DProTaskCreateResponse,
             data=To3DPartTaskRequest(
                 File=TaskFile3DInput(Type=file_format.upper(), Url=model_url),
+                EnableStagedGeneration=True,
             ),
             is_rate_limited=_is_tencent_rate_limited,
         )

comfy_api_nodes/util/_helpers.py

@@ -11,9 +11,11 @@ from io import BytesIO
 from yarl import URL
 
 from comfy.cli_args import args
+from comfy.comfy_api_env import normalize_comfy_api_base
 from comfy.deploy_environment import get_deploy_environment
 from comfy.model_management import processing_interrupted
 from comfy_api.latest import IO
+from comfyui_version import __version__ as comfyui_version
 
 from .common_exceptions import ProcessingInterrupted
 
@@ -59,11 +61,12 @@ def get_comfy_api_headers(node_cls: type[IO.ComfyNode]) -> dict[str, str]:
         **get_auth_header(node_cls),
         "Comfy-Env": get_deploy_environment(),
         "Comfy-Usage-Source": get_usage_source(node_cls),
+        "Comfy-Core-Version": comfyui_version,
     }
 
 
 def default_base_url() -> str:
-    return getattr(args, "comfy_api_base", "https://api.comfy.org")
+    return normalize_comfy_api_base(getattr(args, "comfy_api_base", "https://api.comfy.org"))
 
 
 async def sleep_with_interrupt(

comfy_execution/caching.py

@@ -503,6 +503,8 @@ RAM_CACHE_DEFAULT_RAM_USAGE = 0.05
 
 RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER = 1.3
 
+RAM_CACHE_LARGE_INTERMEDIATE = 512 * 1024 ** 2
+
 
 def all_outputs_dynamic(outputs):
     if outputs is None:
@@ -517,7 +519,6 @@ def all_outputs_dynamic(outputs):
 
     return True
 
-
 class RAMPressureCache(LRUCache):
 
     def __init__(self, key_class, enable_providers=False):
@@ -539,9 +540,9 @@ class RAMPressureCache(LRUCache):
         self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
         super().set_local(node_id, value)
 
-    def ram_release(self, target, free_active=False):
+    def ram_release(self, target, free_active=False, min_entry_size=0):
         if psutil.virtual_memory().available >= target:
-            return
+            return 0
 
         clean_list = []
 
@@ -555,8 +556,9 @@ class RAMPressureCache(LRUCache):
             oom_score = RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER ** (self.generation - self.used_generation[key])
 
             ram_usage = RAM_CACHE_DEFAULT_RAM_USAGE
+            oom_ram_usage = ram_usage
             def scan_list_for_ram_usage(outputs):
-                nonlocal ram_usage
+                nonlocal ram_usage, oom_ram_usage
                 if outputs is None:
                     return
                 for output in outputs:
@@ -564,19 +566,26 @@ class RAMPressureCache(LRUCache):
                         scan_list_for_ram_usage(output)
                     elif isinstance(output, torch.Tensor) and output.device.type == 'cpu':
                         ram_usage += output.numel() * output.element_size()
+                        oom_ram_usage += output.numel() * output.element_size()
                     elif isinstance(output, ModelPatcher) and self.used_generation[key] != self.generation:
                         #old ModelPatchers are the first to go
-                        ram_usage = 1e30
+                        oom_ram_usage = 1e30
             scan_list_for_ram_usage(cache_entry.outputs)
 
-            oom_score *= ram_usage
+            if ram_usage < min_entry_size:
+                continue
+
+            oom_score *= oom_ram_usage
             #In the case where we have no information on the node ram usage at all,
             #break OOM score ties on the last touch timestamp (pure LRU)
-            bisect.insort(clean_list, (oom_score, self.timestamps[key], key))
+            bisect.insort(clean_list, (oom_score, self.timestamps[key], key, ram_usage))
 
+        freed = 0
         while psutil.virtual_memory().available < target and clean_list:
-            _, _, key = clean_list.pop()
+            _, _, key, ram_usage = clean_list.pop()
             del self.cache[key]
             self.used_generation.pop(key, None)
             self.timestamps.pop(key, None)
             self.children.pop(key, None)
+            freed += ram_usage
+        return freed

comfy_execution/graph.py

@@ -3,11 +3,12 @@ from typing import Type, Literal
 import nodes
 import asyncio
 import inspect
-from comfy_execution.graph_utils import is_link, ExecutionBlocker
+from comfy_execution.graph_utils import is_link, ExecutionBlocker, ExecutionFailureBlocker
 from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputTypeOptions
 
 # NOTE: ExecutionBlocker code got moved to graph_utils.py to prevent torch being imported too soon during unit tests
 ExecutionBlocker = ExecutionBlocker
+ExecutionFailureBlocker = ExecutionFailureBlocker
 
 class DependencyCycleError(Exception):
     pass
@@ -201,19 +202,28 @@ class ExecutionList(TopologicalSort):
         self.staged_node_id = None
         self.execution_cache = {}
         self.execution_cache_listeners = {}
+        self.transient_cache = {}
+        self.failure_tainted_parents = set()
 
     def is_cached(self, node_id):
-        return self.output_cache.get_local(node_id) is not None
+        return node_id in self.transient_cache or self.output_cache.get_local(node_id) is not None
+
+    def _get_cache_value(self, node_id):
+        if node_id in self.transient_cache:
+            return self.transient_cache[node_id]
+        return self.output_cache.get_local(node_id)
 
     def cache_link(self, from_node_id, to_node_id):
         if to_node_id not in self.execution_cache:
             self.execution_cache[to_node_id] = {}
-        self.execution_cache[to_node_id][from_node_id] = self.output_cache.get_local(from_node_id)
+        self.execution_cache[to_node_id][from_node_id] = self._get_cache_value(from_node_id)
         if from_node_id not in self.execution_cache_listeners:
             self.execution_cache_listeners[from_node_id] = set()
         self.execution_cache_listeners[from_node_id].add(to_node_id)
 
     def get_cache(self, from_node_id, to_node_id):
+        if from_node_id in self.transient_cache:
+            return self.transient_cache[from_node_id]
         if to_node_id not in self.execution_cache:
             return None
         value = self.execution_cache[to_node_id].get(from_node_id)
@@ -223,7 +233,9 @@ class ExecutionList(TopologicalSort):
         self.output_cache.set_local(from_node_id, value)
         return value
 
-    def cache_update(self, node_id, value):
+    def cache_update(self, node_id, value, transient=False):
+        if transient:
+            self.transient_cache[node_id] = value
         if node_id in self.execution_cache_listeners:
             for to_node_id in self.execution_cache_listeners[node_id]:
                 if to_node_id in self.execution_cache:
@@ -233,6 +245,25 @@ class ExecutionList(TopologicalSort):
         super().add_strong_link(from_node_id, from_socket, to_node_id)
         self.cache_link(from_node_id, to_node_id)
 
+    def add_completion_link(self, from_node_id, to_node_id):
+        # Block to_node_id until from_node_id finishes, without consuming any of its output sockets.
+        if not self.is_cached(from_node_id):
+            self.add_node(from_node_id)
+            if to_node_id not in self.blocking[from_node_id]:
+                self.blocking[from_node_id][to_node_id] = {}
+                self.blockCount[to_node_id] += 1
+
+    def mark_failure_tainted(self, node_id):
+        # Taint all ephemeral ancestors of a failed or failure-blocked node so dynamically-expanded parents are
+        # never cached as reusable when part of their expansion did not complete.
+        parent_id = self.dynprompt.get_parent_node_id(node_id)
+        while parent_id is not None and parent_id not in self.failure_tainted_parents:
+            self.failure_tainted_parents.add(parent_id)
+            parent_id = self.dynprompt.get_parent_node_id(parent_id)
+
+    def is_failure_tainted(self, node_id):
+        return node_id in self.failure_tainted_parents
+
     async def stage_node_execution(self):
         assert self.staged_node_id is None
         if self.is_empty():
@@ -323,7 +354,9 @@ class ExecutionList(TopologicalSort):
         blocked_by = { node_id: {} for node_id in self.pendingNodes }
         for from_node_id in self.blocking:
             for to_node_id in self.blocking[from_node_id]:
-                if True in self.blocking[from_node_id][to_node_id].values():
+                # Strong links have a True socket entry; completion links have no socket entries at all.
+                sockets = self.blocking[from_node_id][to_node_id]
+                if len(sockets) == 0 or True in sockets.values():
                     blocked_by[to_node_id][from_node_id] = True
         to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0]
         while len(to_remove) > 0:

comfy_execution/graph_utils.py

@@ -153,3 +153,9 @@ class ExecutionBlocker:
     """
     def __init__(self, message):
         self.message = message
+
+
+class ExecutionFailureBlocker(ExecutionBlocker):
+    def __init__(self, node_id):
+        super().__init__(None)
+        self.node_id = node_id

comfy_execution/jobs.py

@@ -56,6 +56,9 @@ PREVIEWABLE_MEDIA_TYPES = frozenset({'images', 'video', 'audio', '3d', 'text'})
 # 3D file extensions for preview fallback (no dedicated media_type exists)
 THREE_D_EXTENSIONS = frozenset({'.obj', '.fbx', '.gltf', '.glb', '.usdz'})
 
+# Text file extensions for preview fallback (the formats SaveText can produce)
+TEXT_EXTENSIONS = frozenset({'.txt', '.md', '.json'})
+
 
 def has_3d_extension(filename: str) -> bool:
     lower = filename.lower()
@@ -143,9 +146,10 @@ def is_previewable(media_type: str, item: dict) -> bool:
     Maintains backwards compatibility with existing logic.
 
     Priority:
-    1. media_type is 'images', 'video', 'audio', or '3d'
+    1. media_type is 'images', 'video', 'audio', '3d', or 'text'
     2. format field starts with 'video/' or 'audio/'
     3. filename has a 3D extension (.obj, .fbx, .gltf, .glb, .usdz)
+    4. filename has a text extension (.txt, .md, .json, ...)
     """
     if media_type in PREVIEWABLE_MEDIA_TYPES:
         return True
@@ -156,10 +160,12 @@ def is_previewable(media_type: str, item: dict) -> bool:
     if fmt and (fmt.startswith('video/') or fmt.startswith('audio/')):
         return True
 
-    # Check for 3D files by extension
+    # Check for 3D and text files by extension
     filename = item.get('filename', '').lower()
     if any(filename.endswith(ext) for ext in THREE_D_EXTENSIONS):
         return True
+    if any(filename.endswith(ext) for ext in TEXT_EXTENSIONS):
+        return True
 
     return False
 
@@ -198,10 +204,14 @@ def normalize_history_item(prompt_id: str, history_item: dict, include_outputs:
     outputs_count, preview_output = get_outputs_summary(outputs)
 
     execution_error = None
+    execution_errors = []
     execution_start_time = None
     execution_end_time = None
+    execution_success = None
     was_interrupted = False
+    execution_summary = {}
     if status_info:
+        execution_summary = status_info.get('execution_summary') or {}
         messages = status_info.get('messages', [])
         for entry in messages:
             if isinstance(entry, (list, tuple)) and len(entry) >= 2:
@@ -211,10 +221,22 @@ def normalize_history_item(prompt_id: str, history_item: dict, include_outputs:
                         execution_start_time = event_data.get('timestamp')
                     elif event_name in ('execution_success', 'execution_error', 'execution_interrupted'):
                         execution_end_time = event_data.get('timestamp')
-                        if event_name == 'execution_error':
+                        if event_name == 'execution_success':
+                            execution_success = event_data
+                        elif event_name == 'execution_error':
                             execution_error = event_data
                         elif event_name == 'execution_interrupted':
                             was_interrupted = True
+                    elif event_name == 'execution_node_error':
+                        execution_errors.append(event_data)
+
+    completion_status = execution_summary.get('completion_status')
+    if completion_status is None and execution_success is not None:
+        completion_status = execution_success.get('completion_status', 'success')
+    if completion_status is None and status_str == 'success':
+        completion_status = 'success'
+    has_errors = execution_summary.get('has_errors', bool(execution_errors)) if completion_status is not None else None
+    execution_error_count = execution_summary.get('execution_error_count', len(execution_errors)) if completion_status is not None else None
 
     if status_str == 'success':
         status = JobStatus.COMPLETED
@@ -231,6 +253,9 @@ def normalize_history_item(prompt_id: str, history_item: dict, include_outputs:
         'execution_start_time': execution_start_time,
         'execution_end_time': execution_end_time,
         'execution_error': execution_error,
+        'completion_status': completion_status,
+        'has_errors': has_errors,
+        'execution_error_count': execution_error_count,
         'outputs_count': outputs_count,
         'preview_output': preview_output,
         'workflow_id': workflow_id,
@@ -239,6 +264,7 @@ def normalize_history_item(prompt_id: str, history_item: dict, include_outputs:
     if include_outputs:
         job['outputs'] = normalize_outputs(outputs)
         job['execution_status'] = status_info
+        job['execution_errors'] = execution_errors
         job['workflow'] = {
             'prompt': prompt,
             'extra_data': extra_data,
@@ -255,6 +281,10 @@ def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
     Preview priority (matching frontend):
     1. type="output" with previewable media
     2. Any previewable media
+
+    Text content entries (strings under 'text') are preview-only metadata,
+    matching the frontend's METADATA_KEYS: they can serve as the fallback
+    preview but are not counted as outputs.
     """
     count = 0
     preview_output = None
@@ -275,7 +305,6 @@ def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
                     if normalized is None:
                         # Not a 3D file string — check for text preview
                         if media_type == 'text':
-                            count += 1
                             if preview_output is None:
                                 if isinstance(item, tuple):
                                     text_value = item[0] if item else ''

comfy_execution/progress.py

@@ -17,6 +17,7 @@ class NodeState(Enum):
     Running = "running"
     Finished = "finished"
     Error = "error"
+    Blocked = "blocked"
 
 
 class NodeProgressState(TypedDict):
@@ -301,17 +302,25 @@ class ProgressRegistry:
                     node_id, value, max_value, entry, self.prompt_id, image
                 )
 
-    def finish_progress(self, node_id: str) -> None:
-        """Finish progress tracking for a node"""
+    def _finish_progress(self, node_id: str, state: NodeState) -> None:
         entry = self.ensure_entry(node_id)
-        entry["state"] = NodeState.Finished
+        entry["state"] = state
         entry["value"] = entry["max"]
 
-        # Notify all enabled handlers
         for handler in self.handlers.values():
             if handler.enabled:
                 handler.finish_handler(node_id, entry, self.prompt_id)
 
+    def finish_progress(self, node_id: str) -> None:
+        """Finish progress tracking for a node"""
+        self._finish_progress(node_id, NodeState.Finished)
+
+    def error_progress(self, node_id: str) -> None:
+        self._finish_progress(node_id, NodeState.Error)
+
+    def block_progress(self, node_id: str) -> None:
+        self._finish_progress(node_id, NodeState.Blocked)
+
     def reset_handlers(self) -> None:
         """Reset all handlers"""
         for handler in self.handlers.values():

comfy_extras/nodes_audio.py

@@ -298,6 +298,7 @@ class PreviewAudio(IO.ComfyNode):
             search_aliases=["play audio"],
             display_name="Preview Audio",
             category="audio",
+            description="Preview the audio without saving it to the ComfyUI output directory.",
             inputs=[
                 IO.Audio.Input("audio"),
             ],

comfy_extras/nodes_bounding_boxes.py

@@ -1,3 +1,5 @@
+import json
+
 import numpy as np
 import torch
 from PIL import Image, ImageDraw, ImageEnhance, ImageFont
@@ -166,6 +168,111 @@ def boxes_to_regions(boxes, width: int, height: int) -> list:
     return regions
 
 
+def normalize_incoming_boxes(bboxes) -> list:
+    if isinstance(bboxes, dict):
+        frame = [bboxes]
+    elif not isinstance(bboxes, list) or not bboxes:
+        frame = []
+    elif isinstance(bboxes[0], dict):
+        frame = bboxes
+    else:
+        frame = bboxes[0] if isinstance(bboxes[0], list) else []
+    boxes = []
+    for box in frame:
+        if not isinstance(box, dict):
+            continue
+        norm = {
+            "x": box.get("x", 0),
+            "y": box.get("y", 0),
+            "width": box.get("width", 0),
+            "height": box.get("height", 0),
+        }
+        meta = box.get("metadata")
+        if isinstance(meta, dict):
+            norm["metadata"] = meta
+        boxes.append(norm)
+    return boxes
+
+
+def _looks_like_element(box: dict) -> bool:
+    bbox = box.get("bbox")
+    return isinstance(bbox, (list, tuple)) and len(bbox) == 4
+
+
+def _looks_like_bbox(box: dict) -> bool:
+    return all(key in box for key in ("x", "y", "width", "height"))
+
+
+def elements_to_boxes(elements: list, width: int, height: int) -> list:
+    boxes = []
+    for element in elements:
+        if not isinstance(element, dict):
+            continue
+        bbox = element.get("bbox")
+        if not (isinstance(bbox, (list, tuple)) and len(bbox) == 4):
+            raise ValueError("bboxes element is missing a valid 'bbox' [ymin, xmin, ymax, xmax]")
+        try:
+            ymin, xmin, ymax, xmax = (float(v) / 1000.0 for v in bbox)
+        except (TypeError, ValueError):
+            raise ValueError("bboxes element 'bbox' must contain four numbers")
+        etype = "text" if element.get("type") == "text" else "obj"
+        boxes.append({
+            "x": round(min(xmin, xmax) * width),
+            "y": round(min(ymin, ymax) * height),
+            "width": round(abs(xmax - xmin) * width),
+            "height": round(abs(ymax - ymin) * height),
+            "metadata": {
+                "type": etype,
+                "text": element.get("text", "") if etype == "text" else "",
+                "desc": element.get("desc", ""),
+                "palette": element.get("color_palette", []) or [],
+            },
+        })
+    return boxes
+
+
+def boxes_from_input(data, width: int, height: int) -> list:
+    if data is None:
+        return []
+    if isinstance(data, str):
+        text = data.strip()
+        if not text:
+            return []
+        try:
+            data = json.loads(text)
+        except (ValueError, TypeError) as exc:
+            raise ValueError(f"bboxes string input is not valid JSON: {exc}") from exc
+    if isinstance(data, dict):
+        if _looks_like_element(data):
+            return elements_to_boxes([data], width, height)
+        if _looks_like_bbox(data):
+            return normalize_incoming_boxes(data)
+        raise ValueError(
+            "bboxes dict must be a bounding box (x, y, width, height) or an element (with a 'bbox')"
+        )
+    if not isinstance(data, list):
+        raise ValueError(
+            "bboxes input must be bounding boxes, elements, or a JSON string, "
+            f"got {type(data).__name__}"
+        )
+    if not data:
+        return []
+    first = data[0]
+    if isinstance(first, list):
+        return normalize_incoming_boxes(data)
+    if isinstance(first, dict):
+        if _looks_like_element(first):
+            return elements_to_boxes(data, width, height)
+        if _looks_like_bbox(first):
+            return normalize_incoming_boxes(data)
+        raise ValueError(
+            "bboxes items must be bounding boxes (x, y, width, height) or elements (with a 'bbox')"
+        )
+    raise ValueError(
+        f"bboxes list must contain bounding boxes or elements, got {type(first).__name__}"
+    )
+
+
 def _norm_bbox(region: dict) -> list[int]:
     def grid(value: float) -> int:
         return max(0, min(1000, round(value * 1000)))
@@ -217,29 +324,48 @@ class CreateBoundingBoxes(io.ComfyNode):
                     optional=True,
                     tooltip="Optional image used as background in the canvas and preview.",
                 ),
+                io.MultiType.Input(
+                    "bboxes",
+                    [io.BoundingBox, io.Array, io.String],
+                    optional=True,
+                    tooltip="Bounding boxes, elements, or a JSON string to initialize the canvas. A new upstream value initializes the canvas; edits made on the canvas take priority and are kept until the upstream value changes again.",
+                ),
                 io.Int.Input("width", default=1024, min=64, max=16384, step=16,
                              tooltip="Width of the canvas and the pixel grid for the bounding boxes."),
                 io.Int.Input("height", default=1024, min=64, max=16384, step=16,
                              tooltip="Height of the canvas and the pixel grid for the bounding boxes."),
                 editor_state,
+                io.BoundingBoxes.Input(
+                    "last_incoming",
+                    optional=True,
+                    tooltip="Internal state managed by the canvas: the upstream bboxes value that last initialized it. Leave empty to re-initialize the canvas from the bboxes input on the next run.",
+                ),
             ],
             outputs=[
                 io.Image.Output(display_name="preview"),
                 io.BoundingBox.Output(display_name="bboxes"),
                 io.Array.Output(display_name="elements"),
             ],
+            is_output_node=True,
             is_experimental=True,
         )
 
     @classmethod
-    def execute(cls, width, height, editor_state=None, background=None) -> io.NodeOutput:
-        regions = boxes_to_regions(editor_state, width, height)
+    def execute(cls, width, height, editor_state=None, last_incoming=None, background=None, bboxes=None) -> io.NodeOutput:
+        incoming = boxes_from_input(bboxes, width, height)
+        applied = last_incoming if isinstance(last_incoming, list) else []
+        upstream_changed = bool(incoming) and incoming != applied
+        source = incoming if upstream_changed else (editor_state or [])
+        regions = boxes_to_regions(source, width, height)
         preview = render_preview(regions, width, height, _bg_from_image(background))
+        ui = {"dims": [width, height]}
+        if incoming:
+            ui["input_bboxes"] = incoming
         return io.NodeOutput(
             preview,
             fractions_to_bbox_frame(regions, width, height),
             build_elements(regions),
-            ui={"dims": [width, height]},
+            ui=ui,
         )
 
 

comfy_extras/nodes_images.py

@@ -844,15 +844,18 @@ class ImageMergeTileList(IO.ComfyNode):
 # Format specifications
 # ---------------------------------------------------------------------------
 
-# Maps (file_format, bit_depth, has_alpha) -> (numpy dtype scale, av pixel format,
-# stream pix_fmt). Keeps the encode path declarative instead of branchy.
+# Maps (file_format, bit_depth, num_channels) -> (quantization scale, numpy dtype,
+# av frame pix_fmt, stream pix_fmt). Keeps the encode path declarative instead of branchy.
 _FORMAT_SPECS = {
-    ("png", "8-bit", False):  {"scale": 255.0,   "dtype": np.uint8,   "frame_fmt": "rgb24",     "stream_fmt": "rgb24"},
-    ("png", "8-bit", True):   {"scale": 255.0,   "dtype": np.uint8,   "frame_fmt": "rgba",      "stream_fmt": "rgba"},
-    ("png", "16-bit", False): {"scale": 65535.0, "dtype": np.uint16,  "frame_fmt": "rgb48le",   "stream_fmt": "rgb48be"},
-    ("png", "16-bit", True):  {"scale": 65535.0, "dtype": np.uint16,  "frame_fmt": "rgba64le",  "stream_fmt": "rgba64be"},
-    ("exr", "32-bit float", False): {"scale": 1.0, "dtype": np.float32, "frame_fmt": "gbrpf32le",  "stream_fmt": "gbrpf32le"},
-    ("exr", "32-bit float", True):  {"scale": 1.0, "dtype": np.float32, "frame_fmt": "gbrapf32le", "stream_fmt": "gbrapf32le"},
+    ("png", "8-bit", 1):  {"scale": 255.0,   "dtype": np.uint8,   "frame_fmt": "gray",      "stream_fmt": "gray"},
+    ("png", "8-bit", 3):  {"scale": 255.0,   "dtype": np.uint8,   "frame_fmt": "rgb24",     "stream_fmt": "rgb24"},
+    ("png", "8-bit", 4):  {"scale": 255.0,   "dtype": np.uint8,   "frame_fmt": "rgba",      "stream_fmt": "rgba"},
+    ("png", "16-bit", 1): {"scale": 65535.0, "dtype": np.uint16,  "frame_fmt": "gray16le",  "stream_fmt": "gray16be"},
+    ("png", "16-bit", 3): {"scale": 65535.0, "dtype": np.uint16,  "frame_fmt": "rgb48le",   "stream_fmt": "rgb48be"},
+    ("png", "16-bit", 4): {"scale": 65535.0, "dtype": np.uint16,  "frame_fmt": "rgba64le",  "stream_fmt": "rgba64be"},
+    ("exr", "32-bit float", 1): {"scale": 1.0, "dtype": np.float32, "frame_fmt": "grayf32le",  "stream_fmt": "grayf32le"},
+    ("exr", "32-bit float", 3): {"scale": 1.0, "dtype": np.float32, "frame_fmt": "gbrpf32le",  "stream_fmt": "gbrpf32le"},
+    ("exr", "32-bit float", 4): {"scale": 1.0, "dtype": np.float32, "frame_fmt": "gbrapf32le", "stream_fmt": "gbrapf32le"},
 }
 
 
@@ -891,10 +894,11 @@ def hlg_to_linear(t: torch.Tensor) -> torch.Tensor:
         return torch.cat([hlg_to_linear(rgb), alpha], dim=-1)
 
     # Piecewise: sqrt branch below 0.5, log branch above.
-    # Clamp inside the log branch so negative / out-of-range values don't blow up;
+    # Clamp the log branch at the 0.5 branch point (not above it) so the
+    # unselected lane stays finite in exp() without altering selected values;
     # values above 1.0 are allowed and extrapolate naturally.
     low = (t ** 2) / 3.0
-    high = (torch.exp((t.clamp(min=_HLG_C) - _HLG_C) / _HLG_A) + _HLG_B) / 12.0
+    high = (torch.exp((t.clamp(min=0.5) - _HLG_C) / _HLG_A) + _HLG_B) / 12.0
     return torch.where(t <= 0.5, low, high)
 
 
@@ -1087,7 +1091,8 @@ def _encode_image(
     bit_depth: str,
     colorspace: str,
 ) -> bytes:
-    """Encode a single HxWxC tensor to PNG or EXR bytes in memory.
+    """Encode a single HxWxC (or channel-less HxW grayscale) tensor to PNG or
+    EXR bytes in memory. Grayscale is written as single-channel PNG / Y-only EXR.
 
     For EXR the input is interpreted according to `colorspace` and converted
     to scene-linear (EXR's convention) before writing:
@@ -1101,10 +1106,16 @@ def _encode_image(
     For PNG, colorspace selection does not modify pixels — PNG is delivered
     sRGB-encoded and there is no PNG path for wide-gamut HDR in this node.
     """
+    if img_tensor.ndim == 2:
+        img_tensor = img_tensor.unsqueeze(-1)  # Some nodes emit grayscale as (H, W) with no channel dim, mask-style.
     height, width, num_channels = img_tensor.shape
-    has_alpha = num_channels == 4
 
-    spec = _FORMAT_SPECS[(file_format, bit_depth, has_alpha)]
+    spec = _FORMAT_SPECS.get((file_format, bit_depth, num_channels))
+    if spec is None:
+        raise ValueError(
+            f"No {file_format}/{bit_depth} encoder for {num_channels}-channel images: "
+            "supported channel counts are 1 (grayscale), 3 (RGB) and 4 (RGBA)."
+        )
 
     if spec["dtype"] == np.float32:
         # EXR path: preserve full range, no clamp.

comfy_extras/nodes_load_3d.py

@@ -61,14 +61,10 @@ class Load3D(IO.ComfyNode):
 
     @classmethod
     def execute(cls, model_file, image, **kwargs) -> IO.NodeOutput:
-        image_path = folder_paths.get_annotated_filepath(image['image'])
-        mask_path = folder_paths.get_annotated_filepath(image['mask'])
-        normal_path = folder_paths.get_annotated_filepath(image['normal'])
-
         load_image_node = nodes.LoadImage()
-        output_image, ignore_mask = load_image_node.load_image(image=image_path)
-        ignore_image, output_mask = load_image_node.load_image(image=mask_path)
-        normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path)
+        output_image, ignore_mask = load_image_node.load_image(image=image['image'])
+        ignore_image, output_mask = load_image_node.load_image(image=image['mask'])
+        normal_image, ignore_mask2 = load_image_node.load_image(image=image['normal'])
 
         video = None
 
@@ -96,6 +92,7 @@ class Preview3D(IO.ComfyNode):
             search_aliases=["view mesh", "3d viewer"],
             display_name="Preview 3D & Animation",
             category="3d",
+            description="Preview a 3D model file without saving it to the ComfyUI output directory.",
             is_experimental=True,
             is_output_node=True,
             inputs=[
@@ -140,6 +137,7 @@ class Preview3DAdvanced(IO.ComfyNode):
             display_name="Preview 3D (Advanced)",
             search_aliases=["preview 3d", "3d viewer", "view mesh", "frame 3d", "3d camera output"],
             category="3d",
+            description="Preview a 3D model file without saving it to the ComfyUI output directory.",
             is_experimental=True,
             is_output_node=True,
             inputs=[
@@ -176,8 +174,9 @@ class Preview3DAdvanced(IO.ComfyNode):
         filename = f"preview3d_advanced_{uuid.uuid4().hex}.{model_3d.format}"
         model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
 
+        viewport_state = viewport_state if isinstance(viewport_state, dict) else {}
         camera_info_input = kwargs.get("camera_info", None)
-        camera_info = camera_info_input if camera_info_input is not None else viewport_state['camera_info']
+        camera_info = camera_info_input if camera_info_input is not None else viewport_state.get('camera_info')
         model_3d_info_input = kwargs.get("model_3d_info", None)
         model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
         return IO.NodeOutput(
@@ -197,6 +196,7 @@ class PreviewGaussianSplat(IO.ComfyNode):
             node_id="PreviewGaussianSplat",
             display_name="Preview Splat",
             category="3d",
+            description="Preview a gaussian splat 3D file without saving it to the ComfyUI output directory.",
             is_experimental=True,
             is_output_node=True,
             search_aliases=[
@@ -244,8 +244,9 @@ class PreviewGaussianSplat(IO.ComfyNode):
         filename = f"preview_splat_{uuid.uuid4().hex}.{model_3d.format}"
         model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
 
+        viewport_state = viewport_state if isinstance(viewport_state, dict) else {}
         camera_info_input = kwargs.get("camera_info", None)
-        camera_info = camera_info_input if camera_info_input is not None else viewport_state['camera_info']
+        camera_info = camera_info_input if camera_info_input is not None else viewport_state.get('camera_info')
         model_3d_info_input = kwargs.get("model_3d_info", None)
         model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
         return IO.NodeOutput(
@@ -265,6 +266,7 @@ class PreviewPointCloud(IO.ComfyNode):
             node_id="PreviewPointCloud",
             display_name="Preview Point Cloud",
             category="3d",
+            description="Preview a point cloud 3D file without saving it to the ComfyUI output directory.",
             is_experimental=True,
             is_output_node=True,
             search_aliases=[
@@ -303,8 +305,9 @@ class PreviewPointCloud(IO.ComfyNode):
         filename = f"preview_pointcloud_{uuid.uuid4().hex}.{model_3d.format}"
         model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename))
 
+        viewport_state = viewport_state if isinstance(viewport_state, dict) else {}
         camera_info_input = kwargs.get("camera_info", None)
-        camera_info = camera_info_input if camera_info_input is not None else viewport_state['camera_info']
+        camera_info = camera_info_input if camera_info_input is not None else viewport_state.get('camera_info')
         model_3d_info_input = kwargs.get("model_3d_info", None)
         model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
         return IO.NodeOutput(
@@ -375,8 +378,9 @@ class Load3DAdvanced(IO.ComfyNode):
         file_3d = None
         if model_file and model_file != "none":
             file_3d = Types.File3D(folder_paths.get_annotated_filepath(model_file))
+        viewport_state = viewport_state if isinstance(viewport_state, dict) else {}
         model_3d_info = viewport_state.get('model_3d_info', [])
-        return IO.NodeOutput(file_3d, model_3d_info, viewport_state['camera_info'], width, height)
+        return IO.NodeOutput(file_3d, model_3d_info, viewport_state.get('camera_info'), width, height)
 
 
 class Load3DExtension(ComfyExtension):

comfy_extras/nodes_mask.py

@@ -419,17 +419,18 @@ class MaskPreview(IO.ComfyNode):
             search_aliases=["show mask", "view mask", "inspect mask", "debug mask"],
             display_name="Preview Mask",
             category="image/mask",
-            description="Saves the input images to your ComfyUI output directory.",
+            description="Preview the masks without saving them to the ComfyUI output directory.",
             inputs=[
                 IO.Mask.Input("mask"),
             ],
             hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
             is_output_node=True,
+            outputs=[IO.Mask.Output(display_name="mask")]
         )
 
     @classmethod
     def execute(cls, mask, filename_prefix="ComfyUI") -> IO.NodeOutput:
-        return IO.NodeOutput(ui=UI.PreviewMask(mask))
+        return IO.NodeOutput(mask, ui=UI.PreviewMask(mask))
 
 
 class MaskExtension(ComfyExtension):

comfy_extras/nodes_preview_any.py

@@ -18,6 +18,7 @@ class PreviewAny():
 
     CATEGORY = "utilities"
     SEARCH_ALIASES = ["show output", "inspect", "debug", "print value", "show text"]
+    DESCRIPTION = "Preview any input value as text."
 
     def main(self, source=None):
         torch.set_printoptions(edgeitems=6)

comfy_extras/nodes_primitive.py

@@ -10,11 +10,10 @@ class String(io.ComfyNode):
         return io.Schema(
             node_id="PrimitiveString",
             search_aliases=["text", "string", "text box", "prompt"],
-            display_name="Text String (DEPRECATED)",
+            display_name="Text",
             category="utilities/primitive",
             inputs=[io.String.Input("value")],
-            outputs=[io.String.Output()],
-            is_deprecated=True
+            outputs=[io.String.Output()]
         )
 
     @classmethod
@@ -28,7 +27,7 @@ class StringMultiline(io.ComfyNode):
         return io.Schema(
             node_id="PrimitiveStringMultiline",
             search_aliases=["text", "string", "text multiline", "string multiline", "text box", "prompt"],
-            display_name="Input Text",
+            display_name="Text (Multiline)",
             category="utilities/primitive",
             essentials_category="Basics",
             inputs=[io.String.Input("value", multiline=True)],

comfy_extras/nodes_save_3d.py

@@ -13,7 +13,7 @@ from typing_extensions import override
 
 import folder_paths
 from comfy.cli_args import args
-from comfy_api.latest import ComfyExtension, IO, Types
+from comfy_api.latest import ComfyExtension, IO, Types, UI
 
 
 def pack_variable_mesh_batch(vertices, faces, colors=None, uvs=None, texture=None, unlit=False):
@@ -406,10 +406,165 @@ class SaveGLB(IO.ComfyNode):
         return IO.NodeOutput(ui={"3d": results})
 
 
+def _save_file3d_to_output(model_3d: Types.File3D, filename_prefix: str) -> str:
+    full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
+        filename_prefix, folder_paths.get_output_directory()
+    )
+    ext = model_3d.format or "glb"
+    saved_filename = f"{filename}_{counter:05}.{ext}"
+    model_3d.save_to(os.path.join(full_output_folder, saved_filename))
+    return f"{subfolder}/{saved_filename}" if subfolder else saved_filename
+
+
+def execute_save_3d_advanced(model_3d, viewport_state, width, height, filename_prefix, kwargs) -> IO.NodeOutput:
+    model_file = _save_file3d_to_output(model_3d, filename_prefix)
+    viewport_state = viewport_state if isinstance(viewport_state, dict) else {}
+    camera_info_input = kwargs.get("camera_info", None)
+    camera_info = camera_info_input if camera_info_input is not None else viewport_state.get('camera_info')
+    model_3d_info_input = kwargs.get("model_3d_info", None)
+    model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
+    return IO.NodeOutput(
+        model_3d,
+        model_3d_info,
+        camera_info,
+        width,
+        height,
+        ui=UI.PreviewUI3DAdvanced(model_file, camera_info, model_3d_info),
+    )
+
+
+class Save3DAdvanced(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Save3DAdvanced",
+            display_name="Save 3D (Advanced)",
+            search_aliases=["save 3d", "export 3d model", "save mesh advanced"],
+            category="3d",
+            is_experimental=True,
+            is_output_node=True,
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DGLB,
+                        IO.File3DGLTF,
+                        IO.File3DFBX,
+                        IO.File3DOBJ,
+                        IO.File3DSTL,
+                        IO.File3DUSDZ,
+                        IO.File3DAny,
+                    ],
+                    tooltip="3D model file from an upstream 3D node.",
+                ),
+                IO.String.Input("filename_prefix", default="3d/ComfyUI"),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, filename_prefix: str, **kwargs) -> IO.NodeOutput:
+        return execute_save_3d_advanced(model_3d, viewport_state, width, height, filename_prefix, kwargs)
+
+
+class SaveGaussianSplat(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveGaussianSplat",
+            display_name="Save Splat",
+            search_aliases=["save splat", "save gaussian splat", "export gaussian", "export splat"],
+            category="3d",
+            is_experimental=True,
+            is_output_node=True,
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DSplatAny,
+                        IO.File3DPLY,
+                        IO.File3DSPLAT,
+                        IO.File3DSPZ,
+                        IO.File3DKSPLAT,
+                    ],
+                    tooltip="A gaussian splat 3D file.",
+                ),
+                IO.String.Input("filename_prefix", default="3d/ComfyUI"),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DSplatAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, filename_prefix: str, **kwargs) -> IO.NodeOutput:
+        return execute_save_3d_advanced(model_3d, viewport_state, width, height, filename_prefix, kwargs)
+
+
+class SavePointCloud(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SavePointCloud",
+            display_name="Save Point Cloud",
+            search_aliases=["save point cloud", "save pointcloud", "export point cloud"],
+            category="3d",
+            is_experimental=True,
+            is_output_node=True,
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DPointCloudAny,
+                        IO.File3DPLY,
+                    ],
+                    tooltip="Point cloud file (.ply)",
+                ),
+                IO.String.Input("filename_prefix", default="3d/ComfyUI"),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DPointCloudAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, filename_prefix: str, **kwargs) -> IO.NodeOutput:
+        return execute_save_3d_advanced(model_3d, viewport_state, width, height, filename_prefix, kwargs)
+
+
 class Save3DExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
-        return [SaveGLB]
+        return [SaveGLB, Save3DAdvanced, SaveGaussianSplat, SavePointCloud]
 
 
 async def comfy_entrypoint() -> Save3DExtension:

comfy_extras/nodes_seedvr.py

@@ -0,0 +1,614 @@
+import logging
+
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+import torch
+
+import comfy.model_management
+from comfy.ldm.seedvr.color_fix import (
+    adain_color_transfer,
+    lab_color_transfer,
+    wavelet_color_transfer,
+)
+from comfy.ldm.seedvr.constants import (
+    BYTEDANCE_VAE_SPATIAL_DOWNSAMPLE,
+    SEEDVR2_ADAIN_SCALE_MULTIPLIER,
+    SEEDVR2_CHUNK_GIB_PER_MPX_FRAME,
+    SEEDVR2_CHUNK_RESERVED_GIB,
+    SEEDVR2_CHUNK_SIGMA_GIB,
+    SEEDVR2_CHUNK_SIGMA_K,
+    SEEDVR2_COLOR_MEM_HEADROOM,
+    SEEDVR2_DTYPE_BYTES_FLOOR,
+    SEEDVR2_LAB_SCALE_MULTIPLIER,
+    SEEDVR2_LATENT_CHANNELS,
+    SEEDVR2_OOM_BACKOFF_DIVISOR,
+    SEEDVR2_WAVELET_SCALE_MULTIPLIER,
+)
+
+from torchvision.transforms import functional as TVF
+from torchvision.transforms.functional import InterpolationMode
+
+
+_SEEDVR2_INVALID_MODEL_MSG_PREFIX = "SeedVR2Conditioning: model object does not match expected SeedVR2 structure"
+_ATTR_MISSING = object()
+
+
+def _resolve_seedvr2_diffusion_model(model):
+    inner = getattr(model, "model", _ATTR_MISSING)
+    if inner is _ATTR_MISSING:
+        raise RuntimeError(
+            f"{_SEEDVR2_INVALID_MODEL_MSG_PREFIX}: input has no 'model' attribute "
+            f"(got type {type(model).__name__})."
+        )
+    if inner is None:
+        raise RuntimeError(
+            f"{_SEEDVR2_INVALID_MODEL_MSG_PREFIX}: input.model is None "
+            f"(input type {type(model).__name__})."
+        )
+    diffusion_model = getattr(inner, "diffusion_model", _ATTR_MISSING)
+    if diffusion_model is _ATTR_MISSING:
+        raise RuntimeError(
+            f"{_SEEDVR2_INVALID_MODEL_MSG_PREFIX}: 'model.model' has no "
+            f"'diffusion_model' attribute (got type {type(inner).__name__})."
+        )
+    if diffusion_model is None:
+        raise RuntimeError(
+            f"{_SEEDVR2_INVALID_MODEL_MSG_PREFIX}: 'model.model.diffusion_model' "
+            f"is None (model.model type {type(inner).__name__})."
+        )
+    return diffusion_model
+
+
+def div_pad(image, factor):
+    height_factor, width_factor = factor
+    height, width = image.shape[-2:]
+
+    pad_height = (height_factor - (height % height_factor)) % height_factor
+    pad_width = (width_factor - (width % width_factor)) % width_factor
+
+    if pad_height == 0 and pad_width == 0:
+        return image
+
+    padding = (0, pad_width, 0, pad_height)
+    return torch.nn.functional.pad(image, padding, mode='constant', value=0.0)
+
+def cut_videos(videos):
+    t = videos.size(1)
+    if t < 1:
+        raise ValueError("SeedVR2Preprocess expected at least one frame.")
+    if t == 1:
+        return videos
+    if t <= 4:
+        padding = videos[:, -1:].repeat(1, 4 - t + 1, 1, 1, 1)
+        return torch.cat([videos, padding], dim=1)
+    if (t - 1) % 4 == 0:
+        return videos
+    padding = videos[:, -1:].repeat(1, 4 - ((t - 1) % 4), 1, 1, 1)
+    videos = torch.cat([videos, padding], dim=1)
+    if (videos.size(1) - 1) % 4 != 0:
+        raise ValueError(f"SeedVR2Preprocess failed to pad video length to 4n+1; got {videos.size(1)} frames.")
+    return videos
+
+def _seedvr2_input_shorter_edge(images, node_name):
+    if images.dim() == 4:
+        return min(images.shape[1], images.shape[2])
+    if images.dim() == 5:
+        return min(images.shape[2], images.shape[3])
+    raise ValueError(
+        f"{node_name}: expected 4-D or 5-D IMAGE tensor, "
+        f"got shape {tuple(images.shape)}"
+    )
+
+
+def _seedvr2_pad(images, upscaled_shorter_edge, node_name):
+    if upscaled_shorter_edge < 2:
+        raise ValueError(
+            f"{node_name}: input shorter edge must be at least 2 pixels; "
+            f"got {upscaled_shorter_edge}."
+        )
+    if images.shape[-1] > 3:
+        images = images[..., :3]
+    if images.dim() == 4:
+        # Comfy video components arrive as a 4-D IMAGE frame sequence:
+        # (frames, H, W, C). SeedVR2 consumes that as one video.
+        images = images.unsqueeze(0)
+    elif images.dim() != 5:
+        raise ValueError(
+            f"{node_name}: expected 4-D or 5-D IMAGE tensor, "
+            f"got shape {tuple(images.shape)}"
+        )
+    images = images.permute(0, 1, 4, 2, 3)
+
+    b, t, c, h, w = images.shape
+    images = images.reshape(b * t, c, h, w)
+
+    images = torch.clamp(images, 0.0, 1.0)
+    images = div_pad(images, (16, 16))
+    _, _, new_h, new_w = images.shape
+
+    images = images.reshape(b, t, c, new_h, new_w)
+    images = cut_videos(images)
+    images_bthwc = images.permute(0, 1, 3, 4, 2).contiguous()
+
+    return io.NodeOutput(images_bthwc)
+
+
+class SeedVR2Preprocess(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SeedVR2Preprocess",
+            display_name="Pre-Process SeedVR2 Input",
+            category="image/pre-processors",
+            description="Pad a resized image for SeedVR2 model. Alpha channel is dropped. The node Post-Process SeedVR2 Output re-applies it from the original resized image.",
+            search_aliases=["seedvr2", "upscale", "video upscale", "pad", "preprocess"],
+            inputs=[
+                io.Image.Input("resized_images", tooltip="The resized image to process."),
+            ],
+            outputs=[
+                io.Image.Output("images", tooltip="The padded image for VAE encoding."),
+            ]
+        )
+
+    @classmethod
+    def execute(cls, resized_images):
+        upscaled_shorter_edge = _seedvr2_input_shorter_edge(resized_images, "SeedVR2Preprocess")
+        return _seedvr2_pad(
+            resized_images, upscaled_shorter_edge, "SeedVR2Preprocess",
+        )
+
+
+class SeedVR2PostProcessing(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SeedVR2PostProcessing",
+            display_name="Post-Process SeedVR2 Output",
+            category="image/post-processors",
+            description="Align the generated image with the original resized image and apply color correction.",
+            search_aliases=["seedvr2", "upscale", "color correction", "color match", "postprocess"],
+            inputs=[
+                io.Image.Input("images", tooltip="The generated image to process."),
+                io.Image.Input("original_resized_images", tooltip="The original resized image before pre-processing, used as reference."),
+                io.Combo.Input("color_correction_method", options=["lab", "wavelet", "adain", "none"], default="lab", tooltip="Method to match the generated image colors to the original image. lab: transfer color in CIELAB space, preserving detail (most faithful). wavelet: transfer low-frequency color, keeping upscaled high-frequency detail. adain: match per-channel mean/std (fastest, global tint). none: skip color transfer (geometry alignment only)."),
+            ],
+            outputs=[io.Image.Output(display_name="images", tooltip="The aligned, color-corrected image.")],
+        )
+
+    @classmethod
+    def execute(cls, images, original_resized_images, color_correction_method):
+        alpha_input = None
+        if original_resized_images.shape[-1] == 4:
+            alpha_input = original_resized_images[..., 3:4]
+            original_resized_images = original_resized_images[..., :3]
+        decoded_5d, decoded_was_4d = cls._as_bthwc(images)
+        reference_full, _ = cls._as_bthwc(original_resized_images)
+        decoded_5d = cls._restore_reference_batch_time(decoded_5d, reference_full)
+
+        b = min(decoded_5d.shape[0], reference_full.shape[0])
+        t = min(decoded_5d.shape[1], reference_full.shape[1])
+        reference_h = reference_full.shape[2]
+        reference_w = reference_full.shape[3]
+
+        decoded_5d = decoded_5d[:b, :t, :, :, :]
+        target_h = min(decoded_5d.shape[2], reference_h)
+        target_w = min(decoded_5d.shape[3], reference_w)
+        decoded_5d = decoded_5d[:, :, :target_h, :target_w, :]
+        if color_correction_method in ("lab", "wavelet", "adain"):
+            reference_5d = reference_full[:b, :t, :, :, :]
+            reference_5d = cls._resize_reference(reference_5d, target_h, target_w)
+            output_device = decoded_5d.device
+            decoded_raw = cls._to_seedvr2_raw(decoded_5d)
+            reference_raw = cls._to_seedvr2_raw(reference_5d)
+            decoded_flat = decoded_raw.permute(0, 1, 4, 2, 3).reshape(b * t, decoded_raw.shape[4], target_h, target_w)
+            reference_flat = reference_raw.permute(0, 1, 4, 2, 3).reshape(b * t, reference_raw.shape[4], target_h, target_w)
+            output = cls._color_transfer_chunked(
+                decoded_flat, reference_flat, output_device, color_correction_method,
+            )
+            output = output.reshape(b, t, output.shape[1], output.shape[2], output.shape[3]).permute(0, 1, 3, 4, 2)
+            output = output.add(1.0).div(2.0).clamp(0.0, 1.0)
+        elif color_correction_method == "none":
+            output = decoded_5d
+        else:
+            raise ValueError(f"SeedVR2PostProcessing: unknown color_correction_method {color_correction_method!r}")
+
+        if alpha_input is not None:
+            alpha_5d, _ = cls._as_bthwc(alpha_input)
+            alpha_5d = alpha_5d[:output.shape[0], :output.shape[1], :output.shape[2], :output.shape[3], :]
+            output = torch.cat([output, alpha_5d.to(dtype=output.dtype, device=output.device)], dim=-1)
+        h2 = output.shape[-3] - (output.shape[-3] % 2)
+        w2 = output.shape[-2] - (output.shape[-2] % 2)
+        output = output[:, :, :h2, :w2, :]
+        if decoded_was_4d:
+            output = output.reshape(-1, output.shape[-3], output.shape[-2], output.shape[-1])
+        return io.NodeOutput(output)
+
+    @staticmethod
+    def _as_bthwc(images):
+        if images.ndim == 4:
+            return images.unsqueeze(0), True
+        if images.ndim == 5:
+            return images, False
+        raise ValueError(
+            f"SeedVR2PostProcessing: expected 4-D or 5-D IMAGE tensor, got shape {tuple(images.shape)}"
+        )
+
+    @staticmethod
+    def _restore_reference_batch_time(decoded, reference):
+        if decoded.shape[0] != 1:
+            return decoded
+        ref_b, ref_t = reference.shape[:2]
+        if ref_b < 1 or decoded.shape[1] % ref_b != 0:
+            return decoded
+        decoded_t = decoded.shape[1] // ref_b
+        if decoded_t < ref_t:
+            return decoded
+        return decoded.reshape(ref_b, decoded_t, decoded.shape[2], decoded.shape[3], decoded.shape[4])
+
+    @staticmethod
+    def _to_seedvr2_raw(images):
+        return images.mul(2.0).sub(1.0)
+
+    @staticmethod
+    def _color_transfer_on_vae_device(decoded_flat, reference_flat, output_device, transfer_fn):
+        color_device = comfy.model_management.vae_device()
+        decoded_flat = decoded_flat.to(device=color_device)
+        reference_flat = reference_flat.to(device=color_device)
+        output = transfer_fn(decoded_flat, reference_flat)
+        return output.to(device=output_device)
+
+    @staticmethod
+    def _lab_color_transfer_on_vae_device(decoded_flat, reference_flat, output_device):
+        color_device = comfy.model_management.vae_device()
+        result = None
+        for start in range(decoded_flat.shape[0]):
+            decoded_frame = decoded_flat[start:start + 1].to(device=color_device).clone()
+            reference_frame = reference_flat[start:start + 1].to(device=color_device).clone()
+            output = lab_color_transfer(decoded_frame, reference_frame).to(device=output_device)
+            if result is None:
+                result = torch.empty(
+                    (decoded_flat.shape[0],) + tuple(output.shape[1:]),
+                    device=output_device,
+                    dtype=output.dtype,
+                )
+            result[start:start + 1].copy_(output)
+        if result is None:
+            raise ValueError("SeedVR2PostProcessing: LAB color correction requires at least one frame.")
+        return result
+
+    @classmethod
+    def _color_transfer_chunked(cls, decoded_flat, reference_flat, output_device, color_correction_method):
+        chunk_size = cls._estimate_color_correction_chunk_size(decoded_flat, color_correction_method)
+        while True:
+            try:
+                return cls._run_color_transfer_chunks(
+                    decoded_flat, reference_flat, output_device, color_correction_method, chunk_size,
+                )
+            except Exception as e:
+                comfy.model_management.raise_non_oom(e)
+                if chunk_size <= 1:
+                    raise RuntimeError(
+                        "SeedVR2PostProcessing: color correction OOM at one frame; "
+                        f"color_correction_method={color_correction_method}, shape={tuple(decoded_flat.shape)}."
+                    ) from e
+                chunk_size = max(1, chunk_size // SEEDVR2_OOM_BACKOFF_DIVISOR)
+
+    @classmethod
+    def _run_color_transfer_chunks(cls, decoded_flat, reference_flat, output_device, color_correction_method, chunk_size):
+        result = None
+        for start in range(0, decoded_flat.shape[0], chunk_size):
+            end = min(start + chunk_size, decoded_flat.shape[0])
+            decoded_chunk = decoded_flat[start:end]
+            reference_chunk = reference_flat[start:end]
+            if color_correction_method == "lab":
+                output = cls._lab_color_transfer_on_vae_device(decoded_chunk, reference_chunk, output_device)
+            elif color_correction_method == "wavelet":
+                output = cls._color_transfer_on_vae_device(
+                    decoded_chunk, reference_chunk, output_device, wavelet_color_transfer,
+                )
+            else:
+                output = cls._color_transfer_on_vae_device(
+                    decoded_chunk, reference_chunk, output_device, adain_color_transfer,
+                )
+            if result is None:
+                result = torch.empty(
+                    (decoded_flat.shape[0],) + tuple(output.shape[1:]),
+                    device=output_device,
+                    dtype=output.dtype,
+                )
+            result[start:end].copy_(output)
+        if result is None:
+            raise ValueError("SeedVR2PostProcessing: color correction requires at least one frame.")
+        return result
+
+    @classmethod
+    def _estimate_color_correction_chunk_size(cls, decoded_flat, color_correction_method):
+        multiplier = cls._color_correction_memory_multiplier(color_correction_method)
+        frames = decoded_flat.shape[0]
+        _, channels, height, width = decoded_flat.shape
+        dtype_bytes = max(decoded_flat.element_size(), SEEDVR2_DTYPE_BYTES_FLOOR)
+        bytes_per_frame = height * width * channels * dtype_bytes * multiplier
+        if bytes_per_frame <= 0:
+            return frames
+        color_device = comfy.model_management.vae_device()
+        free_memory = comfy.model_management.get_free_memory(color_device)
+        chunk_size = int((free_memory * SEEDVR2_COLOR_MEM_HEADROOM) // bytes_per_frame)
+        return max(1, min(frames, chunk_size))
+
+    @staticmethod
+    def _color_correction_memory_multiplier(color_correction_method):
+        if color_correction_method == "lab":
+            return SEEDVR2_LAB_SCALE_MULTIPLIER
+        if color_correction_method == "wavelet":
+            return SEEDVR2_WAVELET_SCALE_MULTIPLIER
+        if color_correction_method == "adain":
+            return SEEDVR2_ADAIN_SCALE_MULTIPLIER
+        raise ValueError(f"SeedVR2PostProcessing: unknown color_correction_method {color_correction_method!r}")
+
+    @staticmethod
+    def _resize_reference(reference, height, width):
+        if reference.shape[2] == height and reference.shape[3] == width:
+            return reference
+        b, t = reference.shape[:2]
+        reference_flat = reference.permute(0, 1, 4, 2, 3).reshape(b * t, reference.shape[4], reference.shape[2], reference.shape[3])
+        resized = TVF.resize(
+            reference_flat,
+            size=(height, width),
+            interpolation=InterpolationMode.BICUBIC,
+            antialias=not (isinstance(reference_flat, torch.Tensor) and reference_flat.device.type == "mps"),
+        )
+        return resized.reshape(b, t, resized.shape[1], height, width).permute(0, 1, 3, 4, 2)
+
+
+class SeedVR2Conditioning(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SeedVR2Conditioning",
+            display_name="Apply SeedVR2 Conditioning",
+            category="model/conditioning",
+            description="Build SeedVR2 positive/negative conditioning from a VAE latent.",
+            search_aliases=["seedvr2", "upscale", "conditioning"],
+            inputs=[
+                io.Model.Input("model", tooltip="The SeedVR2 model."),
+                io.Latent.Input("vae_conditioning", display_name="latent"),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive", tooltip="The positive conditioning for sampling."),
+                io.Conditioning.Output(display_name="negative", tooltip="The negative conditioning for sampling."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, vae_conditioning) -> io.NodeOutput:
+
+        vae_conditioning = vae_conditioning["samples"]
+        if vae_conditioning.ndim != 5:
+            raise ValueError(
+                "SeedVR2Conditioning expects a 5-D VAE latent in Comfy "
+                f"channel-first layout; got shape {tuple(vae_conditioning.shape)}."
+            )
+        if vae_conditioning.shape[1] != SEEDVR2_LATENT_CHANNELS:
+            if vae_conditioning.shape[-1] == SEEDVR2_LATENT_CHANNELS:
+                raise ValueError(
+                    "SeedVR2Conditioning expects SeedVR2 VAE latents in Comfy "
+                    f"channel-first layout (B, {SEEDVR2_LATENT_CHANNELS}, T, H, W); "
+                    f"got channel-last shape {tuple(vae_conditioning.shape)}."
+                )
+            raise ValueError(
+                "SeedVR2Conditioning expects SeedVR2 VAE latents with "
+                f"{SEEDVR2_LATENT_CHANNELS} channels; got shape {tuple(vae_conditioning.shape)}."
+            )
+        vae_conditioning = vae_conditioning.movedim(1, -1).contiguous()
+        model = _resolve_seedvr2_diffusion_model(model)
+        pos_cond = model.positive_conditioning
+        neg_cond = model.negative_conditioning
+
+        mask = vae_conditioning.new_ones(vae_conditioning.shape[:-1] + (1,))
+        condition = torch.cat((vae_conditioning, mask), dim=-1)
+        condition = condition.movedim(-1, 1)
+
+        negative = [[neg_cond.unsqueeze(0), {"condition": condition}]]
+        positive = [[pos_cond.unsqueeze(0), {"condition": condition}]]
+
+        return io.NodeOutput(positive, negative)
+
+def _seedvr2_chunk_crossfade_weights(overlap, device, dtype):
+    """Descending previous-chunk weights across the overlap (next chunk gets ``1 - w``): a Hann fade over the middle third, flat shoulders on the outer thirds."""
+    ramp = torch.linspace(0.0, 1.0, steps=overlap, device=device, dtype=dtype)
+    ramp = ((ramp - 1.0 / 3.0) / (1.0 / 3.0)).clamp(0.0, 1.0)
+    return 0.5 + 0.5 * torch.cos(torch.pi * ramp)
+
+
+class SeedVR2TemporalChunk(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SeedVR2TemporalChunk",
+            display_name="Split SeedVR2 Latent",
+            category="model/latent/batch",
+            description="Split a SeedVR2 video latent into overlapping temporal chunks small enough to sample one at a time within VRAM, wiring latents outputs to both Apply SeedVR2 Conditioning and the sampler latent input before recombining with Merge SeedVR2 Latents.",
+            search_aliases=["seedvr2", "split", "chunk", "temporal", "video upscale", "rebatch"],
+            inputs=[
+                io.Latent.Input("latent", tooltip="The VAE-encoded SeedVR2 latent to split."),
+                io.Int.Input("temporal_overlap", default=0, min=0, max=16384,
+                             tooltip="Latent frames shared between adjacent chunks and crossfaded at merge; 0 = no overlap."),
+                io.DynamicCombo.Input("chunking_mode",
+                                      tooltip="manual = use frames_per_chunk exactly; auto = predict the largest chunk that fits free VRAM.",
+                                      options=[
+                                          io.DynamicCombo.Option("auto", []),
+                                          io.DynamicCombo.Option("manual", [
+                                              io.Int.Input("frames_per_chunk", default=21, min=1, max=16384, step=4,
+                                                           tooltip="Pixel frames per temporal chunk (4n+1: 1, 5, 9, 13, ...)."),
+                                          ]),
+                                      ]),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="latents", is_output_list=True,
+                                 tooltip="The temporal chunks in sequence order."),
+                io.Int.Output(display_name="temporal_overlap",
+                              tooltip="The effective latent-frame overlap between adjacent chunks, for Merge SeedVR2 Latents."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, latent, temporal_overlap, chunking_mode) -> io.NodeOutput:
+        samples = latent["samples"]
+        if samples.ndim != 5:
+            raise ValueError(
+                f"SeedVR2TemporalChunk: expected a 5-D video latent (B, C, T, H, W); "
+                f"got shape {tuple(samples.shape)}."
+            )
+        if samples.shape[1] != SEEDVR2_LATENT_CHANNELS:
+            raise ValueError(
+                f"SeedVR2TemporalChunk: expected {SEEDVR2_LATENT_CHANNELS} latent channels; "
+                f"got shape {tuple(samples.shape)}."
+            )
+        if temporal_overlap < 0:
+            raise ValueError(
+                f"SeedVR2TemporalChunk: temporal_overlap must be >= 0; got {temporal_overlap}."
+            )
+        mode = chunking_mode["chunking_mode"]
+        if mode not in ("auto", "manual"):
+            raise ValueError(
+                f"SeedVR2TemporalChunk: chunking_mode must be 'auto' or 'manual'; "
+                f"got {mode!r}."
+            )
+        t_latent = samples.shape[2]
+        t_pixel = 4 * (t_latent - 1) + 1
+
+        if mode == "auto":
+            free_gb = comfy.model_management.get_free_memory(
+                comfy.model_management.get_torch_device()) / (1024 ** 3)
+            mpx_per_frame = (samples.shape[0] * samples.shape[3] * samples.shape[4]) * (BYTEDANCE_VAE_SPATIAL_DOWNSAMPLE ** 2) / 1e6
+            budget_gb = free_gb - SEEDVR2_CHUNK_RESERVED_GIB - SEEDVR2_CHUNK_SIGMA_K * SEEDVR2_CHUNK_SIGMA_GIB
+            chunk_latent_max = max(1, int(budget_gb / (SEEDVR2_CHUNK_GIB_PER_MPX_FRAME * mpx_per_frame)))
+            frames_per_chunk = min(4 * (chunk_latent_max - 1) + 1, t_pixel)
+            logging.info(
+                "SeedVR2TemporalChunk auto: free=%.2fGiB, %.2fMpx -> frames_per_chunk=%d (t_pixel=%d).",
+                free_gb, mpx_per_frame, frames_per_chunk, t_pixel,
+            )
+        else:
+            frames_per_chunk = chunking_mode["frames_per_chunk"]
+            if frames_per_chunk < 1 or (frames_per_chunk - 1) % 4 != 0:
+                raise ValueError(
+                    f"SeedVR2TemporalChunk: frames_per_chunk must be a 4n+1 pixel-frame count "
+                    f"(1, 5, 9, 13, 17, 21, ...); got {frames_per_chunk}."
+                )
+
+        if t_pixel <= frames_per_chunk:
+            return io.NodeOutput([latent], 0)
+
+        chunk_latent = (frames_per_chunk - 1) // 4 + 1
+        temporal_overlap = min(temporal_overlap, chunk_latent - 1)
+        step = chunk_latent - temporal_overlap
+
+        chunks = []
+        for start in range(0, t_latent, step):
+            end = min(start + chunk_latent, t_latent)
+            chunk = latent.copy()
+            chunk["samples"] = samples[:, :, start:end].contiguous()
+            chunks.append(chunk)
+            if end >= t_latent:
+                break
+        return io.NodeOutput(chunks, temporal_overlap)
+
+
+class SeedVR2TemporalMerge(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SeedVR2TemporalMerge",
+            display_name="Merge SeedVR2 Latents",
+            category="model/latent/batch",
+            is_input_list=True,
+            description="Recombine sampled SeedVR2 latent temporal chunks into one latent, crossfading each overlap with a Hann window sized by the temporal_overlap wired from Split SeedVR2 Latent.",
+            search_aliases=["seedvr2", "merge", "temporal", "hann", "crossfade"],
+            inputs=[
+                io.Latent.Input("latents", tooltip="The sampled temporal chunks in sequence order."),
+                io.Int.Input("temporal_overlap", default=0, min=0, max=16384, force_input=True,
+                             tooltip="The temporal_overlap output of Split SeedVR2 Latent. 0 = plain concatenation."),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="latent", tooltip="The recombined full-length latent."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, latents, temporal_overlap) -> io.NodeOutput:
+        temporal_overlap = temporal_overlap[0]
+        if temporal_overlap < 0:
+            raise ValueError(
+                f"SeedVR2TemporalMerge: temporal_overlap must be >= 0; got {temporal_overlap}."
+            )
+        chunks = [entry["samples"] for entry in latents]
+        first = chunks[0]
+        if first.ndim != 5:
+            raise ValueError(
+                f"SeedVR2TemporalMerge: expected 5-D video latents (B, C, T, H, W); "
+                f"chunk 0 has shape {tuple(first.shape)}."
+            )
+        for i, chunk in enumerate(chunks[1:], start=1):
+            if chunk.shape[:2] != first.shape[:2] or chunk.shape[3:] != first.shape[3:]:
+                raise ValueError(
+                    f"SeedVR2TemporalMerge: chunk {i} shape {tuple(chunk.shape)} does not "
+                    f"match chunk 0 shape {tuple(first.shape)} outside the temporal axis."
+                )
+            if i < len(chunks) - 1 and chunk.shape[2] != first.shape[2]:
+                raise ValueError(
+                    f"SeedVR2TemporalMerge: chunk {i} has {chunk.shape[2]} latent frames but "
+                    f"chunk 0 has {first.shape[2]}; only the final chunk may be shorter."
+                )
+
+        out = latents[0].copy()
+        out.pop("noise_mask", None)
+
+        if len(chunks) == 1:
+            out["samples"] = first
+            return io.NodeOutput(out)
+        if temporal_overlap == 0:
+            out["samples"] = torch.cat(chunks, dim=2)
+            return io.NodeOutput(out)
+
+        chunk_latent = first.shape[2]
+        step = chunk_latent - min(temporal_overlap, chunk_latent - 1)
+        t_total = step * (len(chunks) - 1) + chunks[-1].shape[2]
+        b, c, _, h, w = first.shape
+        merged = torch.empty((b, c, t_total, h, w), device=first.device, dtype=first.dtype)
+
+        merged[:, :, :chunk_latent] = first
+        filled = chunk_latent
+        for i, chunk in enumerate(chunks[1:], start=1):
+            start = i * step
+            end = start + chunk.shape[2]
+            # Crossfade width is bounded by the previous fill frontier and by a runt
+            # final chunk shorter than the configured overlap.
+            fade = min(filled - start, chunk.shape[2])
+            if fade > 0:
+                w_prev = _seedvr2_chunk_crossfade_weights(
+                    fade, chunk.device, chunk.dtype).view(1, 1, fade, 1, 1)
+                merged[:, :, start:start + fade] = (
+                    merged[:, :, start:start + fade] * w_prev + chunk[:, :, :fade] * (1.0 - w_prev)
+                )
+                merged[:, :, start + fade:end] = chunk[:, :, fade:]
+            else:
+                merged[:, :, start:end] = chunk
+            filled = end
+
+        out["samples"] = merged
+        return io.NodeOutput(out)
+
+
+class SeedVRExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SeedVR2Conditioning,
+            SeedVR2Preprocess,
+            SeedVR2PostProcessing,
+            SeedVR2TemporalChunk,
+            SeedVR2TemporalMerge,
+        ]
+
+async def comfy_entrypoint() -> SeedVRExtension:
+    return SeedVRExtension()

comfy_extras/nodes_text.py

@@ -0,0 +1,71 @@
+import os
+import json
+from typing_extensions import override
+from comfy_api.latest import io, ComfyExtension, ui
+import folder_paths
+
+
+class SaveTextNode(io.ComfyNode):
+    """Save text content to .txt, .md, or .json."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SaveText",
+            search_aliases=["save text", "write text", "export text"],
+            display_name="Save Text",
+            category="text",
+            description="Save text content to a file in the output directory.",
+            inputs=[
+                io.String.Input("text", force_input=True),
+                io.String.Input("filename_prefix", default="ComfyUI"),
+                io.Combo.Input("format", options=["txt", "md", "json"], default="txt"),
+            ],
+            outputs=[io.String.Output(display_name="text")],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, text, filename_prefix, format):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
+            filename_prefix,
+            folder_paths.get_output_directory(),
+            1,
+            1,
+        )
+
+        file = f"{filename}_{counter:05}.{format}"
+        filepath = os.path.join(full_output_folder, file)
+
+        if format == "json":
+            # tries to pretty print otherwise saves normally
+            try:
+                data = json.loads(text)
+                with open(filepath, "w", encoding="utf-8") as f:
+                    json.dump(data, f, indent=2, ensure_ascii=False)
+            except json.JSONDecodeError:
+                with open(filepath, "w", encoding="utf-8") as f:
+                    f.write(text)
+        else:
+            with open(filepath, "w", encoding="utf-8") as f:
+                f.write(text)
+
+        return io.NodeOutput(
+            text,
+            ui={
+                "text": (text,),
+                "files": [
+                    ui.SavedResult(file, subfolder, io.FolderType.output)
+                ]
+            }
+        )
+
+class TextExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SaveTextNode
+        ]
+
+async def comfy_entrypoint() -> TextExtension:
+    return TextExtension()

comfy_extras/nodes_video.py

@@ -81,7 +81,7 @@ class SaveVideo(io.ComfyNode):
             display_name="Save Video",
             category="video",
             essentials_category="Basics",
-            description="Saves the input images to your ComfyUI output directory.",
+            description="Saves the input videos to your ComfyUI output directory.",
             inputs=[
                 io.Video.Input("video", tooltip="The video to save."),
                 io.String.Input("filename_prefix", default="video/ComfyUI", tooltip="The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."),

execution.py

@@ -29,11 +29,16 @@ from comfy_execution.caching import (
     HierarchicalCache,
     LRUCache,
     RAMPressureCache,
+    RAM_CACHE_LARGE_INTERMEDIATE,
 )
 from comfy_execution.graph import (
+    DependencyCycleError,
     DynamicPrompt,
     ExecutionBlocker,
+    ExecutionFailureBlocker,
     ExecutionList,
+    NodeInputError,
+    NodeNotFoundError,
     get_input_info,
 )
 from comfy_execution.graph_utils import GraphBuilder, is_link
@@ -50,6 +55,16 @@ class ExecutionResult(Enum):
     SUCCESS = 0
     FAILURE = 1
     PENDING = 2
+    BLOCKED = 3
+
+
+NODE_FAILURE_POLICY_FAIL_FAST = "fail_fast"
+NODE_FAILURE_POLICY_CONTINUE_INDEPENDENT = "continue_independent"
+NODE_FAILURE_POLICIES = frozenset({
+    NODE_FAILURE_POLICY_FAIL_FAST,
+    NODE_FAILURE_POLICY_CONTINUE_INDEPENDENT,
+})
+NODE_FAILURE_POLICY_EXTRA_DATA_KEY = "_node_failure_policy"
 
 class DuplicateNodeError(Exception):
     pass
@@ -103,6 +118,47 @@ class CacheEntry(NamedTuple):
     outputs: list
 
 
+def _failure_blocker_state(value):
+    if isinstance(value, ExecutionFailureBlocker):
+        return True, False
+    if isinstance(value, dict):
+        values = value.values()
+    elif isinstance(value, (list, tuple)):
+        values = value
+    else:
+        return False, True
+
+    has_failure_blocker = False
+    has_normal_value = False
+    for child in values:
+        child_failure, child_normal = _failure_blocker_state(child)
+        has_failure_blocker = has_failure_blocker or child_failure
+        has_normal_value = has_normal_value or child_normal
+        if has_failure_blocker and has_normal_value:
+            break
+    return has_failure_blocker, has_normal_value
+
+
+def _tag_node_raised(ex):
+    # Marks an exception as raised by node code (vs execution machinery). Best effort: an
+    # exception class rejecting attribute assignment must not mask the original error.
+    try:
+        ex._node_raised = True
+    except Exception:
+        pass
+
+
+def _is_recoverable_node_failure(ex):
+    if isinstance(ex, (
+        comfy.model_management.InterruptProcessingException,
+        DependencyCycleError,
+        NodeInputError,
+        NodeNotFoundError,
+    )):
+        return False
+    return not comfy.model_management.is_oom(ex)
+
+
 class CacheType(Enum):
     CLASSIC = 0
     LRU = 1
@@ -151,7 +207,7 @@ class CacheSet:
         }
         return result
 
-SENSITIVE_EXTRA_DATA_KEYS = ("auth_token_comfy_org", "api_key_comfy_org")
+SENSITIVE_EXTRA_DATA_KEYS = ("auth_token_comfy_org", "api_key_comfy_org", NODE_FAILURE_POLICY_EXTRA_DATA_KEY)
 
 def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=None, extra_data={}):
     is_v3 = issubclass(class_def, _ComfyNodeInternal)
@@ -254,16 +310,22 @@ async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, f
     async def process_inputs(inputs, index=None, input_is_list=False):
         if allow_interrupt:
             nodes.before_node_execution()
-        execution_block = None
+        # Prefer a runtime-failure blocker over a plain user blocker so failure
+        # taint and retry semantics are not masked by input ordering.
+        blocked_value = None
         for k, v in inputs.items():
-            if input_is_list:
-                for e in v:
-                    if isinstance(e, ExecutionBlocker):
-                        v = e
+            values = v if input_is_list else (v,)
+            for e in values:
+                if isinstance(e, ExecutionBlocker):
+                    if blocked_value is None or isinstance(e, ExecutionFailureBlocker):
+                        blocked_value = e
+                    if isinstance(blocked_value, ExecutionFailureBlocker):
                         break
-            if isinstance(v, ExecutionBlocker):
-                execution_block = execution_block_cb(v) if execution_block_cb else v
+            if isinstance(blocked_value, ExecutionFailureBlocker):
                 break
+        execution_block = None
+        if blocked_value is not None:
+            execution_block = execution_block_cb(blocked_value) if execution_block_cb else blocked_value
         if execution_block is None:
             if pre_execute_cb is not None and index is not None:
                 pre_execute_cb(index)
@@ -289,7 +351,11 @@ async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, f
             if inspect.iscoroutinefunction(f):
                 async def async_wrapper(f, prompt_id, unique_id, list_index, args):
                     with CurrentNodeContext(prompt_id, unique_id, list_index):
-                        return await f(**args)
+                        try:
+                            return await f(**args)
+                        except Exception as ex:
+                            _tag_node_raised(ex)
+                            raise
                 task = asyncio.create_task(async_wrapper(f, prompt_id, unique_id, index, args=inputs))
                 # Give the task a chance to execute without yielding
                 await asyncio.sleep(0)
@@ -300,7 +366,11 @@ async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, f
                     results.append(task)
             else:
                 with CurrentNodeContext(prompt_id, unique_id, index):
-                    result = f(**inputs)
+                    try:
+                        result = f(**inputs)
+                    except Exception as ex:
+                        _tag_node_raised(ex)
+                        raise
                 results.append(result)
         else:
             results.append(execution_block)
@@ -447,11 +517,16 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
         execution_list.cache_update(unique_id, cached)
         return (ExecutionResult.SUCCESS, None, None)
 
+    continue_on_failure = extra_data.get(NODE_FAILURE_POLICY_EXTRA_DATA_KEY) == NODE_FAILURE_POLICY_CONTINUE_INDEPENDENT
     input_data_all = None
+    failure_blocked_invocations = 0
+    successful_invocations = 0
+    resumed_subgraph = False
     try:
         if unique_id in pending_async_nodes:
+            pending_results, failure_blocked_invocations, successful_invocations = pending_async_nodes[unique_id]
             results = []
-            for r in pending_async_nodes[unique_id]:
+            for r in pending_results:
                 if isinstance(r, asyncio.Task):
                     try:
                         results.append(r.result())
@@ -464,7 +539,8 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
             del pending_async_nodes[unique_id]
             output_data, output_ui, has_subgraph = get_output_from_returns(results, class_def)
         elif unique_id in pending_subgraph_results:
-            cached_results = pending_subgraph_results[unique_id]
+            cached_results, failure_blocked_invocations = pending_subgraph_results[unique_id]
+            resumed_subgraph = True
             resolved_outputs = []
             for is_subgraph, result in cached_results:
                 if not is_subgraph:
@@ -517,6 +593,9 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                     return (ExecutionResult.PENDING, None, None)
 
             def execution_block_cb(block):
+                nonlocal failure_blocked_invocations
+                if isinstance(block, ExecutionFailureBlocker):
+                    failure_blocked_invocations += 1
                 if block.message is not None:
                     mes = {
                         "prompt_id": prompt_id,
@@ -535,6 +614,8 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                 else:
                     return block
             def pre_execute_cb(call_index):
+                nonlocal successful_invocations
+                successful_invocations += 1
                 # TODO - How to handle this with async functions without contextvars (which requires Python 3.12)?
                 GraphBuilder.set_default_prefix(unique_id, call_index, 0)
 
@@ -549,7 +630,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
                     comfy_aimdo.model_vbar.vbars_reset_watermark_limits()
 
             if has_pending_tasks:
-                pending_async_nodes[unique_id] = output_data
+                pending_async_nodes[unique_id] = (output_data, failure_blocked_invocations, successful_invocations)
                 unblock = execution_list.add_external_block(unique_id)
                 async def await_completion():
                     tasks = [x for x in output_data if isinstance(x, asyncio.Task)]
@@ -604,14 +685,26 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
             for node_id in new_output_ids:
                 execution_list.add_node(node_id)
                 execution_list.cache_link(node_id, unique_id)
+                if continue_on_failure:
+                    # Wait for expanded output nodes before finishing, so a failure inside the expansion taints
+                    # this node before its outputs can be cached as reusable.
+                    execution_list.add_completion_link(node_id, unique_id)
             for link in new_output_links:
                 execution_list.add_strong_link(link[0], link[1], unique_id)
-            pending_subgraph_results[unique_id] = cached_outputs
+            pending_subgraph_results[unique_id] = (cached_outputs, failure_blocked_invocations)
             return (ExecutionResult.PENDING, None, None)
 
         cache_entry = CacheEntry(ui=ui_outputs.get(unique_id), outputs=output_data)
-        execution_list.cache_update(unique_id, cache_entry)
-        await caches.outputs.set(unique_id, cache_entry)
+        if continue_on_failure:
+            has_failure_blocker, has_normal_output = _failure_blocker_state(output_data)
+        else:
+            has_failure_blocker, has_normal_output = False, True
+        failure_tainted = has_failure_blocker or failure_blocked_invocations > 0 or execution_list.is_failure_tainted(unique_id)
+        if failure_tainted:
+            execution_list.mark_failure_tainted(unique_id)
+        execution_list.cache_update(unique_id, cache_entry, transient=failure_tainted)
+        if not failure_tainted:
+            await caches.outputs.set(unique_id, cache_entry)
 
     except comfy.model_management.InterruptProcessingException as iex:
         logging.info("Processing interrupted")
@@ -648,11 +741,23 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
             "exception_message": "{}\n{}".format(ex, tips),
             "exception_type": exception_type,
             "traceback": traceback.format_tb(tb),
-            "current_inputs": input_data_formatted
+            "current_inputs": input_data_formatted,
+            "node_raised": getattr(ex, "_node_raised", False),
         }
 
         return (ExecutionResult.FAILURE, error_details, ex)
 
+    if resumed_subgraph:
+        fully_failure_blocked = has_failure_blocker and not has_normal_output
+    else:
+        fully_failure_blocked = successful_invocations == 0 and (
+            failure_blocked_invocations > 0
+            or (has_failure_blocker and not has_normal_output)
+        )
+    if fully_failure_blocked:
+        get_progress_state().block_progress(unique_id)
+        return (ExecutionResult.BLOCKED, None, None)
+
     get_progress_state().finish_progress(unique_id)
     executed.add(unique_id)
 
@@ -669,6 +774,7 @@ class PromptExecutor:
         self.caches = CacheSet(cache_type=self.cache_type, cache_args=self.cache_args)
         self.status_messages = []
         self.success = True
+        self.execution_summary = None
 
     def add_message(self, event, data: dict, broadcast: bool):
         data = {
@@ -707,6 +813,21 @@ class PromptExecutor:
             }
             self.add_message("execution_error", mes, broadcast=False)
 
+    def handle_node_execution_error(self, prompt_id, prompt, current_outputs, executed, error):
+        node_id = error["node_id"]
+        mes = {
+            "prompt_id": prompt_id,
+            "node_id": node_id,
+            "node_type": prompt[node_id]["class_type"],
+            "executed": list(executed),
+            "exception_message": error["exception_message"],
+            "exception_type": error["exception_type"],
+            "traceback": error["traceback"],
+            "current_inputs": error["current_inputs"],
+            "current_outputs": list(current_outputs),
+        }
+        self.add_message("execution_node_error", mes, broadcast=False)
+
     def _notify_prompt_lifecycle(self, event: str, prompt_id: str):
         if not _has_cache_providers():
             return
@@ -727,6 +848,8 @@ class PromptExecutor:
         set_preview_method(extra_data.get("preview_method"))
 
         nodes.interrupt_processing(False)
+        self.success = True
+        self.execution_summary = None
 
         if "client_id" in extra_data:
             self.server.client_id = extra_data["client_id"]
@@ -771,35 +894,75 @@ class PromptExecutor:
                 executed = set()
                 execution_list = ExecutionList(dynamic_prompt, self.caches.outputs)
                 current_outputs = self.caches.outputs.all_node_ids()
+                output_targets = set(execute_outputs)
+                failed_node_ids = set()
+                blocked_node_ids = set()
+                blocked_output_node_ids = set()
+                successful_output_node_ids = set()
+                node_failures = []
+                continue_independent = extra_data.get(
+                    NODE_FAILURE_POLICY_EXTRA_DATA_KEY,
+                    NODE_FAILURE_POLICY_FAIL_FAST,
+                ) == NODE_FAILURE_POLICY_CONTINUE_INDEPENDENT
                 for node_id in list(execute_outputs):
                     execution_list.add_node(node_id)
 
                 while not execution_list.is_empty():
                     node_id, error, ex = await execution_list.stage_node_execution()
                     if error is not None:
+                        self.success = False
                         self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
                         break
 
                     assert node_id is not None, "Node ID should not be None at this point"
                     result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_node_outputs)
-                    self.success = result != ExecutionResult.FAILURE
                     if result == ExecutionResult.FAILURE:
-                        self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
-                        break
+                        if continue_independent and error.get("node_raised") and _is_recoverable_node_failure(ex):
+                            self.handle_node_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error)
+                            real_node_id = error["node_id"]
+                            failed_node_ids.add(real_node_id)
+                            node_failures.append((error, ex))
+                            blocker = ExecutionFailureBlocker(real_node_id)
+                            class_type = dynamic_prompt.get_node(node_id)["class_type"]
+                            class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+                            cache_entry = CacheEntry(
+                                ui=None,
+                                outputs=[[blocker] for _ in class_def.RETURN_TYPES],
+                            )
+                            execution_list.cache_update(node_id, cache_entry, transient=True)
+                            execution_list.mark_failure_tainted(node_id)
+                            get_progress_state().error_progress(node_id)
+                            execution_list.complete_node_execution()
+                        else:
+                            self.success = False
+                            self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
+                            break
                     elif result == ExecutionResult.PENDING:
                         execution_list.unstage_node_execution()
+                    elif result == ExecutionResult.BLOCKED:
+                        real_node_id = dynamic_prompt.get_real_node_id(node_id)
+                        blocked_node_ids.add(real_node_id)
+                        if node_id in output_targets:
+                            blocked_output_node_ids.add(real_node_id)
+                        execution_list.complete_node_execution()
                     else: # result == ExecutionResult.SUCCESS:
+                        if node_id in output_targets:
+                            successful_output_node_ids.add(dynamic_prompt.get_real_node_id(node_id))
                         execution_list.complete_node_execution()
 
                     if self.cache_type == CacheType.RAM_PRESSURE:
                         ram_release_callback(ram_inactive_headroom)
                         ram_shortfall = ram_headroom - psutil.virtual_memory().available
-                        freed = comfy.model_management.free_pins(ram_shortfall + 512 * (1024 ** 2))
-                        if freed < ram_shortfall:
-                            if freed > 64 * (1024 ** 2):
-                                # AIMDO MEM_DECOMMIT can outrun psutil.available catching up.
-                                time.sleep(0.05)
-                            ram_release_callback(ram_headroom, free_active=True)
+                        if ram_shortfall > 0:
+                            freed = ram_release_callback(ram_headroom, free_active=True, min_entry_size=RAM_CACHE_LARGE_INTERMEDIATE)
+                            ram_shortfall -= freed
+                        if comfy.model_management.should_free_pins_for_ram_pressure(ram_shortfall):
+                            freed = comfy.model_management.free_pins(ram_shortfall + 512 * (1024 ** 2))
+                            if freed < ram_shortfall:
+                                if freed > 64 * (1024 ** 2):
+                                    # AIMDO MEM_DECOMMIT can outrun psutil.available catching up.
+                                    time.sleep(0.05)
+                                ram_release_callback(ram_headroom, free_active=True)
                 else:
                     # Only execute when the while-loop ends without break
                     # Send cached UI for intermediate output nodes that weren't executed
@@ -812,7 +975,36 @@ class PromptExecutor:
                         if cached is not None:
                             display_node_id = dynamic_prompt.get_display_node_id(node_id)
                             _send_cached_ui(self.server, node_id, display_node_id, cached, prompt_id, ui_node_outputs)
-                    self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)
+
+                    if node_failures:
+                        self.execution_summary = {
+                            "has_errors": True,
+                            "execution_error_count": len(node_failures),
+                            "failed_node_ids": sorted(failed_node_ids)[:100],
+                            "blocked_node_ids": sorted(blocked_node_ids)[:100],
+                            "blocked_output_node_ids": sorted(blocked_output_node_ids)[:100],
+                            "successful_output_node_ids": sorted(successful_output_node_ids)[:100],
+                        }
+                        if successful_output_node_ids:
+                            self.execution_summary["completion_status"] = "partial_success"
+                            self.add_message(
+                                "execution_success",
+                                {"prompt_id": prompt_id, **self.execution_summary},
+                                broadcast=False,
+                            )
+                        else:
+                            self.success = False
+                            last_error, last_ex = node_failures[-1]
+                            self.handle_execution_error(
+                                prompt_id,
+                                dynamic_prompt.original_prompt,
+                                current_outputs,
+                                executed,
+                                last_error,
+                                last_ex,
+                            )
+                    else:
+                        self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)
 
                 ui_outputs = {}
                 meta_outputs = {}
@@ -1270,6 +1462,7 @@ class PromptQueue:
         status_str: Literal['success', 'error']
         completed: bool
         messages: List[str]
+        execution_summary: Optional[dict] = None
 
     def task_done(self, item_id, history_result,
                   status: Optional['PromptQueue.ExecutionStatus'], process_item=None):
@@ -1281,6 +1474,8 @@ class PromptQueue:
             status_dict: Optional[dict] = None
             if status is not None:
                 status_dict = copy.deepcopy(status._asdict())
+                if status_dict.get("execution_summary") is None:
+                    del status_dict["execution_summary"]
 
             if process_item is not None:
                 prompt = process_item(prompt)

main.py

@@ -366,7 +366,8 @@ def prompt_worker(q, server_instance):
                         status=execution.PromptQueue.ExecutionStatus(
                             status_str='success' if e.success else 'error',
                             completed=e.success,
-                            messages=e.status_messages), process_item=remove_sensitive)
+                            messages=e.status_messages,
+                            execution_summary=e.execution_summary), process_item=remove_sensitive)
             if server_instance.client_id is not None:
                 server_instance.send_sync("executing", {"node": None, "prompt_id": prompt_id}, server_instance.client_id)
 

nodes.py

@@ -1709,6 +1709,7 @@ class PreviewImage(SaveImage):
         self.compress_level = 1
 
     SEARCH_ALIASES = ["preview", "preview image", "show image", "view image", "display image", "image viewer"]
+    DESCRIPTION = "Preview the images without saving them to the ComfyUI output directory."
 
     @classmethod
     def INPUT_TYPES(s):
@@ -2458,6 +2459,7 @@ async def init_builtin_extra_nodes():
         "nodes_camera_trajectory.py",
         "nodes_edit_model.py",
         "nodes_tcfg.py",
+        "nodes_seedvr.py",
         "nodes_context_windows.py",
         "nodes_qwen.py",
         "nodes_boogu.py",
@@ -2503,6 +2505,7 @@ async def init_builtin_extra_nodes():
         "nodes_triposplat.py",
         "nodes_depth_anything_3.py",
         "nodes_seed.py",
+        "nodes_text.py",
     ]
 
     import_failed = []

openapi.yaml

@@ -922,6 +922,13 @@ components:
                 number:
                     description: Priority number for the queue (lower numbers have higher priority)
                     type: number
+                node_failure_policy:
+                    default: fail_fast
+                    description: Controls whether a runtime node failure terminates the prompt or only blocks dependent nodes
+                    enum:
+                        - fail_fast
+                        - continue_independent
+                    type: string
                 partial_execution_targets:
                     description: List of node names to execute
                     items:
@@ -3297,6 +3304,12 @@ paths:
                             schema:
                                 $ref: '#/components/schemas/ErrorResponse'
                     description: Invalid request parameters
+                "401":
+                    content:
+                        application/json:
+                            schema:
+                                $ref: '#/components/schemas/ErrorResponse'
+                    description: Unauthorized - Authentication required
                 "500":
                     content:
                         application/json:

requirements.txt

@@ -1,6 +1,6 @@
 comfyui-frontend-package==1.45.20
-comfyui-workflow-templates==0.11.6
-comfyui-embedded-docs==0.5.7
+comfyui-workflow-templates==0.11.9
+comfyui-embedded-docs==0.5.8
 torch
 torchsde
 torchvision
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy>=2.0.0
 filelock
 av>=16.0.0
-comfy-kitchen==0.2.16
+comfy-kitchen==0.2.19
 comfy-aimdo==0.4.10
 requests
 simpleeval>=1.0.0

server.py

@@ -39,6 +39,7 @@ from comfy.deploy_environment import get_deploy_environment
 import comfy.utils
 import comfy.model_management
 from comfy_api import feature_flags
+from comfy.comfy_api_env import get_environment_overrides
 import node_helpers
 from comfyui_version import __version__
 from app.frontend_management import FrontendManager, parse_version
@@ -46,6 +47,7 @@ from comfy_api.internal import _ComfyNodeInternal
 from app.assets.seeder import asset_seeder
 from app.assets.api.routes import register_assets_routes
 from app.assets.services.ingest import register_file_in_place
+from app.assets.services.path_utils import get_known_subfolder_tags
 from app.assets.services.asset_management import resolve_hash_to_path
 
 from app.user_manager import UserManager
@@ -441,7 +443,9 @@ class PromptServer():
                 if args.enable_assets:
                     try:
                         tag = image_upload_type if image_upload_type in ("input", "output") else "input"
-                        result = register_file_in_place(abs_path=filepath, name=filename, tags=[tag])
+                        tags = [tag]
+                        tags.extend(get_known_subfolder_tags(subfolder))
+                        result = register_file_in_place(abs_path=filepath, name=filename, tags=tags)
                         resp["asset"] = {
                             "id": result.ref.id,
                             "name": result.ref.name,
@@ -724,7 +728,11 @@ class PromptServer():
 
         @routes.get("/features")
         async def get_features(request):
-            return web.json_response(feature_flags.get_server_features())
+            features = feature_flags.get_server_features()
+            overrides = get_environment_overrides()
+            if overrides:
+                features.update(overrides)
+            return web.json_response(features)
 
         @routes.get("/prompt")
         async def get_prompt(request):
@@ -1089,6 +1097,19 @@ class PromptServer():
                 if "partial_execution_targets" in json_data:
                     partial_execution_targets = json_data["partial_execution_targets"]
 
+                node_failure_policy = json_data.get(
+                    "node_failure_policy",
+                    execution.NODE_FAILURE_POLICY_FAIL_FAST,
+                )
+                if not isinstance(node_failure_policy, str) or node_failure_policy not in execution.NODE_FAILURE_POLICIES:
+                    error = {
+                        "type": "invalid_node_failure_policy",
+                        "message": "node_failure_policy must be 'fail_fast' or 'continue_independent'",
+                        "details": f"Invalid node_failure_policy: {node_failure_policy!r}",
+                        "extra_info": {},
+                    }
+                    return web.json_response({"error": error, "node_errors": {}}, status=400)
+
                 self.node_replace_manager.apply_replacements(prompt)
 
                 valid = await execution.validate_prompt(prompt_id, prompt, partial_execution_targets)
@@ -1096,6 +1117,10 @@ class PromptServer():
                 if "extra_data" in json_data:
                     extra_data = json_data["extra_data"]
 
+                extra_data.pop(execution.NODE_FAILURE_POLICY_EXTRA_DATA_KEY, None)
+                if node_failure_policy == execution.NODE_FAILURE_POLICY_CONTINUE_INDEPENDENT:
+                    extra_data[execution.NODE_FAILURE_POLICY_EXTRA_DATA_KEY] = node_failure_policy
+
                 if "client_id" in json_data:
                     extra_data["client_id"] = json_data["client_id"]
 

tests-unit/app_test/model_manager_test.py

@@ -24,6 +24,28 @@ def app(model_manager):
     app.add_routes(routes)
     return app
 
+async def test_get_model_folders_includes_registered_extensions(aiohttp_client, app, tmp_path):
+    """Folders expose their registered extension set verbatim; an empty list
+    means match-all (filter_files_extensions semantics)."""
+    with patch('folder_paths.folder_names_and_paths', {
+        'test_checkpoints': ([str(tmp_path)], {'.safetensors', '.ckpt'}),
+        'test_configs': ([str(tmp_path)], ['.yaml']),
+        'test_match_all': ([str(tmp_path)], set()),
+        'configs': ([str(tmp_path)], ['.yaml']),
+    }):
+        client = await aiohttp_client(app)
+        response = await client.get('/experiment/models')
+
+        assert response.status == 200
+        folders = {f['name']: f for f in await response.json()}
+
+        assert 'configs' not in folders  # blocklisted
+        assert folders['test_checkpoints']['folders'] == [str(tmp_path)]
+        assert folders['test_checkpoints']['extensions'] == ['.ckpt', '.safetensors']
+        assert folders['test_configs']['extensions'] == ['.yaml']
+        # Match-all registrations are exposed honestly, not substituted.
+        assert folders['test_match_all']['extensions'] == []
+
 async def test_get_model_preview_safetensors(aiohttp_client, app, tmp_path):
     img = Image.new('RGB', (100, 100), 'white')
     img_byte_arr = BytesIO()

tests-unit/app_test/test_migrations.py

@@ -8,6 +8,7 @@ upgrade/downgrade for 0003+.
 """
 
 import os
+import sqlite3
 
 import pytest
 from alembic import command
@@ -30,6 +31,12 @@ def _make_config(db_path: str) -> Config:
     return cfg
 
 
+def _sqlite_path(cfg: Config) -> str:
+    url = cfg.get_main_option("sqlalchemy.url")
+    assert url is not None and url.startswith("sqlite:///")
+    return url.removeprefix("sqlite:///")
+
+
 @pytest.fixture
 def migration_db(tmp_path):
     """Yield an alembic Config pre-upgraded to the baseline revision."""
@@ -55,3 +62,26 @@ def test_upgrade_downgrade_cycle(migration_db):
     command.upgrade(migration_db, "head")
     command.downgrade(migration_db, _BASELINE)
     command.upgrade(migration_db, "head")
+
+
+def test_case_sensitive_tags_downgrade_normalizes_existing_tags(migration_db):
+    """Downgrading 0005 folds mixed-case tag vocabulary before restoring CHECK."""
+    command.upgrade(migration_db, "0005_allow_case_sensitive_tags")
+
+    db_path = _sqlite_path(migration_db)
+    with sqlite3.connect(db_path) as conn:
+        conn.execute("INSERT INTO tags(name) VALUES (?)", ("NewTag",))
+        conn.execute("INSERT INTO tags(name) VALUES (?)", ("newtag",))
+        conn.execute("INSERT INTO tags(name) VALUES (?)", ("model_type:LLM",))
+
+    command.downgrade(migration_db, "0004_drop_tag_type")
+
+    with sqlite3.connect(db_path) as conn:
+        tags = {row[0] for row in conn.execute("SELECT name FROM tags")}
+        assert "newtag" in tags
+        assert "model_type:llm" in tags
+        assert "NewTag" not in tags
+        assert "model_type:LLM" not in tags
+
+        with pytest.raises(sqlite3.IntegrityError):
+            conn.execute("INSERT INTO tags(name) VALUES (?)", ("Upper",))

tests-unit/assets_test/conftest.py

@@ -234,7 +234,7 @@ def seeded_asset(request: pytest.FixtureRequest, http: requests.Session, api_bas
     p = getattr(request, "param", {}) or {}
     tags: Optional[list[str]] = p.get("tags")
     if tags is None:
-        tags = ["models", "checkpoints", "unit-tests", "alpha"]
+        tags = ["models", "model_type:checkpoints", "unit-tests", "alpha"]
     meta = {"purpose": "test", "epoch": 1, "flags": ["x", "y"], "nullable": None}
     # 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

tests-unit/assets_test/queries/test_asset_info.py

@@ -133,6 +133,66 @@ class TestListReferencesPage:
         assert total == 1
         assert refs[0].name == "tagged"
 
+    def test_include_tags_filter_ands_persisted_model_tags(self, session: Session):
+        asset = _make_asset(session, "hash-model-tags")
+        checkpoint = _make_reference(session, asset, name="checkpoint")
+        lora = _make_reference(session, asset, name="lora")
+        input_ref = _make_reference(session, asset, name="input")
+        ensure_tags_exist(
+            session,
+            ["models", "model_type:checkpoints", "model_type:loras", "unit-tests"],
+        )
+        add_tags_to_reference(
+            session,
+            reference_id=checkpoint.id,
+            tags=["models", "model_type:checkpoints", "unit-tests"],
+            origin="automatic",
+        )
+        add_tags_to_reference(
+            session,
+            reference_id=lora.id,
+            tags=["models", "model_type:loras", "unit-tests"],
+            origin="automatic",
+        )
+        add_tags_to_reference(
+            session,
+            reference_id=input_ref.id,
+            tags=["unit-tests"],
+        )
+        session.commit()
+
+        refs, _, total = list_references_page(
+            session,
+            include_tags=["models", "model_type:checkpoints", "unit-tests"],
+        )
+
+        assert total == 1
+        assert refs[0].id == checkpoint.id
+
+    def test_include_tags_filter_preserves_model_type_case(self, session: Session):
+        asset = _make_asset(session, "hash-model-case")
+        ref = _make_reference(session, asset, name="llm")
+        ensure_tags_exist(session, ["models", "model_type:LLM"])
+        add_tags_to_reference(
+            session,
+            reference_id=ref.id,
+            tags=["models", "model_type:LLM"],
+            origin="automatic",
+        )
+        session.commit()
+
+        refs, _, total = list_references_page(
+            session, include_tags=["models", "model_type:LLM"]
+        )
+        refs_lower, _, total_lower = list_references_page(
+            session, include_tags=["models", "model_type:llm"]
+        )
+
+        assert total == 1
+        assert refs[0].id == ref.id
+        assert total_lower == 0
+        assert refs_lower == []
+
     def test_exclude_tags_filter(self, session: Session):
         asset = _make_asset(session, "hash1")
         _make_reference(session, asset, name="keep")

tests-unit/assets_test/queries/test_cache_state.py

@@ -176,6 +176,39 @@ class TestUpsertReference:
         ref = session.query(AssetReference).filter_by(file_path=file_path).one()
         assert ref.mtime_ns == final_mtime
 
+    def test_upsert_refreshes_loader_path_on_existing_reference(self, session: Session):
+        """Re-ingesting an existing reference writes the loader_path computed
+        by that ingest, healing NULL or stale values even when nothing else
+        about the row changed."""
+        asset = _make_asset(session, "hash1")
+        file_path = "/models/checkpoints/sub/model.safetensors"
+
+        upsert_reference(
+            session, asset_id=asset.id, file_path=file_path, name="model",
+            mtime_ns=100, loader_path=None,
+        )
+        session.commit()
+
+        created, updated = upsert_reference(
+            session, asset_id=asset.id, file_path=file_path, name="model",
+            mtime_ns=100, loader_path="sub/model.safetensors",
+        )
+        session.commit()
+
+        assert created is False
+        assert updated is True
+        ref = session.query(AssetReference).filter_by(file_path=file_path).one()
+        assert ref.loader_path == "sub/model.safetensors"
+
+        # Identical loader_path is a no-op, not a spurious update.
+        created, updated = upsert_reference(
+            session, asset_id=asset.id, file_path=file_path, name="model",
+            mtime_ns=100, loader_path="sub/model.safetensors",
+        )
+        session.commit()
+        assert created is False
+        assert updated is False
+
     def test_upsert_restores_missing_reference(self, session: Session):
         """Upserting a reference that was marked missing should restore it."""
         asset = _make_asset(session, "hash1")

tests-unit/assets_test/queries/test_tags.py

@@ -58,7 +58,7 @@ class TestEnsureTagsExist:
         session.commit()
 
         tags = session.query(Tag).all()
-        assert {t.name for t in tags} == {"alpha", "beta"}
+        assert {t.name for t in tags} == {"ALPHA", "Beta", "alpha"}
 
     def test_empty_list_is_noop(self, session: Session):
         ensure_tags_exist(session, [])
@@ -258,6 +258,16 @@ class TestListTagsWithUsage:
         tag_names = {name for name, _ in rows}
         assert tag_names == {"alpha", "alphabet"}
 
+    def test_prefix_filter_is_case_sensitive(self, session: Session):
+        ensure_tags_exist(session, ["model_type:LLM", "model_type:llm"])
+        session.commit()
+
+        rows, total = list_tags_with_usage(session, prefix="model_type:L")
+
+        tag_names = {name for name, _ in rows}
+        assert tag_names == {"model_type:LLM"}
+        assert total == 1
+
     def test_order_by_name(self, session: Session):
         ensure_tags_exist(session, ["zebra", "alpha", "middle"])
         session.commit()

tests-unit/assets_test/services/test_asset_response_loader_path.py

@@ -0,0 +1,83 @@
+"""Tests for how _build_asset_response derives the response `loader_path`.
+
+Guards the persist-and-read contract: the response reads the stored
+`loader_path` verbatim, with no read-time recomputation. Like tags, the
+value is a seed-time derivative healed by the scan lifecycle.
+"""
+
+from datetime import datetime
+from pathlib import Path
+from unittest.mock import patch
+
+from app.assets.api.routes import _build_asset_response
+from app.assets.services.schemas import AssetDetailResult, ReferenceData
+
+_TS = datetime(2024, 1, 1, 0, 0, 0)
+
+
+def _make_result(
+    *, file_path: str | None, loader_path: str | None
+) -> AssetDetailResult:
+    ref = ReferenceData(
+        id="ref-1",
+        name="model.safetensors",
+        file_path=file_path,
+        loader_path=loader_path,
+        user_metadata=None,
+        preview_id=None,
+        created_at=_TS,
+        updated_at=_TS,
+        last_access_time=_TS,
+    )
+    return AssetDetailResult(ref=ref, asset=None, tags=[])
+
+
+def test_uses_persisted_loader_path_without_recomputing():
+    """A stored loader_path is returned verbatim, not re-derived from file_path.
+
+    The sentinel value could never be produced by compute_loader_path for this
+    file_path, so seeing it in the response proves the stored column is read.
+    """
+    result = _make_result(
+        file_path="/unmatched/root/model.safetensors",
+        loader_path="SENTINEL/stored.safetensors",
+    )
+
+    resp = _build_asset_response(result)
+
+    assert resp.loader_path == "SENTINEL/stored.safetensors"
+
+
+def test_null_stored_loader_path_is_served_as_null(tmp_path: Path):
+    """No read-time recomputation: a NULL column is served as null even when
+    the path would resolve."""
+    models = tmp_path / "models"
+    ckpt = models / "checkpoints"
+    ckpt.mkdir(parents=True)
+    f = ckpt / "bar.safetensors"
+    f.touch()
+
+    with patch("app.assets.services.path_utils.folder_paths") as mock_fp, patch(
+        "app.assets.services.path_utils.get_comfy_models_folders",
+        return_value=[("checkpoints", [str(ckpt)], {".safetensors"})],
+    ):
+        mock_fp.get_input_directory.return_value = str(tmp_path / "in")
+        mock_fp.get_output_directory.return_value = str(tmp_path / "out")
+        mock_fp.get_temp_directory.return_value = str(tmp_path / "tmp")
+        mock_fp.models_dir = str(models)
+
+        result = _make_result(file_path=str(f), loader_path=None)
+        resp = _build_asset_response(result)
+
+        assert resp.loader_path is None
+        assert resp.display_name == "checkpoints/bar.safetensors"
+
+
+def test_all_path_fields_null_without_file_path():
+    """API-created / hash-only references (no file_path) expose no paths."""
+    result = _make_result(file_path=None, loader_path=None)
+
+    resp = _build_asset_response(result)
+
+    assert resp.loader_path is None
+    assert resp.display_name is None

tests-unit/assets_test/services/test_bulk_ingest.py

@@ -1,10 +1,14 @@
 """Tests for bulk ingest services."""
 
+import os
 from pathlib import Path
+from unittest.mock import patch
 
 from sqlalchemy.orm import Session
 
 from app.assets.database.models import Asset, AssetReference
+from app.assets.database.queries import get_reference_tags
+from app.assets.scanner import build_asset_specs
 from app.assets.services.bulk_ingest import SeedAssetSpec, batch_insert_seed_assets
 
 
@@ -101,6 +105,184 @@ class TestBatchInsertSeedAssets:
             asset = session.query(Asset).filter_by(id=ref.asset_id).first()
             assert asset.mime_type == expected_mime, f"Expected {expected_mime} for {filename}, got {asset.mime_type}"
 
+    def test_duplicate_paths_merge_tags_before_insert(
+        self, session: Session, temp_dir: Path
+    ):
+        """Overlapping model-folder registrations can emit the same path twice."""
+        file_path = temp_dir / "shared.safetensors"
+        file_path.write_bytes(b"shared model")
+
+        specs: list[SeedAssetSpec] = [
+            {
+                "abs_path": str(file_path),
+                "size_bytes": 12,
+                "mtime_ns": 1234567890000000000,
+                "info_name": "Shared Model",
+                "tags": ["models", "model_type:checkpoints"],
+                "fname": "shared.safetensors",
+                "metadata": None,
+                "hash": None,
+                "mime_type": "application/safetensors",
+            },
+            {
+                "abs_path": str(file_path),
+                "size_bytes": 12,
+                "mtime_ns": 1234567890000000000,
+                "info_name": "Shared Model",
+                "tags": ["models", "model_type:diffusion_models"],
+                "fname": "shared.safetensors",
+                "metadata": None,
+                "hash": None,
+                "mime_type": "application/safetensors",
+            },
+        ]
+
+        result = batch_insert_seed_assets(session, specs=specs, owner_id="")
+
+        assert result.inserted_refs == 1
+        assert result.won_paths == 1
+        refs = session.query(AssetReference).all()
+        assert len(refs) == 1
+        assert set(get_reference_tags(session, reference_id=refs[0].id)) == {
+            "models",
+            "model_type:checkpoints",
+            "model_type:diffusion_models",
+        }
+
+    def test_duplicate_paths_are_merged_after_abspath_normalization(
+        self, session: Session, temp_dir: Path, monkeypatch
+    ):
+        """The scanner may emit equivalent paths with different spelling."""
+        file_path = temp_dir / "same-file.safetensors"
+        file_path.write_bytes(b"shared model")
+        monkeypatch.chdir(temp_dir)
+        relative_path = file_path.name
+        absolute_path = os.path.abspath(relative_path)
+
+        specs: list[SeedAssetSpec] = [
+            {
+                "abs_path": relative_path,
+                "size_bytes": 12,
+                "mtime_ns": 1234567890000000000,
+                "info_name": "Shared Model",
+                "tags": ["models", "model_type:checkpoints"],
+                "fname": "same-file.safetensors",
+                "metadata": None,
+                "hash": None,
+                "mime_type": "application/safetensors",
+            },
+            {
+                "abs_path": absolute_path,
+                "size_bytes": 12,
+                "mtime_ns": 1234567890000000000,
+                "info_name": "Shared Model",
+                "tags": ["models", "model_type:diffusion_models"],
+                "fname": "same-file.safetensors",
+                "metadata": None,
+                "hash": None,
+                "mime_type": "application/safetensors",
+            },
+        ]
+
+        result = batch_insert_seed_assets(session, specs=specs, owner_id="")
+
+        assert result.inserted_refs == 1
+        assert result.won_paths == 1
+        refs = session.query(AssetReference).all()
+        assert len(refs) == 1
+        assert refs[0].file_path == absolute_path
+        # loader_path is persisted from the spec's fname (compute_loader_path).
+        assert refs[0].loader_path == "same-file.safetensors"
+        assert set(get_reference_tags(session, reference_id=refs[0].id)) == {
+            "models",
+            "model_type:checkpoints",
+            "model_type:diffusion_models",
+        }
+
+    def test_scanner_duplicate_shared_model_paths_keep_all_model_type_tags(
+        self, session: Session, temp_dir: Path
+    ):
+        """Shared extra model roots make scanner collection emit duplicate paths."""
+        shared_root = temp_dir / "shared"
+        input_dir = temp_dir / "input"
+        output_dir = temp_dir / "output"
+        temp_root = temp_dir / "temp"
+        for directory in (shared_root, input_dir, output_dir, temp_root):
+            directory.mkdir()
+        file_path = shared_root / "dual_use_model.safetensors"
+        file_path.write_bytes(b"shared model")
+
+        with (
+            patch("app.assets.services.path_utils.folder_paths") as mock_fp,
+            patch(
+                "app.assets.services.path_utils.get_comfy_models_folders",
+                return_value=[
+                    ("checkpoints", [str(shared_root)], {".safetensors"}),
+                    ("diffusion_models", [str(shared_root)], {".safetensors"}),
+                ],
+            ),
+        ):
+            mock_fp.get_input_directory.return_value = str(input_dir)
+            mock_fp.get_output_directory.return_value = str(output_dir)
+            mock_fp.get_temp_directory.return_value = str(temp_root)
+
+            specs, tag_pool, skipped = build_asset_specs(
+                paths=[str(file_path), str(file_path)],
+                existing_paths=set(),
+                enable_metadata_extraction=False,
+                compute_hashes=False,
+            )
+
+        assert skipped == 0
+        assert len(specs) == 2
+        assert tag_pool == {
+            "models",
+            "model_type:checkpoints",
+            "model_type:diffusion_models",
+        }
+
+        result = batch_insert_seed_assets(session, specs=specs, owner_id="")
+
+        assert result.inserted_refs == 1
+        assert result.won_paths == 1
+        refs = session.query(AssetReference).all()
+        assert len(refs) == 1
+        assert set(get_reference_tags(session, reference_id=refs[0].id)) == {
+            "models",
+            "model_type:checkpoints",
+            "model_type:diffusion_models",
+        }
+
+    def test_loader_path_persisted_as_null_when_fname_is_none(
+        self, session: Session, temp_dir: Path
+    ):
+        """A file with no in-root loader path (fname=None, e.g. an orphan under
+        models_root) persists loader_path as NULL rather than a synthesized value."""
+        file_path = temp_dir / "orphan.bin"
+        file_path.write_bytes(b"x")
+
+        specs: list[SeedAssetSpec] = [
+            {
+                "abs_path": str(file_path),
+                "size_bytes": 1,
+                "mtime_ns": 1234567890000000000,
+                "info_name": "orphan.bin",
+                "tags": [],
+                "fname": None,
+                "metadata": None,
+                "hash": None,
+                "mime_type": None,
+            }
+        ]
+
+        result = batch_insert_seed_assets(session, specs=specs, owner_id="")
+
+        assert result.inserted_refs == 1
+        refs = session.query(AssetReference).all()
+        assert len(refs) == 1
+        assert refs[0].file_path == str(file_path)
+        assert refs[0].loader_path is None
+
 
 class TestMetadataExtraction:
     def test_extracts_mime_type_for_model_files(self, temp_dir: Path):

tests-unit/assets_test/services/test_ingest.py

@@ -94,6 +94,47 @@ class TestIngestFileFromPath:
         ref_tags = get_reference_tags(session, reference_id=result.reference_id)
         assert set(ref_tags) == {"models", "checkpoints"}
 
+    def test_path_derived_tags_use_automatic_origin(
+        self, mock_create_session, temp_dir: Path, session: Session
+    ):
+        input_dir = temp_dir / "input"
+        output_dir = temp_dir / "output"
+        temp_root = temp_dir / "temp"
+        for directory in (input_dir, output_dir, temp_root):
+            directory.mkdir()
+        file_path = input_dir / "pasted" / "tagged.png"
+        file_path.parent.mkdir()
+        file_path.write_bytes(b"data")
+
+        with (
+            patch("app.assets.services.path_utils.folder_paths") as mock_fp,
+            patch(
+                "app.assets.services.path_utils.get_comfy_models_folders",
+                return_value=[],
+            ),
+        ):
+            mock_fp.get_input_directory.return_value = str(input_dir)
+            mock_fp.get_output_directory.return_value = str(output_dir)
+            mock_fp.get_temp_directory.return_value = str(temp_root)
+
+            result = _ingest_file_from_path(
+                abs_path=str(file_path),
+                asset_hash="blake3:pathorigin",
+                size_bytes=4,
+                mtime_ns=1234567890000000000,
+                info_name="Tagged Asset",
+                tags=["input", "manual-label"],
+            )
+
+        assert result.reference_id is not None
+        links = session.query(AssetReferenceTag).filter_by(
+            asset_reference_id=result.reference_id
+        )
+        origin_by_tag = {link.tag_name: link.origin for link in links}
+        assert origin_by_tag["input"] == "automatic"
+        assert origin_by_tag["pasted"] == "automatic"
+        assert origin_by_tag["manual-label"] == "manual"
+
     def test_idempotent_upsert(self, mock_create_session, temp_dir: Path, session: Session):
         file_path = temp_dir / "dup.bin"
         file_path.write_bytes(b"content")
@@ -288,6 +329,45 @@ class TestIngestExistingFileTagFK:
             assert "output" in ref_tag_names
 
 
+class TestIngestExistingFileLoaderPath:
+    """Outputs saved into a subfolder must persist the subfolder-qualified
+    loader path, not the bare basename (regression: spec["fname"] was
+    os.path.basename)."""
+
+    def test_subfoldered_output_persists_relative_loader_path(
+        self, mock_create_session, temp_dir: Path, session: Session
+    ):
+        input_dir = temp_dir / "input"
+        output_dir = temp_dir / "output"
+        temp_root = temp_dir / "temp"
+        for directory in (input_dir, output_dir, temp_root):
+            directory.mkdir()
+        file_path = output_dir / "sub" / "img_00001_.png"
+        file_path.parent.mkdir()
+        file_path.write_bytes(b"image data")
+
+        with (
+            patch("app.assets.services.path_utils.folder_paths") as mock_fp,
+            patch(
+                "app.assets.services.path_utils.get_comfy_models_folders",
+                return_value=[],
+            ),
+        ):
+            mock_fp.get_input_directory.return_value = str(input_dir)
+            mock_fp.get_output_directory.return_value = str(output_dir)
+            mock_fp.get_temp_directory.return_value = str(temp_root)
+
+            assert ingest_existing_file(abs_path=str(file_path)) is True
+
+        ref = (
+            session.query(AssetReference)
+            .filter_by(file_path=str(file_path))
+            .one()
+        )
+        assert ref.loader_path == "sub/img_00001_.png"
+        assert (ref.user_metadata or {}).get("filename") == "sub/img_00001_.png"
+
+
 class TestIngestImageDimensions:
     """system_metadata should carry {kind, width, height} for image assets."""
 

tests-unit/assets_test/services/test_path_utils.py

@@ -6,7 +6,16 @@ from unittest.mock import patch
 
 import pytest
 
-from app.assets.services.path_utils import get_asset_category_and_relative_path
+from app.assets.services.path_utils import (
+    compute_display_name,
+    compute_loader_path,
+    compute_logical_path,
+    get_asset_category_and_relative_path,
+    get_known_input_subfolder_tags_from_path,
+    get_known_subfolder_tags,
+    get_name_and_tags_from_asset_path,
+    resolve_destination_from_tags,
+)
 
 
 @pytest.fixture
@@ -17,7 +26,8 @@ def fake_dirs():
         input_dir = root_path / "input"
         output_dir = root_path / "output"
         temp_dir = root_path / "temp"
-        models_dir = root_path / "models" / "checkpoints"
+        models_root = root_path / "models"
+        models_dir = models_root / "checkpoints"
         for d in (input_dir, output_dir, temp_dir, models_dir):
             d.mkdir(parents=True)
 
@@ -25,15 +35,17 @@ def fake_dirs():
             mock_fp.get_input_directory.return_value = str(input_dir)
             mock_fp.get_output_directory.return_value = str(output_dir)
             mock_fp.get_temp_directory.return_value = str(temp_dir)
+            mock_fp.models_dir = str(models_root)
 
             with patch(
                 "app.assets.services.path_utils.get_comfy_models_folders",
-                return_value=[("checkpoints", [str(models_dir)])],
+                return_value=[("checkpoints", [str(models_dir)], {".safetensors"})],
             ):
                 yield {
                     "input": input_dir,
                     "output": output_dir,
                     "temp": temp_dir,
+                    "models_root": models_root,
                     "models": models_dir,
                 }
 
@@ -76,6 +88,538 @@ class TestGetAssetCategoryAndRelativePath:
         cat, rel = get_asset_category_and_relative_path(str(f))
         assert cat == "models"
 
+    def test_model_path_tags_include_registered_model_type_only(self, fake_dirs):
+        f = fake_dirs["models"] / "subdir" / "model.safetensors"
+        f.parent.mkdir()
+        f.touch()
+
+        _name, tags = get_name_and_tags_from_asset_path(str(f))
+
+        assert "models" in tags
+        assert "model_type:checkpoints" in tags
+        assert "checkpoints" not in tags
+        assert "subdir" not in tags
+
+    def test_model_type_preserves_registered_folder_case(self, fake_dirs):
+        llm_dir = fake_dirs["models"].parent / "LLM"
+        llm_dir.mkdir()
+        f = llm_dir / "model.safetensors"
+        f.touch()
+
+        with patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[("LLM", [str(llm_dir)], {".safetensors"})],
+        ):
+            _name, tags = get_name_and_tags_from_asset_path(str(f))
+
+        assert "models" in tags
+        assert "model_type:LLM" in tags
+        assert "model_type:llm" not in tags
+
+    def test_path_components_do_not_create_model_type_tags(self, fake_dirs):
+        f = fake_dirs["models"] / "loras" / "model.safetensors"
+        f.parent.mkdir()
+        f.touch()
+
+        _name, tags = get_name_and_tags_from_asset_path(str(f))
+
+        assert "models" in tags
+        assert "model_type:checkpoints" in tags
+        assert "loras" not in tags
+        assert "model_type:loras" not in tags
+
+    def test_shared_root_returns_all_matching_model_type_tags(self, fake_dirs):
+        shared_root = fake_dirs["models"].parent / "shared"
+        shared_root.mkdir()
+        f = shared_root / "foo.safetensors"
+        f.touch()
+
+        with patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[
+                ("checkpoints", [str(shared_root)], {".safetensors"}),
+                ("loras", [str(shared_root)], {".safetensors"}),
+            ],
+        ):
+            _name, tags = get_name_and_tags_from_asset_path(str(f))
+
+        assert "models" in tags
+        assert "model_type:checkpoints" in tags
+        assert "model_type:loras" in tags
+
+    def test_shared_root_model_type_tags_respect_bucket_extensions(self, fake_dirs):
+        """Buckets sharing a base dir only tag files matching their extensions."""
+        shared_root = fake_dirs["models"].parent / "unet"
+        shared_root.mkdir()
+        safetensors_file = shared_root / "wan.safetensors"
+        gguf_file = shared_root / "wan.gguf"
+        safetensors_file.touch()
+        gguf_file.touch()
+
+        with patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[
+                ("diffusion_models", [str(shared_root)], {".safetensors"}),
+                ("unet_gguf", [str(shared_root)], {".gguf"}),
+            ],
+        ):
+            _name, safetensors_tags = get_name_and_tags_from_asset_path(str(safetensors_file))
+            _name, gguf_tags = get_name_and_tags_from_asset_path(str(gguf_file))
+
+        assert "model_type:diffusion_models" in safetensors_tags
+        assert "model_type:unet_gguf" not in safetensors_tags
+        assert "model_type:unet_gguf" in gguf_tags
+        assert "model_type:diffusion_models" not in gguf_tags
+
+    def test_empty_extension_set_tags_any_extension(self, fake_dirs):
+        """Custom buckets registered without extensions accept every file."""
+        custom_root = fake_dirs["models"].parent / "custom_bucket"
+        custom_root.mkdir()
+        f = custom_root / "weights.bin"
+        f.touch()
+
+        with patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[("custom_bucket", [str(custom_root)], set())],
+        ):
+            _name, tags = get_name_and_tags_from_asset_path(str(f))
+
+        assert "models" in tags
+        assert "model_type:custom_bucket" in tags
+
+    def test_no_extension_match_keeps_models_tag_without_model_type(self, fake_dirs):
+        f = fake_dirs["models"] / "notes.txt"
+        f.touch()
+
+        _name, tags = get_name_and_tags_from_asset_path(str(f))
+
+        assert "models" in tags
+        assert not any(tag.startswith("model_type:") for tag in tags)
+
+    def test_output_backed_registered_folder_gets_model_and_output_tags(self, fake_dirs):
+        output_checkpoints_dir = fake_dirs["output"] / "checkpoints"
+        output_checkpoints_dir.mkdir()
+        f = output_checkpoints_dir / "saved.safetensors"
+        f.touch()
+
+        with patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[("checkpoints", [str(output_checkpoints_dir)], {".safetensors"})],
+        ):
+            _name, tags = get_name_and_tags_from_asset_path(str(f))
+
+        assert "models" in tags
+        assert "model_type:checkpoints" in tags
+        assert "output" in tags
+
+    def test_temp_path_tags_include_temp_not_output_or_preview(self, fake_dirs):
+        f = fake_dirs["temp"] / "preview.png"
+        f.touch()
+
+        _name, tags = get_name_and_tags_from_asset_path(str(f))
+
+        assert "temp" in tags
+        assert "output" not in tags
+        assert "preview:true" not in tags
+
+    def test_known_subfolder_tags_are_centralized(self):
+        assert get_known_subfolder_tags("pasted") == ["pasted"]
+        assert get_known_subfolder_tags("arbitrary") == []
+
+    def test_known_input_subfolder_tags_are_path_derived_for_direct_children(self, fake_dirs):
+        f = fake_dirs["input"] / "pasted" / "image.png"
+        f.parent.mkdir()
+        f.touch()
+
+        assert get_known_input_subfolder_tags_from_path(str(f)) == ["pasted"]
+
+        _name, tags = get_name_and_tags_from_asset_path(str(f))
+        assert "input" in tags
+        assert "pasted" in tags
+
+    def test_known_input_subfolder_tags_do_not_apply_to_nested_or_other_roots(self, fake_dirs):
+        nested = fake_dirs["input"] / "pasted" / "session" / "image.png"
+        output = fake_dirs["output"] / "pasted" / "image.png"
+        for path in (nested, output):
+            path.parent.mkdir(parents=True)
+            path.touch()
+
+        assert get_known_input_subfolder_tags_from_path(str(nested)) == []
+        assert get_known_input_subfolder_tags_from_path(str(output)) == []
+
     def test_unknown_path_raises(self, fake_dirs):
         with pytest.raises(ValueError, match="not within"):
             get_asset_category_and_relative_path("/some/random/path.png")
+
+
+class TestResponseStoragePaths:
+    def test_input_file_path_and_display_name_include_subfolder(self, fake_dirs):
+        sub = fake_dirs["input"] / "some" / "folder"
+        sub.mkdir(parents=True)
+        f = sub / "image.png"
+        f.touch()
+
+        assert compute_logical_path(str(f)) == "input/some/folder/image.png"
+        assert compute_display_name(str(f)) == "some/folder/image.png"
+
+    def test_output_file_path_and_display_name_include_subfolder(self, fake_dirs):
+        sub = fake_dirs["output"] / "renders"
+        sub.mkdir()
+        f = sub / "ComfyUI_00001_.png"
+        f.touch()
+
+        assert compute_logical_path(str(f)) == "output/renders/ComfyUI_00001_.png"
+        assert compute_display_name(str(f)) == "renders/ComfyUI_00001_.png"
+
+    def test_temp_file_path_and_display_name(self, fake_dirs):
+        f = fake_dirs["temp"] / "preview.png"
+        f.touch()
+
+        assert compute_logical_path(str(f)) == "temp/preview.png"
+        assert compute_display_name(str(f)) == "preview.png"
+
+    def test_exact_storage_root_has_no_display_name(self, fake_dirs):
+        assert compute_logical_path(str(fake_dirs["input"])) == "input"
+        assert compute_display_name(str(fake_dirs["input"])) is None
+
+    def test_longest_matching_builtin_root_wins(self, fake_dirs, tmp_path: Path):
+        nested_output = fake_dirs["input"] / "nested-output"
+        nested_output.mkdir()
+        f = nested_output / "image.png"
+        f.touch()
+
+        with patch("app.assets.services.path_utils.folder_paths") as mock_fp:
+            mock_fp.get_input_directory.return_value = str(fake_dirs["input"])
+            mock_fp.get_output_directory.return_value = str(nested_output)
+            mock_fp.get_temp_directory.return_value = str(tmp_path / "temp")
+            mock_fp.models_dir = str(fake_dirs["models_root"])
+
+            assert compute_logical_path(str(f)) == "output/image.png"
+            assert compute_display_name(str(f)) == "image.png"
+
+    def test_model_file_path_is_relative_to_physical_models_root(self, fake_dirs):
+        sub = fake_dirs["models"] / "flux"
+        sub.mkdir()
+        f = sub / "model.safetensors"
+        f.touch()
+
+        assert compute_logical_path(str(f)) == "models/checkpoints/flux/model.safetensors"
+        assert compute_display_name(str(f)) == "checkpoints/flux/model.safetensors"
+
+        name, tags = get_name_and_tags_from_asset_path(str(f))
+        assert name == "model.safetensors"
+        assert "models" in tags
+        assert "model_type:checkpoints" in tags
+        assert "checkpoints" not in tags
+        assert "flux" not in tags
+
+    @pytest.mark.parametrize(
+        "folder_name",
+        ["checkpoints", "clip", "vae", "diffusion_models", "loras"],
+    )
+    def test_output_model_folder_uses_output_storage_file_path(self, fake_dirs, folder_name):
+        output_model_dir = fake_dirs["output"] / folder_name
+        output_model_dir.mkdir(exist_ok=True)
+        default_model_dir = fake_dirs["models_root"] / folder_name
+        default_model_dir.mkdir(exist_ok=True)
+        f = output_model_dir / "saved.safetensors"
+        f.touch()
+
+        with patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[
+                (folder_name, [str(default_model_dir), str(output_model_dir)], {".safetensors"})
+            ],
+        ):
+            assert compute_logical_path(str(f)) == f"output/{folder_name}/saved.safetensors"
+            assert compute_display_name(str(f)) == f"{folder_name}/saved.safetensors"
+
+            name, tags = get_name_and_tags_from_asset_path(str(f))
+            assert name == "saved.safetensors"
+            assert "output" in tags
+            assert "models" in tags
+            assert f"model_type:{folder_name}" in tags
+            assert folder_name not in tags
+
+    def test_output_model_subfolder_uses_output_storage_file_path(self, fake_dirs):
+        folder_name = "loras"
+        output_model_dir = fake_dirs["output"] / folder_name
+        subdir = output_model_dir / "experiments"
+        subdir.mkdir(parents=True)
+        f = subdir / "my_lora.safetensors"
+        f.touch()
+
+        with patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[(folder_name, [str(output_model_dir)], {".safetensors"})],
+        ):
+            assert (
+                compute_logical_path(str(f))
+                == "output/loras/experiments/my_lora.safetensors"
+            )
+            assert compute_display_name(str(f)) == "loras/experiments/my_lora.safetensors"
+
+            name, tags = get_name_and_tags_from_asset_path(str(f))
+            assert name == "my_lora.safetensors"
+            assert "output" in tags
+            assert "models" in tags
+            assert "model_type:loras" in tags
+            assert "loras" not in tags
+            assert "experiments" not in tags
+
+    def test_external_model_folder_without_provenance_has_no_file_path(self, tmp_path: Path):
+        external_checkpoints_dir = tmp_path / "external" / "not_named_like_category"
+        external_checkpoints_dir.mkdir(parents=True)
+        f = external_checkpoints_dir / "external.safetensors"
+        f.touch()
+
+        with patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[("checkpoints", [str(external_checkpoints_dir)], {".safetensors"})],
+        ):
+            assert compute_logical_path(str(f)) is None
+            assert compute_display_name(str(f)) is None
+
+            name, tags = get_name_and_tags_from_asset_path(str(f))
+            assert name == "external.safetensors"
+            assert "models" in tags
+            assert "model_type:checkpoints" in tags
+
+    def test_same_relative_model_file_under_multiple_external_roots_has_no_storage_file_path(
+        self, tmp_path: Path
+    ):
+        foo_dir = tmp_path / "foo"
+        bar_dir = tmp_path / "bar"
+        foo_dir.mkdir()
+        bar_dir.mkdir()
+        foo_file = foo_dir / "baz.safetensors"
+        bar_file = bar_dir / "baz.safetensors"
+        foo_file.touch()
+        bar_file.touch()
+
+        with patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[("checkpoints", [str(foo_dir), str(bar_dir)], {".safetensors"})],
+        ):
+            assert compute_logical_path(str(foo_file)) is None
+            assert compute_logical_path(str(bar_file)) is None
+            assert compute_display_name(str(foo_file)) is None
+            assert compute_display_name(str(bar_file)) is None
+
+    def test_output_clip_folder_uses_output_storage_and_text_encoder_tag(self, fake_dirs):
+        output_clip_dir = fake_dirs["output"] / "clip"
+        output_clip_dir.mkdir()
+        f = output_clip_dir / "clip_l.safetensors"
+        f.touch()
+
+        with patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[("text_encoders", [str(output_clip_dir)], {".safetensors"})],
+        ):
+            assert compute_logical_path(str(f)) == "output/clip/clip_l.safetensors"
+            assert compute_display_name(str(f)) == "clip/clip_l.safetensors"
+
+            name, tags = get_name_and_tags_from_asset_path(str(f))
+            assert name == "clip_l.safetensors"
+            assert "output" in tags
+            assert "models" in tags
+            assert "model_type:text_encoders" in tags
+            assert "clip" not in tags
+
+    def test_physical_unet_folder_uses_storage_path_and_diffusion_models_tag(self, fake_dirs):
+        unet_dir = fake_dirs["models_root"] / "unet"
+        diffusion_models_dir = fake_dirs["models_root"] / "diffusion_models"
+        unet_dir.mkdir()
+        diffusion_models_dir.mkdir()
+        f = unet_dir / "wan.safetensors"
+        f.touch()
+
+        with patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[
+                ("diffusion_models", [str(unet_dir), str(diffusion_models_dir)], {".safetensors"})
+            ],
+        ):
+            assert compute_logical_path(str(f)) == "models/unet/wan.safetensors"
+            assert compute_display_name(str(f)) == "unet/wan.safetensors"
+
+            name, tags = get_name_and_tags_from_asset_path(str(f))
+            assert name == "wan.safetensors"
+            assert "models" in tags
+            assert "model_type:diffusion_models" in tags
+            assert "unet" not in tags
+
+    def test_unregistered_file_under_physical_models_root_still_has_storage_file_path(self, fake_dirs):
+        f = fake_dirs["models_root"] / "not_registered" / "orphan.bin"
+        f.parent.mkdir()
+        f.touch()
+
+        assert compute_logical_path(str(f)) == "models/not_registered/orphan.bin"
+        assert compute_display_name(str(f)) == "not_registered/orphan.bin"
+
+    def test_output_checkpoint_folder_without_registration_has_only_output_tag(self, fake_dirs):
+        f = fake_dirs["output"] / "checkpoints" / "saved.safetensors"
+        f.parent.mkdir(exist_ok=True)
+        f.touch()
+
+        with patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[],
+        ):
+            assert compute_logical_path(str(f)) == "output/checkpoints/saved.safetensors"
+            assert compute_display_name(str(f)) == "checkpoints/saved.safetensors"
+
+            name, tags = get_name_and_tags_from_asset_path(str(f))
+            assert name == "saved.safetensors"
+            assert "output" in tags
+            assert "models" not in tags
+            assert not any(tag.startswith("model_type:") for tag in tags)
+
+    def test_unknown_path_returns_none(self):
+        assert compute_logical_path("/some/random/path.png") is None
+        assert compute_display_name("/some/random/path.png") is None
+
+
+class TestLoaderPath:
+    """In-root loader path: relative to the storage root, model category dropped."""
+
+    def test_model_loader_path_drops_category(self, fake_dirs):
+        sub = fake_dirs["models"] / "flux"
+        sub.mkdir()
+        f = sub / "model.safetensors"
+        f.touch()
+
+        # logical_path keeps the category, file_path (loader) drops it
+        assert compute_logical_path(str(f)) == "models/checkpoints/flux/model.safetensors"
+        assert compute_loader_path(str(f)) == "flux/model.safetensors"
+
+    def test_model_loader_path_flat_file(self, fake_dirs):
+        f = fake_dirs["models"] / "model.safetensors"
+        f.touch()
+
+        assert compute_loader_path(str(f)) == "model.safetensors"
+
+    def test_input_loader_path_keeps_subfolders(self, fake_dirs):
+        sub = fake_dirs["input"] / "some" / "folder"
+        sub.mkdir(parents=True)
+        f = sub / "image.png"
+        f.touch()
+
+        assert compute_loader_path(str(f)) == "some/folder/image.png"
+
+    def test_temp_loader_path(self, fake_dirs):
+        f = fake_dirs["temp"] / "preview.png"
+        f.touch()
+
+        assert compute_loader_path(str(f)) == "preview.png"
+
+    def test_unregistered_file_under_models_root_has_no_loader_path(self, fake_dirs):
+        # Under models_root but not within any registered category base.
+        f = fake_dirs["models_root"] / "not_registered" / "orphan.bin"
+        f.parent.mkdir()
+        f.touch()
+
+        # It still has a namespaced logical_path, but no loader path.
+        assert compute_logical_path(str(f)) == "models/not_registered/orphan.bin"
+        assert compute_loader_path(str(f)) is None
+
+    def test_extension_mismatch_in_registered_bucket_has_no_loader_path(self, fake_dirs):
+        # Inside a registered bucket, but the bucket's extension set cannot
+        # load it: no model_type tag, and no loader path either.
+        f = fake_dirs["models"] / "notes.txt"
+        f.touch()
+
+        assert compute_logical_path(str(f)) == "models/checkpoints/notes.txt"
+        assert compute_loader_path(str(f)) is None
+
+    def test_shared_base_loader_path_uses_extension_matching_bucket(self, fake_dirs):
+        shared_root = fake_dirs["models"].parent / "unet"
+        shared_root.mkdir()
+        f = shared_root / "wan.gguf"
+        f.touch()
+
+        with patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[
+                ("diffusion_models", [str(shared_root)], {".safetensors"}),
+                ("unet_gguf", [str(shared_root)], {".gguf"}),
+            ],
+        ):
+            assert compute_loader_path(str(f)) == "wan.gguf"
+
+    def test_match_all_bucket_provides_loader_path_for_any_extension(self, fake_dirs):
+        custom_root = fake_dirs["models"].parent / "custom_bucket"
+        custom_root.mkdir()
+        f = custom_root / "weights.bin"
+        f.touch()
+
+        with patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[("custom_bucket", [str(custom_root)], set())],
+        ):
+            assert compute_loader_path(str(f)) == "weights.bin"
+
+    def test_extra_path_model_has_loader_path_but_no_logical_path(self, tmp_path: Path):
+        """Registered category base outside models_dir (extra_model_paths style).
+
+        Loadable, so loader_path resolves; but it is not under any canonical
+        storage root, so logical_path/display_name are None. This asymmetry is
+        intentional: loader_path resolves every registered model-folder base,
+        logical_path only resolves the canonical storage roots.
+        """
+        extra = tmp_path / "extra_ckpts"
+        extra.mkdir()
+        f = extra / "foo.safetensors"
+        f.touch()
+
+        with patch("app.assets.services.path_utils.folder_paths") as mock_fp, patch(
+            "app.assets.services.path_utils.get_comfy_models_folders",
+            return_value=[("checkpoints", [str(extra)], {".safetensors"})],
+        ):
+            mock_fp.get_input_directory.return_value = str(tmp_path / "in")
+            mock_fp.get_output_directory.return_value = str(tmp_path / "out")
+            mock_fp.get_temp_directory.return_value = str(tmp_path / "tmp")
+            mock_fp.models_dir = str(tmp_path / "models")  # extra is NOT under this
+
+            assert compute_loader_path(str(f)) == "foo.safetensors"
+            assert compute_logical_path(str(f)) is None
+            assert compute_display_name(str(f)) is None
+
+    def test_unknown_path_returns_none(self):
+        assert compute_loader_path("/some/random/path.png") is None
+
+
+class TestResolveDestinationFromTags:
+    def test_extra_tags_are_not_path_components(self, fake_dirs):
+        base_dir, subdirs = resolve_destination_from_tags(["input", "unit-tests", "foo"])
+
+        assert base_dir == os.path.abspath(fake_dirs["input"])
+        assert subdirs == []
+
+    def test_model_upload_rejects_non_writable_registered_folders(self):
+        with tempfile.TemporaryDirectory() as root:
+            root_path = Path(root)
+            checkpoints_dir = root_path / "models" / "checkpoints"
+            configs_dir = root_path / "models" / "configs"
+            custom_nodes_dir = root_path / "custom_nodes"
+            for path in (checkpoints_dir, configs_dir, custom_nodes_dir):
+                path.mkdir(parents=True)
+
+            with patch("app.assets.services.path_utils.folder_paths") as mock_fp:
+                mock_fp.folder_names_and_paths = {
+                    "checkpoints": ([str(checkpoints_dir)], set()),
+                    "configs": ([str(configs_dir)], set()),
+                    "custom_nodes": ([str(custom_nodes_dir)], set()),
+                }
+
+                base_dir, subdirs = resolve_destination_from_tags(
+                    ["models", "model_type:checkpoints"]
+                )
+                assert base_dir == os.path.abspath(checkpoints_dir)
+                assert subdirs == []
+
+                for folder_name in ("configs", "custom_nodes"):
+                    with pytest.raises(ValueError, match="unknown model category"):
+                        resolve_destination_from_tags(
+                            ["models", f"model_type:{folder_name}"]
+                        )

tests-unit/assets_test/test_assets_missing_sync.py

@@ -19,7 +19,8 @@ def test_seed_asset_removed_when_file_is_deleted(
     """Asset without hash (seed) whose file disappears:
        after triggering sync_seed_assets, Asset + AssetInfo disappear.
     """
-    # Create a file directly under input/unit-tests/<case> so tags include "unit-tests"
+    # Create a file directly under input/unit-tests/<case>. Backend tags only
+    # classify the root; nested path components are not exposed as tags.
     case_dir = comfy_tmp_base_dir / root / "unit-tests" / "syncseed"
     case_dir.mkdir(parents=True, exist_ok=True)
     name = f"seed_{uuid.uuid4().hex[:8]}.bin"
@@ -32,7 +33,7 @@ def test_seed_asset_removed_when_file_is_deleted(
     # Verify it is visible via API and carries no hash (seed)
     r1 = http.get(
         api_base + "/api/assets",
-        params={"include_tags": "unit-tests,syncseed", "name_contains": name},
+        params={"include_tags": root, "name_contains": name},
         timeout=120,
     )
     body1 = r1.json()
@@ -54,7 +55,7 @@ def test_seed_asset_removed_when_file_is_deleted(
     # It should disappear (AssetInfo and seed Asset gone)
     r2 = http.get(
         api_base + "/api/assets",
-        params={"include_tags": "unit-tests,syncseed", "name_contains": name},
+        params={"include_tags": root, "name_contains": name},
         timeout=120,
     )
     body2 = r2.json()
@@ -132,7 +133,7 @@ def test_hashed_asset_two_asset_infos_both_get_missing(
     second_id = b2["id"]
 
     # Remove the single underlying file
-    p = comfy_tmp_base_dir / "input" / "unit-tests" / "multiinfo" / get_asset_filename(b2["asset_hash"], ".png")
+    p = comfy_tmp_base_dir / "input" / get_asset_filename(created["asset_hash"], ".png")
     assert p.exists()
     p.unlink()
 
@@ -250,8 +251,7 @@ def test_missing_tag_clears_on_fastpass_when_mtime_and_size_match(
 
     a = asset_factory(name, [root, "unit-tests", scope], {}, data)
     aid = a["id"]
-    base = comfy_tmp_base_dir / root / "unit-tests" / scope
-    p = base / get_asset_filename(a["asset_hash"], ".bin")
+    p = comfy_tmp_base_dir / root / get_asset_filename(a["asset_hash"], ".bin")
     st0 = p.stat()
     orig_mtime_ns = getattr(st0, "st_mtime_ns", int(st0.st_mtime * 1_000_000_000))
 

tests-unit/assets_test/test_crud.py

@@ -290,7 +290,7 @@ def test_metadata_filename_is_set_for_seed_asset_without_hash(
 
     r1 = http.get(
         api_base + "/api/assets",
-        params={"include_tags": f"unit-tests,{scope}", "name_contains": name},
+        params={"include_tags": root, "name_contains": name},
         timeout=120,
     )
     body = r1.json()

tests-unit/assets_test/test_downloads.py

@@ -23,7 +23,7 @@ def test_download_svg_forced_to_attachment(http: requests.Session, api_base: str
     svg = b'<svg xmlns="http://www.w3.org/2000/svg"><script>alert(1)</script></svg>'
     files = {"file": ("evil.svg", svg, "image/svg+xml")}
     form_data = {
-        "tags": json.dumps(["models", "checkpoints", "unit-tests", "svgxss"]),
+        "tags": json.dumps(["models", "model_type:checkpoints", "unit-tests", "svgxss"]),
         "name": "evil.svg",
     }
     up = http.post(api_base + "/api/assets", files=files, data=form_data, timeout=120)
@@ -131,7 +131,7 @@ def test_download_chooses_existing_state_and_updates_access_time(
         assert t1 > t0
 
 
-@pytest.mark.parametrize("seeded_asset", [{"tags": ["models", "checkpoints"]}], indirect=True)
+@pytest.mark.parametrize("seeded_asset", [{"tags": ["models", "model_type:checkpoints"]}], indirect=True)
 def test_download_missing_file_returns_404(
     http: requests.Session, api_base: str, comfy_tmp_base_dir: Path, seeded_asset: dict
 ):

tests-unit/assets_test/test_list_cursor.py

@@ -13,7 +13,7 @@ def _seed(asset_factory, make_asset_bytes, count: int, tag: str) -> list[str]:
     for n in names:
         asset_factory(
             n,
-            ["models", "checkpoints", "unit-tests", tag],
+            ["models", "model_type:checkpoints", "unit-tests", tag],
             {},
             make_asset_bytes(n, size=2048),
         )
@@ -208,7 +208,7 @@ def test_cursor_walks_for_non_name_sorts(sort_field, http: requests.Session, api
     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))
+        asset_factory(n, ["models", "model_type:checkpoints", "unit-tests", f"cursor-{sort_field}"], {}, make_asset_bytes(n, size=2048 + i))
         names.append(n)
 
     params = {

tests-unit/assets_test/test_list_filter.py

@@ -11,7 +11,7 @@ def test_list_assets_paging_and_sort(http: requests.Session, api_base: str, asse
     for n in names:
         asset_factory(
             n,
-            ["models", "checkpoints", "unit-tests", "paging"],
+            ["models", "model_type:checkpoints", "unit-tests", "paging"],
             {"epoch": 1},
             make_asset_bytes(n, size=2048),
         )
@@ -45,8 +45,8 @@ def test_list_assets_paging_and_sort(http: requests.Session, api_base: str, asse
 
 
 def test_list_assets_include_exclude_and_name_contains(http: requests.Session, api_base: str, asset_factory):
-    a = asset_factory("inc_a.safetensors", ["models", "checkpoints", "unit-tests", "alpha"], {}, b"X" * 1024)
-    b = asset_factory("inc_b.safetensors", ["models", "checkpoints", "unit-tests", "beta"], {}, b"Y" * 1024)
+    a = asset_factory("inc_a.safetensors", ["models", "model_type:checkpoints", "unit-tests", "alpha"], {}, b"X" * 1024)
+    b = asset_factory("inc_b.safetensors", ["models", "model_type:checkpoints", "unit-tests", "beta"], {}, b"Y" * 1024)
 
     r = http.get(
         api_base + "/api/assets",
@@ -81,7 +81,7 @@ def test_list_assets_include_exclude_and_name_contains(http: requests.Session, a
 
 
 def test_list_assets_sort_by_size_both_orders(http, api_base, asset_factory, make_asset_bytes):
-    t = ["models", "checkpoints", "unit-tests", "lf-size"]
+    t = ["models", "model_type:checkpoints", "unit-tests", "lf-size"]
     n1, n2, n3 = "sz1.safetensors", "sz2.safetensors", "sz3.safetensors"
     asset_factory(n1, t, {}, make_asset_bytes(n1, 1024))
     asset_factory(n2, t, {}, make_asset_bytes(n2, 2048))
@@ -108,7 +108,7 @@ def test_list_assets_sort_by_size_both_orders(http, api_base, asset_factory, mak
 
 
 def test_list_assets_sort_by_updated_at_desc(http, api_base, asset_factory, make_asset_bytes):
-    t = ["models", "checkpoints", "unit-tests", "lf-upd"]
+    t = ["models", "model_type:checkpoints", "unit-tests", "lf-upd"]
     a1 = asset_factory("upd_a.safetensors", t, {}, make_asset_bytes("upd_a", 1200))
     a2 = asset_factory("upd_b.safetensors", t, {}, make_asset_bytes("upd_b", 1200))
 
@@ -131,7 +131,7 @@ def test_list_assets_sort_by_updated_at_desc(http, api_base, asset_factory, make
 
 
 def test_list_assets_sort_by_last_access_time_desc(http, api_base, asset_factory, make_asset_bytes):
-    t = ["models", "checkpoints", "unit-tests", "lf-access"]
+    t = ["models", "model_type:checkpoints", "unit-tests", "lf-access"]
     asset_factory("acc_a.safetensors", t, {}, make_asset_bytes("acc_a", 1100))
     time.sleep(0.02)
     a2 = asset_factory("acc_b.safetensors", t, {}, make_asset_bytes("acc_b", 1100))
@@ -154,14 +154,14 @@ def test_list_assets_sort_by_last_access_time_desc(http, api_base, asset_factory
 
 
 def test_list_assets_include_tags_variants_and_case(http, api_base, asset_factory, make_asset_bytes):
-    t = ["models", "checkpoints", "unit-tests", "lf-include"]
+    t = ["models", "model_type:checkpoints", "unit-tests", "lf-include"]
     a = asset_factory("incvar_alpha.safetensors", [*t, "alpha"], {}, make_asset_bytes("iva"))
     asset_factory("incvar_beta.safetensors", [*t, "beta"], {}, make_asset_bytes("ivb"))
 
-    # CSV + case-insensitive
+    # CSV tag filters are whitespace-trimmed and case-sensitive.
     r1 = http.get(
         api_base + "/api/assets",
-        params={"include_tags": "UNIT-TESTS,LF-INCLUDE,alpha"},
+        params={"include_tags": "unit-tests,lf-include,alpha"},
         timeout=120,
     )
     b1 = r1.json()
@@ -196,14 +196,14 @@ def test_list_assets_include_tags_variants_and_case(http, api_base, asset_factor
 
 
 def test_list_assets_exclude_tags_dedup_and_case(http, api_base, asset_factory, make_asset_bytes):
-    t = ["models", "checkpoints", "unit-tests", "lf-exclude"]
+    t = ["models", "model_type:checkpoints", "unit-tests", "lf-exclude"]
     a = asset_factory("ex_a_alpha.safetensors", [*t, "alpha"], {}, make_asset_bytes("exa", 900))
     asset_factory("ex_b_beta.safetensors", [*t, "beta"], {}, make_asset_bytes("exb", 900))
 
-    # Exclude uppercase should work
+    # Exclude filters are case-sensitive.
     r1 = http.get(
         api_base + "/api/assets",
-        params={"include_tags": "unit-tests,lf-exclude", "exclude_tags": "BETA"},
+        params={"include_tags": "unit-tests,lf-exclude", "exclude_tags": "beta"},
         timeout=120,
     )
     b1 = r1.json()
@@ -225,7 +225,7 @@ def test_list_assets_exclude_tags_dedup_and_case(http, api_base, asset_factory,
 
 
 def test_list_assets_name_contains_case_and_specials(http, api_base, asset_factory, make_asset_bytes):
-    t = ["models", "checkpoints", "unit-tests", "lf-name"]
+    t = ["models", "model_type:checkpoints", "unit-tests", "lf-name"]
     a1 = asset_factory("CaseMix.SAFE", t, {}, make_asset_bytes("cm", 800))
     a2 = asset_factory("case-other.safetensors", t, {}, make_asset_bytes("co", 800))
 
@@ -261,7 +261,7 @@ def test_list_assets_name_contains_case_and_specials(http, api_base, asset_facto
 
 
 def test_list_assets_offset_beyond_total_and_limit_boundary(http, api_base, asset_factory, make_asset_bytes):
-    t = ["models", "checkpoints", "unit-tests", "lf-pagelimits"]
+    t = ["models", "model_type:checkpoints", "unit-tests", "lf-pagelimits"]
     asset_factory("pl1.safetensors", t, {}, make_asset_bytes("pl1", 600))
     asset_factory("pl2.safetensors", t, {}, make_asset_bytes("pl2", 600))
     asset_factory("pl3.safetensors", t, {}, make_asset_bytes("pl3", 600))
@@ -319,7 +319,7 @@ def test_list_assets_name_contains_literal_underscore(
       - foobar.safetensors       (must NOT match)
     """
     scope = f"lf-underscore-{uuid.uuid4().hex[:6]}"
-    tags = ["models", "checkpoints", "unit-tests", scope]
+    tags = ["models", "model_type:checkpoints", "unit-tests", scope]
 
     a = asset_factory("foo_bar.safetensors", tags, {}, make_asset_bytes("a", 700))
     b = asset_factory("fooxbar.safetensors", tags, {}, make_asset_bytes("b", 700))

tests-unit/assets_test/test_metadata_filters.py

@@ -5,7 +5,7 @@ def test_meta_and_across_keys_and_types(
     http, api_base: str, asset_factory, make_asset_bytes
 ):
     name = "mf_and_mix.safetensors"
-    tags = ["models", "checkpoints", "unit-tests", "mf-and"]
+    tags = ["models", "model_type:checkpoints", "unit-tests", "mf-and"]
     meta = {"purpose": "mix", "epoch": 1, "active": True, "score": 1.23}
     asset_factory(name, tags, meta, make_asset_bytes(name, 4096))
 
@@ -41,7 +41,7 @@ def test_meta_and_across_keys_and_types(
 
 def test_meta_type_strictness_int_vs_str_and_bool(http, api_base, asset_factory, make_asset_bytes):
     name = "mf_types.safetensors"
-    tags = ["models", "checkpoints", "unit-tests", "mf-types"]
+    tags = ["models", "model_type:checkpoints", "unit-tests", "mf-types"]
     meta = {"epoch": 1, "active": True}
     asset_factory(name, tags, meta, make_asset_bytes(name))
 
@@ -95,7 +95,7 @@ def test_meta_type_strictness_int_vs_str_and_bool(http, api_base, asset_factory,
 
 def test_meta_any_of_list_of_scalars(http, api_base, asset_factory, make_asset_bytes):
     name = "mf_list_scalars.safetensors"
-    tags = ["models", "checkpoints", "unit-tests", "mf-list"]
+    tags = ["models", "model_type:checkpoints", "unit-tests", "mf-list"]
     meta = {"flags": ["red", "green"]}
     asset_factory(name, tags, meta, make_asset_bytes(name, 3000))
 
@@ -134,7 +134,7 @@ def test_meta_none_semantics_missing_or_null_and_any_of_with_none(
     http, api_base, asset_factory, make_asset_bytes
 ):
     # a1: key missing; a2: explicit null; a3: concrete value
-    t = ["models", "checkpoints", "unit-tests", "mf-none"]
+    t = ["models", "model_type:checkpoints", "unit-tests", "mf-none"]
     a1 = asset_factory("mf_none_missing.safetensors", t, {"x": 1}, make_asset_bytes("a1"))
     a2 = asset_factory("mf_none_null.safetensors", t, {"maybe": None}, make_asset_bytes("a2"))
     a3 = asset_factory("mf_none_value.safetensors", t, {"maybe": "x"}, make_asset_bytes("a3"))
@@ -166,7 +166,7 @@ def test_meta_none_semantics_missing_or_null_and_any_of_with_none(
 
 def test_meta_nested_json_object_equality(http, api_base, asset_factory, make_asset_bytes):
     name = "mf_nested_json.safetensors"
-    tags = ["models", "checkpoints", "unit-tests", "mf-nested"]
+    tags = ["models", "model_type:checkpoints", "unit-tests", "mf-nested"]
     cfg = {"optimizer": "adam", "lr": 0.001, "schedule": {"type": "cosine", "warmup": 100}}
     asset_factory(name, tags, {"config": cfg}, make_asset_bytes(name, 2200))
 
@@ -197,7 +197,7 @@ def test_meta_nested_json_object_equality(http, api_base, asset_factory, make_as
 
 def test_meta_list_of_objects_any_of(http, api_base, asset_factory, make_asset_bytes):
     name = "mf_list_objects.safetensors"
-    tags = ["models", "checkpoints", "unit-tests", "mf-objlist"]
+    tags = ["models", "model_type:checkpoints", "unit-tests", "mf-objlist"]
     transforms = [{"type": "crop", "size": 128}, {"type": "flip", "p": 0.5}]
     asset_factory(name, tags, {"transforms": transforms}, make_asset_bytes(name, 2048))
 
@@ -228,7 +228,7 @@ def test_meta_list_of_objects_any_of(http, api_base, asset_factory, make_asset_b
 
 def test_meta_with_special_and_unicode_keys(http, api_base, asset_factory, make_asset_bytes):
     name = "mf_keys_unicode.safetensors"
-    tags = ["models", "checkpoints", "unit-tests", "mf-keys"]
+    tags = ["models", "model_type:checkpoints", "unit-tests", "mf-keys"]
     meta = {
         "weird.key": "v1",
         "path/like": 7,
@@ -259,7 +259,7 @@ def test_meta_with_special_and_unicode_keys(http, api_base, asset_factory, make_
 
 
 def test_meta_with_zero_and_boolean_lists(http, api_base, asset_factory, make_asset_bytes):
-    t = ["models", "checkpoints", "unit-tests", "mf-zero-bool"]
+    t = ["models", "model_type:checkpoints", "unit-tests", "mf-zero-bool"]
     a0 = asset_factory("mf_zero_count.safetensors", t, {"count": 0}, make_asset_bytes("z", 1025))
     a1 = asset_factory("mf_bool_list.safetensors", t, {"choices": [True, False]}, make_asset_bytes("b", 1026))
 
@@ -286,7 +286,7 @@ def test_meta_with_zero_and_boolean_lists(http, api_base, asset_factory, make_as
 
 def test_meta_mixed_list_types_and_strictness(http, api_base, asset_factory, make_asset_bytes):
     name = "mf_mixed_list.safetensors"
-    tags = ["models", "checkpoints", "unit-tests", "mf-mixed"]
+    tags = ["models", "model_type:checkpoints", "unit-tests", "mf-mixed"]
     meta = {"mix": ["1", 1, True, None]}
     asset_factory(name, tags, meta, make_asset_bytes(name, 1999))
 
@@ -311,7 +311,7 @@ def test_meta_mixed_list_types_and_strictness(http, api_base, asset_factory, mak
 
 def test_meta_unknown_key_and_none_behavior_with_scope_tags(http, api_base, asset_factory, make_asset_bytes):
     # Use a unique scope tag to avoid interference
-    t = ["models", "checkpoints", "unit-tests", "mf-unknown-scope"]
+    t = ["models", "model_type:checkpoints", "unit-tests", "mf-unknown-scope"]
     x = asset_factory("mf_unknown_a.safetensors", t, {"k1": 1}, make_asset_bytes("ua"))
     y = asset_factory("mf_unknown_b.safetensors", t, {"k2": 2}, make_asset_bytes("ub"))
 
@@ -340,13 +340,13 @@ def test_meta_with_tags_include_exclude_and_name_contains(http, api_base, asset_
     # alpha matches epoch=1; beta has epoch=2
     a = asset_factory(
         "mf_tag_alpha.safetensors",
-        ["models", "checkpoints", "unit-tests", "mf-tag", "alpha"],
+        ["models", "model_type:checkpoints", "unit-tests", "mf-tag", "alpha"],
         {"epoch": 1},
         make_asset_bytes("alpha"),
     )
     b = asset_factory(
         "mf_tag_beta.safetensors",
-        ["models", "checkpoints", "unit-tests", "mf-tag", "beta"],
+        ["models", "model_type:checkpoints", "unit-tests", "mf-tag", "beta"],
         {"epoch": 2},
         make_asset_bytes("beta"),
     )
@@ -367,7 +367,7 @@ def test_meta_with_tags_include_exclude_and_name_contains(http, api_base, asset_
 
 def test_meta_sort_and_paging_under_filter(http, api_base, asset_factory, make_asset_bytes):
     # Three assets in same scope with different sizes and a common filter key
-    t = ["models", "checkpoints", "unit-tests", "mf-sort"]
+    t = ["models", "model_type:checkpoints", "unit-tests", "mf-sort"]
     n1, n2, n3 = "mf_sort_1.safetensors", "mf_sort_2.safetensors", "mf_sort_3.safetensors"
     asset_factory(n1, t, {"group": "g"}, make_asset_bytes(n1, 1024))
     asset_factory(n2, t, {"group": "g"}, make_asset_bytes(n2, 2048))

tests-unit/assets_test/test_prune_orphaned_assets.py

@@ -29,7 +29,7 @@ def create_seed_file(comfy_tmp_base_dir: Path):
 def find_asset(http: requests.Session, api_base: str):
     """Query API for assets matching scope and optional name."""
     def _find(scope: str, name: str | None = None) -> list[dict]:
-        params = {"include_tags": f"unit-tests,{scope}"}
+        params = {"limit": "500"}
         if name:
             params["name_contains"] = name
         r = http.get(f"{api_base}/api/assets", params=params, timeout=120)
@@ -91,7 +91,7 @@ def test_hashed_asset_not_pruned_when_file_missing(
     data = make_asset_bytes("test", 2048)
     a = asset_factory("test.bin", ["input", "unit-tests", scope], {}, data)
 
-    path = comfy_tmp_base_dir / "input" / "unit-tests" / scope / get_asset_filename(a["asset_hash"], ".bin")
+    path = comfy_tmp_base_dir / "input" / get_asset_filename(a["asset_hash"], ".bin")
     path.unlink()
 
     trigger_sync_seed_assets(http, api_base)
@@ -108,18 +108,20 @@ def test_prune_across_multiple_roots(
 ):
     """Prune correctly handles assets across input and output roots."""
     scope = f"multi-{uuid.uuid4().hex[:6]}"
-    input_fp = create_seed_file("input", scope, "input.bin")
-    create_seed_file("output", scope, "output.bin")
+    input_name = f"{scope}-input.bin"
+    output_name = f"{scope}-output.bin"
+    input_fp = create_seed_file("input", scope, input_name)
+    create_seed_file("output", scope, output_name)
 
     trigger_sync_seed_assets(http, api_base)
-    assert len(find_asset(scope)) == 2
+    assert find_asset(scope, input_name)
+    assert find_asset(scope, output_name)
 
     input_fp.unlink()
     trigger_sync_seed_assets(http, api_base)
 
-    remaining = find_asset(scope)
-    assert len(remaining) == 1
-    assert remaining[0]["name"] == "output.bin"
+    assert not find_asset(scope, input_name)
+    assert find_asset(scope, output_name)
 
 
 @pytest.mark.parametrize("dirname", ["100%_done", "my_folder_name", "has spaces"])

tests-unit/assets_test/test_tags_api.py

@@ -10,9 +10,9 @@ def test_tags_present(http: requests.Session, api_base: str, seeded_asset: dict)
     body1 = r1.json()
     assert r1.status_code == 200
     names = [t["name"] for t in body1["tags"]]
-    # A few system tags from migration should exist:
+    # A few selected contract tags should exist.
     assert "models" in names
-    assert "checkpoints" in names
+    assert "model_type:checkpoints" in names
 
     # Only used tags before we add anything new from this test cycle
     r2 = http.get(api_base + "/api/tags", params={"include_zero": "false"}, timeout=120)
@@ -21,7 +21,7 @@ def test_tags_present(http: requests.Session, api_base: str, seeded_asset: dict)
     # We already seeded one asset via fixture, so used tags must be non-empty
     used_names = [t["name"] for t in body2["tags"]]
     assert "models" in used_names
-    assert "checkpoints" in used_names
+    assert "model_type:checkpoints" in used_names
 
     # Prefix filter should refine the list
     r3 = http.get(api_base + "/api/tags", params={"include_zero": "false", "prefix": "uni"}, timeout=120)
@@ -45,7 +45,7 @@ def test_tags_empty_usage(http: requests.Session, api_base: str, asset_factory,
     body1 = r1.json()
     assert r1.status_code == 200
     names = [t["name"] for t in body1["tags"]]
-    assert "models" in names and "checkpoints" in names
+    assert "models" in names and "model_type:checkpoints" in names
 
     # Create a short-lived asset under input with a unique custom tag
     scope = f"tags-empty-usage-{uuid.uuid4().hex[:6]}"
@@ -89,28 +89,28 @@ def test_tags_empty_usage(http: requests.Session, api_base: str, asset_factory,
 def test_add_and_remove_tags(http: requests.Session, api_base: str, seeded_asset: dict):
     aid = seeded_asset["id"]
 
-    # Add tags with duplicates and mixed case
-    payload_add = {"tags": ["NewTag", "unit-tests", "newtag", "BETA"]}
+    # Add tags with duplicates while preserving source case.
+    payload_add = {"tags": ["NewTag", "unit-tests", "NewTag", "BETA"]}
     r1 = http.post(f"{api_base}/api/assets/{aid}/tags", json=payload_add, timeout=120)
     b1 = r1.json()
     assert r1.status_code == 200, b1
-    # normalized, deduplicated; 'unit-tests' was already present from the seed
-    assert set(b1["added"]) == {"newtag", "beta"}
+    # stripped, deduplicated; 'unit-tests' was already present from the seed
+    assert set(b1["added"]) == {"NewTag", "BETA"}
     assert set(b1["already_present"]) == {"unit-tests"}
-    assert "newtag" in b1["total_tags"] and "beta" in b1["total_tags"]
+    assert "NewTag" in b1["total_tags"] and "BETA" in b1["total_tags"]
 
     rg = http.get(f"{api_base}/api/assets/{aid}", timeout=120)
     g = rg.json()
     assert rg.status_code == 200
     tags_now = set(g["tags"])
-    assert {"newtag", "beta"}.issubset(tags_now)
+    assert {"NewTag", "BETA"}.issubset(tags_now)
 
     # Remove a tag and a non-existent tag
-    payload_del = {"tags": ["newtag", "does-not-exist"]}
+    payload_del = {"tags": ["NewTag", "does-not-exist"]}
     r2 = http.delete(f"{api_base}/api/assets/{aid}/tags", json=payload_del, timeout=120)
     b2 = r2.json()
     assert r2.status_code == 200
-    assert set(b2["removed"]) == {"newtag"}
+    assert set(b2["removed"]) == {"NewTag"}
     assert set(b2["not_present"]) == {"does-not-exist"}
 
     # Verify remaining tags after deletion
@@ -118,8 +118,44 @@ def test_add_and_remove_tags(http: requests.Session, api_base: str, seeded_asset
     g2 = rg2.json()
     assert rg2.status_code == 200
     tags_later = set(g2["tags"])
-    assert "newtag" not in tags_later
-    assert "beta" in tags_later  # still present
+    assert "NewTag" not in tags_later
+    assert "BETA" in tags_later  # still present
+
+
+def test_add_system_looking_tags_allowed_as_labels(
+    http: requests.Session, api_base: str, seeded_asset: dict
+):
+    aid = seeded_asset["id"]
+
+    response = http.post(
+        f"{api_base}/api/assets/{aid}/tags",
+        json={
+            "tags": [
+                "models",
+                "model_type:manual",
+                "model:true",
+                "models:foo",
+                "input:true",
+                "output:true",
+                "uploaded:true",
+                "temp:true",
+                "temporary",
+            ]
+        },
+        timeout=120,
+    )
+    body = response.json()
+
+    assert response.status_code == 200, body
+    assert "models" in body["total_tags"]
+    assert "model_type:manual" in body["total_tags"]
+    assert "model:true" in body["total_tags"]
+    assert "models:foo" in body["total_tags"]
+    assert "input:true" in body["total_tags"]
+    assert "output:true" in body["total_tags"]
+    assert "uploaded:true" in body["total_tags"]
+    assert "temp:true" in body["total_tags"]
+    assert "temporary" in body["total_tags"]
 
 
 def test_tags_list_order_and_prefix(http: requests.Session, api_base: str, seeded_asset: dict):

tests-unit/assets_test/test_uploads.py

@@ -1,11 +1,14 @@
 import json
 import uuid
 from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
 
 import requests
 import pytest
 
+from app.assets.api.schemas_in import UploadAssetSpec
 from app.assets.api.schemas_out import Asset, AssetCreated
+from helpers import get_asset_filename
 
 
 def test_asset_created_inherits_hash_field():
@@ -20,9 +23,18 @@ def test_asset_created_inherits_hash_field():
     assert AssetCreated.model_fields["hash"].annotation == Asset.model_fields["hash"].annotation
 
 
+def test_upload_asset_spec_ignores_subfolder_field():
+    spec = UploadAssetSpec.model_validate(
+        {"tags": ["input"], "subfolder": "pasted", "name": "image.png"}
+    )
+
+    assert "subfolder" not in UploadAssetSpec.model_fields
+    assert not hasattr(spec, "subfolder")
+
+
 def test_upload_ok_duplicate_reference(http: requests.Session, api_base: str, make_asset_bytes):
     name = "dup_a.safetensors"
-    tags = ["models", "checkpoints", "unit-tests", "alpha"]
+    tags = ["models", "model_type:checkpoints", "unit-tests", "alpha"]
     meta = {"purpose": "dup"}
     data = make_asset_bytes(name)
     files = {"file": (name, data, "application/octet-stream")}
@@ -43,6 +55,8 @@ def test_upload_ok_duplicate_reference(http: requests.Session, api_base: str, ma
     assert a2["asset_hash"] == a1["asset_hash"]
     assert a2["hash"] == a1["hash"]
     assert a2["id"] != a1["id"]  # new reference with same content
+    assert a2.get("loader_path") is None
+    assert a2.get("display_name") is None
 
     # Third upload with the same data but different name also creates new AssetReference
     files = {"file": (name, data, "application/octet-stream")}
@@ -53,12 +67,14 @@ def test_upload_ok_duplicate_reference(http: requests.Session, api_base: str, ma
     assert a3["asset_hash"] == a1["asset_hash"]
     assert a3["id"] != a1["id"]
     assert a3["id"] != a2["id"]
+    assert a3.get("loader_path") is None
+    assert a3.get("display_name") is None
 
 
 def test_upload_fastpath_from_existing_hash_no_file(http: requests.Session, api_base: str):
     # Seed a small file first
     name = "fastpath_seed.safetensors"
-    tags = ["models", "checkpoints", "unit-tests"]
+    tags = ["input", "unit-tests"]
     meta = {}
     files = {"file": (name, b"B" * 1024, "application/octet-stream")}
     form = {"tags": json.dumps(tags), "name": name, "user_metadata": json.dumps(meta)}
@@ -69,9 +85,10 @@ def test_upload_fastpath_from_existing_hash_no_file(http: requests.Session, api_
     assert b1["hash"] == h
 
     # Now POST /api/assets with only hash and no file
+    hash_only_tags = ["models", "checkpoints", "unit-tests", "hash-labels"]
     files = [
         ("hash", (None, h)),
-        ("tags", (None, json.dumps(tags))),
+        ("tags", (None, json.dumps(hash_only_tags))),
         ("name", (None, "fastpath_copy.safetensors")),
         ("user_metadata", (None, json.dumps({"purpose": "copy"}))),
     ]
@@ -81,6 +98,53 @@ def test_upload_fastpath_from_existing_hash_no_file(http: requests.Session, api_
     assert b2["created_new"] is False
     assert b2["asset_hash"] == h
     assert b2["hash"] == h
+    assert "models" in b2["tags"]
+    assert "checkpoints" in b2["tags"]
+    assert "uploaded" not in b2["tags"]
+    assert not any(tag.startswith("model_type:") for tag in b2["tags"])
+    assert b2.get("loader_path") is None
+    assert b2.get("display_name") is None
+
+    rg = http.get(f"{api_base}/api/assets/{b2['id']}", timeout=120)
+    detail = rg.json()
+    assert rg.status_code == 200, detail
+    assert detail.get("loader_path") is None
+    assert detail.get("display_name") is None
+
+
+def test_create_from_hash_with_model_tags_does_not_synthesize_loader_path(
+    http: requests.Session, api_base: str
+):
+    seed_name = "from_hash_seed.safetensors"
+    seed_tags = ["models", "model_type:checkpoints", "unit-tests"]
+    files = {"file": (seed_name, b"D" * 1024, "application/octet-stream")}
+    form = {
+        "tags": json.dumps(seed_tags),
+        "name": seed_name,
+        "user_metadata": json.dumps({}),
+    }
+    seed_r = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
+    seed = seed_r.json()
+    assert seed_r.status_code == 201, seed
+
+    payload = {
+        "hash": seed["asset_hash"],
+        "name": "from_hash_copy.safetensors",
+        "tags": ["models", "model_type:checkpoints", "unit-tests", "spoofed"],
+    }
+    created_r = http.post(api_base + "/api/assets/from-hash", json=payload, timeout=120)
+    created = created_r.json()
+    assert created_r.status_code == 201, created
+    assert created["created_new"] is False
+    assert created["asset_hash"] == seed["asset_hash"]
+    assert created.get("loader_path") is None
+    assert created.get("display_name") is None
+
+    detail_r = http.get(f"{api_base}/api/assets/{created['id']}", timeout=120)
+    detail = detail_r.json()
+    assert detail_r.status_code == 200, detail
+    assert detail.get("loader_path") is None
+    assert detail.get("display_name") is None
 
 
 def test_upload_fastpath_with_known_hash_and_file(
@@ -88,7 +152,7 @@ def test_upload_fastpath_with_known_hash_and_file(
 ):
     # Seed
     files = {"file": ("seed.safetensors", b"C" * 128, "application/octet-stream")}
-    form = {"tags": json.dumps(["models", "checkpoints", "unit-tests", "fp"]), "name": "seed.safetensors", "user_metadata": json.dumps({})}
+    form = {"tags": json.dumps(["models", "model_type:checkpoints", "unit-tests", "fp"]), "name": "seed.safetensors", "user_metadata": json.dumps({})}
     r1 = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
     b1 = r1.json()
     assert r1.status_code == 201, b1
@@ -104,11 +168,49 @@ def test_upload_fastpath_with_known_hash_and_file(
     assert b2["created_new"] is False
     assert b2["asset_hash"] == h
     assert b2["hash"] == h
+    assert "checkpoints" in b2["tags"]
+    assert "uploaded" not in b2["tags"]
+    assert not any(tag == "model_type:checkpoints" for tag in b2["tags"])
+
+
+def test_duplicate_byte_upload_is_reference_only_and_does_not_need_destination(
+    http: requests.Session, api_base: str
+):
+    data = b"duplicate-reference-only" * 64
+    seed_files = {"file": ("duplicate-seed.bin", data, "application/octet-stream")}
+    seed_form = {
+        "tags": json.dumps(["input", "unit-tests", "duplicate-seed"]),
+        "name": "duplicate-seed.bin",
+        "user_metadata": json.dumps({}),
+    }
+    seed_response = http.post(api_base + "/api/assets", data=seed_form, files=seed_files, timeout=120)
+    seed = seed_response.json()
+    assert seed_response.status_code == 201, seed
+
+    duplicate_files = {"file": ("duplicate-copy.bin", data, "application/octet-stream")}
+    duplicate_form = {
+        "tags": json.dumps(["not-a-destination", "unit-tests", "duplicate-copy"]),
+        "name": "duplicate-copy.bin",
+        "user_metadata": json.dumps({}),
+    }
+    duplicate_response = http.post(
+        api_base + "/api/assets", data=duplicate_form, files=duplicate_files, timeout=120
+    )
+    duplicate = duplicate_response.json()
+
+    assert duplicate_response.status_code == 200, duplicate
+    assert duplicate["created_new"] is False
+    assert duplicate["asset_hash"] == seed["asset_hash"]
+    assert "not-a-destination" in duplicate["tags"]
+    assert "uploaded" not in duplicate["tags"]
+    assert "input" not in duplicate["tags"]
+    assert duplicate.get("loader_path") is None
+    assert duplicate.get("display_name") is None
 
 
 def test_upload_multiple_tags_fields_are_merged(http: requests.Session, api_base: str):
     data = [
-        ("tags", "models,checkpoints"),
+        ("tags", "models,model_type:checkpoints"),
         ("tags", json.dumps(["unit-tests", "alpha"])),
         ("name", "merge.safetensors"),
         ("user_metadata", json.dumps({"u": 1})),
@@ -124,7 +226,71 @@ def test_upload_multiple_tags_fields_are_merged(http: requests.Session, api_base
     detail = rg.json()
     assert rg.status_code == 200, detail
     tags = set(detail["tags"])
-    assert {"models", "checkpoints", "unit-tests", "alpha"}.issubset(tags)
+    assert {"models", "model_type:checkpoints", "unit-tests", "alpha"}.issubset(tags)
+
+
+@pytest.mark.parametrize(
+    (
+        "tags",
+        "extension",
+        "expected_display_prefix",
+    ),
+    [
+        (["input", "unit-tests"], ".png", ""),
+        (
+            ["models", "model_type:checkpoints", "unit-tests"],
+            ".safetensors",
+            "checkpoints/",
+        ),
+    ],
+)
+def test_upload_response_includes_loader_path_and_display_name(
+    tags: list[str],
+    extension: str,
+    expected_display_prefix: str,
+    http: requests.Session,
+    api_base: str,
+    make_asset_bytes,
+):
+    scope = f"response-paths-{uuid.uuid4().hex[:6]}"
+    scoped_tags = [*tags, scope]
+    name = f"asset_response_path{extension}"
+
+    files = {"file": (name, make_asset_bytes(name, 1024), "application/octet-stream")}
+    form = {
+        "tags": json.dumps(scoped_tags),
+        "name": name,
+        "user_metadata": json.dumps({}),
+    }
+    created_r = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
+    created = created_r.json()
+    assert created_r.status_code in (200, 201), created
+    stored_filename = get_asset_filename(created["asset_hash"], extension)
+    expected_suffix = stored_filename
+    expected_display_name = f"{expected_display_prefix}{expected_suffix}"
+    # In-root loader path: model category dropped, no subfolders here -> just the filename.
+    expected_loader_path = expected_suffix
+
+    assert created["loader_path"] == expected_loader_path
+    assert created["display_name"] == expected_display_name
+    assert "logical_path" not in created
+
+    detail_r = http.get(f"{api_base}/api/assets/{created['id']}", timeout=120)
+    detail = detail_r.json()
+    assert detail_r.status_code == 200, detail
+    assert detail["loader_path"] == expected_loader_path
+    assert detail["display_name"] == expected_display_name
+
+    list_r = http.get(
+        api_base + "/api/assets",
+        params={"include_tags": f"unit-tests,{scope}", "limit": "50"},
+        timeout=120,
+    )
+    listed = list_r.json()
+    assert list_r.status_code == 200, listed
+    match = next(a for a in listed["assets"] if a["id"] == created["id"])
+    assert match["loader_path"] == expected_loader_path
+    assert match["display_name"] == expected_display_name
 
 
 @pytest.mark.parametrize("root", ["input", "output"])
@@ -192,16 +358,55 @@ def test_create_from_hash_endpoint_404(http: requests.Session, api_base: str):
     assert body["error"]["code"] == "ASSET_NOT_FOUND"
 
 
+def test_create_from_hash_accepts_arbitrary_system_looking_tags(
+    http: requests.Session, api_base: str
+):
+    files = {"file": ("hash-seed.bin", b"hash-seed" * 64, "application/octet-stream")}
+    form = {
+        "tags": json.dumps(["input", "unit-tests", "hash-seed"]),
+        "name": "hash-seed.bin",
+        "user_metadata": json.dumps({}),
+    }
+    seed_response = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
+    seed = seed_response.json()
+    assert seed_response.status_code == 201, seed
+
+    response = http.post(
+        api_base + "/api/assets/from-hash",
+        json={
+            "hash": seed["asset_hash"],
+            "name": "hash-copy.bin",
+            "tags": [
+                "models",
+                "model:true",
+                "models:foo",
+                "temporary:true",
+                "unit-tests",
+                "hash-copy",
+            ],
+        },
+        timeout=120,
+    )
+    body = response.json()
+
+    assert response.status_code == 201, body
+    assert "models" in body["tags"]
+    assert "model:true" in body["tags"]
+    assert "models:foo" in body["tags"]
+    assert "temporary:true" in body["tags"]
+    assert "uploaded" not in body["tags"]
+
+
 def test_upload_zero_byte_rejected(http: requests.Session, api_base: str):
     files = {"file": ("empty.safetensors", b"", "application/octet-stream")}
-    form = {"tags": json.dumps(["models", "checkpoints", "unit-tests", "edge"]), "name": "empty.safetensors", "user_metadata": json.dumps({})}
+    form = {"tags": json.dumps(["models", "model_type:checkpoints", "unit-tests", "edge"]), "name": "empty.safetensors", "user_metadata": json.dumps({})}
     r = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
     body = r.json()
     assert r.status_code == 400
     assert body["error"]["code"] == "EMPTY_UPLOAD"
 
 
-def test_upload_invalid_root_tag_rejected(http: requests.Session, api_base: str):
+def test_upload_rejects_arbitrary_labels_without_required_destination_role(http: requests.Session, api_base: str):
     files = {"file": ("badroot.bin", b"A" * 64, "application/octet-stream")}
     form = {"tags": json.dumps(["not-a-root", "whatever"]), "name": "badroot.bin", "user_metadata": json.dumps({})}
     r = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
@@ -212,7 +417,7 @@ def test_upload_invalid_root_tag_rejected(http: requests.Session, api_base: str)
 
 def test_upload_user_metadata_must_be_json(http: requests.Session, api_base: str):
     files = {"file": ("badmeta.bin", b"A" * 128, "application/octet-stream")}
-    form = {"tags": json.dumps(["models", "checkpoints", "unit-tests", "edge"]), "name": "badmeta.bin", "user_metadata": "{not json}"}
+    form = {"tags": json.dumps(["models", "model_type:checkpoints", "unit-tests", "edge"]), "name": "badmeta.bin", "user_metadata": "{not json}"}
     r = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
     body = r.json()
     assert r.status_code == 400
@@ -228,7 +433,7 @@ def test_upload_requires_multipart(http: requests.Session, api_base: str):
 
 def test_upload_missing_file_and_hash(http: requests.Session, api_base: str):
     files = [
-        ("tags", (None, json.dumps(["models", "checkpoints", "unit-tests"]))),
+        ("tags", (None, json.dumps(["models", "model_type:checkpoints", "unit-tests"]))),
         ("name", (None, "x.safetensors")),
     ]
     r = http.post(api_base + "/api/assets", files=files, timeout=120)
@@ -237,17 +442,33 @@ def test_upload_missing_file_and_hash(http: requests.Session, api_base: str):
     assert body["error"]["code"] == "MISSING_FILE"
 
 
-def test_upload_models_unknown_category(http: requests.Session, api_base: str):
+def test_upload_models_unknown_model_type(http: requests.Session, api_base: str):
     files = {"file": ("m.safetensors", b"A" * 128, "application/octet-stream")}
-    form = {"tags": json.dumps(["models", "no_such_category", "unit-tests"]), "name": "m.safetensors"}
+    form = {"tags": json.dumps(["models", "model_type:no_such_category", "unit-tests"]), "name": "m.safetensors"}
     r = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
     body = r.json()
-    assert r.status_code == 400
+    assert r.status_code == 400, body
     assert body["error"]["code"] == "INVALID_BODY"
-    assert body["error"]["message"].startswith("unknown models category")
 
 
-def test_upload_models_requires_category(http: requests.Session, api_base: str):
+@pytest.mark.parametrize("model_type", ["configs", "custom_nodes"])
+def test_upload_models_rejects_non_model_registered_folder(
+    model_type: str, http: requests.Session, api_base: str
+):
+    files = {"file": ("not-a-model.py", b"A" * 128, "application/octet-stream")}
+    form = {
+        "tags": json.dumps(["models", f"model_type:{model_type}", "unit-tests"]),
+        "name": "not-a-model.py",
+    }
+
+    response = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
+    body = response.json()
+
+    assert response.status_code == 400, body
+    assert body["error"]["code"] == "INVALID_BODY"
+
+
+def test_upload_models_requires_model_type(http: requests.Session, api_base: str):
     files = {"file": ("nocat.safetensors", b"A" * 64, "application/octet-stream")}
     form = {"tags": json.dumps(["models"]), "name": "nocat.safetensors", "user_metadata": json.dumps({})}
     r = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
@@ -256,13 +477,152 @@ def test_upload_models_requires_category(http: requests.Session, api_base: str):
     assert body["error"]["code"] == "INVALID_BODY"
 
 
-def test_upload_tags_traversal_guard(http: requests.Session, api_base: str):
+def test_upload_extra_tags_are_labels_not_path_components(http: requests.Session, api_base: str):
     files = {"file": ("evil.safetensors", b"A" * 256, "application/octet-stream")}
-    form = {"tags": json.dumps(["models", "checkpoints", "unit-tests", "..", "zzz"]), "name": "evil.safetensors"}
+    form = {"tags": json.dumps(["models", "model_type:checkpoints", "unit-tests", "..", "zzz"]), "name": "evil.safetensors"}
     r = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
     body = r.json()
-    assert r.status_code == 400
-    assert body["error"]["code"] in ("BAD_REQUEST", "INVALID_BODY")
+    assert r.status_code == 201, body
+    assert ".." in body["tags"]
+    assert "zzz" in body["tags"]
+    assert "models" in body["tags"]
+    assert "model_type:checkpoints" in body["tags"]
+
+
+@pytest.mark.parametrize(
+    ("subfolder", "expected_tag", "unexpected_tags"),
+    [
+        ("custom/session", None, {"custom", "session"}),
+        ("pasted", "pasted", set()),
+    ],
+)
+def test_upload_image_accepts_arbitrary_subfolder_but_only_known_values_become_tags(
+    http: requests.Session,
+    api_base: str,
+    comfy_tmp_base_dir: Path,
+    subfolder: str,
+    expected_tag: str | None,
+    unexpected_tags: set[str],
+):
+    name = f"upload-image-{uuid.uuid4().hex}.png"
+    files = {"image": (name, b"image-upload" * 64, "image/png")}
+    form = {"type": "input", "subfolder": subfolder}
+
+    response = http.post(api_base + "/upload/image", data=form, files=files, timeout=120)
+    body = response.json()
+
+    assert response.status_code == 200, body
+    assert body["subfolder"] == subfolder
+    assert (comfy_tmp_base_dir / "input" / subfolder / body["name"]).exists()
+
+    asset = body["asset"]
+    tags = set(asset["tags"])
+    assert "input" in tags
+    assert "uploaded" in tags
+    if expected_tag:
+        assert expected_tag in tags
+    assert tags.isdisjoint(unexpected_tags)
+
+
+def test_multipart_upload_accepts_system_looking_extra_labels(
+    http: requests.Session, api_base: str
+):
+    files = {"file": ("relaxed-labels.bin", b"relaxed" * 64, "application/octet-stream")}
+    form = {
+        "tags": json.dumps(
+            [
+                "input",
+                "unit-tests",
+                "model:true",
+                "models:foo",
+                "temporary",
+                "uploaded:true",
+            ]
+        ),
+        "name": "relaxed-labels.bin",
+        "user_metadata": json.dumps({}),
+    }
+    response = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
+    body = response.json()
+
+    assert response.status_code == 201, body
+    assert "input" in body["tags"]
+    assert "model:true" in body["tags"]
+    assert "models:foo" in body["tags"]
+    assert "temporary" in body["tags"]
+    assert "uploaded:true" in body["tags"]
+
+
+def test_multipart_upload_rejects_ambiguous_destination_roles(
+    http: requests.Session, api_base: str
+):
+    files = {"file": ("ambiguous.bin", b"ambiguous" * 64, "application/octet-stream")}
+    form = {
+        "tags": json.dumps(["input", "output", "unit-tests"]),
+        "name": "ambiguous.bin",
+        "user_metadata": json.dumps({}),
+    }
+    response = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
+    body = response.json()
+
+    assert response.status_code == 400, body
+    assert body["error"]["code"] == "INVALID_BODY"
+
+
+def test_multipart_upload_rejects_multiple_model_types_for_models_destination(
+    http: requests.Session, api_base: str
+):
+    files = {"file": ("ambiguous-model.safetensors", b"ambiguous-model" * 64, "application/octet-stream")}
+    form = {
+        "tags": json.dumps(
+            ["models", "model_type:checkpoints", "model_type:loras", "unit-tests"]
+        ),
+        "name": "ambiguous-model.safetensors",
+        "user_metadata": json.dumps({}),
+    }
+    response = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
+    body = response.json()
+
+    assert response.status_code == 400, body
+    assert body["error"]["code"] == "INVALID_BODY"
+
+
+@pytest.mark.parametrize(
+    ("tags", "expected_root", "extension"),
+    [
+        (["input", "unit-tests", "upload-location-input"], "input", ".bin"),
+        (["output", "unit-tests", "upload-location-output"], "output", ".bin"),
+        (
+            ["models", "model_type:checkpoints", "unit-tests", "upload-location-model"],
+            "models/checkpoints",
+            ".safetensors",
+        ),
+    ],
+)
+def test_multipart_upload_role_selects_write_location(
+    http: requests.Session,
+    api_base: str,
+    comfy_tmp_base_dir: Path,
+    tags: list[str],
+    expected_root: str,
+    extension: str,
+):
+    role = next(tag for tag in tags if tag in {"input", "models", "output"})
+    name = f"{role}-role-upload{extension}"
+    files = {"file": (name, f"{role}-role-bytes".encode() * 64, "application/octet-stream")}
+    form = {
+        "tags": json.dumps(tags),
+        "name": name,
+        "user_metadata": json.dumps({}),
+    }
+
+    response = http.post(api_base + "/api/assets", data=form, files=files, timeout=120)
+    body = response.json()
+
+    assert response.status_code == 201, body
+    stored_name = get_asset_filename(body["asset_hash"], extension)
+    expected_disk_path = comfy_tmp_base_dir / expected_root / stored_name
+    assert expected_disk_path.exists()
 
 
 def test_upload_empty_tags_rejected(http: requests.Session, api_base: str):

tests-unit/comfy_extras_test/test_seedvr2_conditioning.py

@@ -0,0 +1,186 @@
+"""SeedVR2 conditioning node regression tests."""
+
+import importlib
+import sys
+from unittest.mock import MagicMock
+
+import pytest
+import torch
+import torch.nn as nn
+
+from comfy.cli_args import args as cli_args
+from comfy.ldm.seedvr.constants import SEEDVR2_LATENT_CHANNELS
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+
+_SENTINEL = object()
+_TARGETS = (
+    ("comfy.model_management", "comfy"),
+    ("comfy_extras.nodes_seedvr", "comfy_extras"),
+)
+
+
+def _import_nodes_seedvr_isolated():
+    """Import comfy_extras.nodes_seedvr with comfy.model_management mocked."""
+    priors = []
+    for mod_name, parent_name in _TARGETS:
+        prior_mod = sys.modules.get(mod_name, _SENTINEL)
+        parent = sys.modules.get(parent_name)
+        attr = mod_name.split(".")[-1]
+        prior_attr = (
+            getattr(parent, attr, _SENTINEL) if parent is not None else _SENTINEL
+        )
+        priors.append((mod_name, parent_name, attr, prior_mod, prior_attr))
+
+    mock_mm = MagicMock()
+    for fn in (
+        "xformers_enabled", "xformers_enabled_vae",
+        "pytorch_attention_enabled", "pytorch_attention_enabled_vae",
+        "sage_attention_enabled", "flash_attention_enabled",
+        "is_intel_xpu",
+    ):
+        getattr(mock_mm, fn).return_value = False
+    tv = torch.version.__version__.split(".")
+    mock_mm.torch_version_numeric = (int(tv[0]), int(tv[1]))
+    mock_mm.WINDOWS = False
+    sys.modules["comfy.model_management"] = mock_mm
+    if sys.modules.get("comfy") is None:
+        importlib.import_module("comfy")
+    comfy_pkg = sys.modules.get("comfy")
+    if comfy_pkg is not None:
+        setattr(comfy_pkg, "model_management", mock_mm)
+    nodes_seedvr = sys.modules.get("comfy_extras.nodes_seedvr") or (
+        importlib.import_module("comfy_extras.nodes_seedvr")
+    )
+
+    def _restore():
+        for mod_name, parent_name, attr, prior_mod, prior_attr in priors:
+            if prior_mod is _SENTINEL:
+                sys.modules.pop(mod_name, None)
+            else:
+                sys.modules[mod_name] = prior_mod
+            parent = sys.modules.get(parent_name)
+            if parent is None:
+                continue
+            if prior_attr is _SENTINEL:
+                if hasattr(parent, attr):
+                    delattr(parent, attr)
+            else:
+                setattr(parent, attr, prior_attr)
+
+    return nodes_seedvr, _restore
+
+
+class _Rope(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.freqs = nn.Parameter(torch.zeros(4))
+
+
+class _Block(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.rope = _Rope()
+
+
+class _DiffusionModel(nn.Module):
+    def __init__(self, n_blocks=3, conditioning_dtype=torch.float32):
+        super().__init__()
+        self.blocks = nn.ModuleList([_Block() for _ in range(n_blocks)])
+        self.register_buffer("positive_conditioning", torch.ones((2, 4), dtype=conditioning_dtype))
+        self.register_buffer("negative_conditioning", torch.zeros((3, 4), dtype=conditioning_dtype))
+
+
+class _ModelInner:
+    def __init__(self, diffusion_model):
+        self.diffusion_model = diffusion_model
+
+
+class _ModelPatcher:
+    def __init__(self, diffusion_model):
+        self.model = _ModelInner(diffusion_model)
+
+
+def test_seedvr2_conditioning_schema_exposes_conditioning_outputs():
+    nodes_seedvr, restore = _import_nodes_seedvr_isolated()
+    try:
+        schema = nodes_seedvr.SeedVR2Conditioning.define_schema()
+        assert [input_item.id for input_item in schema.inputs] == [
+            "model",
+            "vae_conditioning",
+        ]
+        assert schema.inputs[1].display_name == "latent"
+        assert [output.display_name for output in schema.outputs] == [
+            "positive",
+            "negative",
+        ]
+    finally:
+        restore()
+
+
+def test_seedvr2_conditioning_rejects_wrong_latent_channels():
+    nodes_seedvr, restore = _import_nodes_seedvr_isolated()
+    try:
+        patcher = _ModelPatcher(_DiffusionModel())
+        vae_conditioning = {"samples": torch.zeros(1, 8, 2, 2, 2)}
+
+        with pytest.raises(ValueError, match=f"{SEEDVR2_LATENT_CHANNELS} channels"):
+            nodes_seedvr.SeedVR2Conditioning.execute(patcher, vae_conditioning)
+    finally:
+        restore()
+
+
+def test_seedvr2_conditioning_returns_conditioning_deterministically():
+    nodes_seedvr, restore = _import_nodes_seedvr_isolated()
+    try:
+        diffusion_model = _DiffusionModel()
+        patcher = _ModelPatcher(diffusion_model)
+        samples = torch.arange(
+            1,
+            1 + SEEDVR2_LATENT_CHANNELS * 3 * 2 * 2,
+            dtype=torch.float32,
+        ).reshape(1, SEEDVR2_LATENT_CHANNELS, 3, 2, 2)
+        vae_conditioning = {"samples": samples}
+
+        first_positive, first_negative = (
+            nodes_seedvr.SeedVR2Conditioning.execute(
+                patcher,
+                vae_conditioning,
+            )
+        )
+        second_positive, second_negative = (
+            nodes_seedvr.SeedVR2Conditioning.execute(
+                patcher,
+                vae_conditioning,
+            )
+        )
+
+        channel_last = samples.movedim(1, -1).contiguous()
+        expected_condition = torch.cat(
+            [
+                channel_last,
+                torch.ones((*channel_last.shape[:-1], 1)),
+            ],
+            dim=-1,
+        ).movedim(-1, 1)
+
+        assert torch.equal(
+            first_positive[0][1]["condition"],
+            expected_condition,
+        )
+        assert torch.equal(
+            second_positive[0][1]["condition"],
+            expected_condition,
+        )
+        assert torch.equal(
+            first_negative[0][1]["condition"],
+            expected_condition,
+        )
+        assert torch.equal(
+            second_negative[0][1]["condition"],
+            expected_condition,
+        )
+    finally:
+        restore()

tests-unit/comfy_extras_test/test_seedvr2_nodes.py

@@ -0,0 +1,55 @@
+import importlib
+import inspect
+import sys
+from unittest.mock import MagicMock, patch
+
+import torch
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+
+def test_seedvr_node_signature_matches_schema():
+    mock_mm = MagicMock()
+    mock_mm.xformers_enabled.return_value = False
+    mock_mm.xformers_enabled_vae.return_value = False
+    mock_mm.sage_attention_enabled.return_value = False
+    mock_mm.flash_attention_enabled.return_value = False
+
+    sentinel = object()
+    prior_cpu = cli_args.cpu
+    cli_args.cpu = True
+    prior_module = sys.modules.get("comfy_extras.nodes_seedvr", sentinel)
+    comfy_pkg = sys.modules.get("comfy")
+    prior_mm_attr = getattr(comfy_pkg, "model_management", sentinel) if comfy_pkg else sentinel
+
+    with patch.dict(sys.modules, {"comfy.model_management": mock_mm}):
+        if comfy_pkg is not None:
+            setattr(comfy_pkg, "model_management", mock_mm)
+        sys.modules.pop("comfy_extras.nodes_seedvr", None)
+        try:
+            nodes_seedvr = importlib.import_module("comfy_extras.nodes_seedvr")
+            for node_cls in (nodes_seedvr.SeedVR2Preprocess, nodes_seedvr.SeedVR2PostProcessing, nodes_seedvr.SeedVR2Conditioning):
+                schema_ids = [i.id for i in node_cls.define_schema().inputs]
+                exec_params = [
+                    p for p in inspect.signature(node_cls.execute).parameters.keys()
+                    if p != "cls"
+                ]
+                assert schema_ids == exec_params, (
+                    f"{node_cls.__name__} schema/execute drift: "
+                    f"schema_ids={schema_ids}, exec_params={exec_params}"
+                )
+        finally:
+            cli_args.cpu = prior_cpu
+            if prior_module is sentinel:
+                sys.modules.pop("comfy_extras.nodes_seedvr", None)
+            else:
+                sys.modules["comfy_extras.nodes_seedvr"] = prior_module
+            if comfy_pkg is not None:
+                if prior_mm_attr is sentinel:
+                    if hasattr(comfy_pkg, "model_management"):
+                        delattr(comfy_pkg, "model_management")
+                else:
+                    setattr(comfy_pkg, "model_management", prior_mm_attr)

tests-unit/comfy_extras_test/test_seedvr2_post_processing.py

@@ -0,0 +1,51 @@
+from unittest.mock import patch
+
+import pytest
+import torch
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+from comfy_extras import nodes_seedvr  # noqa: E402
+
+
+def _schema_ids(items):
+    return [item.id for item in items]
+
+
+def test_seedvr2_post_processing_schema():
+    schema = nodes_seedvr.SeedVR2PostProcessing.define_schema()
+
+    assert _schema_ids(schema.inputs) == ["images", "original_resized_images", "color_correction_method"]
+    assert schema.inputs[2].options == ["lab", "wavelet", "adain", "none"]
+    assert schema.inputs[2].default == "lab"
+    assert schema.outputs[0].get_io_type() == "IMAGE"
+
+
+def test_seedvr2_post_processing_oom_error_uses_color_correction_method(monkeypatch):
+    decoded = torch.full((1, 3, 4, 4), 0.25)
+    reference = torch.full((1, 3, 4, 4), 0.75)
+
+    def _lab(content, style):
+        raise torch.cuda.OutOfMemoryError("CUDA out of memory")
+
+    monkeypatch.setattr(nodes_seedvr.comfy.model_management, "vae_device", lambda: torch.device("cpu"))
+    monkeypatch.setattr(nodes_seedvr.comfy.model_management, "get_free_memory", lambda device: 1_000_000)
+
+    with patch.object(nodes_seedvr, "lab_color_transfer", _lab):
+        with pytest.raises(RuntimeError) as excinfo:
+            nodes_seedvr.SeedVR2PostProcessing._color_transfer_chunked(
+                decoded, reference, torch.device("cpu"), "lab",
+            )
+    assert "color_correction_method=lab" in str(excinfo.value)
+    assert " method=lab" not in str(excinfo.value)
+
+
+def test_seedvr2_post_processing_unknown_color_correction_method_raises():
+    decoded = torch.zeros(1, 2, 4, 4, 3)
+    original = torch.zeros(1, 2, 4, 4, 3)
+    with pytest.raises(ValueError) as excinfo:
+        nodes_seedvr.SeedVR2PostProcessing.execute(decoded, original, "bogus")
+    assert "color_correction_method" in str(excinfo.value)

tests-unit/comfy_extras_test/test_seedvr2_temporal_chunk.py

@@ -0,0 +1,77 @@
+"""SeedVR2 temporal chunk/merge node regression tests."""
+
+import pytest
+import torch
+
+from comfy.cli_args import args as cli_args
+from comfy.ldm.seedvr.constants import (
+    BYTEDANCE_VAE_SPATIAL_DOWNSAMPLE,
+    SEEDVR2_CHUNK_GIB_PER_MPX_FRAME,
+    SEEDVR2_CHUNK_RESERVED_GIB,
+    SEEDVR2_CHUNK_SIGMA_GIB,
+    SEEDVR2_CHUNK_SIGMA_K,
+    SEEDVR2_LATENT_CHANNELS,
+)
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+import comfy.model_management  # noqa: E402
+from comfy_extras.nodes_seedvr import SeedVR2TemporalChunk, SeedVR2TemporalMerge, _seedvr2_chunk_crossfade_weights  # noqa: E402
+
+def _latent(t_latent, h=8, w=8, b=1):
+    g = torch.Generator().manual_seed(7)
+    return {"samples": torch.randn(b, SEEDVR2_LATENT_CHANNELS, t_latent, h, w, generator=g)}
+
+def _split(latent, frames_per_chunk, temporal_overlap, chunking_mode="manual"):
+    combo = {"chunking_mode": chunking_mode}
+    if chunking_mode != "auto":
+        combo["frames_per_chunk"] = frames_per_chunk
+    return SeedVR2TemporalChunk.execute(latent, temporal_overlap, combo).args
+
+def _merge(chunks, temporal_overlap):
+    return SeedVR2TemporalMerge.execute(chunks, [temporal_overlap]).args[0]
+
+def test_chunk_temporal_windows_and_validation():
+    with pytest.raises(ValueError, match="4n\\+1"):
+        _split(_latent(9), 20, 0)
+    with pytest.raises(ValueError, match="5-D"):
+        _split({"samples": torch.zeros(1, SEEDVR2_LATENT_CHANNELS * 9, 8, 8)}, 21, 0)
+    with pytest.raises(ValueError, match="chunking_mode"):
+        _split(_latent(13), 21, 0, "adaptive")
+    latent = _latent(13)
+    chunks, overlap = _split(latent, 21, 2)  # chunk_latent=6, step=4 -> [0:6], [4:10], [8:13]
+    assert overlap == 2 and [c["samples"].shape[2] for c in chunks] == [6, 6, 5]
+    assert all(torch.equal(c["samples"], latent["samples"][:, :, s:e]) for c, (s, e) in zip(chunks, [(0, 6), (4, 10), (8, 13)]))
+    assert len(_split(_latent(13), 21, 999)[0]) == 8  # overlap clamps to chunk_latent-1 -> step=1
+    assert (r := _split(_latent(5), 21, 3)) and len(r[0]) == 1 and r[1] == 0  # t_pixel <= 21: passthrough
+
+def test_chunk_auto_mode_applies_vram_law(monkeypatch):
+    mpx_per_frame = (32 * 32) * (BYTEDANCE_VAE_SPATIAL_DOWNSAMPLE ** 2) / 1e6
+    free_gb = (
+        SEEDVR2_CHUNK_RESERVED_GIB
+        + SEEDVR2_CHUNK_SIGMA_K * SEEDVR2_CHUNK_SIGMA_GIB
+        + 5.1 * SEEDVR2_CHUNK_GIB_PER_MPX_FRAME * mpx_per_frame
+    )
+    monkeypatch.setattr(comfy.model_management, "get_free_memory", lambda dev=None: free_gb * (1024 ** 3))
+    assert [c["samples"].shape[2] for c in _split(_latent(13, h=32, w=32), 1, 0, "auto")[0]] == [5, 5, 3]
+    assert _split(_latent(13, h=32, w=32, b=2), 1, 0, "auto")[0][0]["samples"].shape[2] == 2  # batch halves the chunk
+
+def test_merge_crossfade_and_reassembly():
+    latent = _latent(13)
+    latent["noise_mask"] = torch.rand(1, 1, 13, 8, 8)
+    latent["batch_index"] = [0]
+    merged = _merge(_split(latent, 21, 0)[0], 0)
+    assert torch.equal(merged["samples"], latent["samples"])
+    assert "noise_mask" not in merged and merged["batch_index"] == [0]
+    assert torch.allclose(_merge(_split(latent, 21, 3)[0], 3)["samples"], latent["samples"], atol=1e-6)
+    w = _seedvr2_chunk_crossfade_weights(3, merged["samples"].device, merged["samples"].dtype)
+    assert w[0] == 1.0 and w[-1] == 0.0 and torch.all(w[:-1] >= w[1:])
+    ones, zeros = {"samples": torch.ones(1, SEEDVR2_LATENT_CHANNELS, 6, 8, 8)}, {"samples": torch.zeros(1, SEEDVR2_LATENT_CHANNELS, 6, 8, 8)}
+    fused = _merge([ones, zeros], 3)["samples"]  # overlap equals w: prev fades out, next fades in
+    assert torch.equal(fused[:, :, 3:6], w.view(1, 1, 3, 1, 1).expand(1, SEEDVR2_LATENT_CHANNELS, 3, 8, 8))
+    assert torch.equal(fused[:, :, :3], ones["samples"][:, :, :3]) and torch.equal(fused[:, :, 6:], zeros["samples"][:, :, :3])
+    short = _split(latent, 21, 2)[0]
+    short[0]["samples"] = short[0]["samples"][:, :, :4]
+    with pytest.raises(ValueError, match="only the final chunk may be shorter"):
+        _merge(short, 2)

tests-unit/comfy_quant/test_mixed_precision.py

@@ -15,7 +15,7 @@ if not has_gpu():
     args.cpu = True
 
 from comfy import ops
-from comfy.quant_ops import QuantizedTensor
+from comfy.quant_ops import QUANT_ALGOS, QuantizedTensor
 import comfy.utils
 
 
@@ -283,7 +283,59 @@ class TestMixedPrecisionOps(unittest.TestCase):
         saved = model.state_dict()
         saved_conf = json.loads(saved["layer.comfy_quant"].numpy().tobytes())
         self.assertTrue(saved_conf["convrot"])
+
+    def test_convrot_w4a4_loads_into_params(self):
+        """ConvRot W4A4 checkpoints must load as the dedicated kitchen layout."""
+        if "convrot_w4a4" not in QUANT_ALGOS:
+            self.skipTest("comfy_kitchen does not provide ConvRot W4A4")
+
+        torch.manual_seed(456)
+        layer_quant_config = {
+            "layer": {
+                "format": "convrot_w4a4",
+                "convrot_groupsize": 256,
+                "linear_dtype": "int8",
+            }
+        }
+        weight = torch.randn(16, 256, dtype=torch.bfloat16)
+        bias = torch.randn(16, dtype=torch.bfloat16)
+        q_weight = QuantizedTensor.from_float(
+            weight,
+            "TensorCoreConvRotW4A4Layout",
+            convrot_groupsize=256,
+            quant_group_size=64,
+        )
+        state_dict = {
+            "layer.weight": q_weight._qdata,
+            "layer.bias": bias,
+            "layer.weight_scale": q_weight._params.scale,
+        }
+
+        state_dict, _ = comfy.utils.convert_old_quants(
+            state_dict,
+            metadata={"_quantization_metadata": json.dumps({"layers": layer_quant_config})},
+        )
+        model = torch.nn.Module()
+        model.layer = ops.mixed_precision_ops({}).Linear(256, 16, device="cpu", dtype=torch.bfloat16)
+        model.load_state_dict(state_dict, strict=False)
+
+        self.assertIsInstance(model.layer.weight, QuantizedTensor)
+        self.assertEqual(model.layer.weight._layout_cls, "TensorCoreConvRotW4A4Layout")
+        self.assertEqual(model.layer.weight._params.convrot_groupsize, 256)
+        self.assertEqual(model.layer.weight._params.quant_group_size, 64)
+        self.assertEqual(model.layer.weight._params.linear_dtype, "int8")
+
+        input_tensor = torch.randn(4, 256, dtype=torch.bfloat16)
+        loaded_out = model.layer(input_tensor)
+        ref_out = torch.nn.functional.linear(input_tensor, q_weight, bias)
+        self.assertTrue(torch.equal(loaded_out, ref_out))
+
+        saved = model.state_dict()
+        saved_conf = json.loads(saved["layer.comfy_quant"].numpy().tobytes())
+        self.assertEqual(saved_conf["format"], "convrot_w4a4")
         self.assertEqual(saved_conf["convrot_groupsize"], 256)
+        self.assertEqual(saved_conf["linear_dtype"], "int8")
+        self.assertNotIn("quant_group_size", saved_conf)
 
 if __name__ == "__main__":
     unittest.main()

tests-unit/comfy_test/model_detection_test.py

@@ -2,7 +2,7 @@ from collections import defaultdict
 
 import torch
 
-from comfy.model_detection import detect_unet_config, model_config_from_unet_config
+from comfy.model_detection import detect_unet_config, model_config_from_unet, model_config_from_unet_config
 import comfy.supported_models
 
 
@@ -73,6 +73,49 @@ def _make_flux_schnell_comfyui_sd():
     return sd
 
 
+def _make_seedvr2_7b_separate_mm_sd():
+    return {
+        "blocks.35.mlp.vid.proj_out.weight": torch.empty(3072, 1),
+        "positive_conditioning": torch.empty(58, 5120),
+        "negative_conditioning": torch.empty(64, 5120),
+    }
+
+
+def _make_seedvr2_7b_shared_mm_sd():
+    return {
+        "blocks.35.mlp.all.proj_in_gate.weight": torch.empty(1, 1),
+        "positive_conditioning": torch.empty(58, 5120),
+        "negative_conditioning": torch.empty(64, 5120),
+    }
+
+
+def _make_seedvr2_3b_shared_mm_sd():
+    return {
+        "blocks.31.mlp.all.proj_in_gate.weight": torch.empty(1, 1),
+        "positive_conditioning": torch.empty(58, 5120),
+        "negative_conditioning": torch.empty(64, 5120),
+    }
+
+
+def _make_pid_v1_5_sd(latent_proj_channels=16):
+    sd = {
+        "pixel_embedder.proj.weight": torch.empty(16, 3, device="meta"),
+        "lq_proj.latent_proj.0.weight": torch.empty(1024, latent_proj_channels, 3, 3, device="meta"),
+        "lq_proj.pit_head.weight": torch.empty(1536, 1024, device="meta"),
+        "lq_proj.gate_modules.0.content_proj.weight": torch.empty(1, 3072, device="meta"),
+        "pixel_blocks.0.attn.q_norm.weight": torch.empty(72, device="meta"),
+        "pixel_blocks.0.adaLN_modulation.0.weight": torch.empty(24576, 1536, device="meta"),
+        "pixel_blocks.0.adaLN_modulation.0.bias": torch.empty(24576, device="meta"),
+    }
+    for i in range(7):
+        sd[f"lq_proj.gate_modules.{i}.log_alpha"] = torch.empty((), device="meta")
+    return sd
+
+
+def _add_model_diffusion_prefix(sd):
+    return {f"model.diffusion_model.{k}": v for k, v in sd.items()}
+
+
 class TestModelDetection:
     """Verify that first-match model detection selects the correct model
     based on list ordering and unet_config specificity."""
@@ -125,6 +168,96 @@ class TestModelDetection:
         assert model_config is not None
         assert type(model_config).__name__ == "FluxSchnell"
 
+    def test_seedvr2_7b_separate_mm_detection_config(self):
+        sd = _make_seedvr2_7b_separate_mm_sd()
+        unet_config = detect_unet_config(sd, "")
+
+        assert unet_config is not None
+        assert unet_config["image_model"] == "seedvr2"
+        assert unet_config["vid_dim"] == 3072
+        assert unet_config["heads"] == 24
+        assert unet_config["num_layers"] == 36
+        assert unet_config["mm_layers"] == 36
+        assert unet_config["mlp_type"] == "normal"
+        assert unet_config["rope_type"] == "rope3d"
+        assert unet_config["rope_dim"] == 64
+
+    def test_seedvr2_7b_shared_mm_detection_config(self):
+        sd = _make_seedvr2_7b_shared_mm_sd()
+        unet_config = detect_unet_config(sd, "")
+
+        assert unet_config is not None
+        assert unet_config["image_model"] == "seedvr2"
+        assert unet_config["vid_dim"] == 3072
+        assert unet_config["heads"] == 24
+        assert unet_config["num_layers"] == 36
+        assert unet_config["mm_layers"] == 10
+        assert unet_config["mlp_type"] == "swiglu"
+        assert unet_config["rope_type"] == "rope3d"
+        assert unet_config["rope_dim"] == 64
+
+    def test_seedvr2_3b_shared_mm_detection_config(self):
+        sd = _make_seedvr2_3b_shared_mm_sd()
+        unet_config = detect_unet_config(sd, "")
+
+        assert unet_config is not None
+        assert unet_config["image_model"] == "seedvr2"
+        assert unet_config["vid_dim"] == 2560
+        assert unet_config["heads"] == 20
+        assert unet_config["num_layers"] == 32
+        assert unet_config["mlp_type"] == "swiglu"
+
+    def test_seedvr2_model_match_requires_conditioning_tensors(self):
+        sd = _make_seedvr2_7b_shared_mm_sd()
+        unet_config = detect_unet_config(sd, "")
+
+        assert type(model_config_from_unet_config(unet_config, sd)).__name__ == "SeedVR2"
+
+        del sd["positive_conditioning"]
+        assert model_config_from_unet_config(unet_config, sd) is None
+
+    def test_seedvr2_model_match_accepts_full_checkpoint_prefix(self):
+        sd = _add_model_diffusion_prefix(_make_seedvr2_7b_shared_mm_sd())
+
+        assert type(model_config_from_unet(sd, "model.diffusion_model.")).__name__ == "SeedVR2"
+
+    def test_pid_v1_5_detection(self):
+        sd = _make_pid_v1_5_sd()
+        unet_config = detect_unet_config(sd, "")
+
+        assert unet_config == {
+            "image_model": "pid",
+            "lq_latent_channels": 16,
+            "lq_hidden_dim": 1024,
+            "latent_spatial_down_factor": 8,
+            "lq_interval": 2,
+            "lq_latent_unpatchify_factor": 1,
+            "lq_conv_padding_mode": "replicate",
+            "lq_gate_per_token": True,
+            "pit_lq_inject": True,
+            "rope_ref_h": 2048,
+            "rope_ref_w": 2048,
+        }
+        assert type(model_config_from_unet_config(unet_config, sd)).__name__ == "PiD"
+
+    def test_pid_v1_5_flux2_detection(self):
+        unet_config = detect_unet_config(_make_pid_v1_5_sd(latent_proj_channels=32), "")
+
+        assert unet_config["lq_latent_channels"] == 128
+        assert unet_config["latent_spatial_down_factor"] == 16
+        assert unet_config["lq_latent_unpatchify_factor"] == 2
+
+    def test_pid_v1_5_pixel_adaln_conversion(self):
+        sd = _make_pid_v1_5_sd()
+        model_config = model_config_from_unet_config(detect_unet_config(sd, ""), sd)
+        processed = model_config.process_unet_state_dict(sd)
+
+        assert processed["pixel_blocks.0.attn.q_norm.weight"].shape == (72,)
+        assert processed["pixel_blocks.0.adaLN_modulation_msa.weight"].shape == (12288, 1536)
+        assert processed["pixel_blocks.0.adaLN_modulation_mlp.weight"].shape == (12288, 1536)
+        assert processed["pixel_blocks.0.adaLN_modulation_msa.bias"].shape == (12288,)
+        assert processed["pixel_blocks.0.adaLN_modulation_mlp.bias"].shape == (12288,)
+
     def test_unet_config_and_required_keys_combination_is_unique(self):
         """Each model in the registry must have a unique combination of
         ``unet_config`` and ``required_keys``. If two models share the same

tests-unit/comfy_test/seedvr_vae_forward_test.py

@@ -0,0 +1,74 @@
+"""Regression tests for the SeedVR2 VAE forward return contract."""
+
+import pytest
+import torch
+import torch.nn as nn
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+from comfy.ldm.seedvr.vae import SEEDVR2_LATENT_CHANNELS, VideoAutoencoderKL  # noqa: E402
+
+
+_LATENT_SHAPE = (1, SEEDVR2_LATENT_CHANNELS, 2, 2, 2)
+_DECODED_SHAPE = (1, 3, 5, 16, 16)
+_INPUT_ENCODE_SHAPE = (1, 3, 5, 16, 16)
+_INPUT_DECODE_SHAPE = _LATENT_SHAPE
+
+
+class _StubVAE(VideoAutoencoderKL):
+    def __init__(self):
+        nn.Module.__init__(self)
+        self._encode_out = torch.zeros(*_LATENT_SHAPE)
+        self._decode_out = torch.zeros(*_DECODED_SHAPE)
+
+    def encode(self, x, return_dict=True):
+        return self._encode_out
+
+    def decode_(self, z, return_dict=True):
+        return self._decode_out
+
+
+def test_forward_encode_returns_tensor():
+    vae = _StubVAE()
+    x = torch.zeros(*_INPUT_ENCODE_SHAPE)
+    result = vae.forward(x, mode="encode")
+    assert type(result) is torch.Tensor
+    assert result.shape == torch.Size(_LATENT_SHAPE)
+
+
+def test_forward_decode_returns_tensor():
+    vae = _StubVAE()
+    z = torch.zeros(*_INPUT_DECODE_SHAPE)
+    result = vae.forward(z, mode="decode")
+    assert type(result) is torch.Tensor
+    assert result.shape == torch.Size(_DECODED_SHAPE)
+
+
+class _TupleReturningStubVAE(VideoAutoencoderKL):
+    def __init__(self):
+        nn.Module.__init__(self)
+        self._encode_tensor = torch.zeros(*_LATENT_SHAPE)
+        self._decode_tensor = torch.zeros(*_DECODED_SHAPE)
+
+    def encode(self, x, return_dict=True):
+        return (self._encode_tensor,)
+
+    def decode_(self, z, return_dict=True):
+        return (self._decode_tensor,)
+
+
+def test_forward_all_unwraps_one_tuple_at_each_step():
+    vae = _TupleReturningStubVAE()
+    x = torch.zeros(*_INPUT_ENCODE_SHAPE)
+    result = vae.forward(x, mode="all")
+    assert type(result) is torch.Tensor
+    assert result.shape == torch.Size(_DECODED_SHAPE)
+
+
+def test_forward_rejects_unknown_mode():
+    vae = _StubVAE()
+    with pytest.raises(ValueError, match="Unknown SeedVR2 VAE forward mode"):
+        vae.forward(torch.zeros(*_INPUT_ENCODE_SHAPE), mode="bogus")

tests-unit/comfy_test/test_seedvr2_dtype.py

@@ -0,0 +1,79 @@
+import torch
+import torch.nn as nn
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+import comfy.sd
+import comfy.supported_models
+import comfy.ldm.seedvr.model as seedvr_model
+import comfy.ldm.seedvr.vae as seedvr_vae
+
+
+def test_seedvr2_fp16_manual_cast_only_for_bf16_device(monkeypatch):
+    bf16_device = object()
+    fp16_device = object()
+
+    monkeypatch.setattr(
+        comfy.supported_models.comfy.model_management,
+        "should_use_bf16",
+        lambda device=None: device is bf16_device,
+    )
+
+    bf16_config = comfy.supported_models.SeedVR2({"image_model": "seedvr2"})
+    bf16_config.set_inference_dtype(torch.float16, None, device=bf16_device)
+    assert bf16_config.manual_cast_dtype is torch.bfloat16
+
+    fp16_config = comfy.supported_models.SeedVR2({"image_model": "seedvr2"})
+    fp16_config.set_inference_dtype(torch.float16, None, device=fp16_device)
+    assert fp16_config.manual_cast_dtype is None
+
+
+def test_seedvr2_text_conditioning_accepts_cfg1_single_branch():
+    context = torch.arange(6, dtype=torch.float32).reshape(1, 3, 2)
+
+    txt, txt_shape = seedvr_model.NaDiT._resolve_text_conditioning(object(), context, [0])
+
+    torch.testing.assert_close(txt, context.squeeze(0))
+    torch.testing.assert_close(txt_shape, torch.tensor([[3]], device=context.device))
+
+
+def test_seedvr2_vae_decode_memory_covers_full_frame_lab_transfer():
+    wrapper = seedvr_vae.VideoAutoencoderKLWrapper.__new__(seedvr_vae.VideoAutoencoderKLWrapper)
+    latent_channels = seedvr_vae.SEEDVR2_LATENT_CHANNELS
+    estimate = wrapper.comfy_memory_used_decode((1, latent_channels, 26, 120, 160))
+    old_estimate = latent_channels * 120 * 160 * (4 * 8 * 8) * 2
+
+    assert estimate == 101 * 960 * 1280 * 160
+    assert estimate > 15 * 1024 ** 3
+    assert estimate > old_estimate * 100
+
+
+def test_seedvr2_vae_encode_preserves_compute_dtype(monkeypatch):
+    wrapper = seedvr_vae.VideoAutoencoderKLWrapper.__new__(seedvr_vae.VideoAutoencoderKLWrapper)
+    nn.Module.__init__(wrapper)
+    wrapper._dummy = nn.Parameter(torch.empty(1, dtype=torch.float16))
+    input_dtype = None
+
+    def encode(self, x):
+        nonlocal input_dtype
+        input_dtype = x.dtype
+        return x
+
+    monkeypatch.setattr(seedvr_vae.VideoAutoencoderKL, "encode", encode)
+
+    x = torch.zeros((1, 3, 1, 8, 8), dtype=torch.float32)
+    wrapper._encode_with_raw_latent(x)
+
+    assert input_dtype == torch.float32
+
+
+def test_seedvr2_vae_ops_cast_weights_to_compute_dtype():
+    attention = seedvr_vae.Attention(query_dim=4, heads=1, dim_head=4).to(torch.float16)
+    hidden_states = torch.zeros((1, 2, 4), dtype=torch.float32)
+
+    output = attention(hidden_states)
+
+    assert output.dtype == torch.float32

tests-unit/comfy_test/test_seedvr2_internals.py

@@ -0,0 +1,169 @@
+"""SeedVR2 internals regression tests."""
+
+from __future__ import annotations
+
+from unittest.mock import patch
+
+import pytest
+import torch
+
+from comfy.cli_args import args
+
+if not torch.cuda.is_available():
+    args.cpu = True
+
+import comfy.ldm.seedvr.model as seedvr_model  # noqa: E402
+import comfy.ldm.seedvr.vae as vae_mod  # noqa: E402
+import comfy.ldm.modules.attention as attention  # noqa: E402
+import comfy.ops as comfy_ops  # noqa: E402
+from comfy.ldm.seedvr.vae import (  # noqa: E402
+    causal_norm_wrapper,
+    set_norm_limit,
+)
+from comfy.ldm.seedvr.attention import var_attention_optimized_split  # noqa: E402
+
+
+_NUM_CHANNELS = 8
+_NUM_GROUPS = 4
+_TENSOR_SHAPE = (1, 8, 2, 4, 4)
+
+_GROUPNORM_SUBCLASSES = [
+    pytest.param(comfy_ops.disable_weight_init.GroupNorm, id="disable_weight_init"),
+    pytest.param(comfy_ops.manual_cast.GroupNorm, id="manual_cast"),
+]
+
+
+@pytest.mark.parametrize("groupnorm_cls", _GROUPNORM_SUBCLASSES)
+def test_seedvr_groupnorm_low_limit_uses_chunked_groupnorm_path(groupnorm_cls):
+    real_group_norm = vae_mod.F.group_norm
+    set_norm_limit(1e-9)
+    try:
+        gn = groupnorm_cls(num_channels=_NUM_CHANNELS, num_groups=_NUM_GROUPS)
+        gn.eval()
+
+        forward_hook_calls = []
+
+        def _hook(module, inputs, output):
+            forward_hook_calls.append(tuple(inputs[0].shape))
+
+        spy_calls = []
+
+        def _group_norm_spy(input_tensor, num_groups_arg, *args, **kwargs):
+            spy_calls.append({"num_groups": int(num_groups_arg)})
+            return real_group_norm(input_tensor, num_groups_arg, *args, **kwargs)
+
+        handle = gn.register_forward_hook(_hook)
+        try:
+            with patch.object(vae_mod.F, "group_norm", side_effect=_group_norm_spy):
+                out_tensor = causal_norm_wrapper(gn, torch.randn(*_TENSOR_SHAPE))
+        finally:
+            handle.remove()
+
+        full_calls = len(forward_hook_calls)
+        chunked_calls = sum(1 for entry in spy_calls if entry["num_groups"] < _NUM_GROUPS)
+
+        assert tuple(int(s) for s in out_tensor.shape) == _TENSOR_SHAPE
+        assert full_calls == 0, (
+            f"low-limit GroupNorm gate must NOT take the full-forward path; got full_calls={full_calls}"
+        )
+        assert chunked_calls > 0, (
+            f"low-limit GroupNorm gate must take the chunked path; got chunked_calls={chunked_calls}"
+        )
+    finally:
+        set_norm_limit(None)
+
+
+def test_seedvr2_7b_swin_attention_forward_uses_optimized_var_attention(monkeypatch):
+    dim = 8
+    heads = 2
+    head_dim = 4
+    attn = seedvr_model.NaSwinAttention(
+        vid_dim=dim,
+        txt_dim=dim,
+        heads=heads,
+        head_dim=head_dim,
+        qk_bias=False,
+        qk_norm=comfy_ops.disable_weight_init.RMSNorm,
+        qk_norm_eps=1e-6,
+        rope_type=None,
+        rope_dim=head_dim,
+        shared_weights=False,
+        window=(2, 1, 1),
+        window_method="720pwin_by_size_bysize",
+        version=True,
+        device="cpu",
+        dtype=torch.float32,
+        operations=comfy_ops.disable_weight_init,
+    )
+    generator = torch.Generator(device="cpu").manual_seed(11)
+    vid = torch.randn(8, dim, generator=generator)
+    txt = torch.randn(3, dim, generator=generator)
+    vid_shape = torch.tensor([[2, 2, 2]], dtype=torch.long)
+    txt_shape = torch.tensor([[3]], dtype=torch.long)
+    calls = []
+
+    def fake_optimized_var_attention(**kwargs):
+        calls.append(kwargs)
+        return kwargs["q"]
+
+    monkeypatch.setattr(seedvr_model, "optimized_var_attention", fake_optimized_var_attention)
+
+    vid_out, txt_out = attn(vid, txt, vid_shape, txt_shape, seedvr_model.Cache(disable=True))
+
+    assert tuple(vid_out.shape) == (8, dim)
+    assert tuple(txt_out.shape) == (3, dim)
+    assert len(calls) == 1
+    call = calls[0]
+    assert tuple(call["q"].shape) == (14, heads, head_dim)
+    assert tuple(call["k"].shape) == (14, heads, head_dim)
+    assert tuple(call["v"].shape) == (14, heads, head_dim)
+    assert call["heads"] == heads
+    assert call["skip_reshape"] is True
+    assert call["skip_output_reshape"] is True
+    assert call["cu_seqlens_q"] == [0, 7, 14]
+    assert call["cu_seqlens_k"] == [0, 7, 14]
+
+
+def test_var_attention_optimized_split_calls_dense_backend_per_window(monkeypatch):
+    heads = 2
+    head_dim = 3
+    q = torch.arange(30, dtype=torch.float32).reshape(5, heads, head_dim)
+    k = q + 100
+    v = q + 200
+    cu = [0, 2, 5]
+    calls = []
+
+    def fake_optimized_attention(q_arg, k_arg, v_arg, heads_arg, **kwargs):
+        calls.append(
+            {
+                "q_shape": tuple(q_arg.shape),
+                "k_shape": tuple(k_arg.shape),
+                "v_shape": tuple(v_arg.shape),
+                "heads": heads_arg,
+                "kwargs": kwargs,
+            }
+        )
+        return q_arg + v_arg
+
+    monkeypatch.setattr(attention, "optimized_attention", fake_optimized_attention)
+
+    out = var_attention_optimized_split(
+        q,
+        k,
+        v,
+        heads,
+        cu,
+        cu,
+        skip_reshape=True,
+        skip_output_reshape=True,
+    )
+
+    assert tuple(out.shape) == (5, heads, head_dim)
+    assert len(calls) == 2
+    assert calls[0]["q_shape"] == (1, heads, 2, head_dim)
+    assert calls[1]["q_shape"] == (1, heads, 3, head_dim)
+    assert all(call["heads"] == heads for call in calls)
+    assert all(call["kwargs"]["skip_reshape"] is True for call in calls)
+    assert all(call["kwargs"]["skip_output_reshape"] is True for call in calls)
+    torch.testing.assert_close(out, q + v, rtol=0, atol=0)
+

tests-unit/comfy_test/test_seedvr2_model.py

@@ -0,0 +1,320 @@
+"""SeedVR2 model, latent-format, and VAE graph regression tests."""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock
+
+import pytest
+import torch
+from torch import nn
+
+from comfy.cli_args import args
+
+if not torch.cuda.is_available():
+    args.cpu = True
+
+import comfy  # noqa: E402
+import comfy.latent_formats  # noqa: E402
+import comfy.ldm.seedvr.model as seedvr_model  # noqa: E402
+import comfy.ldm.seedvr.vae as seedvr_vae_mod  # noqa: E402
+import comfy.model_management  # noqa: E402
+import comfy.ops as comfy_ops  # noqa: E402
+import comfy.sample  # noqa: E402
+import comfy.sd as sd_mod  # noqa: E402
+import nodes as nodes_mod  # noqa: E402
+from comfy.ldm.seedvr.model import NaDiT  # noqa: E402
+
+
+_LATENT_CHANNELS = seedvr_vae_mod.SEEDVR2_LATENT_CHANNELS
+
+
+def _make_standin(positive_conditioning):
+    class _StandIn(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.register_buffer(
+                "positive_conditioning", positive_conditioning
+            )
+
+        _resolve_text_conditioning = NaDiT._resolve_text_conditioning
+
+    return _StandIn()
+
+
+class _StubModule(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+
+
+def _capture_last_layer_flags(monkeypatch, vid_dim: int, txt_in_dim: int) -> list[bool]:
+    flags = []
+
+    class _Block(_StubModule):
+        def __init__(self, *args, **kwargs):
+            flags.append(kwargs["is_last_layer"])
+            super().__init__()
+
+    monkeypatch.setattr(seedvr_model, "NaPatchIn", _StubModule)
+    monkeypatch.setattr(seedvr_model, "NaPatchOut", _StubModule)
+    monkeypatch.setattr(seedvr_model, "TimeEmbedding", _StubModule)
+    monkeypatch.setattr(seedvr_model, "NaMMSRTransformerBlock", _Block)
+
+    seedvr_model.NaDiT(
+        norm_eps=1e-5,
+        num_layers=4,
+        mlp_type="normal",
+        vid_dim=vid_dim,
+        txt_in_dim=txt_in_dim,
+        heads=24,
+        mm_layers=3,
+        operations=comfy_ops.disable_weight_init,
+    )
+
+    return flags
+
+
+class _Model:
+    def __init__(self, latent_format):
+        self._latent_format = latent_format
+
+    def get_model_object(self, name):
+        assert name == "latent_format"
+        return self._latent_format
+
+
+class _Patcher:
+    def get_free_memory(self, device):
+        return 1024 * 1024 * 1024
+
+
+class _EncodeWrapper(seedvr_vae_mod.VideoAutoencoderKLWrapper):
+    def __init__(self, encoded):
+        nn.Module.__init__(self)
+        self.encoded = encoded
+        self.spatial_downsample_factor = 8
+        self.temporal_downsample_factor = 4
+        self.seen = []
+
+    def encode(self, x):
+        self.seen.append(tuple(x.shape))
+        return self.encoded.to(device=x.device, dtype=x.dtype)
+
+
+class _DecodeWrapper(seedvr_vae_mod.VideoAutoencoderKLWrapper):
+    def __init__(self):
+        nn.Module.__init__(self)
+        self.spatial_downsample_factor = 8
+        self.temporal_downsample_factor = 4
+        self.calls = []
+
+    def decode(self, z, seedvr2_tiling=None):
+        self.calls.append({"shape": tuple(z.shape), "seedvr2_tiling": seedvr2_tiling})
+        if z.ndim == 4:
+            b, tc, h, w = z.shape
+            t = tc // _LATENT_CHANNELS
+        else:
+            b, _, t, h, w = z.shape
+        return torch.zeros(b, 3, t, h * 8, w * 8, dtype=z.dtype, device=z.device)
+
+
+def test_seedvr2_wrapper_public_encode_returns_tensor(monkeypatch):
+    raw_latent = torch.full((1, _LATENT_CHANNELS, 1, 4, 5), 2.0)
+    seen_shapes = []
+
+    def base_encode(self, x):
+        seen_shapes.append(tuple(x.shape))
+        return raw_latent.to(device=x.device, dtype=x.dtype)
+
+    monkeypatch.setattr(seedvr_vae_mod.VideoAutoencoderKL, "encode", base_encode)
+
+    vae = seedvr_vae_mod.VideoAutoencoderKLWrapper.__new__(seedvr_vae_mod.VideoAutoencoderKLWrapper)
+    nn.Module.__init__(vae)
+    vae._dummy = nn.Parameter(torch.zeros((), dtype=torch.float32))
+
+    latent = vae.encode(torch.zeros(1, 3, 32, 40))
+
+    assert type(latent) is torch.Tensor
+    assert tuple(latent.shape) == (1, _LATENT_CHANNELS, 4, 5)
+    assert seen_shapes == [(1, 3, 1, 32, 40)]
+
+
+def test_seedvr2_wrapper_private_encode_helper_keeps_raw_latent(monkeypatch):
+    raw_latent = torch.full((1, _LATENT_CHANNELS, 1, 4, 5), 3.0)
+
+    def base_encode(self, x):
+        return raw_latent.to(device=x.device, dtype=x.dtype)
+
+    monkeypatch.setattr(seedvr_vae_mod.VideoAutoencoderKL, "encode", base_encode)
+
+    vae = seedvr_vae_mod.VideoAutoencoderKLWrapper.__new__(seedvr_vae_mod.VideoAutoencoderKLWrapper)
+    nn.Module.__init__(vae)
+    vae._dummy = nn.Parameter(torch.zeros((), dtype=torch.float32))
+
+    latent, raw = vae._encode_with_raw_latent(torch.zeros(1, 3, 32, 40))
+
+    assert tuple(latent.shape) == (1, _LATENT_CHANNELS, 4, 5)
+    assert tuple(raw.shape) == (1, _LATENT_CHANNELS, 1, 4, 5)
+    assert torch.equal(raw, raw_latent)
+
+
+def _make_vae(wrapper):
+    vae = sd_mod.VAE.__new__(sd_mod.VAE)
+    vae.first_stage_model = wrapper
+    vae.device = torch.device("cpu")
+    vae.output_device = torch.device("cpu")
+    vae.vae_dtype = torch.float32
+    vae.latent_channels = _LATENT_CHANNELS
+    vae.latent_dim = 3
+    vae.downscale_ratio = (lambda a: max(0, (a + 3) // 4), 8, 8)
+    vae.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
+    vae.output_channels = 3
+    vae.disable_offload = True
+    vae.extra_1d_channel = None
+    vae.crop_input = False
+    vae.not_video = False
+    vae.handles_tiling = isinstance(wrapper, seedvr_vae_mod.VideoAutoencoderKLWrapper)
+    vae.format_encoded = wrapper.comfy_format_encoded
+    vae.patcher = _Patcher()
+    vae.process_input = lambda image: image
+    vae.process_output = lambda image: image.add(1.0).div(2.0).clamp(0.0, 1.0)
+    vae.vae_output_dtype = lambda: torch.float32
+    vae.memory_used_encode = lambda shape, dtype: 1
+    vae.memory_used_decode = lambda shape, dtype: 1
+    vae.throw_exception_if_invalid = lambda: None
+    vae.vae_encode_crop_pixels = lambda pixels: pixels
+    vae.spacial_compression_decode = lambda: 8
+    vae.temporal_compression_decode = lambda: 4
+    return vae
+
+
+def test_missing_context_falls_back_to_positive_buffer():
+    pos_buffer = torch.full((58, 5120), 7.0)
+    standin = _make_standin(pos_buffer)
+    txt, txt_shape = standin._resolve_text_conditioning(None)
+    assert txt.shape == (58, 5120)
+    assert (txt == 7.0).all(), (
+        "fallback path must use the positive_conditioning buffer "
+        "verbatim, not a zero tensor"
+    )
+    assert txt_shape.shape == (1, 1)
+    assert txt_shape[0, 0].item() == 58
+
+
+def test_seedvr2_7b_keeps_final_block_text_path(monkeypatch):
+    assert _capture_last_layer_flags(monkeypatch, vid_dim=3072, txt_in_dim=3072) == [
+        False,
+        False,
+        False,
+        False,
+    ]
+
+
+def test_seedvr2_7b_rope3d_matches_wrapper_oracle():
+    rope = seedvr_model.get_na_rope("rope3d", dim=64)
+    generator = torch.Generator(device="cpu").manual_seed(0)
+    q = torch.randn(4, 2, 128, generator=generator)
+    k = torch.randn(4, 2, 128, generator=generator)
+    shape = torch.tensor([[1, 2, 2]], dtype=torch.long)
+    freqs = rope.get_axial_freqs(1, 2, 2).reshape(4, -1)
+
+    expected_q = seedvr_model._apply_seedvr2_rotary_emb(
+        freqs,
+        q.permute(1, 0, 2).float(),
+    ).to(q.dtype).permute(1, 0, 2)
+    expected_k = seedvr_model._apply_seedvr2_rotary_emb(
+        freqs,
+        k.permute(1, 0, 2).float(),
+    ).to(k.dtype).permute(1, 0, 2)
+
+    actual_q, actual_k = rope(q.clone(), k.clone(), shape, seedvr_model.Cache(disable=True))
+
+    torch.testing.assert_close(actual_q, expected_q, rtol=0, atol=0)
+    torch.testing.assert_close(actual_k, expected_k, rtol=0, atol=0)
+
+
+def test_seedvr2_forward_requires_conditioning_latents():
+    model = NaDiT.__new__(NaDiT)
+    x = torch.zeros(1, _LATENT_CHANNELS, 1, 4, 5)
+
+    with pytest.raises(ValueError, match="requires conditioning latents"):
+        NaDiT.forward(model, x, timestep=torch.tensor([1.0]), context=None)
+
+
+def test_seedvr2_latent_format_uses_native_video_latent_shape():
+    latent_format = comfy.latent_formats.SeedVR2()
+    latent_image = torch.zeros(1, 1, 4, 5)
+
+    fixed = comfy.sample.fix_empty_latent_channels(_Model(latent_format), latent_image)
+
+    assert latent_format.latent_channels == _LATENT_CHANNELS
+    assert latent_format.latent_dimensions == 3
+    assert fixed.shape == (1, _LATENT_CHANNELS, 1, 4, 5)
+
+
+def test_seedvr2_model_requires_native_5d_latent():
+    latent = torch.zeros(1, _LATENT_CHANNELS, 2, 4, 5)
+    assert NaDiT._check_seedvr2_video_latent(latent, _LATENT_CHANNELS, "latent") is latent
+
+    with pytest.raises(ValueError, match="5-D native latent"):
+        NaDiT._check_seedvr2_video_latent(torch.zeros(1, _LATENT_CHANNELS * 2, 4, 5), _LATENT_CHANNELS, "latent")
+
+
+def test_seedvr2_encode_and_encode_tiled_preserve_native_latent_contract(monkeypatch):
+    monkeypatch.setattr(sd_mod.model_management, "load_models_gpu", lambda *a, **k: None)
+
+    encoded = torch.full((1, _LATENT_CHANNELS, 2, 4, 5), 2.0)
+    vae = _make_vae(_EncodeWrapper(encoded))
+    pixels = torch.zeros(1, 5, 32, 40, 3)
+
+    node_output = nodes_mod.VAEEncode().encode(vae, pixels)[0]
+    node_latent = node_output["samples"]
+    assert set(node_output) == {"samples"}
+    assert tuple(node_latent.shape) == (1, _LATENT_CHANNELS, 2, 4, 5)
+    assert node_latent.dtype == torch.float32
+    assert node_latent.stride()[-1] == 1
+    assert torch.equal(node_latent, torch.full_like(node_latent, 2.0 * seedvr_vae_mod.BYTEDANCE_VAE_SCALING_FACTOR))
+
+    tiled = torch.full((1, _LATENT_CHANNELS, 2, 4, 5), 3.0)
+    monkeypatch.setattr(seedvr_vae_mod, "tiled_vae", MagicMock(return_value=tiled))
+    tiled_output = nodes_mod.VAEEncodeTiled().encode(
+        vae,
+        pixels,
+        tile_size=512,
+        overlap=64,
+        temporal_size=16,
+        temporal_overlap=4,
+    )[0]
+    tiled_latent = tiled_output["samples"]
+    assert set(tiled_output) == {"samples"}
+    assert tuple(tiled_latent.shape) == (1, _LATENT_CHANNELS, 2, 4, 5)
+    assert tiled_latent.dtype == torch.float32
+    assert torch.equal(tiled_latent, torch.full_like(tiled_latent, 3.0 * seedvr_vae_mod.BYTEDANCE_VAE_SCALING_FACTOR))
+
+
+def test_vaedecode_tiled_spatial_applies_temporal_discarded(monkeypatch):
+    monkeypatch.setattr(sd_mod.model_management, "load_models_gpu", lambda *a, **k: None)
+    vae = _make_vae(_DecodeWrapper())
+
+    nodes_mod.VAEDecodeTiled().decode(
+        vae,
+        {"samples": torch.zeros(1, _LATENT_CHANNELS, 2, 4, 5)},
+        tile_size=512,
+        overlap=64,
+        temporal_size=16,
+        temporal_overlap=4,
+    )
+
+    # Spatial inputs flow through; temporal inputs are discarded as public tiling
+    # knobs, but SeedVR2's internal MemoryState causal slicing is left intact.
+    assert vae.first_stage_model.calls == [
+        {
+            "shape": (1, _LATENT_CHANNELS, 2, 4, 5),
+            "seedvr2_tiling": {
+                "enable_tiling": True,
+                "tile_size": (512, 512),
+                "tile_overlap": (64, 64),
+                "temporal_size": None,
+                "temporal_overlap": None,
+            },
+        }
+    ]

tests-unit/comfy_test/test_seedvr2_vae_decode.py

@@ -0,0 +1,94 @@
+from unittest.mock import patch
+
+import pytest
+import torch
+import torch.nn as nn
+
+from comfy.cli_args import args as cli_args
+
+if not torch.cuda.is_available():
+    cli_args.cpu = True
+
+import comfy.ldm.seedvr.vae as vae_mod  # noqa: E402
+from comfy_extras import nodes_seedvr  # noqa: E402
+
+
+_LATENT_CHANNELS = vae_mod.SEEDVR2_LATENT_CHANNELS
+
+
+def _make_wrapper() -> vae_mod.VideoAutoencoderKLWrapper:
+    wrapper = vae_mod.VideoAutoencoderKLWrapper.__new__(
+        vae_mod.VideoAutoencoderKLWrapper
+    )
+    nn.Module.__init__(wrapper)
+    return wrapper
+
+
+def _fingerprint_decode_(self, z, return_dict=True):
+    b = int(z.shape[0])
+    t = int(z.shape[2])
+    h = int(z.shape[3])
+    w = int(z.shape[4])
+    out = torch.empty(b, 3, t, h * 8, w * 8)
+    for batch_idx in range(b):
+        out[batch_idx].fill_(float(batch_idx + 1))
+    return out
+
+
+def _decode_with_patches(wrapper, z):
+    with patch.object(vae_mod.VideoAutoencoderKL, "decode_", _fingerprint_decode_):
+        return wrapper.decode(z)
+
+
+def test_decode_b2_t3_multi_frame_batch_unchanged():
+    wrapper = _make_wrapper()
+
+    out = _decode_with_patches(wrapper, torch.zeros(2, _LATENT_CHANNELS * 3, 2, 2))
+
+    assert tuple(out.shape) == (2, 3, 3, 16, 16)
+
+
+class _Wrapper(vae_mod.VideoAutoencoderKLWrapper):
+    def __init__(self):
+        nn.Module.__init__(self)
+        self.calls = []
+
+    def parameters(self):
+        return iter([torch.nn.Parameter(torch.zeros(()))])
+
+def _decode_stub(self, latent):
+    self.calls.append(tuple(latent.shape))
+    return torch.zeros(latent.shape[0], 3, latent.shape[2], latent.shape[3] * 8, latent.shape[4] * 8)
+
+
+def test_seedvr2_wrapper_decode_accepts_5d_channel_first_latents_without_preprocessor_state():
+    wrapper = _Wrapper()
+
+    with patch.object(vae_mod.VideoAutoencoderKL, "decode_", _decode_stub):
+        out = wrapper.decode(torch.zeros(1, _LATENT_CHANNELS, 2, 4, 5))
+
+    assert tuple(out.shape) == (1, 3, 2, 32, 40)
+    assert wrapper.calls == [(1, _LATENT_CHANNELS, 2, 4, 5)]
+
+
+def test_seedvr2_wrapper_decode_rejects_wrong_rank_latents():
+    wrapper = _Wrapper()
+
+    with pytest.raises(RuntimeError, match=r"latent input must be 4-D collapsed .* or 5-D"):
+        wrapper.decode(torch.zeros(1, _LATENT_CHANNELS, 4))
+
+
+def _t_padded(t_in: int) -> int:
+    if t_in == 1:
+        return 1
+    if t_in <= 4:
+        return 5
+    if (t_in - 1) % 4 == 0:
+        return t_in
+    return t_in + (4 - ((t_in - 1) % 4))
+
+
+@pytest.mark.parametrize("t_in", [1, 5, 9])
+def test_t_padded_matches_cut_videos(t_in):
+    dummy = torch.zeros(1, t_in, 1, 1, 1)
+    assert nodes_seedvr.cut_videos(dummy).shape[1] == _t_padded(t_in)