Merge b0eb165473 into 55a15f87ce

* feat(assets): add namespaced model type tags

* fix(assets): mark path-derived upload tags automatic

* fix(assets): merge duplicate scan specs

* test(assets): make duplicate path normalization portable

* feat(assets): add loader_path as the authoritative loader locator (#14796)

* fix(assets): filter model_type tags by bucket extension sets

Buckets sharing a base directory (e.g. diffusion_models and a custom
unet_gguf) tagged every file in the directory regardless of whether the
bucket could load it, so .safetensors files were tagged
model_type:unet_gguf and vice versa. Carry each bucket's registered
extension set through get_comfy_models_folders and only emit a
model_type tag when the file extension matches, keeping the empty-set
match-all convention from folder_paths.filter_files_extensions.

Files under a model base matching no bucket now keep only the models
tag instead of every directory-matching model_type tag.

* feat(assets): replace response file_path with persisted loader_path

The old file_path response field was a namespaced storage locator
(models/checkpoints/foo.safetensors): not an absolute path, not unique
identity, and not the value a loader consumes. Nothing needs that shape
on the wire (hash/ID-based locating is the long-term direction), so it
is dropped rather than renamed; the storage-root matching stays internal,
powering display_name.

What loaders DO need is the in-root loader path (category dropped:
models/checkpoints/foo/bar.safetensors -> foo/bar.safetensors). Serve it
as a first-class loader_path field, persisted on asset_references
(migration 0006) and written by every ingest pipeline at insert, so
responses read the column verbatim.

Like the model_type tags, loader_path is a seed-time derivative of the
model folder registry, maintained by the same scan lifecycle (new files seed
fresh values, pruning retires rows whose bucket disappeared). Rows
predating the column serve a null loader_path; databases from before
this stack already need recreating for the base branch's tag changes.

loader_path resolves every registered base including extra_model_paths
entries; display_name only the canonical storage roots. A file can
therefore be loadable with no display name (extra-path models) or the
reverse (unregistered files under the models root), and loader_path is
null exactly when no loader can resolve the file.

* test(assets): lock loader_path matrix (asymmetry, null, persist/read)

Cover the behaviour that has no production change but is easy to regress:
the extra-path asymmetry (loadable but no storage namespace), null
loader_path persistence for orphan files, and the response reading the
stored column with a compute fallback for un-backfilled rows.

* fix(assets): persist subfolder-qualified loader_path for ingested outputs

ingest_existing_file built its seed spec with the file's basename, so
outputs saved into a subfolder persisted loader_path (and the
user_metadata filename that preview URLs split for their subfolder
param) as just the basename: the served locator pointed at a file that
does not exist at that path. Scanner and seeder specs already derive
fname via compute_loader_path; use the same derivation here.

* fix(assets): only extension-matching buckets contribute a loader_path

The model-base match in get_asset_category_and_relative_path ignored
each bucket's extension set, so a file inside a registered base whose
extension the bucket cannot load (e.g. a .txt uploaded into
model_type:checkpoints) advertised a loader_path that no loader list
would ever resolve, while the tag side of the same stack already
excluded it. Apply the extension check used for backend tags (empty set
accepts any extension), keeping loader_path null exactly when no loader
can resolve the file.

* fix(assets): refresh loader_path when re-ingesting an existing reference

upsert_reference only wrote loader_path on the INSERT branch, so
re-ingesting an existing reference (an output overwritten in place, or a
file re-registered after its loader_path derivation changed) kept the
stale or NULL value forever. Write it on the UPDATE branch too, with a
null-safe change guard so a loader_path difference alone is enough to
trigger the update, and identical values stay a no-op.

* fix(assets): repair semantic merge breakage from #14796 and master

Two textually-clean but semantically-broken merges:

- routes.py lost its folder_paths import when #14796's import block
  superseded the base's, while the content-type hardening added via the
  base's master merge still calls folder_paths.is_dangerous_content_type.
- master's SVG download-hardening test uploads with the pre-namespacing
  bare checkpoints tag, which this branch's destination validation
  rejects; use model_type:checkpoints.

---------

Co-authored-by: guill <jacob.e.segal@gmail.com>

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,

comfy/ldm/joyimage/model.py

@@ -0,0 +1,535 @@
+# https://github.com/jdopensource/JoyAI-Image-Edit (Apache 2.0)
+import math
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.ops
+import comfy.patcher_extension
+from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps
+from comfy.ldm.modules.attention import optimized_attention
+
+
+def _apply_rotary_emb(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cis: Tuple[torch.Tensor, torch.Tensor],
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    ndim = xq.ndim
+    shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(xq.shape)]
+    cos = freqs_cis[0].view(*shape)
+    sin = freqs_cis[1].view(*shape)
+
+    def _rotate_half(x):
+        x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1)
+        return torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+
+    xq_out = (xq.float() * cos + _rotate_half(xq) * sin).type_as(xq)
+    xk_out = (xk.float() * cos + _rotate_half(xk) * sin).type_as(xk)
+    return xq_out, xk_out
+
+
+class JoyImageModulate(nn.Module):
+    def __init__(self, hidden_size: int, factor: int, dtype=None, device=None):
+        super().__init__()
+        self.factor = factor
+        self.modulate_table = nn.Parameter(
+            torch.empty(1, factor, hidden_size, dtype=dtype, device=device)
+        )
+
+    def forward(self, x: torch.Tensor) -> list:
+        if x.ndim != 3:
+            x = x.unsqueeze(1)
+        table = comfy.ops.cast_to_input(self.modulate_table, x)
+        return [o.squeeze(1) for o in (table + x).chunk(self.factor, dim=1)]
+
+
+class JoyImageFeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        inner_dim: int,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.net = nn.ModuleList([
+            _GeluApproximate(dim, inner_dim, dtype=dtype, device=device, operations=operations),
+            nn.Identity(),
+            operations.Linear(inner_dim, dim, bias=True, dtype=dtype, device=device),
+        ])
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        for module in self.net:
+            x = module(x)
+        return x
+
+
+class _GeluApproximate(nn.Module):
+    def __init__(self, dim_in: int, dim_out: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.proj = operations.Linear(dim_in, dim_out, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return F.gelu(self.proj(x), approximate="tanh")
+
+
+class JoyImageAttention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        eps: float = 1e-6,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.num_attention_heads = num_attention_heads
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.img_attn_qkv = operations.Linear(dim, inner_dim * 3, bias=True, dtype=dtype, device=device)
+        self.img_attn_q_norm = operations.RMSNorm(attention_head_dim, eps=eps, dtype=dtype, device=device)
+        self.img_attn_k_norm = operations.RMSNorm(attention_head_dim, eps=eps, dtype=dtype, device=device)
+        self.img_attn_proj = operations.Linear(inner_dim, dim, bias=True, dtype=dtype, device=device)
+
+        self.txt_attn_qkv = operations.Linear(dim, inner_dim * 3, bias=True, dtype=dtype, device=device)
+        self.txt_attn_q_norm = operations.RMSNorm(attention_head_dim, eps=eps, dtype=dtype, device=device)
+        self.txt_attn_k_norm = operations.RMSNorm(attention_head_dim, eps=eps, dtype=dtype, device=device)
+        self.txt_attn_proj = operations.Linear(inner_dim, dim, bias=True, dtype=dtype, device=device)
+
+    def forward(
+        self,
+        img: torch.Tensor,
+        txt: torch.Tensor,
+        image_rotary_emb: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]],
+        transformer_options={},
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        heads = self.num_attention_heads
+
+        img_q, img_k, img_v = self.img_attn_qkv(img).chunk(3, dim=-1)
+        txt_q, txt_k, txt_v = self.txt_attn_qkv(txt).chunk(3, dim=-1)
+
+        img_q = img_q.unflatten(-1, (heads, -1))
+        img_k = img_k.unflatten(-1, (heads, -1))
+        img_v = img_v.unflatten(-1, (heads, -1))
+        txt_q = txt_q.unflatten(-1, (heads, -1))
+        txt_k = txt_k.unflatten(-1, (heads, -1))
+        txt_v = txt_v.unflatten(-1, (heads, -1))
+
+        img_q = self.img_attn_q_norm(img_q)
+        img_k = self.img_attn_k_norm(img_k)
+        txt_q = self.txt_attn_q_norm(txt_q)
+        txt_k = self.txt_attn_k_norm(txt_k)
+
+        if image_rotary_emb is not None:
+            vis_freqs, txt_freqs = image_rotary_emb
+            if vis_freqs is not None:
+                img_q, img_k = _apply_rotary_emb(img_q, img_k, vis_freqs)
+            if txt_freqs is not None:
+                txt_q, txt_k = _apply_rotary_emb(txt_q, txt_k, txt_freqs)
+
+        joint_q = torch.cat([img_q, txt_q], dim=1)
+        joint_k = torch.cat([img_k, txt_k], dim=1)
+        joint_v = torch.cat([img_v, txt_v], dim=1)
+
+        joint_q = joint_q.flatten(2, 3)
+        joint_k = joint_k.flatten(2, 3)
+        joint_v = joint_v.flatten(2, 3)
+
+        joint_out = optimized_attention(joint_q, joint_k, joint_v, heads=heads, transformer_options=transformer_options)
+        joint_out = joint_out.to(joint_q.dtype)
+
+        seq_img = img.shape[1]
+        img_out = joint_out[:, :seq_img, :]
+        txt_out = joint_out[:, seq_img:, :]
+
+        img_out = self.img_attn_proj(img_out)
+        txt_out = self.txt_attn_proj(txt_out)
+        return img_out, txt_out
+
+
+class JoyImageTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        mlp_width_ratio: float = 4.0,
+        eps: float = 1e-6,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+        mlp_hidden_dim = int(dim * mlp_width_ratio)
+
+        self.img_mod = JoyImageModulate(dim, factor=6, dtype=dtype, device=device)
+        self.img_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
+        self.img_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
+        self.img_mlp = JoyImageFeedForward(dim, inner_dim=mlp_hidden_dim, dtype=dtype, device=device, operations=operations)
+
+        self.txt_mod = JoyImageModulate(dim, factor=6, dtype=dtype, device=device)
+        self.txt_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
+        self.txt_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device)
+        self.txt_mlp = JoyImageFeedForward(dim, inner_dim=mlp_hidden_dim, dtype=dtype, device=device, operations=operations)
+
+        self.attn = JoyImageAttention(
+            dim=dim,
+            num_attention_heads=num_attention_heads,
+            attention_head_dim=attention_head_dim,
+            eps=eps,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        image_rotary_emb: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]] = None,
+        transformer_options={},
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        (
+            img_mod1_shift,
+            img_mod1_scale,
+            img_mod1_gate,
+            img_mod2_shift,
+            img_mod2_scale,
+            img_mod2_gate,
+        ) = self.img_mod(temb)
+        (
+            txt_mod1_shift,
+            txt_mod1_scale,
+            txt_mod1_gate,
+            txt_mod2_shift,
+            txt_mod2_scale,
+            txt_mod2_gate,
+        ) = self.txt_mod(temb)
+
+        img_normed = self.img_norm1(hidden_states)
+        txt_normed = self.txt_norm1(encoder_hidden_states)
+        img_modulated = img_normed * (1 + img_mod1_scale.unsqueeze(1)) + img_mod1_shift.unsqueeze(1)
+        txt_modulated = txt_normed * (1 + txt_mod1_scale.unsqueeze(1)) + txt_mod1_shift.unsqueeze(1)
+
+        img_attn, txt_attn = self.attn(img_modulated, txt_modulated, image_rotary_emb, transformer_options=transformer_options)
+
+        hidden_states = hidden_states + img_attn * img_mod1_gate.unsqueeze(1)
+        encoder_hidden_states = encoder_hidden_states + txt_attn * txt_mod1_gate.unsqueeze(1)
+
+        img_ffn_normed = self.img_norm2(hidden_states)
+        txt_ffn_normed = self.txt_norm2(encoder_hidden_states)
+        img_ffn_input = img_ffn_normed * (1 + img_mod2_scale.unsqueeze(1)) + img_mod2_shift.unsqueeze(1)
+        txt_ffn_input = txt_ffn_normed * (1 + txt_mod2_scale.unsqueeze(1)) + txt_mod2_shift.unsqueeze(1)
+        hidden_states = hidden_states + self.img_mlp(img_ffn_input) * img_mod2_gate.unsqueeze(1)
+        encoder_hidden_states = encoder_hidden_states + self.txt_mlp(txt_ffn_input) * txt_mod2_gate.unsqueeze(1)
+
+        return hidden_states, encoder_hidden_states
+
+
+class JoyImageTimeTextImageEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        time_freq_dim: int,
+        time_proj_dim: int,
+        text_embed_dim: int,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.timesteps_proj = Timesteps(num_channels=time_freq_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.time_embedder = TimestepEmbedding(
+            in_channels=time_freq_dim,
+            time_embed_dim=dim,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+        self.act_fn = nn.SiLU()
+        self.time_proj = operations.Linear(dim, time_proj_dim, bias=True, dtype=dtype, device=device)
+        self.text_embedder = _PixArtAlphaTextProjection(
+            text_embed_dim, dim, dtype=dtype, device=device, operations=operations,
+        )
+
+    def forward(self, timestep: torch.Tensor, encoder_hidden_states: torch.Tensor):
+        timestep = self.timesteps_proj(timestep)
+        temb = self.time_embedder(timestep.to(dtype=encoder_hidden_states.dtype)).type_as(encoder_hidden_states)
+        timestep_proj = self.time_proj(self.act_fn(temb))
+        encoder_hidden_states = self.text_embedder(encoder_hidden_states)
+        return temb, timestep_proj, encoder_hidden_states
+
+
+class _PixArtAlphaTextProjection(nn.Module):
+    def __init__(self, in_features: int, hidden_size: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.linear_1 = operations.Linear(in_features, hidden_size, bias=True, dtype=dtype, device=device)
+        self.act_1 = nn.GELU(approximate="tanh")
+        self.linear_2 = operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device)
+
+    def forward(self, caption: torch.Tensor) -> torch.Tensor:
+        return self.linear_2(self.act_1(self.linear_1(caption)))
+
+
+class JoyImageTransformer3DModel(nn.Module):
+    def __init__(
+        self,
+        patch_size: list = [1, 2, 2],
+        in_channels: int = 16,
+        out_channels: Optional[int] = None,
+        hidden_size: int = 3072,
+        num_attention_heads: int = 24,
+        text_dim: int = 4096,
+        mlp_width_ratio: float = 4.0,
+        num_layers: int = 20,
+        rope_dim_list: list = [16, 56, 56],
+        rope_type: str = "rope",
+        theta: int = 256,
+        image_model=None,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.dtype = dtype
+        self.out_channels = out_channels or in_channels
+        self.patch_size = list(patch_size)
+        self.hidden_size = hidden_size
+        self.num_attention_heads = num_attention_heads
+        self.rope_dim_list = list(rope_dim_list)
+        self.rope_type = rope_type
+        self.theta = theta
+
+        if hidden_size % num_attention_heads != 0:
+            raise ValueError(
+                f"hidden_size ({hidden_size}) must be divisible by num_attention_heads ({num_attention_heads})"
+            )
+        attention_head_dim = hidden_size // num_attention_heads
+        if sum(self.rope_dim_list) != attention_head_dim:
+            raise ValueError(
+                f"sum(rope_dim_list) ({sum(self.rope_dim_list)}) must equal head_dim ({attention_head_dim})"
+            )
+
+        self.img_in = operations.Conv3d(
+            in_channels,
+            hidden_size,
+            kernel_size=tuple(self.patch_size),
+            stride=tuple(self.patch_size),
+            dtype=dtype,
+            device=device,
+        )
+
+        self.condition_embedder = JoyImageTimeTextImageEmbedding(
+            dim=hidden_size,
+            time_freq_dim=256,
+            time_proj_dim=hidden_size * 6,
+            text_embed_dim=text_dim,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        self.double_blocks = nn.ModuleList([
+            JoyImageTransformerBlock(
+                dim=hidden_size,
+                num_attention_heads=num_attention_heads,
+                attention_head_dim=attention_head_dim,
+                mlp_width_ratio=mlp_width_ratio,
+                dtype=dtype,
+                device=device,
+                operations=operations,
+            )
+            for _ in range(num_layers)
+        ])
+
+        self.norm_out = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.proj_out = operations.Linear(
+            hidden_size,
+            self.out_channels * math.prod(self.patch_size),
+            bias=True,
+            dtype=dtype,
+            device=device,
+        )
+
+    def _get_rotary_pos_embed_for_range(
+        self,
+        start: Tuple[int, int, int],
+        stop: Tuple[int, int, int],
+        device=None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # 3D RoPE for the patch grid range [start, stop) over (t, h, w). Token order after
+        # reshape(-1) is (t, h, w), matching the img_in Conv3d flatten.
+        head_dim = self.hidden_size // self.num_attention_heads
+        rope_dim_list = self.rope_dim_list
+        if rope_dim_list is None:
+            rope_dim_list = [head_dim // 3 for _ in range(3)]
+        if sum(rope_dim_list) != head_dim:
+            raise ValueError("sum(rope_dim_list) should equal head_dim")
+
+        grids = [torch.arange(start[i], stop[i], dtype=torch.float32, device=device) for i in range(3)]
+        mesh = torch.stack(torch.meshgrid(*grids, indexing="ij"), dim=0)
+
+        cos_parts, sin_parts = [], []
+        for i, dim in enumerate(rope_dim_list):
+            pos = mesh[i].reshape(-1)
+            freqs = 1.0 / (self.theta ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device)[: (dim // 2)] / dim))
+            angles = torch.outer(pos, freqs)
+            cos_parts.append(angles.cos().repeat_interleave(2, dim=1))
+            sin_parts.append(angles.sin().repeat_interleave(2, dim=1))
+
+        return torch.cat(cos_parts, dim=1), torch.cat(sin_parts, dim=1)
+
+    def get_rotary_pos_embed_for_components(
+        self,
+        component_sizes,
+        device=None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Per-component 3D RoPE. component_sizes is a list of (t, h, w) patch grid sizes in
+        # sequence order [target, ref0, ref1, ...]; h/w restart at 0 for each component while t
+        # continues from the running offset, giving every image its own temporal position band.
+        cos_parts, sin_parts = [], []
+        t_offset = 0
+        for (t, h, w) in component_sizes:
+            cos_emb, sin_emb = self._get_rotary_pos_embed_for_range(
+                start=(t_offset, 0, 0),
+                stop=(t_offset + t, h, w),
+                device=device,
+            )
+            cos_parts.append(cos_emb)
+            sin_parts.append(sin_emb)
+            t_offset += t
+        return torch.cat(cos_parts, dim=0), torch.cat(sin_parts, dim=0)
+
+    def unpatchify(self, x: torch.Tensor, t: int, h: int, w: int) -> torch.Tensor:
+        c = self.out_channels
+        pt, ph, pw = self.patch_size
+        if t * h * w != x.shape[1]:
+            raise ValueError(f"Expected t*h*w ({t * h * w}) to equal x.shape[1] ({x.shape[1]})")
+        x = x.reshape(x.shape[0], t, h, w, pt, ph, pw, c)
+        x = x.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        return x.reshape(x.shape[0], c, t * pt, h * ph, w * pw)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        ref_latents=None,
+        transformer_options={},
+        **kwargs,
+    ) -> torch.Tensor:
+        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
+            self._forward,
+            self,
+            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
+        ).execute(hidden_states, timestep, encoder_hidden_states, ref_latents, transformer_options, **kwargs)
+
+    def _forward(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        ref_latents=None,
+        transformer_options={},
+        **kwargs,
+    ) -> torch.Tensor:
+        # The target noise latent and each reference latent are independently patchified by img_in
+        # (Conv3d) and concatenated along the sequence dim, in the order [target, ref0, ref1, ...].
+        # RoPE is built per component so references may differ in resolution. Only the leading
+        # target segment (tt*th*tw tokens) is projected back out; reference tokens are dropped.
+        # A single reference is simply the len(ref_latents) == 1 case.
+        if hidden_states.ndim != 5:
+            raise ValueError(f"JoyImage transformer expects 5D (B,C,T,H,W) hidden_states; got shape {tuple(hidden_states.shape)}")
+
+        _, _, ot, oh, ow = hidden_states.shape
+        pt, ph, pw = self.patch_size
+        if ot % pt != 0 or oh % ph != 0 or ow % pw != 0:
+            raise ValueError(
+                f"JoyImage: target latent spatial/temporal shape {(ot, oh, ow)} must be divisible by patch_size {tuple(self.patch_size)}"
+            )
+        tt = ot // pt
+        th = oh // ph
+        tw = ow // pw
+
+        components = [hidden_states]
+        if ref_latents is not None:
+            for r in ref_latents:
+                if r.ndim != 5:
+                    raise ValueError(f"JoyImage: each reference latent must be 5D (B,C,T,H,W); got shape {tuple(r.shape)}")
+                components.append(r)
+
+        component_sizes = []
+        img_tokens = []
+        for comp in components:
+            _, _, ct, ch, cw = comp.shape
+            if ct % pt != 0 or ch % ph != 0 or cw % pw != 0:
+                raise ValueError(
+                    f"JoyImage: component shape {(ct, ch, cw)} must be divisible by patch_size {tuple(self.patch_size)}"
+                )
+            component_sizes.append((ct // pt, ch // ph, cw // pw))
+            tokens = self.img_in(comp).flatten(2).transpose(1, 2)  # (B, n_i, D)
+            img_tokens.append(tokens)
+
+        img = torch.cat(img_tokens, dim=1)
+
+        _, vec, txt = self.condition_embedder(timestep, encoder_hidden_states)
+        vec = vec.unflatten(1, (6, -1))
+
+        vis_cos, vis_sin = self.get_rotary_pos_embed_for_components(
+            component_sizes,
+            device=hidden_states.device,
+        )
+        vis_freqs = (vis_cos, vis_sin)
+        txt_freqs = None
+        image_rotary_emb = (vis_freqs, txt_freqs)
+
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.double_blocks)
+        transformer_options["block_type"] = "double"
+        for i, block in enumerate(self.double_blocks):
+            transformer_options["block_index"] = i
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"], out["txt"] = block(
+                        hidden_states=args["img"],
+                        encoder_hidden_states=args["txt"],
+                        temb=args["vec"],
+                        image_rotary_emb=args["pe"],
+                        transformer_options=args.get("transformer_options"),
+                    )
+                    return out
+
+                out = blocks_replace[("double_block", i)]({"img": img,
+                                                           "txt": txt,
+                                                           "vec": vec,
+                                                           "pe": image_rotary_emb,
+                                                           "transformer_options": transformer_options},
+                                                          {"original_block": block_wrap})
+                txt = out["txt"]
+                img = out["img"]
+            else:
+                img, txt = block(
+                    hidden_states=img,
+                    encoder_hidden_states=txt,
+                    temb=vec,
+                    image_rotary_emb=image_rotary_emb,
+                    transformer_options=transformer_options,
+                )
+
+        img = self.proj_out(self.norm_out(img))
+        target_tokens = tt * th * tw
+        img = img[:, :target_tokens, :]
+        img = self.unpatchify(img, tt, th, tw)
+        return img

comfy/model_base.py

@@ -57,6 +57,7 @@ import comfy.ldm.ace.model
 import comfy.ldm.omnigen.omnigen2
 import comfy.ldm.boogu.model
 import comfy.ldm.qwen_image.model
+import comfy.ldm.joyimage.model
 import comfy.ldm.ideogram4.model
 import comfy.ldm.krea2.model
 import comfy.ldm.kandinsky5.model
@@ -2264,6 +2265,95 @@ class QwenImage(BaseModel):
             out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
         return out
 
+class JoyImage(BaseModel):
+    # The noise latent and every reference latent are concatenated as a token sequence inside the
+    # transformer. A single-reference edit is just the len(ref_latents) == 1 case. The required CFG
+    # guidance rescale is applied by the JoyImageGuidanceRescale node (comfy_extras/nodes_joyimage.py).
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.joyimage.model.JoyImageTransformer3DModel)
+        self.memory_usage_factor_conds = ("ref_latents",)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        ref_latents = kwargs.get("reference_latents", None)
+        if ref_latents is None or len(ref_latents) == 0:
+            raise ValueError(
+                "JoyImageEdit is an edit model: every conditioning (positive AND negative) must carry "
+                "reference_latents. Wire the same reference image(s) and vae into both the positive and "
+                "negative TextEncodeJoyImageEdit / TextEncodeJoyImageEditPlus nodes. Empty negative "
+                "prompts still need the image(s) and vae."
+            )
+        latents = []
+        for lat in ref_latents:
+            latents.append(self.process_latent_in(lat))
+        out['ref_latents'] = comfy.conds.CONDList(latents)
+        return out
+
+    def extra_conds_shapes(self, **kwargs):
+        out = {}
+        ref_latents = kwargs.get("reference_latents", None)
+        if ref_latents is not None:
+            out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
+        return out
+
+    def _apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
+        # Pass the noise latent and the reference latents to the transformer, which patchifies each
+        # component and concatenates them along the sequence dim. References may be any resolution.
+        if c_concat is not None:
+            raise ValueError("JoyImage does not support c_concat / noise_concat conditioning")
+        transformer_options = transformer_options.copy()
+        sigma = t
+        xc = self.model_sampling.calculate_input(sigma, x)
+        context = c_crossattn
+        dtype = self.get_dtype_inference()
+        xc = xc.to(dtype)
+        device = xc.device
+        t_in = self.model_sampling.timestep(t).float()
+        if context is not None:
+            context = comfy.model_management.cast_to_device(context, device, dtype)
+
+        extra_conds = {}
+        for o in kwargs:
+            extra = kwargs[o]
+            if hasattr(extra, "dtype"):
+                extra = convert_tensor(extra, dtype, device)
+            elif isinstance(extra, list):
+                ex = []
+                for ext in extra:
+                    ex.append(convert_tensor(ext, dtype, device))
+                extra = ex
+            extra_conds[o] = extra
+
+        ref_latents = extra_conds.pop("ref_latents", None)
+        if ref_latents is None or len(ref_latents) == 0:
+            raise ValueError("JoyImageEdit forward requires ref_latents; got none.")
+
+        if xc.ndim != 5:
+            raise ValueError("JoyImageEdit: noise latent must be 5D (B,C,T,H,W); got shape {}.".format(tuple(xc.shape)))
+
+        refs = []
+        for r in ref_latents:
+            if r.ndim != 5:
+                raise ValueError(
+                    "JoyImageEdit: each reference latent must be 5D (B,C,T,H,W); got shape {}.".format(tuple(r.shape))
+                )
+            refs.append(r)
+
+        if control is not None:
+            raise ValueError("JoyImageEdit: control (ControlNet) is not supported by the transformer.")
+
+        # The transformer's forward signature is (hidden_states, timestep, encoder_hidden_states,
+        # ref_latents, transformer_options); it does not accept control/other extra_conds.
+        if extra_conds:
+            raise ValueError("JoyImageEdit: unexpected extra_conds keys {} reached the transformer.".format(list(extra_conds.keys())))
+
+        noise_pred = self.diffusion_model(xc, t_in, context, ref_latents=refs, transformer_options=transformer_options)
+
+        return self.model_sampling.calculate_denoised(sigma, noise_pred.float(), x)
+
 class Ideogram4(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ideogram4.model.Ideogram4Transformer2DModel)

comfy/model_detection.py

@@ -827,6 +827,27 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["default_ref_method"] = "negative_index"
         return dit_config
 
+    # JoyImageEdit: dual-stream double_blocks with img_attn_qkv, a condition_embedder
+    # time_embedder, and a 5D Conv3d img_in (kernel [1,2,2]).
+    if (
+        '{}double_blocks.0.attn.img_attn_qkv.weight'.format(key_prefix) in state_dict_keys
+        and '{}condition_embedder.time_embedder.linear_1.weight'.format(key_prefix) in state_dict_keys
+        and '{}img_in.weight'.format(key_prefix) in state_dict_keys
+        and len(state_dict['{}img_in.weight'.format(key_prefix)].shape) == 5
+    ):
+        img_in = state_dict['{}img_in.weight'.format(key_prefix)]
+        dit_config = {}
+        dit_config["image_model"] = "joyimage"
+        dit_config["in_channels"] = img_in.shape[1]
+        dit_config["hidden_size"] = img_in.shape[0]
+        dit_config["patch_size"] = list(img_in.shape[2:])
+        dit_config["num_layers"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
+        head_dim = state_dict['{}double_blocks.0.attn.img_attn_q_norm.weight'.format(key_prefix)].shape[0]
+        dit_config["num_attention_heads"] = dit_config["hidden_size"] // head_dim
+        # text_dim from the text-embedder input projection
+        dit_config["text_dim"] = state_dict['{}condition_embedder.text_embedder.linear_1.weight'.format(key_prefix)].shape[1]
+        return dit_config
+
     if '{}embed_image_indicator.weight'.format(key_prefix) in state_dict_keys:  # Ideogram 4
         dit_config = {}
         dit_config["image_model"] = "ideogram4"

comfy/sd.py

@@ -75,6 +75,7 @@ import comfy.text_encoders.gemma4
 import comfy.text_encoders.cogvideo
 import comfy.text_encoders.sa3
 import comfy.text_encoders.gpt_oss
+import comfy.text_encoders.joyimage
 
 import comfy.model_patcher
 import comfy.lora
@@ -1255,7 +1256,10 @@ class VAE:
             return None
 
     def is_dynamic(self):
-        return self.patcher.is_dynamic()
+        # A VAE built from a state dict with no detectable VAE weights returns early
+        # from __init__ ("No VAE weights detected") before self.patcher is assigned.
+        patcher = getattr(self, "patcher", None)
+        return patcher is not None and patcher.is_dynamic()
 
 class StyleModel:
     def __init__(self, model, device="cpu"):
@@ -1310,6 +1314,7 @@ class CLIPType(Enum):
     IDEOGRAM4 = 30
     BOOGU = 31
     KREA2 = 32
+    JOYIMAGE = 33
 
 
 
@@ -1639,6 +1644,10 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
                 clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
                 clip_target.clip = comfy.text_encoders.krea2.te(**llama_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.krea2.Krea2Tokenizer
+            elif clip_type == CLIPType.JOYIMAGE and te_model == TEModel.QWEN3VL_8B:  # JoyImageEdit: full Qwen3-VL-8B, edit-conditioning template + drop_idx.
+                clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."})
+                clip_target.clip = comfy.text_encoders.joyimage.te(**llama_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.joyimage.JoyImageTokenizer
             elif clip_type in (CLIPType.FLUX, CLIPType.FLUX2):  # Flux2 Klein reuses the Qwen3-VL LM (3-layer tap -> 12288); visual unused.
                 klein_model_type = "qwen3_8b" if te_model == TEModel.QWEN3VL_8B else "qwen3_4b"
                 clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type=klein_model_type)

comfy/supported_models.py

@@ -1877,6 +1877,45 @@ class QwenImage(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.qwen_image.QwenImageTokenizer, comfy.text_encoders.qwen_image.te(**hunyuan_detect))
 
+class JoyImage(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "joyimage",
+    }
+
+    # multiplier=1000: the transformer's time embedding is trained on t in [0,1000].
+    # ModelSamplingDiscreteFlow.timestep(sigma)=sigma*multiplier yields that range; the
+    # multiplier cancels in the sigma table, so it only rescales the timestep value.
+    sampling_settings = {
+        "multiplier": 1000,
+        "shift": 1.5,
+    }
+
+    memory_usage_factor = 1.8
+
+    unet_extra_config = {
+        "theta": 10000,
+        "rope_dim_list": [16, 56, 56],
+    }
+
+    latent_format = latent_formats.Wan21  # AutoencoderKLWan: z_dim=16, scale_factor_spatial=8, scale_factor_temporal=4.
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.JoyImage(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        # Imported lazily so this module stays importable without the text-encoder deps loaded;
+        # the import is only resolved when a checkpoint is actually loaded.
+        import comfy.text_encoders.joyimage
+        pref = self.text_encoder_key_prefix[0]
+        qwen3vl_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3vl.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.joyimage.JoyImageTokenizer, comfy.text_encoders.joyimage.te(**qwen3vl_detect))
+
 class HunyuanImage21(HunyuanVideo):
     unet_config = {
         "image_model": "hunyuan_video",
@@ -2354,6 +2393,7 @@ models = [
     Omnigen2,
     Boogu,
     QwenImage,
+    JoyImage,
     Ideogram4,
     Krea2,
     Flux2,

comfy/text_encoders/joyimage.py

@@ -0,0 +1,203 @@
+"""JoyImageEdit text encoder: a stock Qwen3-VL-8B multimodal stack feeding the
+JoyImageEdit DiT, built on `comfy.text_encoders.qwen3vl` with the
+JoyImage-specific prompt templates, system-prompt strip, image preprocessing,
+and conditioning-path multimodal handling.
+"""
+
+import torch
+
+from comfy import sd1_clip
+import comfy.text_encoders.qwen_vl
+from comfy.text_encoders.qwen3vl import Qwen3VL, Qwen3VLTokenizer
+
+# Prompt templates for the text-only and image-conditioned modes. The image-conditioned template
+# wraps the user text with one `<|vision_start|><|image_pad|><|vision_end|>` block per reference
+# image (no separator between blocks); `{vision}` is filled with the N concatenated blocks and
+# `{prompt}` with the user text.
+JOYIMAGE_TEMPLATE_TEXT = (
+    "<|im_start|>system\n \\nDescribe the image by detailing the color, shape, size, texture, "
+    "quantity, text, spatial relationships of the objects and background:<|im_end|>\n"
+    "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+)
+
+JOYIMAGE_TEMPLATE_IMAGE = (
+    "<|im_start|>system\n \\nDescribe the image by detailing the color, shape, size, texture, "
+    "quantity, text, spatial relationships of the objects and background:<|im_end|>\n"
+    "<|im_start|>user\n{vision}{prompt}<|im_end|>\n<|im_start|>assistant\n"
+)
+
+# A single vision block; N copies are concatenated to condition on N reference images.
+JOYIMAGE_VISION_BLOCK = "<|vision_start|><|image_pad|><|vision_end|>"
+
+# Number of leading template tokens (system prompt + the user block's opening
+# `<|im_start|>`) stripped from the encoded output by
+# JoyImageTEModel.encode_token_weights, so the kept sequence begins at the
+# `user` token.
+JOYIMAGE_DROP_IDX = 34
+
+# Special-token ids (vocab shared with Qwen2.5 / Qwen3, vocab_size 151936).
+IMAGE_PAD_TOKEN = 151655
+PAD_TOKEN = 151643
+
+
+class Qwen3VL8B_JoyImage(Qwen3VL):
+    """JoyImage Qwen3-VL-8B encoder.
+
+    Stock `qwen3vl_8b` config (text dims 4096 / 36L / 32H / 8 kv; interleaved
+    3D MRoPE rope_dims=[24,20,20], rope_theta=5e6; vision 1152/4304, depth 27,
+    patch_size 16, deepstack_visual_indexes=[8,16,24]).
+    """
+
+    model_type = "qwen3vl_8b"
+
+    def preprocess_embed(self, embed, device):
+        # Run the vision tower with JoyImage's bicubic+clamp preprocessing and
+        # return ``(merged, {"grid", "deepstack"})``.
+        if embed["type"] == "image":
+            image, grid = comfy.text_encoders.qwen_vl.process_qwen2vl_images(
+                embed["data"], min_pixels=65536, max_pixels=16777216, patch_size=16,
+                image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5],
+                interpolation="bicubic", clamp=True,
+            )
+            merged, deepstack = self.visual(image.to(device, dtype=torch.float32), grid)
+            return merged, {"grid": grid, "deepstack": deepstack}
+        return None, None
+
+    def forward(self, x, attention_mask=None, embeds=None, num_tokens=None,
+                intermediate_output=None, final_layer_norm_intermediate=True,
+                dtype=None, embeds_info=()):
+        # The conditioning path must build the 3D MRoPE position ids for the
+        # image-token block and inject the deepstack visual features.
+        # `build_image_inputs` returns the kwargs the decoder expects:
+        # (position_ids, visual_pos_masks, deepstack).
+        if embeds is not None:
+            position_ids, visual_pos_masks, deepstack = self.build_image_inputs(embeds, embeds_info)
+        else:
+            position_ids, visual_pos_masks, deepstack = None, None, None
+        return self.model(
+            x,
+            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,
+            deepstack_embeds=deepstack,
+            visual_pos_masks=visual_pos_masks,
+        )
+
+
+class JoyImageTokenizer(Qwen3VLTokenizer):
+    """JoyImageEdit tokenizer.
+
+    ``tokenize_with_weights(text, images=[...])`` selects the image-conditioned
+    template when one or more image tensors are passed, emitting one
+    ``<|vision_start|><|image_pad|><|vision_end|>`` block per image (N blocks
+    for N reference images), otherwise the text-only template. Each
+    ``<|image_pad|>`` token in the formatted prompt is replaced with an
+    embedding marker so `SDClipModel.process_tokens` routes each image through
+    `Qwen3VL8B_JoyImage.preprocess_embed`; ``drop_idx=34`` leading template
+    tokens are stripped downstream by `JoyImageTEModel.encode_token_weights`.
+    No ``<think>`` block is appended.
+    """
+
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(
+            embedding_directory=embedding_directory, tokenizer_data=tokenizer_data,
+            model_type="qwen3vl_8b",
+        )
+        self.llama_template = JOYIMAGE_TEMPLATE_TEXT
+        self.llama_template_images = JOYIMAGE_TEMPLATE_IMAGE
+
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None,
+                              images=[], **kwargs):
+        if text.startswith("<|im_start|>"):
+            llama_text = text
+        elif llama_template is not None:
+            llama_text = llama_template.format(text)
+        elif len(images) > 0:
+            # One vision block per reference image.
+            vision = JOYIMAGE_VISION_BLOCK * len(images)
+            llama_text = self.llama_template_images.format(vision=vision, prompt=text)
+        else:
+            llama_text = self.llama_template.format(text)
+
+        # Tokenize the already-rendered template via the grandparent
+        # (SD1Tokenizer); calling `super()` would re-apply the Qwen3VL template.
+        tokens = sd1_clip.SD1Tokenizer.tokenize_with_weights(
+            self, llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs,
+        )
+
+        key_name = next(iter(tokens))
+        embed_count = 0
+        qwen_tokens = tokens[key_name]
+        for r in qwen_tokens:
+            for i in range(len(r)):
+                if r[i][0] == IMAGE_PAD_TOKEN:
+                    if len(images) > embed_count:
+                        r[i] = ({"type": "image", "data": images[embed_count],
+                                 "original_type": "image"},) + r[i][1:]
+                        embed_count += 1
+        if embed_count != len(images):
+            raise ValueError(
+                f"JoyImageTokenizer: prompt had {embed_count} <|image_pad|> placeholders "
+                f"but {len(images)} image(s) were supplied. Either pre-format the prompt "
+                f"with `<|vision_start|><|image_pad|><|vision_end|>` per image or pass an "
+                f"image-free prompt."
+            )
+        return tokens
+
+
+class _JoyImageClipModel(sd1_clip.SDClipModel):
+    """Qwen3-VL multimodal encoder wrapper.
+
+    Conditions on the **pre-final-norm** output of the last decoder layer
+    (``layer="hidden", layer_idx=-1, layer_norm_hidden_state=False``). The
+    post-norm ``last_hidden_state`` differs by ~10x in scale and produces broken
+    DiT outputs, so these flags must not be changed.
+    """
+
+    def __init__(self, device="cpu", layer="hidden", layer_idx=-1, dtype=None,
+                 attention_mask=True, model_options={}):
+        super().__init__(
+            device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={},
+            dtype=dtype, special_tokens={"pad": PAD_TOKEN}, layer_norm_hidden_state=False,
+            model_class=Qwen3VL8B_JoyImage, enable_attention_masks=attention_mask,
+            return_attention_masks=attention_mask, model_options=model_options,
+        )
+
+
+class JoyImageTEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(
+            device=device, dtype=dtype, name="qwen3vl_8b",
+            clip_model=_JoyImageClipModel, model_options=model_options,
+        )
+
+    def encode_token_weights(self, token_weight_pairs):
+        out, pooled, extra = super().encode_token_weights(token_weight_pairs)
+        # Strip the JOYIMAGE_DROP_IDX-token system-prompt prefix from both the
+        # embedding sequence and the attention mask.
+        if out.shape[1] <= JOYIMAGE_DROP_IDX:
+            raise ValueError(
+                f"JoyImageTEModel: encoded sequence length {out.shape[1]} is shorter "
+                f"than drop_idx={JOYIMAGE_DROP_IDX}; the prompt did not include the "
+                f"template prefix."
+            )
+        out = out[:, JOYIMAGE_DROP_IDX:]
+        if "attention_mask" in extra:
+            extra["attention_mask"] = extra["attention_mask"][:, JOYIMAGE_DROP_IDX:]
+        return out, pooled, extra
+
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+    class JoyImageTEModel_(JoyImageTEModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_quantization_metadata is not None:
+                model_options = model_options.copy()
+                model_options["quantization_metadata"] = llama_quantization_metadata
+            if dtype_llama is not None:
+                dtype = dtype_llama
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return JoyImageTEModel_

comfy/text_encoders/qwen_vl.py

@@ -15,6 +15,8 @@ def process_qwen2vl_images(
     merge_size: int = 2,
     image_mean: list = None,
     image_std: list = None,
+    interpolation: str = "bilinear",
+    clamp: bool = False,
 ):
     if image_mean is None:
         image_mean = [0.48145466, 0.4578275, 0.40821073]
@@ -47,9 +49,11 @@ def process_qwen2vl_images(
     img_resized = F.interpolate(
         img.unsqueeze(0),
         size=(h_bar, w_bar),
-        mode='bilinear',
+        mode=interpolation,
         align_corners=False
     ).squeeze(0)
+    if clamp:
+        img_resized = img_resized.clamp(0.0, 1.0)
 
     normalized = img_resized.clone()
     for c in range(3):

comfy_api/feature_flags.py

@@ -100,6 +100,7 @@ def _parse_cli_feature_flags() -> dict[str, Any]:
 # Default server capabilities
 _CORE_FEATURE_FLAGS: dict[str, Any] = {
     "supports_preview_metadata": 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

@@ -24,8 +24,8 @@ class Seedream4TaskCreationRequest(BaseModel):
     image: list[str] | None = Field(None, description="Image URLs")
     size: str = Field(...)
     seed: int = Field(..., ge=0, le=2147483647)
-    sequential_image_generation: str = Field("disabled")
-    sequential_image_generation_options: Seedream4Options = Field(Seedream4Options(max_images=15))
+    sequential_image_generation: str | None = Field("disabled")
+    sequential_image_generation_options: Seedream4Options | None = Field(Seedream4Options(max_images=15))
     watermark: bool = Field(False)
     output_format: str | None = None
 
@@ -261,6 +261,19 @@ _PRESETS_SEEDREAM_4K = [
 
 _CUSTOM_PRESET = [("Custom", None, None)]
 
+_PRESETS_SEEDREAM_2K_PRO = [
+    ("(2K) 2048x2048 (1:1)", 2048, 2048),
+    ("(2K) 1728x2304 (3:4)", 1728, 2304),
+    ("(2K) 2304x1728 (4:3)", 2304, 1728),
+    # ("(2K) 2848x1600 (16:9)", 2848, 1600),  # 4,556,800 px - temporarily unavailable
+    # ("(2K) 1600x2848 (9:16)", 1600, 2848),  # 4,556,800 px - temporarily unavailable
+    ("(2K) 1664x2496 (2:3)", 1664, 2496),
+    ("(2K) 2496x1664 (3:2)", 2496, 1664),
+    # ("(2K) 3136x1344 (21:9)", 3136, 1344),  # 4,214,784 px - temporarily unavailable
+]
+RECOMMENDED_PRESETS_SEEDREAM_5_PRO = (
+    _PRESETS_SEEDREAM_1K + _PRESETS_SEEDREAM_2K_PRO + _CUSTOM_PRESET
+)
 RECOMMENDED_PRESETS_SEEDREAM_5_LITE = (
     _PRESETS_SEEDREAM_2K + _PRESETS_SEEDREAM_3K + _PRESETS_SEEDREAM_4K + _CUSTOM_PRESET
 )

comfy_api_nodes/nodes_bytedance.py

@@ -16,6 +16,7 @@ from comfy_api_nodes.apis.bytedance import (
     RECOMMENDED_PRESETS_SEEDREAM_4_0,
     RECOMMENDED_PRESETS_SEEDREAM_4_5,
     RECOMMENDED_PRESETS_SEEDREAM_5_LITE,
+    RECOMMENDED_PRESETS_SEEDREAM_5_PRO,
     SEEDANCE2_REF_VIDEO_PIXEL_LIMITS,
     VIDEO_TASKS_EXECUTION_TIME,
     GetAssetResponse,
@@ -80,12 +81,14 @@ _VERIFICATION_POLL_TIMEOUT_SEC = 120
 _VERIFICATION_POLL_INTERVAL_SEC = 3
 
 SEEDREAM_MODELS = {
+    "seedream 5.0 pro": "seedream-5-0-pro-260628",
     "seedream 5.0 lite": "seedream-5-0-260128",
     "seedream-4-5-251128": "seedream-4-5-251128",
     "seedream-4-0-250828": "seedream-4-0-250828",
 }
 
 SEEDREAM_PRESETS = {
+    "seedream-5-0-pro-260628": RECOMMENDED_PRESETS_SEEDREAM_5_PRO,
     "seedream-5-0-260128": RECOMMENDED_PRESETS_SEEDREAM_5_LITE,
     "seedream-4-5-251128": RECOMMENDED_PRESETS_SEEDREAM_4_5,
     "seedream-4-0-250828": RECOMMENDED_PRESETS_SEEDREAM_4_0,
@@ -743,8 +746,15 @@ class ByteDanceSeedreamNode(IO.ComfyNode):
         return IO.NodeOutput(torch.cat([await download_url_to_image_tensor(i) for i in urls]))
 
 
-def _seedream_model_inputs(*, max_ref_images: int, presets: list):
-    return [
+def _seedream_model_inputs(
+    *,
+    max_ref_images: int,
+    presets: list,
+    max_width: int = 6240,
+    max_height: int = 4992,
+    supports_batch: bool = True,
+):
+    inputs = [
         IO.Combo.Input(
             "size_preset",
             options=[label for label, _, _ in presets],
@@ -754,7 +764,7 @@ def _seedream_model_inputs(*, max_ref_images: int, presets: list):
             "width",
             default=2048,
             min=1024,
-            max=6240,
+            max=max_width,
             step=2,
             tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`",
         ),
@@ -762,22 +772,27 @@ def _seedream_model_inputs(*, max_ref_images: int, presets: list):
             "height",
             default=2048,
             min=1024,
-            max=4992,
+            max=max_height,
             step=2,
             tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`",
         ),
-        IO.Int.Input(
-            "max_images",
-            default=1,
-            min=1,
-            max=max_ref_images,
-            step=1,
-            display_mode=IO.NumberDisplay.number,
-            tooltip="Maximum number of images to generate. With 1, exactly one image is produced. "
-            "With >1, the model generates between 1 and max_images related images "
-            "(e.g., story scenes, character variations). "
-            "Total images (input + generated) cannot exceed 15.",
-        ),
+    ]
+    if supports_batch:
+        inputs.append(
+            IO.Int.Input(
+                "max_images",
+                default=1,
+                min=1,
+                max=max_ref_images,
+                step=1,
+                display_mode=IO.NumberDisplay.number,
+                tooltip="Maximum number of images to generate. With 1, exactly one image is produced. "
+                "With >1, the model generates between 1 and max_images related images "
+                "(e.g., story scenes, character variations). "
+                "Total images (input + generated) cannot exceed 15.",
+            )
+        )
+    inputs.append(
         IO.Autogrow.Input(
             "images",
             template=IO.Autogrow.TemplateNames(
@@ -787,14 +802,18 @@ def _seedream_model_inputs(*, max_ref_images: int, presets: list):
             ),
             tooltip=f"Optional reference image(s) for image-to-image or multi-reference generation. "
             f"Up to {max_ref_images} images.",
-        ),
-        IO.Boolean.Input(
-            "fail_on_partial",
-            default=False,
-            tooltip="If enabled, abort execution if any requested images are missing or return an error.",
-            advanced=True,
-        ),
-    ]
+        )
+    )
+    if supports_batch:
+        inputs.append(
+            IO.Boolean.Input(
+                "fail_on_partial",
+                default=False,
+                tooltip="If enabled, abort execution if any requested images are missing or return an error.",
+                advanced=True,
+            )
+        )
+    return inputs
 
 
 class ByteDanceSeedreamNodeV2(IO.ComfyNode):
@@ -816,6 +835,16 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
                 IO.DynamicCombo.Input(
                     "model",
                     options=[
+                        IO.DynamicCombo.Option(
+                            "seedream 5.0 pro",
+                            _seedream_model_inputs(
+                                max_ref_images=10,
+                                presets=RECOMMENDED_PRESETS_SEEDREAM_5_PRO,
+                                max_width=3136,
+                                max_height=2496,
+                                supports_batch=False,
+                            ),
+                        ),
                         IO.DynamicCombo.Option(
                             "seedream 5.0 lite",
                             _seedream_model_inputs(max_ref_images=14, presets=RECOMMENDED_PRESETS_SEEDREAM_5_LITE),
@@ -857,15 +886,27 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model"]),
+                depends_on=IO.PriceBadgeDepends(
+                    widgets=["model", "model.size_preset", "model.width", "model.height"]
+                ),
                 expr="""
                 (
-                  $price := $contains(widgets.model, "5.0 lite") ? 0.035 :
-                            $contains(widgets.model, "4-5") ? 0.04 : 0.03;
+                  $sp := $lookup(widgets, "model.size_preset");
+                  $px := $lookup(widgets, "model.width") * $lookup(widgets, "model.height");
+                  $isPro := $contains(widgets.model, "5.0 pro");
+                  $price := $isPro
+                              ? (
+                                  $contains($sp, "custom")
+                                    ? ($px <= 2360000 ? 0.045 : 0.09)
+                                    : ($contains($sp, "1k") ? 0.045 : 0.09)
+                                )
+                              : $contains(widgets.model, "5.0 lite") ? 0.035
+                              : $contains(widgets.model, "4-5") ? 0.04
+                              : 0.03;
                   {
-                    "type":"usd",
+                    "type": "usd",
                     "usd": $price,
-                    "format": { "suffix":" x images/Run", "approximate": true }
+                    "format": { "suffix": $isPro ? "/Image" : " x images/Run", "approximate": true }
                   }
                 )
                 """,
@@ -883,6 +924,7 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
         validate_string(prompt, strip_whitespace=True, min_length=1)
         model_id = SEEDREAM_MODELS[model["model"]]
         presets = SEEDREAM_PRESETS[model_id]
+        is_pro = "seedream-5-0-pro" in model_id
 
         size_preset = model.get("size_preset", presets[0][0])
         width = model.get("width", 2048)
@@ -902,19 +944,29 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
 
         out_num_pixels = w * h
         mp_provided = out_num_pixels / 1_000_000.0
-        if ("seedream-4-5" in model_id or "seedream-5-0" in model_id) and out_num_pixels < 3686400:
-            raise ValueError(
-                f"Minimum image resolution for the selected model is 3.68MP, but {mp_provided:.2f}MP provided."
-            )
-        if "seedream-4-0" in model_id and out_num_pixels < 921600:
-            raise ValueError(
-                f"Minimum image resolution that the selected model can generate is 0.92MP, "
-                f"but {mp_provided:.2f}MP provided."
-            )
-        if out_num_pixels > 16_777_216:
-            raise ValueError(
-                f"Maximum image resolution for the selected model is 16.78MP, but {mp_provided:.2f}MP provided."
-            )
+        if is_pro:
+            if out_num_pixels < 921_600:
+                raise ValueError(
+                    f"Minimum image resolution for the selected model is 0.92MP, but {mp_provided:.2f}MP provided."
+                )
+            if out_num_pixels > 4_194_304:
+                raise ValueError(
+                    f"Maximum image resolution for the selected model is 4.19MP, but {mp_provided:.2f}MP provided."
+                )
+        else:
+            if ("seedream-4-5" in model_id or "seedream-5-0" in model_id) and out_num_pixels < 3_686_400:
+                raise ValueError(
+                    f"Minimum image resolution for the selected model is 3.68MP, but {mp_provided:.2f}MP provided."
+                )
+            if "seedream-4-0" in model_id and out_num_pixels < 921_600:
+                raise ValueError(
+                    f"Minimum image resolution that the selected model can generate is 0.92MP, "
+                    f"but {mp_provided:.2f}MP provided."
+                )
+            if out_num_pixels > 16_777_216:
+                raise ValueError(
+                    f"Maximum image resolution for the selected model is 16.78MP, but {mp_provided:.2f}MP provided."
+                )
 
         image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
         n_input_images = sum(get_number_of_images(t) for t in image_tensors)
@@ -950,8 +1002,8 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
                 image=reference_images_urls,
                 size=f"{w}x{h}",
                 seed=seed,
-                sequential_image_generation=sequential_image_generation,
-                sequential_image_generation_options=Seedream4Options(max_images=max_images),
+                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,
             ),
         )

comfy_extras/nodes_joyimage.py

@@ -0,0 +1,198 @@
+import node_helpers
+import comfy.utils
+import torch
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+# fmt: off
+BUCKETS_1024 = [
+    (512, 1792), (512, 1856), (512, 1920), (512, 1984), (512, 2048),
+    (576, 1600), (576, 1664), (576, 1728), (576, 1792),
+    (640, 1472), (640, 1536), (640, 1600),
+    (704, 1344), (704, 1408), (704, 1472),
+    (768, 1216), (768, 1280), (768, 1344),
+    (832, 1152), (832, 1216),
+    (896, 1088), (896, 1152),
+    (960, 1024), (960, 1088),
+    (1024, 960), (1024, 1024),
+    (1088, 896), (1088, 960),
+    (1152, 832), (1152, 896),
+    (1216, 768), (1216, 832),
+    (1280, 768),
+    (1344, 704), (1344, 768),
+    (1408, 704),
+    (1472, 640), (1472, 704),
+    (1536, 640),
+    (1600, 576), (1600, 640),
+    (1664, 576),
+    (1728, 576),
+    (1792, 512), (1792, 576),
+    (1856, 512),
+    (1920, 512),
+    (1984, 512),
+    (2048, 512),
+]
+# fmt: on
+
+
+def _find_best_bucket(height: int, width: int) -> tuple[int, int]:
+    target_ratio = height / width
+    return min(BUCKETS_1024, key=lambda hw: abs(hw[0] / hw[1] - target_ratio))
+
+
+class TextEncodeJoyImageEdit(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="TextEncodeJoyImageEdit",
+            category="advanced/conditioning",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.Vae.Input("vae"),
+                io.Image.Input("image"),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+                io.Image.Output(display_name="image"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, prompt, vae, image) -> io.NodeOutput:
+        samples = image.movedim(-1, 1)
+        src_h, src_w = samples.shape[2], samples.shape[3]
+        bucket_h, bucket_w = _find_best_bucket(src_h, src_w)
+
+        resized = comfy.utils.common_upscale(samples, bucket_w, bucket_h, "bilinear", "center")
+        resized_image = resized.movedim(1, -1)[:, :, :, :3]
+
+        tokens = clip.tokenize(prompt, images=[resized_image])
+        conditioning = clip.encode_from_tokens_scheduled(tokens)
+
+        ref_latent = vae.encode(resized_image)
+        conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [ref_latent]}, append=True)
+
+        # Return the bucketed image so VAEEncode can build a matching-size init latent;
+        # the bucket size isn't reproducible outside this node.
+        return io.NodeOutput(conditioning, resized_image)
+
+
+class TextEncodeJoyImageEditPlus(io.ComfyNode):
+    """JoyImageEdit multi-image (Plus) text-encode node.
+
+    Accepts 1-6 optional reference images. Each supplied image is
+    bucket-resized independently (same buckets/resize as the single-image
+    node), VAE-encoded, and appended in order to
+    ``conditioning["reference_latents"]`` (image1 → ref0, image2 → ref1, ...).
+    All resized images are passed to the VL tower in one call; the tokenizer
+    emits one ``<|vision_start|><|image_pad|><|vision_end|>`` block per image.
+    """
+
+    MAX_IMAGES = 6
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="TextEncodeJoyImageEditPlus",
+            category="advanced/conditioning",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.Vae.Input("vae"),
+                io.Image.Input("image1", optional=True),
+                io.Image.Input("image2", optional=True),
+                io.Image.Input("image3", optional=True),
+                io.Image.Input("image4", optional=True),
+                io.Image.Input("image5", optional=True),
+                io.Image.Input("image6", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+                io.Image.Output(display_name="image"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, prompt, vae, image1=None, image2=None, image3=None,
+                image4=None, image5=None, image6=None) -> io.NodeOutput:
+        images = [image1, image2, image3, image4, image5, image6]
+        supplied = [img for img in images if img is not None]
+        if len(supplied) == 0:
+            raise ValueError(
+                "TextEncodeJoyImageEditPlus requires at least one reference image."
+            )
+
+        resized_images = []
+        ref_latents = []
+        for image in supplied:
+            samples = image.movedim(-1, 1)
+            src_h, src_w = samples.shape[2], samples.shape[3]
+            bucket_h, bucket_w = _find_best_bucket(src_h, src_w)
+
+            resized = comfy.utils.common_upscale(samples, bucket_w, bucket_h, "bilinear", "center")
+            resized_image = resized.movedim(1, -1)[:, :, :, :3]
+            resized_images.append(resized_image)
+            ref_latents.append(vae.encode(resized_image))
+
+        tokens = clip.tokenize(prompt, images=resized_images)
+        conditioning = clip.encode_from_tokens_scheduled(tokens)
+        conditioning = node_helpers.conditioning_set_values(
+            conditioning, {"reference_latents": ref_latents}, append=True,
+        )
+
+        # The last reference sets the target resolution; return it for VAEEncode
+        # since the bucket size isn't reproducible outside this node.
+        return io.NodeOutput(conditioning, resized_images[-1])
+
+
+class JoyImageGuidanceRescale(io.ComfyNode):
+    """CFG combine + per-token L2 norm rescale required by JoyImageEdit.
+
+    Wire this onto the model before sampling: JoyImageEdit's diffusers pipeline
+    rescales the combined noise prediction back to the conditional branch's norm
+    (comb * ||cond|| / ||comb||), the same rescale CFGNorm's pre_cfg branch does.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="JoyImageGuidanceRescale",
+            category="model/patch",
+            inputs=[
+                io.Model.Input("model"),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model) -> io.NodeOutput:
+        def guidance_rescale(args):
+            cond = args["cond"]
+            uncond = args["uncond"]
+            cond_scale = args["cond_scale"]
+            comb = uncond + cond_scale * (cond - uncond)
+            cond_norm = torch.norm(cond, dim=1, keepdim=True)
+            comb_norm = torch.norm(comb, dim=1, keepdim=True)
+            return comb * (cond_norm / comb_norm.clamp_min(1e-6))
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(guidance_rescale)
+        return io.NodeOutput(m)
+
+
+class JoyImageExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            TextEncodeJoyImageEdit,
+            TextEncodeJoyImageEditPlus,
+            JoyImageGuidanceRescale,
+        ]
+
+
+async def comfy_entrypoint() -> JoyImageExtension:
+    return JoyImageExtension()

nodes.py

@@ -992,7 +992,7 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
-                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image", "cogvideox", "lens", "pixeldit", "ideogram4", "boogu", "krea2"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image", "cogvideox", "lens", "pixeldit", "ideogram4", "boogu", "krea2", "joyimage"], ),
                               },
                 "optional": {
                               "device": (["default", "cpu"], {"advanced": True}),
@@ -2460,6 +2460,7 @@ async def init_builtin_extra_nodes():
         "nodes_tcfg.py",
         "nodes_context_windows.py",
         "nodes_qwen.py",
+        "nodes_joyimage.py",
         "nodes_boogu.py",
         "nodes_chroma_radiance.py",
         "nodes_pid.py",

openapi.yaml

@@ -7,18 +7,18 @@ components:
                     description: Timestamp when the asset was created
                     format: date-time
                     type: string
-                display_name:
-                    description: Display name of the asset. Mirrors name for backwards compatibility.
-                    nullable: true
-                    type: string
-                file_path:
-                    description: Relative path in global-namespace-root form (e.g. "models/checkpoints/flux.safetensors")
-                    nullable: true
-                    type: string
                 hash:
                     description: Blake3 hash of the asset content.
                     pattern: ^blake3:[a-f0-9]{64}$
                     type: string
+                loader_path:
+                    description: The value a loader consumes to load this asset. Null when no loader can resolve the file.
+                    nullable: true
+                    type: string
+                display_name:
+                    description: Human-facing label for the asset. Not unique.
+                    nullable: true
+                    type: string
                 id:
                     description: Unique identifier for the asset
                     format: uuid
@@ -144,14 +144,6 @@ components:
         AssetUpdated:
             description: Response returned when an existing asset is successfully updated.
             properties:
-                display_name:
-                    description: Display name of the asset. Mirrors name for backwards compatibility.
-                    nullable: true
-                    type: string
-                file_path:
-                    description: Relative path in global-namespace-root form (e.g. "models/checkpoints/flux.safetensors")
-                    nullable: true
-                    type: string
                 hash:
                     description: Blake3 hash of the asset content.
                     pattern: ^blake3:[a-f0-9]{64}$
@@ -1644,7 +1636,7 @@ paths:
                                     format: uuid
                                     type: string
                                 tags:
-                                    description: JSON-encoded array of freeform tag strings, e.g. '["models","checkpoint"]'. Common types include "models", "input", "output", and "temp", but any tag can be used in any order.
+                                    description: JSON-encoded array of tag strings. For new byte uploads, include exactly one destination role (`input`, `output`, or `models`); `models` uploads also require exactly one `model_type:<folder_name>` tag. Extra tags are stored as labels and do not create path components.
                                     type: string
                                 user_metadata:
                                     description: Custom JSON metadata as a string
@@ -1829,7 +1821,7 @@ paths:
                     content:
                         application/json:
                             schema:
-                                $ref: '#/components/schemas/AssetUpdated'
+                                $ref: '#/components/schemas/Asset'
                     description: Asset updated successfully
                 "400":
                     content:
@@ -2470,6 +2462,9 @@ paths:
                                     supports_preview_metadata:
                                         description: Whether the server supports preview metadata
                                         type: boolean
+                                    supports_model_type_tags:
+                                        description: Whether the server supports namespaced model type asset tags
+                                        type: boolean
                                 type: object
                     description: Success
                     headers:

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.45.20
-comfyui-workflow-templates==0.11.2
+comfyui-workflow-templates==0.11.6
 comfyui-embedded-docs==0.5.7
 torch
 torchsde

server.py

@@ -46,6 +46,7 @@ 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 +442,9 @@ 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,

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/feature_flags_test.py

@@ -29,6 +29,8 @@ class TestFeatureFlags:
         features = get_server_features()
         assert "supports_preview_metadata" in features
         assert features["supports_preview_metadata"] is True
+        assert "supports_model_type_tags" in features
+        assert features["supports_model_type_tags"] is True
         assert "max_upload_size" in features
         assert isinstance(features["max_upload_size"], (int, float))
 

tests-unit/websocket_feature_flags_test.py

@@ -12,6 +12,8 @@ class TestWebSocketFeatureFlags:
         # Check expected server features
         assert "supports_preview_metadata" in features
         assert features["supports_preview_metadata"] is True
+        assert "supports_model_type_tags" in features
+        assert features["supports_model_type_tags"] is True
         assert "max_upload_size" in features
         assert isinstance(features["max_upload_size"], (int, float))
 
@@ -75,3 +77,5 @@ class TestWebSocketFeatureFlags:
         assert server_message["type"] == "feature_flags"
         assert "supports_preview_metadata" in server_message["data"]
         assert server_message["data"]["supports_preview_metadata"] is True
+        assert "supports_model_type_tags" in server_message["data"]
+        assert server_message["data"]["supports_model_type_tags"] is True