Merge branch 'Comfy-Org:master' into feature/krea2-ref-latents

.coderabbit.yaml

@@ -4,12 +4,12 @@ early_access: false
 tone_instructions: "Only comment on issues introduced by this PR's changes. Do not flag pre-existing problems in moved, re-indented, or reformatted code."
 
 reviews:
-  profile: "chill"
-  request_changes_workflow: false
+  profile: "assertive"
+  request_changes_workflow: true
   high_level_summary: false
   poem: false
   review_status: false
-  review_details: false
+  review_details: true
   commit_status: true
   collapse_walkthrough: true
   changed_files_summary: false
@@ -39,6 +39,14 @@ reviews:
     - path: "**"
       instructions: |
         IMPORTANT: Only comment on issues directly introduced by this PR's code changes.
+        Treat AGENTS.md as mandatory repository policy, not optional style guidance.
+        Flag PR changes that violate AGENTS.md even when the code is otherwise functional.
+        In particular, enforce architecture boundaries, dtype/device/memory rules,
+        interface contracts, import style, no unnecessary try/except blocks, no inline
+        imports, no outbound internet paths in core ComfyUI, and narrow scoped fixes.
+        Prefer direct findings over suggestions when a rule is violated. Only ignore
+        AGENTS.md when it clearly conflicts with a newer explicit maintainer instruction
+        in the PR.
         Do NOT flag pre-existing issues in code that was merely moved, re-indented,
         de-indented, or reformatted without logic changes. If code appears in the diff
         only due to whitespace or structural reformatting (e.g., removing a `with:` block),
@@ -123,5 +131,10 @@ chat:
 
 knowledge_base:
   opt_out: false
+  code_guidelines:
+    enabled: true
+    filePatterns:
+      - files: "AGENTS.md"
+        applyTo: "**"
   learnings:
     scope: "auto"

.github/workflows/cla.yml

@@ -0,0 +1,93 @@
+name: CLA Assistant
+
+on:
+  issue_comment:
+    types: [created]
+  pull_request_target:
+    types: [opened, synchronize, closed]
+
+permissions:
+  actions: write
+  contents: read # 'read' is enough because signatures live in a REMOTE repo
+  pull-requests: write
+  statuses: write
+
+jobs:
+  cla-assistant:
+    runs-on: ubuntu-latest
+    steps:
+      # The CLA action normally requires every commit author in a PR to sign.
+      # We only want the PR author to sign, so we allowlist all other committers
+      # by computing them from the PR's commits and excluding the PR author.
+      - name: Build author-only allowlist
+        id: allowlist
+        if: >
+          github.event_name == 'pull_request_target' ||
+          (github.event_name == 'issue_comment' && github.event.issue.pull_request && (
+            github.event.comment.body == 'recheck' ||
+            github.event.comment.body == 'I have read and agree to the Contributor License Agreement'
+          ))
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          PR_NUMBER: ${{ github.event.pull_request.number || github.event.issue.number }}
+          PR_AUTHOR: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
+          BASE_ALLOWLIST: action@github.com,actions-user,ampagent,claude,comfy-pr-bot,GitHub Action,github-actions,github-actions[bot],Glary Bot,Glary-Bot,*[bot]
+        # For each commit emit the GitHub login when the author/committer email resolves to a GitHub account
+        # otherwise fall back to the raw git name.
+        run: |
+          others=$(gh api "repos/${{ github.repository }}/pulls/${PR_NUMBER}/commits" --paginate \
+            --jq '.[] | (.author.login // .commit.author.name // empty), (.committer.login // .commit.committer.name // empty)' \
+            | sort -u | grep -vix "${PR_AUTHOR}" | paste -sd, -)
+          if [ -n "$others" ]; then
+            echo "allowlist=${BASE_ALLOWLIST},${others}" >> "$GITHUB_OUTPUT"
+          else
+            echo "allowlist=${BASE_ALLOWLIST}" >> "$GITHUB_OUTPUT"
+          fi
+
+      - name: CLA Assistant
+        # Run on PR events, on "recheck" comment, or when someone posts the signing phrase.
+        # IMPORTANT: this phrase must match `custom-pr-sign-comment` below.
+        if: >
+          github.event_name == 'pull_request_target' ||
+          (github.event_name == 'issue_comment' && github.event.issue.pull_request && (
+            github.event.comment.body == 'recheck' ||
+            github.event.comment.body == 'I have read and agree to the Contributor License Agreement'
+          ))
+        uses: contributor-assistant/github-action@ca4a40a7d1004f18d9960b404b97e5f30a505a08 # v2.6.1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          # PAT required to write to the centralized signatures repo.
+          PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
+        with:
+          # Where the CLA document lives (shown to contributors)
+          path-to-document: https://github.com/Comfy-Org/comfy-cla/blob/main/comfyui_icla.md
+
+          # Centralized signature storage
+          remote-organization-name: comfy-org
+          remote-repository-name: comfy-cla
+          path-to-signatures: signatures/cla.json
+          branch: main
+
+          # Only the PR author must sign: bots plus every non-author committer
+          # are allowlisted via the "Build author-only allowlist" step above.
+          # *[bot] is a catch-all for any GitHub App bot account.
+          allowlist: ${{ steps.allowlist.outputs.allowlist }}
+
+          # Custom PR comment messages
+          custom-notsigned-prcomment: |
+            🎉 Thank you for your contribution, we really appreciate it! 🎉
+
+            Like many open source projects, we require contributors to sign our [Contributor License Agreement (CLA)](https://github.com/Comfy-Org/comfy-cla/blob/main/comfyui_icla.md). A CLA makes the ownership of contributions explicit, so contributors and the project share a clear understanding of how the code can be used. By signing, you:
+
+            - Confirm that you own your contribution.
+            - Keep the right to reuse your own code.
+            - Grant us a copyright license to include and share it within our projects.
+
+            CLAs are standard practice across major open source projects including those under the Apache Software Foundation and the Linux Foundation. Ours is based on the Apache Software Foundation's CLA. Most importantly, it would enable us to relicense the project under a more permissive license in the future, giving the project and its community greater flexibility.
+
+            ✍ **To sign, please post a new comment on this PR with exactly the following text:** ✍
+
+          custom-pr-sign-comment: I have read and agree to the Contributor License Agreement
+
+          custom-allsigned-prcomment: |
+            ✅ All contributors have signed the CLA. Thank you! This PR is ready to be merged.

AGENTS.md

@@ -127,6 +127,8 @@
 - Do not add unnecessary `try`/`except` blocks. Use them for optional dependency,
   platform, or backend capability detection only when the program has a useful
   fallback. Prefer specific exception types when changing new code.
+- If a library version is pinned in `requirements.txt`, do not add code to
+  ComfyUI to handle older versions of that library.
 - Remove any workarounds for PyTorch versions that ComfyUI no longer officially
   supports. Deprecated workarounds include catching an exception and rerunning
   the same op with the input cast to float. If a workaround does not have a

CLAUDE.md

@@ -1 +0,0 @@
-AGENTS.md

README.md

@@ -229,7 +229,7 @@ Python 3.14 works but some custom nodes may have issues. The free threaded varia
 
 Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
 
-torch 2.4 and above is supported but some features and optimizations might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old.
+torch 2.5 is minimally supported but using a newer version is extremely recommended. Some features and optimizations might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old. If your pytorch is more than 6 months old, please update it.
 
 ### Instructions:
 

alembic_db/versions/0005_allow_case_sensitive_tags.py

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

alembic_db/versions/0006_add_loader_path.py

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

app/assets/api/routes.py

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

app/assets/api/schemas_in.py

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

app/assets/api/schemas_out.py

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

app/assets/api/upload.py

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

app/assets/database/models.py

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

app/assets/database/queries/asset_reference.py

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

app/assets/database/queries/tags.py

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

app/assets/helpers.py

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

app/assets/scanner.py

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

app/assets/services/asset_management.py

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

app/assets/services/bulk_ingest.py

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

app/assets/services/ingest.py

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

app/assets/services/path_utils.py

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

app/assets/services/schemas.py

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

app/model_manager.py

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

comfy/cli_args.py

@@ -92,6 +92,7 @@ parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE"
 parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
 parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.")
 parser.add_argument("--enable-triton-backend", action="store_true", help="ComfyUI will enable the use of Triton backend in comfy-kitchen. Is disabled at launch by default.")
+parser.add_argument("--disable-triton-backend", action="store_true", help="Force-disable the comfy-kitchen Triton backend, overriding the automatic ROCm/AMD default and --enable-triton-backend.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"
@@ -225,6 +226,7 @@ parser.add_argument(
 )
 
 parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path. Overrides --base-directory.")
+parser.add_argument("--models-directory", type=is_valid_directory, default=None, help="Set the ComfyUI models directory. Overrides the models folder in --base-directory.")
 
 parser.add_argument("--enable-compress-response-body", action="store_true", help="Enable compressing response body.")
 

comfy/comfy_api_env.py

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

comfy/latent_formats.py

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

comfy/ldm/ace/ace_step15.py

@@ -217,10 +217,7 @@ class AceStepAttention(nn.Module):
                 cos, sin = position_embeddings
                 query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
-        n_rep = self.num_heads // self.num_kv_heads
-        if n_rep > 1:
-            key_states = key_states.repeat_interleave(n_rep, dim=1)
-            value_states = value_states.repeat_interleave(n_rep, dim=1)
+        gqa_kwargs = {"enable_gqa": True} if self.num_heads != self.num_kv_heads else {}
 
         attn_bias = None
         if self.sliding_window is not None and not self.is_cross_attention:
@@ -244,7 +241,7 @@ class AceStepAttention(nn.Module):
             else:
                 attn_bias = window_bias
 
-        attn_output = optimized_attention(query_states, key_states, value_states, self.num_heads, attn_bias, skip_reshape=True, low_precision_attention=False)
+        attn_output = optimized_attention(query_states, key_states, value_states, self.num_heads, attn_bias, skip_reshape=True, low_precision_attention=False, **gqa_kwargs)
         attn_output = self.o_proj(attn_output)
 
         return attn_output

comfy/ldm/audio/dit.py

@@ -425,19 +425,16 @@ class Attention(nn.Module):
         if n == 1 and causal:
             causal = False
 
-        if h != kv_h:
-            # Repeat interleave kv_heads to match q_heads
-            heads_per_kv_head = h // kv_h
-            k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
+        gqa_kwargs = {"enable_gqa": True} if h != kv_h else {}
 
         if self.differential:
             q, q_diff = q.unbind(dim=1)
             k, k_diff = k.unbind(dim=1)
-            out      = optimized_attention(q,      k,      v, h, skip_reshape=True, low_precision_attention=False, transformer_options=transformer_options)
-            out_diff = optimized_attention(q_diff, k_diff, v, h, skip_reshape=True, low_precision_attention=False, transformer_options=transformer_options)
+            out      = optimized_attention(q,      k,      v, h, skip_reshape=True, low_precision_attention=False, transformer_options=transformer_options, **gqa_kwargs)
+            out_diff = optimized_attention(q_diff, k_diff, v, h, skip_reshape=True, low_precision_attention=False, transformer_options=transformer_options, **gqa_kwargs)
             out = out - out_diff
         else:
-            out = optimized_attention(q, k, v, h, skip_reshape=True, low_precision_attention=False, transformer_options=transformer_options)
+            out = optimized_attention(q, k, v, h, skip_reshape=True, low_precision_attention=False, transformer_options=transformer_options, **gqa_kwargs)
 
         out = self.to_out(out)
 

comfy/ldm/boogu/model.py

@@ -74,11 +74,8 @@ class BooguDoubleStreamProcessor(nn.Module):
         key = key.transpose(1, 2)
         value = value.transpose(1, 2)
 
-        if attn.kv_heads < attn.heads:
-            key = key.repeat_interleave(attn.heads // attn.kv_heads, dim=1)
-            value = value.repeat_interleave(attn.heads // attn.kv_heads, dim=1)
-
-        hidden_states = optimized_attention_masked(query, key, value, attn.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options)
+        gqa_kwargs = {"enable_gqa": True} if attn.kv_heads < attn.heads else {}
+        hidden_states = optimized_attention_masked(query, key, value, attn.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options, **gqa_kwargs)
 
         # Split back to instruction/image, apply per-stream output projections, recombine.
         instruct_hidden_states = self.instruct_out(hidden_states[:, :L_instruct])

comfy/ldm/modules/attention.py

@@ -1,5 +1,6 @@
 import math
 import sys
+import inspect
 
 import torch
 import torch.nn.functional as F
@@ -14,16 +15,16 @@ from .sub_quadratic_attention import efficient_dot_product_attention
 
 from comfy import model_management
 
-TORCH_HAS_GQA = model_management.torch_version_numeric >= (2, 5)
-
 if model_management.xformers_enabled():
     import xformers
     import xformers.ops
 
 SAGE_ATTENTION_IS_AVAILABLE = False
+SAGE_ATTENTION_SUPPORTS_MASK = False
 try:
     from sageattention import sageattn
     SAGE_ATTENTION_IS_AVAILABLE = True
+    SAGE_ATTENTION_SUPPORTS_MASK = "attn_mask" in inspect.signature(sageattn).parameters
 except ImportError as e:
     if model_management.sage_attention_enabled():
         if e.name == "sageattention":
@@ -89,6 +90,44 @@ def default(val, d):
         return val
     return d
 
+def _gqa_repeat_factor(query_heads, key_heads, value_heads):
+    if key_heads != value_heads:
+        raise ValueError(f"Key/value head count mismatch for GQA: {key_heads} != {value_heads}")
+    if query_heads == key_heads:
+        return 1
+    if query_heads % key_heads != 0:
+        raise ValueError(f"Query heads must be divisible by key/value heads for GQA: {query_heads} vs {key_heads}")
+    return query_heads // key_heads
+
+def _repeat_kv_for_gqa(k, v, query_heads, head_dim):
+    n_rep = _gqa_repeat_factor(query_heads, k.shape[head_dim], v.shape[head_dim])
+    if n_rep > 1:
+        k = k.repeat_interleave(n_rep, dim=head_dim)
+        v = v.repeat_interleave(n_rep, dim=head_dim)
+    return k, v
+
+def _heads_from_dim(tensor, dim_head, name):
+    inner_dim = tensor.shape[-1]
+    if inner_dim % dim_head != 0:
+        raise ValueError(f"{name} inner dimension {inner_dim} is not divisible by head dimension {dim_head}")
+    return inner_dim // dim_head
+
+def _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, enable_gqa=False, expand_kv=True):
+    q = q.unsqueeze(3).reshape(b, -1, heads, dim_head)
+    if enable_gqa:
+        key_heads = _heads_from_dim(k, dim_head, "Key")
+        value_heads = _heads_from_dim(v, dim_head, "Value")
+    else:
+        key_heads = heads
+        value_heads = heads
+    k = k.unsqueeze(3).reshape(b, -1, key_heads, dim_head)
+    v = v.unsqueeze(3).reshape(b, -1, value_heads, dim_head)
+    if enable_gqa:
+        _gqa_repeat_factor(heads, key_heads, value_heads)
+        if expand_kv:
+            k, v = _repeat_kv_for_gqa(k, v, heads, -2)
+    return q, k, v
+
 
 # feedforward
 class GEGLU(nn.Module):
@@ -152,28 +191,19 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
         b, _, dim_head = q.shape
         dim_head //= heads
 
-    if kwargs.get("enable_gqa", False) and q.shape[-3] != k.shape[-3]:
-        n_rep = q.shape[-3] // k.shape[-3]
-        k = k.repeat_interleave(n_rep, dim=-3)
-        v = v.repeat_interleave(n_rep, dim=-3)
-
     scale = kwargs.get("scale", dim_head ** -0.5)
 
     h = heads
     if skip_reshape:
-         q, k, v = map(
+        if kwargs.get("enable_gqa", False):
+            k, v = _repeat_kv_for_gqa(k, v, q.shape[-3], -3)
+        q, k, v = map(
             lambda t: t.reshape(b * heads, -1, dim_head),
             (q, k, v),
         )
     else:
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(b, -1, heads, dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b * heads, -1, dim_head)
-            .contiguous(),
-            (q, k, v),
-        )
+        q, k, v = _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, kwargs.get("enable_gqa", False))
+        q, k, v = map(lambda t: t.permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head).contiguous(), (q, k, v))
 
     # force cast to fp32 to avoid overflowing
     if attn_precision == torch.float32:
@@ -231,13 +261,16 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None,
         query = query * (kwargs["scale"] * dim_head ** 0.5)
 
     if skip_reshape:
+        if kwargs.get("enable_gqa", False):
+            key, value = _repeat_kv_for_gqa(key, value, query.shape[-3], -3)
         query = query.reshape(b * heads, -1, dim_head)
         value = value.reshape(b * heads, -1, dim_head)
         key = key.reshape(b * heads, -1, dim_head).movedim(1, 2)
     else:
-        query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
-        value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
-        key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
+        query, key, value = _reshape_qkv_to_heads(query, key, value, b, heads, dim_head, kwargs.get("enable_gqa", False))
+        query = query.permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+        value = value.permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+        key = key.permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
 
 
     dtype = query.dtype
@@ -304,19 +337,15 @@ def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
     scale = kwargs.get("scale", dim_head ** -0.5)
 
     if skip_reshape:
-         q, k, v = map(
+        if kwargs.get("enable_gqa", False):
+            k, v = _repeat_kv_for_gqa(k, v, q.shape[-3], -3)
+        q, k, v = map(
             lambda t: t.reshape(b * heads, -1, dim_head),
             (q, k, v),
         )
     else:
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(b, -1, heads, dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b * heads, -1, dim_head)
-            .contiguous(),
-            (q, k, v),
-        )
+        q, k, v = _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, kwargs.get("enable_gqa", False))
+        q, k, v = map(lambda t: t.permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head).contiguous(), (q, k, v))
 
     r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
 
@@ -438,7 +467,7 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
             disabled_xformers = True
 
     if disabled_xformers:
-        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, **kwargs)
+        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, skip_output_reshape=skip_output_reshape, **kwargs)
 
     if skip_reshape:
         # b h k d -> b k h d
@@ -446,13 +475,12 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
             lambda t: t.permute(0, 2, 1, 3),
             (q, k, v),
         )
+        if kwargs.get("enable_gqa", False):
+            k, v = _repeat_kv_for_gqa(k, v, q.shape[-2], -2)
     # actually do the reshaping
     else:
         dim_head //= heads
-        q, k, v = map(
-            lambda t: t.reshape(b, -1, heads, dim_head),
-            (q, k, v),
-        )
+        q, k, v = _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, kwargs.get("enable_gqa", False))
 
     if mask is not None:
         # add a singleton batch dimension
@@ -474,7 +502,7 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
         mask = mask_out[..., :mask.shape[-1]]
         mask = mask.expand(b, heads, -1, -1)
 
-    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask, scale=kwargs.get("scale", None))
 
     if skip_output_reshape:
         out = out.permute(0, 2, 1, 3)
@@ -498,10 +526,8 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
     else:
         b, _, dim_head = q.shape
         dim_head //= heads
-        q, k, v = map(
-            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
-            (q, k, v),
-        )
+        q, k, v = _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, kwargs.get("enable_gqa", False), expand_kv=False)
+        q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v))
 
     if mask is not None:
         # add a batch dimension if there isn't already one
@@ -511,9 +537,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
         if mask.ndim == 3:
             mask = mask.unsqueeze(1)
 
-    # Pass through extra SDPA kwargs (scale, enable_gqa) if provided
-    # enable_gqa requires PyTorch 2.5+; older versions use manual KV expansion above
-    sdpa_keys = ("scale", "enable_gqa") if TORCH_HAS_GQA else ("scale",)
+    sdpa_keys = ("scale", "enable_gqa")
     sdpa_extra = {k: v for k, v in kwargs.items() if k in sdpa_keys}
 
     if SDP_BATCH_LIMIT >= b:
@@ -541,20 +565,19 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
 
 @wrap_attn
 def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
-    if kwargs.get("low_precision_attention", True) is False:
+    if kwargs.get("low_precision_attention", True) is False or (mask is not None and not SAGE_ATTENTION_SUPPORTS_MASK):
         return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=skip_reshape, skip_output_reshape=skip_output_reshape, **kwargs)
 
     exception_fallback = False
     if skip_reshape:
         b, _, _, dim_head = q.shape
         tensor_layout = "HND"
+        if kwargs.get("enable_gqa", False):
+            k, v = _repeat_kv_for_gqa(k, v, q.shape[-3], -3)
     else:
         b, _, dim_head = q.shape
         dim_head //= heads
-        q, k, v = map(
-            lambda t: t.view(b, -1, heads, dim_head),
-            (q, k, v),
-        )
+        q, k, v = _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, kwargs.get("enable_gqa", False))
         tensor_layout = "NHD"
 
     if mask is not None:
@@ -565,8 +588,12 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
         if mask.ndim == 3:
             mask = mask.unsqueeze(1)
 
+    sage_kwargs = {"is_causal": False, "tensor_layout": tensor_layout, "sm_scale": kwargs.get("scale", None), "smooth_k": False}
+    if mask is not None:
+        sage_kwargs["attn_mask"] = mask
+
     try:
-        out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
+        out = sageattn(q, k, v, **sage_kwargs)
     except Exception as e:
         logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e))
         exception_fallback = True
@@ -616,7 +643,6 @@ def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
                 skip_output_reshape=skip_output_reshape,
                 **kwargs
             )
-        q_s, k_s, v_s = q, k, v
         N = q.shape[2]
         dim_head = D
     else:
@@ -642,11 +668,15 @@ def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
                 **kwargs
             )
 
-    if not skip_reshape:
-        q_s, k_s, v_s = map(
-            lambda t: t.view(B, -1, heads, dim_head).permute(0, 2, 1, 3).contiguous(),
-            (q, k, v),
-        )
+    if skip_reshape:
+        q_s = q
+        if kwargs.get("enable_gqa", False):
+            k_s, v_s = _repeat_kv_for_gqa(k, v, H, -3)
+        else:
+            k_s, v_s = k, v
+    else:
+        q_s, k_s, v_s = _reshape_qkv_to_heads(q, k, v, B, heads, dim_head, kwargs.get("enable_gqa", False))
+        q_s, k_s, v_s = map(lambda t: t.permute(0, 2, 1, 3).contiguous(), (q_s, k_s, v_s))
         B, H, L, D = q_s.shape
 
     try:
@@ -662,7 +692,7 @@ def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
                 q, k, v, heads,
                 mask=mask,
                 attn_precision=attn_precision,
-                skip_reshape=False,
+                skip_reshape=skip_reshape,
                 skip_output_reshape=skip_output_reshape,
                 **kwargs
             )
@@ -681,19 +711,20 @@ def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
 try:
     @torch.library.custom_op("flash_attention::flash_attn", mutates_args=())
     def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
-                    dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
-        return flash_attn_func(q, k, v, dropout_p=dropout_p, causal=causal)
+                    dropout_p: float = 0.0, causal: bool = False, softmax_scale: float = -1.0) -> torch.Tensor:
+        softmax_scale_arg = None if softmax_scale == -1.0 else softmax_scale
+        return flash_attn_func(q, k, v, dropout_p=dropout_p, causal=causal, softmax_scale=softmax_scale_arg)
 
 
     @flash_attn_wrapper.register_fake
-    def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False):
+    def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False, softmax_scale=-1.0):
         # Output shape is the same as q
         return q.new_empty(q.shape)
 except AttributeError as error:
     FLASH_ATTN_ERROR = error
 
     def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
-                    dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
+                    dropout_p: float = 0.0, causal: bool = False, softmax_scale: float = -1.0) -> torch.Tensor:
         assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}"
 
 @wrap_attn
@@ -703,10 +734,8 @@ def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
     else:
         b, _, dim_head = q.shape
         dim_head //= heads
-        q, k, v = map(
-            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
-            (q, k, v),
-        )
+        q, k, v = _reshape_qkv_to_heads(q, k, v, b, heads, dim_head, kwargs.get("enable_gqa", False), expand_kv=False)
+        q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v))
 
     if mask is not None:
         # add a batch dimension if there isn't already one
@@ -725,10 +754,16 @@ def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
             v.transpose(1, 2),
             dropout_p=0.0,
             causal=False,
+            softmax_scale=kwargs.get("scale", -1.0),
         ).transpose(1, 2)
     except Exception as e:
         logging.warning(f"Flash Attention failed, using default SDPA: {e}")
-        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+        sdpa_extra = {}
+        if kwargs.get("enable_gqa", False):
+            sdpa_extra["enable_gqa"] = True
+        if "scale" in kwargs:
+            sdpa_extra["scale"] = kwargs["scale"]
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False, **sdpa_extra)
     if not skip_output_reshape:
         out = (
             out.transpose(1, 2).reshape(b, -1, heads * dim_head)
@@ -1209,5 +1244,3 @@ class SpatialVideoTransformer(SpatialTransformer):
             x = self.proj_out(x)
         out = x + x_in
         return out
-
-

comfy/ldm/modules/diffusionmodules/model.py

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

comfy/ldm/omnigen/omnigen2.py

@@ -141,11 +141,8 @@ class Attention(nn.Module):
         key = key.transpose(1, 2)
         value = value.transpose(1, 2)
 
-        if self.kv_heads < self.heads:
-            key = key.repeat_interleave(self.heads // self.kv_heads, dim=1)
-            value = value.repeat_interleave(self.heads // self.kv_heads, dim=1)
-
-        hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options)
+        gqa_kwargs = {"enable_gqa": True} if self.kv_heads < self.heads else {}
+        hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options, **gqa_kwargs)
         hidden_states = self.to_out[0](hidden_states)
         return hidden_states
 

comfy/ldm/seedvr/attention.py

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

comfy/ldm/seedvr/color_fix.py

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

comfy/ldm/seedvr/constants.py

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

comfy/model_base.py

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

comfy/model_detection.py

@@ -598,6 +598,44 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
         return dit_config
 
+    seedvr2_7b_separate_key = "{}blocks.35.mlp.vid.proj_out.weight".format(key_prefix)
+    if seedvr2_7b_separate_key in state_dict_keys and state_dict[seedvr2_7b_separate_key].shape[0] == 3072: # seedvr2 7b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 3072
+        dit_config["heads"] = 24
+        dit_config["num_layers"] = 36
+        # This checkpoint uses separate vid/txt MMModule keys in every block.
+        dit_config["mm_layers"] = 36
+        dit_config["norm_eps"] = 1e-5
+        dit_config["rope_type"] = "rope3d"
+        dit_config["rope_dim"] = 64
+        dit_config["mlp_type"] = "normal"
+        return dit_config
+    if "{}blocks.35.mlp.all.proj_in_gate.weight".format(key_prefix) in state_dict_keys: # seedvr2 7b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 3072
+        dit_config["heads"] = 24
+        dit_config["num_layers"] = 36
+        # This checkpoint uses shared all.* MMModule keys after the initial blocks.
+        dit_config["mm_layers"] = 10
+        dit_config["norm_eps"] = 1e-5
+        dit_config["rope_type"] = "rope3d"
+        dit_config["rope_dim"] = 64
+        dit_config["mlp_type"] = "swiglu"
+        return dit_config
+    if "{}blocks.31.mlp.all.proj_in_gate.weight".format(key_prefix) in state_dict_keys: # seedvr2 3b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 2560
+        dit_config["heads"] = 20
+        dit_config["num_layers"] = 32
+        dit_config["norm_eps"] = 1.0e-05
+        dit_config["mlp_type"] = "swiglu"
+        dit_config["vid_out_norm"] = True
+        return dit_config
+
     if '{}head.modulation'.format(key_prefix) in state_dict_keys:  # Wan 2.1
         dit_config = {}
         dit_config["image_model"] = "wan2.1"
@@ -1119,9 +1157,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
     return unet_config
 
-def model_config_from_unet_config(unet_config, state_dict=None):
+
+def model_config_from_unet_config(unet_config, state_dict=None, unet_key_prefix=""):
     for model_config in comfy.supported_models.models:
-        if model_config.matches(unet_config, state_dict):
+        if model_config.matches(unet_config, state_dict, unet_key_prefix=unet_key_prefix):
             return model_config(unet_config)
 
     logging.error("no match {}".format(unet_config))
@@ -1131,7 +1170,7 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
     unet_config = detect_unet_config(state_dict, unet_key_prefix, metadata=metadata)
     if unet_config is None:
         return None
-    model_config = model_config_from_unet_config(unet_config, state_dict)
+    model_config = model_config_from_unet_config(unet_config, state_dict, unet_key_prefix)
     if model_config is None and use_base_if_no_match:
         model_config = comfy.supported_models_base.BASE(unet_config)
 

comfy/model_management.py

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

comfy/ops.py

@@ -174,6 +174,8 @@ def cast_modules_with_vbar(comfy_modules, dtype, device, bias_dtype, non_blockin
                     elif xfer_dest2 is not None:
                         xfer_source.prepare(xfer_dest2, stream, copy=True, commit=False)
                         return
+                    else:
+                        return
                 comfy.model_management.cast_to_gathered(xfer_source, xfer_dest, non_blocking=non_blocking, stream=stream, r2=xfer_dest2)
 
         def handle_pin(m, pin, source, dest, subset="weights", size=None):
@@ -1102,6 +1104,21 @@ def _load_quantized_module(module, super_load, state_dict, prefix, local_metadat
                 scales["convrot_groupsize"] = int(
                     layer_conf.get("convrot_groupsize", params_conf.get("convrot_groupsize", 256))
                 )
+        elif module.quant_format == "convrot_w4a4":
+            scale = pop_scale("weight_scale")
+            if scale is None:
+                raise ValueError(f"Missing ConvRot W4A4 weight scale for layer {layer_name}")
+            params_conf = layer_conf.get("params", {})
+            if not isinstance(params_conf, dict):
+                params_conf = {}
+            scales = {
+                "scale": scale,
+                "convrot_groupsize": int(
+                    layer_conf.get("convrot_groupsize", params_conf.get("convrot_groupsize", 256))
+                ),
+                "quant_group_size": 64,
+                "linear_dtype": layer_conf.get("linear_dtype", params_conf.get("linear_dtype", "int4")),
+            }
         else:
             raise ValueError(f"Unsupported quantization format: {module.quant_format}")
 
@@ -1148,6 +1165,11 @@ def _quantized_weight_state_dict(module, sd, prefix, extra_quant_conf=None, extr
         if module.quant_format == "int8_tensorwise" and getattr(params, "convrot", False):
             quant_conf["convrot"] = True
             quant_conf["convrot_groupsize"] = getattr(params, "convrot_groupsize", 256)
+        elif module.quant_format == "convrot_w4a4":
+            quant_conf["convrot_groupsize"] = getattr(params, "convrot_groupsize", 256)
+            linear_dtype = getattr(params, "linear_dtype", "int4")
+            if linear_dtype != "int4":
+                quant_conf["linear_dtype"] = linear_dtype
         if extra_quant_conf:
             quant_conf.update(extra_quant_conf)
         sd[f"{prefix}comfy_quant"] = torch.tensor(list(json.dumps(quant_conf).encode("utf-8")), dtype=torch.uint8)
@@ -1235,7 +1257,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 run_every_op()
 
                 input_shape = input.shape
-                reshaped_3d = False
+                reshaped_nd = False
                 #If cast needs to apply lora, it should be done in the compute dtype
                 compute_dtype = input.dtype
 
@@ -1272,12 +1294,12 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 # Inference path (unchanged)
                 if _use_quantized and quantize_input:
 
-                    # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others)
-                    input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input
+                    # Reshape >=3D tensors to 2D for quantization (needed for NVFP4 and others)
+                    input_reshaped = input.reshape(-1, input_shape[-1]) if input.ndim >= 3 else input
 
                     # Fall back to non-quantized for non-2D tensors
                     if input_reshaped.ndim == 2:
-                        reshaped_3d = input.ndim == 3
+                        reshaped_nd = input.ndim >= 3
                         # dtype is now implicit in the layout class
                         scale = getattr(self, 'input_scale', None)
                         if scale is not None:
@@ -1292,9 +1314,9 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                     weight_only_quant=weight_only_quant,
                 )
 
-                # Reshape output back to 3D if input was 3D
-                if reshaped_3d:
-                    output = output.reshape((input_shape[0], input_shape[1], self.weight.shape[0]))
+                # Reshape output back to original rank if input was >2D
+                if reshaped_nd:
+                    output = output.reshape((*input_shape[:-1], self.weight.shape[0]))
 
                 return output
 
@@ -1428,6 +1450,12 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 }
                 if hasattr(params, "block_scale"): # NVFP4
                     kwargs["block_scale"] = params.block_scale[i]
+                if hasattr(params, "quant_group_size"):
+                    kwargs["quant_group_size"] = params.quant_group_size
+                if hasattr(params, "convrot_groupsize"):
+                    kwargs["convrot_groupsize"] = params.convrot_groupsize
+                if hasattr(params, "linear_dtype"):
+                    kwargs["linear_dtype"] = params.linear_dtype
                 return QuantizedTensor(weight._qdata[i], weight._layout_cls, type(params)(**kwargs))
 
             def state_dict(self, *args, destination=None, prefix="", **kwargs):

comfy/quant_ops.py

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

comfy/sd.py

@@ -16,6 +16,7 @@ import comfy.ldm.cosmos.vae
 import comfy.ldm.wan.vae
 import comfy.ldm.wan.vae2_2
 import comfy.ldm.hunyuan3d.vae
+import comfy.ldm.seedvr.vae
 import comfy.ldm.triposplat.vae
 import comfy.ldm.ace.vae.music_dcae_pipeline
 import comfy.ldm.cogvideo.vae
@@ -468,9 +469,13 @@ class CLIP:
     def decode(self, token_ids, skip_special_tokens=True):
         return self.tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
 
+    def is_dynamic(self):
+        return self.patcher.is_dynamic()
+
 class VAE:
     def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None):
-        if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
+        is_seedvr2_vae = "decoder.up_blocks.2.upsamplers.0.upscale_conv.weight" in sd
+        if not is_seedvr2_vae and 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
             sd = diffusers_convert.convert_vae_state_dict(sd)
 
         if model_management.is_amd():
@@ -497,6 +502,8 @@ class VAE:
         self.upscale_index_formula = None
         self.extra_1d_channel = None
         self.crop_input = True
+        self.handles_tiling = False
+        self.format_encoded = None
 
         self.audio_sample_rate = 44100
 
@@ -543,6 +550,22 @@ class VAE:
                 self.first_stage_model = StageC_coder()
                 self.downscale_ratio = 32
                 self.latent_channels = 16
+            elif "decoder.up_blocks.2.upsamplers.0.upscale_conv.weight" in sd: # seedvr2
+                self.first_stage_model = comfy.ldm.seedvr.vae.VideoAutoencoderKLWrapper()
+                self.latent_channels = comfy.ldm.seedvr.vae.SEEDVR2_LATENT_CHANNELS
+                self.latent_dim = 3
+                self.disable_offload = True
+                self.memory_used_decode = lambda shape, dtype: self.first_stage_model.comfy_memory_used_decode(shape)
+                self.memory_used_encode = lambda shape, dtype: (max(shape[2], 5) * shape[3] * shape[4] * 64) * model_management.dtype_size(dtype)
+                self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+                self.handles_tiling = True
+                self.format_encoded = self.first_stage_model.comfy_format_encoded
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
+                self.downscale_index_formula = (4, 8, 8)
+                self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
+                self.upscale_index_formula = (4, 8, 8)
+                self.process_input = lambda image: image * 2.0 - 1.0
+                self.crop_input = False
             elif "decoder.conv_in.weight" in sd:
                 if sd['decoder.conv_in.weight'].shape[1] == 64:
                     ddconfig = {"block_out_channels": [128, 256, 512, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 32, "downsample_match_channel": True, "upsample_match_channel": True}
@@ -1009,6 +1032,10 @@ class VAE:
         decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, index_formulas=self.upscale_index_formula, output_device=self.output_device))
 
+    def _decode_tiled_owned(self, samples, **kwargs):
+        out = self.first_stage_model.decode_tiled(samples.to(self.vae_dtype).to(self.device), **kwargs)
+        return self.process_output(out.to(device=self.output_device, dtype=self.vae_output_dtype(), copy=True))
+
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
         steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
         steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
@@ -1045,6 +1072,25 @@ class VAE:
         encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).to(dtype=self.vae_output_dtype())
         return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device)
 
+    def _encode_tiled_owned(self, pixel_samples, **kwargs):
+        x = self.process_input(pixel_samples).to(self.vae_dtype).to(self.device)
+        out = self.first_stage_model.encode_tiled(x, **kwargs)
+        return out.to(device=self.output_device, dtype=self.vae_output_dtype())
+
+    def _owned_tiled_args(self, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
+        args = {}
+        if tile_x is not None:
+            args["tile_x"] = tile_x
+        if tile_y is not None:
+            args["tile_y"] = tile_y
+        if overlap is not None:
+            args["overlap"] = overlap
+        if tile_t is not None:
+            args["tile_t"] = tile_t
+        if overlap_t is not None:
+            args["overlap_t"] = overlap_t
+        return args
+
     def decode(self, samples_in, vae_options={}):
         self.throw_exception_if_invalid()
         pixel_samples = None
@@ -1092,11 +1138,19 @@ class VAE:
                 if dims == 1 or self.extra_1d_channel is not None:
                     pixel_samples = self.decode_tiled_1d(samples_in)
                 elif dims == 2:
-                    pixel_samples = self.decode_tiled_(samples_in)
+                    if self.handles_tiling:
+                        tile = 256 // self.spacial_compression_decode()
+                        overlap = tile // 4
+                        pixel_samples = self._decode_tiled_owned(samples_in, tile_x=tile, tile_y=tile, overlap=overlap)
+                    else:
+                        pixel_samples = self.decode_tiled_(samples_in)
                 elif dims == 3:
                     tile = 256 // self.spacial_compression_decode()
                     overlap = tile // 4
-                    pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
+                    if self.handles_tiling:
+                        pixel_samples = self._decode_tiled_owned(samples_in, tile_x=tile, tile_y=tile, overlap=overlap)
+                    else:
+                        pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
 
         pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
         return pixel_samples
@@ -1115,7 +1169,9 @@ class VAE:
             args["overlap"] = overlap
 
         with model_management.cuda_device_context(self.device):
-            if dims == 1 or self.extra_1d_channel is not None:
+            if self.handles_tiling and dims in (2, 3):
+                output = self._decode_tiled_owned(samples, **self._owned_tiled_args(tile_x, tile_y, overlap, tile_t, overlap_t))
+            elif dims == 1 or self.extra_1d_channel is not None:
                 args.pop("tile_y")
                 output = self.decode_tiled_1d(samples, **args)
             elif dims == 2:
@@ -1176,12 +1232,17 @@ class VAE:
                 if self.latent_dim == 3:
                     tile = 256
                     overlap = tile // 4
-                    samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
+                    if self.handles_tiling:
+                        samples = self._encode_tiled_owned(pixel_samples, tile_x=tile, tile_y=tile, overlap=overlap)
+                    else:
+                        samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
                 elif self.latent_dim == 1 or self.extra_1d_channel is not None:
                     samples = self.encode_tiled_1d(pixel_samples)
                 else:
                     samples = self.encode_tiled_(pixel_samples)
 
+        if self.format_encoded is not None:
+            samples = self.format_encoded(samples)
         return samples
 
     def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
@@ -1189,7 +1250,7 @@ class VAE:
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
         dims = self.latent_dim
         pixel_samples = pixel_samples.movedim(-1, 1)
-        if dims == 3:
+        if dims == 3 and pixel_samples.ndim < 5:
             if not self.not_video:
                 pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
             else:
@@ -1213,21 +1274,27 @@ class VAE:
             elif dims == 2:
                 samples = self.encode_tiled_(pixel_samples, **args)
             elif dims == 3:
-                if tile_t is not None:
-                    tile_t_latent = max(2, self.downscale_ratio[0](tile_t))
+                if self.handles_tiling:
+                    samples = self._encode_tiled_owned(pixel_samples, **self._owned_tiled_args(tile_x, tile_y, overlap, tile_t, overlap_t))
                 else:
-                    tile_t_latent = 9999
-                args["tile_t"] = self.upscale_ratio[0](tile_t_latent)
+                    if tile_t is not None:
+                        tile_t_latent = max(2, self.downscale_ratio[0](tile_t))
+                    else:
+                        tile_t_latent = 9999
+                    args["tile_t"] = self.upscale_ratio[0](tile_t_latent)
 
-                if overlap_t is None:
-                    args["overlap"] = (1, overlap, overlap)
-                else:
-                    args["overlap"] = (self.upscale_ratio[0](max(1, min(tile_t_latent // 2, self.downscale_ratio[0](overlap_t)))), overlap, overlap)
-                maximum = pixel_samples.shape[2]
-                maximum = self.upscale_ratio[0](self.downscale_ratio[0](maximum))
+                    spatial_overlap = overlap if overlap is not None else 64
+                    if overlap_t is None:
+                        args["overlap"] = (1, spatial_overlap, spatial_overlap)
+                    else:
+                        args["overlap"] = (self.upscale_ratio[0](max(1, min(tile_t_latent // 2, self.downscale_ratio[0](overlap_t)))), spatial_overlap, spatial_overlap)
+                    maximum = pixel_samples.shape[2]
+                    maximum = self.upscale_ratio[0](self.downscale_ratio[0](maximum))
 
-                samples = self.encode_tiled_3d(pixel_samples[:,:,:maximum], **args)
+                    samples = self.encode_tiled_3d(pixel_samples[:,:,:maximum], **args)
 
+        if self.format_encoded is not None:
+            samples = self.format_encoded(samples)
         return samples
 
     def get_sd(self):
@@ -1251,6 +1318,11 @@ class VAE:
         except:
             return None
 
+    def is_dynamic(self):
+        # 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"):
@@ -1890,7 +1962,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
         manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
     else:
         manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
-    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
+    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype, device=load_device)
 
     if model_config.clip_vision_prefix is not None:
         if output_clipvision:
@@ -2031,7 +2103,7 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None, disable
         manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
     else:
         manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
-    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
+    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype, device=load_device)
 
     if custom_operations is not None:
         model_config.custom_operations = custom_operations

comfy/supported_models.py

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

comfy/supported_models_base.py

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

comfy/text_encoders/gemma4.py

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

comfy/text_encoders/gpt_oss.py

@@ -12,7 +12,7 @@ import torch.nn.functional as F
 
 import comfy.ops
 from comfy import sd1_clip
-from comfy.ldm.modules.attention import TORCH_HAS_GQA, optimized_attention_for_device
+from comfy.ldm.modules.attention import optimized_attention_for_device
 from comfy.text_encoders.llama import RMSNorm, apply_rope
 
 
@@ -110,10 +110,6 @@ def _attention_with_sinks(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, sin
     putting the sink logit in the mask at that column.
     """
 
-    if num_kv_groups > 1 and not TORCH_HAS_GQA:
-        k = k.repeat_interleave(num_kv_groups, dim=1)
-        v = v.repeat_interleave(num_kv_groups, dim=1)
-
     B, _, S_q, D = q.shape
     H_kv = k.shape[1]
     S_kv = k.shape[-2]

comfy/text_encoders/llama.py

@@ -550,10 +550,8 @@ class Attention(nn.Module):
                 xv = xv[:, :, -sliding_window:]
                 attention_mask = attention_mask[..., -sliding_window:] if attention_mask is not None else None
 
-        xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
-        xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
-
-        output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True)
+        gqa_kwargs = {"enable_gqa": True} if self.num_heads != self.num_kv_heads else {}
+        output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True, **gqa_kwargs)
         return self.o_proj(output), present_key_value
 
 class MLP(nn.Module):
@@ -937,22 +935,41 @@ class BaseGenerate:
             return torch.argmax(logits, dim=-1, keepdim=True)
 
         # Sampling mode
-        if repetition_penalty != 1.0:
-            for i in range(logits.shape[0]):
-                for token_id in set(token_history):
-                    logits[i, token_id] *= repetition_penalty if logits[i, token_id] < 0 else 1/repetition_penalty
-
-        if presence_penalty is not None and presence_penalty != 0.0:
-            for i in range(logits.shape[0]):
-                for token_id in set(token_history):
-                    logits[i, token_id] -= presence_penalty
+        if len(token_history) > 0 and (repetition_penalty != 1.0 or (presence_penalty is not None and presence_penalty != 0.0)):
+            token_ids = torch.tensor(list(set(token_history)), device=logits.device)
+            token_logits = logits[:, token_ids]
+            if repetition_penalty != 1.0:
+                token_logits = torch.where(token_logits < 0, token_logits * repetition_penalty, token_logits / repetition_penalty)
+            if presence_penalty is not None and presence_penalty != 0.0:
+                token_logits = token_logits - presence_penalty
+            logits[:, token_ids] = token_logits
 
         if temperature != 1.0:
             logits = logits / temperature
 
         if top_k > 0:
-            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
-            logits[indices_to_remove] = torch.finfo(logits.dtype).min
+            top_k = min(top_k, logits.shape[-1])
+            logits, top_indices = torch.topk(logits, top_k)
+
+            if min_p > 0.0:
+                probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)
+                top_probs, _ = probs_before_filter.max(dim=-1, keepdim=True)
+                min_threshold = min_p * top_probs
+                indices_to_remove = probs_before_filter < min_threshold
+                logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+            if top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+                cumulative_probs = torch.cumsum(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[..., 0] = False
+                indices_to_remove = torch.zeros_like(logits, dtype=torch.bool)
+                indices_to_remove.scatter_(1, sorted_indices, sorted_indices_to_remove)
+                logits[indices_to_remove] = torch.finfo(logits.dtype).min
+
+            probs = torch.nn.functional.softmax(logits, dim=-1)
+            next_token = torch.multinomial(probs, num_samples=1, generator=generator)
+            return top_indices.gather(1, next_token)
 
         if min_p > 0.0:
             probs_before_filter = torch.nn.functional.softmax(logits, dim=-1)

comfy/text_encoders/qwen35.py

@@ -366,12 +366,8 @@ class GatedAttention(nn.Module):
                     xv = torch.cat((past_value[:, :, :index], xv), dim=2)
                 present_key_value = (xk, xv, index + num_tokens)
 
-        # Expand KV heads for GQA
-        if self.num_heads != self.num_kv_heads:
-            xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
-            xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
-
-        output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True)
+        gqa_kwargs = {"enable_gqa": True} if self.num_heads != self.num_kv_heads else {}
+        output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True, **gqa_kwargs)
         output = output * gate.sigmoid()
 
         return self.o_proj(output), present_key_value

comfy/text_encoders/qwen3vl.py

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

comfy_api/feature_flags.py

@@ -100,6 +100,7 @@ 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/latest/_input_impl/video_types.py

@@ -281,11 +281,18 @@ class VideoFromFile(VideoInput):
         video_done = False
         audio_done = True
 
-        if len(container.streams.audio):
-            audio_stream = container.streams.audio[-1]
+        # Use the last decodable audio stream. Streams FFmpeg has no decoder for have no codec context,
+        # and decoding their packets crashes the process. (e.g. APAC spatial-audio track in iPhone)
+        audio_stream = next(
+            (s for s in reversed(container.streams.audio) if s.codec_context is not None),
+            None,
+        )
+        if audio_stream is not None:
             streams += [audio_stream]
             resampler = av.audio.resampler.AudioResampler(format='fltp')
             audio_done = False
+        elif len(container.streams.audio):
+            logging.warning("No decodable audio stream found in video; ignoring audio.")
 
         for packet in container.demux(*streams):
             if video_done and audio_done:
@@ -457,10 +464,13 @@ class VideoFromFile(VideoInput):
                         else:
                             output_container.metadata[key] = json.dumps(value)
 
-                # Add streams to the new container
+                # Add streams to the new container. Streams with no codec context cannot be used as an output template.
                 stream_map = {}
                 for stream in streams:
                     if isinstance(stream, (av.VideoStream, av.AudioStream, SubtitleStream)):
+                        if stream.codec_context is None:
+                            logging.warning("Skipping %s stream %d with unsupported codec", stream.type, stream.index)
+                            continue
                         out_stream = output_container.add_stream_from_template(template=stream, opaque=True)
                         stream_map[stream] = out_stream
 

comfy_api_nodes/apis/bytedance.py

@@ -17,6 +17,10 @@ class Seedream4Options(BaseModel):
     max_images: int = Field(15)
 
 
+class Seedream5OptimizePromptOptions(BaseModel):
+    thinking: Literal["auto", "enabled", "disabled"] = Field(...)
+
+
 class Seedream4TaskCreationRequest(BaseModel):
     model: str = Field(...)
     prompt: str = Field(...)
@@ -24,10 +28,11 @@ 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
+    optimize_prompt_options: Seedream5OptimizePromptOptions | None = None
 
 
 class ImageTaskCreationResponse(BaseModel):
@@ -261,6 +266,19 @@ _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/apis/hunyuan3d.py

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

comfy_api_nodes/nodes_bytedance.py

@@ -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,
@@ -33,6 +34,7 @@ from comfy_api_nodes.apis.bytedance import (
     SeedanceVirtualLibraryCreateAssetRequest,
     Seedream4Options,
     Seedream4TaskCreationRequest,
+    Seedream5OptimizePromptOptions,
     TaskAudioContent,
     TaskAudioContentUrl,
     TaskCreationResponse,
@@ -80,12 +82,14 @@ _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 +747,15 @@ 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 +765,7 @@ 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 +773,27 @@ 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 +803,18 @@ 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 +836,16 @@ 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),
@@ -846,6 +876,17 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
                     tooltip='Whether to add an "AI generated" watermark to the image.',
                     advanced=True,
                 ),
+                IO.Boolean.Input(
+                    "thinking",
+                    default=True,
+                    tooltip=(
+                        "Enable the model's prompt-optimization reasoning ('thinking') for better adherence. "
+                        "Can substantially increase generation time — notably on Seedream 5.0 Pro. "
+                        "Can only be disabled for text-to-image (not when reference images are provided)."
+                    ),
+                    optional=True,
+                    advanced=True,
+                ),
             ],
             outputs=[
                 IO.Image.Output(),
@@ -857,15 +898,27 @@ 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 }
                   }
                 )
                 """,
@@ -879,10 +932,12 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
         model: dict,
         seed: int = 0,
         watermark: bool = False,
+        thinking: bool = True,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
         model_id = SEEDREAM_MODELS[model["model"]]
         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 +957,29 @@ 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)
@@ -927,6 +992,10 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
             raise ValueError(
                 "The maximum number of generated images plus the number of reference images cannot exceed 15."
             )
+        if not thinking and n_input_images > 0:
+            raise ValueError(
+                "'thinking' can only be disabled for text-to-image; enable it when using reference images."
+            )
 
         reference_images_urls: list[str] = []
         if image_tensors:
@@ -940,6 +1009,9 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode):
                 wait_label="Uploading reference images",
             )
 
+        optimize_prompt_options = None
+        if n_input_images == 0:
+            optimize_prompt_options = Seedream5OptimizePromptOptions(thinking="enabled" if thinking else "disabled")
         response = await sync_op(
             cls,
             ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"),
@@ -950,9 +1022,10 @@ 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,
+                optimize_prompt_options=optimize_prompt_options,
             ),
         )
         if len(response.data) == 1:

comfy_api_nodes/nodes_hunyuan3d.py

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

comfy_api_nodes/util/_helpers.py

@@ -11,6 +11,7 @@ from io import BytesIO
 from yarl import URL
 
 from comfy.cli_args import args
+from comfy.comfy_api_env import normalize_comfy_api_base
 from comfy.deploy_environment import get_deploy_environment
 from comfy.model_management import processing_interrupted
 from comfy_api.latest import IO
@@ -63,7 +64,7 @@ def get_comfy_api_headers(node_cls: type[IO.ComfyNode]) -> dict[str, str]:
 
 
 def default_base_url() -> str:
-    return getattr(args, "comfy_api_base", "https://api.comfy.org")
+    return normalize_comfy_api_base(getattr(args, "comfy_api_base", "https://api.comfy.org"))
 
 
 async def sleep_with_interrupt(

comfy_api_nodes/util/request_logger.py

@@ -9,6 +9,7 @@ from typing import Any
 import folder_paths
 
 logger = logging.getLogger(__name__)
+_SENSITIVE_HEADERS = {"authorization", "x-api-key"}
 
 
 def get_log_directory():
@@ -73,6 +74,10 @@ def _format_data_for_logging(data: Any) -> str:
     return str(data)
 
 
+def _redact_headers(headers: dict) -> dict:
+    return {k: ("***" if k.lower() in _SENSITIVE_HEADERS else v) for k, v in headers.items()}
+
+
 def log_request_response(
     operation_id: str,
     request_method: str,
@@ -101,7 +106,7 @@ def log_request_response(
         log_content.append(f"Method: {request_method}")
         log_content.append(f"URL: {request_url}")
         if request_headers:
-            log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}")
+            log_content.append(f"Headers:\n{_format_data_for_logging(_redact_headers(request_headers))}")
         if request_params:
             log_content.append(f"Params:\n{_format_data_for_logging(request_params)}")
         if request_data is not None:

comfy_api_nodes/util/upload_helpers.py

@@ -158,7 +158,14 @@ async def upload_video_to_comfyapi(
 
     # Convert VideoInput to BytesIO using specified container/codec
     video_bytes_io = BytesIO()
-    video.save_to(video_bytes_io, format=container, codec=codec)
+    try:
+        video.save_to(video_bytes_io, format=container, codec=codec)
+    except Exception as e:
+        raise ValueError(
+            f"Could not convert the input video to {container.value.upper()} for upload; "
+            f"the file may be corrupted or use an unsupported codec. "
+            f"Try re-exporting it as MP4 (H.264). Original error: {e}"
+        ) from e
     video_bytes_io.seek(0)
 
     return await upload_file_to_comfyapi(cls, video_bytes_io, filename, upload_mime_type, wait_label)

comfy_execution/caching.py

@@ -503,6 +503,22 @@ RAM_CACHE_DEFAULT_RAM_USAGE = 0.05
 
 RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER = 1.3
 
+RAM_CACHE_LARGE_INTERMEDIATE = 512 * 1024 ** 2
+
+
+def all_outputs_dynamic(outputs):
+    if outputs is None:
+        return False
+
+    for output in outputs:
+        if isinstance(output, (list, tuple)):
+            if not all_outputs_dynamic(output):
+                return False
+        elif not hasattr(output, "is_dynamic") or not output.is_dynamic():
+            return False
+
+    return True
+
 class RAMPressureCache(LRUCache):
 
     def __init__(self, key_class, enable_providers=False):
@@ -524,20 +540,25 @@ class RAMPressureCache(LRUCache):
         self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time()
         super().set_local(node_id, value)
 
-    def ram_release(self, target, free_active=False):
+    def ram_release(self, target, free_active=False, min_entry_size=0):
         if psutil.virtual_memory().available >= target:
-            return
+            return 0
 
         clean_list = []
 
         for key, cache_entry in self.cache.items():
             if not free_active and self.used_generation[key] == self.generation:
                 continue
-            oom_score =  RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER ** (self.generation - self.used_generation[key])
+
+            if all_outputs_dynamic(cache_entry.outputs) and self.used_generation[key] == self.generation:
+                continue
+
+            oom_score = RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER ** (self.generation - self.used_generation[key])
 
             ram_usage = RAM_CACHE_DEFAULT_RAM_USAGE
+            oom_ram_usage = ram_usage
             def scan_list_for_ram_usage(outputs):
-                nonlocal ram_usage
+                nonlocal ram_usage, oom_ram_usage
                 if outputs is None:
                     return
                 for output in outputs:
@@ -545,19 +566,26 @@ class RAMPressureCache(LRUCache):
                         scan_list_for_ram_usage(output)
                     elif isinstance(output, torch.Tensor) and output.device.type == 'cpu':
                         ram_usage += output.numel() * output.element_size()
+                        oom_ram_usage += output.numel() * output.element_size()
                     elif isinstance(output, ModelPatcher) and self.used_generation[key] != self.generation:
                         #old ModelPatchers are the first to go
-                        ram_usage = 1e30
+                        oom_ram_usage = 1e30
             scan_list_for_ram_usage(cache_entry.outputs)
 
-            oom_score *= ram_usage
+            if ram_usage < min_entry_size:
+                continue
+
+            oom_score *= oom_ram_usage
             #In the case where we have no information on the node ram usage at all,
             #break OOM score ties on the last touch timestamp (pure LRU)
-            bisect.insort(clean_list, (oom_score, self.timestamps[key], key))
+            bisect.insort(clean_list, (oom_score, self.timestamps[key], key, ram_usage))
 
+        freed = 0
         while psutil.virtual_memory().available < target and clean_list:
-            _, _, key = clean_list.pop()
+            _, _, key, ram_usage = clean_list.pop()
             del self.cache[key]
             self.used_generation.pop(key, None)
             self.timestamps.pop(key, None)
             self.children.pop(key, None)
+            freed += ram_usage
+        return freed

comfy_execution/jobs.py

@@ -56,6 +56,9 @@ PREVIEWABLE_MEDIA_TYPES = frozenset({'images', 'video', 'audio', '3d', 'text'})
 # 3D file extensions for preview fallback (no dedicated media_type exists)
 THREE_D_EXTENSIONS = frozenset({'.obj', '.fbx', '.gltf', '.glb', '.usdz'})
 
+# Text file extensions for preview fallback (the formats SaveText can produce)
+TEXT_EXTENSIONS = frozenset({'.txt', '.md', '.json'})
+
 
 def has_3d_extension(filename: str) -> bool:
     lower = filename.lower()
@@ -143,9 +146,10 @@ def is_previewable(media_type: str, item: dict) -> bool:
     Maintains backwards compatibility with existing logic.
 
     Priority:
-    1. media_type is 'images', 'video', 'audio', or '3d'
+    1. media_type is 'images', 'video', 'audio', '3d', or 'text'
     2. format field starts with 'video/' or 'audio/'
     3. filename has a 3D extension (.obj, .fbx, .gltf, .glb, .usdz)
+    4. filename has a text extension (.txt, .md, .json, ...)
     """
     if media_type in PREVIEWABLE_MEDIA_TYPES:
         return True
@@ -156,10 +160,12 @@ def is_previewable(media_type: str, item: dict) -> bool:
     if fmt and (fmt.startswith('video/') or fmt.startswith('audio/')):
         return True
 
-    # Check for 3D files by extension
+    # Check for 3D and text files by extension
     filename = item.get('filename', '').lower()
     if any(filename.endswith(ext) for ext in THREE_D_EXTENSIONS):
         return True
+    if any(filename.endswith(ext) for ext in TEXT_EXTENSIONS):
+        return True
 
     return False
 
@@ -255,6 +261,10 @@ def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
     Preview priority (matching frontend):
     1. type="output" with previewable media
     2. Any previewable media
+
+    Text content entries (strings under 'text') are preview-only metadata,
+    matching the frontend's METADATA_KEYS: they can serve as the fallback
+    preview but are not counted as outputs.
     """
     count = 0
     preview_output = None
@@ -275,7 +285,6 @@ def get_outputs_summary(outputs: dict) -> tuple[int, Optional[dict]]:
                     if normalized is None:
                         # Not a 3D file string — check for text preview
                         if media_type == 'text':
-                            count += 1
                             if preview_output is None:
                                 if isinstance(item, tuple):
                                     text_value = item[0] if item else ''

comfy_extras/nodes_audio.py

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

comfy_extras/nodes_bounding_boxes.py

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

comfy_extras/nodes_color.py

@@ -16,23 +16,30 @@ class ColorToRGBInt(io.ComfyNode):
             ],
             outputs=[
                 io.Int.Output(display_name="rgb_int"),
-                io.Color.Output(display_name="hex")
+                io.Color.Output(display_name="hex"),
+                io.Float.Output(display_name="alpha"),
             ],
         )
 
     @classmethod
     def execute(cls, color: str) -> io.NodeOutput:
-        # expect format #RRGGBB
-        if len(color) != 7 or color[0] != "#":
-            raise ValueError("Color must be in format #RRGGBB")
+        # expect format #RRGGBB or #RRGGBBAA
+        if len(color) not in (7, 9) or color[0] != "#":
+            raise ValueError("Color must be in format #RRGGBB or #RRGGBBAA")
         try:
             int(color[1:], 16)
         except ValueError:
-            raise ValueError("Color must be in format #RRGGBB") from None
+            raise ValueError("Color must be in format #RRGGBB or #RRGGBBAA") from None
+
+        alpha = 1.0
+        if len(color) == 9:
+            alpha = int(color[7:9], 16) / 255.0
+            color = color[:7]
+
         r, g, b = hex_to_rgb(color)
 
         rgb_int = r * 256 * 256 + g * 256 + b
-        return io.NodeOutput(rgb_int, color)
+        return io.NodeOutput(rgb_int, color, alpha)
 
 
 class ColorExtension(ComfyExtension):

comfy_extras/nodes_load_3d.py

@@ -61,14 +61,10 @@ class Load3D(IO.ComfyNode):
 
     @classmethod
     def execute(cls, model_file, image, **kwargs) -> IO.NodeOutput:
-        image_path = folder_paths.get_annotated_filepath(image['image'])
-        mask_path = folder_paths.get_annotated_filepath(image['mask'])
-        normal_path = folder_paths.get_annotated_filepath(image['normal'])
-
         load_image_node = nodes.LoadImage()
-        output_image, ignore_mask = load_image_node.load_image(image=image_path)
-        ignore_image, output_mask = load_image_node.load_image(image=mask_path)
-        normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path)
+        output_image, ignore_mask = load_image_node.load_image(image=image['image'])
+        ignore_image, output_mask = load_image_node.load_image(image=image['mask'])
+        normal_image, ignore_mask2 = load_image_node.load_image(image=image['normal'])
 
         video = None
 
@@ -96,6 +92,7 @@ class Preview3D(IO.ComfyNode):
             search_aliases=["view mesh", "3d viewer"],
             display_name="Preview 3D & Animation",
             category="3d",
+            description="Preview a 3D model file without saving it to the ComfyUI output directory.",
             is_experimental=True,
             is_output_node=True,
             inputs=[
@@ -140,6 +137,7 @@ class Preview3DAdvanced(IO.ComfyNode):
             display_name="Preview 3D (Advanced)",
             search_aliases=["preview 3d", "3d viewer", "view mesh", "frame 3d", "3d camera output"],
             category="3d",
+            description="Preview a 3D model file without saving it to the ComfyUI output directory.",
             is_experimental=True,
             is_output_node=True,
             inputs=[
@@ -197,6 +195,7 @@ class PreviewGaussianSplat(IO.ComfyNode):
             node_id="PreviewGaussianSplat",
             display_name="Preview Splat",
             category="3d",
+            description="Preview a gaussian splat 3D file without saving it to the ComfyUI output directory.",
             is_experimental=True,
             is_output_node=True,
             search_aliases=[
@@ -265,6 +264,7 @@ class PreviewPointCloud(IO.ComfyNode):
             node_id="PreviewPointCloud",
             display_name="Preview Point Cloud",
             category="3d",
+            description="Preview a point cloud 3D file without saving it to the ComfyUI output directory.",
             is_experimental=True,
             is_output_node=True,
             search_aliases=[

comfy_extras/nodes_mask.py

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

comfy_extras/nodes_preview_any.py

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

comfy_extras/nodes_primitive.py

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

comfy_extras/nodes_save_3d.py

@@ -13,7 +13,7 @@ from typing_extensions import override
 
 import folder_paths
 from comfy.cli_args import args
-from comfy_api.latest import ComfyExtension, IO, Types
+from comfy_api.latest import ComfyExtension, IO, Types, UI
 
 
 def pack_variable_mesh_batch(vertices, faces, colors=None, uvs=None, texture=None, unlit=False):
@@ -406,10 +406,164 @@ class SaveGLB(IO.ComfyNode):
         return IO.NodeOutput(ui={"3d": results})
 
 
+def _save_file3d_to_output(model_3d: Types.File3D, filename_prefix: str) -> str:
+    full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
+        filename_prefix, folder_paths.get_output_directory()
+    )
+    ext = model_3d.format or "glb"
+    saved_filename = f"{filename}_{counter:05}.{ext}"
+    model_3d.save_to(os.path.join(full_output_folder, saved_filename))
+    return f"{subfolder}/{saved_filename}" if subfolder else saved_filename
+
+
+def execute_save_3d_advanced(model_3d, viewport_state, width, height, filename_prefix, kwargs) -> IO.NodeOutput:
+    model_file = _save_file3d_to_output(model_3d, filename_prefix)
+    camera_info_input = kwargs.get("camera_info", None)
+    camera_info = camera_info_input if camera_info_input is not None else viewport_state['camera_info']
+    model_3d_info_input = kwargs.get("model_3d_info", None)
+    model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', [])
+    return IO.NodeOutput(
+        model_3d,
+        model_3d_info,
+        camera_info,
+        width,
+        height,
+        ui=UI.PreviewUI3DAdvanced(model_file, camera_info, model_3d_info),
+    )
+
+
+class Save3DAdvanced(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="Save3DAdvanced",
+            display_name="Save 3D (Advanced)",
+            search_aliases=["save 3d", "export 3d model", "save mesh advanced"],
+            category="3d",
+            is_experimental=True,
+            is_output_node=True,
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DGLB,
+                        IO.File3DGLTF,
+                        IO.File3DFBX,
+                        IO.File3DOBJ,
+                        IO.File3DSTL,
+                        IO.File3DUSDZ,
+                        IO.File3DAny,
+                    ],
+                    tooltip="3D model file from an upstream 3D node.",
+                ),
+                IO.String.Input("filename_prefix", default="3d/ComfyUI"),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, filename_prefix: str, **kwargs) -> IO.NodeOutput:
+        return execute_save_3d_advanced(model_3d, viewport_state, width, height, filename_prefix, kwargs)
+
+
+class SaveGaussianSplat(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SaveGaussianSplat",
+            display_name="Save Splat",
+            search_aliases=["save splat", "save gaussian splat", "export gaussian", "export splat"],
+            category="3d",
+            is_experimental=True,
+            is_output_node=True,
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DSplatAny,
+                        IO.File3DPLY,
+                        IO.File3DSPLAT,
+                        IO.File3DSPZ,
+                        IO.File3DKSPLAT,
+                    ],
+                    tooltip="A gaussian splat 3D file.",
+                ),
+                IO.String.Input("filename_prefix", default="3d/ComfyUI"),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DSplatAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, filename_prefix: str, **kwargs) -> IO.NodeOutput:
+        return execute_save_3d_advanced(model_3d, viewport_state, width, height, filename_prefix, kwargs)
+
+
+class SavePointCloud(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="SavePointCloud",
+            display_name="Save Point Cloud",
+            search_aliases=["save point cloud", "save pointcloud", "export point cloud"],
+            category="3d",
+            is_experimental=True,
+            is_output_node=True,
+            inputs=[
+                IO.MultiType.Input(
+                    "model_3d",
+                    types=[
+                        IO.File3DPointCloudAny,
+                        IO.File3DPLY,
+                    ],
+                    tooltip="Point cloud file (.ply)",
+                ),
+                IO.String.Input("filename_prefix", default="3d/ComfyUI"),
+                IO.Load3D.Input("viewport_state"),
+                IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True),
+                IO.Load3DCamera.Input("camera_info", optional=True, advanced=True),
+                IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+                IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+            ],
+            outputs=[
+                IO.File3DPointCloudAny.Output(display_name="model_3d"),
+                IO.Load3DModelInfo.Output(display_name="model_3d_info"),
+                IO.Load3DCamera.Output(display_name="camera_info"),
+                IO.Int.Output(display_name="width"),
+                IO.Int.Output(display_name="height"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model_3d: Types.File3D, viewport_state, width: int, height: int, filename_prefix: str, **kwargs) -> IO.NodeOutput:
+        return execute_save_3d_advanced(model_3d, viewport_state, width, height, filename_prefix, kwargs)
+
+
 class Save3DExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
-        return [SaveGLB]
+        return [SaveGLB, Save3DAdvanced, SaveGaussianSplat, SavePointCloud]
 
 
 async def comfy_entrypoint() -> Save3DExtension:

comfy_extras/nodes_seedvr.py

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

comfy_extras/nodes_text.py

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

comfy_extras/nodes_text_overlay.py

@@ -0,0 +1,150 @@
+import numpy as np
+import torch
+from PIL import Image as PILImage, ImageColor, ImageDraw, ImageFont
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, IO
+
+
+class TextOverlay(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="TextOverlay",
+            display_name="Draw Text Overlay",
+            category="text",
+            description="Draw text overlay on an image or batch of images.",
+            search_aliases=["text", "label", "caption", "subtitle", "watermark", "title", "addlabel", "overlay"],
+            inputs=[
+                IO.Image.Input("images"),
+                IO.String.Input("text", multiline=True, default=""),
+                IO.Float.Input("font_size", default=5.0, min=0.5, max=50.0, step=0.5, tooltip="Font size as a percentage of the image height."),
+                IO.Color.Input("color", default="#ffffff", tooltip="Color of the text."),
+                IO.Combo.Input("position", options=["top", "bottom"], default="top"),
+                IO.Combo.Input("align", options=["left", "center", "right"], default="left"),
+                IO.Boolean.Input("outline", default=True, tooltip="Draw a black outline around the text."),
+            ],
+            outputs=[IO.Image.Output(display_name="images")],
+        )
+
+    @classmethod
+    def execute(cls, images, text, font_size, color, position, align, outline) -> IO.NodeOutput:
+        if text.strip() == "":
+            return IO.NodeOutput(images)
+
+        text = text.replace("\\n", "\n").replace("\\t", "\t")
+
+        text_rgba = cls.parse_color_to_rgba(color)
+        outline_rgba = (0, 0, 0, 255) if outline else (0, 0, 0, 0)
+
+        # Render the overlay once and composite it across all frames in the batch
+        height = images.shape[1]
+        width = images.shape[2]
+        overlay_rgb, overlay_alpha = cls.render_overlay_text(width, height, text, position, align, font_size, text_rgba, outline_rgba)
+        overlay_rgb = overlay_rgb.to(device=images.device, dtype=images.dtype)
+        overlay_alpha = overlay_alpha.to(device=images.device, dtype=images.dtype)
+
+        result = images * (1.0 - overlay_alpha) + overlay_rgb * overlay_alpha
+        return IO.NodeOutput(result)
+
+    @staticmethod
+    def parse_color_to_rgba(color_string):
+        parsed = ImageColor.getrgb(color_string)
+
+        if len(parsed) == 3:
+            return (*parsed, 255)
+
+        return parsed
+
+    @classmethod
+    def render_overlay_text(cls, width, height, text, position, align, font_size, text_rgba, outline_rgba):
+        line_spacing = 1.2
+        margin_percent = 1.0
+        min_font_percent = 2.0
+        min_font_pixels = 10
+        outline_thickness_factor = 0.04
+
+        # Draw onto a transparent layer so the result can be alpha-composited over any frame.
+        layer = PILImage.new("RGBA", (width, height), (0, 0, 0, 0))
+        draw = ImageDraw.Draw(layer)
+
+        margin = int(round(margin_percent / 100.0 * min(width, height)))
+        max_width = max(1, width - 2 * margin)
+        max_height = max(1, height - 2 * margin)
+
+        # Font scales with resolution, then shrinks to fit the height.
+        size = max(1, int(round(font_size / 100.0 * height)))
+        floor = min(size, max(min_font_pixels, int(round(min_font_percent / 100.0 * height))))
+
+        while True:
+            font = ImageFont.load_default(size=size)
+            stroke = max(1, int(round(size * outline_thickness_factor))) if outline_rgba[3] > 0 else 0
+            block = "\n".join(cls.wrap_text(text, font, max_width))
+            # convert line spacing to pixel spacing
+            single = draw.textbbox((0, 0), "Ay", font=font, stroke_width=stroke)
+            double = draw.multiline_textbbox((0, 0), "Ay\nAy", font=font, spacing=0, stroke_width=stroke)
+            natural_advance = (double[3] - double[1]) - (single[3] - single[1])
+            pixel_spacing = int(round(size * line_spacing - natural_advance))
+            box = draw.multiline_textbbox((0, 0), block, font=font, spacing=pixel_spacing, stroke_width=stroke)
+            block_height = box[3] - box[1]
+
+            if block_height <= max_height or size <= floor:
+                break
+
+            size = max(floor, int(size * 0.9))
+
+        anchor_h, x = {"left": ("l", margin), "center": ("m", width / 2), "right": ("r", width - margin)}[align]
+
+        # Offset y so the rendered text sits flush against the margin
+        if position == "bottom":
+            y = height - margin - box[3]
+        else:
+            y = margin - box[1]
+
+        draw.multiline_text((x, y), block, font=font, fill=text_rgba, anchor=anchor_h + "a",
+                            align=align, spacing=pixel_spacing, stroke_width=stroke, stroke_fill=outline_rgba)
+
+        overlay = np.array(layer).astype(np.float32) / 255.0
+        overlay_rgb = torch.from_numpy(overlay[:, :, :3])
+        overlay_alpha = torch.from_numpy(overlay[:, :, 3:4])
+        return overlay_rgb, overlay_alpha
+
+    @staticmethod
+    def wrap_text(text, font, max_width):
+        lines = []
+        for raw_line in text.split("\n"):
+            words = raw_line.split()
+            if not words:
+                lines.append("")
+                continue
+            current = ""
+            # Break the line into words and split words that are too long
+            for word in words:
+                while font.getlength(word) > max_width and len(word) > 1:
+                    cut = 1
+                    while cut < len(word) and font.getlength(word[:cut + 1]) <= max_width:
+                        cut += 1
+                    if current:
+                        lines.append(current)
+                        current = ""
+                    lines.append(word[:cut])
+                    word = word[cut:]
+                candidate = word if not current else current + " " + word
+                if not current or font.getlength(candidate) <= max_width:
+                    current = candidate
+                else:
+                    lines.append(current)
+                    current = word
+            if current:
+                lines.append(current)
+        return lines
+
+
+class TextOverlayExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [TextOverlay]
+
+
+async def comfy_entrypoint() -> TextOverlayExtension:
+    return TextOverlayExtension()

execution.py

@@ -29,6 +29,7 @@ from comfy_execution.caching import (
     HierarchicalCache,
     LRUCache,
     RAMPressureCache,
+    RAM_CACHE_LARGE_INTERMEDIATE,
 )
 from comfy_execution.graph import (
     DynamicPrompt,
@@ -794,12 +795,16 @@ class PromptExecutor:
                     if self.cache_type == CacheType.RAM_PRESSURE:
                         ram_release_callback(ram_inactive_headroom)
                         ram_shortfall = ram_headroom - psutil.virtual_memory().available
-                        freed = comfy.model_management.free_pins(ram_shortfall + 512 * (1024 ** 2))
-                        if freed < ram_shortfall:
-                            if freed > 64 * (1024 ** 2):
-                                # AIMDO MEM_DECOMMIT can outrun psutil.available catching up.
-                                time.sleep(0.05)
-                            ram_release_callback(ram_headroom, free_active=True)
+                        if ram_shortfall > 0:
+                            freed = ram_release_callback(ram_headroom, free_active=True, min_entry_size=RAM_CACHE_LARGE_INTERMEDIATE)
+                            ram_shortfall -= freed
+                        if comfy.model_management.should_free_pins_for_ram_pressure(ram_shortfall):
+                            freed = comfy.model_management.free_pins(ram_shortfall + 512 * (1024 ** 2))
+                            if freed < ram_shortfall:
+                                if freed > 64 * (1024 ** 2):
+                                    # AIMDO MEM_DECOMMIT can outrun psutil.available catching up.
+                                    time.sleep(0.05)
+                                ram_release_callback(ram_headroom, free_active=True)
                 else:
                     # Only execute when the while-loop ends without break
                     # Send cached UI for intermediate output nodes that weren't executed

folder_paths.py

@@ -17,7 +17,11 @@ if args.base_directory:
 else:
     base_path = os.path.dirname(os.path.realpath(__file__))
 
-models_dir = os.path.join(base_path, "models")
+if args.models_directory:
+    models_dir = os.path.abspath(args.models_directory)
+else:
+    models_dir = os.path.join(base_path, "models")
+
 folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions)
 folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"])
 

main.py

@@ -131,6 +131,10 @@ def apply_custom_paths():
     if args.base_directory:
         logging.info(f"Setting base directory to: {folder_paths.base_path}")
 
+    # --models-directory
+    if args.models_directory:
+        logging.info(f"Setting models directory to: {folder_paths.models_dir}")
+
     # --output-directory, --input-directory, --user-directory
     if args.output_directory:
         output_dir = os.path.abspath(args.output_directory)

nodes.py

@@ -1709,6 +1709,7 @@ class PreviewImage(SaveImage):
         self.compress_level = 1
 
     SEARCH_ALIASES = ["preview", "preview image", "show image", "view image", "display image", "image viewer"]
+    DESCRIPTION = "Preview the images without saving them to the ComfyUI output directory."
 
     @classmethod
     def INPUT_TYPES(s):
@@ -2458,6 +2459,7 @@ async def init_builtin_extra_nodes():
         "nodes_camera_trajectory.py",
         "nodes_edit_model.py",
         "nodes_tcfg.py",
+        "nodes_seedvr.py",
         "nodes_context_windows.py",
         "nodes_qwen.py",
         "nodes_boogu.py",
@@ -2478,6 +2480,7 @@ async def init_builtin_extra_nodes():
         "nodes_glsl.py",
         "nodes_lora_debug.py",
         "nodes_textgen.py",
+        "nodes_text_overlay.py",
         "nodes_color.py",
         "nodes_toolkit.py",
         "nodes_replacements.py",
@@ -2502,6 +2505,7 @@ async def init_builtin_extra_nodes():
         "nodes_triposplat.py",
         "nodes_depth_anything_3.py",
         "nodes_seed.py",
+        "nodes_text.py",
     ]
 
     import_failed = []

openapi.yaml

@@ -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}$
@@ -783,6 +775,14 @@ components:
         ModelFolder:
             description: Represents a folder containing models
             properties:
+                extensions:
+                    description: The folder's registered file-extension allowlist. An empty array means the folder accepts any extension (match-all).
+                    example:
+                        - .ckpt
+                        - .safetensors
+                    items:
+                        type: string
+                    type: array
                 folders:
                     description: List of paths where models of this type are stored
                     example:
@@ -1644,7 +1644,7 @@ 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 +1829,7 @@ paths:
                     content:
                         application/json:
                             schema:
-                                $ref: '#/components/schemas/AssetUpdated'
+                                $ref: '#/components/schemas/Asset'
                     description: Asset updated successfully
                 "400":
                     content:
@@ -2470,6 +2470,9 @@ 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

@@ -1,6 +1,6 @@
 comfyui-frontend-package==1.45.20
-comfyui-workflow-templates==0.11.2
-comfyui-embedded-docs==0.5.6
+comfyui-workflow-templates==0.11.6
+comfyui-embedded-docs==0.5.7
 torch
 torchsde
 torchvision
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy>=2.0.0
 filelock
 av>=16.0.0
-comfy-kitchen==0.2.16
+comfy-kitchen==0.2.18
 comfy-aimdo==0.4.10
 requests
 simpleeval>=1.0.0

server.py

@@ -39,6 +39,7 @@ from comfy.deploy_environment import get_deploy_environment
 import comfy.utils
 import comfy.model_management
 from comfy_api import feature_flags
+from comfy.comfy_api_env import get_environment_overrides
 import node_helpers
 from comfyui_version import __version__
 from app.frontend_management import FrontendManager, parse_version
@@ -46,6 +47,7 @@ from comfy_api.internal import _ComfyNodeInternal
 from app.assets.seeder import asset_seeder
 from app.assets.api.routes import register_assets_routes
 from app.assets.services.ingest import register_file_in_place
+from app.assets.services.path_utils import get_known_subfolder_tags
 from app.assets.services.asset_management import resolve_hash_to_path
 
 from app.user_manager import UserManager
@@ -441,7 +443,9 @@ class PromptServer():
                 if args.enable_assets:
                     try:
                         tag = image_upload_type if image_upload_type in ("input", "output") else "input"
-                        result = register_file_in_place(abs_path=filepath, name=filename, tags=[tag])
+                        tags = [tag]
+                        tags.extend(get_known_subfolder_tags(subfolder))
+                        result = register_file_in_place(abs_path=filepath, name=filename, tags=tags)
                         resp["asset"] = {
                             "id": result.ref.id,
                             "name": result.ref.name,
@@ -724,7 +728,11 @@ class PromptServer():
 
         @routes.get("/features")
         async def get_features(request):
-            return web.json_response(feature_flags.get_server_features())
+            features = feature_flags.get_server_features()
+            overrides = get_environment_overrides()
+            if overrides:
+                features.update(overrides)
+            return web.json_response(features)
 
         @routes.get("/prompt")
         async def get_prompt(request):

tests-unit/app_test/model_manager_test.py

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

tests-unit/app_test/test_migrations.py

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

tests-unit/assets_test/conftest.py

@@ -234,7 +234,7 @@ def seeded_asset(request: pytest.FixtureRequest, http: requests.Session, api_bas
     p = getattr(request, "param", {}) or {}
     tags: Optional[list[str]] = p.get("tags")
     if tags is None:
-        tags = ["models", "checkpoints", "unit-tests", "alpha"]
+        tags = ["models", "model_type:checkpoints", "unit-tests", "alpha"]
     meta = {"purpose": "test", "epoch": 1, "flags": ["x", "y"], "nullable": None}
     # Unique content per test so the seed always creates a fresh asset (201).
     # Delete is now always a soft delete, so content from a prior test survives

tests-unit/assets_test/queries/test_asset_info.py

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

tests-unit/assets_test/queries/test_cache_state.py

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

tests-unit/assets_test/queries/test_tags.py

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

tests-unit/assets_test/services/test_asset_response_loader_path.py

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

tests-unit/assets_test/services/test_bulk_ingest.py

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

tests-unit/assets_test/services/test_ingest.py

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

tests-unit/assets_test/services/test_path_utils.py

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

tests-unit/assets_test/test_assets_missing_sync.py

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

tests-unit/assets_test/test_crud.py

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

tests-unit/assets_test/test_downloads.py

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

tests-unit/assets_test/test_list_cursor.py

@@ -13,7 +13,7 @@ def _seed(asset_factory, make_asset_bytes, count: int, tag: str) -> list[str]:
     for n in names:
         asset_factory(
             n,
-            ["models", "checkpoints", "unit-tests", tag],
+            ["models", "model_type:checkpoints", "unit-tests", tag],
             {},
             make_asset_bytes(n, size=2048),
         )
@@ -208,7 +208,7 @@ def test_cursor_walks_for_non_name_sorts(sort_field, http: requests.Session, api
     names = []
     for i in range(4):
         n = f"cursor_{sort_field}_{i:02d}.safetensors"
-        asset_factory(n, ["models", "checkpoints", "unit-tests", f"cursor-{sort_field}"], {}, make_asset_bytes(n, size=2048 + i))
+        asset_factory(n, ["models", "model_type:checkpoints", "unit-tests", f"cursor-{sort_field}"], {}, make_asset_bytes(n, size=2048 + i))
         names.append(n)
 
     params = {

tests-unit/assets_test/test_list_filter.py

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

tests-unit/assets_test/test_metadata_filters.py

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

tests-unit/assets_test/test_prune_orphaned_assets.py

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

tests-unit/assets_test/test_tags_api.py

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

tests-unit/assets_test/test_uploads.py

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

tests-unit/comfy_extras_test/test_seedvr2_conditioning.py

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

tests-unit/comfy_extras_test/test_seedvr2_nodes.py

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

tests-unit/comfy_extras_test/test_seedvr2_post_processing.py

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