ComfyUI v0.28.1

* [Partner Nodes] feat(HeyGen): add Avatar, Talking Photo, Create Avatar, Video Translate and TTS nodes

Signed-off-by: bigcat88 <bigcat88@icloud.com>

* [Partner Nodes] fix(HeyGen): display only Avatars supported by engine

Signed-off-by: bigcat88 <bigcat88@icloud.com>

* [Partner Nodes] remove 4K option

---------

Signed-off-by: bigcat88 <bigcat88@icloud.com>
Co-authored-by: Alexis Rolland <alexisrolland@hotmail.com>

.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/hidream_o1/attention.py

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

comfy/ldm/modules/attention.py

@@ -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
             )
@@ -679,21 +709,22 @@ def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
     return out
 
 try:
-    @torch.library.custom_op("flash_attention::flash_attn", mutates_args=())
+    @torch.library.custom_op("comfy::flash_attn", mutates_args=())
     def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
-                    dropout_p: float = 0.0, causal: bool = False) -> 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/pixeldit/model.py

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

comfy/ldm/pixeldit/pid.py

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

comfy/ldm/seedvr/attention.py

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

comfy/ldm/seedvr/color_fix.py

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

comfy/ldm/seedvr/constants.py

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

comfy/model_base.py

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

comfy/model_detection.py

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

comfy/model_management.py

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

comfy/ops.py

@@ -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/heygen.py

@@ -0,0 +1,452 @@
+# (label, avatar_id, avatar_type, supported engines)
+HEYGEN_AVATAR_LOOKS: list[tuple[str, str, str, tuple[str, ...]]] = [
+    (
+        "Annie Lounge Standing Side",
+        "Annie_Lounge_Standing_Side_public",
+        "studio_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Yara Modern Lecture Hall",
+        "fd6814ecc5e143cd899e615a80eaa2dc",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Brandon Business Sitting Front",
+        "Brandon_Business_Sitting_Front_public",
+        "studio_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Caroline Business Sitting Side",
+        "Caroline_Business_Sitting_Side_public",
+        "studio_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Ursula Lawyer Angle 4",
+        "f7173d2bb8584c00bfec6905c5e9a492",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Sofia Corporate Presenter 01 Angle 3",
+        "fe563971fd2d438e957372dac9e2be8c",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Seoyeon Health Nutrition Coach Angle 3",
+        "fe3c5d5028d941398d064b8fc64a2dea",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Sanne Fitness Coach Angle 4",
+        "d967f935a8bf4a0c8f0bccfd66c501d2",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    ("Sander", "f5cd7b94056f495ca0610602d64a9aa3", "photo_avatar", ("avatar_v", "avatar_iv", "avatar_iii")),
+    (
+        "Rupert Personal Development Coach Angle 4",
+        "f57b3e626adb4bc997b38f64884adce4",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Olivier Professor Angle 2",
+        "f6659bbb094b459c87c967edbb9ee481",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Obi Health Nutrition Coach Angle 5",
+        "f3dc2c38201d414382f506d2d8e8d029",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Matilda Modern Office Setting",
+        "fda889ac354a440da8dbecc410981273",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Mateo Traditional Law Office",
+        "ff172d6c499c4e47ba6fcc5de631e9fc",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Marlon Inviting Armchair Setting",
+        "f5a57db099ab462daa3e7c604a05dacc",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Margaret Professor Angle 1",
+        "fb472bc29ab04bcca576e3703978fecb",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Marek Therapy Coach Angle 3",
+        "e197768703f1463a93dc25ada1f421fb",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Maeve Warm, Professional Setting",
+        "faf66681d8cc48dc82c4283200b3e782",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    (
+        "Lorenzo Professor Angle 5",
+        "fc268dc244bb40d7a554663ce723dcf0",
+        "photo_avatar",
+        ("avatar_v", "avatar_iv", "avatar_iii"),
+    ),
+    ("Luca", "Luca_public", "studio_avatar", ("avatar_iii",)),
+    ("Bruce", "Bruce_public", "studio_avatar", ("avatar_iii",)),
+    ("Nico", "Nico_public", "studio_avatar", ("avatar_iii",)),
+    ("Lisa", "Lisa_public", "studio_avatar", ("avatar_iii",)),
+    ("Sophie", "Sophie_public", "studio_avatar", ("avatar_iii",)),
+    ("Aiko", "Aiko_public", "studio_avatar", ("avatar_iii",)),
+    ("Rebecca (portrait)", "Rebecca_public", "studio_avatar", ("avatar_iii",)),
+    ("Daphne in Grey blazer (portrait)", "Daphne_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Bryce in Black t-shirt", "Bryce_public_5", "studio_avatar", ("avatar_iii",)),
+    ("Diora in White shirt", "Diora_public_3", "studio_avatar", ("avatar_iii",)),
+    ("Freja in White blazer", "Freja_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Albert in Blue blazer", "Albert_public_2", "studio_avatar", ("avatar_iii",)),
+    ("Emery in Red blazer", "Emery_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Minho in Blue shirt", "Minho_public_6", "studio_avatar", ("avatar_iii",)),
+    ("Aditya in Brown blazer", "Aditya_public_4", "studio_avatar", ("avatar_iii",)),
+    ("Nadim in Blue blazer", "Nadim_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Iker in Black blazer", "Iker_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Nour in Black blazer", "Nour_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Saskia in Blue blazer", "Saskia_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Lucien in Blue blazer", "Lucien_public_1", "studio_avatar", ("avatar_iii",)),
+    ("Esmond in Blue suit", "Esmond_public_3", "studio_avatar", ("avatar_iii",)),
+    ("Jinwoo in Blue suit", "Jinwoo_public_5", "studio_avatar", ("avatar_iii",)),
+    ("Annelore in Red sweater (portrait)", "Annelore_public_3", "studio_avatar", ("avatar_iii",)),
+    ("Bastien in Blue shirt", "Bastien_public_4", "studio_avatar", ("avatar_iii",)),
+    ("Zosia in Khaki blazer", "Zosia_public_3", "studio_avatar", ("avatar_iii",)),
+    ("Tahlia in Dark blue suit", "Tahlia_public_4", "studio_avatar", ("avatar_iii",)),
+]
+HEYGEN_AVATAR_OPTIONS = [x[0] for x in HEYGEN_AVATAR_LOOKS]
+HEYGEN_AVATAR_MAP = {x[0]: (x[1], x[2], x[3]) for x in HEYGEN_AVATAR_LOOKS}
+
+# (label, voice_id) — Starfish-compatible voices for the TTS endpoint
+HEYGEN_VOICE_TTS: list[tuple[str, str]] = [
+    ("Chill Brian (English, male)", "d2f4f24783d04e22ab49ee8fdc3715e0"),
+    ("Zain (English, female)", "0047732240584155b1588455313e78ec"),
+    ("Narrator Mateo - Excited 🤩 (Spanish, male)", "0077225a877e457db4572ccaf245910b"),
+    ("Aria (English, female)", "007e1378fc454a9f976db570ba6164a7"),
+    ("Caryns (English, female)", "0082e70326864107823605db0d77f5e0"),
+    ("Klara (English, female)", "01209fdcd1c24a109c86dc24ee0f71c0"),
+    ("Bold Kasia - Excited 🤩 (Polish, female)", "015482a78b9a46ebae74bd0beb17765b"),
+    ("Shaun (English, male)", "01c42cddcfdc4665a57b8d89cba8ffc1"),
+    ("Senthil (English, male)", "01d674cfd32b4728a3fddd21b7e7d543"),
+    ("Cody (English, male)", "01f98ed43e6140349f47dbd37a416827"),
+    ("Saffron (English, female)", "0258bbc2cd8648cfa357adfb833f6d7b"),
+    ("Blanka - Lifelike (English, female)", "02880d1c6fd94b7799d91135581ed810"),
+    ("Rami (English, male)", "02d5366a90af4c7a87157808ff352e33"),
+    ("Rhodes (English, female)", "02dce0a169b3460084b6c914d18fb2c8"),
+    ("Michelle - Voice 1 (English, female)", "02X8sHnuxFpsq1caYWN0"),
+    ("Autumn - UGC 3 (English, female)", "03dca9ebfca441dba55fb14afa0791b7"),
+    ("Reassuring Rupert (English, male)", "03fcf8ecb0a94b6b94e9007edb7c35f8"),
+    ("Rose - UGC -2  (English, female)", "0495e14c2bd74eb3aeeef03583e0bce5"),
+    ("Derya - Lifelike - Broadcaster 🎙️ (English, female)", "04d0ae1d0af2489ca7d3bb402a39a890"),
+    ("Dynamic Derek (English, male)", "0516c2d857eb425c94e90b068241914e"),
+    ("Lotte (English, female)", "052fcfb83d1a4c2f8d0368c226fea4b9"),
+    ("Thanos - Broadcaster 🎙️ (English, male)", "054af44a167344d0af2722fdfef08d17"),
+    ("Marcia (English, female)", "05f19352e8f74b0392a8f411eba40de1"),
+    ("Camden (English, male)", "06468055edd4458aa131a1dfd813c1e9"),
+    ("Rumi (English, female)", "06672207805f41a9ad0af6797f8aa14b"),
+    ("Pippa (English, female)", "06b68c4dbb544935b9af984e80efa4fb"),
+    ("William Prescott - Broadcaster 🎙️ (English, male)", "06c816b952f14fa9b3a6c42aa151f731"),
+    ("Sammy (English, female)", "06e6facd99654b9dbb9308f67bf3a31c"),
+    ("Breezy Bagus (Indonesian, male)", "06e81a5d7c8b41818d3f0b38f7cf15a1"),
+    ("Ben (English, male)", "07ca39b243184dbcb82e7e0f0e524b21"),
+    ("Smooth Dev (English, male)", "07d2ba65847541feb97abc9b60181555"),
+    ("Daran inside booth (English, male)", "080d9383c0314056aef392892e009806"),
+    ("Peppy Stella (English, female)", "084760b4922a44599575c770070ec2d7"),
+    ("Silas (English, male)", "08f561403ec846dbbd8c691cc448f45a"),
+    ("Aditya (English, male)", "09c3d65e44e247dd8b78a97a903feb58"),
+    ("Christy (English, female)", "09d88c036bf449fa905900c08b235a37"),
+    ("Elio (English, male)", "0a0b38624ac64ec6afcd5842a977ca10"),
+    ("Luminous Laksh (Hindi, male)", "0adc547b76a5401c856274c379904eb7"),
+    ("Jeff (English, male)", "0add542e349f4ccaba6ecb3b7ced6034"),
+    ("Tahlia Brooks - Excited 🤩 (English, female)", "0b440d1ac2454d69a73302fc806522b1"),
+    ("Riya Mehta (Hindi, female)", "0b464b2f4e2249a4b5a05e60eaf41e7e"),
+    ("Ben Hart (English, male)", "0b47b5a637e944f9bfd49913999b344b"),
+    ("Skylar (English, female)", "0bbfbda5aa924a68a9d1da7b8496052a"),
+    ("Relaxed Reece (English, male)", "0c2151d538844c70a8b096de533f2828"),
+    ("Daniel (English, male)", "0c23804af39a4946ac6fda42bfff2738"),
+    ("Melani (English, female)", "0c54c6399ad64551a304e1a346677723"),
+    ("Clover (English, female)", "0ccb0bea067d4449ad367baeed7ea2e9"),
+    ("Pedro Lima - Serious 😐 (Portuguese, male)", "0d0e23e8170446e38b18a7380b2d30a8"),
+    ("Ana Carvalho (Portuguese, female)", "0d23c5b2f6004e909802a2e8bfcd52c2"),
+    ("Confident Connor - Excited 🤩 (English, male)", "0dd34c3eb79247238219eea35aeb58cd"),
+    ("Vibrant Victor (Spanish, male)", "1062976ea8bf42f4adc27c7e868b8fde"),
+    ("Young Olivier (French, male)", "1c5dc9a8f8cf4de0932f91d75f43a15d"),
+    ("Émile Noir (French, male)", "25a6a67280574d3da78e97b1935ebfc7"),
+    ("Steadfast Stefan (German, male)", "0eb85e6e8710473b82f7e88609ba3053"),
+    ("Deep Dieter (German, male)", "118949676b0a46629d1ad52981c3ef84"),
+    ("Serene Marco (Italian, male)", "72e922488a614041b5ab5f6ee07e3deb"),
+    ("Murmuring Matteo (Italian, male)", "755902b751654f30a6ef49e8bbcacfec"),
+    ("Gail in car (Multilingual, female)", "0214ac51f93e420f8711d568dcfbc50e"),
+    ("Daran outside walking (Multilingual, male)", "0ac81e725f4948dfa9638ceca216bcfa"),
+    ("BOB - Voice 1 (Chinese, unknown)", "dMkR1XwIkarpNqWUJLnX"),
+    ("Hakeem Hassan (Arabic, male)", "61a4359785664d01a59664ceb87ce6d4"),
+    ("Rami Idris (Arabic, male)", "a0bd2e5d41a74643be47ac75ca9171a2"),
+    ("Bold Kasia - Friendly 😊 (Polish, female)", "331624aec8b24a6c9287b8e16bdf54e8"),
+    ("Tranquil Tulin (Turkish, female)", "61646c861eb64e2d9036d8db51385356"),
+    ("Dynamic Derya (Turkish, female)", "664b73058b784aa89ddb2924c141d441"),
+    ("Quiet Dewa (Indonesian, male)", "1fa1193cf1d74f27ba58531c07ef9862"),
+    ("Cuong (Vietnamese, male)", "8af68d7ea38f4e7ca05cf46c3f7a590b"),
+]
+HEYGEN_VOICE_TTS_OPTIONS = [x[0] for x in HEYGEN_VOICE_TTS]
+HEYGEN_VOICE_TTS_MAP = dict(HEYGEN_VOICE_TTS)
+
+# (label, voice_id) — top-ranked voices for video narration (any engine)
+HEYGEN_VOICE_GENERAL: list[tuple[str, str]] = [
+    ("Cassidy (English, female)", "16a09e4706f74997ba4ed05ea11470f6"),
+    ("Hope (English, female)", "42d00d4aac5441279d8536cd6b52c53c"),
+    ("Archer (English, male)", "453c20e1525a429080e2ad9e4b26f2cd"),
+    ("Brittney (English, female)", "4754e1ec667544b0bd18cdf4bec7d6a7"),
+    ("Mark (English, male)", "5d8c378ba8c3434586081a52ac368738"),
+    ("Andrew (English, male)", "6be73833ef9a4eb0aeee399b8fe9d62b"),
+    ("Spuds Oxley (English, male)", "76940a9adcd0490a9ce2cfe9a64a2664"),
+    ("Patrick (English, male)", "7e157ec62c9c45f1adca12faae72c86f"),
+    ("David Castlemore (English, male)", "828b59f834fd4c7188da322b6d9b6c75"),
+    ("Michael C (English, male)", "8661cd40d6c44c709e2d0031c0186ada"),
+    ("Adam Stone (English, male)", "88bb9ee1c81b466eb2a08fdde86d3619"),
+    ("Alex (English, male)", "897d6a9b2c844f56aa077238768fe10a"),
+    ("Monika Sogam (English, female)", "97dd67ab8ce242b6a9e7689cb00c6414"),
+    ("Jessica Anne Bogart (English, female)", "b966c31caf124c2a99f19ff1479c964f"),
+    ("John Doe (English, male)", "c4a8ceb7a2954500bc047fb092bcff3f"),
+    ("Ivy (English, female)", "cef3bc4e0a84424cafcde6f2cf466c97"),
+    ("Chill Brian (English, male)", "d2f4f24783d04e22ab49ee8fdc3715e0"),
+    ("Allison (English, female)", "f8c69e517f424cafaecde32dde57096b"),
+    ("Mia Starset (Norwegian, female)", "000466f8ac6d47a49f5743d50b3778de"),
+    ("William Shanks (Spanish, male)", "001248bb63f847888d37b766ee8b3a47"),
+    ("Zain (English, female)", "0047732240584155b1588455313e78ec"),
+    ("Jora Slobod (Romanian, male)", "00631519159a402ab5d8f719e51532bb"),
+    ("Narrator Mateo - Excited 🤩 (Spanish, male)", "0077225a877e457db4572ccaf245910b"),
+    ("Aria (English, female)", "007e1378fc454a9f976db570ba6164a7"),
+    ("Caryns (English, female)", "0082e70326864107823605db0d77f5e0"),
+    ("Klara (English, female)", "01209fdcd1c24a109c86dc24ee0f71c0"),
+    ("Son Tran (Vietnamese, male)", "0132f85950a94d11ba180f885101bf84"),
+    ("Bold Kasia - Excited 🤩 (Polish, female)", "015482a78b9a46ebae74bd0beb17765b"),
+    ("Marc Aurèle (French, male)", "018a94cf15574491a0bab7f6799ac15b"),
+    ("Shaun (English, male)", "01c42cddcfdc4665a57b8d89cba8ffc1"),
+    ("Senthil (English, male)", "01d674cfd32b4728a3fddd21b7e7d543"),
+    ("Cody (English, male)", "01f98ed43e6140349f47dbd37a416827"),
+    ("Saffron (English, female)", "0258bbc2cd8648cfa357adfb833f6d7b"),
+    ("Blanka - Lifelike (English, female)", "02880d1c6fd94b7799d91135581ed810"),
+    ("Rami (English, male)", "02d5366a90af4c7a87157808ff352e33"),
+    ("Rhodes (English, female)", "02dce0a169b3460084b6c914d18fb2c8"),
+    ("Michelle - Voice 1 (English, female)", "02X8sHnuxFpsq1caYWN0"),
+    ("Tuba (, female)", "034ca0c32b6542028748d6d365d90d6a"),
+    ("Autumn - UGC 3 (English, female)", "03dca9ebfca441dba55fb14afa0791b7"),
+    ("Reassuring Rupert (English, male)", "03fcf8ecb0a94b6b94e9007edb7c35f8"),
+]
+HEYGEN_VOICE_GENERAL_OPTIONS = [x[0] for x in HEYGEN_VOICE_GENERAL]
+HEYGEN_VOICE_GENERAL_MAP = dict(HEYGEN_VOICE_GENERAL)
+
+HEYGEN_TRANSLATE_LANGUAGES = [
+    "English",
+    "Spanish",
+    "Spanish (Spain)",
+    "Spanish (Mexico)",
+    "French",
+    "French (France)",
+    "German",
+    "German (Germany)",
+    "Portuguese",
+    "Portuguese (Brazil)",
+    "Italian",
+    "Italian (Italy)",
+    "Japanese",
+    "Japanese (Japan)",
+    "Korean",
+    "Chinese (Mandarin, Simplified)",
+    "Arabic",
+    "Hindi",
+    "Hindi (India)",
+    "Russian",
+    "Russian (Russia)",
+    "Dutch",
+    "Polish",
+    "Turkish",
+    "Indonesian",
+    "Vietnamese",
+    "Ukrainian",
+    "Afrikaans (South Africa)",
+    "Albanian (Albania)",
+    "Amharic (Ethiopia)",
+    "Arabic (Algeria)",
+    "Arabic (Bahrain)",
+    "Arabic (Egypt)",
+    "Arabic (Iraq)",
+    "Arabic (Jordan)",
+    "Arabic (Kuwait)",
+    "Arabic (Lebanon)",
+    "Arabic (Libya)",
+    "Arabic (Morocco)",
+    "Arabic (Oman)",
+    "Arabic (Qatar)",
+    "Arabic (Saudi Arabia)",
+    "Arabic (Syria)",
+    "Arabic (Tunisia)",
+    "Arabic (United Arab Emirates)",
+    "Arabic (World)",
+    "Arabic (Yemen)",
+    "Armenian (Armenia)",
+    "Azerbaijani (Latin, Azerbaijan)",
+    "Bangla (Bangladesh)",
+    "Basque",
+    "Belarusian (Belarus)",
+    "Bengali (India)",
+    "Bosnian (Bosnia and Herzegovina)",
+    "Bulgarian",
+    "Bulgarian (Bulgaria)",
+    "Burmese (Myanmar)",
+    "Catalan",
+    "Chinese (Cantonese, Traditional)",
+    "Chinese (Jilu Mandarin, Simplified)",
+    "Chinese (Northeastern Mandarin, Simplified)",
+    "Chinese (Southwestern Mandarin, Simplified)",
+    "Chinese (Taiwanese Mandarin, Traditional)",
+    "Chinese (Wu, Simplified)",
+    "Chinese (Zhongyuan Mandarin Henan, Simplified)",
+    "Chinese (Zhongyuan Mandarin Shaanxi, Simplified)",
+    "Croatian",
+    "Croatian (Croatia)",
+    "Czech",
+    "Czech (Czechia)",
+    "Danish",
+    "Danish (Denmark)",
+    "Dutch (Belgium)",
+    "Dutch (Netherlands)",
+    "English (Australia)",
+    "English (Canada)",
+    "English (Hong Kong SAR)",
+    "English (India)",
+    "English (Ireland)",
+    "English (Kenya)",
+    "English (New Zealand)",
+    "English (Nigeria)",
+    "English (Philippines)",
+    "English (Singapore)",
+    "English (South Africa)",
+    "English (Tanzania)",
+    "English (UK)",
+    "English (United States)",
+    "Estonian (Estonia)",
+    "Filipino",
+    "Filipino (Cebuano)",
+    "Filipino (Philippines)",
+    "Finnish",
+    "Finnish (Finland)",
+    "French (Belgium)",
+    "French (Canada)",
+    "French (Switzerland)",
+    "Galician",
+    "Georgian (Georgia)",
+    "German (Austria)",
+    "German (Switzerland)",
+    "Greek",
+    "Greek (Greece)",
+    "Gujarati (India)",
+    "Haitian Creole (Haiti)",
+    "Hebrew (Israel)",
+    "Hungarian (Hungary)",
+    "Icelandic (Iceland)",
+    "Indonesian (Indonesia)",
+    "Irish (Ireland)",
+    "Javanese (Latin, Indonesia)",
+    "Kannada (India)",
+    "Kazakh (Kazakhstan)",
+    "Khmer (Cambodia)",
+    "Konkani (India)",
+    "Korean (Korea)",
+    "Lao (Laos)",
+    "Latin (Vatican City)",
+    "Latvian (Latvia)",
+    "Lithuanian (Lithuania)",
+    "Luxembourgish (Luxembourg)",
+    "Macedonian (North Macedonia)",
+    "Maithili (India)",
+    "Malagasy (Madagascar)",
+    "Malay",
+    "Malay (Malaysia)",
+    "Malayalam (India)",
+    "Maltese (Malta)",
+    "Mandarin",
+    "Marathi (India)",
+    "Mongolian (Mongolia)",
+    "Nepali (Nepal)",
+    "Norwegian Bokmål (Norway)",
+    "Norwegian Nynorsk (Norway)",
+    "Odia (India)",
+    "Pashto (Afghanistan)",
+    "Persian (Iran)",
+    "Polish (Poland)",
+    "Portuguese (Portugal)",
+    "Punjabi (India)",
+    "Romanian",
+    "Romanian (Romania)",
+    "Serbian (Latin, Serbia)",
+    "Sindhi (India)",
+    "Sinhala (Sri Lanka)",
+    "Slovak",
+    "Slovak (Slovakia)",
+    "Slovenian (Slovenia)",
+    "Somali (Somalia)",
+    "Spanish (Argentina)",
+    "Spanish (Bolivia)",
+    "Spanish (Chile)",
+    "Spanish (Colombia)",
+    "Spanish (Costa Rica)",
+    "Spanish (Cuba)",
+    "Spanish (Dominican Republic)",
+    "Spanish (Ecuador)",
+    "Spanish (El Salvador)",
+    "Spanish (Equatorial Guinea)",
+    "Spanish (Guatemala)",
+    "Spanish (Honduras)",
+    "Spanish (Latin America)",
+    "Spanish (Nicaragua)",
+    "Spanish (Panama)",
+    "Spanish (Paraguay)",
+    "Spanish (Peru)",
+    "Spanish (Puerto Rico)",
+    "Spanish (United States)",
+    "Spanish (Uruguay)",
+    "Spanish (Venezuela)",
+    "Sundanese (Indonesia)",
+    "Swahili (Kenya)",
+    "Swahili (Tanzania)",
+    "Swedish",
+    "Swedish (Sweden)",
+    "Tamil",
+    "Tamil (India)",
+    "Tamil (Malaysia)",
+    "Tamil (Singapore)",
+    "Tamil (Sri Lanka)",
+    "Telugu (India)",
+    "Thai (Thailand)",
+    "Turkish (Türkiye)",
+    "Ukrainian (Ukraine)",
+    "Urdu (India)",
+    "Urdu (Pakistan)",
+    "Uzbek (Latin, Uzbekistan)",
+    "Vietnamese (Vietnam)",
+    "Welsh (United Kingdom)",
+    "Zulu (South Africa)",
+]

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/apis/openai.py

@@ -128,7 +128,7 @@ class OpenAIResponse(ModelResponseProperties, ResponseProperties):
     parallel_tool_calls: bool | None = Field(True)
     status: str | None = Field(
         None,
-        description="One of `completed`, `failed`, `in_progress`, or `incomplete`.",
+        description="One of `completed`, `failed`, `in_progress`, `incomplete`, `queued`, or `cancelled`.",
     )
     usage: ResponseUsage | None = Field(None)
 

comfy_api_nodes/apis/sync_so.py

@@ -0,0 +1,49 @@
+from pydantic import BaseModel, Field
+
+
+class SyncInputItem(BaseModel):
+    type: str = Field(..., description="Input kind: 'video', 'image' or 'audio'.")
+    url: str = Field(...)
+
+
+class SyncActiveSpeakerDetection(BaseModel):
+    auto_detect: bool | None = Field(
+        None, description="Detect the active speaker automatically. Video input only; rejected for images."
+    )
+    frame_number: int | None = Field(
+        None, description="Frame used for manual speaker selection. Must be 0 for image inputs."
+    )
+    coordinates: list[int] | None = Field(
+        None, description="Pixel [x, y] of the speaker's face in the frame selected by frame_number."
+    )
+
+
+class SyncGenerationOptions(BaseModel):
+    sync_mode: str | None = Field(
+        None,
+        description="How to resolve an audio/video duration mismatch: "
+        "cut_off, bounce, loop, silence or remap. Ignored for image inputs.",
+    )
+    i2v_prompt: str | None = Field(
+        None, description="Motion prompt for image-to-video generation. Image input only."
+    )
+    active_speaker_detection: SyncActiveSpeakerDetection | None = Field(None)
+
+
+class SyncGenerationRequest(BaseModel):
+    model: str = Field(..., description="Generation model, e.g. 'sync-3'.")
+    input: list[SyncInputItem] = Field(
+        ..., description="Exactly one visual input (video or image) plus one audio input."
+    )
+    options: SyncGenerationOptions | None = Field(None)
+
+
+class SyncGeneration(BaseModel):
+    """Subset of the Generation object returned by POST /v2/generate and GET /v2/generate/{id}."""
+
+    id: str = Field(...)
+    status: str = Field(..., description="PENDING | PROCESSING | COMPLETED | FAILED | REJECTED")
+    outputUrl: str | None = Field(None)
+    outputDuration: float | None = Field(None)
+    error: str | None = Field(None, description="Human-readable failure message.")
+    errorCode: str | None = Field(None, description="Stable machine-readable code from the GET /v2/errors catalog.")

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_gemini.py

@@ -274,6 +274,10 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
         input_tokens_price = 0.25
         output_text_tokens_price = 1.50
         output_image_tokens_price = 0.0
+    elif response.modelVersion == "gemini-3.5-flash":
+        input_tokens_price = 1.50
+        output_text_tokens_price = 9.0
+        output_image_tokens_price = 0.0
     elif response.modelVersion in ("gemini-3-pro-image-preview", "gemini-3-pro-image"):
         input_tokens_price = 2
         output_text_tokens_price = 12.0
@@ -619,11 +623,12 @@ class GeminiNode(IO.ComfyNode):
 
 GEMINI_V2_MODELS: dict[str, str] = {
     "Gemini 3.1 Pro": "gemini-3.1-pro-preview",
+    "Gemini 3.5 Flash": "gemini-3.5-flash",
     "Gemini 3.1 Flash-Lite": "gemini-3.1-flash-lite-preview",
 }
 
 
-def _gemini_text_model_inputs(thinking_default: str) -> list[Input]:
+def _gemini_text_model_inputs(thinking_default: str, thinking_options: list[str] | None = None) -> list[Input]:
     """Per-model inputs revealed by the model DynamicCombo (shared media + sampling controls)."""
     return [
         IO.Autogrow.Input(
@@ -661,7 +666,7 @@ def _gemini_text_model_inputs(thinking_default: str) -> list[Input]:
         ),
         IO.Combo.Input(
             "thinking_level",
-            options=["LOW", "HIGH"],
+            options=thinking_options or ["LOW", "HIGH"],
             default=thinking_default,
             tooltip="How hard the model reasons internally before answering. "
             "HIGH improves quality on difficult tasks but costs more (thinking) tokens and is slower.",
@@ -719,6 +724,10 @@ class GeminiNodeV2(IO.ComfyNode):
                 IO.DynamicCombo.Input(
                     "model",
                     options=[
+                        IO.DynamicCombo.Option(
+                            "Gemini 3.5 Flash",
+                            _gemini_text_model_inputs("MEDIUM", ["MINIMAL", "LOW", "MEDIUM", "HIGH"]),
+                        ),
                         IO.DynamicCombo.Option("Gemini 3.1 Pro", _gemini_text_model_inputs("HIGH")),
                         IO.DynamicCombo.Option("Gemini 3.1 Flash-Lite", _gemini_text_model_inputs("LOW")),
                     ],
@@ -759,7 +768,13 @@ class GeminiNodeV2(IO.ComfyNode):
                     "type": "list_usd",
                     "usd": [0.00025, 0.0015],
                     "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
-                  } : {
+                  }
+                  : $contains($m, "3.5 flash") ? {
+                    "type": "list_usd",
+                    "usd": [0.0015, 0.009],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : {
                     "type": "list_usd",
                     "usd": [0.002, 0.012],
                     "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
@@ -1133,7 +1148,9 @@ class GeminiImage2(IO.ComfyNode):
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=True, min_length=1)
         if model == "Nano Banana 2 (Gemini 3.1 Flash Image)":
-            model = "gemini-3.1-flash-image-preview"
+            model = "gemini-3.1-flash-image"
+        elif model == "gemini-3-pro-image-preview":
+            model = "gemini-3-pro-image"
 
         parts: list[GeminiPart] = [GeminiPart(text=prompt)]
         if images is not None:
@@ -1507,7 +1524,7 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
         validate_string(prompt, strip_whitespace=True, min_length=1)
         model_choice = model["model"]
         if model_choice == "Nano Banana 2 (Gemini 3.1 Flash Image)":
-            model_id = "gemini-3.1-flash-image-preview"
+            model_id = "gemini-3.1-flash-image"
         elif model_choice == "Nano Banana 2 Lite":
             model_id = "gemini-3.1-flash-lite-image"
         else:

comfy_api_nodes/nodes_heygen.py

@@ -0,0 +1,799 @@
+import uuid
+
+import torch
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.heygen import (
+    HEYGEN_AVATAR_MAP,
+    HEYGEN_AVATAR_OPTIONS,
+    HEYGEN_TRANSLATE_LANGUAGES,
+    HEYGEN_VOICE_GENERAL_MAP,
+    HEYGEN_VOICE_GENERAL_OPTIONS,
+    HEYGEN_VOICE_TTS_MAP,
+    HEYGEN_VOICE_TTS_OPTIONS,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    audio_bytes_to_audio_input,
+    download_url_as_bytesio,
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    downscale_image_tensor_by_max_side,
+    get_number_of_images,
+    poll_op_raw,
+    sync_op_raw,
+    upload_audio_to_comfyapi,
+    upload_image_to_comfyapi,
+    upload_images_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_string,
+)
+from server import PromptServer
+
+_VIDEOS_PATH = "/proxy/heygen/v3/videos"
+_TRANSLATIONS_PATH = "/proxy/heygen/v3/video-translations"
+_SPEECH_PATH = "/proxy/heygen/v3/voices/speech"
+_AVATARS_PATH = "/proxy/heygen/v3/avatars"
+_LOOKS_PATH = "/proxy/heygen/v3/avatars/looks"
+
+_DEFAULT_VOICE_OPTION = "(avatar's default voice)"
+
+_AVATARS_BY_ENGINE = {
+    e: [label for label, (_aid, _atype, engines) in HEYGEN_AVATAR_MAP.items() if e in engines]
+    for e in ("avatar_iv", "avatar_iii", "avatar_v")
+}
+
+
+async def _apply_speech_source(cls: type[IO.ComfyNode], payload: dict, speech: dict, require_voice: bool) -> None:
+    """Fill script/audio speech fields of a /v3/videos payload from the DynamicCombo dict."""
+    if speech["speech"] == "audio":
+        payload["audio_url"] = await upload_audio_to_comfyapi(
+            cls, speech["audio"], container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg"
+        )
+    elif speech["speech"] == "script":
+        validate_string(speech["text"], strip_whitespace=True, min_length=1, max_length=5000)
+        payload["script"] = speech["text"]
+        voice_id = speech.get("custom_voice_id", "").strip()
+        if not voice_id and speech["voice"] != _DEFAULT_VOICE_OPTION:
+            voice_id = HEYGEN_VOICE_GENERAL_MAP[speech["voice"]]
+        if voice_id:
+            payload["voice_id"] = voice_id
+        elif require_voice:
+            raise ValueError("A voice is required when driving the video with a text script.")
+        speed = speech.get("voice_speed", 1.0)
+        if speed != 1.0:
+            payload["voice_settings"] = {"speed": round(speed, 2)}
+
+
+async def _create_and_poll_video(cls: type[IO.ComfyNode], payload: dict) -> dict:
+    """POST a /v3/videos payload, poll until terminal, and return the final video data."""
+    created = await sync_op_raw(
+        cls,
+        ApiEndpoint(path=_VIDEOS_PATH, method="POST", headers={"Idempotency-Key": uuid.uuid4().hex}),
+        data=payload,
+    )
+    video_id = (created.get("data") or {}).get("video_id")
+    if not video_id:
+        raise ValueError(f"HeyGen did not return a video_id: {created}")
+    final = await poll_op_raw(
+        cls,
+        ApiEndpoint(path=f"{_VIDEOS_PATH}/{video_id}"),
+        status_extractor=lambda r: (r.get("data") or {}).get("status"),
+        queued_statuses=["pending", "waiting"],
+        poll_interval=5.0,
+    )
+    data = final["data"]
+    if not data.get("video_url"):
+        raise ValueError(f"HeyGen returned no video_url for video {video_id}.")
+    return data
+
+
+async def _resolve_avatar(
+    cls: type[IO.ComfyNode], avatar_label: str, custom_avatar_id: str, engine_choice: str
+) -> tuple[str, str | None]:
+    """Resolve (avatar_id, engine_type) from the combo/override + engine widgets."""
+    custom_avatar_id = custom_avatar_id.strip()
+    if custom_avatar_id:
+        look = (
+            await sync_op_raw(
+                cls,
+                ApiEndpoint(path=f"{_LOOKS_PATH}/{custom_avatar_id}"),
+                final_label_on_success=None,
+            )
+        ).get("data") or {}
+        avatar_id = custom_avatar_id
+        avatar_label = look.get("name") or custom_avatar_id
+        supported = look.get("supported_api_engines") or []
+    else:
+        avatar_id, avatar_type, supported = HEYGEN_AVATAR_MAP[avatar_label]
+
+    if engine_choice == "auto":
+        engine = next((e for e in ("avatar_iv", "avatar_iii", "avatar_v") if e in supported), None)
+    else:
+        engine = engine_choice
+        if supported and engine not in supported:
+            raise ValueError(
+                f"Avatar '{avatar_label}' does not support the {engine} engine "
+                f"(supported: {', '.join(supported)}). Set engine to 'auto' to pick "
+                "a compatible engine automatically."
+            )
+    return avatar_id, engine
+
+
+class HeyGenTalkingPhotoNode(IO.ComfyNode):
+    """Animate a still image of a person into a lip-synced talking video."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="HeyGenTalkingPhotoNode",
+            display_name="HeyGen Talking Photo",
+            category="partner/video/HeyGen",
+            description="Animate any image of a person into a lip-synced talking video "
+            "(HeyGen Avatar IV). Drive it with a text script or your own audio.",
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="Image of a person to animate. Downscaled automatically if larger than 2K.",
+                ),
+                IO.DynamicCombo.Input(
+                    "speech",
+                    display_name="speech source",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "script",
+                            [
+                                IO.String.Input(
+                                    "text",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Text for the avatar to speak (up to 5000 characters). "
+                                    "The generated speech must be at least 1 second long.",
+                                ),
+                                IO.Combo.Input(
+                                    "voice",
+                                    options=HEYGEN_VOICE_GENERAL_OPTIONS,
+                                    tooltip="Voice for the script (HeyGen's most popular voices).",
+                                ),
+                                IO.String.Input(
+                                    "custom_voice_id",
+                                    default="",
+                                    optional=True,
+                                    tooltip="Optional HeyGen voice ID. When set, overrides the voice selected above. "
+                                    "Any voice from HeyGen's library (2000+) can be used.",
+                                ),
+                                IO.Float.Input(
+                                    "voice_speed",
+                                    default=1.0,
+                                    min=0.5,
+                                    max=1.5,
+                                    step=0.05,
+                                    optional=True,
+                                    tooltip="Speech speed multiplier.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "audio",
+                            [
+                                IO.Audio.Input(
+                                    "audio",
+                                    tooltip="Audio for the avatar to lip-sync, up to 10 minutes.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Drive the avatar with a text script (HeyGen text-to-speech) or your own audio.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["720p", "1080p"],
+                    default="1080p",
+                    optional=True,
+                    tooltip="Output video resolution.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["auto", "16:9", "9:16", "1:1", "4:5", "5:4"],
+                    default="auto",
+                    optional=True,
+                    tooltip="Output aspect ratio. 'auto' follows the input image.",
+                ),
+                IO.Combo.Input(
+                    "expressiveness",
+                    options=["low", "medium", "high"],
+                    default="low",
+                    optional=True,
+                    tooltip="How expressive the animated face and gestures are.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    optional=True,
+                    tooltip="Not sent to HeyGen; change it to force a re-run.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.0715,"format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        speech: dict,
+        resolution: str = "1080p",
+        aspect_ratio: str = "auto",
+        expressiveness: str = "low",
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        image = downscale_image_tensor_by_max_side(image, max_side=2000)
+        image_url = await upload_image_to_comfyapi(cls, image, mime_type="image/png", total_pixels=None)
+        payload = {
+            "type": "image",
+            "image": {"type": "url", "url": image_url},
+            "resolution": resolution,
+            "aspect_ratio": aspect_ratio,
+            "expressiveness": expressiveness,
+            "title": "ComfyUI Talking Photo",
+        }
+        await _apply_speech_source(cls, payload, speech, require_voice=True)
+        video = await _create_and_poll_video(cls, payload)
+        return IO.NodeOutput(await download_url_to_video_output(video["video_url"]))
+
+
+class HeyGenAvatarVideoNode(IO.ComfyNode):
+    """Generate a presenter video from a HeyGen avatar look."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="HeyGenAvatarVideoNode",
+            display_name="HeyGen Avatar Video",
+            category="partner/video/HeyGen",
+            description="Generate a talking-presenter video from a HeyGen avatar. "
+            "Includes HeyGen's most popular public avatars; any look ID can be supplied as an override.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "engine",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "auto",
+                            [
+                                IO.Combo.Input(
+                                    "avatar",
+                                    options=HEYGEN_AVATAR_OPTIONS,
+                                    tooltip="Avatar look to present the video (curated from HeyGen's "
+                                    "public library). The best engine the look supports is chosen "
+                                    "automatically.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "avatar_iv",
+                            [
+                                IO.Combo.Input(
+                                    "avatar",
+                                    options=_AVATARS_BY_ENGINE["avatar_iv"],
+                                    tooltip="Avatar looks that support the Avatar IV engine.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "avatar_iii",
+                            [
+                                IO.Combo.Input(
+                                    "avatar",
+                                    options=_AVATARS_BY_ENGINE["avatar_iii"],
+                                    tooltip="Avatar looks that support the Avatar III engine.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "avatar_v",
+                            [
+                                IO.Combo.Input(
+                                    "avatar",
+                                    options=_AVATARS_BY_ENGINE["avatar_v"],
+                                    tooltip="Avatar looks that support the Avatar V engine.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Rendering engine; each choice lists only the avatars that support it. "
+                    "'auto' offers every avatar and picks its best engine (Avatar IV preferred). "
+                    "Avatar V is highest fidelity, Avatar III is the most affordable.",
+                ),
+                IO.String.Input(
+                    "custom_avatar_id",
+                    default="",
+                    optional=True,
+                    tooltip="Optional HeyGen avatar look ID. When set, overrides the avatar selected above. "
+                    "Any of HeyGen's 3000+ public looks (or your private avatars) can be used.",
+                ),
+                IO.DynamicCombo.Input(
+                    "speech",
+                    display_name="speech source",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "script",
+                            [
+                                IO.String.Input(
+                                    "text",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Text for the avatar to speak (up to 5000 characters). "
+                                    "The generated speech must be at least 1 second long.",
+                                ),
+                                IO.Combo.Input(
+                                    "voice",
+                                    options=[_DEFAULT_VOICE_OPTION] + HEYGEN_VOICE_GENERAL_OPTIONS,
+                                    tooltip="Voice for the script. The default option uses the voice HeyGen assigned to the avatar.",
+                                ),
+                                IO.String.Input(
+                                    "custom_voice_id",
+                                    default="",
+                                    optional=True,
+                                    tooltip="Optional HeyGen voice ID. When set, overrides the voice selected above. "
+                                    "Any voice from HeyGen's library (2000+) can be used.",
+                                ),
+                                IO.Float.Input(
+                                    "voice_speed",
+                                    default=1.0,
+                                    min=0.5,
+                                    max=1.5,
+                                    step=0.05,
+                                    optional=True,
+                                    tooltip="Speech speed multiplier.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "audio",
+                            [
+                                IO.Audio.Input(
+                                    "audio",
+                                    tooltip="Audio for the avatar to lip-sync, up to 10 minutes.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Drive the avatar with a text script (HeyGen text-to-speech) or your own audio.",
+                ),
+                IO.Combo.Input(
+                    "resolution",
+                    options=["720p", "1080p"],
+                    default="1080p",
+                    optional=True,
+                    tooltip="Output video resolution.",
+                ),
+                IO.Combo.Input(
+                    "aspect_ratio",
+                    options=["auto", "16:9", "9:16", "1:1", "4:5", "5:4"],
+                    default="auto",
+                    optional=True,
+                    tooltip="Output aspect ratio. 'auto' follows the avatar's source footage.",
+                ),
+                IO.String.Input(
+                    "background_color",
+                    default="",
+                    optional=True,
+                    tooltip="Optional solid background color as a hex code (e.g. '#00ff00'). "
+                    "Leave empty for the avatar's own background.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    optional=True,
+                    tooltip="Not sent to HeyGen; change it to force a re-run.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["engine"]),
+                expr="""
+                widgets.engine = "avatar_iii"
+                  ? {"type":"range_usd","min_usd":0.023881,"max_usd":0.061919,"format":{"suffix":"/second"}}
+                  : widgets.engine = "avatar_v"
+                  ? {"type":"usd","usd":0.095381,"format":{"suffix":"/second"}}
+                  : widgets.engine = "avatar_iv"
+                  ? {"type":"range_usd","min_usd":0.0715,"max_usd":0.095381,"format":{"suffix":"/second"}}
+                  : {"type":"range_usd","min_usd":0.023881,"max_usd":0.095381,"format":{"suffix":"/second"}}
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        engine: dict,
+        speech: dict,
+        custom_avatar_id: str = "",
+        resolution: str = "1080p",
+        aspect_ratio: str = "auto",
+        background_color: str = "",
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        avatar_id, engine_type = await _resolve_avatar(cls, engine["avatar"], custom_avatar_id, engine["engine"])
+        payload = {
+            "type": "avatar",
+            "avatar_id": avatar_id,
+            "resolution": resolution,
+            "aspect_ratio": aspect_ratio,
+            "title": "ComfyUI Avatar Video",
+        }
+        if engine_type:
+            payload["engine"] = {"type": engine_type}
+        background_color = background_color.strip()
+        if background_color:
+            if not background_color.startswith("#"):
+                raise ValueError("background_color must be a hex color code like '#00ff00'.")
+            payload["background"] = {"type": "color", "value": background_color}
+        await _apply_speech_source(cls, payload, speech, require_voice=False)
+        video = await _create_and_poll_video(cls, payload)
+        return IO.NodeOutput(await download_url_to_video_output(video["video_url"]))
+
+
+class HeyGenCreateAvatarNode(IO.ComfyNode):
+    """Create a reusable HeyGen avatar from a photo or a text prompt."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="HeyGenCreateAvatarNode",
+            display_name="HeyGen Create Avatar",
+            category="partner/video/HeyGen",
+            description="Create your own reusable HeyGen avatar from a photo of a person or "
+            "from a text prompt (a generated character). Feed the resulting avatar_id into "
+            "HeyGen Avatar Video's custom_avatar_id — and save the ID somewhere to reuse the "
+            "avatar in future workflows.",
+            inputs=[
+                IO.DynamicCombo.Input(
+                    "source",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "prompt",
+                            [
+                                IO.String.Input(
+                                    "prompt",
+                                    multiline=True,
+                                    default="",
+                                    tooltip="Description of the avatar to generate (up to 1000 characters).",
+                                ),
+                                IO.Autogrow.Input(
+                                    "reference_images",
+                                    template=IO.Autogrow.TemplateNames(
+                                        IO.Image.Input("ref_image"),
+                                        names=[f"ref_image_{i}" for i in range(1, 4)],
+                                        min=0,
+                                    ),
+                                    tooltip="Up to 3 reference images guiding the generated look.",
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option(
+                            "photo",
+                            [
+                                IO.Image.Input(
+                                    "identity_photo",
+                                    tooltip="Photo of the person to turn into an avatar. "
+                                    "Downscaled automatically if larger than 2K.",
+                                ),
+                            ],
+                        ),
+                    ],
+                    tooltip="Generate a new character from a text prompt, or create the avatar "
+                    "from a connected photo of a person.",
+                ),
+            ],
+            outputs=[
+                IO.String.Output(
+                    display_name="avatar_id",
+                    tooltip="Avatar look ID. Pass it to HeyGen Avatar Video's custom_avatar_id; "
+                    "save it to reuse the avatar later.",
+                ),
+                IO.Image.Output(display_name="preview"),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":1.43}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        source: dict,
+    ) -> IO.NodeOutput:
+        payload: dict = {"name": "ComfyUI Avatar"}
+        if source["source"] == "photo":
+            image = downscale_image_tensor_by_max_side(source["identity_photo"], max_side=2000)
+            image_url = await upload_image_to_comfyapi(cls, image, mime_type="image/png", total_pixels=None)
+            payload["type"] = "photo"
+            payload["file"] = {"type": "url", "url": image_url}
+        else:
+            validate_string(source["prompt"], strip_whitespace=True, min_length=1, max_length=1000)
+            payload["type"] = "prompt"
+            payload["prompt"] = source["prompt"]
+            ref_tensors = [t for t in (source.get("reference_images") or {}).values() if t is not None]
+            if ref_tensors:
+                n_images = sum(get_number_of_images(t) for t in ref_tensors)
+                if n_images > 3:
+                    raise ValueError(f"HeyGen accepts at most 3 reference images; got {n_images}.")
+                scaled = [downscale_image_tensor_by_max_side(t, max_side=2000) for t in ref_tensors]
+                ref_urls = await upload_images_to_comfyapi(
+                    cls, scaled, max_images=3, mime_type="image/png", total_pixels=None
+                )
+                payload["reference_images"] = [{"type": "url", "url": u} for u in ref_urls]
+        created = await sync_op_raw(
+            cls,
+            ApiEndpoint(path=_AVATARS_PATH, method="POST"),
+            data=payload,
+        )
+        look_id = ((created.get("data") or {}).get("avatar_item") or {}).get("id")
+        if not look_id:
+            raise ValueError(f"HeyGen did not return an avatar: {created}")
+        final = await poll_op_raw(
+            cls,
+            ApiEndpoint(path=f"{_LOOKS_PATH}/{look_id}"),
+            # A missing status means the look needed no training and is ready.
+            status_extractor=lambda r: (r.get("data") or {}).get("status") or "completed",
+            failed_statuses=["failed", "pending_consent"],
+            poll_interval=5.0,
+        )
+        data = final["data"]
+        if data.get("preview_image_url"):
+            preview = await download_url_to_image_tensor(data["preview_image_url"])
+        else:
+            preview = torch.zeros(1, 64, 64, 3)
+        PromptServer.instance.send_progress_text(
+            f"Please save the avatar_id for reuse.\n\navatar_id: {look_id}",
+            cls.hidden.unique_id,
+        )
+        return IO.NodeOutput(look_id, preview)
+
+
+class HeyGenVideoTranslateNode(IO.ComfyNode):
+    """Translate a spoken video into another language with voice cloning and lip sync."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="HeyGenVideoTranslateNode",
+            display_name="HeyGen Video Translate",
+            category="partner/video/HeyGen",
+            description="Translate a spoken video into another language. Clones the original "
+            "speaker's voice and re-animates the mouth to match the translated speech.",
+            inputs=[
+                IO.Video.Input(
+                    "video",
+                    tooltip="Video with speech to translate.",
+                ),
+                IO.Combo.Input(
+                    "output_language",
+                    options=HEYGEN_TRANSLATE_LANGUAGES,
+                    tooltip="Target language for the translated video.",
+                ),
+                IO.Combo.Input(
+                    "mode",
+                    options=["speed", "precision"],
+                    default="speed",
+                    tooltip="'speed' is faster; 'precision' produces higher-quality lip sync at twice the price.",
+                ),
+                IO.Boolean.Input(
+                    "translate_audio_only",
+                    default=False,
+                    optional=True,
+                    tooltip="Only swap the audio track, keeping the original mouth movements (no lip sync).",
+                ),
+                IO.Int.Input(
+                    "speaker_count",
+                    default=0,
+                    min=0,
+                    max=10,
+                    optional=True,
+                    tooltip="Number of speakers in the video. 0 = detect automatically.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    optional=True,
+                    tooltip="Not sent to HeyGen; change it to force a re-run.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["mode"]),
+                expr="""{"type":"usd","usd": widgets.mode = "precision" ? 0.095381 : 0.047619,"""
+                """"format":{"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        output_language: str,
+        mode: str,
+        translate_audio_only: bool = False,
+        speaker_count: int = 0,
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        video_url = await upload_video_to_comfyapi(cls, video)
+        payload = {
+            "video": {"type": "url", "url": video_url},
+            "output_languages": [output_language],
+            "mode": mode,
+            "translate_audio_only": translate_audio_only,
+            "title": "ComfyUI Video Translate",
+        }
+        if speaker_count > 0:
+            payload["speaker_num"] = speaker_count
+        created = await sync_op_raw(
+            cls,
+            ApiEndpoint(path=_TRANSLATIONS_PATH, method="POST"),
+            data=payload,
+        )
+        translation_ids = (created.get("data") or {}).get("video_translation_ids") or []
+        if not translation_ids:
+            raise ValueError(f"HeyGen did not return a translation ID: {created}")
+        final = await poll_op_raw(
+            cls,
+            ApiEndpoint(path=f"{_TRANSLATIONS_PATH}/{translation_ids[0]}"),
+            status_extractor=lambda r: (r.get("data") or {}).get("status"),
+            queued_statuses=["pending"],
+            poll_interval=5.0,
+        )
+        data = final["data"]
+        if not data.get("video_url"):
+            raise ValueError(f"HeyGen returned no video_url for translation {translation_ids[0]}.")
+        return IO.NodeOutput(await download_url_to_video_output(data["video_url"]))
+
+
+class HeyGenTextToSpeechNode(IO.ComfyNode):
+    """Synthesize speech audio from text with HeyGen's Starfish TTS engine."""
+
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="HeyGenTextToSpeechNode",
+            display_name="HeyGen Text to Speech",
+            category="partner/audio/HeyGen",
+            description="Generate speech audio from text using HeyGen's Starfish TTS engine. "
+            "Includes HeyGen's most popular voices across 17 languages.",
+            inputs=[
+                IO.String.Input(
+                    "text",
+                    multiline=True,
+                    default="",
+                    tooltip="Text to synthesize (up to 5000 characters). The generated speech "
+                    "must be at least 1 second long.",
+                ),
+                IO.Combo.Input(
+                    "voice",
+                    options=HEYGEN_VOICE_TTS_OPTIONS,
+                    tooltip="Voice to use (curated from HeyGen's most popular Starfish-compatible voices).",
+                ),
+                IO.String.Input(
+                    "custom_voice_id",
+                    default="",
+                    optional=True,
+                    tooltip="Optional HeyGen voice ID. When set, overrides the voice selected above. "
+                    "The voice must support the Starfish engine.",
+                ),
+                IO.Float.Input(
+                    "speed",
+                    default=1.0,
+                    min=0.5,
+                    max=2.0,
+                    step=0.05,
+                    optional=True,
+                    tooltip="Speech speed multiplier.",
+                ),
+                IO.Boolean.Input(
+                    "ssml",
+                    default=False,
+                    optional=True,
+                    tooltip="Treat the text as SSML markup (for pauses, emphasis, and pronunciation control).",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    optional=True,
+                    tooltip="Not sent to HeyGen; change it to force a re-run.",
+                ),
+            ],
+            outputs=[IO.Audio.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.00095381,"format":{"approximate":true,"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        text: str,
+        voice: str,
+        custom_voice_id: str = "",
+        speed: float = 1.0,
+        ssml: bool = False,
+        seed: int = 0,
+    ) -> IO.NodeOutput:
+        validate_string(text, strip_whitespace=True, min_length=1, max_length=5000)
+        payload = {
+            "text": text,
+            "voice_id": custom_voice_id.strip() or HEYGEN_VOICE_TTS_MAP[voice],
+            "speed": round(speed, 2),
+        }
+        if ssml:
+            payload["input_type"] = "ssml"
+        response = await sync_op_raw(
+            cls,
+            ApiEndpoint(path=_SPEECH_PATH, method="POST"),
+            data=payload,
+        )
+        audio_url = (response.get("data") or {}).get("audio_url")
+        if not audio_url:
+            raise ValueError(f"HeyGen did not return an audio_url: {response}")
+        audio_bytes = await download_url_as_bytesio(audio_url)
+        return IO.NodeOutput(audio_bytes_to_audio_input(audio_bytes.getvalue()))
+
+
+class HeyGenExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            HeyGenTalkingPhotoNode,
+            HeyGenAvatarVideoNode,
+            HeyGenCreateAvatarNode,
+            HeyGenVideoTranslateNode,
+            HeyGenTextToSpeechNode,
+        ]
+
+
+async def comfy_entrypoint() -> HeyGenExtension:
+    return HeyGenExtension()

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/nodes_openai.py

@@ -41,6 +41,9 @@ STARTING_POINT_ID_PATTERN = r"<starting_point_id:(.*)>"
 
 
 class SupportedOpenAIModel(str, Enum):
+    gpt_5_6_sol = "gpt-5.6-sol"
+    gpt_5_6_terra = "gpt-5.6-terra"
+    gpt_5_6_luna = "gpt-5.6-luna"
     gpt_5_5_pro = "gpt-5.5-pro"
     gpt_5_5 = "gpt-5.5"
     gpt_5 = "gpt-5"
@@ -1063,6 +1066,21 @@ class OpenAIChatNode(IO.ComfyNode):
                     "usd": [0.002, 0.008],
                     "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
                   }
+                  : $contains($m, "gpt-5.6-terra") ? {
+                    "type": "list_usd",
+                    "usd": [0.0025, 0.015],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gpt-5.6-luna") ? {
+                    "type": "list_usd",
+                    "usd": [0.001, 0.006],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
+                  : $contains($m, "gpt-5.6") ? {
+                    "type": "list_usd",
+                    "usd": [0.005, 0.03],
+                    "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" }
+                  }
                   : $contains($m, "gpt-5.5-pro") ? {
                     "type": "list_usd",
                     "usd": [0.03, 0.18],

comfy_api_nodes/nodes_sync_so.py

@@ -0,0 +1,391 @@
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.sync_so import (
+    SyncActiveSpeakerDetection,
+    SyncGeneration,
+    SyncGenerationOptions,
+    SyncGenerationRequest,
+    SyncInputItem,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_video_output,
+    downscale_image_tensor,
+    downscale_image_tensor_by_max_side,
+    get_image_dimensions,
+    get_number_of_images,
+    poll_op,
+    sync_op,
+    upload_audio_to_comfyapi,
+    upload_image_to_comfyapi,
+    upload_video_to_comfyapi,
+    validate_audio_duration,
+)
+
+
+class SyncLipSyncNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="SyncLipSyncNode",
+            display_name="sync.so Lip Sync",
+            category="partner/video/sync.so",
+            description=(
+                "Re-sync mouth movement in a video to new speech audio using sync.so. "
+                "Handles close-ups, profiles and obstructions automatically while preserving "
+                "the speaker's expression. Cost scales with output duration."
+            ),
+            inputs=[
+                IO.Video.Input(
+                    "video",
+                    tooltip="Footage of the speaker to re-sync. Up to 4K (4096x2160); "
+                    "a constant frame rate of 24/25/30 fps works best.",
+                ),
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Speech audio to sync the mouth to.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "sync-3",
+                            [
+                                IO.Combo.Input(
+                                    "sync_mode",
+                                    options=["bounce", "cut_off", "loop", "silence", "remap"],
+                                    default="bounce",
+                                    tooltip=(
+                                        "How to handle a duration mismatch between video and audio; "
+                                        "this also sets the output length. "
+                                        "bounce: video plays forward then backward until the audio ends "
+                                        "(output = audio length). "
+                                        "loop: video restarts until the audio ends (output = audio length). "
+                                        "remap: video is time-stretched to match the audio (output = audio length). "
+                                        "cut_off: the longer track is trimmed (output = shorter length). "
+                                        "silence: nothing is trimmed; the shorter track is padded "
+                                        "(output = longer length)."
+                                    ),
+                                ),
+                                IO.Combo.Input(
+                                    "speaker_selection",
+                                    options=["default", "auto-detect", "coordinates"],
+                                    default="default",
+                                    tooltip=(
+                                        "Which face to lipsync when several people are visible. "
+                                        "default: let the model decide. "
+                                        "auto-detect: detect and follow the active speaker. "
+                                        "coordinates: target the face at pixel (speaker_x, speaker_y) "
+                                        "in the frame chosen by speaker_frame."
+                                    ),
+                                ),
+                                IO.Int.Input(
+                                    "speaker_frame",
+                                    default=0,
+                                    min=0,
+                                    max=1_000_000,
+                                    advanced=True,
+                                    tooltip="Video frame used to locate the speaker. "
+                                    "Only used when speaker_selection is 'coordinates'.",
+                                ),
+                                IO.Int.Input(
+                                    "speaker_x",
+                                    default=0,
+                                    min=0,
+                                    max=4096,
+                                    advanced=True,
+                                    tooltip="X pixel coordinate of the speaker's face. "
+                                    "Only used when speaker_selection is 'coordinates'.",
+                                ),
+                                IO.Int.Input(
+                                    "speaker_y",
+                                    default=0,
+                                    min=0,
+                                    max=4096,
+                                    advanced=True,
+                                    tooltip="Y pixel coordinate of the speaker's face. "
+                                    "Only used when speaker_selection is 'coordinates'.",
+                                ),
+                            ],
+                        )
+                    ],
+                    tooltip="sync.so generation model.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.19019,"format":{"approximate":true,"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        audio: Input.Audio,
+        seed: int,
+        model: dict,
+    ) -> IO.NodeOutput:
+        try:
+            width, height = video.get_dimensions()
+        except Exception:
+            width = height = None
+        if width and height and (max(width, height) > 4096 or width * height > 4096 * 2160):
+            raise ValueError(
+                f"sync.so rejects videos above 4K (4096x2160); got {width}x{height}. Downscale the video first."
+            )
+        validate_audio_duration(audio, max_duration=600)
+
+        if model["speaker_selection"] == "auto-detect":
+            speaker_detection = SyncActiveSpeakerDetection(auto_detect=True)
+        elif model["speaker_selection"] == "coordinates":
+            speaker_detection = SyncActiveSpeakerDetection(
+                frame_number=model["speaker_frame"],
+                coordinates=[model["speaker_x"], model["speaker_y"]],
+            )
+        else:
+            speaker_detection = None
+
+        video_url = await upload_video_to_comfyapi(cls, video, max_duration=600)
+        audio_url = await upload_audio_to_comfyapi(cls, audio)
+
+        generation = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/synclabs/v2/generate", method="POST"),
+            response_model=SyncGeneration,
+            data=SyncGenerationRequest(
+                model=model["model"],
+                input=[
+                    SyncInputItem(type="video", url=video_url),
+                    SyncInputItem(type="audio", url=audio_url),
+                ],
+                options=SyncGenerationOptions(
+                    sync_mode=model["sync_mode"],
+                    active_speaker_detection=speaker_detection,
+                ),
+            ),
+        )
+        generation = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/synclabs/v2/generate/{generation.id}"),
+            response_model=SyncGeneration,
+            status_extractor=lambda g: g.status,
+            completed_statuses=["COMPLETED", "FAILED", "REJECTED"],
+            failed_statuses=[],
+            queued_statuses=["PENDING"],
+            poll_interval=10.0,
+        )
+        if generation.status != "COMPLETED":
+            code = f" [{generation.errorCode}]" if generation.errorCode else ""
+            raise ValueError(
+                f"sync.so generation {generation.status.lower()}{code}: "
+                f"{generation.error or 'no error details provided'}"
+            )
+        if not generation.outputUrl:
+            raise ValueError("sync.so generation completed but no output URL was returned.")
+        return IO.NodeOutput(await download_url_to_video_output(generation.outputUrl))
+
+
+class SyncTalkingImageNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> IO.Schema:
+        return IO.Schema(
+            node_id="SyncTalkingImageNode",
+            display_name="sync.so Talking Image",
+            category="partner/video/sync.so",
+            description=(
+                "Animate a still portrait into a talking video driven by speech audio, "
+                "using sync.so's sync-3 model. The output duration matches the audio. "
+                "Cost scales with output duration."
+            ),
+            inputs=[
+                IO.Image.Input(
+                    "image",
+                    tooltip="A single image with a clearly visible face, up to 4K (4096x2160).",
+                ),
+                IO.Audio.Input(
+                    "audio",
+                    tooltip="Speech audio driving the talking video; the output duration matches it. "
+                    "Chain any TTS node here to drive the animation from text.",
+                ),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Optional guidance for how the portrait comes to life, e.g. "
+                    "'make the subject smile and look at the camera'. "
+                    "Leave empty for natural talking motion.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed controls whether the node should re-run; "
+                    "results are non-deterministic regardless of seed.",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "sync-3",
+                            [
+                                IO.Combo.Input(
+                                    "speaker_selection",
+                                    options=["default", "coordinates"],
+                                    default="default",
+                                    tooltip=(
+                                        "Which face to animate when several people are visible. "
+                                        "default: let the model decide. "
+                                        "coordinates: target the face at pixel (speaker_x, speaker_y) "
+                                        "in the image. Auto-detection is not supported for images."
+                                    ),
+                                ),
+                                IO.Int.Input(
+                                    "speaker_x",
+                                    default=0,
+                                    min=0,
+                                    max=4096,
+                                    advanced=True,
+                                    tooltip="X pixel coordinate of the speaker's face. "
+                                    "Only used when speaker_selection is 'coordinates'.",
+                                ),
+                                IO.Int.Input(
+                                    "speaker_y",
+                                    default=0,
+                                    min=0,
+                                    max=4096,
+                                    advanced=True,
+                                    tooltip="Y pixel coordinate of the speaker's face. "
+                                    "Only used when speaker_selection is 'coordinates'.",
+                                ),
+                                IO.Boolean.Input(
+                                    "auto_downscale",
+                                    default=True,
+                                    advanced=True,
+                                    tooltip="Automatically downscale the image if it exceeds the 4K "
+                                    "(4096x2160) input limit; speaker coordinates are scaled to match. "
+                                    "When disabled, an oversized image raises an error instead.",
+                                ),
+                            ],
+                        )
+                    ],
+                    tooltip="sync.so generation model. Image input is exclusive to sync-3.",
+                ),
+            ],
+            outputs=[IO.Video.Output()],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                expr="""{"type":"usd","usd":0.19019,"format":{"approximate":true,"suffix":"/second"}}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        image: Input.Image,
+        audio: Input.Audio,
+        prompt: str,
+        seed: int,
+        model: dict,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) != 1:
+            raise ValueError("Exactly one image is required; got a batch. Pick one frame first.")
+        validate_audio_duration(audio, max_duration=600)
+
+        height, width = get_image_dimensions(image)
+        speaker_x, speaker_y = model["speaker_x"], model["speaker_y"]
+        if max(width, height) > 4096 or width * height > 4096 * 2160:
+            if not model["auto_downscale"]:
+                raise ValueError(
+                    f"sync.so rejects images above 4K (4096x2160); got {width}x{height}. "
+                    "Downscale the image first or enable auto_downscale."
+                )
+            image = downscale_image_tensor(image, total_pixels=4096 * 2160)
+            image = downscale_image_tensor_by_max_side(image, max_side=4096)
+            new_height, new_width = get_image_dimensions(image)
+            # speaker coordinates are given in the original image's pixel space
+            speaker_x = min(new_width - 1, round(speaker_x * new_width / width))
+            speaker_y = min(new_height - 1, round(speaker_y * new_height / height))
+
+        if model["speaker_selection"] == "coordinates":
+            speaker_detection = SyncActiveSpeakerDetection(
+                frame_number=0,  # images have a single frame; auto_detect is rejected by the API
+                coordinates=[speaker_x, speaker_y],
+            )
+        else:
+            speaker_detection = None
+
+        image_url = await upload_image_to_comfyapi(cls, image, mime_type="image/png", total_pixels=None)
+        audio_url = await upload_audio_to_comfyapi(cls, audio)
+
+        generation = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/synclabs/v2/generate", method="POST"),
+            response_model=SyncGeneration,
+            data=SyncGenerationRequest(
+                model=model["model"],
+                input=[
+                    SyncInputItem(type="image", url=image_url),
+                    SyncInputItem(type="audio", url=audio_url),
+                ],
+                options=SyncGenerationOptions(
+                    i2v_prompt=prompt.strip() or None,
+                    active_speaker_detection=speaker_detection,
+                ),
+            ),
+        )
+        generation = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/synclabs/v2/generate/{generation.id}"),
+            response_model=SyncGeneration,
+            status_extractor=lambda g: g.status,
+            completed_statuses=["COMPLETED", "FAILED", "REJECTED"],
+            failed_statuses=[],
+            queued_statuses=["PENDING"],
+            poll_interval=10.0,
+        )
+        if generation.status != "COMPLETED":
+            code = f" [{generation.errorCode}]" if generation.errorCode else ""
+            raise ValueError(
+                f"sync.so generation {generation.status.lower()}{code}: "
+                f"{generation.error or 'no error details provided'}"
+            )
+        if not generation.outputUrl:
+            raise ValueError("sync.so generation completed but no output URL was returned.")
+        return IO.NodeOutput(await download_url_to_video_output(generation.outputUrl))
+
+
+class SyncExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            SyncLipSyncNode,
+            SyncTalkingImageNode,
+        ]
+
+
+async def comfy_entrypoint() -> SyncExtension:
+    return SyncExtension()

comfy_api_nodes/util/_helpers.py

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

comfy_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_images.py

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

comfy_extras/nodes_load_3d.py

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

comfy_extras/nodes_mask.py

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

comfy_extras/nodes_preview_any.py

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

comfy_extras/nodes_primitive.py

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

comfy_extras/nodes_save_3d.py

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

comfy_extras/nodes_seedvr.py

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

comfy_extras/nodes_text.py

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

comfy_extras/nodes_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()

comfy_extras/nodes_video.py

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

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.27.0"
+__version__ = "0.28.1"

execution.py

@@ -29,6 +29,7 @@ from comfy_execution.caching import (
     HierarchicalCache,
     LRUCache,
     RAMPressureCache,
+    RAM_CACHE_LARGE_INTERMEDIATE,
 )
 from comfy_execution.graph import (
     DynamicPrompt,
@@ -425,12 +426,12 @@ def _is_intermediate_output(dynprompt, node_id):
 
 
 def _send_cached_ui(server, node_id, display_node_id, cached, prompt_id, ui_outputs):
+    if cached.ui is not None:
+        ui_outputs[node_id] = cached.ui
     if server.client_id is None:
         return
     cached_ui = cached.ui or {}
     server.send_sync("executed", { "node": node_id, "display_node": display_node_id, "output": cached_ui.get("output", None), "prompt_id": prompt_id }, server.client_id)
-    if cached.ui is not None:
-        ui_outputs[node_id] = cached.ui
 
 async def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_outputs):
     unique_id = current_item
@@ -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

@@ -3297,6 +3297,12 @@ paths:
                             schema:
                                 $ref: '#/components/schemas/ErrorResponse'
                     description: Invalid request parameters
+                "401":
+                    content:
+                        application/json:
+                            schema:
+                                $ref: '#/components/schemas/ErrorResponse'
+                    description: Unauthorized - Authentication required
                 "500":
                     content:
                         application/json:

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.27.0"
+version = "0.28.1"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

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-frontend-package==1.45.21
+comfyui-workflow-templates==0.11.11
+comfyui-embedded-docs==0.5.8
 torch
 torchsde
 torchvision
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy>=2.0.0
 filelock
 av>=16.0.0
-comfy-kitchen==0.2.16
+comfy-kitchen==0.2.20
 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."""