diff --git a/.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt b/.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt index 2cbb00d99..2c72c8a13 100755 --- a/.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt +++ b/.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt @@ -1,5 +1,4 @@ -As of the time of writing this you need this driver for best results: -https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-7-1-1.html +As of the time of writing this you need a recent driver. Updating to the latest driver is recommended. HOW TO RUN: @@ -7,9 +6,9 @@ If you have a AMD gpu: run_amd_gpu.bat -If you have memory issues you can try disabling the smart memory management by running comfyui with: +If you have memory issues you can try enabling the new dynamic memory management by running comfyui with: -run_amd_gpu_disable_smart_memory.bat +run_amd_gpu_enable_dynamic_vram.bat IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints diff --git a/.github/workflows/check-line-endings.yml b/.github/workflows/check-line-endings.yml index eeb594d6c..a69a24a87 100644 --- a/.github/workflows/check-line-endings.yml +++ b/.github/workflows/check-line-endings.yml @@ -17,7 +17,7 @@ jobs: - name: Check for Windows line endings (CRLF) run: | # Get the list of changed files in the PR - CHANGED_FILES=$(git diff --name-only ${{ github.event.pull_request.base.sha }}..${{ github.event.pull_request.head.sha }}) + CHANGED_FILES=$(git diff --name-only ${{ github.event.pull_request.base.sha }}..${{ github.event.pull_request.head.sha }} -- ':!.ci') # Flag to track if CRLF is found CRLF_FOUND=false diff --git a/README.md b/README.md index dc2389266..bcec86377 100644 --- a/README.md +++ b/README.md @@ -140,7 +140,7 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang - Commits outside of the stable release tags may be very unstable and break many custom nodes. - Serves as the foundation for the desktop release -2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)** +2. **[Comfy Desktop](https://github.com/Comfy-Org/Comfy-Desktop)** - Builds a new release using the latest stable core version 3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)** @@ -309,7 +309,7 @@ After this you should have everything installed and can proceed to running Comfy #### Apple Mac silicon -You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version. +You can install ComfyUI in Apple Mac silicon (M1, M2, M3 or M4) with any recent macOS version. 1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly). 1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux. @@ -364,7 +364,7 @@ For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step | Flag | Description | |------|-------------| | `--enable-manager` | Enable ComfyUI-Manager | -| `--enable-manager-legacy-ui` | Use the legacy manager UI instead of the new UI (requires `--enable-manager`) | +| `--enable-manager-legacy-ui` | Use the legacy manager UI instead of the new UI (implies `--enable-manager`) | | `--disable-manager-ui` | Disable the manager UI and endpoints while keeping background features like security checks and scheduled installation completion (requires `--enable-manager`) | @@ -382,11 +382,7 @@ For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 pyt ### AMD ROCm Tips -You can enable experimental memory efficient attention on recent pytorch in ComfyUI on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that I can enable it by default. - -```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention``` - -You can also try setting this env variable `PYTORCH_TUNABLEOP_ENABLED=1` which might speed things up at the cost of a very slow initial run. +You can try setting this env variable `PYTORCH_TUNABLEOP_ENABLED=1` which might speed things up at the cost of a very slow initial run. # Notes @@ -462,16 +458,6 @@ To use the most up-to-date frontend version: This approach allows you to easily switch between the stable fortnightly release and the cutting-edge daily updates, or even specific versions for testing purposes. -### Accessing the Legacy Frontend - -If you need to use the legacy frontend for any reason, you can access it using the following command line argument: - -``` ---front-end-version Comfy-Org/ComfyUI_legacy_frontend@latest -``` - -This will use a snapshot of the legacy frontend preserved in the [ComfyUI Legacy Frontend repository](https://github.com/Comfy-Org/ComfyUI_legacy_frontend). - # QA ### Which GPU should I buy for this? diff --git a/alembic_db/versions/0004_drop_tag_type.py b/alembic_db/versions/0004_drop_tag_type.py new file mode 100644 index 000000000..582bec4e8 --- /dev/null +++ b/alembic_db/versions/0004_drop_tag_type.py @@ -0,0 +1,39 @@ +""" +Drop the vestigial tags.tag_type column. + +tag_type was always "user" in practice — no code path ever set it to anything +else (no system/seeded classification was ever wired up) and nothing queried it. +The column, its index (ix_tags_tag_type), and the corresponding API field were +dead weight, so they are removed. + +Revision ID: 0004_drop_tag_type +Revises: 0003_add_metadata_job_id +Create Date: 2026-06-03 +""" + +from alembic import op +import sqlalchemy as sa + +revision = "0004_drop_tag_type" +down_revision = "0003_add_metadata_job_id" +branch_labels = None +depends_on = None + + +def upgrade() -> None: + with op.batch_alter_table("tags") as batch_op: + batch_op.drop_index("ix_tags_tag_type") + batch_op.drop_column("tag_type") + + +def downgrade() -> None: + with op.batch_alter_table("tags") as batch_op: + batch_op.add_column( + sa.Column( + "tag_type", + sa.String(length=32), + nullable=False, + server_default="user", + ) + ) + batch_op.create_index("ix_tags_tag_type", ["tag_type"]) diff --git a/app/assets/api/routes.py b/app/assets/api/routes.py index 6555974e9..7ef462f5c 100644 --- a/app/assets/api/routes.py +++ b/app/assets/api/routes.py @@ -39,6 +39,7 @@ from app.assets.services import ( update_asset_metadata, upload_from_temp_path, ) +from app.assets.services.cursor import InvalidCursorError from app.assets.services.tagging import list_tag_histogram ROUTES = web.RouteTableDef() @@ -174,7 +175,7 @@ def _build_asset_response(result: schemas.AssetDetailResult | schemas.UploadResu user_metadata=result.ref.user_metadata or {}, metadata=result.ref.system_metadata, job_id=result.ref.job_id, - prompt_id=result.ref.job_id, # deprecated: mirrors job_id for cloud compat + prompt_id=result.ref.job_id, # deprecated alias of job_id, kept for compatibility created_at=result.ref.created_at, updated_at=result.ref.updated_at, last_access_time=result.ref.last_access_time, @@ -211,24 +212,37 @@ async def list_assets_route(request: web.Request) -> web.Response: order_candidate = (q.order or "desc").lower() order = order_candidate if order_candidate in {"asc", "desc"} else "desc" - result = list_assets_page( - owner_id=USER_MANAGER.get_request_user_id(request), - include_tags=q.include_tags, - exclude_tags=q.exclude_tags, - name_contains=q.name_contains, - metadata_filter=q.metadata_filter, - limit=q.limit, - offset=q.offset, - sort=sort, - order=order, - ) + try: + result = list_assets_page( + owner_id=USER_MANAGER.get_request_user_id(request), + include_tags=q.include_tags, + exclude_tags=q.exclude_tags, + name_contains=q.name_contains, + metadata_filter=q.metadata_filter, + limit=q.limit, + offset=q.offset, + sort=sort, + order=order, + after=q.after, + ) + except InvalidCursorError as e: + return _build_error_response(400, "INVALID_CURSOR", str(e)) summaries = [_build_asset_response(item) for item in result.items] + # has_more semantics differ by mode: + # - cursor mode: a non-empty next_cursor means there are more results. + # - offset mode: derived from total - (offset + page size). + if q.after is not None: + has_more = result.next_cursor is not None + else: + has_more = (q.offset + len(summaries)) < result.total + payload = schemas_out.AssetsList( assets=summaries, total=result.total, - has_more=(q.offset + len(summaries)) < result.total, + has_more=has_more, + next_cursor=result.next_cursor, ) return web.json_response(payload.model_dump(mode="json", exclude_none=True)) @@ -519,18 +533,14 @@ async def update_asset_route(request: web.Request) -> web.Response: @_require_assets_feature_enabled async def delete_asset_route(request: web.Request) -> web.Response: reference_id = str(uuid.UUID(request.match_info["id"])) - delete_content_param = request.query.get("delete_content") - delete_content = ( - False - if delete_content_param is None - else delete_content_param.lower() not in {"0", "false", "no"} - ) try: + # Deleting an asset is a soft delete of the reference; the underlying + # content is preserved (it may be shared with other references). deleted = delete_asset_reference( reference_id=reference_id, owner_id=USER_MANAGER.get_request_user_id(request), - delete_content_if_orphan=delete_content, + delete_content_if_orphan=False, ) except Exception: logging.exception( @@ -575,8 +585,8 @@ async def get_tags(request: web.Request) -> web.Response: ) tags = [ - schemas_out.TagUsage(name=name, count=count, type=tag_type) - for (name, tag_type, count) in rows + schemas_out.TagUsage(name=name, count=count) + for (name, count) in rows ] payload = schemas_out.TagsList( tags=tags, total=total, has_more=(query.offset + len(tags)) < total diff --git a/app/assets/api/schemas_in.py b/app/assets/api/schemas_in.py index 186a6ae1e..af666746d 100644 --- a/app/assets/api/schemas_in.py +++ b/app/assets/api/schemas_in.py @@ -59,6 +59,11 @@ class ListAssetsQuery(BaseModel): limit: conint(ge=1, le=500) = 20 offset: conint(ge=0) = 0 + # Opaque keyset cursor. When supplied, `offset` is ignored. Cursor pagination + # is supported for sort values `created_at`, `updated_at`, `name`, `size`. + # Supplying `after` together with `sort=last_access_time` returns + # 400 INVALID_CURSOR; that sort only supports offset/limit. + after: str | None = None sort: Literal["name", "created_at", "updated_at", "size", "last_access_time"] = ( "created_at" diff --git a/app/assets/api/schemas_out.py b/app/assets/api/schemas_out.py index 0e748b907..4e38e19d1 100644 --- a/app/assets/api/schemas_out.py +++ b/app/assets/api/schemas_out.py @@ -41,12 +41,13 @@ class AssetsList(BaseModel): assets: list[Asset] total: int has_more: bool + # Opaque cursor for the next page. Omitted when there are no more results. + next_cursor: str | None = None class TagUsage(BaseModel): name: str count: int - type: str class TagsList(BaseModel): diff --git a/app/assets/database/models.py b/app/assets/database/models.py index a3af8a192..9b61d309a 100644 --- a/app/assets/database/models.py +++ b/app/assets/database/models.py @@ -227,7 +227,6 @@ class Tag(Base): __tablename__ = "tags" name: Mapped[str] = mapped_column(String(512), primary_key=True) - tag_type: Mapped[str] = mapped_column(String(32), nullable=False, default="user") asset_reference_links: Mapped[list[AssetReferenceTag]] = relationship( back_populates="tag", @@ -240,7 +239,5 @@ class Tag(Base): overlaps="asset_reference_links,tag_links,tags,asset_reference", ) - __table_args__ = (Index("ix_tags_tag_type", "tag_type"),) - def __repr__(self) -> str: return f"" diff --git a/app/assets/database/queries/asset_reference.py b/app/assets/database/queries/asset_reference.py index 8b90ae511..792411800 100644 --- a/app/assets/database/queries/asset_reference.py +++ b/app/assets/database/queries/asset_reference.py @@ -266,9 +266,18 @@ def list_references_page( metadata_filter: dict | None = None, sort: str | None = None, order: str | None = None, + after_cursor_value: object | None = None, + after_cursor_id: str | None = None, ) -> tuple[list[AssetReference], dict[str, list[str]], int]: """List references with pagination, filtering, and sorting. + When ``after_cursor_value``/``after_cursor_id`` are supplied the query uses + keyset pagination — ``offset`` is ignored and a WHERE clause selects rows + strictly after the given ``(sort_col, id)`` position in the active sort + direction. The cursor value must already be typed for the column + (datetime for time sorts, int for size, str for name); the caller decodes + the opaque cursor string and resolves to the typed value. + Returns (references, tag_map, total_count). """ base = ( @@ -297,9 +306,31 @@ def list_references_page( "size": Asset.size_bytes, } sort_col = sort_map.get(sort, AssetReference.created_at) - sort_exp = sort_col.desc() if order == "desc" else sort_col.asc() + descending = order == "desc" - base = base.order_by(sort_exp).limit(limit).offset(offset) + # Keyset WHERE: (sort_col, id) strictly less-than / greater-than the cursor. + # Equivalent to: sort_col v OR (sort_col = v AND id cursor_id). + if after_cursor_value is not None and after_cursor_id is not None: + if descending: + keyset = sa.or_( + sort_col < after_cursor_value, + sa.and_(sort_col == after_cursor_value, AssetReference.id < after_cursor_id), + ) + else: + keyset = sa.or_( + sort_col > after_cursor_value, + sa.and_(sort_col == after_cursor_value, AssetReference.id > after_cursor_id), + ) + base = base.where(keyset) + + # Secondary ORDER BY id (matching the primary direction) gives the keyset + # comparison a deterministic tiebreaker on duplicate sort_col values. + id_exp = AssetReference.id.desc() if descending else AssetReference.id.asc() + sort_exp = sort_col.desc() if descending else sort_col.asc() + + base = base.order_by(sort_exp, id_exp).limit(limit) + if after_cursor_id is None: + base = base.offset(offset) count_stmt = ( select(sa.func.count()) diff --git a/app/assets/database/queries/tags.py b/app/assets/database/queries/tags.py index f4126dba8..d41d73a10 100644 --- a/app/assets/database/queries/tags.py +++ b/app/assets/database/queries/tags.py @@ -55,13 +55,11 @@ def validate_tags_exist(session: Session, tags: list[str]) -> None: raise ValueError(f"Unknown tags: {missing}") -def ensure_tags_exist( - session: Session, names: Iterable[str], tag_type: str = "user" -) -> None: +def ensure_tags_exist(session: Session, names: Iterable[str]) -> None: wanted = normalize_tags(list(names)) if not wanted: return - rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))] + rows = [{"name": n} for n in list(dict.fromkeys(wanted))] ins = ( sqlite.insert(Tag) .values(rows) @@ -97,7 +95,7 @@ def set_reference_tags( to_remove = [t for t in current if t not in desired] if to_add: - ensure_tags_exist(session, to_add, tag_type="user") + ensure_tags_exist(session, to_add) session.add_all( [ AssetReferenceTag( @@ -142,7 +140,7 @@ def add_tags_to_reference( return AddTagsResult(added=[], already_present=[], total_tags=total) if create_if_missing: - ensure_tags_exist(session, norm, tag_type="user") + ensure_tags_exist(session, norm) current = set(get_reference_tags(session, reference_id)) @@ -289,7 +287,6 @@ def list_tags_with_usage( q = ( select( Tag.name, - Tag.tag_type, func.coalesce(counts_sq.c.cnt, 0).label("count"), ) .select_from(Tag) @@ -331,7 +328,7 @@ def list_tags_with_usage( rows = (session.execute(q.limit(limit).offset(offset))).all() total = (session.execute(total_q)).scalar_one() - rows_norm = [(name, ttype, int(count or 0)) for (name, ttype, count) in rows] + rows_norm = [(name, int(count or 0)) for (name, count) in rows] return rows_norm, int(total or 0) diff --git a/app/assets/scanner.py b/app/assets/scanner.py index ebb6869af..2c1e97840 100644 --- a/app/assets/scanner.py +++ b/app/assets/scanner.py @@ -33,6 +33,7 @@ from app.assets.services.file_utils import ( verify_file_unchanged, ) 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, @@ -354,7 +355,7 @@ def insert_asset_specs(specs: list[SeedAssetSpec], tag_pool: set[str]) -> int: return 0 with create_session() as sess: if tag_pool: - ensure_tags_exist(sess, tag_pool, tag_type="user") + ensure_tags_exist(sess, tag_pool) result = batch_insert_seed_assets(sess, specs=specs, owner_id="") sess.commit() return result.inserted_refs @@ -506,6 +507,10 @@ def enrich_asset( if extract_metadata and metadata: system_metadata = metadata.to_user_metadata() + if mime_type and mime_type.startswith("image/"): + dims = extract_image_dimensions(file_path, mime_type=mime_type) + if dims: + system_metadata.update(dims) set_reference_system_metadata(session, reference_id, system_metadata) if full_hash: diff --git a/app/assets/services/asset_management.py b/app/assets/services/asset_management.py index 5aefd9956..d4e4fc61c 100644 --- a/app/assets/services/asset_management.py +++ b/app/assets/services/asset_management.py @@ -1,8 +1,19 @@ import contextlib import mimetypes import os +from datetime import timezone from typing import Sequence +from app.assets.services.cursor import ( + CursorPayload, + InvalidCursorError, + decode_cursor, + decode_cursor_int, + decode_cursor_time, + encode_cursor, + encode_cursor_from_time, +) + from app.assets.database.models import Asset from app.assets.database.queries import ( @@ -149,6 +160,16 @@ def delete_asset_reference( owner_id: str, delete_content_if_orphan: bool = True, ) -> bool: + """Delete an asset reference. + + With ``delete_content_if_orphan=False`` (a soft delete), the reference is + hidden and the underlying content is preserved. With ``True``, the content + is also removed once it becomes orphaned. + + Note: the public DELETE /api/assets/{id} endpoint always soft-deletes + (passes ``False``); the orphan-reclamation path is intentionally + internal-only, retained for a future GC/admin caller. + """ with create_session() as session: if not delete_content_if_orphan: # Soft delete: mark the reference as deleted but keep everything @@ -242,6 +263,11 @@ def get_asset_by_hash(asset_hash: str) -> AssetData | None: return extract_asset_data(asset) +# Sort fields that support cursor pagination. `last_access_time` is not +# in this list — it falls back to offset/limit. +_CURSOR_SORT_FIELDS = ("created_at", "updated_at", "name", "size") + + def list_assets_page( owner_id: str = "", include_tags: Sequence[str] | None = None, @@ -252,7 +278,39 @@ def list_assets_page( offset: int = 0, sort: str = "created_at", order: str = "desc", + after: str | None = None, ) -> ListAssetsResult: + """List assets with optional cursor pagination. + + When ``after`` is supplied it overrides ``offset``. The cursor's sort field + must match ``sort`` and be in the cursor-supported allowlist; mismatches + raise InvalidCursorError so the handler can map to 400 INVALID_CURSOR. + """ + cursor_value: object | None = None + cursor_id: str | None = None + # Mint next_cursor on every page where the sort is cursor-supported, not + # only when the request itself arrived with a cursor. Otherwise a first + # request (no `after`) returns next_cursor=None and the client can never + # enter cursor mode. + mint_cursor = sort in _CURSOR_SORT_FIELDS + + if after is not None: + if sort not in _CURSOR_SORT_FIELDS: + raise InvalidCursorError( + f"cursor pagination is not supported for sort={sort!r}" + ) + payload = decode_cursor(after, _CURSOR_SORT_FIELDS, expected_order=order) + if payload.sort_field != sort: + raise InvalidCursorError( + f"cursor sort field {payload.sort_field!r} does not match request sort {sort!r}" + ) + cursor_value, cursor_id = _resolve_cursor_value(payload), payload.id + + # Over-fetch by one row so we can distinguish "exactly `limit` rows total + # remaining" from "more rows past this page" without a second query. Drop + # the sentinel before returning. + fetch_limit = limit + 1 if mint_cursor else limit + with create_session() as session: refs, tag_map, total = list_references_page( session, @@ -261,12 +319,22 @@ def list_assets_page( exclude_tags=exclude_tags, name_contains=name_contains, metadata_filter=metadata_filter, - limit=limit, + limit=fetch_limit, offset=offset, sort=sort, order=order, + after_cursor_value=cursor_value, + after_cursor_id=cursor_id, ) + next_cursor: str | None = None + if mint_cursor and len(refs) > limit: + # There's at least one more row past this page — mint a cursor from + # the last row of the page (i.e. index `limit - 1`, since we + # over-fetched), and drop the sentinel. + next_cursor = _encode_next_cursor(refs[limit - 1], sort, order) + refs = refs[:limit] + items: list[AssetSummaryData] = [] for ref in refs: items.append( @@ -277,7 +345,39 @@ def list_assets_page( ) ) - return ListAssetsResult(items=items, total=total) + return ListAssetsResult(items=items, total=total, next_cursor=next_cursor) + + +def _resolve_cursor_value(payload: CursorPayload) -> object: + """Map a decoded cursor payload to a column-typed Python value.""" + if payload.sort_field in ("created_at", "updated_at"): + # DB stores naive UTC; strip tzinfo so the comparison binds against a + # `TIMESTAMP WITHOUT TIME ZONE` column without an offset shift. + return decode_cursor_time(payload).replace(tzinfo=None) + if payload.sort_field == "size": + return decode_cursor_int(payload) + return payload.value # name, str-typed + + +def _encode_next_cursor(ref, sort: str, order: str) -> str | None: + """Mint a cursor pointing at *ref* for the given sort dimension. + + Returns None when the boundary row carries a NULL sort value (e.g. an asset + record whose size_bytes hasn't been backfilled). Continuing pagination + across a NULL boundary is undefined under keyset ordering — better to + truncate cleanly here than to mint a cursor that mis-positions. + """ + if sort == "name": + return encode_cursor("name", ref.name, ref.id, order=order) + if sort == "size": + if ref.asset is None or ref.asset.size_bytes is None: + return None + return encode_cursor("size", str(ref.asset.size_bytes), ref.id, order=order) + # created_at / updated_at — DB datetimes are naive UTC; attach tz before encoding. + value = ref.created_at if sort == "created_at" else ref.updated_at + if value is None: + return None + return encode_cursor_from_time(sort, value.replace(tzinfo=timezone.utc), ref.id, order=order) def resolve_hash_to_path( diff --git a/app/assets/services/cursor.py b/app/assets/services/cursor.py new file mode 100644 index 000000000..6c7791528 --- /dev/null +++ b/app/assets/services/cursor.py @@ -0,0 +1,213 @@ +"""Opaque keyset-pagination cursor for /api/assets. + +Payload JSON uses short keys to keep the encoded length small: + + {"s": , "v": , "id": , "o": } + +The `o` key binds the cursor to the sort direction it was minted under, +so replaying a `desc` cursor against an `asc` request fails with +``INVALID_CURSOR`` rather than silently walking the wrong direction. +`o` is mandatory on every payload — a cursor without it is rejected as +malformed. + +Encoding is base64url with no padding. Cursors are opaque tokens: the +payload format is internal to this server, and clients must treat a +cursor as a black box handed back via `next_cursor`. No byte-level +compatibility with any other implementation is required. + +Time values are serialized as Unix microseconds (UTC) — microsecond +precision is sufficient to round-trip the timestamps stored by the +database without rounding rows in the same millisecond bucket. +""" +from __future__ import annotations + +import base64 +import json +from dataclasses import dataclass +from datetime import datetime, timezone +from typing import Iterable, Optional + + +class InvalidCursorError(ValueError): + """Raised on a malformed, oversized, or unsupported-sort-field cursor. + + Map to a 400 response with code ``INVALID_CURSOR`` at the handler. + """ + + +# Wire-format length caps. Cursors are user-controlled, so caps protect the +# decode path from oversized allocations and downstream SQL predicates from +# unbounded strings. +# +# MAX_CURSOR_VALUE_LENGTH is 512 to fit the `AssetReference.name` column max +# (`String(512)`) — otherwise a long-named asset would mint a cursor the same +# server then refuses on the next request. +# +# MAX_ENCODED_CURSOR_LENGTH is the decode-path guard, sized comfortably above +# the largest cursor the per-field caps can produce. Worst case is value + id +# at their caps with every character JSON-escaping to the six-byte `\uXXXX` +# form (control characters), which is ~5.2 KB once base64url-encoded. At 8192 +# the encoder can never mint a cursor that exceeds it, so a freshly minted +# cursor always decodes on the next request and there is no user-visible +# "cursor too long" failure. +MAX_ENCODED_CURSOR_LENGTH = 8192 +MAX_CURSOR_VALUE_LENGTH = 512 +MAX_CURSOR_ID_LENGTH = 128 + + +@dataclass(frozen=True) +class CursorPayload: + sort_field: str + value: str + id: str + order: str + + +_VALID_ORDERS = ("asc", "desc") + + +def encode_cursor(sort_field: str, value: str, id: str, order: str = "desc") -> str: + """Encode a cursor payload as a base64url (no-padding) string. + + `order` binds the cursor to the sort direction it was minted under so a + later request with a flipped `order` query parameter is rejected with + ``INVALID_CURSOR`` rather than silently walking the wrong direction. + """ + if order not in _VALID_ORDERS: + raise InvalidCursorError(f"order must be one of {_VALID_ORDERS}, got {order!r}") + # Symmetric input validation: the encoder must reject anything the + # decoder rejects, or the same server will mint cursors it then 400s on + # the next request. + if not id: + raise InvalidCursorError("id must be non-empty") + if len(id) > MAX_CURSOR_ID_LENGTH: + raise InvalidCursorError("id exceeds maximum length") + if len(value) > MAX_CURSOR_VALUE_LENGTH: + raise InvalidCursorError("value exceeds maximum length") + payload = {"s": sort_field, "v": value, "id": id, "o": order} + raw = json.dumps(payload, separators=(",", ":"), ensure_ascii=False) + # No mint-time length guard is needed: the per-field caps above bound the + # encoded length well below MAX_ENCODED_CURSOR_LENGTH (see its definition), + # so the encoder can never produce a cursor the decode path would reject. + return base64.urlsafe_b64encode(raw.encode("utf-8")).rstrip(b"=").decode("ascii") + + +def encode_cursor_from_time(sort_field: str, t: datetime, id: str, order: str = "desc") -> str: + """Encode a time-typed cursor at Unix microsecond precision. + + Accepts an aware datetime (any timezone) and normalizes to UTC. Naive + datetimes are rejected so callers can't accidentally encode the local + wall-clock value of a UTC-stored timestamp. + """ + if t.tzinfo is None: + raise ValueError("encode_cursor_from_time requires an aware datetime") + micros = _datetime_to_unix_micros(t.astimezone(timezone.utc)) + return encode_cursor(sort_field, str(micros), id, order=order) + + +def decode_cursor( + cursor: str, + allowed_sort_fields: Iterable[str], + expected_order: str | None = None, +) -> CursorPayload: + """Parse an opaque cursor. + + ``allowed_sort_fields`` is the endpoint's accepted sort-field list — a + cursor carrying a field outside this set is rejected so a cursor minted + for one column can't be replayed against another (e.g. a ``created_at`` + timestamp string compared against a ``name`` column). + + ``expected_order`` (``"asc"``/``"desc"``), when supplied, must match the + payload's ``o`` field. ``o`` is required on every payload; a cursor + missing it is rejected as malformed. + + Passing no allowed fields rejects every cursor. + """ + if len(cursor) > MAX_ENCODED_CURSOR_LENGTH: + raise InvalidCursorError("cursor exceeds maximum length") + + try: + # urlsafe_b64decode requires correct padding; we strip on encode, so + # restore the trailing '=' pad here. + padding = "=" * (-len(cursor) % 4) + raw = base64.urlsafe_b64decode(cursor + padding) + except (ValueError, base64.binascii.Error) as e: + raise InvalidCursorError(f"encoding: {e}") from e + + try: + decoded = json.loads(raw) + except (json.JSONDecodeError, UnicodeDecodeError) as e: + raise InvalidCursorError(f"payload: {e}") from e + + if not isinstance(decoded, dict): + raise InvalidCursorError("payload: expected object") + + sort_field = decoded.get("s") + value = decoded.get("v") + id = decoded.get("id") + order = decoded.get("o") + + if not isinstance(sort_field, str) or not isinstance(value, str) or not isinstance(id, str): + raise InvalidCursorError("payload: missing or non-string s/v/id") + + if id == "": + raise InvalidCursorError("missing id") + if len(id) > MAX_CURSOR_ID_LENGTH: + raise InvalidCursorError("id exceeds maximum length") + if len(value) > MAX_CURSOR_VALUE_LENGTH: + raise InvalidCursorError("value exceeds maximum length") + + if sort_field not in allowed_sort_fields: + raise InvalidCursorError(f"unsupported sort field {sort_field!r}") + + if not isinstance(order, str): + raise InvalidCursorError("missing or non-string o") + if order not in _VALID_ORDERS: + raise InvalidCursorError(f"unsupported order {order!r}") + if expected_order is not None and order != expected_order: + raise InvalidCursorError( + f"cursor order {order!r} does not match request order {expected_order!r}" + ) + + return CursorPayload(sort_field=sort_field, value=value, id=id, order=order) + + +def decode_cursor_time(payload: Optional[CursorPayload]) -> datetime: + """Parse a time-typed cursor value as Unix microseconds, returning UTC.""" + if payload is None: + raise InvalidCursorError("nil cursor payload") + try: + micros = int(payload.value) + except ValueError as e: + raise InvalidCursorError(f"value is not a valid timestamp: {e}") from e + try: + return _unix_micros_to_datetime(micros) + except (OverflowError, OSError, ValueError) as e: + # Crafted out-of-range microseconds (e.g. > datetime.MAX_YEAR) blow up + # in fromtimestamp / datetime construction. Map to 400, not 500. + raise InvalidCursorError(f"value is out of representable range: {e}") from e + + +def decode_cursor_int(payload: Optional[CursorPayload]) -> int: + """Parse a cursor value as a base-10 integer.""" + if payload is None: + raise InvalidCursorError("nil cursor payload") + try: + return int(payload.value) + except ValueError as e: + raise InvalidCursorError(f"value is not a valid integer: {e}") from e + + +_EPOCH = datetime(1970, 1, 1, tzinfo=timezone.utc) + + +def _datetime_to_unix_micros(t: datetime) -> int: + """Convert an aware UTC datetime to Unix microseconds (integer math).""" + delta = t - _EPOCH + return (delta.days * 86_400 + delta.seconds) * 1_000_000 + delta.microseconds + + +def _unix_micros_to_datetime(micros: int) -> datetime: + """Convert Unix microseconds to a UTC datetime, preserving precision.""" + seconds, micro_remainder = divmod(micros, 1_000_000) + return datetime.fromtimestamp(seconds, tz=timezone.utc).replace(microsecond=micro_remainder) diff --git a/app/assets/services/image_dimensions.py b/app/assets/services/image_dimensions.py new file mode 100644 index 000000000..ccd97399a --- /dev/null +++ b/app/assets/services/image_dimensions.py @@ -0,0 +1,63 @@ +"""Image dimension extraction for asset ingest. + +Reads only the image header via Pillow to capture width/height cheaply, +without a full pixel decode. Returns a metadata dict suitable for merging +into ``AssetReference.system_metadata``. +""" +from __future__ import annotations + +import logging +from typing import Any + +logger = logging.getLogger(__name__) + + +def extract_image_dimensions( + file_path: str, mime_type: str | None = None +) -> dict[str, Any] | None: + """Extract image dimensions for the file at ``file_path``. + + Args: + file_path: Absolute path to a file on disk. + mime_type: Optional MIME type hint. When provided and not prefixed + with ``image/``, extraction is skipped without touching the file. + + Returns: + ``{"kind": "image", "width": W, "height": H}`` when the file is a + recognizable image with positive dimensions, otherwise ``None``. + + The dict shape is intended to be merged into ``system_metadata`` so the + asset response surfaces ``metadata.kind`` plus dimension fields for image + assets. Forward-compatible: future media kinds (e.g. ``"video"`` with + duration/fps) can extend this shape without schema changes. + """ + if mime_type is not None and not mime_type.startswith("image/"): + return None + + try: + from PIL import Image, UnidentifiedImageError + except ImportError: + logger.debug( + "Pillow not available; skipping image dimension extraction for %s", + file_path, + ) + return None + + try: + with Image.open(file_path) as img: + width, height = img.size + except (OSError, UnidentifiedImageError, ValueError) as exc: + logger.debug( + "Failed to read image dimensions from %s: %s", file_path, exc + ) + return None + + if ( + not isinstance(width, int) + or not isinstance(height, int) + or width <= 0 + or height <= 0 + ): + return None + + return {"kind": "image", "width": width, "height": height} diff --git a/app/assets/services/ingest.py b/app/assets/services/ingest.py index f0b070517..3b6dc237c 100644 --- a/app/assets/services/ingest.py +++ b/app/assets/services/ingest.py @@ -17,9 +17,11 @@ from app.assets.database.queries import ( get_reference_by_file_path, get_reference_tags, get_or_create_reference, + list_references_by_asset_id, reference_exists, remove_missing_tag_for_asset_id, set_reference_metadata, + set_reference_system_metadata, set_reference_tags, update_asset_hash_and_mime, upsert_asset, @@ -29,6 +31,7 @@ from app.assets.database.queries import ( from app.assets.helpers import get_utc_now, normalize_tags 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, get_name_and_tags_from_asset_path, @@ -118,6 +121,14 @@ def _ingest_file_from_path( user_metadata=user_metadata, ) + _maybe_store_image_dimensions( + session, + reference_id=reference_id, + file_path=locator, + mime_type=mime_type, + current_system_metadata=ref.system_metadata, + ) + try: remove_missing_tag_for_asset_id(session, asset_id=asset.id) except Exception: @@ -288,6 +299,13 @@ def _register_existing_asset( user_metadata=new_meta, ) + _backfill_image_dimensions_from_siblings( + session, + asset_id=asset.id, + new_reference_id=ref.id, + current_system_metadata=ref.system_metadata, + ) + if tags is not None: set_reference_tags( session, @@ -334,6 +352,87 @@ def _update_metadata_with_filename( ) +_IMAGE_DIMENSION_KEYS = ("kind", "width", "height") + + +def _maybe_store_image_dimensions( + session: Session, + reference_id: str, + file_path: str, + mime_type: str | None, + current_system_metadata: dict | None, +) -> None: + """Populate ``kind``/``width``/``height`` on system_metadata for image refs. + + Non-image MIME types are a no-op. Pre-existing keys (e.g. enricher-written + safetensors metadata, download provenance) are preserved by merge. + """ + if not mime_type or not mime_type.startswith("image/"): + return + + dims = extract_image_dimensions(file_path, mime_type=mime_type) + if not dims: + return + + current = current_system_metadata or {} + merged = dict(current) + merged.update(dims) + if merged != current: + set_reference_system_metadata( + session, + reference_id=reference_id, + system_metadata=merged, + ) + + +def _backfill_image_dimensions_from_siblings( + session: Session, + asset_id: str, + new_reference_id: str, + current_system_metadata: dict | None, +) -> None: + """Copy image dimension keys from any sibling reference of the same asset. + + The from-hash path doesn't read the file bytes, so dimensions can't be + extracted there directly. When another reference of the same asset already + carries image dimensions, copy them onto the new reference so consumers + see consistent metadata regardless of how the asset was registered. + + Best-effort: missing siblings, non-image siblings, or absent dimension + keys leave the target reference unchanged. + """ + current = current_system_metadata or {} + if current.get("kind") == "image" and "width" in current and "height" in current: + return + + for sibling in list_references_by_asset_id(session, asset_id): + if sibling.id == new_reference_id: + continue + meta = sibling.system_metadata or {} + if meta.get("kind") != "image": + continue + width = meta.get("width") + height = meta.get("height") + if ( + type(width) is not int + or type(height) is not int + or width <= 0 + or height <= 0 + ): + continue + merged = dict(current) + merged["kind"] = "image" + merged["width"] = width + merged["height"] = height + if merged != current: + set_reference_system_metadata( + session, + reference_id=new_reference_id, + system_metadata=merged, + ) + return + + def _sanitize_filename(name: str | None, fallback: str) -> str: n = os.path.basename((name or "").strip() or fallback) return n if n else fallback diff --git a/app/assets/services/schemas.py b/app/assets/services/schemas.py index 0eb128f58..4d2af8a02 100644 --- a/app/assets/services/schemas.py +++ b/app/assets/services/schemas.py @@ -56,7 +56,6 @@ class IngestResult: class TagUsage(NamedTuple): name: str - tag_type: str count: int @@ -71,6 +70,7 @@ class AssetSummaryData: class ListAssetsResult: items: list[AssetSummaryData] total: int + next_cursor: str | None = None @dataclass(frozen=True) diff --git a/app/assets/services/tagging.py b/app/assets/services/tagging.py index 37b612753..5fa39d26a 100644 --- a/app/assets/services/tagging.py +++ b/app/assets/services/tagging.py @@ -75,7 +75,7 @@ def list_tags( owner_id=owner_id, ) - return [TagUsage(name, tag_type, count) for name, tag_type, count in rows], total + return [TagUsage(name, count) for name, count in rows], total def list_tag_histogram( diff --git a/blueprints/Character Replacement (SCAIL-2 Base).json b/blueprints/Character Replacement (SCAIL-2 Base).json new file mode 100644 index 000000000..61803df65 --- /dev/null +++ b/blueprints/Character Replacement (SCAIL-2 Base).json @@ -0,0 +1,4191 @@ +{ + "revision": 0, + "last_node_id": 410, + "last_link_id": 0, + "nodes": [ + { + "id": 410, + "type": "35331397-69fb-40ad-b99a-7f17b1a53017", + "pos": [ + 2450, + 5670 + ], + "size": [ + 490, + 1120 + ], + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [ + { + "label": "pose_video", + "localized_name": "video", + "name": "video", + "type": "VIDEO", + "link": null + }, + { + "label": "reference_image", + "localized_name": "images", + "name": "images", + "type": "IMAGE", + "link": null + }, + { + "label": "prompt", + "name": "text", + "type": "STRING", + "widget": { + "name": "text" + }, + "link": null + }, + { + "label": "segment_index", + "name": "value", + "type": "INT", + "widget": { + "name": "value" + }, + "link": null + }, + { + "label": "replace_mode", + "name": "value_2", + "type": "BOOLEAN", + "widget": { + "name": "value_2" + }, + "link": null + }, + { + "label": "width", + "name": "value_3", + "type": "INT", + "widget": { + "name": "value_3" + }, + "link": null + }, + { + "label": "height", + "name": "value_4", + "type": "INT", + "widget": { + "name": "value_4" + }, + "link": null + }, + { + "label": "frame_count", + "name": "length", + "type": "INT", + "widget": { + "name": "length" + }, + "link": null + }, + { + "name": "previous_frame_count", + "type": "INT", + "widget": { + "name": "previous_frame_count" + }, + "link": null + }, + { + "name": "pose_strength", + "type": "FLOAT", + "widget": { + "name": "pose_strength" + }, + "link": null + }, + { + "name": "pose_start", + "type": "FLOAT", + "widget": { + "name": "pose_start" + }, + "link": null + }, + { + "name": "pose_end", + "type": "FLOAT", + "widget": { + "name": "pose_end" + }, + "link": null + }, + { + "label": "turbo_mode", + "name": "value_5", + "type": "BOOLEAN", + "widget": { + "name": "value_5" + }, + "link": null + }, + { + "name": "unet_name", + "type": "COMBO", + "widget": { + "name": "unet_name" + }, + "link": null + }, + { + "label": "distill_lora", + "name": "lora_name", + "type": "COMBO", + "widget": { + "name": "lora_name" + }, + "link": null + }, + { + "label": "dpo_lora", + "name": "lora_name_1", + "type": "COMBO", + "widget": { + "name": "lora_name_1" + }, + "link": null + }, + { + "name": "clip_name", + "type": "COMBO", + "widget": { + "name": "clip_name" + }, + "link": null + }, + { + "name": "vae_name", + "type": "COMBO", + "widget": { + "name": "vae_name" + }, + "link": null + }, + { + "label": "clip_vision", + "name": "clip_name_1", + "type": "COMBO", + "widget": { + "name": "clip_name_1" + }, + "link": null + }, + { + "label": "sam3_video_object", + "name": "text_1", + "type": "STRING", + "widget": { + "name": "text_1" + }, + "link": null + }, + { + "label": "sam3_image_object", + "name": "text_2", + "type": "STRING", + "widget": { + "name": "text_2" + }, + "link": null + }, + { + "label": "sam3_model", + "name": "ckpt_name", + "type": "COMBO", + "widget": { + "name": "ckpt_name" + }, + "link": null + }, + { + "name": "noise_seed", + "type": "INT", + "widget": { + "name": "noise_seed" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": "output", + "name": "output", + "type": "IMAGE", + "links": [] + } + ], + "properties": { + "proxyWidgets": [ + [ + "405", + "text" + ], + [ + "391", + "value" + ], + [ + "398", + "value" + ], + [ + "387", + "value" + ], + [ + "388", + "value" + ], + [ + "386", + "length" + ], + [ + "406", + "previous_frame_count" + ], + [ + "406", + "pose_strength" + ], + [ + "406", + "pose_start" + ], + [ + "406", + "pose_end" + ], + [ + "402", + "value" + ], + [ + "374", + "unet_name" + ], + [ + "367", + "lora_name" + ], + [ + "408", + "lora_name" + ], + [ + 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It outputs depth maps, camera poses, and optionally 3D Gaussian parameters for novel view synthesis." + } +} \ No newline at end of file diff --git a/comfy/background_removal/birefnet.py b/comfy/background_removal/birefnet.py index df54b2b90..78a80246e 100644 --- a/comfy/background_removal/birefnet.py +++ b/comfy/background_removal/birefnet.py @@ -105,7 +105,7 @@ class WindowAttention(nn.Module): relative_position_bias = self.relative_position_bias_table[self.relative_position_index.long().view(-1)].view( self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + relative_position_bias = comfy.ops.cast_to_input(relative_position_bias.permute(2, 0, 1).contiguous(), attn) # nH, Wh*Ww, Wh*Ww attn = attn + relative_position_bias.unsqueeze(0) if mask is not None: diff --git a/comfy/cli_args.py b/comfy/cli_args.py index 9bda414d1..e3099a230 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -115,6 +115,7 @@ cache_group.add_argument("--cache-ram", nargs='*', type=float, default=[], metav cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.") cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.") cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.") +cache_group.add_argument("--high-ram", action="store_true", help="Can improve performance slightly on high RAM or on systems where pagefile use is preferred over model loading.") attn_group = parser.add_mutually_exclusive_group() attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.") @@ -133,7 +134,7 @@ upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disabl parser.add_argument("--enable-manager", action="store_true", help="Enable the ComfyUI-Manager feature.") manager_group = parser.add_mutually_exclusive_group() manager_group.add_argument("--disable-manager-ui", action="store_true", help="Disables only the ComfyUI-Manager UI and endpoints. Scheduled installations and similar background tasks will still operate.") -manager_group.add_argument("--enable-manager-legacy-ui", action="store_true", help="Enables the legacy UI of ComfyUI-Manager") +manager_group.add_argument("--enable-manager-legacy-ui", action="store_true", help="Enables the legacy UI of ComfyUI-Manager. Implies --enable-manager.") vram_group = parser.add_mutually_exclusive_group() @@ -144,11 +145,13 @@ vram_group.add_argument("--novram", action="store_true", help="When lowvram isn' vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).") parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.") +parser.add_argument("--vram-headroom", type=float, default=0, help="Set the amount of vram in GB for DynamicVRAM to maintain as extra headroom above default. ComfyUI will try and keep this much VRAM completely free and unused, even counting VRAM from other apps.") parser.add_argument("--async-offload", nargs='?', const=2, type=int, default=None, metavar="NUM_STREAMS", help="Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.") parser.add_argument("--disable-async-offload", action="store_true", help="Disable async weight offloading.") parser.add_argument("--disable-dynamic-vram", action="store_true", help="Disable dynamic VRAM and use estimate based model loading.") parser.add_argument("--enable-dynamic-vram", action="store_true", help="Enable dynamic VRAM on systems where it's not enabled by default.") +parser.add_argument("--fast-disk", action="store_true", help="Prefer disk-backed dynamic loading and offload over unpinned RAM. Can be faster for users with fast NVME disks.") parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.") @@ -165,6 +168,8 @@ class PerformanceFeature(enum.Enum): parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. This is used to test new features so using it might crash your comfyui. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature)))) +parser.add_argument("--debug-hang", action="store_true", help="Enable stack trace dumps on Ctrl-C for debugging hangs.") + parser.add_argument("--disable-pinned-memory", action="store_true", help="Disable pinned memory use.") parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.") @@ -246,6 +251,9 @@ else: if args.cache_ram is not None and len(args.cache_ram) > 2: parser.error("--cache-ram accepts at most two values: active GB and inactive GB") +if args.high_ram: + args.cache_classic = True + if args.windows_standalone_build: args.auto_launch = True @@ -255,6 +263,10 @@ if args.disable_auto_launch: if args.force_fp16: args.fp16_unet = True +# '--enable-manager-legacy-ui' is meaningless unless the manager is enabled, so imply '--enable-manager'. +if args.enable_manager_legacy_ui: + args.enable_manager = True + # '--fast' is not provided, use an empty set if args.fast is None: diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py index 1691fca81..ce8924a11 100644 --- a/comfy/clip_vision.py +++ b/comfy/clip_vision.py @@ -9,6 +9,7 @@ import comfy.model_management import comfy.utils import comfy.clip_model import comfy.image_encoders.dino2 +import comfy.image_encoders.dino3 class Output: def __getitem__(self, key): @@ -23,12 +24,16 @@ IMAGE_ENCODERS = { "siglip_vision_model": comfy.clip_model.CLIPVisionModelProjection, "siglip2_vision_model": comfy.clip_model.CLIPVisionModelProjection, "dinov2": comfy.image_encoders.dino2.Dinov2Model, + "dinov3": comfy.image_encoders.dino3.DINOv3ViTModel, } class ClipVisionModel(): def __init__(self, json_config): - with open(json_config) as f: - config = json.load(f) + if isinstance(json_config, dict): + config = json_config + else: + with open(json_config) as f: + config = json.load(f) self.image_size = config.get("image_size", 224) self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073]) @@ -134,6 +139,8 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False): json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_giant.json") elif 'encoder.layer.23.layer_scale2.lambda1' in sd: json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_large.json") + elif 'layer.0.mlp.gate_proj.weight' in sd and 'layer.31.norm1.weight' in sd: # Dinov3 ViT-H/16+ (SwiGLU gated MLP, 32 layers) + json_config = comfy.image_encoders.dino3.DINOV3_VITH_CONFIG else: return None diff --git a/comfy/context_windows.py b/comfy/context_windows.py index db57537a2..5f9899c67 100644 --- a/comfy/context_windows.py +++ b/comfy/context_windows.py @@ -8,6 +8,8 @@ from abc import ABC, abstractmethod import logging import comfy.model_management import comfy.patcher_extension +import comfy.utils +import comfy.conds if TYPE_CHECKING: from comfy.model_base import BaseModel from comfy.model_patcher import ModelPatcher @@ -51,12 +53,18 @@ class ContextHandlerABC(ABC): class IndexListContextWindow(ContextWindowABC): - def __init__(self, index_list: list[int], dim: int=0, total_frames: int=0): + def __init__(self, index_list: list[int], dim: int=0, total_frames: int=0, modality_windows: dict=None, context_overlap: int=0): self.index_list = index_list self.context_length = len(index_list) + self.context_overlap = context_overlap self.dim = dim self.total_frames = total_frames self.center_ratio = (min(index_list) + max(index_list)) / (2 * total_frames) + self.modality_windows = modality_windows # dict of {mod_idx: IndexListContextWindow} + self.guide_frames_indices: list[int] = [] + self.guide_overlap_info: list[tuple[int, int]] = [] + self.guide_kf_local_positions: list[int] = [] + self.guide_downscale_factors: list[int] = [] def get_tensor(self, full: torch.Tensor, device=None, dim=None, retain_index_list=[]) -> torch.Tensor: if dim is None: @@ -85,6 +93,11 @@ class IndexListContextWindow(ContextWindowABC): region_idx = int(self.center_ratio * num_regions) return min(max(region_idx, 0), num_regions - 1) + def get_window_for_modality(self, modality_idx: int) -> 'IndexListContextWindow': + if modality_idx == 0: + return self + return self.modality_windows[modality_idx] + class IndexListCallbacks: EVALUATE_CONTEXT_WINDOWS = "evaluate_context_windows" @@ -148,6 +161,172 @@ def slice_cond(cond_value, window: IndexListContextWindow, x_in: torch.Tensor, d return cond_value._copy_with(sliced) +def compute_guide_overlap(guide_entries: list[dict], keyframe_idxs: torch.Tensor, temporal_downscale_ratio: int, window_index_list: list[int]): + """Compute which concatenated guide frames overlap with a context window. + + Each guide's latent-space start is derived from its first token's pixel-t-start + in keyframe_idxs (shape (B, [t,h,w], num_tokens, [start, end])), divided by the + model's temporal_downscale_ratio. + + Args: + guide_entries: list of guide_attention_entry dicts + keyframe_idxs: per-token pixel coords cond tensor for the modality + temporal_downscale_ratio: model's pixel-to-latent temporal compression ratio + window_index_list: the window's frame indices into the video portion + + Returns: + suffix_indices: indices into the guide_frames tensor for frame selection + overlap_info: list of (entry_idx, overlap_count) for guide_attention_entries adjustment + kf_local_positions: window-local frame positions for keyframe_idxs regeneration + total_overlap: total number of overlapping guide frames + """ + window_set = set(window_index_list) + window_list = list(window_index_list) + suffix_indices = [] + overlap_info = [] + kf_local_positions = [] + suffix_base = 0 + token_offset = 0 + + for entry_idx, entry in enumerate(guide_entries): + first_t_pixel = int(keyframe_idxs[0, 0, token_offset, 0].item()) + latent_start = (first_t_pixel + temporal_downscale_ratio - 1) // temporal_downscale_ratio + guide_len = entry["latent_shape"][0] + entry_overlap = 0 + + for local_offset in range(guide_len): + video_pos = latent_start + local_offset + if video_pos in window_set: + suffix_indices.append(suffix_base + local_offset) + kf_local_positions.append(window_list.index(video_pos)) + entry_overlap += 1 + + if entry_overlap > 0: + overlap_info.append((entry_idx, entry_overlap)) + suffix_base += guide_len + token_offset += entry["pre_filter_count"] + + return suffix_indices, overlap_info, kf_local_positions, len(suffix_indices) + + +@dataclass +class WindowingState: + """Per-modality context windowing state for each step, + built using IndexListContextHandler._build_window_state(). + For non-multimodal models the lists are length 1 + """ + latents: list[torch.Tensor] # per-modality working latents (guide frames stripped) + guide_latents: list[torch.Tensor | None] # per-modality guide frames stripped from latents + guide_entries: list[list[dict] | None] # per-modality guide_attention_entry metadata + keyframe_idxs: list[torch.Tensor | None] # per-modality keyframe_idxs tensor for guide latent_start derivation + latent_shapes: list | None # original packed shapes for unpack/pack (None if not multimodal) + dim: int = 0 # primary modality temporal dim for context windowing + is_multimodal: bool = False + temporal_downscale_ratio: int = 1 # model's pixel-to-latent temporal compression ratio + + def prepare_window(self, window: IndexListContextWindow, model) -> IndexListContextWindow: + """Reformat window for multimodal contexts by deriving per-modality index lists. + Non-multimodal contexts return the input window unchanged. + """ + if not self.is_multimodal: + return window + + x = self.latents[0] + primary_total = self.latent_shapes[0][self.dim] + primary_overlap = window.context_overlap + map_shapes = self.latent_shapes + if x.size(self.dim) != primary_total: + map_shapes = list(self.latent_shapes) + video_shape = list(self.latent_shapes[0]) + video_shape[self.dim] = x.size(self.dim) + map_shapes[0] = torch.Size(video_shape) + try: + per_modality_indices = model.map_context_window_to_modalities( + window.index_list, map_shapes, self.dim) + except AttributeError: + raise NotImplementedError( + f"{type(model).__name__} must implement map_context_window_to_modalities for multimodal context windows.") + modality_windows = {} + for mod_idx in range(1, len(self.latents)): + modality_total_frames = self.latents[mod_idx].shape[self.dim] + ratio = modality_total_frames / primary_total if primary_total > 0 else 1 + modality_overlap = max(round(primary_overlap * ratio), 0) + modality_windows[mod_idx] = IndexListContextWindow( + per_modality_indices[mod_idx], dim=self.dim, + total_frames=modality_total_frames, + context_overlap=modality_overlap) + return IndexListContextWindow( + window.index_list, dim=self.dim, total_frames=x.shape[self.dim], + modality_windows=modality_windows, context_overlap=primary_overlap) + + def slice_for_window(self, window: IndexListContextWindow, retain_index_list: list[int], device=None) -> tuple[list[torch.Tensor], list[int]]: + """Slice latents for a context window, injecting guide frames where applicable. + For multimodal contexts, uses the modality-specific windows derived in prepare_window(). + """ + sliced = [] + guide_frame_counts = [] + for idx in range(len(self.latents)): + modality_window = window.get_window_for_modality(idx) + retain = retain_index_list if idx == 0 else [] + s = modality_window.get_tensor(self.latents[idx], device, retain_index_list=retain) + if self.guide_entries[idx] is not None: + s, ng = self._inject_guide_frames(s, modality_window, modality_idx=idx) + else: + ng = 0 + sliced.append(s) + guide_frame_counts.append(ng) + return sliced, guide_frame_counts + + def strip_guide_frames(self, out_per_modality: list[list[torch.Tensor]], guide_frame_counts: list[int], window: IndexListContextWindow): + """Strip injected guide frames from per-cond, per-modality outputs in place.""" + for idx in range(len(self.latents)): + if guide_frame_counts[idx] > 0: + window_len = len(window.get_window_for_modality(idx).index_list) + for ci in range(len(out_per_modality)): + out_per_modality[ci][idx] = out_per_modality[ci][idx].narrow(self.dim, 0, window_len) + + def _inject_guide_frames(self, latent_slice: torch.Tensor, window: IndexListContextWindow, modality_idx: int = 0) -> tuple[torch.Tensor, int]: + guide_entries = self.guide_entries[modality_idx] + guide_frames = self.guide_latents[modality_idx] + keyframe_idxs = self.keyframe_idxs[modality_idx] + suffix_idx, overlap_info, kf_local_pos, guide_frame_count = compute_guide_overlap( + guide_entries, keyframe_idxs, self.temporal_downscale_ratio, window.index_list) + # Shift keyframe positions to account for causal_window_fix anchor occupying sub-pos 0. + anchor_idx = getattr(window, 'causal_anchor_index', None) + if anchor_idx is not None and anchor_idx >= 0: + kf_local_pos = [p + 1 for p in kf_local_pos] + window.guide_frames_indices = suffix_idx + window.guide_overlap_info = overlap_info + window.guide_kf_local_positions = kf_local_pos + + # Derive per-overlap-entry latent_downscale_factor from guide entry latent_shape vs guide frame spatial dims. + # guide_frames has full (post-dilation) spatial dims; entry["latent_shape"] has pre-dilation dims. + guide_downscale_factors = [] + if guide_frame_count > 0: + full_H = guide_frames.shape[3] + for entry_idx, _ in overlap_info: + entry_H = guide_entries[entry_idx]["latent_shape"][1] + guide_downscale_factors.append(full_H // entry_H) + window.guide_downscale_factors = guide_downscale_factors + + if guide_frame_count > 0: + idx = tuple([slice(None)] * self.dim + [suffix_idx]) + return torch.cat([latent_slice, guide_frames[idx]], dim=self.dim), guide_frame_count + return latent_slice, 0 + + def patch_latent_shapes(self, sub_conds, new_shapes): + if not self.is_multimodal: + return + + for cond_list in sub_conds: + if cond_list is None: + continue + for cond_dict in cond_list: + model_conds = cond_dict.get('model_conds', {}) + if 'latent_shapes' in model_conds: + model_conds['latent_shapes'] = comfy.conds.CONDConstant(new_shapes) + + @dataclass class ContextSchedule: name: str @@ -162,7 +341,7 @@ ContextResults = collections.namedtuple("ContextResults", ['window_idx', 'sub_co class IndexListContextHandler(ContextHandlerABC): def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1, closed_loop: bool=False, dim:int=0, freenoise: bool=False, cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False, - causal_window_fix: bool=True): + latent_retain_index_list: list[int]=[], causal_window_fix: bool=True): self.context_schedule = context_schedule self.fuse_method = fuse_method self.context_length = context_length @@ -174,17 +353,118 @@ class IndexListContextHandler(ContextHandlerABC): self.freenoise = freenoise self.cond_retain_index_list = [int(x.strip()) for x in cond_retain_index_list.split(",")] if cond_retain_index_list else [] self.split_conds_to_windows = split_conds_to_windows + self.latent_retain_index_list = [int(x.strip()) for x in latent_retain_index_list.split(",")] if latent_retain_index_list else [] self.causal_window_fix = causal_window_fix self.callbacks = {} + @staticmethod + def _get_latent_shapes(conds): + for cond_list in conds: + if cond_list is None: + continue + for cond_dict in cond_list: + model_conds = cond_dict.get('model_conds', {}) + if 'latent_shapes' in model_conds: + return model_conds['latent_shapes'].cond + return None + + @staticmethod + def _get_guide_entries(conds): + for cond_list in conds: + if cond_list is None: + continue + for cond_dict in cond_list: + model_conds = cond_dict.get('model_conds', {}) + entries = model_conds.get('guide_attention_entries') + if entries is not None and hasattr(entries, 'cond') and entries.cond: + return entries.cond + return None + + @staticmethod + def _get_keyframe_idxs(conds): + for cond_list in conds: + if cond_list is None: + continue + for cond_dict in cond_list: + model_conds = cond_dict.get('model_conds', {}) + kf = model_conds.get('keyframe_idxs') + if kf is not None and hasattr(kf, 'cond') and kf.cond is not None: + return kf.cond + return None + + def _apply_freenoise(self, noise: torch.Tensor, conds: list[list[dict]], seed: int) -> torch.Tensor: + """Apply FreeNoise shuffling, scaling context length/overlap per-modality by frame ratio. + If guide frames are present on the primary modality, only the video portion is shuffled. + """ + guide_entries = self._get_guide_entries(conds) + guide_count = sum(e["latent_shape"][0] for e in guide_entries) if guide_entries else 0 + + latent_shapes = self._get_latent_shapes(conds) + if latent_shapes is not None and len(latent_shapes) > 1: + modalities = comfy.utils.unpack_latents(noise, latent_shapes) + primary_total = latent_shapes[0][self.dim] + primary_video_count = modalities[0].size(self.dim) - guide_count + apply_freenoise(modalities[0].narrow(self.dim, 0, primary_video_count), self.dim, self.context_length, self.context_overlap, seed) + for i in range(1, len(modalities)): + mod_total = latent_shapes[i][self.dim] + ratio = mod_total / primary_total if primary_total > 0 else 1 + mod_ctx_len = max(round(self.context_length * ratio), 1) + mod_ctx_overlap = max(round(self.context_overlap * ratio), 0) + modalities[i] = apply_freenoise(modalities[i], self.dim, mod_ctx_len, mod_ctx_overlap, seed) + noise, _ = comfy.utils.pack_latents(modalities) + return noise + video_count = noise.size(self.dim) - guide_count + apply_freenoise(noise.narrow(self.dim, 0, video_count), self.dim, self.context_length, self.context_overlap, seed) + return noise + + def _build_window_state(self, x_in: torch.Tensor, conds: list[list[dict]], model: BaseModel) -> WindowingState: + """Build windowing state for the current step, including unpacking latents and extracting guide frame info from conds.""" + latent_shapes = self._get_latent_shapes(conds) + is_multimodal = latent_shapes is not None and len(latent_shapes) > 1 + unpacked_latents = comfy.utils.unpack_latents(x_in, latent_shapes) if is_multimodal else [x_in] + + unpacked_latents_list = list(unpacked_latents) + guide_latents_list = [None] * len(unpacked_latents) + guide_entries_list = [None] * len(unpacked_latents) + keyframe_idxs_list = [None] * len(unpacked_latents) + + extracted_guide_entries = self._get_guide_entries(conds) + extracted_keyframe_idxs = self._get_keyframe_idxs(conds) + + # Strip guide frames (only from first modality for now) + if extracted_guide_entries is not None: + guide_count = sum(e["latent_shape"][0] for e in extracted_guide_entries) + if guide_count > 0: + x = unpacked_latents[0] + latent_count = x.size(self.dim) - guide_count + unpacked_latents_list[0] = x.narrow(self.dim, 0, latent_count) + guide_latents_list[0] = x.narrow(self.dim, latent_count, guide_count) + guide_entries_list[0] = extracted_guide_entries + keyframe_idxs_list[0] = extracted_keyframe_idxs + + + return WindowingState( + latents=unpacked_latents_list, + guide_latents=guide_latents_list, + guide_entries=guide_entries_list, + keyframe_idxs=keyframe_idxs_list, + latent_shapes=latent_shapes, + dim=self.dim, + is_multimodal=is_multimodal, + temporal_downscale_ratio=model.latent_format.temporal_downscale_ratio) + def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool: - # for now, assume first dim is batch - should have stored on BaseModel in actual implementation - if x_in.size(self.dim) > self.context_length: - logging.info(f"Using context windows {self.context_length} with overlap {self.context_overlap} for {x_in.size(self.dim)} frames.") + window_state = self._build_window_state(x_in, conds, model) # build window_state to check frame counts, will be built again in execute + total_frame_count = window_state.latents[0].size(self.dim) + if total_frame_count > self.context_length: + logging.info(f"\nUsing context windows: Context length {self.context_length} with overlap {self.context_overlap} for {total_frame_count} frames.") if self.cond_retain_index_list: logging.info(f"Retaining original cond for indexes: {self.cond_retain_index_list}") + if self.latent_retain_index_list: + logging.info(f"Retaining original latent for indexes: {self.latent_retain_index_list}") return True + logging.info(f"\nNot using context windows since context length ({self.context_length}) exceeds input frames ({total_frame_count}).") return False def prepare_control_objects(self, control: ControlBase, device=None) -> ControlBase: @@ -275,7 +555,9 @@ class IndexListContextHandler(ContextHandlerABC): return resized_cond def set_step(self, timestep: torch.Tensor, model_options: dict[str]): - mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep[0], rtol=0.0001) + sample_sigmas = model_options["transformer_options"]["sample_sigmas"] + current_timestep = timestep[0].to(sample_sigmas.dtype) + mask = torch.isclose(sample_sigmas, current_timestep, rtol=0.0001) matches = torch.nonzero(mask) if torch.numel(matches) == 0: return # substep from multi-step sampler: keep self._step from the last full step @@ -284,54 +566,98 @@ class IndexListContextHandler(ContextHandlerABC): def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]: full_length = x_in.size(self.dim) # TODO: choose dim based on model context_windows = self.context_schedule.func(full_length, self, model_options) - context_windows = [IndexListContextWindow(window, dim=self.dim, total_frames=full_length) for window in context_windows] + context_windows = [IndexListContextWindow(window, dim=self.dim, total_frames=full_length, context_overlap=self.context_overlap) for window in context_windows] return context_windows def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]): self._model = model self.set_step(timestep, model_options) - context_windows = self.get_context_windows(model, x_in, model_options) - enumerated_context_windows = list(enumerate(context_windows)) - conds_final = [torch.zeros_like(x_in) for _ in conds] + window_state = self._build_window_state(x_in, conds, model) + num_modalities = len(window_state.latents) + + context_windows = self.get_context_windows(model, window_state.latents[0], model_options) + enumerated_context_windows = list(enumerate(context_windows)) + total_windows = len(enumerated_context_windows) + + # Initialize per-modality accumulators (length 1 for single-modality) + accum = [[torch.zeros_like(m) for _ in conds] for m in window_state.latents] if self.fuse_method.name == ContextFuseMethods.RELATIVE: - counts_final = [torch.ones(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds] + counts = [[torch.ones(get_shape_for_dim(m, self.dim), device=m.device) for _ in conds] for m in window_state.latents] else: - counts_final = [torch.zeros(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds] - biases_final = [([0.0] * x_in.shape[self.dim]) for _ in conds] + counts = [[torch.zeros(get_shape_for_dim(m, self.dim), device=m.device) for _ in conds] for m in window_state.latents] + biases = [[([0.0] * m.shape[self.dim]) for _ in conds] for m in window_state.latents] for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_START, self.callbacks): callback(self, model, x_in, conds, timestep, model_options) + # accumulate results from each context window for enum_window in enumerated_context_windows: - results = self.evaluate_context_windows(calc_cond_batch, model, x_in, conds, timestep, [enum_window], model_options) + results = self.evaluate_context_windows( + calc_cond_batch, model, x_in, conds, timestep, [enum_window], + model_options, window_state=window_state, total_windows=total_windows) for result in results: - self.combine_context_window_results(x_in, result.sub_conds_out, result.sub_conds, result.window, result.window_idx, len(enumerated_context_windows), timestep, - conds_final, counts_final, biases_final) + # result.sub_conds_out is per-cond, per-modality: list[list[Tensor]] + for mod_idx in range(num_modalities): + mod_out = [result.sub_conds_out[ci][mod_idx] for ci in range(len(conds))] + modality_window = result.window.get_window_for_modality(mod_idx) + self.combine_context_window_results( + window_state.latents[mod_idx], mod_out, result.sub_conds, modality_window, + result.window_idx, total_windows, timestep, + accum[mod_idx], counts[mod_idx], biases[mod_idx]) + + # fuse accumulated results into final conds try: - # finalize conds - if self.fuse_method.name == ContextFuseMethods.RELATIVE: - # relative is already normalized, so return as is - del counts_final - return conds_final - else: - # normalize conds via division by context usage counts - for i in range(len(conds_final)): - conds_final[i] /= counts_final[i] - del counts_final - return conds_final + result_out = [] + for ci in range(len(conds)): + finalized = [] + for mod_idx in range(num_modalities): + if self.fuse_method.name != ContextFuseMethods.RELATIVE: + accum[mod_idx][ci] /= counts[mod_idx][ci] + f = accum[mod_idx][ci] + + # if guide frames were injected, append them to the end of the fused latents for the next step + if window_state.guide_latents[mod_idx] is not None: + f = torch.cat([f, window_state.guide_latents[mod_idx]], dim=self.dim) + finalized.append(f) + + # pack modalities together if needed + if window_state.is_multimodal and len(finalized) > 1: + packed, _ = comfy.utils.pack_latents(finalized) + else: + packed = finalized[0] + + result_out.append(packed) + return result_out finally: for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_CLEANUP, self.callbacks): callback(self, model, x_in, conds, timestep, model_options) - def evaluate_context_windows(self, calc_cond_batch: Callable, model: BaseModel, x_in: torch.Tensor, conds, timestep: torch.Tensor, enumerated_context_windows: list[tuple[int, IndexListContextWindow]], - model_options, device=None, first_device=None): + def evaluate_context_windows(self, calc_cond_batch: Callable, model: BaseModel, x_in: torch.Tensor, conds, + timestep: torch.Tensor, enumerated_context_windows: list[tuple[int, IndexListContextWindow]], + model_options, window_state: WindowingState, total_windows: int = None, + device=None, first_device=None): + """Evaluate context windows and return per-cond, per-modality outputs in ContextResults.sub_conds_out + + For each window: + 1. Builds windows (for each modality if multimodal) + 2. Slices window for each modality + 3. Injects concatenated latent guide frames where present + 4. Packs together if needed and calls model + 5. Unpacks and strips any guides from outputs + """ + x = window_state.latents[0] + results: list[ContextResults] = [] for window_idx, window in enumerated_context_windows: # allow processing to end between context window executions for faster Cancel comfy.model_management.throw_exception_if_processing_interrupted() - # causal_window_fix: prepend a pre-window frame that will be stripped post-forward + # prepare the window accounting for multimodal windows + window = window_state.prepare_window(window, model) + + # causal_window_fix: prepend a pre-window frame that will be stripped post-forward. + # Set anchor before slice_for_window so the latent slice and downstream cond slices both pick it up. anchor_applied = False if self.causal_window_fix: anchor_idx = window.index_list[0] - 1 @@ -339,27 +665,46 @@ class IndexListContextHandler(ContextHandlerABC): window.causal_anchor_index = anchor_idx anchor_applied = True + # slice the window for each modality, injecting guide frames where applicable + sliced, guide_frame_counts_per_modality = window_state.slice_for_window(window, self.latent_retain_index_list, device) + for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EVALUATE_CONTEXT_WINDOWS, self.callbacks): callback(self, model, x_in, conds, timestep, model_options, window_idx, window, model_options, device, first_device) - # update exposed params + logging.info(f"Context window {window_idx + 1}/{total_windows or len(enumerated_context_windows)}: frames {window.index_list[0]}-{window.index_list[-1]} of {x.shape[self.dim]}" + + (f" (+{guide_frame_counts_per_modality[0]} guide frames)" if guide_frame_counts_per_modality[0] > 0 else "") + ) + + # if multimodal, pack modalities together + if window_state.is_multimodal and len(sliced) > 1: + sub_x, sub_shapes = comfy.utils.pack_latents(sliced) + else: + sub_x, sub_shapes = sliced[0], [sliced[0].shape] + + # get resized conds for window model_options["transformer_options"]["context_window"] = window - # get subsections of x, timestep, conds - sub_x = window.get_tensor(x_in, device) - sub_timestep = window.get_tensor(timestep, device, dim=0) - sub_conds = [self.get_resized_cond(cond, x_in, window, device) for cond in conds] + sub_timestep = window.get_tensor(timestep, dim=0) + sub_conds = [self.get_resized_cond(cond, x, window) for cond in conds] + # if multimodal, patch latent_shapes in conds for correct unpacking in model + window_state.patch_latent_shapes(sub_conds, sub_shapes) + + # call model on window sub_conds_out = calc_cond_batch(model, sub_conds, sub_x, sub_timestep, model_options) - if device is not None: - for i in range(len(sub_conds_out)): - sub_conds_out[i] = sub_conds_out[i].to(x_in.device) - # strip causal_window_fix anchor if applied + # unpack outputs + out_per_modality = [comfy.utils.unpack_latents(sub_conds_out[i], sub_shapes) for i in range(len(sub_conds_out))] + + # strip causal_window_fix anchor from primary modality before guide strip so window_len math stays correct if anchor_applied: - for i in range(len(sub_conds_out)): - sub_conds_out[i] = sub_conds_out[i].narrow(self.dim, 1, sub_conds_out[i].shape[self.dim] - 1) + for ci in range(len(out_per_modality)): + t = out_per_modality[ci][0] + out_per_modality[ci][0] = t.narrow(self.dim, 1, t.shape[self.dim] - 1) - results.append(ContextResults(window_idx, sub_conds_out, sub_conds, window)) + # strip injected guide frames + window_state.strip_guide_frames(out_per_modality, guide_frame_counts_per_modality, window) + + results.append(ContextResults(window_idx, out_per_modality, sub_conds, window)) return results @@ -383,7 +728,7 @@ class IndexListContextHandler(ContextHandlerABC): biases_final[i][idx] = bias_total + bias else: # add conds and counts based on weights of fuse method - weights = get_context_weights(window.context_length, x_in.shape[self.dim], window.index_list, self, sigma=timestep) + weights = get_context_weights(window.context_length, x_in.shape[self.dim], window.index_list, self, sigma=timestep, context_overlap=window.context_overlap) weights_tensor = match_weights_to_dim(weights, x_in, self.dim, device=x_in.device) for i in range(len(sub_conds_out)): window.add_window(conds_final[i], sub_conds_out[i] * weights_tensor) @@ -393,16 +738,22 @@ class IndexListContextHandler(ContextHandlerABC): callback(self, x_in, sub_conds_out, sub_conds, window, window_idx, total_windows, timestep, conds_final, counts_final, biases_final) -def _prepare_sampling_wrapper(executor, model, noise_shape: torch.Tensor, *args, **kwargs): - # limit noise_shape length to context_length for more accurate vram use estimation +def _prepare_sampling_wrapper(executor, model, noise_shape: torch.Tensor, conds, *args, **kwargs): + # Scale noise_shape to a single context window so VRAM estimation budgets per-window. model_options = kwargs.get("model_options", None) if model_options is None: raise Exception("model_options not found in prepare_sampling_wrapper; this should never happen, something went wrong.") handler: IndexListContextHandler = model_options.get("context_handler", None) if handler is not None: noise_shape = list(noise_shape) - noise_shape[handler.dim] = min(noise_shape[handler.dim], handler.context_length) - return executor(model, noise_shape, *args, **kwargs) + is_packed = len(noise_shape) == 3 and noise_shape[1] == 1 + if is_packed: + # TODO: latent_shapes cond isn't attached yet at this point, so we can't compute a + # per-window flat latent here. Skipping the clamp over-estimates but prevents immediate OOM. + pass + elif handler.dim < len(noise_shape) and noise_shape[handler.dim] > handler.context_length: + noise_shape[handler.dim] = min(noise_shape[handler.dim], handler.context_length) + return executor(model, noise_shape, conds, *args, **kwargs) def create_prepare_sampling_wrapper(model: ModelPatcher): @@ -422,11 +773,12 @@ def _sampler_sample_wrapper(executor, guider, sigmas, extra_args, callback, nois raise Exception("context_handler not found in sampler_sample_wrapper; this should never happen, something went wrong.") if not handler.freenoise: return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs) - noise = apply_freenoise(noise, handler.dim, handler.context_length, handler.context_overlap, extra_args["seed"]) + + conds = [guider.conds.get('positive', guider.conds.get('negative', []))] + noise = handler._apply_freenoise(noise, conds, extra_args["seed"]) return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs) - def create_sampler_sample_wrapper(model: ModelPatcher): model.add_wrapper_with_key( comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE, @@ -434,7 +786,6 @@ def create_sampler_sample_wrapper(model: ModelPatcher): _sampler_sample_wrapper ) - def match_weights_to_dim(weights: list[float], x_in: torch.Tensor, dim: int, device=None) -> torch.Tensor: total_dims = len(x_in.shape) weights_tensor = torch.Tensor(weights).to(device=device) @@ -580,8 +931,9 @@ def get_matching_context_schedule(context_schedule: str) -> ContextSchedule: return ContextSchedule(context_schedule, func) -def get_context_weights(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, sigma: torch.Tensor=None): - return handler.fuse_method.func(length, sigma=sigma, handler=handler, full_length=full_length, idxs=idxs) +def get_context_weights(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, sigma: torch.Tensor=None, context_overlap: int=None): + context_overlap = handler.context_overlap if context_overlap is None else context_overlap + return handler.fuse_method.func(length, sigma=sigma, handler=handler, full_length=full_length, idxs=idxs, context_overlap=context_overlap) def create_weights_flat(length: int, **kwargs) -> list[float]: @@ -599,18 +951,18 @@ def create_weights_pyramid(length: int, **kwargs) -> list[float]: weight_sequence = list(range(1, max_weight, 1)) + [max_weight] + list(range(max_weight - 1, 0, -1)) return weight_sequence -def create_weights_overlap_linear(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, **kwargs): +def create_weights_overlap_linear(length: int, full_length: int, idxs: list[int], context_overlap: int, **kwargs): # based on code in Kijai's WanVideoWrapper: https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/dbb2523b37e4ccdf45127e5ae33e31362f755c8e/nodes.py#L1302 # only expected overlap is given different weights weights_torch = torch.ones((length)) # blend left-side on all except first window if min(idxs) > 0: - ramp_up = torch.linspace(1e-37, 1, handler.context_overlap) - weights_torch[:handler.context_overlap] = ramp_up + ramp_up = torch.linspace(1e-37, 1, context_overlap) + weights_torch[:context_overlap] = ramp_up # blend right-side on all except last window if max(idxs) < full_length-1: - ramp_down = torch.linspace(1, 1e-37, handler.context_overlap) - weights_torch[-handler.context_overlap:] = ramp_down + ramp_down = torch.linspace(1, 1e-37, context_overlap) + weights_torch[-context_overlap:] = ramp_down return weights_torch class ContextFuseMethods: diff --git a/comfy/image_encoders/dino2.py b/comfy/image_encoders/dino2.py index ee86f8309..53e4fdb6c 100644 --- a/comfy/image_encoders/dino2.py +++ b/comfy/image_encoders/dino2.py @@ -1,7 +1,13 @@ import torch +import torch.nn.functional as F + from comfy.text_encoders.bert import BertAttention import comfy.model_management from comfy.ldm.modules.attention import optimized_attention_for_device +from comfy.ldm.depth_anything_3.reference_view_selector import ( + select_reference_view, reorder_by_reference, restore_original_order, + THRESH_FOR_REF_SELECTION, +) class Dino2AttentionOutput(torch.nn.Module): @@ -14,13 +20,41 @@ class Dino2AttentionOutput(torch.nn.Module): class Dino2AttentionBlock(torch.nn.Module): - def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations): + def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations, + qk_norm=False): super().__init__() + self.heads = heads + self.head_dim = embed_dim // heads self.attention = BertAttention(embed_dim, heads, dtype, device, operations) self.output = Dino2AttentionOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations) + if qk_norm: + self.q_norm = operations.LayerNorm(self.head_dim, dtype=dtype, device=device) + self.k_norm = operations.LayerNorm(self.head_dim, dtype=dtype, device=device) + else: + self.q_norm = None + self.k_norm = None - def forward(self, x, mask, optimized_attention): - return self.output(self.attention(x, mask, optimized_attention)) + def forward(self, x, mask, optimized_attention, pos=None, rope=None): + # Fast path used by the existing CLIP-vision DINOv2 (no DA3 extensions). + if self.q_norm is None and rope is None: + return self.output(self.attention(x, mask, optimized_attention)) + + # DA3 path: do QKV manually so we can apply per-head QK-norm and 2D RoPE. + attn = self.attention + B, N, C = x.shape + h = self.heads + d = self.head_dim + q = attn.query(x).view(B, N, h, d).transpose(1, 2) + k = attn.key(x).view(B, N, h, d).transpose(1, 2) + v = attn.value(x).view(B, N, h, d).transpose(1, 2) + if self.q_norm is not None: + q = self.q_norm(q) + k = self.k_norm(k) + if rope is not None and pos is not None: + q = rope(q, pos) + k = rope(k, pos) + out = optimized_attention(q, k, v, h, mask=mask, skip_reshape=True) + return self.output(out) class LayerScale(torch.nn.Module): @@ -64,9 +98,11 @@ class SwiGLUFFN(torch.nn.Module): class Dino2Block(torch.nn.Module): - def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn): + def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn, + qk_norm=False): super().__init__() - self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations) + self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations, + qk_norm=qk_norm) self.layer_scale1 = LayerScale(dim, dtype, device, operations) self.layer_scale2 = LayerScale(dim, dtype, device, operations) if use_swiglu_ffn: @@ -76,19 +112,90 @@ class Dino2Block(torch.nn.Module): self.norm1 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) self.norm2 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) - def forward(self, x, optimized_attention): - x = x + self.layer_scale1(self.attention(self.norm1(x), None, optimized_attention)) + def forward(self, x, optimized_attention, pos=None, rope=None, attn_mask=None): + x = x + self.layer_scale1(self.attention(self.norm1(x), attn_mask, optimized_attention, + pos=pos, rope=rope)) x = x + self.layer_scale2(self.mlp(self.norm2(x))) return x -class Dino2Encoder(torch.nn.Module): - def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn): +# ----------------------------------------------------------------------------- +# 2D Rotary position embedding (DA3 extension) +# ----------------------------------------------------------------------------- + + +class _PositionGetter: + """Cache (h, w) -> flat (y, x) position grid used to feed ``rope``.""" + + def __init__(self): + self._cache: dict = {} + + def __call__(self, batch_size: int, height: int, width: int, device) -> torch.Tensor: + key = (height, width, device) + if key not in self._cache: + y = torch.arange(height, device=device) + x = torch.arange(width, device=device) + self._cache[key] = torch.cartesian_prod(y, x) + cached = self._cache[key] + return cached.view(1, height * width, 2).expand(batch_size, -1, -1).clone() + + +class RotaryPositionEmbedding2D(torch.nn.Module): + """2D RoPE used by DA3-Small/Base. No learnable parameters.""" + + def __init__(self, frequency: float = 100.0): super().__init__() - self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn) - for _ in range(num_layers)]) + self.base_frequency = frequency + self._freq_cache: dict = {} + + def _components(self, dim: int, seq_len: int, device, dtype): + key = (dim, seq_len, device, dtype) + if key not in self._freq_cache: + exp = torch.arange(0, dim, 2, device=device).float() / dim + inv_freq = 1.0 / (self.base_frequency ** exp) + pos = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) + ang = torch.einsum("i,j->ij", pos, inv_freq) + ang = ang.to(dtype) + ang = torch.cat((ang, ang), dim=-1) + self._freq_cache[key] = (ang.cos().to(dtype), ang.sin().to(dtype)) + return self._freq_cache[key] + + @staticmethod + def _rotate(x: torch.Tensor) -> torch.Tensor: + d = x.shape[-1] + x1, x2 = x[..., : d // 2], x[..., d // 2:] + return torch.cat((-x2, x1), dim=-1) + + def _apply_1d(self, tokens, positions, cos_c, sin_c): + cos = F.embedding(positions, cos_c)[:, None, :, :] + sin = F.embedding(positions, sin_c)[:, None, :, :] + return (tokens * cos) + (self._rotate(tokens) * sin) + + def forward(self, tokens: torch.Tensor, positions: torch.Tensor) -> torch.Tensor: + feature_dim = tokens.size(-1) // 2 + max_pos = int(positions.max()) + 1 + cos_c, sin_c = self._components(feature_dim, max_pos, tokens.device, tokens.dtype) + v, h = tokens.chunk(2, dim=-1) + v = self._apply_1d(v, positions[..., 0], cos_c, sin_c) + h = self._apply_1d(h, positions[..., 1], cos_c, sin_c) + return torch.cat((v, h), dim=-1) + + +class Dino2Encoder(torch.nn.Module): + def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn, + qknorm_start: int = -1): + super().__init__() + self.layer = torch.nn.ModuleList([ + Dino2Block( + dim, num_heads, layer_norm_eps, dtype, device, operations, + use_swiglu_ffn=use_swiglu_ffn, + qk_norm=(qknorm_start != -1 and i >= qknorm_start), + ) + for i in range(num_layers) + ]) def forward(self, x, intermediate_output=None): + # Backward-compat path used by ``ClipVisionModel`` (no DA3 extensions). optimized_attention = optimized_attention_for_device(x.device, False, small_input=True) if intermediate_output is not None: @@ -122,16 +229,27 @@ class Dino2PatchEmbeddings(torch.nn.Module): class Dino2Embeddings(torch.nn.Module): - def __init__(self, dim, dtype, device, operations): + def __init__(self, dim, dtype, device, operations, + patch_size: int = 14, image_size: int = 518, + use_mask_token: bool = True, + num_camera_tokens: int = 0): super().__init__() - patch_size = 14 - image_size = 518 self.patch_size = patch_size + self.image_size = image_size self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations) self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device)) self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device)) # mask_token is a pre-training param, kept only so strict loading accepts the key. - self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device)) + if use_mask_token: + self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device)) + else: + self.mask_token = None + if num_camera_tokens > 0: + # DA3 stores (ref_token, src_token) pairs that get injected at the + # alt-attn boundary; see ``Dinov2Model._inject_camera_token``. + self.camera_token = torch.nn.Parameter(torch.empty(1, num_camera_tokens, dim, dtype=dtype, device=device)) + else: + self.camera_token = None def interpolate_pos_encoding(self, x, h_pixels, w_pixels): pos_embed = comfy.model_management.cast_to_device(self.position_embeddings, x.device, torch.float32) @@ -140,12 +258,22 @@ class Dino2Embeddings(torch.nn.Module): patch_pos = pos_embed[:, 1:] N = patch_pos.shape[1] M = int(N ** 0.5) + assert N == M * M, f"DINOv2 position grid must be square, got N={N} patches (sqrt={M})" h0 = h_pixels // self.patch_size w0 = w_pixels // self.patch_size - scale_factor = ((h0 + 0.1) / M, (w0 + 0.1) / M) # +0.1 matches upstream DINOv2's FP-rounding workaround so the interpolate output size lands on (h0, w0). + # +0.1 matches upstream DINOv2's FP-rounding workaround so the interpolate output size lands on (h0, w0). + # scale_factor is (height_scale, width_scale) -- height MUST come first; + # swapping these only happens to work for square inputs and breaks + # non-square paths like DA3-Small / DA3-Base multi-view. + scale_factor = ((h0 + 0.1) / M, (w0 + 0.1) / M) patch_pos = patch_pos.reshape(1, M, M, -1).permute(0, 3, 1, 2) patch_pos = torch.nn.functional.interpolate(patch_pos, scale_factor=scale_factor, mode="bicubic", antialias=False) + assert (h0, w0) == patch_pos.shape[-2:], ( + f"Interpolated pos-embed grid {tuple(patch_pos.shape[-2:])} does not match " + f"target patch grid ({h0}, {w0}) for input {h_pixels}x{w_pixels} (patch_size={self.patch_size}); " + f"check scale_factor axis order and +0.1 rounding workaround" + ) patch_pos = patch_pos.permute(0, 2, 3, 1).flatten(1, 2) return torch.cat((class_pos, patch_pos), dim=1).to(x.dtype) @@ -168,12 +296,51 @@ class Dinov2Model(torch.nn.Module): heads = config_dict["num_attention_heads"] layer_norm_eps = config_dict["layer_norm_eps"] use_swiglu_ffn = config_dict["use_swiglu_ffn"] + patch_size = config_dict.get("patch_size", 14) + image_size = config_dict.get("image_size", 518) + use_mask_token = config_dict.get("use_mask_token", True) - self.embeddings = Dino2Embeddings(dim, dtype, device, operations) - self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn) + # DA3 extensions (all default to disabled). + self.alt_start = config_dict.get("alt_start", -1) + self.qknorm_start = config_dict.get("qknorm_start", -1) + self.rope_start = config_dict.get("rope_start", -1) + self.cat_token = config_dict.get("cat_token", False) + rope_freq = config_dict.get("rope_freq", 100.0) + + self.embed_dim = dim + self.patch_size = patch_size + self.num_register_tokens = 0 + self.patch_start_idx = 1 + + if self.rope_start != -1 and rope_freq > 0: + self.rope = RotaryPositionEmbedding2D(frequency=rope_freq) + self._position_getter = _PositionGetter() + else: + self.rope = None + self._position_getter = None + + # camera_token shape: (1, 2, dim) -> (ref_token, src_token). + num_cam_tokens = 2 if self.alt_start != -1 else 0 + + self.embeddings = Dino2Embeddings( + dim, dtype, device, operations, + patch_size=patch_size, image_size=image_size, + use_mask_token=use_mask_token, num_camera_tokens=num_cam_tokens, + ) + self.encoder = Dino2Encoder( + dim, heads, layer_norm_eps, num_layers, dtype, device, operations, + use_swiglu_ffn=use_swiglu_ffn, + qknorm_start=self.qknorm_start, + ) self.layernorm = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) def forward(self, pixel_values, attention_mask=None, intermediate_output=None): + if self.alt_start != -1: + raise RuntimeError( + "Dinov2Model.forward() is the backward-compatible CLIP-vision path and does not " + "apply DA3 extensions (RoPE, alternating attention, camera-token injection). " + "Use get_intermediate_layers_da3() for Depth Anything 3 models." + ) x = self.embeddings(pixel_values) x, i = self.encoder(x, intermediate_output=intermediate_output) x = self.layernorm(x) @@ -181,6 +348,7 @@ class Dinov2Model(torch.nn.Module): return x, i, pooled_output, None def get_intermediate_layers(self, pixel_values, indices, apply_norm=True): + """Single-view multi-layer feature extraction.""" x = self.embeddings(pixel_values) optimized_attention = optimized_attention_for_device(x.device, False, small_input=True) n_layers = len(self.encoder.layer) @@ -197,3 +365,132 @@ class Dinov2Model(torch.nn.Module): if i >= max_idx: break return [cache[i] for i in resolved] + + # ------------------------------------------------------------------ + # Depth Anything 3 forward + # ------------------------------------------------------------------ + def _prepare_rope_positions(self, B, S, H, W, device): + if self.rope is None: + return None, None + ph, pw = H // self.patch_size, W // self.patch_size + pos = self._position_getter(B * S, ph, pw, device=device) + # Shift so the cls/cam token at position 0 is reserved for "no diff". + pos = pos + 1 + cls_pos = torch.zeros(B * S, self.patch_start_idx, 2, device=device, dtype=pos.dtype) + # Per-view local: real grid positions for patches, 0 for cls token. + pos_local = torch.cat([cls_pos, pos], dim=1) + # Global (across views): same grid positions; cls token still at 0, + # but patches share the same positions in every view. + pos_global = torch.cat([cls_pos, torch.zeros_like(pos) + 1], dim=1) + return pos_local, pos_global + + def _inject_camera_token(self, x: torch.Tensor, B: int, S: int, cam_token: "torch.Tensor | None") -> torch.Tensor: + # x: (B, S, N, C). Replace token at index 0 with the camera token. + if cam_token is not None: + inj = cam_token + else: + ct = comfy.model_management.cast_to_device(self.embeddings.camera_token, x.device, x.dtype) + ref_token = ct[:, :1].expand(B, -1, -1) + src_token = ct[:, 1:].expand(B, max(S - 1, 0), -1) + inj = torch.cat([ref_token, src_token], dim=1) + x = x.clone() + x[:, :, 0] = inj + return x + + def get_intermediate_layers_da3(self, pixel_values, out_layers, cam_token=None, ref_view_strategy="saddle_balanced", export_feat_layers=None): + """Multi-view multi-layer feature extraction used by Depth Anything 3.""" + if pixel_values.ndim == 4: + pixel_values = pixel_values.unsqueeze(1) + assert pixel_values.ndim == 5 and pixel_values.shape[2] == 3, \ + f"expected (B,3,H,W) or (B,S,3,H,W); got {tuple(pixel_values.shape)}" + B, S, _, H, W = pixel_values.shape + + # Patch + cls + (interpolated) pos embed for each view. + x = pixel_values.reshape(B * S, 3, H, W) + x = self.embeddings(x) # (B*S, 1+N, C) + x = x.reshape(B, S, x.shape[-2], x.shape[-1]) # (B, S, 1+N, C) + + pos_local, pos_global = self._prepare_rope_positions(B, S, H, W, x.device) + # optimized_attention is only used by blocks without QK-norm/RoPE + # (vanilla DINOv2 path); enabling-aware blocks fall through to SDPA. + optimized_attention = optimized_attention_for_device(x.device, False, small_input=True) + + out_set = set(out_layers) + export_set = set(export_feat_layers) if export_feat_layers else set() + outputs: list[torch.Tensor] = [] + aux_outputs: list[torch.Tensor] = [] + local_x = x + b_idx = None + + + for i, blk in enumerate(self.encoder.layer): + apply_rope = self.rope is not None and i >= self.rope_start + block_rope = self.rope if apply_rope else None + l_pos = pos_local if apply_rope else None + g_pos = pos_global if apply_rope else None + + # Reference-view selection threshold: matches the upstream constant + # THRESH_FOR_REF_SELECTION = 3. Skipped when a user-supplied + # cam_token is provided (camera info already pins the geometry). + if (self.alt_start != -1 and i == self.alt_start - 1 and S >= THRESH_FOR_REF_SELECTION and cam_token is None): + b_idx = select_reference_view(x, strategy=ref_view_strategy) + x = reorder_by_reference(x, b_idx) + local_x = reorder_by_reference(local_x, b_idx) + + if self.alt_start != -1 and i == self.alt_start: + x = self._inject_camera_token(x, B, S, cam_token) + + if self.alt_start != -1 and i >= self.alt_start and (i % 2 == 1): + # Global attention across views: flatten S into the seq dim. + t = x.reshape(B, S * x.shape[-2], x.shape[-1]) + p = g_pos.reshape(B, S * g_pos.shape[-2], g_pos.shape[-1]) if g_pos is not None else None + t = blk(t, optimized_attention=optimized_attention, pos=p, rope=block_rope) + x = t.reshape(B, S, x.shape[-2], x.shape[-1]) + else: + # Per-view local attention. + t = x.reshape(B * S, x.shape[-2], x.shape[-1]) + p = l_pos.reshape(B * S, l_pos.shape[-2], l_pos.shape[-1]) if l_pos is not None else None + t = blk(t, optimized_attention=optimized_attention, pos=p, rope=block_rope) + x = t.reshape(B, S, x.shape[-2], x.shape[-1]) + local_x = x + + if i in out_set: + if self.cat_token: + out_x = torch.cat([local_x, x], dim=-1) + else: + out_x = x + # Restore original view order on the way out so heads see views + # in the user's expected order. + if b_idx is not None and self.alt_start != -1: + out_x = restore_original_order(out_x, b_idx) + outputs.append(out_x) + + if i in export_set: + aux = x + if b_idx is not None and self.alt_start != -1: + aux = restore_original_order(aux, b_idx) + aux_outputs.append(aux) + + # Apply final norm. When cat_token is set, only the right half + # ("global" features) is normalised; the left half is left as-is to + # match the upstream DA3 head signature. + normed: list[torch.Tensor] = [] + cls_tokens: list[torch.Tensor] = [] + for out_x in outputs: + cls_tokens.append(out_x[:, :, 0]) + if out_x.shape[-1] == self.embed_dim: + normed.append(self.layernorm(out_x)) + elif out_x.shape[-1] == self.embed_dim * 2: + left = out_x[..., :self.embed_dim] + right = self.layernorm(out_x[..., self.embed_dim:]) + normed.append(torch.cat([left, right], dim=-1)) + else: + raise ValueError(f"Unexpected token width: {out_x.shape[-1]}") + + # Drop cls/cam token from the patch sequence. + normed = [o[..., 1 + self.num_register_tokens:, :] for o in normed] + + # Final layernorm + drop cls token from auxiliary features too. + aux_normed = [self.layernorm(o)[..., 1 + self.num_register_tokens:, :] + for o in aux_outputs] + return list(zip(normed, cls_tokens)), aux_normed diff --git a/comfy/image_encoders/dino3.py b/comfy/image_encoders/dino3.py new file mode 100644 index 000000000..ad29b06f8 --- /dev/null +++ b/comfy/image_encoders/dino3.py @@ -0,0 +1,259 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +import comfy.ops +from comfy.ldm.modules.attention import optimized_attention_for_device +from comfy.image_encoders.dino2 import LayerScale as DINOv3ViTLayerScale + + +# DINOv3 ViT-H/16+ (SwiGLU) +DINOV3_VITH_CONFIG = { + "model_type": "dinov3", + "num_hidden_layers": 32, + "hidden_size": 1280, + "num_attention_heads": 20, + "num_register_tokens": 4, + "intermediate_size": 5120, + "layer_norm_eps": 1e-5, + "num_channels": 3, + "patch_size": 16, + "rope_theta": 100.0, + "use_gated_mlp": True, + "gated_mlp_act": "silu", + "image_size": 1024, + "image_mean": [0.485, 0.456, 0.406], + "image_std": [0.229, 0.224, 0.225], +} + + +class DINOv3ViTMLP(nn.Module): + def __init__(self, hidden_size, intermediate_size, mlp_bias, device, dtype, operations): + super().__init__() + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.up_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=mlp_bias, device=device, dtype=dtype) + self.down_proj = operations.Linear(self.intermediate_size, self.hidden_size, bias=mlp_bias, device=device, dtype=dtype) + self.act_fn = torch.nn.GELU() + + def forward(self, x): + return self.down_proj(self.act_fn(self.up_proj(x))) + + +def rotate_half(x): + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, **kwargs): + num_tokens = q.shape[-2] + num_patches = sin.shape[-2] + num_prefix_tokens = num_tokens - num_patches + + q_prefix_tokens, q_patches = q.split((num_prefix_tokens, num_patches), dim=-2) + k_prefix_tokens, k_patches = k.split((num_prefix_tokens, num_patches), dim=-2) + + q_patches = (q_patches * cos) + (rotate_half(q_patches) * sin) + k_patches = (k_patches * cos) + (rotate_half(k_patches) * sin) + + q = torch.cat((q_prefix_tokens, q_patches), dim=-2) + k = torch.cat((k_prefix_tokens, k_patches), dim=-2) + + return q, k + + +class DINOv3ViTAttention(nn.Module): + def __init__(self, hidden_size, num_attention_heads, device, dtype, operations): + super().__init__() + self.embed_dim = hidden_size + self.num_heads = num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + + self.k_proj = operations.Linear(self.embed_dim, self.embed_dim, bias=False, device=device, dtype=dtype) # key_bias = False + self.v_proj = operations.Linear(self.embed_dim, self.embed_dim, bias=True, device=device, dtype=dtype) + self.q_proj = operations.Linear(self.embed_dim, self.embed_dim, bias=True, device=device, dtype=dtype) + self.o_proj = operations.Linear(self.embed_dim, self.embed_dim, bias=True, device=device, dtype=dtype) + + def forward(self, hidden_states, attention_mask=None, position_embeddings=None, **kwargs): + batch_size, patches, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2) + + if position_embeddings is not None: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + attn = optimized_attention_for_device(query_states.device, mask=False) + attn_output = attn( + query_states, key_states, value_states, self.num_heads, attention_mask, + skip_reshape=True, skip_output_reshape=True, low_precision_attention=False, + ) + + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(batch_size, patches, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output + + +class DINOv3ViTGatedMLP(nn.Module): + def __init__(self, hidden_size, intermediate_size, mlp_bias, device, dtype, operations, act="silu"): + super().__init__() + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.gate_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=mlp_bias, device=device, dtype=dtype) + self.up_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=mlp_bias, device=device, dtype=dtype) + self.down_proj = operations.Linear(self.intermediate_size, self.hidden_size, bias=mlp_bias, device=device, dtype=dtype) + self.act_fn = torch.nn.SiLU() if act == "silu" else torch.nn.GELU() + + def forward(self, x): + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + +def get_patches_center_coordinates(num_patches_h, num_patches_w, dtype, device): + coords_h = torch.arange(0.5, num_patches_h, dtype=dtype, device=device) + coords_w = torch.arange(0.5, num_patches_w, dtype=dtype, device=device) + coords_h = coords_h / num_patches_h + coords_w = coords_w / num_patches_w + coords = torch.stack(torch.meshgrid(coords_h, coords_w, indexing="ij"), dim=-1) + coords = coords.flatten(0, 1) + coords = 2.0 * coords - 1.0 + return coords + + +class DINOv3ViTRopePositionEmbedding(nn.Module): + inv_freq: torch.Tensor + + def __init__(self, rope_theta, hidden_size, num_attention_heads, patch_size, device, dtype): + super().__init__() + self.base = rope_theta + self.head_dim = hidden_size // num_attention_heads + self.patch_size = patch_size + + inv_freq = 1 / self.base ** torch.arange(0, 1, 4 / self.head_dim, dtype=torch.float32, device=device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + def forward(self, pixel_values): + _, _, height, width = pixel_values.shape + num_patches_h = height // self.patch_size + num_patches_w = width // self.patch_size + + patch_coords = get_patches_center_coordinates(num_patches_h, num_patches_w, dtype=torch.float32, device=pixel_values.device) + self.inv_freq = self.inv_freq.to(pixel_values.device) + angles = 2 * math.pi * patch_coords[:, :, None] * self.inv_freq[None, None, :] + angles = angles.flatten(1, 2) + angles = angles.tile(2) + cos = torch.cos(angles).to(dtype=pixel_values.dtype) + sin = torch.sin(angles).to(dtype=pixel_values.dtype) + return cos, sin + + +class DINOv3ViTEmbeddings(nn.Module): + def __init__(self, hidden_size, num_register_tokens, num_channels, patch_size, dtype, device, operations): + super().__init__() + self.cls_token = nn.Parameter(torch.empty(1, 1, hidden_size, device=device, dtype=dtype)) + self.mask_token = nn.Parameter(torch.empty(1, 1, hidden_size, device=device, dtype=dtype)) + self.register_tokens = nn.Parameter(torch.empty(1, num_register_tokens, hidden_size, device=device, dtype=dtype)) + self.patch_embeddings = operations.Conv2d( + num_channels, hidden_size, kernel_size=patch_size, stride=patch_size, device=device, dtype=dtype + ) + + def forward(self, pixel_values, bool_masked_pos=None): + batch_size = pixel_values.shape[0] + + patch_embeddings = self.patch_embeddings(pixel_values) + patch_embeddings = patch_embeddings.flatten(2).transpose(1, 2) + + if bool_masked_pos is not None: + mask_token = comfy.ops.cast_to_input(self.mask_token, patch_embeddings) + patch_embeddings = torch.where(bool_masked_pos.unsqueeze(-1), mask_token, patch_embeddings) + + cls_token = comfy.ops.cast_to_input(self.cls_token.expand(batch_size, -1, -1), patch_embeddings) + register_tokens = comfy.ops.cast_to_input(self.register_tokens.expand(batch_size, -1, -1), patch_embeddings) + embeddings = torch.cat([cls_token, register_tokens, patch_embeddings], dim=1) + return embeddings + + +class DINOv3ViTLayer(nn.Module): + def __init__(self, hidden_size, layer_norm_eps, use_gated_mlp, mlp_bias, intermediate_size, + num_attention_heads, device, dtype, operations, gated_mlp_act="silu"): + super().__init__() + self.norm1 = operations.LayerNorm(hidden_size, eps=layer_norm_eps, device=device, dtype=dtype) + self.attention = DINOv3ViTAttention(hidden_size, num_attention_heads, device=device, dtype=dtype, operations=operations) + self.layer_scale1 = DINOv3ViTLayerScale(hidden_size, device=device, dtype=dtype, operations=None) + + self.norm2 = operations.LayerNorm(hidden_size, eps=layer_norm_eps, device=device, dtype=dtype) + if use_gated_mlp: + self.mlp = DINOv3ViTGatedMLP(hidden_size, intermediate_size, mlp_bias, device=device, dtype=dtype, operations=operations, act=gated_mlp_act) + else: + self.mlp = DINOv3ViTMLP(hidden_size, intermediate_size=intermediate_size, mlp_bias=mlp_bias, device=device, dtype=dtype, operations=operations) + self.layer_scale2 = DINOv3ViTLayerScale(hidden_size, device=device, dtype=dtype, operations=None) + + def forward(self, hidden_states, attention_mask=None, position_embeddings=None): + residual = hidden_states + hidden_states = self.norm1(hidden_states) + hidden_states = self.attention(hidden_states, attention_mask=attention_mask, position_embeddings=position_embeddings) + hidden_states = self.layer_scale1(hidden_states) + hidden_states = hidden_states + residual + + residual = hidden_states + hidden_states = self.norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = self.layer_scale2(hidden_states) + hidden_states = hidden_states + residual + return hidden_states + + +class DINOv3ViTModel(nn.Module): + def __init__(self, config, dtype, device, operations): + super().__init__() + num_hidden_layers = config["num_hidden_layers"] + hidden_size = config["hidden_size"] + num_attention_heads = config["num_attention_heads"] + num_register_tokens = config["num_register_tokens"] + intermediate_size = config["intermediate_size"] + layer_norm_eps = config["layer_norm_eps"] + num_channels = config["num_channels"] + patch_size = config["patch_size"] + rope_theta = config["rope_theta"] + use_gated_mlp = config.get("use_gated_mlp", False) + gated_mlp_act = config.get("gated_mlp_act", "silu") + + self.embeddings = DINOv3ViTEmbeddings( + hidden_size, num_register_tokens, num_channels=num_channels, patch_size=patch_size, + dtype=dtype, device=device, operations=operations + ) + self.rope_embeddings = DINOv3ViTRopePositionEmbedding( + rope_theta, hidden_size, num_attention_heads, patch_size=patch_size, dtype=dtype, device=device + ) + self.layer = nn.ModuleList([ + DINOv3ViTLayer(hidden_size, layer_norm_eps, use_gated_mlp=use_gated_mlp, mlp_bias=True, + intermediate_size=intermediate_size, num_attention_heads=num_attention_heads, + dtype=dtype, device=device, operations=operations, gated_mlp_act=gated_mlp_act) + for _ in range(num_hidden_layers)]) + self.norm = operations.LayerNorm(hidden_size, eps=layer_norm_eps, dtype=dtype, device=device) + + def get_input_embeddings(self): + return self.embeddings.patch_embeddings + + def forward(self, pixel_values, bool_masked_pos=None, **kwargs): + hidden_states = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos) + position_embeddings = self.rope_embeddings(pixel_values) + + for layer_module in self.layer: + hidden_states = layer_module(hidden_states, position_embeddings=position_embeddings) + + if kwargs.get("skip_norm_elementwise", False): + sequence_output = F.layer_norm(hidden_states, hidden_states.shape[-1:]) + else: + norm = self.norm.to(hidden_states.device) + sequence_output = norm(hidden_states) + pooled_output = sequence_output[:, 0, :] + return sequence_output, None, pooled_output, None diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 12a934d71..bbdfd4bc2 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -239,6 +239,16 @@ class Flux2(LatentFormat): def process_out(self, latent): return latent +class TripoSplat(LatentFormat): + # Sequence latent (B, 8192, 16) the camera token rides alongside as a second nested latent + latent_channels = 16 + + def process_in(self, latent): + return latent + + def process_out(self, latent): + return latent + class Mochi(LatentFormat): latent_channels = 12 latent_dimensions = 3 diff --git a/comfy/ldm/boogu/model.py b/comfy/ldm/boogu/model.py new file mode 100644 index 000000000..966f3c583 --- /dev/null +++ b/comfy/ldm/boogu/model.py @@ -0,0 +1,321 @@ +# Boogu-Image-0.1 transformer +# Architecture is an OmniGen2 derivative (see comfy/ldm/omnigen/omnigen2.py) with an +# added dual-stream ("double_stream") stage before the single-stream layers, conditioned +# by a Qwen3-VL multimodal LLM. Reuses the OmniGen2/Lumina building blocks and the Flux +# RoPE core, the only new component is the double-stream block + the hybrid forward order. + +from typing import Optional, Tuple + +import torch +import torch.nn as nn +from einops import rearrange + +import comfy.ldm.common_dit +import comfy.ldm.omnigen.omnigen2 +from comfy.ldm.modules.attention import optimized_attention_masked +from comfy.ldm.omnigen.omnigen2 import ( + OmniGen2RotaryPosEmbed, + Lumina2CombinedTimestepCaptionEmbedding, + LuminaRMSNormZero, + LuminaLayerNormContinuous, + LuminaFeedForward, + Attention, + OmniGen2TransformerBlock, + apply_rotary_emb, +) + +class BooguDoubleStreamProcessor(nn.Module): + # Joint attention over [instruct ; img] with separate per-stream q/k/v and output projections. + def __init__(self, dim, head_dim, heads, kv_heads, dtype=None, device=None, operations=None): + super().__init__() + query_dim = head_dim * heads + kv_dim = head_dim * kv_heads + + self.img_to_q = operations.Linear(query_dim, query_dim, bias=False, dtype=dtype, device=device) + self.img_to_k = operations.Linear(query_dim, kv_dim, bias=False, dtype=dtype, device=device) + self.img_to_v = operations.Linear(query_dim, kv_dim, bias=False, dtype=dtype, device=device) + + self.instruct_to_q = operations.Linear(query_dim, query_dim, bias=False, dtype=dtype, device=device) + self.instruct_to_k = operations.Linear(query_dim, kv_dim, bias=False, dtype=dtype, device=device) + self.instruct_to_v = operations.Linear(query_dim, kv_dim, bias=False, dtype=dtype, device=device) + + self.instruct_out = operations.Linear(query_dim, query_dim, bias=False, dtype=dtype, device=device) + self.img_out = operations.Linear(query_dim, query_dim, bias=False, dtype=dtype, device=device) + + def forward(self, attn, img_hidden_states, instruct_hidden_states, rotary_emb, attention_mask=None, transformer_options={}): + batch_size = img_hidden_states.shape[0] + L_instruct = instruct_hidden_states.shape[1] + + img_q = self.img_to_q(img_hidden_states) + img_k = self.img_to_k(img_hidden_states) + img_v = self.img_to_v(img_hidden_states) + + instruct_q = self.instruct_to_q(instruct_hidden_states) + instruct_k = self.instruct_to_k(instruct_hidden_states) + instruct_v = self.instruct_to_v(instruct_hidden_states) + + # Concatenate instruction first, then image (matches reference processor order). + query = torch.cat([instruct_q, img_q], dim=1) + key = torch.cat([instruct_k, img_k], dim=1) + value = torch.cat([instruct_v, img_v], dim=1) + + query = query.view(batch_size, -1, attn.heads, attn.dim_head) + key = key.view(batch_size, -1, attn.kv_heads, attn.dim_head) + value = value.view(batch_size, -1, attn.kv_heads, attn.dim_head) + + query = attn.norm_q(query) + key = attn.norm_k(key) + + if rotary_emb is not None: + query = apply_rotary_emb(query, rotary_emb) + key = apply_rotary_emb(key, rotary_emb) + + query = query.transpose(1, 2) + 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) + + # Split back to instruction/image, apply per-stream output projections, recombine. + instruct_hidden_states = self.instruct_out(hidden_states[:, :L_instruct]) + img_hidden_states = self.img_out(hidden_states[:, L_instruct:]) + hidden_states = torch.cat([instruct_hidden_states, img_hidden_states], dim=1) + + hidden_states = attn.to_out[0](hidden_states) + return hidden_states + + +class BooguJointAttention(nn.Module): + # Holds the shared q/k RMSNorm + final output projection + def __init__(self, dim, head_dim, heads, kv_heads, eps=1e-5, dtype=None, device=None, operations=None): + super().__init__() + self.heads = heads + self.kv_heads = kv_heads + self.dim_head = head_dim + self.scale = head_dim ** -0.5 + + self.norm_q = operations.RMSNorm(head_dim, eps=eps, dtype=dtype, device=device) + self.norm_k = operations.RMSNorm(head_dim, eps=eps, dtype=dtype, device=device) + self.to_out = nn.Sequential( + operations.Linear(heads * head_dim, dim, bias=False, dtype=dtype, device=device), + nn.Dropout(0.0), + ) + self.processor = BooguDoubleStreamProcessor(dim, head_dim, heads, kv_heads, dtype=dtype, device=device, operations=operations) + + def forward(self, img_hidden_states, instruct_hidden_states, rotary_emb, attention_mask=None, transformer_options={}): + return self.processor(self, img_hidden_states, instruct_hidden_states, rotary_emb, attention_mask, transformer_options=transformer_options) + + +class BooguDoubleStreamBlock(nn.Module): + # Dual-stream block: joint attention over [instruct ; img] + image self-attention, each stream with its own modulation/MLP. + def __init__(self, dim, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, dtype=None, device=None, operations=None): + super().__init__() + head_dim = dim // num_attention_heads + + self.img_instruct_attn = BooguJointAttention(dim, head_dim, num_attention_heads, num_kv_heads, eps=1e-5, dtype=dtype, device=device, operations=operations) + self.img_self_attn = Attention( + query_dim=dim, dim_head=head_dim, heads=num_attention_heads, kv_heads=num_kv_heads, + eps=1e-5, bias=False, dtype=dtype, device=device, operations=operations, + ) + + self.img_feed_forward = LuminaFeedForward(dim=dim, inner_dim=4 * dim, multiple_of=multiple_of, dtype=dtype, device=device, operations=operations) + self.instruct_feed_forward = LuminaFeedForward(dim=dim, inner_dim=4 * dim, multiple_of=multiple_of, dtype=dtype, device=device, operations=operations) + + self.img_norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations) + self.img_norm2 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations) + self.img_norm3 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations) + self.instruct_norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations) + self.instruct_norm2 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations) + + self.img_attn_norm = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + self.img_self_attn_norm = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + self.img_ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + self.img_ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + + self.instruct_attn_norm = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + self.instruct_ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + self.instruct_ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + + def forward(self, img_hidden_states, instruct_hidden_states, joint_rotary_emb, img_rotary_emb, temb, joint_attention_mask=None, img_attention_mask=None, transformer_options={}): + L_instruct = instruct_hidden_states.shape[1] + + img_norm1_out, img_gate_msa, img_scale_mlp, img_gate_mlp = self.img_norm1(img_hidden_states, temb) + img_norm2_out, img_shift_mlp, _, _ = self.img_norm2(img_hidden_states, temb) + img_norm3_out, img_gate_self, _, _ = self.img_norm3(img_hidden_states, temb) + + instruct_norm1_out, instruct_gate_msa, instruct_scale_mlp, instruct_gate_mlp = self.instruct_norm1(instruct_hidden_states, temb) + instruct_norm2_out, instruct_shift_mlp, _, _ = self.instruct_norm2(instruct_hidden_states, temb) + + joint_attn_out = self.img_instruct_attn(img_norm1_out, instruct_norm1_out, joint_rotary_emb, joint_attention_mask, transformer_options=transformer_options) + instruct_attn_out = joint_attn_out[:, :L_instruct] + img_attn_out = joint_attn_out[:, L_instruct:] + + img_self_attn_out = self.img_self_attn(img_norm3_out, img_norm3_out, img_attention_mask, img_rotary_emb, transformer_options=transformer_options) + + img_hidden_states = img_hidden_states + img_gate_msa.unsqueeze(1).tanh() * self.img_attn_norm(img_attn_out) + img_hidden_states = img_hidden_states + img_gate_self.unsqueeze(1).tanh() * self.img_self_attn_norm(img_self_attn_out) + img_mlp_input = (1 + img_scale_mlp.unsqueeze(1)) * img_norm2_out + img_shift_mlp.unsqueeze(1) + img_mlp_out = self.img_feed_forward(self.img_ffn_norm1(img_mlp_input)) + img_hidden_states = img_hidden_states + img_gate_mlp.unsqueeze(1).tanh() * self.img_ffn_norm2(img_mlp_out) + + instruct_hidden_states = instruct_hidden_states + instruct_gate_msa.unsqueeze(1).tanh() * self.instruct_attn_norm(instruct_attn_out) + instruct_mlp_input = (1 + instruct_scale_mlp.unsqueeze(1)) * instruct_norm2_out + instruct_shift_mlp.unsqueeze(1) + instruct_mlp_out = self.instruct_feed_forward(self.instruct_ffn_norm1(instruct_mlp_input)) + instruct_hidden_states = instruct_hidden_states + instruct_gate_mlp.unsqueeze(1).tanh() * self.instruct_ffn_norm2(instruct_mlp_out) + + return img_hidden_states, instruct_hidden_states + + +class BooguTransformer2DModel(nn.Module): + def __init__( + self, + patch_size: int = 2, + in_channels: int = 16, + out_channels: Optional[int] = None, + hidden_size: int = 3360, + num_layers: int = 32, + num_double_stream_layers: int = 8, + num_refiner_layers: int = 2, + num_attention_heads: int = 28, + num_kv_heads: int = 7, + multiple_of: int = 256, + ffn_dim_multiplier: Optional[float] = None, + norm_eps: float = 1e-5, + axes_dim_rope: Tuple[int, int, int] = (40, 40, 40), + axes_lens: Tuple[int, int, int] = (2048, 1664, 1664), + instruction_feat_dim: int = 4096, + timestep_scale: float = 1000.0, + image_model=None, + device=None, dtype=None, operations=None, + ): + super().__init__() + + self.patch_size = patch_size + self.out_channels = out_channels or in_channels + self.hidden_size = hidden_size + self.dtype = dtype + + self.rope_embedder = OmniGen2RotaryPosEmbed( + theta=10000, + axes_dim=axes_dim_rope, + axes_lens=axes_lens, + patch_size=patch_size, + ) + + self.x_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device) + self.ref_image_patch_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device) + + self.time_caption_embed = Lumina2CombinedTimestepCaptionEmbedding( + hidden_size=hidden_size, + text_feat_dim=instruction_feat_dim, + norm_eps=norm_eps, + timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations + ) + + self.noise_refiner = nn.ModuleList([ + OmniGen2TransformerBlock(hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations) + for _ in range(num_refiner_layers) + ]) + + self.ref_image_refiner = nn.ModuleList([ + OmniGen2TransformerBlock(hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations) + for _ in range(num_refiner_layers) + ]) + + self.context_refiner = nn.ModuleList([ + OmniGen2TransformerBlock(hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, modulation=False, dtype=dtype, device=device, operations=operations) + for _ in range(num_refiner_layers) + ]) + + self.double_stream_layers = nn.ModuleList([ + BooguDoubleStreamBlock(hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, dtype=dtype, device=device, operations=operations) + for _ in range(num_double_stream_layers) + ]) + + self.single_stream_layers = nn.ModuleList([ + OmniGen2TransformerBlock(hidden_size, num_attention_heads, num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations) + for _ in range(num_layers) + ]) + + self.norm_out = LuminaLayerNormContinuous( + embedding_dim=hidden_size, + conditioning_embedding_dim=min(hidden_size, 1024), + elementwise_affine=False, + eps=1e-6, + out_dim=patch_size * patch_size * self.out_channels, dtype=dtype, device=device, operations=operations + ) + + self.image_index_embedding = nn.Parameter(torch.empty(5, hidden_size, device=device, dtype=dtype)) + + # Patchify/refine helpers are identical to OmniGen2; reuse via bound methods. + flat_and_pad_to_seq = comfy.ldm.omnigen.omnigen2.OmniGen2Transformer2DModel.flat_and_pad_to_seq + img_patch_embed_and_refine = comfy.ldm.omnigen.omnigen2.OmniGen2Transformer2DModel.img_patch_embed_and_refine + + def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, transformer_options={}, **kwargs): + B, C, H, W = x.shape + hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) + _, _, H_padded, W_padded = hidden_states.shape + timestep = 1.0 - timesteps + text_hidden_states = context + text_attention_mask = attention_mask + ref_image_hidden_states = ref_latents + device = hidden_states.device + + temb, text_hidden_states = self.time_caption_embed(timestep, text_hidden_states, hidden_states[0].dtype) + + ( + hidden_states, ref_image_hidden_states, + img_mask, ref_img_mask, + l_effective_ref_img_len, l_effective_img_len, + ref_img_sizes, img_sizes, + ) = self.flat_and_pad_to_seq(hidden_states, ref_image_hidden_states) + + ( + context_rotary_emb, ref_img_rotary_emb, noise_rotary_emb, + rotary_emb, encoder_seq_lengths, seq_lengths, + ) = self.rope_embedder( + hidden_states.shape[0], text_hidden_states.shape[1], [num_tokens] * text_hidden_states.shape[0], + l_effective_ref_img_len, l_effective_img_len, + ref_img_sizes, img_sizes, device, + ) + + for layer in self.context_refiner: + text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb, transformer_options=transformer_options) + + img_len = hidden_states.shape[1] + combined_img_hidden_states = self.img_patch_embed_and_refine( + hidden_states, ref_image_hidden_states, + img_mask, ref_img_mask, + noise_rotary_emb, ref_img_rotary_emb, + l_effective_ref_img_len, l_effective_img_len, + temb, + transformer_options=transformer_options, + ) + + # Double-stream stage: the image self-attention only sees the [ref ; noise] tokens, + # which sit after the instruction tokens in the joint rope. + L_instruct = text_hidden_states.shape[1] + combined_img_rotary_emb = rotary_emb[:, L_instruct:] + for layer in self.double_stream_layers: + combined_img_hidden_states, text_hidden_states = layer( + combined_img_hidden_states, text_hidden_states, + rotary_emb, combined_img_rotary_emb, temb, + joint_attention_mask=None, img_attention_mask=None, + transformer_options=transformer_options, + ) + + hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1) + + for layer in self.single_stream_layers: + hidden_states = layer(hidden_states, None, rotary_emb, temb, transformer_options=transformer_options) + + hidden_states = self.norm_out(hidden_states, temb) + + p = self.patch_size + output = rearrange(hidden_states[:, -img_len:], 'b (h w) (p1 p2 c) -> b c (h p1) (w p2)', h=H_padded // p, w=W_padded // p, p1=p, p2=p)[:, :, :H, :W] + + return -output diff --git a/comfy/ldm/chroma_radiance/model.py b/comfy/ldm/chroma_radiance/model.py index 4fb56165e..86af98d36 100644 --- a/comfy/ldm/chroma_radiance/model.py +++ b/comfy/ldm/chroma_radiance/model.py @@ -38,6 +38,8 @@ class ChromaRadianceParams(ChromaParams): # None means use the same dtype as the model. nerf_embedder_dtype: Optional[torch.dtype] use_x0: bool + # Use sequential txt_ids instead of zeros + use_sequential_txt_ids: bool class ChromaRadiance(Chroma): """ @@ -162,6 +164,9 @@ class ChromaRadiance(Chroma): if params.use_x0: self.register_buffer("__x0__", torch.tensor([])) + if params.use_sequential_txt_ids: + self.register_buffer("__sequential__", torch.tensor([])) + @property def _nerf_final_layer(self) -> nn.Module: if self.params.nerf_final_head_type == "linear": @@ -313,6 +318,9 @@ class ChromaRadiance(Chroma): img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) + # Radiance after 2026-05-22 uses sequential txt_ids instead of zeros + if params.use_sequential_txt_ids: + txt_ids[:, :, 0] = torch.arange(context.shape[1], device=x.device, dtype=x.dtype).unsqueeze(0).expand(bs, -1) img_out = self.forward_orig( img, diff --git a/comfy/ldm/colormap.py b/comfy/ldm/colormap.py new file mode 100644 index 000000000..1f4d88bd9 --- /dev/null +++ b/comfy/ldm/colormap.py @@ -0,0 +1,25 @@ +"""Colormap utilities for depth and geometry visualisation.""" + +from __future__ import annotations + +import torch + + +def turbo(x: torch.Tensor) -> torch.Tensor: + """Anton Mikhailov polynomial approximation of the Turbo colormap. + + Args: + x: Float tensor with values in [0, 1]. + + Returns: + RGB tensor of the same shape as ``x`` with a trailing size-3 dimension. + """ + x = x.clamp(0.0, 1.0) + x2 = x * x + x3 = x2 * x + x4 = x2 * x2 + x5 = x4 * x + r = 0.13572138 + 4.61539260*x - 42.66032258*x2 + 132.13108234*x3 - 152.94239396*x4 + 59.28637943*x5 + g = 0.09140261 + 2.19418839*x + 4.84296658*x2 - 14.18503333*x3 + 4.27729857*x4 + 2.82956604*x5 + b = 0.10667330 + 12.64194608*x - 60.58204836*x2 + 110.36276771*x3 - 89.90310912*x4 + 27.34824973*x5 + return torch.stack([r, g, b], dim=-1).clamp(0.0, 1.0) diff --git a/comfy/ldm/cosmos/predict2.py b/comfy/ldm/cosmos/predict2.py index 30a36ad49..aec874815 100644 --- a/comfy/ldm/cosmos/predict2.py +++ b/comfy/ldm/cosmos/predict2.py @@ -14,6 +14,7 @@ from torchvision import transforms import comfy.patcher_extension from comfy.ldm.modules.attention import optimized_attention import comfy.ldm.common_dit +import comfy.quant_ops # ---------------------- Feed Forward Network ----------------------- @@ -514,7 +515,7 @@ class Block(nn.Module): h=H, w=W, ) - x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype) + x_B_T_H_W_D = torch.addcmul(x_B_T_H_W_D, gate_self_attn_B_T_1_1_D.to(residual_dtype), result_B_T_H_W_D.to(residual_dtype)) def _x_fn( _x_B_T_H_W_D: torch.Tensor, @@ -547,7 +548,7 @@ class Block(nn.Module): shift_cross_attn_B_T_1_1_D, transformer_options=transformer_options, ) - x_B_T_H_W_D = result_B_T_H_W_D.to(residual_dtype) * gate_cross_attn_B_T_1_1_D.to(residual_dtype) + x_B_T_H_W_D + x_B_T_H_W_D = torch.addcmul(x_B_T_H_W_D, gate_cross_attn_B_T_1_1_D.to(residual_dtype), result_B_T_H_W_D.to(residual_dtype)) normalized_x_B_T_H_W_D = _fn( x_B_T_H_W_D, @@ -556,7 +557,7 @@ class Block(nn.Module): shift_mlp_B_T_1_1_D, ) result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D.to(compute_dtype)) - x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D.to(residual_dtype) * result_B_T_H_W_D.to(residual_dtype) + x_B_T_H_W_D = torch.addcmul(x_B_T_H_W_D, gate_mlp_B_T_1_1_D.to(residual_dtype), result_B_T_H_W_D.to(residual_dtype)) return x_B_T_H_W_D diff --git a/comfy/ldm/depth_anything_3/camera.py b/comfy/ldm/depth_anything_3/camera.py new file mode 100644 index 000000000..65a57d66f --- /dev/null +++ b/comfy/ldm/depth_anything_3/camera.py @@ -0,0 +1,177 @@ +"""Camera-token encoder and decoder for Depth Anything 3.""" + +from __future__ import annotations + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from comfy.ldm.modules.attention import optimized_attention_for_device +from .transform import affine_inverse, extri_intri_to_pose_encoding + + +# ----------------------------------------------------------------------- +# Building blocks (mirror depth_anything_3.model.utils.{attention,block}) +# ----------------------------------------------------------------------- + + +class _Mlp(nn.Module): + """Standard 2-layer MLP with GELU. Matches upstream ``utils.attention.Mlp``.""" + + def __init__(self, in_features, hidden_features=None, out_features=None, *, device=None, dtype=None, operations=None): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = operations.Linear(in_features, hidden_features, bias=True, device=device, dtype=dtype) + self.fc2 = operations.Linear(hidden_features, out_features, bias=True, device=device, dtype=dtype) + + def forward(self, x): + return self.fc2(F.gelu(self.fc1(x))) + + +class _LayerScale(nn.Module): + """Per-channel learnable scaling. Matches upstream LayerScale.""" + + def __init__(self, dim, *, device=None, dtype=None): + super().__init__() + self.gamma = nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) + + def forward(self, x): + return x * self.gamma.to(dtype=x.dtype, device=x.device) + + +class _Attention(nn.Module): + """ Self-attention with fused QKV projection. Mirrors upstream utils.attention.Attention; + Layout matches the HF safetensors (attn.qkv.{weight,bias} and attn.proj.{weight,bias}).""" + + def __init__(self, dim, num_heads, *, device=None, dtype=None, operations=None): + super().__init__() + assert dim % num_heads == 0 + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.qkv = operations.Linear(dim, dim * 3, bias=True, device=device, dtype=dtype) + self.proj = operations.Linear(dim, dim, bias=True, device=device, dtype=dtype) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, C) + q, k, v = qkv.unbind(2) # each (B, N, C) + attn_fn = optimized_attention_for_device(x.device, small_input=True) + out = attn_fn(q, k, v, heads=self.num_heads) + return self.proj(out) + + +class _Block(nn.Module): + """Pre-norm transformer block with LayerScale. Used by :class:CameraEnc. Layout follows upstream utils.block.Block.""" + + def __init__(self, dim, num_heads, mlp_ratio=4, init_values=0.01, *, device=None, dtype=None, operations=None): + super().__init__() + self.norm1 = operations.LayerNorm(dim, device=device, dtype=dtype) + self.attn = _Attention(dim, num_heads, device=device, dtype=dtype, operations=operations) + self.ls1 = _LayerScale(dim, device=device, dtype=dtype) if init_values else nn.Identity() + self.norm2 = operations.LayerNorm(dim, device=device, dtype=dtype) + self.mlp = _Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), device=device, dtype=dtype, operations=operations) + self.ls2 = _LayerScale(dim, device=device, dtype=dtype) if init_values else nn.Identity() + + def forward(self, x): + x = x + self.ls1(self.attn(self.norm1(x))) + x = x + self.ls2(self.mlp(self.norm2(x))) + return x + + +class CameraEnc(nn.Module): + """Encode per-view (extrinsics, intrinsics) into a camera token. + + Maps a 9-D pose-encoding vector through a small MLP up to the backbone's + ``embed_dim``, then runs ``trunk_depth`` transformer blocks. The output + has shape ``(B, S, embed_dim)`` and is injected at block ``alt_start`` + of the DINOv2 backbone in place of the cls token. + + Parameters mirror the upstream ``cam_enc.py`` so HF weights load directly. + """ + + def __init__( + self, + dim_out: int = 1024, + dim_in: int = 9, + trunk_depth: int = 4, + target_dim: int = 9, + num_heads: int = 16, + mlp_ratio: int = 4, + init_values: float = 0.01, + *, + device=None, dtype=None, operations=None, + **_kwargs, + ): + super().__init__() + self.target_dim = target_dim + self.trunk_depth = trunk_depth + self.trunk = nn.Sequential(*[ + _Block(dim_out, num_heads=num_heads, mlp_ratio=mlp_ratio, + init_values=init_values, + device=device, dtype=dtype, operations=operations) + for _ in range(trunk_depth) + ]) + self.token_norm = operations.LayerNorm(dim_out, device=device, dtype=dtype) + self.trunk_norm = operations.LayerNorm(dim_out, device=device, dtype=dtype) + self.pose_branch = _Mlp( + in_features=dim_in, + hidden_features=dim_out // 2, + out_features=dim_out, + device=device, dtype=dtype, operations=operations, + ) + + def forward(self, extrinsics: torch.Tensor, intrinsics: torch.Tensor, + image_size_hw) -> torch.Tensor: + """Encode camera parameters into ``(B, S, dim_out)`` tokens.""" + c2ws = affine_inverse(extrinsics) + pose_encoding = extri_intri_to_pose_encoding(c2ws, intrinsics, image_size_hw) + tokens = self.pose_branch(pose_encoding.to(self.pose_branch.fc1.weight.dtype)) + tokens = self.token_norm(tokens) + tokens = self.trunk(tokens) + tokens = self.trunk_norm(tokens) + return tokens + + +class CameraDec(nn.Module): + """Decode the final cam token into a 9-D pose encoding. + + Output layout: ``[T(3), quat_xyzw(4), fov_h, fov_w]``. The translation is + always predicted by the network; the quaternion and FoV can either be + predicted or supplied via ``camera_encoding`` (used at training time + when GT cameras are available -- not exercised at inference here). + + Parameters mirror the upstream ``cam_dec.py`` so HF weights load directly. + """ + + def __init__(self, dim_in: int = 1536, + *, device=None, dtype=None, operations=None, **_kwargs): + super().__init__() + d = dim_in + self.backbone = nn.Sequential( + operations.Linear(d, d, device=device, dtype=dtype), + nn.ReLU(), + operations.Linear(d, d, device=device, dtype=dtype), + nn.ReLU(), + ) + self.fc_t = operations.Linear(d, 3, device=device, dtype=dtype) + self.fc_qvec = operations.Linear(d, 4, device=device, dtype=dtype) + self.fc_fov = nn.Sequential( + operations.Linear(d, 2, device=device, dtype=dtype), + nn.ReLU(), + ) + + def forward(self, feat: torch.Tensor, + camera_encoding: "torch.Tensor | None" = None) -> torch.Tensor: + """Decode ``(B, N, dim_in)`` cam tokens into ``(B, N, 9)`` pose enc.""" + B, N = feat.shape[:2] + feat = feat.reshape(B * N, -1) + feat = self.backbone(feat) + out_t = self.fc_t(feat.float()).reshape(B, N, 3) + if camera_encoding is None: + out_qvec = self.fc_qvec(feat.float()).reshape(B, N, 4) + out_fov = self.fc_fov(feat.float()).reshape(B, N, 2) + else: + out_qvec = camera_encoding[..., 3:7] + out_fov = camera_encoding[..., -2:] + return torch.cat([out_t, out_qvec, out_fov], dim=-1) diff --git a/comfy/ldm/depth_anything_3/dpt.py b/comfy/ldm/depth_anything_3/dpt.py new file mode 100644 index 000000000..fb940873b --- /dev/null +++ b/comfy/ldm/depth_anything_3/dpt.py @@ -0,0 +1,489 @@ +"""DPT / DualDPT heads for Depth Anything 3.""" + +from __future__ import annotations + +from typing import List, Optional, Sequence, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class Permute(nn.Module): + def __init__(self, dims: Tuple[int, ...]): + super().__init__() + self.dims = dims + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return x.permute(*self.dims) + + +def _custom_interpolate( + x: torch.Tensor, + size: Optional[Tuple[int, int]] = None, + scale_factor: Optional[float] = None, + mode: str = "bilinear", + align_corners: bool = True, +) -> torch.Tensor: + if size is None: + assert scale_factor is not None + size = (int(x.shape[-2] * scale_factor), int(x.shape[-1] * scale_factor)) + INT_MAX = 1610612736 + total = size[0] * size[1] * x.shape[0] * x.shape[1] + if total > INT_MAX: + chunks = torch.chunk(x, chunks=(total // INT_MAX) + 1, dim=0) + outs = [F.interpolate(c, size=size, mode=mode, align_corners=align_corners) for c in chunks] + return torch.cat(outs, dim=0).contiguous() + return F.interpolate(x, size=size, mode=mode, align_corners=align_corners) + + +def _create_uv_grid(width: int, height: int, aspect_ratio: float, dtype, device) -> torch.Tensor: + """Normalised UV grid spanning (-x_span, -y_span)..(x_span, y_span).""" + diag_factor = (aspect_ratio ** 2 + 1.0) ** 0.5 + span_x = aspect_ratio / diag_factor + span_y = 1.0 / diag_factor + left_x = -span_x * (width - 1) / width + right_x = span_x * (width - 1) / width + top_y = -span_y * (height - 1) / height + bottom_y = span_y * (height - 1) / height + x_coords = torch.linspace(left_x, right_x, steps=width, dtype=dtype, device=device) + y_coords = torch.linspace(top_y, bottom_y, steps=height, dtype=dtype, device=device) + uu, vv = torch.meshgrid(x_coords, y_coords, indexing="xy") + return torch.stack((uu, vv), dim=-1) # (H, W, 2) + + +def _make_sincos_pos_embed(embed_dim: int, pos: torch.Tensor, omega_0: float = 100.0) -> torch.Tensor: + omega = torch.arange(embed_dim // 2, dtype=torch.float32, device=pos.device) + omega = 1.0 / omega_0 ** (omega / (embed_dim / 2.0)) + pos = pos.reshape(-1) + out = torch.einsum("m,d->md", pos, omega) + return torch.cat([out.sin(), out.cos()], dim=1).float() + + +def _position_grid_to_embed(pos_grid: torch.Tensor, embed_dim: int, omega_0: float = 100.0) -> torch.Tensor: + H, W, _ = pos_grid.shape + pos_flat = pos_grid.reshape(-1, 2) + emb_x = _make_sincos_pos_embed(embed_dim // 2, pos_flat[:, 0], omega_0=omega_0) + emb_y = _make_sincos_pos_embed(embed_dim // 2, pos_flat[:, 1], omega_0=omega_0) + emb = torch.cat([emb_x, emb_y], dim=-1) + return emb.view(H, W, embed_dim) + + +def _add_pos_embed(x: torch.Tensor, W: int, H: int, ratio: float = 0.1) -> torch.Tensor: + """Stateless UV positional embedding added to a feature map (B, C, h, w).""" + pw, ph = x.shape[-1], x.shape[-2] + pe = _create_uv_grid(pw, ph, aspect_ratio=W / H, dtype=x.dtype, device=x.device) + pe = _position_grid_to_embed(pe, x.shape[1]) * ratio + pe = pe.permute(2, 0, 1)[None].expand(x.shape[0], -1, -1, -1).to(dtype=x.dtype) + return x + pe + + +def _apply_activation(x: torch.Tensor, activation: str) -> torch.Tensor: + act = (activation or "linear").lower() + if act == "exp": + return torch.exp(x) + if act == "expp1": + return torch.exp(x) + 1 + if act == "expm1": + return torch.expm1(x) + if act == "relu": + return torch.relu(x) + if act == "sigmoid": + return torch.sigmoid(x) + if act == "softplus": + return F.softplus(x) + if act == "tanh": + return torch.tanh(x) + return x + + +# ----------------------------------------------------------------------------- +# Fusion building blocks +# ----------------------------------------------------------------------------- + + +class ResidualConvUnit(nn.Module): + def __init__(self, features: int, device=None, dtype=None, operations=None): + super().__init__() + self.conv1 = operations.Conv2d(features, features, 3, 1, 1, bias=True, device=device, dtype=dtype) + self.conv2 = operations.Conv2d(features, features, 3, 1, 1, bias=True, device=device, dtype=dtype) + self.activation = nn.ReLU(inplace=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + out = self.activation(x) + out = self.conv1(out) + out = self.activation(out) + out = self.conv2(out) + return out + x + + +class FeatureFusionBlock(nn.Module): + def __init__(self, features: int, has_residual: bool = True, align_corners: bool = True, device=None, dtype=None, operations=None): + super().__init__() + self.align_corners = align_corners + self.has_residual = has_residual + if has_residual: + self.resConfUnit1 = ResidualConvUnit(features, device=device, dtype=dtype, operations=operations) + else: + self.resConfUnit1 = None + self.resConfUnit2 = ResidualConvUnit(features, device=device, dtype=dtype, operations=operations) + self.out_conv = operations.Conv2d(features, features, 1, 1, 0, bias=True, device=device, dtype=dtype) + + def forward(self, *xs: torch.Tensor, size: Optional[Tuple[int, int]] = None) -> torch.Tensor: + y = xs[0] + if self.has_residual and len(xs) > 1 and self.resConfUnit1 is not None: + y = y + self.resConfUnit1(xs[1]) + y = self.resConfUnit2(y) + if size is None: + up_kwargs = {"scale_factor": 2.0} + else: + up_kwargs = {"size": size} + y = _custom_interpolate(y, **up_kwargs, mode="bilinear", align_corners=self.align_corners) + y = self.out_conv(y) + return y + + +class _Scratch(nn.Module): + """Container that mirrors upstream ``scratch`` attribute layout.""" + + +def _make_scratch(in_shape: List[int], out_shape: int, device=None, dtype=None, operations=None) -> _Scratch: + scratch = _Scratch() + scratch.layer1_rn = operations.Conv2d(in_shape[0], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype) + scratch.layer2_rn = operations.Conv2d(in_shape[1], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype) + scratch.layer3_rn = operations.Conv2d(in_shape[2], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype) + scratch.layer4_rn = operations.Conv2d(in_shape[3], out_shape, 3, 1, 1, bias=False, device=device, dtype=dtype) + return scratch + + +def _make_fusion_block(features: int, has_residual: bool = True, device=None, dtype=None, operations=None) -> FeatureFusionBlock: + return FeatureFusionBlock(features, has_residual=has_residual, align_corners=True, device=device, dtype=dtype, operations=operations) + + +# ----------------------------------------------------------------------------- +# DPT (single head + optional sky head) -- used by DA3Mono/Metric +# ----------------------------------------------------------------------------- + + +class DPT(nn.Module): + """Single-head DPT used by DA3Mono-Large and DA3Metric-Large.""" + + def __init__( + self, + dim_in: int, + patch_size: int = 14, + output_dim: int = 1, + activation: str = "exp", + conf_activation: str = "expp1", + features: int = 256, + out_channels: Sequence[int] = (256, 512, 1024, 1024), + pos_embed: bool = False, + down_ratio: int = 1, + head_name: str = "depth", + use_sky_head: bool = True, + sky_name: str = "sky", + sky_activation: str = "relu", + norm_type: str = "idt", + device=None, dtype=None, operations=None, + ): + super().__init__() + self.patch_size = patch_size + self.activation = activation + self.conf_activation = conf_activation + self.pos_embed = pos_embed + self.down_ratio = down_ratio + self.head_main = head_name + self.sky_name = sky_name + self.out_dim = output_dim + self.has_conf = output_dim > 1 + self.use_sky_head = use_sky_head + self.sky_activation = sky_activation + self.intermediate_layer_idx: Tuple[int, int, int, int] = (0, 1, 2, 3) + + if norm_type == "layer": + self.norm = operations.LayerNorm(dim_in, device=device, dtype=dtype) + else: + self.norm = nn.Identity() + + out_channels = list(out_channels) + self.projects = nn.ModuleList([ + operations.Conv2d(dim_in, oc, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype) + for oc in out_channels + ]) + self.resize_layers = nn.ModuleList([ + operations.ConvTranspose2d(out_channels[0], out_channels[0], kernel_size=4, stride=4, padding=0, device=device, dtype=dtype), + operations.ConvTranspose2d(out_channels[1], out_channels[1], kernel_size=2, stride=2, padding=0, device=device, dtype=dtype), + nn.Identity(), + operations.Conv2d(out_channels[3], out_channels[3], kernel_size=3, stride=2, padding=1, device=device, dtype=dtype), + ]) + + self.scratch = _make_scratch(out_channels, features, device=device, dtype=dtype, operations=operations) + self.scratch.refinenet1 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations) + self.scratch.refinenet2 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations) + self.scratch.refinenet3 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations) + self.scratch.refinenet4 = _make_fusion_block(features, has_residual=False, device=device, dtype=dtype, operations=operations) + + head_features_1 = features + head_features_2 = 32 + self.scratch.output_conv1 = operations.Conv2d( + head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1, + device=device, dtype=dtype, + ) + self.scratch.output_conv2 = nn.Sequential( + operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype), + nn.ReLU(inplace=False), + operations.Conv2d(head_features_2, output_dim, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype), + ) + + if self.use_sky_head: + self.scratch.sky_output_conv2 = nn.Sequential( + operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype), + nn.ReLU(inplace=False), + operations.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype), + ) + + def forward(self, feats: List[torch.Tensor], H: int, W: int, patch_start_idx: int = 0, **_kwargs) -> dict: + # feats[i][0] is the patch-token tensor with shape (B, S, N_patch, C) + B, S, N, C = feats[0][0].shape + feats_flat = [feat[0].reshape(B * S, N, C) for feat in feats] + + ph, pw = H // self.patch_size, W // self.patch_size + resized = [] + for stage_idx, take_idx in enumerate(self.intermediate_layer_idx): + x = feats_flat[take_idx][:, patch_start_idx:] + x = self.norm(x) + x = x.permute(0, 2, 1).contiguous().reshape(B * S, C, ph, pw) + x = self.projects[stage_idx](x) + if self.pos_embed: + x = _add_pos_embed(x, W, H) + x = self.resize_layers[stage_idx](x) + resized.append(x) + + l1_rn = self.scratch.layer1_rn(resized[0]) + l2_rn = self.scratch.layer2_rn(resized[1]) + l3_rn = self.scratch.layer3_rn(resized[2]) + l4_rn = self.scratch.layer4_rn(resized[3]) + + out = self.scratch.refinenet4(l4_rn, size=l3_rn.shape[2:]) + out = self.scratch.refinenet3(out, l3_rn, size=l2_rn.shape[2:]) + out = self.scratch.refinenet2(out, l2_rn, size=l1_rn.shape[2:]) + out = self.scratch.refinenet1(out, l1_rn) + + h_out = int(ph * self.patch_size / self.down_ratio) + w_out = int(pw * self.patch_size / self.down_ratio) + + fused = self.scratch.output_conv1(out) + fused = _custom_interpolate(fused, (h_out, w_out), mode="bilinear", align_corners=True) + if self.pos_embed: + fused = _add_pos_embed(fused, W, H) + feat = fused + + main_logits = self.scratch.output_conv2(feat) + outs = {} + if self.has_conf: + fmap = main_logits.permute(0, 2, 3, 1) + pred = _apply_activation(fmap[..., :-1], self.activation) + conf = _apply_activation(fmap[..., -1], self.conf_activation) + outs[self.head_main] = pred.squeeze(-1).view(B, S, *pred.shape[1:-1]) + outs[f"{self.head_main}_conf"] = conf.view(B, S, *conf.shape[1:]) + else: + pred = _apply_activation(main_logits, self.activation) + outs[self.head_main] = pred.squeeze(1).view(B, S, *pred.shape[2:]) + + if self.use_sky_head: + sky_logits = self.scratch.sky_output_conv2(feat) + if self.sky_activation.lower() == "sigmoid": + sky = torch.sigmoid(sky_logits) + elif self.sky_activation.lower() == "relu": + sky = F.relu(sky_logits) + else: + sky = sky_logits + outs[self.sky_name] = sky.squeeze(1).view(B, S, *sky.shape[2:]) + + return outs + + +# ----------------------------------------------------------------------------- +# DualDPT (depth + auxiliary "ray" head) -- used by DA3-Small / DA3-Base +# ----------------------------------------------------------------------------- + + +class DualDPT(nn.Module): + """Two-head DPT used by DA3-Small / DA3-Base.""" + + def __init__( + self, + dim_in: int, + patch_size: int = 14, + output_dim: int = 2, + activation: str = "exp", + conf_activation: str = "expp1", + features: int = 256, + out_channels: Sequence[int] = (256, 512, 1024, 1024), + pos_embed: bool = True, + down_ratio: int = 1, + aux_pyramid_levels: int = 4, + aux_out1_conv_num: int = 5, + head_names: Tuple[str, str] = ("depth", "ray"), + device=None, dtype=None, operations=None, + ): + super().__init__() + self.patch_size = patch_size + self.activation = activation + self.conf_activation = conf_activation + self.pos_embed = pos_embed + self.down_ratio = down_ratio + self.aux_levels = aux_pyramid_levels + self.aux_out1_conv_num = aux_out1_conv_num + self.head_main, self.head_aux = head_names + self.intermediate_layer_idx: Tuple[int, int, int, int] = (0, 1, 2, 3) + # Toggle the auxiliary ray branch at runtime. Default off (mono path). + # DepthAnything3Net flips this on when running multi-view + ray-pose. + self.enable_aux: bool = False + + self.norm = operations.LayerNorm(dim_in, device=device, dtype=dtype) + out_channels = list(out_channels) + self.projects = nn.ModuleList([ + operations.Conv2d(dim_in, oc, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype) + for oc in out_channels + ]) + self.resize_layers = nn.ModuleList([ + operations.ConvTranspose2d(out_channels[0], out_channels[0], kernel_size=4, stride=4, padding=0, device=device, dtype=dtype), + operations.ConvTranspose2d(out_channels[1], out_channels[1], kernel_size=2, stride=2, padding=0, device=device, dtype=dtype), + nn.Identity(), + operations.Conv2d(out_channels[3], out_channels[3], kernel_size=3, stride=2, padding=1, device=device, dtype=dtype), + ]) + + self.scratch = _make_scratch(out_channels, features, device=device, dtype=dtype, operations=operations) + # Main fusion chain + self.scratch.refinenet1 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations) + self.scratch.refinenet2 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations) + self.scratch.refinenet3 = _make_fusion_block(features, device=device, dtype=dtype, operations=operations) + self.scratch.refinenet4 = _make_fusion_block(features, has_residual=False, device=device, dtype=dtype, operations=operations) + # Auxiliary fusion chain (separate copies) + self.scratch.refinenet1_aux = _make_fusion_block(features, device=device, dtype=dtype, operations=operations) + self.scratch.refinenet2_aux = _make_fusion_block(features, device=device, dtype=dtype, operations=operations) + self.scratch.refinenet3_aux = _make_fusion_block(features, device=device, dtype=dtype, operations=operations) + self.scratch.refinenet4_aux = _make_fusion_block(features, has_residual=False, device=device, dtype=dtype, operations=operations) + + head_features_1 = features + head_features_2 = 32 + + # Main head neck + final projection + self.scratch.output_conv1 = operations.Conv2d( + head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1, + device=device, dtype=dtype, + ) + self.scratch.output_conv2 = nn.Sequential( + operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype), + nn.ReLU(inplace=False), + operations.Conv2d(head_features_2, output_dim, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype), + ) + + # Aux pre-head per level (multi-level pyramid) + self.scratch.output_conv1_aux = nn.ModuleList([ + self._make_aux_out1_block(head_features_1, device=device, dtype=dtype, operations=operations) + for _ in range(self.aux_levels) + ]) + + # Aux final projection per level (includes LayerNorm permute path). + ln_seq = [Permute((0, 2, 3, 1)), + operations.LayerNorm(head_features_2, device=device, dtype=dtype), + Permute((0, 3, 1, 2))] + self.scratch.output_conv2_aux = nn.ModuleList([ + nn.Sequential( + operations.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1, device=device, dtype=dtype), + *ln_seq, + nn.ReLU(inplace=False), + operations.Conv2d(head_features_2, 7, kernel_size=1, stride=1, padding=0, device=device, dtype=dtype), + ) + for _ in range(self.aux_levels) + ]) + + @staticmethod + def _make_aux_out1_block(in_ch: int, *, device=None, dtype=None, operations=None) -> nn.Sequential: + # aux_out1_conv_num=5 in all Apache-2.0 variants. + return nn.Sequential( + operations.Conv2d(in_ch, in_ch // 2, 3, 1, 1, device=device, dtype=dtype), + operations.Conv2d(in_ch // 2, in_ch, 3, 1, 1, device=device, dtype=dtype), + operations.Conv2d(in_ch, in_ch // 2, 3, 1, 1, device=device, dtype=dtype), + operations.Conv2d(in_ch // 2, in_ch, 3, 1, 1, device=device, dtype=dtype), + operations.Conv2d(in_ch, in_ch // 2, 3, 1, 1, device=device, dtype=dtype), + ) + + def forward(self, feats: List[torch.Tensor], H: int, W: int, patch_start_idx: int = 0, **_kwargs) -> dict: + B, S, N, C = feats[0][0].shape + feats_flat = [feat[0].reshape(B * S, N, C) for feat in feats] + + ph, pw = H // self.patch_size, W // self.patch_size + resized = [] + for stage_idx, take_idx in enumerate(self.intermediate_layer_idx): + x = feats_flat[take_idx][:, patch_start_idx:] + x = self.norm(x) + x = x.permute(0, 2, 1).contiguous().reshape(B * S, C, ph, pw) + x = self.projects[stage_idx](x) + if self.pos_embed: + x = _add_pos_embed(x, W, H) + x = self.resize_layers[stage_idx](x) + resized.append(x) + + l1_rn = self.scratch.layer1_rn(resized[0]) + l2_rn = self.scratch.layer2_rn(resized[1]) + l3_rn = self.scratch.layer3_rn(resized[2]) + l4_rn = self.scratch.layer4_rn(resized[3]) + + # Main pyramid (output_conv1 is applied inside the upstream `_fuse`, + # before interpolation -- replicate that order here). + m = self.scratch.refinenet4(l4_rn, size=l3_rn.shape[2:]) + if self.enable_aux: + a4 = self.scratch.refinenet4_aux(l4_rn, size=l3_rn.shape[2:]) + aux_pyr = [a4] + m = self.scratch.refinenet3(m, l3_rn, size=l2_rn.shape[2:]) + if self.enable_aux: + aux_pyr.append(self.scratch.refinenet3_aux(aux_pyr[-1], l3_rn, size=l2_rn.shape[2:])) + m = self.scratch.refinenet2(m, l2_rn, size=l1_rn.shape[2:]) + if self.enable_aux: + aux_pyr.append(self.scratch.refinenet2_aux(aux_pyr[-1], l2_rn, size=l1_rn.shape[2:])) + m = self.scratch.refinenet1(m, l1_rn) + if self.enable_aux: + aux_pyr.append(self.scratch.refinenet1_aux(aux_pyr[-1], l1_rn)) + m = self.scratch.output_conv1(m) + + h_out = int(ph * self.patch_size / self.down_ratio) + w_out = int(pw * self.patch_size / self.down_ratio) + + m = _custom_interpolate(m, (h_out, w_out), mode="bilinear", align_corners=True) + if self.pos_embed: + m = _add_pos_embed(m, W, H) + main_logits = self.scratch.output_conv2(m) + fmap = main_logits.permute(0, 2, 3, 1) + depth_pred = _apply_activation(fmap[..., :-1], self.activation) + depth_conf = _apply_activation(fmap[..., -1], self.conf_activation) + + outs = { + self.head_main: depth_pred.squeeze(-1).view(B, S, *depth_pred.shape[1:-1]), + f"{self.head_main}_conf": depth_conf.view(B, S, *depth_conf.shape[1:]), + } + + if self.enable_aux: + # Auxiliary "ray" head (multi-level inside) -- only the last level + # is returned. Mirrors upstream ``DualDPT._fuse`` + ``_forward_impl``: + # each aux pyramid level goes through ``output_conv1_aux[i]`` + # (5-layer conv stack that ends at ``features // 2`` channels), + # then the last level optionally gets a pos-embed and finally + # ``output_conv2_aux[-1]``. + aux_processed = [ + self.scratch.output_conv1_aux[i](a) for i, a in enumerate(aux_pyr) + ] + last_aux = aux_processed[-1] + if self.pos_embed: + last_aux = _add_pos_embed(last_aux, W, H) + last_aux_logits = self.scratch.output_conv2_aux[-1](last_aux) + fmap_last = last_aux_logits.permute(0, 2, 3, 1) + # Channels: [ray(6), ray_conf(1)]; ray uses 'linear' activation. + aux_pred = fmap_last[..., :-1] + aux_conf = _apply_activation(fmap_last[..., -1], self.conf_activation) + outs[self.head_aux] = aux_pred.view(B, S, *aux_pred.shape[1:]) + outs[f"{self.head_aux}_conf"] = aux_conf.view(B, S, *aux_conf.shape[1:]) + + return outs diff --git a/comfy/ldm/depth_anything_3/model.py b/comfy/ldm/depth_anything_3/model.py new file mode 100644 index 000000000..f3c8a5ee3 --- /dev/null +++ b/comfy/ldm/depth_anything_3/model.py @@ -0,0 +1,236 @@ +from __future__ import annotations + +from typing import Dict, Optional, Sequence + +import torch +import torch.nn as nn + +from comfy.image_encoders.dino2 import Dinov2Model + +from .camera import CameraDec, CameraEnc +from .dpt import DPT, DualDPT +from .ray_pose import get_extrinsic_from_camray +from .transform import affine_inverse, pose_encoding_to_extri_intri + + +_HEAD_REGISTRY = { + "dpt": DPT, + "dualdpt": DualDPT, +} + + +# Backbone presets (mirror the upstream DINOv2 ViT variants). +_BACKBONE_PRESETS = { + "vits": dict(hidden_size=384, num_hidden_layers=12, num_attention_heads=6, use_swiglu_ffn=False), + "vitb": dict(hidden_size=768, num_hidden_layers=12, num_attention_heads=12, use_swiglu_ffn=False), + "vitl": dict(hidden_size=1024, num_hidden_layers=24, num_attention_heads=16, use_swiglu_ffn=False), + "vitg": dict(hidden_size=1536, num_hidden_layers=40, num_attention_heads=24, use_swiglu_ffn=True), +} + + +def _build_backbone_config( + backbone_name: str, + *, + alt_start: int, + qknorm_start: int, + rope_start: int, + cat_token: bool, +) -> dict: + if backbone_name not in _BACKBONE_PRESETS: + raise ValueError(f"Unknown DINOv2 backbone variant: {backbone_name!r}") + cfg = dict(_BACKBONE_PRESETS[backbone_name]) + cfg.update(dict( + layer_norm_eps=1e-6, + patch_size=14, + image_size=518, + # No mask_token in DA3 weights; omit param to avoid load warnings. + use_mask_token=False, + alt_start=alt_start, + qknorm_start=qknorm_start, + rope_start=rope_start, + cat_token=cat_token, + rope_freq=100.0, + )) + return cfg + + +class DepthAnything3Net(nn.Module): + + PATCH_SIZE = 14 + + def __init__( + self, + # --- Backbone --- + backbone_name: str = "vitl", + out_layers: Sequence[int] = (4, 11, 17, 23), + alt_start: int = -1, + qknorm_start: int = -1, + rope_start: int = -1, + cat_token: bool = False, + # --- Head --- + head_type: str = "dpt", # dpt or dualdpt + head_dim_in: int = 1024, + head_output_dim: int = 1, # 1 = depth only, 2 = depth+conf + head_features: int = 256, + head_out_channels: Sequence[int] = (256, 512, 1024, 1024), + head_use_sky_head: bool = True, # ignored by DualDPT + head_pos_embed: Optional[bool] = None, # default: True for DualDPT, False for DPT + # --- Camera (multi-view) --- + has_cam_enc: bool = False, + has_cam_dec: bool = False, + cam_dim_out: Optional[int] = None, # CameraEnc dim_out (defaults to embed_dim) + cam_dec_dim_in: Optional[int] = None, # CameraDec dim_in (defaults to 2*embed_dim with cat_token) + # ComfyUI plumbing + device=None, dtype=None, operations=None, + **_ignored, + ): + super().__init__() + head_cls = _HEAD_REGISTRY[head_type.lower()] + self.head_type = head_type.lower() + self.has_sky = (self.head_type == "dpt") and head_use_sky_head + self.has_conf = head_output_dim > 1 + self.out_layers = list(out_layers) + + backbone_cfg = _build_backbone_config( + backbone_name, + alt_start=alt_start, + qknorm_start=qknorm_start, + rope_start=rope_start, + cat_token=cat_token, + ) + self.backbone = Dinov2Model(backbone_cfg, dtype, device, operations) + + head_kwargs = dict( + dim_in=head_dim_in, + patch_size=self.PATCH_SIZE, + output_dim=head_output_dim, + features=head_features, + out_channels=tuple(head_out_channels), + device=device, dtype=dtype, operations=operations, + ) + if self.head_type == "dpt": + head_kwargs.update( + use_sky_head=head_use_sky_head, + pos_embed=(False if head_pos_embed is None else head_pos_embed), + ) + else: # dualdpt + head_kwargs.update( + pos_embed=(True if head_pos_embed is None else head_pos_embed), + ) + self.head = head_cls(**head_kwargs) + + # Built only if checkpoint has weights; cam_enc output dim == embed_dim. + embed_dim = backbone_cfg["hidden_size"] + if has_cam_enc: + self.cam_enc = CameraEnc( + dim_out=cam_dim_out if cam_dim_out is not None else embed_dim, + num_heads=max(1, embed_dim // 64), + device=device, dtype=dtype, operations=operations, + ) + else: + self.cam_enc = None + if has_cam_dec: + default_dim = embed_dim * (2 if cat_token else 1) + self.cam_dec = CameraDec( + dim_in=cam_dec_dim_in if cam_dec_dim_in is not None else default_dim, + device=device, dtype=dtype, operations=operations, + ) + else: + self.cam_dec = None + + self.dtype = dtype + + def forward( + self, + image: torch.Tensor, + extrinsics: Optional[torch.Tensor] = None, + intrinsics: Optional[torch.Tensor] = None, + *, + use_ray_pose: bool = False, + ref_view_strategy: str = "saddle_balanced", + export_feat_layers: Optional[Sequence[int]] = None, + **_unused, + ) -> Dict[str, torch.Tensor]: + """Run depth and optionally pose prediction.""" + if image.ndim == 4: + image = image.unsqueeze(1) # (B, 1, 3, H, W) + assert image.ndim == 5 and image.shape[2] == 3, \ + f"image must be (B,3,H,W) or (B,S,3,H,W); got {tuple(image.shape)}" + + B, S, _, H, W = image.shape + assert H % self.PATCH_SIZE == 0 and W % self.PATCH_SIZE == 0, \ + f"image H,W must be multiples of {self.PATCH_SIZE}; got {(H, W)}" + + # Camera-token preparation (multi-view path). + cam_token = None + if extrinsics is not None and intrinsics is not None and self.cam_enc is not None: + cam_token = self.cam_enc(extrinsics, intrinsics, (H, W)) + + # Toggle aux ray output on/off depending on what the caller asked for. + if isinstance(self.head, DualDPT): + self.head.enable_aux = bool(use_ray_pose) + + feats, aux_feats = self.backbone.get_intermediate_layers_da3( + image, self.out_layers, cam_token=cam_token, + ref_view_strategy=ref_view_strategy, + export_feat_layers=export_feat_layers, + ) + head_out = self.head(feats, H=H, W=W, patch_start_idx=0) + + # Pose prediction. + out: Dict[str, torch.Tensor] = {} + if use_ray_pose and "ray" in head_out and "ray_conf" in head_out: + ray = head_out["ray"] + ray_conf = head_out["ray_conf"] + extr_c2w, focal, pp = get_extrinsic_from_camray( + ray, ray_conf, ray.shape[-3], ray.shape[-2], + ) + # Match the upstream output: w2c, drop the homogeneous row. + extr_w2c = affine_inverse(extr_c2w)[:, :, :3, :] + # Build pixel-space intrinsics from the normalised focal/pp output. + intr = torch.eye(3, device=ray.device, dtype=ray.dtype) + intr = intr[None, None].expand(extr_c2w.shape[0], extr_c2w.shape[1], 3, 3).clone() + intr[:, :, 0, 0] = focal[:, :, 0] / 2 * W + intr[:, :, 1, 1] = focal[:, :, 1] / 2 * H + intr[:, :, 0, 2] = pp[:, :, 0] * W * 0.5 + intr[:, :, 1, 2] = pp[:, :, 1] * H * 0.5 + out["extrinsics"] = extr_w2c + out["intrinsics"] = intr + elif self.cam_dec is not None and S > 1: + # Decode the cam-token of the final out_layer into a pose encoding. + cam_feat = feats[-1][1] # (B, S, dim_in_to_cam_dec) + pose_enc = self.cam_dec(cam_feat) + c2w_3x4, intr = pose_encoding_to_extri_intri(pose_enc, (H, W)) + # Match the upstream output convention: w2c (world->camera), 3x4. + c2w_4x4 = torch.cat([ + c2w_3x4, + torch.tensor([0, 0, 0, 1], device=c2w_3x4.device, dtype=c2w_3x4.dtype) + .view(1, 1, 1, 4).expand(B, S, 1, 4), + ], dim=-2) + out["extrinsics"] = affine_inverse(c2w_4x4)[:, :, :3, :] + out["intrinsics"] = intr + + # Flatten the views axis for per-pixel outputs (depth/conf/sky) so the + # per-image consumer keeps its (B*S, H, W) interface. + for k, v in head_out.items(): + if k in ("ray", "ray_conf"): + # Keep multi-view shape for downstream pose work. + out[k] = v + elif v.ndim >= 3 and v.shape[0] == B and v.shape[1] == S: + out[k] = v.reshape(B * S, *v.shape[2:]) + else: + out[k] = v + + if export_feat_layers: + out["aux_features"] = self._reshape_aux_features(aux_feats, H, W) + return out + + def _reshape_aux_features(self, aux_feats, H: int, W: int): + """Reshape (B, S, N, C) aux features into (B, S, h_p, w_p, C).""" + ph, pw = H // self.PATCH_SIZE, W // self.PATCH_SIZE + out = [] + for f in aux_feats: + B, S, N, C = f.shape + assert N == ph * pw, f"aux feature seq mismatch: {N} != {ph}*{pw}" + out.append(f.reshape(B, S, ph, pw, C)) + return out diff --git a/comfy/ldm/depth_anything_3/preprocess.py b/comfy/ldm/depth_anything_3/preprocess.py new file mode 100644 index 000000000..2238bd0d6 --- /dev/null +++ b/comfy/ldm/depth_anything_3/preprocess.py @@ -0,0 +1,128 @@ +"""Input/output preprocessing helpers for Depth Anything 3.""" + +from __future__ import annotations + +from typing import Tuple + +import torch + +import comfy.utils + +PATCH_SIZE = 14 + +# ImageNet normalization constants used during DA3 training. +_IMAGENET_MEAN = torch.tensor([0.485, 0.456, 0.406]) +_IMAGENET_STD = torch.tensor([0.229, 0.224, 0.225]) + + +def _round_to_patch(x: int, patch: int = PATCH_SIZE) -> int: + down = (x // patch) * patch + up = down + patch + return up if abs(up - x) <= abs(x - down) else down + + +def compute_target_size(orig_h: int, orig_w: int, process_res: int, method: str = "upper_bound_resize") -> Tuple[int, int]: + """Compute (target_h, target_w) for a single image. + upper_bound_resize: scale longest side to process_res, then round each dim to nearest multiple of 14 (default upstream method). + lower_bound_resize: scale shortest side to process_res, then round.""" + + if method == "upper_bound_resize": + longest = max(orig_h, orig_w) + scale = process_res / float(longest) + elif method == "lower_bound_resize": + shortest = min(orig_h, orig_w) + scale = process_res / float(shortest) + else: + raise ValueError(f"Unsupported process_res_method: {method}") + + new_w = max(1, _round_to_patch(int(round(orig_w * scale)))) + new_h = max(1, _round_to_patch(int(round(orig_h * scale)))) + return new_h, new_w + + +def preprocess_image(image: torch.Tensor, process_res: int = 504, method: str = "upper_bound_resize") -> torch.Tensor: + assert image.ndim == 4 and image.shape[-1] == 3, f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}" + B, H, W, _ = image.shape + target_h, target_w = compute_target_size(H, W, process_res, method) + + # (B, H, W, 3) -> (B, 3, H, W) + x = image.movedim(-1, 1).contiguous() + if (target_h, target_w) != (H, W): + # Upstream uses cv2 INTER_CUBIC (upscale) / INTER_AREA (downscale). + # Lanczos in ``common_upscale`` is anti-aliased and produces the + # closest pixel-wise match in a sweep across {bilinear, bicubic, + # area, lanczos, bislerp}. Used in both directions for simplicity. + x = comfy.utils.common_upscale(x.float(), target_w, target_h, "lanczos", "disabled",) + x = x.clamp(0.0, 1.0) + + mean = _IMAGENET_MEAN.to(device=x.device, dtype=x.dtype).view(1, 3, 1, 1) + std = _IMAGENET_STD.to(device=x.device, dtype=x.dtype).view(1, 3, 1, 1) + x = (x - mean) / std + return x + + +# ----------------------------------------------------------------------------- +# Output post-processing (sky-aware clipping for Mono/Metric variants) +# ----------------------------------------------------------------------------- + + +def compute_non_sky_mask(sky_prediction: torch.Tensor, threshold: float = 0.3) -> torch.Tensor: + """Boolean mask: True for non-sky pixels (sky probability < threshold).""" + return sky_prediction < threshold + + +def apply_sky_aware_clip(depth: torch.Tensor, sky: torch.Tensor, threshold: float = 0.3, quantile: float = 0.99) -> torch.Tensor: + """Clips sky regions to the 99th percentile of non-sky depth. Returns a new depth tensor.""" + non_sky = compute_non_sky_mask(sky, threshold=threshold) + if non_sky.sum() <= 10 or (~non_sky).sum() <= 10: + return depth.clone() + + non_sky_depth = depth[non_sky] + if non_sky_depth.numel() > 100_000: + idx = torch.randint(0, non_sky_depth.numel(), (100_000,), device=non_sky_depth.device) + sampled = non_sky_depth[idx] + else: + sampled = non_sky_depth + + max_depth = torch.quantile(sampled, quantile) + out = depth.clone() + out[~non_sky] = max_depth + return out + + +def normalize_depth_v2_style(depth: torch.Tensor, sky: torch.Tensor | None = None, low_quantile: float = 0.01, high_quantile: float = 0.99) -> torch.Tensor: + """V2-style normalization computes percentile bounds over non-sky pixels (when available), then maps depth into [0, 1] with near = white (1.0).""" + if sky is not None: + mask = compute_non_sky_mask(sky) + if mask.any(): + valid = depth[mask] + else: + valid = depth.flatten() + else: + valid = depth.flatten() + + if valid.numel() > 100_000: + idx = torch.randint(0, valid.numel(), (100_000,), device=valid.device) + sample = valid[idx] + else: + sample = valid + + lo = torch.quantile(sample, low_quantile) + hi = torch.quantile(sample, high_quantile) + rng = (hi - lo).clamp(min=1e-6) + norm = ((depth - lo) / rng).clamp(0.0, 1.0) + # Nearer pixels are brighter (1.0) + norm = 1.0 - norm + if sky is not None: + # Sky pixels become black (far / unknown) + sky_mask = ~compute_non_sky_mask(sky) + norm = torch.where(sky_mask, torch.zeros_like(norm), norm) + return norm + + +def normalize_depth_min_max(depth: torch.Tensor) -> torch.Tensor: + """Simple per-frame min/max normalization with near=1.0 convention.""" + lo = depth.amin(dim=(-2, -1), keepdim=True) + hi = depth.amax(dim=(-2, -1), keepdim=True) + rng = (hi - lo).clamp(min=1e-6) + return 1.0 - ((depth - lo) / rng).clamp(0.0, 1.0) diff --git a/comfy/ldm/depth_anything_3/ray_pose.py b/comfy/ldm/depth_anything_3/ray_pose.py new file mode 100644 index 000000000..90890f1da --- /dev/null +++ b/comfy/ldm/depth_anything_3/ray_pose.py @@ -0,0 +1,272 @@ +"""Ray-to-pose conversion for the multi-view path of Depth Anything 3.""" + +from __future__ import annotations + +from typing import Optional, Tuple + +import torch + + +# qr/svd use fp32: CUDA often has no fp16/bf16 kernels for these ops. + + +def _ql_decomposition(A: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """Decompose A = Q @ L with Q orthogonal and L lower-triangular. + Implemented in terms of QR by reversing the columns/rows; the standard + trick from the upstream reference. Inputs A are (3, 3).""" + P = torch.tensor([[0, 0, 1], [0, 1, 0], [1, 0, 0]], device=A.device, dtype=A.dtype) + A_tilde = A @ P + # CUDA QR is not implemented for fp16/bf16; upcast just for this call. + Q_tilde, R_tilde = torch.linalg.qr(A_tilde.float()) + Q_tilde = Q_tilde.to(A.dtype) + R_tilde = R_tilde.to(A.dtype) + Q = Q_tilde @ P + L = P @ R_tilde @ P + d = torch.diag(L) + sign = torch.sign(d) + Q = Q * sign[None, :] # scale columns of Q + L = L * sign[:, None] # scale rows of L + return Q, L + + +def _homogenize_points(points: torch.Tensor) -> torch.Tensor: + return torch.cat([points, torch.ones_like(points[..., :1])], dim=-1) + + +# ----------------------------------------------------------------------------- +# Weighted-LSQ + RANSAC homography (batched) +# ----------------------------------------------------------------------------- + + +def _find_homography_weighted_lsq(src_pts: torch.Tensor, dst_pts: torch.Tensor, confident_weight: torch.Tensor,) -> torch.Tensor: + """Solve a single H with weighted least-squares (DLT).""" + N = src_pts.shape[0] + if N < 4: + raise ValueError("At least 4 points are required to compute a homography.") + w = confident_weight.sqrt().unsqueeze(1) # (N, 1) + x = src_pts[:, 0:1] + y = src_pts[:, 1:2] + u = dst_pts[:, 0:1] + v = dst_pts[:, 1:2] + zeros = torch.zeros_like(x) + A1 = torch.cat([-x * w, -y * w, -w, zeros, zeros, zeros, x * u * w, y * u * w, u * w], dim=1) + A2 = torch.cat([zeros, zeros, zeros, -x * w, -y * w, -w, x * v * w, y * v * w, v * w], dim=1) + A = torch.cat([A1, A2], dim=0) # (2N, 9) + # CUDA SVD is not implemented for fp16/bf16; upcast just for this call. + _, _, Vh = torch.linalg.svd(A.float()) + Vh = Vh.to(A.dtype) + H = Vh[-1].reshape(3, 3) + return H / H[-1, -1] + + +def _find_homography_weighted_lsq_batched(src_pts_batch: torch.Tensor, dst_pts_batch: torch.Tensor, confident_weight_batch: torch.Tensor) -> torch.Tensor: + """Batched DLT solver. Inputs (B, K, 2) / (B, K); output (B, 3, 3).""" + B, K, _ = src_pts_batch.shape + w = confident_weight_batch.sqrt().unsqueeze(2) + x = src_pts_batch[:, :, 0:1] + y = src_pts_batch[:, :, 1:2] + u = dst_pts_batch[:, :, 0:1] + v = dst_pts_batch[:, :, 1:2] + zeros = torch.zeros_like(x) + A1 = torch.cat([-x * w, -y * w, -w, zeros, zeros, zeros, x * u * w, y * u * w, u * w], dim=2) + A2 = torch.cat([zeros, zeros, zeros, -x * w, -y * w, -w, x * v * w, y * v * w, v * w], dim=2) + A = torch.cat([A1, A2], dim=1) # (B, 2K, 9) + # CUDA SVD is not implemented for fp16/bf16; upcast just for this call. + _, _, Vh = torch.linalg.svd(A.float()) + Vh = Vh.to(A.dtype) + H = Vh[:, -1].reshape(B, 3, 3) + return H / H[:, 2:3, 2:3] + + +def _ransac_find_homography_weighted_batched( + src_pts: torch.Tensor, # (B, N, 2) + dst_pts: torch.Tensor, # (B, N, 2) + confident_weight: torch.Tensor, # (B, N) + n_sample: int, + n_iter: int = 100, + reproj_threshold: float = 3.0, + num_sample_for_ransac: int = 8, + max_inlier_num: int = 10000, + rand_sample_iters_idx: Optional[torch.Tensor] = None, +) -> torch.Tensor: + """Batched weighted-RANSAC homography estimator. Returns (B, 3, 3) homography matrices.""" + B, N, _ = src_pts.shape + assert N >= 4 + device = src_pts.device + + sorted_idx = torch.argsort(confident_weight, descending=True, dim=1) + candidate_idx = sorted_idx[:, :n_sample] # (B, n_sample) + + if rand_sample_iters_idx is None: + rand_sample_iters_idx = torch.stack( + [torch.randperm(n_sample, device=device)[:num_sample_for_ransac] + for _ in range(n_iter)], + dim=0, + ) + + rand_idx = candidate_idx[:, rand_sample_iters_idx] # (B, n_iter, k) + b_idx = ( + torch.arange(B, device=device) + .view(B, 1, 1) + .expand(B, n_iter, num_sample_for_ransac) + ) + src_b = src_pts[b_idx, rand_idx] + dst_b = dst_pts[b_idx, rand_idx] + w_b = confident_weight[b_idx, rand_idx] + + cB, cN = src_b.shape[:2] + H_batch = _find_homography_weighted_lsq_batched( + src_b.flatten(0, 1), dst_b.flatten(0, 1), w_b.flatten(0, 1), + ).unflatten(0, (cB, cN)) # (B, n_iter, 3, 3) + + src_homo = torch.cat([src_pts, torch.ones(B, N, 1, device=device, dtype=src_pts.dtype)], dim=2) + proj = torch.bmm( + src_homo.unsqueeze(1).expand(B, n_iter, N, 3).reshape(-1, N, 3), + H_batch.reshape(-1, 3, 3).transpose(1, 2), + ) # (B*n_iter, N, 3) + proj_xy = (proj[:, :, :2] / proj[:, :, 2:3]).reshape(B, n_iter, N, 2) + err = ((proj_xy - dst_pts.unsqueeze(1)) ** 2).sum(-1).sqrt() # (B, n_iter, N) + inlier_mask = err < reproj_threshold + score = (inlier_mask * confident_weight.unsqueeze(1)).sum(dim=2) + best_idx = torch.argmax(score, dim=1) + best_inlier_mask = inlier_mask[torch.arange(B, device=device), best_idx] + + # Refit with the inlier set (per-batch, since the inlier counts vary). + H_inlier_list = [] + for b in range(B): + mask = best_inlier_mask[b] + in_src = src_pts[b][mask] + in_dst = dst_pts[b][mask] + in_w = confident_weight[b][mask] + if in_src.shape[0] < 4: + # Fall back to identity when RANSAC fails to find enough inliers. + H_inlier_list.append(torch.eye(3, device=device, dtype=src_pts.dtype)) + continue + sorted_w = torch.argsort(in_w, descending=True) + if len(sorted_w) > max_inlier_num: + keep = max(int(len(sorted_w) * 0.95), max_inlier_num) + sorted_w = sorted_w[:keep][torch.randperm(keep, device=device)[:max_inlier_num]] + H_inlier_list.append( + _find_homography_weighted_lsq(in_src[sorted_w], in_dst[sorted_w], in_w[sorted_w]) + ) + return torch.stack(H_inlier_list, dim=0) + + +# ----------------------------------------------------------------------------- +# Camera-ray utilities +# ----------------------------------------------------------------------------- + + +def _unproject_identity(num_y: int, num_x: int, B: int, S: int, device, dtype) -> torch.Tensor: + """Camera-space unit rays for an identity intrinsic on a 2x2 image plane.""" + dx = 1.0 / num_x + dy = 1.0 / num_y + # Centered camera-space coords directly (skip the K^-1 step since it's + # just a translation by -1 on x and y when K is identity-with-center=1). + y = torch.linspace(-(1 - dy), (1 - dy), num_y, device=device, dtype=dtype) + x = torch.linspace(-(1 - dx), (1 - dx), num_x, device=device, dtype=dtype) + yy, xx = torch.meshgrid(y, x, indexing="ij") + grid = torch.stack((xx, yy), dim=-1) # (h, w, 2) + grid = grid.unsqueeze(0).unsqueeze(0).expand(B, S, num_y, num_x, 2) + return torch.cat([grid, torch.ones_like(grid[..., :1])], dim=-1) + + +def _camray_to_caminfo( + camray: torch.Tensor, # (B, S, h, w, 6) + confidence: Optional[torch.Tensor] = None, # (B, S, h, w) + reproj_threshold: float = 0.2, +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Convert per-pixel camera rays to per-view (R, T, focal, principal).""" + if confidence is None: + confidence = torch.ones_like(camray[..., 0]) + B, S, h, w, _ = camray.shape + device = camray.device + dtype = camray.dtype + + rays_target = camray[..., :3] # (B, S, h, w, 3) + rays_origin = _unproject_identity(h, w, B, S, device, dtype) + + # Flatten (B*S, h*w, *) for the RANSAC routine. + rays_target = rays_target.flatten(0, 1).flatten(1, 2) + rays_origin = rays_origin.flatten(0, 1).flatten(1, 2) + weights = confidence.flatten(0, 1).flatten(1, 2).clone() + + # Project to 2D in homogeneous form (the upstream calls this "perspective division"). + z_thresh = 1e-4 + mask = (rays_target[:, :, 2].abs() > z_thresh) & (rays_origin[:, :, 2].abs() > z_thresh) + weights = torch.where(mask, weights, torch.zeros_like(weights)) + src = rays_origin.clone() + dst = rays_target.clone() + src[..., 0] = torch.where(mask, src[..., 0] / src[..., 2], src[..., 0]) + src[..., 1] = torch.where(mask, src[..., 1] / src[..., 2], src[..., 1]) + dst[..., 0] = torch.where(mask, dst[..., 0] / dst[..., 2], dst[..., 0]) + dst[..., 1] = torch.where(mask, dst[..., 1] / dst[..., 2], dst[..., 1]) + src = src[..., :2] + dst = dst[..., :2] + + N = src.shape[1] + n_iter = 100 + sample_ratio = 0.3 + num_sample_for_ransac = 8 + n_sample = max(num_sample_for_ransac, int(N * sample_ratio)) + rand_idx = torch.stack( + [torch.randperm(n_sample, device=device)[:num_sample_for_ransac] for _ in range(n_iter)], + dim=0, + ) + + # Chunk along the view axis to keep peak memory predictable. + chunk = 2 + A_list = [] + for i in range(0, src.shape[0], chunk): + A = _ransac_find_homography_weighted_batched( + src[i:i + chunk], dst[i:i + chunk], weights[i:i + chunk], + n_sample=n_sample, n_iter=n_iter, + num_sample_for_ransac=num_sample_for_ransac, + reproj_threshold=reproj_threshold, + rand_sample_iters_idx=rand_idx, + max_inlier_num=8000, + ) + # Flip sign on dets that come out < 0 (so that the QL produces a + # right-handed rotation). ``det`` lacks fp16/bf16 CUDA kernels, so + # do the comparison in fp32. + flip = torch.linalg.det(A.float()) < 0 + A = torch.where(flip[:, None, None], -A, A) + A_list.append(A) + A = torch.cat(A_list, dim=0) # (B*S, 3, 3) + + R_list, f_list, pp_list = [], [], [] + for i in range(A.shape[0]): + R, L = _ql_decomposition(A[i]) + L = L / L[2][2] + f_list.append(torch.stack((L[0][0], L[1][1]))) + pp_list.append(torch.stack((L[2][0], L[2][1]))) + R_list.append(R) + R = torch.stack(R_list).reshape(B, S, 3, 3) + focal = torch.stack(f_list).reshape(B, S, 2) + pp = torch.stack(pp_list).reshape(B, S, 2) + + # Translation: confidence-weighted average of camray direction(s). + cf = confidence.flatten(0, 1).flatten(1, 2) + T = (camray.flatten(0, 1).flatten(1, 2)[..., 3:] * cf.unsqueeze(-1)).sum(dim=1) + T = T / cf.sum(dim=-1, keepdim=True) + T = T.reshape(B, S, 3) + + # Match upstream output convention: focal -> 1/focal, pp + 1. + return R, T, 1.0 / focal, pp + 1.0 + + +def get_extrinsic_from_camray( + camray: torch.Tensor, # (B, S, h, w, 6) + conf: torch.Tensor, # (B, S, h, w, 1) or (B, S, h, w) + patch_size_y: int, + patch_size_x: int, +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Wrap a 4x4 extrinsic + per-view focal + principal-point output.""" + if conf.ndim == 5 and conf.shape[-1] == 1: + conf = conf.squeeze(-1) + R, T, focal, pp = _camray_to_caminfo(camray, confidence=conf) + extr = torch.cat([R, T.unsqueeze(-1)], dim=-1) # (B, S, 3, 4) + homo_row = torch.tensor([0, 0, 0, 1], dtype=R.dtype, device=R.device) + homo_row = homo_row.view(1, 1, 1, 4).expand(R.shape[0], R.shape[1], 1, 4) + extr = torch.cat([extr, homo_row], dim=-2) # (B, S, 4, 4) + return extr, focal, pp diff --git a/comfy/ldm/depth_anything_3/reference_view_selector.py b/comfy/ldm/depth_anything_3/reference_view_selector.py new file mode 100644 index 000000000..90f00be92 --- /dev/null +++ b/comfy/ldm/depth_anything_3/reference_view_selector.py @@ -0,0 +1,87 @@ +"""Reference-view selection for the multi-view path of Depth Anything 3.""" + +from __future__ import annotations + +from typing import Literal + +import torch + + +RefViewStrategy = Literal["first", "middle", "saddle_balanced", "saddle_sim_range"] + + +# Per the upstream constants module: ``THRESH_FOR_REF_SELECTION = 3``. +# Reference selection only runs when there are at least this many views. +THRESH_FOR_REF_SELECTION: int = 3 + + +def select_reference_view(x: torch.Tensor, strategy: RefViewStrategy = "saddle_balanced") -> torch.Tensor: + """Pick a reference view index per batch element.""" + B, S, _, _ = x.shape + if S <= 1: + return torch.zeros(B, dtype=torch.long, device=x.device) + if strategy == "first": + return torch.zeros(B, dtype=torch.long, device=x.device) + if strategy == "middle": + return torch.full((B,), S // 2, dtype=torch.long, device=x.device) + + # Feature-based strategies: normalised cls/cam token per view. + img_class_feat = x[:, :, 0] / x[:, :, 0].norm(dim=-1, keepdim=True) # (B,S,C) + + if strategy == "saddle_balanced": + sim = torch.matmul(img_class_feat, img_class_feat.transpose(1, 2)) # (B,S,S) + sim_no_diag = sim - torch.eye(S, device=sim.device).unsqueeze(0) + sim_score = sim_no_diag.sum(dim=-1) / (S - 1) # (B,S) + feat_norm = x[:, :, 0].norm(dim=-1) # (B,S) + feat_var = img_class_feat.var(dim=-1) # (B,S) + + def _normalize(metric): + mn = metric.min(dim=1, keepdim=True).values + mx = metric.max(dim=1, keepdim=True).values + return (metric - mn) / (mx - mn + 1e-8) + + sim_n, norm_n, var_n = _normalize(sim_score), _normalize(feat_norm), _normalize(feat_var) + balance = (sim_n - 0.5).abs() + (norm_n - 0.5).abs() + (var_n - 0.5).abs() + return balance.argmin(dim=1) + + if strategy == "saddle_sim_range": + sim = torch.matmul(img_class_feat, img_class_feat.transpose(1, 2)) + sim_no_diag = sim - torch.eye(S, device=sim.device).unsqueeze(0) + sim_max = sim_no_diag.max(dim=-1).values + sim_min = sim_no_diag.min(dim=-1).values + return (sim_max - sim_min).argmax(dim=1) + + raise ValueError( + f"Unknown reference view selection strategy: {strategy!r}. " + f"Must be one of: 'first', 'middle', 'saddle_balanced', 'saddle_sim_range'" + ) + + +def reorder_by_reference(x: torch.Tensor, b_idx: torch.Tensor) -> torch.Tensor: + """Reorder x so the reference view is at position 0 in axis S.""" + B, S = x.shape[0], x.shape[1] + if S <= 1: + return x + positions = torch.arange(S, device=x.device).unsqueeze(0).expand(B, -1) + b_idx_exp = b_idx.unsqueeze(1) + reorder = torch.where( + (positions > 0) & (positions <= b_idx_exp), + positions - 1, + positions, + ) + reorder[:, 0] = b_idx + batch = torch.arange(B, device=x.device).unsqueeze(1) + return x[batch, reorder] + + +def restore_original_order(x: torch.Tensor, b_idx: torch.Tensor) -> torch.Tensor: + """Inverse of reorder_by_reference.""" + B, S = x.shape[0], x.shape[1] + if S <= 1: + return x + target_positions = torch.arange(S, device=x.device).unsqueeze(0).expand(B, -1) + b_idx_exp = b_idx.unsqueeze(1) + restore = torch.where(target_positions < b_idx_exp, target_positions + 1, target_positions) + restore = torch.scatter(restore, dim=1, index=b_idx_exp, src=torch.zeros_like(b_idx_exp)) + batch = torch.arange(B, device=x.device).unsqueeze(1) + return x[batch, restore] diff --git a/comfy/ldm/depth_anything_3/transform.py b/comfy/ldm/depth_anything_3/transform.py new file mode 100644 index 000000000..b735d7bec --- /dev/null +++ b/comfy/ldm/depth_anything_3/transform.py @@ -0,0 +1,160 @@ +"""Geometry / camera transform helpers for Depth Anything 3.""" + +from __future__ import annotations + +from typing import Tuple + +import torch +import torch.nn.functional as F + + +# ----------------------------------------------------------------------------- +# Affine 4x4 helpers +# ----------------------------------------------------------------------------- + + +def as_homogeneous(ext: torch.Tensor) -> torch.Tensor: + """Promote (...,3,4) extrinsics to (...,4,4) homogeneous form. No-op when the input is already ``(...,4,4)``.""" + if ext.shape[-2:] == (4, 4): + return ext + if ext.shape[-2:] == (3, 4): + ones = torch.zeros_like(ext[..., :1, :4]) + ones[..., 0, 3] = 1.0 + return torch.cat([ext, ones], dim=-2) + raise ValueError(f"Invalid affine shape: {ext.shape}") + + +def affine_inverse(A: torch.Tensor) -> torch.Tensor: + """Inverse of an affine matrix ``[R|T; 0 0 0 1]``.""" + R = A[..., :3, :3] + T = A[..., :3, 3:] + P = A[..., 3:, :] + return torch.cat([torch.cat([R.mT, -R.mT @ T], dim=-1), P], dim=-2) + + +# ----------------------------------------------------------------------------- +# Quaternion <-> rotation matrix (xyzw / scalar-last) +# ----------------------------------------------------------------------------- + + +def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor: + """sqrt(max(0, x)) with a zero subgradient where x == 0.""" + ret = torch.zeros_like(x) + positive_mask = x > 0 + if torch.is_grad_enabled(): + ret[positive_mask] = torch.sqrt(x[positive_mask]) + else: + ret = torch.where(positive_mask, torch.sqrt(x), ret) + return ret + + +def standardize_quaternion(quaternions: torch.Tensor) -> torch.Tensor: + """Force the real part of a unit quaternion (xyzw) to be non-negative.""" + return torch.where(quaternions[..., 3:4] < 0, -quaternions, quaternions) + + +def quat_to_mat(quaternions: torch.Tensor) -> torch.Tensor: + """Convert quaternions (xyzw) to (...,3,3) rotation matrices.""" + i, j, k, r = torch.unbind(quaternions, -1) + two_s = 2.0 / (quaternions * quaternions).sum(-1) + o = torch.stack( + ( + 1 - two_s * (j * j + k * k), + two_s * (i * j - k * r), + two_s * (i * k + j * r), + two_s * (i * j + k * r), + 1 - two_s * (i * i + k * k), + two_s * (j * k - i * r), + two_s * (i * k - j * r), + two_s * (j * k + i * r), + 1 - two_s * (i * i + j * j), + ), + -1, + ) + return o.reshape(quaternions.shape[:-1] + (3, 3)) + + +def mat_to_quat(matrix: torch.Tensor) -> torch.Tensor: + """Convert (...,3,3) rotation matrices to quaternions (xyzw).""" + if matrix.size(-1) != 3 or matrix.size(-2) != 3: + raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.") + + batch_dim = matrix.shape[:-2] + m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind( + matrix.reshape(batch_dim + (9,)), dim=-1 + ) + + q_abs = _sqrt_positive_part( + torch.stack( + [ + 1.0 + m00 + m11 + m22, + 1.0 + m00 - m11 - m22, + 1.0 - m00 + m11 - m22, + 1.0 - m00 - m11 + m22, + ], + dim=-1, + ) + ) + + quat_by_rijk = torch.stack( + [ + torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1), + torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1), + torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1), + torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1), + ], + dim=-2, + ) + + flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device) + quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr)) + + out = quat_candidates[F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :].reshape( + batch_dim + (4,) + ) + # Reorder rijk -> xyzw (i.e. ijkr). + out = out[..., [1, 2, 3, 0]] + return standardize_quaternion(out) + + +# ----------------------------------------------------------------------------- +# Pose-encoding <-> extrinsics + intrinsics +# ----------------------------------------------------------------------------- + + +def extri_intri_to_pose_encoding(extrinsics: torch.Tensor, intrinsics: torch.Tensor, image_size_hw: Tuple[int, int]) -> torch.Tensor: + """Pack (extr, intr, image_size) into the 9-D pose-encoding vector. + extrinsics: camera-to-world (c2w) (B,S,4,4) matrices, + intrinsics: pixel-space (B,S,3,3) matrices, + image_size_hw: is a (H, W) pair. + """ + R = extrinsics[..., :3, :3] + T = extrinsics[..., :3, 3] + quat = mat_to_quat(R) + H, W = image_size_hw + fov_h = 2 * torch.atan((H / 2) / intrinsics[..., 1, 1]) + fov_w = 2 * torch.atan((W / 2) / intrinsics[..., 0, 0]) + return torch.cat([T, quat, fov_h[..., None], fov_w[..., None]], dim=-1).float() + + +def pose_encoding_to_extri_intri(pose_encoding: torch.Tensor, image_size_hw: Tuple[int, int]) -> Tuple[torch.Tensor, torch.Tensor]: + """Inverse of extri_intri_to_pose_encoding.""" + T = pose_encoding[..., :3] + quat = pose_encoding[..., 3:7] + fov_h = pose_encoding[..., 7] + fov_w = pose_encoding[..., 8] + # Normalize to unit quaternion. CameraDec outputs raw values; a near-zero + # quaternion causes two_s = 2/norm² → inf in quat_to_mat → NaN extrinsics. + quat = quat / quat.norm(dim=-1, keepdim=True).clamp(min=1e-6) + R = quat_to_mat(quat) + extrinsics = torch.cat([R, T[..., None]], dim=-1) + H, W = image_size_hw + fy = (H / 2.0) / torch.clamp(torch.tan(fov_h / 2.0), 1e-6) + fx = (W / 2.0) / torch.clamp(torch.tan(fov_w / 2.0), 1e-6) + intrinsics = torch.zeros(pose_encoding.shape[:2] + (3, 3), device=pose_encoding.device, dtype=pose_encoding.dtype) + intrinsics[..., 0, 0] = fx + intrinsics[..., 1, 1] = fy + intrinsics[..., 0, 2] = W / 2 + intrinsics[..., 1, 2] = H / 2 + intrinsics[..., 2, 2] = 1.0 + return extrinsics, intrinsics diff --git a/comfy/ldm/ernie/model.py b/comfy/ldm/ernie/model.py index eba661aec..f158ca1d2 100644 --- a/comfy/ldm/ernie/model.py +++ b/comfy/ldm/ernie/model.py @@ -5,6 +5,7 @@ import torch.nn.functional as F from comfy.ldm.modules.attention import optimized_attention import comfy.model_management +import comfy.quant_ops def rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor: assert dim % 2 == 0 @@ -19,15 +20,6 @@ def rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor: out = torch.stack([torch.cos(out), torch.sin(out)], dim=0) return out.to(dtype=torch.float32, device=pos.device) -def apply_rotary_emb(x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor: - rot_dim = freqs_cis.shape[-1] - x, x_pass = x_in[..., :rot_dim], x_in[..., rot_dim:] - cos_ = freqs_cis[0] - sin_ = freqs_cis[1] - x1, x2 = x.chunk(2, dim=-1) - x_rotated = torch.cat((-x2, x1), dim=-1) - return torch.cat((x * cos_ + x_rotated * sin_, x_pass), dim=-1) - class ErnieImageEmbedND3(nn.Module): def __init__(self, dim: int, theta: int, axes_dim: tuple): super().__init__() @@ -37,8 +29,16 @@ class ErnieImageEmbedND3(nn.Module): def forward(self, ids: torch.Tensor) -> torch.Tensor: emb = torch.cat([rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(3)], dim=-1) - emb = emb.unsqueeze(3) # [2, B, S, 1, head_dim//2] - return torch.stack([emb, emb], dim=-1).reshape(*emb.shape[:-1], -1) # [B, S, 1, head_dim] + cos_ = emb[0] + sin_ = emb[1] + N = cos_.shape[-1] + half = N // 2 + cos_top = cos_[..., :half].repeat_interleave(2, dim=-1) + sin_top = sin_[..., :half].repeat_interleave(2, dim=-1) + cos_bot = cos_[..., half:].repeat_interleave(2, dim=-1) + sin_bot = sin_[..., half:].repeat_interleave(2, dim=-1) + rot = torch.stack([cos_top, -sin_top, sin_bot, cos_bot], dim=-1) + return rot.reshape(*rot.shape[:-1], 2, 2).unsqueeze(2) class ErnieImagePatchEmbedDynamic(nn.Module): def __init__(self, in_channels: int, embed_dim: int, patch_size: int, operations, device=None, dtype=None): @@ -115,8 +115,7 @@ class ErnieImageAttention(nn.Module): key = self.norm_k(key) if image_rotary_emb is not None: - query = apply_rotary_emb(query, image_rotary_emb) - key = apply_rotary_emb(key, image_rotary_emb) + query, key = comfy.quant_ops.ck.apply_rope_split_half(query, key, image_rotary_emb) q_flat = query.reshape(B, S, -1) k_flat = key.reshape(B, S, -1) @@ -274,7 +273,7 @@ class ErnieImageModel(nn.Module): image_ids = image_ids.view(1, N_img, 3).expand(B, -1, -1) - rotary_pos_emb = self.pos_embed(torch.cat([image_ids, text_ids], dim=1)).to(x.dtype) + rotary_pos_emb = self.pos_embed(torch.cat([image_ids, text_ids], dim=1)) del image_ids, text_ids sample = self.time_proj(timesteps).to(dtype) diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py index 6d0aed827..891dea7dd 100644 --- a/comfy/ldm/flux/math.py +++ b/comfy/ldm/flux/math.py @@ -4,7 +4,7 @@ from torch import Tensor from comfy.ldm.modules.attention import optimized_attention import comfy.model_management -import logging +import comfy.quant_ops def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor: @@ -44,21 +44,15 @@ def _apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor): return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis) -try: - import comfy.quant_ops - q_apply_rope = comfy.quant_ops.ck.apply_rope - q_apply_rope1 = comfy.quant_ops.ck.apply_rope1 - def apply_rope(xq, xk, freqs_cis): - if comfy.model_management.in_training: - return _apply_rope(xq, xk, freqs_cis) - else: - return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis) - def apply_rope1(x, freqs_cis): - if comfy.model_management.in_training: - return _apply_rope1(x, freqs_cis) - else: - return q_apply_rope1(x, freqs_cis) -except: - logging.warning("No comfy kitchen, using old apply_rope functions.") - apply_rope = _apply_rope - apply_rope1 = _apply_rope1 +def apply_rope(xq, xk, freqs_cis): + if comfy.model_management.in_training: + return _apply_rope(xq, xk, freqs_cis) + else: + return comfy.quant_ops.ck.apply_rope(xq, xk, freqs_cis) + + +def apply_rope1(x, freqs_cis): + if comfy.model_management.in_training: + return _apply_rope1(x, freqs_cis) + else: + return comfy.quant_ops.ck.apply_rope1(x, freqs_cis) diff --git a/comfy/ldm/ideogram4/model.py b/comfy/ldm/ideogram4/model.py new file mode 100644 index 000000000..4ea5b8aaf --- /dev/null +++ b/comfy/ldm/ideogram4/model.py @@ -0,0 +1,297 @@ +""" +The Ideogram 4 transformer is a NextDiT/Lumina2-family single-stream model +consumes Qwen3-VL hidden-state features (concatenated from 13 layers -> 53248 dims) +packs ``[text tokens, image tokens]`` into one sequence with block-diagonal segment attention and 3D interleaved MRoPE. +""" + +from __future__ import annotations + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import comfy.patcher_extension +from comfy.ldm.lumina.model import FeedForward +from comfy.ldm.modules.attention import optimized_attention_masked +from comfy.text_encoders.llama import apply_rope, precompute_freqs_cis + +# Per-token role indicators +SEQUENCE_PADDING_INDICATOR = -1 +OUTPUT_IMAGE_INDICATOR = 2 +LLM_TOKEN_INDICATOR = 3 +# Image grid coordinates are offset so they never collide with text positions +IMAGE_POSITION_OFFSET = 65536 + + +class Ideogram4Attention(nn.Module): + def __init__(self, hidden_size, num_heads, eps=1e-5, dtype=None, device=None, operations=None): + super().__init__() + self.num_heads = num_heads + self.head_dim = hidden_size // num_heads + self.hidden_size = hidden_size + + self.qkv = operations.Linear(hidden_size, hidden_size * 3, bias=False, dtype=dtype, device=device) + self.norm_q = operations.RMSNorm(self.head_dim, eps=eps, elementwise_affine=True, dtype=dtype, device=device) + self.norm_k = operations.RMSNorm(self.head_dim, eps=eps, elementwise_affine=True, dtype=dtype, device=device) + self.o = operations.Linear(hidden_size, hidden_size, bias=False, dtype=dtype, device=device) + + def forward(self, x, attn_mask, freqs_cis, transformer_options={}): + batch_size, seq_len, _ = x.shape + qkv = self.qkv(x).view(batch_size, seq_len, 3, self.num_heads, self.head_dim) + q, k, v = qkv.unbind(dim=2) + + q = self.norm_q(q) + k = self.norm_k(k) + + # (B, heads, L, head_dim) + q = q.transpose(1, 2) + k = k.transpose(1, 2) + v = v.transpose(1, 2) + + q, k = apply_rope(q, k, freqs_cis) + + out = optimized_attention_masked(q, k, v, self.num_heads, attn_mask, skip_reshape=True, transformer_options=transformer_options) + return self.o(out) + + +class Ideogram4TransformerBlock(nn.Module): + def __init__(self, hidden_size, intermediate_size, num_heads, norm_eps, adaln_dim, dtype=None, device=None, operations=None): + super().__init__() + self.attention = Ideogram4Attention(hidden_size, num_heads, eps=1e-5, dtype=dtype, device=device, operations=operations) + self.feed_forward = FeedForward( + dim=hidden_size, hidden_dim=intermediate_size, multiple_of=1, ffn_dim_multiplier=None, + operation_settings={"operations": operations, "dtype": dtype, "device": device}, + ) + + self.attention_norm1 = operations.RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=True, dtype=dtype, device=device) + self.ffn_norm1 = operations.RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=True, dtype=dtype, device=device) + self.attention_norm2 = operations.RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=True, dtype=dtype, device=device) + self.ffn_norm2 = operations.RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=True, dtype=dtype, device=device) + + self.adaln_modulation = operations.Linear(adaln_dim, 4 * hidden_size, bias=True, dtype=dtype, device=device) + + def forward(self, x, attn_mask, freqs_cis, adaln_input, transformer_options={}): + mod = self.adaln_modulation(adaln_input) + scale_msa, gate_msa, scale_mlp, gate_mlp = mod.chunk(4, dim=-1) + gate_msa = torch.tanh(gate_msa) + gate_mlp = torch.tanh(gate_mlp) + scale_msa = 1.0 + scale_msa + scale_mlp = 1.0 + scale_mlp + + attn_out = self.attention(self.attention_norm1(x) * scale_msa, attn_mask, freqs_cis, transformer_options=transformer_options) + x = x + gate_msa * self.attention_norm2(attn_out) + x = x + gate_mlp * self.ffn_norm2(self.feed_forward(self.ffn_norm1(x) * scale_mlp)) + return x + + +def _sinusoidal_embedding(t, dim, scale=1e4): + t = t.to(torch.float32) + half = dim // 2 + freq = math.log(scale) / (half - 1) + freq = torch.exp(torch.arange(half, dtype=torch.float32, device=t.device) * -freq) + emb = t.unsqueeze(-1) * freq + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) + if dim % 2 == 1: + emb = F.pad(emb, (0, 1)) + return emb + + +class Ideogram4EmbedScalar(nn.Module): + def __init__(self, dim, input_range=(0.0, 1.0), dtype=None, device=None, operations=None): + super().__init__() + self.dim = dim + self.range_min, self.range_max = input_range + self.mlp_in = operations.Linear(dim, dim, bias=True, dtype=dtype, device=device) + self.mlp_out = operations.Linear(dim, dim, bias=True, dtype=dtype, device=device) + + def forward(self, x, dtype): + x = x.to(torch.float32) + scaled = 1e4 * (x - self.range_min) / (self.range_max - self.range_min) + emb = _sinusoidal_embedding(scaled, self.dim) + emb = emb.to(dtype) + emb = F.silu(self.mlp_in(emb)) + return self.mlp_out(emb) + + +class Ideogram4FinalLayer(nn.Module): + def __init__(self, hidden_size, out_channels, adaln_dim, dtype=None, device=None, operations=None): + super().__init__() + self.norm_final = operations.LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, out_channels, bias=True, dtype=dtype, device=device) + self.adaln_modulation = operations.Linear(adaln_dim, hidden_size, bias=True, dtype=dtype, device=device) + + def forward(self, x, c): + scale = 1.0 + self.adaln_modulation(F.silu(c)) + return self.linear(self.norm_final(x) * scale) + + +class Ideogram4Transformer(nn.Module): + """A single Ideogram 4 backbone operating on a packed token sequence.""" + + def __init__(self, emb_dim, num_layers, num_heads, intermediate_size, adaln_dim, + in_channels, llm_features_dim, rope_theta, mrope_section, norm_eps, + dtype=None, device=None, operations=None): + super().__init__() + self.head_dim = emb_dim // num_heads + self.rope_theta = rope_theta + self.mrope_section = tuple(mrope_section) + + self.input_proj = operations.Linear(in_channels, emb_dim, bias=True, dtype=dtype, device=device) + self.llm_cond_norm = operations.RMSNorm(llm_features_dim, eps=1e-6, elementwise_affine=True, dtype=dtype, device=device) + self.llm_cond_proj = operations.Linear(llm_features_dim, emb_dim, bias=True, dtype=dtype, device=device) + self.t_embedding = Ideogram4EmbedScalar(emb_dim, input_range=(0.0, 1.0), dtype=dtype, device=device, operations=operations) + self.adaln_proj = operations.Linear(emb_dim, adaln_dim, bias=True, dtype=dtype, device=device) + + self.embed_image_indicator = operations.Embedding(2, emb_dim, dtype=dtype, device=device) + + self.layers = nn.ModuleList([ + Ideogram4TransformerBlock(emb_dim, intermediate_size, num_heads, norm_eps, adaln_dim, + dtype=dtype, device=device, operations=operations) + for _ in range(num_layers) + ]) + + self.final_layer = Ideogram4FinalLayer(emb_dim, in_channels, adaln_dim, dtype=dtype, device=device, operations=operations) + + def _backbone(self, llm_features, x, t, position_ids, attn_mask, indicator, transformer_options={}): + indicator = indicator.to(torch.long) + output_image_mask = (indicator == OUTPUT_IMAGE_INDICATOR).to(x.dtype).unsqueeze(-1) + + x = x * output_image_mask + h = self.input_proj(x) * output_image_mask + + t_cond = self.t_embedding(t, dtype=x.dtype) + if t.dim() == 1: + t_cond = t_cond.unsqueeze(1) + adaln_input = F.silu(self.adaln_proj(t_cond)) + + # h is zero on the text rows (content lives only on image rows), add writes the text features in place + if llm_features is not None: + L_text = llm_features.shape[1] + text_mask = (indicator[:, :L_text] == LLM_TOKEN_INDICATOR).to(x.dtype).unsqueeze(-1) + llm = self.llm_cond_norm(llm_features * text_mask) + llm = self.llm_cond_proj(llm) * text_mask + h[:, :L_text] = h[:, :L_text] + llm + + h = h + self.embed_image_indicator((indicator == OUTPUT_IMAGE_INDICATOR).to(torch.long), out_dtype=h.dtype) + + # Qwen3-VL interleaved MRoPE; position_ids (B, L, 3) -> (3, L) (same across batch). + freqs_cis = precompute_freqs_cis( + self.head_dim, position_ids[0].transpose(0, 1), self.rope_theta, + rope_dims=self.mrope_section, interleaved_mrope=True, device=position_ids.device, + ) + + if attn_mask is not None and attn_mask.dtype == torch.bool: + attn_mask = torch.zeros_like(attn_mask, dtype=h.dtype).masked_fill_(~attn_mask, -torch.finfo(h.dtype).max) + + for layer in self.layers: + h = layer(h, attn_mask, freqs_cis, adaln_input, transformer_options=transformer_options) + + return self.final_layer(h, adaln_input) + + +class Ideogram4Transformer2DModel(Ideogram4Transformer): + """Ideogram 4 single-stream DiT. + + Runs a packed ``[text, image]`` sequence when text context is supplied, or an image-only sequence when ``context is None``. + """ + + def __init__(self, image_model=None, in_channels=128, num_layers=34, num_attention_heads=18, attention_head_dim=256, intermediate_size=12288, + adaln_dim=512, llm_features_dim=53248, rope_theta=5000000, mrope_section=(24, 20, 20), norm_eps=1e-5, + dtype=None, device=None, operations=None, **kwargs): + emb_dim = num_attention_heads * attention_head_dim + super().__init__( + emb_dim=emb_dim, num_layers=num_layers, num_heads=num_attention_heads, + intermediate_size=intermediate_size, adaln_dim=adaln_dim, in_channels=in_channels, + llm_features_dim=llm_features_dim, rope_theta=rope_theta, mrope_section=mrope_section, + norm_eps=norm_eps, dtype=dtype, device=device, operations=operations) + self.dtype = dtype + self.in_channels = in_channels + self.out_channels = in_channels + # 128-dim token = patch (2x2) * ae_channels (32). + self.patch_size = 2 + self.ae_channels = in_channels // (self.patch_size * self.patch_size) + + def _img_to_tokens(self, x): + B, C, gh, gw = x.shape + x = x.view(B, self.ae_channels, self.patch_size, self.patch_size, gh, gw) + x = x.permute(0, 4, 5, 2, 3, 1) # (B, gh, gw, pi, pj, c) + return x.reshape(B, gh * gw, C) + + def _tokens_to_img(self, tokens, gh, gw): + B = tokens.shape[0] + C = tokens.shape[-1] + x = tokens.reshape(B, gh, gw, self.patch_size, self.patch_size, self.ae_channels) + x = x.permute(0, 5, 3, 4, 1, 2) # (B, c, pi, pj, gh, gw) + return x.reshape(B, C, gh, gw) + + def _image_position_ids(self, gh, gw, device): + h_idx = torch.arange(gh, device=device).view(-1, 1).expand(gh, gw).reshape(-1) + w_idx = torch.arange(gw, device=device).view(1, -1).expand(gh, gw).reshape(-1) + t_idx = torch.zeros_like(h_idx) + return torch.stack([t_idx, h_idx, w_idx], dim=1) + IMAGE_POSITION_OFFSET # (L_img, 3) + + def _run_conditional(self, x_chunk, context_chunk, attn_mask_chunk, t_chunk, gh, gw, transformer_options): + B = x_chunk.shape[0] + device = x_chunk.device + img_tokens = self._img_to_tokens(x_chunk) + L_img = img_tokens.shape[1] + L_text = context_chunk.shape[1] + L = L_text + L_img + latent_dim = img_tokens.shape[-1] + + x_full = torch.zeros(B, L, latent_dim, dtype=img_tokens.dtype, device=device) + x_full[:, L_text:] = img_tokens + + text_pos = torch.arange(L_text, device=device).view(-1, 1).expand(L_text, 3) + img_pos = self._image_position_ids(gh, gw, device) + position_ids = torch.cat([text_pos, img_pos], dim=0).unsqueeze(0).expand(B, L, 3) + + indicator = torch.empty(B, L, dtype=torch.long, device=device) + indicator[:, :L_text] = LLM_TOKEN_INDICATOR + indicator[:, L_text:] = OUTPUT_IMAGE_INDICATOR + + attn_mask = None + if attn_mask_chunk is not None: + segment_ids = torch.ones(B, L, dtype=torch.long, device=device) + pad = (attn_mask_chunk == 0) + segment_ids[:, :L_text][pad] = SEQUENCE_PADDING_INDICATOR + indicator[:, :L_text][pad] = 0 + # Block-diagonal mask from segment ids: (B, 1, L, L), True = attend. + attn_mask = (segment_ids.unsqueeze(2) == segment_ids.unsqueeze(1)).unsqueeze(1) + + out = self._backbone(context_chunk, x_full, t_chunk, position_ids, attn_mask, indicator, + transformer_options=transformer_options) + return self._tokens_to_img(out[:, L_text:], gh, gw) + + def _run_image_only(self, x_chunk, t_chunk, gh, gw, transformer_options): + B = x_chunk.shape[0] + device = x_chunk.device + img_tokens = self._img_to_tokens(x_chunk) + L_img = img_tokens.shape[1] + + position_ids = self._image_position_ids(gh, gw, device).unsqueeze(0).expand(B, L_img, 3) + indicator = torch.full((B, L_img), OUTPUT_IMAGE_INDICATOR, dtype=torch.long, device=device) + + # Image-only sequence is a single segment -> no mask, full attention, no LLM context. + out = self._backbone(None, img_tokens, t_chunk, position_ids, None, indicator, transformer_options=transformer_options) + return self._tokens_to_img(out, gh, gw) + + def forward(self, x, timesteps, context=None, attention_mask=None, transformer_options={}, **kwargs): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options), + ).execute(x, timesteps, context, attention_mask, transformer_options, **kwargs) + + def _forward(self, x, timesteps, context=None, attention_mask=None, transformer_options={}, **kwargs): + bs, c, gh, gw = x.shape + + timesteps = 1.0 - timesteps + + # unconditional pass + if context is None: + return -self._run_image_only(x, timesteps, gh, gw, transformer_options) + + return -self._run_conditional(x, context, attention_mask, timesteps, gh, gw, transformer_options) diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py index e0a4a0f9b..9953b6679 100644 --- a/comfy/ldm/lightricks/model.py +++ b/comfy/ldm/lightricks/model.py @@ -1085,7 +1085,7 @@ class LTXVModel(LTXBaseModel): ) grid_mask = None - if keyframe_idxs is not None: + if keyframe_idxs is not None and keyframe_idxs.shape[2] > 0: additional_args.update({ "orig_patchified_shape": list(x.shape)}) denoise_mask = self.patchifier.patchify(denoise_mask)[0] grid_mask = ~torch.any(denoise_mask < 0, dim=-1)[0] @@ -1330,7 +1330,7 @@ class LTXVModel(LTXBaseModel): x = x * (1 + scale) + shift x = self.proj_out(x) - if keyframe_idxs is not None: + if keyframe_idxs is not None and keyframe_idxs.shape[2] > 0: grid_mask = kwargs["grid_mask"] orig_patchified_shape = kwargs["orig_patchified_shape"] full_x = torch.zeros(orig_patchified_shape, dtype=x.dtype, device=x.device) diff --git a/comfy/ldm/omnigen/omnigen2.py b/comfy/ldm/omnigen/omnigen2.py index 82edc92da..b8da4cf39 100644 --- a/comfy/ldm/omnigen/omnigen2.py +++ b/comfy/ldm/omnigen/omnigen2.py @@ -8,6 +8,7 @@ import torch.nn.functional as F from einops import rearrange, repeat from comfy.ldm.lightricks.model import Timesteps from comfy.ldm.flux.layers import EmbedND +from comfy.ldm.flux.math import apply_rope1 from comfy.ldm.modules.attention import optimized_attention_masked import comfy.model_management import comfy.ldm.common_dit @@ -17,13 +18,11 @@ def apply_rotary_emb(x, freqs_cis): if x.shape[1] == 0: return x - t_ = x.reshape(*x.shape[:-1], -1, 1, 2) - t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1] - return t_out.reshape(*x.shape).to(dtype=x.dtype) + return apply_rope1(x, freqs_cis) def swiglu(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: - return F.silu(x) * y + return F.silu(x, inplace=True).mul_(y) class TimestepEmbedding(nn.Module): diff --git a/comfy/ldm/qwen_image/model.py b/comfy/ldm/qwen_image/model.py index 3462d8108..e49886dd9 100644 --- a/comfy/ldm/qwen_image/model.py +++ b/comfy/ldm/qwen_image/model.py @@ -51,6 +51,18 @@ class FeedForward(nn.Module): return hidden_states +# Addin this back because Nunchaku custom nodes rely on it, see comment here: +# https://github.com/Comfy-Org/ComfyUI/pull/14178#issuecomment-4640475161 +# TODO: Eventually remove this once we natively support SVDQuants +def apply_rotary_emb(x, freqs_cis): + if x.shape[1] == 0: + return x + + t_ = x.reshape(*x.shape[:-1], -1, 1, 2) + t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1] + return t_out.reshape(*x.shape) + + class QwenTimestepProjEmbeddings(nn.Module): def __init__(self, embedding_dim, pooled_projection_dim, use_additional_t_cond=False, dtype=None, device=None, operations=None): super().__init__() diff --git a/comfy/ldm/triposplat/gaussian.py b/comfy/ldm/triposplat/gaussian.py new file mode 100644 index 000000000..a4cd2f62f --- /dev/null +++ b/comfy/ldm/triposplat/gaussian.py @@ -0,0 +1,199 @@ +# TripoSplat 3D gaussian container. Operates on already-decoded +# tensors and exposes them as render-ready tensors (render_tensors) for the generic SPLAT type. +import torch +import torch.nn.functional as F + +import comfy.model_management + + +class GaussianModel: + def __init__(self, aabb: list, sh_degree: int = 0, mininum_kernel_size: float = 0.0, + scaling_bias: float = 0.01, opacity_bias: float = 0.1, + scaling_activation: str = "exp", device=None): + self.sh_degree = sh_degree + self.mininum_kernel_size = mininum_kernel_size + self.scaling_bias = scaling_bias + self.opacity_bias = opacity_bias + self.device = device + self.aabb = torch.tensor(aabb, dtype=torch.float32, device=device) + + if scaling_activation == "exp": + self._scaling_activation = torch.exp + self._inverse_scaling_activation = torch.log + elif scaling_activation == "softplus": + self._scaling_activation = F.softplus + self._inverse_scaling_activation = lambda x: x + torch.log(-torch.expm1(-x)) + + self._opacity_activation = torch.sigmoid + self._inverse_opacity_activation = lambda x: torch.log(x / (1 - x)) + + self.scale_bias = self._inverse_scaling_activation(torch.tensor(self.scaling_bias)).to(self.device) + self.rots_bias = torch.zeros(4, device=self.device) + self.rots_bias[0] = 1 + self.opacity_bias_val = self._inverse_opacity_activation(torch.tensor(self.opacity_bias)).to(self.device) + + self._storage = {} + + def _get_store(self, name): + return self._storage.get(name) + + def _set_store(self, name, value): + self._storage[name] = value + + @property + def _xyz(self): + return self._get_store("_xyz") + @_xyz.setter + def _xyz(self, value): + if value is None: + self._set_store("_xyz", None) + self._set_store("xyz", None) + return + self._set_store("_xyz", value) + self._set_store("xyz", value * self.aabb[None, 3:] + self.aabb[None, :3]) + + @property + def get_xyz(self): + return self._get_store("xyz") + + @property + def _features_dc(self): + return self._get_store("_features_dc") + @_features_dc.setter + def _features_dc(self, value): + self._set_store("_features_dc", value) + + @property + def _opacity(self): + return self._get_store("_opacity") + @_opacity.setter + def _opacity(self, value): + if value is None: + self._set_store("_opacity", None) + self._set_store("opacity", None) + return + self._set_store("_opacity", value) + self._set_store("opacity", self._opacity_activation(value + self.opacity_bias_val)) + + @property + def get_opacity(self): + return self._get_store("opacity") + + @property + def _scaling(self): + return self._get_store("_scaling") + @_scaling.setter + def _scaling(self, value): + if value is None: + self._set_store("_scaling", None) + self._set_store("scaling", None) + return + self._set_store("_scaling", value) + s = self._scaling_activation(value + self.scale_bias) + s = torch.square(s) + self.mininum_kernel_size ** 2 + self._set_store("scaling", torch.sqrt(s)) + + @property + def get_scaling(self): + return self._get_store("scaling") + + @property + def _rotation(self): + return self._get_store("_rotation") + @_rotation.setter + def _rotation(self, value): + self._set_store("_rotation", value) + + _DEFAULT_TRANSFORM = [[1, 0, 0], [0, 0, -1], [0, 1, 0]] + + def render_tensors(self): + # Render-ready (activated, world-space) tensors for the generic SPLAT type. The axis transform + # (a 3x3 rotation, object frame -> viewer Y-up) is baked into positions and rotations. + # Returns float tensors on the intermediate device: positions (N,3), scales (N,3) linear, + # rotations (N,4) wxyz, opacities (N,1) in [0,1], sh (N,K,3) coefficients. + xyz = self.get_xyz.float() + scaling = self.get_scaling.float() + opacity = self.get_opacity.float() + rotation = (self._rotation + self.rots_bias[None, :]).float() + sh = self._features_dc.float() # (N, K, 3) + T = torch.as_tensor(self._DEFAULT_TRANSFORM, dtype=torch.float32, device=xyz.device) + xyz = xyz @ T.T + rotation = _matrix_to_quat(torch.matmul(T, _quat_to_matrix(rotation))) + rotation = rotation / torch.linalg.norm(rotation, dim=-1, keepdim=True) + out_device = comfy.model_management.intermediate_device() + return ( + xyz.to(out_device).contiguous(), scaling.to(out_device).contiguous(), + rotation.to(out_device).contiguous(), opacity.to(out_device).contiguous(), + sh.to(out_device).contiguous(), + ) + + +def _quat_to_matrix(q): + q = q / torch.linalg.norm(q, dim=-1, keepdim=True) + w, x, y, z = q[:, 0], q[:, 1], q[:, 2], q[:, 3] + R = torch.stack([ + 1 - 2*(y*y + z*z), 2*(x*y - w*z), 2*(x*z + w*y), + 2*(x*y + w*z), 1 - 2*(x*x + z*z), 2*(y*z - w*x), + 2*(x*z - w*y), 2*(y*z + w*x), 1 - 2*(x*x + y*y), + ], dim=-1).reshape(-1, 3, 3) + return R + + +def _matrix_to_quat(R): + trace = R[:, 0, 0] + R[:, 1, 1] + R[:, 2, 2] + q = torch.zeros((R.shape[0], 4), dtype=R.dtype, device=R.device) + s = torch.sqrt(torch.clamp(trace + 1, min=0)) * 2 + q[:, 0] = 0.25 * s + denom = torch.where(s != 0, s, torch.ones_like(s)) + q[:, 1] = (R[:, 2, 1] - R[:, 1, 2]) / denom + q[:, 2] = (R[:, 0, 2] - R[:, 2, 0]) / denom + q[:, 3] = (R[:, 1, 0] - R[:, 0, 1]) / denom + m01 = (R[:, 0, 0] >= R[:, 1, 1]) & (R[:, 0, 0] >= R[:, 2, 2]) & (s == 0) + s1 = torch.sqrt(torch.clamp(1 + R[:, 0, 0] - R[:, 1, 1] - R[:, 2, 2], min=0)) * 2 + q[m01, 0] = (R[m01, 2, 1] - R[m01, 1, 2]) / s1[m01] + q[m01, 1] = 0.25 * s1[m01] + q[m01, 2] = (R[m01, 0, 1] + R[m01, 1, 0]) / s1[m01] + q[m01, 3] = (R[m01, 0, 2] + R[m01, 2, 0]) / s1[m01] + m11 = (R[:, 1, 1] > R[:, 0, 0]) & (R[:, 1, 1] >= R[:, 2, 2]) & (s == 0) + s2 = torch.sqrt(torch.clamp(1 + R[:, 1, 1] - R[:, 0, 0] - R[:, 2, 2], min=0)) * 2 + q[m11, 0] = (R[m11, 0, 2] - R[m11, 2, 0]) / s2[m11] + q[m11, 1] = (R[m11, 0, 1] + R[m11, 1, 0]) / s2[m11] + q[m11, 2] = 0.25 * s2[m11] + q[m11, 3] = (R[m11, 1, 2] + R[m11, 2, 1]) / s2[m11] + m21 = (R[:, 2, 2] > R[:, 0, 0]) & (R[:, 2, 2] > R[:, 1, 1]) & (s == 0) + s3 = torch.sqrt(torch.clamp(1 + R[:, 2, 2] - R[:, 0, 0] - R[:, 1, 1], min=0)) * 2 + q[m21, 0] = (R[m21, 1, 0] - R[m21, 0, 1]) / s3[m21] + q[m21, 1] = (R[m21, 0, 2] + R[m21, 2, 0]) / s3[m21] + q[m21, 2] = (R[m21, 1, 2] + R[m21, 2, 1]) / s3[m21] + q[m21, 3] = 0.25 * s3[m21] + return q / torch.linalg.norm(q, dim=-1, keepdim=True) + + +def build_gaussian_models(decoder, points_pred: dict, pred: dict): + # Assemble GaussianModels from the elastic decoder layout. decoder is the ElasticGaussianFixedlenDecoder + # (carries layout / rep_config / _get_offset) + x = points_pred + offset = decoder._get_offset(pred['features']) + h = pred["features"] + ret = [] + for i in range(h.shape[0]): + g = GaussianModel( + sh_degree=0, + aabb=[-0.5, -0.5, -0.5, 1.0, 1.0, 1.0], + mininum_kernel_size=decoder.rep_config['filter_kernel_size_3d'], + scaling_bias=decoder.rep_config['scaling_bias'], + opacity_bias=decoder.rep_config['opacity_bias'], + scaling_activation=decoder.rep_config['scaling_activation'], + device=h.device, + ) + _x = x["points"][i, :, None, :] + for k, v in decoder.layout.items(): + if k == '_xyz': + setattr(g, k, (offset[i] + _x).flatten(0, 1)) + elif k in ('_xyz_center', '_offset_scale'): + continue + else: + feats = h[i][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape']).flatten(0, 1) + setattr(g, k, feats * decoder.rep_config['lr'][k]) + ret.append(g) + return ret diff --git a/comfy/ldm/triposplat/model.py b/comfy/ldm/triposplat/model.py new file mode 100644 index 000000000..d8a531772 --- /dev/null +++ b/comfy/ldm/triposplat/model.py @@ -0,0 +1,326 @@ +# TripoSplat flow-matching denoiser (LatentSeqMMFlowModel). Registered as a ModelType.FLOW arch and +# driven by the standard KSampler; jointly denoises the (B, 8192, 16) latent and a (B, 1, 5) camera token +# carried as a 2-element nested latent. +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import comfy.model_management +import comfy.patcher_extension +import comfy.rmsnorm +from comfy.ldm.modules.attention import optimized_attention +from comfy.ldm.flux.math import apply_rope + + +class MultiHeadRMSNorm(nn.Module): + def __init__(self, dim, heads, dtype=None, device=None): + super().__init__() + self.gamma = nn.Parameter(torch.empty(heads, dim, dtype=dtype, device=device)) + + def forward(self, x): + x = comfy.rmsnorm.rms_norm(x) + return x * comfy.model_management.cast_to(self.gamma, x.dtype, x.device) + + +# Positional embeddings + +class RePo3DRotaryEmbedding(nn.Module): + def __init__(self, model_channels, num_heads, head_dim, repo_hidden_ratio=0.125, max_freq=16.0, + dtype=None, device=None, operations=None): + super().__init__() + self.num_heads = num_heads + self.head_dim = head_dim + repo_hidden_size = int(model_channels * repo_hidden_ratio) + self.norm = operations.LayerNorm(model_channels, dtype=dtype, device=device) + self.gate_map = operations.Linear(model_channels, repo_hidden_size, bias=False, dtype=dtype, device=device) + self.content_map = operations.Linear(model_channels, repo_hidden_size, bias=False, dtype=dtype, device=device) + self.act = nn.SiLU() + self.final_map = operations.Linear(repo_hidden_size, 3 * num_heads, bias=False, dtype=dtype, device=device) + self.dim_0 = 2 * (head_dim // 6) + self.dim_1 = 2 * (head_dim // 6) + self.dim_2 = head_dim - self.dim_0 - self.dim_1 + dims = [self.dim_0, self.dim_1, self.dim_2] + freqs_list = [] + for d in dims: + freq_dim = d // 2 + freqs_list.append(torch.linspace(1.0, float(max_freq), steps=freq_dim, dtype=torch.float32)) + self.freqs_0 = nn.Parameter(freqs_list[0]) + self.freqs_1 = nn.Parameter(freqs_list[1]) + self.freqs_2 = nn.Parameter(freqs_list[2]) + + def forward(self, hidden_states): + h = self.norm(hidden_states) + feat = self.act(self.gate_map(h)) * self.content_map(h) + out = self.final_map(feat) + B, L, _ = out.shape + delta_pos = out.reshape(B, L, self.num_heads, 3) + f0 = comfy.model_management.cast_to(self.freqs_0, torch.float32, out.device) + f1 = comfy.model_management.cast_to(self.freqs_1, torch.float32, out.device) + f2 = comfy.model_management.cast_to(self.freqs_2, torch.float32, out.device) + ang_0 = delta_pos[..., 0].unsqueeze(-1) * f0 * torch.pi + ang_1 = delta_pos[..., 1].unsqueeze(-1) * f1 * torch.pi + ang_2 = delta_pos[..., 2].unsqueeze(-1) * f2 * torch.pi + ang = torch.cat([ang_0, ang_1, ang_2], dim=-1).float() # (B, L, heads, head_dim/2) + cos, sin = ang.cos(), ang.sin() + return torch.stack([cos, -sin, sin, cos], dim=-1).reshape(*ang.shape, 2, 2) + + +class PcdAbsolutePositionEmbedder(nn.Module): + # Sinusoidal absolute position embedding. Two fixed schedules are used in TripoSplat: + # "pow2" (flow-model latent anchors) and "log2" (octree / gaussian decoders). + def __init__(self, channels: int, in_channels: int = 3, max_res: int = 16, schedule: str = "pow2"): + super().__init__() + self.channels = channels + self.in_channels = in_channels + self.max_res = max_res + self.schedule = schedule + self.freq_dim = channels // in_channels // 2 + + def _freqs(self, device): + if self.schedule == "pow2": + freqs_2exp = torch.arange(self.max_res, dtype=torch.float32, device=device) + res_dim = max(0, self.freq_dim - self.max_res) + freqs_res = (torch.arange(res_dim, dtype=torch.float32, device=device) / max(res_dim, 1) * self.max_res + if res_dim > 0 else torch.empty(0, device=device)) + freqs = torch.cat([freqs_2exp, freqs_res], dim=0)[:self.freq_dim] + return torch.pow(2.0, freqs) * 2.0 # *2 folds this schedule's 2*pi into the shared *pi below + logs = torch.linspace(0.0, float(self.max_res), steps=self.freq_dim, dtype=torch.float32, device=device) + return torch.pow(2.0, logs) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + orig_dtype = x.dtype + x = x.float() + *dims, D = x.shape + out = torch.outer(x.reshape(-1), self._freqs(x.device)) * torch.pi + out = torch.cat([out.sin(), out.cos()], dim=-1).reshape(*dims, -1) + if out.shape[-1] < self.channels: + out = torch.cat([out, torch.zeros(*dims, self.channels - out.shape[-1], + device=out.device, dtype=out.dtype)], dim=-1) + return out.to(orig_dtype) + + +def attention(q, k, v, transformer_options=None): + # q, k, v: (B, L, heads, dim) -> (B, L, heads, dim). Shared optimized_attention call convention. + out = optimized_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), heads=q.shape[2], + skip_reshape=True, skip_output_reshape=True, low_precision_attention=False, + transformer_options=transformer_options) + return out.transpose(1, 2) + + +# Transformer building blocks + +class MLP(nn.Module): + def __init__(self, in_channels, hidden_channels, out_channels, dtype=None, device=None, operations=None): + super().__init__() + self.mlp = nn.Sequential( + operations.Linear(in_channels, hidden_channels, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(hidden_channels, out_channels, dtype=dtype, device=device), + ) + + def forward(self, x): + return self.mlp(x) + + +class RopeMultiHeadAttention(nn.Module): + def __init__(self, channels, num_heads, qkv_bias=True, qk_rms_norm=False, use_rope=False, + dtype=None, device=None, operations=None): + super().__init__() + self.channels = channels + self.num_heads = num_heads + self.head_dim = channels // num_heads + self.qk_rms_norm = qk_rms_norm + self.use_rope = use_rope + self.qkv = operations.Linear(channels, channels * 3, bias=qkv_bias, dtype=dtype, device=device) + if self.qk_rms_norm: + self.q_norm = MultiHeadRMSNorm(self.head_dim, num_heads, dtype=dtype, device=device) + self.k_norm = MultiHeadRMSNorm(self.head_dim, num_heads, dtype=dtype, device=device) + self.out = operations.Linear(channels, channels, dtype=dtype, device=device) + + def forward(self, x, rope_emb=None, transformer_options=None): + B, L, C = x.shape + qkv = self.qkv(x).reshape(B, L, 3, self.num_heads, self.head_dim) + q, k, v = qkv.unbind(2) + if self.use_rope: + q, k = apply_rope(q, k, rope_emb) + if self.qk_rms_norm: + q = self.q_norm(q) + k = self.k_norm(k) + h = attention(q, k, v, transformer_options) # (B, L, heads, dim) + return self.out(h.reshape(B, L, C)) + + +class UnifiedTransformerBlock(nn.Module): + def __init__(self, channels, num_heads, mlp_ratio=4.0, + use_rope=False, qk_rms_norm=False, qkv_bias=True, + modulation=True, share_mod=False, + dtype=None, device=None, operations=None): + super().__init__() + self.modulation = modulation + self.share_mod = share_mod + self.norm1 = operations.LayerNorm(channels, elementwise_affine=not modulation, eps=1e-6, dtype=dtype, device=device) + self.norm2 = operations.LayerNorm(channels, elementwise_affine=not modulation, eps=1e-6, dtype=dtype, device=device) + self.attn = RopeMultiHeadAttention(channels, num_heads=num_heads, + qkv_bias=qkv_bias, use_rope=use_rope, qk_rms_norm=qk_rms_norm, + dtype=dtype, device=device, operations=operations) + self.mlp = MLP(channels, int(channels * mlp_ratio), channels, dtype=dtype, device=device, operations=operations) + if modulation: + if not share_mod: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), operations.Linear(channels, 6 * channels, bias=True, dtype=dtype, device=device)) + self.shift_table = nn.Parameter(torch.empty(1, 6 * channels, dtype=dtype, device=device)) + + def forward(self, x, mod=None, rotary_emb=None, transformer_options=None): + if self.modulation: + if not self.share_mod: + mod = self.adaLN_modulation(mod) + mod = mod + comfy.model_management.cast_to(self.shift_table, mod.dtype, mod.device) + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1) + h = torch.addcmul(shift_msa.unsqueeze(1), self.norm1(x), 1 + scale_msa.unsqueeze(1)) + x = torch.addcmul(x, self.attn(h, rope_emb=rotary_emb, transformer_options=transformer_options), gate_msa.unsqueeze(1)) + h = torch.addcmul(shift_mlp.unsqueeze(1), self.norm2(x), 1 + scale_mlp.unsqueeze(1)) + x = torch.addcmul(x, self.mlp(h), gate_mlp.unsqueeze(1)) + else: + x = x + self.attn(self.norm1(x), rope_emb=rotary_emb, transformer_options=transformer_options) + x = x + self.mlp(self.norm2(x)) + return x + + +class TimestepEmbedder(nn.Module): + def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): + super().__init__() + self.mlp = nn.Sequential( + operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + half = dim // 2 + freqs = torch.exp(-np.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + emb = self.timestep_embedding(t, self.frequency_embedding_size) + return self.mlp(emb.to(self.mlp[0].weight.dtype)) + + +class LatentSeqMMFlowModel(nn.Module): + def __init__(self, image_model=None, q_token_length=8192, in_channels=16, model_channels=1024, + cond_channels=1280, out_channels=16, num_blocks=24, num_refiner_blocks=2, + num_heads=None, num_head_channels=64, cam_channels=5, cond2_channels=128, + mlp_ratio=4, share_mod=True, qk_rms_norm=True, + dtype=None, device=None, operations=None, **kwargs): + super().__init__() + self.dtype = dtype + self.q_token_length = q_token_length + self.in_channels = in_channels + self.cam_channels = cam_channels + self.model_channels = model_channels + self.cond_channels = cond_channels + self.cond2_channels = cond2_channels + self.out_channels = out_channels + self.num_blocks = num_blocks + self.num_refiner_blocks = num_refiner_blocks + self.num_heads = num_heads or model_channels // num_head_channels + self.mlp_ratio = mlp_ratio + self.share_mod = share_mod + self.qk_rms_norm = qk_rms_norm + + factory_kwargs = dict(dtype=dtype, device=device) + op_kwargs = dict(operations=operations, **factory_kwargs) + + self.t_embedder = TimestepEmbedder(model_channels, **op_kwargs) + if share_mod: + self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(model_channels, 6 * model_channels, bias=True, **factory_kwargs)) + + self.input_layer = operations.Linear(in_channels, model_channels, **factory_kwargs) + self.cond_embedder = operations.Linear(cond_channels, model_channels, **factory_kwargs) + self.cond_embedder2 = operations.Linear(cond2_channels, model_channels, **factory_kwargs) if cond2_channels is not None else None + + # Fixed Sobol (low-discrepancy) 3D anchor positions for the latent tokens, used as positional encoding. + # The embedder is parameter-free and the anchors are fixed, precompute once. + sobol_seq = torch.quasirandom.SobolEngine(dimension=3, scramble=True, seed=123).draw(q_token_length) + pos_emb = PcdAbsolutePositionEmbedder(model_channels)(sobol_seq.unsqueeze(0)) + self.register_buffer("pos_emb", pos_emb, persistent=False) + + # RePo3DRotaryEmbedding layers for the refiner and main blocks + repo_kwargs = dict(num_heads=self.num_heads, head_dim=num_head_channels, **op_kwargs) + self.noise_repo_layers = nn.ModuleList( + [RePo3DRotaryEmbedding(model_channels, **repo_kwargs) for _ in range(num_refiner_blocks)]) + self.context_repo_layers = nn.ModuleList( + [RePo3DRotaryEmbedding(model_channels, **repo_kwargs) for _ in range(num_refiner_blocks)]) + self.repo_layers = nn.ModuleList( + [RePo3DRotaryEmbedding(model_channels, **repo_kwargs) for _ in range(num_blocks)]) + + # Refiner blocks + block_kwargs = dict(num_heads=self.num_heads, mlp_ratio=self.mlp_ratio, use_rope=True, qk_rms_norm=self.qk_rms_norm, **op_kwargs) + self.noise_refiner = nn.ModuleList( + [UnifiedTransformerBlock(model_channels, modulation=True, share_mod=self.share_mod, **block_kwargs) for _ in range(num_refiner_blocks)]) + self.context_refiner = nn.ModuleList( + [UnifiedTransformerBlock(model_channels, modulation=False, **block_kwargs) for _ in range(num_refiner_blocks)]) + + self.cam_refiner = MLP(self.cam_channels, model_channels, model_channels, **op_kwargs) + + self.blocks = nn.ModuleList( + [UnifiedTransformerBlock(model_channels, modulation=True, share_mod=self.share_mod, **block_kwargs) for _ in range(num_blocks)]) + + self.shift_table = nn.Parameter(torch.empty(1, 2, model_channels, **factory_kwargs)) + self.out_layer = operations.Linear(model_channels, out_channels, **factory_kwargs) + self.cam_out_layer = operations.Linear(model_channels, cam_channels, **factory_kwargs) + + def forward(self, x, t, context=None, ref_latents=None, transformer_options={}, **kwargs): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) + ).execute(x, t, context, ref_latents, transformer_options, **kwargs) + + def _forward(self, x, t, context=None, ref_latents=None, transformer_options={}, **kwargs): + # x is the unpacked nested latent: [latent (B,8192,in_channels), camera (B,1,cam_channels)]. + # context == feature1. + z, camera = x[0], x[1] + feat1 = context + + h_x = self.input_layer(z) + h_cond = self.cond_embedder(feat1) + if ref_latents is not None and self.cond_embedder2 is not None: + # Flatten the Flux2 VAE latent (B,128,h,w) to a token sequence and front-pad to feat1's length + # (the pad count = feat1's prefix tokens: DINOv3 cls + registers), then add to the context. + feat2 = ref_latents[0].flatten(2).transpose(1, 2) + feat2 = F.pad(feat2, (0, 0, feat1.shape[1] - feat2.shape[1], 0)) + h_cond = h_cond + self.cond_embedder2(feat2.to(h_cond.dtype)) + t_emb = self.t_embedder(t) + t_mod = self.adaLN_modulation(t_emb) if self.share_mod else t_emb + + h_x = h_x + self.pos_emb.to(z) + + for i, block in enumerate(self.noise_refiner): + h_x = block(h_x, mod=t_mod, rotary_emb=self.noise_repo_layers[i](h_x), transformer_options=transformer_options) + + for i, block in enumerate(self.context_refiner): + h_cond = block(h_cond, mod=None, rotary_emb=self.context_repo_layers[i](h_cond), transformer_options=transformer_options) + + cam = camera.to(z) + h_cam = self.cam_refiner(cam) + h = torch.cat([h_x, h_cond, h_cam], dim=1) + + for i, block in enumerate(self.blocks): + h = block(h, mod=t_mod, rotary_emb=self.repo_layers[i](h), transformer_options=transformer_options) + + h_x = F.layer_norm(h[:, :z.shape[1]].float(), h.shape[-1:]).to(z) + h_cam = F.layer_norm(h[:, -cam.shape[1]:].float(), h.shape[-1:]).to(z) + + shift, scale = (comfy.model_management.cast_to(self.shift_table, t_emb.dtype, t_emb.device) + t_emb.unsqueeze(1)).chunk(2, dim=1) + scale = 1 + scale + h_x = torch.addcmul(shift, h_x, scale) + h_cam = torch.addcmul(shift, h_cam, scale) + + return self.out_layer(h_x), self.cam_out_layer(h_cam) diff --git a/comfy/ldm/triposplat/preview.py b/comfy/ldm/triposplat/preview.py new file mode 100644 index 000000000..6a942bb53 --- /dev/null +++ b/comfy/ldm/triposplat/preview.py @@ -0,0 +1,91 @@ +# Live preview for TripoSplat: decode an x0 estimate into a coarse gaussian splat and render it with a perspective orbit camera. +import numpy as np +from PIL import Image + +_C0 = 0.28209479177387814 +_LATENT_TOKENS = 8192 # q_token_length +_LATENT_CH = 16 # in_channels +_OBJECT_TO_VIEWER = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]], np.float32) # object frame -> viewer Y-up frame + + +def _view_matrix(yaw_deg, pitch_deg): + y, p = np.radians(yaw_deg), np.radians(pitch_deg) + Ry = np.array([[np.cos(y), 0, np.sin(y)], [0, 1, 0], [-np.sin(y), 0, np.cos(y)]], np.float32) + Rx = np.array([[1, 0, 0], [0, np.cos(p), -np.sin(p)], [0, np.sin(p), np.cos(p)]], np.float32) + return Rx @ Ry + + +def render_splat(xyz, rgb, scale, opacity=None, yaw=35.0, pitch=30.0, size=320, min_px=2, gain=1.0, + max_px=9, min_opacity=0.0, fov=35.0, dist=2.2): + # Project gaussian centers with a perspective camera and paint each as a filled disk whose screen + # radius follows the gaussian's world-space scale, composited with a nearest-wins z-buffer. + # gain scales the footprint (≈ std spanned), `min_px`/`max_px` clamp the on-screen radius. + + pts = xyz.astype(np.float32) @ _OBJECT_TO_VIEWER.T + v = pts @ _view_matrix(yaw, pitch).T + zc = v[:, 2] + dist + keep = zc > 1e-2 + if opacity is not None and min_opacity > 0.0: # culls gaussians with very low opacity + keep = keep & (opacity > min_opacity) + v, zc, scale = v[keep], zc[keep], scale[keep] + col = (np.clip(rgb, 0, 1)[:, :3] * 255).astype(np.uint8)[keep] + if v.shape[0] == 0: + return Image.fromarray(np.zeros((size, size, 3), np.uint8)) + f = (size / 2) / np.tan(np.radians(fov) / 2) + cx = size / 2 + f * v[:, 0] / zc + cy = size / 2 + f * v[:, 1] / zc + radius = np.clip(np.round(f * scale / zc * gain), min_px, max_px).astype(np.int32) + + # Expand each splat to its disk pixels, bucketed by integer radius so it stays vectorized. + px, py, pz, pc = [], [], [], [] + for r in range(int(radius.min()), int(radius.max()) + 1): + m = radius == r + if not m.any(): + continue + dy, dx = np.mgrid[-r:r + 1, -r:r + 1] + disk = (dx * dx + dy * dy) <= r * r + ox, oy = dx[disk], dy[disk] + px.append((cx[m, None] + ox).ravel()) + py.append((cy[m, None] + oy).ravel()) + pz.append(np.repeat(zc[m], ox.size)) + pc.append(np.repeat(col[m], ox.size, axis=0)) + px, py = np.concatenate(px), np.concatenate(py) + pz, pc = np.concatenate(pz), np.concatenate(pc) + xi = np.clip(px, 0, size - 1).astype(np.int64) + yi = np.clip(py, 0, size - 1).astype(np.int64) + + # Nearest-wins z-buffer: pack (quantized depth, source index), per-pixel min picks the closest + # splat, then decode the winning index back to its color. + pid = yi * size + xi + q = np.clip((pz * 1024.0).astype(np.int64), 0, (1 << 20) - 1) # near = small + key = (q << 32) | np.arange(pid.size, dtype=np.int64) + buf = np.full(size * size, 1 << 62, np.int64) + np.minimum.at(buf, pid, key) + img = np.zeros((size * size, 3), np.uint8) + hit = buf < (1 << 62) + img[hit] = pc[buf[hit] & 0xFFFFFFFF] + return Image.fromarray(img.reshape(size, size, 3)) + + +def _extract_latent(x0): + # x0 from the sampler callback is the nested latent packed to (B, 1, TOKENS*CH + 1*5); + # the plain single-latent case is (B, TOKENS, CH). Return the (B, TOKENS, CH) latent stream. + if x0.ndim == 3 and x0.shape[1] == _LATENT_TOKENS and x0.shape[2] == _LATENT_CH: + return x0 + flat = x0.reshape(x0.shape[0], -1) + return flat[:, :_LATENT_TOKENS * _LATENT_CH].reshape(x0.shape[0], _LATENT_TOKENS, _LATENT_CH) + + +def decode_x0_to_image(decoder, x0, cfg): + # Decode x0 at a coarse octree level / few gaussians and render a preview image. + latent = _extract_latent(x0) + fsm = decoder.first_stage_model + gaussian = fsm.decode(latent.to(decoder.device, decoder.vae_dtype), + num_gaussians=cfg.get("gaussians", 16384), level=cfg.get("level", 5))[0] + xyz = gaussian.get_xyz.float().cpu().numpy() + rgb = gaussian._features_dc.float().cpu().numpy()[:, 0, :] * _C0 + 0.5 + scale = gaussian.get_scaling.float().cpu().numpy().max(axis=1) # per-splat world radius (largest axis) + opacity = gaussian.get_opacity.float().cpu().numpy()[:, 0] + return render_splat(xyz, rgb, scale, opacity=opacity, yaw=cfg.get("yaw", 35.0), pitch=cfg.get("pitch", 30.0), + size=cfg.get("size", 320), min_px=1, gain=1.0, max_px=cfg.get("point_size", 3), + min_opacity=0.01) diff --git a/comfy/ldm/triposplat/vae.py b/comfy/ldm/triposplat/vae.py new file mode 100644 index 000000000..e5ed9fd36 --- /dev/null +++ b/comfy/ldm/triposplat/vae.py @@ -0,0 +1,382 @@ +# TripoSplat gaussian decoder ("VAE"): an octree probability decoder picks point coords, then an +# elastic-gaussian decoder predicts per-point gaussian params. OctreeGaussianDecoder.decode() returns +# a Gaussian. The octree sampler uses the global torch RNG (no generator) like upstream, so seed it for repeatable decodes. +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +import comfy.model_management +import comfy.ops +from .gaussian import build_gaussian_models +from .model import MultiHeadRMSNorm, MLP, PcdAbsolutePositionEmbedder, attention + + +# Quasi-random sampling utilities (pure functions, dtype/device-agnostic) + +PRIMES = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53] + + +def radical_inverse(base, n): + val = 0 + inv_base = 1.0 / base + inv_base_n = inv_base + while n > 0: + digit = n % base + val += digit * inv_base_n + n //= base + inv_base_n *= inv_base + return val + + +def halton_sequence(dim, n): + return [radical_inverse(PRIMES[i], n) for i in range(dim)] + + +def hammersley_sequence(dim, n, num_samples): + return [n / num_samples] + halton_sequence(dim - 1, n) + + +def sample_probs(probs, counts, generator=None): + # Systematic resampling: distribute counts[r] draws across the P bins of row r + batch_shape = counts.shape + R = counts.numel() + P = probs.size(-1) + device = probs.device + probs = probs.reshape(R, P).to(torch.float32).clamp_min(0) + counts = counts.reshape(R).to(device=device, dtype=torch.long) + + row_sums = probs.sum(1, keepdim=True) + probs = torch.where(row_sums == 0, probs.new_tensor(1.0 / P), probs / row_sums.clamp_min(1)) + cdf = probs.cumsum(dim=1).clamp(max=1.0 - 1e-12) + + Nmax = int(counts.max()) + if Nmax == 0: + return counts.new_zeros(*batch_shape, P) + cnt = counts.clamp_min(1).float().unsqueeze(1) # (R, 1) + grid = torch.arange(Nmax, device=device, dtype=torch.float32).unsqueeze(0) # (1, Nmax) + u = (torch.rand(R, 1, generator=generator).to(device) + grid) / cnt # (R, Nmax) systematic samples (CPU-seeded) + idx = torch.searchsorted(cdf, u.clamp(max=1.0 - 1e-12)).clamp_max(P - 1) + weight = (grid < counts.unsqueeze(1)).to(cdf.dtype) # mask out j >= counts[r] + out = torch.zeros(R, P, dtype=torch.float32, device=device) + out.scatter_add_(1, idx, weight) + return out.to(torch.long).view(*batch_shape, P) + + +class MultiHeadAttention(nn.Module): + def __init__(self, channels, num_heads, ctx_channels=None, type="self", qkv_bias=True, qk_rms_norm=False, + dtype=None, device=None, operations=None): + super().__init__() + assert channels % num_heads == 0 + self.channels = channels + self.head_dim = channels // num_heads + self.ctx_channels = ctx_channels if ctx_channels is not None else channels + self.num_heads = num_heads + self._type = type + self.qk_rms_norm = qk_rms_norm + if self._type == "self": + self.to_qkv = operations.Linear(channels, channels * 3, bias=qkv_bias, dtype=dtype, device=device) + else: + self.to_q = operations.Linear(channels, channels, bias=qkv_bias, dtype=dtype, device=device) + self.to_kv = operations.Linear(self.ctx_channels, channels * 2, bias=qkv_bias, dtype=dtype, device=device) + if self.qk_rms_norm: + self.q_rms_norm = MultiHeadRMSNorm(self.head_dim, num_heads, dtype=dtype, device=device) + self.k_rms_norm = MultiHeadRMSNorm(self.head_dim, num_heads, dtype=dtype, device=device) + self.to_out = operations.Linear(channels, channels, dtype=dtype, device=device) + + def forward(self, x, context=None): + B, L, C = x.shape + if self._type == "self": + q, k, v = self.to_qkv(x).reshape(B, L, 3, self.num_heads, -1).unbind(dim=2) + else: + Lkv = context.shape[1] + q = self.to_q(x).reshape(B, L, self.num_heads, -1) + k, v = self.to_kv(context).reshape(B, Lkv, 2, self.num_heads, -1).unbind(dim=2) + if self.qk_rms_norm: + q = self.q_rms_norm(q) + k = self.k_rms_norm(k) + h = attention(q, k, v) + return self.to_out(h.reshape(B, L, -1)) + + +# Octree probability decoder + +class LevelEmbedder(nn.Module): + def __init__(self, hidden_size, frequency_embedding_size=256, max_period=1024, + dtype=None, device=None, operations=None): + super().__init__() + self.mlp = nn.Sequential( + operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), + ) + self.frequency_embedding_size = frequency_embedding_size + self.max_period = max_period + + @staticmethod + def level_embedding(t, dim, max_period=1024): + half = dim // 2 + freqs = torch.exp(-np.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(device=t.device) + args = t[:, None].float() * freqs[None] * 2 * torch.pi + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + emb = self.level_embedding(t, self.frequency_embedding_size, self.max_period) + return self.mlp(emb.to(self.mlp[0].weight.dtype)) + + +class ModulatedTransformerCrossOnlyBlock(nn.Module): + def __init__(self, channels, ctx_channels, num_heads, mlp_ratio=4.0, share_mod=False, + qk_rms_norm_cross=True, qkv_bias=True, dtype=None, device=None, operations=None): + super().__init__() + self.share_mod = share_mod + self.norm1 = operations.LayerNorm(channels, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.norm2 = operations.LayerNorm(channels, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.cross_attn = MultiHeadAttention(channels, ctx_channels=ctx_channels, num_heads=num_heads, + type="cross", qkv_bias=qkv_bias, + qk_rms_norm=qk_rms_norm_cross, dtype=dtype, device=device, operations=operations) + self.mlp = MLP(channels, int(channels * mlp_ratio), channels, dtype=dtype, device=device, operations=operations) + if not share_mod: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), operations.Linear(channels, 6 * channels, bias=True, dtype=dtype, device=device)) + + def forward(self, x, mod, context): + if self.share_mod: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = mod.chunk(6, dim=1) + else: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(mod).chunk(6, dim=1) + h = torch.addcmul(shift_msa.unsqueeze(1), self.norm1(x), 1 + scale_msa.unsqueeze(1)) + x = torch.addcmul(x, self.cross_attn(h, context), gate_msa.unsqueeze(1)) + h = torch.addcmul(shift_mlp.unsqueeze(1), self.norm2(x), 1 + scale_mlp.unsqueeze(1)) + x = torch.addcmul(x, self.mlp(h), gate_mlp.unsqueeze(1)) + return x + + +class OctreeProbabilityFixedlenDecoder(nn.Module): + # Cross-attention transformer over octree coords -> per-node 8-way child occupancy logits. + def __init__(self, model_channels=1024, cond_channels=16, num_blocks=4, num_heads=16, + num_head_channels=64, mlp_ratio=4.0, share_mod=True, + qk_rms_norm_cross=True, dtype=None, device=None, operations=None): + super().__init__() + self.model_channels = model_channels + self.cond_channels = cond_channels + self.num_blocks = num_blocks + self.num_heads = num_heads or model_channels // num_head_channels + self.mlp_ratio = mlp_ratio + self.share_mod = share_mod + self.qk_rms_norm_cross = qk_rms_norm_cross + self.input_layer = operations.Linear(model_channels, model_channels, dtype=dtype, device=device) + self.l_embedder = LevelEmbedder(model_channels, dtype=dtype, device=device, operations=operations) + if share_mod: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), operations.Linear(model_channels, 6 * model_channels, bias=True, dtype=dtype, device=device)) + if cond_channels is not None: + self.blocks = nn.ModuleList([ + ModulatedTransformerCrossOnlyBlock( + model_channels, ctx_channels=cond_channels, num_heads=self.num_heads, + mlp_ratio=self.mlp_ratio, qk_rms_norm_cross=self.qk_rms_norm_cross, + share_mod=self.share_mod, dtype=dtype, device=device, operations=operations) + for _ in range(num_blocks) + ]) + self.out_proj = operations.Linear(model_channels, 8, dtype=dtype, device=device) + self.in_proj = operations.Linear(3, model_channels, dtype=dtype, device=device) + self.pos_embedder = PcdAbsolutePositionEmbedder(channels=model_channels, in_channels=3, max_res=10, schedule="log2") + + def forward(self, x, l, cond): + d = next(self.parameters()).dtype + B, L, _ = x.shape + h = self.in_proj(x.to(d)) + self.pos_embedder(x.reshape(-1, 3)).reshape(B, L, -1).to(d) + h = self.input_layer(h) + l_emb = self.l_embedder(l) + if self.share_mod: + l_emb = self.adaLN_modulation(l_emb) + cond = cond.to(d) + for block in self.blocks: + h = block(h, l_emb, cond) + h = F.layer_norm(h.float(), h.shape[-1:]).to(d) + logits = self.out_proj(h) + return {"logits": logits, "probs": torch.softmax(logits, dim=-1)} + + @staticmethod + def sample(model, cond, num_points, level, temperature=1.0, generator=None): + B = cond.shape[0] + device = cond.device + child_offset = torch.tensor([[i, j, k] for k in [0, 1] for j in [0, 1] for i in [0, 1]], + dtype=torch.long, device=device) + prev_coords_int = torch.zeros(B, 1, 3, dtype=torch.long, device=device) + prev_counts = torch.full((B, 1), num_points, dtype=torch.long, device=device) + prev_log_probs = torch.zeros(B, 1, dtype=torch.float32, device=device) + batch_indices_range = torch.arange(B, device=device).unsqueeze(1) + + for lv in range(1, level + 1): + res_p = 1 << (lv - 1) + res = 1 << lv + parent_coords_norm = (prev_coords_int.to(torch.float32) + 0.5) / res_p + res_tensor = torch.full((B,), res, dtype=torch.long, device=device) + pred_logits = model(parent_coords_norm, res_tensor, cond)["logits"] / temperature + pred_probs = torch.softmax(pred_logits, dim=-1) + pred_log_probs = torch.log_softmax(pred_logits, dim=-1) + sampled = sample_probs(pred_probs, prev_counts, generator=generator).flatten(1, 2) + pred_log_probs = pred_log_probs.flatten(1, 2) + prev_log_probs_expanded = prev_log_probs.repeat_interleave(8, dim=1) + child_coords_int = (prev_coords_int[:, :, None, :] * 2 + child_offset[None, None, :, :]).flatten(1, 2) + mask = sampled > 0 + max_valid = mask.sum(dim=1).max().item() + scatter_indices = mask.cumsum(dim=1) - 1 + valid_scatter_indices = scatter_indices[mask] + valid_batch_indices = batch_indices_range.expand_as(mask)[mask] + next_prev_coords_int = torch.zeros(B, max_valid, 3, dtype=child_coords_int.dtype, device=device) + next_prev_coords_int[valid_batch_indices, valid_scatter_indices] = child_coords_int[mask] + next_prev_counts = torch.zeros(B, max_valid, dtype=sampled.dtype, device=device) + next_prev_counts[valid_batch_indices, valid_scatter_indices] = sampled[mask] + next_prev_log_probs = torch.zeros(B, max_valid, dtype=prev_log_probs.dtype, device=device) + next_prev_log_probs[valid_batch_indices, valid_scatter_indices] = (prev_log_probs_expanded + pred_log_probs)[mask] + prev_coords_int = next_prev_coords_int + prev_counts = next_prev_counts + prev_log_probs = next_prev_log_probs + + res = 1 << level + prev_log_probs = torch.repeat_interleave(prev_log_probs.flatten(0, 1), prev_counts.flatten(0, 1), dim=0).reshape(B, num_points) + coords_int = torch.repeat_interleave(prev_coords_int.flatten(0, 1), prev_counts.flatten(0, 1), dim=0).reshape(B, num_points, -1) + rand = torch.rand(coords_int.shape, dtype=torch.float32, generator=generator).to(device) + coords_norm = (coords_int.to(torch.float32) + rand) / res + return {"points": coords_norm, "log_probs": prev_log_probs} + + +# Elastic gaussian decoder + +class TransformerCrossBlock(nn.Module): + def __init__(self, channels, ctx_channels, num_heads, mlp_ratio=4.0, + qk_rms_norm=True, qk_rms_norm_cross=True, qkv_bias=True, + dtype=None, device=None, operations=None): + super().__init__() + self.norm1 = operations.LayerNorm(channels, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.norm2 = operations.LayerNorm(channels, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) + self.norm3 = operations.LayerNorm(channels, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.self_attn = MultiHeadAttention(channels, num_heads=num_heads, type="self", qkv_bias=qkv_bias, + qk_rms_norm=qk_rms_norm, dtype=dtype, device=device, operations=operations) + self.cross_attn = MultiHeadAttention(channels, ctx_channels=ctx_channels, num_heads=num_heads, type="cross", + qkv_bias=qkv_bias, qk_rms_norm=qk_rms_norm_cross, dtype=dtype, device=device, operations=operations) + self.mlp = MLP(channels, int(channels * mlp_ratio), channels, dtype=dtype, device=device, operations=operations) + + def forward(self, x, context): + x = x + self.self_attn(self.norm1(x)) + x = x + self.cross_attn(self.norm2(x), context) + x = x + self.mlp(self.norm3(x)) + return x + + +class ElasticGaussianFixedlenDecoder(nn.Module): + # Cross-attention transformer over sampled octree points -> per-point gaussian params. + def __init__(self, in_channels=3, model_channels=1024, cond_channels=16, num_blocks=16, num_heads=16, + num_head_channels=64, mlp_ratio=4.0, *, representation_config=None, + qk_rms_norm=True, qk_rms_norm_cross=True, dtype=None, device=None, operations=None): + super().__init__() + self.rep_config = representation_config or dict( + lr=dict(_xyz=1.0, _features_dc=1.0, _opacity=1.0, _scaling=1.0, _rotation=0.1), + perturb_offset=True, perturbe_size=1.5, offset_scale=0.05, num_gaussians=32, + filter_kernel_size_3d=0.0009, scaling_bias=0.004, opacity_bias=0.1, + scaling_activation="softplus", + ) + self.out_channels = self._calc_layout() + self.model_channels = model_channels + self.cond_channels = cond_channels + self.num_blocks = num_blocks + self.num_heads = num_heads or model_channels // num_head_channels + self.mlp_ratio = mlp_ratio + self.input_layer = operations.Linear(model_channels, model_channels, dtype=dtype, device=device) + if cond_channels is not None: + self.blocks = nn.ModuleList([ + TransformerCrossBlock(model_channels, ctx_channels=cond_channels, + num_heads=self.num_heads, mlp_ratio=self.mlp_ratio, + qk_rms_norm=qk_rms_norm, qk_rms_norm_cross=qk_rms_norm_cross, + dtype=dtype, device=device, operations=operations) + for _ in range(num_blocks) + ]) + self.in_proj = operations.Linear(in_channels, model_channels, dtype=dtype, device=device) + self.pos_embedder = PcdAbsolutePositionEmbedder(channels=model_channels, in_channels=3, max_res=10, schedule="log2") + self.out_proj = operations.Linear(model_channels, self.out_channels, dtype=dtype, device=device) + self._build_perturbation() + + def _calc_layout(self): + ng = self.rep_config['num_gaussians'] + self.layout = { + '_xyz': {'shape': (ng, 3), 'size': ng * 3}, + '_features_dc': {'shape': (ng, 1, 3), 'size': ng * 3}, + '_scaling': {'shape': (ng, 3), 'size': ng * 3}, + '_rotation': {'shape': (ng, 4), 'size': ng * 4}, + '_opacity': {'shape': (ng, 1), 'size': ng}, + } + self.layout['_offset_scale'] = {'shape': (ng, 1), 'size': ng} + start = 0 + for k, v in self.layout.items(): + v['range'] = (start, start + v['size']) + start += v['size'] + return start + + def _build_perturbation(self): + ng = self.rep_config['num_gaussians'] + perturbation = torch.tensor([hammersley_sequence(3, i, ng) for i in range(ng)]).float() + perturbation = torch.atanh((perturbation * 2 - 1) / self.rep_config['perturbe_size']) + self.register_buffer('points_offset_perturbation', perturbation) + base = torch.tensor(self.rep_config['offset_scale']) + self.register_buffer('base_offset_scale', torch.log(torch.exp(base) - 1.0)) + + def _get_offset(self, h): + B = h.shape[0] + r = self.layout['_offset_scale']['range'] + _offset_scale = F.softplus( + h[:, :, r[0]:r[1]].reshape(B, -1, *self.layout['_offset_scale']['shape']) + + comfy.model_management.cast_to(self.base_offset_scale, h.dtype, h.device)) + + r = self.layout['_xyz']['range'] + offset = h[:, :, r[0]:r[1]].reshape(B, -1, *self.layout['_xyz']['shape']) + offset = offset * self.rep_config['lr']['_xyz'] + if self.rep_config['perturb_offset']: + offset = offset + comfy.model_management.cast_to(self.points_offset_perturbation, offset.dtype, offset.device) + offset = torch.tanh(offset) * 0.5 * self.rep_config['perturbe_size'] + offset = offset * _offset_scale + return offset + + def forward(self, x=None, cond=None): + pcd = x["points"] + d = next(self.parameters()).dtype + B, L, _ = pcd.shape + h = self.in_proj(pcd.to(d)) + self.pos_embedder(pcd.reshape(-1, 3)).reshape(B, L, -1).to(d) + h = self.input_layer(h) + cond = cond.to(d) + for block in self.blocks: + h = block(h, cond) + h = F.layer_norm(h.float(), h.shape[-1:]).to(h.dtype) + return {"features": self.out_proj(h)} + + +# Combined octree gaussian decoder (comfy first-stage model) + +class OctreeGaussianDecoder(nn.Module): + _MAX_VOXEL_LEVEL = 8 + + def __init__(self, dtype=None, device=None, operations=None): + super().__init__() + if operations is None: + operations = comfy.ops.disable_weight_init + self.octree = OctreeProbabilityFixedlenDecoder(dtype=dtype, device=device, operations=operations) + self.gs = ElasticGaussianFixedlenDecoder(dtype=dtype, device=device, operations=operations) + + @property + def gaussians_per_point(self) -> int: + return self.gs.rep_config['num_gaussians'] + + def decode(self, latent: torch.Tensor, num_gaussians: int, level: int = None, generator=None): + # level defaults to the full octree depth, a lower level is cheaper (coarser) for live previews. + # generator (a CPU torch.Generator) makes the octree sampling reproducible without touching global RNG. + level = self._MAX_VOXEL_LEVEL if level is None else level + num_decoder_tokens = max(1, num_gaussians // self.gaussians_per_point) + points_pred = OctreeProbabilityFixedlenDecoder.sample( + self.octree, latent, num_points=num_decoder_tokens, level=level, temperature=1.0, generator=generator, + ) + pred = self.gs(x=points_pred, cond=latent) + return build_gaussian_models(self.gs, points_pred, pred) # one GaussianModel per batch item diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py index 70dfe7b16..1c9782a38 100644 --- a/comfy/ldm/wan/model.py +++ b/comfy/ldm/wan/model.py @@ -8,7 +8,7 @@ from einops import rearrange from comfy.ldm.modules.attention import optimized_attention from comfy.ldm.flux.layers import EmbedND -from comfy.ldm.flux.math import apply_rope1 +from comfy.ldm.flux.math import apply_rope1, rope import comfy.ldm.common_dit import comfy.model_management import comfy.patcher_extension @@ -570,6 +570,14 @@ class WanModel(torch.nn.Module): full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2) x = torch.concat((full_ref, x), dim=1) + # In-context reference (Bernini) + context_latents = kwargs.get("context_latents", None) + main_len = x.shape[1] + if context_latents is not None: + for lat in context_latents: + cl = self.patch_embedding(lat.float().to(x.device)).to(x.dtype).flatten(2).transpose(1, 2) + x = torch.cat([x, cl], dim=1) + # context context = self.text_embedding(context) @@ -599,6 +607,9 @@ class WanModel(torch.nn.Module): # head x = self.head(x, e) + if context_latents is not None: + x = x[:, :main_len] + if full_ref is not None: x = x[:, full_ref.shape[1]:] @@ -606,7 +617,7 @@ class WanModel(torch.nn.Module): x = self.unpatchify(x, grid_sizes) return x - def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}): + def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}, source_id=0): patch_size = self.patch_size t_len = ((t + (patch_size[0] // 2)) // patch_size[0]) h_len = ((h + (patch_size[1] // 2)) // patch_size[1]) @@ -638,6 +649,13 @@ class WanModel(torch.nn.Module): img_ids = img_ids.reshape(1, -1, img_ids.shape[-1]) freqs = self.rope_embedder(img_ids).movedim(1, 2) + + # In-context reference: a non-zero source_id composes an extra rotation into the spatial rope + if source_id: + d = self.dim // self.num_heads + pos = torch.tensor([[float(source_id)]], device=freqs.device, dtype=torch.float32) + id_rot = rope(pos, d, self.rope_embedder.theta).reshape(1, 1, 1, d // 2, 2, 2).to(freqs.dtype) + freqs = torch.einsum('...ij,...jk->...ik', freqs, id_rot) return freqs def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs): @@ -661,6 +679,15 @@ class WanModel(torch.nn.Module): t_len += 1 freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options) + + # In-context reference: one rope block per stream, each with it's own source_id (1, 2, ...) to distinguish from the target (id 0). + context_latents = kwargs.get("context_latents", None) + if context_latents is not None: + context_latents = [comfy.ldm.common_dit.pad_to_patch_size(lat, self.patch_size) for lat in context_latents] + for i, lat in enumerate(context_latents): + freqs = torch.cat([freqs, self.rope_encode(lat.shape[-3], lat.shape[-2], lat.shape[-1], device=x.device, dtype=x.dtype, transformer_options=transformer_options, source_id=i + 1)], dim=1) + kwargs = {**kwargs, "context_latents": context_latents} + return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, **kwargs)[:, :, :t, :h, :w] def unpatchify(self, x, grid_sizes): @@ -1631,13 +1658,15 @@ class SCAILWanModel(WanModel): self.patch_embedding_pose = operations.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=torch.float32) - def forward_orig(self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, pose_latents=None, reference_latent=None, **kwargs): + def forward_orig(self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, pose_latents=None, reference_latent=None, ref_mask_latents=None, sam_latents=None, **kwargs): if reference_latent is not None: x = torch.cat((reference_latent, x), dim=2) # embeddings x = self.patch_embedding(x.float()).to(x.dtype) + if ref_mask_latents is not None: # SCAIL-2 additive mask stream (one identity mask frame per reference, then video) + x = x + self.patch_embedding_mask(ref_mask_latents.float()).to(x.dtype) grid_sizes = x.shape[2:] transformer_options["grid_sizes"] = grid_sizes x = x.flatten(2).transpose(1, 2) @@ -1645,6 +1674,8 @@ class SCAILWanModel(WanModel): scail_pose_seq_len = 0 if pose_latents is not None: scail_x = self.patch_embedding_pose(pose_latents.float()).to(x.dtype) + if sam_latents is not None: # SCAIL-2 additive mask stream + scail_x = scail_x + self.patch_embedding_mask(sam_latents.float()).to(x.dtype) scail_x = scail_x.flatten(2).transpose(1, 2) scail_pose_seq_len = scail_x.shape[1] x = torch.cat([x, scail_x], dim=1) @@ -1695,16 +1726,44 @@ class SCAILWanModel(WanModel): return x - def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, pose_latents=None, reference_latent=None, transformer_options={}): + # ref_mask_flag is a scalar bool (CONDConstant, SCAIL-2 only). False => replacement mode, + # which places ref/pose via H/W rope shifts instead of the animation-mode temporal offset. + # reference_latent may stack several frames: the last is the primary reference adjacent to the video, the earlier frames are additional references. + def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, pose_latents=None, reference_latent=None, ref_mask_flag=None, transformer_options={}): + ref_t_patches = 0 + if reference_latent is not None: + ref_t_patches = (reference_latent.shape[2] + (self.patch_size[0] // 2)) // self.patch_size[0] + + if ref_mask_flag is not None and not bool(ref_mask_flag): + REF_ROPE_H = 120.0 + POSE_ROPE_W = 120.0 + + main_t_patches = t - ref_t_patches + video_t_start = max(ref_t_patches - 1, 0) + + parts = [] + if ref_t_patches > 0: + ref_tf = {"rope_options": {"shift_y": REF_ROPE_H, "shift_x": 0.0, "scale_y": 1.0, "scale_x": 1.0}} + parts.append(super().rope_encode(ref_t_patches, h, w, t_start=0, device=device, dtype=dtype, transformer_options=ref_tf)) + if main_t_patches > 0: + parts.append(super().rope_encode(main_t_patches, h, w, t_start=video_t_start, device=device, dtype=dtype, transformer_options=transformer_options)) + + if pose_latents is not None: + F_pose, H_pose, W_pose = pose_latents.shape[-3], pose_latents.shape[-2], pose_latents.shape[-1] + h_scale = h / H_pose + w_scale = w / W_pose + h_shift = (h_scale - 1) / 2 + w_shift = (w_scale - 1) / 2 + pose_tf = {"rope_options": {"shift_y": h_shift, "shift_x": POSE_ROPE_W + w_shift, "scale_y": h_scale, "scale_x": w_scale}} + parts.append(super().rope_encode(F_pose, H_pose, W_pose, t_start=video_t_start, device=device, dtype=dtype, transformer_options=pose_tf)) + + return torch.cat(parts, dim=1) + main_freqs = super().rope_encode(t, h, w, t_start=t_start, steps_t=steps_t, steps_h=steps_h, steps_w=steps_w, device=device, dtype=dtype, transformer_options=transformer_options) if pose_latents is None: return main_freqs - ref_t_patches = 0 - if reference_latent is not None: - ref_t_patches = (reference_latent.shape[2] + (self.patch_size[0] // 2)) // self.patch_size[0] - F_pose, H_pose, W_pose = pose_latents.shape[-3], pose_latents.shape[-2], pose_latents.shape[-1] # if pose is at half resolution, scale_y/scale_x=2 stretches the position range to cover the same RoPE extent as the main frames @@ -1719,12 +1778,16 @@ class SCAILWanModel(WanModel): return torch.cat([main_freqs, pose_freqs], dim=1) - def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, pose_latents=None, **kwargs): + def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, pose_latents=None, ref_mask_latents=None, sam_latents=None, **kwargs): bs, c, t, h, w = x.shape x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size) if pose_latents is not None: pose_latents = comfy.ldm.common_dit.pad_to_patch_size(pose_latents, self.patch_size) + if ref_mask_latents is not None: # SCAIL-2 + ref_mask_latents = comfy.ldm.common_dit.pad_to_patch_size(ref_mask_latents, self.patch_size) + if sam_latents is not None: # SCAIL-2 + sam_latents = comfy.ldm.common_dit.pad_to_patch_size(sam_latents, self.patch_size) t_len = t if time_dim_concat is not None: @@ -1737,5 +1800,15 @@ class SCAILWanModel(WanModel): reference_latent = comfy.ldm.common_dit.pad_to_patch_size(kwargs.pop("reference_latent"), self.patch_size) t_len += reference_latent.shape[2] - freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent) - return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent, **kwargs)[:, :, :t, :h, :w] + ref_mask_flag = kwargs.pop("ref_mask_flag", None) # SCAIL-2 + + freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent, ref_mask_flag=ref_mask_flag) + return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent, ref_mask_latents=ref_mask_latents, sam_latents=sam_latents, **kwargs)[:, :, :t, :h, :w] + + +class SCAIL2WanModel(SCAILWanModel): + """SCAIL-2: SCAIL-Preview + an additive binary multi-identity mask stream.""" + + def __init__(self, model_type="scail2", patch_size=(1, 2, 2), in_dim=20, mask_in_dim=28, dim=5120, operations=None, device=None, dtype=None, **kwargs): + super().__init__(model_type=model_type, patch_size=patch_size, in_dim=in_dim, dim=dim, operations=operations, device=device, dtype=dtype, **kwargs) + self.patch_embedding_mask = operations.Conv3d(mask_in_dim, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=torch.float32) diff --git a/comfy/lora.py b/comfy/lora.py index 4e0ea29e0..2c8d0f0bf 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -357,6 +357,12 @@ def model_lora_keys_unet(model, key_map={}): key_lora = k[len("diffusion_model."):-len(".weight")] key_map["transformer.{}".format(key_lora)] = k + if isinstance(model, (comfy.model_base.LTXV, comfy.model_base.LTXAV)): + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")] + key_map["{}".format(key_lora)] = k + return key_map diff --git a/comfy/memory_management.py b/comfy/memory_management.py index 962addb27..e032b7dcd 100644 --- a/comfy/memory_management.py +++ b/comfy/memory_management.py @@ -4,6 +4,7 @@ import dataclasses import torch from typing import NamedTuple +import comfy_aimdo.host_buffer from comfy.quant_ops import QuantizedTensor @@ -17,21 +18,18 @@ class TensorFileSlice(NamedTuple): def read_tensor_file_slice_into(tensor, destination, stream=None, destination2=None): if isinstance(tensor, QuantizedTensor): - if not isinstance(destination, QuantizedTensor): - return False - if tensor._layout_cls != destination._layout_cls: - return False - - if not read_tensor_file_slice_into(tensor._qdata, destination._qdata, stream=stream, + if not read_tensor_file_slice_into(tensor._qdata, + destination._qdata if destination is not None else None, stream=stream, destination2=(destination2._qdata if destination2 is not None else None)): return False - dst_orig_dtype = destination._params.orig_dtype - destination._params.copy_from(tensor._params, non_blocking=False) - destination._params = dataclasses.replace(destination._params, orig_dtype=dst_orig_dtype) + if destination is not None: + dst_orig_dtype = destination._params.orig_dtype + destination._params.copy_from(tensor._params, non_blocking=False) + destination._params = dataclasses.replace(destination._params, orig_dtype=dst_orig_dtype) if destination2 is not None: dst_orig_dtype = destination2._params.orig_dtype - destination2._params.copy_from(destination._params, non_blocking=True) + destination2._params.copy_from(destination._params if destination is not None else tensor._params, non_blocking=True) destination2._params = dataclasses.replace(destination2._params, orig_dtype=dst_orig_dtype) return True @@ -39,10 +37,15 @@ def read_tensor_file_slice_into(tensor, destination, stream=None, destination2=N if info is None: return False + if destination is not None and destination.device.type != "cpu" and destination2 is None: + destination2 = destination + destination = None + file_obj = info.file_ref - if (destination.device.type != "cpu" - or file_obj is None - or destination.numel() * destination.element_size() < info.size + if (file_obj is None + or (destination is None and destination2 is None) + or (destination is not None and (destination.device.type != "cpu" or destination.numel() * destination.element_size() < info.size)) + or (destination2 is not None and (destination2.device.type == "cpu" or destination2.numel() * destination2.element_size() < info.size)) or tensor.numel() * tensor.element_size() != info.size or tensor.storage_offset() != 0 or not tensor.is_contiguous()): @@ -51,6 +54,14 @@ def read_tensor_file_slice_into(tensor, destination, stream=None, destination2=N if info.size == 0: return True + if destination is None: + stream_ptr = getattr(stream, "cuda_stream", 0) if stream is not None else 0 + comfy_aimdo.host_buffer.read_file_to_device(file_obj, info.offset, info.size, + stream_ptr, destination2.data_ptr(), + destination2.device.index, + mark_cold=False) + return True + hostbuf = getattr(destination.untyped_storage(), "_comfy_hostbuf", None) if hostbuf is not None: stream_ptr = getattr(stream, "cuda_stream", 0) if stream is not None else 0 @@ -63,6 +74,9 @@ def read_tensor_file_slice_into(tensor, destination, stream=None, destination2=N device=None if destination2 is None else destination2.device.index) return True + if not hasattr(file_obj, "seek") or not hasattr(file_obj, "readinto"): + return False + buf_type = ctypes.c_ubyte * info.size view = memoryview(buf_type.from_address(destination.data_ptr())) diff --git a/comfy/model_base.py b/comfy/model_base.py index 205178911..264dbb9b3 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -21,6 +21,7 @@ import comfy.ldm.hunyuan3dv2_1.hunyuandit import torch import logging import comfy.ldm.lightricks.av_model +import comfy.ldm.lightricks.symmetric_patchifier import comfy.context_windows from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep from comfy.ldm.cascade.stage_c import StageC @@ -46,6 +47,7 @@ import comfy.ldm.wan.model_animate import comfy.ldm.wan.ar_model import comfy.ldm.wan.model_wandancer import comfy.ldm.hunyuan3d.model +import comfy.ldm.triposplat.model import comfy.ldm.hidream.model import comfy.ldm.chroma.model import comfy.ldm.chroma_radiance.model @@ -53,7 +55,9 @@ import comfy.ldm.pixeldit.model import comfy.ldm.pixeldit.pid import comfy.ldm.ace.model import comfy.ldm.omnigen.omnigen2 +import comfy.ldm.boogu.model import comfy.ldm.qwen_image.model +import comfy.ldm.ideogram4.model import comfy.ldm.kandinsky5.model import comfy.ldm.anima.model import comfy.ldm.ace.ace_step15 @@ -63,6 +67,7 @@ import comfy.ldm.ernie.model import comfy.ldm.sam3.detector import comfy.ldm.hidream_o1.model from comfy.ldm.hidream_o1.conditioning import build_extra_conds +import comfy.ldm.depth_anything_3.model import comfy.model_management import comfy.patcher_extension @@ -1200,6 +1205,127 @@ class LTXAV(BaseModel): def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): return latent_image + def map_context_window_to_modalities(self, primary_indices, latent_shapes, dim): + result = [primary_indices] + if len(latent_shapes) < 2: + return result + + video_total = latent_shapes[0][dim] + + for i in range(1, len(latent_shapes)): + mod_total = latent_shapes[i][dim] + # Map each primary index to its proportional range of modality indices and + # concatenate in order. Preserves wrapped/strided geometry so the modality + # attends to the same temporal regions as the primary window. + mod_indices = [] + seen = set() + for v_idx in primary_indices: + a_start = min(int(round(v_idx * mod_total / video_total)), mod_total - 1) + a_end = min(int(round((v_idx + 1) * mod_total / video_total)), mod_total) + if a_end <= a_start: + a_end = a_start + 1 + for a in range(a_start, a_end): + if a not in seen: + seen.add(a) + mod_indices.append(a) + result.append(mod_indices) + + return result + + @staticmethod + def _get_guide_entries(conds): + for cond_list in conds: + if cond_list is None: + continue + for cond_dict in cond_list: + model_conds = cond_dict.get('model_conds', {}) + entries = model_conds.get('guide_attention_entries') + if entries is not None and hasattr(entries, 'cond') and entries.cond: + return entries.cond + return None + + def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): + # Audio denoise mask — slice using audio modality window + if cond_key == "audio_denoise_mask" and hasattr(window, 'modality_windows') and window.modality_windows: + audio_window = window.modality_windows.get(1) + if audio_window is not None and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor): + sliced = audio_window.get_tensor(cond_value.cond, device, dim=2) + return cond_value._copy_with(sliced) + + # Video denoise mask — split into video + guide portions, slice each + if cond_key == "denoise_mask" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor): + cond_tensor = cond_value.cond + guide_count = cond_tensor.size(window.dim) - x_in.size(window.dim) + if guide_count > 0: + T_video = x_in.size(window.dim) + video_mask = cond_tensor.narrow(window.dim, 0, T_video) + guide_mask = cond_tensor.narrow(window.dim, T_video, guide_count) + sliced_video = window.get_tensor(video_mask, device, retain_index_list=retain_index_list) + suffix_indices = window.guide_frames_indices + if suffix_indices: + idx = tuple([slice(None)] * window.dim + [suffix_indices]) + sliced_guide = guide_mask[idx].to(device) + return cond_value._copy_with(torch.cat([sliced_video, sliced_guide], dim=window.dim)) + else: + return cond_value._copy_with(sliced_video) + + # Keyframe indices — regenerate pixel coords for window, select guide positions + if cond_key == "keyframe_idxs": + kf_local_pos = window.guide_kf_local_positions + if not kf_local_pos: + return cond_value._copy_with(cond_value.cond[:, :, :0, :]) # empty + H, W = x_in.shape[3], x_in.shape[4] + window_len = len(window.index_list) + # account for causal_window_fix anchor in coord space size + anchor_idx = getattr(window, 'causal_anchor_index', None) + if anchor_idx is not None and anchor_idx >= 0: + window_len += 1 + patchifier = self.diffusion_model.patchifier + latent_coords = patchifier.get_latent_coords(window_len, H, W, 1, cond_value.cond.device) + scale_factors = self.diffusion_model.vae_scale_factors + pixel_coords = comfy.ldm.lightricks.symmetric_patchifier.latent_to_pixel_coords( + latent_coords, + scale_factors, + causal_fix=self.diffusion_model.causal_temporal_positioning) + tokens = [] + for pos in kf_local_pos: + tokens.extend(range(pos * H * W, (pos + 1) * H * W)) + pixel_coords = pixel_coords[:, :, tokens, :] + + # Adjust spatial end positions for dilated (downscaled) guides. + # Each guide entry may have a different downscale factor; expand the + # per-entry factor to cover all tokens belonging to that entry. + downscale_factors = window.guide_downscale_factors + overlap_info = window.guide_overlap_info + if downscale_factors: + per_token_factor = [] + for (entry_idx, overlap_count), dsf in zip(overlap_info, downscale_factors): + per_token_factor.extend([dsf] * (overlap_count * H * W)) + factor_tensor = torch.tensor(per_token_factor, device=pixel_coords.device, dtype=pixel_coords.dtype) + spatial_end_offset = (factor_tensor.unsqueeze(0).unsqueeze(0).unsqueeze(-1) - 1) * torch.tensor( + scale_factors[1:], device=pixel_coords.device, dtype=pixel_coords.dtype, + ).view(1, -1, 1, 1) + pixel_coords[:, 1:, :, 1:] += spatial_end_offset + + B = cond_value.cond.shape[0] + if B > 1: + pixel_coords = pixel_coords.expand(B, -1, -1, -1) + return cond_value._copy_with(pixel_coords) + + # Guide attention entries — adjust per-guide counts based on window overlap + if cond_key == "guide_attention_entries": + overlap_info = window.guide_overlap_info + H, W = x_in.shape[3], x_in.shape[4] + new_entries = [] + for entry_idx, overlap_count in overlap_info: + e = cond_value.cond[entry_idx] + new_entries.append({**e, + "pre_filter_count": overlap_count * H * W, + "latent_shape": [overlap_count, H, W]}) + return cond_value._copy_with(new_entries) + + return None + class HunyuanVideo(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo) @@ -1516,8 +1642,26 @@ class WAN21(BaseModel): if reference_latents is not None: out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1])[:, :, 0]) + # In-context reference conditioning (Bernini) + context_latents = kwargs.get("context_latents", None) + if context_latents is not None: + out['context_latents'] = comfy.conds.CONDList([self.process_latent_in(l) for l in context_latents]) + return out + def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): + # In-context cond slicing (Bernini) + if cond_key == "context_latents" and isinstance(getattr(cond_value, "cond", None), list): + dim = window.dim + out = [] + for lat in cond_value.cond: + if lat.ndim > dim and lat.shape[dim] > 1 and lat.shape[dim] == x_in.shape[dim]: + out.append(window.get_tensor(lat, device, dim=dim, retain_index_list=retain_index_list)) + else: + out.append(lat.to(device)) + return cond_value._copy_with(out) + return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list) + class WAN21_CausalAR(WAN21): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): @@ -1726,10 +1870,14 @@ class WAN21_SCAIL(WAN21): reference_latents = kwargs.get("reference_latents", None) if reference_latents is not None: - ref_latent = self.process_latent_in(reference_latents[-1]) - ref_mask = torch.ones_like(ref_latent[:, :4]) - ref_latent = torch.cat([ref_latent, ref_mask], dim=1) - out['reference_latent'] = comfy.conds.CONDRegular(ref_latent) + # SCAIL-2 multi-reference: reference_latents[0] is the primary ref, [1:] are additional + # references. Stack as [additional..., primary] so the primary stays adjacent to the video. + ordered = list(reference_latents[1:]) + list(reference_latents[:1]) + stacked = [] + for lat in ordered: + lat = self.process_latent_in(lat) + stacked.append(torch.cat([lat, torch.ones_like(lat[:, :4])], dim=1)) + out['reference_latent'] = comfy.conds.CONDRegular(torch.cat(stacked, dim=2)) pose_latents = kwargs.get("pose_video_latent", None) if pose_latents is not None: @@ -1752,6 +1900,99 @@ class WAN21_SCAIL(WAN21): return out +class WAN21_SCAIL2(WAN21_SCAIL): + """SCAIL-2: SCAIL-Preview + an additive binary multi-identity mask stream.""" + + def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): + super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.SCAIL2WanModel) + self.memory_usage_factor_conds = ("reference_latent", "pose_latents", "ref_mask_latents", "sam_latents") + self.memory_usage_shape_process = { + "pose_latents": lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]], + "sam_latents": lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]], + } + self.image_to_video = image_to_video + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + + driving_mask_28ch = kwargs.get("driving_mask_28ch", None) + if driving_mask_28ch is not None: + out['sam_latents'] = comfy.conds.CONDRegular(driving_mask_28ch.movedim(1, 2).contiguous()) + + # ref_mask_28ch holds one identity mask per stacked reference frame (additional refs first, then the primary ref), followed by zeros over the video frames. + ref_mask_28ch = kwargs.get("ref_mask_28ch", None) + if ref_mask_28ch is not None: + out['ref_mask_latents'] = comfy.conds.CONDRegular(ref_mask_28ch.movedim(1, 2).contiguous()) + + ref_mask_flag = kwargs.get("ref_mask_flag", None) + if ref_mask_flag is not None: + out['ref_mask_flag'] = comfy.conds.CONDConstant(ref_mask_flag) + + return out + + def extra_conds_shapes(self, **kwargs): + out = super().extra_conds_shapes(**kwargs) + driving_mask_28ch = kwargs.get("driving_mask_28ch", None) + if driving_mask_28ch is not None: + s = driving_mask_28ch.shape + out['sam_latents'] = [s[0], 28, s[1], s[3], s[4]] + ref_mask_28ch = kwargs.get("ref_mask_28ch", None) + if ref_mask_28ch is not None: + s = ref_mask_28ch.shape + out['ref_mask_latents'] = [s[0], 28, s[1], s[3], s[4]] + return out + + def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]): + if cond_key in ("sam_latents", "pose_latents"): + # Return sliced view omitting retain_index_list + return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_offset=0) + if cond_key == "ref_mask_latents" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor): + # The ref mask is N leading ref frames padded with frames of zeros, so just grab the first frames for all windows + full_ref_mask = cond_value.cond + video_frame_count = x_in.shape[2] + ref_frame_count = full_ref_mask.shape[2] - video_frame_count + if ref_frame_count < 1: + return None + window_length = len(window.index_list) + + # Account for the causal anchor frame if it exists + anchor_index = getattr(window, "causal_anchor_index", None) + if anchor_index is not None and anchor_index >= 0: + window_length += 1 + + window_ref_mask = full_ref_mask[:, :, :window_length + ref_frame_count].to(device) + return cond_value._copy_with(window_ref_mask) + + return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list) + + def concat_cond(self, **kwargs): + # The 4 extra channels are the history_mask (1 at clean-anchor frames). + noise = kwargs.get("noise", None) + extra_channels = self.diffusion_model.patch_embedding.weight.shape[1] - noise.shape[1] + if extra_channels != 4: + return super().concat_cond(**kwargs) + + mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + if mask is None: + return torch.zeros_like(noise)[:, :4] + + device = kwargs["device"] + if mask.shape[1] != 4: + mask = torch.mean(mask, dim=1, keepdim=True) + mask = 1.0 - mask + mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + if mask.shape[-3] < noise.shape[-3]: + mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0) + if mask.shape[1] == 1: + mask = mask.repeat(1, 4, 1, 1, 1) + mask = utils.resize_to_batch_size(mask, noise.shape[0]) + return mask + + def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): + # Hold anchor constant across all sigmas instead of base sigma*noise + (1-sigma)*latent_image. + return latent_image + + class WAN22_WanDancer(WAN21): def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=True, device=None): super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model_wandancer.WanDancerModel) @@ -1806,6 +2047,24 @@ class Hunyuan3Dv2_1(BaseModel): out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) return out +class TripoSplat(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.triposplat.model.LatentSeqMMFlowModel) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) # DINOv3 token sequence -> cross-attention context. + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + ref_latents = kwargs.get("reference_latents", None) # Flux2 VAE image latent -> additive second conditioning. + if ref_latents is not None: + out['ref_latents'] = comfy.conds.CONDList(list(ref_latents)) + latent_shapes = kwargs.get("latent_shapes", None) # {latent, camera} nested latent + if latent_shapes is not None: + out['latent_shapes'] = comfy.conds.CONDConstant(latent_shapes) + return out + + class HiDream(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hidream.model.HiDreamImageTransformer2DModel) @@ -1967,6 +2226,11 @@ class Omnigen2(BaseModel): out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) return out +class Boogu(Omnigen2): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super(Omnigen2, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.boogu.model.BooguTransformer2DModel) + self.memory_usage_factor_conds = ("ref_latents",) + class QwenImage(BaseModel): def __init__(self, model_config, model_type=ModelType.FLUX, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.qwen_image.model.QwenImageTransformer2DModel) @@ -1999,6 +2263,21 @@ class QwenImage(BaseModel): out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) return out +class Ideogram4(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ideogram4.model.Ideogram4Transformer2DModel) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + if torch.numel(attention_mask) != attention_mask.sum(): + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out + class HunyuanImage21(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo) @@ -2192,6 +2471,12 @@ class RT_DETR_v4(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.rt_detr.rtdetr_v4.RTv4) + +class DepthAnything3(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, + unet_model=comfy.ldm.depth_anything_3.model.DepthAnything3Net) + class ErnieImage(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ernie.model.ErnieImageModel) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index f0db7d388..b773f0393 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -313,6 +313,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["use_x0"] = True else: dit_config["use_x0"] = False + if "{}__sequential__".format(key_prefix) in state_dict_keys: # sequential txt_ids + dit_config["use_sequential_txt_ids"] = True + else: + dit_config["use_sequential_txt_ids"] = False else: dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys dit_config["yak_mlp"] = '{}double_blocks.0.img_mlp.gate_proj.weight'.format(key_prefix) in state_dict_keys @@ -626,6 +630,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["model_type"] = "humo" elif '{}face_adapter.fuser_blocks.0.k_norm.weight'.format(key_prefix) in state_dict_keys: dit_config["model_type"] = "animate" + elif '{}patch_embedding_mask.weight'.format(key_prefix) in state_dict_keys: + dit_config["model_type"] = "scail2" elif '{}patch_embedding_pose.weight'.format(key_prefix) in state_dict_keys: dit_config["model_type"] = "scail" elif '{}patch_embedding_global.weight'.format(key_prefix) in state_dict_keys: @@ -676,6 +682,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["guidance_cond_proj_dim"] = None#f"{key_prefix}t_embedder.cond_proj.weight" in state_dict_keys return dit_config + if '{}cam_out_layer.weight'.format(key_prefix) in state_dict_keys and '{}repo_layers.0.final_map.weight'.format(key_prefix) in state_dict_keys: # TripoSplat + return {"image_model": "triposplat"} + if '{}t_embedder1.mlp.0.weight'.format(key_prefix) in state_dict_keys and '{}x_embedder.proj1.weight'.format(key_prefix) in state_dict_keys: # HiDream-O1 return {"image_model": "hidream_o1"} @@ -752,6 +761,16 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): return dit_config + if '{}double_stream_layers.0.img_instruct_attn.processor.img_to_q.weight'.format(key_prefix) in state_dict_keys: # Boogu-Image (OmniGen2 derivative + dual-stream stage) + dit_config = {} + dit_config["image_model"] = "boogu" + dit_config["hidden_size"] = state_dict['{}x_embedder.weight'.format(key_prefix)].shape[0] + dit_config["num_layers"] = count_blocks(state_dict_keys, '{}single_stream_layers.'.format(key_prefix) + '{}.') + dit_config["num_double_stream_layers"] = count_blocks(state_dict_keys, '{}double_stream_layers.'.format(key_prefix) + '{}.') + dit_config["num_refiner_layers"] = count_blocks(state_dict_keys, '{}noise_refiner.'.format(key_prefix) + '{}.') + dit_config["instruction_feat_dim"] = state_dict['{}time_caption_embed.caption_embedder.0.weight'.format(key_prefix)].shape[0] + return dit_config + if '{}time_caption_embed.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: # Omnigen2 dit_config = {} dit_config["image_model"] = "omnigen2" @@ -808,6 +827,13 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["default_ref_method"] = "negative_index" return dit_config + if '{}embed_image_indicator.weight'.format(key_prefix) in state_dict_keys: # Ideogram 4 + dit_config = {} + dit_config["image_model"] = "ideogram4" + dit_config["in_channels"] = state_dict['{}input_proj.weight'.format(key_prefix)].shape[1] + dit_config["num_layers"] = count_blocks(state_dict_keys, '{}layers.'.format(key_prefix) + '{}.') + return dit_config + if '{}visual_transformer_blocks.0.cross_attention.key_norm.weight'.format(key_prefix) in state_dict_keys: # Kandinsky 5 dit_config = {} model_dim = state_dict['{}visual_embeddings.in_layer.bias'.format(key_prefix)].shape[0] @@ -846,6 +872,95 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["enc_h"] = state_dict['{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix)].shape[0] return dit_config + # Depth Anything 3 (repackaged to ComfyUI's native Dinov2Model layout via scripts/convert_da3.py) + if '{}backbone.embeddings.patch_embeddings.projection.weight'.format(key_prefix) in state_dict_keys: + dit_config = {} + dit_config["image_model"] = "DepthAnything3" + + patch_w = state_dict['{}backbone.embeddings.patch_embeddings.projection.weight'.format(key_prefix)] + embed_dim = patch_w.shape[0] + depth = count_blocks(state_dict_keys, '{}backbone.encoder.layer.'.format(key_prefix) + '{}.') + + # Backbone preset is determined by embed_dim (matches vits/vitb/vitl/vitg). + backbone_name = {384: "vits", 768: "vitb", 1024: "vitl", 1536: "vitg"}.get(embed_dim) + if backbone_name is None: + return None + dit_config["backbone_name"] = backbone_name + + # Detect DA3 extensions on top of vanilla DINOv2. + has_camera_token = '{}backbone.embeddings.camera_token'.format(key_prefix) in state_dict_keys + # qk-norm shows up as `attention.q_norm.weight` on enabled blocks. + qknorm_indices = [ + i for i in range(depth) + if '{}backbone.encoder.layer.{}.attention.q_norm.weight'.format(key_prefix, i) in state_dict_keys + ] + qknorm_start = qknorm_indices[0] if qknorm_indices else -1 + + # The DA3 main-series configs always set alt_start == qknorm_start == rope_start. + # cat_token=True is implied by the presence of camera_token. + if has_camera_token: + dit_config["alt_start"] = qknorm_start + dit_config["rope_start"] = qknorm_start + dit_config["qknorm_start"] = qknorm_start + dit_config["cat_token"] = True + else: + dit_config["alt_start"] = -1 + dit_config["rope_start"] = -1 + dit_config["qknorm_start"] = -1 + dit_config["cat_token"] = False + + # Detect head type and config. + has_aux = '{}head.scratch.refinenet1_aux.out_conv.weight'.format(key_prefix) in state_dict_keys + dit_config["head_dim_in"] = state_dict['{}head.projects.0.weight'.format(key_prefix)].shape[1] + dit_config["head_features"] = state_dict['{}head.scratch.refinenet1.out_conv.weight'.format(key_prefix)].shape[0] + dit_config["head_out_channels"] = [ + state_dict['{}head.projects.{}.weight'.format(key_prefix, i)].shape[0] + for i in range(4) + ] + if has_aux: + # DualDPT: dim_in = 2 * embed_dim (because cat_token doubles token width). + dit_config["head_type"] = "dualdpt" + dit_config["head_output_dim"] = 2 + dit_config["head_use_sky_head"] = False + else: + dit_config["head_type"] = "dpt" + dit_config["head_output_dim"] = state_dict[ + '{}head.scratch.output_conv2.2.weight'.format(key_prefix) + ].shape[0] + dit_config["head_use_sky_head"] = ( + '{}head.scratch.sky_output_conv2.0.weight'.format(key_prefix) in state_dict_keys + ) + + # out_layers: hard-coded per upstream YAML config (depth-aware default). + if depth >= 24: + # vitl: depths used vary between DA3-Large (DualDPT) and Mono/Metric (DPT). + if has_aux: + dit_config["out_layers"] = [11, 15, 19, 23] + else: + dit_config["out_layers"] = [4, 11, 17, 23] + else: + # vits/vitb: 12 blocks + dit_config["out_layers"] = [5, 7, 9, 11] + + # Camera encoder/decoder presence (multi-view + pose path). + has_cam_enc = '{}cam_enc.token_norm.weight'.format(key_prefix) in state_dict_keys + has_cam_dec = '{}cam_dec.fc_t.weight'.format(key_prefix) in state_dict_keys + dit_config["has_cam_enc"] = has_cam_enc + dit_config["has_cam_dec"] = has_cam_dec + if has_cam_enc: + cam_enc_w = state_dict.get( + '{}cam_enc.pose_branch.fc2.weight'.format(key_prefix) + ) + if cam_enc_w is not None: + dit_config["cam_dim_out"] = cam_enc_w.shape[0] + if has_cam_dec: + cam_dec_w = state_dict.get( + '{}cam_dec.fc_t.weight'.format(key_prefix) + ) + if cam_dec_w is not None: + dit_config["cam_dec_dim_in"] = cam_dec_w.shape[1] + return dit_config + if '{}layers.0.mlp.linear_fc2.weight'.format(key_prefix) in state_dict_keys: # Ernie Image dit_config = {} dit_config["image_model"] = "ernie" diff --git a/comfy/model_management.py b/comfy/model_management.py index 83cb5d277..f2569b0ea 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -542,8 +542,10 @@ try: except: pass -if torch.cuda.is_available() and torch.backends.cudnn.is_available() and PerformanceFeature.AutoTune in args.fast: - torch.backends.cudnn.benchmark = True + +def set_cudnn_benchmark(): + if torch.cuda.is_available() and torch.backends.cudnn.is_available(): + torch.backends.cudnn.benchmark = PerformanceFeature.AutoTune in args.fast try: if torch_version_numeric >= (2, 5): @@ -649,15 +651,19 @@ def free_pins(size, evict_active=False): return freed_total def ensure_pin_budget(size, evict_active=False): - shortfall = size + comfy.memory_management.RAM_CACHE_HEADROOM / 2 - psutil.virtual_memory().available + if args.high_ram: + return True + if args.fast_disk: + shortfall = TOTAL_PINNED_MEMORY + size - MAX_PINNED_MEMORY + else: + shortfall = size + max(comfy.memory_management.RAM_CACHE_HEADROOM / 2, 2048 * 1024 ** 2) - psutil.virtual_memory().available if shortfall <= 0: return True to_free = shortfall + PIN_PRESSURE_HYSTERESIS return free_pins(to_free, evict_active=evict_active) >= shortfall -def ensure_pin_registerable(size, evict_active=False): - shortfall = TOTAL_PINNED_MEMORY + size - MAX_PINNED_MEMORY +def free_registrations(shortfall, evict_active=True): if MAX_PINNED_MEMORY <= 0: return False if shortfall <= 0: @@ -666,12 +672,22 @@ def ensure_pin_registerable(size, evict_active=False): shortfall += REGISTERABLE_PIN_HYSTERESIS for loaded_model in reversed(current_loaded_models): model = loaded_model.model - if model is not None and model.is_dynamic() and (evict_active or not model.model.dynamic_pins[model.load_device]["active"]): + if model is not None and model.is_dynamic() and not model.model.dynamic_pins[model.load_device]["active"]: shortfall -= model.unregister_inactive_pins(shortfall) if shortfall <= 0: return True + if evict_active: + for loaded_model in current_loaded_models: + model = loaded_model.model + if model is not None and model.is_dynamic() and model.model.dynamic_pins[model.load_device]["active"]: + shortfall -= model.unregister_inactive_pins(shortfall) + if shortfall <= 0: + return True return shortfall <= REGISTERABLE_PIN_HYSTERESIS +def ensure_pin_registerable(size, evict_active=True): + return free_registrations(TOTAL_PINNED_MEMORY + size - MAX_PINNED_MEMORY, evict_active=evict_active) + class LoadedModel: def __init__(self, model: ModelPatcher): self._set_model(model) @@ -811,9 +827,9 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins for x in can_unload_sorted: i = x[-1] memory_to_free = 1e32 - if current_loaded_models[i].model.is_dynamic() and (not DISABLE_SMART_MEMORY or device is None): + if not DISABLE_SMART_MEMORY or device is None: memory_to_free = 0 if device is None else memory_required - get_free_memory(device) - if for_dynamic: + if current_loaded_models[i].model.is_dynamic() and for_dynamic: #don't actually unload dynamic models for the sake of other dynamic models #as that works on-demand. memory_required -= current_loaded_models[i].model.loaded_size() @@ -825,6 +841,10 @@ def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins for i in sorted(unloaded_model, reverse=True): unloaded_models.append(current_loaded_models.pop(i)) + if not for_dynamic and pins_required > 0: + ensure_pin_budget(pins_required) + ensure_pin_registerable(pins_required) + if len(unloaded_model) > 0: soft_empty_cache() elif device is not None: @@ -887,15 +907,19 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu model_to_unload.model_finalizer.detach() total_memory_required = {} + total_pins_required = {} for loaded_model in models_to_load: device = loaded_model.device total_memory_required[device] = total_memory_required.get(device, 0) + loaded_model.model_memory_required(device) + if not loaded_model.model.is_dynamic(): + total_pins_required[device] = total_pins_required.get(device, 0) + loaded_model.model_memory() for device in total_memory_required: if device != torch.device("cpu"): free_memory(total_memory_required[device] * 1.1 + extra_mem, device, - for_dynamic=free_for_dynamic) + for_dynamic=free_for_dynamic, + pins_required=total_pins_required.get(device, 0)) for device in total_memory_required: if device != torch.device("cpu"): @@ -953,8 +977,6 @@ def loaded_models(only_currently_used=False): def cleanup_models_gc(): do_gc = False - reset_cast_buffers() - for i in range(len(current_loaded_models)): cur = current_loaded_models[i] if cur.is_dead(): @@ -1298,7 +1320,6 @@ STREAM_CAST_BUFFERS = {} LARGEST_CASTED_WEIGHT = (None, 0) STREAM_AIMDO_CAST_BUFFERS = {} LARGEST_AIMDO_CASTED_WEIGHT = (None, 0) -STREAM_PIN_BUFFERS = {} DEFAULT_AIMDO_CAST_BUFFER_RESERVATION_SIZE = 16 * 1024 ** 3 @@ -1341,42 +1362,13 @@ def get_aimdo_cast_buffer(offload_stream, device): STREAM_AIMDO_CAST_BUFFERS[offload_stream] = cast_buffer return cast_buffer -def get_pin_buffer(offload_stream): - pin_buffer = STREAM_PIN_BUFFERS.get(offload_stream, None) - if pin_buffer is None: - pin_buffer = comfy_aimdo.host_buffer.HostBuffer(0, 0, pinned_hostbuf_size(8 * 1024**3), mark_cold=False) - STREAM_PIN_BUFFERS[offload_stream] = pin_buffer - elif offload_stream is not None: - event = getattr(pin_buffer, "_comfy_event", None) - if event is not None: - event.synchronize() - delattr(pin_buffer, "_comfy_event") - return pin_buffer - -def resize_pin_buffer(pin_buffer, size): - global TOTAL_PINNED_MEMORY - old_size = pin_buffer.size - if size <= old_size: - return True - growth = size - old_size - comfy.memory_management.extra_ram_release(comfy.memory_management.RAM_CACHE_HEADROOM) - ensure_pin_budget(growth, evict_active=True) - ensure_pin_registerable(growth, evict_active=True) - try: - pin_buffer.extend(size=size, reallocate=True) - except RuntimeError: - return False - TOTAL_PINNED_MEMORY += pin_buffer.size - old_size - return True - def reset_cast_buffers(): - global TOTAL_PINNED_MEMORY global LARGEST_CASTED_WEIGHT global LARGEST_AIMDO_CASTED_WEIGHT LARGEST_CASTED_WEIGHT = (None, 0) LARGEST_AIMDO_CASTED_WEIGHT = (None, 0) - for offload_stream in set(STREAM_CAST_BUFFERS) | set(STREAM_AIMDO_CAST_BUFFERS) | set(STREAM_PIN_BUFFERS): + for offload_stream in set(STREAM_CAST_BUFFERS) | set(STREAM_AIMDO_CAST_BUFFERS): if offload_stream is not None: offload_stream.synchronize() synchronize() @@ -1385,20 +1377,24 @@ def reset_cast_buffers(): mmap_obj.bounce() DIRTY_MMAPS.clear() - for pin_buffer in STREAM_PIN_BUFFERS.values(): - TOTAL_PINNED_MEMORY -= pin_buffer.size - TOTAL_PINNED_MEMORY = max(0, TOTAL_PINNED_MEMORY) - for loaded_model in current_loaded_models: model = loaded_model.model if model is not None and model.is_dynamic(): - model.model.dynamic_pins[model.load_device]["active"] = False + pin_state = model.model.dynamic_pins[model.load_device] + + if pin_state["active"]: + *_, buckets = pin_state["weights"] + for size, bucket in list(buckets.items()): + bucket[:] = [ entry for entry in bucket if entry[-1] is not None ] + if not bucket: + del buckets[size] + + pin_state["active"] = False model.partially_unload_ram(1e30, subsets=[ "patches" ]) - model.model.dynamic_pins[model.load_device]["patches"] = (comfy_aimdo.host_buffer.HostBuffer(0, 8 * 1024 * 1024, pinned_hostbuf_size(model.model_size())), [], [-1], [0]) + model.model.dynamic_pins[model.load_device]["patches"] = (comfy_aimdo.host_buffer.HostBuffer(0, 8 * 1024 * 1024, pinned_hostbuf_size(model.model_size())), [], [-1], [0], [0], {}) STREAM_CAST_BUFFERS.clear() STREAM_AIMDO_CAST_BUFFERS.clear() - STREAM_PIN_BUFFERS.clear() soft_empty_cache() def get_offload_stream(device): @@ -1451,7 +1447,7 @@ def cast_to_gathered(tensors, r, non_blocking=False, stream=None, r2=None): if hasattr(wf_context, "as_context"): wf_context = wf_context.as_context(stream) - dest_views = comfy.memory_management.interpret_gathered_like(tensors, r) + dest_views = comfy.memory_management.interpret_gathered_like(tensors, r) if r is not None else [None] * len(tensors) dest2_views = comfy.memory_management.interpret_gathered_like(tensors, r2) if r2 is not None else None with wf_context: for tensor in tensors: @@ -1463,9 +1459,10 @@ def cast_to_gathered(tensors, r, non_blocking=False, stream=None, r2=None): continue storage = tensor._qdata.untyped_storage() if isinstance(tensor, comfy.quant_ops.QuantizedTensor) else tensor.untyped_storage() mark_mmap_dirty(storage) - dest_view.copy_(tensor, non_blocking=non_blocking) + if dest_view is not None: + dest_view.copy_(tensor, non_blocking=non_blocking) if dest2_view is not None: - dest2_view.copy_(dest_view, non_blocking=non_blocking) + dest2_view.copy_(tensor if dest_view is None else dest_view, non_blocking=non_blocking) def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None, r=None): @@ -1516,6 +1513,8 @@ if not args.disable_pinned_memory: PINNING_ALLOWED_TYPES = set(["Tensor", "Parameter", "QuantizedTensor"]) def pinned_hostbuf_size(size): + if args.high_ram: + return max(0, int(size * 2)) return max(0, int(min(size, MAX_PINNED_MEMORY) * 2)) def discard_cuda_async_error(): @@ -1738,6 +1737,13 @@ def is_device_xpu(device): def is_device_cuda(device): return is_device_type(device, 'cuda') +def set_torch_device(device): + """Set the current device for the given torch device. Supports CUDA and XPU.""" + if is_device_cuda(device): + torch.cuda.set_device(device) + elif is_device_xpu(device): + torch.xpu.set_device(device) + def is_directml_enabled(): global directml_enabled if directml_enabled: diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index d7a2fb704..2814563db 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -381,10 +381,11 @@ class ModelPatcher: def get_clone_model_override(self): return self.model, (self.backup, self.backup_buffers, self.object_patches_backup, self.pinned) - def clone(self, disable_dynamic=False, model_override=None): + def clone(self, disable_dynamic=False, model_override=None, force_deepcopy=False): class_ = self.__class__ - if self.is_dynamic() and disable_dynamic: - class_ = ModelPatcher + if self.is_dynamic() and disable_dynamic or force_deepcopy: + if self.is_dynamic() and disable_dynamic: + class_ = ModelPatcher if model_override is None: if self.cached_patcher_init is None: raise RuntimeError("Cannot create non-dynamic delegate: cached_patcher_init is not initialized.") @@ -1728,8 +1729,8 @@ class ModelPatcherDynamic(ModelPatcher): """ if device not in self.model.dynamic_pins: self.model.dynamic_pins[device] = { - "weights": (comfy_aimdo.host_buffer.HostBuffer(0, 0, 0), [], [-1], [0]), - "patches": (comfy_aimdo.host_buffer.HostBuffer(0, 0, 0), [], [-1], [0]), + "weights": (comfy_aimdo.host_buffer.HostBuffer(0, 0, 0), [], [-1], [0], [0], {}), + "patches": (comfy_aimdo.host_buffer.HostBuffer(0, 0, 0), [], [-1], [0], [0], {}), "hostbufs_initialized": False, "failed": False, "active": False, @@ -1806,8 +1807,8 @@ class ModelPatcherDynamic(ModelPatcher): pin_state = self.model.dynamic_pins[self.load_device] if not pin_state["hostbufs_initialized"]: hostbuf_size = comfy.model_management.pinned_hostbuf_size(self.model_size()) - pin_state["weights"] = (comfy_aimdo.host_buffer.HostBuffer(0, 64 * 1024 * 1024, hostbuf_size), [], [-1], [0]) - pin_state["patches"] = (comfy_aimdo.host_buffer.HostBuffer(0, 8 * 1024 * 1024, hostbuf_size), [], [-1], [0]) + pin_state["weights"] = (comfy_aimdo.host_buffer.HostBuffer(0, 64 * 1024 * 1024, hostbuf_size), [], [-1], [0], [0], {}) + pin_state["patches"] = (comfy_aimdo.host_buffer.HostBuffer(0, 8 * 1024 * 1024, hostbuf_size), [], [-1], [0], [0], {}) pin_state["hostbufs_initialized"] = True pin_state["failed"] = False pin_state["active"] = True @@ -1949,18 +1950,16 @@ class ModelPatcherDynamic(ModelPatcher): return freed def loaded_ram_size(self): - return (self.model.dynamic_pins[self.load_device]["weights"][0].size + - self.model.dynamic_pins[self.load_device]["patches"][0].size) + return (self.model.dynamic_pins[self.load_device]["weights"][0].size) def pinned_memory_size(self): - return (self.model.dynamic_pins[self.load_device]["weights"][3][0] + - self.model.dynamic_pins[self.load_device]["patches"][3][0]) + return (self.model.dynamic_pins[self.load_device]["weights"][3][0]) def unregister_inactive_pins(self, ram_to_unload, subsets=[ "weights", "patches" ]): freed = 0 pin_state = self.model.dynamic_pins[self.load_device] for subset in subsets: - hostbuf, stack, stack_split, pinned_size = pin_state[subset] + hostbuf, stack, stack_split, pinned_size, *_ = pin_state[subset] split = stack_split[0] while split >= 0: module, offset = stack[split] @@ -1985,10 +1984,12 @@ class ModelPatcherDynamic(ModelPatcher): freed = 0 pin_state = self.model.dynamic_pins[self.load_device] for subset in subsets: - hostbuf, stack, stack_split, pinned_size = pin_state[subset] + hostbuf, stack, stack_split, pinned_size, *_ = pin_state[subset] while len(stack) > 0: module, offset = stack.pop() size = module._pin.numel() * module._pin.element_size() + module._pin_balancer_entry[-1] = None + del module._pin_balancer_entry del module._pin hostbuf.truncate(offset, do_unregister=module._pin_registered) stack_split[0] = min(stack_split[0], len(stack) - 1) diff --git a/comfy/model_prefetch.py b/comfy/model_prefetch.py index 72e11dec6..aa6d22d77 100644 --- a/comfy/model_prefetch.py +++ b/comfy/model_prefetch.py @@ -1,4 +1,5 @@ import comfy_aimdo.model_vbar +import comfy.memory_management import comfy.model_management import comfy.ops @@ -50,7 +51,17 @@ def prefetch_queue_pop(queue, device, module): if hasattr(s, "_v"): comfy_modules.append(s) + registerable_size = 0 + for s in comfy_modules: + registerable_size += comfy.memory_management.vram_aligned_size([s.weight, s.bias]) + for param_key in ("weight", "bias"): + lowvram_fn = getattr(s, param_key + "_lowvram_function", None) + if lowvram_fn is not None: + registerable_size += lowvram_fn.memory_required() + offload_stream = comfy.ops.cast_modules_with_vbar(comfy_modules, None, device, None, True) + if not comfy.model_management.args.fast_disk: + comfy.model_management.ensure_pin_registerable(registerable_size) comfy.model_management.sync_stream(device, offload_stream) queue[0] = (offload_stream, (prefetch, comfy_modules)) diff --git a/comfy/multigpu.py b/comfy/multigpu.py index e7f5b3d6f..2b6d8260d 100644 --- a/comfy/multigpu.py +++ b/comfy/multigpu.py @@ -17,7 +17,7 @@ class MultiGPUThreadPool: """Persistent thread pool for multi-GPU work distribution. Maintains one worker thread per extra GPU device. Each thread calls - torch.cuda.set_device() once at startup so that compiled kernel caches + set_torch_device() once at startup so that compiled kernel caches (inductor/triton) stay warm across diffusion steps. """ @@ -37,7 +37,7 @@ class MultiGPUThreadPool: def _worker_loop(self, device: torch.device, work_q: queue.Queue, result_q: queue.Queue): try: - torch.cuda.set_device(device) + comfy.model_management.set_torch_device(device) except Exception as e: logging.error(f"MultiGPUThreadPool: failed to set device {device}: {e}") while True: @@ -54,6 +54,8 @@ class MultiGPUThreadPool: try: result = fn(*args, **kwargs) result_q.put((result, None)) + except comfy.model_management.InterruptProcessingException as e: + result_q.put((None, e)) except Exception as e: result_q.put((None, e)) diff --git a/comfy/ops.py b/comfy/ops.py index 56445be8d..3f088a962 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -76,8 +76,6 @@ except: cast_to = comfy.model_management.cast_to #TODO: remove once no more references -STREAM_PIN_BUFFER_HEADROOM = 8 * 1024 * 1024 - def cast_to_input(weight, input, non_blocking=False, copy=True): return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy) @@ -94,9 +92,6 @@ def cast_modules_with_vbar(comfy_modules, dtype, device, bias_dtype, non_blockin offload_stream = None cast_buffer = None cast_buffer_offset = 0 - stream_pin_hostbuf = None - stream_pin_offset = 0 - stream_pin_queue = [] def ensure_offload_stream(module, required_size, check_largest): nonlocal offload_stream @@ -130,22 +125,6 @@ def cast_modules_with_vbar(comfy_modules, dtype, device, bias_dtype, non_blockin cast_buffer_offset += buffer_size return buffer - def get_stream_pin_buffer_offset(buffer_size): - nonlocal stream_pin_hostbuf - nonlocal stream_pin_offset - - if buffer_size == 0 or offload_stream is None: - return None - - if stream_pin_hostbuf is None: - stream_pin_hostbuf = comfy.model_management.get_pin_buffer(offload_stream) - if stream_pin_hostbuf is None: - return None - - offset = stream_pin_offset - stream_pin_offset += buffer_size - return offset - for s in comfy_modules: signature = comfy_aimdo.model_vbar.vbar_fault(s._v) resident = comfy_aimdo.model_vbar.vbar_signature_compare(signature, s._v_signature) @@ -184,33 +163,27 @@ def cast_modules_with_vbar(comfy_modules, dtype, device, bias_dtype, non_blockin if xfer_dest is None: xfer_dest = get_cast_buffer(dest_size) - def cast_maybe_lowvram_patch(xfer_source, xfer_dest, stream): + def cast_maybe_lowvram_patch(xfer_source, xfer_dest, stream, xfer_dest2=None): if xfer_source is not None: if getattr(xfer_source, "is_lowvram_patch", False): - xfer_source.prepare(xfer_dest, stream, copy=True, commit=False) - else: - comfy.model_management.cast_to_gathered(xfer_source, xfer_dest, non_blocking=non_blocking, stream=stream) + if xfer_dest is not None: + xfer_source.prepare(xfer_dest, stream, copy=True, commit=False) + xfer_source = [ xfer_dest ] + xfer_dest = xfer_dest2 + xfer_dest2 = None + elif xfer_dest2 is not None: + xfer_source.prepare(xfer_dest2, stream, copy=True, commit=False) + 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): if pin is not None: cast_maybe_lowvram_patch([pin], dest, offload_stream) return - if signature is None: + if signature is None or args.high_ram: comfy.pinned_memory.pin_memory(m, subset=subset, size=size) pin = comfy.pinned_memory.get_pin(m, subset=subset) - if pin is not None: - if isinstance(source, list): - comfy.model_management.cast_to_gathered(source, pin, non_blocking=non_blocking, stream=offload_stream, r2=dest) - else: - cast_maybe_lowvram_patch(source, pin, None) - cast_maybe_lowvram_patch([ pin ], dest, offload_stream) - return - if pin is None: - pin_offset = get_stream_pin_buffer_offset(size) - if pin_offset is not None: - stream_pin_queue.append((source, pin_offset, size, dest)) - return - cast_maybe_lowvram_patch(source, dest, offload_stream) + cast_maybe_lowvram_patch(source, pin, offload_stream, xfer_dest2=dest) handle_pin(s, pin, xfer_source, xfer_dest, size=dest_size) @@ -232,23 +205,6 @@ def cast_modules_with_vbar(comfy_modules, dtype, device, bias_dtype, non_blockin prefetch["needs_cast"] = needs_cast s._prefetch = prefetch - if stream_pin_offset > 0: - if stream_pin_hostbuf.size < stream_pin_offset: - if not comfy.model_management.resize_pin_buffer(stream_pin_hostbuf, stream_pin_offset + STREAM_PIN_BUFFER_HEADROOM): - for xfer_source, _, _, xfer_dest in stream_pin_queue: - cast_maybe_lowvram_patch(xfer_source, xfer_dest, offload_stream) - return offload_stream - stream_pin_tensor = comfy_aimdo.torch.hostbuf_to_tensor(stream_pin_hostbuf) - stream_pin_tensor.untyped_storage()._comfy_hostbuf = stream_pin_hostbuf - for xfer_source, pin_offset, pin_size, xfer_dest in stream_pin_queue: - pin = stream_pin_tensor[pin_offset:pin_offset + pin_size] - if isinstance(xfer_source, list): - comfy.model_management.cast_to_gathered(xfer_source, pin, non_blocking=non_blocking, stream=offload_stream, r2=xfer_dest) - else: - cast_maybe_lowvram_patch(xfer_source, pin, None) - comfy.model_management.cast_to_gathered([ pin ], xfer_dest, non_blocking=non_blocking, stream=offload_stream) - stream_pin_hostbuf._comfy_event = offload_stream.record_event() - return offload_stream @@ -343,21 +299,21 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of non_blocking = comfy.model_management.device_supports_non_blocking(device) - if hasattr(s, "_v"): + if hasattr(s, "_v") and comfy.model_management.is_device_cpu(device): #vbar doesn't support CPU weights, but some custom nodes have weird paths #that might switch the layer to the CPU and expect it to work. We have to take #a clone conservatively as we are mmapped and some SFT files are packed misaligned #If you are a custom node author reading this, please move your layer to the GPU #or declare your ModelPatcher as CPU in the first place. - if comfy.model_management.is_device_cpu(device): - materialize_meta_param(s, ["weight", "bias"]) - weight = s.weight.to(dtype=dtype, copy=True) - if isinstance(weight, QuantizedTensor): - weight = weight.dequantize() - bias = s.bias.to(dtype=bias_dtype, copy=True) if s.bias is not None else None - return format_return((weight, bias, (None, None, None)), offloadable) + materialize_meta_param(s, ["weight", "bias"]) + weight = s.weight.to(dtype=dtype, copy=True) + if isinstance(weight, QuantizedTensor): + weight = weight.dequantize() + bias = s.bias.to(dtype=bias_dtype, copy=True) if s.bias is not None else None + return format_return((weight, bias, (None, None, None)), offloadable) + elif hasattr(s, "_v") and s.weight.device != device: prefetched = hasattr(s, "_prefetch") offload_stream = None offload_device = None diff --git a/comfy/pinned_memory.py b/comfy/pinned_memory.py index 0e8f573ba..cb77c517a 100644 --- a/comfy/pinned_memory.py +++ b/comfy/pinned_memory.py @@ -1,17 +1,55 @@ +import bisect + import comfy.model_management import comfy.memory_management +import comfy.utils import comfy_aimdo.host_buffer import comfy_aimdo.torch import torch from comfy.cli_args import args +def _add_to_bucket(module, buckets, size, priority): + bucket = buckets.setdefault(size, []) + entry = [-priority, 0, module] + entry[1] = id(entry) + bisect.insort(bucket, entry) + module._pin_balancer_entry = entry + +def _steal_pin(module, stack, buckets, size, priority): + bucket = buckets.get(size) + if bucket is None: + return False + + while bucket and bucket[-1][-1] is None: + bucket.pop() + if not bucket: + del buckets[size] + return False + + if priority <= -bucket[-1][0]: + return False + + *_, victim = bucket.pop() + module._pin = victim._pin + module._pin_registered = victim._pin_registered + module._pin_stack_index = victim._pin_stack_index + stack[module._pin_stack_index] = (module, stack[module._pin_stack_index][1]) + + victim._pin_registered = False + del victim._pin + del victim._pin_stack_index + del victim._pin_balancer_entry + + _add_to_bucket(module, buckets, size, priority) + return True + def get_pin(module, subset="weights"): pin = getattr(module, "_pin", None) if pin is None or module._pin_registered or args.disable_pinned_memory: return pin - _, _, stack_split, pinned_size = module._pin_state[subset] + _, _, stack_split, pinned_size, *_ = module._pin_state[subset] size = pin.nbytes comfy.model_management.ensure_pin_registerable(size) @@ -31,33 +69,51 @@ def pin_memory(module, subset="weights", size=None): return pin = get_pin(module, subset) - if pin is not None or pin_state["failed"]: + if pin is not None: return - hostbuf, stack, stack_split, pinned_size = pin_state[subset] + hostbuf, stack, stack_split, pinned_size, counter, buckets = pin_state[subset] if size is None: size = comfy.memory_management.vram_aligned_size([ module.weight, module.bias ]) offset = hostbuf.size - registerable_size = size + max(0, hostbuf.size - pinned_size[0]) + registerable_size = size + priority = getattr(module, "_pin_balancer_priority", None) + + if priority is None: + priority = comfy.utils.bit_reverse_range(counter[0], 16) + counter[0] += 1 + module._pin_balancer_priority = priority comfy.memory_management.extra_ram_release(comfy.memory_management.RAM_CACHE_HEADROOM) if (not comfy.model_management.ensure_pin_budget(size) or not comfy.model_management.ensure_pin_registerable(registerable_size)): - pin_state["failed"] = True - return False + return _steal_pin(module, stack, buckets, size, priority) + extended = False try: - hostbuf.extend(size=size) + hostbuf.extend(size=size, register=False) + extended = True + pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf)[offset:offset + size] + pin.untyped_storage()._comfy_hostbuf = hostbuf + if torch.cuda.cudart().cudaHostRegister(pin.data_ptr(), size, 1) != 0: + comfy.model_management.discard_cuda_async_error() + comfy.model_management.free_registrations(size) + if torch.cuda.cudart().cudaHostRegister(pin.data_ptr(), size, 1) != 0: + comfy.model_management.discard_cuda_async_error() + del pin + hostbuf.truncate(offset, do_unregister=False) + return _steal_pin(module, stack, buckets, size, priority) except RuntimeError: - pin_state["failed"] = True - return False + if extended: + hostbuf.truncate(offset, do_unregister=False) + return _steal_pin(module, stack, buckets, size, priority) - module._pin = comfy_aimdo.torch.hostbuf_to_tensor(hostbuf)[offset:offset + size] - module._pin.untyped_storage()._comfy_hostbuf = hostbuf + module._pin = pin stack.append((module, offset)) module._pin_registered = True module._pin_stack_index = len(stack) - 1 stack_split[0] = max(stack_split[0], module._pin_stack_index) comfy.model_management.TOTAL_PINNED_MEMORY += size pinned_size[0] += size + _add_to_bucket(module, buckets, size, priority) return True diff --git a/comfy/samplers.py b/comfy/samplers.py index e31277f7b..25c5a855f 100755 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -464,10 +464,7 @@ def _calc_cond_batch_multigpu(model: BaseModel, conds: list[list[dict]], x_in: t def _handle_batch(device: torch.device, batch_tuple: tuple[comfy.hooks.HookGroup, tuple], results: list[thread_result]): try: - # TODO: non-NVIDIA support -- guard with `if device.type == "cuda":` once - # we extend multigpu QA beyond CUDA. Unconditional call crashes on - # XPU/NPU/MPS/CPU/DirectML backends. - torch.cuda.set_device(device) + comfy.model_management.set_torch_device(device) model_current: BaseModel = model_options["multigpu_clones"][device].model # run every hooked_to_run separately with torch.no_grad(): diff --git a/comfy/sd.py b/comfy/sd.py index c7869a97c..8e36e7b69 100644 --- a/comfy/sd.py +++ b/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.triposplat.vae import comfy.ldm.ace.vae.music_dcae_pipeline import comfy.ldm.cogvideo.vae import comfy.ldm.hunyuan_video.vae @@ -57,6 +58,7 @@ import comfy.text_encoders.omnigen2 import comfy.text_encoders.qwen_image import comfy.text_encoders.hunyuan_image import comfy.text_encoders.z_image +import comfy.text_encoders.ideogram4 import comfy.text_encoders.ovis import comfy.text_encoders.kandinsky5 import comfy.text_encoders.jina_clip_2 @@ -65,6 +67,8 @@ import comfy.text_encoders.anima import comfy.text_encoders.ace15 import comfy.text_encoders.longcat_image import comfy.text_encoders.qwen35 +import comfy.text_encoders.qwen3vl +import comfy.text_encoders.boogu import comfy.text_encoders.ernie import comfy.text_encoders.gemma4 import comfy.text_encoders.cogvideo @@ -894,6 +898,16 @@ class VAE: #Force cast it for --disable-dynamic-vram users until there is a true core fix. if not comfy.memory_management.aimdo_enabled: self.disable_offload = True + elif "gs.base_offset_scale" in sd and "octree.out_proj.weight" in sd: # TripoSplat octree gaussian decoder + self.first_stage_model = comfy.ldm.triposplat.vae.OctreeGaussianDecoder() + self.latent_channels = 16 + self.latent_dim = 1 + self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] + # The generic VAE.encode/decode path isn't used: VAEDecodeTripoSplat calls the gaussian + # decoder directly (structured GaussianSplat objects, not a tensor and reserves VRAM itself from num_gaussians. + def _no_generic_io(*args, **kwargs): + raise RuntimeError("TripoSplat gaussian decoder: use the 'TripoSplat Decode' (VAEDecodeTripoSplat)") + self.memory_used_encode = self.memory_used_decode = _no_generic_io else: logging.warning("WARNING: No VAE weights detected, VAE not initalized.") self.first_stage_model = None @@ -1297,6 +1311,8 @@ class CLIPType(Enum): COGVIDEOX = 27 LENS = 28 PIXELDIT = 29 + IDEOGRAM4 = 30 + BOOGU = 31 @@ -1350,6 +1366,8 @@ class TEModel(Enum): GEMMA_4_31B = 31 T5_GEMMA = 32 GPT_OSS_20B = 33 + QWEN3VL_4B = 34 + QWEN3VL_8B = 35 def detect_te_model(sd): @@ -1411,6 +1429,8 @@ def detect_te_model(sd): if weight.shape[0] == 5120: return TEModel.QWEN35_27B return TEModel.QWEN35_2B + if "model.visual.deepstack_merger_list.0.norm.weight" in sd: # DeepStack is unique to Qwen3-VL + return TEModel.QWEN3VL_4B if sd["model.visual.merger.linear_fc2.weight"].shape[0] == 2560 else TEModel.QWEN3VL_8B if "model.layers.0.post_attention_layernorm.weight" in sd: weight = sd['model.layers.0.post_attention_layernorm.weight'] if 'model.layers.0.self_attn.q_norm.weight' in sd: @@ -1595,8 +1615,12 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip clip_target.clip = comfy.text_encoders.ovis.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.ovis.OvisTokenizer elif te_model == TEModel.QWEN3_8B: - clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type="qwen3_8b") - clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer8B + if clip_type == CLIPType.IDEOGRAM4: + clip_target.clip = comfy.text_encoders.ideogram4.te(**llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.ideogram4.Ideogram4Tokenizer + else: + clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type="qwen3_8b") + clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer8B elif te_model == TEModel.JINA_CLIP_2: clip_target.clip = comfy.text_encoders.jina_clip_2.JinaClip2TextModelWrapper clip_target.tokenizer = comfy.text_encoders.jina_clip_2.JinaClip2TokenizerWrapper @@ -1605,6 +1629,24 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip qwen35_type = {TEModel.QWEN35_08B: "qwen35_08b", TEModel.QWEN35_2B: "qwen35_2b", TEModel.QWEN35_4B: "qwen35_4b", TEModel.QWEN35_9B: "qwen35_9b", TEModel.QWEN35_27B: "qwen35_27b"}[te_model] clip_target.clip = comfy.text_encoders.qwen35.te(**llama_detect(clip_data), model_type=qwen35_type) clip_target.tokenizer = comfy.text_encoders.qwen35.tokenizer(model_type=qwen35_type) + elif te_model in (TEModel.QWEN3VL_4B, TEModel.QWEN3VL_8B): + if clip_type == CLIPType.IDEOGRAM4 and te_model == TEModel.QWEN3VL_8B: # Ideogram4 reuses the full Qwen3-VL-8B (13-layer tap for conditioning + multimodal generate). + clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."}) + clip_target.clip = comfy.text_encoders.ideogram4.te_qwen3vl(**llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.ideogram4.Ideogram4Qwen3VLTokenizer + elif clip_type == CLIPType.BOOGU and te_model == TEModel.QWEN3VL_8B: # Boogu-Image: full Qwen3-VL-8B, last hidden state, no-think template. + clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."}) + clip_target.clip = comfy.text_encoders.boogu.te(**llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.boogu.BooguTokenizer + elif clip_type in (CLIPType.FLUX, CLIPType.FLUX2): # Flux2 Klein reuses the Qwen3-VL LM (3-layer tap -> 12288); visual unused. + klein_model_type = "qwen3_8b" if te_model == TEModel.QWEN3VL_8B else "qwen3_4b" + clip_target.clip = comfy.text_encoders.flux.klein_te(**llama_detect(clip_data), model_type=klein_model_type) + clip_target.tokenizer = comfy.text_encoders.flux.KleinTokenizer8B if te_model == TEModel.QWEN3VL_8B else comfy.text_encoders.flux.KleinTokenizer + else: + clip_data[0] = comfy.utils.state_dict_prefix_replace(clip_data[0], {"model.language_model.": "model.", "model.visual.": "visual.", "lm_head.": "model.lm_head."}) + qwen3vl_type = {TEModel.QWEN3VL_4B: "qwen3vl_4b", TEModel.QWEN3VL_8B: "qwen3vl_8b"}[te_model] + clip_target.clip = comfy.text_encoders.qwen3vl.te(**llama_detect(clip_data), model_type=qwen3vl_type) + clip_target.tokenizer = comfy.text_encoders.qwen3vl.tokenizer(model_type=qwen3vl_type) elif te_model == TEModel.QWEN3_06B: clip_target.clip = comfy.text_encoders.anima.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.anima.AnimaTokenizer diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 00941da53..cc05908ee 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -24,6 +24,8 @@ import comfy.text_encoders.qwen_image import comfy.text_encoders.hunyuan_image import comfy.text_encoders.kandinsky5 import comfy.text_encoders.z_image +import comfy.text_encoders.ideogram4 +import comfy.text_encoders.boogu import comfy.text_encoders.anima import comfy.text_encoders.ace15 import comfy.text_encoders.longcat_image @@ -1449,6 +1451,17 @@ class WAN21_SCAIL(WAN21_T2V): out = model_base.WAN21_SCAIL(self, image_to_video=False, device=device) return out + +class WAN21_SCAIL2(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "scail2", + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN21_SCAIL2(self, image_to_video=False, device=device) + return out + class WAN22_WanDancer(WAN21_T2V): unet_config = { "image_model": "wan2.1", @@ -1538,6 +1551,30 @@ class Hunyuan3Dv2mini(Hunyuan3Dv2): latent_format = latent_formats.Hunyuan3Dv2mini +class TripoSplat(supported_models_base.BASE): + # Image -> 3D gaussian splat flow denoiser + unet_config = { + "image_model": "triposplat", + } + + unet_extra_config = {} + + sampling_settings = { + "shift": 3.0, + } + + memory_usage_factor = 0.6 + + latent_format = latent_formats.TripoSplat + + supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + def get_model(self, state_dict, prefix="", device=None): + return model_base.TripoSplat(self, device=device) + + def clip_target(self, state_dict={}): + return None + class HiDream(supported_models_base.BASE): unet_config = { "image_model": "hidream", @@ -1722,6 +1759,65 @@ class Omnigen2(supported_models_base.BASE): hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_3b.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.omnigen2.Omnigen2Tokenizer, comfy.text_encoders.omnigen2.te(**hunyuan_detect)) +class Boogu(Omnigen2): + unet_config = { + "image_model": "boogu", + } + + sampling_settings = { + "multiplier": 1.0, + "shift": 3.16, + } + + memory_usage_factor = 2.15 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Boogu(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3vl_8b.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.boogu.BooguTokenizer, comfy.text_encoders.boogu.te(**hunyuan_detect)) + +class Ideogram4(supported_models_base.BASE): + unet_config = { + "image_model": "ideogram4", + } + + sampling_settings = { + "multiplier": 1.0, + "shift": 1.0, + } + + memory_usage_factor = 11.6 + + unet_extra_config = { + "num_attention_heads": 18, + "attention_head_dim": 256, + "intermediate_size": 12288, + "adaln_dim": 512, + "llm_features_dim": 53248, + "rope_theta": 5000000, + "mrope_section": [24, 20, 20], + "norm_eps": 1e-5, + } + latent_format = latent_formats.Flux2 + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Ideogram4(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3vl_8b.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.ideogram4.Ideogram4Tokenizer, comfy.text_encoders.ideogram4.te(**hunyuan_detect)) + class QwenImage(supported_models_base.BASE): unet_config = { "image_model": "qwen_image", @@ -1982,6 +2078,23 @@ class RT_DETR_v4(supported_models_base.BASE): return None +class DepthAnything3(supported_models_base.BASE): + unet_config = { + "image_model": "DepthAnything3", + } + + # Mono path: no num_heads / num_head_channels needed. + unet_extra_config = {} + + supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + def get_model(self, state_dict, prefix="", device=None): + return model_base.DepthAnything3(self, device=device) + + def clip_target(self, state_dict={}): + return None + + class ErnieImage(supported_models_base.BASE): unet_config = { "image_model": "ernie", @@ -2196,10 +2309,12 @@ models = [ WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, + WAN21_SCAIL2, WAN22_WanDancer, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, + TripoSplat, HiDream, HiDreamO1, Chroma, @@ -2207,7 +2322,9 @@ models = [ ACEStep, ACEStep15, Omnigen2, + Boogu, QwenImage, + Ideogram4, Flux2, Lens, Kandinsky5Image, @@ -2221,4 +2338,5 @@ models = [ CogVideoX_I2V, CogVideoX_T2V, SVD_img2vid, + DepthAnything3, ] diff --git a/comfy/text_encoders/boogu.py b/comfy/text_encoders/boogu.py new file mode 100644 index 000000000..d9de92f10 --- /dev/null +++ b/comfy/text_encoders/boogu.py @@ -0,0 +1,58 @@ +"""Boogu-Image text encoder: full Qwen3-VL-8B, last hidden state (4096-dim). + +Boogu uses the final hidden state of Qwen3-VL as the per-token instruction feature +(num_instruction_feature_layers=1, reduce_type=mean -> just the last layer). +The model itself is the standard Qwen3-VL TE, only the chat template differs +(a fixed system prompt and no block). +""" + +import comfy.text_encoders.qwen3vl +from comfy import sd1_clip + + +# System prompts from the reference pipeline (pipeline_boogu.py). +# T2I (non-empty instruction, no image) uses the helpful-assistant prompt +# everything else (the CFG negative / "drop" condition, and any image case) uses the TI2I "describe" prompt. +BOOGU_T2I_SYSTEM = "You are a helpful assistant that generates high-quality images based on user instructions. The instructions are as follows." +BOOGU_DROP_SYSTEM = "Describe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate." + + +class BooguTokenizer(comfy.text_encoders.qwen3vl.Qwen3VLTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type="qwen3vl_8b") + # apply_chat_template without add_generation_prompt + self.llama_template = "<|im_start|>system\n" + BOOGU_T2I_SYSTEM + "<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n" + self.llama_template_images = "<|im_start|>system\n" + BOOGU_DROP_SYSTEM + "<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n" + # Reference SYSTEM_PROMPT_DROP: used for the empty negative/uncond instruction. + self.llama_template_drop = "<|im_start|>system\n" + BOOGU_DROP_SYSTEM + "<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n" + + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=True, **kwargs): + if llama_template is None and len(images) == 0 and text.strip() == "": + llama_template = self.llama_template_drop + # Boogu conditions on the no-think template; thinking=True drops the empty block qwen3vl adds by default. + return super().tokenize_with_weights(text, return_word_ids=return_word_ids, llama_template=llama_template, images=images, prevent_empty_text=prevent_empty_text, thinking=thinking, **kwargs) + + +class BooguQwen3VLClipModel(comfy.text_encoders.qwen3vl.Qwen3VLClipModel): + def __init__(self, device="cpu", dtype=None, attention_mask=True, model_options={}, model_type="qwen3vl_8b"): + super().__init__(device=device, dtype=dtype, attention_mask=attention_mask, model_options=model_options, model_type=model_type) + # apply the final RMSNorm to the tapped last layer + self.layer_norm_hidden_state = True + + +class BooguTEModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + clip_model = lambda **kw: BooguQwen3VLClipModel(**kw, model_type="qwen3vl_8b") + super().__init__(device=device, dtype=dtype, name="qwen3vl_8b", clip_model=clip_model, model_options=model_options) + + +def te(dtype_llama=None, llama_quantization_metadata=None): + class BooguTEModel_(BooguTEModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if dtype_llama is not None: + dtype = dtype_llama + if llama_quantization_metadata is not None: + model_options = model_options.copy() + model_options["quantization_metadata"] = llama_quantization_metadata + super().__init__(device=device, dtype=dtype, model_options=model_options) + return BooguTEModel_ diff --git a/comfy/text_encoders/ideogram4.py b/comfy/text_encoders/ideogram4.py new file mode 100644 index 000000000..151b43c53 --- /dev/null +++ b/comfy/text_encoders/ideogram4.py @@ -0,0 +1,120 @@ +"""Ideogram 4 text encoder: Qwen3-VL-8B language model, 13-layer tap. + +Ideogram 4 conditions on the concatenation of hidden states from 13 layers of +Qwen3-VL (layers 0,3,...,33,35), giving a 4096*13 = 53248-dim feature per token. +""" + +import os + +from transformers import Qwen2Tokenizer + +import comfy.text_encoders.llama +import comfy.text_encoders.qwen3vl +from comfy import sd1_clip + +# Reference taps outputs of layers (0,3,...,35); comfy captures layer inputs, offset by +1. +IDEOGRAM4_TAP_LAYERS = [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 36] + + +class Qwen3VLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") + super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, + embedding_size=4096, embedding_key='qwen3vl_8b', tokenizer_class=Qwen2Tokenizer, + has_start_token=False, has_end_token=False, pad_to_max_length=False, + max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data) + + +class Ideogram4Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, + name="qwen3vl_8b", tokenizer=Qwen3VLTokenizer) + + self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" + + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs): + if text.startswith('<|im_start|>'): + llama_text = text + elif llama_template is None: + llama_text = self.llama_template.format(text) + else: + llama_text = llama_template.format(text) + return super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs) + + +# Qwen3-VL-8B = 5e6 (vs plain Qwen3-8B's 1e6) +# final_norm/lm_head off -> Ideogram only reads raw tapped hidden states +QWEN3VL_8B_CONFIG = {"rope_theta": 5000000.0, "final_norm": False, "lm_head": False} + + +class Qwen3VL8BModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="hidden", layer_idx=None, dtype=None, attention_mask=True, model_options={}): + super().__init__(device=device, layer=IDEOGRAM4_TAP_LAYERS, layer_idx=None, + textmodel_json_config=dict(QWEN3VL_8B_CONFIG), + dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, + model_class=comfy.text_encoders.llama.Qwen3_8B, + enable_attention_masks=attention_mask, return_attention_masks=attention_mask, + model_options=model_options) + + +class Ideogram4TEModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="qwen3vl_8b", clip_model=Qwen3VL8BModel, model_options=model_options) + + def encode_token_weights(self, token_weight_pairs): + out, pooled, extra = super().encode_token_weights(token_weight_pairs) + b, n, seq, h = out.shape # (B, n_taps=13, seq, 4096) stacked in ascending layer order. + out = out.permute(0, 2, 3, 1).reshape(b, seq, h * n) # (B, seq, 4096*13). permute -> (B, seq, H, taps). + return out, pooled, extra + + +def te(dtype_llama=None, llama_quantization_metadata=None): + class Ideogram4TEModel_(Ideogram4TEModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if dtype_llama is not None: + dtype = dtype_llama + if llama_quantization_metadata is not None: + model_options = model_options.copy() + model_options["quantization_metadata"] = llama_quantization_metadata + super().__init__(device=device, dtype=dtype, model_options=model_options) + return Ideogram4TEModel_ + + +# Full Qwen3-VL-8B variant with vision + +class Ideogram4Qwen3VLClipModel(comfy.text_encoders.qwen3vl.Qwen3VLClipModel): + def __init__(self, device="cpu", dtype=None, attention_mask=True, model_options={}): + super().__init__(device=device, layer=IDEOGRAM4_TAP_LAYERS, layer_idx=None, dtype=dtype, + attention_mask=attention_mask, model_options=model_options, model_type="qwen3vl_8b") + + +class Ideogram4Qwen3VLTEModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="qwen3vl_8b", clip_model=Ideogram4Qwen3VLClipModel, model_options=model_options) + + def encode_token_weights(self, token_weight_pairs): + out, pooled, extra = super().encode_token_weights(token_weight_pairs) + b, n, seq, h = out.shape # (B, n_taps=13, seq, 4096), ascending layer order. + out = out.permute(0, 2, 3, 1).reshape(b, seq, h * n) # (B, seq, 4096*13 = 53248). + return out, pooled, extra + + +class Ideogram4Qwen3VLTokenizer(comfy.text_encoders.qwen3vl.Qwen3VLTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type="qwen3vl_8b") + + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=True, **kwargs): + # Ideogram 4 conditions on the no-think template; default thinking=True drops the empty think block qwen3vl adds. + return super().tokenize_with_weights(text, return_word_ids=return_word_ids, llama_template=llama_template, images=images, prevent_empty_text=prevent_empty_text, thinking=thinking, **kwargs) + + +def te_qwen3vl(dtype_llama=None, llama_quantization_metadata=None): + class Ideogram4Qwen3VLTEModel_(Ideogram4Qwen3VLTEModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if dtype_llama is not None: + dtype = dtype_llama + if llama_quantization_metadata is not None: + model_options = model_options.copy() + model_options["quantization_metadata"] = llama_quantization_metadata + super().__init__(device=device, dtype=dtype, model_options=model_options) + return Ideogram4Qwen3VLTEModel_ diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py index 5087228ca..e9f38a9a2 100644 --- a/comfy/text_encoders/llama.py +++ b/comfy/text_encoders/llama.py @@ -251,6 +251,19 @@ class Qwen3_8BConfig: lm_head: bool = True stop_tokens = [151643, 151645] +@dataclass +class Qwen3VL_8BConfig(Qwen3_8BConfig): + max_position_embeddings: int = 262144 + rope_theta: float = 5000000.0 + rope_dims = [24, 20, 20] + interleaved_mrope = True + +@dataclass +class Qwen3VL_4BConfig(Qwen3VL_8BConfig): + hidden_size: int = 2560 + intermediate_size: int = 9728 + lm_head: bool = False # 4B ties word embeddings + @dataclass class Ovis25_2BConfig: vocab_size: int = 151936 @@ -703,7 +716,8 @@ class Llama2_(nn.Module): interleaved_mrope=getattr(self.config, "interleaved_mrope", False), device=device) - def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None, input_ids=None): + def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, + dtype=None, position_ids=None, embeds_info=[], past_key_values=None, input_ids=None,deepstack_embeds=None, visual_pos_masks=None): if embeds is not None: x = embeds else: @@ -767,6 +781,10 @@ class Llama2_(nn.Module): if current_kv is not None: next_key_values.append(current_kv) + # DeepStack: add per-layer visual features into the first len() decoder layers at image positions (Qwen3-VL) + if deepstack_embeds is not None and i < len(deepstack_embeds): + x[visual_pos_masks] = x[visual_pos_masks] + deepstack_embeds[i].to(x) + if i == intermediate_output: intermediate = x.clone() @@ -860,7 +878,7 @@ class BaseGenerate: torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0)) return past_key_values - def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0, presence_penalty=0.0, initial_input_ids=None): + def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0, presence_penalty=0.0, initial_input_ids=None, position_ids=None, deepstack_embeds=None, visual_pos_masks=None): device = embeds.device if stop_tokens is None: @@ -884,10 +902,18 @@ class BaseGenerate: generated_token_ids = [] pbar = comfy.utils.ProgressBar(max_length) + # MRoPE: prefill uses explicit 3D position_ids, decode continues from the last position + next_pos = int(position_ids[:, -1].max()) + 1 if position_ids is not None else None + # Generation loop current_input_ids = initial_input_ids for step in tqdm(range(max_length), desc="Generating tokens"): - x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values, input_ids=current_input_ids) + # DeepStack visual features are injected on the prefill only; gemma4's forward lacks these kwargs. + extra = {} + if step == 0 and deepstack_embeds is not None: + extra["deepstack_embeds"] = deepstack_embeds + extra["visual_pos_masks"] = visual_pos_masks + x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values, input_ids=current_input_ids, position_ids=position_ids, **extra) logits = self.logits(x)[:, -1] next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample, presence_penalty=presence_penalty) token_id = next_token[0].item() @@ -895,6 +921,9 @@ class BaseGenerate: embeds = self.model.embed_tokens(next_token).to(execution_dtype) current_input_ids = next_token if initial_input_ids is not None else None + if next_pos is not None: # advance MRoPE position for the next (decode) step + position_ids = torch.tensor([[next_pos]], device=device) + next_pos += 1 pbar.update(1) if token_id in stop_tokens: diff --git a/comfy/text_encoders/qwen35.py b/comfy/text_encoders/qwen35.py index 416ce9d18..71a17990f 100644 --- a/comfy/text_encoders/qwen35.py +++ b/comfy/text_encoders/qwen35.py @@ -3,7 +3,6 @@ import torch.nn as nn import torch.nn.functional as F from dataclasses import dataclass, field import os -import math import comfy.model_management from comfy.ldm.modules.attention import optimized_attention_for_device @@ -563,6 +562,8 @@ class Qwen35VisionModel(nn.Module): for _ in range(config["depth"]) ]) self.merger = Qwen35VisionPatchMerger(self.hidden_size, self.spatial_merge_size, config["out_hidden_size"], device=device, dtype=dtype, ops=ops) + self.deepstack_visual_indexes = [] # DeepStack, per-layer visual features (Qwen3-VL) + self.deepstack_merger_list = None def rot_pos_emb(self, grid_thw): merge_size = self.spatial_merge_size @@ -664,9 +665,14 @@ class Qwen35VisionModel(nn.Module): ).cumsum(dim=0, dtype=torch.int32) cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0) optimized_attention = optimized_attention_for_device(x.device, mask=False, small_input=True) - for blk in self.blocks: + deepstack_features = [] + for layer_num, blk in enumerate(self.blocks): x = blk(x, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings, optimized_attention=optimized_attention) + if self.deepstack_merger_list is not None and layer_num in self.deepstack_visual_indexes: + deepstack_features.append(self.deepstack_merger_list[self.deepstack_visual_indexes.index(layer_num)](x)) merged = self.merger(x) + if self.deepstack_merger_list is not None: + return merged, deepstack_features return merged # Model Wrapper @@ -690,30 +696,7 @@ class Qwen35(BaseLlama, BaseGenerate, torch.nn.Module): return None, None def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[], past_key_values=None): - grid = None - position_ids = None - offset = 0 - for e in embeds_info: - if e.get("type") == "image": - grid = e.get("extra", None) - start = e.get("index") - if position_ids is None: - position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device) - position_ids[:, :start] = torch.arange(0, start, device=embeds.device) - end = e.get("size") + start - len_max = int(grid.max()) // 2 - start_next = len_max + start - position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device) - position_ids[0, start:end] = start + offset - max_d = int(grid[0][1]) // 2 - position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start] - max_d = int(grid[0][2]) // 2 - position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start] - offset += len_max - (end - start) - - if grid is None: - position_ids = None - + position_ids = comfy.text_encoders.qwen_vl.qwen2vl_mrope_position_ids(embeds_info, embeds.shape[1], embeds.device) return super().forward(x, attention_mask=attention_mask, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=final_layer_norm_intermediate, dtype=dtype, position_ids=position_ids, past_key_values=past_key_values) def init_kv_cache(self, batch, max_cache_len, device, execution_dtype): diff --git a/comfy/text_encoders/qwen3vl.py b/comfy/text_encoders/qwen3vl.py new file mode 100644 index 000000000..59c9aae6d --- /dev/null +++ b/comfy/text_encoders/qwen3vl.py @@ -0,0 +1,193 @@ +import os + +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import Qwen2Tokenizer + +from comfy import sd1_clip +import comfy.text_encoders.qwen_vl +from .qwen35 import Qwen35VisionModel +from .llama import BaseLlama, BaseQwen3, BaseGenerate, Llama2_, Qwen3VL_4BConfig, Qwen3VL_8BConfig + + +QWEN3VL_VISION = { + "qwen3vl_4b": dict(hidden_size=1024, intermediate_size=4096, depth=24, deepstack_visual_indexes=[5, 11, 17]), + "qwen3vl_8b": dict(hidden_size=1152, intermediate_size=4304, depth=27, deepstack_visual_indexes=[8, 16, 24]), +} +QWEN3VL_VISION_COMMON = dict(num_heads=16, patch_size=16, temporal_patch_size=2, in_channels=3, + spatial_merge_size=2, num_position_embeddings=2304) + +QWEN3VL_CONFIGS = {"qwen3vl_4b": Qwen3VL_4BConfig, "qwen3vl_8b": Qwen3VL_8BConfig} + + +class Qwen3VLDeepstackMerger(nn.Module): + # DeepStack merger: postshuffle LayerNorm (applied after spatial merge), unlike the main merger. + def __init__(self, hidden_size, spatial_merge_size, out_hidden_size, device=None, dtype=None, ops=None): + super().__init__() + self.merge_dim = hidden_size * (spatial_merge_size ** 2) + self.norm = ops.LayerNorm(self.merge_dim, eps=1e-6, device=device, dtype=dtype) + self.linear_fc1 = ops.Linear(self.merge_dim, self.merge_dim, device=device, dtype=dtype) + self.linear_fc2 = ops.Linear(self.merge_dim, out_hidden_size, device=device, dtype=dtype) + + def forward(self, x): + x = self.norm(x.view(-1, self.merge_dim)) + return self.linear_fc2(F.gelu(self.linear_fc1(x))) + + +class Qwen3VLVisionModel(Qwen35VisionModel): + # Qwen3.5 vision + DeepStack + def __init__(self, config, device=None, dtype=None, ops=None): + super().__init__(config, device=device, dtype=dtype, ops=ops) + self.deepstack_visual_indexes = config["deepstack_visual_indexes"] + self.deepstack_merger_list = nn.ModuleList([ + Qwen3VLDeepstackMerger(self.hidden_size, self.spatial_merge_size, config["out_hidden_size"], device=device, dtype=dtype, ops=ops) + for _ in self.deepstack_visual_indexes + ]) + + +class Qwen3VL(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module): + model_type = "qwen3vl_8b" + + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + config = QWEN3VL_CONFIGS[self.model_type](**config_dict) + self.num_layers = config.num_hidden_layers + self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) + vision_config = {**QWEN3VL_VISION_COMMON, **QWEN3VL_VISION[self.model_type], "out_hidden_size": config.hidden_size} + self.visual = Qwen3VLVisionModel(vision_config, device=device, dtype=dtype, ops=operations) + self.dtype = dtype + + def preprocess_embed(self, embed, device): + if embed["type"] == "image": + # Qwen3-VL normalizes to [-1, 1] (mean/std 0.5), unlike Qwen2.5-VL's CLIP normalization. + image, grid = comfy.text_encoders.qwen_vl.process_qwen2vl_images(embed["data"], patch_size=16, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5]) + merged, deepstack = self.visual(image.to(device, dtype=torch.float32), grid) + return merged, {"grid": grid, "deepstack": deepstack} + return None, None + + def build_image_inputs(self, embeds, embeds_info): + # Returns (position_ids, visual_pos_masks, deepstack) for the prompt + images = sorted([e for e in embeds_info if e.get("type") == "image"], key=lambda e: e["index"]) + if len(images) == 0: + return None, None, None + + device = embeds.device + seq = embeds.shape[1] + position_ids = comfy.text_encoders.qwen_vl.qwen2vl_mrope_position_ids(embeds_info, seq, device) + + # DeepStack: mask of image positions + per-vision-layer features to inject there. + visual_pos_masks = torch.zeros((1, seq), dtype=torch.bool, device=device) + deepstack = None + for e in images: + start = e["index"] + end = e["size"] + start + visual_pos_masks[0, start:end] = True + ds = e["extra"]["deepstack"] + if deepstack is None: + deepstack = [d for d in ds] + else: + deepstack = [torch.cat([deepstack[i], ds[i]], dim=0) for i in range(len(ds))] + return position_ids, visual_pos_masks, deepstack + + +def _make_qwen3vl_model(model_type): + class Qwen3VL_(Qwen3VL): + pass + Qwen3VL_.model_type = model_type + return Qwen3VL_ + + +class Qwen3VLClipModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="hidden", layer_idx=-1, dtype=None, attention_mask=True, model_options={}, model_type="qwen3vl_8b"): + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, + dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, + model_class=_make_qwen3vl_model(model_type), enable_attention_masks=attention_mask, + return_attention_masks=attention_mask, model_options=model_options) + + def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty=0.0): + if isinstance(tokens, dict): + tokens = next(iter(tokens.values())) + tokens_only = [[t[0] for t in b] for b in tokens] + embeds, _, _, embeds_info = self.process_tokens(tokens_only, self.execution_device) + position_ids, visual_pos_masks, deepstack = self.transformer.build_image_inputs(embeds, embeds_info) + return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, + presence_penalty=presence_penalty, position_ids=position_ids, + visual_pos_masks=visual_pos_masks, deepstack_embeds=deepstack) + + +class Qwen3VLTEModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, model_type="qwen3vl_8b"): + clip_model = lambda **kw: Qwen3VLClipModel(**kw, model_type=model_type) + super().__init__(device=device, dtype=dtype, name=model_type, clip_model=clip_model, model_options=model_options) + + +class Qwen3VLSDTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}, embedding_size=4096, embedding_key="qwen3vl_8b"): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") + super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=embedding_size, embedding_key=embedding_key, tokenizer_class=Qwen2Tokenizer, + has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data) + + +class Qwen3VLTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}, model_type="qwen3vl_8b"): + embedding_size = 2560 if model_type == "qwen3vl_4b" else 4096 + tokenizer = lambda *a, **kw: Qwen3VLSDTokenizer(*a, **kw, embedding_size=embedding_size, embedding_key=model_type) + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name=model_type, tokenizer=tokenizer) + self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" + self.llama_template_images = "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n" + + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, thinking=False, **kwargs): + image = kwargs.get("image", None) + if image is not None and len(images) == 0: + images = [image[i:i + 1] for i in range(image.shape[0])] + + skip_template = text.startswith('<|im_start|>') + if prevent_empty_text and text == '': + text = ' ' + + if skip_template: + llama_text = text + else: + if llama_template is not None: + template = llama_template + elif len(images) == 0: + template = self.llama_template + else: + template = self.llama_template_images + if len(images) > 1: + vision_block = "<|vision_start|><|image_pad|><|vision_end|>" + template = template.replace(vision_block, vision_block * len(images), 1) + llama_text = template.format(text) + if not thinking: # Qwen3 convention: empty think block suppresses reasoning + llama_text += "\n\n\n\n" + + tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs) + key_name = next(iter(tokens)) + embed_count = 0 + for r in tokens[key_name]: + for i in range(len(r)): + if r[i][0] == 151655: # <|image_pad|> + if len(images) > embed_count: + r[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"},) + r[i][1:] + embed_count += 1 + return tokens + + +def tokenizer(model_type="qwen3vl_8b"): + class Qwen3VLTokenizer_(Qwen3VLTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type=model_type) + return Qwen3VLTokenizer_ + + +def te(dtype_llama=None, llama_quantization_metadata=None, model_type="qwen3vl_8b"): + class Qwen3VLTEModel_(Qwen3VLTEModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if dtype_llama is not None: + dtype = dtype_llama + if llama_quantization_metadata is not None: + model_options = model_options.copy() + model_options["quantization_metadata"] = llama_quantization_metadata + super().__init__(device=device, dtype=dtype, model_options=model_options, model_type=model_type) + return Qwen3VLTEModel_ diff --git a/comfy/text_encoders/qwen_vl.py b/comfy/text_encoders/qwen_vl.py index 98c350a12..924eb6ad8 100644 --- a/comfy/text_encoders/qwen_vl.py +++ b/comfy/text_encoders/qwen_vl.py @@ -88,6 +88,32 @@ def process_qwen2vl_images( return flatten_patches, image_grid_thw +def qwen2vl_mrope_position_ids(embeds_info, seq_len, device): + # (3, seq_len) T/H/W MRoPE position ids: text runs sequentially, each image span gets its grid positions. + # Returns None when there are no image embeds. `extra` is the image grid_thw, or a dict carrying it under "grid". + position_ids = None + offset = 0 + for e in embeds_info: + if e.get("type") == "image": + extra = e.get("extra", None) + grid = extra["grid"] if isinstance(extra, dict) else extra + start = e.get("index") + if position_ids is None: + position_ids = torch.zeros((3, seq_len), device=device) + position_ids[:, :start] = torch.arange(0, start, device=device) + end = e.get("size") + start + len_max = int(grid.max()) // 2 + start_next = len_max + start + position_ids[:, end:] = torch.arange(start_next + offset, start_next + (seq_len - end) + offset, device=device) + position_ids[0, start:end] = start + offset + max_d = int(grid[0][1]) // 2 + position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start] + max_d = int(grid[0][2]) // 2 + position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start] + offset += len_max - (end - start) + return position_ids + + class VisionPatchEmbed(nn.Module): def __init__( self, diff --git a/comfy/utils.py b/comfy/utils.py index 49ae12b06..09d783fff 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -85,9 +85,9 @@ _TYPES = { def load_safetensors(ckpt): import comfy_aimdo.model_mmap - f = open(ckpt, "rb", buffering=0) file_lock = threading.Lock() model_mmap = comfy_aimdo.model_mmap.ModelMMAP(ckpt) + f = model_mmap.get_file_handle() file_size = os.path.getsize(ckpt) mv = memoryview((ctypes.c_uint8 * file_size).from_address(model_mmap.get())) @@ -1452,3 +1452,10 @@ def deepcopy_list_dict(obj, memo=None): memo[obj_id] = res return res + +def bit_reverse_range(index, bits): + result = 0 + for _ in range(bits): + result = (result << 1) | (index & 1) + index >>= 1 + return result diff --git a/comfy_api/feature_flags.py b/comfy_api/feature_flags.py index adb5a3144..0f30608a9 100644 --- a/comfy_api/feature_flags.py +++ b/comfy_api/feature_flags.py @@ -25,6 +25,11 @@ CLI_FEATURE_FLAG_REGISTRY: dict[str, FeatureFlagInfo] = { "default": False, "description": "Show the sign-in button in the frontend even when not signed in", }, + "enable_telemetry": { + "type": "bool", + "default": False, + "description": "Signal the frontend that telemetry collection is enabled", + }, } diff --git a/comfy_api/latest/__init__.py b/comfy_api/latest/__init__.py index e0a585b10..294ad425e 100644 --- a/comfy_api/latest/__init__.py +++ b/comfy_api/latest/__init__.py @@ -5,7 +5,7 @@ from comfy_api.internal.singleton import ProxiedSingleton from comfy_api.internal.async_to_sync import create_sync_class from ._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput from ._input_impl import VideoFromFile, VideoFromComponents -from ._util import VideoCodec, VideoContainer, VideoComponents, MESH, VOXEL, File3D +from ._util import VideoCodec, VideoContainer, VideoComponents, MESH, VOXEL, SPLAT, File3D from . import _io_public as io from . import _ui_public as ui from comfy_execution.utils import get_executing_context @@ -143,6 +143,7 @@ class Types: VideoComponents = VideoComponents MESH = MESH VOXEL = VOXEL + SPLAT = SPLAT File3D = File3D diff --git a/comfy_api/latest/_input/video_types.py b/comfy_api/latest/_input/video_types.py index 451e9526e..e2e99521f 100644 --- a/comfy_api/latest/_input/video_types.py +++ b/comfy_api/latest/_input/video_types.py @@ -27,10 +27,13 @@ class VideoInput(ABC): path: Union[str, IO[bytes]], format: VideoContainer = VideoContainer.AUTO, codec: VideoCodec = VideoCodec.AUTO, - metadata: Optional[dict] = None + metadata: Optional[dict] = None, + bit_depth: int | None = None, ): """ Abstract method to save the video input to a file. + + bit_depth selects the encoded bit depth; None keeps the video's native depth. """ pass @@ -65,6 +68,12 @@ class VideoInput(ABC): buffer.seek(0) return buffer + def get_active_trim_window(self) -> tuple[float, float]: + """Return the active trim as ``(start_time, duration)`` in seconds (start_time normalized + to ``>= 0``; ``duration == 0`` means "until the end"). Default: no trim; trimmable subclasses override. + """ + return 0.0, 0.0 + # Provide a default implementation, but subclasses can provide optimized versions # if possible. def get_dimensions(self) -> tuple[int, int]: @@ -77,6 +86,14 @@ class VideoInput(ABC): components = self.get_components() return components.images.shape[2], components.images.shape[1] + def get_bit_depth(self) -> int: + """ + Returns the bit depth of the video (e.g. 8 or 10). + + Default implementation returns 8; subclasses report their real depth. + """ + return 8 + def get_duration(self) -> float: """ Returns the duration of the video in seconds. diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py index 99e67d363..6c69256ab 100644 --- a/comfy_api/latest/_input_impl/video_types.py +++ b/comfy_api/latest/_input_impl/video_types.py @@ -52,6 +52,12 @@ def get_open_write_kwargs( return open_kwargs +def video_stream_bit_depth(stream) -> int: + if stream is None or stream.format is None or not stream.format.components: + return 8 + return max(component.bits for component in stream.format.components) + + class VideoFromFile(VideoInput): """ Class representing video input from a file. @@ -75,6 +81,12 @@ class VideoFromFile(VideoInput): self.__file.seek(0) return self.__file + def get_active_trim_window(self) -> tuple[float, float]: + start_time = self.__start_time + if start_time < 0: + start_time = max(self._get_raw_duration() + start_time, 0.0) + return float(start_time), float(self.__duration) + def get_dimensions(self) -> tuple[int, int]: """ Returns the dimensions of the video input. @@ -91,6 +103,13 @@ class VideoFromFile(VideoInput): return stream.width, stream.height raise ValueError(f"No video stream found in file '{self.__file}'") + def get_bit_depth(self) -> int: + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) # Reset the BytesIO object to the beginning + with av.open(self.__file, mode="r") as container: + video_stream = container.streams.video[0] if len(container.streams.video) > 0 else None + return video_stream_bit_depth(video_stream) + def get_duration(self) -> float: """ Returns the duration of the video in seconds. @@ -251,6 +270,7 @@ class VideoFromFile(VideoInput): image_format = 'gbrpf32le' process_image_format = lambda a: a + align_graph = None audio = None streams = [video_stream] @@ -304,7 +324,28 @@ class VideoFromFile(VideoInput): checked_alpha = True - img = frame.to_ndarray(format=image_format) # shape: (H, W, 4) + # Fix non-deterministic video decode when the video width is not a multiple of 32 + # For non-yuvj pixel formats: most H.264/H.265 video and static images (e.g. lossy WebP via LoadImage) + # Pad both axes to a multiple of 32 and smear the border so the alignment padding never bleeds into the cropped edges + if image_format in ('gbrpf32le', 'gbrapf32le') and frame.width % 32 != 0: + if align_graph is None: + pad_w = ((frame.width + 31) // 32) * 32 + pad_h = ((frame.height + 31) // 32) * 32 + g = av.filter.Graph() + g_src = g.add_buffer(width=frame.width, height=frame.height, + format=frame.format.name, time_base=video_stream.time_base) + g_pad = g.add('pad', f'{pad_w}:{pad_h}:0:0') + g_fill = g.add('fillborders', f'left=0:right={pad_w - frame.width}:top=0:bottom={pad_h - frame.height}:mode=smear') + g_sink = g.add('buffersink') + g_src.link_to(g_pad) + g_pad.link_to(g_fill) + g_fill.link_to(g_sink) + g.configure() + align_graph = (g, g_src, g_sink) + align_graph[1].push(frame) + img = np.ascontiguousarray(align_graph[2].pull().to_ndarray(format=image_format)[:frame.height, :frame.width]) + else: + img = frame.to_ndarray(format=image_format) if frame.rotation != 0: k = int(round(frame.rotation // 90)) img = np.rot90(img, k=k, axes=(0, 1)).copy() @@ -371,25 +412,32 @@ class VideoFromFile(VideoInput): format: VideoContainer = VideoContainer.AUTO, codec: VideoCodec = VideoCodec.AUTO, metadata: Optional[dict] = None, + bit_depth: int | None = None, ): if isinstance(self.__file, io.BytesIO): self.__file.seek(0) # Reset the BytesIO object to the beginning with av.open(self.__file, mode='r') as container: container_format = container.format.name - video_encoding = container.streams.video[0].codec.name if len(container.streams.video) > 0 else None + video_stream = container.streams.video[0] if len(container.streams.video) > 0 else None + video_encoding = video_stream.codec.name if video_stream is not None else None + source_bit_depth = video_stream_bit_depth(video_stream) reuse_streams = True if format != VideoContainer.AUTO and format not in container_format.split(","): reuse_streams = False if codec != VideoCodec.AUTO and codec != video_encoding and video_encoding is not None: reuse_streams = False + if bit_depth is not None and video_encoding is not None and bit_depth != source_bit_depth: + reuse_streams = False if self.__start_time or self.__duration: reuse_streams = False if not reuse_streams: + if bit_depth is None: + bit_depth = source_bit_depth components = self.get_components_internal(container) video = VideoFromComponents(components) return video.save_to( - path, format=format, codec=codec, metadata=metadata + path, format=format, codec=codec, metadata=metadata, bit_depth=bit_depth, ) streams = container.streams @@ -445,8 +493,10 @@ class VideoFromComponents(VideoInput): Class representing video input from tensors. """ - def __init__(self, components: VideoComponents): + def __init__(self, components: VideoComponents, bit_depth: int = 8): self.__components = components + # Tensor components have no inherent bit depth; this is the depth used when encoding. + self.__bit_depth = bit_depth def get_components(self) -> VideoComponents: return VideoComponents( @@ -455,18 +505,26 @@ class VideoFromComponents(VideoInput): frame_rate=self.__components.frame_rate, ) + def get_bit_depth(self) -> int: + return self.__bit_depth + def save_to( self, path: str, format: VideoContainer = VideoContainer.AUTO, codec: VideoCodec = VideoCodec.AUTO, metadata: Optional[dict] = None, + bit_depth: int | None = None, ): """Save the video to a file path or BytesIO buffer.""" if format != VideoContainer.AUTO and format != VideoContainer.MP4: raise ValueError("Only MP4 format is supported for now") if codec != VideoCodec.AUTO and codec != VideoCodec.H264: raise ValueError("Only H264 codec is supported for now") + # None means "use the depth this video was created with" (CreateVideo's choice). + if bit_depth is None: + bit_depth = self.__bit_depth + is_10bit = bit_depth >= 10 extra_kwargs = {} if isinstance(format, VideoContainer) and format != VideoContainer.AUTO: extra_kwargs["format"] = format.value @@ -482,10 +540,11 @@ class VideoFromComponents(VideoInput): frame_rate = Fraction(round(self.__components.frame_rate * 1000), 1000) # Create a video stream + pix_fmt = "yuv420p10le" if is_10bit else "yuv420p" video_stream = output.add_stream('h264', rate=frame_rate) video_stream.width = self.__components.images.shape[2] video_stream.height = self.__components.images.shape[1] - video_stream.pix_fmt = 'yuv420p' + video_stream.pix_fmt = pix_fmt # Create an audio stream audio_sample_rate = 1 @@ -499,9 +558,14 @@ class VideoFromComponents(VideoInput): # Encode video for i, frame in enumerate(self.__components.images): - img = (frame * 255).clamp(0, 255).byte().cpu().numpy() # shape: (H, W, 3) - frame = av.VideoFrame.from_ndarray(img, format='rgb24') - frame = frame.reformat(format='yuv420p') # Convert to YUV420P as required by h264 + if is_10bit: + # 16-bit RGB keeps float precision through the conversion to 10-bit YUV. + img = (frame.float() * 65535).clamp(0, 65535).cpu().numpy().astype(np.uint16) # shape: (H, W, 3) + frame = av.VideoFrame.from_ndarray(img, format="rgb48le") + else: + img = (frame * 255).clamp(0, 255).byte().cpu().numpy() # shape: (H, W, 3) + frame = av.VideoFrame.from_ndarray(img, format='rgb24') + frame = frame.reformat(format=pix_fmt) packet = video_stream.encode(frame) output.mux(packet) diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py index 19d8176b0..012fae3ac 100644 --- a/comfy_api/latest/_io.py +++ b/comfy_api/latest/_io.py @@ -28,7 +28,7 @@ if TYPE_CHECKING: from comfy_api.internal import (_ComfyNodeInternal, _NodeOutputInternal, classproperty, copy_class, first_real_override, is_class, prune_dict, shallow_clone_class) from comfy_execution.graph_utils import ExecutionBlocker -from ._util import MESH, VOXEL, SVG as _SVG, File3D +from ._util import MESH, VOXEL, SPLAT, SVG as _SVG, File3D class FolderType(str, Enum): @@ -684,6 +684,10 @@ class Voxel(ComfyTypeIO): class Mesh(ComfyTypeIO): Type = MESH +@comfytype(io_type="SPLAT") +class Splat(ComfyTypeIO): + Type = SPLAT + @comfytype(io_type="FILE_3D") class File3DAny(ComfyTypeIO): @@ -727,6 +731,42 @@ class File3DUSDZ(ComfyTypeIO): Type = File3D +@comfytype(io_type="FILE_3D_PLY") +class File3DPLY(ComfyTypeIO): + """PLY format 3D file - point cloud or Gaussian splat.""" + Type = File3D + + +@comfytype(io_type="FILE_3D_SPLAT") +class File3DSPLAT(ComfyTypeIO): + """SPLAT format 3D file - 3D Gaussian splat.""" + Type = File3D + + +@comfytype(io_type="FILE_3D_SPZ") +class File3DSPZ(ComfyTypeIO): + """SPZ format 3D file - compressed 3D Gaussian splat.""" + Type = File3D + + +@comfytype(io_type="FILE_3D_KSPLAT") +class File3DKSPLAT(ComfyTypeIO): + """KSPLAT format 3D file - 3D Gaussian splat.""" + Type = File3D + + +@comfytype(io_type="FILE_3D_SPLAT_ANY") +class File3DSplatAny(ComfyTypeIO): + """General 3D Gaussian splat file type - accepts any supported splat container (.ply / .spz / .splat / .ksplat).""" + Type = File3D + + +@comfytype(io_type="FILE_3D_POINT_CLOUD_ANY") +class File3DPointCloudAny(ComfyTypeIO): + """General point cloud file type - accepts any supported point cloud container (currently .ply).""" + Type = File3D + + @comfytype(io_type="HOOKS") class Hooks(ComfyTypeIO): if TYPE_CHECKING: @@ -1360,7 +1400,8 @@ class V3Data(TypedDict): class HiddenHolder: def __init__(self, unique_id: str, prompt: Any, extra_pnginfo: Any, dynprompt: Any, - auth_token_comfy_org: str, api_key_comfy_org: str, **kwargs): + auth_token_comfy_org: str, api_key_comfy_org: str, + comfy_usage_source: str = None, **kwargs): self.unique_id = unique_id """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).""" self.prompt = prompt @@ -1373,6 +1414,8 @@ class HiddenHolder: """AUTH_TOKEN_COMFY_ORG is a token acquired from signing into a ComfyOrg account on frontend.""" self.api_key_comfy_org = api_key_comfy_org """API_KEY_COMFY_ORG is an API Key generated by ComfyOrg that allows skipping signing into a ComfyOrg account on frontend.""" + self.comfy_usage_source = comfy_usage_source + """COMFY_USAGE_SOURCE identifies the client that submitted the prompt (e.g. comfyui-frontend, comfy-cli, comfyui-mcp); forwarded to API nodes' upstream requests via the Comfy-Usage-Source header.""" def __getattr__(self, key: str): '''If hidden variable not found, return None.''' @@ -1389,6 +1432,7 @@ class HiddenHolder: dynprompt=d.get(Hidden.dynprompt, None), auth_token_comfy_org=d.get(Hidden.auth_token_comfy_org, None), api_key_comfy_org=d.get(Hidden.api_key_comfy_org, None), + comfy_usage_source=d.get(Hidden.comfy_usage_source, None), ) @classmethod @@ -1411,6 +1455,8 @@ class Hidden(str, Enum): """AUTH_TOKEN_COMFY_ORG is a token acquired from signing into a ComfyOrg account on frontend.""" api_key_comfy_org = "API_KEY_COMFY_ORG" """API_KEY_COMFY_ORG is an API Key generated by ComfyOrg that allows skipping signing into a ComfyOrg account on frontend.""" + comfy_usage_source = "COMFY_USAGE_SOURCE" + """COMFY_USAGE_SOURCE identifies the client that submitted the prompt (e.g. comfyui-frontend, comfy-cli, comfyui-mcp); forwarded to API nodes' upstream requests via the Comfy-Usage-Source header.""" @dataclass @@ -1614,6 +1660,8 @@ class Schema: self.hidden.append(Hidden.auth_token_comfy_org) if Hidden.api_key_comfy_org not in self.hidden: self.hidden.append(Hidden.api_key_comfy_org) + if Hidden.comfy_usage_source not in self.hidden: + self.hidden.append(Hidden.comfy_usage_source) # if is an output_node, will need prompt and extra_pnginfo if self.is_output_node: if Hidden.prompt not in self.hidden: @@ -2296,6 +2344,7 @@ __all__ = [ "LossMap", "Voxel", "Mesh", + "Splat", "File3DAny", "File3DGLB", "File3DGLTF", @@ -2303,6 +2352,12 @@ __all__ = [ "File3DOBJ", "File3DSTL", "File3DUSDZ", + "File3DPLY", + "File3DSPLAT", + "File3DSPZ", + "File3DKSPLAT", + "File3DSplatAny", + "File3DPointCloudAny", "Hooks", "HookKeyframes", "TimestepsRange", diff --git a/comfy_api/latest/_ui.py b/comfy_api/latest/_ui.py index e238cdf3c..b48713d41 100644 --- a/comfy_api/latest/_ui.py +++ b/comfy_api/latest/_ui.py @@ -285,7 +285,7 @@ class AudioSaveHelper: results = [] for batch_number, waveform in enumerate(audio["waveform"].cpu()): filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) - file = f"{filename_with_batch_num}_{counter:05}_.{format}" + file = f"{filename_with_batch_num}_{counter:05}.{format}" output_path = os.path.join(full_output_folder, file) # Use original sample rate initially @@ -452,6 +452,16 @@ class PreviewUI3D(_UIOutput): return {"result": [self.model_file, self.camera_info, self.bg_image_path]} +class PreviewUI3DAdvanced(_UIOutput): + def __init__(self, model_file, camera_info, model_3d_info): + self.model_file = model_file + self.camera_info = camera_info + self.model_3d_info = model_3d_info + + def as_dict(self): + return {"result": [self.model_file, self.camera_info, self.model_3d_info]} + + class PreviewText(_UIOutput): def __init__(self, value: str, **kwargs): self.value = value @@ -471,5 +481,6 @@ __all__ = [ "PreviewAudio", "PreviewVideo", "PreviewUI3D", + "PreviewUI3DAdvanced", "PreviewText", ] diff --git a/comfy_api/latest/_util/__init__.py b/comfy_api/latest/_util/__init__.py index 115baf392..b27f5a97e 100644 --- a/comfy_api/latest/_util/__init__.py +++ b/comfy_api/latest/_util/__init__.py @@ -1,5 +1,5 @@ from .video_types import VideoContainer, VideoCodec, VideoComponents -from .geometry_types import VOXEL, MESH, File3D +from .geometry_types import VOXEL, MESH, SPLAT, File3D from .image_types import SVG __all__ = [ @@ -9,6 +9,7 @@ __all__ = [ "VideoComponents", "VOXEL", "MESH", + "SPLAT", "File3D", "SVG", ] diff --git a/comfy_api/latest/_util/geometry_types.py b/comfy_api/latest/_util/geometry_types.py index cdde60b10..84a18d69a 100644 --- a/comfy_api/latest/_util/geometry_types.py +++ b/comfy_api/latest/_util/geometry_types.py @@ -11,13 +11,32 @@ class VOXEL: self.data = data +class SPLAT: + """A batch of 3D Gaussian splats in render-ready (activated, world-space) form. + + Tensors are (B, N, ...) and zero-padded to a common N across the batch; `counts` (B,) holds the + real per-item lengths (None when rows are uniform and no slicing is needed). SH coefficients are + stored as (B, N, K, 3) with K = (sh_degree + 1)**2; the DC (diffuse) term is sh[..., 0, :]. + """ + + def __init__(self, positions: torch.Tensor, scales: torch.Tensor, rotations: torch.Tensor, + opacities: torch.Tensor, sh: torch.Tensor, counts: torch.Tensor | None = None): + self.positions = positions # (B, N, 3) world-space centers + self.scales = scales # (B, N, 3) linear (positive) per-axis std + self.rotations = rotations # (B, N, 4) quaternion wxyz (normalized) + self.opacities = opacities # (B, N, 1) in [0, 1] + self.sh = sh # (B, N, K, 3) spherical-harmonic color coefficients + self.counts = counts # (B,) real lengths, or None + + class MESH: def __init__(self, vertices: torch.Tensor, faces: torch.Tensor, uvs: torch.Tensor | None = None, vertex_colors: torch.Tensor | None = None, texture: torch.Tensor | None = None, vertex_counts: torch.Tensor | None = None, - face_counts: torch.Tensor | None = None): + face_counts: torch.Tensor | None = None, + unlit: bool = False): assert (vertex_counts is None) == (face_counts is None), \ "vertex_counts and face_counts must be provided together (both or neither)" @@ -30,6 +49,8 @@ class MESH: # these hold the real per-item lengths (B,). None means rows are uniform and no slicing is needed. self.vertex_counts = vertex_counts self.face_counts = face_counts + # Render flat / emissive (no scene lighting) when saved, e.g. for gaussian-splat-derived meshes. + self.unlit = unlit class File3D: diff --git a/comfy_api_nodes/apis/__init__.py b/comfy_api_nodes/apis/__init__.py index 9c4cfb9b6..9a7049ea2 100644 --- a/comfy_api_nodes/apis/__init__.py +++ b/comfy_api_nodes/apis/__init__.py @@ -1310,13 +1310,6 @@ class KlingTaskStatus(str, Enum): failed = 'failed' -class KlingTextToVideoModelName(str, Enum): - kling_v1 = 'kling-v1' - kling_v1_6 = 'kling-v1-6' - kling_v2_1_master = 'kling-v2-1-master' - kling_v2_5_turbo = 'kling-v2-5-turbo' - - class KlingVideoGenAspectRatio(str, Enum): field_16_9 = '16:9' field_9_16 = '9:16' @@ -5179,7 +5172,7 @@ class KlingText2VideoRequest(BaseModel): duration: Optional[KlingVideoGenDuration] = '5' external_task_id: Optional[str] = Field(None, description='Customized Task ID') mode: Optional[KlingVideoGenMode] = 'std' - model_name: Optional[KlingTextToVideoModelName] = 'kling-v1' + model_name: Optional[str] = 'kling-v1' negative_prompt: Optional[str] = Field( None, description='Negative text prompt', max_length=2500 ) diff --git a/comfy_api_nodes/apis/bfl.py b/comfy_api_nodes/apis/bfl.py index f0665fa09..4c950da84 100644 --- a/comfy_api_nodes/apis/bfl.py +++ b/comfy_api_nodes/apis/bfl.py @@ -1,71 +1,72 @@ from enum import Enum -from typing import Any, Dict, Optional +from typing import Any -from pydantic import BaseModel, Field, confloat, conint - - -class BFLOutputFormat(str, Enum): - png = 'png' - jpeg = 'jpeg' +from pydantic import BaseModel, Field class BFLFluxExpandImageRequest(BaseModel): - prompt: str = Field(..., description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' - ) - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - top: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the top of the image') - bottom: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the bottom of the image') - left: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the left side of the image') - right: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the right side of the image') - steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') - guidance: confloat(ge=1.5, le=100) = Field(..., description='Guidance strength for the image generation process') - safety_tolerance: Optional[conint(ge=0, le=6)] = Field( - 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' - ) - output_format: Optional[BFLOutputFormat] = Field( - BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] - ) - image: str = Field(None, description='A Base64-encoded string representing the image you wish to expand') + prompt: str = Field(...) + prompt_upsampling: bool | None = Field(None) + seed: int | None = Field(None) + top: int = Field(...) + bottom: int = Field(...) + left: int = Field(...) + right: int = Field(...) + steps: int = Field(...) + guidance: float = Field(...) + safety_tolerance: int = Field(6) + output_format: str = Field("png") + image: str = Field(None, description="A Base64-encoded string representing the image you wish to expand") class BFLFluxFillImageRequest(BaseModel): - prompt: str = Field(..., description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + prompt: str = Field(...) + prompt_upsampling: bool | None = Field(None) + seed: int | None = Field(None) + steps: int = Field(...) + guidance: float = Field(...) + safety_tolerance: int = Field(6) + output_format: str = Field("png") + image: str = Field( + None, description="Base64-encoded string representing the image to modify. Can contain alpha mask if desired.", ) - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') - guidance: confloat(ge=1.5, le=100) = Field(..., description='Guidance strength for the image generation process') - safety_tolerance: Optional[conint(ge=0, le=6)] = Field( - 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + mask: str = Field( + None, description="Base64-encoded string representing the mask of the areas you wish to modify." ) - output_format: Optional[BFLOutputFormat] = Field( - BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + + +class BFLFluxEraseRequest(BaseModel): + image: str = Field(..., description="A Base64-encoded string representing the image to erase from.") + mask: str = Field( + ..., + description="A Base64-encoded black/white mask matching the input dimensions; " + "white (255) marks areas to remove, black (0) marks areas to preserve.", ) - image: str = Field(None, description='A Base64-encoded string representing the image you wish to modify. Can contain alpha mask if desired.') - mask: str = Field(None, description='A Base64-encoded string representing the mask of the areas you with to modify.') + dilate_pixels: int = Field(10) + seed: int | None = Field(None) + output_format: str = Field("png") + + +class BFLFluxVTORequest(BaseModel): + prompt: str = Field( + ..., description="Natural-language styling instruction. Required field, but may be an empty string." + ) + person: str = Field(..., description="A Base64-encoded string representing the person image.") + garment: str = Field(..., description="A Base64-encoded string representing the garment reference image.") + seed: int | None = Field(None) + safety_tolerance: int = Field(5) + output_format: str = Field("png") class BFLFluxProGenerateRequest(BaseModel): - prompt: str = Field(..., description='The text prompt for image generation.') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' - ) - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - width: conint(ge=256, le=1440) = Field(1024, description='Width of the generated image in pixels. Must be a multiple of 32.') - height: conint(ge=256, le=1440) = Field(768, description='Height of the generated image in pixels. Must be a multiple of 32.') - safety_tolerance: Optional[conint(ge=0, le=6)] = Field( - 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' - ) - output_format: Optional[BFLOutputFormat] = Field( - BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] - ) - image_prompt: Optional[str] = Field(None, description='Optional image to remix in base64 format') - # image_prompt_strength: Optional[confloat(ge=0.0, le=1.0)] = Field( - # None, description='Blend between the prompt and the image prompt.' - # ) + prompt: str = Field(...) + prompt_upsampling: bool | None = Field(None) + seed: int | None = Field(None) + width: int = Field(1024, description="Must be a multiple of 32.") + height: int = Field(768, description="Must be a multiple of 32.") + safety_tolerance: int = Field(6) + output_format: str = Field("png") + image_prompt: str | None = Field(None, description="Optional image to remix in base64 format") class Flux2ProGenerateRequest(BaseModel): @@ -83,55 +84,37 @@ class Flux2ProGenerateRequest(BaseModel): input_image_7: str | None = Field(None, description="Base64 encoded image for image-to-image generation") input_image_8: str | None = Field(None, description="Base64 encoded image for image-to-image generation") input_image_9: str | None = Field(None, description="Base64 encoded image for image-to-image generation") - safety_tolerance: int | None = Field( - 5, description="Tolerance level for input and output moderation. Value 0 being most strict.", ge=0, le=5 - ) - output_format: str | None = Field( - "png", description="Output format for the generated image. Can be 'jpeg' or 'png'." - ) + safety_tolerance: int = Field(5) + output_format: str = Field("png") class BFLFluxKontextProGenerateRequest(BaseModel): - prompt: str = Field(..., description='The text prompt for what you wannt to edit.') - input_image: Optional[str] = Field(None, description='Image to edit in base64 format') - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - guidance: confloat(ge=0.1, le=99.0) = Field(..., description='Guidance strength for the image generation process') - steps: conint(ge=1, le=150) = Field(..., description='Number of steps for the image generation process') - safety_tolerance: Optional[conint(ge=0, le=2)] = Field( - 2, description='Tolerance level for input and output moderation. Between 0 and 2, 0 being most strict, 6 being least strict. Defaults to 2.' - ) - output_format: Optional[BFLOutputFormat] = Field( - BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] - ) - aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' - ) + prompt: str = Field(...) + input_image: str | None = Field(None, description="Image to edit in base64 format") + seed: int | None = Field(None) + guidance: float = Field(...) + steps: int = Field(...) + safety_tolerance: int = Field(2) + output_format: str = Field("png") + aspect_ratio: str | None = Field(None) + prompt_upsampling: bool | None = Field(None) class BFLFluxProUltraGenerateRequest(BaseModel): - prompt: str = Field(..., description='The text prompt for image generation.') - prompt_upsampling: Optional[bool] = Field( - None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' - ) - seed: Optional[int] = Field(None, description='The seed value for reproducibility.') - aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.') - safety_tolerance: Optional[conint(ge=0, le=6)] = Field( - 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' - ) - output_format: Optional[BFLOutputFormat] = Field( - BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] - ) - raw: Optional[bool] = Field(None, description='Generate less processed, more natural-looking images.') - image_prompt: Optional[str] = Field(None, description='Optional image to remix in base64 format') - image_prompt_strength: Optional[confloat(ge=0.0, le=1.0)] = Field( - None, description='Blend between the prompt and the image prompt.' - ) + prompt: str = Field(...) + prompt_upsampling: bool | None = Field(None) + seed: int | None = Field(None) + aspect_ratio: str | None = Field(None) + safety_tolerance: int = Field(6) + output_format: str = Field("png") + raw: bool | None = Field(None) + image_prompt: str | None = Field(None, description="Optional image to remix in base64 format") + image_prompt_strength: float | None = Field(None) class BFLFluxProGenerateResponse(BaseModel): - id: str = Field(..., description="The unique identifier for the generation task.") - polling_url: str = Field(..., description="URL to poll for the generation result.") + id: str = Field(...) + polling_url: str = Field(...) cost: float | None = Field(None, description="Price in cents") @@ -145,7 +128,7 @@ class BFLStatus(str, Enum): class BFLFluxStatusResponse(BaseModel): - id: str = Field(..., description="The unique identifier for the generation task.") - status: BFLStatus = Field(..., description="The status of the task.") - result: Optional[Dict[str, Any]] = Field(None, description="The result of the task (null if not completed).") - progress: Optional[float] = Field(None, description="The progress of the task (0.0 to 1.0).", ge=0.0, le=1.0) + id: str = Field(...) + status: BFLStatus = Field(...) + result: dict[str, Any] | None = Field(None) + progress: float | None = Field(None, ge=0.0, le=1.0) diff --git a/comfy_api_nodes/apis/bria.py b/comfy_api_nodes/apis/bria.py index e08a519a8..7a98428c3 100644 --- a/comfy_api_nodes/apis/bria.py +++ b/comfy_api_nodes/apis/bria.py @@ -97,3 +97,28 @@ class BriaRemoveVideoBackgroundResult(BaseModel): class BriaRemoveVideoBackgroundResponse(BaseModel): status: str = Field(...) result: BriaRemoveVideoBackgroundResult | None = Field(None) + + +class BriaVideoGreenScreenRequest(BaseModel): + video: str = Field(..., description="Publicly accessible URL of the input video.") + green_shade: str = Field( + default="broadcast_green", + description="Solid chroma-key shade applied behind the foreground " + "(broadcast_green, chroma_green, or blue_screen).", + ) + output_container_and_codec: str = Field(...) + preserve_audio: bool = Field(True) + seed: int = Field(...) + + +class BriaVideoReplaceBackgroundRequest(BaseModel): + video: str = Field(..., description="Publicly accessible URL of the input (foreground) video.") + background_url: str = Field( + ..., + description="Publicly accessible URL of the background image or video to composite behind " + "the foreground. Stretched to the foreground frame; match its aspect ratio for " + "undistorted results.", + ) + output_container_and_codec: str = Field(...) + preserve_audio: bool = Field(True) + seed: int = Field(...) diff --git a/comfy_api_nodes/apis/gemini.py b/comfy_api_nodes/apis/gemini.py index 22879fe18..caaba8f36 100644 --- a/comfy_api_nodes/apis/gemini.py +++ b/comfy_api_nodes/apis/gemini.py @@ -108,13 +108,19 @@ class GeminiVideoMetadata(BaseModel): startOffset: GeminiOffset | None = Field(None) +class GeminiThinkingConfig(BaseModel): + includeThoughts: bool | None = Field(None) + thinkingLevel: str = Field(...) + + class GeminiGenerationConfig(BaseModel): - maxOutputTokens: int | None = Field(None, ge=16, le=8192) + maxOutputTokens: int | None = Field(None, ge=16, le=65536) seed: int | None = Field(None) stopSequences: list[str] | None = Field(None) temperature: float | None = Field(None, ge=0.0, le=2.0) topK: int | None = Field(None, ge=1) topP: float | None = Field(None, ge=0.0, le=1.0) + thinkingConfig: GeminiThinkingConfig | None = Field(None) class GeminiImageOutputOptions(BaseModel): @@ -128,11 +134,6 @@ class GeminiImageConfig(BaseModel): imageOutputOptions: GeminiImageOutputOptions = Field(default_factory=GeminiImageOutputOptions) -class GeminiThinkingConfig(BaseModel): - includeThoughts: bool | None = Field(None) - thinkingLevel: str = Field(...) - - class GeminiImageGenerationConfig(GeminiGenerationConfig): responseModalities: list[str] | None = Field(None) imageConfig: GeminiImageConfig | None = Field(None) diff --git a/comfy_api_nodes/apis/ideogram.py b/comfy_api_nodes/apis/ideogram.py index 737e18e3b..c5ad9559f 100644 --- a/comfy_api_nodes/apis/ideogram.py +++ b/comfy_api_nodes/apis/ideogram.py @@ -290,3 +290,19 @@ class IdeogramV3Request(BaseModel): None, description='Optional masks for character reference images. When provided, must match the number of character_reference_images. Each mask should be a grayscale image of the same dimensions as the corresponding character reference image. The images should be in JPEG, PNG or WebP format.' ) + + +class IdeogramV4Request(BaseModel): + text_prompt: str | None = Field( + None, + description="Natural-language prompt; Magic Prompt is applied automatically. " + "Supply exactly one of text_prompt or json_prompt.", + ) + json_prompt: dict[str, Any] | None = Field( + None, + description="Structured V4 prompt object consumed directly (disables Magic Prompt). " + "Supply exactly one of text_prompt or json_prompt.", + ) + resolution: str | None = Field(None, description="Output resolution in WIDTHxHEIGHT (e.g. '2048x2048').") + rendering_speed: str | None = Field(None, description="Rendering speed: 'TURBO', 'DEFAULT', or 'QUALITY'.") + enable_copyright_detection: bool | None = Field(None, description="Opt into post-generation copyright detection.") diff --git a/comfy_api_nodes/apis/kling.py b/comfy_api_nodes/apis/kling.py index fe0f97cb3..2c98c23b7 100644 --- a/comfy_api_nodes/apis/kling.py +++ b/comfy_api_nodes/apis/kling.py @@ -149,3 +149,59 @@ class MotionControlRequest(BaseModel): character_orientation: str = Field(...) mode: str = Field(..., description="'pro' or 'std'") model_name: str = Field(...) + + +class Kling3TurboSettings(BaseModel): + resolution: str = Field("720p", description="'720p' or '1080p'") + aspect_ratio: str | None = Field(None, description="'16:9'/'9:16'/'1:1'; text-to-video only") + duration: int = Field(5, description="3-15 second") + + +class Kling3TurboText2VideoRequest(BaseModel): + prompt: str = Field(..., description="<=3072 chars; may use multi-shot 'shot n, m, words; ...'") + settings: Kling3TurboSettings | None = Field(None) + + +class Kling3TurboContent(BaseModel): + type: str = Field(..., description="'prompt' or 'first_frame'") + text: str | None = Field(None, description="for type=prompt; <=2500 chars") + url: str | None = Field(None, description="for type=first_frame") + + +class Kling3TurboImage2VideoRequest(BaseModel): + contents: list[Kling3TurboContent] = Field(..., description="prompt + first_frame materials") + settings: Kling3TurboSettings | None = Field(None) + + +class Kling3TurboCreateData(BaseModel): + id: str | None = Field(None, description="Task ID") + status: str | None = Field(None) + message: str | None = Field(None) + + +class Kling3TurboCreateResponse(BaseModel): + code: int | None = Field(None) + message: str | None = Field(None) + request_id: str | None = Field(None) + data: Kling3TurboCreateData | None = Field(None) + + +class Kling3TurboOutput(BaseModel): + type: str | None = Field(None, description="'video', 'image', 'audio', ...") + id: str | None = Field(None) + url: str | None = Field(None) + duration: str | None = Field(None) + + +class Kling3TurboTaskData(BaseModel): + id: str | None = Field(None) + status: str | None = Field(None, description="submitted | processing | succeeded | failed") + message: str | None = Field(None) + outputs: list[Kling3TurboOutput] | None = Field(None) + + +class Kling3TurboQueryResponse(BaseModel): + code: int | None = Field(None) + message: str | None = Field(None) + request_id: str | None = Field(None) + data: list[Kling3TurboTaskData] | None = Field(None) diff --git a/comfy_api_nodes/apis/luma.py b/comfy_api_nodes/apis/luma.py index 8c6db2022..2465c3b37 100644 --- a/comfy_api_nodes/apis/luma.py +++ b/comfy_api_nodes/apis/luma.py @@ -10,6 +10,7 @@ from pydantic import BaseModel, Field, confloat class LumaIO: LUMA_REF = "LUMA_REF" LUMA_CONCEPTS = "LUMA_CONCEPTS" + LUMA_RAY32_KEYFRAME = "LUMA_RAY32_KEYFRAME" class LumaReference: @@ -20,13 +21,14 @@ class LumaReference: def create_api_model(self, download_url: str): return LumaImageRef(url=download_url, weight=self.weight) + class LumaReferenceChain: - def __init__(self, first_ref: LumaReference=None): + def __init__(self, first_ref: LumaReference = None): self.refs: list[LumaReference] = [] if first_ref: self.refs.append(first_ref) - def add(self, luma_ref: LumaReference=None): + def add(self, luma_ref: LumaReference = None): self.refs.append(luma_ref) def create_api_model(self, download_urls: list[str], max_refs=4): @@ -124,7 +126,7 @@ def get_luma_concepts(include_none=False): "pull_out", "aerial", "crane_up", - "eye_level" + "eye_level", ] @@ -162,8 +164,8 @@ class LumaVideoModelOutputDuration(str, Enum): class LumaGenerationType(str, Enum): - video = 'video' - image = 'image' + video = "video" + image = "image" class LumaState(str, Enum): @@ -174,86 +176,109 @@ class LumaState(str, Enum): class LumaAssets(BaseModel): - video: Optional[str] = Field(None, description='The URL of the video') - image: Optional[str] = Field(None, description='The URL of the image') - progress_video: Optional[str] = Field(None, description='The URL of the progress video') + video: Optional[str] = Field(None, description="The URL of the video") + image: Optional[str] = Field(None, description="The URL of the image") + progress_video: Optional[str] = Field(None, description="The URL of the progress video") class LumaImageRef(BaseModel): """Used for image gen""" - url: str = Field(..., description='The URL of the image reference') - weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference') + + url: str = Field(..., description="The URL of the image reference") + weight: confloat(ge=0.0, le=1.0) = Field(..., description="The weight of the image reference") class LumaImageReference(BaseModel): """Used for video gen""" - type: Optional[str] = Field('image', description='Input type, defaults to image') - url: str = Field(..., description='The URL of the image') + + type: Optional[str] = Field("image", description="Input type, defaults to image") + url: str = Field(..., description="The URL of the image") class LumaModifyImageRef(BaseModel): - url: str = Field(..., description='The URL of the image reference') - weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference') + url: str = Field(..., description="The URL of the image reference") + weight: confloat(ge=0.0, le=1.0) = Field(..., description="The weight of the image reference") class LumaCharacterRef(BaseModel): - identity0: LumaImageIdentity = Field(..., description='The image identity object') + identity0: LumaImageIdentity = Field(..., description="The image identity object") class LumaImageIdentity(BaseModel): - images: list[str] = Field(..., description='The URLs of the image identity') + images: list[str] = Field(..., description="The URLs of the image identity") class LumaGenerationReference(BaseModel): - type: str = Field('generation', description='Input type, defaults to generation') - id: str = Field(..., description='The ID of the generation') + type: str = Field("generation", description="Input type, defaults to generation") + id: str = Field(..., description="The ID of the generation") class LumaKeyframes(BaseModel): - frame0: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='') - frame1: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='') + frame0: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description="") + frame1: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description="") class LumaConceptObject(BaseModel): - key: str = Field(..., description='Camera Concept name') + key: str = Field(..., description="Camera Concept name") class LumaImageGenerationRequest(BaseModel): - prompt: str = Field(..., description='The prompt of the generation') - model: LumaImageModel = Field(LumaImageModel.photon_1, description='The image model used for the generation') - aspect_ratio: Optional[LumaAspectRatio] = Field(LumaAspectRatio.ratio_16_9, description='The aspect ratio of the generation') - image_ref: Optional[list[LumaImageRef]] = Field(None, description='List of image reference objects') - style_ref: Optional[list[LumaImageRef]] = Field(None, description='List of style reference objects') - character_ref: Optional[LumaCharacterRef] = Field(None, description='The image identity object') - modify_image_ref: Optional[LumaModifyImageRef] = Field(None, description='The modify image reference object') + prompt: str = Field(..., description="The prompt of the generation") + model: LumaImageModel = Field(LumaImageModel.photon_1, description="The image model used for the generation") + aspect_ratio: Optional[LumaAspectRatio] = Field(LumaAspectRatio.ratio_16_9) + image_ref: Optional[list[LumaImageRef]] = Field(None, description="List of image reference objects") + style_ref: Optional[list[LumaImageRef]] = Field(None, description="List of style reference objects") + character_ref: Optional[LumaCharacterRef] = Field(None, description="The image identity object") + modify_image_ref: Optional[LumaModifyImageRef] = Field(None, description="The modify image reference object") class LumaGenerationRequest(BaseModel): - prompt: str = Field(..., description='The prompt of the generation') - model: LumaVideoModel = Field(LumaVideoModel.ray_2, description='The video model used for the generation') - duration: Optional[LumaVideoModelOutputDuration] = Field(None, description='The duration of the generation') - aspect_ratio: Optional[LumaAspectRatio] = Field(None, description='The aspect ratio of the generation') - resolution: Optional[LumaVideoOutputResolution] = Field(None, description='The resolution of the generation') - loop: Optional[bool] = Field(None, description='Whether to loop the video') - keyframes: Optional[LumaKeyframes] = Field(None, description='The keyframes of the generation') - concepts: Optional[list[LumaConceptObject]] = Field(None, description='Camera Concepts to apply to generation') + prompt: str = Field(..., description="The prompt of the generation") + model: LumaVideoModel = Field(LumaVideoModel.ray_2, description="The video model used for the generation") + duration: Optional[LumaVideoModelOutputDuration] = Field(None, description="The duration of the generation") + aspect_ratio: Optional[LumaAspectRatio] = Field(None, description="The aspect ratio of the generation") + resolution: Optional[LumaVideoOutputResolution] = Field(None, description="The resolution of the generation") + loop: Optional[bool] = Field(None, description="Whether to loop the video") + keyframes: Optional[LumaKeyframes] = Field(None, description="The keyframes of the generation") + concepts: Optional[list[LumaConceptObject]] = Field(None, description="Camera Concepts to apply to generation") class LumaGeneration(BaseModel): - id: str = Field(..., description='The ID of the generation') - generation_type: LumaGenerationType = Field(..., description='Generation type, image or video') - state: LumaState = Field(..., description='The state of the generation') - failure_reason: Optional[str] = Field(None, description='The reason for the state of the generation') - created_at: str = Field(..., description='The date and time when the generation was created') - assets: Optional[LumaAssets] = Field(None, description='The assets of the generation') - model: str = Field(..., description='The model used for the generation') - request: Union[LumaGenerationRequest, LumaImageGenerationRequest] = Field(..., description="The request used for the generation") + id: str = Field(..., description="The ID of the generation") + generation_type: LumaGenerationType = Field(..., description="Generation type, image or video") + state: LumaState = Field(..., description="The state of the generation") + failure_reason: Optional[str] = Field(None, description="The reason for the state of the generation") + created_at: str = Field(..., description="The date and time when the generation was created") + assets: Optional[LumaAssets] = Field(None, description="The assets of the generation") + model: str = Field(..., description="The model used for the generation") + request: Union[LumaGenerationRequest, LumaImageGenerationRequest] = Field(...) class Luma2ImageRef(BaseModel): url: str | None = None data: str | None = None media_type: str | None = None + generation_id: str | None = Field(None, description="reference a prior generation (extend / source reuse)") + + +class Luma2VideoEdit(BaseModel): + """Edit controls for Ray 3.2 ``video_edit`` generations.""" + + auto_controls: bool | None = Field(None, description="derive a conditioning schedule from the source (recommended)") + strength: str | None = Field(None, description="'adhere_1' .. 'reimagine_3'; constrained by IO.Combo") + + +class Luma2VideoOptions(BaseModel): + """Ray 3.2 ``video`` output settings (text / image / keyframe / edit / extend).""" + + resolution: str | None = Field(None, description="360p | 540p | 720p | 1080p") + duration: str | None = Field(None, description="5s | 10s") + loop: bool | None = Field(None) + start_frame: Luma2ImageRef | None = Field(None) + end_frame: Luma2ImageRef | None = Field(None) + keyframes: list[Luma2ImageRef] | None = Field(None) + keyframe_indexes: list[int] | None = Field(None) + edit: Luma2VideoEdit | None = Field(None) class Luma2GenerationRequest(BaseModel): @@ -266,6 +291,7 @@ class Luma2GenerationRequest(BaseModel): web_search: bool | None = None image_ref: list[Luma2ImageRef] | None = None source: Luma2ImageRef | None = None + video: Luma2VideoOptions | None = Field(None) class Luma2Generation(BaseModel): @@ -277,3 +303,31 @@ class Luma2Generation(BaseModel): output: list[LumaImageReference] | None = None failure_reason: str | None = None failure_code: str | None = None + + +# --- Ray 3.2 multi-keyframe chain --- + +LUMA_KEYFRAME_MODE_FRACTION = "fraction" # value in [0.0, 1.0] of the output video duration +LUMA_KEYFRAME_MODE_SECONDS = "seconds" # absolute time, in seconds, from the start of the output + + +class LumaRay32KeyframeItem: + """One guide image anchored at a position on the Ray 3.2 output timeline.""" + + def __init__(self, image: torch.Tensor, mode: str, value: float): + self.image = image + self.mode = mode # LUMA_KEYFRAME_MODE_FRACTION | LUMA_KEYFRAME_MODE_SECONDS + self.value = value + + +class LumaRay32KeyframeChain: + def __init__(self): + self.items: list[LumaRay32KeyframeItem] = [] + + def add(self, item: LumaRay32KeyframeItem) -> None: + self.items.append(item) + + def clone(self) -> "LumaRay32KeyframeChain": + c = LumaRay32KeyframeChain() + c.items = list(self.items) + return c diff --git a/comfy_api_nodes/apis/runway.py b/comfy_api_nodes/apis/runway.py index df6f2b845..6878aa6f0 100644 --- a/comfy_api_nodes/apis/runway.py +++ b/comfy_api_nodes/apis/runway.py @@ -67,15 +67,6 @@ class RunwayImageToVideoResponse(BaseModel): id: Optional[str] = Field(None, description='Task ID') -class RunwayTaskStatusEnum(str, Enum): - SUCCEEDED = 'SUCCEEDED' - RUNNING = 'RUNNING' - FAILED = 'FAILED' - PENDING = 'PENDING' - CANCELLED = 'CANCELLED' - THROTTLED = 'THROTTLED' - - class RunwayTaskStatusResponse(BaseModel): createdAt: datetime = Field(..., description='Task creation timestamp') id: str = Field(..., description='Task ID') @@ -86,7 +77,7 @@ class RunwayTaskStatusResponse(BaseModel): ge=0.0, le=1.0, ) - status: RunwayTaskStatusEnum + status: str = Field(..., description="SUCCEEDED, RUNNING, FAILED, PENDING, CANCELLED or THROTTLED") class Model4(str, Enum): @@ -125,3 +116,144 @@ class RunwayTextToImageRequest(BaseModel): class RunwayTextToImageResponse(BaseModel): id: Optional[str] = Field(None, description='Task ID') + + +class RunwayAleph2IO: + """Custom socket types for chaining Aleph2 guidance images.""" + + KEYFRAME = "RUNWAY_ALEPH2_KEYFRAME" + PROMPT_IMAGE = "RUNWAY_ALEPH2_PROMPT_IMAGE" + + +# Keyframe timing modes (anchored to the INPUT video). Stored on the chain item and used to +# choose the request model below. The values match the Aleph2 keyframe union field names. +KEYFRAME_MODE_SECONDS = "seconds" # absolute time, in seconds, from the start of the input video +KEYFRAME_MODE_AT = "at" # fraction [0.0, 1.0] of the input video duration + +# Prompt-image position modes (anchored to the OUTPUT video). Values match the Aleph2 position `type`. +PROMPT_IMAGE_MODE_TIMESTAMP = "timestamp" # absolute time, in seconds, from the start of the output video +PROMPT_IMAGE_MODE_POSITION = "position" # fraction [0.0, 1.0] of the output video duration + + +class RunwayAleph2KeyframeItem: + """A guidance image anchored to a point of the INPUT video (one Aleph2 ``keyframe``).""" + + def __init__(self, image, mode: str, value: float): + self.image = image + self.mode = mode # KEYFRAME_MODE_SECONDS | KEYFRAME_MODE_AT + self.value = value + + +class RunwayAleph2KeyframeChain: + """An ordered collection of keyframes, built by chaining Runway Aleph2 Keyframe nodes.""" + + def __init__(self): + self.items: list[RunwayAleph2KeyframeItem] = [] + + def add(self, item: RunwayAleph2KeyframeItem) -> None: + self.items.append(item) + + def clone(self) -> "RunwayAleph2KeyframeChain": + c = RunwayAleph2KeyframeChain() + c.items = list(self.items) + return c + + +class RunwayAleph2PromptImageItem: + """A guidance image anchored to a point of the OUTPUT video (one Aleph2 ``promptImage``).""" + + def __init__(self, image, mode: str, value: float): + self.image = image + self.mode = mode # PROMPT_IMAGE_MODE_TIMESTAMP | PROMPT_IMAGE_MODE_POSITION + self.value = value + + +class RunwayAleph2PromptImageChain: + """An ordered collection of prompt images, built by chaining Runway Aleph2 Prompt Image nodes.""" + + def __init__(self): + self.items: list[RunwayAleph2PromptImageItem] = [] + + def add(self, item: RunwayAleph2PromptImageItem) -> None: + self.items.append(item) + + def clone(self) -> "RunwayAleph2PromptImageChain": + c = RunwayAleph2PromptImageChain() + c.items = list(self.items) + return c + + +class RunwayAleph2KeyframeSeconds(BaseModel): + seconds: float = Field( + ..., + description="Absolute timestamp in seconds from the start of the input video when this guidance image should apply.", + ge=0.0, + ) + uri: str = Field(...) + + +class RunwayAleph2KeyframeAt(BaseModel): + at: float = Field( + ..., + description="Position as a fraction [0.0, 1.0] of the input video duration.", + ge=0.0, + le=1.0, + ) + uri: str = Field(...) + + +class RunwayAleph2TimestampPosition(BaseModel): + type: str = Field(default="timestamp") + timestampSeconds: float = Field( + ..., + description="Absolute timestamp in seconds from the start of the output video.", + ge=0.0, + ) + + +class RunwayAleph2RelativePosition(BaseModel): + type: str = Field(default="position") + positionPercentage: float = Field( + ..., + description="Position as a fraction [0.0, 1.0] of the total output video duration.", + ge=0.0, + le=1.0, + ) + + +class RunwayAleph2PromptImage(BaseModel): + position: RunwayAleph2TimestampPosition | RunwayAleph2RelativePosition + uri: str = Field(...) + + +class RunwayAleph2ContentModeration(BaseModel): + publicFigureThreshold: str = Field( + ..., + description='When set to "low", the content moderation system is less strict about ' + 'recognizable public figures. One of "auto" or "low".', + ) + + +class RunwayAleph2Request(BaseModel): + model: str = Field(default="aleph2") + promptText: str = Field( + ..., + description="A non-empty string describing what should appear in the output.", + min_length=1, + max_length=1000, + ) + videoUri: str = Field(...) + seed: int = Field(..., description="Random seed for generation", ge=0, le=4294967295) + contentModeration: RunwayAleph2ContentModeration = Field(...) + keyframes: list[RunwayAleph2KeyframeSeconds | RunwayAleph2KeyframeAt] | None = Field( + None, + description="Timed guidance images placed at specific points in the input video. Up to 5.", + ) + promptImage: list[RunwayAleph2PromptImage] | None = Field( + None, + description="Up to 5 image keyframes for guiding the edit at specific points in the output video.", + ) + + +class RunwayAleph2Response(BaseModel): + id: str | None = Field(None, description="Task ID") diff --git a/comfy_api_nodes/apis/tripo.py b/comfy_api_nodes/apis/tripo.py index 7ac81d42c..79913997a 100644 --- a/comfy_api_nodes/apis/tripo.py +++ b/comfy_api_nodes/apis/tripo.py @@ -208,6 +208,10 @@ class TripoMultiviewToModelRequest(BaseModel): quad: bool | None = Field(False, description="Whether to apply quad to the generated model") +class TripoTexturePrompt(BaseModel): + text: str | None = Field(None, description="Text guidance for texture generation") + + class TripoTextureModelRequest(BaseModel): type: TripoTaskType = Field(TripoTaskType.TEXTURE_MODEL, description="Type of task") original_model_task_id: str = Field(..., description="The task ID of the original model") @@ -219,6 +223,11 @@ class TripoTextureModelRequest(BaseModel): texture_alignment: TripoTextureAlignment | None = Field( TripoTextureAlignment.ORIGINAL_IMAGE, description="The texture alignment method" ) + texture_prompt: TripoTexturePrompt | None = Field( + None, + description="Optional guidance for texturing. Required in practice for imported models, " + "which carry no source image to infer texture from.", + ) class TripoRefineModelRequest(BaseModel): @@ -307,6 +316,17 @@ class TripoP1MultiviewToModelRequest(TripoP1CommonRequest): orientation: str | None = None +class TripoImportModelRequest(BaseModel): + """Request for the comfy-api composite import endpoint (/proxy/tripo/v2/openapi/import). + + The model file is uploaded to ComfyUI API storage first; the backend downloads it from + `url`, re-uploads it to Tripo's storage and creates the import_model task server-side. + """ + + url: str = Field(..., description="ComfyUI API storage download URL of the model file") + format: str = Field(..., description='File format: "glb", "fbx", "obj" or "stl"') + + class TripoTaskOutput(BaseModel): model: str | None = Field(None, description="URL to the model") base_model: str | None = Field(None, description="URL to the base model") diff --git a/comfy_api_nodes/nodes_anthropic.py b/comfy_api_nodes/nodes_anthropic.py index 7805c96ce..87a870553 100644 --- a/comfy_api_nodes/nodes_anthropic.py +++ b/comfy_api_nodes/nodes_anthropic.py @@ -155,7 +155,7 @@ class ClaudeNode(IO.ComfyNode): return IO.Schema( node_id="ClaudeNode", display_name="Anthropic Claude", - category="text/partner/Anthropic", + category="partner/text/Anthropic", essentials_category="Text Generation", description="Generate text responses with Anthropic's Claude models. " "Provide a text prompt and optionally one or more images for multimodal context.", diff --git a/comfy_api_nodes/nodes_beeble.py b/comfy_api_nodes/nodes_beeble.py index f1082884c..d863c2130 100644 --- a/comfy_api_nodes/nodes_beeble.py +++ b/comfy_api_nodes/nodes_beeble.py @@ -206,7 +206,7 @@ class BeebleSwitchXVideoEdit(IO.ComfyNode): return IO.Schema( node_id="BeebleSwitchXVideoEdit", display_name="Beeble SwitchX Video Edit", - category="video/partner/Beeble", + category="partner/video/Beeble", description=( "Edit a video with Beeble SwitchX. Switches anything in the scene (background, " "lighting, costume) while preserving the original subject's pixels and motion. " @@ -302,7 +302,7 @@ class BeebleSwitchXImageEdit(IO.ComfyNode): return IO.Schema( node_id="BeebleSwitchXImageEdit", display_name="Beeble SwitchX Image Edit", - category="image/partner/Beeble", + category="partner/image/Beeble", description=( "Edit a single image with Beeble SwitchX. Switches anything in the scene " "(background, lighting, costume) while preserving the original subject's pixels. " diff --git a/comfy_api_nodes/nodes_bfl.py b/comfy_api_nodes/nodes_bfl.py index f1a5dc5f0..259c54ef9 100644 --- a/comfy_api_nodes/nodes_bfl.py +++ b/comfy_api_nodes/nodes_bfl.py @@ -4,17 +4,20 @@ from typing_extensions import override from comfy_api.latest import IO, ComfyExtension, Input from comfy_api_nodes.apis.bfl import ( + BFLFluxEraseRequest, BFLFluxExpandImageRequest, BFLFluxFillImageRequest, BFLFluxKontextProGenerateRequest, BFLFluxProGenerateResponse, BFLFluxProUltraGenerateRequest, BFLFluxStatusResponse, + BFLFluxVTORequest, BFLStatus, Flux2ProGenerateRequest, ) from comfy_api_nodes.util import ( ApiEndpoint, + convert_mask_to_image, download_url_to_image_tensor, get_number_of_images, poll_op, @@ -22,19 +25,11 @@ from comfy_api_nodes.util import ( sync_op, tensor_to_base64_string, validate_aspect_ratio_string, + validate_image_dimensions, validate_string, ) -def convert_mask_to_image(mask: Input.Image): - """ - Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image. - """ - mask = mask.unsqueeze(-1) - mask = torch.cat([mask] * 3, dim=-1) - return mask - - class FluxProUltraImageNode(IO.ComfyNode): @classmethod @@ -42,7 +37,7 @@ class FluxProUltraImageNode(IO.ComfyNode): return IO.Schema( node_id="FluxProUltraImageNode", display_name="Flux 1.1 [pro] Ultra Image", - category="image/partner/BFL", + category="partner/image/BFL", description="Generates images using Flux Pro 1.1 Ultra via api based on prompt and resolution.", inputs=[ IO.String.Input( @@ -160,7 +155,7 @@ class FluxKontextProImageNode(IO.ComfyNode): return IO.Schema( node_id=cls.NODE_ID, display_name=cls.DISPLAY_NAME, - category="image/partner/BFL", + category="partner/image/BFL", description="Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio.", inputs=[ IO.String.Input( @@ -282,7 +277,7 @@ class FluxProExpandNode(IO.ComfyNode): return IO.Schema( node_id="FluxProExpandNode", display_name="Flux.1 Expand Image", - category="image/partner/BFL", + category="partner/image/BFL", description="Outpaints image based on prompt.", inputs=[ IO.Image.Input("image"), @@ -419,7 +414,7 @@ class FluxProFillNode(IO.ComfyNode): return IO.Schema( node_id="FluxProFillNode", display_name="Flux.1 Fill Image", - category="image/partner/BFL", + category="partner/image/BFL", description="Inpaints image based on mask and prompt.", inputs=[ IO.Image.Input("image"), @@ -519,6 +514,174 @@ class FluxProFillNode(IO.ComfyNode): return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"])) +class FluxEraseNode(IO.ComfyNode): + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="FluxEraseNode", + display_name="Flux Erase Image", + category="partner/image/BFL", + description="Removes the masked object from an image and reconstructs the background. " + "Paint the mask over what you want to erase.", + inputs=[ + IO.Image.Input("image"), + IO.Mask.Input("mask", tooltip="White areas are removed; black areas are preserved."), + IO.Int.Input( + "dilate_pixels", + default=10, + min=0, + max=25, + tooltip="Expands the mask boundaries to ensure clean coverage of the object's edges.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + optional=True, + ), + ], + outputs=[IO.Image.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":"range_usd","min_usd":0.03,"max_usd":0.06,"format":{"approximate":true}}""", + ), + ) + + @classmethod + async def execute( + cls, + image: Input.Image, + mask: Input.Image, + dilate_pixels: int = 10, + seed: int = 0, + ) -> IO.NodeOutput: + validate_image_dimensions(image, min_width=256, min_height=256) + mask = resize_mask_to_image(mask, image) + mask = tensor_to_base64_string(convert_mask_to_image(mask)) + initial_response = await sync_op( + cls, + ApiEndpoint(path="/proxy/bfl/v1/flux-tools/erase-v1", method="POST"), + response_model=BFLFluxProGenerateResponse, + data=BFLFluxEraseRequest( + image=tensor_to_base64_string(image[:, :, :, :3]), # make sure image will have alpha channel removed + mask=mask, + dilate_pixels=dilate_pixels, + seed=seed, + ), + ) + + def price_extractor(_r: BaseModel) -> float | None: + return None if initial_response.cost is None else initial_response.cost / 100 + + response = await poll_op( + cls, + ApiEndpoint(initial_response.polling_url), + response_model=BFLFluxStatusResponse, + status_extractor=lambda r: r.status, + progress_extractor=lambda r: r.progress, + price_extractor=price_extractor, + completed_statuses=[BFLStatus.ready], + failed_statuses=[ + BFLStatus.request_moderated, + BFLStatus.content_moderated, + BFLStatus.error, + BFLStatus.task_not_found, + ], + queued_statuses=[], + ) + return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"])) + + +class FluxVTONode(IO.ComfyNode): + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="FluxVTONode", + display_name="Flux Virtual Try-On", + category="partner/image/BFL", + description="Virtual try-on: dresses the person in the provided garment.", + inputs=[ + IO.Image.Input("person", tooltip="Image of the person to dress."), + IO.Image.Input("garment", tooltip="Image of the garment to apply."), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Optional natural-language styling instruction (e.g. how the garment should fit).", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + ), + ], + outputs=[IO.Image.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":"range_usd","min_usd":0.0375,"max_usd":0.075,"format":{"approximate":true}}""", + ), + ) + + @classmethod + async def execute( + cls, + person: Input.Image, + garment: Input.Image, + prompt: str = "", + seed: int = 0, + ) -> IO.NodeOutput: + initial_response = await sync_op( + cls, + ApiEndpoint(path="/proxy/bfl/v1/flux-tools/vto-v1", method="POST"), + response_model=BFLFluxProGenerateResponse, + data=BFLFluxVTORequest( + prompt=prompt, + person=tensor_to_base64_string(person[:, :, :, :3]), + garment=tensor_to_base64_string(garment[:, :, :, :3]), + seed=seed, + ), + ) + + def price_extractor(_r: BaseModel) -> float | None: + return None if initial_response.cost is None else initial_response.cost / 100 + + response = await poll_op( + cls, + ApiEndpoint(initial_response.polling_url), + response_model=BFLFluxStatusResponse, + status_extractor=lambda r: r.status, + progress_extractor=lambda r: r.progress, + price_extractor=price_extractor, + completed_statuses=[BFLStatus.ready], + failed_statuses=[ + BFLStatus.request_moderated, + BFLStatus.content_moderated, + BFLStatus.error, + BFLStatus.task_not_found, + ], + queued_statuses=[], + ) + return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"])) + + class Flux2ProImageNode(IO.ComfyNode): NODE_ID = "Flux2ProImageNode" @@ -545,7 +708,7 @@ class Flux2ProImageNode(IO.ComfyNode): return IO.Schema( node_id=cls.NODE_ID, display_name=cls.DISPLAY_NAME, - category="image/partner/BFL", + category="partner/image/BFL", description="Generates images synchronously based on prompt and resolution.", inputs=[ IO.String.Input( @@ -716,7 +879,7 @@ class Flux2ImageNode(IO.ComfyNode): return IO.Schema( node_id="Flux2ImageNode", display_name="Flux.2 Image", - category="image/partner/BFL", + category="partner/image/BFL", description="Generate images via Flux.2 [pro] or Flux.2 [max] from a prompt and optional reference images.", inputs=[ IO.String.Input( @@ -853,6 +1016,8 @@ class BFLExtension(ComfyExtension): FluxKontextMaxImageNode, FluxProExpandNode, FluxProFillNode, + FluxEraseNode, + FluxVTONode, Flux2ProImageNode, Flux2MaxImageNode, Flux2ImageNode, diff --git a/comfy_api_nodes/nodes_bria.py b/comfy_api_nodes/nodes_bria.py index 53e763210..090154afb 100644 --- a/comfy_api_nodes/nodes_bria.py +++ b/comfy_api_nodes/nodes_bria.py @@ -1,14 +1,19 @@ +import av +import torch +from av.codec import CodecContext from typing_extensions import override from comfy_api.latest import IO, ComfyExtension, Input from comfy_api_nodes.apis.bria import ( BriaEditImageRequest, + BriaImageEditResponse, BriaRemoveBackgroundRequest, BriaRemoveBackgroundResponse, BriaRemoveVideoBackgroundRequest, BriaRemoveVideoBackgroundResponse, - BriaImageEditResponse, BriaStatusResponse, + BriaVideoGreenScreenRequest, + BriaVideoReplaceBackgroundRequest, InputModerationSettings, ) from comfy_api_nodes.util import ( @@ -31,7 +36,7 @@ class BriaImageEditNode(IO.ComfyNode): return IO.Schema( node_id="BriaImageEditNode", display_name="Bria FIBO Image Edit", - category="image/partner/Bria", + category="partner/image/Bria", description="Edit images using Bria latest model", inputs=[ IO.Combo.Input("model", options=["FIBO"]), @@ -169,7 +174,7 @@ class BriaRemoveImageBackground(IO.ComfyNode): return IO.Schema( node_id="BriaRemoveImageBackground", display_name="Bria Remove Image Background", - category="image/partner/Bria", + category="partner/image/Bria", description="Remove the background from an image using Bria RMBG 2.0.", inputs=[ IO.Image.Input("image"), @@ -245,7 +250,7 @@ class BriaRemoveVideoBackground(IO.ComfyNode): return IO.Schema( node_id="BriaRemoveVideoBackground", display_name="Bria Remove Video Background", - category="video/partner/Bria", + category="partner/video/Bria", description="Remove the background from a video using Bria. ", inputs=[ IO.Video.Input("video"), @@ -284,7 +289,7 @@ class BriaRemoveVideoBackground(IO.ComfyNode): ], is_api_node=True, price_badge=IO.PriceBadge( - expr="""{"type":"usd","usd":0.14,"format":{"suffix":"/second"}}""", + expr="""{"type":"usd","usd":0.0042,"format":{"suffix":"/second"}}""", ), ) @@ -316,6 +321,251 @@ class BriaRemoveVideoBackground(IO.ComfyNode): return IO.NodeOutput(await download_url_to_video_output(response.result.video_url)) +class BriaVideoGreenScreen(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="BriaVideoGreenScreen", + display_name="Bria Video Green Screen", + category="partner/video/Bria", + description="Replace a video's background with a solid chroma-key screen using Bria.", + inputs=[ + IO.Video.Input("video"), + IO.Combo.Input( + "green_shade", + options=["broadcast_green", "chroma_green", "blue_screen"], + tooltip="Solid chroma-key shade applied behind the foreground: " + "broadcast_green (#00B140), chroma_green (#00FF00), or blue_screen (#0000FF).", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed controls whether the node should re-run; " + "results are non-deterministic regardless of seed.", + ), + ], + 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":0.0042,"format":{"suffix":"/second"}}""", + ), + ) + + @classmethod + async def execute( + cls, + video: Input.Video, + green_shade: str, + seed: int, + ) -> IO.NodeOutput: + validate_video_duration(video, max_duration=60.0) + response = await sync_op( + cls, + ApiEndpoint(path="/proxy/bria/v2/video/edit/green_screen", method="POST"), + data=BriaVideoGreenScreenRequest( + video=await upload_video_to_comfyapi(cls, video), + green_shade=green_shade, + output_container_and_codec="mp4_h264", + seed=seed, + ), + response_model=BriaStatusResponse, + ) + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"), + status_extractor=lambda r: r.status, + response_model=BriaRemoveVideoBackgroundResponse, + ) + return IO.NodeOutput(await download_url_to_video_output(response.result.video_url)) + + +class BriaVideoReplaceBackground(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="BriaVideoReplaceBackground", + display_name="Bria Video Replace Background", + category="partner/video/Bria", + description="Replace a video's background with a supplied image or video using Bria. " + "The output keeps the foreground's resolution and frame rate; a background with a " + "different aspect ratio is stretched to fit, so match it for undistorted results.", + inputs=[ + IO.Video.Input("video", tooltip="Foreground video whose background is replaced."), + IO.Image.Input( + "background_image", + optional=True, + tooltip="Background image to composite behind the foreground. " + "Provide either a background image or a background video, not both.", + ), + IO.Video.Input( + "background_video", + optional=True, + tooltip="Background video to composite behind the foreground. " + "Provide either a background image or a background video, not both.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed controls whether the node should re-run; " + "results are non-deterministic regardless of seed.", + ), + ], + 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":0.0042,"format":{"suffix":"/second"}}""", + ), + ) + + @classmethod + async def execute( + cls, + video: Input.Video, + seed: int, + background_image: Input.Image | None = None, + background_video: Input.Video | None = None, + ) -> IO.NodeOutput: + if (background_image is None) == (background_video is None): + raise ValueError("Provide either a background image or a background video, not both.") + validate_video_duration(video, max_duration=60.0) + if background_video is not None: + validate_video_duration(background_video, max_duration=60.0) + background_url = await upload_video_to_comfyapi(cls, background_video, wait_label="Uploading background") + else: + # Bria's replace_background 500s on RGBA, so drop the alpha channel before upload. + background_url = await upload_image_to_comfyapi( + cls, background_image[:, :, :, :3], wait_label="Uploading background" + ) + response = await sync_op( + cls, + ApiEndpoint(path="/proxy/bria/v2/video/edit/replace_background", method="POST"), + data=BriaVideoReplaceBackgroundRequest( + video=await upload_video_to_comfyapi(cls, video), + background_url=background_url, + output_container_and_codec="mp4_h264", + seed=seed, + ), + response_model=BriaStatusResponse, + ) + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"), + status_extractor=lambda r: r.status, + response_model=BriaRemoveVideoBackgroundResponse, + ) + return IO.NodeOutput(await download_url_to_video_output(response.result.video_url)) + + +def _video_to_images_and_mask(video: Input.Video) -> tuple[Input.Image, Input.Mask]: + """Decode a transparent webm (VP9 + alpha) into image frames and an alpha mask. + + VP9 keeps its alpha in a side layer that PyAV's default vp9 decoder drops, so the frames + are decoded with libvpx-vp9. Returns RGB images [B,H,W,3] in 0..1 and a mask [B,H,W] + following the Load Image convention (1 = transparent) for compositing or Save WEBM. + """ + rgb_frames: list[torch.Tensor] = [] + alpha_frames: list[torch.Tensor] = [] + with av.open(video.get_stream_source(), mode="r") as container: + stream = container.streams.video[0] + decoder = CodecContext.create("libvpx-vp9", "r") if stream.codec_context.name == "vp9" else None + for packet in container.demux(stream): + for frame in (decoder.decode(packet) if decoder is not None else packet.decode()): + rgba = torch.from_numpy(frame.to_ndarray(format="rgba")).float() / 255.0 + rgb_frames.append(rgba[..., :3]) + alpha_frames.append(rgba[..., 3]) + images = torch.stack(rgb_frames) if rgb_frames else torch.zeros(0, 0, 0, 3) + mask = (1.0 - torch.stack(alpha_frames)) if alpha_frames else torch.zeros((images.shape[0], 64, 64)) + return images, mask + + +class BriaTransparentVideoBackground(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="BriaTransparentVideoBackground", + display_name="Bria Remove Video Background (Transparent)", + category="partner/video/Bria", + description="Remove the background from a video using Bria and return the cut-out frames " + "plus an alpha mask. Connect both to a compositing node, or feed them to Save WEBM to " + "write a transparent video.", + inputs=[ + IO.Video.Input("video"), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed controls whether the node should re-run; " + "results are non-deterministic regardless of seed.", + ), + ], + outputs=[ + IO.Image.Output(display_name="images"), + IO.Mask.Output(display_name="mask"), + ], + 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":0.0042,"format":{"suffix":"/second"}}""", + ), + ) + + @classmethod + async def execute( + cls, + video: Input.Video, + seed: int, + ) -> IO.NodeOutput: + validate_video_duration(video, max_duration=60.0) + response = await sync_op( + cls, + ApiEndpoint(path="/proxy/bria/v2/video/edit/remove_background", method="POST"), + data=BriaRemoveVideoBackgroundRequest( + video=await upload_video_to_comfyapi(cls, video), + background_color="Transparent", + output_container_and_codec="webm_vp9", + seed=seed, + ), + response_model=BriaStatusResponse, + ) + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"), + status_extractor=lambda r: r.status, + response_model=BriaRemoveVideoBackgroundResponse, + ) + video_out = await download_url_to_video_output(response.result.video_url) + images, mask = _video_to_images_and_mask(video_out) + return IO.NodeOutput(images, mask) + + class BriaExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: @@ -323,6 +573,9 @@ class BriaExtension(ComfyExtension): BriaImageEditNode, BriaRemoveImageBackground, BriaRemoveVideoBackground, + BriaVideoGreenScreen, + BriaVideoReplaceBackground, + BriaTransparentVideoBackground, ] diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py index 3711bac1d..c30ddc446 100644 --- a/comfy_api_nodes/nodes_bytedance.py +++ b/comfy_api_nodes/nodes_bytedance.py @@ -7,6 +7,7 @@ from io import BytesIO import torch from typing_extensions import override +from comfy.utils import common_upscale from comfy_api.latest import IO, ComfyExtension, Input, Types from comfy_api_nodes.apis.bytedance import ( RECOMMENDED_PRESETS, @@ -131,6 +132,44 @@ def _prepare_seedance_image(image: Input.Image) -> Input.Image: return image +# Supported output aspect ratios, used to pre-size FLF frames to matching pixel pair to avoid the 1080p stretch jump. +SEEDANCE2_RATIO_WH = { + "16:9": (16, 9), + "4:3": (4, 3), + "1:1": (1, 1), + "3:4": (3, 4), + "9:16": (9, 16), + "21:9": (21, 9), +} +SEEDANCE2_RES_SHORT_SIDE = {"480p": 480, "720p": 720, "1080p": 1080} + + +def _seedance2_target_dims(resolution: str, ratio: str, image: torch.Tensor) -> tuple[int, int]: + """Exact supported output (width, height) for (resolution, ratio). + + The shorter side equals the resolution number (e.g. 1080p 16:9 -> 1920x1080). For ratio + "adaptive" (or any unexpected value) the ratio is derived from the image's own aspect, snapped + to the nearest supported ratio, so the output keeps the frame's orientation. + """ + short = SEEDANCE2_RES_SHORT_SIDE[resolution] + if ratio not in SEEDANCE2_RATIO_WH: + aspect = image.shape[-2] / image.shape[-3] # W / H; tensor is (B, H, W, C) + ratio = min(SEEDANCE2_RATIO_WH, key=lambda k: abs(SEEDANCE2_RATIO_WH[k][0] / SEEDANCE2_RATIO_WH[k][1] - aspect)) + rw, rh = SEEDANCE2_RATIO_WH[ratio] + if rw >= rh: # landscape or square: shorter side is the height + out_w, out_h = round(short * rw / rh), short + else: # portrait: shorter side is the width + out_w, out_h = short, round(short * rh / rw) + return out_w - out_w % 2, out_h - out_h % 2 + + +def _resize_to_exact(image: torch.Tensor, width: int, height: int) -> torch.Tensor: + """Center-crop to the target aspect and resize to exactly width x height (lanczos).""" + samples = image.movedim(-1, 1) # (B, H, W, C) -> (B, C, H, W) + resized = common_upscale(samples, width, height, "lanczos", "center") + return resized.movedim(1, -1) + + async def _resolve_reference_assets( cls: type[IO.ComfyNode], asset_ids: list[str], @@ -368,7 +407,7 @@ class ByteDanceImageNode(IO.ComfyNode): return IO.Schema( node_id="ByteDanceImageNode", display_name="ByteDance Image", - category="image/partner/ByteDance", + category="partner/image/ByteDance", description="Generate images using ByteDance models via api based on prompt", inputs=[ IO.Combo.Input("model", options=["seedream-3-0-t2i-250415"]), @@ -492,7 +531,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode): return IO.Schema( node_id="ByteDanceSeedreamNode", display_name="ByteDance Seedream 4.5 & 5.0", - category="image/partner/ByteDance", + category="partner/image/ByteDance", description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.", inputs=[ IO.Combo.Input( @@ -754,7 +793,7 @@ class ByteDanceSeedreamNodeV2(IO.ComfyNode): return IO.Schema( node_id="ByteDanceSeedreamNodeV2", display_name="ByteDance Seedream 4.5 & 5.0", - category="image/partner/ByteDance", + category="partner/image/ByteDance", description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.", inputs=[ IO.String.Input( @@ -920,7 +959,7 @@ class ByteDanceTextToVideoNode(IO.ComfyNode): return IO.Schema( node_id="ByteDanceTextToVideoNode", display_name="ByteDance Text to Video", - category="video/partner/ByteDance", + category="partner/video/ByteDance", description="Generate video using ByteDance models via api based on prompt", inputs=[ IO.Combo.Input( @@ -1048,7 +1087,7 @@ class ByteDanceImageToVideoNode(IO.ComfyNode): return IO.Schema( node_id="ByteDanceImageToVideoNode", display_name="ByteDance Image to Video", - category="video/partner/ByteDance", + category="partner/video/ByteDance", description="Generate video using ByteDance models via api based on image and prompt", inputs=[ IO.Combo.Input( @@ -1185,7 +1224,7 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode): return IO.Schema( node_id="ByteDanceFirstLastFrameNode", display_name="ByteDance First-Last-Frame to Video", - category="video/partner/ByteDance", + category="partner/video/ByteDance", description="Generate video using prompt and first and last frames.", inputs=[ IO.Combo.Input( @@ -1333,7 +1372,7 @@ class ByteDanceImageReferenceNode(IO.ComfyNode): return IO.Schema( node_id="ByteDanceImageReferenceNode", display_name="ByteDance Reference Images to Video", - category="video/partner/ByteDance", + category="partner/video/ByteDance", description="Generate video using prompt and reference images.", inputs=[ IO.Combo.Input( @@ -1576,7 +1615,7 @@ class ByteDance2TextToVideoNode(IO.ComfyNode): return IO.Schema( node_id="ByteDance2TextToVideoNode", display_name="ByteDance Seedance 2.0 Text to Video", - category="video/partner/ByteDance", + category="partner/video/ByteDance", description="Generate video using Seedance 2.0 models based on a text prompt.", inputs=[ IO.DynamicCombo.Input( @@ -1677,7 +1716,7 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode): return IO.Schema( node_id="ByteDance2FirstLastFrameNode", display_name="ByteDance Seedance 2.0 First-Last-Frame to Video", - category="video/partner/ByteDance", + category="partner/video/ByteDance", description="Generate video using Seedance 2.0 from a first frame image and optional last frame image.", inputs=[ IO.DynamicCombo.Input( @@ -1790,10 +1829,28 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode): if last_frame is not None and last_frame_asset_id: raise ValueError("Provide only one of last_frame or last_frame_asset_id, not both.") - if first_frame is not None: - first_frame = _prepare_seedance_image(first_frame) - if last_frame is not None: - last_frame = _prepare_seedance_image(last_frame) + request_ratio = model["ratio"] + if first_frame_asset_id or last_frame_asset_id: + if first_frame is not None: + first_frame = _prepare_seedance_image(first_frame) + if last_frame is not None: + last_frame = _prepare_seedance_image(last_frame) + else: + # The 1080p FLF stretch fix (pre-size frames to a supported pixel pair + submit ratio="adaptive") + # only applies to local image inputs we can resize. + request_ratio = "adaptive" + target_dims: tuple[int, int] | None = None + if first_frame is not None: + validate_image_aspect_ratio(first_frame, (2, 5), (5, 2), strict=False) # 0.4 to 2.5 + validate_image_dimensions(first_frame, min_width=300, min_height=300) + target_dims = _seedance2_target_dims(model["resolution"], model["ratio"], first_frame) + first_frame = _resize_to_exact(first_frame, *target_dims) + if last_frame is not None: + validate_image_aspect_ratio(last_frame, (2, 5), (5, 2), strict=False) # 0.4 to 2.5 + validate_image_dimensions(last_frame, min_width=300, min_height=300) + if target_dims is None: + target_dims = _seedance2_target_dims(model["resolution"], model["ratio"], last_frame) + last_frame = _resize_to_exact(last_frame, *target_dims) asset_ids_to_resolve = [a for a in (first_frame_asset_id, last_frame_asset_id) if a] image_assets: dict[str, str] = {} @@ -1844,7 +1901,7 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode): content=content, generate_audio=model["generate_audio"], resolution=model["resolution"], - ratio=model["ratio"], + ratio=request_ratio, duration=model["duration"], seed=seed, watermark=watermark, @@ -1944,7 +2001,7 @@ class ByteDance2ReferenceNode(IO.ComfyNode): return IO.Schema( node_id="ByteDance2ReferenceNode", display_name="ByteDance Seedance 2.0 Reference to Video", - category="video/partner/ByteDance", + category="partner/video/ByteDance", description="Generate, edit, or extend video using Seedance 2.0 with reference images, " "videos, and audio. Supports multimodal reference, video editing, and video extension.", inputs=[ @@ -2241,7 +2298,7 @@ class ByteDanceCreateImageAsset(IO.ComfyNode): return IO.Schema( node_id="ByteDanceCreateImageAsset", display_name="ByteDance Create Image Asset", - category="image/partner/ByteDance", + category="partner/image/ByteDance", description=( "Create a Seedance 2.0 personal image asset. Uploads the input image and " "registers it in the given asset group. If group_id is empty, runs a real-person " @@ -2308,7 +2365,7 @@ class ByteDanceCreateVideoAsset(IO.ComfyNode): return IO.Schema( node_id="ByteDanceCreateVideoAsset", display_name="ByteDance Create Video Asset", - category="video/partner/ByteDance", + category="partner/video/ByteDance", description=( "Create a Seedance 2.0 personal video asset. Uploads the input video and " "registers it in the given asset group. If group_id is empty, runs a real-person " diff --git a/comfy_api_nodes/nodes_bytedance_llm.py b/comfy_api_nodes/nodes_bytedance_llm.py index 007cac45f..cb41defa0 100644 --- a/comfy_api_nodes/nodes_bytedance_llm.py +++ b/comfy_api_nodes/nodes_bytedance_llm.py @@ -144,7 +144,7 @@ class ByteDanceSeedNode(IO.ComfyNode): return IO.Schema( node_id="ByteDanceSeedNode", display_name="ByteDance Seed", - category="text/partner/ByteDance", + category="partner/text/ByteDance", essentials_category="Text Generation", description="Generate text responses with ByteDance's Seed 2.0 models. " "Provide a text prompt and optionally one or more images or videos for multimodal context.", diff --git a/comfy_api_nodes/nodes_elevenlabs.py b/comfy_api_nodes/nodes_elevenlabs.py index 37eeb2601..eba578a45 100644 --- a/comfy_api_nodes/nodes_elevenlabs.py +++ b/comfy_api_nodes/nodes_elevenlabs.py @@ -69,7 +69,7 @@ class ElevenLabsSpeechToText(IO.ComfyNode): return IO.Schema( node_id="ElevenLabsSpeechToText", display_name="ElevenLabs Speech to Text", - category="audio/partner/ElevenLabs", + category="partner/audio/ElevenLabs", description="Transcribe audio to text. " "Supports automatic language detection, speaker diarization, and audio event tagging.", inputs=[ @@ -210,7 +210,7 @@ class ElevenLabsVoiceSelector(IO.ComfyNode): return IO.Schema( node_id="ElevenLabsVoiceSelector", display_name="ElevenLabs Voice Selector", - category="audio/partner/ElevenLabs", + category="partner/audio/ElevenLabs", description="Select a predefined ElevenLabs voice for text-to-speech generation.", inputs=[ IO.Combo.Input( @@ -239,7 +239,7 @@ class ElevenLabsTextToSpeech(IO.ComfyNode): return IO.Schema( node_id="ElevenLabsTextToSpeech", display_name="ElevenLabs Text to Speech", - category="audio/partner/ElevenLabs", + category="partner/audio/ElevenLabs", description="Convert text to speech.", inputs=[ IO.Custom(ELEVENLABS_VOICE).Input( @@ -414,7 +414,7 @@ class ElevenLabsAudioIsolation(IO.ComfyNode): return IO.Schema( node_id="ElevenLabsAudioIsolation", display_name="ElevenLabs Voice Isolation", - category="audio/partner/ElevenLabs", + category="partner/audio/ElevenLabs", description="Remove background noise from audio, isolating vocals or speech.", inputs=[ IO.Audio.Input( @@ -459,7 +459,7 @@ class ElevenLabsTextToSoundEffects(IO.ComfyNode): return IO.Schema( node_id="ElevenLabsTextToSoundEffects", display_name="ElevenLabs Text to Sound Effects", - category="audio/partner/ElevenLabs", + category="partner/audio/ElevenLabs", description="Generate sound effects from text descriptions.", inputs=[ IO.String.Input( @@ -555,7 +555,7 @@ class ElevenLabsInstantVoiceClone(IO.ComfyNode): return IO.Schema( node_id="ElevenLabsInstantVoiceClone", display_name="ElevenLabs Instant Voice Clone", - category="audio/partner/ElevenLabs", + category="partner/audio/ElevenLabs", description="Create a cloned voice from audio samples. " "Provide 1-8 audio recordings of the voice to clone.", inputs=[ @@ -658,7 +658,7 @@ class ElevenLabsSpeechToSpeech(IO.ComfyNode): return IO.Schema( node_id="ElevenLabsSpeechToSpeech", display_name="ElevenLabs Speech to Speech", - category="audio/partner/ElevenLabs", + category="partner/audio/ElevenLabs", description="Transform speech from one voice to another while preserving the original content and emotion.", inputs=[ IO.Custom(ELEVENLABS_VOICE).Input( @@ -793,7 +793,7 @@ class ElevenLabsTextToDialogue(IO.ComfyNode): return IO.Schema( node_id="ElevenLabsTextToDialogue", display_name="ElevenLabs Text to Dialogue", - category="audio/partner/ElevenLabs", + category="partner/audio/ElevenLabs", description="Generate multi-speaker dialogue from text. Each dialogue entry has its own text and voice.", inputs=[ IO.Float.Input( diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py index 3cfd541b2..a63625ada 100644 --- a/comfy_api_nodes/nodes_gemini.py +++ b/comfy_api_nodes/nodes_gemini.py @@ -5,10 +5,9 @@ See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/infer import base64 import os -from enum import Enum from fnmatch import fnmatch from io import BytesIO -from typing import Literal +from typing import Any, Literal import torch from typing_extensions import override @@ -19,6 +18,7 @@ from comfy_api_nodes.apis.gemini import ( GeminiContent, GeminiFileData, GeminiGenerateContentRequest, + GeminiGenerationConfig, GeminiGenerateContentResponse, GeminiImageConfig, GeminiImageGenerateContentRequest, @@ -40,13 +40,18 @@ from comfy_api_nodes.util import ( get_number_of_images, sync_op, tensor_to_base64_string, + upload_audio_to_comfyapi, + upload_image_to_comfyapi, upload_images_to_comfyapi, + upload_video_to_comfyapi, validate_string, video_to_base64_string, ) GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini" GEMINI_MAX_INPUT_FILE_SIZE = 20 * 1024 * 1024 # 20 MB +GEMINI_URL_INPUT_BUDGET = 10 +GEMINI_MAX_INLINE_BYTES = 18 * 1024 * 1024 GEMINI_IMAGE_SYS_PROMPT = ( "You are an expert image-generation engine. You must ALWAYS produce an image.\n" "Interpret all user input—regardless of " @@ -72,15 +77,6 @@ GEMINI_IMAGE_2_PRICE_BADGE = IO.PriceBadge( ) -class GeminiImageModel(str, Enum): - """ - Gemini Image Model Names allowed by comfy-api - """ - - gemini_2_5_flash_image_preview = "gemini-2.5-flash-image-preview" - gemini_2_5_flash_image = "gemini-2.5-flash-image" - - async def create_image_parts( cls: type[IO.ComfyNode], images: Input.Image | list[Input.Image], @@ -237,21 +233,15 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N if not response.modelVersion: return None # Define prices (Cost per 1,000,000 tokens), see https://cloud.google.com/vertex-ai/generative-ai/pricing - if response.modelVersion in ("gemini-2.5-pro-preview-05-06", "gemini-2.5-pro"): + if response.modelVersion == "gemini-2.5-pro": input_tokens_price = 1.25 output_text_tokens_price = 10.0 output_image_tokens_price = 0.0 - elif response.modelVersion in ( - "gemini-2.5-flash-preview-04-17", - "gemini-2.5-flash", - ): + elif response.modelVersion == "gemini-2.5-flash": input_tokens_price = 0.30 output_text_tokens_price = 2.50 output_image_tokens_price = 0.0 - elif response.modelVersion in ( - "gemini-2.5-flash-image-preview", - "gemini-2.5-flash-image", - ): + elif response.modelVersion == "gemini-2.5-flash-image": input_tokens_price = 0.30 output_text_tokens_price = 2.50 output_image_tokens_price = 30.0 @@ -285,6 +275,140 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N return final_price / 1_000_000.0 +def create_video_parts(video_input: Input.Video) -> list[GeminiPart]: + """Convert a single video input to Gemini API compatible parts (inline MP4/H.264).""" + base_64_string = video_to_base64_string( + video_input, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264 + ) + return [ + GeminiPart( + inlineData=GeminiInlineData( + mimeType=GeminiMimeType.video_mp4, + data=base_64_string, + ) + ) + ] + + +def create_audio_parts(audio_input: Input.Audio) -> list[GeminiPart]: + """Convert an audio input to Gemini API compatible parts (one inline MP3 part per batch item).""" + audio_parts: list[GeminiPart] = [] + for batch_index in range(audio_input["waveform"].shape[0]): + # Recreate an IO.AUDIO object for the given batch dimension index + audio_at_index = Input.Audio( + waveform=audio_input["waveform"][batch_index].unsqueeze(0), + sample_rate=audio_input["sample_rate"], + ) + # Convert to MP3 format for compatibility with Gemini API + audio_bytes = audio_to_base64_string( + audio_at_index, + container_format="mp3", + codec_name="libmp3lame", + ) + audio_parts.append( + GeminiPart( + inlineData=GeminiInlineData( + mimeType=GeminiMimeType.audio_mp3, + data=audio_bytes, + ) + ) + ) + return audio_parts + + +def _flatten_images(images: list[Input.Image]) -> list[torch.Tensor]: + """Expand any batched image tensors into individual (H, W, C) frames, preserving order.""" + frames: list[torch.Tensor] = [] + for img in images: + if len(img.shape) == 4: + frames.extend(img[i] for i in range(img.shape[0])) + else: + frames.append(img) + return frames + + +def _flatten_audio(audios: list[Input.Audio]) -> list[Input.Audio]: + """Expand any batched audio inputs into individual single-clip audio inputs, preserving order.""" + clips: list[Input.Audio] = [] + for audio in audios: + waveform = audio["waveform"] + for i in range(waveform.shape[0]): + clips.append(Input.Audio(waveform=waveform[i].unsqueeze(0), sample_rate=audio["sample_rate"])) + return clips + + +async def _media_url_part(cls: type[IO.ComfyNode], kind: str, payload: Any) -> GeminiPart: + """Upload a single media unit to ComfyAPI storage and return a fileData (URL) part.""" + if kind == "image": + url = await upload_image_to_comfyapi(cls, payload, mime_type="image/png", wait_label="Uploading image") + return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.image_png, fileUri=url)) + if kind == "audio": + url = await upload_audio_to_comfyapi( + cls, payload, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mp3" + ) + return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.audio_mp3, fileUri=url)) + url = await upload_video_to_comfyapi(cls, payload, wait_label="Uploading video") + return GeminiPart(fileData=GeminiFileData(mimeType=GeminiMimeType.video_mp4, fileUri=url)) + + +def _media_inline_part(kind: str, payload: Any) -> tuple[GeminiPart, int]: + """Encode a single media unit as an inline base64 part; returns (part, base64_length).""" + if kind == "image": + data = tensor_to_base64_string(payload, mime_type="image/webp") + mime = GeminiMimeType.image_webp + elif kind == "audio": + data = audio_to_base64_string(payload, container_format="mp3", codec_name="libmp3lame") + mime = GeminiMimeType.audio_mp3 + else: + data = video_to_base64_string( + payload, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264 + ) + mime = GeminiMimeType.video_mp4 + return GeminiPart(inlineData=GeminiInlineData(mimeType=mime, data=data)), len(data) + + +async def build_gemini_media_parts( + cls: type[IO.ComfyNode], + images: list[Input.Image], + audios: list[Input.Audio], + videos: list[Input.Video], + *, + url_budget: int = GEMINI_URL_INPUT_BUDGET, + max_inline_bytes: int = GEMINI_MAX_INLINE_BYTES, +) -> list[GeminiPart]: + """Build Gemini parts for multimodal inputs (images, audio, video). + + fileData URLs are preferred for every media type: the upload is fetched directly by the + model, keeping the request body tiny regardless of media size. The URL budget is shared + across all media and assigned largest-first (video, then audio, then images), so that if it + is ever exhausted the inline-base64 overflow is limited to the smallest items. Total inline + payload is capped by `max_inline_bytes`. + """ + units: list[tuple[str, Any]] = ( + [("video", v) for v in videos] + + [("audio", a) for a in _flatten_audio(audios)] + + [("image", f) for f in _flatten_images(images)] + ) + + parts: list[GeminiPart] = [] + url_used = 0 + inline_bytes = 0 + for kind, payload in units: + if url_used < url_budget: + parts.append(await _media_url_part(cls, kind, payload)) + url_used += 1 + continue + part, nbytes = _media_inline_part(kind, payload) + inline_bytes += nbytes + if inline_bytes > max_inline_bytes: + raise ValueError( + f"Too much media to send inline (over {max_inline_bytes // (1024 * 1024)}MB after the first " + f"{url_budget} inputs are uploaded as URLs). Reduce the number or size of attached media." + ) + parts.append(part) + return parts + + class GeminiNode(IO.ComfyNode): """ Node to generate text responses from a Gemini model. @@ -300,7 +424,7 @@ class GeminiNode(IO.ComfyNode): return IO.Schema( node_id="GeminiNode", display_name="Google Gemini", - category="text/partner/Gemini", + category="partner/text/Gemini", description="Generate text responses with Google's Gemini AI model. " "You can provide multiple types of inputs (text, images, audio, video) " "as context for generating more relevant and meaningful responses.", @@ -315,8 +439,6 @@ class GeminiNode(IO.ComfyNode): IO.Combo.Input( "model", options=[ - "gemini-2.5-pro-preview-05-06", - "gemini-2.5-flash-preview-04-17", "gemini-2.5-pro", "gemini-2.5-flash", "gemini-3-pro-preview", @@ -407,58 +529,9 @@ class GeminiNode(IO.ComfyNode): ) """, ), + is_deprecated=True, ) - @classmethod - def create_video_parts(cls, video_input: Input.Video) -> list[GeminiPart]: - """Convert video input to Gemini API compatible parts.""" - - base_64_string = video_to_base64_string( - video_input, container_format=Types.VideoContainer.MP4, codec=Types.VideoCodec.H264 - ) - return [ - GeminiPart( - inlineData=GeminiInlineData( - mimeType=GeminiMimeType.video_mp4, - data=base_64_string, - ) - ) - ] - - @classmethod - def create_audio_parts(cls, audio_input: Input.Audio) -> list[GeminiPart]: - """ - Convert audio input to Gemini API compatible parts. - - Args: - audio_input: Audio input from ComfyUI, containing waveform tensor and sample rate. - - Returns: - List of GeminiPart objects containing the encoded audio. - """ - audio_parts: list[GeminiPart] = [] - for batch_index in range(audio_input["waveform"].shape[0]): - # Recreate an IO.AUDIO object for the given batch dimension index - audio_at_index = Input.Audio( - waveform=audio_input["waveform"][batch_index].unsqueeze(0), - sample_rate=audio_input["sample_rate"], - ) - # Convert to MP3 format for compatibility with Gemini API - audio_bytes = audio_to_base64_string( - audio_at_index, - container_format="mp3", - codec_name="libmp3lame", - ) - audio_parts.append( - GeminiPart( - inlineData=GeminiInlineData( - mimeType=GeminiMimeType.audio_mp3, - data=audio_bytes, - ) - ) - ) - return audio_parts - @classmethod async def execute( cls, @@ -482,9 +555,9 @@ class GeminiNode(IO.ComfyNode): if images is not None: parts.extend(await create_image_parts(cls, images)) if audio is not None: - parts.extend(cls.create_audio_parts(audio)) + parts.extend(create_audio_parts(audio)) if video is not None: - parts.extend(cls.create_video_parts(video)) + parts.extend(create_video_parts(video)) if files is not None: parts.extend(files) @@ -512,6 +585,210 @@ class GeminiNode(IO.ComfyNode): return IO.NodeOutput(output_text or "Empty response from Gemini model...") +GEMINI_V2_MODELS: dict[str, str] = { + "Gemini 3.1 Pro": "gemini-3.1-pro-preview", + "Gemini 3.1 Flash-Lite": "gemini-3.1-flash-lite-preview", +} + + +def _gemini_text_model_inputs(thinking_default: str) -> list[Input]: + """Per-model inputs revealed by the model DynamicCombo (shared media + sampling controls).""" + return [ + IO.Autogrow.Input( + "images", + template=IO.Autogrow.TemplateNames( + IO.Image.Input("image"), + names=[f"image_{i}" for i in range(1, 17)], + min=0, + ), + tooltip="Optional image(s) to use as context for the model. Up to 16 images.", + ), + IO.Autogrow.Input( + "audio", + template=IO.Autogrow.TemplateNames( + IO.Audio.Input("audio"), + names=["audio_1"], + min=0, + ), + tooltip="Optional audio clip to use as context for the model.", + ), + IO.Autogrow.Input( + "video", + template=IO.Autogrow.TemplateNames( + IO.Video.Input("video"), + names=["video_1"], + min=0, + ), + tooltip="Optional video clip to use as context for the model.", + ), + IO.Custom("GEMINI_INPUT_FILES").Input( + "files", + optional=True, + tooltip="Optional file(s) to use as context for the model. " + "Accepts inputs from the Gemini Input Files node.", + ), + IO.Combo.Input( + "thinking_level", + options=["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.", + ), + IO.Float.Input( + "temperature", + default=1.0, + min=0.0, + max=2.0, + step=0.01, + tooltip="Controls randomness. Lower is more focused/deterministic, higher is more creative.", + advanced=True, + ), + IO.Float.Input( + "top_p", + default=0.95, + min=0.0, + max=1.0, + step=0.01, + tooltip="Nucleus sampling: sample from the smallest token set whose cumulative probability reaches top_p.", + advanced=True, + ), + IO.Int.Input( + "max_output_tokens", + default=32768, + min=16, + max=65536, + tooltip="Maximum tokens to generate, including the model's internal thinking. " + "With thinking_level HIGH, a low value can leave no room for the answer; raise this if " + "responses come back empty or truncated. The model stops early when finished, so a higher " + "cap costs nothing extra for short replies.", + advanced=True, + ), + ] + + +class GeminiNodeV2(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="GeminiNodeV2", + display_name="Google Gemini", + category="partner/text/Gemini", + essentials_category="Text Generation", + description="Generate text responses with Google's Gemini models. Provide a text prompt and, " + "optionally, one or more images, audio clips, videos, or files as multimodal context.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text input to the model. Include detailed instructions, questions, or context.", + ), + IO.DynamicCombo.Input( + "model", + options=[ + 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")), + ], + tooltip="The Gemini model used to generate the response.", + ), + IO.Int.Input( + "seed", + default=42, + min=0, + max=2147483647, + control_after_generate=True, + tooltip="Seed for sampling. Set to 0 for a random seed. Deterministic output isn't guaranteed.", + ), + IO.String.Input( + "system_prompt", + multiline=True, + default="", + optional=True, + advanced=True, + tooltip="Foundational instructions that dictate the model's behavior.", + ), + ], + outputs=[ + IO.String.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=["model"]), + expr=""" + ( + $m := widgets.model; + $contains($m, "lite") ? { + "type": "list_usd", + "usd": [0.00025, 0.0015], + "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" } + } : { + "type": "list_usd", + "usd": [0.002, 0.012], + "format": { "approximate": true, "separator": "-", "suffix": " per 1K tokens" } + } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + prompt: str, + model: dict, + seed: int, + system_prompt: str = "", + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + model_id = GEMINI_V2_MODELS[model["model"]] + + parts: list[GeminiPart] = [GeminiPart(text=prompt)] + images = [t for t in (model.get("images") or {}).values() if t is not None] + audios = [a for a in (model.get("audio") or {}).values() if a is not None] + videos = [v for v in (model.get("video") or {}).values() if v is not None] + if images or audios or videos: + parts.extend(await build_gemini_media_parts(cls, images, audios, videos)) + files = model.get("files") + if files is not None: + parts.extend(files) + + gemini_system_prompt = None + if system_prompt: + gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None) + + response = await sync_op( + cls, + endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model_id}", method="POST"), + data=GeminiGenerateContentRequest( + contents=[ + GeminiContent( + role=GeminiRole.user, + parts=parts, + ) + ], + generationConfig=GeminiGenerationConfig( + temperature=model["temperature"], + topP=model["top_p"], + maxOutputTokens=model["max_output_tokens"], + seed=seed if seed > 0 else None, + thinkingConfig=GeminiThinkingConfig(thinkingLevel=model["thinking_level"]), + ), + systemInstruction=gemini_system_prompt, + ), + response_model=GeminiGenerateContentResponse, + price_extractor=calculate_tokens_price, + ) + + output_text = get_text_from_response(response) + return IO.NodeOutput(output_text or "Empty response from Gemini model...") + + class GeminiInputFiles(IO.ComfyNode): """ Loads and formats input files for use with the Gemini API. @@ -541,7 +818,7 @@ class GeminiInputFiles(IO.ComfyNode): return IO.Schema( node_id="GeminiInputFiles", display_name="Gemini Input Files", - category="text/partner/Gemini", + category="partner/text/Gemini", description="Loads and prepares input files to include as inputs for Gemini LLM nodes. " "The files will be read by the Gemini model when generating a response. " "The contents of the text file count toward the token limit. " @@ -598,7 +875,7 @@ class GeminiImage(IO.ComfyNode): return IO.Schema( node_id="GeminiImageNode", display_name="Nano Banana (Google Gemini Image)", - category="image/partner/Gemini", + category="partner/image/Gemini", description="Edit images synchronously via Google API.", inputs=[ IO.String.Input( @@ -609,8 +886,7 @@ class GeminiImage(IO.ComfyNode): ), IO.Combo.Input( "model", - options=GeminiImageModel, - default=GeminiImageModel.gemini_2_5_flash_image, + options=["gemini-2.5-flash-image"], tooltip="The Gemini model to use for generating responses.", ), IO.Int.Input( @@ -731,7 +1007,7 @@ class GeminiImage2(IO.ComfyNode): return IO.Schema( node_id="GeminiImage2Node", display_name="Nano Banana Pro (Google Gemini Image)", - category="image/partner/Gemini", + category="partner/image/Gemini", description="Generate or edit images synchronously via Google Vertex API.", inputs=[ IO.String.Input( @@ -869,7 +1145,7 @@ class GeminiNanoBanana2(IO.ComfyNode): return IO.Schema( node_id="GeminiNanoBanana2", display_name="Nano Banana 2", - category="image/partner/Gemini", + category="partner/image/Gemini", description="Generate or edit images synchronously via Google Vertex API.", inputs=[ IO.String.Input( @@ -1085,7 +1361,7 @@ class GeminiNanoBanana2V2(IO.ComfyNode): return IO.Schema( node_id="GeminiNanoBanana2V2", display_name="Nano Banana 2", - category="image/partner/Gemini", + category="partner/image/Gemini", description="Generate or edit images synchronously via Google Vertex API.", inputs=[ IO.String.Input( @@ -1129,6 +1405,26 @@ class GeminiNanoBanana2V2(IO.ComfyNode): tooltip="Foundational instructions that dictate an AI's behavior.", advanced=True, ), + IO.Float.Input( + "temperature", + default=1.0, + min=0.0, + max=2.0, + step=0.01, + optional=True, + tooltip="Controls randomness in generation. Lower is more focused/deterministic.", + advanced=True, + ), + IO.Float.Input( + "top_p", + default=0.95, + min=0.0, + max=1.0, + step=0.01, + optional=True, + tooltip="Nucleus sampling threshold. Lower is more focused, higher more diverse.", + advanced=True, + ), ], outputs=[ IO.Image.Output(), @@ -1165,6 +1461,8 @@ class GeminiNanoBanana2V2(IO.ComfyNode): seed: int, response_modalities: str, system_prompt: str = "", + temperature: float = 1.0, + top_p: float = 0.95, ) -> IO.NodeOutput: validate_string(prompt, strip_whitespace=True, min_length=1) model_choice = model["model"] @@ -1204,6 +1502,8 @@ class GeminiNanoBanana2V2(IO.ComfyNode): responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]), imageConfig=image_config, thinkingConfig=GeminiThinkingConfig(thinkingLevel=model["thinking_level"]), + temperature=temperature, + topP=top_p, ), systemInstruction=gemini_system_prompt, ), @@ -1222,6 +1522,7 @@ class GeminiExtension(ComfyExtension): async def get_node_list(self) -> list[type[IO.ComfyNode]]: return [ GeminiNode, + GeminiNodeV2, GeminiImage, GeminiImage2, GeminiNanoBanana2, diff --git a/comfy_api_nodes/nodes_grok.py b/comfy_api_nodes/nodes_grok.py index a41da42f3..2ae529813 100644 --- a/comfy_api_nodes/nodes_grok.py +++ b/comfy_api_nodes/nodes_grok.py @@ -29,6 +29,11 @@ from comfy_api_nodes.util import ( ) +_GROK_VIDEO_MODEL_API_IDS = { + "grok-imagine-video-1.5": "grok-imagine-video-1.5-preview", +} + + def _extract_grok_price(response) -> float | None: if response.usage and response.usage.cost_in_usd_ticks is not None: return response.usage.cost_in_usd_ticks / 10_000_000_000 @@ -49,7 +54,7 @@ class GrokImageNode(IO.ComfyNode): return IO.Schema( node_id="GrokImageNode", display_name="Grok Image", - category="image/partner/Grok", + category="partner/image/Grok", description="Generate images using Grok based on a text prompt", inputs=[ IO.Combo.Input( @@ -223,7 +228,7 @@ class GrokImageEditNode(IO.ComfyNode): return IO.Schema( node_id="GrokImageEditNode", display_name="Grok Image Edit", - category="image/partner/Grok", + category="partner/image/Grok", description="Modify an existing image based on a text prompt", inputs=[ IO.Combo.Input( @@ -364,7 +369,7 @@ class GrokImageEditNodeV2(IO.ComfyNode): return IO.Schema( node_id="GrokImageEditNodeV2", display_name="Grok Image Edit", - category="image/partner/Grok", + category="partner/image/Grok", description="Modify an existing image based on a text prompt", inputs=[ IO.String.Input( @@ -501,10 +506,14 @@ class GrokVideoNode(IO.ComfyNode): return IO.Schema( node_id="GrokVideoNode", display_name="Grok Video", - category="video/partner/Grok", + category="partner/video/Grok", description="Generate video from a prompt or an image", inputs=[ - IO.Combo.Input("model", options=["grok-imagine-video"]), + IO.Combo.Input( + "model", + options=["grok-imagine-video", "grok-imagine-video-1.5"], + tooltip="grok-imagine-video-1.5 currently always requires an input image.", + ), IO.String.Input( "prompt", multiline=True, @@ -540,7 +549,11 @@ class GrokVideoNode(IO.ComfyNode): tooltip="Seed to determine if node should re-run; " "actual results are nondeterministic regardless of seed.", ), - IO.Image.Input("image", optional=True), + IO.Image.Input( + "image", + optional=True, + tooltip="Optional starting image for grok-imagine-video. Required for grok-imagine-video-1.5.", + ), ], outputs=[ IO.Video.Output(), @@ -552,12 +565,16 @@ class GrokVideoNode(IO.ComfyNode): ], is_api_node=True, price_badge=IO.PriceBadge( - depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution"], inputs=["image"]), + depends_on=IO.PriceBadgeDepends(widgets=["model", "duration", "resolution"], inputs=["image"]), expr=""" ( - $rate := widgets.resolution = "720p" ? 0.07 : 0.05; + $is15 := $contains(widgets.model, "1.5"); + $rate := $is15 + ? (widgets.resolution = "720p" ? 0.2002 : 0.1144) + : (widgets.resolution = "720p" ? 0.07 : 0.05); + $imgCost := $is15 ? 0.0143 : 0.002; $base := $rate * widgets.duration; - {"type":"usd","usd": inputs.image.connected ? $base + 0.002 : $base} + {"type":"usd","usd": inputs.image.connected ? $base + $imgCost : $base} ) """, ), @@ -574,6 +591,8 @@ class GrokVideoNode(IO.ComfyNode): seed: int, image: Input.Image | None = None, ) -> IO.NodeOutput: + if image is None and model == "grok-imagine-video-1.5": + raise ValueError(f"The '{model}' model requires an input image; connect one to the 'image' input.") image_url = None if image is not None: if get_number_of_images(image) != 1: @@ -584,7 +603,7 @@ class GrokVideoNode(IO.ComfyNode): cls, ApiEndpoint(path="/proxy/xai/v1/videos/generations", method="POST"), data=VideoGenerationRequest( - model=model, + model=_GROK_VIDEO_MODEL_API_IDS.get(model, model), image=image_url, prompt=prompt, resolution=resolution, @@ -599,7 +618,7 @@ class GrokVideoNode(IO.ComfyNode): ApiEndpoint(path=f"/proxy/xai/v1/videos/{initial_response.request_id}"), status_extractor=lambda r: r.status if r.status is not None else "complete", response_model=VideoStatusResponse, - price_extractor=_extract_grok_price, + price_extractor=_extract_grok_video_price if model == "grok-imagine-video-1.5" else _extract_grok_price, ) return IO.NodeOutput(await download_url_to_video_output(response.video.url)) @@ -611,7 +630,7 @@ class GrokVideoEditNode(IO.ComfyNode): return IO.Schema( node_id="GrokVideoEditNode", display_name="Grok Video Edit", - category="video/partner/Grok", + category="partner/video/Grok", description="Edit an existing video based on a text prompt.", inputs=[ IO.Combo.Input("model", options=["grok-imagine-video"]), @@ -689,7 +708,7 @@ class GrokVideoReferenceNode(IO.ComfyNode): return IO.Schema( node_id="GrokVideoReferenceNode", display_name="Grok Reference-to-Video", - category="video/partner/Grok", + category="partner/video/Grok", description="Generate video guided by reference images as style and content references.", inputs=[ IO.String.Input( @@ -822,7 +841,7 @@ class GrokVideoExtendNode(IO.ComfyNode): return IO.Schema( node_id="GrokVideoExtendNode", display_name="Grok Video Extend", - category="video/partner/Grok", + category="partner/video/Grok", description="Extend an existing video with a seamless continuation based on a text prompt.", inputs=[ IO.String.Input( diff --git a/comfy_api_nodes/nodes_hitpaw.py b/comfy_api_nodes/nodes_hitpaw.py index 22e679c29..062d3cf1d 100644 --- a/comfy_api_nodes/nodes_hitpaw.py +++ b/comfy_api_nodes/nodes_hitpaw.py @@ -71,7 +71,7 @@ class HitPawGeneralImageEnhance(IO.ComfyNode): return IO.Schema( node_id="HitPawGeneralImageEnhance", display_name="HitPaw General Image Enhance", - category="image/partner/HitPaw", + category="partner/image/HitPaw", description="Upscale low-resolution images to super-resolution, eliminate artifacts and noise. " f"Maximum output: {MAX_MP_GENERATIVE} megapixels.", inputs=[ @@ -201,7 +201,7 @@ class HitPawVideoEnhance(IO.ComfyNode): return IO.Schema( node_id="HitPawVideoEnhance", display_name="HitPaw Video Enhance", - category="video/partner/HitPaw", + category="partner/video/HitPaw", description="Upscale low-resolution videos to high resolution, eliminate artifacts and noise. " "Prices shown are per second of video.", inputs=[ diff --git a/comfy_api_nodes/nodes_hunyuan3d.py b/comfy_api_nodes/nodes_hunyuan3d.py index 826a3bd2d..fcd27b7fb 100644 --- a/comfy_api_nodes/nodes_hunyuan3d.py +++ b/comfy_api_nodes/nodes_hunyuan3d.py @@ -123,7 +123,7 @@ class TencentTextToModelNode(IO.ComfyNode): return IO.Schema( node_id="TencentTextToModelNode", display_name="Hunyuan3D: Text to Model", - category="3d/partner/Tencent", + category="partner/3d/Tencent", essentials_category="3D", inputs=[ IO.Combo.Input( @@ -242,7 +242,7 @@ class TencentImageToModelNode(IO.ComfyNode): return IO.Schema( node_id="TencentImageToModelNode", display_name="Hunyuan3D: Image(s) to Model", - category="3d/partner/Tencent", + category="partner/3d/Tencent", essentials_category="3D", inputs=[ IO.Combo.Input( @@ -415,7 +415,7 @@ class TencentModelTo3DUVNode(IO.ComfyNode): return IO.Schema( node_id="TencentModelTo3DUVNode", display_name="Hunyuan3D: Model to UV", - category="3d/partner/Tencent", + category="partner/3d/Tencent", description="Perform UV unfolding on a 3D model to generate UV texture. " "Input model must have less than 30000 faces.", inputs=[ @@ -505,7 +505,7 @@ class Tencent3DTextureEditNode(IO.ComfyNode): return IO.Schema( node_id="Tencent3DTextureEditNode", display_name="Hunyuan3D: 3D Texture Edit", - category="3d/partner/Tencent", + category="partner/3d/Tencent", description="After inputting the 3D model, perform 3D model texture redrawing.", inputs=[ IO.MultiType.Input( @@ -594,7 +594,7 @@ class Tencent3DPartNode(IO.ComfyNode): return IO.Schema( node_id="Tencent3DPartNode", display_name="Hunyuan3D: 3D Part", - category="3d/partner/Tencent", + category="partner/3d/Tencent", description="Automatically perform component identification and generation based on the model structure.", inputs=[ IO.MultiType.Input( @@ -666,7 +666,7 @@ class TencentSmartTopologyNode(IO.ComfyNode): return IO.Schema( node_id="TencentSmartTopologyNode", display_name="Hunyuan3D: Smart Topology", - category="3d/partner/Tencent", + category="partner/3d/Tencent", description="Perform smart retopology on a 3D model. " "Supports GLB/OBJ formats; max 200MB; recommended for high-poly models.", inputs=[ diff --git a/comfy_api_nodes/nodes_ideogram.py b/comfy_api_nodes/nodes_ideogram.py index edd9b9435..3b914a850 100644 --- a/comfy_api_nodes/nodes_ideogram.py +++ b/comfy_api_nodes/nodes_ideogram.py @@ -10,6 +10,7 @@ from comfy_api_nodes.apis.ideogram import ( ImageRequest, IdeogramV3Request, IdeogramV3EditRequest, + IdeogramV4Request, ) from comfy_api_nodes.util import ( ApiEndpoint, @@ -17,6 +18,7 @@ from comfy_api_nodes.util import ( download_url_as_bytesio, resize_mask_to_image, sync_op, + validate_string, ) V1_V1_RES_MAP = { @@ -234,7 +236,7 @@ class IdeogramV1(IO.ComfyNode): return IO.Schema( node_id="IdeogramV1", display_name="Ideogram V1", - category="image/partner/Ideogram", + category="partner/image/Ideogram", description="Generates images using the Ideogram V1 model.", inputs=[ IO.String.Input( @@ -360,7 +362,7 @@ class IdeogramV2(IO.ComfyNode): return IO.Schema( node_id="IdeogramV2", display_name="Ideogram V2", - category="image/partner/Ideogram", + category="partner/image/Ideogram", description="Generates images using the Ideogram V2 model.", inputs=[ IO.String.Input( @@ -526,7 +528,7 @@ class IdeogramV3(IO.ComfyNode): return IO.Schema( node_id="IdeogramV3", display_name="Ideogram V3", - category="image/partner/Ideogram", + category="partner/image/Ideogram", description="Generates images using the Ideogram V3 model. " "Supports both regular image generation from text prompts and image editing with mask.", inputs=[ @@ -798,6 +800,119 @@ class IdeogramV3(IO.ComfyNode): return IO.NodeOutput(await download_and_process_images(image_urls)) +class IdeogramV4(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="IdeogramV4", + display_name="Ideogram V4", + category="partner/image/Ideogram", + description="Generates images using the Ideogram 4.0 model from a text prompt.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text prompt for the image generation.", + ), + IO.Combo.Input( + "resolution", + options=[ + "Auto", + "2048x2048 (1:1)", + "1440x2880 (1:2)", + "2880x1440 (2:1)", + "1664x2496 (2:3)", + "2496x1664 (3:2)", + "1792x2240 (4:5)", + "2240x1792 (5:4)", + "1440x2560 (9:16)", + "2560x1440 (16:9)", + "1600x2560 (5:8)", + "2560x1600 (8:5)", + "1728x2304 (3:4)", + "2304x1728 (4:3)", + "1296x3168 (9:22)", + "3168x1296 (22:9)", + "1152x2944 (9:23)", + "2944x1152 (23:9)", + "1248x3328 (3:8)", + "3328x1248 (8:3)", + "1280x3072 (5:12)", + "3072x1280 (12:5)", + ], + default="Auto", + ), + IO.Combo.Input( + "rendering_speed", + options=["DEFAULT", "TURBO", "QUALITY"], + default="DEFAULT", + tooltip="Controls the trade-off between generation speed and quality.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + control_after_generate=True, + display_mode=IO.NumberDisplay.number, + ), + ], + outputs=[ + IO.Image.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=["rendering_speed"]), + expr=""" + ( + $speed := widgets.rendering_speed; + $price := + $contains($speed,"turbo") ? 0.0429 : + $contains($speed,"quality") ? 0.143 : + 0.0858; + {"type":"usd","usd": $price} + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + prompt: str, + resolution: str, + rendering_speed: str, + seed: int, + ): + validate_string(prompt, strip_whitespace=True, min_length=1) + response = await sync_op( + cls, + ApiEndpoint(path="/proxy/ideogram/ideogram-v4/generate", method="POST"), + response_model=IdeogramGenerateResponse, + data=IdeogramV4Request( + text_prompt=prompt, + resolution=resolution.split(" ")[0] if resolution != "Auto" else None, + rendering_speed=rendering_speed, + ), + max_retries=1, + ) + + if not response.data or len(response.data) == 0: + raise Exception("No images were generated in the response") + image_urls = [image_data.url for image_data in response.data if image_data.url] + if not image_urls: + raise Exception("No image URLs were generated in the response") + return IO.NodeOutput(await download_and_process_images(image_urls)) + + class IdeogramExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: @@ -805,6 +920,7 @@ class IdeogramExtension(ComfyExtension): IdeogramV1, IdeogramV2, IdeogramV3, + IdeogramV4, ] diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py index 9925ec548..b27de2549 100644 --- a/comfy_api_nodes/nodes_kling.py +++ b/comfy_api_nodes/nodes_kling.py @@ -60,6 +60,12 @@ from comfy_api_nodes.apis.kling import ( OmniProImageRequest, OmniProReferences2VideoRequest, OmniProText2VideoRequest, + Kling3TurboSettings, + Kling3TurboText2VideoRequest, + Kling3TurboContent, + Kling3TurboImage2VideoRequest, + Kling3TurboCreateResponse, + Kling3TurboQueryResponse, TaskStatusResponse, TextToVideoWithAudioRequest, ) @@ -436,7 +442,7 @@ async def execute_text2video( negative_prompt=negative_prompt if negative_prompt else None, duration=KlingVideoGenDuration(duration), mode=KlingVideoGenMode(model_mode), - model_name=KlingVideoGenModelName(model_name), + model_name=model_name, cfg_scale=cfg_scale, aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), camera_control=camera_control, @@ -642,7 +648,7 @@ class KlingCameraControls(IO.ComfyNode): return IO.Schema( node_id="KlingCameraControls", display_name="Kling Camera Controls", - category="video/partner/Kling", + category="partner/video/Kling", description="Allows specifying configuration options for Kling Camera Controls and motion control effects.", inputs=[ IO.Combo.Input("camera_control_type", options=KlingCameraControlType), @@ -762,7 +768,7 @@ class KlingTextToVideoNode(IO.ComfyNode): return IO.Schema( node_id="KlingTextToVideoNode", display_name="Kling Text to Video", - category="video/partner/Kling", + category="partner/video/Kling", description="Kling Text to Video Node", inputs=[ IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"), @@ -849,7 +855,7 @@ class OmniProTextToVideoNode(IO.ComfyNode): return IO.Schema( node_id="KlingOmniProTextToVideoNode", display_name="Kling 3.0 Omni Text to Video", - category="video/partner/Kling", + category="partner/video/Kling", description="Use text prompts to generate videos with the latest Kling model.", inputs=[ IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]), @@ -998,7 +1004,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode): return IO.Schema( node_id="KlingOmniProFirstLastFrameNode", display_name="Kling 3.0 Omni First-Last-Frame to Video", - category="video/partner/Kling", + category="partner/video/Kling", description="Use a start frame, an optional end frame, or reference images with the latest Kling model.", inputs=[ IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]), @@ -1205,7 +1211,7 @@ class OmniProImageToVideoNode(IO.ComfyNode): return IO.Schema( node_id="KlingOmniProImageToVideoNode", display_name="Kling 3.0 Omni Image to Video", - category="video/partner/Kling", + category="partner/video/Kling", description="Use up to 7 reference images to generate a video with the latest Kling model.", inputs=[ IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]), @@ -1374,7 +1380,7 @@ class OmniProVideoToVideoNode(IO.ComfyNode): return IO.Schema( node_id="KlingOmniProVideoToVideoNode", display_name="Kling 3.0 Omni Video to Video", - category="video/partner/Kling", + category="partner/video/Kling", description="Use a video and up to 4 reference images to generate a video with the latest Kling model.", inputs=[ IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-video-o1"]), @@ -1485,7 +1491,7 @@ class OmniProEditVideoNode(IO.ComfyNode): return IO.Schema( node_id="KlingOmniProEditVideoNode", display_name="Kling 3.0 Omni Edit Video", - category="video/partner/Kling", + category="partner/video/Kling", essentials_category="Video Generation", description="Edit an existing video with the latest model from Kling.", inputs=[ @@ -1593,7 +1599,7 @@ class OmniProImageNode(IO.ComfyNode): return IO.Schema( node_id="KlingOmniProImageNode", display_name="Kling 3.0 Omni Image", - category="image/partner/Kling", + category="partner/image/Kling", description="Create or edit images with the latest model from Kling.", inputs=[ IO.Combo.Input("model_name", options=["kling-v3-omni", "kling-image-o1"]), @@ -1721,7 +1727,7 @@ class KlingCameraControlT2VNode(IO.ComfyNode): return IO.Schema( node_id="KlingCameraControlT2VNode", display_name="Kling Text to Video (Camera Control)", - category="video/partner/Kling", + category="partner/video/Kling", description="Transform text into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original text.", inputs=[ IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"), @@ -1783,7 +1789,7 @@ class KlingImage2VideoNode(IO.ComfyNode): return IO.Schema( node_id="KlingImage2VideoNode", display_name="Kling Image(First Frame) to Video", - category="video/partner/Kling", + category="partner/video/Kling", inputs=[ IO.Image.Input("start_frame", tooltip="The reference image used to generate the video."), IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"), @@ -1882,7 +1888,7 @@ class KlingCameraControlI2VNode(IO.ComfyNode): return IO.Schema( node_id="KlingCameraControlI2VNode", display_name="Kling Image to Video (Camera Control)", - category="video/partner/Kling", + category="partner/video/Kling", description="Transform still images into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original image.", inputs=[ IO.Image.Input( @@ -1953,7 +1959,7 @@ class KlingStartEndFrameNode(IO.ComfyNode): return IO.Schema( node_id="KlingStartEndFrameNode", display_name="Kling Start-End Frame to Video", - category="video/partner/Kling", + category="partner/video/Kling", description="Generate a video sequence that transitions between your provided start and end images. The node creates all frames in between, producing a smooth transformation from the first frame to the last.", inputs=[ IO.Image.Input( @@ -2047,7 +2053,7 @@ class KlingVideoExtendNode(IO.ComfyNode): return IO.Schema( node_id="KlingVideoExtendNode", display_name="Kling Video Extend", - category="video/partner/Kling", + category="partner/video/Kling", description="Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes.", inputs=[ IO.String.Input( @@ -2128,7 +2134,7 @@ class KlingDualCharacterVideoEffectNode(IO.ComfyNode): return IO.Schema( node_id="KlingDualCharacterVideoEffectNode", display_name="Kling Dual Character Video Effects", - category="video/partner/Kling", + category="partner/video/Kling", description="Achieve different special effects when generating a video based on the effect_scene. First image will be positioned on left side, second on right side of the composite.", inputs=[ IO.Image.Input("image_left", tooltip="Left side image"), @@ -2218,7 +2224,7 @@ class KlingSingleImageVideoEffectNode(IO.ComfyNode): return IO.Schema( node_id="KlingSingleImageVideoEffectNode", display_name="Kling Video Effects", - category="video/partner/Kling", + category="partner/video/Kling", description="Achieve different special effects when generating a video based on the effect_scene.", inputs=[ IO.Image.Input( @@ -2291,7 +2297,7 @@ class KlingLipSyncAudioToVideoNode(IO.ComfyNode): return IO.Schema( node_id="KlingLipSyncAudioToVideoNode", display_name="Kling Lip Sync Video with Audio", - category="video/partner/Kling", + category="partner/video/Kling", essentials_category="Video Generation", description="Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length.", inputs=[ @@ -2343,7 +2349,7 @@ class KlingLipSyncTextToVideoNode(IO.ComfyNode): return IO.Schema( node_id="KlingLipSyncTextToVideoNode", display_name="Kling Lip Sync Video with Text", - category="video/partner/Kling", + category="partner/video/Kling", description="Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length.", inputs=[ IO.Video.Input("video"), @@ -2411,7 +2417,7 @@ class KlingVirtualTryOnNode(IO.ComfyNode): return IO.Schema( node_id="KlingVirtualTryOnNode", display_name="Kling Virtual Try On", - category="image/partner/Kling", + category="partner/image/Kling", description="Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human. You can merge multiple clothing item pictures into one image with a white background.", inputs=[ IO.Image.Input("human_image"), @@ -2478,7 +2484,7 @@ class KlingImageGenerationNode(IO.ComfyNode): return IO.Schema( node_id="KlingImageGenerationNode", display_name="Kling 3.0 Image", - category="image/partner/Kling", + category="partner/image/Kling", description="Kling Image Generation Node. Generate an image from a text prompt with an optional reference image.", inputs=[ IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"), @@ -2615,7 +2621,7 @@ class TextToVideoWithAudio(IO.ComfyNode): return IO.Schema( node_id="KlingTextToVideoWithAudio", display_name="Kling 2.6 Text to Video with Audio", - category="video/partner/Kling", + category="partner/video/Kling", inputs=[ IO.Combo.Input("model_name", options=["kling-v2-6"]), IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt."), @@ -2683,7 +2689,7 @@ class ImageToVideoWithAudio(IO.ComfyNode): return IO.Schema( node_id="KlingImageToVideoWithAudio", display_name="Kling 2.6 Image(First Frame) to Video with Audio", - category="video/partner/Kling", + category="partner/video/Kling", inputs=[ IO.Combo.Input("model_name", options=["kling-v2-6"]), IO.Image.Input("start_frame"), @@ -2753,7 +2759,7 @@ class MotionControl(IO.ComfyNode): return IO.Schema( node_id="KlingMotionControl", display_name="Kling Motion Control", - category="video/partner/Kling", + category="partner/video/Kling", inputs=[ IO.String.Input("prompt", multiline=True), IO.Image.Input("reference_image"), @@ -2847,6 +2853,67 @@ class MotionControl(IO.ComfyNode): return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url)) +def build_turbo_shot_prompt(multi_prompt: list[MultiPromptEntry]) -> str: + """Render storyboard entries into the Turbo multi-shot prompt 'shot n, m, words; ...'.""" + return "; ".join(f"shot {i}, {int(e.duration)}, {e.prompt}" for i, e in enumerate(multi_prompt, 1)) + ";" + + +def _turbo_video_url(response: Kling3TurboQueryResponse) -> str: + """Extract the result video URL from a /tasks response (data[].outputs[] where type == 'video').""" + task = response.data[0] if response.data else None + if task and task.outputs: + for output in task.outputs: + if output.type == "video" and output.url: + return output.url + raise RuntimeError(f"Kling 3.0 Turbo task finished without a video output: {response.model_dump()}") + + +async def execute_kling_turbo( + cls: type[IO.ComfyNode], + *, + prompt: str, + resolution: str, + aspect_ratio: str, + duration: int, + start_frame: torch.Tensor | None, +) -> IO.NodeOutput: + """Create + poll a Kling 3.0 Turbo task. Image-to-video when start_frame is given, else text-to-video.""" + if start_frame is not None: + validate_image_dimensions(start_frame, min_width=300, min_height=300) + validate_image_aspect_ratio(start_frame, (1, 2.5), (2.5, 1)) + contents = [Kling3TurboContent(type="first_frame", url=tensor_to_base64_string(start_frame))] + if prompt: + contents.insert(0, Kling3TurboContent(type="prompt", text=prompt)) + create = await sync_op( + cls, + ApiEndpoint(path="/proxy/kling/image-to-video/kling-3.0-turbo", method="POST"), + response_model=Kling3TurboCreateResponse, + data=Kling3TurboImage2VideoRequest( + contents=contents, + settings=Kling3TurboSettings(resolution=resolution, duration=duration), # i2v: no aspect_ratio + ), + ) + else: + create = await sync_op( + cls, + ApiEndpoint(path="/proxy/kling/text-to-video/kling-3.0-turbo", method="POST"), + response_model=Kling3TurboCreateResponse, + data=Kling3TurboText2VideoRequest( + prompt=prompt, + settings=Kling3TurboSettings(resolution=resolution, aspect_ratio=aspect_ratio, duration=duration), + ), + ) + if not (create.data and create.data.id): + raise RuntimeError(f"Kling 3.0 Turbo create failed. Code: {create.code}, Message: {create.message}") + final_response = await poll_op( + cls, + ApiEndpoint(path="/proxy/kling/tasks", query_params={"task_ids": create.data.id}), + response_model=Kling3TurboQueryResponse, + status_extractor=lambda r: (r.data[0].status if r.data else None), + ) + return IO.NodeOutput(await download_url_to_video_output(_turbo_video_url(final_response))) + + class KlingVideoNode(IO.ComfyNode): @classmethod @@ -2854,7 +2921,7 @@ class KlingVideoNode(IO.ComfyNode): return IO.Schema( node_id="KlingVideoNode", display_name="Kling 3.0 Video", - category="video/partner/Kling", + category="partner/video/Kling", description="Generate videos with Kling V3. " "Supports text-to-video and image-to-video with optional storyboard multi-prompt and audio generation.", inputs=[ @@ -2884,7 +2951,11 @@ class KlingVideoNode(IO.ComfyNode): ], tooltip="Generate a series of video segments with individual prompts and durations.", ), - IO.Boolean.Input("generate_audio", default=True), + IO.Boolean.Input( + "generate_audio", + default=True, + tooltip="'kling-3.0-turbo' always generates native audio, so the audio toggle is ignored.", + ), IO.DynamicCombo.Input( "model", options=[ @@ -2899,6 +2970,17 @@ class KlingVideoNode(IO.ComfyNode): ), ], ), + IO.DynamicCombo.Option( + "kling-3.0-turbo", + [ + IO.Combo.Input("resolution", options=["1080p", "720p"], default="720p"), + IO.Combo.Input( + "aspect_ratio", + options=["16:9", "9:16", "1:1"], + tooltip="Ignored in image-to-video mode.", + ), + ], + ), ], tooltip="Model and generation settings.", ), @@ -2930,6 +3012,7 @@ class KlingVideoNode(IO.ComfyNode): price_badge=IO.PriceBadge( depends_on=IO.PriceBadgeDepends( widgets=[ + "model", "model.resolution", "generate_audio", "multi_shot", @@ -2944,14 +3027,7 @@ class KlingVideoNode(IO.ComfyNode): ), expr=""" ( - $rates := { - "4k": {"off": 0.42, "on": 0.42}, - "1080p": {"off": 0.112, "on": 0.168}, - "720p": {"off": 0.084, "on": 0.126} - }; $res := $lookup(widgets, "model.resolution"); - $audio := widgets.generate_audio ? "on" : "off"; - $rate := $lookup($lookup($rates, $res), $audio); $ms := widgets.multi_shot; $isSb := $ms != "disabled"; $n := $isSb ? $number($substring($ms, 0, 1)) : 0; @@ -2962,7 +3038,18 @@ class KlingVideoNode(IO.ComfyNode): $d5 := $n >= 5 ? $lookup(widgets, "multi_shot.storyboard_5_duration") : 0; $d6 := $n >= 6 ? $lookup(widgets, "multi_shot.storyboard_6_duration") : 0; $dur := $isSb ? $d1 + $d2 + $d3 + $d4 + $d5 + $d6 : $lookup(widgets, "multi_shot.duration"); - {"type":"usd","usd": $rate * $dur} + widgets.model = "kling-3.0-turbo" + ? {"type":"usd","usd": ($res = "1080p" ? 0.14 : 0.112) * $dur} + : ( + $rates := { + "4k": {"off": 0.42, "on": 0.42}, + "1080p": {"off": 0.112, "on": 0.168}, + "720p": {"off": 0.084, "on": 0.126} + }; + $audio := widgets.generate_audio ? "on" : "off"; + $rate := $lookup($lookup($rates, $res), $audio); + {"type":"usd","usd": $rate * $dur} + ) ) """, ), @@ -3015,6 +3102,17 @@ class KlingVideoNode(IO.ComfyNode): duration = multi_shot["duration"] validate_string(multi_shot["prompt"], min_length=1, max_length=2500) + if model["model"] == "kling-3.0-turbo": + turbo_prompt = build_turbo_shot_prompt(multi_prompt_list) if custom_multi_shot else multi_shot["prompt"] + return await execute_kling_turbo( + cls, + prompt=turbo_prompt, + resolution=model["resolution"], + aspect_ratio=model["aspect_ratio"], + duration=duration, + start_frame=start_frame, + ) + if start_frame is not None: validate_image_dimensions(start_frame, min_width=300, min_height=300) validate_image_aspect_ratio(start_frame, (1, 2.5), (2.5, 1)) @@ -3077,7 +3175,7 @@ class KlingFirstLastFrameNode(IO.ComfyNode): return IO.Schema( node_id="KlingFirstLastFrameNode", display_name="Kling 3.0 First-Last-Frame to Video", - category="video/partner/Kling", + category="partner/video/Kling", description="Generate videos with Kling V3 using first and last frames.", inputs=[ IO.String.Input("prompt", multiline=True, default=""), @@ -3202,7 +3300,7 @@ class KlingAvatarNode(IO.ComfyNode): return IO.Schema( node_id="KlingAvatarNode", display_name="Kling Avatar 2.0", - category="video/partner/Kling", + category="partner/video/Kling", description="Generate broadcast-style digital human videos from a single photo and an audio file.", inputs=[ IO.Image.Input( diff --git a/comfy_api_nodes/nodes_krea.py b/comfy_api_nodes/nodes_krea.py index be04a272b..b9e6268f2 100644 --- a/comfy_api_nodes/nodes_krea.py +++ b/comfy_api_nodes/nodes_krea.py @@ -42,9 +42,11 @@ async def _upload_image_to_krea_assets(cls: type[IO.ComfyNode], image: Input.Ima _MODEL_MEDIUM = "Krea 2 Medium" +_MODEL_MEDIUM_TURBO = "Krea 2 Medium Turbo" _MODEL_LARGE = "Krea 2 Large" _MODEL_ENDPOINTS: dict[str, str] = { _MODEL_MEDIUM: "/proxy/krea/generate/image/krea/krea-2/medium", + _MODEL_MEDIUM_TURBO: "/proxy/krea/generate/image/krea/krea-2/medium-turbo", _MODEL_LARGE: "/proxy/krea/generate/image/krea/krea-2/large", } @@ -57,7 +59,7 @@ _UUID_RE = re.compile(r"^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F def _krea_model_inputs() -> list: - """Nested inputs shared by both Krea 2 Medium and Large under the DynamicCombo.""" + """Nested inputs shared by Krea 2 Medium, Medium Turbo and Large under the DynamicCombo.""" return [ IO.Combo.Input( "aspect_ratio", @@ -106,7 +108,7 @@ class Krea2ImageNode(IO.ComfyNode): return IO.Schema( node_id="Krea2ImageNode", display_name="Krea 2 Image", - category="image/partner/Krea", + category="partner/image/Krea", description=( "Generate images via Krea 2 — pick Medium (expressive illustrations) or " "Large (expressive photorealism). Supports an optional moodboard and up " @@ -123,6 +125,7 @@ class Krea2ImageNode(IO.ComfyNode): "model", options=[ IO.DynamicCombo.Option(_MODEL_MEDIUM, _krea_model_inputs()), + IO.DynamicCombo.Option(_MODEL_MEDIUM_TURBO, _krea_model_inputs()), IO.DynamicCombo.Option(_MODEL_LARGE, _krea_model_inputs()), ], tooltip="Krea 2 Medium is best for expressive illustrations; " @@ -151,14 +154,15 @@ class Krea2ImageNode(IO.ComfyNode): ), expr=""" ( - $isLarge := widgets.model = "krea 2 large"; + $rates := { + "krea 2 medium turbo": {"text": 0.015, "style": 0.0175, "moodboard": 0.02}, + "krea 2 medium": {"text": 0.03, "style": 0.035, "moodboard": 0.04}, + "krea 2 large": {"text": 0.06, "style": 0.065, "moodboard": 0.07} + }; + $r := $lookup($rates, widgets.model); $hasMoodboard := $length($lookup(widgets, "model.moodboard_id")) > 0; $hasStyle := $lookup(inputs, "model.style_reference").connected; - $usd := $hasMoodboard - ? ($isLarge ? 0.07 : 0.04) - : ($hasStyle - ? ($isLarge ? 0.065 : 0.035) - : ($isLarge ? 0.06 : 0.03)); + $usd := $hasMoodboard ? $r.moodboard : ($hasStyle ? $r.style : $r.text); {"type":"usd","usd": $usd} ) """, @@ -229,7 +233,7 @@ class Krea2StyleReferenceNode(IO.ComfyNode): return IO.Schema( node_id="Krea2StyleReferenceNode", display_name="Krea 2 Style Reference", - category="image/partner/Krea", + category="partner/image/Krea", description=( "Add an image style reference to a Krea 2 generation. Chain multiple Krea 2 " "Style Reference nodes (max 10) and feed the final `style_reference` output " diff --git a/comfy_api_nodes/nodes_ltxv.py b/comfy_api_nodes/nodes_ltxv.py index 01791d354..878e04b4e 100644 --- a/comfy_api_nodes/nodes_ltxv.py +++ b/comfy_api_nodes/nodes_ltxv.py @@ -50,7 +50,7 @@ class TextToVideoNode(IO.ComfyNode): return IO.Schema( node_id="LtxvApiTextToVideo", display_name="LTXV Text To Video", - category="video/partner/LTXV", + category="partner/video/LTXV", description="Professional-quality videos with customizable duration and resolution.", inputs=[ IO.Combo.Input("model", options=list(MODELS_MAP.keys())), @@ -127,7 +127,7 @@ class ImageToVideoNode(IO.ComfyNode): return IO.Schema( node_id="LtxvApiImageToVideo", display_name="LTXV Image To Video", - category="video/partner/LTXV", + category="partner/video/LTXV", description="Professional-quality videos with customizable duration and resolution based on start image.", inputs=[ IO.Image.Input("image", tooltip="First frame to be used for the video."), diff --git a/comfy_api_nodes/nodes_luma.py b/comfy_api_nodes/nodes_luma.py index 08ae9904c..cdfa32d8b 100644 --- a/comfy_api_nodes/nodes_luma.py +++ b/comfy_api_nodes/nodes_luma.py @@ -3,9 +3,13 @@ from typing_extensions import override from comfy_api.latest import IO, ComfyExtension, Input from comfy_api_nodes.apis.luma import ( + LUMA_KEYFRAME_MODE_FRACTION, + LUMA_KEYFRAME_MODE_SECONDS, Luma2Generation, Luma2GenerationRequest, Luma2ImageRef, + Luma2VideoEdit, + Luma2VideoOptions, LumaAspectRatio, LumaCharacterRef, LumaConceptChain, @@ -18,6 +22,8 @@ from comfy_api_nodes.apis.luma import ( LumaIO, LumaKeyframes, LumaModifyImageRef, + LumaRay32KeyframeChain, + LumaRay32KeyframeItem, LumaReference, LumaReferenceChain, LumaVideoModel, @@ -33,6 +39,7 @@ from comfy_api_nodes.util import ( sync_op, upload_image_to_comfyapi, upload_images_to_comfyapi, + upload_video_to_comfyapi, validate_string, ) @@ -46,7 +53,7 @@ class LumaReferenceNode(IO.ComfyNode): return IO.Schema( node_id="LumaReferenceNode", display_name="Luma Reference", - category="image/partner/Luma", + category="partner/image/Luma", description="Holds an image and weight for use with Luma Generate Image node.", inputs=[ IO.Image.Input( @@ -85,7 +92,7 @@ class LumaConceptsNode(IO.ComfyNode): return IO.Schema( node_id="LumaConceptsNode", display_name="Luma Concepts", - category="video/partner/Luma", + category="partner/video/Luma", description="Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes.", inputs=[ IO.Combo.Input( @@ -134,7 +141,7 @@ class LumaImageGenerationNode(IO.ComfyNode): return IO.Schema( node_id="LumaImageNode", display_name="Luma Text to Image", - category="image/partner/Luma", + category="partner/image/Luma", description="Generates images synchronously based on prompt and aspect ratio.", inputs=[ IO.String.Input( @@ -278,7 +285,7 @@ class LumaImageModifyNode(IO.ComfyNode): return IO.Schema( node_id="LumaImageModifyNode", display_name="Luma Image to Image", - category="image/partner/Luma", + category="partner/image/Luma", description="Modifies images synchronously based on prompt and aspect ratio.", inputs=[ IO.Image.Input( @@ -371,7 +378,7 @@ class LumaTextToVideoGenerationNode(IO.ComfyNode): return IO.Schema( node_id="LumaVideoNode", display_name="Luma Text to Video", - category="video/partner/Luma", + category="partner/video/Luma", description="Generates videos synchronously based on prompt and output_size.", inputs=[ IO.String.Input( @@ -472,7 +479,7 @@ class LumaImageToVideoGenerationNode(IO.ComfyNode): return IO.Schema( node_id="LumaImageToVideoNode", display_name="Luma Image to Video", - category="video/partner/Luma", + category="partner/video/Luma", description="Generates videos synchronously based on prompt, input images, and output_size.", inputs=[ IO.String.Input( @@ -692,7 +699,10 @@ async def _luma2_upload_image_refs( async def _luma2_submit_and_poll( cls: type[IO.ComfyNode], request: Luma2GenerationRequest, -) -> Input.Image: + *, + estimated_duration: int | None = None, +) -> Luma2Generation: + """Submit a Luma Agents generation and poll until done; returns the completed generation.""" initial = await sync_op( cls, ApiEndpoint(path="/proxy/luma_2/generations", method="POST"), @@ -700,21 +710,21 @@ async def _luma2_submit_and_poll( data=request, ) if not initial.id: - raise RuntimeError("Luma 2 API did not return a generation id.") + raise RuntimeError("Luma API did not return a generation id.") final = await poll_op( cls, ApiEndpoint(path=f"/proxy/luma_2/generations/{initial.id}", method="GET"), response_model=Luma2Generation, status_extractor=lambda r: r.state, progress_extractor=lambda r: None, + estimated_duration=estimated_duration, ) - if not final.output: + if not final.output or not final.output[0].url: msg = final.failure_reason or "no output returned" - raise RuntimeError(f"Luma 2 generation failed: {msg}") - url = final.output[0].url - if not url: - raise RuntimeError("Luma 2 generation completed without an output URL.") - return await download_url_to_image_tensor(url) + if final.failure_code: + msg = f"{msg} [{final.failure_code}]" + raise RuntimeError(f"Luma generation failed: {msg}") + return final class LumaImageNode(IO.ComfyNode): @@ -724,7 +734,7 @@ class LumaImageNode(IO.ComfyNode): return IO.Schema( node_id="LumaImageNode2", display_name="Luma UNI-1 Image", - category="image/partner/Luma", + category="partner/image/Luma", description="Generate images from text using the Luma UNI-1 model.", inputs=[ IO.String.Input( @@ -843,7 +853,8 @@ class LumaImageNode(IO.ComfyNode): web_search=model["web_search"], image_ref=await _luma2_upload_image_refs(cls, model.get("image_ref"), max_count=9), ) - return IO.NodeOutput(await _luma2_submit_and_poll(cls, request)) + final = await _luma2_submit_and_poll(cls, request) + return IO.NodeOutput(await download_url_to_image_tensor(final.output[0].url)) class LumaImageEditNode(IO.ComfyNode): @@ -853,7 +864,7 @@ class LumaImageEditNode(IO.ComfyNode): return IO.Schema( node_id="LumaImageEditNode2", display_name="Luma UNI-1 Image Edit", - category="image/partner/Luma", + category="partner/image/Luma", description="Edit an existing image with a text prompt using the Luma UNI-1 model.", inputs=[ IO.Image.Input( @@ -929,7 +940,533 @@ class LumaImageEditNode(IO.ComfyNode): web_search=model["web_search"], image_ref=await _luma2_upload_image_refs(cls, model.get("image_ref"), max_count=8), ) - return IO.NodeOutput(await _luma2_submit_and_poll(cls, request)) + final = await _luma2_submit_and_poll(cls, request) + return IO.NodeOutput(await download_url_to_image_tensor(final.output[0].url)) + + +_BADGE_RAY32_VIDEO = IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["resolution", "duration"]), + expr=""" + ( + $p := { + "360p": {"5s": 0.06, "10s": 0.18}, + "540p": {"5s": 0.15, "10s": 0.45}, + "720p": {"5s": 0.3, "10s": 0.9}, + "1080p": {"5s": 1.2, "10s": 3.6} + }; + {"type": "usd", "usd": $lookup($lookup($p, widgets.resolution), widgets.duration)} + ) + """, +) + +_BADGE_RAY32_VIDEO_5S = IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["resolution"]), + expr=""" + ( + $p := {"360p": 0.06, "540p": 0.15, "720p": 0.3, "1080p": 1.2}; + {"type": "usd", "usd": $lookup($p, widgets.resolution)} + ) + """, +) + +_BADGE_RAY32_EDIT = IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["resolution"]), + expr=""" + ( + $p := { + "360p": {"min": 0.54, "max": 1.08}, + "540p": {"min": 0.72, "max": 1.44}, + "720p": {"min": 1.08, "max": 2.16}, + "1080p": {"min": 2.16, "max": 4.32} + }; + $r := $lookup($p, widgets.resolution); + {"type": "range_usd", "min_usd": $r.min, "max_usd": $r.max, "format": {"note": "(by source length)"}} + ) + """, +) + +_BADGE_RAY32_REFRAME = IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["resolution"]), + expr=""" + ( + $p := {"360p": 0.03, "540p": 0.06, "720p": 0.12, "1080p": 0.36}; + {"type": "usd", "usd": $lookup($p, widgets.resolution), "format": {"suffix": "/second"}} + ) + """, +) + + +def _ray32_seed_input() -> IO.Input: + return IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="Seed to determine if node should re-run; results are nondeterministic regardless of seed.", + ) + + +async def _ray32_generate(cls: type[IO.ComfyNode], request: Luma2GenerationRequest) -> IO.NodeOutput: + """Run a ray-3.2 generation and return (video, generation_id).""" + final = await _luma2_submit_and_poll(cls, request, estimated_duration=120) + video = await download_url_to_video_output(final.output[0].url) + return IO.NodeOutput(video, final.id or "") + + +class LumaRay32TextToVideoNode(IO.ComfyNode): + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="LumaRay32TextToVideoNode", + display_name="Luma Ray 3.2 Text to Video", + category="partner/video/Luma", + description="Generate a video from a text prompt using Luma's Ray 3.2 model.", + inputs=[ + IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."), + IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1", "4:3", "3:4", "21:9"]), + IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"), + IO.Combo.Input("duration", options=["5s", "10s"]), + IO.Boolean.Input( + "loop", + default=False, + tooltip="Make the video loop seamlessly. Only available with 5s duration.", + ), + _ray32_seed_input(), + ], + outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=_BADGE_RAY32_VIDEO, + ) + + @classmethod + async def execute( + cls, prompt: str, aspect_ratio: str, resolution: str, duration: str, loop: bool, seed: int + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000) + if loop and duration == "10s": + raise ValueError("Looping is only available with 5s duration on Ray 3.2.") + request = Luma2GenerationRequest( + prompt=prompt, + model="ray-3.2", + type="video", + aspect_ratio=aspect_ratio, + video=Luma2VideoOptions(resolution=resolution, duration=duration, loop=loop or None), + ) + return await _ray32_generate(cls, request) + + +class LumaRay32ImageToVideoNode(IO.ComfyNode): + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="LumaRay32ImageToVideoNode", + display_name="Luma Ray 3.2 Image to Video", + category="partner/video/Luma", + description="Generate a video from a start and/or end frame using Luma's Ray 3.2 model. " + "Image-anchored generations are always 5 seconds.", + inputs=[ + IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."), + IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"), + IO.Boolean.Input( + "loop", + default=False, + tooltip="Make the video loop seamlessly. Not available when an end_frame is set.", + ), + _ray32_seed_input(), + IO.Image.Input("start_frame", optional=True, tooltip="First frame of the generated video."), + IO.Image.Input("end_frame", optional=True, tooltip="Last frame of the generated video."), + ], + outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=_BADGE_RAY32_VIDEO_5S, + ) + + @classmethod + async def execute( + cls, + prompt: str, + resolution: str, + loop: bool, + seed: int, + start_frame: torch.Tensor | None = None, + end_frame: torch.Tensor | None = None, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000) + if start_frame is None and end_frame is None: + raise ValueError("Provide at least one of start_frame / end_frame.") + if loop and end_frame is not None: + raise ValueError("Looping is not available when an end_frame is set.") + video = Luma2VideoOptions(resolution=resolution, duration="5s", loop=loop or None) + if start_frame is not None: + url = await upload_image_to_comfyapi(cls, start_frame, mime_type="image/png") + video.start_frame = Luma2ImageRef(url=url) + if end_frame is not None: + url = await upload_image_to_comfyapi(cls, end_frame, mime_type="image/png") + video.end_frame = Luma2ImageRef(url=url) + request = Luma2GenerationRequest(prompt=prompt, model="ray-3.2", type="video", video=video) + return await _ray32_generate(cls, request) + + +class LumaRay32KeyframeNode(IO.ComfyNode): + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="LumaRay32KeyframeNode", + display_name="Luma Ray 3.2 Keyframe", + category="partner/video/Luma", + description="Anchor a guide image to a position on the Ray 3.2 output video timeline. Connect this to " + "the 'keyframes' input of the Luma Ray 3.2 Keyframes to Video node; chain several together via the " + "optional 'keyframes' input below.", + inputs=[ + IO.Image.Input("image", tooltip="Guide image to place at the chosen moment of the output video."), + IO.DynamicCombo.Input( + "position", + options=[ + IO.DynamicCombo.Option( + "Fraction of duration (0.0-1.0)", + [ + IO.Float.Input( + "fraction", + default=0.0, + min=0.0, + max=1.0, + step=0.01, + display_mode=IO.NumberDisplay.number, + tooltip="Where in the output video this image applies " "(0.0 = start, 1.0 = end).", + ), + ], + ), + IO.DynamicCombo.Option( + "Absolute time (seconds)", + [ + IO.Float.Input( + "seconds", + default=0.0, + min=0.0, + max=10.0, + step=0.1, + display_mode=IO.NumberDisplay.number, + tooltip="Time in seconds from the start of the output video where this " + "image applies.", + ), + ], + ), + ], + tooltip="How to place this image on the output video's timeline.", + ), + IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Input( + "keyframes", + optional=True, + tooltip="Optional earlier keyframes to chain with this one.", + ), + ], + outputs=[IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Output(display_name="keyframes")], + ) + + @classmethod + def execute( + cls, + image: torch.Tensor, + position: dict, + keyframes: LumaRay32KeyframeChain | None = None, + ) -> IO.NodeOutput: + chain = keyframes.clone() if keyframes is not None else LumaRay32KeyframeChain() + if position["position"] == "Absolute time (seconds)": + mode, value = LUMA_KEYFRAME_MODE_SECONDS, float(position["seconds"]) + else: + mode, value = LUMA_KEYFRAME_MODE_FRACTION, float(position["fraction"]) + chain.add(LumaRay32KeyframeItem(image=image, mode=mode, value=value)) + return IO.NodeOutput(chain) + + +class LumaRay32KeyframesToVideoNode(IO.ComfyNode): + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="LumaRay32KeyframesToVideoNode", + display_name="Luma Ray 3.2 Keyframes to Video", + category="partner/video/Luma", + description="Generate a video that interpolates through a sequence of guide images, each anchored to a " + "position on the timeline, using Luma Ray 3.2. Build the sequence with Luma Ray 3.2 Keyframe nodes " + "(at least 2).", + inputs=[ + IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the video generation."), + IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"), + IO.Combo.Input("duration", options=["5s", "10s"]), + _ray32_seed_input(), + IO.Custom(LumaIO.LUMA_RAY32_KEYFRAME).Input( + "keyframes", + tooltip="Keyframe sequence from Luma Ray 3.2 Keyframe nodes (at least 2).", + ), + ], + outputs=[IO.Video.Output(), IO.String.Output(display_name="generation_id")], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=_BADGE_RAY32_VIDEO, + ) + + @classmethod + async def execute( + cls, + prompt: str, + resolution: str, + duration: str, + seed: int, + keyframes: LumaRay32KeyframeChain | None = None, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000) + items = keyframes.items if keyframes is not None else [] + if len(items) < 2: + raise ValueError( + "Connect at least 2 Luma Ray 3.2 Keyframe nodes " + "(use Luma Ray 3.2 Image to Video for a single start/end frame)." + ) + if len(items) > 64: + raise ValueError(f"Ray 3.2 supports at most 64 keyframes; got {len(items)}.") + maxframe = 120 if duration == "5s" else 240 + duration_seconds = maxframe / 24 # 5.0 or 10.0 + # Resolve each keyframe to an output-frame index, then order by position + # (so the user can chain keyframes in any order — the position is what places them) + placed: list[tuple[int, torch.Tensor]] = [] + for item in items: + if item.mode == LUMA_KEYFRAME_MODE_SECONDS: + if item.value > duration_seconds: + raise ValueError( + f"Keyframe position {item.value:g}s is past the end of the {duration} video; " + f"use 0-{duration_seconds:g}s (or switch the keyframe to fraction mode)." + ) + idx = round(item.value * 24) + else: + idx = round(item.value * maxframe) + placed.append((max(0, min(maxframe, idx)), item.image)) + placed.sort(key=lambda p: p[0]) + indexes = [idx for idx, _ in placed] + for a, b in zip(indexes, indexes[1:]): + if a == b: + raise ValueError( + f"Two keyframes resolve to the same output frame ({a}) for a {duration} video " + f"(valid range 0-{maxframe}); give each keyframe a distinct position." + ) + refs: list[Luma2ImageRef] = [] + for _, image in placed: + url = await upload_image_to_comfyapi(cls, image, mime_type="image/png") + refs.append(Luma2ImageRef(url=url)) + request = Luma2GenerationRequest( + prompt=prompt, + model="ray-3.2", + type="video", + video=Luma2VideoOptions(resolution=resolution, duration=duration, keyframes=refs, keyframe_indexes=indexes), + ) + return await _ray32_generate(cls, request) + + +class LumaRay32VideoEditNode(IO.ComfyNode): + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="LumaRay32VideoEditNode", + display_name="Luma Ray 3.2 Video Edit", + category="partner/video/Luma", + description="Re-render an existing video under a new prompt using Luma Ray 3.2 (restyle, relight, add " + "or remove elements) while keeping the original motion. Source video up to 18 seconds; the edited " + "video keeps the source's length.", + inputs=[ + IO.Video.Input("video", tooltip="Source video to edit. Up to 18 seconds."), + IO.String.Input("prompt", multiline=True, default="", tooltip="Describes the desired edit."), + IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"), + IO.Combo.Input( + "strength", + options=[ + "auto", + "adhere_1", + "adhere_2", + "adhere_3", + "flex_1", + "flex_2", + "flex_3", + "reimagine_1", + "reimagine_2", + "reimagine_3", + ], + default="auto", + tooltip="How strongly to preserve vs. reimagine the source. 'auto' lets Ray 3.2 choose; " + "adhere_* preserves the most, flex_* is balanced, reimagine_* changes the most.", + ), + _ray32_seed_input(), + ], + outputs=[ + IO.Video.Output(), + IO.String.Output(display_name="generation_id"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=_BADGE_RAY32_EDIT, + ) + + @classmethod + async def execute( + cls, video: Input.Video, prompt: str, resolution: str, strength: str, seed: int + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1, max_length=6000) + try: + duration = "5s" if video.get_duration() <= 5.0 else "10s" + except Exception: + duration = "10s" + source_url = await upload_video_to_comfyapi(cls, video, max_duration=18) + edit = Luma2VideoEdit(auto_controls=True) if strength == "auto" else Luma2VideoEdit(strength=strength) + request = Luma2GenerationRequest( + prompt=prompt, + model="ray-3.2", + type="video_edit", + source=Luma2ImageRef(url=source_url, media_type="video/mp4"), + video=Luma2VideoOptions(resolution=resolution, duration=duration, edit=edit), + ) + return await _ray32_generate(cls, request) + + +class LumaRay32VideoReframeNode(IO.ComfyNode): + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="LumaRay32VideoReframeNode", + display_name="Luma Ray 3.2 Video Reframe", + category="partner/video/Luma", + description="Change the aspect ratio of an existing video, using Luma Ray 3.2 to fill the newly " + "exposed canvas areas. Source video up to 30 seconds. Billed per second of output.", + inputs=[ + IO.Video.Input("video", tooltip="Source video to reframe. Up to 30 seconds."), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Describes how the newly exposed canvas areas should be filled.", + ), + IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1", "4:3", "3:4", "21:9"]), + IO.Combo.Input("resolution", options=["360p", "540p", "720p", "1080p"], default="720p"), + _ray32_seed_input(), + ], + outputs=[ + IO.Video.Output(), + IO.String.Output(display_name="generation_id"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=_BADGE_RAY32_REFRAME, + ) + + @classmethod + async def execute( + cls, video: Input.Video, prompt: str, aspect_ratio: str, resolution: str, seed: int + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=False, min_length=1, max_length=6000) + if resolution == "1080p" and aspect_ratio in {"9:16", "3:4"}: + raise ValueError("1080p is not available for vertical aspect ratios (9:16, 3:4) when reframing.") + source_url = await upload_video_to_comfyapi(cls, video, max_duration=30) + request = Luma2GenerationRequest( + prompt=prompt, + model="ray-3.2", + type="video_reframe", + aspect_ratio=aspect_ratio, + source=Luma2ImageRef(url=source_url, media_type="video/mp4"), + video=Luma2VideoOptions(resolution=resolution), + ) + return await _ray32_generate(cls, request) + + +class LumaRay32ExtendVideoNode(IO.ComfyNode): + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="LumaRay32ExtendVideoNode", + display_name="Luma Ray 3.2 Extend Video", + category="partner/video/Luma", + description="Extend a previous Ray 3.2 generation forward (continue after it) or backward (lead-in " + "before it). Connect the generation_id output of a prior Luma Ray 3.2 node." + " Extensions are always 5 seconds.", + inputs=[ + IO.String.Input( + "source_generation_id", + default="", + tooltip="generation_id of the prior Ray 3.2 video to extend." + " Connect the generation_id output of another Luma Ray 3.2 node.", + ), + IO.DynamicCombo.Input( + "direction", + options=[ + IO.DynamicCombo.Option( + "Forward (continue after)", + [ + IO.Boolean.Input( + "loop", + default=False, + tooltip="Loop the extended video seamlessly (forward extend only).", + ), + ], + ), + IO.DynamicCombo.Option("Backward (lead-in before)", []), + ], + tooltip="Forward continues after the prior clip; backward is prepended before it.", + ), + IO.String.Input("prompt", multiline=True, default="", tooltip="Text prompt for the new content."), + IO.Combo.Input("resolution", options=["540p", "720p", "1080p"], default="720p"), + _ray32_seed_input(), + ], + outputs=[ + IO.Video.Output(), + IO.String.Output(display_name="generation_id"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=_BADGE_RAY32_VIDEO_5S, + ) + + @classmethod + async def execute( + cls, source_generation_id: str, direction: dict, prompt: str, resolution: str, seed: int + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=False, min_length=1, max_length=6000) + gen_id = (source_generation_id or "").strip() + if not gen_id: + raise ValueError( + "source_generation_id is required (connect the generation_id output of a prior Luma Ray 3.2 node)." + ) + video = Luma2VideoOptions(resolution=resolution, duration="5s") + ref = Luma2ImageRef(generation_id=gen_id) + if direction["direction"] == "Forward (continue after)": + video.start_frame = ref + if direction.get("loop"): + video.loop = True + else: + video.end_frame = ref + request = Luma2GenerationRequest(prompt=prompt, model="ray-3.2", type="video", video=video) + return await _ray32_generate(cls, request) class LumaExtension(ComfyExtension): @@ -944,6 +1481,13 @@ class LumaExtension(ComfyExtension): LumaConceptsNode, LumaImageNode, LumaImageEditNode, + LumaRay32TextToVideoNode, + LumaRay32ImageToVideoNode, + LumaRay32KeyframeNode, + LumaRay32KeyframesToVideoNode, + LumaRay32VideoEditNode, + LumaRay32VideoReframeNode, + LumaRay32ExtendVideoNode, ] diff --git a/comfy_api_nodes/nodes_magnific.py b/comfy_api_nodes/nodes_magnific.py index a6aeb194a..4ce4735df 100644 --- a/comfy_api_nodes/nodes_magnific.py +++ b/comfy_api_nodes/nodes_magnific.py @@ -61,7 +61,7 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode): return IO.Schema( node_id="MagnificImageUpscalerCreativeNode", display_name="Magnific Image Upscale (Creative)", - category="image/partner/Magnific", + category="partner/image/Magnific", description="Prompt‑guided enhancement, stylization, and 2x/4x/8x/16x upscaling. " "Maximum output: 25.3 megapixels.", inputs=[ @@ -240,7 +240,7 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode): return IO.Schema( node_id="MagnificImageUpscalerPreciseV2Node", display_name="Magnific Image Upscale (Precise V2)", - category="image/partner/Magnific", + category="partner/image/Magnific", description="High-fidelity upscaling with fine control over sharpness, grain, and detail. " "Maximum output: 10060×10060 pixels.", inputs=[ @@ -400,7 +400,7 @@ class MagnificImageStyleTransferNode(IO.ComfyNode): return IO.Schema( node_id="MagnificImageStyleTransferNode", display_name="Magnific Image Style Transfer", - category="image/partner/Magnific", + category="partner/image/Magnific", description="Transfer the style from a reference image to your input image.", inputs=[ IO.Image.Input("image", tooltip="The image to apply style transfer to."), @@ -549,7 +549,7 @@ class MagnificImageRelightNode(IO.ComfyNode): return IO.Schema( node_id="MagnificImageRelightNode", display_name="Magnific Image Relight", - category="image/partner/Magnific", + category="partner/image/Magnific", description="Relight an image with lighting adjustments and optional reference-based light transfer.", inputs=[ IO.Image.Input("image", tooltip="The image to relight."), @@ -789,7 +789,7 @@ class MagnificImageSkinEnhancerNode(IO.ComfyNode): return IO.Schema( node_id="MagnificImageSkinEnhancerNode", display_name="Magnific Image Skin Enhancer", - category="image/partner/Magnific", + category="partner/image/Magnific", description="Skin enhancement for portraits with multiple processing modes.", inputs=[ IO.Image.Input("image", tooltip="The portrait image to enhance."), diff --git a/comfy_api_nodes/nodes_meshy.py b/comfy_api_nodes/nodes_meshy.py index 4fb670404..3a24f1095 100644 --- a/comfy_api_nodes/nodes_meshy.py +++ b/comfy_api_nodes/nodes_meshy.py @@ -33,7 +33,7 @@ class MeshyTextToModelNode(IO.ComfyNode): return IO.Schema( node_id="MeshyTextToModelNode", display_name="Meshy: Text to Model", - category="3d/partner/Meshy", + category="partner/3d/Meshy", inputs=[ IO.Combo.Input("model", options=["latest"]), IO.String.Input("prompt", multiline=True, default=""), @@ -145,7 +145,7 @@ class MeshyRefineNode(IO.ComfyNode): return IO.Schema( node_id="MeshyRefineNode", display_name="Meshy: Refine Draft Model", - category="3d/partner/Meshy", + category="partner/3d/Meshy", description="Refine a previously created draft model.", inputs=[ IO.Combo.Input("model", options=["latest"]), @@ -240,7 +240,7 @@ class MeshyImageToModelNode(IO.ComfyNode): return IO.Schema( node_id="MeshyImageToModelNode", display_name="Meshy: Image to Model", - category="3d/partner/Meshy", + category="partner/3d/Meshy", inputs=[ IO.Combo.Input("model", options=["latest"]), IO.Image.Input("image"), @@ -405,7 +405,7 @@ class MeshyMultiImageToModelNode(IO.ComfyNode): return IO.Schema( node_id="MeshyMultiImageToModelNode", display_name="Meshy: Multi-Image to Model", - category="3d/partner/Meshy", + category="partner/3d/Meshy", inputs=[ IO.Combo.Input("model", options=["latest"]), IO.Autogrow.Input( @@ -575,7 +575,7 @@ class MeshyRigModelNode(IO.ComfyNode): return IO.Schema( node_id="MeshyRigModelNode", display_name="Meshy: Rig Model", - category="3d/partner/Meshy", + category="partner/3d/Meshy", description="Provides a rigged character in standard formats. " "Auto-rigging is currently not suitable for untextured meshes, non-humanoid assets, " "or humanoid assets with unclear limb and body structure.", @@ -656,7 +656,7 @@ class MeshyAnimateModelNode(IO.ComfyNode): return IO.Schema( node_id="MeshyAnimateModelNode", display_name="Meshy: Animate Model", - category="3d/partner/Meshy", + category="partner/3d/Meshy", description="Apply a specific animation action to a previously rigged character.", inputs=[ IO.Custom("MESHY_RIGGED_TASK_ID").Input("rig_task_id"), @@ -722,7 +722,7 @@ class MeshyTextureNode(IO.ComfyNode): return IO.Schema( node_id="MeshyTextureNode", display_name="Meshy: Texture Model", - category="3d/partner/Meshy", + category="partner/3d/Meshy", inputs=[ IO.Combo.Input("model", options=["latest"]), IO.Custom("MESHY_TASK_ID").Input("meshy_task_id"), diff --git a/comfy_api_nodes/nodes_minimax.py b/comfy_api_nodes/nodes_minimax.py index 338584148..6250af146 100644 --- a/comfy_api_nodes/nodes_minimax.py +++ b/comfy_api_nodes/nodes_minimax.py @@ -101,7 +101,7 @@ class MinimaxTextToVideoNode(IO.ComfyNode): return IO.Schema( node_id="MinimaxTextToVideoNode", display_name="MiniMax Text to Video", - category="video/partner/MiniMax", + category="partner/video/MiniMax", description="Generates videos synchronously based on a prompt, and optional parameters.", inputs=[ IO.String.Input( @@ -163,7 +163,7 @@ class MinimaxImageToVideoNode(IO.ComfyNode): return IO.Schema( node_id="MinimaxImageToVideoNode", display_name="MiniMax Image to Video", - category="video/partner/MiniMax", + category="partner/video/MiniMax", description="Generates videos synchronously based on an image and prompt, and optional parameters.", inputs=[ IO.Image.Input( @@ -230,7 +230,7 @@ class MinimaxSubjectToVideoNode(IO.ComfyNode): return IO.Schema( node_id="MinimaxSubjectToVideoNode", display_name="MiniMax Subject to Video", - category="video/partner/MiniMax", + category="partner/video/MiniMax", description="Generates videos synchronously based on an image and prompt, and optional parameters.", inputs=[ IO.Image.Input( @@ -294,7 +294,7 @@ class MinimaxHailuoVideoNode(IO.ComfyNode): return IO.Schema( node_id="MinimaxHailuoVideoNode", display_name="MiniMax Hailuo Video", - category="video/partner/MiniMax", + category="partner/video/MiniMax", description="Generates videos from prompt, with optional start frame using the new MiniMax Hailuo-02 model.", inputs=[ IO.String.Input( diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py index 48c739dfe..ad62f2164 100644 --- a/comfy_api_nodes/nodes_openai.py +++ b/comfy_api_nodes/nodes_openai.py @@ -9,6 +9,7 @@ from PIL import Image from typing_extensions import override import folder_paths +from comfy.utils import common_upscale from comfy_api.latest import IO, ComfyExtension, Input from comfy_api_nodes.apis.openai import ( InputFileContent, @@ -62,7 +63,8 @@ async def validate_and_cast_response(response, timeout: int = None) -> torch.Ten timeout: Request timeout in seconds. Defaults to None (no timeout). Returns: - A torch.Tensor representing the image (1, H, W, C). + A torch.Tensor of shape (N, H, W, C) with all returned images; images whose + dimensions differ from the first image's are resized to match it. Raises: ValueError: If the response is not valid. @@ -89,6 +91,14 @@ async def validate_and_cast_response(response, timeout: int = None) -> torch.Ten arr = np.asarray(pil_img).astype(np.float32) / 255.0 image_tensors.append(torch.from_numpy(arr)) + # With size="auto" the API can return images whose dimensions differ by a few pixels within a single response + # resize them to the first image's dimensions so they can be stacked into one batch. + ref_h, ref_w = image_tensors[0].shape[:2] + for i, t in enumerate(image_tensors): + if t.shape[:2] != (ref_h, ref_w): + samples = t.unsqueeze(0).movedim(-1, 1) + samples = common_upscale(samples, ref_w, ref_h, "bilinear", "center") + image_tensors[i] = samples.movedim(1, -1).squeeze(0) return torch.stack(image_tensors, dim=0) @@ -99,7 +109,7 @@ class OpenAIDalle2(IO.ComfyNode): return IO.Schema( node_id="OpenAIDalle2", display_name="OpenAI DALL·E 2", - category="image/partner/OpenAI", + category="partner/image/OpenAI", description="Generates images synchronously via OpenAI's DALL·E 2 endpoint.", inputs=[ IO.String.Input( @@ -249,7 +259,7 @@ class OpenAIDalle3(IO.ComfyNode): return IO.Schema( node_id="OpenAIDalle3", display_name="OpenAI DALL·E 3", - category="image/partner/OpenAI", + category="partner/image/OpenAI", description="Generates images synchronously via OpenAI's DALL·E 3 endpoint.", inputs=[ IO.String.Input( @@ -371,7 +381,7 @@ class OpenAIGPTImage1(IO.ComfyNode): return IO.Schema( node_id="OpenAIGPTImage1", display_name="OpenAI GPT Image 2", - category="image/partner/OpenAI", + category="partner/image/OpenAI", description="Generates images synchronously via OpenAI's GPT Image endpoint.", is_deprecated=True, inputs=[ @@ -695,7 +705,7 @@ class OpenAIGPTImageNodeV2(IO.ComfyNode): return IO.Schema( node_id="OpenAIGPTImageNodeV2", display_name="OpenAI GPT Image 2", - category="image/partner/OpenAI", + category="partner/image/OpenAI", description="Generates images via OpenAI's GPT Image endpoint.", inputs=[ IO.String.Input( @@ -962,7 +972,7 @@ class OpenAIChatNode(IO.ComfyNode): return IO.Schema( node_id="OpenAIChatNode", display_name="OpenAI ChatGPT", - category="text/partner/OpenAI", + category="partner/text/OpenAI", essentials_category="Text Generation", description="Generate text responses from an OpenAI model.", inputs=[ @@ -1201,7 +1211,7 @@ class OpenAIInputFiles(IO.ComfyNode): return IO.Schema( node_id="OpenAIInputFiles", display_name="OpenAI ChatGPT Input Files", - category="text/partner/OpenAI", + category="partner/text/OpenAI", description="Loads and prepares input files (text, pdf, etc.) to include as inputs for the OpenAI Chat Node. The files will be read by the OpenAI model when generating a response. 🛈 TIP: Can be chained together with other OpenAI Input File nodes.", inputs=[ IO.Combo.Input( @@ -1248,7 +1258,7 @@ class OpenAIChatConfig(IO.ComfyNode): return IO.Schema( node_id="OpenAIChatConfig", display_name="OpenAI ChatGPT Advanced Options", - category="text/partner/OpenAI", + category="partner/text/OpenAI", description="Allows specifying advanced configuration options for the OpenAI Chat Nodes.", inputs=[ IO.Combo.Input( diff --git a/comfy_api_nodes/nodes_openrouter.py b/comfy_api_nodes/nodes_openrouter.py index d2ebbef0d..ba98133f0 100644 --- a/comfy_api_nodes/nodes_openrouter.py +++ b/comfy_api_nodes/nodes_openrouter.py @@ -265,7 +265,7 @@ class OpenRouterLLMNode(IO.ComfyNode): return IO.Schema( node_id="OpenRouterLLMNode", display_name="OpenRouter LLM", - category="text/partner/OpenRouter", + category="partner/text/OpenRouter", essentials_category="Text Generation", description=( "Generate text responses through OpenRouter. Routes to a curated set of popular " diff --git a/comfy_api_nodes/nodes_pixverse.py b/comfy_api_nodes/nodes_pixverse.py index 3861cfedd..4c8b723b9 100644 --- a/comfy_api_nodes/nodes_pixverse.py +++ b/comfy_api_nodes/nodes_pixverse.py @@ -53,7 +53,7 @@ class PixverseTemplateNode(IO.ComfyNode): return IO.Schema( node_id="PixverseTemplateNode", display_name="PixVerse Template", - category="video/partner/PixVerse", + category="partner/video/PixVerse", inputs=[ IO.Combo.Input("template", options=list(pixverse_templates.keys())), ], @@ -74,7 +74,7 @@ class PixverseTextToVideoNode(IO.ComfyNode): return IO.Schema( node_id="PixverseTextToVideoNode", display_name="PixVerse Text to Video", - category="video/partner/PixVerse", + category="partner/video/PixVerse", description="Generates videos based on prompt and output_size.", inputs=[ IO.String.Input( @@ -192,7 +192,7 @@ class PixverseImageToVideoNode(IO.ComfyNode): return IO.Schema( node_id="PixverseImageToVideoNode", display_name="PixVerse Image to Video", - category="video/partner/PixVerse", + category="partner/video/PixVerse", description="Generates videos based on prompt and output_size.", inputs=[ IO.Image.Input("image"), @@ -310,7 +310,7 @@ class PixverseTransitionVideoNode(IO.ComfyNode): return IO.Schema( node_id="PixverseTransitionVideoNode", display_name="PixVerse Transition Video", - category="video/partner/PixVerse", + category="partner/video/PixVerse", description="Generates videos based on prompt and output_size.", inputs=[ IO.Image.Input("first_frame"), diff --git a/comfy_api_nodes/nodes_quiver.py b/comfy_api_nodes/nodes_quiver.py index ad045a7ef..34929fa0c 100644 --- a/comfy_api_nodes/nodes_quiver.py +++ b/comfy_api_nodes/nodes_quiver.py @@ -62,7 +62,7 @@ class QuiverTextToSVGNode(IO.ComfyNode): return IO.Schema( node_id="QuiverTextToSVGNode", display_name="Quiver Text to SVG", - category="image/partner/Quiver", + category="partner/image/Quiver", description="Generate an SVG from a text prompt using Quiver AI.", inputs=[ IO.String.Input( @@ -177,7 +177,7 @@ class QuiverImageToSVGNode(IO.ComfyNode): return IO.Schema( node_id="QuiverImageToSVGNode", display_name="Quiver Image to SVG", - category="image/partner/Quiver", + category="partner/image/Quiver", description="Vectorize a raster image into SVG using Quiver AI.", inputs=[ IO.Image.Input( diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py index 07387821d..c44942f50 100644 --- a/comfy_api_nodes/nodes_recraft.py +++ b/comfy_api_nodes/nodes_recraft.py @@ -178,7 +178,7 @@ class RecraftColorRGBNode(IO.ComfyNode): return IO.Schema( node_id="RecraftColorRGB", display_name="Recraft Color RGB", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Create Recraft Color by choosing specific RGB values.", inputs=[ IO.Int.Input("r", default=0, min=0, max=255, tooltip="Red value of color."), @@ -204,7 +204,7 @@ class RecraftControlsNode(IO.ComfyNode): return IO.Schema( node_id="RecraftControls", display_name="Recraft Controls", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Create Recraft Controls for customizing Recraft generation.", inputs=[ IO.Custom(RecraftIO.COLOR).Input("colors", optional=True), @@ -228,7 +228,7 @@ class RecraftStyleV3RealisticImageNode(IO.ComfyNode): return IO.Schema( node_id="RecraftStyleV3RealisticImage", display_name="Recraft Style - Realistic Image", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Select realistic_image style and optional substyle.", inputs=[ IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)), @@ -253,7 +253,7 @@ class RecraftStyleV3DigitalIllustrationNode(RecraftStyleV3RealisticImageNode): return IO.Schema( node_id="RecraftStyleV3DigitalIllustration", display_name="Recraft Style - Digital Illustration", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Select realistic_image style and optional substyle.", inputs=[ IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)), @@ -272,7 +272,7 @@ class RecraftStyleV3VectorIllustrationNode(RecraftStyleV3RealisticImageNode): return IO.Schema( node_id="RecraftStyleV3VectorIllustrationNode", display_name="Recraft Style - Realistic Image", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Select realistic_image style and optional substyle.", inputs=[ IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE)), @@ -291,7 +291,7 @@ class RecraftStyleV3LogoRasterNode(RecraftStyleV3RealisticImageNode): return IO.Schema( node_id="RecraftStyleV3LogoRaster", display_name="Recraft Style - Logo Raster", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Select realistic_image style and optional substyle.", inputs=[ IO.Combo.Input("substyle", options=get_v3_substyles(cls.RECRAFT_STYLE, include_none=False)), @@ -308,7 +308,7 @@ class RecraftStyleInfiniteStyleLibrary(IO.ComfyNode): return IO.Schema( node_id="RecraftStyleV3InfiniteStyleLibrary", display_name="Recraft Style - Infinite Style Library", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Choose style based on preexisting UUID from Recraft's Infinite Style Library.", inputs=[ IO.String.Input("style_id", default="", tooltip="UUID of style from Infinite Style Library."), @@ -331,7 +331,7 @@ class RecraftCreateStyleNode(IO.ComfyNode): return IO.Schema( node_id="RecraftCreateStyleNode", display_name="Recraft Create Style", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Create a custom style from reference images. " "Upload 1-5 images to use as style references. " "Total size of all images is limited to 5 MB.", @@ -400,7 +400,7 @@ class RecraftTextToImageNode(IO.ComfyNode): return IO.Schema( node_id="RecraftTextToImageNode", display_name="Recraft Text to Image", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Generates images synchronously based on prompt and resolution.", inputs=[ IO.String.Input("prompt", multiline=True, default="", tooltip="Prompt for the image generation."), @@ -512,7 +512,7 @@ class RecraftImageToImageNode(IO.ComfyNode): return IO.Schema( node_id="RecraftImageToImageNode", display_name="Recraft Image to Image", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Modify image based on prompt and strength.", inputs=[ IO.Image.Input("image"), @@ -630,7 +630,7 @@ class RecraftImageInpaintingNode(IO.ComfyNode): return IO.Schema( node_id="RecraftImageInpaintingNode", display_name="Recraft Image Inpainting", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Modify image based on prompt and mask.", inputs=[ IO.Image.Input("image"), @@ -732,7 +732,7 @@ class RecraftTextToVectorNode(IO.ComfyNode): return IO.Schema( node_id="RecraftTextToVectorNode", display_name="Recraft Text to Vector", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Generates SVG synchronously based on prompt and resolution.", inputs=[ IO.String.Input("prompt", default="", tooltip="Prompt for the image generation.", multiline=True), @@ -832,7 +832,7 @@ class RecraftVectorizeImageNode(IO.ComfyNode): return IO.Schema( node_id="RecraftVectorizeImageNode", display_name="Recraft Vectorize Image", - category="image/partner/Recraft", + category="partner/image/Recraft", essentials_category="Image Tools", description="Generates SVG synchronously from an input image.", inputs=[ @@ -876,7 +876,7 @@ class RecraftReplaceBackgroundNode(IO.ComfyNode): return IO.Schema( node_id="RecraftReplaceBackgroundNode", display_name="Recraft Replace Background", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Replace background on image, based on provided prompt.", inputs=[ IO.Image.Input("image"), @@ -963,7 +963,7 @@ class RecraftRemoveBackgroundNode(IO.ComfyNode): return IO.Schema( node_id="RecraftRemoveBackgroundNode", display_name="Recraft Remove Background", - category="image/partner/Recraft", + category="partner/image/Recraft", essentials_category="Image Tools", description="Remove background from image, and return processed image and mask.", inputs=[ @@ -1012,7 +1012,7 @@ class RecraftCrispUpscaleNode(IO.ComfyNode): return IO.Schema( node_id="RecraftCrispUpscaleNode", display_name="Recraft Crisp Upscale Image", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Upscale image synchronously.\n" "Enhances a given raster image using ‘crisp upscale’ tool, " "increasing image resolution, making the image sharper and cleaner.", @@ -1058,7 +1058,7 @@ class RecraftCreativeUpscaleNode(RecraftCrispUpscaleNode): return IO.Schema( node_id="RecraftCreativeUpscaleNode", display_name="Recraft Creative Upscale Image", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Upscale image synchronously.\n" "Enhances a given raster image using ‘creative upscale’ tool, " "boosting resolution with a focus on refining small details and faces.", @@ -1086,7 +1086,7 @@ class RecraftV4TextToImageNode(IO.ComfyNode): return IO.Schema( node_id="RecraftV4TextToImageNode", display_name="Recraft V4 Text to Image", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Generates images using Recraft V4 or V4 Pro models.", inputs=[ IO.String.Input( @@ -1210,7 +1210,7 @@ class RecraftV4TextToVectorNode(IO.ComfyNode): return IO.Schema( node_id="RecraftV4TextToVectorNode", display_name="Recraft V4 Text to Vector", - category="image/partner/Recraft", + category="partner/image/Recraft", description="Generates SVG using Recraft V4 or V4 Pro models.", inputs=[ IO.String.Input( diff --git a/comfy_api_nodes/nodes_reve.py b/comfy_api_nodes/nodes_reve.py index 2b15eadd7..177349a8b 100644 --- a/comfy_api_nodes/nodes_reve.py +++ b/comfy_api_nodes/nodes_reve.py @@ -109,7 +109,7 @@ class ReveImageCreateNode(IO.ComfyNode): return IO.Schema( node_id="ReveImageCreateNode", display_name="Reve Image Create", - category="image/partner/Reve", + category="partner/image/Reve", description="Generate images from text descriptions using Reve.", inputs=[ IO.String.Input( @@ -200,7 +200,7 @@ class ReveImageEditNode(IO.ComfyNode): return IO.Schema( node_id="ReveImageEditNode", display_name="Reve Image Edit", - category="image/partner/Reve", + category="partner/image/Reve", description="Edit images using natural language instructions with Reve.", inputs=[ IO.Image.Input("image", tooltip="The image to edit."), @@ -300,7 +300,7 @@ class ReveImageRemixNode(IO.ComfyNode): return IO.Schema( node_id="ReveImageRemixNode", display_name="Reve Image Remix", - category="image/partner/Reve", + category="partner/image/Reve", description="Combine reference images with text prompts to create new images using Reve.", inputs=[ IO.Autogrow.Input( diff --git a/comfy_api_nodes/nodes_rodin.py b/comfy_api_nodes/nodes_rodin.py index e14955661..0375a2123 100644 --- a/comfy_api_nodes/nodes_rodin.py +++ b/comfy_api_nodes/nodes_rodin.py @@ -230,7 +230,7 @@ class Rodin3D_Regular(IO.ComfyNode): return IO.Schema( node_id="Rodin3D_Regular", display_name="Rodin 3D Generate - Regular Generate", - category="3d/partner/Rodin", + category="partner/3d/Rodin", description=cleandoc(cls.__doc__ or ""), inputs=[ IO.Image.Input("Images"), @@ -289,7 +289,7 @@ class Rodin3D_Detail(IO.ComfyNode): return IO.Schema( node_id="Rodin3D_Detail", display_name="Rodin 3D Generate - Detail Generate", - category="3d/partner/Rodin", + category="partner/3d/Rodin", description=cleandoc(cls.__doc__ or ""), inputs=[ IO.Image.Input("Images"), @@ -348,7 +348,7 @@ class Rodin3D_Smooth(IO.ComfyNode): return IO.Schema( node_id="Rodin3D_Smooth", display_name="Rodin 3D Generate - Smooth Generate", - category="3d/partner/Rodin", + category="partner/3d/Rodin", description=cleandoc(cls.__doc__ or ""), inputs=[ IO.Image.Input("Images"), @@ -406,7 +406,7 @@ class Rodin3D_Sketch(IO.ComfyNode): return IO.Schema( node_id="Rodin3D_Sketch", display_name="Rodin 3D Generate - Sketch Generate", - category="3d/partner/Rodin", + category="partner/3d/Rodin", description=cleandoc(cls.__doc__ or ""), inputs=[ IO.Image.Input("Images"), @@ -468,7 +468,7 @@ class Rodin3D_Gen2(IO.ComfyNode): return IO.Schema( node_id="Rodin3D_Gen2", display_name="Rodin 3D Generate - Gen-2 Generate", - category="3d/partner/Rodin", + category="partner/3d/Rodin", description=cleandoc(cls.__doc__ or ""), inputs=[ IO.Image.Input("Images"), @@ -941,7 +941,7 @@ class Rodin3D_Gen25_Image(IO.ComfyNode): return IO.Schema( node_id="Rodin3D_Gen25_Image", display_name="Rodin 3D Gen-2.5 - Image to 3D", - category="3d/partner/Rodin", + category="partner/3d/Rodin", description=( "Generate a 3D model from 1-5 reference images via Rodin Gen-2.5. " "Pick a mode (Fast / Regular / Extreme-High) to tune quality vs. cost." @@ -1035,7 +1035,7 @@ class Rodin3D_Gen25_Text(IO.ComfyNode): return IO.Schema( node_id="Rodin3D_Gen25_Text", display_name="Rodin 3D Gen-2.5 - Text to 3D", - category="3d/partner/Rodin", + category="partner/3d/Rodin", description=( "Generate a 3D model from a text prompt via Rodin Gen-2.5. " "Pick a mode (Fast / Regular / Extreme-High) to tune quality vs. cost." diff --git a/comfy_api_nodes/nodes_runway.py b/comfy_api_nodes/nodes_runway.py index 7357c733e..013a193d9 100644 --- a/comfy_api_nodes/nodes_runway.py +++ b/comfy_api_nodes/nodes_runway.py @@ -30,13 +30,33 @@ from comfy_api_nodes.apis.runway import ( Model4, ReferenceImage, RunwayTextToImageAspectRatioEnum, + RunwayAleph2IO, + RunwayAleph2KeyframeChain, + RunwayAleph2KeyframeItem, + RunwayAleph2PromptImageChain, + RunwayAleph2PromptImageItem, + RunwayAleph2Request, + RunwayAleph2Response, + RunwayAleph2KeyframeSeconds, + RunwayAleph2KeyframeAt, + RunwayAleph2PromptImage, + RunwayAleph2TimestampPosition, + RunwayAleph2RelativePosition, + RunwayAleph2ContentModeration, + KEYFRAME_MODE_SECONDS, + KEYFRAME_MODE_AT, + PROMPT_IMAGE_MODE_TIMESTAMP, + PROMPT_IMAGE_MODE_POSITION, ) from comfy_api_nodes.util import ( image_tensor_pair_to_batch, validate_string, validate_image_dimensions, validate_image_aspect_ratio, + validate_video_duration, upload_images_to_comfyapi, + upload_image_to_comfyapi, + upload_video_to_comfyapi, download_url_to_video_output, download_url_to_image_tensor, ApiEndpoint, @@ -45,6 +65,7 @@ from comfy_api_nodes.util import ( ) PATH_IMAGE_TO_VIDEO = "/proxy/runway/image_to_video" +PATH_VIDEO_TO_VIDEO = "/proxy/runway/video_to_video" PATH_TEXT_TO_IMAGE = "/proxy/runway/text_to_image" PATH_GET_TASK_STATUS = "/proxy/runway/tasks" @@ -53,12 +74,6 @@ AVERAGE_DURATION_FLF_SECONDS = 256 AVERAGE_DURATION_T2I_SECONDS = 41 -class RunwayApiError(Exception): - """Base exception for Runway API errors.""" - - pass - - class RunwayGen4TurboAspectRatio(str, Enum): """Aspect ratios supported for Image to Video API when using gen4_turbo model.""" @@ -84,14 +99,6 @@ def get_video_url_from_task_status(response: TaskStatusResponse) -> str | None: return None -def extract_progress_from_task_status( - response: TaskStatusResponse, -) -> float | None: - if hasattr(response, "progress") and response.progress is not None: - return response.progress * 100 - return None - - def get_image_url_from_task_status(response: TaskStatusResponse) -> str | None: """Returns the image URL from the task status response if it exists.""" if hasattr(response, "output") and len(response.output) > 0: @@ -102,14 +109,13 @@ def get_image_url_from_task_status(response: TaskStatusResponse) -> str | None: async def get_response( cls: type[IO.ComfyNode], task_id: str, estimated_duration: int | None = None ) -> TaskStatusResponse: - """Poll the task status until it is finished then get the response.""" return await poll_op( cls, ApiEndpoint(path=f"{PATH_GET_TASK_STATUS}/{task_id}"), response_model=TaskStatusResponse, - status_extractor=lambda r: r.status.value, + status_extractor=lambda r: r.status, estimated_duration=estimated_duration, - progress_extractor=extract_progress_from_task_status, + progress_extractor=lambda r: r.progress * 100 if r.progress is not None else None, ) @@ -127,7 +133,7 @@ async def generate_video( final_response = await get_response(cls, initial_response.id, estimated_duration) if not final_response.output: - raise RunwayApiError("Runway task succeeded but no video data found in response.") + raise ValueError("Runway task succeeded but no video data found in response.") video_url = get_video_url_from_task_status(final_response) return await download_url_to_video_output(video_url) @@ -140,7 +146,7 @@ class RunwayImageToVideoNodeGen3a(IO.ComfyNode): return IO.Schema( node_id="RunwayImageToVideoNodeGen3a", display_name="Runway Image to Video (Gen3a Turbo)", - category="video/partner/Runway", + category="partner/video/Runway", description="Generate a video from a single starting frame using Gen3a Turbo model. " "Before diving in, review these best practices to ensure that " "your input selections will set your generation up for success: " @@ -234,7 +240,7 @@ class RunwayImageToVideoNodeGen4(IO.ComfyNode): return IO.Schema( node_id="RunwayImageToVideoNodeGen4", display_name="Runway Image to Video (Gen4 Turbo)", - category="video/partner/Runway", + category="partner/video/Runway", description="Generate a video from a single starting frame using Gen4 Turbo model. " "Before diving in, review these best practices to ensure that " "your input selections will set your generation up for success: " @@ -329,7 +335,7 @@ class RunwayFirstLastFrameNode(IO.ComfyNode): return IO.Schema( node_id="RunwayFirstLastFrameNode", display_name="Runway First-Last-Frame to Video", - category="video/partner/Runway", + category="partner/video/Runway", description="Upload first and last keyframes, draft a prompt, and generate a video. " "More complex transitions, such as cases where the Last frame is completely different " "from the First frame, may benefit from the longer 10s duration. " @@ -410,7 +416,7 @@ class RunwayFirstLastFrameNode(IO.ComfyNode): mime_type="image/png", ) if len(download_urls) != 2: - raise RunwayApiError("Failed to upload one or more images to comfy api.") + raise ValueError("Failed to upload one or more images to comfy api.") return IO.NodeOutput( await generate_video( @@ -440,7 +446,7 @@ class RunwayTextToImageNode(IO.ComfyNode): return IO.Schema( node_id="RunwayTextToImageNode", display_name="Runway Text to Image", - category="image/partner/Runway", + category="partner/image/Runway", description="Generate an image from a text prompt using Runway's Gen 4 model. " "You can also include reference image to guide the generation.", inputs=[ @@ -514,11 +520,321 @@ class RunwayTextToImageNode(IO.ComfyNode): estimated_duration=AVERAGE_DURATION_T2I_SECONDS, ) if not final_response.output: - raise RunwayApiError("Runway task succeeded but no image data found in response.") + raise ValueError("Runway task succeeded but no image data found in response.") return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_task_status(final_response))) +_TIMING_ABSOLUTE = "Absolute time (seconds)" +_TIMING_FRACTION = "Fraction of duration (0.0-1.0)" + + +class RunwayAleph2KeyframeNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="RunwayAleph2KeyframeNode", + display_name="Runway Aleph2 Keyframe", + category="partner/video/Runway", + description="Anchor a guidance image to a moment of the input (source) video, so Aleph2 " + "steers the edit at that point of your footage. Connect this to the 'keyframes' input of " + "the Runway Aleph2 Video to Video node; chain several together (up to 5) via the optional " + "'keyframes' input below.", + inputs=[ + IO.Image.Input( + "image", + tooltip="The guidance image to apply at the chosen moment of the input video.", + ), + IO.DynamicCombo.Input( + "timing", + options=[ + IO.DynamicCombo.Option( + _TIMING_ABSOLUTE, + [ + IO.Float.Input( + "seconds", + default=0.0, + min=0.0, + max=30.0, + step=0.1, + display_mode=IO.NumberDisplay.number, + tooltip="Time in seconds from start of the input video where this image applies.", + ), + ], + ), + IO.DynamicCombo.Option( + _TIMING_FRACTION, + [ + IO.Float.Input( + "fraction", + default=0.0, + min=0.0, + max=1.0, + step=0.01, + display_mode=IO.NumberDisplay.number, + tooltip="Where in the input video this image applies, " + "as a fraction of its duration (0.0 = start, 1.0 = end).", + ), + ], + ), + ], + tooltip="How to place this image on the input video's timeline.", + ), + IO.Custom(RunwayAleph2IO.KEYFRAME).Input( + "keyframes", + optional=True, + tooltip="Optional earlier keyframes to chain with this one.", + ), + ], + outputs=[IO.Custom(RunwayAleph2IO.KEYFRAME).Output(display_name="keyframes")], + ) + + @classmethod + def execute( + cls, + image: Input.Image, + timing: dict, + keyframes: RunwayAleph2KeyframeChain | None = None, + ) -> IO.NodeOutput: + chain = keyframes.clone() if keyframes is not None else RunwayAleph2KeyframeChain() + if timing["timing"] == _TIMING_ABSOLUTE: + mode, value = KEYFRAME_MODE_SECONDS, float(timing["seconds"]) + else: + mode, value = KEYFRAME_MODE_AT, float(timing["fraction"]) + chain.add(RunwayAleph2KeyframeItem(image=image, mode=mode, value=value)) + return IO.NodeOutput(chain) + + +class RunwayAleph2PromptImageNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="RunwayAleph2PromptImageNode", + display_name="Runway Aleph2 Prompt Image", + category="partner/video/Runway", + description="Anchor a guidance image to a moment of the output (result) video, to guide what " + "the edited video looks like at that point. Connect this to the 'prompt_images' input of the " + "Runway Aleph2 Video to Video node; chain several together (up to 5) via the optional " + "'prompt_images' input below.", + inputs=[ + IO.Image.Input( + "image", + tooltip="The guidance image to place at the chosen moment of the output video.", + ), + IO.DynamicCombo.Input( + "position", + options=[ + IO.DynamicCombo.Option( + _TIMING_ABSOLUTE, + [ + IO.Float.Input( + "seconds", + default=0.0, + min=0.0, + max=30.0, + step=0.1, + display_mode=IO.NumberDisplay.number, + tooltip="Time in seconds from start of the output video where this image applies.", + ), + ], + ), + IO.DynamicCombo.Option( + _TIMING_FRACTION, + [ + IO.Float.Input( + "fraction", + default=0.0, + min=0.0, + max=1.0, + step=0.01, + display_mode=IO.NumberDisplay.number, + tooltip="Where in the output video this image applies, " + "as a fraction of its duration (0.0 = start, 1.0 = end).", + ), + ], + ), + ], + tooltip="How to place this image on the output video's timeline.", + ), + IO.Custom(RunwayAleph2IO.PROMPT_IMAGE).Input( + "prompt_images", + optional=True, + tooltip="Optional earlier prompt images to chain with this one.", + ), + ], + outputs=[IO.Custom(RunwayAleph2IO.PROMPT_IMAGE).Output(display_name="prompt_images")], + ) + + @classmethod + def execute( + cls, + image: Input.Image, + position: dict, + prompt_images: RunwayAleph2PromptImageChain | None = None, + ) -> IO.NodeOutput: + chain = prompt_images.clone() if prompt_images is not None else RunwayAleph2PromptImageChain() + if position["position"] == _TIMING_ABSOLUTE: + mode, value = PROMPT_IMAGE_MODE_TIMESTAMP, float(position["seconds"]) + else: + mode, value = PROMPT_IMAGE_MODE_POSITION, float(position["fraction"]) + chain.add(RunwayAleph2PromptImageItem(image=image, mode=mode, value=value)) + return IO.NodeOutput(chain) + + +class RunwayAleph2VideoToVideoNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="RunwayAleph2VideoToVideoNode", + display_name="Runway Aleph2 Video to Video", + category="partner/video/Runway", + description="Edit a video with a text prompt using Runway's Aleph2 model. Aleph2 transforms " + "your footage (restyle, relight, add or remove elements, change the viewpoint) while keeping " + "the original motion and timing; the output resolution matches the input video, which must be " + "2-30 seconds at 30 fps or lower. Optionally steer the edit with either keyframes (anchored to " + "the input video) or prompt images (anchored to the output video) - use one or the other, not both.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Describes what should appear in the output (1-1000 characters).", + ), + IO.Video.Input( + "video", + tooltip="Input video to edit. Must be 2-30 seconds at 30 fps or lower.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=4294967295, + step=1, + control_after_generate=True, + display_mode=IO.NumberDisplay.number, + tooltip="Random seed for generation", + ), + IO.Combo.Input( + "public_figure_threshold", + options=["auto", "low"], + default="low", + tooltip="Content moderation for recognizable public figures.", + ), + IO.Custom(RunwayAleph2IO.KEYFRAME).Input( + "keyframes", + optional=True, + tooltip="Guidance images anchored to the input video, from Aleph2 Keyframe nodes (up to 5). " + "Use keyframes or prompt images, not both.", + ), + IO.Custom(RunwayAleph2IO.PROMPT_IMAGE).Input( + "prompt_images", + optional=True, + tooltip="Guidance images anchored to the output video, from Aleph2 Prompt Image nodes (up to 5). " + "Use keyframes or prompt images, not both.", + ), + ], + 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": 0.4004, "format":{"suffix":"/second"}}""", + ), + ) + + @classmethod + async def execute( + cls, + prompt: str, + video: Input.Video, + seed: int, + public_figure_threshold: str = "low", + keyframes: RunwayAleph2KeyframeChain | None = None, + prompt_images: RunwayAleph2PromptImageChain | None = None, + ) -> IO.NodeOutput: + validate_string(prompt, min_length=1, max_length=1000) + validate_video_duration( + video, + min_duration=2.0, + max_duration=30.0, + ) + try: + fps = float(video.get_frame_rate()) + except Exception: + fps = None + if fps is not None and fps > 30.0 + 0.01: + raise ValueError(f"Input video frame rate ({fps:.2f} fps) exceeds Aleph2's maximum of 30 fps.") + + if (keyframes and keyframes.items) and (prompt_images and prompt_images.items): + raise ValueError("Aleph2 accepts either keyframes or prompt images, not both.") + + video_duration: float | None = None + try: + video_duration = video.get_duration() + except Exception: + video_duration = None + + def _check_seconds(value: float, label: str) -> None: + if video_duration is not None and value > video_duration + 0.0001: + raise ValueError(f"{label} {value:.2f}s exceeds the input video duration ({video_duration:.2f}s).") + + video_url = await upload_video_to_comfyapi(cls, video) + + keyframe_models: list[RunwayAleph2KeyframeSeconds | RunwayAleph2KeyframeAt] = [] + if keyframes is not None: + if len(keyframes.items) > 5: + raise ValueError("Aleph2 supports at most 5 keyframes.") + for item in keyframes.items: + image_url = await upload_image_to_comfyapi(cls, item.image, mime_type="image/png") + if item.mode == KEYFRAME_MODE_SECONDS: + _check_seconds(item.value, "Keyframe timestamp") + keyframe_models.append(RunwayAleph2KeyframeSeconds(seconds=item.value, uri=image_url)) + else: + keyframe_models.append(RunwayAleph2KeyframeAt(at=item.value, uri=image_url)) + + prompt_image_models: list[RunwayAleph2PromptImage] = [] + if prompt_images is not None: + if len(prompt_images.items) > 5: + raise ValueError("Aleph2 supports at most 5 prompt images.") + for item in prompt_images.items: + image_url = await upload_image_to_comfyapi(cls, item.image, mime_type="image/png") + position: RunwayAleph2TimestampPosition | RunwayAleph2RelativePosition + if item.mode == PROMPT_IMAGE_MODE_TIMESTAMP: + _check_seconds(item.value, "Prompt image timestamp") + position = RunwayAleph2TimestampPosition(timestampSeconds=item.value) + else: + position = RunwayAleph2RelativePosition(positionPercentage=item.value) + prompt_image_models.append(RunwayAleph2PromptImage(position=position, uri=image_url)) + + initial_response = await sync_op( + cls, + endpoint=ApiEndpoint(path=PATH_VIDEO_TO_VIDEO, method="POST"), + response_model=RunwayAleph2Response, + data=RunwayAleph2Request( + promptText=prompt, + videoUri=video_url, + seed=seed, + contentModeration=RunwayAleph2ContentModeration(publicFigureThreshold=public_figure_threshold), + keyframes=keyframe_models or None, + promptImage=prompt_image_models or None, + ), + ) + + final_response = await get_response(cls, initial_response.id) + if not final_response.output: + raise ValueError("Runway task succeeded but no video data found in response.") + + return IO.NodeOutput(await download_url_to_video_output(get_video_url_from_task_status(final_response))) + + class RunwayExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: @@ -527,6 +843,9 @@ class RunwayExtension(ComfyExtension): RunwayImageToVideoNodeGen3a, RunwayImageToVideoNodeGen4, RunwayTextToImageNode, + RunwayAleph2VideoToVideoNode, + RunwayAleph2KeyframeNode, + RunwayAleph2PromptImageNode, ] diff --git a/comfy_api_nodes/nodes_sonilo.py b/comfy_api_nodes/nodes_sonilo.py index bc31a0074..2ad35531a 100644 --- a/comfy_api_nodes/nodes_sonilo.py +++ b/comfy_api_nodes/nodes_sonilo.py @@ -16,7 +16,7 @@ from comfy_api_nodes.util import ( ) from comfy_api_nodes.util._helpers import ( default_base_url, - get_auth_header, + get_comfy_api_headers, get_node_id, is_processing_interrupted, ) @@ -34,7 +34,7 @@ class SoniloVideoToMusic(IO.ComfyNode): return IO.Schema( node_id="SoniloVideoToMusic", display_name="Sonilo Video to Music", - category="audio/partner/Sonilo", + category="partner/audio/Sonilo", description="Generate music from video content using Sonilo's AI model. " "Analyzes the video and creates matching music.", inputs=[ @@ -99,9 +99,8 @@ class SoniloTextToMusic(IO.ComfyNode): return IO.Schema( node_id="SoniloTextToMusic", display_name="Sonilo Text to Music", - category="audio/partner/Sonilo", - description="Generate music from a text prompt using Sonilo's AI model. " - "Leave duration at 0 to let the model infer it from the prompt.", + category="partner/audio/Sonilo", + description="Generate music from a text prompt using Sonilo's AI model.", inputs=[ IO.String.Input( "prompt", @@ -111,11 +110,10 @@ class SoniloTextToMusic(IO.ComfyNode): ), IO.Int.Input( "duration", - default=0, - min=0, + default=30, + min=1, max=360, - tooltip="Target duration in seconds. Set to 0 to let the model " - "infer the duration from the prompt. Maximum: 6 minutes.", + tooltip="Target duration in seconds. Maximum: 6 minutes.", ), IO.Int.Input( "seed", @@ -136,13 +134,7 @@ class SoniloTextToMusic(IO.ComfyNode): is_api_node=True, price_badge=IO.PriceBadge( depends_on=IO.PriceBadgeDepends(widgets=["duration"]), - expr=""" - ( - widgets.duration > 0 - ? {"type":"usd","usd": 0.005 * widgets.duration} - : {"type":"usd","usd": 0.005, "format":{"suffix":"/second"}} - ) - """, + expr='{"type":"usd","usd": 0.0025 * widgets.duration}', ), ) @@ -150,14 +142,13 @@ class SoniloTextToMusic(IO.ComfyNode): async def execute( cls, prompt: str, - duration: int = 0, + duration: int = 1, seed: int = 0, ) -> IO.NodeOutput: - validate_string(prompt, strip_whitespace=True, min_length=1) + validate_string(prompt, strip_whitespace=True, min_length=1, max_length=1000) form = aiohttp.FormData() form.add_field("prompt", prompt) - if duration > 0: - form.add_field("duration", str(duration)) + form.add_field("duration", str(duration)) audio_bytes = await _stream_sonilo_music( cls, ApiEndpoint(path="/proxy/sonilo/t2m/generate", method="POST"), @@ -174,8 +165,7 @@ async def _stream_sonilo_music( """POST ``form`` to Sonilo, read the NDJSON stream, and return the first stream's audio bytes.""" url = urljoin(default_base_url().rstrip("/") + "/", endpoint.path.lstrip("/")) - headers: dict[str, str] = {} - headers.update(get_auth_header(cls)) + headers = get_comfy_api_headers(cls) headers.update(endpoint.headers) node_id = get_node_id(cls) diff --git a/comfy_api_nodes/nodes_sora.py b/comfy_api_nodes/nodes_sora.py index 83cfca495..4ff1d649f 100644 --- a/comfy_api_nodes/nodes_sora.py +++ b/comfy_api_nodes/nodes_sora.py @@ -34,7 +34,7 @@ class OpenAIVideoSora2(IO.ComfyNode): return IO.Schema( node_id="OpenAIVideoSora2", display_name="OpenAI Sora - Video (DEPRECATED)", - category="video/partner/Sora", + category="partner/video/Sora", description=( "OpenAI video and audio generation.\n\n" "DEPRECATION NOTICE: OpenAI will stop serving the Sora v2 API in September 2026. " diff --git a/comfy_api_nodes/nodes_stability.py b/comfy_api_nodes/nodes_stability.py index a1753d647..9eaba173b 100644 --- a/comfy_api_nodes/nodes_stability.py +++ b/comfy_api_nodes/nodes_stability.py @@ -62,7 +62,7 @@ class StabilityStableImageUltraNode(IO.ComfyNode): return IO.Schema( node_id="StabilityStableImageUltraNode", display_name="Stability AI Stable Image Ultra", - category="image/partner/Stability AI", + category="partner/image/Stability AI", description=cleandoc(cls.__doc__ or ""), inputs=[ IO.String.Input( @@ -197,7 +197,7 @@ class StabilityStableImageSD_3_5Node(IO.ComfyNode): return IO.Schema( node_id="StabilityStableImageSD_3_5Node", display_name="Stability AI Stable Diffusion 3.5 Image", - category="image/partner/Stability AI", + category="partner/image/Stability AI", description=cleandoc(cls.__doc__ or ""), inputs=[ IO.String.Input( @@ -354,7 +354,7 @@ class StabilityUpscaleConservativeNode(IO.ComfyNode): return IO.Schema( node_id="StabilityUpscaleConservativeNode", display_name="Stability AI Upscale Conservative", - category="image/partner/Stability AI", + category="partner/image/Stability AI", description=cleandoc(cls.__doc__ or ""), inputs=[ IO.Image.Input("image"), @@ -457,7 +457,7 @@ class StabilityUpscaleCreativeNode(IO.ComfyNode): return IO.Schema( node_id="StabilityUpscaleCreativeNode", display_name="Stability AI Upscale Creative", - category="image/partner/Stability AI", + category="partner/image/Stability AI", description=cleandoc(cls.__doc__ or ""), inputs=[ IO.Image.Input("image"), @@ -578,7 +578,7 @@ class StabilityUpscaleFastNode(IO.ComfyNode): return IO.Schema( node_id="StabilityUpscaleFastNode", display_name="Stability AI Upscale Fast", - category="image/partner/Stability AI", + category="partner/image/Stability AI", description=cleandoc(cls.__doc__ or ""), inputs=[ IO.Image.Input("image"), @@ -630,7 +630,7 @@ class StabilityTextToAudio(IO.ComfyNode): return IO.Schema( node_id="StabilityTextToAudio", display_name="Stability AI Text To Audio", - category="audio/partner/Stability AI", + category="partner/audio/Stability AI", essentials_category="Audio", description=cleandoc(cls.__doc__ or ""), inputs=[ @@ -708,7 +708,7 @@ class StabilityAudioToAudio(IO.ComfyNode): return IO.Schema( node_id="StabilityAudioToAudio", display_name="Stability AI Audio To Audio", - category="audio/partner/Stability AI", + category="partner/audio/Stability AI", description=cleandoc(cls.__doc__ or ""), inputs=[ IO.Combo.Input( @@ -802,7 +802,7 @@ class StabilityAudioInpaint(IO.ComfyNode): return IO.Schema( node_id="StabilityAudioInpaint", display_name="Stability AI Audio Inpaint", - category="audio/partner/Stability AI", + category="partner/audio/Stability AI", description=cleandoc(cls.__doc__ or ""), inputs=[ IO.Combo.Input( diff --git a/comfy_api_nodes/nodes_topaz.py b/comfy_api_nodes/nodes_topaz.py index d0906ee44..f7ef4cbf6 100644 --- a/comfy_api_nodes/nodes_topaz.py +++ b/comfy_api_nodes/nodes_topaz.py @@ -52,7 +52,7 @@ class TopazImageEnhance(IO.ComfyNode): return IO.Schema( node_id="TopazImageEnhance", display_name="Topaz Image Enhance", - category="image/partner/Topaz", + category="partner/image/Topaz", description="Industry-standard upscaling and image enhancement.", inputs=[ IO.Combo.Input("model", options=["Reimagine"]), @@ -235,7 +235,7 @@ class TopazVideoEnhance(IO.ComfyNode): return IO.Schema( node_id="TopazVideoEnhance", display_name="Topaz Video Enhance (Legacy)", - category="video/partner/Topaz", + category="partner/video/Topaz", description="Breathe new life into video with powerful upscaling and recovery technology.", inputs=[ IO.Video.Input("video"), @@ -475,7 +475,7 @@ class TopazVideoEnhanceV2(IO.ComfyNode): return IO.Schema( node_id="TopazVideoEnhanceV2", display_name="Topaz Video Enhance", - category="video/partner/Topaz", + category="partner/video/Topaz", description="Breathe new life into video with powerful upscaling and recovery technology.", inputs=[ IO.Video.Input("video"), diff --git a/comfy_api_nodes/nodes_tripo.py b/comfy_api_nodes/nodes_tripo.py index 4820e26c1..228fe8a1d 100644 --- a/comfy_api_nodes/nodes_tripo.py +++ b/comfy_api_nodes/nodes_tripo.py @@ -1,6 +1,6 @@ from typing_extensions import override -from comfy_api.latest import IO, ComfyExtension, Input +from comfy_api.latest import IO, ComfyExtension, Input, Types from comfy_api_nodes.apis.tripo import ( TripoAnimateRetargetRequest, TripoAnimateRigRequest, @@ -8,6 +8,7 @@ from comfy_api_nodes.apis.tripo import ( TripoFileEmptyReference, TripoFileReference, TripoImageToModelRequest, + TripoImportModelRequest, TripoModelVersion, TripoMultiviewToModelRequest, TripoOrientation, @@ -21,6 +22,7 @@ from comfy_api_nodes.apis.tripo import ( TripoTaskType, TripoTextToModelRequest, TripoTextureModelRequest, + TripoTexturePrompt, TripoUrlReference, ) from comfy_api_nodes.util import ( @@ -28,6 +30,7 @@ from comfy_api_nodes.util import ( download_url_to_file_3d, poll_op, sync_op, + upload_3d_model_to_comfyapi, upload_images_to_comfyapi, ) @@ -83,7 +86,7 @@ class TripoTextToModelNode(IO.ComfyNode): return IO.Schema( node_id="TripoTextToModelNode", display_name="Tripo: Text to Model", - category="3d/partner/Tripo", + category="partner/3d/Tripo", inputs=[ IO.String.Input("prompt", multiline=True), IO.String.Input("negative_prompt", multiline=True, optional=True), @@ -210,7 +213,7 @@ class TripoImageToModelNode(IO.ComfyNode): return IO.Schema( node_id="TripoImageToModelNode", display_name="Tripo: Image to Model", - category="3d/partner/Tripo", + category="partner/3d/Tripo", inputs=[ IO.Image.Input("image"), IO.Combo.Input( @@ -358,7 +361,7 @@ class TripoMultiviewToModelNode(IO.ComfyNode): return IO.Schema( node_id="TripoMultiviewToModelNode", display_name="Tripo: Multiview to Model", - category="3d/partner/Tripo", + category="partner/3d/Tripo", inputs=[ IO.Image.Input("image"), IO.Image.Input("image_left", optional=True), @@ -518,7 +521,7 @@ class TripoTextureNode(IO.ComfyNode): return IO.Schema( node_id="TripoTextureNode", display_name="Tripo: Texture model", - category="3d/partner/Tripo", + category="partner/3d/Tripo", inputs=[ IO.Custom("MODEL_TASK_ID").Input("model_task_id"), IO.Boolean.Input("texture", default=True, optional=True), @@ -538,6 +541,14 @@ class TripoTextureNode(IO.ComfyNode): optional=True, advanced=True, ), + IO.String.Input( + "texture_prompt", + default="", + multiline=True, + optional=True, + tooltip="Optional text guidance for texturing. Required in practice for imported " + "models (Tripo: Import Model), which carry no source image to infer colors from.", + ), ], outputs=[ IO.String.Output(display_name="model_file"), # for backward compatibility only @@ -571,6 +582,7 @@ class TripoTextureNode(IO.ComfyNode): texture_seed: int | None = None, texture_quality: str | None = None, texture_alignment: str | None = None, + texture_prompt: str = "", ) -> IO.NodeOutput: response = await sync_op( cls, @@ -583,6 +595,7 @@ class TripoTextureNode(IO.ComfyNode): texture_seed=texture_seed, texture_quality=texture_quality, texture_alignment=texture_alignment, + texture_prompt=TripoTexturePrompt(text=texture_prompt.strip()) if texture_prompt.strip() else None, ), ) return await poll_until_finished(cls, response, average_duration=80) @@ -595,7 +608,7 @@ class TripoRefineNode(IO.ComfyNode): return IO.Schema( node_id="TripoRefineNode", display_name="Tripo: Refine Draft model", - category="3d/partner/Tripo", + category="partner/3d/Tripo", description="Refine a draft model created by v1.4 Tripo models only.", inputs=[ IO.Custom("MODEL_TASK_ID").Input("model_task_id", tooltip="Must be a v1.4 Tripo model"), @@ -635,7 +648,7 @@ class TripoRigNode(IO.ComfyNode): return IO.Schema( node_id="TripoRigNode", display_name="Tripo: Rig model", - category="3d/partner/Tripo", + category="partner/3d/Tripo", inputs=[IO.Custom("MODEL_TASK_ID").Input("original_model_task_id")], outputs=[ IO.String.Output(display_name="model_file"), # for backward compatibility only @@ -672,7 +685,7 @@ class TripoRetargetNode(IO.ComfyNode): return IO.Schema( node_id="TripoRetargetNode", display_name="Tripo: Retarget rigged model", - category="3d/partner/Tripo", + category="partner/3d/Tripo", inputs=[ IO.Custom("RIG_TASK_ID").Input("original_model_task_id"), IO.Combo.Input( @@ -737,7 +750,7 @@ class TripoConversionNode(IO.ComfyNode): return IO.Schema( node_id="TripoConversionNode", display_name="Tripo: Convert model", - category="3d/partner/Tripo", + category="partner/3d/Tripo", inputs=[ IO.Custom("MODEL_TASK_ID,RIG_TASK_ID,RETARGET_TASK_ID").Input("original_model_task_id"), IO.Combo.Input("format", options=["GLTF", "USDZ", "FBX", "OBJ", "STL", "3MF"]), @@ -915,6 +928,90 @@ class TripoConversionNode(IO.ComfyNode): return await poll_until_finished(cls, response, average_duration=30) +class TripoImportModelNode(IO.ComfyNode): + """Imports an external 3D model into Tripo, producing a MODEL_TASK_ID for post-processing nodes.""" + + SUPPORTED_FORMATS = ("glb", "fbx", "obj", "stl") + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TripoImportModelNode", + display_name="Tripo: Import Model", + category="partner/3d/Tripo", + description="Import an external 3D model (e.g. from Rodin, Hunyuan3D or a local file) into Tripo " + "to use it with Tripo's post-processing nodes: Texture, Rig, Convert. " + "GLB is recommended: textures survive import only when embedded in the file. " + "Note that texturing an imported model requires a texture prompt.", + inputs=[ + IO.MultiType.Input( + "model_3d", + types=[IO.File3DGLB, IO.File3DFBX, IO.File3DOBJ, IO.File3DSTL, IO.File3DAny], + tooltip="3D model to import (GLB / FBX / OBJ / STL, up to 150 MB). " + "OBJ and STL files carry no embedded textures.", + ), + ], + outputs=[ + IO.Custom("MODEL_TASK_ID").Output(display_name="model task_id"), + ], + 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":"text","text":"Free"}""", + ), + ) + + @classmethod + async def execute(cls, model_3d: Types.File3D) -> IO.NodeOutput: + file_format = (model_3d.format or "").lstrip(".").lower() + if file_format == "gltf": + raise ValueError( + "GLTF (.gltf) references external files and cannot be imported. Export a single-file GLB instead." + ) + if file_format not in cls.SUPPORTED_FORMATS: + raise ValueError( + f"Unsupported 3D format '{file_format or 'unknown'}'. " + f"Tripo import supports: {', '.join(f.upper() for f in cls.SUPPORTED_FORMATS)}." + ) + size = len(model_3d.get_bytes()) + if size > 150 * 1024 * 1024: + raise ValueError(f"Model file is {size / (1024 * 1024):.1f} MB; Tripo import allows up to 150 MB.") + + url = await upload_3d_model_to_comfyapi(cls, model_3d, file_format) + response = await sync_op( + cls, + endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/import", method="POST"), + response_model=TripoTaskResponse, + data=TripoImportModelRequest(url=url, format=file_format), + ) + if response.code != 0: + raise RuntimeError(f"Failed to import model: {response.error}") + + task_id = response.data.task_id + response_poll = await poll_op( + cls, + poll_endpoint=ApiEndpoint(path=f"/proxy/tripo/v2/openapi/task/{task_id}"), + response_model=TripoTaskResponse, + failed_statuses=[ + TripoTaskStatus.FAILED, + TripoTaskStatus.CANCELLED, + TripoTaskStatus.UNKNOWN, + TripoTaskStatus.BANNED, + TripoTaskStatus.EXPIRED, + ], + status_extractor=lambda x: x.data.status, + progress_extractor=lambda x: x.data.progress, + estimated_duration=10, + ) + if response_poll.data.status != TripoTaskStatus.SUCCESS: + raise RuntimeError(f"Failed to import model: {response_poll}") + return IO.NodeOutput(task_id) + + def _p1_price_expr(*, geometry_credits: int, textured_credits: int, detailed_credits: int) -> str: return ( "(" @@ -1051,7 +1148,7 @@ class TripoP1TextToModelNode(IO.ComfyNode): return IO.Schema( node_id="TripoP1TextToModelNode", display_name="Tripo P1: Text to Model", - category="3d/partner/Tripo", + category="partner/3d/Tripo", description="Tripo P1 text-to-3D. Optimized for low-poly, game-ready meshes with stable topology.", inputs=[ IO.String.Input("prompt", multiline=True, tooltip="Up to 1024 characters."), @@ -1122,7 +1219,7 @@ class TripoP1ImageToModelNode(IO.ComfyNode): return IO.Schema( node_id="TripoP1ImageToModelNode", display_name="Tripo P1: Image to Model", - category="3d/partner/Tripo", + category="partner/3d/Tripo", description="Tripo P1 image-to-3D. Optimized for low-poly, game-ready meshes.", inputs=[ IO.Image.Input("image"), @@ -1202,7 +1299,7 @@ class TripoP1MultiviewToModelNode(IO.ComfyNode): return IO.Schema( node_id="TripoP1MultiviewToModelNode", display_name="Tripo P1: Multiview to Model", - category="3d/partner/Tripo", + category="partner/3d/Tripo", description="Tripo P1 multiview-to-3D from 2-4 reference images in [front, left, back, right] order. " "Front is required; any combination of the other three may be omitted.", inputs=[ @@ -1292,6 +1389,7 @@ class TripoExtension(ComfyExtension): TripoP1TextToModelNode, TripoP1ImageToModelNode, TripoP1MultiviewToModelNode, + TripoImportModelNode, TripoTextureNode, TripoRefineNode, TripoRigNode, diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py index 068862397..ed34e928b 100644 --- a/comfy_api_nodes/nodes_veo2.py +++ b/comfy_api_nodes/nodes_veo2.py @@ -45,7 +45,7 @@ class VeoVideoGenerationNode(IO.ComfyNode): return IO.Schema( node_id="VeoVideoGenerationNode", display_name="Google Veo 2 Video Generation", - category="video/partner/Veo", + category="partner/video/Veo", description="Generates videos from text prompts using Google's Veo 2 API", inputs=[ IO.String.Input( @@ -256,7 +256,7 @@ class Veo3VideoGenerationNode(IO.ComfyNode): return IO.Schema( node_id="Veo3VideoGenerationNode", display_name="Google Veo 3 Video Generation", - category="video/partner/Veo", + category="partner/video/Veo", description="Generates videos from text prompts using Google's Veo 3 API", inputs=[ IO.String.Input( @@ -468,7 +468,7 @@ class Veo3FirstLastFrameNode(IO.ComfyNode): return IO.Schema( node_id="Veo3FirstLastFrameNode", display_name="Google Veo 3 First-Last-Frame to Video", - category="video/partner/Veo", + category="partner/video/Veo", description="Generate video using prompt and first and last frames.", inputs=[ IO.String.Input( diff --git a/comfy_api_nodes/nodes_vidu.py b/comfy_api_nodes/nodes_vidu.py index 16f6113de..8c5a43f5b 100644 --- a/comfy_api_nodes/nodes_vidu.py +++ b/comfy_api_nodes/nodes_vidu.py @@ -71,7 +71,7 @@ class ViduTextToVideoNode(IO.ComfyNode): return IO.Schema( node_id="ViduTextToVideoNode", display_name="Vidu Text To Video Generation", - category="video/partner/Vidu", + category="partner/video/Vidu", description="Generate video from a text prompt", inputs=[ IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"), @@ -169,7 +169,7 @@ class ViduImageToVideoNode(IO.ComfyNode): return IO.Schema( node_id="ViduImageToVideoNode", display_name="Vidu Image To Video Generation", - category="video/partner/Vidu", + category="partner/video/Vidu", description="Generate video from image and optional prompt", inputs=[ IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"), @@ -273,7 +273,7 @@ class ViduReferenceVideoNode(IO.ComfyNode): return IO.Schema( node_id="ViduReferenceVideoNode", display_name="Vidu Reference To Video Generation", - category="video/partner/Vidu", + category="partner/video/Vidu", description="Generate video from multiple images and a prompt", inputs=[ IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"), @@ -388,7 +388,7 @@ class ViduStartEndToVideoNode(IO.ComfyNode): return IO.Schema( node_id="ViduStartEndToVideoNode", display_name="Vidu Start End To Video Generation", - category="video/partner/Vidu", + category="partner/video/Vidu", description="Generate a video from start and end frames and a prompt", inputs=[ IO.Combo.Input("model", options=["viduq1"], tooltip="Model name"), @@ -492,7 +492,7 @@ class Vidu2TextToVideoNode(IO.ComfyNode): return IO.Schema( node_id="Vidu2TextToVideoNode", display_name="Vidu2 Text-to-Video Generation", - category="video/partner/Vidu", + category="partner/video/Vidu", description="Generate video from a text prompt", inputs=[ IO.Combo.Input("model", options=["viduq2"]), @@ -584,7 +584,7 @@ class Vidu2ImageToVideoNode(IO.ComfyNode): return IO.Schema( node_id="Vidu2ImageToVideoNode", display_name="Vidu2 Image-to-Video Generation", - category="video/partner/Vidu", + category="partner/video/Vidu", description="Generate a video from an image and an optional prompt.", inputs=[ IO.Combo.Input("model", options=["viduq2-pro-fast", "viduq2-pro", "viduq2-turbo"]), @@ -714,7 +714,7 @@ class Vidu2ReferenceVideoNode(IO.ComfyNode): return IO.Schema( node_id="Vidu2ReferenceVideoNode", display_name="Vidu2 Reference-to-Video Generation", - category="video/partner/Vidu", + category="partner/video/Vidu", description="Generate a video from multiple reference images and a prompt.", inputs=[ IO.Combo.Input("model", options=["viduq2"]), @@ -849,7 +849,7 @@ class Vidu2StartEndToVideoNode(IO.ComfyNode): return IO.Schema( node_id="Vidu2StartEndToVideoNode", display_name="Vidu2 Start/End Frame-to-Video Generation", - category="video/partner/Vidu", + category="partner/video/Vidu", description="Generate a video from a start frame, an end frame, and a prompt.", inputs=[ IO.Combo.Input("model", options=["viduq2-pro-fast", "viduq2-pro", "viduq2-turbo"]), @@ -969,7 +969,7 @@ class ViduExtendVideoNode(IO.ComfyNode): return IO.Schema( node_id="ViduExtendVideoNode", display_name="Vidu Video Extension", - category="video/partner/Vidu", + category="partner/video/Vidu", description="Extend an existing video by generating additional frames.", inputs=[ IO.DynamicCombo.Input( @@ -1138,7 +1138,7 @@ class ViduMultiFrameVideoNode(IO.ComfyNode): return IO.Schema( node_id="ViduMultiFrameVideoNode", display_name="Vidu Multi-Frame Video Generation", - category="video/partner/Vidu", + category="partner/video/Vidu", description="Generate a video with multiple keyframe transitions.", inputs=[ IO.Combo.Input("model", options=["viduq2-pro", "viduq2-turbo"]), @@ -1284,7 +1284,7 @@ class Vidu3TextToVideoNode(IO.ComfyNode): return IO.Schema( node_id="Vidu3TextToVideoNode", display_name="Vidu Q3 Text-to-Video Generation", - category="video/partner/Vidu", + category="partner/video/Vidu", description="Generate video from a text prompt.", inputs=[ IO.DynamicCombo.Input( @@ -1429,7 +1429,7 @@ class Vidu3ImageToVideoNode(IO.ComfyNode): return IO.Schema( node_id="Vidu3ImageToVideoNode", display_name="Vidu Q3 Image-to-Video Generation", - category="video/partner/Vidu", + category="partner/video/Vidu", description="Generate a video from an image and an optional prompt.", inputs=[ IO.DynamicCombo.Input( @@ -1571,7 +1571,7 @@ class Vidu3StartEndToVideoNode(IO.ComfyNode): return IO.Schema( node_id="Vidu3StartEndToVideoNode", display_name="Vidu Q3 Start/End Frame-to-Video Generation", - category="video/partner/Vidu", + category="partner/video/Vidu", description="Generate a video from a start frame, an end frame, and a prompt.", inputs=[ IO.DynamicCombo.Input( diff --git a/comfy_api_nodes/nodes_wan.py b/comfy_api_nodes/nodes_wan.py index a235dc387..b7b97d70f 100644 --- a/comfy_api_nodes/nodes_wan.py +++ b/comfy_api_nodes/nodes_wan.py @@ -61,7 +61,7 @@ class WanTextToImageApi(IO.ComfyNode): return IO.Schema( node_id="WanTextToImageApi", display_name="Wan Text to Image", - category="image/partner/Wan", + category="partner/image/Wan", description="Generates an image based on a text prompt.", inputs=[ IO.Combo.Input( @@ -184,7 +184,7 @@ class WanImageToImageApi(IO.ComfyNode): return IO.Schema( node_id="WanImageToImageApi", display_name="Wan Image to Image", - category="image/partner/Wan", + category="partner/image/Wan", description="Generates an image from one or two input images and a text prompt. " "The output image is currently fixed at 1.6 MP, and its aspect ratio matches the input image(s).", inputs=[ @@ -312,7 +312,7 @@ class WanTextToVideoApi(IO.ComfyNode): return IO.Schema( node_id="WanTextToVideoApi", display_name="Wan Text to Video", - category="video/partner/Wan", + category="partner/video/Wan", description="Generates a video based on a text prompt.", inputs=[ IO.Combo.Input( @@ -495,7 +495,7 @@ class WanImageToVideoApi(IO.ComfyNode): return IO.Schema( node_id="WanImageToVideoApi", display_name="Wan Image to Video", - category="video/partner/Wan", + category="partner/video/Wan", description="Generates a video from the first frame and a text prompt.", inputs=[ IO.Combo.Input( @@ -674,7 +674,7 @@ class WanReferenceVideoApi(IO.ComfyNode): return IO.Schema( node_id="WanReferenceVideoApi", display_name="Wan Reference to Video", - category="video/partner/Wan", + category="partner/video/Wan", description="Use the character and voice from input videos, combined with a prompt, " "to generate a new video that maintains character consistency.", inputs=[ @@ -828,7 +828,7 @@ class Wan2TextToVideoApi(IO.ComfyNode): return IO.Schema( node_id="Wan2TextToVideoApi", display_name="Wan 2.7 Text to Video", - category="video/partner/Wan", + category="partner/video/Wan", description="Generates a video based on a text prompt using the Wan 2.7 model.", inputs=[ IO.DynamicCombo.Input( @@ -981,7 +981,7 @@ class Wan2ImageToVideoApi(IO.ComfyNode): return IO.Schema( node_id="Wan2ImageToVideoApi", display_name="Wan 2.7 Image to Video", - category="video/partner/Wan", + category="partner/video/Wan", description="Generate a video from a first-frame image, with optional last-frame image and audio.", inputs=[ IO.DynamicCombo.Input( @@ -1152,7 +1152,7 @@ class Wan2VideoContinuationApi(IO.ComfyNode): return IO.Schema( node_id="Wan2VideoContinuationApi", display_name="Wan 2.7 Video Continuation", - category="video/partner/Wan", + category="partner/video/Wan", description="Continue a video from where it left off, with optional last-frame control.", inputs=[ IO.DynamicCombo.Input( @@ -1319,7 +1319,7 @@ class Wan2VideoEditApi(IO.ComfyNode): return IO.Schema( node_id="Wan2VideoEditApi", display_name="Wan 2.7 Video Edit", - category="video/partner/Wan", + category="partner/video/Wan", description="Edit a video using text instructions, reference images, or style transfer.", inputs=[ IO.DynamicCombo.Input( @@ -1477,7 +1477,7 @@ class Wan2ReferenceVideoApi(IO.ComfyNode): return IO.Schema( node_id="Wan2ReferenceVideoApi", display_name="Wan 2.7 Reference to Video", - category="video/partner/Wan", + category="partner/video/Wan", description="Generate a video featuring a person or object from reference materials. " "Supports single-character performances and multi-character interactions.", inputs=[ @@ -1651,7 +1651,7 @@ class HappyHorseTextToVideoApi(IO.ComfyNode): return IO.Schema( node_id="HappyHorseTextToVideoApi", display_name="HappyHorse Text to Video", - category="video/partner/Wan", + category="partner/video/Wan", description="Generates a video based on a text prompt using the HappyHorse model.", inputs=[ IO.DynamicCombo.Input( @@ -1775,7 +1775,7 @@ class HappyHorseImageToVideoApi(IO.ComfyNode): return IO.Schema( node_id="HappyHorseImageToVideoApi", display_name="HappyHorse Image to Video", - category="video/partner/Wan", + category="partner/video/Wan", description="Generate a video from a first-frame image using the HappyHorse model.", inputs=[ IO.DynamicCombo.Input( @@ -1905,7 +1905,7 @@ class HappyHorseVideoEditApi(IO.ComfyNode): return IO.Schema( node_id="HappyHorseVideoEditApi", display_name="HappyHorse Video Edit", - category="video/partner/Wan", + category="partner/video/Wan", description="Edit a video using text instructions or reference images with the HappyHorse model. " "Output duration is 3-15s and matches the input video; inputs longer than 15s are truncated.", inputs=[ @@ -2046,7 +2046,7 @@ class HappyHorseReferenceVideoApi(IO.ComfyNode): return IO.Schema( node_id="HappyHorseReferenceVideoApi", display_name="HappyHorse Reference to Video", - category="video/partner/Wan", + category="partner/video/Wan", description="Generate a video featuring a person or object from reference materials with the HappyHorse " "model. Supports single-character performances and multi-character interactions.", inputs=[ diff --git a/comfy_api_nodes/nodes_wavespeed.py b/comfy_api_nodes/nodes_wavespeed.py index a250015c3..5839f9d37 100644 --- a/comfy_api_nodes/nodes_wavespeed.py +++ b/comfy_api_nodes/nodes_wavespeed.py @@ -27,7 +27,7 @@ class WavespeedFlashVSRNode(IO.ComfyNode): return IO.Schema( node_id="WavespeedFlashVSRNode", display_name="FlashVSR Video Upscale", - category="video/partner/WaveSpeed", + category="partner/video/WaveSpeed", description="Fast, high-quality video upscaler that " "boosts resolution and restores clarity for low-resolution or blurry footage.", inputs=[ @@ -98,7 +98,7 @@ class WavespeedImageUpscaleNode(IO.ComfyNode): return IO.Schema( node_id="WavespeedImageUpscaleNode", display_name="WaveSpeed Image Upscale", - category="image/partner/WaveSpeed", + category="partner/image/WaveSpeed", description="Boost image resolution and quality, upscaling photos to 4K or 8K for sharp, detailed results.", inputs=[ IO.Combo.Input("model", options=["SeedVR2", "Ultimate"]), diff --git a/comfy_api_nodes/util/_helpers.py b/comfy_api_nodes/util/_helpers.py index 648defe3d..6b8121cab 100644 --- a/comfy_api_nodes/util/_helpers.py +++ b/comfy_api_nodes/util/_helpers.py @@ -4,11 +4,14 @@ import os import re import time from collections.abc import Callable +from datetime import datetime, timezone +from email.utils import parsedate_to_datetime from io import BytesIO from yarl import URL from comfy.cli_args import args +from comfy.deploy_environment import get_deploy_environment from comfy.model_management import processing_interrupted from comfy_api.latest import IO @@ -35,6 +38,30 @@ def get_auth_header(node_cls: type[IO.ComfyNode]) -> dict[str, str]: return {} +def get_usage_source(node_cls: type[IO.ComfyNode]) -> str: + """Source of the prompt that triggered this API node. + + Defaults to "comfyui-api" when the submitting client didn't identify itself, + i.e. a direct API call to this server. + """ + return node_cls.hidden.comfy_usage_source or "comfyui-api" + + +def get_comfy_api_headers(node_cls: type[IO.ComfyNode]) -> dict[str, str]: + """Common headers (auth, deploy environment, usage source) for Comfy API requests. + + Centralizes the shared header set so every Comfy API request sends a consistent + set and new shared headers only need to be added in one place. Intended for + relative/cloud URLs resolved against ``default_base_url()``; because the result + includes auth, callers must not attach it to arbitrary absolute/presigned URLs. + """ + return { + **get_auth_header(node_cls), + "Comfy-Env": get_deploy_environment(), + "Comfy-Usage-Source": get_usage_source(node_cls), + } + + def default_base_url() -> str: return getattr(args, "comfy_api_base", "https://api.comfy.org") @@ -66,6 +93,32 @@ async def sleep_with_interrupt( await asyncio.sleep(min(1.0, end - now)) +def _retry_after_wait(value: str | None, fallback: float, max_wait: float) -> float: + """Delay before the next retry, honoring a server ``Retry-After`` header.""" + + seconds: float | None = None + if value is not None: + value = value.strip() + if value.isascii() and value.isdigit(): + # delay-seconds form. The ASCII-digit guard keeps exotic Unicode "digit" characters away from float() + # an all-digit string always converts (huge values become inf, never raising). + seconds = float(value) + elif value: + # HTTP-date form. parsedate_to_datetime raises OverflowError (not a ValueError) on absurd years/offsets + try: + parsed = parsedate_to_datetime(value) + except (TypeError, ValueError, OverflowError): + parsed = None + if parsed is not None: + if parsed.tzinfo is None: # naive datetime: HTTP-date is UTC + parsed = parsed.replace(tzinfo=timezone.utc) + delta = (parsed - datetime.now(timezone.utc)).total_seconds() + seconds = delta if delta > 0 else 0.0 + if seconds is None: + return fallback + return min(seconds, max_wait) + + def mimetype_to_extension(mime_type: str) -> str: """Converts a MIME type to a file extension.""" return mime_type.split("/")[-1].lower() diff --git a/comfy_api_nodes/util/client.py b/comfy_api_nodes/util/client.py index 57c501724..66aab17f8 100644 --- a/comfy_api_nodes/util/client.py +++ b/comfy_api_nodes/util/client.py @@ -19,12 +19,11 @@ from comfy import utils from comfy_api.latest import IO from server import PromptServer -from comfy.deploy_environment import get_deploy_environment - from . import request_logger from ._helpers import ( + _retry_after_wait, default_base_url, - get_auth_header, + get_comfy_api_headers, get_node_id, is_processing_interrupted, sleep_with_interrupt, @@ -84,6 +83,7 @@ class _PollUIState: _RETRY_STATUS = {408, 500, 502, 503, 504} # status 429 is handled separately +_MAX_RETRY_AFTER_WAIT = 150.0 # Cap a server Retry-After at this many seconds so a large hint can't block execution COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed", "finished", "done", "complete"] FAILED_STATUSES = ["cancelled", "canceled", "canceling", "fail", "failed", "error"] QUEUED_STATUSES = ["created", "queued", "queueing", "submitted", "initializing", "wait", "in_queue"] @@ -645,8 +645,7 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool): payload_headers = {"Accept": "*/*"} if expect_binary else {"Accept": "application/json"} if not parsed_url.scheme and not parsed_url.netloc: # is URL relative? - payload_headers.update(get_auth_header(cfg.node_cls)) - payload_headers["Comfy-Env"] = get_deploy_environment() + payload_headers.update(get_comfy_api_headers(cfg.node_cls)) if cfg.endpoint.headers: payload_headers.update(cfg.endpoint.headers) @@ -750,6 +749,7 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool): should_retry = True if should_retry: + wait_time = _retry_after_wait(resp.headers.get("Retry-After"), wait_time, _MAX_RETRY_AFTER_WAIT) logging.warning( "HTTP %s %s -> %s. Waiting %.2fs (%s).", method, diff --git a/comfy_api_nodes/util/conversions.py b/comfy_api_nodes/util/conversions.py index 5738df57f..a1b5d599c 100644 --- a/comfy_api_nodes/util/conversions.py +++ b/comfy_api_nodes/util/conversions.py @@ -469,6 +469,11 @@ def _apply_video_scale(video: Input.Video, scale_dims: tuple[int, int]) -> Input input_container = None output_container = None + # get_stream_source() is untrimmed, so apply the trim window in this same pass. + # start_time is normalized (>= 0); duration == 0 means "until the end". + start_time, duration = video.get_active_trim_window() + trimming = bool(start_time or duration) + try: input_source = video.get_stream_source() input_container = av.open(input_source, mode="r") @@ -487,16 +492,45 @@ def _apply_video_scale(video: Input.Video, scale_dims: tuple[int, int]) -> Input audio_stream.layout = stream.layout break + in_video = input_container.streams.video[0] + start_pts = int(start_time / in_video.time_base) if trimming else 0 + end_pts = int((start_time + duration) / in_video.time_base) if duration else None + if start_pts: + input_container.seek(start_pts, stream=in_video) + + encoded = 0 for frame in input_container.decode(video=0): + if trimming: + if frame.pts is None or frame.pts < start_pts: + continue + if end_pts is not None and frame.pts >= end_pts: + break frame = frame.reformat(width=out_w, height=out_h, format="yuv420p") + # Re-wrap as a fresh frame: dropping irregular source timestamps (VFR/AVI/GIF/...) + # lets the encoder assign clean ones and avoids mp4 muxer errors. + frame = av.VideoFrame.from_ndarray(frame.to_ndarray(format="yuv420p"), format="yuv420p") for packet in video_stream.encode(frame): output_container.mux(packet) + encoded += 1 for packet in video_stream.encode(): output_container.mux(packet) + if encoded == 0: + raise ValueError( + f"resize produced no frames (start_time={start_time}, duration={duration} " + "selected nothing from the source)" + ) + if audio_stream is not None: input_container.seek(0) for audio_frame in input_container.decode(audio=0): + if trimming: + if audio_frame.time is None or audio_frame.time < start_time: + continue + if duration and audio_frame.time > start_time + duration: + break + # Carry odd audio time bases the mp4 muxer rejects; reset pts, encoder assigns clean ones (MP3-in-AVI) + audio_frame.pts = None for packet in audio_stream.encode(audio_frame): output_container.mux(packet) for packet in audio_stream.encode(): diff --git a/comfy_api_nodes/util/download_helpers.py b/comfy_api_nodes/util/download_helpers.py index aa588d038..0ec3c6e66 100644 --- a/comfy_api_nodes/util/download_helpers.py +++ b/comfy_api_nodes/util/download_helpers.py @@ -17,7 +17,7 @@ from folder_paths import get_output_directory from . import request_logger from ._helpers import ( default_base_url, - get_auth_header, + get_comfy_api_headers, is_processing_interrupted, sleep_with_interrupt, to_aiohttp_url, @@ -64,7 +64,7 @@ async def download_url_to_bytesio( if cls is None: raise ValueError("For relative 'cloud' paths, the `cls` parameter is required.") url = urljoin(default_base_url().rstrip("/") + "/", url.lstrip("/")) - headers = get_auth_header(cls) + headers = get_comfy_api_headers(cls) while True: attempt += 1 diff --git a/comfy_execution/asset_enrichment.py b/comfy_execution/asset_enrichment.py new file mode 100644 index 000000000..38e9496a8 --- /dev/null +++ b/comfy_execution/asset_enrichment.py @@ -0,0 +1,66 @@ +"""Enrich executed-node output entries with asset id.""" +import logging +import os + + +def enrich_output_with_assets(output_ui: dict) -> dict: + """Register file-type output entries as assets and inject their ``id``. + + Runs at output-processing time, once per produced output, when + --enable-assets is set. Returns a new dict; entries without a resolvable + on-disk file path are left unchanged. Errors are caught per-entry so a + failure never blocks execution or the other entries. + """ + from comfy.cli_args import args + if not args.enable_assets: + return output_ui + + import folder_paths + from app.assets.services.ingest import register_file_in_place, DependencyMissingError + + enriched = {} + for key, entries in output_ui.items(): + if not isinstance(entries, list): + enriched[key] = entries + continue + new_entries = [] + for entry in entries: + if not isinstance(entry, dict) or "filename" not in entry or "type" not in entry: + new_entries.append(entry) + continue + try: + base = folder_paths.get_directory_by_type(entry["type"]) + if base is None: + new_entries.append(entry) + continue + base_abs = os.path.abspath(base) + abs_path = os.path.abspath(os.path.join(base_abs, entry.get("subfolder") or "", entry["filename"])) + try: + if os.path.commonpath([base_abs, abs_path]) != base_abs: + raise ValueError("escapes base") + except ValueError: + logging.warning("Asset enrichment skipped (path escapes base): %s", entry.get("filename")) + new_entries.append(entry) + continue + if not os.path.isfile(abs_path): + new_entries.append(entry) + continue + + # Register unconditionally: the file was just produced, and + # register_file_in_place re-hashes so an overwritten path can + # never carry a stale id. + result = register_file_in_place( + abs_path=abs_path, + name=entry["filename"], + tags=[entry["type"]], + ) + + entry = dict(entry) + entry["id"] = result.ref.id + except DependencyMissingError: + logging.warning("Asset enrichment skipped (blake3 not available): %s", entry.get("filename")) + except Exception: + logging.warning("Failed to enrich output entry with asset id: %s", entry.get("filename"), exc_info=True) + new_entries.append(entry) + enriched[key] = new_entries + return enriched diff --git a/comfy_execution/jobs.py b/comfy_execution/jobs.py index fcd7ef735..fa3ab0faf 100644 --- a/comfy_execution/jobs.py +++ b/comfy_execution/jobs.py @@ -3,11 +3,23 @@ Job utilities for the /api/jobs endpoint. Provides normalization and helper functions for job status tracking. """ -from typing import Optional +import uuid +from typing import Callable, Optional from comfy_api.internal import prune_dict +# Result of classifying a job for cancellation. +# 'running' -> job is currently executing (interrupt it) +# 'pending' -> job is queued but not started (dequeue it) +# 'terminal' -> job already finished (present in history); cancel is a no-op +# 'unknown' -> job id is not present anywhere +CANCEL_RUNNING = 'running' +CANCEL_PENDING = 'pending' +CANCEL_TERMINAL = 'terminal' +CANCEL_UNKNOWN = 'unknown' + + class JobStatus: """Job status constants.""" PENDING = 'pending' @@ -19,6 +31,25 @@ class JobStatus: ALL = [PENDING, IN_PROGRESS, COMPLETED, FAILED, CANCELLED] +def validate_job_id(value) -> str: + """Validate a client-supplied job (prompt) id. + + Job ids must be UUIDs in the canonical lowercase hyphenated form. The id + is stored and compared verbatim everywhere downstream — history keys, + websocket events, and /interrupt matching — so accepting another spelling + would silently rewrite the client's id and then miss every exact-match + lookup. Rejecting loudly beats that. + + Returns the id unchanged. Raises ValueError when the value is not a + string in canonical UUID form. + """ + if not isinstance(value, str): + raise ValueError(f"job id must be a string, got {type(value).__name__}") + if str(uuid.UUID(value)) != value: + raise ValueError("job id must be a UUID in canonical lowercase hyphenated form") + return value + + # Media types that can be previewed in the frontend PREVIEWABLE_MEDIA_TYPES = frozenset({'images', 'video', 'audio', '3d', 'text'}) @@ -387,3 +418,71 @@ def get_all_jobs( jobs = jobs[:limit] return (jobs, total_count) + + +def classify_job_for_cancel(prompt_id: str, running: list, queued: list, history: dict) -> str: + """Classify a job id for cancellation. + + Returns one of CANCEL_RUNNING, CANCEL_PENDING, CANCEL_TERMINAL, CANCEL_UNKNOWN. + + Queue items are tuples whose second element (index 1) is the prompt_id. + History is a dict keyed by prompt_id, so a job present there has already + finished and cancelling it is a no-op. + """ + for item in running: + if item[1] == prompt_id: + return CANCEL_RUNNING + for item in queued: + if item[1] == prompt_id: + return CANCEL_PENDING + if prompt_id in history: + return CANCEL_TERMINAL + return CANCEL_UNKNOWN + + +def cancel_job( + prompt_id: str, + running: list, + queued: list, + history: dict, + interrupt: Callable[[str], bool], + dequeue: Callable[[str], bool], +) -> str: + """Cancel a single job by id, regardless of state. + + Maps the cancel onto the runtime's existing mechanics: + - a running job is interrupted via ``interrupt`` + - a pending job is removed from the queue via ``dequeue`` + - a job that already finished (terminal) is a no-op + - an unknown id is a no-op (callers that need fail-fast behaviour should + validate ids up front with ``classify_job_for_cancel``) + + Both ``interrupt`` and ``dequeue`` take the prompt id and return whether + they acted on a job that was *actually* in that state, so the value returned + here reflects what truly happened rather than the (possibly stale) + classification. This matters around the narrow TOCTOU windows where a job + changes state between the caller's snapshot and the action: + + - a job classified RUNNING may have finished before ``interrupt`` fires: + ``interrupt`` returns False and this returns CANCEL_UNKNOWN (no-op). + - a job classified PENDING may have started executing before ``dequeue`` + fires: ``dequeue`` returns False, ``interrupt`` then catches the now- + running job and this returns CANCEL_RUNNING. If it had simply finished + instead, both return False and this returns CANCEL_UNKNOWN. + + ``interrupt`` must be atomic — interrupt the job only if it is still the one + running — so a cancel can never land on an unrelated prompt that started in + the meantime (see ``execution.PromptQueue.interrupt_if_running``). + """ + classification = classify_job_for_cancel(prompt_id, running, queued, history) + if classification == CANCEL_RUNNING: + return CANCEL_RUNNING if interrupt(prompt_id) else CANCEL_UNKNOWN + if classification == CANCEL_PENDING: + if dequeue(prompt_id): + return CANCEL_PENDING + # Left the pending queue between classification and dequeue: if it + # started executing, interrupt the now-running job; otherwise it has + # already finished and the cancel is a genuine no-op. + return CANCEL_RUNNING if interrupt(prompt_id) else CANCEL_UNKNOWN + # CANCEL_TERMINAL and CANCEL_UNKNOWN are intentional no-ops. + return classification diff --git a/comfy_extras/nodes_ace.py b/comfy_extras/nodes_ace.py index 044077b18..eaf234d5b 100644 --- a/comfy_extras/nodes_ace.py +++ b/comfy_extras/nodes_ace.py @@ -11,7 +11,7 @@ class TextEncodeAceStepAudio(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="TextEncodeAceStepAudio", - category="model/conditioning", + category="model/conditioning/ace", inputs=[ IO.Clip.Input("clip"), IO.String.Input("tags", multiline=True, dynamic_prompts=True), @@ -33,7 +33,7 @@ class TextEncodeAceStepAudio15(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="TextEncodeAceStepAudio1.5", - category="model/conditioning", + category="model/conditioning/ace", inputs=[ IO.Clip.Input("clip"), IO.String.Input("tags", multiline=True, dynamic_prompts=True), @@ -67,7 +67,7 @@ class EmptyAceStepLatentAudio(IO.ComfyNode): return IO.Schema( node_id="EmptyAceStepLatentAudio", display_name="Empty Ace Step 1.0 Latent Audio", - category="model/latent/audio", + category="model/latent/ace", inputs=[ IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1), IO.Int.Input( @@ -90,7 +90,7 @@ class EmptyAceStep15LatentAudio(IO.ComfyNode): return IO.Schema( node_id="EmptyAceStep1.5LatentAudio", display_name="Empty Ace Step 1.5 Latent Audio", - category="model/latent/audio", + category="model/latent/ace", inputs=[ IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.01), IO.Int.Input( @@ -111,8 +111,8 @@ class ReferenceAudio(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="ReferenceTimbreAudio", - display_name="Reference Audio", - category="advanced/conditioning/audio", + display_name="Set Reference Audio", + category="model/conditioning", is_experimental=True, description="This node sets the reference audio for ace step 1.5", inputs=[ diff --git a/comfy_extras/nodes_apg.py b/comfy_extras/nodes_apg.py index 4a352038a..6e69b73f7 100644 --- a/comfy_extras/nodes_apg.py +++ b/comfy_extras/nodes_apg.py @@ -16,7 +16,7 @@ class APG(io.ComfyNode): return io.Schema( node_id="APG", display_name="Adaptive Projected Guidance", - category="model/sampling/custom_sampling", + category="model/sampling/custom", inputs=[ io.Model.Input("model"), io.Float.Input( diff --git a/comfy_extras/nodes_ar_video.py b/comfy_extras/nodes_ar_video.py index c22359eb2..9d8f64b20 100644 --- a/comfy_extras/nodes_ar_video.py +++ b/comfy_extras/nodes_ar_video.py @@ -19,7 +19,7 @@ class EmptyARVideoLatent(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="EmptyARVideoLatent", - category="model/latent/video", + category="model/latent/autoregressive", inputs=[ io.Int.Input("width", default=832, min=16, max=8192, step=16), io.Int.Input("height", default=480, min=16, max=8192, step=16), @@ -85,7 +85,7 @@ class ARVideoI2V(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="ARVideoI2V", - category="model/conditioning/video_models", + category="model/conditioning/autoregressive", inputs=[ io.Model.Input("model"), io.Vae.Input("vae"), diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py index ff078f74c..77f124e28 100644 --- a/comfy_extras/nodes_audio.py +++ b/comfy_extras/nodes_audio.py @@ -16,7 +16,7 @@ class EmptyLatentAudio(IO.ComfyNode): return IO.Schema( node_id="EmptyLatentAudio", display_name="Empty Latent Audio", - category="model/latent/audio", + category="model/latent", essentials_category="Audio", inputs=[ IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1), @@ -41,7 +41,7 @@ class ConditioningStableAudio(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="ConditioningStableAudio", - category="model/conditioning", + category="model/conditioning/stable audio", inputs=[ IO.Conditioning.Input("positive"), IO.Conditioning.Input("negative"), @@ -70,7 +70,7 @@ class VAEEncodeAudio(IO.ComfyNode): node_id="VAEEncodeAudio", search_aliases=["audio to latent"], display_name="VAE Encode Audio", - category="model/latent/audio", + category="model/latent", inputs=[ IO.Audio.Input("audio"), IO.Vae.Input("vae"), @@ -115,7 +115,7 @@ class VAEDecodeAudio(IO.ComfyNode): node_id="VAEDecodeAudio", search_aliases=["latent to audio"], display_name="VAE Decode Audio", - category="model/latent/audio", + category="model/latent", inputs=[ IO.Latent.Input("samples"), IO.Vae.Input("vae"), @@ -137,7 +137,7 @@ class VAEDecodeAudioTiled(IO.ComfyNode): node_id="VAEDecodeAudioTiled", search_aliases=["latent to audio"], display_name="VAE Decode Audio (Tiled)", - category="model/latent/audio", + category="model/latent", inputs=[ IO.Latent.Input("samples"), IO.Vae.Input("vae"), @@ -158,7 +158,7 @@ class SaveAudio(IO.ComfyNode): return IO.Schema( node_id="SaveAudio", search_aliases=["export flac"], - display_name="Save Audio (FLAC)", + display_name="Save Audio (FLAC) (Deprecated)", category="audio", essentials_category="Audio", inputs=[ @@ -167,6 +167,7 @@ class SaveAudio(IO.ComfyNode): ], hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo], is_output_node=True, + is_deprecated=True, ) @classmethod @@ -186,7 +187,7 @@ class SaveAudioMP3(IO.ComfyNode): return IO.Schema( node_id="SaveAudioMP3", search_aliases=["export mp3"], - display_name="Save Audio (MP3)", + display_name="Save Audio (MP3) (Deprecated)", category="audio", essentials_category="Audio", inputs=[ @@ -196,6 +197,7 @@ class SaveAudioMP3(IO.ComfyNode): ], hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo], is_output_node=True, + is_deprecated=True, ) @classmethod @@ -217,7 +219,7 @@ class SaveAudioOpus(IO.ComfyNode): return IO.Schema( node_id="SaveAudioOpus", search_aliases=["export opus"], - display_name="Save Audio (Opus)", + display_name="Save Audio (Opus) (Deprecated)", category="audio", inputs=[ IO.Audio.Input("audio"), @@ -226,6 +228,7 @@ class SaveAudioOpus(IO.ComfyNode): ], hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo], is_output_node=True, + is_deprecated=True, ) @classmethod @@ -241,6 +244,54 @@ class SaveAudioOpus(IO.ComfyNode): save_opus = execute # TODO: remove +class SaveAudioAdvanced(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="SaveAudioAdvanced", + search_aliases=["save audio", "export audio", "output audio", "write audio", "flac", "mp3", "opus"], + display_name="Save Audio (Advanced)", + description="Saves the input audio to your ComfyUI output directory.", + category="audio", + inputs=[ + IO.Audio.Input("audio", tooltip="The audio to save."), + IO.String.Input( + "filename_prefix", + default="audio/ComfyUI", + tooltip=( + "The prefix for the file to save. May include formatting tokens " + "such as %date:yyyy-MM-dd%." + ), + ), + IO.DynamicCombo.Input( + "format", + options=[ + IO.DynamicCombo.Option("flac", []), + IO.DynamicCombo.Option("mp3", [ + IO.Combo.Input("quality", options=["V0", "128k", "320k"], default="V0"), + ]), + IO.DynamicCombo.Option("opus", [ + IO.Combo.Input("quality", options=["64k", "96k", "128k", "192k", "320k"], default="128k"), + ]), + ], + tooltip="The file format in which to save the audio.", + ), + ], + hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo], + is_output_node=True, + ) + + @classmethod + def execute(cls, audio, filename_prefix: str, format: dict) -> IO.NodeOutput: + file_format = format.get("format", None) + quality = format.get("quality", None) + if quality: + ui=UI.AudioSaveHelper.get_save_audio_ui(audio, filename_prefix=filename_prefix, cls=cls, format=file_format, quality=quality) + else: + ui=UI.AudioSaveHelper.get_save_audio_ui(audio, filename_prefix=filename_prefix, cls=cls, format=file_format) + return IO.NodeOutput(ui=ui) + + class PreviewAudio(IO.ComfyNode): @classmethod def define_schema(cls): @@ -822,6 +873,7 @@ class AudioExtension(ComfyExtension): SaveAudio, SaveAudioMP3, SaveAudioOpus, + SaveAudioAdvanced, LoadAudio, PreviewAudio, ConditioningStableAudio, diff --git a/comfy_extras/nodes_bernini.py b/comfy_extras/nodes_bernini.py new file mode 100644 index 000000000..0537e0806 --- /dev/null +++ b/comfy_extras/nodes_bernini.py @@ -0,0 +1,108 @@ +import torch +from typing_extensions import override + +import comfy.model_management +import comfy.utils +import node_helpers +from comfy_api.latest import ComfyExtension, io + + +def _resize_long_edge(image, max_size, stride=16): + """Resize (preserve aspect) so the long edge <= max_size, then snap each side to `stride`""" + h, w = image.shape[1], image.shape[2] + scale = min(max_size / max(h, w), 1.0) + nh = max(stride, round(h * scale / stride) * stride) + nw = max(stride, round(w * scale / stride) * stride) + return comfy.utils.common_upscale(image[:, :, :, :3].movedim(-1, 1), nw, nh, "area", "disabled").movedim(1, -1) + + +class BerniniConditioning(io.ComfyNode): + """Bernini in-context conditioning for a Wan2.2-A14B model. + + Attaches the VAE-encoded source video / reference images to the conditioning + source video first, then each reference image + + The task is inferred from which inputs are connected: + (nothing) -> t2v (text-to-video) + source_video -> v2v (video-to-video) + source_video + ref_images -> rv2v (reference-guided video editing) + ref_images only -> r2v (reference-to-video) + source_video + ref_video -> ads2v (insert image/video into video) + + source_video is the edit base / canvas (resized to width x height). + reference_video is moving content to composite in. + Streams are ordered source_video, reference_video, then reference_images -> source_id (1, 2, 3, ...). + """ + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="BerniniConditioning", + display_name="Bernini Conditioning", + category="model/conditioning/bernini", + description="Conditioning node for Bernini in-context video/image conditioning. It can be used for the following tasks: t2v (text-to-video), v2v (video-to-video), rv2v (reference-guided video editing), r2v (reference-to-video), ads2v (insert image/video into video)." + "Reference images injected as in-context tokens (r2v, rv2v) are encoded independently at their own native aspect ratio (long edge capped at ref_max_size)", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=832, min=16, max=8192, step=16), + io.Int.Input("height", default=480, min=16, max=8192, step=16), + io.Int.Input("length", default=81, min=1, max=8192, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Image.Input("source_video", optional=True, tooltip=("Source video to edit or restyle (v2v, rv2v). Resized to width/height and trimmed to length.")), + io.Image.Input("reference_video", optional=True, tooltip=("Video to insert into the source video (ads2v).")), + io.Autogrow.Input("reference_images", optional=True, + template=io.Autogrow.TemplatePrefix( + input=io.Image.Input("reference_image", tooltip=("Reference image injected as an in-context token (r2v, rv2v).")), + prefix="reference_image_", min=0, max=8)), + io.Int.Input("ref_max_size", default=848, min=16, max=8192, step=16, optional=True, tooltip=( + "Max size for the long edge of reference_video and reference_images. Resized with preserved aspect ratio and snapped to 16px.")), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, batch_size, source_video=None, reference_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput: + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + + # source_video (1), reference_video (2), reference_images (3, 4, ...). + context = [] + if source_video is not None: + vid = comfy.utils.common_upscale(source_video[:length, :, :, :3].movedim(-1, 1), width, height, "area", "center").movedim(1, -1) + context.append(vae.encode(vid[:, :, :, :3])) + + if reference_video is not None: + ref_vid = _resize_long_edge(reference_video[:length], ref_max_size) # moving content, native aspect + context.append(vae.encode(ref_vid[:, :, :, :3])) + + # reference_images is an autogrow dict {reference_image_0: IMAGE, ...}; each slot is a + # separate stream at its own native aspect (a multi-image batch in one slot -> one stream per frame). + if reference_images: + for name in sorted(reference_images): + imgs = reference_images[name] + if imgs is None: + continue + for i in range(imgs.shape[0]): + img = _resize_long_edge(imgs[i:i + 1], ref_max_size) # native aspect per ref + context.append(vae.encode(img[:, :, :, :3])) + + if context: + positive = node_helpers.conditioning_set_values(positive, {"context_latents": context}) + negative = node_helpers.conditioning_set_values(negative, {"context_latents": context}) + + return io.NodeOutput(positive, negative, {"samples": latent}) + + +class BerniniExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [BerniniConditioning,] + + +async def comfy_entrypoint() -> BerniniExtension: + return BerniniExtension() diff --git a/comfy_extras/nodes_bg_removal.py b/comfy_extras/nodes_bg_removal.py index 9dc9ad854..c7b33a821 100644 --- a/comfy_extras/nodes_bg_removal.py +++ b/comfy_extras/nodes_bg_removal.py @@ -36,15 +36,15 @@ class RemoveBackground(IO.ComfyNode): category="image/background removal", description="Generates a foreground mask to remove the background from an image using a background removal model.", inputs=[ - IO.Image.Input("image", tooltip="Input image to remove the background from"), - IO.BackgroundRemoval.Input("bg_removal_model", tooltip="Background removal model used to generate the mask") + IO.BackgroundRemoval.Input("bg_removal_model", tooltip="Background removal model used to generate the mask"), + IO.Image.Input("image", tooltip="Input image to remove the background from") ], outputs=[ IO.Mask.Output("mask", tooltip="Generated foreground mask") ] ) @classmethod - def execute(cls, image, bg_removal_model): + def execute(cls, bg_removal_model, image): mask = bg_removal_model.encode_image(image) return IO.NodeOutput(mask) diff --git a/comfy_extras/nodes_boogu.py b/comfy_extras/nodes_boogu.py new file mode 100644 index 000000000..f3951c290 --- /dev/null +++ b/comfy_extras/nodes_boogu.py @@ -0,0 +1,97 @@ +import math + +import node_helpers +import comfy.utils +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io + + +class TextEncodeBooguEdit(io.ComfyNode): + """Boogu-Image Edit conditioning. + + The edit image is used twice, matching the reference pipeline: + - Qwen3-VL vision tokens (instruction understanding) -> positive only + - VAE reference latent (image identity) -> positive and negative + The ref latent is in both conds so it cancels under CFG (identity preserved); + the vision tokens are only in the positive so CFG amplifies the instruction. + The tokenizer selects the right system prompt automatically (image -> TI2I, + empty negative -> DROP), so no template plumbing is needed here. + """ + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="TextEncodeBooguEdit", + category="model/conditioning/boogu", + inputs=[ + io.Clip.Input("clip"), + io.String.Input("prompt", multiline=True, dynamic_prompts=True), + io.String.Input("negative_prompt", multiline=True, dynamic_prompts=True, advanced=True), + io.Vae.Input("vae"), + io.Autogrow.Input( + "images", + template=io.Autogrow.TemplateNames( + io.Image.Input("image"), + names=[f"image_{i}" for i in range(1, 17)], + min=0, + ), + tooltip="Reference image(s) to edit. Boogu focuses on one reference per sample; more are allowed.", + ), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + ], + ) + + @classmethod + def execute(cls, clip, prompt, negative_prompt, vae=None, images: io.Autogrow.Type = None) -> io.NodeOutput: + ref_latents = [] + images_vl = [] + + images = images or {} + for name in sorted(images, key=lambda n: int(n.rsplit("_", 1)[-1])): + image = images[name] + if image is None: + continue + samples = image.movedim(-1, 1) + + # Vision tower input: the reference caps the VLM image at 384x384 + # (max_vlm_input_pil_pixels in pipeline_boogu.py). + total = int(384 * 384) + scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + s = comfy.utils.common_upscale(samples, width, height, "area", "disabled") + images_vl.append(s.movedim(1, -1)[:, :, :, :3]) + + # Reference latent: align to 16 px (VAE /8 * patch_size 2). + if vae is not None: + total = int(1024 * 1024) + scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) + width = round(samples.shape[3] * scale_by / 16.0) * 16 + height = round(samples.shape[2] * scale_by / 16.0) * 16 + s = comfy.utils.common_upscale(samples, width, height, "area", "disabled") + ref_latents.append(vae.encode(s.movedim(1, -1)[:, :, :, :3])) + + # positive: instruction + vision tokens; negative: empty (no vision). Ref latent on both. + positive = clip.encode_from_tokens_scheduled(clip.tokenize(prompt, images=images_vl)) + negative = clip.encode_from_tokens_scheduled(clip.tokenize(negative_prompt)) + + if len(ref_latents) > 0: + positive = node_helpers.conditioning_set_values(positive, {"reference_latents": ref_latents}, append=True) + negative = node_helpers.conditioning_set_values(negative, {"reference_latents": ref_latents}, append=True) + + return io.NodeOutput(positive, negative) + + +class BooguExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + TextEncodeBooguEdit, + ] + + +async def comfy_entrypoint() -> BooguExtension: + return BooguExtension() diff --git a/comfy_extras/nodes_camera_trajectory.py b/comfy_extras/nodes_camera_trajectory.py index 13a1448f4..280d136af 100644 --- a/comfy_extras/nodes_camera_trajectory.py +++ b/comfy_extras/nodes_camera_trajectory.py @@ -153,7 +153,7 @@ class WanCameraEmbedding(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanCameraEmbedding", - category="model/conditioning/video_models", + category="model/conditioning/wan/camera", inputs=[ io.Combo.Input( "camera_pose", diff --git a/comfy_extras/nodes_chroma_radiance.py b/comfy_extras/nodes_chroma_radiance.py index ca427e5cb..059344f3c 100644 --- a/comfy_extras/nodes_chroma_radiance.py +++ b/comfy_extras/nodes_chroma_radiance.py @@ -13,7 +13,7 @@ class EmptyChromaRadianceLatentImage(io.ComfyNode): def define_schema(cls) -> io.Schema: return io.Schema( node_id="EmptyChromaRadianceLatentImage", - category="model/latent/chroma_radiance", + category="model/latent/chroma radiance", inputs=[ io.Int.Input(id="width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), io.Int.Input(id="height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), @@ -33,7 +33,7 @@ class ChromaRadianceOptions(io.ComfyNode): def define_schema(cls) -> io.Schema: return io.Schema( node_id="ChromaRadianceOptions", - category="model/patch/chroma_radiance", + category="model/patch/chroma radiance", description="Allows setting advanced options for the Chroma Radiance model.", inputs=[ io.Model.Input(id="model"), @@ -65,6 +65,12 @@ class ChromaRadianceOptions(io.ComfyNode): tooltip="Allows overriding the default NeRF tile size. -1 means use the default (32). 0 means use non-tiling mode (may require a lot of VRAM).", advanced=True, ), + io.Boolean.Input( + id="force_sequential_txt_ids", + default=False, + tooltip="Force usage of sequential text token IDs instead of zeroes. Should be used for checkpoints from 2026-05-22 to 2026-06-01 that are trained in this way but do not contain the __sequential__ key in the state dict.", + advanced=True, + ), ], outputs=[io.Model.Output()], ) @@ -78,11 +84,15 @@ class ChromaRadianceOptions(io.ComfyNode): start_sigma: float, end_sigma: float, nerf_tile_size: int, + force_sequential_txt_ids: bool, ) -> io.NodeOutput: radiance_options = {} if nerf_tile_size >= 0: radiance_options["nerf_tile_size"] = nerf_tile_size + if force_sequential_txt_ids: + radiance_options["use_sequential_txt_ids"] = True + if not radiance_options: return io.NodeOutput(model) diff --git a/comfy_extras/nodes_clip_sdxl.py b/comfy_extras/nodes_clip_sdxl.py index 7a001af6f..08fbbd827 100644 --- a/comfy_extras/nodes_clip_sdxl.py +++ b/comfy_extras/nodes_clip_sdxl.py @@ -9,7 +9,8 @@ class CLIPTextEncodeSDXLRefiner(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CLIPTextEncodeSDXLRefiner", - category="advanced/conditioning", + display_name="CLIP Text Encode (SDXL Refiner)", + category="model/conditioning/stable diffusion", inputs=[ io.Float.Input("ascore", default=6.0, min=0.0, max=1000.0, step=0.01), io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION), @@ -30,7 +31,8 @@ class CLIPTextEncodeSDXL(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CLIPTextEncodeSDXL", - category="advanced/conditioning", + display_name="CLIP Text Encode (SDXL)", + category="model/conditioning/stable diffusion", inputs=[ io.Clip.Input("clip"), io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION), diff --git a/comfy_extras/nodes_color.py b/comfy_extras/nodes_color.py index 01a05035e..688254e4e 100644 --- a/comfy_extras/nodes_color.py +++ b/comfy_extras/nodes_color.py @@ -7,29 +7,29 @@ class ColorToRGBInt(io.ComfyNode): def define_schema(cls) -> io.Schema: return io.Schema( node_id="ColorToRGBInt", - display_name="Color to RGB Int", + display_name="Color Picker", category="utilities", - description="Convert a color to a RGB integer value.", + description="Return a color RGB integer value and hexadecimal representation.", inputs=[ io.Color.Input("color"), ], outputs=[ io.Int.Output(display_name="rgb_int"), + io.Color.Output(display_name="hex") ], ) @classmethod - def execute( - cls, - color: str, - ) -> io.NodeOutput: + 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") r = int(color[1:3], 16) g = int(color[3:5], 16) b = int(color[5:7], 16) - return io.NodeOutput(r * 256 * 256 + g * 256 + b) + + rgb_int = r * 256 * 256 + g * 256 + b + return io.NodeOutput(rgb_int, color) class ColorExtension(ComfyExtension): diff --git a/comfy_extras/nodes_context_windows.py b/comfy_extras/nodes_context_windows.py index d9e32b9d9..15d2dc506 100644 --- a/comfy_extras/nodes_context_windows.py +++ b/comfy_extras/nodes_context_windows.py @@ -13,21 +13,22 @@ class ContextWindowsManualNode(io.ComfyNode): description="Manually set context windows.", inputs=[ io.Model.Input("model", tooltip="The model to apply context windows to during sampling."), - io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window.", advanced=True), - io.Int.Input("context_overlap", min=0, default=4, tooltip="The overlap of the context window.", advanced=True), + io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window."), + io.Int.Input("context_overlap", min=0, default=4, tooltip="The overlap of the context window."), io.Combo.Input("context_schedule", options=[ comfy.context_windows.ContextSchedules.STATIC_STANDARD, comfy.context_windows.ContextSchedules.UNIFORM_STANDARD, comfy.context_windows.ContextSchedules.UNIFORM_LOOPED, comfy.context_windows.ContextSchedules.BATCHED, - ], tooltip="The stride of the context window."), - io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules.", advanced=True), + ], default=comfy.context_windows.ContextSchedules.STATIC_STANDARD, tooltip="Step-dependent scheduling algorithm for context windows."), + io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."), io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."), io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."), io.Int.Input("dim", min=0, max=5, default=0, tooltip="The dimension to apply the context windows to."), io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."), - io.String.Input("cond_retain_index_list", default="", tooltip="List of latent indices to retain in the conditioning tensors for each window, for example setting this to '0' will use the initial start image for each window."), + io.String.Input("cond_retain_index_list", default="", tooltip="List of latent indices to retain in the conditioning tensors for each window. For concat-style I2V models (e.g. Wan I2V, HunyuanVideo I2V, Cosmos I2V, SVD) the encoded start image lives in the c_concat conditioning channels; setting this to '0' will retain that start image content at sub-pos 0 of every window."), io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index."), + io.String.Input("latent_retain_index_list", default="", tooltip="List of latent indices to retain in the noise latent itself for each window. Use for workflows where reference content (e.g. a start image) lives directly in the noise latent rather than in separate conditioning channels (e.g. inplace-style I2V like LTXV, AnimateDiff). Independent of cond_retain_index_list."), io.Boolean.Input("causal_window_fix", default=True, tooltip="Whether to add a causal fix frame to non-0-indexed context windows."), ], outputs=[ @@ -38,7 +39,7 @@ class ContextWindowsManualNode(io.ComfyNode): @classmethod def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, dim: int, freenoise: bool, - cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False, causal_window_fix: bool=True) -> io.Model: + cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False, latent_retain_index_list: list[int]=[], causal_window_fix: bool=True) -> io.Model: model = model.clone() model.model_options["context_handler"] = comfy.context_windows.IndexListContextHandler( context_schedule=comfy.context_windows.get_matching_context_schedule(context_schedule), @@ -51,6 +52,7 @@ class ContextWindowsManualNode(io.ComfyNode): freenoise=freenoise, cond_retain_index_list=cond_retain_index_list, split_conds_to_windows=split_conds_to_windows, + latent_retain_index_list=latent_retain_index_list, causal_window_fix=causal_window_fix, ) # make memory usage calculation only take into account the context window latents @@ -65,32 +67,71 @@ class WanContextWindowsManualNode(ContextWindowsManualNode): schema = super().define_schema() schema.node_id = "WanContextWindowsManual" schema.display_name = "WAN Context Windows (Manual)" - schema.description = "Manually set context windows for WAN-like models (dim=2)." + schema.display_name = "Wan Context Windows" + schema.description = "Set context windows for Wan-like models." + schema.category="model/patch/wan" schema.inputs = [ io.Model.Input("model", tooltip="The model to apply context windows to during sampling."), - io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window.", advanced=True), - io.Int.Input("context_overlap", min=0, default=30, tooltip="The overlap of the context window.", advanced=True), + io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window in real frames. Must be 4*n + 1."), + io.Int.Input("context_overlap", min=0, default=30, tooltip="The overlap of the context window in real frames."), io.Combo.Input("context_schedule", options=[ comfy.context_windows.ContextSchedules.STATIC_STANDARD, comfy.context_windows.ContextSchedules.UNIFORM_STANDARD, comfy.context_windows.ContextSchedules.UNIFORM_LOOPED, comfy.context_windows.ContextSchedules.BATCHED, - ], tooltip="The stride of the context window."), + ], default=comfy.context_windows.ContextSchedules.UNIFORM_STANDARD, tooltip="Step-dependent scheduling algorithm for context windows."), io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules.", advanced=True), - io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."), + io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules.", advanced=True), io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."), - io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."), - #io.String.Input("cond_retain_index_list", default="", tooltip="List of latent indices to retain in the conditioning tensors for each window, for example setting this to '0' will use the initial start image for each window."), - #io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index."), + io.Boolean.Input("freenoise", default=True, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending.", advanced=True), + io.Boolean.Input("retain_first_frame", default=False, tooltip="Retain the first I2V frame in every context window (may help retain initial reference)."), + io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index.", advanced=True), ] return schema @classmethod def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, freenoise: bool, - cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False) -> io.Model: - context_length = max(((context_length - 1) // 4) + 1, 1) # at least length 1 - context_overlap = max(((context_overlap - 1) // 4) + 1, 0) # at least overlap 0 - return super().execute(model, context_length, context_overlap, context_schedule, context_stride, closed_loop, fuse_method, dim=2, freenoise=freenoise, cond_retain_index_list=cond_retain_index_list, split_conds_to_windows=split_conds_to_windows) + retain_first_frame: bool=False, split_conds_to_windows: bool=False) -> io.Model: + context_length = max(((context_length - 1) // 4) + 1, 1) # at least length 1 + context_overlap = max(context_overlap // 4, 0) # at least overlap 0 + retain_index_list = "0" if retain_first_frame else "" + return super().execute(model, context_length, context_overlap, context_schedule, context_stride, closed_loop, fuse_method, dim=2, freenoise=freenoise, cond_retain_index_list=retain_index_list, split_conds_to_windows=split_conds_to_windows) + + +class LTXVContextWindowsNode(ContextWindowsManualNode): + @classmethod + def define_schema(cls) -> io.Schema: + schema = super().define_schema() + schema.node_id = "LTXVContextWindows" + schema.display_name = "LTXV Context Windows" + schema.description = "Set context windows for LTXV-like models." + schema.inputs = [ + io.Model.Input("model", tooltip="The model to apply context windows to during sampling."), + io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=8, default=145, tooltip="The length of the context window in real frames. Must be 8*n + 1."), + io.Int.Input("context_overlap", min=0, step=8, default=40, tooltip="The overlap of the context window in real frames."), + io.Combo.Input("context_schedule", options=[ + comfy.context_windows.ContextSchedules.STATIC_STANDARD, + comfy.context_windows.ContextSchedules.UNIFORM_STANDARD, + comfy.context_windows.ContextSchedules.UNIFORM_LOOPED, + comfy.context_windows.ContextSchedules.BATCHED, + ], default=comfy.context_windows.ContextSchedules.UNIFORM_STANDARD, tooltip="Step-dependent scheduling algorithm for context windows."), + io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules.", advanced=True), + io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules.", advanced=True), + io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."), + io.Boolean.Input("freenoise", default=True, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending.", advanced=True), + io.Boolean.Input("retain_first_frame", default=False, tooltip="Retain the first latent frame in every context window (may help retain initial reference)."), + io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index.", advanced=True), + ] + return schema + + @classmethod + def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, fuse_method: str, freenoise: bool, + retain_first_frame: bool=False, split_conds_to_windows: bool=False, context_stride: int=1, closed_loop: bool=False) -> io.Model: + context_length = max(((context_length - 1) // 8) + 1, 1) # at least length 1 + context_overlap = max(context_overlap // 8, 0) # at least overlap 0 + retain_index_list = "0" if retain_first_frame else "" + return super().execute(model, context_length, context_overlap, context_schedule, context_stride, closed_loop, fuse_method, dim=2, freenoise=freenoise, + cond_retain_index_list=retain_index_list, latent_retain_index_list=retain_index_list, split_conds_to_windows=split_conds_to_windows) class ContextWindowsExtension(ComfyExtension): @@ -98,6 +139,7 @@ class ContextWindowsExtension(ComfyExtension): return [ ContextWindowsManualNode, WanContextWindowsManualNode, + LTXVContextWindowsNode, ] def comfy_entrypoint(): diff --git a/comfy_extras/nodes_controlnet.py b/comfy_extras/nodes_controlnet.py index 17d965405..eb476f497 100644 --- a/comfy_extras/nodes_controlnet.py +++ b/comfy_extras/nodes_controlnet.py @@ -9,6 +9,8 @@ class SetUnionControlNetType(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="SetUnionControlNetType", + search_aliases=["set controlnet type", "union controlnet type"], + display_name="Set Union ControlNet Type", category="model/conditioning/controlnet", inputs=[ io.ControlNet.Input("control_net"), @@ -39,6 +41,7 @@ class ControlNetInpaintingAliMamaApply(io.ComfyNode): return io.Schema( node_id="ControlNetInpaintingAliMamaApply", search_aliases=["masked controlnet"], + display_name="Apply ControlNet Inpainting (AliMama)", category="model/conditioning/controlnet", inputs=[ io.Conditioning.Input("positive"), diff --git a/comfy_extras/nodes_cosmos.py b/comfy_extras/nodes_cosmos.py index d754ab442..93cc67a6c 100644 --- a/comfy_extras/nodes_cosmos.py +++ b/comfy_extras/nodes_cosmos.py @@ -13,7 +13,7 @@ class EmptyCosmosLatentVideo(io.ComfyNode): def define_schema(cls) -> io.Schema: return io.Schema( node_id="EmptyCosmosLatentVideo", - category="model/latent/video", + category="model/latent/cosmos", inputs=[ io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16), io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16), @@ -45,7 +45,7 @@ class CosmosImageToVideoLatent(io.ComfyNode): def define_schema(cls) -> io.Schema: return io.Schema( node_id="CosmosImageToVideoLatent", - category="model/conditioning/inpaint", + category="model/conditioning/cosmos", inputs=[ io.Vae.Input("vae"), io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16), @@ -88,7 +88,7 @@ class CosmosPredict2ImageToVideoLatent(io.ComfyNode): def define_schema(cls) -> io.Schema: return io.Schema( node_id="CosmosPredict2ImageToVideoLatent", - category="model/conditioning/inpaint", + category="model/conditioning/cosmos", inputs=[ io.Vae.Input("vae"), io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16), diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index c3346bf09..c9d7e06fc 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -1,5 +1,7 @@ import math import comfy.samplers +import comfy.sampler_helpers +import comfy.patcher_extension import comfy.sample from comfy.k_diffusion import sampling as k_diffusion_sampling from comfy.k_diffusion import sa_solver @@ -727,7 +729,7 @@ class SamplerCustom(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="SamplerCustom", - category="model/sampling/custom_sampling", + category="model/sampling/custom", inputs=[ io.Model.Input("model"), io.Boolean.Input("add_noise", default=True, advanced=True), @@ -894,6 +896,85 @@ class DualCFGGuider(io.ComfyNode): get_guider = execute +class Guider_DualModel(comfy.samplers.CFGGuider): + # Runs the positive (cond) pass on the main model and the negative (uncond) pass on a separate model + def __init__(self, model_patcher, uncond_model_patcher): + super().__init__(model_patcher) + self.uncond_model_patcher = uncond_model_patcher + self.uncond_inner = None + + def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, latent_shapes=None): + self.uncond_inner = None + self.uncond_loaded = [] + self._uncond_neg = None + # skip at cfg 1.0 + if not math.isclose(self.cfg, 1.0): + uc = {"negative": list(map(lambda a: a.copy(), self.conds["negative"]))} + self.uncond_inner, uc, self.uncond_loaded = comfy.sampler_helpers.prepare_sampling( + self.uncond_model_patcher, noise.shape, uc, self.uncond_model_patcher.model_options) + self._uncond_neg = uc["negative"] + self.uncond_model_patcher.pre_run() + try: + return super().outer_sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes) + finally: + if self.uncond_inner is not None: + self.uncond_model_patcher.cleanup() + comfy.sampler_helpers.cleanup_models({"negative": self._uncond_neg}, self.uncond_loaded) + self.uncond_inner = None + + def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=None): + if self.uncond_inner is not None: + li = latent_image + if li is not None and torch.count_nonzero(li) > 0: + li = self.uncond_inner.process_latent_in(li) + self._uncond_conds = comfy.samplers.process_conds( + self.uncond_inner, noise, {"negative": self._uncond_neg}, device, li, denoise_mask, seed, latent_shapes=latent_shapes)["negative"] + return super().inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes) + + def predict_noise(self, x, timestep, model_options={}, seed=None): + positive = self.conds.get("positive", None) + cond = comfy.samplers.calc_cond_batch(self.inner_model, [positive], x, timestep, model_options)[0] + # uncond model not loaded (base cfg==1/no negative), or cfg driven to 1.0 this step -> single model, cond only + if self.uncond_inner is None or (math.isclose(self.cfg, 1.0) and not model_options.get("disable_cfg1_optimization", False)): + return cond + + uncond_model_options = model_options + if "multigpu_clones" in model_options: # TODO: support multigpu instead of just running uncond on a single GPU + uncond_model_options = {k: v for k, v in model_options.items() if k != "multigpu_clones"} + uncond = comfy.samplers.calc_cond_batch(self.uncond_inner, [self._uncond_conds], x, timestep, uncond_model_options)[0] + return comfy.samplers.cfg_function(self.inner_model, cond, uncond, self.cfg, x, timestep, + model_options=model_options, cond=positive, uncond=self._uncond_conds) + +class DualModelGuider(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="DualModelGuider", + display_name="Dual Model CFG Guider", + category="model/sampling/guiders", + is_experimental=True, + inputs=[ + io.Model.Input("model", tooltip="Model used for the positive (conditional) pass."), + io.Model.Input("model_negative", optional=True, tooltip="Model used for the negative (unconditional) pass. Use the same model for ordinary CFG."), + io.Conditioning.Input("positive"), + io.Float.Input("cfg", default=4.0, min=0.0, max=100.0, step=0.1, round=0.01), + io.Conditioning.Input("negative", optional=True, tooltip="Negative conditioning run on the negative model. Leave unconnected for a text-free (image-only) unconditional pass."), + ], + outputs=[io.Guider.Output()], + ) + + @classmethod + def execute(cls, model, positive, cfg, model_negative=None, negative=None) -> io.NodeOutput: + if negative is None: + negative = [[None, {}]] # null cond -> no cross_attn -> model runs image-only + + guider = Guider_DualModel(model, model_negative) if model_negative is not None else comfy.samplers.CFGGuider(model) + guider.set_conds(positive, negative) + guider.set_cfg(cfg) + return io.NodeOutput(guider) + + get_guider = execute + class DisableNoise(io.ComfyNode): @classmethod def define_schema(cls): @@ -934,7 +1015,7 @@ class SamplerCustomAdvanced(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="SamplerCustomAdvanced", - category="model/sampling/custom_sampling", + category="model/sampling/custom", inputs=[ io.Noise.Input("noise"), io.Guider.Input("guider"), @@ -1054,11 +1135,53 @@ class ManualSigmas(io.ComfyNode): sigmas = torch.FloatTensor(sigmas) return io.NodeOutput(sigmas) +class CFGOverride(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="CFGOverride", + display_name="CFG Override", + description="Override cfg to a fixed value over a [start, end] percent (sigma) range. " + "With multiple overrides, the one nearest the sampler wins on overlap.", + category="model/sampling/guiders", + inputs=[ + io.Model.Input("model"), + io.Float.Input("cfg", default=1.0, min=0.0, max=100.0, step=0.1, round=0.01), + io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001), + io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001), + ], + outputs=[io.Model.Output()], + ) + + @classmethod + def execute(cls, model, cfg, start_percent, end_percent) -> io.NodeOutput: + ms = model.get_model_object("model_sampling") + sigma_hi = ms.percent_to_sigma(start_percent) # percent->sigma decreasing, so hi >= lo + sigma_lo = ms.percent_to_sigma(end_percent) + + def predict_noise_wrapper(executor, *args, **kwargs): + sigma = float(args[1].flatten()[0]) # args = (x, timestep, model_options, seed) + if not (sigma_lo <= sigma <= sigma_hi): + return executor(*args, **kwargs) + guider = executor.class_obj # guider.cfg feeds cond_scale + saved = guider.cfg + guider.cfg = cfg + try: + return executor(*args, **kwargs) + finally: + guider.cfg = saved # restore for other steps/overrides + + m = model.clone() + m.add_wrapper(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, predict_noise_wrapper) + return io.NodeOutput(m) + + class CustomSamplersExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[io.ComfyNode]]: return [ SamplerCustom, + CFGOverride, BasicScheduler, KarrasScheduler, ExponentialScheduler, @@ -1087,6 +1210,7 @@ class CustomSamplersExtension(ComfyExtension): SamplingPercentToSigma, CFGGuider, DualCFGGuider, + DualModelGuider, BasicGuider, RandomNoise, DisableNoise, diff --git a/comfy_extras/nodes_dataset.py b/comfy_extras/nodes_dataset.py index 104d16d91..73fe75b7f 100644 --- a/comfy_extras/nodes_dataset.py +++ b/comfy_extras/nodes_dataset.py @@ -411,6 +411,21 @@ class ImageProcessingNode(io.ComfyNode): return has_group + @classmethod + def _ensure_image_list(cls, images): + """Normalize to a flat list of [1, H, W, C] tensors.""" + if isinstance(images, torch.Tensor): + if images.ndim != 4: + raise ValueError(f"Expected 4D image tensor, got shape {tuple(images.shape)}") + return [images[i:i+1] for i in range(images.shape[0])] + + flat = [] + for item in images: + if not isinstance(item, torch.Tensor) or item.ndim != 4: + raise ValueError(f"Expected 4D image tensor, got {type(item).__name__} shape {getattr(item, 'shape', None)}") + flat.extend([item[i:i+1] for i in range(item.shape[0])]) + return flat + @classmethod def define_schema(cls): if cls.node_id is None: @@ -458,6 +473,9 @@ class ImageProcessingNode(io.ComfyNode): """Execute the node. Routes to _process or _group_process based on mode.""" is_group = cls._detect_processing_mode() + if is_group: + images = cls._ensure_image_list(images) + # Extract scalar values from lists for parameters params = {} for k, v in kwargs.items(): @@ -1565,7 +1583,7 @@ class LoadTrainingDataset(io.ComfyNode): shard_path = os.path.join(dataset_dir, shard_file) with open(shard_path, "rb") as f: - shard_data = torch.load(f) + shard_data = torch.load(f, weights_only=True) all_latents.extend(shard_data["latents"]) all_conditioning.extend(shard_data["conditioning"]) diff --git a/comfy_extras/nodes_depth_anything_3.py b/comfy_extras/nodes_depth_anything_3.py new file mode 100644 index 000000000..020112515 --- /dev/null +++ b/comfy_extras/nodes_depth_anything_3.py @@ -0,0 +1,681 @@ +"""ComfyUI nodes for Depth Anything 3. +Model capability matrix: + +Variant head_type has_sky has_conf cam_dec +DA3-Small dualdpt False True yes +DA3-Base dualdpt False True yes +DA3-Mono-Large dpt True False no +DA3-Metric-Large dpt True False no (raw output is metres) +""" + +from __future__ import annotations + +import logging +from typing_extensions import override + +import torch + +import comfy.model_management as mm +import comfy.sd +import folder_paths +from comfy.ldm.colormap import turbo as _turbo +from comfy.ldm.depth_anything_3 import preprocess as da3_preprocess +from comfy_api.latest import ComfyExtension, Types, io +from comfy.ldm.moge.geometry import triangulate_grid_mesh + +DA3ModelType = io.Custom("DA3_MODEL") +DA3Geometry = io.Custom("DA3_GEOMETRY") +DA3PointCloud = io.Custom("DA3_POINT_CLOUD") + +# DA3_GEOMETRY is a dict with these optional keys (absent when the upstream model didn't produce them): +# +# Per-frame tensors - B = batch size in mono mode; B = S (number of views) in multi-view mode. +# "depth": torch.Tensor (B, H, W) -- raw model depth (always present; matches MoGe convention) +# "image": torch.Tensor (B, H, W, 3) -- source image in [0, 1], CPU (always present) +# "mode": str -- "mono" or "multiview" (always present) +# "sky": torch.Tensor (B, H, W) -- sky probability in [0, 1] (Mono/Metric variants only) +# "confidence": torch.Tensor (B, H, W) -- raw model confidence output (Small/Base variants only) +# +# Multi-view only - S = number of views; the leading 1 is the scene dimension from the model. +# "extrinsics": torch.Tensor (1, S, 3, 4) -- world-to-camera [R|t] matrices +# "intrinsics": torch.Tensor (1, S, 3, 3) -- pixel-space intrinsics +# +# DA3_POINT_CLOUD is a dict: +# "points": torch.Tensor (N, 3) -- 3-D coords in glTF convention (Y-up, Z-back) +# "colors": torch.Tensor (N, 3) -- RGB in [0, 1], or None +# "confidence": torch.Tensor (N,) -- raw confidence per point, or None + + +def _da3_unproject(depth: torch.Tensor, K: torch.Tensor) -> torch.Tensor: + """Pixel-space K⁻¹ unprojection: (H,W) depth → (H,W,3) point map in OpenCV space.""" + H, W = depth.shape + u = torch.arange(W, dtype=torch.float32, device=depth.device) + v = torch.arange(H, dtype=torch.float32, device=depth.device) + u, v = torch.meshgrid(u, v, indexing='xy') # both (H, W) + pix = torch.stack([u, v, torch.ones_like(u)], dim=-1) # (H, W, 3) + rays = torch.einsum('ij,hwj->hwi', torch.linalg.inv(K.to(depth.device)), pix) + return rays * depth.unsqueeze(-1) # (H, W, 3) + + +def _da3_default_K(H: int, W: int) -> torch.Tensor: + """Fallback ~60° FOV pinhole K for mono-mode DA3 (no intrinsics in geometry).""" + fx = fy = float(W) * 0.7 + return torch.tensor([[fx, 0.0, (W - 1) / 2.0], + [0.0, fy, (H - 1) / 2.0], + [0.0, 0.0, 1.0]], dtype=torch.float32) + + +def _da3_get_K(geometry: dict, b: int, H: int, W: int) -> torch.Tensor: + """Return pixel-space K for batch element b, falling back to a default estimate.""" + if "intrinsics" in geometry: + # shape (1, S, 3, 3) - leading scene dimension from the multiview head + return geometry["intrinsics"][0, b].float() + logging.getLogger("comfy").warning( + "DA3_GEOMETRY has no intrinsics (mono-mode model). " + "Using a ~60° FOV estimate; 3-D reconstruction may be inaccurate." + ) + return _da3_default_K(H, W) + + +def _da3_get_extrinsic(geometry: dict, b: int) -> torch.Tensor | None: + """Return the world-to-camera extrinsic for batch element b, or None in mono mode. + + The model outputs (1, S, 3, 4) [R|t] matrices; the fallback identity is (4, 4). + _da3_apply_extrinsic handles both shapes via [:3, :3] / [:3, 3] slicing. + """ + if "extrinsics" not in geometry: + return None + return geometry["extrinsics"][0, b].float() + + +def _da3_apply_extrinsic(points_cam: torch.Tensor, E: torch.Tensor) -> torch.Tensor: + """Transform (H,W,3) OpenCV camera-space points to world space.""" + E = E.to(points_cam.device).float() + if not torch.isfinite(E).all(): + logging.getLogger("comfy").warning( + "DA3 extrinsic matrix contains non-finite values (pose estimation may have failed). " + "Falling back to camera-space coordinates." + ) + return points_cam + H, W, _ = points_cam.shape + R = E[:3, :3] # (3, 3) rotation + t = E[:3, 3] # (3,) translation + R_inv = R.T # rotation inverse = transpose for orthogonal R + t_inv = -(R_inv @ t) # (3,) + pts = points_cam.reshape(-1, 3) # (N, 3) + pts_world = pts @ R_inv.T + t_inv # (N, 3) + return pts_world.reshape(H, W, 3) + + +def _normalize_confidence(conf: torch.Tensor) -> torch.Tensor: + """Map raw confidence to [0, 1] per image.""" + B = conf.shape[0] + out = [] + for i in range(B): + c = conf[i] + c_min, c_max = c.min(), c.max() + out.append((c - c_min) / (c_max - c_min) if c_max > c_min else torch.ones_like(c)) + return torch.stack(out, dim=0) + + +def _da3_build_mask(geometry: dict, b: int, H: int, W: int, confidence_threshold: float, use_sky_mask: bool) -> torch.Tensor: + """Build (H,W) bool keep-mask from sky probability and confidence.""" + mask = torch.ones(H, W, dtype=torch.bool) + if use_sky_mask and "sky" in geometry: + mask = mask & (geometry["sky"][b] < 0.5) + if "confidence" in geometry and confidence_threshold > 0.0: + conf_norm = _normalize_confidence(geometry["confidence"][b:b + 1])[0] + mask = mask & (conf_norm >= confidence_threshold) + return mask + + +class LoadDA3Model(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="LoadDA3Model", + display_name="Load Depth Anything 3", + category="model/loaders", + inputs=[ + io.Combo.Input( + "model_name", + options=folder_paths.get_filename_list("geometry_estimation"), + ), + io.Combo.Input( + "weight_dtype", + options=["default", "fp16", "bf16", "fp32"], + default="default", + ), + ], + outputs=[DA3ModelType.Output()], + ) + + @classmethod + def execute(cls, model_name, weight_dtype) -> io.NodeOutput: + model_options = {} + if weight_dtype == "fp16": + model_options["dtype"] = torch.float16 + elif weight_dtype == "bf16": + model_options["dtype"] = torch.bfloat16 + elif weight_dtype == "fp32": + model_options["dtype"] = torch.float32 + + path = folder_paths.get_full_path_or_raise("geometry_estimation", model_name) + model = comfy.sd.load_diffusion_model(path, model_options=model_options) + return io.NodeOutput(model) + + +def _run_da3(model_patcher, image: torch.Tensor, process_res: int, method: str = "upper_bound_resize"): + """Run DA3 on (B,H,W,3), returns depth/conf/sky at original resolution (or None).""" + assert image.ndim == 4 and image.shape[-1] == 3, f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}" + + B, H, W, _ = image.shape + mm.load_model_gpu(model_patcher) + diffusion = model_patcher.model.diffusion_model + device = mm.get_torch_device() + dtype = diffusion.dtype if diffusion.dtype is not None else torch.float32 + + depths, confs, skies = [], [], [] + for i in range(B): + single = image[i:i + 1].to(device) + x = da3_preprocess.preprocess_image(single, process_res=process_res, method=method) + x = x.to(dtype=dtype) + with torch.no_grad(): + out = diffusion(x) + + depth_lr = out["depth"] + depth_full = torch.nn.functional.interpolate( + depth_lr.unsqueeze(1).float(), size=(H, W), + mode="bilinear", align_corners=False, + ).squeeze(1).cpu() + depths.append(depth_full) + + if "depth_conf" in out: + conf_full = torch.nn.functional.interpolate( + out["depth_conf"].unsqueeze(1).float(), size=(H, W), + mode="bilinear", align_corners=False, + ).squeeze(1).cpu() + confs.append(conf_full) + if "sky" in out: + sky_full = torch.nn.functional.interpolate( + out["sky"].unsqueeze(1).float(), size=(H, W), + mode="bilinear", align_corners=False, + ).squeeze(1).cpu() + skies.append(sky_full) + + depth = torch.cat(depths, dim=0) + confidence = torch.cat(confs, dim=0) if confs else None + sky = torch.cat(skies, dim=0) if skies else None + return depth, confidence, sky + + +class DA3Inference(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="DA3Inference", + search_aliases=["depth", "geometry", "da3", "depth anything", "monocular", "pointmap", "sky", "3d", "metric depth", "disparity"], + display_name="Run Depth Anything 3", + category="image/geometry estimation", + description="Run Depth Anything 3 on an image. In multi-view mode each image is treated as a separate view of the same scene.", + inputs=[ + DA3ModelType.Input("da3_model"), + io.Image.Input("image"), + io.Int.Input("resolution", default=504, min=140, max=2520, step=14, + tooltip="Resolution the model runs at (longest side, multiple of 14).\n" + "Lower = faster / less VRAM.\n" + "Higher = more detail.\n" + "Output is upsampled back to the original size."), + io.Combo.Input("resize_method", options=["upper_bound_resize", "lower_bound_resize"], default="upper_bound_resize", + tooltip="upper_bound_resize: scale so the longest side = resolution (caps memory, default).\n" + "lower_bound_resize: scale so the shortest side = resolution (preserves more detail on tall/wide images, uses more memory)."), + io.DynamicCombo.Input("mode", tooltip="mono: single view image (works with any model variant).\n" + "multiview: all images processed together for geometric consistency + camera pose (for Small/Base models only).", + options=[ + io.DynamicCombo.Option("mono", []), + io.DynamicCombo.Option("multiview", [ + io.Combo.Input("ref_view_strategy", options=["saddle_balanced", "saddle_sim_range", "first", "middle"], default="saddle_balanced", + tooltip="Which view acts as the geometric anchor.\n" + "- saddle_balanced: the view most 'average' across all others (best general choice).\n" + "- saddle_sim_range: the view most visually distinct from the others.\n" + "- first / middle: fixed positional picks."), + io.Combo.Input("pose_method", options=["cam_dec", "ray_pose"], default="cam_dec", + tooltip="How the camera field-of-view is estimated (for Small/Base models only).\n" + "- cam_dec: learned from image features.\n" + "- ray_pose: derived geometrically from the model's 3D ray output.\n" + "Affects perspective correctness of the 3D output. Try both if results look distorted."), + ]), + ]), + ], + outputs=[ + DA3Geometry.Output("da3_geometry", tooltip="Dictionary of non-normalized tensors.\n" + "Always has the keys: depth, image, mode.\n" + "Optional keys: sky (for Mono/Metric), confidence (for Small/Base), extrinsics + intrinsics (for multi-view)."), + ], + ) + + @classmethod + def execute(cls, da3_model, image, resolution, resize_method, mode) -> io.NodeOutput: + mode_val = mode["mode"] # "mono" or "multiview" + + if mode_val == "mono": + return cls._execute_mono(da3_model, image, resolution, resize_method) + + # Capability checks for multi-view mode. + diffusion = da3_model.model.diffusion_model + pose_method = mode["pose_method"] + ref_view_strategy = mode["ref_view_strategy"] + + has_cam_dec = diffusion.cam_dec is not None + has_dualdpt = diffusion.head_type == "dualdpt" + + if not has_cam_dec and not has_dualdpt: + raise ValueError( + "multi-view mode requires Small or Base model. The loaded model " + f"(head_type='{diffusion.head_type}') does not support cross-view " + "attention or camera pose estimation. Switch mode to 'mono', or " + "load Small or Base model for mult-view." + ) + + if pose_method == "cam_dec" and not has_cam_dec: + raise ValueError( + "pose_method='cam_dec' requires a camera decoder, but the loaded " + f"model (head_type='{diffusion.head_type}') does not have one. " + "Use pose_method='ray_pose' instead." + ) + if pose_method == "ray_pose" and not has_dualdpt: + raise ValueError( + "pose_method='ray_pose' requires a DualDPT head, but the loaded " + f"model has a '{diffusion.head_type}' head. " + "Use pose_method='cam_dec' instead." + ) + + return cls._execute_multiview( + da3_model, image, resolution, resize_method, + ref_view_strategy, pose_method, + ) + + @classmethod + def _execute_mono(cls, model, image, resolution, resize_method) -> io.NodeOutput: + depth, confidence, sky = _run_da3(model, image, resolution, method=resize_method) + + geometry: dict = { + "depth": depth.contiguous(), + "image": image[..., :3].cpu(), + "mode": "mono", + } + if sky is not None: + geometry["sky"] = sky.contiguous() + if confidence is not None: + geometry["confidence"] = confidence.contiguous() + return io.NodeOutput(geometry) + + @classmethod + def _execute_multiview(cls, model, image, resolution, resize_method, ref_view_strategy, pose_method) -> io.NodeOutput: + assert image.ndim == 4 and image.shape[-1] == 3, \ + f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}" + S, H, W, _ = image.shape + + mm.load_model_gpu(model) + diffusion = model.model.diffusion_model + device = mm.get_torch_device() + dtype = diffusion.dtype if diffusion.dtype is not None else torch.float32 + + # All views in a single forward pass: (1, S, 3, H', W'). + x = image.to(device) + x = da3_preprocess.preprocess_image(x, process_res=resolution, method=resize_method) + x = x.to(dtype=dtype).unsqueeze(0) + + use_ray_pose = (pose_method == "ray_pose") + with torch.no_grad(): + out = diffusion(x, use_ray_pose=use_ray_pose, ref_view_strategy=ref_view_strategy) + + depth = torch.nn.functional.interpolate( + out["depth"].float().unsqueeze(1), size=(H, W), + mode="bilinear", align_corners=False, + ).squeeze(1).cpu() + + sky = None + if "sky" in out: + sky = torch.nn.functional.interpolate( + out["sky"].unsqueeze(1).float(), size=(H, W), + mode="bilinear", align_corners=False, + ).squeeze(1).cpu() + + if "extrinsics" in out and "intrinsics" in out: + extrinsics = out["extrinsics"].float().cpu() + intrinsics = out["intrinsics"].float().cpu() + else: + extrinsics = torch.eye(4)[None, None].expand(1, S, 4, 4).clone() + intrinsics = torch.eye(3)[None, None].expand(1, S, 3, 3).clone() + + geometry: dict = { + "depth": depth.contiguous(), + "image": image[..., :3].cpu(), + "mode": "multiview", + "extrinsics": extrinsics.contiguous(), + "intrinsics": intrinsics.contiguous(), + } + if sky is not None: + geometry["sky"] = sky.contiguous() + if "depth_conf" in out: + conf = torch.nn.functional.interpolate( + out["depth_conf"].unsqueeze(1).float(), size=(H, W), + mode="bilinear", align_corners=False, + ).squeeze(1).cpu() + geometry["confidence"] = conf.contiguous() + return io.NodeOutput(geometry) + + +class DA3Render(io.ComfyNode): + """Render a visualization from a DA3_GEOMETRY packet.""" + + _DEPTH_RENDER_INPUTS = [ + io.Combo.Input("normalization", + options=["v2_style", "min_max", "raw"], + default="v2_style", + tooltip="- v2_style: mean/std normalisation for perceptually balanced results (default).\n" + "- min_max: stretches the full depth range to [0, 1] for maximum contrast.\n" + "- raw: no scaling,preserves metric units for Metric model."), + io.Boolean.Input("apply_sky_clip", default=False, + tooltip="Clip sky-region depth to the 99th percentile of foreground depth before normalisation. " + "Requires a sky key in the da3_geometry input (for Mono/Metric models only)."), + ] + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="DA3Render", + display_name="Render Depth Anything 3", + category="image/geometry estimation", + description="Render a depth map, confidence map, or sky mask from Depth Anything 3 geometry data.", + inputs=[ + DA3Geometry.Input("da3_geometry"), + io.DynamicCombo.Input("output", + tooltip="- depth: normalised greyscale depth image.\n" + "- depth_colored: depth mapped through the Turbo colormap.\n" + "- sky_mask: sky probability in [0, 1] (for Mono/Metric models only).\n" + "- confidence: normalised depth confidence (for Small/Base models only).", + options=[ + io.DynamicCombo.Option("depth", cls._DEPTH_RENDER_INPUTS), + io.DynamicCombo.Option("depth_colored", cls._DEPTH_RENDER_INPUTS), + io.DynamicCombo.Option("sky_mask", [ + io.Boolean.Input("colored", default=False, tooltip="Apply the Turbo colormap to the sky mask."), + ]), + io.DynamicCombo.Option("confidence", [ + io.Boolean.Input("colored", default=False, tooltip="Apply the Turbo colormap to the confidence map."), + ]), + ]), + ], + outputs=[io.Image.Output()], + ) + + @classmethod + def execute(cls, da3_geometry, output) -> io.NodeOutput: + output_val = output["output"] + + if output_val in ("depth", "depth_colored"): + normalization = output["normalization"] + apply_sky_clip = output["apply_sky_clip"] + if apply_sky_clip and "sky" not in da3_geometry: + raise ValueError( + "apply_sky_clip=True requires a sky tensor in the da3_geometry input, but none is present. " + "Run with Mono/Metric models or set apply_sky_clip=False." + ) + depth = da3_geometry["depth"] + sky = da3_geometry.get("sky") + if apply_sky_clip and sky is not None: + depth = torch.stack([ + da3_preprocess.apply_sky_aware_clip(depth[i], sky[i]) + for i in range(depth.shape[0]) + ], dim=0) + grey = cls._depth_to_image(depth, sky, normalization) # (B,H,W,3) greyscale + result = _turbo(grey[..., 0]) if output_val == "depth_colored" else grey + + elif output_val == "sky_mask": + if "sky" not in da3_geometry: + raise ValueError("geometry has no sky output; run with Mono/Metric models.") + sky = da3_geometry["sky"] + if output["colored"]: + result = _turbo(sky) + else: + result = sky.unsqueeze(-1).expand(*sky.shape, 3).contiguous() + + elif output_val == "confidence": + if "confidence" not in da3_geometry: + raise ValueError("da3_geometry has no confidence output; run with Small/Base models.") + conf = _normalize_confidence(da3_geometry["confidence"]) + if output["colored"]: + result = _turbo(conf) + else: + result = conf.unsqueeze(-1).expand(*conf.shape, 3).contiguous() + + else: + raise ValueError(f"Unknown output mode: {output_val}") + + return io.NodeOutput(result.float()) + + @staticmethod + def _depth_to_image(depth: torch.Tensor, sky_for_norm: torch.Tensor | None, normalization: str) -> torch.Tensor: + """Normalise depth and pack as an (B,H,W,3) image tensor.""" + + N = depth.shape[0] + if normalization == "v2_style": + norm = torch.stack([ + da3_preprocess.normalize_depth_v2_style( + depth[i], sky_for_norm[i] if sky_for_norm is not None else None) + for i in range(N) + ], dim=0) + elif normalization == "min_max": + norm = da3_preprocess.normalize_depth_min_max(depth) + else: + norm = depth + + out = norm.unsqueeze(-1).repeat(1, 1, 1, 3) + if normalization != "raw": + out = out.clamp(0.0, 1.0) + return out.contiguous() + + +class DA3GeometryToMesh(io.ComfyNode): + """Convert a DA3_GEOMETRY packet into a Types.MESH by unprojecting depth and triangulating.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="DA3GeometryToMesh", + search_aliases=["da3", "depth anything", "mesh", "geometry", "3d", "triangulate"], + display_name="Convert DA3 Geometry to Mesh", + category="image/geometry estimation", + description="Convert a depth map into a triangulated 3D mesh.", + inputs=[ + DA3Geometry.Input("da3_geometry"), + io.Int.Input("batch_index", default=0, min=0, max=4096, tooltip="Which image of a batch to convert. Per-image vertex counts differ so batches cannot be stacked."), + io.Int.Input("decimation", default=1, min=1, max=8, tooltip="Vertex stride. 1 = full resolution, 2 = half, etc."), + io.Float.Input("discontinuity_threshold", default=0.04, min=0.0, max=1.0, step=0.01, tooltip="Drop triangles whose 3x3 depth span exceeds this fraction. 0 = off."), + io.Float.Input("confidence_threshold", default=0.1, min=0.0, max=1.0, step=0.01, + tooltip="Exclude pixels whose per-image normalised confidence is below this value (0 = keep all, 1 = keep only the single most confident pixel). " + "Used when the geometry has a confidence map (Small/Base models)."), + io.Boolean.Input("use_sky_mask", default=True, tooltip="Exclude sky-probability pixels (sky >= 0.5) from the mesh. Used when the geometry has a sky map (Mono/Metric models)."), + io.Boolean.Input("texture", default=True, tooltip="Use the source image as a base color texture."), + ], + outputs=[io.Mesh.Output()], + ) + + @classmethod + def execute(cls, da3_geometry, batch_index, decimation, discontinuity_threshold, confidence_threshold, use_sky_mask, texture) -> io.NodeOutput: + depth_all = da3_geometry["depth"] # (B, H, W) + B = depth_all.shape[0] + if batch_index >= B: + raise ValueError(f"batch_index {batch_index} is out of range; DA3_GEOMETRY has batch size {B}.") + + depth = depth_all[batch_index] # (H, W) + H, W = depth.shape + + # NaN/inf depth would propagate silently through unproject and produce an + # empty mesh; replace them with 0 here so those pixels are later excluded + # by the isfinite check inside triangulate_grid_mesh. + depth = depth.clone() + n_bad = (~torch.isfinite(depth)).sum().item() + if n_bad: + logging.getLogger("comfy").warning( + f"DA3GeometryToMesh: depth[{batch_index}] has {n_bad} non-finite pixels " + f"({100*n_bad/(H*W):.1f}%) - zeroed before unproject." + ) + depth[~torch.isfinite(depth)] = 0.0 + logging.getLogger("comfy").debug( + f"DA3GeometryToMesh: depth[{batch_index}] range " + f"[{depth.min():.4g}, {depth.max():.4g}], mean={depth.mean():.4g}" + ) + + K = _da3_get_K(da3_geometry, batch_index, H, W) + points = _da3_unproject(depth, K) # (H, W, 3) in OpenCV camera space + + # Apply world-to-camera inverse so multi-view frames share a common world frame. + E = _da3_get_extrinsic(da3_geometry, batch_index) + if E is not None: + points = _da3_apply_extrinsic(points, E) + + # Mask invalid pixels by setting them to inf so triangulate_grid_mesh skips them. + mask = _da3_build_mask(da3_geometry, batch_index, H, W, confidence_threshold, use_sky_mask) + # Also exclude pixels where depth was invalid. + mask = mask & (depth_all[batch_index] > 0) & torch.isfinite(depth_all[batch_index]) + points = points.clone() + points[~mask] = float('inf') + + verts, faces, uvs = triangulate_grid_mesh( + points, + decimation=decimation, + discontinuity_threshold=discontinuity_threshold, + depth=depth, + ) + if verts.shape[0] == 0 or faces.shape[0] == 0: + raise ValueError( + "DA3GeometryToMesh produced an empty mesh. " + "Try raising discontinuity_threshold, lowering confidence_threshold, " + "or disabling use_sky_mask." + ) + + # OpenCV (X right, Y down, Z forward) → glTF (X right, Y up, Z back). + # Same transform as MoGePointMapToMesh perspective branch. + verts = verts * torch.tensor([1.0, -1.0, -1.0], dtype=verts.dtype) + faces = faces[:, [0, 2, 1]].contiguous() + + tex = da3_geometry["image"][batch_index:batch_index + 1] if texture else None + mesh = Types.MESH( + vertices=verts.unsqueeze(0), + faces=faces.unsqueeze(0), + uvs=uvs.unsqueeze(0), + texture=tex, + ) + return io.NodeOutput(mesh) + + +class DA3GeometryToPointCloud(io.ComfyNode): + """Unproject a DA3_GEOMETRY depth map into a filtered DA3_POINT_CLOUD.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="DA3GeometryToPointCloud", + search_aliases=["da3", "depth anything", "point cloud", "pointcloud", "3d", "geometry"], + display_name="Convert DA3 Geometry to Point Cloud", + category="image/geometry estimation", + description="Convert a depth map into a 3D point cloud.", + inputs=[ + DA3Geometry.Input("da3_geometry"), + io.Int.Input("batch_index", default=0, min=0, max=4096, tooltip="Which image of a batch to convert."), + io.Float.Input("confidence_threshold", default=0.1, min=0.0, max=1.0, step=0.01, + tooltip="Exclude pixels whose per-image normalised confidence is below this value (0 = keep all). Used when the geometry has a confidence map (Small/Base models)."), + io.Boolean.Input("use_sky_mask", default=True, + tooltip="Exclude sky-probability pixels (sky >= 0.5). Used when the geometry has a sky map (Mono/Metric models)."), + io.Int.Input("downsample", default=1, min=1, max=16, + tooltip="Take every Nth pixel (1 = full resolution). Higher values give fewer points and faster processing."), + ], + # TODO: add a proper PointCloud output type + outputs=[DA3PointCloud.Output(display_name="point_cloud")], + ) + + @classmethod + def execute(cls, da3_geometry, batch_index, confidence_threshold, use_sky_mask, downsample) -> io.NodeOutput: + depth_all = da3_geometry["depth"] # (B, H, W) + B = depth_all.shape[0] + if batch_index >= B: + raise ValueError(f"batch_index {batch_index} is out of range; DA3_GEOMETRY has batch size {B}.") + + depth = depth_all[batch_index].clone() # (H, W) + depth[~torch.isfinite(depth)] = 0.0 + H, W = depth.shape + + K = _da3_get_K(da3_geometry, batch_index, H, W) + + if downsample > 1: + depth = depth[::downsample, ::downsample].contiguous() + # Scale intrinsics to the downsampled grid. + K = K.clone() + K[0, :] /= downsample + K[1, :] /= downsample + + H_ds, W_ds = depth.shape + points = _da3_unproject(depth, K) # (H_ds, W_ds, 3) in OpenCV camera space + + # Apply world-to-camera inverse so multi-view frames share a common world frame. + E = _da3_get_extrinsic(da3_geometry, batch_index) + if E is not None: + points = _da3_apply_extrinsic(points, E) + + # Rebuild mask at downsampled resolution. + mask = _da3_build_mask(da3_geometry, batch_index, H, W, confidence_threshold, use_sky_mask) + if downsample > 1: + mask = mask[::downsample, ::downsample] + + mask = mask & torch.isfinite(depth) + + # OpenCV → glTF: flip Y and Z. + points_gltf = points.clone() + points_gltf[..., 1] *= -1.0 + points_gltf[..., 2] *= -1.0 + + pts_flat = points_gltf.reshape(-1, 3)[mask.reshape(-1)] + + colors_flat = None + if "image" in da3_geometry: + img = da3_geometry["image"][batch_index] # (H, W, 3) + if downsample > 1: + img = img[::downsample, ::downsample] + colors_flat = img.reshape(-1, 3)[mask.reshape(-1)] + + conf_flat = None + if "confidence" in da3_geometry: + conf = da3_geometry["confidence"][batch_index] # (H, W) + if downsample > 1: + conf = conf[::downsample, ::downsample] + conf_flat = conf.reshape(-1)[mask.reshape(-1)] + + if pts_flat.shape[0] == 0: + raise ValueError( + "DA3GeometryToPointCloud produced zero points after filtering. " + "Try lowering confidence_threshold or disabling use_sky_mask." + ) + + return io.NodeOutput({ + "points": pts_flat, + "colors": colors_flat, + "confidence": conf_flat, + }) + + +class DA3Extension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + LoadDA3Model, + DA3Inference, + DA3Render, + DA3GeometryToMesh, + # DA3GeometryToPointCloud, # Keep this commented out for now until we have a proper PointCloud output type + ] + + +async def comfy_entrypoint() -> DA3Extension: + return DA3Extension() diff --git a/comfy_extras/nodes_easycache.py b/comfy_extras/nodes_easycache.py index 923c2bb05..9e907d371 100644 --- a/comfy_extras/nodes_easycache.py +++ b/comfy_extras/nodes_easycache.py @@ -363,7 +363,7 @@ class EasyCacheNode(io.ComfyNode): node_id="EasyCache", display_name="EasyCache", description="Native EasyCache implementation.", - category="advanced/debug/model", + category="advanced/debug", is_experimental=True, inputs=[ io.Model.Input("model", tooltip="The model to add EasyCache to."), @@ -496,7 +496,7 @@ class LazyCacheNode(io.ComfyNode): node_id="LazyCache", display_name="LazyCache", description="A homebrew version of EasyCache - even 'easier' version of EasyCache to implement. Overall works worse than EasyCache, but better in some rare cases AND universal compatibility with everything in ComfyUI.", - category="advanced/debug/model", + category="advanced/debug", is_experimental=True, inputs=[ io.Model.Input("model", tooltip="The model to add LazyCache to."), diff --git a/comfy_extras/nodes_edit_model.py b/comfy_extras/nodes_edit_model.py index 36da66f34..d0d20ae7a 100644 --- a/comfy_extras/nodes_edit_model.py +++ b/comfy_extras/nodes_edit_model.py @@ -8,7 +8,8 @@ class ReferenceLatent(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="ReferenceLatent", - category="advanced/conditioning/edit_models", + display_name="Set Reference Latent", + category="model/conditioning", description="This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images.", inputs=[ io.Conditioning.Input("conditioning"), diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py index afc663b22..e9986c9e7 100644 --- a/comfy_extras/nodes_flux.py +++ b/comfy_extras/nodes_flux.py @@ -13,7 +13,7 @@ class CLIPTextEncodeFlux(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CLIPTextEncodeFlux", - category="advanced/conditioning/flux", + category="model/conditioning/flux", inputs=[ io.Clip.Input("clip"), io.String.Input("clip_l", multiline=True, dynamic_prompts=True), @@ -40,7 +40,7 @@ class EmptyFlux2LatentImage(io.ComfyNode): return io.Schema( node_id="EmptyFlux2LatentImage", display_name="Empty Flux 2 Latent", - category="model/latent", + category="model/latent/flux", inputs=[ io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), @@ -61,7 +61,7 @@ class FluxGuidance(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="FluxGuidance", - category="advanced/conditioning/flux", + category="model/conditioning/flux", inputs=[ io.Conditioning.Input("conditioning"), io.Float.Input("guidance", default=3.5, min=0.0, max=100.0, step=0.1), @@ -84,7 +84,7 @@ class FluxDisableGuidance(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="FluxDisableGuidance", - category="advanced/conditioning/flux", + category="model/conditioning/flux", description="This node completely disables the guidance embed on Flux and Flux like models", inputs=[ io.Conditioning.Input("conditioning"), @@ -128,7 +128,7 @@ class FluxKontextImageScale(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="FluxKontextImageScale", - category="advanced/conditioning/flux", + category="model/conditioning/flux", description="This node resizes the image to one that is more optimal for flux kontext.", inputs=[ io.Image.Input("image"), @@ -156,7 +156,7 @@ class FluxKontextMultiReferenceLatentMethod(io.ComfyNode): return io.Schema( node_id="FluxKontextMultiReferenceLatentMethod", display_name="Edit Model Reference Method", - category="advanced/conditioning/flux", + category="model/conditioning/flux", inputs=[ io.Conditioning.Input("conditioning"), io.Combo.Input( @@ -245,6 +245,11 @@ class KV_Attn_Input: cache_key = "{}_{}".format(extra_options["block_type"], extra_options["block_index"]) if cache_key in self.cache: kk, vv = self.cache[cache_key] + + # Fix batch size changing. + kk = comfy.utils.repeat_to_batch_size(kk, k.shape[0]) + vv = comfy.utils.repeat_to_batch_size(vv, v.shape[0]) + self.set_cache = False return {"q": q, "k": torch.cat((k, kk), dim=2), "v": torch.cat((v, vv), dim=2)} diff --git a/comfy_extras/nodes_frame_interpolation.py b/comfy_extras/nodes_frame_interpolation.py index 4d5bca17e..44708e5ec 100644 --- a/comfy_extras/nodes_frame_interpolation.py +++ b/comfy_extras/nodes_frame_interpolation.py @@ -77,7 +77,7 @@ class FrameInterpolate(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="FrameInterpolate", - display_name="Frame Interpolate", + display_name="Run Frame Interpolation Model", category="video", search_aliases=["rife", "film", "frame interpolation", "slow motion", "interpolate frames", "vfi"], inputs=[ diff --git a/comfy_extras/nodes_gaussian_splat.py b/comfy_extras/nodes_gaussian_splat.py new file mode 100644 index 000000000..116c14fde --- /dev/null +++ b/comfy_extras/nodes_gaussian_splat.py @@ -0,0 +1,1664 @@ +# Generic utility nodes for the SPLAT type (3D gaussian splats) + +import gzip +import logging +import math +import struct +from io import BytesIO + +import numpy as np +import torch +from typing_extensions import override +from scipy.ndimage import map_coordinates, minimum as _ndi_minimum, maximum as _ndi_maximum +from scipy.sparse import coo_matrix +from scipy.sparse.csgraph import connected_components + +import comfy.model_management +import comfy.utils +from comfy_api.latest import ComfyExtension, IO, Types +from comfy_extras.nodes_save_3d import pack_variable_mesh_batch +from server import PromptServer + +_C0 = 0.28209479177387814 # SH band-0 constant: DC coefficient -> base RGB + + +def _srgb_to_linear(c): + return torch.where(c <= 0.04045, c / 12.92, ((c.clamp_min(0) + 0.055) / 1.055) ** 2.4) + + +def _linear_to_srgb(c): + return torch.where(c <= 0.0031308, c * 12.92, 1.055 * c.clamp_min(0) ** (1 / 2.4) - 0.055) + + +def _real_len(g: Types.SPLAT, i: int) -> int: + # Real splat count of batch item i (honors variable-length `counts`). + return int(g.counts[i].item()) if g.counts is not None else g.positions.shape[1] + + +def _hex_to_rgb(h: str) -> tuple[float, float, float]: + # "#RRGGBB" -> (r,g,b) in [0,1]; falls back to black. + h = h.lstrip("#") + if len(h) != 6: + return (0.0, 0.0, 0.0) + return tuple(int(h[i:i + 2], 16) / 255.0 for i in (0, 2, 4)) + + +def _quantile(x, q): + # torch.quantile errors above 2**24 elements; stride-subsample large inputs for the estimate. + lim = 1 << 24 + if x.numel() > lim: + x = x[:: x.numel() // lim + 1] + return torch.quantile(x, q) + + +def _gaussian_ply_bytes(positions, scales, rotations, opacities, sh) -> bytes: + """Serialize render-ready gaussian tensors as a binary 3DGS .ply. + + positions (N,3) world; scales (N,3) linear; rotations (N,4) quat wxyz; opacities (N,1) in [0,1]; + sh (N,K,3) SH coefficients. Activated values are inverted to the standard 3D gaussian splat storage convention + (log scale, logit opacity). + """ + xyz = positions.cpu().numpy().astype(np.float32) + n = xyz.shape[0] + if n == 0: + raise ValueError("SplatToFile3D: gaussian is empty") + normals = np.zeros_like(xyz) + f = sh.cpu().numpy().astype(np.float32) # (N, K, 3) + f_dc = f[:, 0, :] # (N, 3) + f_rest = f[:, 1:, :].transpose(0, 2, 1).reshape(n, -1) # (N, 3*(K-1)) channel-major + op = opacities.cpu().numpy().astype(np.float32).reshape(n, 1).clip(1e-6, 1 - 1e-6) + op = np.log(op / (1.0 - op)) # inverse sigmoid (logit) + scale = np.log(scales.cpu().numpy().astype(np.float32).clip(min=1e-8)) + rot = rotations.cpu().numpy().astype(np.float32) # (N, 4) + + attrs = (['x', 'y', 'z', 'nx', 'ny', 'nz'] + + [f'f_dc_{i}' for i in range(3)] + + [f'f_rest_{i}' for i in range(f_rest.shape[1])] + + ['opacity'] + [f'scale_{i}' for i in range(3)] + [f'rot_{i}' for i in range(4)]) + elements = np.empty(n, dtype=[(a, 'f4') for a in attrs]) + elements[:] = list(map(tuple, np.concatenate([xyz, normals, f_dc, f_rest, op, scale, rot], axis=1))) + + header = "ply\nformat binary_little_endian 1.0\n" + f"element vertex {n}\n" + header += "".join(f"property float {a}\n" for a in attrs) + "end_header\n" + return header.encode('ascii') + elements.tobytes() + + +# .ksplat (mkkellogg SplatBuffer) level 0, SH degree 0: 4096-byte header, one 1024-byte section header, +# then N 44-byte records. Bucketing/quantization only exist at levels >= 1. See SplatBuffer.js. +_KSPLAT_HEADER_BYTES = 4096 +_KSPLAT_SECTION_HEADER_BYTES = 1024 +_KSPLAT_BYTES_PER_SPLAT = 44 # center 12 + scale 12 + rotation 16 + color(RGBA u8) 4 +_KSPLAT_VERSION = (0, 1) # SplatBuffer CurrentMajor/MinorVersion + + +def _gaussian_ksplat_bytes(positions, scales, rotations, opacities, sh) -> bytes: + """Serialize gaussian tensors as a level-0, SH degree-0 .ksplat (linear scale, opacity in color alpha). + + positions (N,3) world; scales (N,3) linear; rotations (N,4) wxyz; opacities (N,1) in [0,1]; sh (N,K,3). + """ + xyz = positions.cpu().numpy().astype(np.float32) + n = xyz.shape[0] + if n == 0: + raise ValueError("SplatToFile3D: gaussian is empty") + scale = scales.cpu().numpy().astype(np.float32) + rot = rotations.cpu().numpy().astype(np.float32) # wxyz, mirrors the .ply rot order + rot = rot / np.linalg.norm(rot, axis=1, keepdims=True).clip(1e-12) + rgb = np.clip(sh[:, 0, :].cpu().numpy().astype(np.float32) * _C0 + 0.5, 0, 1) + op = opacities.cpu().numpy().astype(np.float32).reshape(n, 1).clip(0, 1) + rgba = np.round(np.concatenate([rgb, op], axis=1) * 255.0).astype(np.uint8) # (N, 4) RGBA + + # 44-byte record: float center(3) + scale(3) + rot(4), then uint8 rgba(4). + floats = np.concatenate([xyz, scale, rot], axis=1).astype(' bytes: + """Serialize gaussian tensors as a gzip-compressed .spz (Niantic v2, SH degree 0, base color only). + + positions (N,3) world; scales (N,3) linear; rotations (N,4) wxyz; opacities (N,1) in [0,1]; sh (N,K,3). + """ + xyz = positions.cpu().numpy().astype(np.float32) + n = xyz.shape[0] + if n == 0: + raise ValueError("SplatToFile3D: gaussian is empty") + + # Positions: fixed point, masked to 24 bits, little-endian 3-byte words. + fixed = 1 << _SPZ_FRACTIONAL_BITS + qi = np.clip(np.round(xyz * fixed), -(1 << 23), (1 << 23) - 1).astype(np.int32) + qu = (qi & 0xFFFFFF).astype(np.uint32) + pos = np.stack([qu & 0xFF, (qu >> 8) & 0xFF, (qu >> 16) & 0xFF], axis=-1).reshape(n, 9).astype(np.uint8) + + alpha = np.round(opacities.cpu().numpy().astype(np.float32).reshape(n) * 255.0).clip(0, 255).astype(np.uint8) + + rgb = sh[:, 0, :].cpu().numpy().astype(np.float32) * _C0 + 0.5 + col = np.round(((rgb - 0.5) / _SPZ_COLOR_SCALE + 0.5) * 255.0).clip(0, 255).astype(np.uint8) # (N,3) + + sln = np.log(scales.cpu().numpy().astype(np.float32).clip(min=1e-9)) + scb = np.round((sln + 10.0) * 16.0).clip(0, 255).astype(np.uint8) # (N,3) inverts exp(b/16-10) + + rot = rotations.cpu().numpy().astype(np.float32) # wxyz + rot = rot / np.linalg.norm(rot, axis=1, keepdims=True).clip(1e-12) + rot[rot[:, 0] < 0] *= -1.0 # canonical w >= 0 (w dropped on decode) + rotb = np.round((rot[:, 1:4] + 1.0) * 127.5).clip(0, 255).astype(np.uint8) # (N,3) x,y,z + + header = bytearray(16) + struct.pack_into(' (positions, scales linear, rotations wxyz, opacities [0,1], sh (N,K,3)) ---- +# Inverse of the writers above and of spark's loaders. ksplat/splat/spz carry base color only (SH degree 0 +# -> K=1); .ply round-trips full SH. None of the formats flip axes, so import is the identity of export. +_PLY_DTYPES = {'char': 'i1', 'uchar': 'u1', 'short': 'i2', 'ushort': 'u2', 'int': 'i4', 'uint': 'u4', + 'float': 'f4', 'double': 'f8', 'int8': 'i1', 'uint8': 'u1', 'int16': 'i2', 'uint16': 'u2', + 'int32': 'i4', 'uint32': 'u4', 'float32': 'f4', 'float64': 'f8'} +_KSPLAT_COMPRESSION = { # level -> (bytesPerCenter, scale, rotation, color, shComponent, defaultScaleRange) + 0: (12, 12, 16, 4, 4, 1), 1: (6, 6, 8, 4, 2, 32767), 2: (6, 6, 8, 4, 1, 32767)} +_KSPLAT_SH_COMPONENTS = {0: 0, 1: 9, 2: 24, 3: 45} + + +def _rgb_to_sh_dc(rgb): + return ((np.asarray(rgb, np.float32) - 0.5) / _C0)[:, None, :] # (N,3) base color -> (N,1,3) SH DC + + +def _norm_quat(q): + return q / np.linalg.norm(q, axis=1, keepdims=True).clip(1e-12) + + +def _parse_ply_gaussian(data: bytes): + end = data.find(b'end_header') + if end < 0: + raise ValueError("File3DToSplat: not a PLY (missing end_header)") + header = data[:end].decode('ascii', 'replace') + body = end + len(b'end_header') + body += 2 if data[body:body + 2] == b'\r\n' else 1 + count, props, in_vertex = 0, [], False + for line in header.splitlines(): + p = line.split() + if not p: + continue + if p[0] == 'format' and p[1] != 'binary_little_endian': + raise ValueError(f"File3DToSplat: unsupported PLY format '{p[1]}' (need binary_little_endian)") + if p[0] == 'element': + in_vertex = p[1] == 'vertex' + if in_vertex: + count = int(p[2]) + elif p[0] == 'property' and in_vertex: + if p[1] == 'list': + raise ValueError("File3DToSplat: PLY vertex has list properties (unsupported)") + props.append((p[2], '<' + _PLY_DTYPES[p[1]])) + arr = np.frombuffer(data, np.dtype(props), count=count, offset=body) + names = arr.dtype.names + c = lambda k: arr[k].astype(np.float32) + n = count + + xyz = np.stack([c('x'), c('y'), c('z')], 1) + if 'scale_0' in names: + scale = np.exp(np.stack([c('scale_0'), c('scale_1'), c('scale_2')], 1)) # 3DGS stores log scale + else: + scale = np.full((n, 3), 0.01, np.float32) + if 'rot_0' in names: + rot = _norm_quat(np.stack([c('rot_0'), c('rot_1'), c('rot_2'), c('rot_3')], 1)) # wxyz + else: + rot = np.tile(np.array([1, 0, 0, 0], np.float32), (n, 1)) + opacity = 1.0 / (1.0 + np.exp(-c('opacity'))) if 'opacity' in names else np.ones(n, np.float32) + + if 'f_dc_0' in names: + dc = np.stack([c('f_dc_0'), c('f_dc_1'), c('f_dc_2')], 1) # (N,3) + rest = sorted((k for k in names if k.startswith('f_rest_')), key=lambda s: int(s.split('_')[-1])) + if rest: + r = np.stack([c(k) for k in rest], 1) # (N, 3*(K-1)) channel-major + kk = r.shape[1] // 3 + 1 + r = r.reshape(n, 3, kk - 1).transpose(0, 2, 1) # -> (N, K-1, 3) + sh = np.concatenate([dc[:, None, :], r], 1) + else: + sh = dc[:, None, :] + elif 'red' in names: + sh = _rgb_to_sh_dc(np.stack([c('red'), c('green'), c('blue')], 1) / 255.0) + else: + sh = np.zeros((n, 1, 3), np.float32) + return xyz, scale, rot, opacity, sh + + +def _parse_splat_gaussian(data: bytes): + # antimatter15 .splat: 32-byte records (f32 xyz, f32 scale, u8 rgba, u8 quat as (b-128)/128 wxyz). + if len(data) % 32 != 0: + raise ValueError("File3DToSplat: .splat size is not a multiple of 32 bytes") + rec = np.frombuffer(data, np.dtype([('xyz', ' 0: + ct, ft = (' full_splats: + lengths = np.frombuffer(data, '> 30) & 3 + q = np.zeros((n, 4), np.float32) # x,y,z,w + remaining, sumsq = combined.copy(), np.zeros(n, np.float64) + for comp in (3, 2, 1, 0): + active = comp != largest + value = (remaining & 0x1FF).astype(np.float64) + sign = (remaining >> 9) & 1 + remaining = np.where(active, remaining >> 10, remaining) + val = (1.0 / math.sqrt(2)) * (value / 0x1FF) + val = np.where(sign == 1, -val, val) + q[active, comp] = val[active] + sumsq += np.where(active, val * val, 0.0) + q[np.arange(n), largest] = np.sqrt(np.clip(1.0 - sumsq, 0, None)) + rot = _norm_quat(np.stack([q[:, 3], q[:, 0], q[:, 1], q[:, 2]], 1)) # xyzw -> wxyz + else: + qb = np.frombuffer(raw, np.uint8, count=n * 3, offset=off).reshape(n, 3).astype(np.float32) + xq = qb / 127.5 - 1.0 + w = np.sqrt(np.clip(1.0 - (xq ** 2).sum(1), 0, None)) + rot = _norm_quat(np.concatenate([w[:, None], xq], 1)) # wxyz + return xyz, scale, rot, alpha, _rgb_to_sh_dc(rgb) + + +_GAUSSIAN_PARSERS = {"ply": _parse_ply_gaussian, "splat": _parse_splat_gaussian, + "ksplat": _parse_ksplat_gaussian, "spz": _parse_spz_gaussian} + + +def _detect_splat_format(data: bytes) -> str: + if data[:3] == b'ply': + return "ply" + if data[:2] == b'\x1f\x8b': # gzip -> spz + return "spz" + if len(data) >= 2 and data[0] == 0 and data[1] >= 1: # ksplat version 0.x header + return "ksplat" + if len(data) % 32 == 0: + return "splat" + raise ValueError("File3DToSplat: could not determine splat format from contents") + + +def _gaussian_item(g: Types.SPLAT, i: int, device): + # Slice batch item i to its real length, as float32 torch tensors on `device` (SH DC -> base RGB). + end = _real_len(g, i) + to = lambda a: a.to(device=device, dtype=torch.float32) + xyz = to(g.positions[i, :end]) + rgb = (to(g.sh[i, :end, 0, :]) * _C0 + 0.5).clamp(0, 1) + opacity = to(g.opacities[i, :end]).reshape(-1) + scale = to(g.scales[i, :end]) + rot = to(g.rotations[i, :end]) + return xyz, rgb, opacity, scale, rot + + +def _quat_to_mat(q): + # q: (N, 4) wxyz, normalized -> (N, 3, 3) + q = q / q.norm(dim=-1, keepdim=True).clamp_min(1e-12) + w, x, y, z = q.unbind(-1) + return torch.stack([ + 1 - 2 * (y * y + z * z), 2 * (x * y - w * z), 2 * (x * z + w * y), + 2 * (x * y + w * z), 1 - 2 * (x * x + z * z), 2 * (y * z - w * x), + 2 * (x * z - w * y), 2 * (y * z + w * x), 1 - 2 * (x * x + y * y), + ], dim=-1).reshape(-1, 3, 3) + + +def _quat_mul(a, b): + # Hamilton product a (x) b, wxyz. + aw, ax, ay, az = a.unbind(-1) + bw, bx, by, bz = b.unbind(-1) + return torch.stack([ + aw * bw - ax * bx - ay * by - az * bz, + aw * bx + ax * bw + ay * bz - az * by, + aw * by - ax * bz + ay * bw + az * bx, + aw * bz + ax * by - ay * bx + az * bw, + ], dim=-1) + + +def _euler_to_quat(rx, ry, rz): + # Degrees, applied as Rz @ Ry @ Rx (rotate about X, then Y, then Z in world). Returns wxyz. + c, s = np.cos(np.radians([rx, ry, rz]) / 2.0), np.sin(np.radians([rx, ry, rz]) / 2.0) + qx = torch.tensor([c[0], s[0], 0.0, 0.0], dtype=torch.float32) + qy = torch.tensor([c[1], 0.0, s[1], 0.0], dtype=torch.float32) + qz = torch.tensor([c[2], 0.0, 0.0, s[2]], dtype=torch.float32) + return _quat_mul(_quat_mul(qz, qy), qx) + + +def _mat_to_quat(m): + # Rotation matrix (..., 3, 3) -> quaternion (..., 4) wxyz. Batched; builds the four candidate quaternions + # and keeps the one with the largest component (numerically stable across all rotations). + m00, m11, m22 = m[..., 0, 0], m[..., 1, 1], m[..., 2, 2] + m21, m12 = m[..., 2, 1], m[..., 1, 2] + m02, m20 = m[..., 0, 2], m[..., 2, 0] + m10, m01 = m[..., 1, 0], m[..., 0, 1] + q2 = torch.stack([1 + m00 + m11 + m22, 1 + m00 - m11 - m22, + 1 - m00 + m11 - m22, 1 - m00 - m11 + m22], -1) # 4 * (w^2, x^2, y^2, z^2) + cand = torch.stack([ + torch.stack([q2[..., 0], m21 - m12, m02 - m20, m10 - m01], -1), + torch.stack([m21 - m12, q2[..., 1], m10 + m01, m02 + m20], -1), + torch.stack([m02 - m20, m10 + m01, q2[..., 2], m12 + m21], -1), + torch.stack([m10 - m01, m02 + m20, m12 + m21, q2[..., 3]], -1), + ], -2) # (...,4,4) candidates, rows = wxyz + sel = q2.argmax(-1) + q = torch.gather(cand, -2, sel[..., None, None].expand(sel.shape + (1, 4)))[..., 0, :] + return q / q.norm(dim=-1, keepdim=True).clamp_min(1e-12) + + +class SplatToFile3D(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="SplatToFile3D", + display_name="Create 3D File (from Splat)", + search_aliases=["gaussian to ply", "splat to file", "export gaussian"], + category="3d/splat", + description="Serialize a gaussian splat to a File3D object for Save / Preview 3D nodes. " + "Supports one item per batch only.", + inputs=[ + IO.Splat.Input("splat"), + IO.Combo.Input("format", options=["ply", "ksplat", "spz"], # TODO: add "splat" when we have a writer for it + tooltip="ply: standard 3D Gaussian Splat with full spherical harmonics. " + "ksplat: mkkellogg SplatBuffer (level 0, uncompressed), base color only " + "spz: Niantic gzip-compressed (~10x smaller), base color only " + ), + ], + outputs=[IO.File3DSplatAny.Output(display_name="model_3d")], + ) + + @classmethod + def execute(cls, splat, format="ply") -> IO.NodeOutput: + if splat.positions.shape[0] > 1: + logging.warning("SplatToFile3D supports one item per batch only. Got %d; using first.", splat.positions.shape[0]) + end = _real_len(splat, 0) + writer = {"ksplat": _gaussian_ksplat_bytes, "spz": _gaussian_spz_bytes}.get(format, _gaussian_ply_bytes) + data = writer(splat.positions[0, :end], splat.scales[0, :end], + splat.rotations[0, :end], splat.opacities[0, :end], splat.sh[0, :end]) + return IO.NodeOutput(Types.File3D(BytesIO(data), file_format=format)) + + +class File3DToSplat(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="File3DToSplat", + display_name="Get Splat", + search_aliases=["load splat", "ply to splat", "import splat", "file to splat"], + category="3d/splat", + description="Parse a splat File3D into a gaussian splat. Inverse of Create 3D File (from Splat). " + "Supported format: PLY, SPLAT, KSPLAT, SPZ. PLY carries full spherical harmonics, " + "the other formats are base color only. Format is auto-detected from the file contents.", + inputs=[ + IO.MultiType.Input( + IO.File3DAny.Input("model_3d"), + types=[IO.File3DSplatAny, IO.File3DPLY, IO.File3DSPLAT, IO.File3DKSPLAT, IO.File3DSPZ], + tooltip="A gaussian splat 3D file", + ), + ], + outputs=[IO.Splat.Output(display_name="splat")], + ) + + @classmethod + def execute(cls, model_3d: Types.File3D) -> IO.NodeOutput: + data = model_3d.get_bytes() + fmt = (model_3d.format or "").lower() + parser = _GAUSSIAN_PARSERS.get(fmt) or _GAUSSIAN_PARSERS[_detect_splat_format(data)] + xyz, scale, rot, opacity, sh = parser(data) + + t = lambda a: torch.from_numpy(np.ascontiguousarray(a)).float() + splat = Types.SPLAT( + t(xyz)[None], # (1, N, 3) + t(scale)[None], # (1, N, 3) linear + t(rot)[None], # (1, N, 4) wxyz + t(opacity).reshape(1, -1, 1), # (1, N, 1) + t(sh)[None], # (1, N, K, 3) + ) + return IO.NodeOutput(splat) + + +def _view_matrix_t(yaw_deg, pitch_deg, device): + y, p = math.radians(yaw_deg), math.radians(pitch_deg) + cy, sy, cp, sp = math.cos(y), math.sin(y), math.cos(p), math.sin(p) + Ry = torch.tensor([[cy, 0, sy], [0, 1, 0], [-sy, 0, cy]], device=device) + Rx = torch.tensor([[1, 0, 0], [0, cp, -sp], [0, sp, cp]], device=device) + return Rx @ Ry + + +def _camera_basis(camera_info, dev): + # Look-at basis in the splat frame, named by their projection rows: right = image +x, up = image +y + # (down, since yflip=1), fwd = view/depth axis (eye -> scene). Load3D is three.js (right-handed, Y-up, + # camera looks down -Z); the splat is 3DGS (Y-down, Z-forward). World -> splat is a 180 deg rotation + # about X: (x, y, z) -> (x, -y, -z) (det +1, no mirror, no axis swap). + pos, tgt = camera_info.get("position", {}), camera_info.get("target", {}) + m = lambda d: torch.tensor([float(d.get("x", 0.0)), -float(d.get("y", 0.0)), -float(d.get("z", 0.0))], device=dev) + eye, target = m(pos), m(tgt) + mv = lambda v: torch.stack([v[0], -v[1], -v[2]]) # same world->splat map, for direction vectors + n = lambda v: v / v.norm().clamp_min(1e-8) + q = camera_info.get("quaternion") + if q: # exact camera world rotation (incl. roll) + qwxyz = torch.tensor([float(q.get("w", 1.0)), float(q.get("x", 0.0)), + float(q.get("y", 0.0)), float(q.get("z", 0.0))], device=dev) + R = _quat_to_mat(qwxyz[None])[0] # columns = camera world axes; looks down local -Z + right = n(mv(R[:, 0])) # camera +X -> image right + up = n(mv(-R[:, 1])) # camera +Y is image up; image-down row is its negative + fwd = n(mv(-R[:, 2])) # camera looks down local -Z -> view direction + return eye, target, right, up, fwd + fwd = n(target - eye) # no quaternion: orbit-consistent, roll-free + yaw = math.degrees(math.atan2(-float(fwd[0]), float(fwd[2]))) + pitch = math.degrees(math.asin(max(-1.0, min(1.0, float(fwd[1]))))) + W = _view_matrix_t(yaw, pitch, dev) + return eye, target, W[0], W[1], W[2] + + +def _lookat_quat_wxyz(position, target, dev): + # three.js lookAt in world frame: camera local +Z = (eye - target), up = world +Y. Returns wxyz. + z = position - target + z = z / z.norm().clamp_min(1e-8) + up0 = torch.tensor([0.0, 1.0, 0.0], device=dev) + if z.dot(up0).abs() > 0.999: # looking straight up/down + up0 = torch.tensor([0.0, 0.0, 1.0], device=dev) + x = torch.linalg.cross(up0, z) + x = x / x.norm().clamp_min(1e-8) + y = torch.linalg.cross(z, x) + R = torch.stack([x, y, z], dim=1) # columns = camera world axes + return _mat_to_quat(R[None])[0] + + +def _lookat_camera_info(position, target, fov, dev, zoom=1.0, camera_type="perspective", roll=0.0): + # Build a camera_info from a world-space (right-handed, Y-up) eye + look-at target; up = world +Y. + pos = torch.as_tensor(position, dtype=torch.float32, device=dev) + tgt = torch.as_tensor(target, dtype=torch.float32, device=dev) + q = _lookat_quat_wxyz(pos, tgt, dev) + if roll: # roll about the view axis (camera local Z) + a = math.radians(roll) + qz = torch.tensor([math.cos(a / 2), 0.0, 0.0, math.sin(a / 2)], device=dev) + q = _quat_mul(q[None], qz[None])[0] + xyz = lambda v: {"x": float(v[0]), "y": float(v[1]), "z": float(v[2])} + return {"position": xyz(pos), "target": xyz(tgt), + "quaternion": {"x": float(q[1]), "y": float(q[2]), "z": float(q[3]), "w": float(q[0])}, + "fov": float(fov), "cameraType": str(camera_type), "zoom": float(zoom)} + + +def _quat_camera_info(position, quat_xyzw, fov, dev, zoom=1.0, camera_type="perspective"): + # camera_info from an explicit world position + camera-rotation quaternion (three.js: looks down local -Z). + pos = torch.as_tensor(position, dtype=torch.float32, device=dev) + qx, qy, qz, qw = (float(c) for c in quat_xyzw) + qwxyz = torch.tensor([qw, qx, qy, qz], dtype=torch.float32, device=dev) + qwxyz = qwxyz / qwxyz.norm().clamp_min(1e-8) + R = _quat_to_mat(qwxyz[None])[0] + tgt = pos - R[:, 2] # look one unit down local -Z + xyz = lambda v: {"x": float(v[0]), "y": float(v[1]), "z": float(v[2])} + return {"position": xyz(pos), "target": xyz(tgt), + "quaternion": {"x": float(qwxyz[1]), "y": float(qwxyz[2]), "z": float(qwxyz[3]), "w": float(qwxyz[0])}, + "fov": float(fov), "cameraType": str(camera_type), "zoom": float(zoom)} + + +def _orbit_camera_info(yaw, pitch, distance, fov, pivot_splat, dev): + # Orbit helper for RenderSplat's default camera: yaw/pitch about `pivot_splat` (splat frame) at `distance`. + # World<->splat is the (x,-y,-z) map, so _camera_basis recovers exactly _view_matrix_t(yaw, pitch). + y, p = math.radians(yaw), math.radians(pitch) + cy, sy, cp, sp = math.cos(y), math.sin(y), math.cos(p), math.sin(p) + fwd_splat = torch.tensor([-cp * sy, sp, cp * cy], device=dev) # == _view_matrix_t(yaw, pitch)[2] + m = lambda v: torch.stack([v[0], -v[1], -v[2]]) # splat<->world (its own inverse) + return _lookat_camera_info(m(pivot_splat - distance * fwd_splat), m(pivot_splat), fov, dev) + + +def _orbit_camera_info_yaw(camera_info, angle_deg, dev): + # Turntable: rigidly rotate a camera_info about world +Y around its target by angle_deg. Returns a new dict. + a = math.radians(angle_deg) + ca, sa = math.cos(a), math.sin(a) + v = lambda d: torch.tensor([float(d.get("x", 0.0)), float(d.get("y", 0.0)), float(d.get("z", 0.0))], device=dev) + pos, tgt = v(camera_info.get("position", {})), v(camera_info.get("target", {})) + Ry = torch.tensor([[ca, 0.0, sa], [0.0, 1.0, 0.0], [-sa, 0.0, ca]], device=dev) + new_pos = tgt + Ry @ (pos - tgt) + q = camera_info.get("quaternion") or {} + qcur = torch.tensor([float(q.get("w", 1.0)), float(q.get("x", 0.0)), + float(q.get("y", 0.0)), float(q.get("z", 0.0))], device=dev) + qy = torch.tensor([math.cos(a / 2), 0.0, math.sin(a / 2), 0.0], device=dev) # world +Y rotation + qn = _quat_mul(qy[None], qcur[None])[0] + xyz = lambda t: {"x": float(t[0]), "y": float(t[1]), "z": float(t[2])} + return {**camera_info, "position": xyz(new_pos), + "quaternion": {"x": float(qn[1]), "y": float(qn[2]), "z": float(qn[3]), "w": float(qn[0])}} + + +def _gauss_blur(x, sigma, dev): + # Separable Gaussian blur of (1, C, H, W). Used to denoise the screen-space normal map. + r = max(1, int(round(3 * sigma))) + k = torch.exp(-0.5 * (torch.arange(-r, r + 1, device=dev, dtype=torch.float32) / sigma) ** 2) + k = k / k.sum() + c = x.shape[1] + x = torch.nn.functional.conv2d(x, k.view(1, 1, 1, -1).expand(c, 1, 1, -1), padding=(0, r), groups=c) + x = torch.nn.functional.conv2d(x, k.view(1, 1, -1, 1).expand(c, 1, -1, 1), padding=(r, 0), groups=c) + return x + + +def _render_gaussian(xyz, rgb, opacity, scale, rot, width, height, splat_scale, bg, camera_info, + sharpen=1.0, headlight_shading=0.0, render_style="color"): + # Perspective-correct anisotropic gaussian splat rasterizer. Each splat is weighted by its 3D Gaussian's + # peak along each pixel's ray (AAA / Hahlbohm), composited front-to-back across depth slabs. `render_style` + # selects the image: color / clay / depth / normal. Returns (image HxWx3, coverage mask HxW) on CPU. + dev = comfy.model_management.get_torch_device() + t = lambda a: torch.as_tensor(a, dtype=torch.float32, device=dev) + idev, idtype = comfy.model_management.intermediate_device(), comfy.model_management.intermediate_dtype() + xyz, rgb, opacity = t(xyz), t(rgb).clamp(0, 1), t(opacity).reshape(-1) + scale, rot = t(scale) * float(splat_scale), t(rot) + do_linear = render_style == "color" # colour blends in linear light, re-encoded at the end + if do_linear: + rgb = _srgb_to_linear(rgb) + flat = width * height + bg_t = t(bg) + bg_comp = _srgb_to_linear(bg_t) if do_linear else bg_t # background blended in the same space as the splats + need_depth = render_style == "depth" + need_normal = render_style in ("normal", "clay") or headlight_shading > 0 + + def background_only(): # no splats to rasterize -> just the background + empty mask + img = bg_t.expand(height, width, 3) if render_style == "color" else torch.zeros(height, width, 3, device=dev) + return img.to(idev, idtype), torch.zeros(height, width, device=idev, dtype=idtype) + + if xyz.shape[0] == 0: # empty input (e.g. all culled by opacity_threshold) + return background_only() + + eye, target, right, up, fwd = _camera_basis(camera_info, dev) # all camera state comes from camera_info + W = torch.stack([right, up, fwd], 0) # rows = camera axes (world -> camera) + cam = (xyz - eye) @ W.T + fov = float(camera_info.get("fov", 0) or 0) or 35.0 + zoom = float(camera_info.get("zoom", 1.0) or 1.0) # three.js digital zoom: scales the focal length + is_ortho = str(camera_info.get("cameraType", "")).lower().startswith("ortho") + xc, yc, zc = cam.unbind(-1) + + keep = zc > 1e-2 + xc, yc, zc, rgb, opacity, scale, rot = (a[keep] for a in (xc, yc, zc, rgb, opacity, scale, rot)) + if xc.shape[0] == 0: # nothing in front of the camera -> background only + return background_only() + if render_style == "clay": + rgb = torch.full_like(rgb, 0.75) # neutral albedo -> shading shows pure geometry + + f = (min(width, height) / 2) / math.tan(math.radians(fov) / 2) * zoom # fov over the smaller axis, x camera zoom + cx0, cy0 = width / 2, height / 2 + + # Camera-space 3D covariance per splat: Sigma = (W Rq) diag(scale^2) (W Rq)^T, plus a tiny relative + # regularizer for a stable inverse (a pixel-size Mip low-pass would over-thicken flat surfels and blur). + Mw = W[None] @ _quat_to_mat(rot) # (N,3,3) world -> camera + cam_cov = (Mw * scale.square()[:, None, :]) @ Mw.transpose(1, 2) + cam_cov = cam_cov + (cam_cov.diagonal(dim1=-2, dim2=-1).mean(-1) * 1e-3)[:, None, None] * torch.eye(3, device=dev) + + # Perspective-correct weighting: peak of the 3D Gaussian along each pixel ray. Precompute Si, Si@mu, mu^T Si mu. + mu = torch.stack([xc, yc, zc], -1) + si = torch.linalg.inv(cam_cov) + simu = (si @ mu[:, :, None])[:, :, 0] # (N,3) + musimu = (mu * simu).sum(-1) # (N,) + s00, s01, s02 = si[:, 0, 0], si[:, 0, 1], si[:, 0, 2] + s11, s12, s22 = si[:, 1, 1], si[:, 1, 2], si[:, 2, 2] + simu0, simu1, simu2 = simu.unbind(-1) + if need_normal: # surfel normal = thinnest axis, oriented toward camera + nrm = Mw[torch.arange(Mw.shape[0], device=dev), :, scale.argmin(-1)] # (N,3) camera-space normal + nrm = nrm * torch.where(nrm[:, 2:3] > 0, -1.0, 1.0) # flip so nz <= 0 (faces camera) + + # Screen centre (exact) + footprint radius from the affine 2D projection (used only to size the kernel). + # The image is +y-down, so the projection's y row is unflipped - it matches the splat frame's +Y. + jm = torch.zeros(xc.shape[0], 2, 3, device=dev) + if is_ortho: # parallel projection: screen = s * (xc, yc) + s = f / float((target - eye).norm().clamp_min(1e-6)) # pixels per world unit at the target plane + cx, cy = cx0 + s * xc, cy0 + s * yc + jm[:, 0, 0] = s + jm[:, 1, 1] = s + else: # perspective: screen = f * (xc, yc) / zc + invz = 1.0 / zc + cx, cy = cx0 + f * xc * invz, cy0 + f * yc * invz + jm[:, 0, 0], jm[:, 0, 2] = f * invz, -f * xc * invz.square() + jm[:, 1, 1], jm[:, 1, 2] = f * invz, -f * yc * invz.square() + cov2 = jm @ cam_cov @ jm.transpose(1, 2) + a, b, c = cov2[:, 0, 0], cov2[:, 0, 1], cov2[:, 1, 1] + max_eig = (a + c) * 0.5 + (((a - c) * 0.5).square() + b * b).clamp_min(0).sqrt() + radius = 3.0 * max_eig.clamp_min(1e-8).sqrt() + K = int(min(max(24, min(width, height) // 16), max(2, math.ceil(_quantile(radius, 0.995).item())))) + + # Per-splat kernel size: bucket splats by radius into a coarse ladder of window sizes (global K stays the cap) so + # small splats (the bulk of it) use a small window. + levels = [L for L in (16, 64, 256) if L < K] + [K] + levels_t = torch.tensor(levels, device=dev, dtype=torch.float32) + grids = [] + for L in levels: + rng = torch.arange(-L, L + 1, device=dev, dtype=torch.float32) + gy, gx = torch.meshgrid(rng, rng, indexing="ij") + grids.append((gx.reshape(-1), gy.reshape(-1))) + blevel = torch.bucketize(radius * (4.0 / 3.0), levels_t).clamp_(max=len(levels) - 1) # window >= ~4 sigma + + n = zc.shape[0] + ns = int(min(256, max(1, n // 1000))) # depth slabs: 1 per ~1000 splats, capped + nl = len(levels) + order = torch.argsort(zc) # front (small zc) -> back -> defines the slabs + bounds = torch.linspace(0, n, ns + 1, device=dev).round().long() + rank = torch.empty(n, dtype=torch.long, device=dev) + rank[order] = torch.arange(n, device=dev) # depth rank of each splat + slab_id = (torch.searchsorted(bounds, rank, right=True) - 1).clamp_(0, ns - 1) + key = slab_id * nl + blevel # group by slab, then kernel level (order-free within) + order = torch.argsort(key) + key = key[order] + + cxr, cyr = cx[order].round(), cy[order].round() + s00, s01, s02 = s00[order], s01[order], s02[order] + s11, s12, s22 = s11[order], s12[order], s22[order] + s01b, s02b, s12b = s01 * 2, s02 * 2, s12 * 2 # doubled cross terms for the fused quadratic forms + simu0, simu1, simu2, musimu = simu0[order], simu1[order], simu2[order], musimu[order] + opacity, rgb = opacity[order], rgb[order] + zc_o = zc[order] if need_depth else None + nrm_o = nrm[order] if need_normal else None + mux_o, muy_o, muz_o = (xc[order], yc[order], zc[order]) if is_ortho else (None, None, None) + + # Pack the per-splat scalars into one tensor so each chunk slices once + common = [cxr, cyr, s00, s11, s22, s01b, s02b, s12b, opacity] + pstack = torch.stack(common + ([s02, s12, mux_o, muy_o, muz_o] if is_ortho else [simu0, simu1, simu2, musimu])) + + # Precompute the (slab, level) run table on-GPU and pull it to the CPU once + starts = torch.cat([torch.zeros(1, dtype=torch.long, device=dev), (key[1:] != key[:-1]).nonzero().flatten() + 1]) + ks = key[starts] + run_lo = starts.tolist() + [n] + run_lev = (ks % nl).tolist() + run_slab = torch.div(ks, nl, rounding_mode="floor").tolist() + slab_runs = [[] for _ in range(ns)] + for r in range(len(run_lev)): + slab_runs[run_slab[r]].append((run_lo[r], run_lo[r + 1], run_lev[r])) + + def splat(lo, hi, ox, oy): # -> pixel idx (m,M), alpha (m,M); weight = 3D Gaussian peak along each pixel's ray + cols = pstack[:, lo:hi, None].unbind(0) + cxr_, cyr_, a00, a11, a22, b01, b02, b12, opa = cols[:9] # a* = Si components; b* = 2 * cross terms + px = cxr_ + ox[None, :] + py = cyr_ + oy[None, :] + valid = (px >= 0) & (px < width) & (py >= 0) & (py < height) + if is_ortho: # parallel ray (0,0,1) from screen point (X, Y, 0); rz constant per splat + c02, c12, mx, my, mz = cols[9:] + rx = (px - cx0) / s - mx + ry = (py - cy0) / s - my + rz = -mz + a22rz = a22 * rz + inx = torch.addcmul(b02 * rz, a00, rx).addcmul_(b01, ry) # a00 rx + b01 ry + b02 rz + rSr = torch.addcmul(a22rz * rz, rx, inx).addcmul_(ry, torch.addcmul(b12 * rz, a11, ry)) + dsr = torch.addcmul(a22rz, c02, rx).addcmul_(c12, ry) + q = torch.addcdiv(rSr, dsr * dsr, a22.clamp_min(1e-12), value=-1).clamp_min_(0) + else: # perspective ray (dx,dy,1) through the camera origin + su0, su1, su2, mus = cols[9:] + dx, dy = (px - cx0) / f, (py - cy0) / f + dsid = torch.addcmul(a22, dx, torch.addcmul(b02, a00, dx)) # a22 + dx*(a00 dx + b02) + dsid = dsid.addcmul_(dy, torch.addcmul(b12, a11, dy)) # + dy*(a11 dy + b12) + dsid = dsid.addcmul_(b01 * dx, dy) # + (2 s01) dx dy + dsimu = torch.addcmul(su2, dx, su0).addcmul_(dy, su1) + q = torch.addcdiv(mus, dsimu * dsimu, dsid.clamp_min(1e-12), value=-1).clamp_min_(0) + alpha = (opa * torch.exp(-0.5 * q) * valid).clamp_(0, 0.999) + idx = py.long().clamp(0, height - 1) * width + px.long().clamp(0, width - 1) + return idx, alpha + + # Front-to-back compositing over the depth slabs set up above. Within a slab the accumulation is a pure + # sum (order-independent), so splats are grouped by kernel level and each level uses its own tight window. + sharp = sharpen != 1.0 # winner-take-more colour blend: dominant splat shows more + cacc = torch.zeros((flat, 3), device=dev) + trans = torch.ones((flat,), device=dev) + a_buf = torch.zeros((flat,), device=dev) # sum alpha -> colour/depth/normal weight (alpha-weighted mean) + tau_buf = torch.zeros((flat,), device=dev) # sum -ln(1-alpha) -> slab opacity = 1-prod(1-alpha) + crgb = torch.zeros((flat, 3), device=dev) # sum alpha^p * rgb -> slab colour + wbuf = torch.zeros((flat,), device=dev) if sharp else None # sum alpha^p -> colour normalizer (sharp only) + dacc = torch.zeros((flat,), device=dev) if need_depth else None # front-weighted depth + nacc = torch.zeros((flat, 3), device=dev) if need_normal else None # front-weighted camera-space normal + zslab = torch.zeros((flat,), device=dev) if need_depth else None + nslab = torch.zeros((flat, 3), device=dev) if need_normal else None + stale = 0 # consecutive fully-occluded slabs -> early-out + for si in range(ns): + runs = slab_runs[si] + if not runs: + continue + a_buf.zero_() + tau_buf.zero_() + crgb.zero_() + if sharp: + wbuf.zero_() + if need_depth: + zslab.zero_() + if need_normal: + nslab.zero_() + for r_lo, r_hi, li in runs: # contiguous same-kernel-level runs in this slab + ox, oy = grids[li] + ch = max(2048, 10_000_000 // ox.shape[0]) # splats/chunk, bounded by this level's kernel size + for lo in range(r_lo, r_hi, ch): + hi = min(lo + ch, r_hi) + idx, alpha = splat(lo, hi, ox, oy) + idx, af = idx.reshape(-1), alpha.reshape(-1) + a_buf.index_add_(0, idx, af) + tau_buf.index_add_(0, idx, (-torch.log1p(-alpha)).reshape(-1)) # -ln(1-alpha), correct opacity merge + apw = alpha.pow(sharpen) if sharp else alpha # bias colour toward the highest-alpha splat + crgb.index_add_(0, idx, (apw[:, :, None] * rgb[lo:hi, None, :]).reshape(-1, 3)) + if sharp: + wbuf.index_add_(0, idx, apw.reshape(-1)) + if need_depth: + zslab.index_add_(0, idx, (alpha * zc_o[lo:hi, None]).reshape(-1)) + if need_normal: + nslab.index_add_(0, idx, (alpha[:, :, None] * nrm_o[lo:hi, None, :]).reshape(-1, 3)) + slab_a = 1 - torch.exp(-tau_buf) # 1 - prod(1-alpha): true opacity of the slab's splats + front = trans * slab_a + denom = wbuf if sharp else a_buf + cacc.addcmul_(front[:, None], crgb / denom.clamp_min(1e-8)[:, None]) # cacc += front * (crgb/denom) + if need_depth or need_normal: + ainv = a_buf.clamp_min(1e-8) # alpha-weighted-mean normalizer (depth/normal only) + if need_depth: + dacc.addcmul_(front, zslab / ainv) + if need_normal: + nacc.addcmul_(front[:, None], nslab / ainv[:, None]) + trans.mul_(1 - slab_a) + if si % 8 == 7: # checkpoint every 8 slabs (a per-slab GPU sync would cost more) + if float(front.max()) < 1e-3: # this checkpoint slab is fully occluded by what is in front + stale += 1 + if stale >= 2: # two occluded checkpoints running -> the rest are too -> stop + break + else: + stale = 0 + + cov = 1 - trans + covg = cov.reshape(height, width) + covm = covg > 0.5 if render_style in ("depth", "normal") else None # silhouette mask (depth/normal styles only) + depth_map = (dacc / cov.clamp_min(1e-6)).reshape(height, width) if need_depth else None + nrm_map = None + if need_normal: + # Per-splat surfel normals are jittery, so do a masked blur + nb = nacc.reshape(height, width, 3).permute(2, 0, 1)[None] + cb = cov.reshape(1, 1, height, width) + nb, cb = _gauss_blur(nb, 1.2, dev), _gauss_blur(cb, 1.2, dev) + normal = (nb / cb.clamp_min(1e-6))[0].permute(1, 2, 0) + nrm_map = normal / normal.norm(dim=-1, keepdim=True).clamp_min(1e-6) + + if render_style == "depth": # near = bright, far = dark, 0 off-object + d = torch.zeros(height, width, device=dev) + if bool(covm.any()): + lo, hi = depth_map[covm].min(), depth_map[covm].max() + d = torch.where(covm, ((hi - depth_map) / (hi - lo).clamp_min(1e-6)).clamp(0, 1), d) + img = d[:, :, None].expand(height, width, 3) + elif render_style == "normal": # OpenGL normal map: +X right, +Y up, +Z to viewer + enc = (nrm_map * t([1.0, -1.0, -1.0]) * 0.5 + 0.5).clamp(0, 1) + img = enc * covm[:, :, None] + else: # color / clay + img = cacc.reshape(height, width, 3) + if render_style == "clay": # studio key light + ambient -> sculpted matte look + kl = t([-0.4, -0.7, -0.6]) # key from screen upper-left, angled toward the viewer + kl = kl / kl.norm() + hl = (0.5 * (nrm_map * kl).sum(-1) + 0.5).clamp(0, 1) # half-Lambert: soft terminator, no harsh dark side + img = img * (0.35 + 0.65 * hl * hl)[:, :, None] # ambient floor + diffuse key + elif headlight_shading > 0: # camera headlight: darken faces turned from view + k = float(headlight_shading) + ndotl = (-nrm_map[:, :, 2]).clamp(0, 1) + img = img * (1 - 0.6 * k + 0.6 * k * ndotl)[:, :, None] + img = img.addcmul_(trans.reshape(height, width, 1), bg_comp) + if do_linear: # back to display space after linear compositing + img = _linear_to_srgb(img) + return img.clamp(0, 1).to(idev, idtype), covg.clamp(0, 1).to(idev, idtype) + + +class RenderSplat(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="RenderSplat", + display_name="Render Splat", + search_aliases=["splat to image", "render splat", "gaussian turntable"], + category="3d/splat", + description="Render a gaussian splat as an image with an anisotropic EWA rasterizer (oriented " + "elliptical splats, antialiased, depth-sorted front-to-back). The camera comes from a " + "camera_info input (Load / Preview 3D, or a Create Camera Info node); leave it empty to " + "auto-frame the splat. Set frames greater than 1 for a turntable batch of images to feed a Video node.", + inputs=[ + IO.Splat.Input("splat"), + IO.Int.Input("width", default=1024, min=64, max=2048, step=8), + IO.Int.Input("height", default=1024, min=64, max=2048, step=8), + IO.Int.Input("frames", default=1, min=-240, max=240, + tooltip="-1, 0, 1 = single still image; >1 = turntable, the camera orbits over a full " + "360 turn (works with any camera_info). Negative value orbits the other way."), + IO.Float.Input("splat_scale", default=1.0, min=0.1, max=5.0, step=0.05, advanced=True, + tooltip="Multiplier on each splat's projected footprint (lower = crisper points, " + "higher = softer/fuller surface)."), + IO.Float.Input("sharpen", default=2.0, min=1.0, max=8.0, step=0.5, + tooltip="Sharpen overlapping splats: 1.0 = physically-correct blend; higher biases " + "each pixel toward its dominant (nearest) splat for crisper texture, without " + "shrinking splats or opening gaps. Non-physical above 1."), + IO.Float.Input("headlight_shading", default=0.0, min=0.0, max=3.0, step=0.05, advanced=True, + tooltip="Diffuse shading from a light at the camera (headlight), using the splat surfel " + "normals: darkens surfaces that turn away from view to reveal form/curvature. " + "0 = flat albedo, 1 = strongest shading."), + IO.Float.Input("opacity_threshold", default=0.0, min=0.0, max=1.0, step=0.01, advanced=True, + tooltip="Cull gaussians with opacity below this (removes faint floaters)."), + IO.Combo.Input("render_style", options=["color", "clay", "depth", "normal"], + tooltip="What the image output shows: color, clay (neutral-albedo shaded), " + "depth (near=bright), normal (OpenGL normal map)."), + IO.Color.Input("background", default="#000000"), + IO.Image.Input("bg_image", optional=True, + tooltip="Optional background plate composited behind the splat (overrides the solid " + "background colour). Resized to the render size; a batch is used per frame, " + "a single image for all. color/clay only."), + IO.Load3DCamera.Input("camera_info", optional=True, + tooltip="Camera to render from - a Load3D / Preview3D camera or a Create Camera " + "Info node. If empty, the splat is auto-framed from a default 3/4 view."), + ], + outputs=[IO.Image.Output(display_name="image"), IO.Mask.Output(display_name="mask")], + ) + + @classmethod + def execute(cls, splat, width, height, frames, splat_scale, sharpen, headlight_shading, + opacity_threshold, background, render_style, camera_info=None, bg_image=None) -> IO.NodeOutput: + bg = _hex_to_rgb(background) + bg_imgs = None + if bg_image is not None: # resize the plate(s) to the render size: (B,H,W,3) + bi = bg_image[... , :3].movedim(-1, 1) # (B,3,H,W) + bi = comfy.utils.common_upscale(bi, width, height, "bicubic", "disabled") + bg_imgs = bi.movedim(1, -1).clamp(0, 1) + n_frames = abs(int(frames)) or 1 # magnitude = frame count (0 -> single still) + orbit_dir = -1.0 if frames < 0 else 1.0 # sign = orbit direction + imgs, masks = [], [] + device = comfy.model_management.get_torch_device() + total = splat.positions.shape[0] * n_frames + pbar = comfy.utils.ProgressBar(total) if total > 1 else None + k = 0 + for i in range(splat.positions.shape[0]): + xyz, rgb, opacity, scale, rot = _gaussian_item(splat, i, device) + if opacity_threshold > 0: + keep = opacity >= opacity_threshold + xyz, rgb, opacity, scale, rot = xyz[keep], rgb[keep], opacity[keep], scale[keep], rot[keep] + base_cam = camera_info + if base_cam is None: # no camera -> default 3/4 view, auto-framed on the splat + center = xyz.mean(0) if xyz.shape[0] else torch.zeros(3, device=device) + extent = (_quantile((xyz - center).norm(dim=-1), 0.99).clamp_min(1e-4) if xyz.shape[0] + else torch.tensor(1.0, device=device)) + dist = float(extent / (math.tan(math.radians(35.0) / 2) * 0.9)) + base_cam = _orbit_camera_info(35.0, 30.0, dist, 35.0, center, device) + for fr in range(n_frames): + cam_fr = (base_cam if n_frames == 1 + else _orbit_camera_info_yaw(base_cam, orbit_dir * 360.0 * fr / n_frames, device)) + bg_k = bg_imgs[k % bg_imgs.shape[0]] if bg_imgs is not None else bg # per-frame plate, or solid colour + img, mask = _render_gaussian(xyz, rgb, opacity, scale, rot, width, height, splat_scale, bg_k, cam_fr, + sharpen=sharpen, headlight_shading=headlight_shading, + render_style=render_style) + imgs.append(img) + masks.append(mask) + k += 1 + if pbar is not None: + pbar.update(1) + return IO.NodeOutput(torch.stack(imgs), torch.stack(masks)) + + +class CreateCameraInfo(IO.ComfyNode): # TODO: move to better file + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="CreateCameraInfo", + display_name="Create Camera Info", + search_aliases=["camera position", "make camera info", "orbit camera", "look at camera"], + category="3d", + description="Build a camera_info" + "Mode 'orbit' aims with yaw/pitch/distance around the target; " + "'look_at' places the camera at world position. Coordinates are the viewer's world space (right-handed,Y-up).", + inputs=[ + IO.DynamicCombo.Input("mode", options=[ + IO.DynamicCombo.Option("orbit", [ + IO.Float.Input("yaw", default=35.0, min=-360.0, max=360.0, step=1.0), + IO.Float.Input("pitch", default=30.0, min=-89.0, max=89.0, step=1.0), + IO.Float.Input("distance", default=4.0, min=0.01, max=1000.0, step=0.01, + tooltip="Camera distance from the target."), + ]), + IO.DynamicCombo.Option("look_at", [ + IO.Float.Input("position_x", default=4.0, min=-1000.0, max=1000.0, step=0.01, + tooltip="Camera position in world space (right-handed, Y-up)."), + IO.Float.Input("position_y", default=4.0, min=-1000.0, max=1000.0, step=0.01), + IO.Float.Input("position_z", default=4.0, min=-1000.0, max=1000.0, step=0.01), + ]), + IO.DynamicCombo.Option("quaternion", [ + IO.Float.Input("position_x", default=4.0, min=-1000.0, max=1000.0, step=0.01, + tooltip="Camera position in world space (right-handed, Y-up)."), + IO.Float.Input("position_y", default=4.0, min=-1000.0, max=1000.0, step=0.01), + IO.Float.Input("position_z", default=4.0, min=-1000.0, max=1000.0, step=0.01), + IO.Float.Input("quat_x", default=0.0, min=-1.0, max=1.0, step=0.001), + IO.Float.Input("quat_y", default=0.0, min=-1.0, max=1.0, step=0.001), + IO.Float.Input("quat_z", default=0.0, min=-1.0, max=1.0, step=0.001), + IO.Float.Input("quat_w", default=1.0, min=-1.0, max=1.0, step=0.001, + tooltip="Camera world-rotation quaternion (three.js: looks down local -Z). Normalized for you."), + ]), + ], tooltip="How to define the camera: orbit angles, an explicit position, or a position + quaternion."), + IO.Float.Input("target_x", default=0.0, min=-1000.0, max=1000.0, step=0.01, advanced=True, + tooltip="Look-at point (orbit pivot / aim). In orbit mode, move it to pan/translate the " + "whole camera. Ignored in quaternion mode. Defaults to the origin."), + IO.Float.Input("target_y", default=0.0, min=-1000.0, max=1000.0, step=0.01, advanced=True), + IO.Float.Input("target_z", default=0.0, min=-1000.0, max=1000.0, step=0.01, advanced=True), + IO.Float.Input("roll", default=0.0, min=-180.0, max=180.0, step=1.0, + tooltip="Camera roll about the view axis, degrees."), + IO.Float.Input("fov", default=35.0, min=1.0, max=120.0, step=1.0, + tooltip="Vertical field of view in degrees."), + IO.Float.Input("zoom", default=1.0, min=0.01, max=100.0, step=0.01, + tooltip="Digital zoom (focal-length multiplier). >1 zooms in without moving the camera."), + IO.Combo.Input("camera_type", options=["perspective", "orthographic"], + tooltip="Projection used by Render Splat: perspective (foreshortening) or orthographic (parallel)."), + ], + outputs=[IO.Load3DCamera.Output(display_name="camera_info")], + ) + + @classmethod + def execute(cls, mode, target_x, target_y, target_z, roll, fov, zoom=1.0, camera_type="perspective") -> IO.NodeOutput: + dev = comfy.model_management.get_torch_device() + kind = mode["mode"] + if kind == "quaternion": # explicit world position + camera rotation + position = [mode["position_x"], mode["position_y"], mode["position_z"]] + quat = [mode["quat_x"], mode["quat_y"], mode["quat_z"], mode["quat_w"]] + return IO.NodeOutput(_quat_camera_info(position, quat, fov, dev, zoom=zoom, camera_type=camera_type)) + target = [target_x, target_y, target_z] # orbit pivot / aim; move it to pan the whole camera + if kind == "orbit": # yaw/pitch/distance about the target (world Y-up) + y, p = math.radians(mode["yaw"]), math.radians(mode["pitch"]) + cy, sy, cp, sp = math.cos(y), math.sin(y), math.cos(p), math.sin(p) + d = mode["distance"] + position = [target_x + d * cp * sy, target_y + d * sp, target_z + d * cp * cy] + else: # look_at: explicit world-space camera position + position = [mode["position_x"], mode["position_y"], mode["position_z"]] + return IO.NodeOutput(_lookat_camera_info(position, target, fov, dev, zoom=zoom, camera_type=camera_type, roll=roll)) + + +class TransformSplat(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TransformSplat", + display_name="Transform Splat", + search_aliases=["move splat", "rotate splat", "scale splat", "gaussian transform"], + category="3d/splat", + description="Translate, rotate, and scale a gaussian splat. " + "Non-uniform scale also reshapes every individual splat, slower process.", + inputs=[ + IO.Splat.Input("splat"), + IO.Float.Input("translate_x", default=0.0, min=-100.0, max=100.0, step=0.01), + IO.Float.Input("translate_y", default=0.0, min=-100.0, max=100.0, step=0.01), + IO.Float.Input("translate_z", default=0.0, min=-100.0, max=100.0, step=0.01), + IO.Float.Input("rotate_x", default=0.0, min=-360.0, max=360.0, step=1.0), + IO.Float.Input("rotate_y", default=0.0, min=-360.0, max=360.0, step=1.0), + IO.Float.Input("rotate_z", default=0.0, min=-360.0, max=360.0, step=1.0), + IO.Float.Input("scale_x", default=1.0, min=0.01, max=100.0, step=0.01), + IO.Float.Input("scale_y", default=1.0, min=0.01, max=100.0, step=0.01), + IO.Float.Input("scale_z", default=1.0, min=0.01, max=100.0, step=0.01), + ], + outputs=[IO.Splat.Output(display_name="splat")], + ) + + @classmethod + def execute(cls, splat, translate_x, translate_y, translate_z, + rotate_x, rotate_y, rotate_z, scale_x, scale_y, scale_z) -> IO.NodeOutput: + pos = splat.positions + dev, dt = pos.device, pos.dtype + q_rot = _euler_to_quat(rotate_x, rotate_y, rotate_z).to(device=dev, dtype=dt) + R = _quat_to_mat(q_rot[None])[0] # (3, 3) node rotation + D = torch.tensor([scale_x, scale_y, scale_z], dtype=dt, device=dev) + A = D[:, None] * R # diag(D) @ R: per-axis scale after rotation + t = torch.tensor([translate_x, translate_y, translate_z], dtype=dt, device=dev) + + positions = pos @ A.T + t # rotate, scale per-axis, then translate + if scale_x == scale_y == scale_z: # uniform: rotation/scale factor out cleanly + scales = splat.scales * scale_x + rotations = _quat_mul(q_rot.expand_as(splat.rotations), splat.rotations) + rotations = rotations / rotations.norm(dim=-1, keepdim=True).clamp_min(1e-12) + else: # non-uniform: transform Sigma = A R s^2 R^T A^T, re-extract + rg = _quat_to_mat(splat.rotations.reshape(-1, 4)) # (M,3,3) per-splat rotation + s2 = splat.scales.reshape(-1, 3).square() + cov = (rg * s2[:, None, :]) @ rg.transpose(-1, -2) # Sigma + cov = A @ cov @ A.T # A Sigma A^T (A broadcast over splats) + lam, V = torch.linalg.eigh(cov) # symmetric -> eigenvalues (asc), orthonormal axes + V = V * torch.where(torch.linalg.det(V) < 0, -1.0, 1.0)[..., None, None] # keep a proper rotation + scales = lam.clamp_min(0).sqrt().reshape(splat.scales.shape) + rotations = _mat_to_quat(V).reshape(splat.rotations.shape) + out = Types.SPLAT(positions, scales, rotations, splat.opacities, splat.sh, + counts=getattr(splat, "counts", None)) + return IO.NodeOutput(out) + + +class GetSplatCount(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="GetSplatCount", + display_name="Get Splat Count", + search_aliases=["splat count", "gaussian count", "number of splats", "splat info"], + category="3d/splat", + description="Returns the number of splats summed across the batch.", + inputs=[IO.Splat.Input("splat")], + outputs=[IO.Splat.Output(display_name="splat"), + IO.Int.Output(display_name="count"), + ], + hidden=[IO.Hidden.unique_id], + ) + + @classmethod + def execute(cls, splat) -> IO.NodeOutput: + count = sum(_real_len(splat, i) for i in range(splat.positions.shape[0])) + if cls.hidden.unique_id: # show the count inline on the node + PromptServer.instance.send_progress_text(f"{count:,} splats", cls.hidden.unique_id) + return IO.NodeOutput(splat, count) + + +def _pad_stack(items, n): + # Stack a list of (Lᵢ, *tail) tensors into (B, n, *tail), zero-padding each row up to n. + tail = items[0].shape[1:] + out = items[0].new_zeros((len(items), n, *tail)) + for i, t in enumerate(items): + out[i, :t.shape[0]] = t + return out + + +def _merge_gaussians(gaussians: list) -> Types.SPLAT: + # Concatenate SPLAT batches along the splat dimension (per item), padding SH to the highest degree. + gs = [g for g in gaussians if g is not None] + if not gs: + raise ValueError("MergeSplat: no gaussians to merge") + b = gs[0].positions.shape[0] + for g in gs: + if g.positions.shape[0] != b: + raise ValueError(f"MergeSplat: batch size mismatch ({b} vs {g.positions.shape[0]}).") + max_k = max(g.sh.shape[2] for g in gs) + + pos_b, scl_b, rot_b, op_b, sh_b, lengths = [], [], [], [], [], [] + for i in range(b): + pos_i, scl_i, rot_i, op_i, sh_i = [], [], [], [], [] + for g in gs: + end = _real_len(g, i) + pos_i.append(g.positions[i, :end]) + scl_i.append(g.scales[i, :end]) + rot_i.append(g.rotations[i, :end]) + op_i.append(g.opacities[i, :end]) + sh = g.sh[i, :end] # (end, K, 3) + if sh.shape[1] < max_k: # zero-pad lower-degree SH + sh = torch.cat([sh, sh.new_zeros(sh.shape[0], max_k - sh.shape[1], sh.shape[2])], dim=1) + sh_i.append(sh) + pos_b.append(torch.cat(pos_i)) + scl_b.append(torch.cat(scl_i)) + rot_b.append(torch.cat(rot_i)) + op_b.append(torch.cat(op_i)) + sh_b.append(torch.cat(sh_i)) + lengths.append(pos_b[-1].shape[0]) + + n = max(lengths) + counts = None + if len(set(lengths)) > 1: + counts = torch.tensor(lengths, device=gs[0].positions.device, dtype=torch.int64) + return Types.SPLAT(_pad_stack(pos_b, n), _pad_stack(scl_b, n), _pad_stack(rot_b, n), + _pad_stack(op_b, n), _pad_stack(sh_b, n), counts=counts) + + +class MergeSplat(IO.ComfyNode): + @classmethod + def define_schema(cls): + # Autogrow: a splat0/splat1/... input list that grows a fresh slot as you connect splats. + splats = IO.Autogrow.TemplatePrefix(IO.Splat.Input("splat"), prefix="splat", min=2, max=32) + return IO.Schema( + node_id="MergeSplat", + display_name="Merge Splats", + search_aliases=["union splat", "densify gaussian", "combine splat", "merge gaussian"], + category="3d/splat", + description="Concatenate any number of gaussian splats into one. Unioning several decodes of the same " + "latent at different seeds densifies the surface, this can improve surface quality when meshing.", + inputs=[IO.Autogrow.Input("splats", template=splats)], + outputs=[IO.Splat.Output(display_name="splat")], + ) + + @classmethod + def execute(cls, splats: IO.Autogrow.Type) -> IO.NodeOutput: + gs = [v for v in splats.values() if v is not None] + if not gs: + raise ValueError("MergeSplat: connect at least one splat.") + return IO.NodeOutput(_merge_gaussians(gs)) + + +def _inverse_covariance(scale, quat): + # Per-splat Sigma^-1 = R diag(1/s^2) R^T. scale (N,3) linear std, quat (N,4) wxyz -> (N,3,3). + q = quat / quat.norm(dim=1, keepdim=True).clamp_min(1e-12) + w, x, y, z = q.unbind(-1) + R = torch.stack([ + 1 - 2 * (y * y + z * z), 2 * (x * y - w * z), 2 * (x * z + w * y), + 2 * (x * y + w * z), 1 - 2 * (x * x + z * z), 2 * (y * z - w * x), + 2 * (x * z - w * y), 2 * (y * z + w * x), 1 - 2 * (x * x + y * y), + ], dim=1).reshape(-1, 3, 3) + inv_s2 = 1.0 / scale.clamp_min(1e-8) ** 2 # (N, 3) + return torch.einsum("nij,nj,nkj->nik", R, inv_s2, R) + + +def _splat_density(xyz, opacity, scale, quat, rgb, res, kernel, device, color_sharpen=1.0, chunk=4096, progress=None, + col_dtype=torch.float16): + # Splat each gaussian as its oriented-covariance disk (3-sigma, opacity-weighted) into a density grid, + # plus a colour volume. Each gaussian uses a voxel window sized to its OWN 3-sigma (capped at `kernel`). + # Colour is weighted by w^color_sharpen: >1 biases each voxel toward its dominant gaussian (crisper + # texture). Returns (density, colour numerator, colour normaliser, origin, voxel). + pad = 4.0 * scale.median() + lo = xyz.amin(0) - pad + hi = xyz.amax(0) + pad + voxel = ((hi - lo).max() / res).clamp_min(1e-8) + dx, dy, dz = (torch.ceil((hi - lo) / voxel).long() + 1).tolist() + + sinv = _inverse_covariance(scale, quat) + kreq = torch.ceil(3.0 * scale.amax(-1) / voxel).long().clamp(1, int(kernel)) # per-gaussian half-width + sharp = color_sharpen != 1.0 + vol = torch.zeros(dx * dy * dz, device=device) # Sum(w) density (surface) + colvol = torch.zeros(dx * dy * dz, 3, device=device, dtype=col_dtype) # Sum(w^p * rgb) colour numerator + wcol = torch.zeros(dx * dy * dz, device=device, dtype=col_dtype) if sharp else None # Sum(w^p) normaliser (p>1) + n, done = xyz.shape[0], 0 + for k in range(1, int(kernel) + 1): + sel = (kreq == k).nonzero(as_tuple=True)[0] + if sel.numel() == 0: + continue + rng = torch.arange(-k, k + 1, device=device, dtype=torch.float32) + off = torch.stack(torch.meshgrid(rng, rng, rng, indexing="ij"), -1).reshape(-1, 3) # (M, 3) + for st in range(0, sel.numel(), chunk): + gi = sel[st:st + chunk] + cc = xyz[gi] + idx = ((cc - lo) / voxel).round()[:, None, :] + off[None] # (b, M, 3) voxel coords + d = (lo + idx * voxel) - cc[:, None, :] # world offset to voxel center + quad = torch.einsum("bmi,bij,bmj->bm", d, sinv[gi], d) + wgt = opacity[gi, None] * torch.exp(-0.5 * quad) + wgt = torch.where(quad < 9.0, wgt, torch.zeros_like(wgt)) # clip beyond 3 sigma + ii = idx.long() + ix = ii[..., 0].clamp(0, dx - 1) + iy = ii[..., 1].clamp(0, dy - 1) + iz = ii[..., 2].clamp(0, dz - 1) + flat = (ix * (dy * dz) + iy * dz + iz).reshape(-1) + vol.index_add_(0, flat, wgt.reshape(-1)) + wp = wgt.pow(color_sharpen) if sharp else wgt # winner-take-more colour weight + colvol.index_add_(0, flat, (wp[..., None] * rgb[gi, None, :]).reshape(-1, 3).to(col_dtype)) + if sharp: + wcol.index_add_(0, flat, wp.reshape(-1).to(col_dtype)) + done += gi.numel() + if progress is not None: + progress(min(1.0, done / max(1, n))) + colnorm = (wcol if sharp else vol).reshape(dx, dy, dz) # p==1 -> Sum(w) == density + return vol.reshape(dx, dy, dz), colvol.reshape(dx, dy, dz, 3), colnorm, lo.cpu().numpy(), float(voxel) + + +def _connected_components_gpu(faces, nv): + # FastSV connected components: grandparent hooking + shortcutting, ~O(log nv) iterations. + # Returns per-vertex component labels (min node id, not densified). + a = torch.cat([faces[:, 0], faces[:, 1]]) # 2F edge endpoints: (v0,v1),(v1,v2) + b = torch.cat([faces[:, 1], faces[:, 2]]) + f = torch.arange(nv, device=faces.device) + while True: + gp = f[f] # grandparent + ga, gb = gp[a], gp[b] + new = f.clone() + new.scatter_reduce_(0, f[a], gb, "amin", include_self=True) # stochastic hooking onto roots + new.scatter_reduce_(0, f[b], ga, "amin", include_self=True) + new.scatter_reduce_(0, a, gb, "amin", include_self=True) # aggressive hooking, both directions + new.scatter_reduce_(0, b, ga, "amin", include_self=True) + new = new[new] # shortcut (path compression) + if torch.equal(new, f): + return f + f = new + + +def _clean_components_gpu(verts, faces, min_verts, device): + # GPU port of _clean_components: FastSV components + scatter reductions. Byte-identical to the numpy path + vt = torch.as_tensor(verts, device=device) + ft = torch.as_tensor(faces, device=device) + nv = vt.shape[0] + _, label = torch.unique(_connected_components_gpu(ft, nv), return_inverse=True) # dense 0..ncomp-1 + ncomp = int(label.max()) + 1 + flabel = label[ft[:, 0]] # component id per face + keep = torch.bincount(label, minlength=ncomp) >= min_verts # per-component vertex-count gate + if int(keep.sum()) > 1: + fcount = torch.bincount(flabel, minlength=ncomp) + largest = int(torch.where(keep, fcount, fcount.new_tensor(-1)).argmax()) + v0, v1, v2 = vt[ft[:, 0]], vt[ft[:, 1]], vt[ft[:, 2]] + cvol = torch.zeros(ncomp, device=device).scatter_add_(0, flabel, (v0 * torch.linalg.cross(v1, v2)).sum(-1)) + idx3 = label[:, None].expand(-1, 3) # per-component vertex bbox + cmin = torch.full((ncomp, 3), float("inf"), device=device).scatter_reduce_(0, idx3, vt, "amin", include_self=True) + cmax = torch.full((ncomp, 3), float("-inf"), device=device).scatter_reduce_(0, idx3, vt, "amax", include_self=True) + tol = 1e-4 * (cmax[largest] - cmin[largest]).max() + enclosed = (cmin >= cmin[largest] - tol).all(1) & (cmax <= cmax[largest] + tol).all(1) + inner = enclosed & (torch.sign(cvol) != torch.sign(cvol[largest])) & (torch.arange(ncomp, device=device) != largest) + keep &= ~inner + faces_k = ft[keep[flabel]] + if faces_k.shape[0] == 0: + return verts[:0], faces[:0] + used = torch.unique(faces_k) # sorted, matches np.unique + remap = torch.full((nv,), -1, dtype=torch.int64, device=device) + remap[used] = torch.arange(used.shape[0], device=device) + return vt[used].cpu().numpy(), remap[faces_k].cpu().numpy() + + +def _clean_components(verts, faces, min_verts, device=None): + # Drop floaters (components with < min_verts vertices) and inner shells - the surfel shell density + # extracts a double wall (outer + inner cavity surface). GPU path (FastSV CC + scatter reductions, ~13x + # faster) when an accelerator has headroom; else numpy/scipy. Both produce byte-identical output. + if device is not None and not comfy.model_management.is_device_cpu(device) and \ + comfy.model_management.get_free_memory(device) > 10 * faces.size * 8: # peak ~8.4x faces bytes + return _clean_components_gpu(verts, faces, min_verts, device) + nv = len(verts) + e = np.concatenate([faces[:, [0, 1]], faces[:, [1, 2]], faces[:, [0, 2]]], 0) + ncomp, label = connected_components(coo_matrix((np.ones(len(e)), (e[:, 0], e[:, 1])), shape=(nv, nv)), directed=False) + flabel = label[faces[:, 0]] # component id per face + keep = np.bincount(label, minlength=ncomp) >= min_verts # per-component vertex-count gate + if keep.sum() > 1: + fcount = np.bincount(flabel, minlength=ncomp) + largest = np.where(keep, fcount, -1).argmax() + v0, v1, v2 = verts[faces[:, 0]], verts[faces[:, 1]], verts[faces[:, 2]] + cvol = np.bincount(flabel, weights=np.einsum("ij,ij->i", v0, np.cross(v1, v2)), minlength=ncomp) # 6*signed vol + cidx = np.arange(ncomp) # per-component vertex bbox via ndimage (~6x faster than ufunc.at) + cmin = np.stack([_ndi_minimum(verts[:, a], label, cidx) for a in range(3)], 1) + cmax = np.stack([_ndi_maximum(verts[:, a], label, cidx) for a in range(3)], 1) + tol = 1e-4 * (cmax[largest] - cmin[largest]).max() + enclosed = (cmin >= cmin[largest] - tol).all(1) & (cmax <= cmax[largest] + tol).all(1) + inner = enclosed & (np.sign(cvol) != np.sign(cvol[largest])) & (np.arange(ncomp) != largest) + keep &= ~inner + faces = faces[keep[flabel]] + if len(faces) == 0: + return verts[:0], faces + used = np.unique(faces) + remap = np.full(nv, -1, np.int64) + remap[used] = np.arange(len(used)) + return verts[used], remap[faces] + + +def _surface_nets(vol, level, voxel, origin, device): + # Vectorized Surface Nets: one dual vertex per sign-changing cell at its edge-crossing mean, quads wound CCW-outward. + # Returns verts (V,3), faces (F,3). + vol = vol.to(device=device, dtype=torch.float32) + dx, dy, dz = vol.shape + origin_t = torch.as_tensor(origin, device=device, dtype=torch.float32) + empty = (np.zeros((0, 3), np.float32), np.zeros((0, 3), np.int64)) + if dx < 2 or dy < 2 or dz < 2: + return empty + + # Active = cells whose 8 corners aren't all in/all out. + inside = vol >= level # (dx,dy,dz) bool + cs8 = [inside[ox:ox + dx - 1, oy:oy + dy - 1, oz:oz + dz - 1] + for ox, oy, oz in ((0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), + (0, 0, 1), (1, 0, 1), (0, 1, 1), (1, 1, 1))] + any_in = cs8[0] | cs8[1] | cs8[2] | cs8[3] | cs8[4] | cs8[5] | cs8[6] | cs8[7] + all_in = cs8[0] & cs8[1] & cs8[2] & cs8[3] & cs8[4] & cs8[5] & cs8[6] & cs8[7] + active = any_in & ~all_in # (cx,cy,cz) straddling cells + nv = int(active.sum()) + if nv == 0: + return empty + + # Active cells only (a thin shell): each dual vertex = mean of its 12 edges' zero-crossings. + del any_in, all_in, cs8 # corner bool grids no longer needed + ac = active.nonzero(as_tuple=False) # (nv,3) cell min-corner indices + offs = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], + [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1]], device=device) + offf = offs.to(torch.float32) + edges = torch.tensor([[0, 1], [0, 2], [0, 4], [1, 3], [1, 5], [2, 3], + [2, 6], [3, 7], [4, 5], [4, 6], [5, 7], [6, 7]], device=device) + e0, e1 = edges[:, 0], edges[:, 1] + oe0, oe1 = offf[e0], offf[e1] # (12,3) edge endpoints + + cstep = 1 << 18 # chunk to bound peak memory (CPU RAM too) + loc = [] + for st in range(0, nv, cstep): + ci = ac[st:st + cstep, None, :] + offs[None] # (m,8,3) + cval = vol[ci[..., 0], ci[..., 1], ci[..., 2]] # (m,8) corner values + csl = cval >= level + v0, v1 = cval[:, e0], cval[:, e1] # (m,12) + cross = (csl[:, e0] != csl[:, e1])[..., None].to(torch.float32) + denom = v1 - v0 + t = torch.where(denom.abs() > 1e-12, (level - v0) / denom, torch.full_like(denom, 0.5)).clamp(0, 1) + pts = torch.lerp(oe0, oe1, t[..., None]) # (m,12,3) local crossings (fused interp) + loc.append((pts * cross).sum(1) / cross.sum(1).clamp_min(1.0)) # (m,3) in [0,1] + local = torch.cat(loc, 0) if len(loc) > 1 else loc[0] # (nv,3) + verts = origin_t + (ac.to(torch.float32) + local) * voxel # world space + del loc, local, ac + + vid = torch.full((dx - 1, dy - 1, dz - 1), -1, dtype=torch.int32, device=device) + vid[active] = torch.arange(nv, dtype=torch.int32, device=device) + del active + + # Each straddling grid edge -> one quad from its 4 cells; `sol` (low-end sign) picks outward winding. + faces = [] + + def emit(cr, sol, a, b, d, c): + valid = cr & (a >= 0) & (b >= 0) & (c >= 0) & (d >= 0) + if not bool(valid.any()): + return + a, b, c, d, sol = a[valid], b[valid], c[valid], d[valid], sol[valid] + p2, p4 = torch.where(sol, b, c), torch.where(sol, c, b) # reverse quad winding where ~sol + faces.append(torch.stack([a, p2, d], 1)) + faces.append(torch.stack([a, d, p4], 1)) + + a = inside[0:dx - 1, 1:dy - 1, 1:dz - 1] + emit(a != inside[1:dx, 1:dy - 1, 1:dz - 1], a, + vid[:, 0:dy - 2, 0:dz - 2], vid[:, 1:dy - 1, 0:dz - 2], + vid[:, 1:dy - 1, 1:dz - 1], vid[:, 0:dy - 2, 1:dz - 1]) + a = inside[1:dx - 1, 0:dy - 1, 1:dz - 1] + emit(a != inside[1:dx - 1, 1:dy, 1:dz - 1], a, + vid[0:dx - 2, :, 0:dz - 2], vid[0:dx - 2, :, 1:dz - 1], + vid[1:dx - 1, :, 1:dz - 1], vid[1:dx - 1, :, 0:dz - 2]) + a = inside[1:dx - 1, 1:dy - 1, 0:dz - 1] + emit(a != inside[1:dx - 1, 1:dy - 1, 1:dz], a, + vid[0:dx - 2, 0:dy - 2, :], vid[1:dx - 1, 0:dy - 2, :], + vid[1:dx - 1, 1:dy - 1, :], vid[0:dx - 2, 1:dy - 1, :]) + + if not faces: + return empty + return verts.cpu().numpy().astype(np.float32), torch.cat(faces, 0).cpu().numpy().astype(np.int64) + + +def _otsu_level(values, bins=256): + # Otsu threshold: the density value that best splits inside/outside (max between-class variance). + hist, edges = np.histogram(values, bins=bins) + hist = hist.astype(np.float64) + centers = (edges[:-1] + edges[1:]) * 0.5 + w = np.cumsum(hist) # background-class weight at each split + mu = np.cumsum(hist * centers) + wf = w[-1] - w # foreground-class weight + mb = mu / np.where(w > 0, w, 1.0) + mf = (mu[-1] - mu) / np.where(wf > 0, wf, 1.0) + var_b = w * wf * (mb - mf) ** 2 # between-class variance + var_b[(w <= 0) | (wf <= 0)] = -1.0 + return float(centers[int(np.argmax(var_b))]) + + +def _taubin_smooth(verts, faces, iters, lam=0.5, mu=-0.53): + # Taubin lambda|mu smoothing: low-pass the mesh surface without the shrinkage of a Laplacian blur + # (the mu inflation pass cancels the lambda pass's volume loss). Uniform (umbrella) weights. + if iters <= 0 or len(verts) == 0 or len(faces) == 0: + return verts + nv = len(verts) + e = np.concatenate([faces[:, [0, 1]], faces[:, [1, 2]], faces[:, [0, 2]]], 0) + e = np.concatenate([e, e[:, ::-1]], 0) # symmetric adjacency + adj = coo_matrix((np.ones(len(e), np.float32), (e[:, 0], e[:, 1])), shape=(nv, nv)).tocsr() + adj.data[:] = 1.0 + deg = np.clip(np.asarray(adj.sum(1)).ravel(), 1.0, None).astype(np.float32)[:, None] + v = verts.astype(np.float32) # fp32 matvec: ~2x faster, sub-micron drift on unit-scale verts + for _ in range(int(iters)): + for fac in (lam, mu): + v = v + np.float32(fac) * ((adj @ v) / deg - v) # fac * (mean(neighbours) - v) + return np.ascontiguousarray(v) + + +def _sample_vertex_colours_gpu(colvol, colnorm, verts, origin, voxel, device): + # GPU trilinear sampling of the colour numerator (3ch) and normaliser (1ch) at vertex grid-coords + # reproduces scipy map_coordinates(order=1, mode='nearest'). Returns col (V,3) numpy. + dx, dy, dz = colnorm.shape + vt = torch.as_tensor(verts, device=device, dtype=torch.float32) + org = torch.as_tensor(origin, device=device, dtype=torch.float32) + gi = (vt - org) / voxel # (V,3) grid-index coords (x,y,z) + size = torch.tensor([dx, dy, dz], device=device, dtype=torch.float32) + g = 2.0 * gi / (size - 1).clamp_min(1.0) - 1.0 # -> [-1,1] (align_corners) + grid = torch.stack([g[:, 2], g[:, 1], g[:, 0]], -1)[None, None, None] # (1,1,1,V,3): grid_sample order (W=z,H=y,D=x) + + def samp(v): # (dx,dy,dz,C) cpu fp16 -> (C,V) fp32 on device + inp = v.to(device).permute(3, 0, 1, 2)[None].float() + o = torch.nn.functional.grid_sample(inp, grid, mode="bilinear", padding_mode="border", align_corners=True) + return o[0, :, 0, 0, :] + num = samp(colvol) # (3,V) + den = samp(colnorm[..., None]) # (1,V) + return (num / den.clamp_min(1e-8)).T.cpu().numpy() # (V,3) + + +def _gaussian_to_mesh(g: Types.SPLAT, i, res, kernel, taubin, level_bias, min_component, min_opacity, color_sharpen, device, progress=None): + # Mesh one splat: density + colour grids -> Surface Nets -> floater removal -> Taubin smoothing -> + # volume-sampled colours. Returns (verts, faces int64, colors in [0,1]), or None if no surface. + rep = progress if progress is not None else (lambda *_: None) + + end = _real_len(g, i) + xyz = g.positions[i, :end].to(device=device, dtype=torch.float32) + scale = g.scales[i, :end].to(device=device, dtype=torch.float32) + quat = g.rotations[i, :end].to(device=device, dtype=torch.float32) + opacity = g.opacities[i, :end].reshape(-1).to(device=device, dtype=torch.float32) + rgb = (g.sh[i, :end, 0, :].to(device=device, dtype=torch.float32) * _C0 + 0.5).clamp(0, 1) + + keep = opacity >= min_opacity + xyz, scale, quat, opacity, rgb = xyz[keep], scale[keep], quat[keep], opacity[keep], rgb[keep] + if xyz.shape[0] == 0: + return None + + vol, colvol, colnorm, origin, voxel = _splat_density(xyz, opacity, scale, quat, rgb, res, kernel, device, + color_sharpen=color_sharpen, + progress=lambda f: rep(0.25 * f)) # density build: 0 -> 25% + # Colour: sample on the GPU (grid_sample) when there's headroom + colour_gpu = not comfy.model_management.is_device_cpu(device) and comfy.model_management.get_free_memory(device) > 6 * vol.numel() * 4 + if colour_gpu: + colvol_cpu, colnorm_cpu = colvol.cpu(), colnorm.half().cpu() # park colours (fp16) off-GPU during meshing + colvol_np = colnorm_np = None + else: + colvol_np = colvol.cpu().numpy().astype(np.float32) # Sum(w^p * rgb) colour numerator (fp16 grid -> fp32) + colnorm_np = colnorm.cpu().numpy().astype(np.float32) # Sum(w^p) colour normaliser + del colvol, colnorm # free the colour grids before iso-surfacing + rep(0.40) + + vmin, vmax = float(vol.min()), float(vol.max()) + occ = vol[vol > vmax * 1e-3] # occupied voxels (skip the empty-space peak) + if occ.numel() == 0: + return None + # Otsu picks the inside/outside split principledly; `level_bias` nudges it (1.0 = auto). Clamp strictly + # inside the data range so a bias can't push the iso off the histogram. + level = min(max(_otsu_level(occ.cpu().numpy()) * level_bias, vmin + 1e-6 * (vmax - vmin)), + vmax - 1e-6 * (vmax - vmin)) + + # Iso-surface on the accelerator when there's headroom: ~15x faster than CPU, identical output. Chunked + # Surface Nets peaks at ~3-3.5x the density grid, so fall back to CPU for large grids / tight VRAM. + sn_dev = device + if not comfy.model_management.is_device_cpu(device) and comfy.model_management.get_free_memory(device) < 6 * vol.numel() * 4: + sn_dev = torch.device("cpu") + vol = vol.cpu() + verts, faces = _surface_nets(vol, level, voxel, origin, sn_dev) + del vol + rep(0.55) + if min_component > 0 and len(faces) > 0: + verts, faces = _clean_components(verts, faces, min_component, device) + if len(verts) == 0 or len(faces) == 0: + return None + + # Taubin smooths the blocky iso without shrinking it (unlike blurring the density, which rounds features). + verts = _taubin_smooth(verts, faces, taubin) + rep(0.7) + + # Colour each vertex from the co-splatted colour volume: trilinearly sample the numerator Sum(w^p*rgb) + # and normaliser Sum(w^p) separately, then divide. Normalising AFTER interpolation keeps zero-density + # edge voxels from pulling colours toward black, and matches the gaussians that formed the surface. + if colour_gpu: + col = _sample_vertex_colours_gpu(colvol_cpu, colnorm_cpu, verts, origin, voxel, device) + else: + coords = ((verts - origin) / voxel).T # (3, V) grid-index coords, matching volume axes + num = np.stack([map_coordinates(colvol_np[..., c], coords, order=1, mode="nearest") for c in range(3)], -1) + den = map_coordinates(colnorm_np, coords, order=1, mode="nearest") + col = num / np.clip(den, 1e-8, None)[:, None] + rep(1.0) + + # The unlit material's COLOR_0 is linear and the viewer sRGB-encodes it on output; the splat colours + # are display (sRGB) values, so convert sRGB -> linear here to land at the same brightness as the splat. + col = np.clip(col, 0, 1) + col = np.where(col <= 0.04045, col / 12.92, ((col + 0.055) / 1.055) ** 2.4).astype(np.float32) + + # Splat +Y is glTF's -Y: rotate 180 deg about X (negate Y,Z) to land upright. Proper rotation, so + # winding is kept; done after colouring (which works in the splat frame). + verts = np.ascontiguousarray(verts * np.array([1.0, -1.0, -1.0], dtype=np.float32)) + return (torch.from_numpy(verts), torch.from_numpy(faces), torch.from_numpy(col)) + + +class SplatToMesh(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="SplatToMesh", + display_name="Extract Mesh from Splat", + search_aliases=["splat to mesh", "gaussian surface nets", "splat surface", "mesh splat"], + category="3d/splat", + description="Extract a coloured mesh from a gaussian splat.", + inputs=[ + IO.Splat.Input("splat"), + IO.Int.Input("resolution", default=384, min=64, max=768, step=16, + tooltip="Density-grid resolution along the longest axis. Higher = finer surface, " + "more VRAM/time (grows with resolution^3)."), + IO.Int.Input("kernel", default=5, min=1, max=8, + tooltip="Max splat half-width in voxels. Each gaussian is rasterized over a window " + "sized to its own 3-sigma, capped here - small surfels stay cheap, large ones " + "aren't truncated. Raise if sparse splats leave gaps."), + IO.Int.Input("smooth", default=0, min=0, max=60, advanced = True, + tooltip="Taubin mesh-smoothing iterations. Smooths the surface without shrinking it " + "(volume-preserving), unlike blurring the density. 0 = raw surface."), + IO.Float.Input("level", default=0.4, min=0.0, max=2.0, step=0.01, + tooltip="Iso-surface level. Auto-picked by Otsu; this biases it (1.0 = auto, lower = " + "fatter/more-connected surface, higher = thinner/tighter)."), + IO.Int.Input("min_component", default=500, min=0, max=100000, step=50, advanced=True, + tooltip="Drop connected components smaller than this many vertices (0 = keep all). " + "Removes detached floater blobs and the inner shell of the double wall."), + IO.Float.Input("min_opacity", default=0.02, min=0.0, max=1.0, step=0.01, advanced=True, + tooltip="Ignore gaussians fainter than this before meshing."), + IO.Float.Input("color_sharpen", default=2.0, min=1.0, max=8.0, step=0.5, + tooltip="Crisp up the vertex texture: 1.0 = physically-correct blend; higher biases " + "each voxel's colour toward its dominant gaussian instead of averaging " + "neighbours (de-smears the texture). Colour only - geometry is unchanged."), + ], + outputs=[IO.Mesh.Output(display_name="mesh")], + ) + + @classmethod + def execute(cls, splat, resolution, kernel, smooth, level, min_component, min_opacity, color_sharpen) -> IO.NodeOutput: + device = comfy.model_management.get_torch_device() + b = splat.positions.shape[0] + prec = 1000 # each splat owns a 0..prec block of the bar; its callback advances within that block + pbar = comfy.utils.ProgressBar(b * prec) + + verts_l, faces_l, colors_l = [], [], [] + for i in range(b): + cb = lambda f, base=i * prec: pbar.update_absolute(base + int(min(max(f, 0.0), 1.0) * prec)) + res = _gaussian_to_mesh(splat, i, resolution, kernel, smooth, level, min_component, min_opacity, color_sharpen, device, cb) + if res is None: + logging.warning("SplatToMesh: splat %d produced no surface; emitting an empty mesh.", i) + v, f, c = torch.zeros((0, 3)), torch.zeros((0, 3), dtype=torch.int64), torch.zeros((0, 3)) + else: + v, f, c = res + verts_l.append(v) + faces_l.append(f) + colors_l.append(c) + pbar.update_absolute((i + 1) * prec) # snap to block end (covers empty / early-out splats) + # unlit: render flat (emissive-like) so SaveGLB matches the splat instead of lighting/washing it. + return IO.NodeOutput(pack_variable_mesh_batch(verts_l, faces_l, colors=colors_l, unlit=True)) + + +class GaussianExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [SplatToFile3D, File3DToSplat, RenderSplat, CreateCameraInfo, TransformSplat, + GetSplatCount, MergeSplat, SplatToMesh] + + +async def comfy_entrypoint() -> GaussianExtension: + return GaussianExtension() diff --git a/comfy_extras/nodes_hidream.py b/comfy_extras/nodes_hidream.py index e345fe51d..65248561b 100644 --- a/comfy_extras/nodes_hidream.py +++ b/comfy_extras/nodes_hidream.py @@ -11,8 +11,9 @@ class QuadrupleCLIPLoader(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="QuadrupleCLIPLoader", - category="advanced/loaders", - description="[Recipes]\n\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct", + display_name="Load CLIP (Quadruple)", + category="model/loaders", + description="Recipes:\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct", inputs=[ io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")), io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")), @@ -38,8 +39,9 @@ class CLIPTextEncodeHiDream(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CLIPTextEncodeHiDream", + display_name="CLIP Text Encode (HiDream)", search_aliases=["hidream prompt"], - category="advanced/conditioning", + category="model/conditioning/hidream", inputs=[ io.Clip.Input("clip"), io.String.Input("clip_l", multiline=True, dynamic_prompts=True), diff --git a/comfy_extras/nodes_hidream_o1.py b/comfy_extras/nodes_hidream_o1.py index 8648d2e26..85693fce6 100644 --- a/comfy_extras/nodes_hidream_o1.py +++ b/comfy_extras/nodes_hidream_o1.py @@ -14,7 +14,7 @@ class EmptyHiDreamO1LatentImage(io.ComfyNode): return io.Schema( node_id="EmptyHiDreamO1LatentImage", display_name="Empty HiDream-O1 Latent Image", - category="model/latent/image", + category="model/latent/hidream", description=( "Empty pixel-space latent for HiDream-O1-Image. The model was " "trained at ~4 megapixels; lower resolutions go off-distribution " @@ -47,7 +47,7 @@ class HiDreamO1ReferenceImages(io.ComfyNode): return io.Schema( node_id="HiDreamO1ReferenceImages", display_name="HiDream-O1 Reference Images", - category="model/conditioning/image", + category="model/conditioning/hidream", description=( "Attach 1-10 reference images to conditioning, one for edit instruction" "or multiple for subject-driven personalization." @@ -117,7 +117,7 @@ class HiDreamO1PatchSeamSmoothing(io.ComfyNode): return io.Schema( node_id="HiDreamO1PatchSeamSmoothing", display_name="HiDream-O1 Patch Seam Smoothing", - category="advanced/model", + category="model/patch/hidream", is_experimental=True, description=( "Average the model output across multiple shifted patch-grid " diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py index 16fff12af..8df2c8908 100644 --- a/comfy_extras/nodes_hunyuan.py +++ b/comfy_extras/nodes_hunyuan.py @@ -14,7 +14,8 @@ class CLIPTextEncodeHunyuanDiT(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CLIPTextEncodeHunyuanDiT", - category="advanced/conditioning", + display_name="CLIP Text Encode (Hunyuan Image)", + category="model/conditioning/hunyuan image", inputs=[ io.Clip.Input("clip"), io.String.Input("bert", multiline=True, dynamic_prompts=True), @@ -41,7 +42,7 @@ class EmptyHunyuanLatentVideo(io.ComfyNode): return io.Schema( node_id="EmptyHunyuanLatentVideo", display_name="Empty HunyuanVideo 1.0 Latent", - category="model/latent/video", + category="model/latent/hunyuan video", inputs=[ io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16), io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), @@ -67,6 +68,7 @@ class EmptyHunyuanVideo15Latent(EmptyHunyuanLatentVideo): schema = super().define_schema() schema.node_id = "EmptyHunyuanVideo15Latent" schema.display_name = "Empty HunyuanVideo 1.5 Latent" + schema.category = "model/latent/hunyuan video" return schema @classmethod @@ -81,7 +83,7 @@ class HunyuanVideo15ImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="HunyuanVideo15ImageToVideo", - category="model/conditioning/video_models", + category="model/conditioning/hunyuan video", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -132,7 +134,7 @@ class HunyuanVideo15SuperResolution(io.ComfyNode): return io.Schema( node_id="HunyuanVideo15SuperResolution", display_name="Hunyuan Video 1.5 Super Resolution", - category="model/conditioning/video_models", + category="model/conditioning/hunyuan video", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -227,7 +229,7 @@ class HunyuanVideo15LatentUpscaleWithModel(io.ComfyNode): return io.Schema( node_id="HunyuanVideo15LatentUpscaleWithModel", display_name="Hunyuan Video 15 Latent Upscale With Model", - category="model/latent", + category="model/latent/hunyhuan video", inputs=[ io.LatentUpscaleModel.Input("model"), io.Latent.Input("samples"), @@ -276,7 +278,7 @@ class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="TextEncodeHunyuanVideo_ImageToVideo", - category="advanced/conditioning", + category="model/conditioning/hunyuan video", inputs=[ io.Clip.Input("clip"), io.ClipVisionOutput.Input("clip_vision_output"), @@ -308,7 +310,7 @@ class HunyuanImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="HunyuanImageToVideo", - category="model/conditioning/video_models", + category="model/conditioning/hunyuan video", inputs=[ io.Conditioning.Input("positive"), io.Vae.Input("vae"), @@ -359,7 +361,7 @@ class EmptyHunyuanImageLatent(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="EmptyHunyuanImageLatent", - category="model/latent", + category="model/latent/hunyuan image", inputs=[ io.Int.Input("width", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32), io.Int.Input("height", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32), @@ -384,7 +386,7 @@ class HunyuanRefinerLatent(io.ComfyNode): return io.Schema( node_id="HunyuanRefinerLatent", display_name="Hunyuan Latent Refiner", - category="model/conditioning/video_models", + category="model/conditioning/hunyuan video", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), diff --git a/comfy_extras/nodes_hunyuan3d.py b/comfy_extras/nodes_hunyuan3d.py index 60e530626..c5fa946cc 100644 --- a/comfy_extras/nodes_hunyuan3d.py +++ b/comfy_extras/nodes_hunyuan3d.py @@ -12,7 +12,7 @@ class EmptyLatentHunyuan3Dv2(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="EmptyLatentHunyuan3Dv2", - category="model/latent/3d", + category="model/latent/hunyuan 3d", inputs=[ IO.Int.Input("resolution", default=3072, min=1, max=8192), IO.Int.Input("batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."), @@ -35,7 +35,7 @@ class Hunyuan3Dv2Conditioning(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="Hunyuan3Dv2Conditioning", - category="model/conditioning/3d_models", + category="model/conditioning/hunyuan 3d", inputs=[ IO.ClipVisionOutput.Input("clip_vision_output"), ], @@ -60,7 +60,7 @@ class Hunyuan3Dv2ConditioningMultiView(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="Hunyuan3Dv2ConditioningMultiView", - category="model/conditioning/3d_models", + category="model/conditioning/hunyuan 3d", inputs=[ IO.ClipVisionOutput.Input("front", optional=True), IO.ClipVisionOutput.Input("left", optional=True), @@ -97,7 +97,7 @@ class VAEDecodeHunyuan3D(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="VAEDecodeHunyuan3D", - category="model/latent/3d", + category="model/latent/hunyuan 3d", inputs=[ IO.Latent.Input("samples"), IO.Vae.Input("vae"), diff --git a/comfy_extras/nodes_ideogram4.py b/comfy_extras/nodes_ideogram4.py new file mode 100644 index 000000000..4070db17c --- /dev/null +++ b/comfy_extras/nodes_ideogram4.py @@ -0,0 +1,64 @@ +"""Ideogram 4 sampling helper +""" + +import math + +import torch +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io + +_LOGSNR_MIN = -15.0 +_LOGSNR_MAX = 18.0 + + +def _logit_normal_schedule(u, mean, std): + # Reference time (0=noise..1=clean) via the probit/ndtri quantile. + u = torch.as_tensor(u, dtype=torch.float64) + t = 1.0 - torch.special.expit(mean + std * torch.special.ndtri(u)) + t_min = 1.0 / (1.0 + math.exp(0.5 * _LOGSNR_MAX)) + t_max = 1.0 / (1.0 + math.exp(0.5 * _LOGSNR_MIN)) + return t.clamp(t_min, t_max) + + +def ideogram4_sigmas(num_steps, width, height, mu, std): + """Descending sigmas (len num_steps+1) for the reference schedule. + + mu + the resolution term form the logSNR shift; std is the spread. + """ + mean = mu + 0.5 * math.log((width * height) / (512 * 512)) + u = torch.linspace(0.0, 1.0, num_steps + 1, dtype=torch.float64) + sigmas = (1.0 - _logit_normal_schedule(u, mean, std)).flip(0) + sigmas[-1] = 0.0 # clamp leaves ~6e-4; force full denoise + return sigmas.to(torch.float32) + + +class Ideogram4Scheduler(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="Ideogram4Scheduler", + display_name="Ideogram 4 Scheduler", + category="model/sampling/schedulers", + inputs=[ + io.Int.Input("steps", default=20, min=1, max=200), + io.Int.Input("width", default=1024, min=256, max=8192, step=16), + io.Int.Input("height", default=1024, min=256, max=8192, step=16), + io.Float.Input("mu", default=0.0, min=-10.0, max=10.0, step=0.05), + io.Float.Input("std", default=1.75, min=0.1, max=5.0, step=0.05), + ], + outputs=[io.Sigmas.Output()], + ) + + @classmethod + def execute(cls, steps, width, height, mu, std) -> io.NodeOutput: + return io.NodeOutput(ideogram4_sigmas(steps, width, height, mu, std)) + + +class Ideogram4Extension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [Ideogram4Scheduler] + + +async def comfy_entrypoint() -> Ideogram4Extension: + return Ideogram4Extension() diff --git a/comfy_extras/nodes_kandinsky5.py b/comfy_extras/nodes_kandinsky5.py index 015965498..96cca0386 100644 --- a/comfy_extras/nodes_kandinsky5.py +++ b/comfy_extras/nodes_kandinsky5.py @@ -13,7 +13,7 @@ class Kandinsky5ImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="Kandinsky5ImageToVideo", - category="model/conditioning/video_models", + category="model/conditioning/kandinsky", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -71,7 +71,7 @@ class NormalizeVideoLatentStart(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="NormalizeVideoLatentStart", - category="model/conditioning/video_models", + category="model/conditioning", description="Normalizes the initial frames of a video latent to match the mean and standard deviation of subsequent reference frames. Helps reduce differences between the starting frames and the rest of the video.", inputs=[ io.Latent.Input("latent"), @@ -104,8 +104,9 @@ class CLIPTextEncodeKandinsky5(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CLIPTextEncodeKandinsky5", + display_name="CLIP Text Encode (Kandinsky 5)", search_aliases=["kandinsky prompt"], - category="advanced/conditioning/kandinsky5", + category="model/conditioning/kandinsky", inputs=[ io.Clip.Input("clip"), io.String.Input("clip_l", multiline=True, dynamic_prompts=True), diff --git a/comfy_extras/nodes_latent.py b/comfy_extras/nodes_latent.py index 32da9e8ac..1f93e34d6 100644 --- a/comfy_extras/nodes_latent.py +++ b/comfy_extras/nodes_latent.py @@ -262,6 +262,7 @@ class LatentBatch(io.ComfyNode): return io.Schema( node_id="LatentBatch", search_aliases=["combine latents", "merge latents", "join latents"], + display_name="Batch Latents (DEPRECATED)", category="model/latent/batch", is_deprecated=True, inputs=[ @@ -447,6 +448,7 @@ class ReplaceVideoLatentFrames(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="ReplaceVideoLatentFrames", + display_name="Replace Video Latent Frames", category="model/latent/batch", inputs=[ io.Latent.Input("destination", tooltip="The destination latent where frames will be replaced."), diff --git a/comfy_extras/nodes_load_3d.py b/comfy_extras/nodes_load_3d.py index 6f05f050e..6e3e88471 100644 --- a/comfy_extras/nodes_load_3d.py +++ b/comfy_extras/nodes_load_3d.py @@ -51,6 +51,14 @@ class Load3D(IO.ComfyNode): ], ) + @classmethod + def validate_inputs(cls, model_file, **kwargs) -> bool | str: + if not model_file or model_file == "none": + return True + if not folder_paths.exists_annotated_filepath(model_file): + return f"Invalid 3D model file: {model_file}" + return True + @classmethod def execute(cls, model_file, image, **kwargs) -> IO.NodeOutput: image_path = folder_paths.get_annotated_filepath(image['image']) @@ -124,12 +132,263 @@ class Preview3D(IO.ComfyNode): process = execute # TODO: remove +class Preview3DAdvanced(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="Preview3DAdvanced", + display_name="Preview 3D (Advanced)", + search_aliases=["preview 3d", "3d viewer", "view mesh", "frame 3d", "3d camera output"], + 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.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True), + IO.Load3D.Input("viewport_state"), + 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, **kwargs) -> IO.NodeOutput: + filename = f"preview3d_advanced_{uuid.uuid4().hex}.{model_3d.format}" + model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename)) + + camera_info_input = kwargs.get("camera_info", None) + camera_info = camera_info_input if camera_info_input is not None else viewport_state['camera_info'] + model_3d_info_input = kwargs.get("model_3d_info", None) + model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', []) + return IO.NodeOutput( + model_3d, + model_3d_info, + camera_info, + width, + height, + ui=UI.PreviewUI3DAdvanced(filename, camera_info, model_3d_info), + ) + + +class PreviewGaussianSplat(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="PreviewGaussianSplat", + display_name="Preview Splat", + category="3d", + is_experimental=True, + is_output_node=True, + search_aliases=[ + "view splat", + "view gaussian", + "view gaussian splat", + "preview gaussian", + "preview gaussian splat", + "view 3dgs", + "preview 3dgs", + "preview ply", + "preview spz", + "preview splat", + "preview ksplat", + ], + inputs=[ + IO.MultiType.Input( + "model_3d", + types=[ + IO.File3DSplatAny, + IO.File3DPLY, + IO.File3DSPLAT, + IO.File3DSPZ, + IO.File3DKSPLAT, + ], + tooltip="A gaussian splat 3D file.", + ), + IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True), + IO.Load3D.Input("viewport_state"), + 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, **kwargs) -> IO.NodeOutput: + filename = f"preview_splat_{uuid.uuid4().hex}.{model_3d.format}" + model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename)) + + camera_info_input = kwargs.get("camera_info", None) + camera_info = camera_info_input if camera_info_input is not None else viewport_state['camera_info'] + model_3d_info_input = kwargs.get("model_3d_info", None) + model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', []) + return IO.NodeOutput( + model_3d, + model_3d_info, + camera_info, + width, + height, + ui=UI.PreviewUI3DAdvanced(filename, camera_info, model_3d_info), + ) + + +class PreviewPointCloud(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="PreviewPointCloud", + display_name="Preview Point Cloud", + category="3d", + is_experimental=True, + is_output_node=True, + search_aliases=[ + "view point cloud", + "view pointcloud", + "preview point cloud", + "preview pointcloud", + "preview ply", + ], + inputs=[ + IO.MultiType.Input( + "model_3d", + types=[ + IO.File3DPointCloudAny, + IO.File3DPLY, + ], + tooltip="Point cloud file (.ply)", + ), + IO.Load3DModelInfo.Input("model_3d_info", optional=True, advanced=True), + IO.Load3D.Input("viewport_state"), + 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, **kwargs) -> IO.NodeOutput: + filename = f"preview_pointcloud_{uuid.uuid4().hex}.{model_3d.format}" + model_3d.save_to(os.path.join(folder_paths.get_temp_directory(), filename)) + + camera_info_input = kwargs.get("camera_info", None) + camera_info = camera_info_input if camera_info_input is not None else viewport_state['camera_info'] + model_3d_info_input = kwargs.get("model_3d_info", None) + model_3d_info = model_3d_info_input if model_3d_info_input is not None else viewport_state.get('model_3d_info', []) + return IO.NodeOutput( + model_3d, + model_3d_info, + camera_info, + width, + height, + ui=UI.PreviewUI3DAdvanced(filename, camera_info, model_3d_info), + ) + + +MESH_EXTENSIONS = {'.gltf', '.glb', '.obj', '.fbx', '.stl'} + + +class Load3DAdvanced(IO.ComfyNode): + @classmethod + def define_schema(cls): + input_dir = os.path.join(folder_paths.get_input_directory(), "3d") + os.makedirs(input_dir, exist_ok=True) + + input_path = Path(input_dir) + base_path = Path(folder_paths.get_input_directory()) + + files = [ + normalize_path(str(file_path.relative_to(base_path))) + for file_path in input_path.rglob("*") + if file_path.suffix.lower() in MESH_EXTENSIONS + ] + return IO.Schema( + node_id="Load3DAdvanced", + display_name="Load 3D (Advanced)", + category="3d", + search_aliases=[ + "load mesh", + "load gltf", + "load glb", + "load obj", + "load fbx", + "load stl", + ], + is_experimental=True, + inputs=[ + IO.Combo.Input("model_file", options=["none"] + sorted(files), upload=IO.UploadType.model), + IO.Load3D.Input("viewport_state"), + 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 validate_inputs(cls, model_file, **kwargs) -> bool | str: + if not model_file or model_file == "none": + return True + if not folder_paths.exists_annotated_filepath(model_file): + return f"Invalid 3D model file: {model_file}" + return True + + @classmethod + def execute(cls, model_file, viewport_state, width: int, height: int, **kwargs) -> IO.NodeOutput: + file_3d = None + if model_file and model_file != "none": + file_3d = Types.File3D(folder_paths.get_annotated_filepath(model_file)) + model_3d_info = viewport_state.get('model_3d_info', []) + return IO.NodeOutput(file_3d, model_3d_info, viewport_state['camera_info'], width, height) + + class Load3DExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: return [ Load3D, + Load3DAdvanced, Preview3D, + Preview3DAdvanced, + PreviewGaussianSplat, + PreviewPointCloud, ] diff --git a/comfy_extras/nodes_logic.py b/comfy_extras/nodes_logic.py index 95f6ab848..13c1685f7 100644 --- a/comfy_extras/nodes_logic.py +++ b/comfy_extras/nodes_logic.py @@ -89,7 +89,8 @@ class SwitchNode(io.ComfyNode): template = io.MatchType.Template("switch") return io.Schema( node_id="ComfySwitchNode", - display_name="Switch", + search_aliases=["if", "then", "switch", "conditional", "branch"], + display_name="If/Else Switch", category="utilities/logic", is_experimental=True, inputs=[ diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py index 6d6078abe..85d76ecef 100644 --- a/comfy_extras/nodes_lt.py +++ b/comfy_extras/nodes_lt.py @@ -25,7 +25,7 @@ class GetICLoRAParameters(io.ComfyNode): display_name="Get IC-LoRA Parameters", description="Extracts IC-LoRA parameters from the safetensors metadata of a LoRA-loaded " "model and outputs them for LTXVAddGuide (eg. reference_downscale_factor).", - category="model/conditioning/video_models", + category="model/conditioning/ltxv", search_aliases=["ic-lora", "ic lora", "iclora", "downscale factor", "reference downscale"], inputs=[ io.Model.Input( @@ -62,7 +62,7 @@ class EmptyLTXVLatentVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="EmptyLTXVLatentVideo", - category="model/latent/video/ltxv", + category="model/latent/ltxv", inputs=[ io.Int.Input("width", default=768, min=64, max=nodes.MAX_RESOLUTION, step=32), io.Int.Input("height", default=512, min=64, max=nodes.MAX_RESOLUTION, step=32), @@ -86,7 +86,7 @@ class LTXVImgToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="LTXVImgToVideo", - category="model/conditioning/video_models", + category="model/conditioning/ltxv", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -131,7 +131,7 @@ class LTXVImgToVideoInplace(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="LTXVImgToVideoInplace", - category="model/conditioning/video_models", + category="model/conditioning/ltxv", inputs=[ io.Vae.Input("vae"), io.Image.Input("image"), @@ -251,7 +251,7 @@ class LTXVAddGuide(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="LTXVAddGuide", - category="model/conditioning/video_models", + category="model/conditioning/ltxv", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -498,7 +498,7 @@ class LTXVCropGuides(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="LTXVCropGuides", - category="model/conditioning/video_models", + category="model/conditioning/ltxv", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -542,7 +542,7 @@ class LTXVConditioning(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="LTXVConditioning", - category="model/conditioning/video_models", + category="model/conditioning/ltxv", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -566,7 +566,7 @@ class ModelSamplingLTXV(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="ModelSamplingLTXV", - category="advanced/model", + category="model/patch/ltxv", inputs=[ io.Model.Input("model"), io.Float.Input("max_shift", default=2.05, min=0.0, max=100.0, step=0.01), @@ -746,7 +746,7 @@ class LTXVConcatAVLatent(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="LTXVConcatAVLatent", - category="model/latent/video/ltxv", + category="model/latent/ltxv", inputs=[ io.Latent.Input("video_latent"), io.Latent.Input("audio_latent"), @@ -781,7 +781,7 @@ class LTXVSeparateAVLatent(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="LTXVSeparateAVLatent", - category="model/latent/video/ltxv", + category="model/latent/ltxv", description="LTXV Separate AV Latent", inputs=[ io.Latent.Input("av_latent"), @@ -814,7 +814,7 @@ class LTXVReferenceAudio(io.ComfyNode): return io.Schema( node_id="LTXVReferenceAudio", display_name="LTXV Reference Audio (ID-LoRA)", - category="model/conditioning/audio", + category="model/conditioning/ltxv", description="Set reference audio for ID-LoRA speaker identity transfer. Encodes a reference audio clip into the conditioning and optionally patches the model with identity guidance (extra forward pass without reference, amplifying the speaker identity effect).", inputs=[ io.Model.Input("model"), diff --git a/comfy_extras/nodes_lt_audio.py b/comfy_extras/nodes_lt_audio.py index 052186083..2d774a0a3 100644 --- a/comfy_extras/nodes_lt_audio.py +++ b/comfy_extras/nodes_lt_audio.py @@ -40,7 +40,7 @@ class LTXVAudioVAEEncode(VAEEncodeAudio): return io.Schema( node_id="LTXVAudioVAEEncode", display_name="LTXV Audio VAE Encode", - category="model/latent/audio", + category="model/latent/ltxv", inputs=[ io.Audio.Input("audio", tooltip="The audio to be encoded."), io.Vae.Input( @@ -63,7 +63,7 @@ class LTXVAudioVAEDecode(io.ComfyNode): return io.Schema( node_id="LTXVAudioVAEDecode", display_name="LTXV Audio VAE Decode", - category="model/latent/audio", + category="model/latent/ltxv", inputs=[ io.Latent.Input("samples", tooltip="The latent to be decoded."), io.Vae.Input( @@ -96,7 +96,7 @@ class LTXVEmptyLatentAudio(io.ComfyNode): return io.Schema( node_id="LTXVEmptyLatentAudio", display_name="LTXV Empty Latent Audio", - category="model/latent/audio", + category="model/latent/ltxv", inputs=[ io.Int.Input( "frames_number", @@ -168,9 +168,9 @@ class LTXAVTextEncoderLoader(io.ComfyNode): def define_schema(cls) -> io.Schema: return io.Schema( node_id="LTXAVTextEncoderLoader", - display_name="LTXV Audio Text Encoder Loader", - category="advanced/loaders", - description="[Recipes]\n\nltxav: gemma 3 12B", + display_name="Load LTXV Audio Text Encoder", + category="model/loaders", + description="Recipes:\nltxav: gemma 3 12B", inputs=[ io.Combo.Input( "text_encoder", diff --git a/comfy_extras/nodes_lt_upsampler.py b/comfy_extras/nodes_lt_upsampler.py index be9a36e69..ef36109d1 100644 --- a/comfy_extras/nodes_lt_upsampler.py +++ b/comfy_extras/nodes_lt_upsampler.py @@ -13,7 +13,7 @@ class LTXVLatentUpsampler(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="LTXVLatentUpsampler", - category="model/latent/video", + category="model/latent/ltxv", is_experimental=True, inputs=[ IO.Latent.Input("samples"), diff --git a/comfy_extras/nodes_lumina2.py b/comfy_extras/nodes_lumina2.py index c060a86a0..bc543c242 100644 --- a/comfy_extras/nodes_lumina2.py +++ b/comfy_extras/nodes_lumina2.py @@ -9,7 +9,7 @@ class RenormCFG(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="RenormCFG", - category="advanced/model", + category="model/patch", inputs=[ io.Model.Input("model"), io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01, advanced=True), @@ -80,8 +80,8 @@ class CLIPTextEncodeLumina2(io.ComfyNode): return io.Schema( node_id="CLIPTextEncodeLumina2", search_aliases=["lumina prompt"], - display_name="CLIP Text Encode for Lumina2", - category="model/conditioning", + display_name="CLIP Text Encode (Lumina 2)", + category="model/conditioning/lumina", description="Encodes a system prompt and a user prompt using a CLIP model into an embedding " "that can be used to guide the diffusion model towards generating specific images.", inputs=[ diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py index 52484697a..76af338de 100644 --- a/comfy_extras/nodes_mask.py +++ b/comfy_extras/nodes_mask.py @@ -53,6 +53,7 @@ class LatentCompositeMasked(IO.ComfyNode): return IO.Schema( node_id="LatentCompositeMasked", search_aliases=["overlay latent", "layer latent", "paste latent", "inpaint latent"], + display_name="Latent Composite Masked", category="model/latent", inputs=[ IO.Latent.Input("destination"), diff --git a/comfy_extras/nodes_math.py b/comfy_extras/nodes_math.py index 873ee7b51..0883c65ac 100644 --- a/comfy_extras/nodes_math.py +++ b/comfy_extras/nodes_math.py @@ -102,11 +102,18 @@ class MathExpressionNode(io.ComfyNode): f"Math Expression '{expression}' must evaluate to a numeric result, " f"got {type(result).__name__}: {result!r}" ) - if not math.isfinite(result): + try: + float_result = float(result) + except OverflowError: + raise ValueError( + f"Math Expression '{expression}' produced a result too large to " + f"represent as a float: {result}" + ) from None + if not math.isfinite(float_result): raise ValueError( f"Math Expression '{expression}' produced a non-finite result: {result}" ) - return io.NodeOutput(float(result), int(result), bool(result)) + return io.NodeOutput(float_result, int(result), bool(result)) class MathExtension(ComfyExtension): diff --git a/comfy_extras/nodes_mochi.py b/comfy_extras/nodes_mochi.py index 3dcea6ab3..3aaf23e69 100644 --- a/comfy_extras/nodes_mochi.py +++ b/comfy_extras/nodes_mochi.py @@ -10,7 +10,7 @@ class EmptyMochiLatentVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="EmptyMochiLatentVideo", - category="model/latent/video", + category="model/latent/mochi", inputs=[ io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16), io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py index b27ac1296..a336ba079 100644 --- a/comfy_extras/nodes_model_advanced.py +++ b/comfy_extras/nodes_model_advanced.py @@ -59,7 +59,7 @@ class ModelSamplingDiscrete: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch" def patch(self, model, sampling, zsnr): m = model.clone() @@ -97,7 +97,7 @@ class ModelSamplingStableCascade: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch/stable cascade" def patch(self, model, shift): m = model.clone() @@ -123,7 +123,7 @@ class ModelSamplingSD3: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch/stable diffusion" def patch(self, model, shift, multiplier=1000): m = model.clone() @@ -150,6 +150,7 @@ class ModelSamplingAuraFlow(ModelSamplingSD3): }} FUNCTION = "patch_aura" + CATEGORY = "model/patch" def patch_aura(self, model, shift): return self.patch(model, shift, multiplier=1.0) @@ -167,7 +168,7 @@ class ModelSamplingFlux: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch/flux" def patch(self, model, max_shift, base_shift, width, height): m = model.clone() @@ -202,7 +203,7 @@ class ModelSamplingContinuousEDM: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch" def patch(self, model, sampling, sigma_max, sigma_min): m = model.clone() @@ -247,7 +248,7 @@ class ModelSamplingContinuousV: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch" def patch(self, model, sampling, sigma_max, sigma_min): m = model.clone() @@ -273,7 +274,7 @@ class RescaleCFG: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch" def patch(self, model, multiplier): def rescale_cfg(args): @@ -314,7 +315,7 @@ class ModelNoiseScale: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/model" + CATEGORY = "model/patch" def patch(self, model, noise_scale): m = model.clone() @@ -337,7 +338,7 @@ class ModelComputeDtype: RETURN_TYPES = ("MODEL",) FUNCTION = "patch" - CATEGORY = "advanced/debug/model" + CATEGORY = "advanced/debug" def patch(self, model, dtype): m = model.clone() diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py index b6b29e34a..962d2a0bb 100644 --- a/comfy_extras/nodes_model_merging.py +++ b/comfy_extras/nodes_model_merging.py @@ -21,7 +21,7 @@ class ModelMergeSimple: RETURN_TYPES = ("MODEL",) FUNCTION = "merge" - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def merge(self, model1, model2, ratio): m = model1.clone() @@ -40,7 +40,7 @@ class ModelSubtract: RETURN_TYPES = ("MODEL",) FUNCTION = "merge" - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def merge(self, model1, model2, multiplier): m = model1.clone() @@ -58,7 +58,7 @@ class ModelAdd: RETURN_TYPES = ("MODEL",) FUNCTION = "merge" - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def merge(self, model1, model2): m = model1.clone() @@ -78,7 +78,7 @@ class CLIPMergeSimple: RETURN_TYPES = ("CLIP",) FUNCTION = "merge" - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def merge(self, clip1, clip2, ratio): m = clip1.clone() @@ -101,7 +101,7 @@ class CLIPSubtract: RETURN_TYPES = ("CLIP",) FUNCTION = "merge" - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def merge(self, clip1, clip2, multiplier): m = clip1.clone() @@ -123,7 +123,7 @@ class CLIPAdd: RETURN_TYPES = ("CLIP",) FUNCTION = "merge" - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def merge(self, clip1, clip2): m = clip1.clone() @@ -147,7 +147,7 @@ class ModelMergeBlocks: RETURN_TYPES = ("MODEL",) FUNCTION = "merge" - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def merge(self, model1, model2, **kwargs): m = model1.clone() @@ -242,7 +242,7 @@ class CheckpointSave: FUNCTION = "save" OUTPUT_NODE = True - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None): save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) @@ -261,7 +261,7 @@ class CLIPSave: FUNCTION = "save" OUTPUT_NODE = True - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None): prompt_info = "" @@ -318,7 +318,7 @@ class VAESave: FUNCTION = "save" OUTPUT_NODE = True - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None): full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) @@ -353,7 +353,7 @@ class ModelSave: FUNCTION = "save" OUTPUT_NODE = True - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" def save(self, model, filename_prefix, prompt=None, extra_pnginfo=None): save_checkpoint(model, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) diff --git a/comfy_extras/nodes_model_merging_model_specific.py b/comfy_extras/nodes_model_merging_model_specific.py index 55eb3ccfe..2fa684b3a 100644 --- a/comfy_extras/nodes_model_merging_model_specific.py +++ b/comfy_extras/nodes_model_merging_model_specific.py @@ -1,7 +1,7 @@ import comfy_extras.nodes_model_merging class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): arg_dict = { "model1": ("MODEL",), @@ -27,7 +27,7 @@ class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks): class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -53,7 +53,7 @@ class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -77,7 +77,7 @@ class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): class ModelMergeAuraflow(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -104,7 +104,7 @@ class ModelMergeAuraflow(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -130,7 +130,7 @@ class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -153,7 +153,7 @@ class ModelMergeSD35_Large(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -175,7 +175,7 @@ class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -197,7 +197,7 @@ class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -221,7 +221,7 @@ class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -245,7 +245,7 @@ class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" DESCRIPTION = "1.3B model has 30 blocks, 14B model has 40 blocks. Image to video model has the extra img_emb." @classmethod @@ -269,7 +269,7 @@ class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} class ModelMergeCosmosPredict2_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -292,7 +292,7 @@ class ModelMergeCosmosPredict2_2B(comfy_extras.nodes_model_merging.ModelMergeBlo return {"required": arg_dict} class ModelMergeCosmosPredict2_14B(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): @@ -315,7 +315,7 @@ class ModelMergeCosmosPredict2_14B(comfy_extras.nodes_model_merging.ModelMergeBl return {"required": arg_dict} class ModelMergeQwenImage(comfy_extras.nodes_model_merging.ModelMergeBlocks): - CATEGORY = "advanced/model_merging/model_specific" + CATEGORY = "model/merging/model specific" @classmethod def INPUT_TYPES(s): diff --git a/comfy_extras/nodes_model_patch.py b/comfy_extras/nodes_model_patch.py index bdccbf8c4..3f785c8b5 100644 --- a/comfy_extras/nodes_model_patch.py +++ b/comfy_extras/nodes_model_patch.py @@ -232,7 +232,7 @@ class ModelPatchLoader: FUNCTION = "load_model_patch" EXPERIMENTAL = True - CATEGORY = "advanced/loaders" + CATEGORY = "model/loaders" def load_model_patch(self, name): model_patch_path = folder_paths.get_full_path_or_raise("model_patches", name) @@ -479,7 +479,7 @@ class QwenImageDiffsynthControlnet: FUNCTION = "diffsynth_controlnet" EXPERIMENTAL = True - CATEGORY = "advanced/loaders/qwen" + CATEGORY = "model/patch/qwen" def diffsynth_controlnet(self, model, model_patch, vae, image=None, strength=1.0, inpaint_image=None, mask=None): model_patched = model.clone() @@ -512,7 +512,7 @@ class ZImageFunControlnet(QwenImageDiffsynthControlnet): }, "optional": {"image": ("IMAGE",), "inpaint_image": ("IMAGE",), "mask": ("MASK",)}} - CATEGORY = "advanced/loaders/zimage" + CATEGORY = "model/patch/z-image" class UsoStyleProjectorPatch: def __init__(self, model_patch, encoded_image): @@ -675,3 +675,11 @@ NODE_CLASS_MAPPINGS = { "USOStyleReference": USOStyleReference, "SUPIRApply": SUPIRApply, } + +NODE_DISPLAY_NAME_MAPPINGS = { + "ModelPatchLoader": "Load Model Patch", + "QwenImageDiffsynthControlnet": "Apply Qwen Image DiffSynth ControlNet", + "ZImageFunControlnet": "Apply Z-Image Fun ControlNet", + "USOStyleReference": "Apply USO Style Reference", + "SUPIRApply": "Apply SUPIR Patch", +} diff --git a/comfy_extras/nodes_moge.py b/comfy_extras/nodes_moge.py index 422949531..a63f0414b 100644 --- a/comfy_extras/nodes_moge.py +++ b/comfy_extras/nodes_moge.py @@ -8,6 +8,7 @@ import folder_paths from comfy_api.latest import ComfyExtension, Types, io from typing_extensions import override +from comfy.ldm.colormap import turbo as _turbo from comfy.ldm.moge.model import MoGeModel from comfy.ldm.moge.geometry import triangulate_grid_mesh from comfy.ldm.moge.panorama import get_panorama_cameras, split_panorama_image, merge_panorama_depth, spherical_uv_to_directions, _uv_grid @@ -27,19 +28,6 @@ MoGeGeometry = io.Custom("MOGE_GEOMETRY") # "image": torch.Tensor (B, H, W, 3) in [0, 1], CPU (always present) -def _turbo(x: torch.Tensor) -> torch.Tensor: - """Anton Mikhailov polynomial approximation of the turbo colormap.""" - x = x.clamp(0.0, 1.0) - x2 = x * x - x3 = x2 * x - x4 = x2 * x2 - x5 = x4 * x - r = 0.13572138 + 4.61539260*x - 42.66032258*x2 + 132.13108234*x3 - 152.94239396*x4 + 59.28637943*x5 - g = 0.09140261 + 2.19418839*x + 4.84296658*x2 - 14.18503333*x3 + 4.27729857*x4 + 2.82956604*x5 - b = 0.10667330 + 12.64194608*x - 60.58204836*x2 + 110.36276771*x3 - 89.90310912*x4 + 27.34824973*x5 - return torch.stack([r, g, b], dim=-1).clamp(0.0, 1.0) - - def _normals_from_points(points: torch.Tensor) -> torch.Tensor: """Camera-space surface normals from a (B, H, W, 3) point map (v1 fallback).""" finite = torch.isfinite(points).all(dim=-1) diff --git a/comfy_extras/nodes_pid.py b/comfy_extras/nodes_pid.py index 811b9ae8e..a3ffd9671 100644 --- a/comfy_extras/nodes_pid.py +++ b/comfy_extras/nodes_pid.py @@ -14,15 +14,13 @@ class PiDConditioning(io.ComfyNode): return io.Schema( node_id="PiDConditioning", display_name="PiD Conditioning", - category="advanced/conditioning", - description=( - "Attaches a latent and a degrade_sigma scalar to a CONDITIONING for PiD decoding/upscaling" - ), + category="model/conditioning", + description=("Attaches a latent and a degrade_sigma scalar to a CONDITIONING for PiD decoding/upscaling"), inputs=[ io.Conditioning.Input("positive"), io.Latent.Input("latent", tooltip="latent (from VAEEncode or a KSampler)."), - io.Combo.Input("latent_format", options=["flux", "sd3"], default="flux", - tooltip="Flux1 and Flux2 latents auto-detected from channel dim, sd3 has to be selected manually."), + io.Combo.Input("latent_format", options=["flux", "sd3", "sdxl", "qwenimage"], default="flux", + tooltip="Flux1 (16-ch) and Flux2 (128-ch) latents are auto-detected from channel dim under 'flux'. For SD3 (16-ch), SDXL (4-ch), or QwenImage (16-ch), select manually."), io.Float.Input( "degrade_sigma", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="0 = clean latent. Increase to denoise corrupted latent outputs.", @@ -36,9 +34,17 @@ class PiDConditioning(io.ComfyNode): samples = latent["samples"] if latent_format == "flux": fmt_cls = comfy.latent_formats.Flux2 if samples.shape[1] == 128 else comfy.latent_formats.Flux - else: + elif latent_format == "sd3": fmt_cls = comfy.latent_formats.SD3 + elif latent_format == "sdxl": + fmt_cls = comfy.latent_formats.SDXL + elif latent_format == "qwenimage": + fmt_cls = comfy.latent_formats.Wan21 + else: + raise ValueError(f"Unknown latent_format: {latent_format}") lq_latent = fmt_cls().process_in(samples) + if lq_latent.ndim == 5: + lq_latent = lq_latent[:, :, 0] sigma_t = torch.tensor([float(degrade_sigma)], dtype=torch.float32) return io.NodeOutput(node_helpers.conditioning_set_values( positive, {"lq_latent": lq_latent, "degrade_sigma": sigma_t}, diff --git a/comfy_extras/nodes_pixart.py b/comfy_extras/nodes_pixart.py index 2f1b73e60..f878a33b5 100644 --- a/comfy_extras/nodes_pixart.py +++ b/comfy_extras/nodes_pixart.py @@ -7,8 +7,9 @@ class CLIPTextEncodePixArtAlpha(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CLIPTextEncodePixArtAlpha", + display_name="CLIP Text Encode (PixArt Alpha)", search_aliases=["pixart prompt"], - category="advanced/conditioning", + category="model/conditioning/pixart", description="Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma.", inputs=[ io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION), diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py index 3e440433e..763b8a52f 100644 --- a/comfy_extras/nodes_post_processing.py +++ b/comfy_extras/nodes_post_processing.py @@ -616,7 +616,7 @@ class BatchLatentsNode(io.ComfyNode): node_id="BatchLatentsNode", search_aliases=["combine latents", "stack latents", "merge latents"], display_name="Batch Latents", - category="model/latent", + category="model/latent/batch", inputs=[ io.Autogrow.Input("latents", template=autogrow_template) ], diff --git a/comfy_extras/nodes_primitive.py b/comfy_extras/nodes_primitive.py index c44b09098..7f90daf14 100644 --- a/comfy_extras/nodes_primitive.py +++ b/comfy_extras/nodes_primitive.py @@ -10,12 +10,11 @@ class String(io.ComfyNode): return io.Schema( node_id="PrimitiveString", search_aliases=["text", "string", "text box", "prompt"], - display_name="Text String", + display_name="Text String (DEPRECATED)", category="utilities/primitive", - inputs=[ - io.String.Input("value"), - ], + inputs=[io.String.Input("value")], outputs=[io.String.Output()], + is_deprecated=True ) @classmethod @@ -29,12 +28,10 @@ class StringMultiline(io.ComfyNode): return io.Schema( node_id="PrimitiveStringMultiline", search_aliases=["text", "string", "text multiline", "string multiline", "text box", "prompt"], - display_name="Text String (Multiline)", + display_name="Input Text", category="utilities/primitive", essentials_category="Basics", - inputs=[ - io.String.Input("value", multiline=True), - ], + inputs=[io.String.Input("value", multiline=True)], outputs=[io.String.Output()], ) diff --git a/comfy_extras/nodes_qwen.py b/comfy_extras/nodes_qwen.py index 5b92814a4..4960774db 100644 --- a/comfy_extras/nodes_qwen.py +++ b/comfy_extras/nodes_qwen.py @@ -12,7 +12,7 @@ class TextEncodeQwenImageEdit(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="TextEncodeQwenImageEdit", - category="advanced/conditioning", + category="model/conditioning/qwen image", inputs=[ io.Clip.Input("clip"), io.String.Input("prompt", multiline=True, dynamic_prompts=True), @@ -55,7 +55,7 @@ class TextEncodeQwenImageEditPlus(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="TextEncodeQwenImageEditPlus", - category="advanced/conditioning", + category="model/conditioning/qwen image", inputs=[ io.Clip.Input("clip"), io.String.Input("prompt", multiline=True, dynamic_prompts=True), diff --git a/comfy_extras/nodes_resolution.py b/comfy_extras/nodes_resolution.py index dc405291c..083e47ae4 100644 --- a/comfy_extras/nodes_resolution.py +++ b/comfy_extras/nodes_resolution.py @@ -6,24 +6,24 @@ from comfy_api.latest import ComfyExtension, io class AspectRatio(str, Enum): SQUARE = "1:1 (Square)" + PHOTO_V = "2:3 (Portrait Photo)" PHOTO_H = "3:2 (Photo)" + STANDARD_V = "3:4 (Portrait Standard)" STANDARD_H = "4:3 (Standard)" + WIDESCREEN_V = "9:16 (Portrait Widescreen)" WIDESCREEN_H = "16:9 (Widescreen)" ULTRAWIDE_H = "21:9 (Ultrawide)" - PHOTO_V = "2:3 (Portrait Photo)" - STANDARD_V = "3:4 (Portrait Standard)" - WIDESCREEN_V = "9:16 (Portrait Widescreen)" ASPECT_RATIOS: dict[AspectRatio, tuple[int, int]] = { AspectRatio.SQUARE: (1, 1), + AspectRatio.PHOTO_V: (2, 3), AspectRatio.PHOTO_H: (3, 2), + AspectRatio.STANDARD_V: (3, 4), AspectRatio.STANDARD_H: (4, 3), + AspectRatio.WIDESCREEN_V: (9, 16), AspectRatio.WIDESCREEN_H: (16, 9), AspectRatio.ULTRAWIDE_H: (21, 9), - AspectRatio.PHOTO_V: (2, 3), - AspectRatio.STANDARD_V: (3, 4), - AspectRatio.WIDESCREEN_V: (9, 16), } @@ -50,26 +50,35 @@ class ResolutionSelector(io.ComfyNode): min=0.1, max=16.0, step=0.1, - tooltip="Target total megapixels. 1.0 MP ≈ 1024×1024 for square.", + tooltip="Target total megapixels. 1.0 MP ≈ 1024x1024 for square.", + ), + io.Int.Input( + id="multiple", + default=8, + min=8, + max=128, + step=4, + tooltip="Nearest multiple of the result to set the selected resolution to.", + advanced=True, ), ], outputs=[ io.Int.Output( - "width", tooltip="Calculated width in pixels (multiple of 8)." + "width", tooltip="Calculated width in pixels multiplied by the selected multiple." ), io.Int.Output( - "height", tooltip="Calculated height in pixels (multiple of 8)." + "height", tooltip="Calculated height in pixels multiplied by the selected multiple." ), ], ) @classmethod - def execute(cls, aspect_ratio: str, megapixels: float) -> io.NodeOutput: + def execute(cls, aspect_ratio: str, megapixels: float, multiple: int) -> io.NodeOutput: w_ratio, h_ratio = ASPECT_RATIOS[aspect_ratio] total_pixels = megapixels * 1024 * 1024 scale = math.sqrt(total_pixels / (w_ratio * h_ratio)) - width = round(w_ratio * scale / 8) * 8 - height = round(h_ratio * scale / 8) * 8 + width = round(w_ratio * scale / multiple) * multiple + height = round(h_ratio * scale / multiple) * multiple return io.NodeOutput(width, height) diff --git a/comfy_extras/nodes_rtdetr.py b/comfy_extras/nodes_rtdetr.py index e5a9b3902..653f3af2f 100644 --- a/comfy_extras/nodes_rtdetr.py +++ b/comfy_extras/nodes_rtdetr.py @@ -14,7 +14,7 @@ class RTDETR_detect(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="RTDETR_detect", - display_name="RT-DETR Detect", + display_name="Run Real-Time Detection (RT-DETR)", category="image/detection", search_aliases=["bbox", "bounding box", "object detection", "coco"], inputs=[ diff --git a/comfy_extras/nodes_sam3.py b/comfy_extras/nodes_sam3.py index daac52f9b..f88aec925 100644 --- a/comfy_extras/nodes_sam3.py +++ b/comfy_extras/nodes_sam3.py @@ -264,7 +264,7 @@ class SAM3_VideoTrack(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="SAM3_VideoTrack", - display_name="SAM3 Video Track", + display_name="Run SAM3 Video Track", category="image/detection", search_aliases=["sam3", "video", "track", "propagate"], inputs=[ diff --git a/comfy_extras/nodes_save_3d.py b/comfy_extras/nodes_save_3d.py index c03524246..1b6592bb2 100644 --- a/comfy_extras/nodes_save_3d.py +++ b/comfy_extras/nodes_save_3d.py @@ -16,7 +16,7 @@ from comfy.cli_args import args from comfy_api.latest import ComfyExtension, IO, Types -def pack_variable_mesh_batch(vertices, faces, colors=None, uvs=None, texture=None): +def pack_variable_mesh_batch(vertices, faces, colors=None, uvs=None, texture=None, unlit=False): # Pack lists of (Nᵢ, *) vertex/face/color/uv tensors into padded batched tensors, # stashing per-item lengths as runtime attrs so consumers can recover the real slice. # colors and uvs are 1:1 with vertices, so they're padded to max_vertices and read with vertex_counts. @@ -54,7 +54,7 @@ def pack_variable_mesh_batch(vertices, faces, colors=None, uvs=None, texture=Non return Types.MESH(packed_vertices, packed_faces, uvs=packed_uvs, vertex_colors=packed_colors, texture=texture, - vertex_counts=vertex_counts, face_counts=face_counts) + vertex_counts=vertex_counts, face_counts=face_counts, unlit=unlit) def get_mesh_batch_item(mesh, index): @@ -77,7 +77,7 @@ def get_mesh_batch_item(mesh, index): def save_glb(vertices, faces, filepath, metadata=None, - uvs=None, vertex_colors=None, texture_image=None): + uvs=None, vertex_colors=None, texture_image=None, unlit=False): """ Save PyTorch tensor vertices and faces as a GLB file without external dependencies. @@ -234,6 +234,17 @@ def save_glb(vertices, faces, filepath, metadata=None, textures = [] samplers = [] materials = [] + extensions_used = [] + if unlit and texture_png_bytes is None: + # Flat, light-independent shading (KHR_materials_unlit): COLOR_0 is shown as-is, matching how a + # gaussian splat renders (emissive). Without this the viewer lights the mesh and washes the colours. + materials.append({ + "pbrMetallicRoughness": {"baseColorFactor": [1.0, 1.0, 1.0, 1.0], "metallicFactor": 0.0, "roughnessFactor": 1.0}, + "extensions": {"KHR_materials_unlit": {}}, + "doubleSided": True, + }) + extensions_used.append("KHR_materials_unlit") + primitive["material"] = 0 if texture_png_bytes is not None and "TEXCOORD_0" in primitive_attributes: buffer_views.append({ "buffer": 0, @@ -271,6 +282,8 @@ def save_glb(vertices, faces, filepath, metadata=None, gltf["textures"] = textures if materials: gltf["materials"] = materials + if extensions_used: + gltf["extensionsUsed"] = extensions_used if metadata: gltf["asset"]["extras"] = metadata @@ -324,6 +337,12 @@ class SaveGLB(IO.ComfyNode): IO.File3DFBX, IO.File3DSTL, IO.File3DUSDZ, + IO.File3DPLY, + IO.File3DSPLAT, + IO.File3DSPZ, + IO.File3DKSPLAT, + IO.File3DSplatAny, + IO.File3DPointCloudAny, IO.File3DAny, ], tooltip="Mesh or 3D file to save", @@ -376,7 +395,8 @@ class SaveGLB(IO.ComfyNode): save_glb(vertices_i, faces_i, os.path.join(full_output_folder, f), metadata, uvs=uvs_i, vertex_colors=v_colors, - texture_image=tex_img) + texture_image=tex_img, + unlit=getattr(mesh, "unlit", False)) results.append({ "filename": f, "subfolder": subfolder, diff --git a/comfy_extras/nodes_scail.py b/comfy_extras/nodes_scail.py new file mode 100644 index 000000000..55c9897e3 --- /dev/null +++ b/comfy_extras/nodes_scail.py @@ -0,0 +1,351 @@ +"""SCAIL / SCAIL-2 nodes: the WanSCAILToVideo conditioning node and the SAM3 +preprocessing that turns video tracks into the bundle the SCAIL-2 model consumes.""" + +from typing_extensions import override + +import torch +import torch.nn.functional as F + +import nodes +import node_helpers +import comfy.model_management +import comfy.utils +from comfy_api.latest import ComfyExtension, io +from comfy.ldm.sam3.tracker import unpack_masks + +SAM3TrackData = io.Custom("SAM3_TRACK_DATA") + + +# Model was trained on these exact colors; deviating degrades multi-identity quality. +DEFAULT_PALETTE = [ + (0.0, 0.0, 1.0), # Blue + (1.0, 0.0, 0.0), # Red + (0.0, 1.0, 0.0), # Green + (1.0, 0.0, 1.0), # Magenta + (0.0, 1.0, 1.0), # Cyan + (1.0, 1.0, 0.0), # Yellow +] + + +def _unpack(track_data): + packed = track_data["packed_masks"] + if packed is None or packed.shape[1] == 0: + return None + return unpack_masks(packed) + + +def _first_appearance_cx_area(masks_bool): + """Per object: first frame it appears in, plus centroid-x and area in that frame.""" + m = masks_bool.float() + T, H, W = m.shape[0], m.shape[-2], m.shape[-1] + grid_x = torch.arange(W, device=m.device, dtype=m.dtype).view(1, 1, 1, W) + area_t = m.sum(dim=(-1, -2)) + cx_t = (m * grid_x).sum(dim=(-1, -2)) / area_t.clamp(min=1) + present = area_t > 0 + frame_idx = torch.arange(T, device=m.device).unsqueeze(1) + first_t = torch.where(present, frame_idx, T).amin(dim=0) + sel = first_t.clamp(max=T - 1).unsqueeze(0) + cx = cx_t.gather(0, sel).squeeze(0) + area = area_t.gather(0, sel).squeeze(0) + return first_t.tolist(), (cx / W).tolist(), (area / (H * W)).tolist() + + +def _subset_track_data(track_data, obj_indices): + out = dict(track_data) + packed = track_data["packed_masks"] + if packed is None or not obj_indices: + out["packed_masks"] = None + if "scores" in out: + out["scores"] = [] + return out + out["packed_masks"] = packed[:, obj_indices].contiguous() + scores = track_data.get("scores") + if scores is not None: + out["scores"] = [scores[i] for i in obj_indices if i < len(scores)] + return out + + +def _render_colored_masks(track_data, background="black"): + packed = track_data["packed_masks"] + H, W = track_data["orig_size"] + device = comfy.model_management.intermediate_device() + dtype = comfy.model_management.intermediate_dtype() + bg_rgb = (1.0, 1.0, 1.0) if background.startswith("white") else (0.0, 0.0, 0.0) + if packed is None or packed.shape[1] == 0: + T = track_data.get("n_frames", 1) if packed is None else packed.shape[0] + out = torch.empty(T, H, W, 3, device=device, dtype=dtype) + out[..., 0], out[..., 1], out[..., 2] = bg_rgb[0], bg_rgb[1], bg_rgb[2] + return out + T, N_obj = packed.shape[0], packed.shape[1] + colors = torch.tensor( + [DEFAULT_PALETTE[i % len(DEFAULT_PALETTE)] for i in range(N_obj)], + device=device, dtype=dtype, + ) + masks_full = unpack_masks(packed.to(device)).float() + Hm, Wm = masks_full.shape[-2], masks_full.shape[-1] + masks_full = F.interpolate( + masks_full.view(T * N_obj, 1, Hm, Wm), size=(H, W), mode="nearest" + ).view(T, N_obj, H, W) > 0.5 + any_mask = masks_full.any(dim=1) + color_overlay = colors[masks_full.to(torch.uint8).argmax(dim=1)] + bg_tensor = torch.tensor(bg_rgb, device=device, dtype=color_overlay.dtype).view(1, 1, 1, 3) + return torch.where(any_mask.unsqueeze(-1), color_overlay, bg_tensor.expand_as(color_overlay)) + + +def _render_mask_as_identity(mask, background="black"): + """Plain comfy MASK (B,H,W) or (H,W) -> (B,H,W,3) rendered as a single identity (palette[0]) + on the given background. A batch is treated as multiple views of that one subject.""" + device = comfy.model_management.intermediate_device() + dtype = comfy.model_management.intermediate_dtype() + if mask.ndim == 2: + mask = mask.unsqueeze(0) + mask = mask.to(device=device, dtype=dtype) + B, H, W = mask.shape + bg_rgb = (1.0, 1.0, 1.0) if background.startswith("white") else (0.0, 0.0, 0.0) + color = torch.tensor(DEFAULT_PALETTE[0], device=device, dtype=dtype).view(1, 1, 1, 3) + bg = torch.tensor(bg_rgb, device=device, dtype=dtype).view(1, 1, 1, 3) + return torch.where((mask > 0.5).unsqueeze(-1), color.expand(B, H, W, 3), bg.expand(B, H, W, 3)) + + +def _extract_mask_to_28ch(rgb_video): + """Colored RGB mask (T, H, W, 3) in [0, 1] -> SCAIL-2 28-channel binary latent + (1, T_lat, 28, H_lat, W_lat). 7 per-color binary channels (white/r/g/b/y/m/c) + threshold-extracted at 225/255, 8x spatial downsample, 4-frame temporal stacking.""" + T, H, W, _ = rgb_video.shape + _ON_THRESH = 225.0 / 255.0 + mask = rgb_video.movedim(-1, 1).float() + R = (mask[:, 0:1] > _ON_THRESH).float() + G = (mask[:, 1:2] > _ON_THRESH).float() + B = (mask[:, 2:3] > _ON_THRESH).float() + nR, nG, nB = 1 - R, 1 - G, 1 - B + binary_7ch = torch.cat([ + R * G * B, # white + R * nG * nB, # red + nR * G * nB, # green + nR * nG * B, # blue + R * G * nB, # yellow + R * nG * B, # magenta + nR * G * B, # cyan + ], dim=1) + H_lat, W_lat = H, W + for _ in range(3): + H_lat = (H_lat + 1) // 2 + W_lat = (W_lat + 1) // 2 + binary_7ch = torch.nn.functional.interpolate(binary_7ch, size=(H_lat, W_lat), mode='area') + T_latent = (T - 1) // 4 + 1 + padded = torch.cat([binary_7ch[:1].repeat(4, 1, 1, 1), binary_7ch[1:]], dim=0) + out = padded.view(T_latent, 28, H_lat, W_lat) + return out.unsqueeze(0) + + +class WanSCAILToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanSCAILToVideo", + category="model/conditioning/wan/scail", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=512, min=32, max=nodes.MAX_RESOLUTION, step=32), + io.Int.Input("height", default=896, min=32, max=nodes.MAX_RESOLUTION, step=32), + io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Image.Input("pose_video", optional=True, tooltip="Video used for pose conditioning. Will be downscaled to half the resolution of the main video."), + io.Image.Input("pose_video_mask", optional=True, tooltip="SCAIL-2 only. Colored per-identity SAM3 mask video at the same resolution as pose_video."), + io.Boolean.Input("replacement_mode", default=False, optional=True, tooltip="SCAIL-2 only. False = Animation Mode (pose_video_mask should have black background). True = Replacement Mode (pose_video_mask should have white background)."), + io.Float.Input("pose_strength", default=1.0, min=0.0, max=10.0, step=0.01, tooltip="Strength of the pose latent."), + io.Float.Input("pose_start", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="Start step of the pose conditioning."), + io.Float.Input("pose_end", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="End step of the pose conditioning."), + io.Image.Input("reference_image", optional=True, tooltip="Reference image. The first image is the primary reference (composite all identities onto it). SCAIL-2: extra batch images are used as additional views (back view, close-up, occluded background), each needing a matching reference_image_mask in that identity's color."), + io.Image.Input("reference_image_mask", optional=True, tooltip="SCAIL-2 only. Colored reference mask, batch matching reference_image (first = primary reference mask, rest = identity masks for the additional reference_image)."), + io.ClipVisionOutput.Input("clip_vision_output", optional=True, tooltip="CLIP vision features for conditioning. Model is trained with stretch resize to aspect ratio."), + io.Int.Input("video_frame_offset", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1, tooltip="Cumulative output frame this chunk begins at. Wire from the previous chunk's video_frame_offset output."), + io.Int.Input("previous_frame_count", default=5, min=1, max=nodes.MAX_RESOLUTION, step=4, tooltip="Tail frames of previous_frames to anchor. SCAIL-2 trained at 5 (81-frame chunks, 76-frame step)."), + io.Image.Input("previous_frames", optional=True, tooltip="SCAIL-2 only. Full decoded output of the previous chunk. Only the last previous_frame_count are used as the extension anchor."), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent", tooltip="Empty latent of the generation size."), + io.Int.Output(display_name="video_frame_offset", tooltip="Adjusted offset + length. Wire into the next chunk."), + ], + is_experimental=True, + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, batch_size, pose_strength, pose_start, pose_end, + video_frame_offset, previous_frame_count, replacement_mode=False, reference_image=None, clip_vision_output=None, pose_video=None, + pose_video_mask=None, reference_image_mask=None, previous_frames=None) -> io.NodeOutput: + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + noise_mask = None + + ref_mask_flag = not replacement_mode + positive = node_helpers.conditioning_set_values(positive, {"ref_mask_flag": ref_mask_flag}) + negative = node_helpers.conditioning_set_values(negative, {"ref_mask_flag": ref_mask_flag}) + + prev_trimmed = None + if previous_frames is not None and previous_frames.shape[0] > 0: + prev_trimmed = previous_frames[-previous_frame_count:] + video_frame_offset -= prev_trimmed.shape[0] + video_frame_offset = max(0, video_frame_offset) + + if reference_image is not None: + ref_imgs = comfy.utils.common_upscale(reference_image.movedim(-1, 1), width, height, "bicubic", "center").movedim(1, -1) + n_ref = ref_imgs.shape[0] + # SCAIL-2 multi-reference: the first image is the primary ref, the rest are additional references. + + # Replacement Mode: composite each ref on black bg using its mask as alpha matte + if replacement_mode and reference_image_mask is not None: + rm = comfy.utils.common_upscale(reference_image_mask.movedim(-1, 1), width, height, "nearest-exact", "center").movedim(1, -1) + rm = rm[[min(i, rm.shape[0] - 1) for i in range(n_ref)]] + is_char = (rm[..., :3].max(dim=-1, keepdim=True).values > 0.1).to(ref_imgs.dtype) + ref_imgs = ref_imgs * is_char + # encode each ref individually so each stays a single latent frame (a batched encode would be treated as a video) + ref_latents = [vae.encode(ref_imgs[i:i + 1, :, :, :3]) for i in range(n_ref)] + positive = node_helpers.conditioning_set_values(positive, {"reference_latents": ref_latents}, append=True) + negative = node_helpers.conditioning_set_values(negative, {"reference_latents": ref_latents}, append=True) + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + if pose_video is not None: + if pose_video.shape[0] <= video_frame_offset: + pose_video = None + else: + pose_video = pose_video[video_frame_offset:] + if pose_video_mask is not None: + if pose_video_mask.shape[0] <= video_frame_offset: + pose_video_mask = None + else: + pose_video_mask = pose_video_mask[video_frame_offset:] + + # Truncate pose+mask jointly to the shorter of the two, capped at length. + ts = [v.shape[0] for v in (pose_video, pose_video_mask) if v is not None] + if ts: + T_kept = ((min(min(ts), length) - 1) // 4) * 4 + 1 + if pose_video is not None: + pose_video = pose_video[:T_kept] + if pose_video_mask is not None: + pose_video_mask = pose_video_mask[:T_kept] + + if pose_video is not None: + pose_video = comfy.utils.common_upscale(pose_video[:length].movedim(-1, 1), width // 2, height // 2, "area", "center").movedim(1, -1) + pose_video_latent = vae.encode(pose_video[:, :, :, :3]) * pose_strength + positive = node_helpers.conditioning_set_values_with_timestep_range(positive, {"pose_video_latent": pose_video_latent}, pose_start, pose_end) + negative = node_helpers.conditioning_set_values_with_timestep_range(negative, {"pose_video_latent": pose_video_latent}, pose_start, pose_end) + + if pose_video_mask is not None: + mask_video_hw = comfy.utils.common_upscale(pose_video_mask[:length].movedim(-1, 1), width // 2, height // 2, "area", "center").movedim(1, -1) + driving_mask_28ch = _extract_mask_to_28ch(mask_video_hw) + positive = node_helpers.conditioning_set_values(positive, {"driving_mask_28ch": driving_mask_28ch}) + negative = node_helpers.conditioning_set_values(negative, {"driving_mask_28ch": driving_mask_28ch}) + + # The ref mask binds reference frames to identities, so it only applies when there's a reference image. + if reference_image_mask is not None and reference_image is not None: + ref_mask_hw = comfy.utils.common_upscale(reference_image_mask.movedim(-1, 1), width, height, "nearest-exact", "center").movedim(1, -1) + n_masks = ref_mask_hw.shape[0] + n_ref = reference_image.shape[0] + + add_masks = [_extract_mask_to_28ch(ref_mask_hw[min(i, n_masks - 1)][None]) for i in range(1, n_ref)] + ref_mask_1f = _extract_mask_to_28ch(ref_mask_hw[:1]) + zeros = torch.zeros((1, latent.shape[2], 28, ref_mask_1f.shape[-2], ref_mask_1f.shape[-1]), device=ref_mask_1f.device, dtype=ref_mask_1f.dtype) + ref_mask_28ch = torch.cat(add_masks + [ref_mask_1f, zeros], dim=1) + positive = node_helpers.conditioning_set_values(positive, {"ref_mask_28ch": ref_mask_28ch}) + negative = node_helpers.conditioning_set_values(negative, {"ref_mask_28ch": ref_mask_28ch}) + + if prev_trimmed is not None: + pf = comfy.utils.common_upscale(prev_trimmed.movedim(-1, 1), width, height, "bicubic", "center").movedim(1, -1) + prev_latent = vae.encode(pf[:, :, :, :3]) + prev_latent_frames = min(prev_latent.shape[2], latent.shape[2]) + latent[:, :, :prev_latent_frames] = prev_latent[:, :, :prev_latent_frames].to(latent.dtype) + noise_mask = torch.ones((1, 1, latent.shape[2], latent.shape[-2], latent.shape[-1]), device=latent.device, dtype=latent.dtype) + noise_mask[:, :, :prev_latent_frames] = 0.0 + + out_latent = {"samples": latent} + if noise_mask is not None: + out_latent["noise_mask"] = noise_mask + return io.NodeOutput(positive, negative, out_latent, video_frame_offset + length) + + +class SCAIL2ColoredMask(io.ComfyNode): + """Render SAM3 tracks for the driving pose video and reference image(s) into the + colored masks WanSCAILToVideo consumes. Shared `sort_by` keeps each identity on the + same color across both outputs. + """ + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="SCAIL2ColoredMask", + display_name="Create SCAIL-2 Colored Mask", + category="model/conditioning/wan/scail", + inputs=[ + SAM3TrackData.Input("driving_track_data", tooltip="SAM3 track of the driving pose video. Will be rendered into the pose_video_mask output."), + io.MultiType.Input("ref_track_data", [SAM3TrackData, io.Mask], optional=True, display_name="reference_masks", + tooltip="SAM3 track of the reference image(s) (one identity per object, colored in batch order), or a plain MASK of the reference subject (rendered as a single identity)."), + io.String.Input("object_indices", default="", + tooltip="Comma-separated list of person indices to include (e.g. '0,2,3'). Applied to both reference and pose video masks. Empty = all."), + io.Combo.Input("sort_by", options=["none", "left_to_right", "area"], default="left_to_right", + tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). Objects that appear in earlier frames always come first; within a frame, left_to_right = leftmost object (by centroid at first appearance) gets the first color, area = biggest object (by mask area at first appearance) gets the first color; none = keep SAM3's order."), + io.Boolean.Input("replacement_mode", default=False, + tooltip="False = Animation Mode (pose_video_mask has black background, reference_image_mask has white background). " + "True = Replacement Mode (pose_video_mask has white background, reference_image_mask has black background)."), + ], + outputs=[ + io.Image.Output("pose_video_mask"), + io.Image.Output("reference_image_mask"), + ], + is_experimental=True, + ) + + @classmethod + def execute(cls, driving_track_data, object_indices, sort_by, replacement_mode, ref_track_data=None): + def _prep(td): + masks_bool = _unpack(td) + if sort_by != "none" and masks_bool is not None: + first_t, cx, area = _first_appearance_cx_area(masks_bool) + if sort_by == "left_to_right": + order = sorted(range(len(cx)), key=lambda i: (first_t[i], cx[i])) + else: # "area" + order = sorted(range(len(area)), key=lambda i: (first_t[i], -area[i])) + td = _subset_track_data(td, order) + if object_indices.strip(): + indices = [int(i.strip()) for i in object_indices.split(",") if i.strip().isdigit()] + packed = td.get("packed_masks") + n_obj = packed.shape[1] if packed is not None else 0 + indices = [i for i in indices if 0 <= i < n_obj] + td = _subset_track_data(td, indices) + return td + + drv = _prep(driving_track_data) + # Animation: driving=black, ref=white. Replacement: driving=white, ref=black. + mask_video = _render_colored_masks(drv, "white" if replacement_mode else "black") + ref_bg = "black" if replacement_mode else "white" + + if ref_track_data is not None: + if isinstance(ref_track_data, torch.Tensor): # plain comfy MASK + reference_image_mask = _render_mask_as_identity(ref_track_data, ref_bg) + else: + reference_image_mask = _render_colored_masks(_prep(ref_track_data), ref_bg) + else: + H, W = drv["orig_size"] + fill_value = 1.0 if ref_bg == "white" else 0.0 + reference_image_mask = torch.full((1, H, W, 3), fill_value, device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) + + return io.NodeOutput(mask_video, reference_image_mask) + + +class SCAILExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + WanSCAILToVideo, + SCAIL2ColoredMask, + ] + + +async def comfy_entrypoint() -> SCAILExtension: + return SCAILExtension() diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py index 38cbf117b..40e84656b 100644 --- a/comfy_extras/nodes_sd3.py +++ b/comfy_extras/nodes_sd3.py @@ -13,8 +13,9 @@ class TripleCLIPLoader(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="TripleCLIPLoader", - category="advanced/loaders", - description="[Recipes]\n\nsd3: clip-l, clip-g, t5", + display_name="Load CLIP (Triple)", + category="model/loaders", + description="Recipes:\nsd3: clip-l, clip-g, t5", inputs=[ io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")), io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")), @@ -41,7 +42,7 @@ class EmptySD3LatentImage(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="EmptySD3LatentImage", - category="model/latent/sd3", + category="model/latent/stable diffusion", inputs=[ io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), @@ -66,7 +67,8 @@ class CLIPTextEncodeSD3(io.ComfyNode): return io.Schema( node_id="CLIPTextEncodeSD3", search_aliases=["sd3 prompt"], - category="advanced/conditioning", + display_name="CLIP Text Encode (SD3)", + category="model/conditioning/stable diffusion", inputs=[ io.Clip.Input("clip"), io.String.Input("clip_l", multiline=True, dynamic_prompts=True), diff --git a/comfy_extras/nodes_sdpose.py b/comfy_extras/nodes_sdpose.py index 20d459b00..d1cbff2a6 100644 --- a/comfy_extras/nodes_sdpose.py +++ b/comfy_extras/nodes_sdpose.py @@ -96,8 +96,12 @@ class KeypointDraw: # Body connections - matching DWPose limbSeq (1-indexed, converted to 0-indexed) self.body_limbSeq = [ [2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], - [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], - [1, 16], [16, 18] + [10, 11], [2, 12], [12, 13], [13, 14] + ] + + # Head connections (1-indexed, converted to 0-indexed) + self.head_edges = [ + [2, 1], [1, 15], [15, 17], [1, 16], [16, 18] ] # Colors matching DWPose @@ -215,7 +219,7 @@ class KeypointDraw: return unique_pts if len(unique_pts) > 1 else [[center[0], center[1]], [center[0], center[1]]] def draw_wholebody_keypoints(self, canvas, keypoints, scores=None, threshold=0.3, - draw_body=True, draw_feet=True, draw_face=True, draw_hands=True, stick_width=4, face_point_size=3): + draw_body=True, draw_head=True, draw_feet=True, draw_face=True, draw_hands=True, stick_width=4, face_point_size=3): """ Draw wholebody keypoints (134 keypoints after processing) in DWPose style. @@ -237,9 +241,17 @@ class KeypointDraw: """ H, W, C = canvas.shape - # Draw body limbs - if draw_body and len(keypoints) >= 18: - for i, limb in enumerate(self.body_limbSeq): + # Draw body limbs & head connections + if (draw_body or draw_head) and len(keypoints) >= 18: + colorIndexOffset = 0 + edges = [] + if draw_body: + edges += self.body_limbSeq + else: + colorIndexOffset += len(self.body_limbSeq) + if draw_head: + edges += self.head_edges + for i, limb in enumerate(edges): # Convert from 1-indexed to 0-indexed idx1, idx2 = limb[0] - 1, limb[1] - 1 @@ -262,11 +274,17 @@ class KeypointDraw: polygon = self.draw.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stick_width), int(angle), 0, 360, 1) - self.draw.fillConvexPoly(canvas, polygon, self.colors[i % len(self.colors)]) + self.draw.fillConvexPoly(canvas, polygon, self.colors[(i + colorIndexOffset) % len(self.colors)]) - # Draw body keypoints - if draw_body and len(keypoints) >= 18: + # Draw body & head keypoints + if (draw_body or draw_head) and len(keypoints) >= 18: + head_keypoints = {0, 14, 15, 16, 17} # nose, eyes, ears + neck_point = 1 for i in range(18): + if not draw_head and i in head_keypoints: + continue + if not draw_body and i not in head_keypoints and i != neck_point: + continue if scores is not None and scores[i] < threshold: continue x, y = int(keypoints[i][0]), int(keypoints[i][1]) @@ -365,6 +383,7 @@ class SDPoseDrawKeypoints(io.ComfyNode): io.Int.Input("stick_width", default=4, min=1, max=10, step=1), io.Int.Input("face_point_size", default=3, min=1, max=10, step=1), io.Float.Input("score_threshold", default=0.3, min=0.0, max=1.0, step=0.01), + io.Boolean.Input("draw_head", default=True), ], outputs=[ io.Image.Output(), @@ -372,7 +391,7 @@ class SDPoseDrawKeypoints(io.ComfyNode): ) @classmethod - def execute(cls, keypoints, draw_body, draw_hands, draw_face, draw_feet, stick_width, face_point_size, score_threshold) -> io.NodeOutput: + def execute(cls, keypoints, draw_body, draw_hands, draw_face, draw_feet, stick_width, face_point_size, score_threshold, draw_head) -> io.NodeOutput: if not keypoints: return io.NodeOutput(torch.zeros((1, 64, 64, 3), dtype=torch.float32)) height = keypoints[0]["canvas_height"] @@ -405,7 +424,7 @@ class SDPoseDrawKeypoints(io.ComfyNode): canvas = drawer.draw_wholebody_keypoints( canvas, kp, sc, threshold=score_threshold, - draw_body=draw_body, draw_feet=draw_feet, + draw_body=draw_body, draw_head=draw_head, draw_feet=draw_feet, draw_face=draw_face, draw_hands=draw_hands, stick_width=stick_width, face_point_size=face_point_size, ) diff --git a/comfy_extras/nodes_sdupscale.py b/comfy_extras/nodes_sdupscale.py index ea283e971..5c247fb49 100644 --- a/comfy_extras/nodes_sdupscale.py +++ b/comfy_extras/nodes_sdupscale.py @@ -9,7 +9,7 @@ class SD_4XUpscale_Conditioning(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="SD_4XUpscale_Conditioning", - category="model/conditioning/upscale_diffusion", + category="model/conditioning/stable diffusion upscaler", inputs=[ io.Image.Input("images"), io.Conditioning.Input("positive"), diff --git a/comfy_extras/nodes_stable3d.py b/comfy_extras/nodes_stable3d.py index 8a6e5b726..b0eba819b 100644 --- a/comfy_extras/nodes_stable3d.py +++ b/comfy_extras/nodes_stable3d.py @@ -27,7 +27,7 @@ class StableZero123_Conditioning(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StableZero123_Conditioning", - category="model/conditioning/3d_models", + category="model/conditioning/stable zero123", inputs=[ io.ClipVision.Input("clip_vision"), io.Image.Input("init_image"), @@ -65,7 +65,7 @@ class StableZero123_Conditioning_Batched(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StableZero123_Conditioning_Batched", - category="model/conditioning/3d_models", + category="model/conditioning/stable zero123", inputs=[ io.ClipVision.Input("clip_vision"), io.Image.Input("init_image"), @@ -112,7 +112,7 @@ class SV3D_Conditioning(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="SV3D_Conditioning", - category="model/conditioning/3d_models", + category="model/conditioning/stable video 3d", inputs=[ io.ClipVision.Input("clip_vision"), io.Image.Input("init_image"), diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py index e55f248ae..6a78ffb47 100644 --- a/comfy_extras/nodes_stable_cascade.py +++ b/comfy_extras/nodes_stable_cascade.py @@ -29,7 +29,7 @@ class StableCascade_EmptyLatentImage(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StableCascade_EmptyLatentImage", - category="model/latent/stable_cascade", + category="model/latent/stable cascade", inputs=[ io.Int.Input("width", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8), io.Int.Input("height", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8), @@ -58,7 +58,7 @@ class StableCascade_StageC_VAEEncode(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StableCascade_StageC_VAEEncode", - category="model/latent/stable_cascade", + category="model/latent/stable cascade", inputs=[ io.Image.Input("image"), io.Vae.Input("vae"), @@ -93,7 +93,7 @@ class StableCascade_StageB_Conditioning(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StableCascade_StageB_Conditioning", - category="model/conditioning/stable_cascade", + category="model/conditioning/stable cascade", inputs=[ io.Conditioning.Input("conditioning"), io.Latent.Input("stage_c"), diff --git a/comfy_extras/nodes_textgen.py b/comfy_extras/nodes_textgen.py index d52faf815..5a947d5c5 100644 --- a/comfy_extras/nodes_textgen.py +++ b/comfy_extras/nodes_textgen.py @@ -35,7 +35,7 @@ class TextGenerate(io.ComfyNode): io.Image.Input("image", optional=True), io.Image.Input("video", optional=True, tooltip="Video frames as image batch. Assumed to be 24 FPS; subsampled to 1 FPS internally."), io.Audio.Input("audio", optional=True), - io.Int.Input("max_length", default=256, min=1, max=2048), + io.Int.Input("max_length", default=512, min=1, max=32768), io.DynamicCombo.Input("sampling_mode", options=sampling_options, display_name="Sampling Mode"), io.Boolean.Input("thinking", optional=True, default=False, tooltip="Operate in thinking mode if the model supports it."), io.Boolean.Input("use_default_template", optional=True, default=True, tooltip="Use the built in system prompt/template if the model has one.", advanced=True), diff --git a/comfy_extras/nodes_train.py b/comfy_extras/nodes_train.py index 046eeaaf5..a27217b80 100644 --- a/comfy_extras/nodes_train.py +++ b/comfy_extras/nodes_train.py @@ -15,6 +15,7 @@ import comfy.sampler_helpers import comfy.sd import comfy.utils import comfy.model_management +from comfy.conds import CONDRegular, CONDList from comfy.cli_args import args, PerformanceFeature import comfy_extras.nodes_custom_sampler import folder_paths @@ -120,6 +121,11 @@ def process_cond_list(d, prefix=""): process_cond_list(v, f"{prefix}.{k}") elif isinstance(v, torch.Tensor): d[k] = v.clone() + elif isinstance(v, CONDList): + v.cond = [t.detach() if isinstance(t, torch.Tensor) else t for t in v.cond] + elif isinstance(v, CONDRegular): + if isinstance(v.cond, torch.Tensor): + v.cond = v.cond.detach() elif isinstance(v, (list, tuple)): for index, item in enumerate(v): process_cond_list(item, f"{prefix}.{k}.{index}") @@ -1143,45 +1149,45 @@ class TrainLoraNode(io.ComfyNode): # Process conditioning positive = _process_conditioning(positive) - # Setup model and dtype - mp = model.clone() - use_grad_scaler = False - lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype) - if training_dtype != "none": - dtype = node_helpers.string_to_torch_dtype(training_dtype) - mp.set_model_compute_dtype(dtype) - else: - # Detect model's native dtype for autocast - model_dtype = mp.model.get_dtype() - if model_dtype == torch.float16: - dtype = torch.float16 - # GradScaler only supports float16 gradients, not bfloat16. - # Only enable it when lora params will also be in float16. - if lora_dtype != torch.bfloat16: - use_grad_scaler = True - # Warn about fp16 accumulation instability during training - if PerformanceFeature.Fp16Accumulation in args.fast: - logging.warning( - "WARNING: FP16 model detected with fp16_accumulation enabled. " - "This combination can be numerically unstable during training and may cause NaN values. " - "Suggested fixes: 1) Set training_dtype to 'bf16', or 2) Disable fp16_accumulation (remove from --fast flags)." - ) - else: - # For fp8, bf16, or other dtypes, use bf16 autocast - dtype = torch.bfloat16 - - # Prepare latents and compute counts - latents_dtype = dtype if dtype not in (None,) else torch.bfloat16 - latents, num_images, multi_res = _prepare_latents_and_count( - latents, latents_dtype, bucket_mode - ) - - # Validate and expand conditioning - positive = _validate_and_expand_conditioning(positive, num_images, bucket_mode) - with torch.inference_mode(False): + # Setup model and dtype + mp = model.clone(force_deepcopy=True) + use_grad_scaler = False + lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype) + if training_dtype != "none": + dtype = node_helpers.string_to_torch_dtype(training_dtype) + mp.set_model_compute_dtype(dtype) + else: + # Detect model's native dtype for autocast + model_dtype = mp.model.get_dtype() + if model_dtype == torch.float16: + dtype = torch.float16 + # GradScaler only supports float16 gradients, not bfloat16. + # Only enable it when lora params will also be in float16. + if lora_dtype != torch.bfloat16: + use_grad_scaler = True + # Warn about fp16 accumulation instability during training + if PerformanceFeature.Fp16Accumulation in args.fast: + logging.warning( + "WARNING: FP16 model detected with fp16_accumulation enabled. " + "This combination can be numerically unstable during training and may cause NaN values. " + "Suggested fixes: 1) Set training_dtype to 'bf16', or 2) Disable fp16_accumulation (remove from --fast flags)." + ) + else: + # For fp8, bf16, or other dtypes, use bf16 autocast + dtype = torch.bfloat16 + + # Prepare latents and compute counts + latents_dtype = dtype if dtype not in (None,) else torch.bfloat16 + latents, num_images, multi_res = _prepare_latents_and_count( + latents, latents_dtype, bucket_mode + ) + + # Validate and expand conditioning + positive = _validate_and_expand_conditioning(positive, num_images, bucket_mode) + # Setup models for training - mp.model.requires_grad_(False) + mp.model.requires_grad_(False).train() # Load existing LoRA weights if provided existing_weights, existing_steps = _load_existing_lora(existing_lora) @@ -1361,7 +1367,7 @@ class SaveLoRA(io.ComfyNode): node_id="SaveLoRA", search_aliases=["export lora"], display_name="Save LoRA Weights", - category="advanced/model_merging", + category="model/merging", is_experimental=True, is_output_node=True, inputs=[ diff --git a/comfy_extras/nodes_triposplat.py b/comfy_extras/nodes_triposplat.py new file mode 100644 index 000000000..7bf4703fe --- /dev/null +++ b/comfy_extras/nodes_triposplat.py @@ -0,0 +1,270 @@ +# TripoSplat nodes: image -> 3D gaussian splat + +import logging + +import torch +import torch.nn.functional as F +from typing_extensions import override + +import comfy.model_management +import comfy.nested_tensor +import comfy.patcher_extension +import comfy.utils +from comfy_api.latest import ComfyExtension, IO, Types + + +_Q_TOKEN_LENGTH = 8192 +_LATENT_CHANNELS = 16 +_CAM_CHANNELS = 5 +_DINOV3_MEAN = [0.485, 0.456, 0.406] +_DINOV3_STD = [0.229, 0.224, 0.225] +_NUM_GAUSSIANS_MIN = 32768 +_NUM_GAUSSIANS_MAX = 1048576 + + +def _preprocess(image: torch.Tensor, mask: torch.Tensor, erode_radius: int, size: int) -> torch.Tensor: + # Match original preprocessing: + # resize min side to `size` -> erode alpha -> alpha bbox -> 1.2x square crop -> resize -> composite on black. + rgb = image[..., :3].clamp(0, 1).movedim(-1, 0) # (3, H, W) + alpha = mask.clamp(0, 1)[None] # (1, H, W) + rgba = torch.cat([rgb, alpha], 0)[None] # (1, 4, H, W) + + h, w = rgba.shape[-2:] + s = size / min(w, h) + rgba = comfy.utils.common_upscale(rgba, max(1, round(w * s)), max(1, round(h * s)), "lanczos", "disabled").clamp(0, 1) + + a = rgba[:, 3:4] + if erode_radius > 0: + # min filter over a (2r+1) window == morphological erosion of the alpha matte. + a = -F.max_pool2d(-a, 2 * erode_radius + 1, stride=1, padding=erode_radius) + rgba = torch.cat([rgba[:, :3], a], 1) + + ys, xs = torch.nonzero(a[0, 0] > 0, as_tuple=True) + if xs.numel() == 0: + raise ValueError("TripoSplatPreprocessImage: mask is empty (no foreground pixels).") + x0, x1 = int(xs.min()), int(xs.max()) + y0, y1 = int(ys.min()), int(ys.max()) + cx, cy = (x0 + x1) / 2, (y0 + y1) / 2 + half = max(x1 - x0, y1 - y0) / 2 * 1.2 + left, upper, right, lower = int(cx - half), int(cy - half), int(cx + half), int(cy + half) + + H, W = rgba.shape[-2:] + crop = rgba.new_zeros((1, 4, lower - upper, right - left)) # out-of-bounds stays 0, matching PIL.crop + sx0, sy0, sx1, sy1 = max(left, 0), max(upper, 0), min(right, W), min(lower, H) + if sx1 > sx0 and sy1 > sy0: + crop[:, :, sy0 - upper:sy1 - upper, sx0 - left:sx1 - left] = rgba[:, :, sy0:sy1, sx0:sx1] + + crop = comfy.utils.common_upscale(crop, size, size, "lanczos", "disabled").clamp(0, 1) + out = (crop[:, :3] * crop[:, 3:4])[0].movedim(0, -1) # composite over black == rgb * alpha + return out.unsqueeze(0) # (1, 1024, 1024, 3) + + +class TripoSplatPreprocessImage(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TripoSplatPreprocessImage", + display_name="TripoSplat Preprocess Image", + category="model/conditioning/triposplat", + description="Crop center each image to a square canvas on a black background and add padding.", + inputs=[ + IO.Image.Input("image"), + IO.Mask.Input("mask"), + IO.Int.Input("erode_radius", default=1, min=0, max=16, + tooltip="Erode the alpha matte by this pixel radius before cropping (avoids border bleed)."), + IO.Int.Input("size", default=1024, min=256, max=4096, step=16, + tooltip="Square image size. The model is trained at 1024; other sizes run but are off-distribution."), + ], + outputs=[IO.Image.Output(display_name="image")], + ) + + @classmethod + def execute(cls, image, mask, erode_radius, size) -> IO.NodeOutput: + size = max(16, (int(size) // 16) * 16) # DINOv3 patch / Flux2 VAE stride is 16 + if mask.shape[0] != image.shape[0]: + mask = comfy.utils.repeat_to_batch_size(mask, image.shape[0]) + if tuple(mask.shape[1:]) != tuple(image.shape[1:3]): + mask = F.interpolate(mask[:, None].float(), size=tuple(image.shape[1:3]), mode="bilinear", align_corners=False)[:, 0] + prepared = torch.cat([_preprocess(image[i], mask[i], erode_radius, size) for i in range(image.shape[0])], dim=0) + return IO.NodeOutput(prepared) + + +class TripoSplatConditioning(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TripoSplatConditioning", + display_name="TripoSplat Conditioning", + category="model/conditioning/triposplat", + description="Encode the image with DINOv3 and the Flux2 VAE into TripoSplat positive/negative " + "conditioning, and create the fixed size noise target (latent + camera) for the KSampler", + inputs=[ + IO.ClipVision.Input("clip_vision", tooltip="DINOv3 ViT-H/16+ image encoder"), + IO.Vae.Input("vae", tooltip="Flux2 VAE"), + IO.Image.Input("image"), + ], + outputs=[ + IO.Conditioning.Output(display_name="positive"), + IO.Conditioning.Output(display_name="negative"), + IO.Latent.Output(display_name="latent", tooltip="The fixed size noise target (latent +camera)."), + ], + ) + + @classmethod + def execute(cls, clip_vision, vae, image) -> IO.NodeOutput: + # feature1: DINOv3 token sequence (cls + registers + patches), ImageNet-normalized, with a final non-affine layer norm on top + comfy.model_management.load_model_gpu(clip_vision.patcher) + device = clip_vision.load_device + img = image.movedim(-1, 1).to(device) # (B,3,H,W) in [0,1] + mean = torch.tensor(_DINOV3_MEAN, device=device).view(1, 3, 1, 1) + std = torch.tensor(_DINOV3_STD, device=device).view(1, 3, 1, 1) + img = (img - mean) / std + seq = clip_vision.model(pixel_values=img.float())[0] + feature1 = F.layer_norm(seq.float(), seq.shape[-1:]).to(comfy.model_management.intermediate_device()) + + # Second conditioning: the Flux2 VAE latent of the image, carried as a standard reference_latents entry + ref = vae.encode(image).to(comfy.model_management.intermediate_device()) # (B, 128, H, W) + b = ref.shape[0] + + positive = [[feature1, {"reference_latents": [ref]}]] + negative = [[torch.zeros_like(feature1), {"reference_latents": [torch.zeros_like(ref)]}]] + + # Fixed noise target: the latent is a constant-shape (8192, 16) shape-code + a (1, 5) camera token + dev = comfy.model_management.intermediate_device() + latent_seq = torch.zeros([b, _Q_TOKEN_LENGTH, _LATENT_CHANNELS], device=dev) + camera = torch.zeros([b, 1, _CAM_CHANNELS], device=dev) + samples = comfy.nested_tensor.NestedTensor((latent_seq, camera)) + return IO.NodeOutput(positive, negative, {"samples": samples}) + + +class VAEDecodeTripoSplat(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="VAEDecodeTripoSplat", + display_name="TripoSplat Decode", + category="3d/latent", + description="Decode the sampled TripoSplat latent into a 3D gaussian splat. " + "Modify the number of gaussians to vary the density.", + inputs=[ + IO.Latent.Input("samples"), + IO.Vae.Input("vae", tooltip="TripoSplat VAE decoder"), + IO.Int.Input("num_gaussians", default=262144, min=_NUM_GAUSSIANS_MIN, max=_NUM_GAUSSIANS_MAX, step=32, + tooltip="Number of gaussians to produce (rounded to a multiple of 32). " + "262144 matches the octree's point density; higher oversamples the same points " + "(denser, but no new detail) and costs proportionally more VRAM/time."), + IO.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff, + tooltip="Seeds the octree point sampler (global RNG) for deterministic decodes."), + ], + outputs=[IO.Splat.Output(display_name="splat")], + ) + + @classmethod + def execute(cls, samples, vae, num_gaussians, seed) -> IO.NodeOutput: + s = samples["samples"] + latent = s.unbind()[0] if getattr(s, "is_nested", False) else s # take the latent stream, drop camera + + decoder = vae.first_stage_model + gpp = decoder.gaussians_per_point + n = max(_NUM_GAUSSIANS_MIN, min(_NUM_GAUSSIANS_MAX, int(num_gaussians))) + if n % gpp != 0: + n = round(n / gpp) * gpp + + dtype_size = comfy.model_management.dtype_size(vae.vae_dtype) + hidden = decoder.gs.model_channels + cond_tokens = latent.shape[1] + memory_required = (cond_tokens * 4 + (n // gpp) * 10) * hidden * dtype_size + comfy.model_management.load_models_gpu([vae.patcher], memory_required=memory_required) + latent = latent.to(device=vae.device, dtype=vae.vae_dtype) + generator = torch.Generator(device="cpu").manual_seed(seed) + parts = [g.render_tensors() for g in decoder.decode(latent, num_gaussians=n, generator=generator)] + positions, scales, rotations, opacities, sh = (torch.stack(t) for t in zip(*parts)) + return IO.NodeOutput(Types.SPLAT(positions, scales, rotations, opacities, sh)) + + +class TripoSplatSamplingPreview(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TripoSplatSamplingPreview", + display_name="TripoSplat Sampling Preview", + category="3d/latent", + description="Patch the TripoSplat model for the standard Ksampler node to show a live decoded " + "gaussian splat preview at each step.", + inputs=[ + IO.Model.Input("model"), + IO.Vae.Input("vae", tooltip="TripoSplat VAE decoder"), + IO.Int.Input("octree_level", default=5, min=2, max=8, advanced=True, + tooltip="Octree depth for the preview decode (lower = cheaper/coarser)."), + IO.Int.Input("num_gaussians", default=16384, min=1024, max=262144, step=32, + tooltip="Number of gaussians to produce for the preview (rounded to a multiple of 32)."), + IO.Float.Input("yaw", default=90.0, min=-360.0, max=360.0, step=1.0, tooltip="Preview camera yaw in degrees.", advanced=True,), + IO.Float.Input("pitch", default=15.0, min=-89.0, max=89.0, step=1.0, tooltip="Preview camera pitch in degrees.", advanced=True,), + IO.Int.Input("point_size", default=3, min=1, max=16, + tooltip="Maximum splat radius in pixels. Each gaussian is sized from its scale and capped here; " + "lower = finer/pointier, higher = chunkier."), + ], + outputs=[IO.Model.Output()], + ) + + @classmethod + def execute(cls, model, vae, octree_level, num_gaussians, yaw, pitch, point_size) -> IO.NodeOutput: + from comfy.ldm.triposplat.preview import decode_x0_to_image + cfg = {"gaussians": num_gaussians, "level": octree_level, "yaw": yaw, "pitch": pitch, + "point_size": point_size} + + fsm = vae.first_stage_model + cond_tokens = model.model.diffusion_model.q_token_length + memory_required = (cond_tokens * 4 + (num_gaussians // fsm.gaussians_per_point) * 10) * fsm.gs.model_channels * comfy.model_management.dtype_size(vae.vae_dtype) + + # Live preview via WrappersMP.OUTER_SAMPLE + ProgressBar + # The wrapper augments the sampler's own callback to decode x0 -> gaussian splat -> preview image each step + def outer_sample_wrapper(executor, *args, **kwargs): + args = list(args) + cb_idx = 5 # outer_sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) + orig_cb = args[cb_idx] if len(args) > cb_idx else kwargs.get("callback") + state = {"ok": True, "pbar": None, "loaded": False} + + def callback(step, x0, x, total_steps): + if orig_cb is not None: + orig_cb(step, x0, x, total_steps) + if not state["ok"]: + return + try: + if not state["loaded"]: + loaded_models = comfy.model_management.loaded_models(only_currently_used=True) + loaded_models.append(vae.patcher) + comfy.model_management.load_models_gpu(loaded_models, memory_required=memory_required) + state["loaded"] = True + img = decode_x0_to_image(vae, x0, cfg) + if state["pbar"] is None: + state["pbar"] = comfy.utils.ProgressBar(total_steps) + state["pbar"].update_absolute(step + 1, total_steps, ("JPEG", img, 512)) + except Exception as e: + logging.warning("TripoSplatSamplingPreview: preview failed, disabling ({})".format(e)) + state["ok"] = False + + if len(args) > cb_idx: + args[cb_idx] = callback + else: + kwargs["callback"] = callback + return executor(*args, **kwargs) + + m = model.clone() + m.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "triposplat_sampling_preview", outer_sample_wrapper) + return IO.NodeOutput(m) + + +class TripoSplatExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + TripoSplatPreprocessImage, + TripoSplatConditioning, + VAEDecodeTripoSplat, + TripoSplatSamplingPreview, + ] + + +async def comfy_entrypoint() -> TripoSplatExtension: + return TripoSplatExtension() diff --git a/comfy_extras/nodes_video.py b/comfy_extras/nodes_video.py index ae1d826d5..8d76af1c1 100644 --- a/comfy_extras/nodes_video.py +++ b/comfy_extras/nodes_video.py @@ -19,7 +19,7 @@ class SaveWEBM(io.ComfyNode): category="video", is_experimental=True, inputs=[ - io.Image.Input("images"), + io.Image.Input("images", tooltip="RGBA images are saved with their alpha channel as transparency (vp9 codec only)."), io.String.Input("filename_prefix", default="ComfyUI"), io.Combo.Input("codec", options=["vp9", "av1"]), io.Float.Input("fps", default=24.0, min=0.01, max=1000.0, step=0.01), @@ -45,18 +45,25 @@ class SaveWEBM(io.ComfyNode): for x in cls.hidden.extra_pnginfo: container.metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x]) + # Save transparency when the images carry an alpha channel (RGBA) and the codec supports it. + # vp9 -> yuva420p; other codecs have no usable alpha path, so the alpha is ignored. + save_alpha = images.shape[-1] == 4 and codec == "vp9" + codec_map = {"vp9": "libvpx-vp9", "av1": "libsvtav1"} stream = container.add_stream(codec_map[codec], rate=Fraction(round(fps * 1000), 1000)) stream.width = images.shape[-2] stream.height = images.shape[-3] - stream.pix_fmt = "yuv420p10le" if codec == "av1" else "yuv420p" + stream.pix_fmt = "yuva420p" if save_alpha else ("yuv420p10le" if codec == "av1" else "yuv420p") stream.bit_rate = 0 stream.options = {'crf': str(crf)} if codec == "av1": stream.options["preset"] = "6" for frame in images: - frame = av.VideoFrame.from_ndarray(torch.clamp(frame[..., :3] * 255, min=0, max=255).to(device=torch.device("cpu"), dtype=torch.uint8).numpy(), format="rgb24") + if save_alpha: + frame = av.VideoFrame.from_ndarray(torch.clamp(frame[..., :4] * 255, min=0, max=255).to(device=torch.device("cpu"), dtype=torch.uint8).numpy(), format="rgba") + else: + frame = av.VideoFrame.from_ndarray(torch.clamp(frame[..., :3] * 255, min=0, max=255).to(device=torch.device("cpu"), dtype=torch.uint8).numpy(), format="rgb24") for packet in stream.encode(frame): container.mux(packet) container.mux(stream.encode()) @@ -127,6 +134,17 @@ class CreateVideo(io.ComfyNode): io.Image.Input("images", tooltip="The images to create a video from."), io.Float.Input("fps", default=30.0, min=1.0, max=120.0, step=1.0), io.Audio.Input("audio", optional=True, tooltip="The audio to add to the video."), + io.Int.Input( + "bit_depth", + min=8, + max=10, + default=8, + step=2, + tooltip="Bit depth of the created video. 10-bit keeps smoother gradients with less" + " banding, but some players and downstream nodes may not support it.", + optional=True, + display_mode=io.NumberDisplay.number, + ), ], outputs=[ io.Video.Output(), @@ -134,9 +152,14 @@ class CreateVideo(io.ComfyNode): ) @classmethod - def execute(cls, images: Input.Image, fps: float, audio: Optional[Input.Audio] = None) -> io.NodeOutput: + def execute( + cls, images: Input.Image, fps: float, audio: Optional[Input.Audio] = None, bit_depth: int = 8, + ) -> io.NodeOutput: return io.NodeOutput( - InputImpl.VideoFromComponents(Types.VideoComponents(images=images, audio=audio, frame_rate=Fraction(fps))) + InputImpl.VideoFromComponents( + Types.VideoComponents(images=images, audio=audio, frame_rate=Fraction(fps)), + bit_depth=bit_depth, + ) ) class GetVideoComponents(io.ComfyNode): @@ -147,7 +170,7 @@ class GetVideoComponents(io.ComfyNode): search_aliases=["extract frames", "split video", "video to images", "demux"], display_name="Get Video Components", category="video", - description="Extracts all components from a video: frames, audio, and framerate.", + description="Extracts all components from a video: frames, audio, framerate, and bit depth.", inputs=[ io.Video.Input("video", tooltip="The video to extract components from."), ], @@ -155,13 +178,14 @@ class GetVideoComponents(io.ComfyNode): io.Image.Output(display_name="images"), io.Audio.Output(display_name="audio"), io.Float.Output(display_name="fps"), + io.Int.Output(display_name="bit_depth"), ], ) @classmethod def execute(cls, video: Input.Video) -> io.NodeOutput: components = video.get_components() - return io.NodeOutput(components.images, components.audio, float(components.frame_rate)) + return io.NodeOutput(components.images, components.audio, float(components.frame_rate), video.get_bit_depth()) class LoadVideo(io.ComfyNode): @@ -209,13 +233,8 @@ class VideoSlice(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="Video Slice", - display_name="Video Slice", - search_aliases=[ - "trim video duration", - "skip first frames", - "frame load cap", - "start time", - ], + display_name="Trim Video", + search_aliases=["trim video duration", "skip first frames", "frame load cap", "start time"], category="video", essentials_category="Video Tools", inputs=[ diff --git a/comfy_extras/nodes_video_model.py b/comfy_extras/nodes_video_model.py index 0d6cae6a8..01d48d4d4 100644 --- a/comfy_extras/nodes_video_model.py +++ b/comfy_extras/nodes_video_model.py @@ -41,7 +41,7 @@ class SVD_img2vid_Conditioning: FUNCTION = "encode" - CATEGORY = "model/conditioning/video_models" + CATEGORY = "model/conditioning/stable video" def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level): output = clip_vision.encode_image(init_image) @@ -108,7 +108,7 @@ class VideoTriangleCFGGuidance: return (m, ) class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave): - CATEGORY = "advanced/model_merging" + CATEGORY = "model/merging" @classmethod def INPUT_TYPES(s): @@ -138,7 +138,7 @@ class ConditioningSetAreaPercentageVideo: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" def append(self, conditioning, width, height, temporal, x, y, z, strength): c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", temporal, height, width, z, y, x), @@ -160,4 +160,5 @@ NODE_DISPLAY_NAME_MAPPINGS = { "ImageOnlyCheckpointLoader": "Load Checkpoint Image Only (img2vid model)", "VideoLinearCFGGuidance": "Video Linear CFG Guidance", "VideoTriangleCFGGuidance": "Video Triangle CFG Guidance", + "ConditioningSetAreaPercentageVideo": "Conditioning (Set Area with Percentage for Video)", } diff --git a/comfy_extras/nodes_void.py b/comfy_extras/nodes_void.py index b43154b8d..7527baf43 100644 --- a/comfy_extras/nodes_void.py +++ b/comfy_extras/nodes_void.py @@ -175,7 +175,7 @@ class VOIDInpaintConditioning(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="VOIDInpaintConditioning", - category="model/conditioning/video_models", + category="model/conditioning/void", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -288,7 +288,7 @@ class VOIDWarpedNoise(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="VOIDWarpedNoise", - category="model/latent/video", + category="model/latent/void", inputs=[ OpticalFlow.Input( "optical_flow", @@ -393,7 +393,7 @@ class VOIDWarpedNoiseSource(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="VOIDWarpedNoiseSource", - category="model/sampling/noise", + category="model/latent/void", inputs=[ io.Latent.Input("warped_noise", tooltip="Warped noise latent from VOIDWarpedNoise"), diff --git a/comfy_extras/nodes_wan.py b/comfy_extras/nodes_wan.py index 67d3a8443..0e47a58df 100644 --- a/comfy_extras/nodes_wan.py +++ b/comfy_extras/nodes_wan.py @@ -18,7 +18,7 @@ class WanImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanImageToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -66,7 +66,7 @@ class WanFunControlToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanFunControlToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/fun control", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -119,7 +119,7 @@ class Wan22FunControlToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="Wan22FunControlToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/fun control", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -184,7 +184,7 @@ class WanFirstLastFrameToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanFirstLastFrameToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -256,7 +256,7 @@ class WanFunInpaintToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanFunInpaintToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/fun inpaint", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -288,7 +288,7 @@ class WanVaceToVideo(io.ComfyNode): return io.Schema( node_id="WanVaceToVideo", search_aliases=["video conditioning", "video control"], - category="model/conditioning/video_models", + category="model/conditioning/wan/vace", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -375,7 +375,8 @@ class TrimVideoLatent(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="TrimVideoLatent", - category="model/latent/video", + display_name="Trim Video Latent", + category="model/latent", inputs=[ io.Latent.Input("samples"), io.Int.Input("trim_amount", default=0, min=0, max=99999), @@ -398,7 +399,7 @@ class WanCameraImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanCameraImageToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/camera", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -452,7 +453,7 @@ class WanPhantomSubjectToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanPhantomSubjectToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/phantom subject", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -707,7 +708,7 @@ class WanTrackToVideo(io.ComfyNode): return io.Schema( node_id="WanTrackToVideo", search_aliases=["motion tracking", "trajectory video", "point tracking", "keypoint animation"], - category="model/conditioning/video_models", + category="model/conditioning/wan/move", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -951,7 +952,7 @@ class WanSoundImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanSoundImageToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/sound", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -984,7 +985,7 @@ class WanSoundImageToVideoExtend(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanSoundImageToVideoExtend", - category="model/conditioning/video_models", + category="model/conditioning/wan/sound", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -1046,7 +1047,7 @@ class WanHuMoImageToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanHuMoImageToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/humo", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -1112,7 +1113,7 @@ class WanAnimateToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanAnimateToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/animate", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), @@ -1252,7 +1253,7 @@ class Wan22ImageToVideoLatent(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="Wan22ImageToVideoLatent", - category="model/conditioning/inpaint", + category="model/conditioning/wan", inputs=[ io.Vae.Input("vae"), io.Int.Input("width", default=1280, min=32, max=nodes.MAX_RESOLUTION, step=32), @@ -1302,7 +1303,7 @@ class WanInfiniteTalkToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanInfiniteTalkToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/infinite talk", inputs=[ io.DynamicCombo.Input("mode", options=[ io.DynamicCombo.Option("single_speaker", []), @@ -1456,63 +1457,6 @@ class WanInfiniteTalkToVideo(io.ComfyNode): return io.NodeOutput(model_patched, positive, negative, out_latent, trim_image) -class WanSCAILToVideo(io.ComfyNode): - @classmethod - def define_schema(cls): - return io.Schema( - node_id="WanSCAILToVideo", - category="model/conditioning/video_models", - inputs=[ - io.Conditioning.Input("positive"), - io.Conditioning.Input("negative"), - io.Vae.Input("vae"), - io.Int.Input("width", default=512, min=32, max=nodes.MAX_RESOLUTION, step=32), - io.Int.Input("height", default=896, min=32, max=nodes.MAX_RESOLUTION, step=32), - io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), - io.Int.Input("batch_size", default=1, min=1, max=4096), - io.ClipVisionOutput.Input("clip_vision_output", optional=True), - io.Image.Input("reference_image", optional=True), - io.Image.Input("pose_video", optional=True, tooltip="Video used for pose conditioning. Will be downscaled to half the resolution of the main video."), - io.Float.Input("pose_strength", default=1.0, min=0.0, max=10.0, step=0.01, tooltip="Strength of the pose latent."), - io.Float.Input("pose_start", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="Start step to use pose conditioning."), - io.Float.Input("pose_end", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="End step to use pose conditioning."), - ], - outputs=[ - io.Conditioning.Output(display_name="positive"), - io.Conditioning.Output(display_name="negative"), - io.Latent.Output(display_name="latent", tooltip="Empty latent of the generation size."), - ], - is_experimental=True, - ) - - @classmethod - def execute(cls, positive, negative, vae, width, height, length, batch_size, pose_strength, pose_start, pose_end, reference_image=None, clip_vision_output=None, pose_video=None) -> io.NodeOutput: - latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - - ref_latent = None - if reference_image is not None: - reference_image = comfy.utils.common_upscale(reference_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) - ref_latent = vae.encode(reference_image[:, :, :, :3]) - - if ref_latent is not None: - positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True) - negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [torch.zeros_like(ref_latent)]}, append=True) - - if clip_vision_output is not None: - positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) - negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) - - if pose_video is not None: - pose_video = comfy.utils.common_upscale(pose_video[:length].movedim(-1, 1), width // 2, height // 2, "area", "center").movedim(1, -1) - pose_video_latent = vae.encode(pose_video[:, :, :, :3]) * pose_strength - positive = node_helpers.conditioning_set_values_with_timestep_range(positive, {"pose_video_latent": pose_video_latent}, pose_start, pose_end) - negative = node_helpers.conditioning_set_values_with_timestep_range(negative, {"pose_video_latent": pose_video_latent}, pose_start, pose_end) - - out_latent = {} - out_latent["samples"] = latent - return io.NodeOutput(positive, negative, out_latent) - - class WanExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[io.ComfyNode]]: @@ -1533,7 +1477,6 @@ class WanExtension(ComfyExtension): WanAnimateToVideo, Wan22ImageToVideoLatent, WanInfiniteTalkToVideo, - WanSCAILToVideo, ] async def comfy_entrypoint() -> WanExtension: diff --git a/comfy_extras/nodes_wandancer.py b/comfy_extras/nodes_wandancer.py index a96885745..fdb2b5e57 100644 --- a/comfy_extras/nodes_wandancer.py +++ b/comfy_extras/nodes_wandancer.py @@ -713,7 +713,7 @@ class WanDancerEncodeAudio(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanDancerEncodeAudio", - category="model/conditioning/video_models", + category="model/conditioning/wan/dancer", inputs=[ io.Audio.Input("audio"), io.Int.Input("video_frames", default=149, min=1, max=nodes.MAX_RESOLUTION, step=4), @@ -787,7 +787,7 @@ class WanDancerVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanDancerVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/dancer", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), diff --git a/comfy_extras/nodes_wanmove.py b/comfy_extras/nodes_wanmove.py index 2db064922..d1f924a40 100644 --- a/comfy_extras/nodes_wanmove.py +++ b/comfy_extras/nodes_wanmove.py @@ -247,7 +247,7 @@ class WanMoveVisualizeTracks(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanMoveVisualizeTracks", - category="model/conditioning/video_models", + category="model/conditioning/wan/move", inputs=[ io.Image.Input("images"), io.Tracks.Input("tracks", optional=True), @@ -283,7 +283,7 @@ class WanMoveTracksFromCoords(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanMoveTracksFromCoords", - category="model/conditioning/video_models", + category="model/conditioning/wan/move", inputs=[ io.String.Input("track_coords", force_input=True, default="[]", optional=True), io.Mask.Input("track_mask", optional=True), @@ -325,7 +325,8 @@ class GenerateTracks(io.ComfyNode): return io.Schema( node_id="GenerateTracks", search_aliases=["motion paths", "camera movement", "trajectory"], - category="model/conditioning/video_models", + display_name="Generate Video Tracks", + category="model/conditioning/wan/move", inputs=[ io.Int.Input("width", default=832, min=16, max=4096, step=16), io.Int.Input("height", default=480, min=16, max=4096, step=16), @@ -434,7 +435,7 @@ class WanMoveConcatTrack(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanMoveConcatTrack", - category="model/conditioning/video_models", + category="model/conditioning/wan/move", inputs=[ io.Tracks.Input("tracks_1"), io.Tracks.Input("tracks_2", optional=True), @@ -463,7 +464,7 @@ class WanMoveTrackToVideo(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="WanMoveTrackToVideo", - category="model/conditioning/video_models", + category="model/conditioning/wan/move", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), diff --git a/comfy_extras/nodes_zimage.py b/comfy_extras/nodes_zimage.py index 70ddc4afa..ce946b377 100644 --- a/comfy_extras/nodes_zimage.py +++ b/comfy_extras/nodes_zimage.py @@ -10,7 +10,7 @@ class TextEncodeZImageOmni(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="TextEncodeZImageOmni", - category="advanced/conditioning", + category="model/conditioning/z-image", is_experimental=True, inputs=[ io.Clip.Input("clip"), diff --git a/comfyui_version.py b/comfyui_version.py index 0bb0f780c..cee317f3d 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.22.0" +__version__ = "0.25.0" diff --git a/cuda_malloc.py b/cuda_malloc.py index f7651981c..8c4422db8 100644 --- a/cuda_malloc.py +++ b/cuda_malloc.py @@ -2,6 +2,7 @@ import os import importlib.util from comfy.cli_args import args, PerformanceFeature import subprocess +import re #Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import. def get_gpu_names(): @@ -77,11 +78,24 @@ try: except: pass +def get_raw_cuda_version(version_str): + match = re.search(r'\+cu(\d+)', version_str) + if match: + try: + return int(match.group(1)) + except: + pass + return None + if not args.cuda_malloc: try: if int(version[0]) >= 2 and "+cu" in version: # enable by default for torch version 2.0 and up only on cuda torch if PerformanceFeature.AutoTune not in args.fast: # Autotune has issues with cuda malloc - args.cuda_malloc = cuda_malloc_supported() + cuda_version = get_raw_cuda_version(version) + if cuda_version is not None and cuda_version >= 130: + args.cuda_malloc = True + else: + args.cuda_malloc = cuda_malloc_supported() except: pass diff --git a/execution.py b/execution.py index 5246d651c..c45317593 100644 --- a/execution.py +++ b/execution.py @@ -40,6 +40,7 @@ from comfy_execution.graph_utils import GraphBuilder, is_link from comfy_execution.validation import validate_node_input from comfy_execution.progress import get_progress_state, reset_progress_state, add_progress_handler, WebUIProgressHandler from comfy_execution.utils import CurrentNodeContext +from comfy_execution.asset_enrichment import enrich_output_with_assets from comfy_api.internal import _ComfyNodeInternal, _NodeOutputInternal, first_real_override, is_class, make_locked_method_func from comfy_api.latest import io, _io from comfy_execution.cache_provider import _has_cache_providers, _get_cache_providers, _logger as _cache_logger @@ -199,6 +200,8 @@ def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt= hidden_inputs_v3[io.Hidden.auth_token_comfy_org] = extra_data.get("auth_token_comfy_org", None) if io.Hidden.api_key_comfy_org.name in hidden: hidden_inputs_v3[io.Hidden.api_key_comfy_org] = extra_data.get("api_key_comfy_org", None) + if io.Hidden.comfy_usage_source.name in hidden: + hidden_inputs_v3[io.Hidden.comfy_usage_source] = extra_data.get("comfy_usage_source", None) else: if "hidden" in valid_inputs: h = valid_inputs["hidden"] @@ -215,6 +218,8 @@ def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt= input_data_all[x] = [extra_data.get("auth_token_comfy_org", None)] if h[x] == "API_KEY_COMFY_ORG": input_data_all[x] = [extra_data.get("api_key_comfy_org", None)] + if h[x] == "COMFY_USAGE_SOURCE": + input_data_all[x] = [extra_data.get("comfy_usage_source", None)] v3_data["hidden_inputs"] = hidden_inputs_v3 return input_data_all, missing_keys, v3_data @@ -418,6 +423,7 @@ def _is_intermediate_output(dynprompt, node_id): class_def = nodes.NODE_CLASS_MAPPINGS[class_type] return getattr(class_def, 'HAS_INTERMEDIATE_OUTPUT', False) + def _send_cached_ui(server, node_id, display_node_id, cached, prompt_id, ui_outputs): if server.client_id is None: return @@ -552,6 +558,10 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed, asyncio.create_task(await_completion()) return (ExecutionResult.PENDING, None, None) if len(output_ui) > 0: + # Enrich at output-processing time (not in the send path) so assets + # are registered even when no client is connected, and the asset id + # flows into ui_outputs and the cache alongside the raw entries. + output_ui = enrich_output_with_assets(output_ui) ui_outputs[unique_id] = { "meta": { "node_id": unique_id, @@ -1298,6 +1308,25 @@ class PromptQueue: queued = copy.copy(self.queue) return (running, queued) + def interrupt_if_running(self, prompt_id): + """Interrupt the running prompt with this id, atomically. + + Checks the live running set and signals the interrupt under the queue + mutex, so the worker cannot move the job to done (and start the next + prompt) in between. Returns True if a matching job was running and an + interrupt was signalled, False otherwise. The atomicity is what keeps a + cancel from landing on an unrelated prompt that started after a separate + is-running check: the global interrupt flag is reset at the start of + every prompt (execute_async), so a job that finishes before consuming + the flag cannot leak the interrupt onto its successor. + """ + with self.mutex: + for item in self.currently_running.values(): + if item[1] == prompt_id: + nodes.interrupt_processing() + return True + return False + def get_tasks_remaining(self): with self.mutex: return len(self.queue) + len(self.currently_running) diff --git a/extra_model_paths.yaml.example b/extra_model_paths.yaml.example index 9c395c0b2..6a31d8a63 100644 --- a/extra_model_paths.yaml.example +++ b/extra_model_paths.yaml.example @@ -8,21 +8,37 @@ # # You can use is_default to mark that these folders should be listed first, and used as the default dirs for eg downloads # #is_default: true # checkpoints: models/checkpoints/ +# configs: models/configs/ +# loras: models/loras/ +# vae: models/vae/ # text_encoders: | # models/text_encoders/ -# models/clip/ # legacy location still supported -# clip_vision: models/clip_vision/ -# configs: models/configs/ -# controlnet: models/controlnet/ +# models/clip/ # diffusion_models: | -# models/diffusion_models -# models/unet +# models/unet/ +# models/diffusion_models/ +# clip_vision: models/clip_vision/ +# style_models: models/style_models/ # embeddings: models/embeddings/ -# loras: models/loras/ +# diffusers: models/diffusers/ +# vae_approx: models/vae_approx/ +# controlnet: | +# models/controlnet/ +# models/t2i_adapter/ +# gligen: models/gligen/ # upscale_models: models/upscale_models/ -# vae: models/vae/ -# audio_encoders: models/audio_encoders/ +# latent_upscale_models: models/latent_upscale_models/ +# custom_nodes: custom_nodes/ +# hypernetworks: models/hypernetworks/ +# photomaker: models/photomaker/ +# classifiers: models/classifiers/ # model_patches: models/model_patches/ +# audio_encoders: models/audio_encoders/ +# background_removal: models/background_removal/ +# frame_interpolation: models/frame_interpolation/ +# geometry_estimation: models/geometry_estimation/ +# optical_flow: models/optical_flow/ +# detection: models/detection/ #config for a1111 ui @@ -45,8 +61,7 @@ # controlnet: models/ControlNet -# For a full list of supported keys (style_models, vae_approx, hypernetworks, photomaker, -# model_patches, audio_encoders, classifiers, etc.) see folder_paths.py. +# For the canonical list of supported keys and extensions, see folder_paths.py. #other_ui: # base_path: path/to/ui diff --git a/main.py b/main.py index bce451a83..ad5c11e16 100644 --- a/main.py +++ b/main.py @@ -26,6 +26,7 @@ import utils.extra_config from utils.mime_types import init_mime_types import faulthandler import logging +import signal import sys from comfy_execution.progress import get_progress_state from comfy_execution.utils import get_executing_context @@ -37,12 +38,28 @@ if __name__ == "__main__": os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1' os.environ['DO_NOT_TRACK'] = '1' -faulthandler.enable(file=sys.stderr, all_threads=False) +faulthandler.enable(file=sys.stderr, all_threads=args.debug_hang) +if __name__ == "__main__" and args.debug_hang: + dumping_traceback = False + + def dump_traceback_on_sigint(signum, frame): + global dumping_traceback + if dumping_traceback: + raise KeyboardInterrupt + dumping_traceback = True + faulthandler.dump_traceback(file=sys.stderr, all_threads=True) + raise KeyboardInterrupt + + signal.signal(signal.SIGINT, dump_traceback_on_sigint) import comfy_aimdo.control if enables_dynamic_vram(): - comfy_aimdo.control.init() + try: + comfy_aimdo.control.init(simple_vram_headroom=None if args.reserve_vram is None else int(args.reserve_vram * 1024 ** 3)) + except TypeError: + # comfy-aimdo 0.4.9 protocol. + comfy_aimdo.control.init() if os.name == "nt": os.environ['MIMALLOC_PURGE_DELAY'] = '0' @@ -110,6 +127,10 @@ def apply_custom_paths(): for config_path in itertools.chain(*args.extra_model_paths_config): utils.extra_config.load_extra_path_config(config_path) + # --base-directory + if args.base_directory: + logging.info(f"Setting base directory to: {folder_paths.base_path}") + # --output-directory, --input-directory, --user-directory if args.output_directory: output_dir = os.path.abspath(args.output_directory) @@ -218,23 +239,30 @@ import comfy.model_patcher if args.enable_dynamic_vram or (enables_dynamic_vram() and comfy.model_management.is_nvidia() and not comfy.model_management.is_wsl()): if (not args.enable_dynamic_vram) and (comfy.model_management.torch_version_numeric < (2, 8)): logging.warning("Unsupported Pytorch detected. DynamicVRAM support requires Pytorch version 2.8 or later. Falling back to legacy ModelPatcher. VRAM estimates may be unreliable especially on Windows") - elif comfy_aimdo.control.init_devices(d.index for d in comfy.model_management.get_all_torch_devices()): - if args.verbose == 'DEBUG': - comfy_aimdo.control.set_log_debug() - elif args.verbose == 'CRITICAL': - comfy_aimdo.control.set_log_critical() - elif args.verbose == 'ERROR': - comfy_aimdo.control.set_log_error() - elif args.verbose == 'WARNING': - comfy_aimdo.control.set_log_warning() - else: #INFO - comfy_aimdo.control.set_log_info() - - comfy.model_patcher.CoreModelPatcher = comfy.model_patcher.ModelPatcherDynamic - comfy.memory_management.aimdo_enabled = True - logging.info("DynamicVRAM support detected and enabled") else: - logging.warning("No working comfy-aimdo install detected. DynamicVRAM support disabled. Falling back to legacy ModelPatcher. VRAM estimates may be unreliable especially on Windows") + try: + aimdo_initialized = comfy_aimdo.control.init_devices((d.index, int(args.vram_headroom * 1024 ** 3)) for d in comfy.model_management.get_all_torch_devices()) + except TypeError: + # comfy-aimdo 0.4.9 protocol. + aimdo_initialized = comfy_aimdo.control.init_devices(d.index for d in comfy.model_management.get_all_torch_devices()) + + if aimdo_initialized: + if args.verbose == 'DEBUG': + comfy_aimdo.control.set_log_debug() + elif args.verbose == 'CRITICAL': + comfy_aimdo.control.set_log_critical() + elif args.verbose == 'ERROR': + comfy_aimdo.control.set_log_error() + elif args.verbose == 'WARNING': + comfy_aimdo.control.set_log_warning() + else: #INFO + comfy_aimdo.control.set_log_info() + + comfy.model_patcher.CoreModelPatcher = comfy.model_patcher.ModelPatcherDynamic + comfy.memory_management.aimdo_enabled = True + logging.info("DynamicVRAM support detected and enabled") + else: + logging.warning("No working comfy-aimdo install detected. DynamicVRAM support disabled. Falling back to legacy ModelPatcher. VRAM estimates may be unreliable especially on Windows") def cuda_malloc_warning(): @@ -464,13 +492,6 @@ def start_comfyui(asyncio_loop=None): folder_paths.set_temp_directory(temp_dir) cleanup_temp() - if args.windows_standalone_build: - try: - import new_updater - new_updater.update_windows_updater() - except: - pass - if not asyncio_loop: asyncio_loop = asyncio.new_event_loop() asyncio.set_event_loop(asyncio_loop) @@ -484,6 +505,11 @@ def start_comfyui(asyncio_loop=None): init_custom_nodes=(not args.disable_all_custom_nodes) or len(args.whitelist_custom_nodes) > 0, init_api_nodes=not args.disable_api_nodes )) + + # Re-apply Comfy's cuDNN benchmark policy after custom-node imports. Benchmark + # mode can request near-card-sized autotune workspaces, and some custom nodes set it at import time. + comfy.model_management.set_cudnn_benchmark() + hook_breaker_ac10a0.restore_functions() cuda_malloc_warning() diff --git a/manager_requirements.txt b/manager_requirements.txt index a079d3492..13786bb35 100644 --- a/manager_requirements.txt +++ b/manager_requirements.txt @@ -1 +1 @@ -comfyui_manager==4.2.1 +comfyui_manager==4.2.2 diff --git a/new_updater.py b/new_updater.py deleted file mode 100644 index 9a203acdd..000000000 --- a/new_updater.py +++ /dev/null @@ -1,35 +0,0 @@ -import os -import shutil - -base_path = os.path.dirname(os.path.realpath(__file__)) - - -def update_windows_updater(): - top_path = os.path.dirname(base_path) - updater_path = os.path.join(base_path, ".ci/update_windows/update.py") - bat_path = os.path.join(base_path, ".ci/update_windows/update_comfyui.bat") - - dest_updater_path = os.path.join(top_path, "update/update.py") - dest_bat_path = os.path.join(top_path, "update/update_comfyui.bat") - dest_bat_deps_path = os.path.join(top_path, "update/update_comfyui_and_python_dependencies.bat") - - try: - with open(dest_bat_path, 'rb') as f: - contents = f.read() - except: - return - - if not contents.startswith(b"..\\python_embeded\\python.exe .\\update.py"): - return - - shutil.copy(updater_path, dest_updater_path) - try: - with open(dest_bat_deps_path, 'rb') as f: - contents = f.read() - contents = contents.replace(b'..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\', b'call update_comfyui.bat nopause') - with open(dest_bat_deps_path, 'wb') as f: - f.write(contents) - except: - pass - shutil.copy(bat_path, dest_bat_path) - print("Updated the windows standalone package updater.") # noqa: T201 diff --git a/nodes.py b/nodes.py index 528bf316f..b1a663f4c 100644 --- a/nodes.py +++ b/nodes.py @@ -20,8 +20,6 @@ from PIL.PngImagePlugin import PngInfo import numpy as np import safetensors.torch -sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy")) - import comfy.diffusers_load import comfy.samplers import comfy.sample @@ -87,7 +85,7 @@ class ConditioningCombine: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "combine" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" SEARCH_ALIASES = ["combine", "merge conditioning", "combine prompts", "merge prompts", "mix prompts", "add prompt"] def combine(self, conditioning_1, conditioning_2): @@ -104,7 +102,7 @@ class ConditioningAverage : RETURN_TYPES = ("CONDITIONING",) FUNCTION = "addWeighted" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength): out = [] @@ -143,7 +141,7 @@ class ConditioningConcat: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "concat" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" def concat(self, conditioning_to, conditioning_from): out = [] @@ -176,7 +174,7 @@ class ConditioningSetArea: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" def append(self, conditioning, width, height, x, y, strength): c = node_helpers.conditioning_set_values(conditioning, {"area": (height // 8, width // 8, y // 8, x // 8), @@ -197,7 +195,7 @@ class ConditioningSetAreaPercentage: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" def append(self, conditioning, width, height, x, y, strength): c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x), @@ -214,7 +212,7 @@ class ConditioningSetAreaStrength: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" def append(self, conditioning, strength): c = node_helpers.conditioning_set_values(conditioning, {"strength": strength}) @@ -234,7 +232,7 @@ class ConditioningSetMask: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "append" - CATEGORY = "model/conditioning" + CATEGORY = "model/conditioning/transform" def append(self, conditioning, mask, set_cond_area, strength): set_area_to_bounds = False @@ -257,7 +255,7 @@ class ConditioningZeroOut: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "zero_out" - CATEGORY = "advanced/conditioning" + CATEGORY = "model/conditioning/transform" def zero_out(self, conditioning): c = [] @@ -283,11 +281,10 @@ class ConditioningSetTimestepRange: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "set_range" - CATEGORY = "advanced/conditioning" + CATEGORY = "model/conditioning/transform" def set_range(self, conditioning, start, end): - c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start, - "end_percent": end}) + c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start, "end_percent": end}) return (c, ) class VAEDecode: @@ -389,7 +386,7 @@ class VAEEncodeForInpaint: RETURN_TYPES = ("LATENT",) FUNCTION = "encode" - CATEGORY = "model/latent/inpaint" + CATEGORY = "model/latent" def encode(self, vae, pixels, mask, grow_mask_by=6): downscale_ratio = vae.spacial_compression_encode() @@ -438,7 +435,7 @@ class InpaintModelConditioning: RETURN_NAMES = ("positive", "negative", "latent") FUNCTION = "encode" - CATEGORY = "model/conditioning/inpaint" + CATEGORY = "model/conditioning" def encode(self, positive, negative, pixels, vae, mask, noise_mask=True): x = (pixels.shape[1] // 8) * 8 @@ -576,7 +573,7 @@ class CheckpointLoader: RETURN_TYPES = ("MODEL", "CLIP", "VAE") FUNCTION = "load_checkpoint" - CATEGORY = "advanced/loaders" + CATEGORY = "model/loaders" DEPRECATED = True def load_checkpoint(self, config_name, ckpt_name): @@ -622,8 +619,9 @@ class DiffusersLoader: return {"required": {"model_path": (paths,), }} RETURN_TYPES = ("MODEL", "CLIP", "VAE") FUNCTION = "load_checkpoint" + DEPRECATED = True - CATEGORY = "advanced/loaders/deprecated" + CATEGORY = "model/loaders" def load_checkpoint(self, model_path, output_vae=True, output_clip=True): for search_path in folder_paths.get_folder_paths("diffusers"): @@ -949,7 +947,7 @@ class UNETLoader: RETURN_TYPES = ("MODEL",) FUNCTION = "load_unet" - CATEGORY = "advanced/loaders" + CATEGORY = "model/loaders" def load_unet(self, unet_name, weight_dtype): model_options = {} @@ -969,7 +967,7 @@ class CLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ), - "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image", "cogvideox", "lens", "pixeldit"], ), + "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis", "longcat_image", "cogvideox", "lens", "pixeldit", "ideogram4", "boogu"], ), }, "optional": { "device": (["default", "cpu"], {"advanced": True}), @@ -977,9 +975,9 @@ class CLIPLoader: RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" - CATEGORY = "advanced/loaders" + CATEGORY = "model/loaders" - DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B\nlens: gpt-oss-20b\n pixeldit: gemma 2 2B elm" + DESCRIPTION = "Recipes:\nsd: clip-l\nstable cascade: clip-g\nsd3: t5 xxl / clip-g / clip-l\nstable audio: t5 base\nmochi: t5 xxl\ncogvideox: t5 xxl (226-token padding)\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\nhidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B\nlens: gpt-oss-20b\npixeldit: gemma 2 2B elm" def load_clip(self, clip_name, type="stable_diffusion", device="default"): clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION) @@ -1005,9 +1003,9 @@ class DualCLIPLoader: RETURN_TYPES = ("CLIP",) FUNCTION = "load_clip" - CATEGORY = "advanced/loaders" + CATEGORY = "model/loaders" - DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama\nhunyuan_image: qwen2.5vl 7b and byt5 small\nnewbie: gemma-3-4b-it, jina clip v2" + DESCRIPTION = "Recipes:\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama\nhunyuan_image: qwen2.5vl 7b and byt5 small\nnewbie: gemma-3-4b-it, jina clip v2" def load_clip(self, clip_name1, clip_name2, type, device="default"): clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION) @@ -1088,7 +1086,7 @@ class StyleModelApply: RETURN_TYPES = ("CONDITIONING",) FUNCTION = "apply_stylemodel" - CATEGORY = "model/conditioning/style_model" + CATEGORY = "model/conditioning" def apply_stylemodel(self, conditioning, style_model, clip_vision_output, strength, strength_type): cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0) @@ -1518,13 +1516,11 @@ class LatentCrop: class SetLatentNoiseMask: @classmethod def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), - "mask": ("MASK",), - }} + return {"required": { "samples": ("LATENT",), "mask": ("MASK",), }} RETURN_TYPES = ("LATENT",) FUNCTION = "set_mask" - CATEGORY = "model/latent/inpaint" + CATEGORY = "model/latent" def set_mask(self, samples, mask): s = samples.copy() @@ -2045,7 +2041,7 @@ NODE_CLASS_MAPPINGS = { "ImageBatch": ImageBatch, "ImagePadForOutpaint": ImagePadForOutpaint, "EmptyImage": EmptyImage, - "ConditioningAverage": ConditioningAverage , + "ConditioningAverage": ConditioningAverage, "ConditioningCombine": ConditioningCombine, "ConditioningConcat": ConditioningConcat, "ConditioningSetArea": ConditioningSetArea, @@ -2101,6 +2097,7 @@ NODE_DISPLAY_NAME_MAPPINGS = { "LoraLoader": "Load LoRA (Model and CLIP)", "LoraLoaderModelOnly": "Load LoRA", "CLIPLoader": "Load CLIP", + "DualCLIPLoader": "Load CLIP (Dual)", "ControlNetLoader": "Load ControlNet Model", "DiffControlNetLoader": "Load ControlNet Model (diff)", "StyleModelLoader": "Load Style Model", @@ -2108,6 +2105,7 @@ NODE_DISPLAY_NAME_MAPPINGS = { "UNETLoader": "Load Diffusion Model", "unCLIPCheckpointLoader": "Load unCLIP Checkpoint", "GLIGENLoader": "Load GLIGEN Model", + "DiffusersLoader": "Load Diffusers Model (DEPRECATED)", # Conditioning "CLIPVisionEncode": "CLIP Vision Encode", "StyleModelApply": "Apply Style Model", @@ -2115,12 +2113,16 @@ NODE_DISPLAY_NAME_MAPPINGS = { "CLIPSetLastLayer": "CLIP Set Last Layer", "ConditioningCombine": "Conditioning (Combine)", "ConditioningAverage ": "Conditioning (Average)", + "ConditioningAverage": "Conditioning (Average)", "ConditioningConcat": "Conditioning (Concat)", "ConditioningSetArea": "Conditioning (Set Area)", "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)", + "ConditioningSetAreaStrength": "Conditioning (Set Area Strength)", "ConditioningSetMask": "Conditioning (Set Mask)", "ControlNetApply": "Apply ControlNet (DEPRECATED)", "ControlNetApplyAdvanced": "Apply ControlNet", + "GLIGENTextBoxApply": "Apply GLIGEN Text Box", + "ConditioningZeroOut": "Conditioning Zero Out", # Latent "VAEEncodeForInpaint": "VAE Encode (for Inpainting)", "SetLatentNoiseMask": "Set Latent Noise Mask", @@ -2134,7 +2136,7 @@ NODE_DISPLAY_NAME_MAPPINGS = { "LatentUpscaleBy": "Upscale Latent By", "LatentComposite": "Latent Composite", "LatentBlend": "Latent Blend", - "LatentFromBatch" : "Latent From Batch", + "LatentFromBatch" : "Get Latent From Batch", "RepeatLatentBatch": "Repeat Latent Batch", # Image "EmptyImage": "Empty Image", @@ -2295,6 +2297,9 @@ async def init_external_custom_nodes(): Returns: None """ + # TODO: remove at some point when custom nodes don't break. + sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy")) + base_node_names = set(NODE_CLASS_MAPPINGS.keys()) node_paths = folder_paths.get_folder_paths("custom_nodes") node_import_times = [] @@ -2362,6 +2367,7 @@ async def init_builtin_extra_nodes(): "nodes_model_downscale.py", "nodes_images.py", "nodes_video_model.py", + "nodes_ideogram4.py", "nodes_train.py", "nodes_dataset.py", "nodes_sag.py", @@ -2403,6 +2409,7 @@ async def init_builtin_extra_nodes(): "nodes_video.py", "nodes_lumina2.py", "nodes_wan.py", + "nodes_bernini.py", "nodes_lotus.py", "nodes_hunyuan3d.py", "nodes_primitive.py", @@ -2419,6 +2426,7 @@ async def init_builtin_extra_nodes(): "nodes_tcfg.py", "nodes_context_windows.py", "nodes_qwen.py", + "nodes_boogu.py", "nodes_chroma_radiance.py", "nodes_pid.py", "nodes_model_patch.py", @@ -2449,12 +2457,16 @@ async def init_builtin_extra_nodes(): "nodes_rtdetr.py", "nodes_frame_interpolation.py", "nodes_sam3.py", + "nodes_scail.py", "nodes_void.py", "nodes_wandancer.py", "nodes_hidream_o1.py", "nodes_save_3d.py", "nodes_moge.py", "nodes_mediapipe.py", + "nodes_gaussian_splat.py", + "nodes_triposplat.py", + "nodes_depth_anything_3.py", ] import_failed = [] diff --git a/openapi.yaml b/openapi.yaml index f801a39d9..380e4476e 100644 --- a/openapi.yaml +++ b/openapi.yaml @@ -1,11749 +1,5089 @@ -openapi: 3.1.0 -info: - title: ComfyUI API - description: | - API for ComfyUI - A powerful and modular stable diffusion GUI and backend. - - This API allows you to interact with ComfyUI programmatically, including: - - Submitting and managing workflow executions - - Querying node/object information - - Uploading and viewing files - - Managing user settings and data - - Asset management (feature-gated) - - ## Dual-path routing - Every route registered via `self.routes` in the ComfyUI server is available at - both its bare path (e.g. `/prompt`) and an `/api`-prefixed path (e.g. `/api/prompt`). - This spec uses the `/api`-prefixed versions as canonical. - - ## Multi-user mode - When ComfyUI is started with `--multi-user`, the `Comfy-User` header identifies - the active user for settings, userdata, and history isolation. This is **not** a - security mechanism — it is an organisational convenience with no authentication - or authorisation behind it. - version: 1.0.0 - license: - name: GNU General Public License v3.0 - url: https://github.com/comfyanonymous/ComfyUI/blob/master/LICENSE - -servers: - - url: / - description: Default ComfyUI server (typically http://127.0.0.1:8188) - -tags: - - name: prompt - description: Workflow submission and prompt info - - name: queue - description: Queue inspection and management - - name: history - description: Execution history - - name: upload - description: File upload endpoints - - name: view - description: File viewing / download - - name: system - description: System stats and feature flags - - name: node - description: Node / object_info definitions - - name: model - description: Model folder and file listing - - name: user - description: User management (multi-user mode) - - name: userdata - description: Per-user file storage - - name: settings - description: Per-user settings - - name: extensions - description: Frontend extension JS files - - name: subgraph - description: Global subgraph blueprints - - name: internal - description: Internal / debug endpoints - - name: assets - description: Asset management (feature-gated behind enable-assets) - - - name: auth - description: Authentication and session management (cloud-only) - - name: billing - description: Billing, subscriptions, and payment management (cloud-only) - - name: workspace - description: Workspace and team management (cloud-only) - - name: hub - description: "ComfyUI Hub: profiles, shared workflows, and labels (cloud-only)" - - name: workflows - description: Cloud workflow management and versioning (cloud-only) - - name: task - description: Background task management (cloud-only) - - name: runtime-only - description: Operations served exclusively by the cloud runtime with no local equivalent - -paths: - # --------------------------------------------------------------------------- - # WebSocket - # --------------------------------------------------------------------------- - /ws: - get: - operationId: connectWebSocket - tags: [system] - summary: WebSocket connection for real-time updates - description: | - Upgrades to a WebSocket connection that streams execution progress, - node status, and output messages. The server sends an initial `status` - message with the session ID (SID) on connect. - - ## Message types (server → client) - The server sends JSON messages with a `type` field. See the - `x-websocket-messages` list below for the schema of each message type. - parameters: - - name: clientId - in: query - required: false - schema: - type: string - description: Client identifier. If omitted the server assigns one. - responses: - "101": - description: WebSocket upgrade successful - '401': - description: Unauthorized - x-websocket-messages: - - type: status - schema: - $ref: "#/components/schemas/StatusWsMessage" - - type: progress - schema: - $ref: "#/components/schemas/ProgressWsMessage" - - type: progress_text - schema: - $ref: "#/components/schemas/ProgressTextWsMessage" - - type: progress_state - schema: - $ref: "#/components/schemas/ProgressStateWsMessage" - - type: executing - schema: - $ref: "#/components/schemas/ExecutingWsMessage" - - type: executed - schema: - $ref: "#/components/schemas/ExecutedWsMessage" - - type: execution_start - schema: - $ref: "#/components/schemas/ExecutionStartWsMessage" - - type: execution_success - schema: - $ref: "#/components/schemas/ExecutionSuccessWsMessage" - - type: execution_cached - schema: - $ref: "#/components/schemas/ExecutionCachedWsMessage" - - type: execution_interrupted - schema: - $ref: "#/components/schemas/ExecutionInterruptedWsMessage" - - type: execution_error - schema: - $ref: "#/components/schemas/ExecutionErrorWsMessage" - - type: logs - schema: - $ref: "#/components/schemas/LogsWsMessage" - - type: notification - schema: - $ref: "#/components/schemas/NotificationWsMessage" - - type: feature_flags - schema: - $ref: "#/components/schemas/FeatureFlagsWsMessage" - - type: asset_download - schema: - $ref: "#/components/schemas/AssetDownloadWsMessage" - - type: asset_export - schema: - $ref: "#/components/schemas/AssetExportWsMessage" - - # --------------------------------------------------------------------------- - # Prompt - # --------------------------------------------------------------------------- - /api/prompt: - get: - operationId: getPromptInfo - tags: [prompt] - summary: Get queue status - description: Returns how many items remain in the execution queue. - responses: - "200": - description: Queue info - content: - application/json: - schema: - $ref: "#/components/schemas/PromptInfo" - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - post: - operationId: executePrompt - tags: [prompt] - summary: Submit a workflow for execution - description: Submits a workflow for execution. The server validates the graph, assigns a `prompt_id`, and enqueues it. Clients listen on `/ws` for execution progress and output messages. - requestBody: - required: true - content: - application/json: - schema: - $ref: "#/components/schemas/PromptRequest" - responses: - "200": - description: Prompt accepted - content: - application/json: - schema: - $ref: "#/components/schemas/PromptResponse" - "400": - description: Validation or node errors - content: - application/json: - schema: - $ref: "#/components/schemas/PromptErrorResponse" - - '402': - description: Payment required - Insufficient credits - content: - application/json: - schema: - $ref: '#/components/schemas/PromptErrorResponse' - '429': - description: Payment required - User has not paid - content: - application/json: - schema: - $ref: '#/components/schemas/PromptErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/PromptErrorResponse' - '503': - description: Service unavailable - content: - application/json: - schema: - $ref: '#/components/schemas/PromptErrorResponse' - # --------------------------------------------------------------------------- - # Queue - # --------------------------------------------------------------------------- - /api/queue: - get: - operationId: getQueueInfo - tags: [queue] - summary: Get running and pending queue items - description: Returns the server's current execution queue, split into the currently-running prompt and the list of pending prompts. - responses: - "200": - description: Queue contents - content: - application/json: - schema: - $ref: "#/components/schemas/QueueInfo" - '400': - description: Invalid request parameters - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Invalid request parameters - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - post: - operationId: manageQueue - tags: [queue] - summary: Clear or delete items from the queue - description: Mutates the execution queue. Supports clearing all queued prompts or deleting individual prompts by ID. - requestBody: - required: true - content: - application/json: - schema: - $ref: "#/components/schemas/QueueManageRequest" - responses: - "200": - description: Queue updated - content: - application/json: - schema: - $ref: "#/components/schemas/QueueManageResponse" - '400': - description: Invalid request parameters - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/interrupt: - post: - operationId: interruptJob - tags: [queue] - summary: Interrupt current execution - description: Interrupts the prompt that is currently executing. The next queued prompt (if any) will start immediately after. - requestBody: - required: false - content: - application/json: - schema: - type: object - properties: - prompt_id: - type: string - format: uuid - description: "If provided, only interrupts this specific running prompt. Otherwise interrupts all." - responses: - "200": - description: Interrupt signal sent - - '401': - description: Unauthorized - Authentication required - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/free: - post: - operationId: freeMemory - tags: [queue] - summary: Free GPU memory and/or unload models - description: Frees GPU memory by unloading models and/or freeing the resident model cache, controlled by the request flags. - requestBody: - required: false - content: - application/json: - schema: - type: object - properties: - unload_models: - type: boolean - description: Unload all models from VRAM/RAM - free_memory: - type: boolean - description: Run garbage collection and free cached memory - responses: - "200": - description: Memory freed - - # --------------------------------------------------------------------------- - # Jobs - # --------------------------------------------------------------------------- - /api/jobs: - get: - operationId: listJobs - tags: [queue] - summary: List jobs with filtering and pagination - description: Returns a paginated list of completed prompt executions, newest first. - parameters: - - name: status - in: query - schema: - type: string - description: Filter by job status - - name: workflow_id - in: query - schema: - type: string - description: Filter by workflow ID - - name: sort_by - in: query - schema: - type: string - description: Field to sort by - - name: sort_order - in: query - schema: - type: string - enum: [asc, desc] - description: Sort direction - - name: limit - in: query - schema: - type: integer - description: Maximum number of results (default is unlimited/None) - - name: offset - in: query - schema: - type: integer - default: 0 - description: Pagination offset - responses: - "200": - description: Jobs list - content: - application/json: - schema: - type: object - properties: - jobs: - type: array - items: - $ref: "#/components/schemas/JobEntry" - pagination: - $ref: "#/components/schemas/PaginationInfo" - - '401': - description: Unauthorized - Authentication required - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/jobs/{job_id}: - get: - operationId: getJobDetail - tags: [queue] - summary: Get a single job by ID - description: Returns the full record for a single completed prompt execution, including its outputs, status, and metadata. - parameters: - - name: job_id - in: path - description: The job (prompt) ID to fetch. - required: true - schema: - type: string - format: uuid - responses: - "200": - description: Job detail - content: - application/json: - schema: - $ref: "#/components/schemas/JobDetailResponse" - "404": - description: Job not found - - '401': - description: Unauthorized - Authentication required - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '403': - description: Forbidden - Job does not belong to user - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - # --------------------------------------------------------------------------- - # History - # --------------------------------------------------------------------------- - /api/history: - get: - operationId: getPromptHistory - tags: [history] - summary: Get execution history - deprecated: true - description: | - **Deprecated.** Superseded by `GET /api/jobs`, which returns the same - execution records in a paginated, filterable format. Planned for removal - no earlier than a future major release; sunset timeline TBD. - - Returns a dictionary keyed by prompt_id. Each value is a HistoryEntry - containing prompt metadata, outputs, status, and node meta. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - - name: max_items - in: query - schema: - type: integer - description: Maximum number of history entries to return - - name: offset - in: query - schema: - type: integer - description: Pagination offset (number of entries to skip) - responses: - "200": - description: History dictionary keyed by prompt_id - content: - application/json: - schema: - type: object - additionalProperties: - $ref: "#/components/schemas/HistoryEntry" - '404': - description: "Not Found \u2014 use /api/history_v2 instead" - post: - operationId: manageHistory - tags: [history] - summary: Clear or delete history entries - deprecated: true - description: | - **Deprecated.** Superseded by the forthcoming job-management endpoints - under `/api/jobs`. Planned for removal no earlier than a future major - release; sunset timeline TBD. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - requestBody: - required: true - content: - application/json: - schema: - $ref: "#/components/schemas/HistoryManageRequest" - responses: - "200": - description: History updated - - '400': - description: Invalid request parameters - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '401': - description: Unauthorized - Authentication required - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/history/{prompt_id}: - get: - operationId: getHistoryByPromptId - tags: [history] - summary: Get history for a specific prompt - deprecated: true - description: | - **Deprecated.** Superseded by `GET /api/jobs/{job_id}`, which returns - the same execution record. Planned for removal no earlier than a future - major release; sunset timeline TBD. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - - name: prompt_id - in: path - description: The prompt ID to fetch history for. - required: true - schema: - type: string - format: uuid - responses: - "200": - description: Single-entry history dictionary. Returns an empty object `{}` if the prompt_id is not found. - content: - application/json: - schema: - type: object - additionalProperties: - $ref: "#/components/schemas/HistoryEntry" - - '404': - description: "Not Found \u2014 use /api/jobs/{prompt_id} instead" - # --------------------------------------------------------------------------- - # Upload - # --------------------------------------------------------------------------- - /api/upload/image: - post: - operationId: uploadImage - tags: [upload] - summary: Upload an image file - description: Uploads an image file into one of the input/output/temp directories so it can be referenced by workflow nodes. - requestBody: - required: true - content: - multipart/form-data: - schema: - type: object - required: - - image - properties: - image: - type: string - format: binary - description: Image file to upload - type: - type: string - enum: [input, temp, output] - default: input - description: Target directory type - overwrite: - type: string - description: 'Set to "true" to overwrite existing files' - subfolder: - type: string - description: Subfolder within the target directory - responses: - "200": - description: Upload result - content: - application/json: - schema: - $ref: "#/components/schemas/UploadResult" - "400": - description: No file provided or invalid request - - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/upload/mask: - post: - operationId: uploadMask - tags: [upload] - deprecated: true - summary: Upload a mask image (deprecated) - description: | - Deprecated. Clients should composite the mask onto the source image - client-side and upload the resulting image via POST /api/upload/image - instead. This endpoint will continue to function for older clients, - but will not receive new features. - - Uploads a mask image associated with a previously-uploaded reference image. - requestBody: - required: true - content: - multipart/form-data: - schema: - type: object - required: - - image - - original_ref - properties: - image: - type: string - format: binary - description: Mask image (alpha channel is used) - original_ref: - type: object - description: Reference to the original image file - required: - - filename - properties: - filename: - type: string - description: Filename of the original image - additionalProperties: true - type: - type: string - enum: [input, temp, output] - default: input - description: Target directory type - overwrite: - type: string - description: 'Set to "true" to overwrite existing files' - subfolder: - type: string - description: Subfolder within the target directory - responses: - "200": - description: Upload result - content: - application/json: - schema: - $ref: "#/components/schemas/UploadResult" - "400": - description: No file provided or invalid request - - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - # --------------------------------------------------------------------------- - # View - # --------------------------------------------------------------------------- - /api/view: - get: - operationId: viewFile - tags: [view] - summary: View or download a file - description: Serves a file (image, audio, or video) from the input/output/temp directory identified by the query parameters. - parameters: - - name: filename - in: query - required: true - schema: - type: string - description: Name of the file to view - - name: type - in: query - schema: - type: string - enum: [input, output, temp] - default: output - description: Directory type - - name: subfolder - in: query - schema: - type: string - description: Subfolder within the directory - - name: preview - in: query - schema: - type: string - description: Preview format hint (e.g. "webp;90") - - name: channel - in: query - schema: - type: string - enum: [rgba, rgb, a] - description: Channel extraction mode - responses: - "200": - description: File content - content: - image/*: - schema: - type: string - format: binary - video/*: - schema: - type: string - format: binary - audio/*: - schema: - type: string - format: binary - application/octet-stream: - schema: - type: string - format: binary - "404": - description: File not found - - '302': - description: Redirect to GCS signed URL - headers: - Location: - description: Signed URL to access the file in GCS - schema: - type: string - Cache-Control: - description: Cache directive for the redirect response - schema: - type: string - Vary: - description: Headers that affect response caching - schema: - type: string - '400': - description: Invalid request parameters - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/view_metadata/{folder_name}: - get: - operationId: viewMetadata - tags: [view] - summary: Get metadata for a file (e.g. safetensors header) - description: Returns embedded metadata parsed from a file in the given folder — for example, the header of a safetensors model. - parameters: - - name: folder_name - in: path - required: true - schema: - type: string - description: Folder type (output, input, temp, etc.) - - name: filename - in: query - required: true - schema: - type: string - description: Filename to read metadata from - responses: - "200": - description: File metadata - content: - application/json: - schema: - type: object - additionalProperties: true - "404": - description: File or metadata not found - - # --------------------------------------------------------------------------- - # System - # --------------------------------------------------------------------------- - /api/system_stats: - get: - operationId: getSystemStats - tags: [system] - summary: Get system statistics - description: Returns hardware, Python, VRAM, and runtime statistics for the running ComfyUI process. - responses: - "200": - description: System stats - content: - application/json: - schema: - $ref: "#/components/schemas/SystemStatsResponse" - - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/features: - get: - operationId: getFeatures - tags: [system] - summary: Get enabled feature flags - description: Returns a dictionary of feature flag names to their enabled state. Cloud deployments may include additional typed fields alongside the boolean flags. - responses: - "200": - description: Feature flags - content: - application/json: - schema: - type: object - additionalProperties: - type: boolean - properties: - max_upload_size: - type: integer - format: int64 - minimum: 0 - description: "Maximum file upload size in bytes." - free_tier_credits: - type: integer - format: int32 - minimum: 0 - nullable: true - x-runtime: [cloud] - description: "[cloud-only] Credits available to free-tier users. Local ComfyUI returns null." - posthog_api_host: - type: string - format: uri - nullable: true - x-runtime: [cloud] - description: "[cloud-only] PostHog analytics proxy URL for frontend telemetry. Local ComfyUI returns null." - max_concurrent_jobs: - type: integer - format: int32 - minimum: 0 - nullable: true - x-runtime: [cloud] - description: "[cloud-only] Maximum concurrent jobs the authenticated user can run. Local ComfyUI returns null." - workflow_templates_version: - type: string - nullable: true - x-runtime: [cloud] - description: "[cloud-only] Version identifier for the workflow templates bundle. Local ComfyUI returns null." - workflow_templates_source: - type: string - nullable: true - enum: [dynamic_config_override, workflow_templates_version_json] - x-runtime: [cloud] - description: "[cloud-only] How the templates version was resolved. Local ComfyUI returns null." - - # --------------------------------------------------------------------------- - # Node / Object Info - # --------------------------------------------------------------------------- - /api/object_info: - get: - operationId: getNodeInfo - tags: [node] - summary: Get all node definitions - description: | - Returns a dictionary of every registered node class, keyed by class name. - Each value is a NodeInfo object describing inputs, outputs, category, etc. - responses: - "200": - description: All node definitions - content: - application/json: - schema: - type: object - additionalProperties: - $ref: "#/components/schemas/NodeInfo" - - /api/object_info/{node_class}: - get: - operationId: getObjectInfoByClass - tags: [node] - summary: Get a single node definition - description: Returns the `NodeInfo` definition for a single registered node class. - parameters: - - name: node_class - in: path - required: true - schema: - type: string - description: Node class name (e.g. "KSampler") - responses: - "200": - description: Single node definition - content: - application/json: - schema: - type: object - additionalProperties: - $ref: "#/components/schemas/NodeInfo" - "404": - description: Node class not found - - /api/embeddings: - get: - operationId: getEmbeddings - tags: [node] - summary: List available embedding names - description: Returns the list of text-encoder embeddings available on disk. - responses: - "200": - description: Embedding names - content: - application/json: - schema: - type: array - items: - type: string - - # --------------------------------------------------------------------------- - # Models - # --------------------------------------------------------------------------- - /api/models: - get: - operationId: getModelTypes - tags: [model] - summary: List model folder type names - description: Returns an array of model type names (e.g. checkpoints, loras, vae). - responses: - "200": - description: Model type names - content: - application/json: - schema: - type: array - items: - type: string - - '404': - description: "Not Found \u2014 use /api/experiment/models instead" - /api/models/{folder}: - get: - operationId: getModelsByFolder - tags: [model] - summary: List model filenames in a folder - description: Returns the names of model files in the given folder. This endpoint predates `/api/experiment/models/{folder}` and returns names only — prefer the experiment endpoint for new integrations. - parameters: - - name: folder - in: path - required: true - schema: - type: string - description: Model folder type name - responses: - "200": - description: Model filenames - content: - application/json: - schema: - type: array - items: - type: string - "404": - description: Unknown folder type - - /api/experiment/models: - get: - operationId: getModelFolders - tags: [model] - summary: List model folders with paths - description: Returns an array of model folder objects with name and folder paths. - responses: - "200": - description: Model folders - content: - application/json: - schema: - type: array - items: - $ref: "#/components/schemas/ModelFolder" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/experiment/models/{folder}: - get: - operationId: getModelsInFolder - tags: [model] - summary: List model files with metadata - description: Returns the model files in the given folder with richer metadata (path index, mtime, size) than the legacy `/api/models/{folder}` endpoint. - parameters: - - name: folder - in: path - required: true - schema: - type: string - description: Model folder type name - responses: - "200": - description: Model files with metadata - content: - application/json: - schema: - type: array - items: - $ref: "#/components/schemas/ModelFile" - "404": - description: Unknown folder type - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/experiment/models/preview/{folder}/{path_index}/{filename}: - get: - operationId: getModelPreview - tags: [model] - summary: Get model preview image - description: Returns the preview image associated with a model file, if one exists alongside the model on disk. - parameters: - - name: folder - in: path - required: true - schema: - type: string - description: Model folder type name - - name: path_index - in: path - required: true - schema: - type: integer - description: Path index within the folder - - name: filename - in: path - required: true - schema: - type: string - description: Model filename - responses: - "200": - description: Preview image (WebP) - content: - image/webp: - schema: - type: string - format: binary - "404": - description: Preview not found - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - # --------------------------------------------------------------------------- - # Users - # --------------------------------------------------------------------------- - /api/users: - get: - operationId: getUsersInfo - tags: [user] - summary: Get user storage info - description: | - Returns user storage configuration. In single-user mode returns - `{"storage": "server", "migrated": true/false}`. In multi-user mode - returns `{"storage": "server", "users": {"user_id": "user_dir", ...}}`. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - responses: - "200": - description: User info - content: - application/json: - schema: - type: object - properties: - storage: - type: string - description: Storage backend type (always "server") - migrated: - type: boolean - description: Whether migration from browser storage is complete (single-user) - users: - type: object - additionalProperties: - type: string - description: Map of user_id to directory name (multi-user) - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - post: - operationId: createUser - tags: [user] - summary: Create a new user (multi-user mode) - description: Creates a new user entry. Only meaningful when ComfyUI is running in multi-user mode. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - username - properties: - username: - type: string - description: Username for the new user - responses: - "200": - description: Created user ID - content: - application/json: - schema: - type: string - description: The generated user_id - "400": - description: Username already exists or invalid - - # --------------------------------------------------------------------------- - # Userdata - # --------------------------------------------------------------------------- - /api/userdata: - get: - operationId: getUserdata - tags: [userdata] - summary: List files in a userdata directory - description: Lists files in the authenticated user's data directory. Returns either filename strings or full objects depending on the `full_info` query parameter. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - - name: dir - in: query - required: true - schema: - type: string - description: Directory path relative to the user's data folder - - name: recurse - in: query - schema: - type: boolean - description: Recurse into subdirectories - - name: full_info - in: query - schema: - type: boolean - description: Return full file info objects instead of just names - - name: split - in: query - schema: - type: boolean - description: Split paths into directory components - responses: - "200": - description: File listing - content: - application/json: - schema: - $ref: "#/components/schemas/GetUserDataResponseFull" - "404": - description: Directory not found - - '400': - description: Bad request (e.g., invalid filename). - content: - text/plain: - schema: - type: string - '401': - description: Unauthorized. - content: - text/plain: - schema: - type: string - '500': - description: General error - content: - text/plain: - schema: - type: string - /api/v2/userdata: - get: - operationId: listUserdataV2 - tags: [userdata] - summary: List files in userdata (v2 format) - description: Lists files in the authenticated user's data directory using the v2 response shape, which always returns full objects. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - - name: path - in: query - schema: - type: string - description: Directory path relative to user data root - responses: - "200": - description: File listing with metadata - content: - application/json: - schema: - type: array - items: - type: object - properties: - name: - type: string - path: - type: string - type: - type: string - enum: [file, directory] - size: - type: integer - modified: - type: number - description: Unix timestamp - - '404': - description: "Not Found \u2014 use /api/userdata instead" - /api/userdata/{file}: - get: - operationId: getUserdataFile - tags: [userdata] - summary: Read a userdata file - description: Reads the contents of a file from the authenticated user's data directory. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - - name: file - in: path - required: true - schema: - type: string - description: File path relative to user data directory - responses: - "200": - description: File content - content: - application/octet-stream: - schema: - type: string - format: binary - "404": - description: File not found - '400': - description: Bad request (e.g., invalid filename). - content: - text/plain: - schema: - type: string - '401': - description: Unauthorized. - content: - text/plain: - schema: - type: string - '500': - description: General error - content: - text/plain: - schema: - type: string - post: - operationId: postUserdataFile - tags: [userdata] - summary: Write or create a userdata file - description: Writes (creates or replaces) a file in the authenticated user's data directory. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - - name: file - in: path - required: true - schema: - type: string - description: File path relative to user data directory - - name: overwrite - in: query - schema: - type: boolean - description: Allow overwriting existing files - - name: full_info - in: query - schema: - type: boolean - description: Return full file info in response - requestBody: - required: true - content: - application/octet-stream: - schema: - type: string - format: binary - application/json: - schema: {} - responses: - "200": - description: File written - content: - application/json: - schema: - $ref: "#/components/schemas/UserDataResponseFull" - "409": - description: File exists and overwrite not set - '400': - description: Missing or invalid 'file' parameter. - content: - text/plain: - schema: - type: string - '401': - description: Unauthorized. - content: - text/plain: - schema: - type: string - '403': - description: The requested path is not allowed. - content: - text/plain: - schema: - type: string - '500': - description: General error - content: - text/plain: - schema: - type: string - delete: - operationId: deleteUserdataFile - tags: [userdata] - summary: Delete a userdata file - description: Deletes a file from the authenticated user's data directory. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - - name: file - in: path - required: true - schema: - type: string - description: File path relative to user data directory - responses: - "204": - description: File deleted - "404": - description: File not found - - '401': - description: Unauthorized. - content: - text/plain: - schema: - type: string - '500': - description: Internal server error. - content: - text/plain: - schema: - type: string - /api/userdata/{file}/move/{dest}: - post: - operationId: moveUserdataFile - tags: [userdata] - summary: Move or rename a userdata file - description: Renames or moves a file within the authenticated user's data directory. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - - name: file - in: path - required: true - schema: - type: string - description: Source file path - - name: dest - in: path - required: true - schema: - type: string - description: Destination file path - - name: overwrite - in: query - schema: - type: boolean - description: Allow overwriting at destination - - name: full_info - in: query - schema: - type: boolean - description: Return full file info in response - responses: - "200": - description: File moved - content: - application/json: - schema: - $ref: "#/components/schemas/UserDataResponseFull" - "404": - description: Source file not found - "409": - description: Destination exists and overwrite not set - - '400': - description: Missing or invalid parameters. - content: - text/plain: - schema: - type: string - '401': - description: Unauthorized. - content: - text/plain: - schema: - type: string - '500': - description: General error - content: - text/plain: - schema: - type: string - # --------------------------------------------------------------------------- - # Settings - # --------------------------------------------------------------------------- - /api/settings: - get: - operationId: getAllSettings - tags: [settings] - summary: Get all user settings - description: Returns all settings for the authenticated user. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - responses: - "200": - description: Settings object - content: - application/json: - schema: - type: object - additionalProperties: true - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - post: - operationId: updateMultipleSettings - tags: [settings] - summary: Update user settings (partial merge) - description: Replaces the authenticated user's settings with the provided object. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - requestBody: - required: true - content: - application/json: - schema: - type: object - additionalProperties: true - description: Partial settings to merge - responses: - "200": - description: Settings updated - - '400': - description: Invalid request - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/settings/{id}: - get: - operationId: getSettingById - tags: [settings] - summary: Get a single setting by key - description: Returns the value of a single setting, identified by key. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - - name: id - in: path - required: true - schema: - type: string - description: Setting key - responses: - "200": - description: Setting value (null if the setting does not exist) - content: - application/json: - schema: - nullable: true - description: The setting value (any JSON type), or null if not set - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '404': - description: Setting not found - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - post: - operationId: updateSettingById - tags: [settings] - summary: Set a single setting value - description: Sets the value of a single setting, identified by key. - parameters: - - $ref: "#/components/parameters/ComfyUserHeader" - - name: id - in: path - required: true - schema: - type: string - description: Setting key - requestBody: - required: true - content: - application/json: - schema: - description: The setting value (any JSON type) - responses: - "200": - description: Setting updated - - '400': - description: Invalid request - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - # --------------------------------------------------------------------------- - # Extensions / Templates / i18n - # --------------------------------------------------------------------------- - /api/extensions: - get: - operationId: getExtensions - tags: [extensions] - summary: List frontend extension JS file paths - description: Returns the list of frontend extension JS URLs registered by custom nodes, to be loaded by the frontend on startup. - responses: - "200": - description: Array of JS file paths - content: - application/json: - schema: - type: array - items: - type: string - description: Relative path to extension JS file - - /api/workflow_templates: - get: - operationId: getWorkflowTemplates - tags: [extensions] - summary: Get workflow template mappings - description: Returns a map of custom node names to their provided workflow template names. - responses: - "200": - description: Template mappings - content: - application/json: - schema: - type: object - additionalProperties: - type: array - items: - type: string - description: Map of node pack name to array of template names - - /api/i18n: - get: - operationId: getI18n - tags: [extensions] - summary: Get internationalisation translation strings - description: Returns the URLs of translation files contributed by custom nodes, keyed by locale. - responses: - "200": - description: Translation map - content: - application/json: - schema: - type: object - additionalProperties: true - description: Nested map of locale to translation key-value pairs - - # --------------------------------------------------------------------------- - # Subgraphs - # --------------------------------------------------------------------------- - /api/global_subgraphs: - get: - operationId: getGlobalSubgraphs - tags: [subgraph] - summary: List global subgraph blueprints - description: Returns a dictionary of subgraph IDs to their metadata. - responses: - "200": - description: Subgraph metadata dictionary - content: - application/json: - schema: - type: object - additionalProperties: - $ref: "#/components/schemas/GlobalSubgraphInfo" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/global_subgraphs/{id}: - get: - operationId: getGlobalSubgraph - tags: [subgraph] - summary: Get a global subgraph with full data - description: Returns the blueprint for a globally-registered subgraph, used by the frontend to materialize the subgraph node. - parameters: - - name: id - in: path - required: true - schema: - type: string - description: Subgraph identifier - responses: - "200": - description: Full subgraph data - content: - application/json: - schema: - $ref: "#/components/schemas/GlobalSubgraphData" - "404": - description: Subgraph not found - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - # --------------------------------------------------------------------------- - # Node Replacements - # --------------------------------------------------------------------------- - /api/node_replacements: - get: - operationId: getNodeReplacements - tags: [node] - summary: Get node replacement mappings - description: | - Returns a dictionary mapping deprecated or replaced node class names - to their replacement node information. - responses: - "200": - description: Replacement mappings - content: - application/json: - schema: - type: object - additionalProperties: true - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - # --------------------------------------------------------------------------- - # Internal (x-internal: true) - # --------------------------------------------------------------------------- - /internal/logs: - get: - operationId: getInternalLogs - tags: [internal] - summary: Get server logs as text - description: Returns structured ComfyUI log entries from the in-memory log buffer. - x-internal: true - responses: - "200": - description: Log text - content: - text/plain: - schema: - type: string - - /internal/logs/raw: - get: - operationId: getInternalLogsRaw - tags: [internal] - summary: Get raw structured log entries - description: Returns the raw ComfyUI log buffer as text, together with metadata about the current size limit. - x-internal: true - responses: - "200": - description: Structured log data - content: - application/json: - schema: - type: object - properties: - entries: - type: array - items: - type: object - properties: - t: - type: number - description: Timestamp - m: - type: string - description: Message - size: - type: object - properties: - cols: - type: integer - rows: - type: integer - - /internal/logs/subscribe: - patch: - operationId: subscribeToLogs - tags: [internal] - summary: Subscribe or unsubscribe a WebSocket client to log streaming - description: Subscribes or unsubscribes the current client from live log streaming over the WebSocket. - x-internal: true - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - clientId - - enabled - properties: - clientId: - type: string - description: WebSocket client ID - enabled: - type: boolean - description: Enable or disable log streaming for this client - responses: - "200": - description: Subscription updated - - /internal/folder_paths: - get: - operationId: getInternalFolderPaths - tags: [internal] - summary: Get configured folder paths - description: Returns the filesystem paths ComfyUI is configured to load models and other assets from, keyed by folder type. - x-internal: true - responses: - "200": - description: Dictionary of folder type to paths - content: - application/json: - schema: - type: object - additionalProperties: - type: array - items: - type: array - items: - type: string - description: Map of folder type name to list of [path, ...] entries - - /internal/files/{directory_type}: - get: - operationId: getFiles - tags: [internal] - summary: List files in a directory type - description: Lists the files present in one of ComfyUI's known directories (input, output, or temp). - x-internal: true - parameters: - - name: directory_type - in: path - required: true - schema: - type: string - description: Directory type (e.g. output, input, temp) - responses: - "200": - description: Array of filenames - content: - application/json: - schema: - type: array - items: - type: string - - '400': - description: Invalid directory type - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - # --------------------------------------------------------------------------- - # Assets (x-feature-gate: enable-assets) - # --------------------------------------------------------------------------- - /api/assets/hash/{hash}: - head: - operationId: checkAssetByHash - tags: [assets] - summary: Check if an asset with the given hash exists - description: Returns 204 if an asset with the given content hash already exists, 404 otherwise. Used by clients to deduplicate uploads before transferring bytes. - x-feature-gate: enable-assets - parameters: - - name: hash - in: path - required: true - schema: - type: string - description: "Blake3 hash of the asset (e.g. blake3:abc123...)" - responses: - "200": - description: Asset exists - "404": - description: No asset with this hash - - '400': - description: Invalid hash format - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/assets: - get: - operationId: listAssets - tags: [assets] - summary: List assets with filtering and pagination - description: Returns a paginated list of assets, optionally filtered by tags, name, or other query parameters. - x-feature-gate: enable-assets - parameters: - - name: limit - in: query - schema: - type: integer - default: 50 - - name: offset - in: query - schema: - type: integer - default: 0 - - name: include_tags - in: query - schema: - type: array - items: - type: string - style: form - explode: true - description: Tags that assets must have (AND logic) - - name: exclude_tags - in: query - schema: - type: array - items: - type: string - style: form - explode: true - description: Tags that assets must not have - - name: name_contains - in: query - schema: - type: string - description: Filter assets whose name contains this substring - - name: metadata_filter - in: query - schema: - type: string - description: JSON-encoded metadata key/value filter - - name: sort - in: query - schema: - type: string - description: Field to sort by - - name: order - in: query - schema: - type: string - enum: [asc, desc] - description: Sort direction - - name: include_public - in: query - schema: - type: boolean - x-runtime: [cloud] - description: "[cloud-only] Include workspace-public assets in addition to the caller's own." - - name: asset_hash - in: query - schema: - type: string - x-runtime: [cloud] - description: "[cloud-only] Filter by exact content hash." - responses: - "200": - description: Asset list - content: - application/json: - schema: - $ref: "#/components/schemas/ListAssetsResponse" - '400': - description: Invalid request parameters - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - post: - operationId: uploadAsset - tags: [assets] - summary: Upload a new asset - description: Uploads a new asset (binary content plus metadata) and registers it in the asset database. - x-feature-gate: enable-assets - requestBody: - required: true - content: - multipart/form-data: - schema: - type: object - required: - - file - properties: - file: - type: string - format: binary - description: Asset file to upload - name: - type: string - description: Display name for the asset - tags: - type: string - description: Comma-separated tags - user_metadata: - type: string - description: JSON-encoded user metadata - hash: - type: string - description: "Blake3 hash of the file content (e.g. blake3:abc123...)" - mime_type: - type: string - description: MIME type of the file (overrides auto-detected type) - preview_id: - type: string - format: uuid - description: ID of an existing asset to use as the preview image - id: - type: string - format: uuid - nullable: true - x-runtime: [cloud] - description: "[cloud-only] Client-supplied asset ID for idempotent creation. If an asset with this ID already exists, the existing asset is returned." - application/json: - schema: - type: object - x-runtime: [cloud] - description: "[cloud-only] URL-based asset upload. Caller supplies a URL instead of a file body; the server fetches the content." - required: - - url - properties: - url: - type: string - format: uri - description: "[cloud-only] URL of the file to import as an asset" - name: - type: string - description: Display name for the asset - tags: - type: string - description: Comma-separated tags - user_metadata: - type: string - description: JSON-encoded user metadata - hash: - type: string - description: "Blake3 hash of the file content (e.g. blake3:abc123...)" - mime_type: - type: string - description: MIME type of the file (overrides auto-detected type) - preview_id: - type: string - format: uuid - description: ID of an existing asset to use as the preview image - id: - type: string - format: uuid - nullable: true - x-runtime: [cloud] - description: "[cloud-only] Client-supplied asset ID for idempotent creation. If an asset with this ID already exists, the existing asset is returned." - responses: - "201": - description: Asset created - content: - application/json: - schema: - $ref: "#/components/schemas/AssetCreated" - - '200': - description: Asset already exists (returned existing asset) - content: - application/json: - schema: - $ref: '#/components/schemas/AssetCreated' - '400': - description: Invalid request (bad file, invalid URL, invalid content type, etc.) - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '403': - description: Source URL requires authentication or access denied - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '404': - description: Source URL not found - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '413': - description: File too large - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '415': - description: Unsupported media type - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '422': - description: Download failed due to network error or timeout - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/assets/from-hash: - post: - operationId: createAssetFromHash - tags: [assets] - summary: Create an asset reference from an existing hash - description: Registers a new asset that references existing content by hash, without re-uploading the bytes. - x-feature-gate: enable-assets - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - hash - - name - properties: - hash: - type: string - description: Blake3 hash of existing content - name: - type: string - description: Display name - tags: - type: array - items: - type: string - user_metadata: - type: object - additionalProperties: true - mime_type: - type: string - nullable: true - x-runtime: [cloud] - description: "[cloud-only] MIME type of the content, so the type is preserved without re-inspecting content. Ignored by local ComfyUI." - responses: - "201": - description: Asset created from hash - content: - application/json: - schema: - $ref: "#/components/schemas/AssetCreated" - - '200': - description: Asset reference already exists (returned existing) - content: - application/json: - schema: - $ref: '#/components/schemas/AssetCreated' - '400': - description: Invalid request (bad hash format, invalid tags, etc.) - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '404': - description: Source asset with given hash not found - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/assets/{id}: - get: - operationId: getAssetById - tags: [assets] - summary: Get asset metadata - description: Returns the metadata for a single asset. - x-feature-gate: enable-assets - parameters: - - name: id - in: path - description: The asset ID. - required: true - schema: - type: string - format: uuid - responses: - "200": - description: Asset metadata - content: - application/json: - schema: - $ref: "#/components/schemas/Asset" - "404": - description: Asset not found - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - put: - operationId: updateAsset - tags: [assets] - summary: Update asset metadata - description: Updates the mutable metadata of an asset (name, tags, etc.). Binary content is immutable. - x-feature-gate: enable-assets - parameters: - - name: id - in: path - description: The asset ID. - required: true - schema: - type: string - format: uuid - requestBody: - required: true - content: - application/json: - schema: - type: object - properties: - name: - type: string - description: New display name for the asset - user_metadata: - type: object - additionalProperties: true - description: Custom user metadata to set - preview_id: - type: string - format: uuid - description: ID of the asset to use as the preview - mime_type: - type: string - nullable: true - x-runtime: [cloud] - description: "[cloud-only] MIME type override when auto-detection was wrong. Ignored by local ComfyUI." - responses: - "200": - description: Asset updated - content: - application/json: - schema: - $ref: "#/components/schemas/AssetUpdated" - '400': - description: Invalid request (no fields provided) - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '404': - description: Asset not found - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - delete: - operationId: deleteAsset - tags: [assets] - summary: Delete an asset - description: Removes an asset entry. Depending on the server configuration, the underlying content may also be deleted. - x-feature-gate: enable-assets - parameters: - - name: id - in: path - description: The asset ID. - required: true - schema: - type: string - format: uuid - - name: delete_content - in: query - schema: - type: boolean - description: Also delete the underlying content file - responses: - "204": - description: Asset deleted - - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '404': - description: Asset not found - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '409': - description: Asset cannot be deleted because it is referenced by another resource (e.g., workflow version) - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/assets/{id}/content: - get: - operationId: getAssetContent - tags: [assets] - summary: Download asset file content - description: Returns the binary content of an asset. Supports range requests. - x-feature-gate: enable-assets - parameters: - - name: id - in: path - description: The asset ID. - required: true - schema: - type: string - format: uuid - responses: - "200": - description: Asset file content - content: - application/octet-stream: - schema: - type: string - format: binary - "404": - description: Asset not found - - /api/assets/{id}/tags: - post: - operationId: addAssetTags - tags: [assets] - summary: Add tags to an asset - description: Adds one or more tags to an asset. - x-feature-gate: enable-assets - parameters: - - name: id - in: path - description: The asset ID. - required: true - schema: - type: string - format: uuid - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - tags - properties: - tags: - type: array - items: - type: string - responses: - "200": - description: Tags added - content: - application/json: - schema: - $ref: "#/components/schemas/TagsModificationResponse" - '400': - description: Invalid request - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '404': - description: Asset not found - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '422': - description: Validation error (e.g., reserved tag) - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - delete: - operationId: removeAssetTags - tags: [assets] - summary: Remove tags from an asset - description: Removes one or more tags from an asset. - x-feature-gate: enable-assets - parameters: - - name: id - in: path - description: The asset ID. - required: true - schema: - type: string - format: uuid - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - tags - properties: - tags: - type: array - items: - type: string - responses: - "200": - description: Tags removed - content: - application/json: - schema: - $ref: "#/components/schemas/TagsModificationResponse" - - '400': - description: Invalid request - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '404': - description: Asset not found - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '422': - description: Validation error (e.g., reserved tag) - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/tags: - get: - operationId: listTags - tags: [assets] - summary: List all known tags with counts - description: Returns the list of all tags known to the asset database, with counts. - x-feature-gate: enable-assets - parameters: - - name: limit - in: query - schema: - type: integer - - name: offset - in: query - schema: - type: integer - - name: search - in: query - schema: - type: string - description: Search term for tag name - responses: - "200": - description: Tag list - content: - application/json: - schema: - $ref: "#/components/schemas/ListTagsResponse" - - '400': - description: Invalid request parameters - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/assets/tags/refine: - get: - operationId: getAssetTagHistogram - tags: [assets] - summary: Get tag counts for assets matching current filters - description: Returns suggested additional tags that would refine a filtered asset query, together with the count of assets each tag would select. - x-feature-gate: enable-assets - parameters: - - name: include_tags - in: query - schema: - type: array - items: - type: string - style: form - explode: true - description: Tags that assets must have (AND logic) - - name: exclude_tags - in: query - schema: - type: array - items: - type: string - style: form - explode: true - description: Tags that assets must not have - - name: name_contains - in: query - schema: - type: string - description: Filter assets whose name contains this substring - - name: metadata_filter - in: query - schema: - type: string - description: JSON-encoded metadata key/value filter - - name: limit - in: query - schema: - type: integer - - name: offset - in: query - schema: - type: integer - - name: sort - in: query - schema: - type: string - description: Field to sort by - - name: order - in: query - schema: - type: string - enum: [asc, desc] - description: Sort direction - responses: - "200": - description: Tag histogram - content: - application/json: - schema: - $ref: "#/components/schemas/AssetTagHistogramResponse" - - '400': - description: Invalid request parameters - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/assets/seed: - post: - operationId: seedAssets - tags: [assets] - summary: Trigger asset scan/seed from filesystem - description: Starts a background job that scans the configured directories and registers any assets not yet present in the asset database. - x-feature-gate: enable-assets - requestBody: - required: false - content: - application/json: - schema: - type: object - properties: - roots: - type: array - items: - type: string - description: Root folder paths to scan (if omitted, scans all) - responses: - "200": - description: Seed started - content: - application/json: - schema: - type: object - properties: - status: - type: string - - /api/assets/seed/status: - get: - operationId: getAssetSeedStatus - tags: [assets] - summary: Get asset scan progress - description: Returns the progress and status of the most recently-started asset seed job. - x-feature-gate: enable-assets - responses: - "200": - description: Scan progress - content: - application/json: - schema: - type: object - additionalProperties: true - description: Scan progress details (files scanned, total, status, etc.) - - /api/assets/seed/cancel: - post: - operationId: cancelAssetSeed - tags: [assets] - summary: Cancel an in-progress asset scan - description: Requests cancellation of the currently-running asset seed job. - x-feature-gate: enable-assets - responses: - "200": - description: Scan cancelled - content: - application/json: - schema: - type: object - properties: - status: - type: string - - /api/assets/prune: - post: - operationId: pruneAssets - tags: [assets] - summary: Mark assets whose backing files no longer exist on disk - description: Starts a background job that removes asset entries whose underlying content no longer exists on disk. - x-feature-gate: enable-assets - responses: - "200": - description: Prune result - content: - application/json: - schema: - type: object - properties: - status: - type: string - marked: - type: integer - description: Number of assets marked as missing - - # =========================================================================== - # Cloud-runtime FE-facing operations - # - # These operations are served by the cloud runtime. The local runtime returns - # 404 for all of these paths. Each operation is tagged x-runtime: [cloud]. - # =========================================================================== - - # --------------------------------------------------------------------------- - # Jobs / prompts (cloud) - # --------------------------------------------------------------------------- - /api/jobs/{job_id}/cancel: - post: - operationId: cancelJob - tags: [queue] - summary: Cancel a running or pending job - description: "[cloud-only] Requests cancellation of a job. If the job is currently executing, execution is interrupted. If it is pending in the queue, it is removed." - x-runtime: [cloud] - parameters: - - name: job_id - in: path - required: true - schema: - type: string - format: uuid - description: The job ID to cancel. - responses: - "200": - description: Cancellation accepted - content: - application/json: - schema: - $ref: "#/components/schemas/JobCancelResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '400': - description: Bad Request - job_id is not a valid UUID (emitted by request validation before the handler runs) - content: - application/json: - schema: - $ref: '#/components/schemas/BindingErrorResponse' - '500': - description: Internal server error - cancellation failed - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/job/{job_id}/status: - get: - operationId: getJobStatus - tags: [queue] - summary: Get status of a cloud job - deprecated: true - description: | - **Deprecated.** This endpoint is superseded by `GET /api/jobs/{job_id}`. - Clients should migrate; the endpoint is retained for backward - compatibility but will be removed in a future release. - x-runtime: [cloud] - parameters: - - name: job_id - in: path - required: true - schema: - type: string - format: uuid - description: The job ID to check status for. - responses: - "200": - description: Job status - content: - application/json: - schema: - $ref: "#/components/schemas/JobStatusResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '403': - description: Forbidden - job belongs to another user - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/prompt/{prompt_id}: - get: - operationId: getCloudPrompt - tags: [prompt] - summary: Get a cloud prompt by ID - description: "[cloud-only] Returns the full prompt record for a cloud-executed prompt, including the submitted workflow graph and execution metadata." - x-runtime: [cloud] - parameters: - - name: prompt_id - in: path - required: true - schema: - type: string - format: uuid - description: The prompt ID to fetch. - responses: - "200": - description: Cloud prompt detail - content: - application/json: - schema: - $ref: "#/components/schemas/CloudPrompt" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - /api/history_v2: - get: - operationId: getHistory - tags: [history] - summary: Get paginated execution history (v2) - deprecated: true - description: | - **Deprecated.** This endpoint is superseded by `GET /api/jobs`. - Clients should migrate; the endpoint is retained for backward - compatibility but will be removed in a future release. - x-runtime: [cloud] - parameters: - - name: limit - in: query - schema: - type: integer - default: 20 - description: Maximum number of results - - name: offset - in: query - schema: - type: integer - default: 0 - description: Pagination offset - - name: status - in: query - schema: - type: string - description: Filter by execution status - responses: - "200": - description: History list - content: - application/json: - schema: - $ref: "#/components/schemas/HistoryResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/history_v2/{prompt_id}: - get: - operationId: getHistoryForPrompt - tags: [history] - summary: Get v2 history for a specific prompt - deprecated: true - description: | - **Deprecated.** This endpoint is superseded by `GET /api/jobs/{prompt_id}`. - Clients should migrate; the endpoint is retained for backward - compatibility but will be removed in a future release. - x-runtime: [cloud] - parameters: - - name: prompt_id - in: path - required: true - schema: - type: string - format: uuid - description: The prompt ID to fetch history for. - responses: - "200": - description: History entry - content: - application/json: - schema: - $ref: "#/components/schemas/HistoryDetailResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/logs: - get: - operationId: getLogs - tags: [system] - summary: Get cloud execution logs - deprecated: true - description: | - **Deprecated.** This endpoint returns a static placeholder response and - provides no real log data. It is retained only to avoid breaking clients - that still call it. Clients should remove their dependency; the endpoint - will be removed in a future release. - x-runtime: [cloud] - parameters: - - name: job_id - in: query - schema: - type: string - description: Filter logs by job ID - - name: limit - in: query - schema: - type: integer - default: 100 - description: Maximum number of log entries - - name: offset - in: query - schema: - type: integer - default: 0 - description: Pagination offset - responses: - "200": - description: Log entries - content: - application/json: - schema: - $ref: "#/components/schemas/LogsResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - # --------------------------------------------------------------------------- - # Assets extensions (cloud) - # --------------------------------------------------------------------------- - /api/assets/download: - post: - operationId: createAssetDownload - tags: [assets] - summary: Download assets to cloud runtime - description: "[cloud-only] Initiates a download of one or more assets to the cloud runtime environment. Returns a task ID for tracking download progress via WebSocket." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - assets - properties: - assets: - type: array - items: - $ref: "#/components/schemas/AssetDownloadRequest" - description: Assets to download - responses: - "202": - description: Download task accepted - content: - application/json: - schema: - type: object - required: - - task_id - - status - properties: - task_id: - type: string - format: uuid - description: ID of the download task; use to poll status. - status: - type: string - enum: [created, running, completed, failed] - description: Current task status (typically `created` on initial creation). - message: - type: string - description: Human-readable task message. - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '200': - description: File already exists in storage - asset created/returned immediately - content: - application/json: - schema: - $ref: '#/components/schemas/AssetCreated' - '422': - description: Validation errors - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/assets/export: - post: - operationId: createAssetExport - tags: [assets] - summary: Export assets as a downloadable archive - description: "[cloud-only] Initiates a bulk export of assets. Returns a task ID for tracking progress via WebSocket. When complete, the export can be downloaded via the exports endpoint." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - properties: - job_ids: - type: array - items: - type: string - description: Job IDs whose associated assets should all be included in the ZIP bundle. - asset_ids: - type: array - items: - type: string - format: uuid - description: Asset IDs to include in the ZIP bundle. Additive to assets associated with provided job IDs. - export_name: - type: string - description: Name for the export archive - naming_strategy: - type: string - enum: [group_by_job_id, preserve, asset_id, group_by_job_time] - default: group_by_job_time - description: "Strategy for naming files in the ZIP: group by job ID, preserve original names, use the asset ID, or group by job creation time." - job_asset_name_filters: - type: object - additionalProperties: - type: array - minItems: 1 - items: - type: string - description: Optional per-job asset name filters. When provided for a job ID, only assets whose name matches one of the listed names are included. - responses: - "202": - description: Export task accepted - content: - application/json: - schema: - type: object - required: - - task_id - - status - properties: - task_id: - type: string - format: uuid - description: ID of the export task; use to poll status. - status: - type: string - enum: [created, running, completed, failed] - description: Current task status (typically `created` on initial creation). - message: - type: string - description: Human-readable task message. - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/assets/exports/{exportName}: - get: - operationId: getAssetExport - tags: [assets] - summary: Download a completed asset export - description: "[cloud-only] Returns the archive file for a completed asset export." - x-runtime: [cloud] - parameters: - - name: exportName - in: path - required: true - schema: - type: string - description: Name of the export to download - responses: - "200": - description: Export archive file - content: - application/zip: - schema: - type: string - format: binary - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '400': - description: Invalid export name - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/assets/from-workflow: - post: - operationId: postAssetsFromWorkflow - tags: [assets] - summary: Create asset records from a workflow execution - description: "[cloud-only] Registers output files from a workflow execution as assets in the asset database." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - prompt_id - properties: - prompt_id: - type: string - format: uuid - description: Prompt ID whose outputs should be registered as assets - tags: - type: array - items: - type: string - description: Tags to apply to the created assets - responses: - "200": - description: Assets created or referenced - content: - application/json: - schema: - type: object - properties: - assets: - type: array - items: - $ref: "#/components/schemas/Asset" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/assets/import: - post: - operationId: importPublishedAssets - tags: [assets] - summary: "[cloud-only] Import published assets into the caller's library" - description: | - [cloud-only] Imports the specified published assets into the caller's asset library. New DB records reference the same storage objects; no file copying occurs. Assets the caller already owns (by hash) are deduplicated. The `id` field on each returned `AssetInfo` is the caller's newly-created private asset ID, not the published asset ID supplied in the request. - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - $ref: "#/components/schemas/ImportPublishedAssetsRequest" - responses: - "200": - description: Successfully imported assets - content: - application/json: - schema: - $ref: "#/components/schemas/ImportPublishedAssetsResponse" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/assets/remote-metadata: - get: - operationId: getRemoteAssetMetadata - tags: [assets] - summary: Fetch metadata for a remote asset URL - description: "[cloud-only] Fetches and returns metadata (content type, size, filename) for a remote URL without downloading the full content." - x-runtime: [cloud] - parameters: - - name: url - in: query - required: true - schema: - type: string - format: uri - description: URL to inspect - responses: - "200": - description: Remote metadata - content: - application/json: - schema: - $ref: "#/components/schemas/AssetMetadataResponse" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '422': - description: Failed to retrieve metadata from source - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - # --------------------------------------------------------------------------- - # Custom nodes / hub (cloud) - # --------------------------------------------------------------------------- - /api/experiment/nodes: - get: - operationId: getNodeInfoSchema - tags: [runtime-only] - summary: Get pre-rendered node info schema - description: "[cloud-only] Returns the static ComfyUI object_info schema, identical for every caller, rendered once at startup with empty model/user-file context. Served by a raw HTTP handler that writes pre-rendered bytes with ETag + Cache-Control validators for RFC 7232 conditional GETs." - x-runtime: [cloud] - parameters: - - name: If-None-Match - in: header - required: false - schema: - type: string - description: Entity tag previously returned by this endpoint. When present and matching, the server returns 304 Not Modified. - responses: - "200": - description: Node info schema - headers: - ETag: - schema: - type: string - description: Entity tag for conditional request validation - Cache-Control: - schema: - type: string - description: Cache directives for the response - content: - application/json: - schema: - type: object - additionalProperties: - $ref: "#/components/schemas/NodeInfo" - "304": - description: Not Modified — returned when the client sends a matching If-None-Match header - post: - operationId: installCloudNode - tags: [node] - summary: Install a custom node package - description: "[cloud-only] Installs a custom node package in the cloud runtime by ID or repository URL." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - id - properties: - id: - type: string - description: Node package ID or repository URL - version: - type: string - description: Specific version to install - responses: - "200": - description: Node installed - content: - application/json: - schema: - $ref: "#/components/schemas/CloudNode" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - /api/experiment/nodes/{id}: - get: - operationId: getNodeByID - tags: [runtime-only] - summary: Get a single node definition by ID - description: "[cloud-only] Returns one node's definition from the pre-indexed object_info schema. Served by a raw HTTP handler that writes pre-rendered bytes with ETag + Cache-Control validators for RFC 7232 conditional GETs." - x-runtime: [cloud] - parameters: - - name: id - in: path - required: true - schema: - type: string - description: Node class identifier - - name: If-None-Match - in: header - required: false - schema: - type: string - description: Entity tag previously returned by this endpoint. When present and matching, the server returns 304 Not Modified. - responses: - "200": - description: Single node definition - headers: - ETag: - schema: - type: string - description: Entity tag for conditional request validation - Cache-Control: - schema: - type: string - description: Cache directives for the response - content: - application/json: - schema: - $ref: "#/components/schemas/NodeInfo" - "304": - description: Not Modified — returned when the client sends a matching If-None-Match header - "404": - description: Node not found - delete: - operationId: uninstallCloudNode - tags: [node] - summary: Uninstall a custom node package - description: "[cloud-only] Removes a custom node package from the cloud runtime." - x-runtime: [cloud] - parameters: - - name: id - in: path - required: true - schema: - type: string - description: Custom node package ID - responses: - "204": - description: Node uninstalled - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - /api/hub/assets/upload-url: - post: - operationId: createHubAssetUploadUrl - tags: [hub] - summary: Get a pre-signed upload URL for a hub asset - description: "[cloud-only] Returns a pre-signed URL that can be used to upload an asset file directly to storage." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - filename - - content_type - properties: - filename: - type: string - description: Name of the file to upload - content_type: - type: string - description: MIME type of the file - size: - type: integer - format: int64 - description: File size in bytes - responses: - "200": - description: Upload URL - content: - application/json: - schema: - type: object - properties: - upload_url: - type: string - format: uri - description: Pre-signed upload URL - asset_url: - type: string - format: uri - description: Public URL after upload completes - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '404': - description: Not found - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/hub/labels: - get: - operationId: listHubLabels - tags: [hub] - summary: List available hub labels - description: "[cloud-only] Returns the list of labels/categories available for tagging hub content." - x-runtime: [cloud] - responses: - "200": - description: Label list - content: - application/json: - schema: - $ref: "#/components/schemas/HubLabelListResponse" - '400': - description: Bad request (e.g. invalid type parameter) - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/hub/profiles: - get: - operationId: listHubProfiles - tags: [hub] - summary: List hub user profiles - description: "[cloud-only] Returns a paginated list of public hub user profiles." - x-runtime: [cloud] - parameters: - - name: limit - in: query - schema: - type: integer - description: Maximum number of results - - name: offset - in: query - schema: - type: integer - description: Pagination offset - - name: search - in: query - schema: - type: string - description: Search by username or display name - responses: - "200": - description: Profile list - content: - application/json: - schema: - type: object - properties: - profiles: - type: array - items: - $ref: "#/components/schemas/HubProfile" - total: - type: integer - has_more: - type: boolean - post: - operationId: createHubProfile - tags: [hub] - summary: Create a Hub profile - description: "[cloud-only] Creates a hub profile for the specified workspace. Username is immutable after creation." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - $ref: "#/components/schemas/CreateHubProfileRequest" - responses: - "201": - description: Hub profile created - content: - application/json: - schema: - $ref: "#/components/schemas/HubProfile" - "400": - description: Bad request (e.g. invalid username) - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "409": - description: Username already taken or profile already exists - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/hub/profiles/{username}: - get: - operationId: getHubProfile - tags: [hub] - summary: Get a hub profile by username - description: "[cloud-only] Returns the public hub profile for the given username." - x-runtime: [cloud] - parameters: - - name: username - in: path - required: true - schema: - type: string - description: Hub username - responses: - "200": - description: Profile - content: - application/json: - schema: - $ref: "#/components/schemas/HubProfile" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/hub/profiles/check: - get: - operationId: checkHubUsername - tags: [hub] - summary: Check if a hub username is available - description: "[cloud-only] Returns whether the given username is available for registration." - x-runtime: [cloud] - parameters: - - name: username - in: query - required: true - schema: - type: string - description: Username to check - responses: - "200": - description: Availability result - content: - application/json: - schema: - type: object - properties: - available: - type: boolean - username: - type: string - - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '404': - description: Not found - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/hub/profiles/me: - get: - operationId: getMyHubProfile - tags: [hub] - summary: Get the authenticated user's hub profile - description: "[cloud-only] Returns the hub profile of the currently authenticated user." - x-runtime: [cloud] - responses: - "200": - description: Profile - content: - application/json: - schema: - $ref: "#/components/schemas/HubProfile" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '404': - description: No hub profile exists - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - put: - operationId: updateMyHubProfile - tags: [hub] - summary: Update the authenticated user's hub profile - description: "[cloud-only] Updates the hub profile of the currently authenticated user." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - properties: - username: - type: string - display_name: - type: string - bio: - type: string - avatar_url: - type: string - format: uri - links: - type: array - items: - type: string - format: uri - responses: - "200": - description: Updated profile - content: - application/json: - schema: - $ref: "#/components/schemas/HubProfile" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "409": - description: Conflict - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - /api/hub/workflows: - get: - operationId: listHubWorkflows - tags: [hub] - summary: List published hub workflows - description: "[cloud-only] Returns a paginated list of publicly shared workflows on the hub." - x-runtime: [cloud] - parameters: - - name: limit - in: query - schema: - type: integer - description: Maximum number of results - - name: offset - in: query - schema: - type: integer - description: Pagination offset - - name: sort - in: query - schema: - type: string - description: Sort field (e.g. created_at, likes) - - name: order - in: query - schema: - type: string - enum: [asc, desc] - description: Sort direction - - name: search - in: query - schema: - type: string - description: Search by title or description - - name: labels - in: query - schema: - type: string - description: Filter by label IDs (comma-separated) - responses: - "200": - description: Hub workflow list - content: - application/json: - schema: - $ref: "#/components/schemas/HubWorkflowListResponse" - '400': - description: Bad request (e.g. malformed pagination cursor) - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '404': - description: Profile not found (when filtering by username) - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - post: - operationId: publishHubWorkflow - tags: [hub] - summary: Publish a workflow to the hub - description: "[cloud-only] Publishes a workflow to the hub with metadata, thumbnail, and sample images." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - $ref: "#/components/schemas/PublishHubWorkflowRequest" - responses: - "200": - description: Workflow published to hub - content: - application/json: - schema: - $ref: "#/components/schemas/HubWorkflowDetail" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Workflow or profile not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/hub/workflows/{share_id}: - get: - operationId: getHubWorkflow - tags: [hub] - summary: Get a published hub workflow by share ID - description: "[cloud-only] Returns the full details of a published workflow on the hub." - x-runtime: [cloud] - parameters: - - name: share_id - in: path - required: true - schema: - type: string - description: Workflow share ID - responses: - "200": - description: Hub workflow - content: - application/json: - schema: - $ref: "#/components/schemas/HubWorkflowDetail" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '413': - description: Workflow JSON too large - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - delete: - operationId: deleteHubWorkflow - tags: [hub] - summary: Unpublish a workflow from the hub - description: "[cloud-only] Removes a workflow from the hub listing." - x-runtime: [cloud] - parameters: - - name: share_id - in: path - required: true - schema: - type: string - description: Workflow share ID - responses: - "204": - description: Successfully unpublished - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Workflow not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/hub/workflows/index: - get: - operationId: listHubWorkflowIndex - tags: [hub] - summary: Get the hub workflow index - description: "[cloud-only] Returns the lightweight index of all hub workflows for client-side search and navigation." - x-runtime: [cloud] - responses: - "200": - description: Workflow index - content: - application/json: - schema: - type: array - items: - $ref: "#/components/schemas/HubWorkflowIndexEntry" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - # --------------------------------------------------------------------------- - # Workflows (cloud) - # --------------------------------------------------------------------------- - /api/workflows: - get: - operationId: listWorkflows - tags: [workflows] - summary: List cloud workflows - description: "[cloud-only] Returns a paginated list of the authenticated user's cloud workflows." - x-runtime: [cloud] - parameters: - - name: limit - in: query - schema: - type: integer - description: Maximum number of results - - name: offset - in: query - schema: - type: integer - description: Pagination offset - - name: sort - in: query - schema: - type: string - description: Sort field - - name: order - in: query - schema: - type: string - enum: [asc, desc] - description: Sort direction - - name: search - in: query - schema: - type: string - description: Search by workflow name - responses: - "200": - description: Workflow list - content: - application/json: - schema: - $ref: "#/components/schemas/WorkflowListResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - post: - operationId: createWorkflow - tags: [workflows] - summary: Create a new cloud workflow - description: "[cloud-only] Creates a new cloud workflow with the provided name and optional initial content." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - name - properties: - name: - type: string - description: Workflow name - description: - type: string - description: Workflow description - content: - type: object - additionalProperties: true - description: Initial workflow graph JSON - responses: - "201": - description: Workflow created - content: - application/json: - schema: - $ref: "#/components/schemas/WorkflowResponse" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '422': - description: Validation error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/workflows/{workflow_id}: - get: - operationId: getWorkflow - tags: [workflows] - summary: Get a cloud workflow by ID - description: "[cloud-only] Returns the metadata for a cloud workflow." - x-runtime: [cloud] - parameters: - - name: workflow_id - in: path - required: true - schema: - type: string - format: uuid - description: The workflow ID. - responses: - "200": - description: Workflow detail - content: - application/json: - schema: - $ref: "#/components/schemas/WorkflowResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '403': - description: Forbidden - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - patch: - operationId: updateWorkflow - tags: [workflows] - summary: Update a cloud workflow - description: "[cloud-only] Updates the metadata (name, description) of an existing cloud workflow." - x-runtime: [cloud] - parameters: - - name: workflow_id - in: path - required: true - schema: - type: string - format: uuid - description: The workflow ID. - requestBody: - required: true - content: - application/json: - schema: - type: object - properties: - name: - type: string - description: - type: string - responses: - "200": - description: Workflow updated - content: - application/json: - schema: - $ref: "#/components/schemas/WorkflowResponse" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '422': - description: Validation error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - delete: - operationId: deleteWorkflow - tags: [workflows] - summary: Delete a cloud workflow - description: "[cloud-only] Deletes a cloud workflow and all its versions." - x-runtime: [cloud] - parameters: - - name: workflow_id - in: path - required: true - schema: - type: string - format: uuid - description: The workflow ID. - responses: - "204": - description: Workflow deleted - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/workflows/{workflow_id}/content: - get: - operationId: getWorkflowContent - tags: [workflows] - summary: Get the content of a cloud workflow - description: "[cloud-only] Returns the full workflow graph JSON for the latest version of a cloud workflow." - x-runtime: [cloud] - parameters: - - name: workflow_id - in: path - required: true - schema: - type: string - format: uuid - description: The workflow ID. - - name: version_id - in: query - schema: - type: string - description: Specific version ID to fetch - responses: - "200": - description: Workflow content - content: - application/json: - schema: - type: object - additionalProperties: true - description: The full workflow graph JSON - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '403': - description: Forbidden - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - put: - operationId: updateCloudWorkflowContent - tags: [workflows] - summary: Update the content of a cloud workflow - description: "[cloud-only] Saves new workflow graph JSON as a new version of the cloud workflow." - x-runtime: [cloud] - parameters: - - name: workflow_id - in: path - required: true - schema: - type: string - format: uuid - description: The workflow ID. - requestBody: - required: true - content: - application/json: - schema: - type: object - additionalProperties: true - description: The workflow graph JSON to save - responses: - "200": - description: Content updated - content: - application/json: - schema: - $ref: "#/components/schemas/CloudWorkflowVersion" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - /api/workflows/{workflow_id}/fork: - post: - operationId: forkWorkflow - tags: [workflows] - summary: Fork a cloud workflow - description: "[cloud-only] Creates a copy of a cloud workflow under the authenticated user's account." - x-runtime: [cloud] - parameters: - - name: workflow_id - in: path - required: true - schema: - type: string - format: uuid - description: The workflow ID to fork. - requestBody: - required: false - content: - application/json: - schema: - type: object - properties: - name: - type: string - description: Name for the forked workflow (defaults to original name) - responses: - "201": - description: Forked workflow - content: - application/json: - schema: - $ref: "#/components/schemas/WorkflowResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '403': - description: Forbidden - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '422': - description: Validation error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/workflows/{workflow_id}/versions: - get: - operationId: listCloudWorkflowVersions - tags: [workflows] - summary: List versions of a cloud workflow - description: "[cloud-only] Returns the version history of a cloud workflow." - x-runtime: [cloud] - parameters: - - name: workflow_id - in: path - required: true - schema: - type: string - format: uuid - description: The workflow ID. - - name: limit - in: query - schema: - type: integer - description: Maximum number of results - - name: offset - in: query - schema: - type: integer - description: Pagination offset - responses: - "200": - description: Version list - content: - application/json: - schema: - type: object - properties: - versions: - type: array - items: - $ref: "#/components/schemas/CloudWorkflowVersion" - total: - type: integer - has_more: - type: boolean - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - post: - operationId: createWorkflowVersion - tags: [workflows] - summary: Create a new cloud workflow version - description: "[cloud-only] Creates a new workflow version with updated workflow JSON. Uses optimistic concurrency via base_version." - x-runtime: [cloud] - parameters: - - name: workflow_id - in: path - required: true - schema: - type: string - format: uuid - description: The workflow ID. - requestBody: - required: true - content: - application/json: - schema: - $ref: "#/components/schemas/CreateWorkflowVersionRequest" - responses: - "201": - description: Version created - content: - application/json: - schema: - $ref: "#/components/schemas/WorkflowVersionResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden — not the workflow owner - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "409": - description: Version conflict — base_version does not match latest - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '422': - description: Validation error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/workflows/published/{share_id}: - get: - operationId: getPublishedWorkflow - tags: [workflows] - summary: Get a published workflow by share ID - description: "[cloud-only] Returns a publicly published cloud workflow by its share identifier." - x-runtime: [cloud] - parameters: - - name: share_id - in: path - required: true - schema: - type: string - description: The workflow share ID. - responses: - "200": - description: Published workflow - content: - application/json: - schema: - $ref: "#/components/schemas/PublishedWorkflowDetail" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '413': - description: Workflow JSON too large - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - # --------------------------------------------------------------------------- - # Auth / session (cloud) - # --------------------------------------------------------------------------- - /api/auth/session: - get: - operationId: getAuthSession - tags: [auth] - summary: Get the current authentication session - description: "[cloud-only] Returns the current session state for the authenticated user, including user identity and active workspace." - x-runtime: [cloud] - responses: - "200": - description: Session info - content: - application/json: - schema: - $ref: "#/components/schemas/AuthSession" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - post: - operationId: createSession - tags: [auth] - summary: Create a session cookie - description: "[cloud-only] Creates a session cookie from the bearer token in the Authorization header. Returns a Set-Cookie header with a secure HttpOnly session cookie. Cookie authentication is not allowed for this endpoint." - x-runtime: [cloud] - responses: - "200": - description: Session created - content: - application/json: - schema: - $ref: "#/components/schemas/CreateSessionResponse" - "400": - description: Bad request — invalid or expired ID token - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - delete: - operationId: deleteSession - tags: [auth] - summary: Delete session cookie (logout) - description: "[cloud-only] Clears the session cookie and optionally revokes the session on the server." - x-runtime: [cloud] - responses: - "200": - description: Session deleted - content: - application/json: - schema: - $ref: "#/components/schemas/DeleteSessionResponse" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/auth/token: - post: - operationId: exchangeToken - tags: [auth] - summary: Exchange credentials for an access token - description: "[cloud-only] Exchanges authentication credentials (e.g. an authorization code) for an access token." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - grant_type - properties: - grant_type: - type: string - enum: [authorization_code, refresh_token] - description: OAuth2 grant type - code: - type: string - description: Authorization code (for authorization_code grant) - refresh_token: - type: string - description: Refresh token (for refresh_token grant) - redirect_uri: - type: string - format: uri - description: Redirect URI used in the authorization request - responses: - "200": - description: Token response - content: - application/json: - schema: - $ref: "#/components/schemas/ExchangeTokenResponse" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '404': - description: Workspace not found or user not a member - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /.well-known/jwks.json: - get: - operationId: getJwks - tags: [auth] - summary: Get JSON Web Key Set - description: "[cloud-only] Returns the JSON Web Key Set (JWKS) used to verify JWTs issued by the cloud authentication service." - x-runtime: [cloud] - responses: - "200": - description: JWKS - content: - application/json: - schema: - $ref: "#/components/schemas/JwksResponse" - - # --------------------------------------------------------------------------- - # OAuth 2.1 / RFC 7591 Dynamic Client Registration (cloud) - # --------------------------------------------------------------------------- - /.well-known/oauth-authorization-server: - get: - operationId: getOAuthAuthorizationServer - tags: [auth] - summary: "[cloud-only] OAuth 2.1 authorization-server metadata (RFC 8414)" - description: "[cloud-only] Public metadata document for OAuth 2.1 clients. Cached 5 minutes." - x-runtime: [cloud] - security: [] - responses: - "200": - description: Authorization-server metadata - content: - application/json: - schema: - $ref: "#/components/schemas/OAuthAuthorizationServerMetadata" - "404": - description: OAuth disabled - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - /.well-known/oauth-protected-resource: - get: - operationId: getOAuthProtectedResource - tags: [auth] - summary: "[cloud-only] OAuth 2.1 protected-resource metadata (RFC 9728)" - description: "[cloud-only] Public metadata describing the currently advertised protected resource. Cached 5 minutes." - x-runtime: [cloud] - security: [] - responses: - "200": - description: Protected-resource metadata - content: - application/json: - schema: - $ref: "#/components/schemas/OAuthProtectedResourceMetadata" - "404": - description: OAuth disabled or no active resource configured - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - /oauth/authorize: - get: - operationId: getOAuthAuthorize - tags: [auth] - summary: "[cloud-only] Begin or resume an OAuth 2.1 authorization request" - description: | - [cloud-only] Two modes: - - **Initial entry** (OAuth params present): validates client/redirect/resource/scopes, persists a server-side authorization-request row, and either redirects (no session / unverified email) to the configured frontend login URL carrying only the opaque `oauth_request_id`, or returns the JSON consent challenge for the frontend to render. - - **Resume** (`oauth_request_id` present): loads the server-side row, fails closed if expired/consumed/unknown, returns the JSON consent challenge. Browser-replayed OAuth params are intentionally ignored. - - The frontend renders the consent UI from the JSON payload and POSTs the user's decision back to this endpoint. - x-runtime: [cloud] - security: [] - parameters: - - { name: response_type, in: query, required: false, schema: { type: string } } - - { name: client_id, in: query, required: false, schema: { type: string } } - - { name: redirect_uri, in: query, required: false, schema: { type: string } } - - { name: scope, in: query, required: false, schema: { type: string } } - - name: state - in: query - required: false - schema: { type: string } - description: | - RFC 6749 §10.12 marks `state` as RECOMMENDED. Cloud hardening makes it REQUIRED on the initial-entry path (omitted only on the resume path where `oauth_request_id` is supplied instead). This parameter is `required: false` at the spec level only because the operation is dual-mode (initial entry vs. resume); the runtime rejects empty `state` on the initial-entry path with a stable `invalid_request` 400. - - { name: code_challenge, in: query, required: false, schema: { type: string } } - - { name: code_challenge_method, in: query, required: false, schema: { type: string } } - - { name: resource, in: query, required: false, schema: { type: string } } - - { name: oauth_request_id, in: query, required: false, schema: { type: string } } - responses: - "200": - description: Consent challenge payload (session present, email verified). Frontend renders the consent UI from this payload and POSTs back to /oauth/authorize. - content: - application/json: - schema: - $ref: "#/components/schemas/OAuthConsentChallenge" - "302": - description: Redirect to login (no session / unverified email) or to registered redirect_uri (pre-validated client error) - headers: - Location: - schema: - type: string - "400": - description: Invalid authorize request (pre-redirect failure — unknown client, redirect mismatch, malformed params) - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: OAuth disabled - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - post: - operationId: postOAuthAuthorize - tags: [auth] - summary: "[cloud-only] Submit OAuth consent decision" - description: | - [cloud-only] JSON-only consent submission. The handler verifies the per-row CSRF token, atomically marks the authorization request consumed (single-use covers both allow and deny paths), then returns the redirect URL the browser must navigate to. The URL contains either `code` + original `state` for allow, or the RFC 6749 §5.2 error and `state` for deny. - - Workspace membership is re-checked at submission time. Consent is persisted keyed by `(user_id, client_id, resource_id, workspace_id)`; broadening the previously approved scope set requires a fresh consent flow. - x-runtime: [cloud] - security: [] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: [oauth_request_id, csrf_token, decision, workspace_id] - properties: - oauth_request_id: { type: string, format: uuid } - csrf_token: { type: string } - decision: { type: string, enum: [allow, deny] } - workspace_id: { type: string } - responses: - "200": - description: Redirect URL for the frontend to navigate to (allow → with code+state; deny → with error+state) - content: - application/json: - schema: - $ref: "#/components/schemas/OAuthAuthorizeRedirectResponse" - "400": - description: Bad request (CSRF mismatch, expired/consumed request, inaccessible workspace) - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Scope broadening on consent re-grant — fresh consent flow required - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: OAuth disabled - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - /oauth/token: - post: - operationId: postOAuthToken - tags: [auth] - summary: "[cloud-only] Exchange authorization code or refresh token for a resource-bound access token" - description: | - [cloud-only] OAuth 2.1 token endpoint (RFC 6749 §3.2). Public clients only — `client_secret` is rejected. - - Two grant types are supported: - - `authorization_code` — exchanges the code minted by `/oauth/authorize` (with PKCE verifier) for an access token + first refresh token. Single-use; reuse fails closed. - - `refresh_token` — rotates the refresh token. Old token immediately invalid; presenting an already-rotated token revokes the entire token family and emits a security metric. - - Both grant types re-validate canonical user state, current workspace membership, and the resource's active flag at every mint. A code or refresh token bound to a deactivated resource fails closed. - - Errors follow RFC 6749 §5.2. Logs never contain raw codes, refresh tokens, or minted tokens. - - Per RFC 6749 §5.1, every 200 and 400 response carries `Cache-Control: no-store` and `Pragma: no-cache` so intermediaries cannot cache token-bearing or state-change-reason responses. - x-runtime: [cloud] - security: [] - requestBody: - required: true - content: - application/x-www-form-urlencoded: - schema: - type: object - required: [grant_type, client_id] - properties: - grant_type: { type: string, enum: [authorization_code, refresh_token] } - client_id: { type: string } - code: { type: string } - redirect_uri: { type: string } - code_verifier: { type: string } - refresh_token: { type: string } - scope: { type: string } - client_secret: { type: string } - responses: - "200": - description: New token pair - headers: - Cache-Control: - schema: - type: string - description: 'Always "no-store" per RFC 6749 §5.1' - Pragma: - schema: - type: string - description: 'Always "no-cache" per RFC 6749 §5.1' - content: - application/json: - schema: - $ref: "#/components/schemas/OAuthTokenResponse" - "400": - description: RFC 6749 §5.2 error - headers: - Cache-Control: - schema: - type: string - description: 'Always "no-store" per RFC 6749 §5.1' - Pragma: - schema: - type: string - description: 'Always "no-cache" per RFC 6749 §5.1' - content: - application/json: - schema: - $ref: "#/components/schemas/OAuthTokenError" - "404": - description: OAuth disabled - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - /oauth/register: - post: - operationId: postOAuthRegister - tags: [auth] - summary: "[cloud-only] Dynamic Client Registration (RFC 7591)" - description: | - [cloud-only] Public, unauthenticated, insert-only RFC 7591 §3.1 client registration. Used by MCP-spec-compliant clients to self-register a public OAuth client without operator involvement. - - Policy: - - - Public clients only — `token_endpoint_auth_method` is forced to `none`. Confidential-client registration is out of scope this phase. - - Server-owned `resource_grants`. Caller-supplied `scope` or `resource_grants` is rejected as `invalid_client_metadata` (would be a privilege-escalation surface). Dynamic clients receive the same scopes the active resource publishes. - - Application-type-aware redirect URI policy. `application_type=native` accepts loopback (`127.0.0.1`, `::1`, `localhost`) and reverse-DNS-shaped custom schemes; `application_type=web` accepts HTTPS to hosts in an operator-controlled allowlist only. `application_type` is REQUIRED on the request — missing or empty rejects with `invalid_client_metadata`. - - Anti-impersonation: reserved client names are rejected from third parties via NFKC-folded compare. - - Generated `client_id` carries a stable prefix to distinguish dynamic from seeded clients in audit logs. - - Cache-Control: `no-store` on every 201 and 400 response (the response carries fresh credentials and rejection reasons). - x-runtime: [cloud] - security: [] - requestBody: - required: true - content: - application/json: - schema: - $ref: "#/components/schemas/OAuthRegisterRequest" - responses: - "201": - description: Registered. Body echoes the metadata RFC 7591 §3.2.1 requires. - headers: - Cache-Control: - schema: - type: string - description: 'Always "no-store"' - Pragma: - schema: - type: string - description: 'Always "no-cache"' - content: - application/json: - schema: - $ref: "#/components/schemas/OAuthRegisterResponse" - "400": - description: RFC 7591 §3.2.2 invalid client metadata - headers: - Cache-Control: - schema: - type: string - description: 'Always "no-store"' - Pragma: - schema: - type: string - description: 'Always "no-cache"' - content: - application/json: - schema: - $ref: "#/components/schemas/OAuthRegisterError" - "404": - description: OAuth disabled - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "503": - description: No active resource is configured — DCR cannot mint a usable client until an active resource row is seeded. - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - # --------------------------------------------------------------------------- - # Billing (cloud) - # --------------------------------------------------------------------------- - /api/billing/balance: - get: - operationId: getBillingBalance - tags: [billing] - summary: Get current credit balance - description: "[cloud-only] Returns the authenticated user's current credit balance and usage summary." - x-runtime: [cloud] - responses: - "200": - description: Balance info - content: - application/json: - schema: - $ref: "#/components/schemas/BillingBalanceResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/billing/events: - get: - operationId: getBillingEvents - tags: [billing] - summary: List billing events - description: "[cloud-only] Returns a paginated list of billing events (charges, credits, refunds) for the authenticated user." - x-runtime: [cloud] - parameters: - - name: limit - in: query - schema: - type: integer - description: Maximum number of results - - name: offset - in: query - schema: - type: integer - description: Pagination offset - - name: type - in: query - schema: - type: string - description: Filter by event type - responses: - "200": - description: Billing events - content: - application/json: - schema: - $ref: "#/components/schemas/BillingEventsResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/billing/ops/{id}: - get: - operationId: getBillingOpStatus - tags: [billing] - summary: Get a billing operation by ID - description: "[cloud-only] Returns details of a specific billing operation." - x-runtime: [cloud] - parameters: - - name: id - in: path - required: true - schema: - type: string - description: The billing operation ID. - responses: - "200": - description: Billing operation - content: - application/json: - schema: - $ref: "#/components/schemas/BillingOpStatusResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/billing/payment-portal: - post: - operationId: getPaymentPortal - tags: [billing] - summary: Create a payment portal session - description: "[cloud-only] Creates a Stripe customer portal session for managing payment methods and invoices. Returns a URL to redirect the user to." - x-runtime: [cloud] - responses: - "200": - description: Portal session - content: - application/json: - schema: - type: object - properties: - url: - type: string - format: uri - description: Stripe portal URL - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '400': - description: Bad request (e.g., missing return_url) - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/billing/plans: - get: - operationId: getBillingPlans - tags: [billing] - summary: List available billing plans - description: "[cloud-only] Returns the list of available subscription plans and their pricing." - x-runtime: [cloud] - responses: - "200": - description: Plan list - content: - application/json: - schema: - type: array - items: - $ref: "#/components/schemas/BillingPlan" - - '401': - description: Unauthorized - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/billing/preview-subscribe: - post: - operationId: previewSubscribe - tags: [billing] - summary: Preview a subscription change - description: "[cloud-only] Returns a preview of what a subscription change would cost, including prorations." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - plan_id - properties: - plan_id: - type: string - description: ID of the plan to preview - responses: - "200": - description: Subscription preview - content: - application/json: - schema: - $ref: "#/components/schemas/PreviewSubscribeResponse" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/billing/status: - get: - operationId: getBillingStatus - tags: [billing] - summary: Get billing status - description: "[cloud-only] Returns the authenticated user's current billing and subscription status." - x-runtime: [cloud] - responses: - "200": - description: Billing status - content: - application/json: - schema: - $ref: "#/components/schemas/BillingStatusResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '404': - description: Workspace not found - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/billing/subscribe: - post: - operationId: subscribe - tags: [billing] - summary: Subscribe to a billing plan - description: "[cloud-only] Creates a new subscription to the specified billing plan." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - plan_id - properties: - plan_id: - type: string - description: ID of the plan to subscribe to - payment_method_id: - type: string - description: Stripe payment method ID - responses: - "200": - description: Subscription created - content: - application/json: - schema: - $ref: "#/components/schemas/SubscribeResponse" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/billing/subscription/cancel: - post: - operationId: cancelSubscription - tags: [billing] - summary: Cancel the active subscription - description: "[cloud-only] Cancels the authenticated user's active subscription. The subscription remains active until the end of the current billing period." - x-runtime: [cloud] - responses: - "200": - description: Subscription cancelled - content: - application/json: - schema: - $ref: "#/components/schemas/CancelSubscriptionResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '400': - description: Invalid request (e.g., no active subscription) - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/billing/subscription/resubscribe: - post: - operationId: resubscribe - tags: [billing] - summary: Resubscribe after cancellation - description: "[cloud-only] Reactivates a subscription that was previously cancelled but has not yet expired." - x-runtime: [cloud] - responses: - "200": - description: Subscription reactivated - content: - application/json: - schema: - $ref: "#/components/schemas/ResubscribeResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '400': - description: Invalid request (e.g., no active subscription, not in cancellation grace period) - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/billing/topup: - post: - operationId: createTopup - tags: [billing] - summary: Purchase additional credits - description: "[cloud-only] Purchases a one-time credit top-up using the user's payment method on file." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - amount - properties: - amount: - type: integer - description: Number of credits to purchase - responses: - "200": - description: Top-up successful - content: - application/json: - schema: - $ref: "#/components/schemas/CreateTopupResponse" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - # --------------------------------------------------------------------------- - # Workspace (cloud) - # --------------------------------------------------------------------------- - /api/workspace/api-keys: - get: - operationId: listWorkspaceAPIKeys - tags: [workspace] - summary: List workspace API keys - description: "[cloud-only] Returns the list of API keys for the current workspace." - x-runtime: [cloud] - responses: - "200": - description: API key list - content: - application/json: - schema: - type: array - items: - $ref: "#/components/schemas/WorkspaceApiKey" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - post: - operationId: createWorkspaceAPIKey - tags: [workspace] - summary: Create a workspace API key - description: "[cloud-only] Creates a new API key for the current workspace." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - name - properties: - name: - type: string - description: Display name for the API key - description: - type: string - description: User-provided description of the key's purpose - maxLength: 5000 - responses: - "201": - description: API key created - content: - application/json: - schema: - $ref: "#/components/schemas/CreateWorkspaceAPIKeyResponse" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '404': - description: Workspace not found - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '422': - description: Validation error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '429': - description: Key limit reached - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/workspace/api-keys/{id}: - delete: - operationId: revokeWorkspaceAPIKey - tags: [workspace] - summary: Delete a workspace API key - description: "[cloud-only] Revokes and deletes a workspace API key." - x-runtime: [cloud] - parameters: - - name: id - in: path - required: true - schema: - type: string - description: The API key ID. - responses: - "204": - description: API key deleted - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/workspace/invites: - get: - operationId: listWorkspaceInvites - tags: [workspace] - summary: List pending workspace invites - description: "[cloud-only] Returns the list of pending invitations for the current workspace." - x-runtime: [cloud] - responses: - "200": - description: Invite list - content: - application/json: - schema: - type: array - items: - $ref: "#/components/schemas/WorkspaceInvite" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - post: - operationId: createWorkspaceInvite - tags: [workspace] - summary: Invite a user to the workspace - description: "[cloud-only] Creates an invitation for a user to join the current workspace." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - email - properties: - email: - type: string - format: email - description: Email address to invite - role: - type: string - enum: [admin, member] - description: Role to assign - responses: - "201": - description: Invite created - content: - application/json: - schema: - $ref: "#/components/schemas/PendingInvite" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "409": - description: Conflict - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '404': - description: Workspace not found - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '422': - description: Validation error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/workspace/invites/{inviteId}: - delete: - operationId: revokeWorkspaceInvite - tags: [workspace] - summary: Cancel a workspace invite - description: "[cloud-only] Cancels a pending workspace invitation." - x-runtime: [cloud] - parameters: - - name: inviteId - in: path - required: true - schema: - type: string - description: The invite ID. - responses: - "204": - description: Invite cancelled - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/workspace/leave: - post: - operationId: leaveWorkspace - tags: [workspace] - summary: Leave the current workspace - description: "[cloud-only] Removes the authenticated user from the current workspace." - x-runtime: [cloud] - responses: - "204": - description: Left workspace - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '404': - description: Workspace not found or not a member - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/workspace/members: - get: - operationId: listWorkspaceMembers - tags: [workspace] - summary: List workspace members - description: "[cloud-only] Returns the list of members in the current workspace." - x-runtime: [cloud] - responses: - "200": - description: Member list - content: - application/json: - schema: - type: array - items: - $ref: "#/components/schemas/WorkspaceMember" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '404': - description: Workspace not found - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '422': - description: Validation error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/workspace/members/{user_id}/api-keys: - get: - operationId: listMemberApiKeys - tags: [workspace] - summary: List API keys for a workspace member - description: "[cloud-only] Returns the API keys belonging to a specific workspace member. Requires admin role." - x-runtime: [cloud] - parameters: - - name: user_id - in: path - required: true - schema: - type: string - description: The member's user ID. - responses: - "200": - description: API key list - content: - application/json: - schema: - type: array - items: - $ref: "#/components/schemas/WorkspaceApiKey" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - delete: - operationId: bulkRevokeWorkspaceMemberAPIKeys - tags: [workspace] - summary: Bulk revoke a member's API keys - description: "[cloud-only] Revokes all active API keys for a specific workspace member. Only workspace owners can perform this action." - x-runtime: [cloud] - parameters: - - name: user_id - in: path - required: true - schema: - type: string - minLength: 1 - description: The member's user ID. - responses: - "200": - description: Keys revoked - content: - application/json: - schema: - $ref: "#/components/schemas/BulkRevokeAPIKeysResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden — must be workspace owner - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '422': - description: Validation error (e.g. empty user_id) - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/workspace/members/{userId}: - patch: - operationId: updateWorkspaceMember - tags: [workspace] - summary: Update a workspace member's role - description: "[cloud-only] Updates the role of a workspace member. Requires admin role." - x-runtime: [cloud] - parameters: - - name: userId - in: path - required: true - schema: - type: string - description: The member's user ID. - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - role - properties: - role: - type: string - enum: [admin, member] - description: New role to assign - responses: - "200": - description: Member updated - content: - application/json: - schema: - $ref: "#/components/schemas/WorkspaceMember" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - delete: - operationId: removeWorkspaceMember - tags: [workspace] - summary: Remove a member from the workspace - description: "[cloud-only] Removes a member from the current workspace. Requires admin role." - x-runtime: [cloud] - parameters: - - name: userId - in: path - required: true - schema: - type: string - description: The member's user ID. - responses: - "204": - description: Member removed - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/workspaces: - get: - operationId: listWorkspaces - tags: [workspace] - summary: List workspaces the user belongs to - description: "[cloud-only] Returns the list of workspaces the authenticated user is a member of." - x-runtime: [cloud] - responses: - "200": - description: Workspace list - content: - application/json: - schema: - type: array - items: - $ref: "#/components/schemas/Workspace" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '404': - description: Feature not enabled for user - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - post: - operationId: createWorkspace - tags: [workspace] - summary: Create a new workspace - description: "[cloud-only] Creates a new workspace. The authenticated user becomes the owner." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - name - properties: - name: - type: string - description: Workspace name - responses: - "201": - description: Workspace created - content: - application/json: - schema: - $ref: "#/components/schemas/Workspace" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '404': - description: Feature not enabled for user - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '422': - description: Validation error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/workspaces/{id}: - get: - operationId: getWorkspace - tags: [workspace] - summary: Get a workspace by ID - description: "[cloud-only] Returns details of a workspace the user is a member of." - x-runtime: [cloud] - parameters: - - name: id - in: path - required: true - schema: - type: string - description: The workspace ID. - responses: - "200": - description: Workspace detail - content: - application/json: - schema: - $ref: "#/components/schemas/Workspace" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - patch: - operationId: updateWorkspace - tags: [workspace] - summary: Update workspace settings - description: "[cloud-only] Updates the name or settings of a workspace. Requires admin role." - x-runtime: [cloud] - parameters: - - name: id - in: path - required: true - schema: - type: string - description: The workspace ID. - requestBody: - required: true - content: - application/json: - schema: - type: object - properties: - name: - type: string - description: New workspace name - responses: - "200": - description: Workspace updated - content: - application/json: - schema: - $ref: "#/components/schemas/Workspace" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '422': - description: Validation error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - delete: - operationId: deleteWorkspace - tags: [workspace] - summary: Delete a workspace - description: "[cloud-only] Soft-deletes a workspace. Requires owner role. Personal workspaces cannot be deleted." - x-runtime: [cloud] - parameters: - - name: id - in: path - required: true - schema: - type: string - description: The workspace ID. - responses: - "204": - description: Workspace deleted - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "403": - description: Forbidden — must be workspace owner - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - # --------------------------------------------------------------------------- - # User / settings / misc (cloud) - # --------------------------------------------------------------------------- - /api/feedback: - post: - operationId: submitFeedback - tags: [user] - summary: Submit user feedback - description: "[cloud-only] Submits feedback from the user about their experience with the cloud runtime." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - $ref: "#/components/schemas/FeedbackRequest" - responses: - "201": - description: Feedback submitted - content: - application/json: - schema: - type: object - properties: - id: - type: string - status: - type: string - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/files/mask-layers: - get: - operationId: getMaskLayers - tags: [assets] - summary: Get related mask layer filenames - description: "[cloud-only] Given a mask file (any of the 4 layers), returns all related mask layer filenames. Used by the mask editor to load the paint, mask, and painted layers when reopening a previously edited mask." - x-runtime: [cloud] - parameters: - - name: filename - in: query - required: true - schema: - type: string - description: Hash filename of any mask layer file - responses: - "200": - description: Related mask layers - content: - application/json: - schema: - type: object - properties: - mask: - type: string - description: Filename of the mask layer - nullable: true - paint: - type: string - description: Filename of the paint strokes layer - nullable: true - painted: - type: string - description: Filename of the painted image layer - nullable: true - painted_masked: - type: string - description: Filename of the final composite layer - nullable: true - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: File not found or not a mask file - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - /api/internal/cloud_analytics: - post: - operationId: postCloudAnalytics - tags: [internal] - summary: Post client analytics events - description: "[cloud-only] Receives analytics events from the frontend for processing by the cloud analytics pipeline." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - events - properties: - events: - type: array - items: - type: object - required: - - event_name - properties: - event_name: - type: string - timestamp: - type: string - format: date-time - properties: - type: object - additionalProperties: true - responses: - "200": - description: Events accepted - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/invites/{token}/accept: - post: - operationId: acceptWorkspaceInvite - tags: [workspace] - summary: Accept a workspace invitation - description: "[cloud-only] Accepts a workspace invitation using the invite token. The authenticated user is added to the workspace." - x-runtime: [cloud] - parameters: - - name: token - in: path - required: true - schema: - type: string - description: The invitation token. - responses: - "200": - description: Invite accepted - content: - application/json: - schema: - $ref: "#/components/schemas/AcceptInviteResponse" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '403': - description: Email does not match invite - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '409': - description: Already a member of this workspace - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/secrets: - get: - operationId: listSecrets - tags: [settings] - summary: List user secrets - description: "[cloud-only] Returns the list of secrets (API keys for third-party services) stored for the authenticated user. Secret values are redacted." - x-runtime: [cloud] - responses: - "200": - description: Secret list - content: - application/json: - schema: - type: array - items: - $ref: "#/components/schemas/SecretMeta" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '503': - description: Service unavailable - feature is disabled - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - post: - operationId: createSecret - tags: [settings] - summary: Create or update a secret - description: "[cloud-only] Stores a new secret or updates an existing one. Secrets are encrypted at rest." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - required: - - name - - value - properties: - name: - type: string - description: Secret name (unique per user) - value: - type: string - description: Secret value - responses: - "201": - description: Secret created - content: - application/json: - schema: - $ref: "#/components/schemas/SecretResponse" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '409': - description: Conflict - secret with this name or provider already exists - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '422': - description: Validation error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '503': - description: Service unavailable - secrets feature disabled - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/secrets/{id}: - get: - operationId: getSecret - tags: [settings] - summary: Get secret metadata - description: "[cloud-only] Returns metadata for a specific secret. Does not return the plaintext secret value." - x-runtime: [cloud] - parameters: - - name: id - in: path - required: true - schema: - type: string - format: uuid - description: The secret ID. - responses: - "200": - description: Secret metadata - content: - application/json: - schema: - $ref: "#/components/schemas/SecretResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '403': - description: Forbidden - user does not own this secret - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '503': - description: Service unavailable - secrets feature disabled - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - patch: - operationId: updateSecret - tags: [settings] - summary: Update a secret - description: "[cloud-only] Updates an existing secret's name and/or value. Both fields are optional; only provided fields are updated." - x-runtime: [cloud] - parameters: - - name: id - in: path - required: true - schema: - type: string - format: uuid - description: The secret ID. - requestBody: - required: true - content: - application/json: - schema: - $ref: "#/components/schemas/UpdateSecretRequest" - responses: - "200": - description: Secret updated - content: - application/json: - schema: - $ref: "#/components/schemas/SecretResponse" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "409": - description: Conflict — a secret with this name already exists - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '403': - description: Forbidden - user does not own this secret - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '503': - description: Service unavailable - secrets feature disabled - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - delete: - operationId: deleteSecret - tags: [settings] - summary: Delete a secret - description: "[cloud-only] Permanently deletes a stored secret." - x-runtime: [cloud] - parameters: - - name: id - in: path - required: true - schema: - type: string - description: The secret ID. - responses: - "204": - description: Secret deleted - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '403': - description: Forbidden - user does not own this secret - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '503': - description: Service unavailable - secrets feature disabled - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/user: - get: - operationId: getUser - tags: [user] - summary: Get the authenticated cloud user - description: "[cloud-only] Returns the profile and account information for the currently authenticated user." - x-runtime: [cloud] - responses: - "200": - description: User profile - content: - application/json: - schema: - $ref: "#/components/schemas/UserResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - put: - operationId: updateCloudUser - tags: [user] - summary: Update the authenticated cloud user profile - description: "[cloud-only] Updates the profile information for the currently authenticated user." - x-runtime: [cloud] - requestBody: - required: true - content: - application/json: - schema: - type: object - properties: - display_name: - type: string - avatar_url: - type: string - format: uri - responses: - "200": - description: Updated profile - content: - application/json: - schema: - $ref: "#/components/schemas/CloudUser" - "400": - description: Bad request - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - /api/userdata/{file}/publish: - get: - operationId: getUserdataFilePublish - tags: [userdata] - summary: Get publish info for a userdata file - description: "[cloud-only] Returns the publish status and share info for a userdata workflow file." - x-runtime: [cloud] - parameters: - - name: file - in: path - required: true - schema: - type: string - description: File path relative to user data directory - responses: - "200": - description: Publish info (publish_time is null if never published) - content: - application/json: - schema: - $ref: "#/components/schemas/WorkflowPublishInfo" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Workflow not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - post: - operationId: postUserdataFilePublish - tags: [userdata] - summary: Publish a userdata file to the cloud - description: "[cloud-only] Makes a userdata file available via a public URL for sharing or embedding." - x-runtime: [cloud] - parameters: - - name: file - in: path - required: true - schema: - type: string - description: File path relative to user data directory - responses: - "200": - description: Published file URL - content: - application/json: - schema: - type: object - properties: - url: - type: string - format: uri - description: Public URL of the published file - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '400': - description: Bad request - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/vhs/queryvideo: - get: - operationId: getVhsQueryVideo - tags: [view] - summary: Query VHS video metadata - description: "[cloud-only] Returns metadata about a video file processed by the VHS (Video Helper Suite) integration." - x-runtime: [cloud] - parameters: - - name: filename - in: query - required: true - schema: - type: string - description: Video filename - - name: type - in: query - schema: - type: string - enum: [input, output, temp] - description: Directory type - - name: subfolder - in: query - schema: - type: string - description: Subfolder within the directory - responses: - "200": - description: Video metadata - content: - application/json: - schema: - type: object - additionalProperties: true - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '400': - description: 'Missing required query parameter. Produced by the oapi-codegen - wrapper via echo.NewHTTPError, so the body shape matches Echo''s - default HTTPError serialization rather than ErrorResponse. - ' - content: - application/json: - schema: - $ref: '#/components/schemas/BindingErrorResponse' - /api/vhs/viewaudio: - get: - operationId: viewVhsAudio - tags: [view] - summary: View or download VHS audio - description: "[cloud-only] Returns audio content from a VHS-processed file." - x-runtime: [cloud] - parameters: - - name: filename - in: query - required: true - schema: - type: string - description: Audio filename - - name: type - in: query - schema: - type: string - enum: [input, output, temp] - description: Directory type - - name: subfolder - in: query - schema: - type: string - description: Subfolder within the directory - responses: - "200": - description: Audio content - content: - audio/*: - schema: - type: string - format: binary - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - /api/vhs/viewvideo: - get: - operationId: viewVhsVideo - tags: [view] - summary: View or download VHS video - description: "[cloud-only] Returns video content from a VHS-processed file." - x-runtime: [cloud] - parameters: - - name: filename - in: query - required: true - schema: - type: string - description: Video filename - - name: type - in: query - schema: - type: string - enum: [input, output, temp] - description: Directory type - - name: subfolder - in: query - schema: - type: string - description: Subfolder within the directory - responses: - "200": - description: Video content - content: - video/*: - schema: - type: string - format: binary - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - /api/viewvideo: - get: - operationId: viewVideo - tags: [view] - summary: View or download a video file - deprecated: true - description: | - **Deprecated.** This endpoint is an alias of `GET /api/view` added for - legacy history-queue video playback. Callers should use `/api/view` - directly; the endpoint is retained for backward compatibility but will - be removed in a future release. - x-runtime: [cloud] - parameters: - - name: filename - in: query - required: true - schema: - type: string - description: Video filename - - name: type - in: query - schema: - type: string - enum: [input, output, temp] - description: Directory type - - name: subfolder - in: query - schema: - type: string - description: Subfolder within the directory - responses: - "200": - description: Video content - content: - video/*: - schema: - type: string - format: binary - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - /api/tasks: - get: - operationId: listTasks - tags: [task] - summary: List background tasks - description: "[cloud-only] Retrieve a paginated list of background tasks for the authenticated user. Supports filtering by task type, status, and creation time." - x-runtime: [cloud] - parameters: - - name: task_name - in: query - schema: - type: string - description: Filter by task type name (exact match). - - name: idempotency_key - in: query - schema: - type: string - description: Filter by idempotency key (exact match). - - name: status - in: query - schema: - type: string - description: Filter by one or more statuses (comma-separated). - - name: created_after - in: query - schema: - type: string - format: date-time - description: Filter tasks created after this timestamp. - - name: created_before - in: query - schema: - type: string - format: date-time - description: Filter tasks created before this timestamp. - - name: sort_order - in: query - schema: - type: string - enum: [asc, desc] - default: desc - description: Sort direction by create_time. - - name: offset - in: query - schema: - type: integer - minimum: 0 - default: 0 - description: Pagination offset (0-based). - - name: limit - in: query - schema: - type: integer - minimum: 1 - maximum: 100 - default: 20 - description: Maximum items per page (1-100). - responses: - "200": - description: Tasks retrieved - content: - application/json: - schema: - $ref: "#/components/schemas/TasksListResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "422": - description: Validation error - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' - /api/tasks/{task_id}: - get: - operationId: getTask - tags: [task] - summary: Get task details - description: "[cloud-only] Retrieve full details for a specific background task." - x-runtime: [cloud] - parameters: - - name: task_id - in: path - required: true - schema: - type: string - format: uuid - description: Task identifier (UUID). - responses: - "200": - description: Task details - content: - application/json: - schema: - $ref: "#/components/schemas/TaskResponse" - "401": - description: Unauthorized - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - "404": - description: Task not found - content: - application/json: - schema: - $ref: "#/components/schemas/CloudError" - - '500': - description: Internal server error - content: - application/json: - schema: - $ref: '#/components/schemas/ErrorResponse' components: - parameters: - ComfyUserHeader: - name: Comfy-User - in: header - required: false - schema: - type: string - description: | - Identifies the active user in multi-user mode. Used for settings, - userdata, and history isolation. This is not a security mechanism — - it is an organisational convenience with no authentication behind it. - - schemas: - # ------------------------------------------------------------------- - # Prompt - # ------------------------------------------------------------------- - PromptRequest: - type: object - description: A workflow submission. Wraps the prompt graph plus optional client identifier and extra per-request data. - required: - - prompt - properties: - prompt: - type: object - description: | - The workflow graph to execute. Keys are node IDs (strings); - values are objects with class_type and inputs. - additionalProperties: true - number: - type: number - description: Priority number for the queue (lower numbers have higher priority) - front: - type: boolean - description: If true, adds the prompt to the front of the queue - extra_data: - type: object - description: Extra data associated with the prompt (e.g. extra_pnginfo) - additionalProperties: true - client_id: - type: string - description: WebSocket client ID to receive progress updates - prompt_id: - type: string - format: uuid - description: "Client-supplied prompt ID. Server generates a UUID if omitted." - partial_execution_targets: - type: array - items: - type: string - description: List of node IDs to execute (partial graph execution) - workflow_id: - type: string - format: uuid - nullable: true - x-runtime: [cloud] - description: "[cloud-only] Cloud workflow entity ID for tracking and gallery association. Ignored by local ComfyUI." - workflow_version_id: - type: string - format: uuid - nullable: true - x-runtime: [cloud] - description: "[cloud-only] Cloud workflow version ID for pinning execution to a specific version. Ignored by local ComfyUI." - - PromptResponse: - type: object - description: Server acknowledgement of a workflow submission. Includes the assigned `prompt_id` and current queue position. - properties: - prompt_id: - type: string - format: uuid - description: Unique identifier for the prompt execution - number: - type: number - description: Priority number in the queue - node_errors: - type: object - description: Validation errors keyed by node ID - additionalProperties: - $ref: "#/components/schemas/NodeError" - error: - description: Top-level prompt error (string message or structured error) - oneOf: - - type: string - - $ref: "#/components/schemas/PromptError" - - PromptErrorResponse: - type: object - description: Error response when prompt validation fails - additionalProperties: true - - PromptError: - type: object - description: Structured prompt validation error - properties: - type: - type: string - message: - type: string - details: - type: string - - Error: - type: object - description: Detailed node-level error - properties: - type: - type: string - message: - type: string - details: - type: string - extra_info: - type: object - properties: - input_name: - type: string - additionalProperties: true - - NodeError: - type: object - description: Error details for a single node - properties: - errors: - type: array - items: - $ref: "#/components/schemas/Error" - class_type: - type: string - description: The node's class type - dependent_outputs: - type: array - items: {} - - PromptInfo: - type: object - description: Summary of a queued or recently-executed prompt, as returned by the queue and history endpoints. - properties: - exec_info: - type: object - properties: - queue_remaining: - type: integer - description: Number of items remaining in the queue - - # ------------------------------------------------------------------- - # Queue - # ------------------------------------------------------------------- - QueueInfo: - type: object - description: Queue information with pending and running items - properties: - queue_running: - type: array - description: Currently running queue items - items: - type: array - description: | - Queue item tuple: [number, prompt_id, prompt, extra_data, outputs_to_execute, sensitive] - items: {} - prefixItems: - - type: number - description: Priority number - - type: string - format: uuid - description: prompt_id - - type: object - description: prompt graph - additionalProperties: true - - type: object - description: extra_data - additionalProperties: true - - type: array - description: outputs_to_execute (list of output node IDs) - items: - type: string - - type: object - description: sensitive data (may be omitted) - additionalProperties: true - queue_pending: - type: array - description: Pending queue items (oldest first) - items: - type: array - description: | - Queue item tuple: [number, prompt_id, prompt, extra_data, outputs_to_execute, sensitive] - items: {} - prefixItems: - - type: number - description: Priority number - - type: string - format: uuid - description: prompt_id - - type: object - description: prompt graph - additionalProperties: true - - type: object - description: extra_data - additionalProperties: true - - type: array - description: outputs_to_execute (list of output node IDs) - items: - type: string - - type: object - description: sensitive data (may be omitted) - additionalProperties: true - - QueueManageRequest: - type: object - description: Request to clear or delete from queue - properties: - clear: - type: boolean - description: If true, clear all pending items - delete: - type: array - items: - type: string - description: Array of prompt IDs to delete from queue - - QueueManageResponse: - type: object - x-runtime: [cloud] - description: >- - [cloud-only] Result of a queue mutation. The Cloud runtime returns which - items were deleted and whether the queue was cleared; local ComfyUI - returns an empty 200 body. - properties: - deleted: - type: array - nullable: true - items: - type: string - description: Prompt IDs that were deleted from the queue. - cleared: - type: boolean - nullable: true - description: Whether the queue was cleared. - - # ------------------------------------------------------------------- - # History - # ------------------------------------------------------------------- - HistoryEntry: - type: object - description: A single execution history entry - properties: - prompt: - type: array - description: | - Prompt tuple: [number, prompt_id, prompt_graph, extra_data, output_node_ids] - items: {} - outputs: - type: object - description: Output data from execution keyed by node ID - additionalProperties: true - status: - type: object - description: Execution status (status_str, completed, messages, etc.) - additionalProperties: true - meta: - type: object - description: Metadata about the execution and nodes - additionalProperties: true - - HistoryManageRequest: - type: object - description: Request to clear or delete history entries - properties: - clear: - type: boolean - description: If true, clear all history - delete: - type: array - items: - type: string - description: Array of prompt IDs to delete from history - - # ------------------------------------------------------------------- - # Jobs - # ------------------------------------------------------------------- - JobEntry: - type: object - description: Lightweight job data for list views - required: - - id - - status - properties: - id: - type: string - format: uuid - description: Unique job identifier (same as prompt_id) - status: - type: string - enum: - - pending - - in_progress - - completed - - failed - - cancelled - description: Current job status - create_time: - type: integer - format: int64 - description: Job creation timestamp (Unix milliseconds). - execution_start_time: - type: integer - format: int64 - description: Workflow execution start timestamp (Unix milliseconds, terminal states only). - execution_end_time: - type: integer - format: int64 - description: Workflow execution end timestamp (Unix milliseconds, terminal states only). - preview_output: - type: object - additionalProperties: true - description: Primary preview output - outputs_count: - type: integer - description: Total number of output files - workflow_id: - type: string - nullable: true - x-runtime: [cloud] - description: "[cloud-only] UUID of the Cloud workflow entity this job is associated with. Local ComfyUI returns null." - execution_error: - x-runtime: [cloud] - description: "[cloud-only] Detailed execution error from ComfyUI for failed jobs. Absent on local ComfyUI." - allOf: - - $ref: "#/components/schemas/ExecutionError" - - JobDetailResponse: - type: object - description: Full job details including workflow and outputs - required: - - id - - status - properties: - id: - type: string - format: uuid - status: - type: string - enum: - - pending - - in_progress - - completed - - failed - - cancelled - workflow: - type: object - additionalProperties: true - description: Full ComfyUI workflow - outputs: - type: object - additionalProperties: true - description: Full outputs object from execution - execution_error: - $ref: "#/components/schemas/ExecutionError" - create_time: - type: integer - format: int64 - description: Job creation timestamp (Unix milliseconds). - update_time: - type: integer - format: int64 - description: Last state-change timestamp (Unix milliseconds). - execution_start_time: - type: integer - format: int64 - description: Workflow execution start timestamp (Unix milliseconds, terminal states only). - execution_end_time: - type: integer - format: int64 - description: Workflow execution end timestamp (Unix milliseconds, terminal states only). - preview_output: - type: object - additionalProperties: true - outputs_count: - type: integer - execution_status: - type: object - additionalProperties: true - execution_meta: - type: object - additionalProperties: true - - ExecutionError: - type: object - description: Detailed execution error from ComfyUI - properties: - node_id: - type: string - description: ID of the node that failed - node_type: - type: string - description: Type name of the node - exception_message: - type: string - description: Human-readable error message - exception_type: - type: string - description: Python exception type - traceback: - type: array - items: - type: string - description: Traceback lines - current_inputs: - type: object - additionalProperties: true - current_outputs: - type: object - additionalProperties: true - - PaginationInfo: - type: object - description: Pagination metadata returned alongside list responses. - properties: - offset: - type: integer - limit: - type: integer - total: - type: integer - has_more: - type: boolean - - # ------------------------------------------------------------------- - # Upload / View - # ------------------------------------------------------------------- - UploadResult: - type: object - description: Response body returned by the image/mask upload endpoints, describing where the uploaded file now lives. - properties: - name: - type: string - description: Saved filename (may be renamed to avoid collisions) - subfolder: - type: string - description: Subfolder the file was saved to - type: - type: string - description: Directory type (input, temp) - - # ------------------------------------------------------------------- - # System - # ------------------------------------------------------------------- - DeviceStats: - type: object - description: GPU/compute device statistics - required: - - name - - type - - index - properties: - name: - type: string - description: Device name - type: - type: string - description: Device type (cuda, mps, cpu, etc.) - index: - type: number - nullable: true - description: | - Device index within its type (e.g. CUDA ordinal for `cuda:0`, - `cuda:1`). `null` for devices with no index, including the CPU - device returned in `--cpu` mode (PyTorch's `torch.device('cpu').index` - is `None`). - vram_total: - type: number - description: Total VRAM in bytes - vram_free: - type: number - description: Free VRAM in bytes - torch_vram_total: - type: number - description: Total PyTorch-managed VRAM in bytes - torch_vram_free: - type: number - description: Free PyTorch-managed VRAM in bytes - - SystemStatsResponse: - type: object - description: Hardware, VRAM, Python, and ComfyUI version information for the running process. - required: - - system - - devices - properties: - system: - type: object - required: - - os - - python_version - - embedded_python - - comfyui_version - - pytorch_version - - argv - - ram_total - - ram_free - properties: - os: - type: string - description: Operating system - python_version: - type: string - description: Python version - embedded_python: - type: boolean - description: Whether using embedded Python - comfyui_version: - type: string - description: ComfyUI version string - pytorch_version: - type: string - description: PyTorch version - required_frontend_version: - type: string - description: Required frontend version - argv: - type: array - items: - type: string - description: Command line arguments - ram_total: - type: number - description: Total RAM in bytes - ram_free: - type: number - description: Free RAM in bytes - installed_templates_version: - type: string - nullable: true - description: Version of the currently installed workflow templates - required_templates_version: - type: string - nullable: true - description: Minimum required workflow templates version for this ComfyUI build - comfy_package_versions: - type: array - description: Installed and required versions for every comfy* package pinned in requirements.txt - items: - type: object - required: - - name - - installed - - required - properties: - name: - type: string - installed: + schemas: + Asset: + description: Represents a user-owned asset (image, video, or other generated output). + properties: + created_at: + description: Timestamp when the asset was created + format: date-time type: string + display_name: + description: Display name of the asset. Mirrors name for backwards compatibility. nullable: true + type: string + file_path: + description: Relative path in global-namespace-root form (e.g. "models/checkpoints/flux.safetensors") + nullable: true + type: string + hash: + description: Blake3 hash of the asset content. + pattern: ^blake3:[a-f0-9]{64}$ + type: string + id: + description: Unique identifier for the asset + format: uuid + type: string + is_immutable: + description: Whether this asset is immutable (cannot be modified or deleted) + type: boolean + job_id: + description: ID of the job that created this asset, if available + format: uuid + nullable: true + type: string + last_access_time: + description: Timestamp when the asset was last accessed + format: date-time + type: string + metadata: + additionalProperties: true + description: System-managed metadata from download sources (HuggingFace, CivitAI, etc.) - read-only, not user-modifiable + readOnly: true + type: object + mime_type: + description: MIME type of the asset + type: string + name: + description: Name of the asset file + type: string + preview_id: + description: ID of the preview asset if available + format: uuid + nullable: true + type: string + preview_url: + description: URL for asset preview/thumbnail + format: uri + type: string + short_url: + description: Durable, owner-gated short link to this asset's content (relative `/api/s/{id}` path). Stable across the underlying signed URL's expiry — resolving it re-mints a fresh signed URL on every request — so it is safe to persist or share into chat, unlike `preview_url`. Only the minting user can resolve it. Omitted when the short-link surface is disabled or the asset has no resolvable content hash. + nullable: true + type: string + x-runtime: + - cloud + size: + description: Size of the asset in bytes + format: int64 + type: integer + tags: + description: Tags associated with the asset + items: + type: string + type: array + updated_at: + description: Timestamp when the asset was last updated + format: date-time + type: string + user_metadata: + additionalProperties: true + description: Custom user metadata for the asset + type: object + required: + - id + - name + - created_at + - updated_at + type: object + AssetCreated: + allOf: + - $ref: '#/components/schemas/Asset' + - properties: + created_new: + description: Whether this was a new asset creation (true) or returned existing (false) + type: boolean required: + - created_new + type: object + description: Response returned when a new asset is successfully created. + AssetInfo: + description: Lightweight asset reference used in workflow publishing payloads. + properties: + id: + description: Asset identifier. type: string + in_library: + description: Whether the caller already owns this asset. + type: boolean + model: + description: Whether this asset is a model. + type: boolean + name: + type: string + preview_url: + description: Signed URL for previewing the asset. + type: string + public: + description: Whether this is a public (platform-provided) asset. + type: boolean + storage_url: + type: string + required: + - id + - name + - preview_url + - storage_url + - model + - public + - in_library + type: object + AssetTagHistogramResponse: + description: Histogram of tag counts used for refining asset search results. + properties: + tag_counts: + additionalProperties: + type: integer + description: Map of tag names to their occurrence counts on matching assets + example: + checkpoint: 32 + lora: 193 + vae: 6 + type: object + required: + - tag_counts + type: object + AssetUpdated: + description: Response returned when an existing asset is successfully updated. + properties: + display_name: + description: Display name of the asset. Mirrors name for backwards compatibility. nullable: true - devices: - type: array - items: - $ref: "#/components/schemas/DeviceStats" - - # ------------------------------------------------------------------- - # Node / Object Info - # ------------------------------------------------------------------- - NodeInfo: - type: object - description: 'Definition of a registered node class: its inputs, outputs, category, and display metadata.' - properties: - input: - type: object - description: Input specifications (required and optional groups) - additionalProperties: true - input_order: - type: object - description: Ordered input names per group - additionalProperties: - type: array - items: - type: string - output: - type: array - items: - type: string - description: Output type names - output_is_list: - type: array - items: - type: boolean - description: Whether each output is a list - output_name: - type: array - items: - type: string - description: Display names of outputs - name: - type: string - description: Internal class name - display_name: - type: string - description: Human-readable display name - description: - type: string - description: Node description - python_module: - type: string - description: Python module implementing the node - category: - type: string - description: Node category path - output_node: - type: boolean - description: Whether this is an output node - output_tooltips: - type: array - items: - type: string - description: Tooltips for each output - deprecated: - type: boolean - description: Whether the node is deprecated - experimental: - type: boolean - description: Whether the node is experimental - api_node: - type: boolean - description: Whether this is an API node - is_input_list: - type: boolean - description: Whether the node accepts list inputs - dev_only: - type: boolean - description: Whether the node is developer-only (hidden in production UI) - has_intermediate_output: - type: boolean - description: Whether the node emits intermediate output during execution - search_aliases: - type: array - items: - type: string - description: Alternative search terms for finding this node - essentials_category: - type: string - nullable: true - description: | - Category override used by the essentials pack. The - `essentials_category` key may be present with a string value, - present and `null`, or absent entirely: - - - V1 nodes: `essentials_category` is **omitted** when the node - class doesn't define an `ESSENTIALS_CATEGORY` attribute, and - **`null`** if the attribute is explicitly set to `None`. - - V3 nodes (`comfy_api.latest.io`): `essentials_category` is - **always present**, and **`null`** for nodes whose `Schema` - doesn't populate it. - - # ------------------------------------------------------------------- - # Models - # ------------------------------------------------------------------- - ModelFolder: - type: object - description: A configured model folder and the list of disk paths it resolves to. - required: - - name - - folders - properties: - name: - type: string - description: Model folder type name (e.g. "checkpoints") - folders: - type: array - items: - type: string - description: Filesystem paths for this model type - - ModelFile: - type: object - description: A single model file in a folder, with filesystem metadata. - required: - - name - - pathIndex - properties: - name: - type: string - description: Model filename - pathIndex: - type: integer - description: Index into the folder's paths array - modified: - type: number - description: File modification timestamp - created: - type: number - description: File creation timestamp - size: - type: integer - format: int64 - description: File size in bytes - - # ------------------------------------------------------------------- - # Subgraphs - # ------------------------------------------------------------------- - GlobalSubgraphInfo: - type: object - description: Metadata for a global subgraph blueprint (without full data) - required: - - source - - name - - info - properties: - source: - type: string - description: Source type ("templates" or "custom_node") - name: - type: string - description: Display name of the subgraph blueprint - info: - type: object - description: Additional information about the subgraph - required: - - node_pack - properties: - node_pack: - type: string - description: The node pack/module providing this subgraph - data: - type: string - description: The full subgraph JSON data (may be empty in list view) - - GlobalSubgraphData: - type: object - description: Full data for a global subgraph blueprint - required: - - source - - name - - info - - data - properties: - source: - type: string - description: Source type ("templates" or "custom_node") - name: - type: string - description: Display name of the subgraph blueprint - info: - type: object - description: Additional information about the subgraph - required: - - node_pack - properties: - node_pack: - type: string - description: The node pack/module providing this subgraph - data: - type: string - description: The full subgraph JSON data as a string - - # ------------------------------------------------------------------- - # Userdata - # ------------------------------------------------------------------- - UserDataResponse: - description: | - Response body for the POST endpoints `/api/userdata/{file}` and - `/api/userdata/{file}/move/{dest}`. Returns a single item whose - shape depends on the `full_info` query parameter. - x-variant-selector: - full_info=true: file-info object (`GetUserDataResponseFullFile`) - default: relative path string - oneOf: - - $ref: "#/components/schemas/GetUserDataResponseFullFile" - - type: string - description: Relative path of the written or moved file. Returned when `full_info` is absent or false. - - ListUserdataResponse: - description: | - Response body for `GET /api/userdata`. The array item shape is - determined by the `full_info` and `split` query parameters. - x-variant-selector: - full_info=true: array of file-info objects (`GetUserDataResponseFullFile`) - split=true: array of `[relative_path, ...path_components]` arrays - default: array of relative path strings - oneOf: - - type: array - items: - $ref: "#/components/schemas/GetUserDataResponseFullFile" - description: Returned when `full_info=true`. - - type: array - items: - type: array - items: - type: string - minItems: 2 - description: | - Returned when `split=true` and `full_info=false`. Each inner - array is `[relative_path, ...path_components]`. - - type: array - items: - type: string - description: Default shape — array of file paths relative to the user data root. - - GetUserDataResponseFullFile: - type: object - description: A single entry in a full-info user data listing. - properties: - path: - type: string - description: File name or path relative to the user directory - created: - type: number - description: Unix timestamp of file creation - size: - type: integer - description: File size in bytes - modified: - type: integer - format: int64 - description: Unix timestamp of last modification in milliseconds - - # ------------------------------------------------------------------- - # Assets - # ------------------------------------------------------------------- - Asset: - type: object - description: A registered asset — an input/output file tracked in the asset database with content hash and metadata. - required: - - id - - name - - size - - created_at - - updated_at - properties: - id: - type: string - format: uuid - description: Unique identifier for the asset - name: - type: string - description: Name of the asset file - hash: - type: string - nullable: true - description: Blake3 content hash of the asset (preferred over asset_hash) - pattern: "^blake3:[a-f0-9]{64}$" - asset_hash: - type: string - nullable: true - deprecated: true - description: "Deprecated: use `hash` instead. Blake3 hash of the asset content." - pattern: "^blake3:[a-f0-9]{64}$" - size: - type: integer - format: int64 - description: Size of the asset in bytes - mime_type: - type: string - description: MIME type of the asset - tags: - type: array - items: - type: string - description: Tags associated with the asset - user_metadata: - type: object - description: Custom user metadata - additionalProperties: true - metadata: - type: object - description: System-managed metadata (read-only) - additionalProperties: true - readOnly: true - preview_url: - type: string - format: uri - description: URL for asset preview/thumbnail - preview_id: - type: string - format: uuid - description: ID of the preview asset if available - prompt_id: - type: string - format: uuid - nullable: true - deprecated: true - description: "Deprecated: use job_id instead. ID of the prompt that created this asset." - job_id: - type: string - format: uuid - nullable: true - description: ID of the job that created this asset - created_at: - type: string - format: date-time - updated_at: - type: string - format: date-time - last_access_time: - type: string - format: date-time - is_immutable: - type: boolean - description: Whether this asset is immutable - - AssetCreated: - description: Response body returned after successfully registering a new asset. - allOf: - - $ref: "#/components/schemas/Asset" - - type: object - required: - - created_new - properties: - created_new: - type: boolean - description: Whether this was a new creation (true) or returned existing (false) - - AssetUpdated: - type: object - description: Response body returned after updating an asset's metadata. - required: - - id - - updated_at - properties: - id: - type: string - format: uuid - name: - type: string - hash: - type: string - nullable: true - description: Blake3 content hash of the asset (preferred over asset_hash) - pattern: "^blake3:[a-f0-9]{64}$" - asset_hash: - type: string - nullable: true - deprecated: true - description: "Deprecated: use `hash` instead. Blake3 hash of the asset content." - pattern: "^blake3:[a-f0-9]{64}$" - tags: - type: array - items: - type: string - mime_type: - type: string - user_metadata: - type: object - additionalProperties: true - prompt_id: - type: string - format: uuid - nullable: true - deprecated: true - description: "Deprecated: use job_id instead. ID of the prompt that created this asset." - job_id: - type: string - format: uuid - nullable: true - description: ID of the job that created this asset - updated_at: - type: string - format: date-time - - ListAssetsResponse: - type: object - description: Paginated list of assets. - required: - - assets - - total - - has_more - properties: - assets: - type: array - items: - $ref: "#/components/schemas/Asset" - total: - type: integer - has_more: - type: boolean - - TagInfo: - type: object - description: A tag known to the asset database, with the number of assets bearing it. - required: - - name - - count - properties: - name: - type: string - count: - type: integer - - ListTagsResponse: - type: object - description: Flat list of all tags, with counts. - required: - - tags - - total - - has_more - properties: - tags: - type: array - items: - $ref: "#/components/schemas/TagInfo" - total: - type: integer - has_more: - type: boolean - - AssetTagHistogramResponse: - type: object - description: Tags that would refine a filtered asset query, with the count of assets each tag would additionally select. - required: - - tag_counts - properties: - tag_counts: - type: object - additionalProperties: - type: integer - description: Map of tag names to occurrence counts - - TagsModificationResponse: - type: object - description: Response body returned after adding or removing tags on an asset. - required: - - total_tags - properties: - added: - type: array - items: - type: string - description: Tags successfully added - removed: - type: array - items: - type: string - description: Tags successfully removed - already_present: - type: array - items: - type: string - description: Tags already present (for add) - not_present: - type: array - items: - type: string - description: Tags not present (for remove) - total_tags: - type: array - items: - type: string - description: All tags on the asset after the operation - - # ------------------------------------------------------------------- - # Result / Output types - # ------------------------------------------------------------------- - ResultItem: - type: object - description: A single output file reference - properties: - filename: - type: string - subfolder: - type: string - type: - type: string - enum: [input, output, temp] - display_name: - type: string - - NodeOutputs: - type: object - description: | - Outputs from a single node execution. Known keys are listed below, - but custom nodes may add arbitrary keys (additionalProperties). - properties: - images: - type: array - items: - $ref: "#/components/schemas/ResultItem" - audio: - type: array - items: - $ref: "#/components/schemas/ResultItem" - video: - type: array - items: - $ref: "#/components/schemas/ResultItem" - animated: - type: array - items: - type: boolean - text: - oneOf: - - type: string - - type: array - items: - type: string - additionalProperties: true - - TerminalSize: - type: object - description: Terminal dimensions - properties: - cols: - type: number - row: - type: number - - LogEntry: - type: object - description: A single log entry - properties: - t: - type: string - description: Timestamp - m: - type: string - description: Log message - - StatusWsMessageStatus: - type: object - description: Inner payload of a `status` WebSocket message, describing the execution queue state. - properties: - exec_info: - type: object - required: - - queue_remaining - properties: - queue_remaining: - type: integer - - StatusWsMessage: - type: object - description: Initial status message sent on connect + queue status updates - properties: - status: - $ref: "#/components/schemas/StatusWsMessageStatus" - sid: - type: string - description: Session ID assigned by the server - - ProgressWsMessage: - type: object - description: Node execution progress (step N of M) - required: - - value - - max - - prompt_id - - node - properties: - value: - type: integer - description: Current step - max: - type: integer - description: Total steps - prompt_id: - type: string - node: - type: string - description: Node ID currently executing - - ProgressTextWsMessage: - type: object - description: Text-based progress update from a node - properties: - nodeId: - type: string - text: - type: string - prompt_id: - type: string - - NodeProgressState: - type: object - description: Progress state for a single node - properties: - value: - type: number - max: - type: number - state: - type: string - enum: [pending, running, finished, error] - node_id: - type: string - prompt_id: - type: string - display_node_id: - type: string - parent_node_id: - type: string - real_node_id: - type: string - - ProgressStateWsMessage: - type: object - description: Bulk progress state for all nodes in a prompt - required: - - prompt_id - - nodes - properties: - prompt_id: - type: string - nodes: - type: object - description: Map of node ID to progress state - additionalProperties: - $ref: "#/components/schemas/NodeProgressState" - - ExecutingWsMessage: - type: object - description: Fired when a node begins execution - required: - - node - - display_node - - prompt_id - properties: - node: - type: string - description: Node ID - display_node: - type: string - description: Display node ID (may differ for subgraphs) - prompt_id: - type: string - - ExecutedWsMessage: - type: object - description: Fired when a node completes execution with output - required: - - node - - display_node - - prompt_id - - output - properties: - node: - type: string - display_node: - type: string - prompt_id: - type: string - output: - $ref: "#/components/schemas/NodeOutputs" - merge: - type: boolean - description: Whether to merge with existing output - - ExecutionWsMessageBase: - type: object - description: Base fields for execution lifecycle messages - required: - - prompt_id - - timestamp - properties: - prompt_id: - type: string - timestamp: - type: integer - description: Unix timestamp in milliseconds - - ExecutionStartWsMessage: - allOf: - - $ref: "#/components/schemas/ExecutionWsMessageBase" - description: Fired when prompt execution begins - - ExecutionSuccessWsMessage: - allOf: - - $ref: "#/components/schemas/ExecutionWsMessageBase" - description: Fired when prompt execution completes successfully - - ExecutionCachedWsMessage: - allOf: - - $ref: "#/components/schemas/ExecutionWsMessageBase" - - type: object - properties: - nodes: - type: array - items: - type: string - description: List of node IDs that were cached - description: Fired when nodes are served from cache - - ExecutionInterruptedWsMessage: - allOf: - - $ref: "#/components/schemas/ExecutionWsMessageBase" - - type: object - properties: - node_id: - type: string - node_type: - type: string - executed: - type: array - items: - type: string - description: Node IDs that completed before interruption - description: Fired when execution is interrupted by user - - ExecutionErrorWsMessage: - allOf: - - $ref: "#/components/schemas/ExecutionWsMessageBase" - - type: object - properties: - node_id: - type: string - node_type: - type: string - executed: - type: array - items: - type: string - exception_message: - type: string - exception_type: - type: string - traceback: - type: array - items: - type: string - current_inputs: {} - current_outputs: {} - description: Fired when a node throws an exception during execution - - LogsWsMessage: - type: object - description: Streaming log entries from the server - properties: - size: - $ref: "#/components/schemas/TerminalSize" - entries: - type: array - items: - $ref: "#/components/schemas/LogEntry" - - NotificationWsMessage: - type: object - description: Server notification (e.g. model download complete) - properties: - value: - type: string - id: - type: string - - FeatureFlagsWsMessage: - type: object - description: Feature flags sent on connect - additionalProperties: true - - AssetDownloadWsMessage: - type: object - description: Asset download progress - required: - - task_id - - asset_name - - bytes_total - - bytes_downloaded - - progress - - status - properties: - task_id: - type: string - asset_name: - type: string - bytes_total: - type: number - bytes_downloaded: - type: number - progress: - type: number - description: 0.0 to 1.0 - status: - type: string - enum: [created, running, completed, failed] - asset_id: - type: string - error: - type: string - - AssetExportWsMessage: - type: object - description: Bulk asset export progress - required: - - task_id - - assets_total - - assets_attempted - - assets_failed - - bytes_total - - bytes_processed - - progress - - status - properties: - task_id: - type: string - export_name: - type: string - assets_total: - type: number - assets_attempted: - type: number - assets_failed: - type: number - bytes_total: - type: number - bytes_processed: - type: number - progress: - type: number - description: 0.0 to 1.0 - status: - type: string - enum: [created, running, completed, failed] - error: - type: string - - # ------------------------------------------------------------------- - # Cloud-runtime schemas - # - # These schemas are exclusively referenced by cloud-runtime operations. - # Tagged x-runtime: [cloud]. - # ------------------------------------------------------------------- - CloudError: - type: object - x-runtime: [cloud] - description: "[cloud-only] Standard error response from cloud endpoints." - required: - - error - properties: - error: - type: string - description: Error message - code: - type: string - description: Machine-readable error code - details: - type: object - additionalProperties: true - description: Additional error context - - CloudJobStatus: - type: object - x-runtime: [cloud] - description: "[cloud-only] Status of a cloud job." - required: - - id - - status - properties: - id: - type: string - format: uuid - status: - type: string - enum: [pending, running, completed, failed, cancelled] - progress: - type: number - minimum: 0 - maximum: 1 - description: "Execution progress (0.0 to 1.0)" - started_at: - type: string - format: date-time - nullable: true - completed_at: - type: string - format: date-time - nullable: true - - CloudPrompt: - type: object - x-runtime: [cloud] - description: "[cloud-only] A cloud-executed prompt record." - required: - - id - - status - properties: - id: - type: string - format: uuid - status: - type: string - workflow: - type: object - additionalProperties: true - outputs: - type: object - additionalProperties: true - created_at: - type: string - format: date-time - completed_at: - type: string - format: date-time - nullable: true - - HistoryV2Response: - type: object - x-runtime: [cloud] - description: "[cloud-only] Paginated execution history in v2 format." - required: - - items - - total - - has_more - properties: - items: - type: array - items: - $ref: "#/components/schemas/HistoryV2Entry" - total: - type: integer - has_more: - type: boolean - - HistoryV2Entry: - type: object - x-runtime: [cloud] - description: "[cloud-only] A single execution history entry in v2 format." - required: - - id - - status - properties: - id: - type: string - format: uuid - status: - type: string - workflow: - type: object - additionalProperties: true - outputs: - type: object - additionalProperties: true - created_at: - type: string - format: date-time - started_at: - type: string - format: date-time - nullable: true - completed_at: - type: string - format: date-time - nullable: true - preview_output: - type: object - additionalProperties: true - - CloudLogsResponse: - type: object - x-runtime: [cloud] - description: "[cloud-only] Paginated cloud execution logs." - required: - - entries - properties: - entries: - type: array - items: - type: object - properties: - timestamp: - type: string - format: date-time - level: - type: string - enum: [debug, info, warn, error] - message: - type: string - job_id: - type: string - format: uuid - total: - type: integer - has_more: - type: boolean - - AssetDownloadRequest: - type: object - x-runtime: [cloud] - description: "[cloud-only] A single asset to download to the cloud runtime." - required: - - asset_id - properties: - asset_id: - type: string - format: uuid - description: ID of the asset to download - target_path: - type: string - description: Target path on the runtime filesystem - - ImportPublishedAssetsRequest: - type: object - x-runtime: [cloud] - description: "[cloud-only] Request body for importing published assets into the caller's library." - required: - - published_asset_ids - properties: - published_asset_ids: - type: array - description: IDs of published assets (inputs and models) to import. - items: - type: string - share_id: - type: string - nullable: true - description: | - Optional. Share ID of the published workflow these assets belong to. When provided (non-null, non-empty): all `published_asset_ids` must belong to this share's workflow version; returns 400 if the share is not found or any asset does not belong to it. When omitted, null, or empty string: no share-scoped validation is performed and the assets are validated only against global rules (preserved for clients that have not yet adopted `share_id`). - - ImportPublishedAssetsResponse: - type: object - x-runtime: [cloud] - description: "[cloud-only] Response after importing published assets. Each returned `AssetInfo.id` is the caller's newly-created private asset ID, not the published asset ID supplied in the request." - required: - - assets - properties: - assets: - type: array - items: - $ref: "#/components/schemas/AssetInfo" - - RemoteAssetMetadata: - type: object - x-runtime: [cloud] - description: "[cloud-only] Metadata fetched from a remote asset URL." - properties: - content_type: - type: string - description: MIME type of the remote file - content_length: - type: integer - format: int64 - description: Size in bytes - filename: - type: string - description: Suggested filename from Content-Disposition or URL - - CloudNode: - type: object - x-runtime: [cloud] - description: "[cloud-only] An installed custom node package in the cloud runtime." - required: - - id - - name - properties: - id: - type: string - name: - type: string - version: - type: string - description: - type: string - author: - type: string - repository: - type: string - format: uri - installed_at: - type: string - format: date-time - enabled: - type: boolean - - HubLabel: - type: object - x-runtime: [cloud] - description: "[cloud-only] A label/category used for tagging hub content." - required: - - id - - name - properties: - id: - type: string - name: - type: string - description: - type: string - color: - type: string - description: Hex color code for the label - - HubProfile: - type: object - x-runtime: [cloud] - description: "[cloud-only] A public user profile on the ComfyUI Hub." - required: - - username - properties: - username: - type: string - display_name: - type: string - bio: - type: string - avatar_url: - type: string - format: uri - links: - type: array - items: - type: string - format: uri - workflow_count: - type: integer - created_at: - type: string - format: date-time - - HubWorkflow: - type: object - x-runtime: [cloud] - description: "[cloud-only] A published workflow on the ComfyUI Hub." - required: - - share_id - - name - properties: - share_id: - type: string - name: - type: string - description: - type: string - author: - $ref: "#/components/schemas/HubProfile" - labels: - type: array - items: - $ref: "#/components/schemas/HubLabel" - thumbnail_url: - type: string - format: uri - content: - type: object - additionalProperties: true - description: Workflow graph JSON - likes: - type: integer - views: - type: integer - forks: - type: integer - created_at: - type: string - format: date-time - updated_at: - type: string - format: date-time - - HubWorkflowList: - type: object - x-runtime: [cloud] - description: "[cloud-only] Paginated list of hub workflows." - required: - - workflows - - total - - has_more - properties: - workflows: - type: array - items: - $ref: "#/components/schemas/HubWorkflow" - total: - type: integer - has_more: - type: boolean - - HubWorkflowIndexEntry: - type: object - x-runtime: [cloud] - description: "[cloud-only] Lightweight entry in the hub workflow index for client-side search." - required: - - share_id - - name - properties: - share_id: - type: string - name: - type: string - author_username: - type: string - labels: - type: array - items: - type: string - likes: - type: integer - updated_at: - type: string - format: date-time - - CloudWorkflow: - type: object - x-runtime: [cloud] - description: "[cloud-only] A cloud-managed workflow with version history." - required: - - id - - name - properties: - id: - type: string - format: uuid - name: - type: string - description: - type: string - share_id: - type: string - nullable: true - description: Public share identifier if published - latest_version_id: - type: string - format: uuid - nullable: true - thumbnail_url: - type: string - format: uri - nullable: true - created_at: - type: string - format: date-time - updated_at: - type: string - format: date-time - - CloudWorkflowList: - type: object - x-runtime: [cloud] - description: "[cloud-only] Paginated list of cloud workflows." - required: - - workflows - - total - - has_more - properties: - workflows: - type: array - items: - $ref: "#/components/schemas/CloudWorkflow" - total: - type: integer - has_more: - type: boolean - - CloudWorkflowVersion: - type: object - x-runtime: [cloud] - description: "[cloud-only] A version of a cloud workflow." - required: - - id - - workflow_id - properties: - id: - type: string - format: uuid - workflow_id: - type: string - format: uuid - version_number: - type: integer - created_at: - type: string - format: date-time - - AuthSession: - type: object - x-runtime: [cloud] - description: "[cloud-only] Current authentication session state." - required: - - user - properties: - user: - $ref: "#/components/schemas/CloudUser" - workspace: - $ref: "#/components/schemas/Workspace" - expires_at: - type: string - format: date-time - - AuthTokenResponse: - type: object - x-runtime: [cloud] - description: "[cloud-only] OAuth2 token response." - required: - - access_token - - token_type - properties: - access_token: - type: string - token_type: - type: string - description: Always "Bearer" - expires_in: - type: integer - description: Token lifetime in seconds - refresh_token: - type: string - nullable: true - scope: - type: string - - JwksResponse: - type: object - x-runtime: [cloud] - description: "[cloud-only] JSON Web Key Set for JWT verification." - required: - - keys - properties: - keys: - type: array - items: - type: object + type: string + file_path: + description: Relative path in global-namespace-root form (e.g. "models/checkpoints/flux.safetensors") + nullable: true + type: string + hash: + description: Blake3 hash of the asset content. + pattern: ^blake3:[a-f0-9]{64}$ + type: string + id: + description: Asset ID + format: uuid + type: string + job_id: + description: ID of the job that created this asset, if available + format: uuid + nullable: true + type: string + mime_type: + description: Updated MIME type of the asset + type: string + name: + description: Updated name of the asset + type: string + tags: + description: Tags associated with the asset + items: + type: string + type: array + updated_at: + description: Timestamp of the update + format: date-time + type: string + user_metadata: + additionalProperties: true + description: Updated custom metadata + type: object required: - - kty - - kid - - use + - id + - updated_at + type: object + CreateWorkflowRequest: + description: Request body for creating a new saved workflow. properties: - kty: - type: string - description: Key type (e.g. RSA) - kid: - type: string - description: Key ID - use: - type: string - description: Key use (e.g. sig) - alg: - type: string - description: Algorithm (e.g. RS256) - n: - type: string - description: RSA modulus (base64url) - e: - type: string - description: RSA exponent (base64url) - additionalProperties: true - - OAuthAuthorizationServerMetadata: - type: object - x-runtime: [cloud] - description: "[cloud-only] OAuth 2.1 authorization-server metadata (RFC 8414)." - required: - - issuer - - authorization_endpoint - - token_endpoint - - jwks_uri - - response_types_supported - - grant_types_supported - - code_challenge_methods_supported - - token_endpoint_auth_methods_supported - properties: - issuer: - type: string - format: uri - authorization_endpoint: - type: string - format: uri - token_endpoint: - type: string - format: uri - jwks_uri: - type: string - format: uri - registration_endpoint: - type: string - format: uri - description: "[cloud-only] RFC 7591 §3.1 Dynamic Client Registration endpoint. Advertised so MCP-spec-compliant clients can auto-discover and self-register without operator involvement. Present only when DCR is enabled." - response_types_supported: - type: array - items: - type: string - grant_types_supported: - type: array - items: - type: string - code_challenge_methods_supported: - type: array - items: - type: string - token_endpoint_auth_methods_supported: - type: array - items: - type: string - scopes_supported: - type: array - items: - type: string - - OAuthProtectedResourceMetadata: - type: object - x-runtime: [cloud] - description: "[cloud-only] OAuth 2.1 protected-resource metadata (RFC 9728)." - required: - - resource - - authorization_servers - - scopes_supported - properties: - resource: - type: string - format: uri - authorization_servers: - type: array - items: - type: string - format: uri - scopes_supported: - type: array - items: - type: string - bearer_methods_supported: - type: array - items: - type: string - - OAuthConsentChallenge: - type: object - x-runtime: [cloud] - description: "[cloud-only] Server-side state describing the OAuth consent decision the user is being asked to make. Returned by GET /oauth/authorize when a valid session exists; the frontend renders the consent UI from this payload and POSTs the decision back. Browser never sees the original OAuth params on resume." - required: - - oauth_request_id - - csrf_token - - client_display_name - - resource_display_name - - scopes - - workspaces - properties: - oauth_request_id: - type: string - format: uuid - description: Opaque server-side identifier for the authorization-request row. Carried back unchanged in the consent submission. - csrf_token: - type: string - description: Per-row CSRF token bound to this authorization request (not to the session). Must be echoed back on POST. - client_display_name: - type: string - description: Human-readable name of the OAuth client requesting authorization. - resource_display_name: - type: string - description: Human-readable name of the protected resource. - scopes: - type: array - description: Scopes the client is requesting for this resource. The frontend should present these for the user to approve. - items: - type: string - workspaces: - type: array - description: Workspaces the user can select from. Membership is re-checked on POST. - items: - $ref: "#/components/schemas/OAuthConsentChallengeWorkspace" - - OAuthConsentChallengeWorkspace: - type: object - x-runtime: [cloud] - description: "[cloud-only] One workspace option presented in the OAuth consent challenge." - required: [id, name, type, role] - properties: - id: { type: string } - name: { type: string } - type: { type: string, enum: [personal, team] } - role: { type: string, enum: [owner, member] } - - OAuthAuthorizeRedirectResponse: - type: object - x-runtime: [cloud] - description: "[cloud-only] Redirect target produced after a JSON consent submission. The frontend must navigate the browser to this URL so custom-scheme client callbacks work without relying on fetch-visible 302 headers." - required: - - redirect_url - properties: - redirect_url: - type: string - format: uri - description: OAuth client redirect URI with either code+state for allow, or error+state for deny. - - OAuthTokenResponse: - type: object - x-runtime: [cloud] - description: "[cloud-only] RFC 6749 §5.1 successful token response." - required: [access_token, token_type, expires_in, refresh_token, scope] - properties: - access_token: - type: string - description: Resource-bound access token (audience matches the protected resource). - token_type: - type: string - enum: [Bearer] - expires_in: - type: integer - description: Access token lifetime in seconds. - refresh_token: - type: string - description: Opaque refresh token. Rotates on every successful refresh; presenting an already-rotated token revokes the entire family. - scope: - type: string - description: Space-delimited scopes granted with this token. - - OAuthTokenError: - type: object - x-runtime: [cloud] - description: "[cloud-only] RFC 6749 §5.2 error response." - required: [error] - properties: - error: - type: string - description: 'RFC 6749 §5.2 error code: invalid_request, invalid_client, invalid_grant, unauthorized_client, unsupported_grant_type, invalid_scope.' - error_description: - type: string - description: Human-readable, no leak of internal storage state. - - OAuthRegisterRequest: - type: object - x-runtime: [cloud] - additionalProperties: false - description: "[cloud-only] RFC 7591 §2 client metadata document. Only the fields the server honors are listed; presence of `scope` or `resource_grants` in the request is rejected (`invalid_client_metadata`) because those are server-owned for dynamic clients." - required: - - redirect_uris - - application_type - properties: - redirect_uris: - type: array - items: - type: string - minItems: 1 - maxItems: 5 - description: 1–5 redirect URIs. Validated against `application_type` policy. - client_name: - type: string - maxLength: 100 - description: Human-readable name shown in the consent UI. Reserved-name list rejects impersonation of major clients. - application_type: - type: string - enum: [native, web] - description: | - RFC 7591 §2 application_type. **REQUIRED** — clients MUST declare intent; the server does not default this field. `native` for desktop / CLI / MCP-spec-strict clients (loopback redirects); `web` for hosted clients (HTTPS only, host must be allowlisted). A missing or explicitly empty `application_type` rejects with `invalid_client_metadata`. - token_endpoint_auth_method: - type: string - enum: [none] - description: 'Public clients only this phase — must be `none` if present. The server forces `none` regardless.' - grant_types: - type: array - items: - type: string - enum: [authorization_code, refresh_token] - description: Optional. Defaults to `["authorization_code","refresh_token"]`. - response_types: - type: array - items: - type: string - enum: [code] - description: Optional. Defaults to `["code"]`. - scope: - type: string - nullable: true - description: "**REJECTED IF PRESENT.** Dynamic clients do not pick scopes — the server assigns scopes from the active resource's published list. Sending `scope` in the registration body is treated as a privilege-escalation attempt and returns `invalid_client_metadata`." - resource_grants: - type: object - nullable: true - additionalProperties: - type: array - items: - type: string - description: "**REJECTED IF PRESENT.** Same reason as `scope`. The set of resources and scopes a dynamic client may request is server-policy, not request-driven." - client_uri: - type: string - nullable: true - description: "**REJECTED IF PRESENT.** Unsupported RFC 7591 metadata for this public-client phase." - logo_uri: - type: string - nullable: true - description: "**REJECTED IF PRESENT.** Unsupported RFC 7591 metadata for this public-client phase." - tos_uri: - type: string - nullable: true - description: "**REJECTED IF PRESENT.** Unsupported RFC 7591 metadata for this public-client phase." - policy_uri: - type: string - nullable: true - description: "**REJECTED IF PRESENT.** Unsupported RFC 7591 metadata for this public-client phase." - software_id: - type: string - nullable: true - description: "**REJECTED IF PRESENT.** Unsupported RFC 7591 metadata for this public-client phase." - software_version: - type: string - nullable: true - description: "**REJECTED IF PRESENT.** Unsupported RFC 7591 metadata for this public-client phase." - contacts: - type: array - nullable: true - items: - type: string - description: "**REJECTED IF PRESENT.** Unsupported RFC 7591 metadata for this public-client phase." - jwks: - type: object - nullable: true - additionalProperties: true - description: "**REJECTED IF PRESENT.** Unsupported RFC 7591 metadata for this public-client phase." - jwks_uri: - type: string - nullable: true - description: "**REJECTED IF PRESENT.** Unsupported RFC 7591 metadata for this public-client phase." - - OAuthRegisterResponse: - type: object - x-runtime: [cloud] - description: "[cloud-only] RFC 7591 §3.2.1 successful registration response." - required: - - client_id - - client_id_issued_at - - redirect_uris - - grant_types - - response_types - - token_endpoint_auth_method - - application_type - properties: - client_id: - type: string - description: Server-generated client_id. - client_id_issued_at: - type: integer - format: int64 - description: Unix timestamp (seconds) when the client was registered. - client_name: - type: string - redirect_uris: - type: array - items: - type: string - grant_types: - type: array - items: - type: string - response_types: - type: array - items: - type: string - token_endpoint_auth_method: - type: string - enum: [none] - application_type: - type: string - enum: [native, web] - - OAuthRegisterError: - type: object - x-runtime: [cloud] - description: "[cloud-only] RFC 7591 §3.2.2 error response." - required: - - error - properties: - error: - type: string - enum: [invalid_redirect_uri, invalid_client_metadata] - error_description: - type: string - nullable: true - - BillingBalance: - type: object - x-runtime: [cloud] - description: "[cloud-only] Current credit balance and usage summary." - required: - - credits_remaining - properties: - credits_remaining: - type: integer - description: Available credits - credits_used: - type: integer - description: Credits used in current billing period - credits_total: - type: integer - description: Total credits allocated in current period - - BillingEvent: - type: object - x-runtime: [cloud] - description: "[cloud-only] A billing event (charge, credit, refund)." - required: - - id - - type - - amount - - created_at - properties: - id: - type: string - type: - type: string - enum: [charge, credit, refund, topup, subscription] - amount: - type: integer - description: Amount in credits - description: - type: string - job_id: - type: string - format: uuid - nullable: true - created_at: - type: string - format: date-time - - BillingEventList: - type: object - x-runtime: [cloud] - description: "[cloud-only] Paginated list of billing events." - required: - - events - - total - - has_more - properties: - events: - type: array - items: - $ref: "#/components/schemas/BillingEvent" - total: - type: integer - has_more: - type: boolean - - BillingOp: - type: object - x-runtime: [cloud] - description: "[cloud-only] A billing operation record." - required: - - id - - status - properties: - id: - type: string - status: - type: string - enum: [pending, completed, failed] - type: - type: string - amount: - type: integer - created_at: - type: string - format: date-time - completed_at: - type: string - format: date-time - nullable: true - - BillingPlan: - type: object - x-runtime: [cloud] - description: "[cloud-only] A subscription plan with pricing details." - required: - - id - - name - properties: - id: - type: string - name: - type: string - description: - type: string - credits_per_month: - type: integer - price_cents: - type: integer - description: Monthly price in cents (USD) - currency: - type: string - default: usd - features: - type: array - items: - type: string - description: List of plan features - - BillingStatus: - type: string - x-runtime: [cloud] - description: "[cloud-only] Overall billing/payment lifecycle status." - enum: - - awaiting_payment_method - - pending_payment - - paid - - payment_failed - - inactive - - BillingSubscription: - type: object - x-runtime: [cloud] - description: "[cloud-only] Active subscription details." - required: - - id - - status - - plan_id - properties: - id: - type: string - status: - type: string - enum: [active, cancelled, past_due, trialing] - plan_id: - type: string - plan_name: - type: string - current_period_start: - type: string - format: date-time - current_period_end: - type: string - format: date-time - cancel_at_period_end: - type: boolean - - SubscriptionPreview: - type: object - x-runtime: [cloud] - description: "[cloud-only] Preview of a subscription change including prorations." - properties: - plan_id: - type: string - plan_name: - type: string - amount_due: - type: integer - description: Amount due in cents - proration_amount: - type: integer - description: Proration adjustment in cents - currency: - type: string - next_billing_date: - type: string - format: date-time - - Workspace: - type: object - x-runtime: [cloud] - description: "[cloud-only] A cloud workspace for team collaboration." - required: - - id - - name - properties: - id: - type: string - name: - type: string - type: - type: string - enum: - - personal - - team - description: Workspace type (personal vs. team). - owner_id: - type: string - member_count: - type: integer - created_at: - type: string - format: date-time - updated_at: - type: string - format: date-time - - WorkspaceMember: - type: object - x-runtime: [cloud] - description: "[cloud-only] A member of a cloud workspace." - required: - - user_id - - role - properties: - user_id: - type: string - email: - type: string - format: email - display_name: - type: string - avatar_url: - type: string - format: uri - role: - type: string - enum: [owner, admin, member] - joined_at: - type: string - format: date-time - - WorkspaceInvite: - type: object - x-runtime: [cloud] - description: "[cloud-only] A pending workspace invitation." - required: - - id - - email - - role - properties: - id: - type: string - email: - type: string - format: email - role: - type: string - enum: [admin, member] - invited_by: - type: string - created_at: - type: string - format: date-time - expires_at: - type: string - format: date-time - - WorkspaceApiKey: - type: object - x-runtime: [cloud] - description: "[cloud-only] A workspace API key (secret value redacted)." - required: - - id - - name - - description - properties: - id: - type: string - name: - type: string - description: - type: string - maxLength: 5000 - description: User-provided description of the key's purpose. Always present in responses; empty string when no description was supplied on create. - prefix: - type: string - description: First few characters of the key for identification - created_at: - type: string - format: date-time - last_used_at: - type: string - format: date-time - nullable: true - created_by: - type: string - - WorkspaceApiKeyCreated: - type: object - x-runtime: [cloud] - description: "[cloud-only] A newly created workspace API key, including the full secret value (shown only once)." - required: - - id - - name - - description - - key - properties: - id: - type: string - name: - type: string - description: - type: string - maxLength: 5000 - description: User-provided description of the key's purpose. Always present in responses; empty string when no description was supplied on create. - key: - type: string - description: Full API key value (only returned on creation) - prefix: - type: string - created_at: - type: string - format: date-time - - CloudUser: - type: object - x-runtime: [cloud] - description: "[cloud-only] A cloud-authenticated user profile." - required: - - id - - email - properties: - id: - type: string - email: - type: string - format: email - display_name: - type: string - avatar_url: - type: string - format: uri - created_at: - type: string - format: date-time - - SecretMeta: - type: object - x-runtime: [cloud] - description: "[cloud-only] Metadata for a stored secret (value is never returned)." - required: - - id - - name - properties: - id: - type: string - name: - type: string - provider: - type: string - description: "[cloud-only] Provider identifier (e.g., huggingface, civitai)." - x-runtime: [cloud] - last_used_at: - type: string - format: date-time - description: "[cloud-only] When the secret was last used for decryption." - x-runtime: [cloud] - created_at: - type: string - format: date-time - updated_at: - type: string - format: date-time - - UpdateSecretRequest: - type: object - x-runtime: [cloud] - description: "[cloud-only] Request body for updating an existing user secret." - properties: - name: - type: string - description: New name for the secret - secret_value: - type: string - description: New secret value (API key, token, etc.) - - CreateSessionResponse: - type: object - x-runtime: [cloud] - description: "[cloud-only] Response after creating a session cookie." - required: - - success - properties: - success: - type: boolean - expiresIn: - type: integer - description: Session expiration time in seconds. - - DeleteSessionResponse: - type: object - x-runtime: [cloud] - description: "[cloud-only] Response after deleting a session cookie." - required: - - success - properties: - success: - type: boolean - - CreateHubProfileRequest: - type: object - x-runtime: [cloud] - description: "[cloud-only] Request body for creating a new Hub profile." - required: - - workspace_id - - username - properties: - workspace_id: - type: string - username: - type: string - description: Unique URL-safe slug. Immutable after creation. - display_name: - type: string - description: - type: string - avatar_token: - type: string - website_urls: - type: array - items: - type: string - - PublishHubWorkflowRequest: - type: object - x-runtime: [cloud] - description: "[cloud-only] Request body for publishing or updating a workflow on the Hub." - required: - - username - - name - - workflow_filename - - asset_ids - properties: - username: - type: string - name: - type: string - workflow_filename: - type: string - asset_ids: - type: array - items: - type: string - description: - type: string - tags: - type: array - items: - type: string - models: - type: array - items: - type: string - custom_nodes: - type: array - items: - type: string - tutorial_url: - type: string - metadata: - type: object - additionalProperties: true - thumbnail_type: - type: string - enum: [image, video, image_comparison] - thumbnail_token_or_url: - type: string - thumbnail_comparison_token_or_url: - type: string - sample_image_tokens_or_urls: - type: array - items: - type: string - - HubWorkflowDetail: - type: object - x-runtime: [cloud] - description: "[cloud-only] Full Hub workflow detail including versions, assets, and statistics." - required: - - share_id - - workflow_id - - name - - workflow_json - - assets - - profile - - status - properties: - share_id: - type: string - workflow_id: - type: string - name: - type: string - status: - type: string - enum: [pending, approved, rejected, deprecated] - description: - type: string - thumbnail_type: - type: string - enum: [image, video, image_comparison] - thumbnail_url: - type: string - thumbnail_comparison_url: - type: string - tutorial_url: - type: string - metadata: - type: object - additionalProperties: true - sample_image_urls: - type: array - items: - type: string - publish_time: - type: string - format: date-time - nullable: true - workflow_json: - type: object - additionalProperties: true - assets: - type: array - items: - $ref: "#/components/schemas/AssetInfo" - profile: - $ref: "#/components/schemas/HubProfile" - - AssetInfo: - type: object - x-runtime: [cloud] - description: "[cloud-only] Lightweight asset reference used in workflow publishing payloads." - required: - - id - - filename - properties: - id: - type: string - filename: - type: string - mime_type: - type: string - size_bytes: - type: integer - format: int64 - - BulkRevokeAPIKeysResponse: - type: object - x-runtime: [cloud] - description: "[cloud-only] Response after bulk-revoking API keys for a workspace member." - required: - - revoked_count - properties: - revoked_count: - type: integer - minimum: 0 - - CreateWorkflowVersionRequest: - type: object - x-runtime: [cloud] - description: "[cloud-only] Request body for creating a new version of a saved workflow." - required: - - base_version - - workflow_json - properties: - base_version: - type: integer - description: Version number this change is based on (for optimistic concurrency). - workflow_json: - type: object - additionalProperties: true - - WorkflowVersionResponse: - type: object - x-runtime: [cloud] - description: "[cloud-only] Metadata for a single workflow version." - required: - - id - - version - - latest_version - - created_by - - created_at - properties: - id: - type: string - version: - type: integer - latest_version: - type: integer - created_by: - type: string - created_at: - type: string - format: date-time - - WorkflowPublishInfo: - type: object - x-runtime: [cloud] - description: "[cloud-only] Publishing metadata for a workflow shared to the Hub." - required: - - workflow_id - - share_id - - listed - - assets - properties: - workflow_id: - type: string - share_id: - type: string - publish_time: - type: string - format: date-time - nullable: true - listed: - type: boolean - assets: - type: array - items: - $ref: "#/components/schemas/AssetInfo" - - TaskEntry: - type: object - x-runtime: [cloud] - description: "[cloud-only] Task data for list views." - required: - - id - - task_name - - status - - create_time - properties: - id: - type: string - format: uuid - task_name: - type: string - status: - type: string - enum: [created, running, completed, failed] - create_time: - type: string - format: date-time - started_at: - type: string - format: date-time - completed_at: - type: string - format: date-time - - TaskResponse: - type: object - x-runtime: [cloud] - description: "[cloud-only] Full task details including payload and result." - required: - - id - - idempotency_key - - task_name - - payload - - status - - create_time - - update_time - properties: - id: - type: string - format: uuid - idempotency_key: - type: string - task_name: - type: string - payload: - type: object - additionalProperties: true - status: - type: string - enum: [created, running, completed, failed] - result: - type: object - additionalProperties: true - create_time: - type: string - format: date-time - update_time: - type: string - format: date-time - started_at: - type: string - format: date-time - completed_at: - type: string - format: date-time - error: - type: string - - TasksListResponse: - type: object - x-runtime: [cloud] - description: "[cloud-only] Paginated list of background tasks for the authenticated user." - required: - - tasks - - pagination - properties: - tasks: - type: array - items: - $ref: "#/components/schemas/TaskEntry" - pagination: - $ref: "#/components/schemas/PaginationInfo" - - # ===== Cloud-only schemas (Comfy-Org/cloud runtime, BE-1106) ===== - AssetDownloadResponse: - type: object - x-runtime: [cloud] - description: '[cloud-only] Acknowledgement of an async asset download task; clients poll GET /api/tasks/{task_id} for status.' - required: - - task_id - - status - properties: - task_id: - type: string - format: uuid - description: Task ID for tracking download progress via GET /api/tasks/{task_id} - status: - type: string - enum: - - created - - running - - completed - - failed - description: Current task status - message: - type: string - description: Human-readable message - example: Download task created. Use task_id to track progress. - - AssetMetadataResponse: - type: object - x-runtime: [cloud] - description: '[cloud-only] Metadata for a remotely hosted asset resolved by URL.' - required: - - content_length - properties: - content_length: - type: integer - format: int64 - description: Size of the asset in bytes (-1 if unknown) - example: 4294967296 - content_type: - type: string - description: MIME type of the asset - example: application/octet-stream - filename: - type: string - description: Suggested filename for the asset from source - example: realistic-vision-v5.safetensors - name: - type: string - description: Display name or title for the asset from source - example: Realistic Vision v5.0 - tags: - type: array - items: - type: string - description: Tags for categorization from source - example: - - models - - checkpoint - preview_image: - type: string - description: Preview image as base64-encoded data URL - example: data:image/jpeg;base64,/9j/4AAQSkZJRg... - validation: - description: Validation results for the file - allOf: - - $ref: '#/components/schemas/ValidationResult' - - BillingBalanceResponse: - type: object - x-runtime: [cloud] - description: '[cloud-only] Current credit balance and usage details for a workspace.' - required: - - amount_micros - - currency - properties: - amount_micros: - type: number - format: double - description: The total remaining balance in microamount (1/1,000,000 of the currency unit) - prepaid_balance_micros: - type: number - format: double - description: The remaining balance from prepaid commits in microamount - cloud_credit_balance_micros: - type: number - format: double - description: The remaining balance from cloud credits in microamount - pending_charges_micros: - type: number - format: double - description: The total amount of pending/unbilled charges from draft invoices in microamount - effective_balance_micros: - type: number - format: double - description: The effective balance (total balance minus pending charges). Can be negative if pending charges exceed - the balance. - currency: - type: string - example: usd - description: Currency code - - BillingPlansResponse: - type: object - x-runtime: [cloud] - description: '[cloud-only] List of available billing plans for subscription.' - required: - - plans - properties: - current_plan_slug: - type: string - description: Current plan slug if subscribed - plans: - type: array - items: - $ref: '#/components/schemas/Plan' - - BillingStatusResponse: - type: object - x-runtime: [cloud] - description: '[cloud-only] Current billing and subscription status for a workspace.' - required: - - is_active - - has_funds - properties: - is_active: - type: boolean - description: Whether the workspace has an active subscription - subscription_status: - type: string - enum: - - active - - ended - - canceled - description: Subscription activity status (scheduled subscriptions are not returned) - subscription_tier: - $ref: '#/components/schemas/SubscriptionTier' - subscription_duration: - $ref: '#/components/schemas/SubscriptionDuration' - plan_slug: - type: string - description: Plan identifier (e.g., standard-monthly, team-pro-annual) - billing_status: - $ref: '#/components/schemas/BillingStatus' - has_funds: - type: boolean - description: Whether the workspace has available credits - cancel_at: - type: string - format: date-time - description: When the subscription will become inactive (if canceled) - renewal_date: - type: string - format: date-time - description: When the current billing period ends and the next one begins - - GetUserDataResponseFull: - type: array - x-runtime: [cloud] - description: '[cloud-only] List of user data file entries (each with path, size, and modification time) returned when full_info=true.' - items: - $ref: '#/components/schemas/GetUserDataResponseFullFile' - - HistoryDetailEntry: - type: object - x-runtime: [cloud] - description: '[cloud-only] History entry with full prompt data' - properties: - prompt: - type: object - description: Full prompt execution data - properties: - priority: - type: number - format: double - description: Execution priority - prompt_id: - type: string - description: The prompt ID - prompt: - type: object - description: The workflow nodes - additionalProperties: true - extra_data: - type: object - description: Additional execution data - additionalProperties: true - outputs_to_execute: - type: array - items: - type: string - description: Output nodes to execute - outputs: - type: object - description: Output data from execution (generated images, files, etc.) - additionalProperties: true - status: - type: object - description: Execution status and timeline information - additionalProperties: true - meta: - type: object - description: Metadata about the execution and nodes - additionalProperties: true - - HistoryDetailResponse: - type: object - x-runtime: [cloud] - description: '[cloud-only] Detailed execution history response for a specific prompt. - - Returns a dictionary with prompt_id as key and full history data as value. - - ' - additionalProperties: - $ref: '#/components/schemas/HistoryDetailEntry' - - HistoryResponse: - type: object - x-runtime: [cloud] - description: '[cloud-only] Execution history response with history array. - - Returns an object with a "history" key containing an array of history entries. - - Each entry includes prompt_id as a property along with execution data. - - ' - required: - - history - properties: - history: - type: array - description: Array of history entries ordered by creation time (newest first) - items: - $ref: '#/components/schemas/HistoryEntry' - - HubLabelInfo: - type: object - x-runtime: [cloud] - description: '[cloud-only] Metadata for a single Hub label.' - required: - - name - - display_name - - type - properties: - name: - type: string - description: Slug identifier. - display_name: - type: string - description: Human-readable display name. - description: - type: string - description: Optional description of the label. - type: - type: string - enum: - - tag - - model - - custom_node - description: Label category. - - HubLabelListResponse: - type: object - x-runtime: [cloud] - description: '[cloud-only] Response wrapper for the available Hub label catalog.' - required: - - labels - properties: - labels: - type: array - items: - $ref: '#/components/schemas/HubLabelInfo' - description: Available labels, optionally filtered by type. - - HubProfileSummary: - type: object - x-runtime: [cloud] - description: '[cloud-only] Abbreviated Hub profile used in workflow listings.' - required: - - username - properties: - username: - type: string - display_name: - type: string - avatar_url: - type: string - description: Public URL of the profile avatar image. - - HubWorkflowListResponse: - type: object - x-runtime: [cloud] - description: '[cloud-only] Paginated list of Hub workflows matching search criteria.' - required: - - workflows - properties: - workflows: - type: array - items: - anyOf: - - $ref: '#/components/schemas/HubWorkflowSummary' - - $ref: '#/components/schemas/HubWorkflowDetail' - description: Array of HubWorkflowSummary (default) or HubWorkflowDetail (when detail=true). - next_cursor: - type: string - description: Cursor for the next page, empty if no more results. - - HubWorkflowStatus: - type: string - x-runtime: [cloud] - description: '[cloud-only] Public workflow status. NULL in the database is represented as pending in API responses.' - enum: - - pending - - approved - - rejected - - deprecated - - HubWorkflowSummary: - type: object - x-runtime: [cloud] - description: '[cloud-only] Abbreviated Hub workflow metadata used in search and listing results.' - required: - - share_id - - name - - profile - - status - properties: - share_id: - type: string - name: - type: string - status: - $ref: '#/components/schemas/HubWorkflowStatus' - description: - type: string - tags: - type: array - items: - $ref: '#/components/schemas/LabelRef' - models: - type: array - items: - $ref: '#/components/schemas/LabelRef' - custom_nodes: - type: array - items: - $ref: '#/components/schemas/LabelRef' - thumbnail_type: - type: string - enum: - - image - - video - - image_comparison - thumbnail_url: - type: string - thumbnail_comparison_url: - type: string - publish_time: - type: string - format: date-time - nullable: true - profile: - $ref: '#/components/schemas/HubProfileSummary' - metadata: - type: object - additionalProperties: true - tutorial_url: - type: string - sample_image_urls: - type: array - items: - type: string - - HubWorkflowTemplateEntry: - type: object - x-runtime: [cloud] - description: '[cloud-only] Entry in the curated workflow template gallery shown on the home page.' - required: - - name - - title - - status - properties: - name: - type: string - description: Slug identifier for the template - title: - type: string - status: - $ref: '#/components/schemas/HubWorkflowStatus' - description: - type: string - tags: - type: array - items: - type: string - models: - type: array - items: - type: string - requiresCustomNodes: - type: array - items: - type: string - thumbnailVariant: - type: string - mediaType: - type: string - mediaSubtype: - type: string - size: - type: integer - format: int64 - description: Workflow asset size in bytes. - vram: - type: integer - format: int64 - description: Approximate VRAM requirement in bytes. - usage: - type: integer - format: int64 - description: Usage count reported upstream. - searchRank: - type: integer - format: int64 - description: Search ranking score reported upstream. - isEssential: - type: boolean - description: Whether the template belongs to a module marked as essential. - openSource: - type: boolean - profile: - $ref: '#/components/schemas/HubProfileSummary' - tutorialUrl: - type: string - logos: - type: array - items: - type: object - additionalProperties: true - date: - type: string - description: Publication date in YYYY-MM-DD format - io: - type: object - properties: - inputs: - type: array - items: - type: object - additionalProperties: true - outputs: - type: array - items: - type: object - additionalProperties: true - includeOnDistributions: - type: array - items: - type: string - thumbnailUrl: - type: string - description: Public URL of the primary thumbnail - thumbnailComparisonUrl: - type: string - description: Public URL of the comparison thumbnail - shareId: - type: string - description: Share ID for linking to the hub workflow detail - extendedDescription: - type: string - description: AI-generated extended description of the workflow - metaDescription: - type: string - description: AI-generated SEO meta description (under 160 chars) - howToUse: - type: array - items: - type: string - description: AI-generated step-by-step usage instructions - suggestedUseCases: - type: array - items: - type: string - description: AI-generated suggested use cases - faqItems: - type: array - items: - type: object + default_view: + description: Default view mode + enum: + - workflow + - app + type: string + description: + description: Description of the workflow + type: string + forked_from_workflow_id: + description: ID of the source workflow if forked + type: string + forked_from_workflow_version_id: + description: ID of the source workflow version if forked + type: string + name: + description: Display name for the workflow + type: string + workflow_json: + additionalProperties: true + description: The ComfyUI workflow JSON + type: object required: - - question - - answer - properties: - question: - type: string - answer: - type: string - description: AI-generated FAQ items - contentTemplate: - type: string - description: Content template used for generation (tutorial, showcase, comparison, breakthrough) - - JobStatusResponse: - type: object - x-runtime: [cloud] - description: '[cloud-only] Job status information' - properties: - id: - type: string - format: uuid - description: The job ID - status: - type: string - enum: - - waiting_to_dispatch - - pending - - in_progress - - completed - - error - - cancelled - description: Current job status - created_at: - type: string - format: date-time - description: When the job was created - updated_at: - type: string - format: date-time - description: When the job was last updated - last_state_update: - type: string - format: date-time - description: When the job status was last changed - assigned_inference: - type: string - nullable: true - description: The inference instance assigned to this job (if any) - error_message: - type: string - nullable: true - description: Error message if the job failed - required: - - id - - status - - created_at - - updated_at - - JobsListResponse: - type: object - x-runtime: [cloud] - description: '[cloud-only] Paginated list of jobs for the authenticated user.' - required: - - jobs - - pagination - properties: - jobs: - type: array - description: Array of jobs ordered by specified sort field - items: - $ref: '#/components/schemas/JobEntry' - pagination: - $ref: '#/components/schemas/PaginationInfo' - - LabelRef: - type: object - x-runtime: [cloud] - description: '[cloud-only] Reference to a Hub label by ID.' - required: - - name - - display_name - properties: - name: - type: string - description: Slug identifier (e.g. "video-generation", "flux"). - display_name: - type: string - description: Human-readable display name (e.g. "Video Generation", "Flux"). - - LogsResponse: - type: array - x-runtime: [cloud] - description: '[cloud-only] System logs response' - items: - type: object - properties: - timestamp: - type: string - format: date-time - description: When the log entry was created - level: - type: string - enum: - - debug - - info - - warn - - error - description: Log level - message: - type: string - description: Log message - source: - type: string - description: Source of the log entry - metadata: + - workflow_json type: object + CreateWorkflowVersionRequest: + description: Request body for creating a new version of a saved workflow. + properties: + base_version: + description: The version number this change is based on (for optimistic concurrency) + type: integer + workflow_json: + additionalProperties: true + description: The updated ComfyUI workflow JSON + type: object + required: + - base_version + - workflow_json + type: object + ErrorResponse: + description: Standard error response with a machine-readable code and human-readable message. + properties: + code: + type: string + details: + additionalProperties: true + description: Optional open object carrying structured, machine-readable context about the error (e.g. offending field names, validation specifics). Absent for most errors; consumers must not assume any particular shape. + type: object + message: + type: string + required: + - code + - message + type: object + ExecutionError: + description: Detailed execution error information from ComfyUI + properties: + current_inputs: + additionalProperties: true + description: Input values at time of failure (empty object if not available) + type: object + current_outputs: + additionalProperties: true + description: Output values at time of failure (empty object if not available) + type: object + exception_message: + description: Human-readable error message + type: string + exception_type: + description: Python exception type (e.g., "RuntimeError") + type: string + node_id: + description: ID of the node that failed + type: string + node_type: + description: Type name of the node (e.g., "KSampler") + type: string + traceback: + description: Array of traceback lines (empty array if not available) + items: + type: string + type: array + required: + - node_id + - node_type + - exception_message + - exception_type + - traceback + - current_inputs + - current_outputs + type: object + FeedbackRequest: + description: Request to submit user feedback + properties: + content: + description: The feedback content or message + type: string + metadata: + additionalProperties: true + description: Additional metadata about the feedback + type: object + rating: + description: User's rating of ComfyUI Cloud experience (1-5 stars) + maximum: 5 + minimum: 1 + type: integer + type: + description: Type of feedback being submitted + enum: + - missing_nodes + - general + - missing_models + type: string + required: + - type + type: object + FeedbackResponse: + description: Response after submitting feedback + type: object + ForkWorkflowRequest: + description: Request body for forking an existing workflow into the user's account. + properties: + name: + description: Name for the forked workflow + type: string + source_version: + description: Version number to fork from + type: integer + required: + - source_version + type: object + GetUserDataResponseFull: + description: List of user data file entries (each with path, size, and modification time) returned when full_info=true. + items: + $ref: '#/components/schemas/GetUserDataResponseFullFile' + type: array + GetUserDataResponseFullFile: + description: Individual file entry within a full user data response. + properties: + modified: + description: UNIX timestamp of the last modification in milliseconds. + format: int64 + type: integer + path: + description: File name or path relative to the user directory. + type: string + size: + description: File size in bytes. + type: integer + type: object + GlobalSubgraphData: + description: Full data for a global subgraph blueprint + properties: + data: + description: The full subgraph JSON data as a string + type: string + info: + description: Additional information about the subgraph + properties: + node_pack: + description: The node pack/module that provides this subgraph + type: string + required: + - node_pack + type: object + name: + description: Display name of the subgraph blueprint + type: string + source: + description: Source type of the subgraph - "templates" for workflow templates or "custom_node" for custom node subgraphs + type: string + required: + - source + - name + - info + - data + type: object + GlobalSubgraphInfo: + description: Metadata for a global subgraph blueprint (without full data) + properties: + data: + description: The full subgraph JSON data (may be empty in list view) + type: string + info: + description: Additional information about the subgraph + properties: + node_pack: + description: The node pack/module that provides this subgraph + type: string + required: + - node_pack + type: object + name: + description: Display name of the subgraph blueprint + type: string + source: + description: Source type of the subgraph - "templates" for workflow templates or "custom_node" for custom node subgraphs + type: string + required: + - source + - name + - info + type: object + HistoryDetailEntry: + description: History entry with full prompt data + properties: + meta: + additionalProperties: true + description: Metadata about the execution and nodes + type: object + outputs: + additionalProperties: true + description: Output data from execution (generated images, files, etc.) + type: object + prompt: + description: Full prompt execution data + properties: + extra_data: + additionalProperties: true + description: Additional execution data + type: object + outputs_to_execute: + description: Output nodes to execute + items: + type: string + type: array + priority: + description: Execution priority + format: double + type: number + prompt: + additionalProperties: true + description: The workflow nodes + type: object + prompt_id: + description: The prompt ID + type: string + type: object + status: + additionalProperties: true + description: Execution status and timeline information + type: object + type: object + HistoryDetailResponse: + additionalProperties: + $ref: '#/components/schemas/HistoryDetailEntry' + description: | + Detailed execution history response for a specific prompt. + Returns a dictionary with prompt_id as key and full history data as value. + type: object + HistoryEntry: + description: History entry with prompt_id and execution data + properties: + create_time: + description: Job creation timestamp (Unix timestamp in milliseconds) + format: int64 + type: integer + meta: + additionalProperties: true + description: Metadata about the execution and nodes + type: object + outputs: + additionalProperties: true + description: Output data from execution (generated images, files, etc.) + type: object + prompt: + description: Filtered prompt execution data (lightweight format) + properties: + extra_data: + additionalProperties: true + description: Additional execution data (workflow removed from extra_pnginfo) + type: object + priority: + description: Execution priority + format: double + type: number + prompt_id: + description: The prompt ID + type: string + type: object + prompt_id: + description: Unique identifier for this prompt execution + type: string + status: + additionalProperties: true + description: Execution status and timeline information + type: object + workflow_id: + description: UUID identifying the workflow graph definition + type: string + required: + - prompt_id + type: object + HistoryManageRequest: + additionalProperties: false + description: Request to manage history operations + properties: + clear: + description: If true, clear all history for the authenticated user + type: boolean + delete: + description: Array of job IDs to delete from history + items: + type: string + type: array + type: object + HistoryResponse: + description: | + Execution history response with history array. + Returns an object with a "history" key containing an array of history entries. + Each entry includes prompt_id as a property along with execution data. + properties: + history: + description: Array of history entries ordered by creation time (newest first) + items: + $ref: '#/components/schemas/HistoryEntry' + type: array + required: + - history + type: object + JobCancelResponse: + description: Response for POST /api/jobs/{job_id}/cancel. Returned on both fresh cancels and idempotent no-ops. + properties: + cancelled: + description: | + True when a cancel event was successfully dispatched by this call. + False when the job was already in a terminal or cancelling state, + in which case the call is a no-op (still 200 — idempotent). + type: boolean + required: + - cancelled + type: object + JobDetailResponse: + description: Full job details including workflow and outputs + properties: + create_time: + description: Job creation timestamp (Unix timestamp in milliseconds) + format: int64 + type: integer + execution_error: + allOf: + - $ref: '#/components/schemas/ExecutionError' + description: Detailed execution error from ComfyUI (only for failed jobs with structured error data) + execution_meta: + additionalProperties: true + description: Node-level execution metadata (only for terminal states) + type: object + execution_status: + additionalProperties: true + description: ComfyUI execution status and timeline (only for terminal states) + type: object + id: + description: Unique job identifier + format: uuid + type: string + outputs: + additionalProperties: true + description: Full outputs object from ComfyUI (only for terminal states) + type: object + outputs_count: + description: Total number of output files (omitted for non-terminal states) + type: integer + preview_output: + additionalProperties: true + description: Primary preview output (only for terminal states) + type: object + status: + description: User-friendly job status + enum: + - pending + - in_progress + - completed + - failed + - cancelled + type: string + update_time: + description: Last update timestamp (Unix timestamp in milliseconds) + format: int64 + type: integer + workflow: + additionalProperties: true + description: | + Full ComfyUI workflow (10-100KB, omitted if not available). + + Sensitive credentials are redacted before the response is returned: + `extra_data.api_key_comfy_org`, when present, is replaced with the + literal string `"[REDACTED]"`. The field is preserved (not removed) + so existence checks still pass, but the value is not usable. + type: object + workflow_id: + description: UUID identifying the workflow graph definition + type: string + required: + - id + - status + - create_time + - update_time + type: object + JobEntry: + description: Lightweight job data for list views (workflow and full outputs excluded) + properties: + create_time: + description: Job creation timestamp (Unix timestamp in milliseconds) + format: int64 + type: integer + execution_end_time: + description: Workflow execution completion timestamp (Unix milliseconds, only present for terminal states) + format: int64 + type: integer + execution_error: + allOf: + - $ref: '#/components/schemas/ExecutionError' + description: Detailed execution error from ComfyUI (only for failed jobs with structured error data) + execution_start_time: + description: Workflow execution start timestamp (Unix milliseconds, only present for terminal states) + format: int64 + type: integer + id: + description: Unique job identifier + format: uuid + type: string + outputs_count: + description: Total number of output files (omitted for non-terminal states) + type: integer + preview_output: + additionalProperties: true + description: Primary preview output (only present for terminal states) + type: object + status: + description: User-friendly job status + enum: + - pending + - in_progress + - completed + - failed + - cancelled + type: string + workflow_id: + description: UUID identifying the workflow graph definition + type: string + required: + - id + - status + - create_time + type: object + JobStatusResponse: + description: Job status information + properties: + assigned_inference: + description: The inference instance assigned to this job (if any) + nullable: true + type: string + created_at: + description: When the job was created + format: date-time + type: string + error_message: + description: Error message if the job failed + nullable: true + type: string + id: + description: The job ID + format: uuid + type: string + last_state_update: + description: When the job status was last changed + format: date-time + type: string + status: + description: Current job status + enum: + - waiting_to_dispatch + - pending + - in_progress + - completed + - error + - cancelled + type: string + updated_at: + description: When the job was last updated + format: date-time + type: string + required: + - id + - status + - created_at + - updated_at + type: object + JobsCancelRequest: + additionalProperties: false + description: Request to cancel multiple jobs by ID. + properties: + job_ids: + description: Job identifiers (UUIDs) to cancel. + items: + format: uuid + type: string + maxItems: 100 + minItems: 1 + type: array + required: + - job_ids + type: object + JobsCancelResponse: + description: Response for POST /api/jobs/cancel. + properties: + cancelled: + description: | + Job IDs for which a cancel event was successfully dispatched by this + call. Jobs already in a terminal or cancelling state are idempotently + skipped and will not appear here. + items: + type: string + type: array + required: + - cancelled + type: object + JobsListResponse: + description: Paginated list of jobs for the authenticated user. + properties: + jobs: + description: Array of jobs ordered by specified sort field + items: + $ref: '#/components/schemas/JobEntry' + type: array + pagination: + $ref: '#/components/schemas/PaginationInfo' + required: + - jobs + - pagination + type: object + ListAssetsResponse: + description: Paginated list of assets belonging to the authenticated user. + properties: + assets: + description: List of assets matching the query + items: + $ref: '#/components/schemas/Asset' + type: array + has_more: + description: Whether more assets are available beyond this page + type: boolean + next_cursor: + description: | + Opaque cursor to pass as the `after` query parameter to fetch the + next page. Omitted from the response when there are no more results. + type: string + total: + description: Total number of assets matching the filters + type: integer + required: + - assets + - total + - has_more + type: object + ListTagsResponse: + description: Paginated list of available asset tags. + properties: + has_more: + description: Whether more tags are available + type: boolean + tags: + description: List of tags + items: + $ref: '#/components/schemas/TagInfo' + type: array + total: + description: Total number of tags + type: integer + required: + - tags + - total + - has_more + type: object + ModelFile: + description: Represents a model file with metadata + properties: + name: + description: The filename of the model + example: model.safetensors + type: string + pathIndex: + description: Index of the path where this model is located + example: 0 + type: integer + required: + - name + - pathIndex + type: object + ModelFolder: + description: Represents a folder containing models + properties: + folders: + description: List of paths where models of this type are stored + example: + - checkpoints + items: + type: string + type: array + name: + description: The name of the model folder + example: checkpoints + type: string + required: + - name + - folders + type: object + NodeInfo: + description: Metadata describing a single ComfyUI node type and its inputs/outputs. + properties: + api_node: + description: Whether this is an API node + type: boolean + category: + description: Category of the node + type: string + deprecated: + description: Whether the node is deprecated + type: boolean + description: + description: Description of the node + type: string + display_name: + description: Display name of the node + type: string + experimental: + description: Whether the node is experimental + type: boolean + input: + additionalProperties: true + description: Input specifications for the node + type: object + input_order: + additionalProperties: + items: + type: string + type: array + description: Order of inputs for display + type: object + name: + description: Internal name of the node + type: string + output: + description: Output types of the node + items: + type: string + type: array + output_is_list: + description: Whether each output is a list + items: + type: boolean + type: array + output_name: + description: Names of the outputs + items: + type: string + type: array + output_node: + description: Whether this is an output node + type: boolean + output_tooltips: + description: Tooltips for outputs + items: + type: string + type: array + python_module: + description: Python module implementing the node + type: string + type: object + PaginationInfo: + description: | + Pagination metadata included in list responses. Supports both legacy + offset/limit pagination and cursor-based pagination. When cursor-based + pagination is used, `next_cursor` is the primary pagination token and + `offset`/`total` may be zero. + properties: + has_more: + description: Whether more items are available beyond this page + type: boolean + limit: + description: Items per page + minimum: 1 + type: integer + next_cursor: + description: | + Opaque cursor for the next page. Pass this value as the `after` + query parameter on the next request. Empty or absent when there + are no more results. + type: string + offset: + deprecated: true + description: 'Current offset (0-based). Deprecated: use cursor-based pagination.' + minimum: 0 + type: integer + total: + description: Total number of items matching filters (may be 0 when using cursor pagination) + minimum: 0 + type: integer + required: + - offset + - limit + - total + - has_more + type: object + PromptErrorResponse: additionalProperties: true - description: Additional log metadata + description: Error response for ComfyUI prompt execution. + type: object + PromptInfo: + description: Metadata about the currently running and queued prompts. + properties: + exec_info: + properties: + queue_remaining: + description: Number of items remaining in the queue + type: integer + type: object + type: object + PromptRequest: + description: Request body for submitting a ComfyUI workflow prompt for execution. + properties: + extra_data: + additionalProperties: true + description: Extra data to be associated with the prompt + type: object + front: + description: If true, adds the prompt to the front of the queue + type: boolean + number: + description: Priority number for the queue (lower numbers have higher priority) + type: number + partial_execution_targets: + description: List of node names to execute + items: + type: string + type: array + prompt: + additionalProperties: true + description: The workflow graph to execute + type: object + workflow_id: + description: UUID identifying the cloud workflow entity to associate with this job + type: string + workflow_version_id: + description: UUID identifying the workflow version to associate with this job + type: string + required: + - prompt + type: object + PromptResponse: + description: Response returned after successfully queuing a workflow prompt. + properties: + node_errors: + additionalProperties: true + description: Any errors in the nodes of the prompt + type: object + number: + description: Priority number in the queue + type: number + prompt_id: + description: Unique identifier for the prompt execution + format: uuid + type: string + type: object + PublishWorkflowAssetsRequest: + description: Request body for publishing workflow assets to the Hub. + properties: + asset_ids: + description: IDs of assets (inputs and models) to snapshot. + items: + type: string + type: array + required: + - asset_ids + type: object + PublishedWorkflowDetail: + description: Full detail of a publicly published workflow on the Hub. + properties: + assets: + description: Published assets with their library status for the caller. + items: + $ref: '#/components/schemas/AssetInfo' + type: array + listed: + type: boolean + name: + description: Human-readable workflow name. + type: string + publish_time: + format: date-time + nullable: true + type: string + share_id: + type: string + workflow_id: + type: string + workflow_json: + additionalProperties: true + description: The workflow JSON content at publish time. + type: object + required: + - share_id + - workflow_id + - name + - listed + - workflow_json + - assets + type: object + QueueInfo: + description: Queue information with pending and running jobs + properties: + queue_pending: + description: Array of pending job items (ordered by creation time, oldest first) + items: + description: | + Queue item tuple format: [job_number, prompt_id, workflow_json, output_node_ids, metadata] + - [0] job_number (integer): Position in queue (1-based) + - [1] prompt_id (string): Job UUID + - [2] workflow_json (object): Full ComfyUI workflow + - [3] output_node_ids (array): Node IDs to return results from + - [4] metadata (object): Contains {create_time: } + items: {} + maxItems: 5 + minItems: 5 + type: array + type: array + queue_running: + description: Array of currently running job items + items: + description: | + Queue item tuple format: [job_number, prompt_id, workflow_json, output_node_ids, metadata] + - [0] job_number (integer): Position in queue (1-based) + - [1] prompt_id (string): Job UUID + - [2] workflow_json (object): Full ComfyUI workflow + - [3] output_node_ids (array): Node IDs to return results from + - [4] metadata (object): Contains {create_time: } + items: {} + maxItems: 5 + minItems: 5 + type: array + type: array + type: object + QueueManageRequest: + additionalProperties: false + description: Request to manage queue operations + properties: + clear: + description: If true, clear all pending jobs from the queue + type: boolean + delete: + description: Array of job IDs to cancel; pending and running jobs transition to cancelled + items: + type: string + type: array + type: object + QueueManageResponse: + description: Response after a queue management action (delete or clear). + properties: + cleared: + description: Whether the queue was cleared + type: boolean + deleted: + description: Array of job IDs that were successfully cancelled + items: + type: string + type: array + type: object + SystemStatsResponse: + description: System statistics response + properties: + devices: + items: + properties: + name: + description: Device name + type: string + type: + description: Device type + type: string + vram_free: + description: Free VRAM in bytes + type: number + vram_total: + description: Total VRAM in bytes + type: number + required: + - name + - type + type: object + type: array + system: + properties: + argv: + description: Command line arguments + items: + type: string + type: array + cloud_version: + description: Cloud ingest service version (commit hash) + type: string + comfyui_frontend_version: + description: ComfyUI frontend version (commit hash or tag) + type: string + comfyui_version: + description: ComfyUI version + type: string + deploy_environment: + description: How this ComfyUI instance is deployed (e.g. cloud, local-git, local-portable, local-desktop) + type: string + embedded_python: + description: Whether using embedded Python + type: boolean + os: + description: Operating system + type: string + python_version: + description: Python version + type: string + pytorch_version: + description: PyTorch version + type: string + ram_free: + description: Free RAM in bytes + type: number + ram_total: + description: Total RAM in bytes + type: number + workflow_templates_version: + description: Workflow templates version + type: string + required: + - os + - python_version + - embedded_python + - comfyui_version + - pytorch_version + - argv + - ram_total + - ram_free + type: object + required: + - system + - devices + type: object + TagInfo: + description: Metadata for a single tag that can be applied to assets. + properties: + count: + description: Number of assets using this tag + type: integer + name: + description: Tag name + type: string + required: + - name + - count + type: object + TagsModificationResponse: + description: Response after adding, updating, or removing tags on an asset. + properties: + added: + description: Tags that were successfully added (for add operation) + items: + type: string + type: array + already_present: + description: Tags that were already present (for add operation) + items: + type: string + type: array + not_present: + description: Tags that were not present (for remove operation) + items: + type: string + type: array + removed: + description: Tags that were successfully removed (for remove operation) + items: + type: string + type: array + total_tags: + description: All tags on the asset after the operation + items: + type: string + type: array + required: + - total_tags + type: object + TaskEntry: + description: Task data for list views + properties: + completed_at: + description: When task completed or failed (null if not finished) + format: date-time + type: string + create_time: + description: Task creation timestamp + format: date-time + type: string + id: + description: Unique task identifier + format: uuid + type: string + started_at: + description: When task execution started (null if not started) + format: date-time + type: string + status: + description: Current task status + enum: + - created + - running + - completed + - failed + type: string + task_name: + description: Task type name (e.g., model_upload) + type: string + required: + - id + - task_name + - status + - create_time + type: object + TaskResponse: + description: Full task details including payload and result + properties: + completed_at: + description: When task completed or failed (null if not finished) + format: date-time + type: string + create_time: + description: Task creation timestamp + format: date-time + type: string + error_message: + description: Error message on failure (null if not failed) + type: string + id: + description: Unique task identifier + format: uuid + type: string + idempotency_key: + description: Caller-provided key for idempotent task creation + type: string + payload: + additionalProperties: true + description: Task input data + type: object + result: + additionalProperties: true + description: Task output data (null if not completed) + type: object + started_at: + description: When task execution started (null if not started) + format: date-time + type: string + status: + description: Current task status + enum: + - created + - running + - completed + - failed + type: string + task_name: + description: Task type name (e.g., model_upload) + type: string + update_time: + description: Task last update timestamp + format: date-time + type: string + required: + - id + - idempotency_key + - task_name + - payload + - status + - create_time + - update_time + type: object + TasksListResponse: + description: Paginated list of background tasks for the authenticated user. + properties: + pagination: + $ref: '#/components/schemas/PaginationInfo' + tasks: + description: Array of tasks ordered by create_time + items: + $ref: '#/components/schemas/TaskEntry' + type: array + required: + - tasks + - pagination + type: object + UpdateWorkflowRequest: + description: Request body for updating an existing saved workflow. + properties: + default_view: + description: New default view mode + enum: + - workflow + - app + type: string + description: + description: New description + type: string + name: + description: New display name + type: string + type: object + UserDataResponseFull: + description: User data listing entry with file metadata (path, size, modification time). + properties: + modified: + description: UNIX timestamp of the last modification in milliseconds. + format: int64 + type: integer + path: + type: string + size: + type: integer + type: object + UserResponse: + description: User information response + properties: + id: + description: Firebase UID of the authenticated user + type: string + status: + description: User status (always "active" for authenticated users) + type: string + required: + - id + - status + type: object + WorkflowForkedFrom: + description: Reference to the parent workflow from which this workflow was forked. + properties: + workflow_id: + type: string + workflow_version_id: + type: string + type: object + WorkflowListResponse: + description: Paginated list of saved workflows. + properties: + data: + items: + $ref: '#/components/schemas/WorkflowResponse' + type: array + pagination: + $ref: '#/components/schemas/PaginationInfo' + required: + - data + - pagination + type: object + WorkflowPublishInfo: + description: Publishing metadata for a workflow shared to the Hub. + properties: + assets: + description: Published assets (inputs and models). + items: + $ref: '#/components/schemas/AssetInfo' + type: array + listed: + type: boolean + publish_time: + format: date-time + nullable: true + type: string + share_id: + type: string + workflow_id: + type: string + required: + - workflow_id + - share_id + - listed + - assets + type: object + WorkflowResponse: + description: Full workflow entity including metadata and version history. + properties: + created_at: + format: date-time + type: string + created_by: + type: string + default_view: + enum: + - workflow + - app + type: string + description: + type: string + forked_from: + $ref: '#/components/schemas/WorkflowForkedFrom' + id: + type: string + latest_version: + type: integer + name: + type: string + updated_at: + format: date-time + type: string + required: + - id + - latest_version + - created_by + - created_at + - updated_at + type: object + WorkflowVersionContentResponse: + description: Full workflow version including the serialized workflow JSON. + properties: + created_at: + format: date-time + type: string + created_by: + type: string + dependency_asset_ids: + items: + type: string + type: array + id: + type: string + version: + type: integer + workflow_json: + additionalProperties: true + type: object + required: + - id + - version + - workflow_json + - created_by + - created_at + type: object + WorkflowVersionResponse: + description: Metadata for a single workflow version. + properties: + created_at: + format: date-time + type: string + created_by: + type: string + id: + type: string + latest_version: + type: integer + version: + type: integer + required: + - id + - version + - latest_version + - created_by + - created_at + type: object + securitySchemes: + ApiKeyAuth: + description: | + API key authentication. Keys are prefixed with 'comfyui-' and can be + generated from user account settings. Example: 'comfyui-abc123...' + in: header + name: X-API-Key + type: apiKey + BearerAuth: + bearerFormat: JWT + description: | + Firebase JWT token authentication. Obtain a token by authenticating + with Firebase and pass it in the Authorization header. + scheme: bearer + type: http + CookieAuth: + description: | + Session cookie authentication. Set automatically after successful + login via the /api/auth/session endpoint. + in: cookie + name: session + type: apiKey +info: + description: | + API for ComfyUI - A powerful and modular UI for Stable Diffusion. - Member: - type: object - x-runtime: [cloud] - description: '[cloud-only] Workspace member with profile and role information.' - required: - - id - - name - - email - - role - - joined_at - properties: - id: - type: string - description: User ID - name: - type: string - description: User's display name - email: - type: string - format: email - description: User's email address - role: - type: string - enum: - - owner - - member - description: User's role in the workspace - joined_at: - type: string - format: date-time - description: When the user joined the workspace + This API allows you to interact with ComfyUI programmatically, including: + - Retrieving prompt information + - Retrieving node information + license: + name: GNU General Public License v3.0 + url: https://github.com/Comfy-Org/ComfyUI/blob/master/LICENSE + title: ComfyUI API + version: 1.0.0 +openapi: 3.0.3 +paths: + /api/assets: + get: + description: | + Retrieves a paginated list of assets belonging to the authenticated user. + Supports filtering by tags, name, metadata, and sorting options. + operationId: listAssets + parameters: + - description: Filter assets that have ALL of these tags + explode: false + in: query + name: include_tags + schema: + items: + type: string + type: array + style: form + - description: Exclude assets that have ANY of these tags + explode: false + in: query + name: exclude_tags + schema: + items: + type: string + type: array + style: form + - description: Filter assets where name contains this substring (case-insensitive) + in: query + name: name_contains + schema: + type: string + - description: JSON object for filtering by metadata fields + in: query + name: metadata_filter + schema: + type: string + - description: Maximum number of assets to return (1-500) + in: query + name: limit + schema: + default: 20 + maximum: 500 + minimum: 1 + type: integer + - description: Number of assets to skip for pagination + in: query + name: offset + schema: + default: 0 + minimum: 0 + type: integer + - description: Field to sort by + in: query + name: sort + schema: + default: created_at + enum: + - name + - created_at + - updated_at + - size + - last_access_time + type: string + - description: Sort order + in: query + name: order + schema: + default: desc + enum: + - asc + - desc + type: string + - description: Whether to include public/shared assets in results + in: query + name: include_public + schema: + default: true + type: boolean + - description: Filter assets by exact content hash. + in: query + name: hash + schema: + type: string + - description: | + Opaque cursor for keyset pagination. Pass the `next_cursor` value + from the previous response to fetch the next page. When provided, + `offset` is ignored. Cursor pagination is only supported with + `sort` values `created_at`, `updated_at`, `name`, or `size`; + requests combining `after` with other sort fields return 400. + The cursor must have been minted under the same `sort` value used + in the follow-up request. + in: query + name: after + schema: + type: string + responses: + "200": + content: + application/json: + schema: + $ref: '#/components/schemas/ListAssetsResponse' + description: Success - Assets returned + "400": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Invalid request parameters + "401": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Unauthorized + "500": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Internal server error + summary: List user assets + tags: + - file + post: + description: | + Creates a new asset from a direct file upload (multipart/form-data) with associated metadata. - OAuthRegisterBadRequestResponse: - x-runtime: [cloud] - description: "[cloud-only] Union of the two 400 shapes /oauth/register can emit. `OAuthRegisterError` is the handler-shaped\ - \ RFC 7591 \xA73.2.2 error; `BindingErrorResponse` is the strict-server binding-layer error fired when the request body\ - \ fails OpenAPI-schema validation before the handler runs.\n" - oneOf: - - $ref: '#/components/schemas/OAuthRegisterError' - - $ref: '#/components/schemas/BindingErrorResponse' + If an asset with the same hash already exists, returns the existing asset. + operationId: createAsset + requestBody: + content: + multipart/form-data: + schema: + properties: + file: + description: The asset file to upload + format: binary + type: string + hash: + description: Content hash of the file. + pattern: ^(blake3|sha256):[a-f0-9]{64}$ + type: string + id: + description: Optional asset ID for idempotent creation. If provided and asset exists, returns existing asset. + format: uuid + type: string + mime_type: + description: MIME type of the asset (e.g., "image/png", "video/mp4") + type: string + name: + description: Display name for the asset + type: string + preview_id: + description: Optional preview asset ID. If not provided, images will use their own ID as preview. + format: uuid + type: string + tags: + description: JSON-encoded array of freeform tag strings, e.g. '["models","checkpoint"]'. Common types include "models", "input", "output", and "temp", but any tag can be used in any order. + type: string + user_metadata: + description: Custom JSON metadata as a string + type: string + required: + - file + type: object + required: true + responses: + "200": + content: + application/json: + schema: + $ref: '#/components/schemas/AssetCreated' + description: | + Asset already existed for this user (deduplicated by content hash); the + existing asset is returned with created_new=false. + "201": + content: + application/json: + schema: + $ref: '#/components/schemas/AssetCreated' + description: Asset created successfully (created_new=true) + "400": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Invalid request (bad file, invalid content type, etc.) + "401": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Unauthorized + "413": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: File too large + "415": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Unsupported media type + "500": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Internal server error + summary: Create a new asset + tags: + - file + /api/assets/{id}: + delete: + description: Deletes the asset record. + operationId: deleteAsset + parameters: + - description: Asset ID + in: path + name: id + required: true + schema: + format: uuid + type: string + responses: + "204": + description: Asset record deleted successfully + "401": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Unauthorized + "404": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Asset not found + "409": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: 'Asset cannot be deleted because it is referenced by another resource, e.g. a workflow version (error code: ASSET_IN_USE)' + "500": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Internal server error + summary: Delete asset + tags: + - file + get: + description: Retrieves detailed information about a specific asset + operationId: getAssetById + parameters: + - description: Asset ID + in: path + name: id + required: true + schema: + format: uuid + type: string + responses: + "200": + content: + application/json: + schema: + $ref: '#/components/schemas/Asset' + description: Asset details retrieved successfully + "401": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Unauthorized + "404": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Asset not found + "500": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Internal server error + summary: Get asset details + tags: + - file + put: + description: | + Updates an asset's metadata. At least one field must be provided. + Only name, mime_type, preview_id, and user_metadata can be updated. + For tag management, use POST (add) and DELETE (remove) /api/assets/{id}/tags. + operationId: updateAsset + parameters: + - description: Asset ID + in: path + name: id + required: true + schema: + format: uuid + type: string + requestBody: + content: + application/json: + schema: + minProperties: 1 + properties: + mime_type: + description: Updated MIME type of the asset + type: string + name: + description: New display name for the asset + type: string + preview_id: + description: Updated preview asset ID + format: uuid + type: string + user_metadata: + additionalProperties: true + description: Updated custom metadata + type: object + type: object + required: true + responses: + "200": + content: + application/json: + schema: + $ref: '#/components/schemas/AssetUpdated' + description: Asset updated successfully + "400": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: | + Invalid request — no fields provided, or `preview_id` is the zero UUID + (`INVALID_PREVIEW_ID`). + "401": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Unauthorized + "404": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: | + Asset not found — returned both when the asset being updated does + not exist and when `preview_id` does not reference an asset + accessible to the caller. + "500": + content: + application/json: + schema: + $ref: '#/components/schemas/ErrorResponse' + description: Internal server error + summary: Update asset metadata + tags: + - file + /api/assets/{id}/content: + get: + description: | + Returns the binary content of an asset by ID. - PendingInvite: - type: object - x-runtime: [cloud] - description: '[cloud-only] An outstanding workspace invitation that has not yet been accepted.' - required: - - id - - email - - invited_at - - expires_at - properties: - id: - type: string - description: Invite ID - email: - type: string - format: email - description: Email address of the invited user - token: - type: string - description: Invite token for constructing invite links. Empty for expired invites. - invited_at: - type: string - format: date-time - description: When the invite was created - expires_at: - type: string - format: date-time - description: When the invite expires + The contract is the same across runtimes — "GET this path and you + receive the asset's bytes" — but the mechanism differs: + - **Local ComfyUI** streams the bytes directly (`200`, + `application/octet-stream`). + - **Cloud** does not proxy large files; it responds `302` with a + `Location` redirect to a short-lived signed storage URL. Clients that + follow redirects (browsers, `fetch`/XHR, ``/`