feat: Add VideoSlice node with lazy operations on VideoInput

- Add VideoOp base class and SliceOp in _input/video_types.py
- Add sliced() method to VideoInput that returns a copy with operation appended
- Each subclass applies operations in get_components() and get_frame_count()
- After materialization, VideoFromFile delegates to internal VideoFromComponents
- Add VideoSlice node that uses video.sliced(start_frame, frame_count)
- Add tests for SliceOp, sliced() behavior, and materialization

README.md

@@ -108,7 +108,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
 - [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
 - Latent previews with [TAESD](#how-to-show-high-quality-previews)
 - Works fully offline: core will never download anything unless you want to.
-- Optional API nodes to use paid models from external providers through the online [Comfy API](https://docs.comfy.org/tutorials/api-nodes/overview).
+- Optional API nodes to use paid models from external providers through the online [Comfy API](https://docs.comfy.org/tutorials/api-nodes/overview) disable with: `--disable-api-nodes`
 - [Config file](extra_model_paths.yaml.example) to set the search paths for models.
 
 Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)
@@ -212,7 +212,7 @@ Python 3.14 works but you may encounter issues with the torch compile node. The
 
 Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
 
-torch 2.4 and above is supported but some features might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old.
+torch 2.4 and above is supported but some features and optimizations might only work on newer versions. We generally recommend using the latest major version of pytorch with the latest cuda version unless it is less than 2 weeks old.
 
 ### Instructions:
 
@@ -229,7 +229,7 @@ AMD users can install rocm and pytorch with pip if you don't have it already ins
 
 ```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.4```
 
-This is the command to install the nightly with ROCm 7.0 which might have some performance improvements:
+This is the command to install the nightly with ROCm 7.1 which might have some performance improvements:
 
 ```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.1```
 
@@ -240,7 +240,7 @@ These have less hardware support than the builds above but they work on windows.
 
 RDNA 3 (RX 7000 series):
 
-```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-dgpu/```
+```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-all/```
 
 RDNA 3.5 (Strix halo/Ryzen AI Max+ 365):
 

app/assets/api/routes.py

@@ -1,20 +1,14 @@
 import logging
 import uuid
-import urllib.parse
-import os
-import contextlib
 from aiohttp import web
 
 from pydantic import ValidationError
 
 import app.assets.manager as manager
-import app.assets.scanner as scanner
 from app import user_manager
 from app.assets.api import schemas_in
 from app.assets.helpers import get_query_dict
 
-import folder_paths
-
 ROUTES = web.RouteTableDef()
 USER_MANAGER: user_manager.UserManager | None = None
 
@@ -34,18 +28,6 @@ def _validation_error_response(code: str, ve: ValidationError) -> web.Response:
     return _error_response(400, code, "Validation failed.", {"errors": ve.json()})
 
 
-@ROUTES.head("/api/assets/hash/{hash}")
-async def head_asset_by_hash(request: web.Request) -> web.Response:
-    hash_str = request.match_info.get("hash", "").strip().lower()
-    if not hash_str or ":" not in hash_str:
-        return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-    algo, digest = hash_str.split(":", 1)
-    if algo != "blake3" or not digest or any(c for c in digest if c not in "0123456789abcdef"):
-        return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-    exists = manager.asset_exists(asset_hash=hash_str)
-    return web.Response(status=200 if exists else 404)
-
-
 @ROUTES.get("/api/assets")
 async def list_assets(request: web.Request) -> web.Response:
     """
@@ -94,321 +76,6 @@ async def get_asset(request: web.Request) -> web.Response:
     return web.json_response(result.model_dump(mode="json"), status=200)
 
 
-@ROUTES.get(f"/api/assets/{{id:{UUID_RE}}}/content")
-async def download_asset_content(request: web.Request) -> web.Response:
-    # question: do we need disposition? could we just stick with one of these?
-    disposition = request.query.get("disposition", "attachment").lower().strip()
-    if disposition not in {"inline", "attachment"}:
-        disposition = "attachment"
-
-    try:
-        abs_path, content_type, filename = manager.resolve_asset_content_for_download(
-            asset_info_id=str(uuid.UUID(request.match_info["id"])),
-            owner_id=USER_MANAGER.get_request_user_id(request),
-        )
-    except ValueError as ve:
-        return _error_response(404, "ASSET_NOT_FOUND", str(ve))
-    except NotImplementedError as nie:
-        return _error_response(501, "BACKEND_UNSUPPORTED", str(nie))
-    except FileNotFoundError:
-        return _error_response(404, "FILE_NOT_FOUND", "Underlying file not found on disk.")
-
-    quoted = (filename or "").replace("\r", "").replace("\n", "").replace('"', "'")
-    cd = f'{disposition}; filename="{quoted}"; filename*=UTF-8\'\'{urllib.parse.quote(filename)}'
-
-    resp = web.FileResponse(abs_path)
-    resp.content_type = content_type
-    resp.headers["Content-Disposition"] = cd
-    return resp
-
-
-@ROUTES.post("/api/assets/from-hash")
-async def create_asset_from_hash(request: web.Request) -> web.Response:
-    try:
-        payload = await request.json()
-        body = schemas_in.CreateFromHashBody.model_validate(payload)
-    except ValidationError as ve:
-        return _validation_error_response("INVALID_BODY", ve)
-    except Exception:
-        return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
-
-    result = manager.create_asset_from_hash(
-        hash_str=body.hash,
-        name=body.name,
-        tags=body.tags,
-        user_metadata=body.user_metadata,
-        owner_id=USER_MANAGER.get_request_user_id(request),
-    )
-    if result is None:
-        return _error_response(404, "ASSET_NOT_FOUND", f"Asset content {body.hash} does not exist")
-    return web.json_response(result.model_dump(mode="json"), status=201)
-
-
-@ROUTES.post("/api/assets")
-async def upload_asset(request: web.Request) -> web.Response:
-    """Multipart/form-data endpoint for Asset uploads."""
-
-    if not (request.content_type or "").lower().startswith("multipart/"):
-        return _error_response(415, "UNSUPPORTED_MEDIA_TYPE", "Use multipart/form-data for uploads.")
-
-    reader = await request.multipart()
-
-    file_present = False
-    file_client_name: str | None = None
-    tags_raw: list[str] = []
-    provided_name: str | None = None
-    user_metadata_raw: str | None = None
-    provided_hash: str | None = None
-    provided_hash_exists: bool | None = None
-
-    file_written = 0
-    tmp_path: str | None = None
-    while True:
-        field = await reader.next()
-        if field is None:
-            break
-
-        fname = getattr(field, "name", "") or ""
-
-        if fname == "hash":
-            try:
-                s = ((await field.text()) or "").strip().lower()
-            except Exception:
-                return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-
-            if s:
-                if ":" not in s:
-                    return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-                algo, digest = s.split(":", 1)
-                if algo != "blake3" or not digest or any(c for c in digest if c not in "0123456789abcdef"):
-                    return _error_response(400, "INVALID_HASH", "hash must be like 'blake3:<hex>'")
-                provided_hash = f"{algo}:{digest}"
-                try:
-                    provided_hash_exists = manager.asset_exists(asset_hash=provided_hash)
-                except Exception:
-                    provided_hash_exists = None  # do not fail the whole request here
-
-        elif fname == "file":
-            file_present = True
-            file_client_name = (field.filename or "").strip()
-
-            if provided_hash and provided_hash_exists is True:
-                # If client supplied a hash that we know exists, drain but do not write to disk
-                try:
-                    while True:
-                        chunk = await field.read_chunk(8 * 1024 * 1024)
-                        if not chunk:
-                            break
-                        file_written += len(chunk)
-                except Exception:
-                    return _error_response(500, "UPLOAD_IO_ERROR", "Failed to receive uploaded file.")
-                continue  # Do not create temp file; we will create AssetInfo from the existing content
-
-            # Otherwise, store to temp for hashing/ingest
-            uploads_root = os.path.join(folder_paths.get_temp_directory(), "uploads")
-            unique_dir = os.path.join(uploads_root, uuid.uuid4().hex)
-            os.makedirs(unique_dir, exist_ok=True)
-            tmp_path = os.path.join(unique_dir, ".upload.part")
-
-            try:
-                with open(tmp_path, "wb") as f:
-                    while True:
-                        chunk = await field.read_chunk(8 * 1024 * 1024)
-                        if not chunk:
-                            break
-                        f.write(chunk)
-                        file_written += len(chunk)
-            except Exception:
-                try:
-                    if os.path.exists(tmp_path or ""):
-                        os.remove(tmp_path)
-                finally:
-                    return _error_response(500, "UPLOAD_IO_ERROR", "Failed to receive and store uploaded file.")
-        elif fname == "tags":
-            tags_raw.append((await field.text()) or "")
-        elif fname == "name":
-            provided_name = (await field.text()) or None
-        elif fname == "user_metadata":
-            user_metadata_raw = (await field.text()) or None
-
-    # If client did not send file, and we are not doing a from-hash fast path -> error
-    if not file_present and not (provided_hash and provided_hash_exists):
-        return _error_response(400, "MISSING_FILE", "Form must include a 'file' part or a known 'hash'.")
-
-    if file_present and file_written == 0 and not (provided_hash and provided_hash_exists):
-        # Empty upload is only acceptable if we are fast-pathing from existing hash
-        try:
-            if tmp_path and os.path.exists(tmp_path):
-                os.remove(tmp_path)
-        finally:
-            return _error_response(400, "EMPTY_UPLOAD", "Uploaded file is empty.")
-
-    try:
-        spec = schemas_in.UploadAssetSpec.model_validate({
-            "tags": tags_raw,
-            "name": provided_name,
-            "user_metadata": user_metadata_raw,
-            "hash": provided_hash,
-        })
-    except ValidationError as ve:
-        try:
-            if tmp_path and os.path.exists(tmp_path):
-                os.remove(tmp_path)
-        finally:
-            return _validation_error_response("INVALID_BODY", ve)
-
-    # Validate models category against configured folders (consistent with previous behavior)
-    if spec.tags and spec.tags[0] == "models":
-        if len(spec.tags) < 2 or spec.tags[1] not in folder_paths.folder_names_and_paths:
-            if tmp_path and os.path.exists(tmp_path):
-                os.remove(tmp_path)
-            return _error_response(
-                400, "INVALID_BODY", f"unknown models category '{spec.tags[1] if len(spec.tags) >= 2 else ''}'"
-            )
-
-    owner_id = USER_MANAGER.get_request_user_id(request)
-
-    # Fast path: if a valid provided hash exists, create AssetInfo without writing anything
-    if spec.hash and provided_hash_exists is True:
-        try:
-            result = manager.create_asset_from_hash(
-                hash_str=spec.hash,
-                name=spec.name or (spec.hash.split(":", 1)[1]),
-                tags=spec.tags,
-                user_metadata=spec.user_metadata or {},
-                owner_id=owner_id,
-            )
-        except Exception:
-            logging.exception("create_asset_from_hash failed for hash=%s, owner_id=%s", spec.hash, owner_id)
-            return _error_response(500, "INTERNAL", "Unexpected server error.")
-
-        if result is None:
-            return _error_response(404, "ASSET_NOT_FOUND", f"Asset content {spec.hash} does not exist")
-
-        # Drain temp if we accidentally saved (e.g., hash field came after file)
-        if tmp_path and os.path.exists(tmp_path):
-            with contextlib.suppress(Exception):
-                os.remove(tmp_path)
-
-        status = 200 if (not result.created_new) else 201
-        return web.json_response(result.model_dump(mode="json"), status=status)
-
-    # Otherwise, we must have a temp file path to ingest
-    if not tmp_path or not os.path.exists(tmp_path):
-        # The only case we reach here without a temp file is: client sent a hash that does not exist and no file
-        return _error_response(404, "ASSET_NOT_FOUND", "Provided hash not found and no file uploaded.")
-
-    try:
-        created = manager.upload_asset_from_temp_path(
-            spec,
-            temp_path=tmp_path,
-            client_filename=file_client_name,
-            owner_id=owner_id,
-            expected_asset_hash=spec.hash,
-        )
-        status = 201 if created.created_new else 200
-        return web.json_response(created.model_dump(mode="json"), status=status)
-    except ValueError as e:
-        if tmp_path and os.path.exists(tmp_path):
-            os.remove(tmp_path)
-        msg = str(e)
-        if "HASH_MISMATCH" in msg or msg.strip().upper() == "HASH_MISMATCH":
-            return _error_response(
-                400,
-                "HASH_MISMATCH",
-                "Uploaded file hash does not match provided hash.",
-            )
-        return _error_response(400, "BAD_REQUEST", "Invalid inputs.")
-    except Exception:
-        if tmp_path and os.path.exists(tmp_path):
-            os.remove(tmp_path)
-        logging.exception("upload_asset_from_temp_path failed for tmp_path=%s, owner_id=%s", tmp_path, owner_id)
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-
-
-@ROUTES.put(f"/api/assets/{{id:{UUID_RE}}}")
-async def update_asset(request: web.Request) -> web.Response:
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
-    try:
-        body = schemas_in.UpdateAssetBody.model_validate(await request.json())
-    except ValidationError as ve:
-        return _validation_error_response("INVALID_BODY", ve)
-    except Exception:
-        return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
-
-    try:
-        result = manager.update_asset(
-            asset_info_id=asset_info_id,
-            name=body.name,
-            tags=body.tags,
-            user_metadata=body.user_metadata,
-            owner_id=USER_MANAGER.get_request_user_id(request),
-        )
-    except (ValueError, PermissionError) as ve:
-        return _error_response(404, "ASSET_NOT_FOUND", str(ve), {"id": asset_info_id})
-    except Exception:
-        logging.exception(
-            "update_asset failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
-            USER_MANAGER.get_request_user_id(request),
-        )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-    return web.json_response(result.model_dump(mode="json"), status=200)
-
-
-@ROUTES.put(f"/api/assets/{{id:{UUID_RE}}}/preview")
-async def set_asset_preview(request: web.Request) -> web.Response:
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
-    try:
-        body = schemas_in.SetPreviewBody.model_validate(await request.json())
-    except ValidationError as ve:
-        return _validation_error_response("INVALID_BODY", ve)
-    except Exception:
-        return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
-
-    try:
-        result = manager.set_asset_preview(
-            asset_info_id=asset_info_id,
-            preview_asset_id=body.preview_id,
-            owner_id=USER_MANAGER.get_request_user_id(request),
-        )
-    except (PermissionError, ValueError) as ve:
-        return _error_response(404, "ASSET_NOT_FOUND", str(ve), {"id": asset_info_id})
-    except Exception:
-        logging.exception(
-            "set_asset_preview failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
-            USER_MANAGER.get_request_user_id(request),
-        )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-    return web.json_response(result.model_dump(mode="json"), status=200)
-
-
-@ROUTES.delete(f"/api/assets/{{id:{UUID_RE}}}")
-async def delete_asset(request: web.Request) -> web.Response:
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
-    delete_content = request.query.get("delete_content")
-    delete_content = True if delete_content is None else delete_content.lower() not in {"0", "false", "no"}
-
-    try:
-        deleted = manager.delete_asset_reference(
-            asset_info_id=asset_info_id,
-            owner_id=USER_MANAGER.get_request_user_id(request),
-            delete_content_if_orphan=delete_content,
-        )
-    except Exception:
-        logging.exception(
-            "delete_asset_reference failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
-            USER_MANAGER.get_request_user_id(request),
-        )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-
-    if not deleted:
-        return _error_response(404, "ASSET_NOT_FOUND", f"AssetInfo {asset_info_id} not found.")
-    return web.Response(status=204)
-
-
 @ROUTES.get("/api/tags")
 async def get_tags(request: web.Request) -> web.Response:
     """
@@ -433,83 +100,3 @@ async def get_tags(request: web.Request) -> web.Response:
         owner_id=USER_MANAGER.get_request_user_id(request),
     )
     return web.json_response(result.model_dump(mode="json"))
-
-@ROUTES.post(f"/api/assets/{{id:{UUID_RE}}}/tags")
-async def add_asset_tags(request: web.Request) -> web.Response:
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
-    try:
-        payload = await request.json()
-        data = schemas_in.TagsAdd.model_validate(payload)
-    except ValidationError as ve:
-        return _error_response(400, "INVALID_BODY", "Invalid JSON body for tags add.", {"errors": ve.errors()})
-    except Exception:
-        return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
-
-    try:
-        result = manager.add_tags_to_asset(
-            asset_info_id=asset_info_id,
-            tags=data.tags,
-            origin="manual",
-            owner_id=USER_MANAGER.get_request_user_id(request),
-        )
-    except (ValueError, PermissionError) as ve:
-        return _error_response(404, "ASSET_NOT_FOUND", str(ve), {"id": asset_info_id})
-    except Exception:
-        logging.exception(
-            "add_tags_to_asset failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
-            USER_MANAGER.get_request_user_id(request),
-        )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-
-    return web.json_response(result.model_dump(mode="json"), status=200)
-
-
-@ROUTES.delete(f"/api/assets/{{id:{UUID_RE}}}/tags")
-async def delete_asset_tags(request: web.Request) -> web.Response:
-    asset_info_id = str(uuid.UUID(request.match_info["id"]))
-    try:
-        payload = await request.json()
-        data = schemas_in.TagsRemove.model_validate(payload)
-    except ValidationError as ve:
-        return _error_response(400, "INVALID_BODY", "Invalid JSON body for tags remove.", {"errors": ve.errors()})
-    except Exception:
-        return _error_response(400, "INVALID_JSON", "Request body must be valid JSON.")
-
-    try:
-        result = manager.remove_tags_from_asset(
-            asset_info_id=asset_info_id,
-            tags=data.tags,
-            owner_id=USER_MANAGER.get_request_user_id(request),
-        )
-    except ValueError as ve:
-        return _error_response(404, "ASSET_NOT_FOUND", str(ve), {"id": asset_info_id})
-    except Exception:
-        logging.exception(
-            "remove_tags_from_asset failed for asset_info_id=%s, owner_id=%s",
-            asset_info_id,
-            USER_MANAGER.get_request_user_id(request),
-        )
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-
-    return web.json_response(result.model_dump(mode="json"), status=200)
-
-
-@ROUTES.post("/api/assets/scan/seed")
-async def seed_assets(request: web.Request) -> web.Response:
-    try:
-        payload = await request.json()
-    except Exception:
-        payload = {}
-
-    try:
-        body = schemas_in.ScheduleAssetScanBody.model_validate(payload)
-    except ValidationError as ve:
-        return _validation_error_response("INVALID_BODY", ve)
-
-    try:
-        scanner.seed_assets(body.roots)
-    except Exception:
-        logging.exception("seed_assets failed for roots=%s", body.roots)
-        return _error_response(500, "INTERNAL", "Unexpected server error.")
-    return web.json_response({"synced": True, "roots": body.roots}, status=200)

app/assets/api/schemas_in.py

@@ -8,10 +8,8 @@ from pydantic import (
     Field,
     conint,
     field_validator,
-    model_validator,
 )
 
-from app.assets.helpers import RootType
 
 class ListAssetsQuery(BaseModel):
     include_tags: list[str] = Field(default_factory=list)
@@ -59,61 +57,6 @@ class ListAssetsQuery(BaseModel):
         return None
 
 
-class UpdateAssetBody(BaseModel):
-    name: str | None = None
-    tags: list[str] | None = None
-    user_metadata: dict[str, Any] | None = None
-
-    @model_validator(mode="after")
-    def _at_least_one(self):
-        if self.name is None and self.tags is None and self.user_metadata is None:
-            raise ValueError("Provide at least one of: name, tags, user_metadata.")
-        if self.tags is not None:
-            if not isinstance(self.tags, list) or not all(isinstance(t, str) for t in self.tags):
-                raise ValueError("Field 'tags' must be an array of strings.")
-        return self
-
-
-class CreateFromHashBody(BaseModel):
-    model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
-
-    hash: str
-    name: str
-    tags: list[str] = Field(default_factory=list)
-    user_metadata: dict[str, Any] = Field(default_factory=dict)
-
-    @field_validator("hash")
-    @classmethod
-    def _require_blake3(cls, v):
-        s = (v or "").strip().lower()
-        if ":" not in s:
-            raise ValueError("hash must be 'blake3:<hex>'")
-        algo, digest = s.split(":", 1)
-        if algo != "blake3":
-            raise ValueError("only canonical 'blake3:<hex>' is accepted here")
-        if not digest or any(c for c in digest if c not in "0123456789abcdef"):
-            raise ValueError("hash digest must be lowercase hex")
-        return s
-
-    @field_validator("tags", mode="before")
-    @classmethod
-    def _tags_norm(cls, v):
-        if v is None:
-            return []
-        if isinstance(v, list):
-            out = [str(t).strip().lower() for t in v if str(t).strip()]
-            seen = set()
-            dedup = []
-            for t in out:
-                if t not in seen:
-                    seen.add(t)
-                    dedup.append(t)
-            return dedup
-        if isinstance(v, str):
-            return [t.strip().lower() for t in v.split(",") if t.strip()]
-        return []
-
-
 class TagsListQuery(BaseModel):
     model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
 
@@ -132,145 +75,6 @@ class TagsListQuery(BaseModel):
         return v.lower() or None
 
 
-class TagsAdd(BaseModel):
-    model_config = ConfigDict(extra="ignore")
-    tags: list[str] = Field(..., min_length=1)
-
-    @field_validator("tags")
-    @classmethod
-    def normalize_tags(cls, v: list[str]) -> list[str]:
-        out = []
-        for t in v:
-            if not isinstance(t, str):
-                raise TypeError("tags must be strings")
-            tnorm = t.strip().lower()
-            if tnorm:
-                out.append(tnorm)
-        seen = set()
-        deduplicated = []
-        for x in out:
-            if x not in seen:
-                seen.add(x)
-                deduplicated.append(x)
-        return deduplicated
-
-
-class TagsRemove(TagsAdd):
-    pass
-
-
-class UploadAssetSpec(BaseModel):
-    """Upload Asset operation.
-    - tags: ordered; first is root ('models'|'input'|'output');
-            if root == 'models', second must be a valid category from folder_paths.folder_names_and_paths
-    - name: display name
-    - user_metadata: arbitrary JSON object (optional)
-    - hash: optional canonical 'blake3:<hex>' provided by the client for validation / fast-path
-
-    Files created via this endpoint are stored on disk using the **content hash** as the filename stem
-    and the original extension is preserved when available.
-    """
-    model_config = ConfigDict(extra="ignore", str_strip_whitespace=True)
-
-    tags: list[str] = Field(..., min_length=1)
-    name: str | None = Field(default=None, max_length=512, description="Display Name")
-    user_metadata: dict[str, Any] = Field(default_factory=dict)
-    hash: str | None = Field(default=None)
-
-    @field_validator("hash", mode="before")
-    @classmethod
-    def _parse_hash(cls, v):
-        if v is None:
-            return None
-        s = str(v).strip().lower()
-        if not s:
-            return None
-        if ":" not in s:
-            raise ValueError("hash must be 'blake3:<hex>'")
-        algo, digest = s.split(":", 1)
-        if algo != "blake3":
-            raise ValueError("only canonical 'blake3:<hex>' is accepted here")
-        if not digest or any(c for c in digest if c not in "0123456789abcdef"):
-            raise ValueError("hash digest must be lowercase hex")
-        return f"{algo}:{digest}"
-
-    @field_validator("tags", mode="before")
-    @classmethod
-    def _parse_tags(cls, v):
-        """
-        Accepts a list of strings (possibly multiple form fields),
-        where each string can be:
-          - JSON array (e.g., '["models","loras","foo"]')
-          - comma-separated ('models, loras, foo')
-          - single token ('models')
-        Returns a normalized, deduplicated, ordered list.
-        """
-        items: list[str] = []
-        if v is None:
-            return []
-        if isinstance(v, str):
-            v = [v]
-
-        if isinstance(v, list):
-            for item in v:
-                if item is None:
-                    continue
-                s = str(item).strip()
-                if not s:
-                    continue
-                if s.startswith("["):
-                    try:
-                        arr = json.loads(s)
-                        if isinstance(arr, list):
-                            items.extend(str(x) for x in arr)
-                            continue
-                    except Exception:
-                        pass  # fallback to CSV parse below
-                items.extend([p for p in s.split(",") if p.strip()])
-        else:
-            return []
-
-        # normalize + dedupe
-        norm = []
-        seen = set()
-        for t in items:
-            tnorm = str(t).strip().lower()
-            if tnorm and tnorm not in seen:
-                seen.add(tnorm)
-                norm.append(tnorm)
-        return norm
-
-    @field_validator("user_metadata", mode="before")
-    @classmethod
-    def _parse_metadata_json(cls, v):
-        if v is None or isinstance(v, dict):
-            return v or {}
-        if isinstance(v, str):
-            s = v.strip()
-            if not s:
-                return {}
-            try:
-                parsed = json.loads(s)
-            except Exception as e:
-                raise ValueError(f"user_metadata must be JSON: {e}") from e
-            if not isinstance(parsed, dict):
-                raise ValueError("user_metadata must be a JSON object")
-            return parsed
-        return {}
-
-    @model_validator(mode="after")
-    def _validate_order(self):
-        if not self.tags:
-            raise ValueError("tags must be provided and non-empty")
-        root = self.tags[0]
-        if root not in {"models", "input", "output"}:
-            raise ValueError("first tag must be one of: models, input, output")
-        if root == "models":
-            if len(self.tags) < 2:
-                raise ValueError("models uploads require a category tag as the second tag")
-        return self
-
-
 class SetPreviewBody(BaseModel):
     """Set or clear the preview for an AssetInfo. Provide an Asset.id or null."""
     preview_id: str | None = None
@@ -288,7 +92,3 @@ class SetPreviewBody(BaseModel):
         except Exception:
             raise ValueError("preview_id must be a UUID")
         return s
-
-
-class ScheduleAssetScanBody(BaseModel):
-    roots: list[RootType] = Field(..., min_length=1)

app/assets/api/schemas_out.py

@@ -29,21 +29,6 @@ class AssetsList(BaseModel):
     has_more: bool
 
 
-class AssetUpdated(BaseModel):
-    id: str
-    name: str
-    asset_hash: str | None = None
-    tags: list[str] = Field(default_factory=list)
-    user_metadata: dict[str, Any] = Field(default_factory=dict)
-    updated_at: datetime | None = None
-
-    model_config = ConfigDict(from_attributes=True)
-
-    @field_serializer("updated_at")
-    def _ser_updated(self, v: datetime | None, _info):
-        return v.isoformat() if v else None
-
-
 class AssetDetail(BaseModel):
     id: str
     name: str
@@ -63,10 +48,6 @@ class AssetDetail(BaseModel):
         return v.isoformat() if v else None
 
 
-class AssetCreated(AssetDetail):
-    created_new: bool
-
-
 class TagUsage(BaseModel):
     name: str
     count: int
@@ -77,17 +58,3 @@ class TagsList(BaseModel):
     tags: list[TagUsage] = Field(default_factory=list)
     total: int
     has_more: bool
-
-
-class TagsAdd(BaseModel):
-    model_config = ConfigDict(str_strip_whitespace=True)
-    added: list[str] = Field(default_factory=list)
-    already_present: list[str] = Field(default_factory=list)
-    total_tags: list[str] = Field(default_factory=list)
-
-
-class TagsRemove(BaseModel):
-    model_config = ConfigDict(str_strip_whitespace=True)
-    removed: list[str] = Field(default_factory=list)
-    not_present: list[str] = Field(default_factory=list)
-    total_tags: list[str] = Field(default_factory=list)

app/assets/database/queries.py

@@ -1,17 +1,9 @@
-import os
-import logging
 import sqlalchemy as sa
 from collections import defaultdict
-from datetime import datetime
-from typing import Iterable, Any
-from sqlalchemy import select, delete, exists, func
-from sqlalchemy.dialects import sqlite
-from sqlalchemy.exc import IntegrityError
+from sqlalchemy import select, exists, func
 from sqlalchemy.orm import Session, contains_eager, noload
-from app.assets.database.models import Asset, AssetInfo, AssetCacheState, AssetInfoMeta, AssetInfoTag, Tag
-from app.assets.helpers import (
-    compute_relative_filename, escape_like_prefix, normalize_tags, project_kv, utcnow
-)
+from app.assets.database.models import Asset, AssetInfo, AssetInfoMeta, AssetInfoTag, Tag
+from app.assets.helpers import escape_like_prefix, normalize_tags
 from typing import Sequence
 
 
@@ -23,22 +15,6 @@ def visible_owner_clause(owner_id: str) -> sa.sql.ClauseElement:
     return AssetInfo.owner_id.in_(["", owner_id])
 
 
-def pick_best_live_path(states: Sequence[AssetCacheState]) -> str:
-    """
-    Return the best on-disk path among cache states:
-      1) Prefer a path that exists with needs_verify == False (already verified).
-      2) Otherwise, pick the first path that exists.
-      3) Otherwise return empty string.
-    """
-    alive = [s for s in states if getattr(s, "file_path", None) and os.path.isfile(s.file_path)]
-    if not alive:
-        return ""
-    for s in alive:
-        if not getattr(s, "needs_verify", False):
-            return s.file_path
-    return alive[0].file_path
-
-
 def apply_tag_filters(
     stmt: sa.sql.Select,
     include_tags: Sequence[str] | None = None,
@@ -66,7 +42,6 @@ def apply_tag_filters(
         )
     return stmt
 
-
 def apply_metadata_filter(
     stmt: sa.sql.Select,
     metadata_filter: dict | None = None,
@@ -119,11 +94,7 @@ def apply_metadata_filter(
     return stmt
 
 
-def asset_exists_by_hash(
-    session: Session,
-    *,
-    asset_hash: str,
-) -> bool:
+def asset_exists_by_hash(session: Session, asset_hash: str) -> bool:
     """
     Check if an asset with a given hash exists in database.
     """
@@ -134,39 +105,9 @@ def asset_exists_by_hash(
     ).first()
     return row is not None
 
-
-def asset_info_exists_for_asset_id(
-    session: Session,
-    *,
-    asset_id: str,
-) -> bool:
-    q = (
-        select(sa.literal(True))
-        .select_from(AssetInfo)
-        .where(AssetInfo.asset_id == asset_id)
-        .limit(1)
-    )
-    return (session.execute(q)).first() is not None
-
-
-def get_asset_by_hash(
-    session: Session,
-    *,
-    asset_hash: str,
-) -> Asset | None:
-    return (
-        session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1))
-    ).scalars().first()
-
-
-def get_asset_info_by_id(
-    session: Session,
-    *,
-    asset_info_id: str,
-) -> AssetInfo | None:
+def get_asset_info_by_id(session: Session, asset_info_id: str) -> AssetInfo | None:
     return session.get(AssetInfo, asset_info_id)
 
-
 def list_asset_infos_page(
     session: Session,
     owner_id: str = "",
@@ -236,7 +177,6 @@ def list_asset_infos_page(
 
     return infos, tag_map, total
 
-
 def fetch_asset_info_asset_and_tags(
     session: Session,
     asset_info_id: str,
@@ -268,494 +208,6 @@ def fetch_asset_info_asset_and_tags(
             tags.append(tag_name)
     return first_info, first_asset, tags
 
-
-def fetch_asset_info_and_asset(
-    session: Session,
-    *,
-    asset_info_id: str,
-    owner_id: str = "",
-) -> tuple[AssetInfo, Asset] | None:
-    stmt = (
-        select(AssetInfo, Asset)
-        .join(Asset, Asset.id == AssetInfo.asset_id)
-        .where(
-            AssetInfo.id == asset_info_id,
-            visible_owner_clause(owner_id),
-        )
-        .limit(1)
-        .options(noload(AssetInfo.tags))
-    )
-    row = session.execute(stmt)
-    pair = row.first()
-    if not pair:
-        return None
-    return pair[0], pair[1]
-
-def list_cache_states_by_asset_id(
-    session: Session, *, asset_id: str
-) -> Sequence[AssetCacheState]:
-    return (
-        session.execute(
-            select(AssetCacheState)
-            .where(AssetCacheState.asset_id == asset_id)
-            .order_by(AssetCacheState.id.asc())
-        )
-    ).scalars().all()
-
-
-def touch_asset_info_by_id(
-    session: Session,
-    *,
-    asset_info_id: str,
-    ts: datetime | None = None,
-    only_if_newer: bool = True,
-) -> None:
-    ts = ts or utcnow()
-    stmt = sa.update(AssetInfo).where(AssetInfo.id == asset_info_id)
-    if only_if_newer:
-        stmt = stmt.where(
-            sa.or_(AssetInfo.last_access_time.is_(None), AssetInfo.last_access_time < ts)
-        )
-    session.execute(stmt.values(last_access_time=ts))
-
-
-def create_asset_info_for_existing_asset(
-    session: Session,
-    *,
-    asset_hash: str,
-    name: str,
-    user_metadata: dict | None = None,
-    tags: Sequence[str] | None = None,
-    tag_origin: str = "manual",
-    owner_id: str = "",
-) -> AssetInfo:
-    """Create or return an existing AssetInfo for an Asset identified by asset_hash."""
-    now = utcnow()
-    asset = get_asset_by_hash(session, asset_hash=asset_hash)
-    if not asset:
-        raise ValueError(f"Unknown asset hash {asset_hash}")
-
-    info = AssetInfo(
-        owner_id=owner_id,
-        name=name,
-        asset_id=asset.id,
-        preview_id=None,
-        created_at=now,
-        updated_at=now,
-        last_access_time=now,
-    )
-    try:
-        with session.begin_nested():
-            session.add(info)
-            session.flush()
-    except IntegrityError:
-        existing = (
-            session.execute(
-                select(AssetInfo)
-                .options(noload(AssetInfo.tags))
-                .where(
-                    AssetInfo.asset_id == asset.id,
-                    AssetInfo.name == name,
-                    AssetInfo.owner_id == owner_id,
-                )
-                .limit(1)
-            )
-        ).unique().scalars().first()
-        if not existing:
-            raise RuntimeError("AssetInfo upsert failed to find existing row after conflict.")
-        return existing
-
-    # metadata["filename"] hack
-    new_meta = dict(user_metadata or {})
-    computed_filename = None
-    try:
-        p = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=asset.id))
-        if p:
-            computed_filename = compute_relative_filename(p)
-    except Exception:
-        computed_filename = None
-    if computed_filename:
-        new_meta["filename"] = computed_filename
-    if new_meta:
-        replace_asset_info_metadata_projection(
-            session,
-            asset_info_id=info.id,
-            user_metadata=new_meta,
-        )
-
-    if tags is not None:
-        set_asset_info_tags(
-            session,
-            asset_info_id=info.id,
-            tags=tags,
-            origin=tag_origin,
-        )
-    return info
-
-
-def set_asset_info_tags(
-    session: Session,
-    *,
-    asset_info_id: str,
-    tags: Sequence[str],
-    origin: str = "manual",
-) -> dict:
-    desired = normalize_tags(tags)
-
-    current = set(
-        tag_name for (tag_name,) in (
-            session.execute(select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id))
-        ).all()
-    )
-
-    to_add = [t for t in desired if t not in current]
-    to_remove = [t for t in current if t not in desired]
-
-    if to_add:
-        ensure_tags_exist(session, to_add, tag_type="user")
-        session.add_all([
-            AssetInfoTag(asset_info_id=asset_info_id, tag_name=t, origin=origin, added_at=utcnow())
-            for t in to_add
-        ])
-        session.flush()
-
-    if to_remove:
-        session.execute(
-            delete(AssetInfoTag)
-            .where(AssetInfoTag.asset_info_id == asset_info_id, AssetInfoTag.tag_name.in_(to_remove))
-        )
-        session.flush()
-
-    return {"added": to_add, "removed": to_remove, "total": desired}
-
-
-def replace_asset_info_metadata_projection(
-    session: Session,
-    *,
-    asset_info_id: str,
-    user_metadata: dict | None = None,
-) -> None:
-    info = session.get(AssetInfo, asset_info_id)
-    if not info:
-        raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-    info.user_metadata = user_metadata or {}
-    info.updated_at = utcnow()
-    session.flush()
-
-    session.execute(delete(AssetInfoMeta).where(AssetInfoMeta.asset_info_id == asset_info_id))
-    session.flush()
-
-    if not user_metadata:
-        return
-
-    rows: list[AssetInfoMeta] = []
-    for k, v in user_metadata.items():
-        for r in project_kv(k, v):
-            rows.append(
-                AssetInfoMeta(
-                    asset_info_id=asset_info_id,
-                    key=r["key"],
-                    ordinal=int(r["ordinal"]),
-                    val_str=r.get("val_str"),
-                    val_num=r.get("val_num"),
-                    val_bool=r.get("val_bool"),
-                    val_json=r.get("val_json"),
-                )
-            )
-    if rows:
-        session.add_all(rows)
-        session.flush()
-
-
-def ingest_fs_asset(
-    session: Session,
-    *,
-    asset_hash: str,
-    abs_path: str,
-    size_bytes: int,
-    mtime_ns: int,
-    mime_type: str | None = None,
-    info_name: str | None = None,
-    owner_id: str = "",
-    preview_id: str | None = None,
-    user_metadata: dict | None = None,
-    tags: Sequence[str] = (),
-    tag_origin: str = "manual",
-    require_existing_tags: bool = False,
-) -> dict:
-    """
-    Idempotently upsert:
-      - Asset by content hash (create if missing)
-      - AssetCacheState(file_path) pointing to asset_id
-      - Optionally AssetInfo + tag links and metadata projection
-    Returns flags and ids.
-    """
-    locator = os.path.abspath(abs_path)
-    now = utcnow()
-
-    if preview_id:
-        if not session.get(Asset, preview_id):
-            preview_id = None
-
-    out: dict[str, Any] = {
-        "asset_created": False,
-        "asset_updated": False,
-        "state_created": False,
-        "state_updated": False,
-        "asset_info_id": None,
-    }
-
-    # 1) Asset by hash
-    asset = (
-        session.execute(select(Asset).where(Asset.hash == asset_hash).limit(1))
-    ).scalars().first()
-    if not asset:
-        vals = {
-            "hash": asset_hash,
-            "size_bytes": int(size_bytes),
-            "mime_type": mime_type,
-            "created_at": now,
-        }
-        res = session.execute(
-            sqlite.insert(Asset)
-            .values(**vals)
-            .on_conflict_do_nothing(index_elements=[Asset.hash])
-        )
-        if int(res.rowcount or 0) > 0:
-            out["asset_created"] = True
-        asset = (
-            session.execute(
-                select(Asset).where(Asset.hash == asset_hash).limit(1)
-            )
-        ).scalars().first()
-        if not asset:
-            raise RuntimeError("Asset row not found after upsert.")
-    else:
-        changed = False
-        if asset.size_bytes != int(size_bytes) and int(size_bytes) > 0:
-            asset.size_bytes = int(size_bytes)
-            changed = True
-        if mime_type and asset.mime_type != mime_type:
-            asset.mime_type = mime_type
-            changed = True
-        if changed:
-            out["asset_updated"] = True
-
-    # 2) AssetCacheState upsert by file_path (unique)
-    vals = {
-        "asset_id": asset.id,
-        "file_path": locator,
-        "mtime_ns": int(mtime_ns),
-    }
-    ins = (
-        sqlite.insert(AssetCacheState)
-        .values(**vals)
-        .on_conflict_do_nothing(index_elements=[AssetCacheState.file_path])
-    )
-
-    res = session.execute(ins)
-    if int(res.rowcount or 0) > 0:
-        out["state_created"] = True
-    else:
-        upd = (
-            sa.update(AssetCacheState)
-            .where(AssetCacheState.file_path == locator)
-            .where(
-                sa.or_(
-                    AssetCacheState.asset_id != asset.id,
-                    AssetCacheState.mtime_ns.is_(None),
-                    AssetCacheState.mtime_ns != int(mtime_ns),
-                )
-            )
-            .values(asset_id=asset.id, mtime_ns=int(mtime_ns))
-        )
-        res2 = session.execute(upd)
-        if int(res2.rowcount or 0) > 0:
-            out["state_updated"] = True
-
-    # 3) Optional AssetInfo + tags + metadata
-    if info_name:
-        try:
-            with session.begin_nested():
-                info = AssetInfo(
-                    owner_id=owner_id,
-                    name=info_name,
-                    asset_id=asset.id,
-                    preview_id=preview_id,
-                    created_at=now,
-                    updated_at=now,
-                    last_access_time=now,
-                )
-                session.add(info)
-                session.flush()
-                out["asset_info_id"] = info.id
-        except IntegrityError:
-            pass
-
-        existing_info = (
-            session.execute(
-                select(AssetInfo)
-                .where(
-                    AssetInfo.asset_id == asset.id,
-                    AssetInfo.name == info_name,
-                    (AssetInfo.owner_id == owner_id),
-                )
-                .limit(1)
-            )
-        ).unique().scalar_one_or_none()
-        if not existing_info:
-            raise RuntimeError("Failed to update or insert AssetInfo.")
-
-        if preview_id and existing_info.preview_id != preview_id:
-            existing_info.preview_id = preview_id
-
-        existing_info.updated_at = now
-        if existing_info.last_access_time < now:
-            existing_info.last_access_time = now
-        session.flush()
-        out["asset_info_id"] = existing_info.id
-
-        norm = [t.strip().lower() for t in (tags or []) if (t or "").strip()]
-        if norm and out["asset_info_id"] is not None:
-            if not require_existing_tags:
-                ensure_tags_exist(session, norm, tag_type="user")
-
-            existing_tag_names = set(
-                name for (name,) in (session.execute(select(Tag.name).where(Tag.name.in_(norm)))).all()
-            )
-            missing = [t for t in norm if t not in existing_tag_names]
-            if missing and require_existing_tags:
-                raise ValueError(f"Unknown tags: {missing}")
-
-            existing_links = set(
-                tag_name
-                for (tag_name,) in (
-                    session.execute(
-                        select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == out["asset_info_id"])
-                    )
-                ).all()
-            )
-            to_add = [t for t in norm if t in existing_tag_names and t not in existing_links]
-            if to_add:
-                session.add_all(
-                    [
-                        AssetInfoTag(
-                            asset_info_id=out["asset_info_id"],
-                            tag_name=t,
-                            origin=tag_origin,
-                            added_at=now,
-                        )
-                        for t in to_add
-                    ]
-                )
-                session.flush()
-
-        # metadata["filename"] hack
-        if out["asset_info_id"] is not None:
-            primary_path = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=asset.id))
-            computed_filename = compute_relative_filename(primary_path) if primary_path else None
-
-            current_meta = existing_info.user_metadata or {}
-            new_meta = dict(current_meta)
-            if user_metadata is not None:
-                for k, v in user_metadata.items():
-                    new_meta[k] = v
-            if computed_filename:
-                new_meta["filename"] = computed_filename
-
-            if new_meta != current_meta:
-                replace_asset_info_metadata_projection(
-                    session,
-                    asset_info_id=out["asset_info_id"],
-                    user_metadata=new_meta,
-                )
-
-    try:
-        remove_missing_tag_for_asset_id(session, asset_id=asset.id)
-    except Exception:
-        logging.exception("Failed to clear 'missing' tag for asset %s", asset.id)
-    return out
-
-
-def update_asset_info_full(
-    session: Session,
-    *,
-    asset_info_id: str,
-    name: str | None = None,
-    tags: Sequence[str] | None = None,
-    user_metadata: dict | None = None,
-    tag_origin: str = "manual",
-    asset_info_row: Any = None,
-) -> AssetInfo:
-    if not asset_info_row:
-        info = session.get(AssetInfo, asset_info_id)
-        if not info:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-    else:
-        info = asset_info_row
-
-    touched = False
-    if name is not None and name != info.name:
-        info.name = name
-        touched = True
-
-    computed_filename = None
-    try:
-        p = pick_best_live_path(list_cache_states_by_asset_id(session, asset_id=info.asset_id))
-        if p:
-            computed_filename = compute_relative_filename(p)
-    except Exception:
-        computed_filename = None
-
-    if user_metadata is not None:
-        new_meta = dict(user_metadata)
-        if computed_filename:
-            new_meta["filename"] = computed_filename
-        replace_asset_info_metadata_projection(
-            session, asset_info_id=asset_info_id, user_metadata=new_meta
-        )
-        touched = True
-    else:
-        if computed_filename:
-            current_meta = info.user_metadata or {}
-            if current_meta.get("filename") != computed_filename:
-                new_meta = dict(current_meta)
-                new_meta["filename"] = computed_filename
-                replace_asset_info_metadata_projection(
-                    session, asset_info_id=asset_info_id, user_metadata=new_meta
-                )
-                touched = True
-
-    if tags is not None:
-        set_asset_info_tags(
-            session,
-            asset_info_id=asset_info_id,
-            tags=tags,
-            origin=tag_origin,
-        )
-        touched = True
-
-    if touched and user_metadata is None:
-        info.updated_at = utcnow()
-        session.flush()
-
-    return info
-
-
-def delete_asset_info_by_id(
-    session: Session,
-    *,
-    asset_info_id: str,
-    owner_id: str,
-) -> bool:
-    stmt = sa.delete(AssetInfo).where(
-        AssetInfo.id == asset_info_id,
-        visible_owner_clause(owner_id),
-    )
-    return int((session.execute(stmt)).rowcount or 0) > 0
-
-
 def list_tags_with_usage(
     session: Session,
     prefix: str | None = None,
@@ -813,163 +265,3 @@ def list_tags_with_usage(
 
     rows_norm = [(name, ttype, int(count or 0)) for (name, ttype, count) in rows]
     return rows_norm, int(total or 0)
-
-
-def ensure_tags_exist(session: Session, names: Iterable[str], tag_type: str = "user") -> None:
-    wanted = normalize_tags(list(names))
-    if not wanted:
-        return
-    rows = [{"name": n, "tag_type": tag_type} for n in list(dict.fromkeys(wanted))]
-    ins = (
-        sqlite.insert(Tag)
-        .values(rows)
-        .on_conflict_do_nothing(index_elements=[Tag.name])
-    )
-    session.execute(ins)
-
-
-def get_asset_tags(session: Session, *, asset_info_id: str) -> list[str]:
-    return [
-        tag_name for (tag_name,) in (
-            session.execute(
-                select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
-            )
-        ).all()
-    ]
-
-
-def add_tags_to_asset_info(
-    session: Session,
-    *,
-    asset_info_id: str,
-    tags: Sequence[str],
-    origin: str = "manual",
-    create_if_missing: bool = True,
-    asset_info_row: Any = None,
-) -> dict:
-    if not asset_info_row:
-        info = session.get(AssetInfo, asset_info_id)
-        if not info:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-    norm = normalize_tags(tags)
-    if not norm:
-        total = get_asset_tags(session, asset_info_id=asset_info_id)
-        return {"added": [], "already_present": [], "total_tags": total}
-
-    if create_if_missing:
-        ensure_tags_exist(session, norm, tag_type="user")
-
-    current = {
-        tag_name
-        for (tag_name,) in (
-            session.execute(
-                sa.select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
-            )
-        ).all()
-    }
-
-    want = set(norm)
-    to_add = sorted(want - current)
-
-    if to_add:
-        with session.begin_nested() as nested:
-            try:
-                session.add_all(
-                    [
-                        AssetInfoTag(
-                            asset_info_id=asset_info_id,
-                            tag_name=t,
-                            origin=origin,
-                            added_at=utcnow(),
-                        )
-                        for t in to_add
-                    ]
-                )
-                session.flush()
-            except IntegrityError:
-                nested.rollback()
-
-    after = set(get_asset_tags(session, asset_info_id=asset_info_id))
-    return {
-        "added": sorted(((after - current) & want)),
-        "already_present": sorted(want & current),
-        "total_tags": sorted(after),
-    }
-
-
-def remove_tags_from_asset_info(
-    session: Session,
-    *,
-    asset_info_id: str,
-    tags: Sequence[str],
-) -> dict:
-    info = session.get(AssetInfo, asset_info_id)
-    if not info:
-        raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-    norm = normalize_tags(tags)
-    if not norm:
-        total = get_asset_tags(session, asset_info_id=asset_info_id)
-        return {"removed": [], "not_present": [], "total_tags": total}
-
-    existing = {
-        tag_name
-        for (tag_name,) in (
-            session.execute(
-                sa.select(AssetInfoTag.tag_name).where(AssetInfoTag.asset_info_id == asset_info_id)
-            )
-        ).all()
-    }
-
-    to_remove = sorted(set(t for t in norm if t in existing))
-    not_present = sorted(set(t for t in norm if t not in existing))
-
-    if to_remove:
-        session.execute(
-            delete(AssetInfoTag)
-            .where(
-                AssetInfoTag.asset_info_id == asset_info_id,
-                AssetInfoTag.tag_name.in_(to_remove),
-            )
-        )
-        session.flush()
-
-    total = get_asset_tags(session, asset_info_id=asset_info_id)
-    return {"removed": to_remove, "not_present": not_present, "total_tags": total}
-
-
-def remove_missing_tag_for_asset_id(
-    session: Session,
-    *,
-    asset_id: str,
-) -> None:
-    session.execute(
-        sa.delete(AssetInfoTag).where(
-            AssetInfoTag.asset_info_id.in_(sa.select(AssetInfo.id).where(AssetInfo.asset_id == asset_id)),
-            AssetInfoTag.tag_name == "missing",
-        )
-    )
-
-
-def set_asset_info_preview(
-    session: Session,
-    *,
-    asset_info_id: str,
-    preview_asset_id: str | None = None,
-) -> None:
-    """Set or clear preview_id and bump updated_at. Raises on unknown IDs."""
-    info = session.get(AssetInfo, asset_info_id)
-    if not info:
-        raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-    if preview_asset_id is None:
-        info.preview_id = None
-    else:
-        # validate preview asset exists
-        if not session.get(Asset, preview_asset_id):
-            raise ValueError(f"Preview Asset {preview_asset_id} not found")
-        info.preview_id = preview_asset_id
-
-    info.updated_at = utcnow()
-    session.flush()

app/assets/helpers.py

@@ -1,6 +1,5 @@
 import contextlib
 import os
-from decimal import Decimal
 from aiohttp import web
 from datetime import datetime, timezone
 from pathlib import Path
@@ -88,40 +87,6 @@ def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
             targets.append((name, paths))
     return targets
 
-def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[str]]:
-    """Validates and maps tags -> (base_dir, subdirs_for_fs)"""
-    root = tags[0]
-    if root == "models":
-        if len(tags) < 2:
-            raise ValueError("at least two tags required for model asset")
-        try:
-            bases = folder_paths.folder_names_and_paths[tags[1]][0]
-        except KeyError:
-            raise ValueError(f"unknown model category '{tags[1]}'")
-        if not bases:
-            raise ValueError(f"no base path configured for category '{tags[1]}'")
-        base_dir = os.path.abspath(bases[0])
-        raw_subdirs = tags[2:]
-    else:
-        base_dir = os.path.abspath(
-            folder_paths.get_input_directory() if root == "input" else folder_paths.get_output_directory()
-        )
-        raw_subdirs = tags[1:]
-    for i in raw_subdirs:
-        if i in (".", ".."):
-            raise ValueError("invalid path component in tags")
-
-    return base_dir, raw_subdirs if raw_subdirs else []
-
-def ensure_within_base(candidate: str, base: str) -> None:
-    cand_abs = os.path.abspath(candidate)
-    base_abs = os.path.abspath(base)
-    try:
-        if os.path.commonpath([cand_abs, base_abs]) != base_abs:
-            raise ValueError("destination escapes base directory")
-    except Exception:
-        raise ValueError("invalid destination path")
-
 def compute_relative_filename(file_path: str) -> str | None:
     """
     Return the model's path relative to the last well-known folder (the model category),
@@ -148,6 +113,7 @@ def compute_relative_filename(file_path: str) -> str | None:
         return "/".join(inside)
     return "/".join(parts)  # input/output: keep all parts
 
+
 def get_relative_to_root_category_path_of_asset(file_path: str) -> tuple[Literal["input", "output", "models"], str]:
     """Given an absolute or relative file path, determine which root category the path belongs to:
       - 'input' if the file resides under `folder_paths.get_input_directory()`
@@ -249,64 +215,3 @@ def collect_models_files() -> list[str]:
             if allowed:
                 out.append(abs_path)
     return out
-
-def is_scalar(v):
-    if v is None:
-        return True
-    if isinstance(v, bool):
-        return True
-    if isinstance(v, (int, float, Decimal, str)):
-        return True
-    return False
-
-def project_kv(key: str, value):
-    """
-    Turn a metadata key/value into typed projection rows.
-    Returns list[dict] with keys:
-      key, ordinal, and one of val_str / val_num / val_bool / val_json (others None)
-    """
-    rows: list[dict] = []
-
-    def _null_row(ordinal: int) -> dict:
-        return {
-            "key": key, "ordinal": ordinal,
-            "val_str": None, "val_num": None, "val_bool": None, "val_json": None
-        }
-
-    if value is None:
-        rows.append(_null_row(0))
-        return rows
-
-    if is_scalar(value):
-        if isinstance(value, bool):
-            rows.append({"key": key, "ordinal": 0, "val_bool": bool(value)})
-        elif isinstance(value, (int, float, Decimal)):
-            num = value if isinstance(value, Decimal) else Decimal(str(value))
-            rows.append({"key": key, "ordinal": 0, "val_num": num})
-        elif isinstance(value, str):
-            rows.append({"key": key, "ordinal": 0, "val_str": value})
-        else:
-            rows.append({"key": key, "ordinal": 0, "val_json": value})
-        return rows
-
-    if isinstance(value, list):
-        if all(is_scalar(x) for x in value):
-            for i, x in enumerate(value):
-                if x is None:
-                    rows.append(_null_row(i))
-                elif isinstance(x, bool):
-                    rows.append({"key": key, "ordinal": i, "val_bool": bool(x)})
-                elif isinstance(x, (int, float, Decimal)):
-                    num = x if isinstance(x, Decimal) else Decimal(str(x))
-                    rows.append({"key": key, "ordinal": i, "val_num": num})
-                elif isinstance(x, str):
-                    rows.append({"key": key, "ordinal": i, "val_str": x})
-                else:
-                    rows.append({"key": key, "ordinal": i, "val_json": x})
-            return rows
-        for i, x in enumerate(value):
-            rows.append({"key": key, "ordinal": i, "val_json": x})
-        return rows
-
-    rows.append({"key": key, "ordinal": 0, "val_json": value})
-    return rows

app/assets/manager.py

@@ -1,34 +1,13 @@
-import os
-import mimetypes
-import contextlib
 from typing import Sequence
 
 from app.database.db import create_session
-from app.assets.api import schemas_out, schemas_in
+from app.assets.api import schemas_out
 from app.assets.database.queries import (
     asset_exists_by_hash,
-    asset_info_exists_for_asset_id,
-    get_asset_by_hash,
-    get_asset_info_by_id,
     fetch_asset_info_asset_and_tags,
-    fetch_asset_info_and_asset,
-    create_asset_info_for_existing_asset,
-    touch_asset_info_by_id,
-    update_asset_info_full,
-    delete_asset_info_by_id,
-    list_cache_states_by_asset_id,
     list_asset_infos_page,
     list_tags_with_usage,
-    get_asset_tags,
-    add_tags_to_asset_info,
-    remove_tags_from_asset_info,
-    pick_best_live_path,
-    ingest_fs_asset,
-    set_asset_info_preview,
 )
-from app.assets.helpers import resolve_destination_from_tags, ensure_within_base
-from app.assets.database.models import Asset
-import app.assets.hashing as hashing
 
 
 def _safe_sort_field(requested: str | None) -> str:
@@ -40,28 +19,11 @@ def _safe_sort_field(requested: str | None) -> str:
     return "created_at"
 
 
-def _get_size_mtime_ns(path: str) -> tuple[int, int]:
-    st = os.stat(path, follow_symlinks=True)
-    return st.st_size, getattr(st, "st_mtime_ns", int(st.st_mtime * 1_000_000_000))
-
-
-def _safe_filename(name: str | None, fallback: str) -> str:
-    n = os.path.basename((name or "").strip() or fallback)
-    if n:
-        return n
-    return fallback
-
-
-def asset_exists(*, asset_hash: str) -> bool:
-    """
-    Check if an asset with a given hash exists in database.
-    """
+def asset_exists(asset_hash: str) -> bool:
     with create_session() as session:
         return asset_exists_by_hash(session, asset_hash=asset_hash)
 
-
 def list_assets(
-    *,
     include_tags: Sequence[str] | None = None,
     exclude_tags: Sequence[str] | None = None,
     name_contains: str | None = None,
@@ -114,12 +76,7 @@ def list_assets(
         has_more=(offset + len(summaries)) < total,
     )
 
-
-def get_asset(
-    *,
-    asset_info_id: str,
-    owner_id: str = "",
-) -> schemas_out.AssetDetail:
+def get_asset(asset_info_id: str, owner_id: str = "") -> schemas_out.AssetDetail:
     with create_session() as session:
         res = fetch_asset_info_asset_and_tags(session, asset_info_id=asset_info_id, owner_id=owner_id)
         if not res:
@@ -140,349 +97,6 @@ def get_asset(
         last_access_time=info.last_access_time,
     )
 
-
-def resolve_asset_content_for_download(
-    *,
-    asset_info_id: str,
-    owner_id: str = "",
-) -> tuple[str, str, str]:
-    with create_session() as session:
-        pair = fetch_asset_info_and_asset(session, asset_info_id=asset_info_id, owner_id=owner_id)
-        if not pair:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-
-        info, asset = pair
-        states = list_cache_states_by_asset_id(session, asset_id=asset.id)
-        abs_path = pick_best_live_path(states)
-        if not abs_path:
-            raise FileNotFoundError
-
-        touch_asset_info_by_id(session, asset_info_id=asset_info_id)
-        session.commit()
-
-    ctype = asset.mime_type or mimetypes.guess_type(info.name or abs_path)[0] or "application/octet-stream"
-    download_name = info.name or os.path.basename(abs_path)
-    return abs_path, ctype, download_name
-
-
-def upload_asset_from_temp_path(
-    spec: schemas_in.UploadAssetSpec,
-    *,
-    temp_path: str,
-    client_filename: str | None = None,
-    owner_id: str = "",
-    expected_asset_hash: str | None = None,
-) -> schemas_out.AssetCreated:
-    try:
-        digest = hashing.blake3_hash(temp_path)
-    except Exception as e:
-        raise RuntimeError(f"failed to hash uploaded file: {e}")
-    asset_hash = "blake3:" + digest
-
-    if expected_asset_hash and asset_hash != expected_asset_hash.strip().lower():
-        raise ValueError("HASH_MISMATCH")
-
-    with create_session() as session:
-        existing = get_asset_by_hash(session, asset_hash=asset_hash)
-        if existing is not None:
-            with contextlib.suppress(Exception):
-                if temp_path and os.path.exists(temp_path):
-                    os.remove(temp_path)
-
-            display_name = _safe_filename(spec.name or (client_filename or ""), fallback=digest)
-            info = create_asset_info_for_existing_asset(
-                session,
-                asset_hash=asset_hash,
-                name=display_name,
-                user_metadata=spec.user_metadata or {},
-                tags=spec.tags or [],
-                tag_origin="manual",
-                owner_id=owner_id,
-            )
-            tag_names = get_asset_tags(session, asset_info_id=info.id)
-            session.commit()
-
-            return schemas_out.AssetCreated(
-                id=info.id,
-                name=info.name,
-                asset_hash=existing.hash,
-                size=int(existing.size_bytes) if existing.size_bytes is not None else None,
-                mime_type=existing.mime_type,
-                tags=tag_names,
-                user_metadata=info.user_metadata or {},
-                preview_id=info.preview_id,
-                created_at=info.created_at,
-                last_access_time=info.last_access_time,
-                created_new=False,
-            )
-
-    base_dir, subdirs = resolve_destination_from_tags(spec.tags)
-    dest_dir = os.path.join(base_dir, *subdirs) if subdirs else base_dir
-    os.makedirs(dest_dir, exist_ok=True)
-
-    src_for_ext = (client_filename or spec.name or "").strip()
-    _ext = os.path.splitext(os.path.basename(src_for_ext))[1] if src_for_ext else ""
-    ext = _ext if 0 < len(_ext) <= 16 else ""
-    hashed_basename = f"{digest}{ext}"
-    dest_abs = os.path.abspath(os.path.join(dest_dir, hashed_basename))
-    ensure_within_base(dest_abs, base_dir)
-
-    content_type = (
-        mimetypes.guess_type(os.path.basename(src_for_ext), strict=False)[0]
-        or mimetypes.guess_type(hashed_basename, strict=False)[0]
-        or "application/octet-stream"
-    )
-
-    try:
-        os.replace(temp_path, dest_abs)
-    except Exception as e:
-        raise RuntimeError(f"failed to move uploaded file into place: {e}")
-
-    try:
-        size_bytes, mtime_ns = _get_size_mtime_ns(dest_abs)
-    except OSError as e:
-        raise RuntimeError(f"failed to stat destination file: {e}")
-
-    with create_session() as session:
-        result = ingest_fs_asset(
-            session,
-            asset_hash=asset_hash,
-            abs_path=dest_abs,
-            size_bytes=size_bytes,
-            mtime_ns=mtime_ns,
-            mime_type=content_type,
-            info_name=_safe_filename(spec.name or (client_filename or ""), fallback=digest),
-            owner_id=owner_id,
-            preview_id=None,
-            user_metadata=spec.user_metadata or {},
-            tags=spec.tags,
-            tag_origin="manual",
-            require_existing_tags=False,
-        )
-        info_id = result["asset_info_id"]
-        if not info_id:
-            raise RuntimeError("failed to create asset metadata")
-
-        pair = fetch_asset_info_and_asset(session, asset_info_id=info_id, owner_id=owner_id)
-        if not pair:
-            raise RuntimeError("inconsistent DB state after ingest")
-        info, asset = pair
-        tag_names = get_asset_tags(session, asset_info_id=info.id)
-        session.commit()
-
-    return schemas_out.AssetCreated(
-        id=info.id,
-        name=info.name,
-        asset_hash=asset.hash,
-        size=int(asset.size_bytes),
-        mime_type=asset.mime_type,
-        tags=tag_names,
-        user_metadata=info.user_metadata or {},
-        preview_id=info.preview_id,
-        created_at=info.created_at,
-        last_access_time=info.last_access_time,
-        created_new=result["asset_created"],
-    )
-
-
-def update_asset(
-    *,
-    asset_info_id: str,
-    name: str | None = None,
-    tags: list[str] | None = None,
-    user_metadata: dict | None = None,
-    owner_id: str = "",
-) -> schemas_out.AssetUpdated:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        if not info_row:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-        if info_row.owner_id and info_row.owner_id != owner_id:
-            raise PermissionError("not owner")
-
-        info = update_asset_info_full(
-            session,
-            asset_info_id=asset_info_id,
-            name=name,
-            tags=tags,
-            user_metadata=user_metadata,
-            tag_origin="manual",
-            asset_info_row=info_row,
-        )
-
-        tag_names = get_asset_tags(session, asset_info_id=asset_info_id)
-        session.commit()
-
-    return schemas_out.AssetUpdated(
-        id=info.id,
-        name=info.name,
-        asset_hash=info.asset.hash if info.asset else None,
-        tags=tag_names,
-        user_metadata=info.user_metadata or {},
-        updated_at=info.updated_at,
-    )
-
-
-def set_asset_preview(
-    *,
-    asset_info_id: str,
-    preview_asset_id: str | None = None,
-    owner_id: str = "",
-) -> schemas_out.AssetDetail:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        if not info_row:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-        if info_row.owner_id and info_row.owner_id != owner_id:
-            raise PermissionError("not owner")
-
-        set_asset_info_preview(
-            session,
-            asset_info_id=asset_info_id,
-            preview_asset_id=preview_asset_id,
-        )
-
-        res = fetch_asset_info_asset_and_tags(session, asset_info_id=asset_info_id, owner_id=owner_id)
-        if not res:
-            raise RuntimeError("State changed during preview update")
-        info, asset, tags = res
-        session.commit()
-
-    return schemas_out.AssetDetail(
-        id=info.id,
-        name=info.name,
-        asset_hash=asset.hash if asset else None,
-        size=int(asset.size_bytes) if asset and asset.size_bytes is not None else None,
-        mime_type=asset.mime_type if asset else None,
-        tags=tags,
-        user_metadata=info.user_metadata or {},
-        preview_id=info.preview_id,
-        created_at=info.created_at,
-        last_access_time=info.last_access_time,
-    )
-
-
-def delete_asset_reference(*, asset_info_id: str, owner_id: str, delete_content_if_orphan: bool = True) -> bool:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        asset_id = info_row.asset_id if info_row else None
-        deleted = delete_asset_info_by_id(session, asset_info_id=asset_info_id, owner_id=owner_id)
-        if not deleted:
-            session.commit()
-            return False
-
-        if not delete_content_if_orphan or not asset_id:
-            session.commit()
-            return True
-
-        still_exists = asset_info_exists_for_asset_id(session, asset_id=asset_id)
-        if still_exists:
-            session.commit()
-            return True
-
-        states = list_cache_states_by_asset_id(session, asset_id=asset_id)
-        file_paths = [s.file_path for s in (states or []) if getattr(s, "file_path", None)]
-
-        asset_row = session.get(Asset, asset_id)
-        if asset_row is not None:
-            session.delete(asset_row)
-
-        session.commit()
-        for p in file_paths:
-            with contextlib.suppress(Exception):
-                if p and os.path.isfile(p):
-                    os.remove(p)
-    return True
-
-
-def create_asset_from_hash(
-    *,
-    hash_str: str,
-    name: str,
-    tags: list[str] | None = None,
-    user_metadata: dict | None = None,
-    owner_id: str = "",
-) -> schemas_out.AssetCreated | None:
-    canonical = hash_str.strip().lower()
-    with create_session() as session:
-        asset = get_asset_by_hash(session, asset_hash=canonical)
-        if not asset:
-            return None
-
-        info = create_asset_info_for_existing_asset(
-            session,
-            asset_hash=canonical,
-            name=_safe_filename(name, fallback=canonical.split(":", 1)[1]),
-            user_metadata=user_metadata or {},
-            tags=tags or [],
-            tag_origin="manual",
-            owner_id=owner_id,
-        )
-        tag_names = get_asset_tags(session, asset_info_id=info.id)
-        session.commit()
-
-    return schemas_out.AssetCreated(
-        id=info.id,
-        name=info.name,
-        asset_hash=asset.hash,
-        size=int(asset.size_bytes),
-        mime_type=asset.mime_type,
-        tags=tag_names,
-        user_metadata=info.user_metadata or {},
-        preview_id=info.preview_id,
-        created_at=info.created_at,
-        last_access_time=info.last_access_time,
-        created_new=False,
-    )
-
-
-def add_tags_to_asset(
-    *,
-    asset_info_id: str,
-    tags: list[str],
-    origin: str = "manual",
-    owner_id: str = "",
-) -> schemas_out.TagsAdd:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        if not info_row:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-        if info_row.owner_id and info_row.owner_id != owner_id:
-            raise PermissionError("not owner")
-        data = add_tags_to_asset_info(
-            session,
-            asset_info_id=asset_info_id,
-            tags=tags,
-            origin=origin,
-            create_if_missing=True,
-            asset_info_row=info_row,
-        )
-        session.commit()
-    return schemas_out.TagsAdd(**data)
-
-
-def remove_tags_from_asset(
-    *,
-    asset_info_id: str,
-    tags: list[str],
-    owner_id: str = "",
-) -> schemas_out.TagsRemove:
-    with create_session() as session:
-        info_row = get_asset_info_by_id(session, asset_info_id=asset_info_id)
-        if not info_row:
-            raise ValueError(f"AssetInfo {asset_info_id} not found")
-        if info_row.owner_id and info_row.owner_id != owner_id:
-            raise PermissionError("not owner")
-
-        data = remove_tags_from_asset_info(
-            session,
-            asset_info_id=asset_info_id,
-            tags=tags,
-        )
-        session.commit()
-    return schemas_out.TagsRemove(**data)
-
-
 def list_tags(
     prefix: str | None = None,
     limit: int = 100,

comfy/clip_vision.py

@@ -66,6 +66,7 @@ class ClipVisionModel():
         outputs = Output()
         outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device())
         outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device())
+        outputs["image_sizes"] = [pixel_values.shape[1:]] * pixel_values.shape[0]
         if self.return_all_hidden_states:
             all_hs = out[1].to(comfy.model_management.intermediate_device())
             outputs["penultimate_hidden_states"] = all_hs[:, -2]

comfy/ldm/anima/model.py

@@ -0,0 +1,202 @@
+from comfy.ldm.cosmos.predict2 import MiniTrainDIT
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+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(x, cos, sin, unsqueeze_dim=1):
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    x_embed = (x * cos) + (rotate_half(x) * sin)
+    return x_embed
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(self, head_dim):
+        super().__init__()
+        self.rope_theta = 10000
+        inv_freq = 1.0 / (self.rope_theta ** (torch.arange(0, head_dim, 2, dtype=torch.int64).to(dtype=torch.float) / head_dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class Attention(nn.Module):
+    def __init__(self, query_dim, context_dim, n_heads, head_dim, device=None, dtype=None, operations=None):
+        super().__init__()
+
+        inner_dim = head_dim * n_heads
+        self.n_heads = n_heads
+        self.head_dim = head_dim
+        self.query_dim = query_dim
+        self.context_dim = context_dim
+
+        self.q_proj = operations.Linear(query_dim, inner_dim, bias=False, device=device, dtype=dtype)
+        self.q_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype)
+
+        self.k_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
+        self.k_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype)
+
+        self.v_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype)
+
+        self.o_proj = operations.Linear(inner_dim, query_dim, bias=False, device=device, dtype=dtype)
+
+    def forward(self, x, mask=None, context=None, position_embeddings=None, position_embeddings_context=None):
+        context = x if context is None else context
+        input_shape = x.shape[:-1]
+        q_shape = (*input_shape, self.n_heads, self.head_dim)
+        context_shape = context.shape[:-1]
+        kv_shape = (*context_shape, self.n_heads, self.head_dim)
+
+        query_states = self.q_norm(self.q_proj(x).view(q_shape)).transpose(1, 2)
+        key_states = self.k_norm(self.k_proj(context).view(kv_shape)).transpose(1, 2)
+        value_states = self.v_proj(context).view(kv_shape).transpose(1, 2)
+
+        if position_embeddings is not None:
+            assert position_embeddings_context is not None
+            cos, sin = position_embeddings
+            query_states = apply_rotary_pos_emb(query_states, cos, sin)
+            cos, sin = position_embeddings_context
+            key_states = apply_rotary_pos_emb(key_states, cos, sin)
+
+        attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=mask)
+
+        attn_output = attn_output.transpose(1, 2).reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output
+
+    def init_weights(self):
+        torch.nn.init.zeros_(self.o_proj.weight)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, source_dim, model_dim, num_heads=16, mlp_ratio=4.0, use_self_attn=False, layer_norm=False, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.use_self_attn = use_self_attn
+
+        if self.use_self_attn:
+            self.norm_self_attn = operations.LayerNorm(model_dim, device=device, dtype=dtype) if layer_norm else operations.RMSNorm(model_dim, eps=1e-6, device=device, dtype=dtype)
+            self.self_attn = Attention(
+                query_dim=model_dim,
+                context_dim=model_dim,
+                n_heads=num_heads,
+                head_dim=model_dim//num_heads,
+                device=device,
+                dtype=dtype,
+                operations=operations,
+            )
+
+        self.norm_cross_attn = operations.LayerNorm(model_dim, device=device, dtype=dtype) if layer_norm else operations.RMSNorm(model_dim, eps=1e-6, device=device, dtype=dtype)
+        self.cross_attn = Attention(
+            query_dim=model_dim,
+            context_dim=source_dim,
+            n_heads=num_heads,
+            head_dim=model_dim//num_heads,
+            device=device,
+            dtype=dtype,
+            operations=operations,
+        )
+
+        self.norm_mlp = operations.LayerNorm(model_dim, device=device, dtype=dtype) if layer_norm else operations.RMSNorm(model_dim, eps=1e-6, device=device, dtype=dtype)
+        self.mlp = nn.Sequential(
+            operations.Linear(model_dim, int(model_dim * mlp_ratio), device=device, dtype=dtype),
+            nn.GELU(),
+            operations.Linear(int(model_dim * mlp_ratio), model_dim, device=device, dtype=dtype)
+        )
+
+    def forward(self, x, context, target_attention_mask=None, source_attention_mask=None, position_embeddings=None, position_embeddings_context=None):
+        if self.use_self_attn:
+            normed = self.norm_self_attn(x)
+            attn_out = self.self_attn(normed, mask=target_attention_mask, position_embeddings=position_embeddings, position_embeddings_context=position_embeddings)
+            x = x + attn_out
+
+        normed = self.norm_cross_attn(x)
+        attn_out = self.cross_attn(normed, mask=source_attention_mask, context=context, position_embeddings=position_embeddings, position_embeddings_context=position_embeddings_context)
+        x = x + attn_out
+
+        x = x + self.mlp(self.norm_mlp(x))
+        return x
+
+    def init_weights(self):
+        torch.nn.init.zeros_(self.mlp[2].weight)
+        self.cross_attn.init_weights()
+
+
+class LLMAdapter(nn.Module):
+    def __init__(
+            self,
+            source_dim=1024,
+            target_dim=1024,
+            model_dim=1024,
+            num_layers=6,
+            num_heads=16,
+            use_self_attn=True,
+            layer_norm=False,
+            device=None,
+            dtype=None,
+            operations=None,
+        ):
+        super().__init__()
+
+        self.embed = operations.Embedding(32128, target_dim, device=device, dtype=dtype)
+        if model_dim != target_dim:
+            self.in_proj = operations.Linear(target_dim, model_dim, device=device, dtype=dtype)
+        else:
+            self.in_proj = nn.Identity()
+        self.rotary_emb = RotaryEmbedding(model_dim//num_heads)
+        self.blocks = nn.ModuleList([
+            TransformerBlock(source_dim, model_dim, num_heads=num_heads, use_self_attn=use_self_attn, layer_norm=layer_norm, device=device, dtype=dtype, operations=operations) for _ in range(num_layers)
+        ])
+        self.out_proj = operations.Linear(model_dim, target_dim, device=device, dtype=dtype)
+        self.norm = operations.RMSNorm(target_dim, eps=1e-6, device=device, dtype=dtype)
+
+    def forward(self, source_hidden_states, target_input_ids, target_attention_mask=None, source_attention_mask=None):
+        if target_attention_mask is not None:
+            target_attention_mask = target_attention_mask.to(torch.bool)
+            if target_attention_mask.ndim == 2:
+                target_attention_mask = target_attention_mask.unsqueeze(1).unsqueeze(1)
+
+        if source_attention_mask is not None:
+            source_attention_mask = source_attention_mask.to(torch.bool)
+            if source_attention_mask.ndim == 2:
+                source_attention_mask = source_attention_mask.unsqueeze(1).unsqueeze(1)
+
+        x = self.in_proj(self.embed(target_input_ids))
+        context = source_hidden_states
+        position_ids = torch.arange(x.shape[1], device=x.device).unsqueeze(0)
+        position_ids_context = torch.arange(context.shape[1], device=x.device).unsqueeze(0)
+        position_embeddings = self.rotary_emb(x, position_ids)
+        position_embeddings_context = self.rotary_emb(x, position_ids_context)
+        for block in self.blocks:
+            x = block(x, context, target_attention_mask=target_attention_mask, source_attention_mask=source_attention_mask, position_embeddings=position_embeddings, position_embeddings_context=position_embeddings_context)
+        return self.norm(self.out_proj(x))
+
+
+class Anima(MiniTrainDIT):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.llm_adapter = LLMAdapter(device=kwargs.get("device"), dtype=kwargs.get("dtype"), operations=kwargs.get("operations"))
+
+    def preprocess_text_embeds(self, text_embeds, text_ids):
+        if text_ids is not None:
+            return self.llm_adapter(text_embeds, text_ids)
+        else:
+            return text_embeds

comfy/ldm/lightricks/vae/audio_vae.py

@@ -103,20 +103,10 @@ class AudioPreprocessor:
             return waveform
         return torchaudio.functional.resample(waveform, source_rate, self.target_sample_rate)
 
-    @staticmethod
-    def normalize_amplitude(
-        waveform: torch.Tensor, max_amplitude: float = 0.5, eps: float = 1e-5
-    ) -> torch.Tensor:
-        waveform = waveform - waveform.mean(dim=2, keepdim=True)
-        peak = torch.max(torch.abs(waveform)) + eps
-        scale = peak.clamp(max=max_amplitude) / peak
-        return waveform * scale
-
     def waveform_to_mel(
         self, waveform: torch.Tensor, waveform_sample_rate: int, device
     ) -> torch.Tensor:
         waveform = self.resample(waveform, waveform_sample_rate)
-        waveform = self.normalize_amplitude(waveform)
 
         mel_transform = torchaudio.transforms.MelSpectrogram(
             sample_rate=self.target_sample_rate,
@@ -189,9 +179,12 @@ class AudioVAE(torch.nn.Module):
         waveform = self.device_manager.move_to_load_device(waveform)
         expected_channels = self.autoencoder.encoder.in_channels
         if waveform.shape[1] != expected_channels:
-            raise ValueError(
-                f"Input audio must have {expected_channels} channels, got {waveform.shape[1]}"
-            )
+            if waveform.shape[1] == 1:
+                waveform = waveform.expand(-1, expected_channels, *waveform.shape[2:])
+            else:
+                raise ValueError(
+                    f"Input audio must have {expected_channels} channels, got {waveform.shape[1]}"
+                )
 
         mel_spec = self.preprocessor.waveform_to_mel(
             waveform, waveform_sample_rate, device=self.device_manager.load_device

comfy/ldm/lightricks/vae/causal_conv3d.py

@@ -1,11 +1,11 @@
 from typing import Tuple, Union
 
+import threading
 import torch
 import torch.nn as nn
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
-
 class CausalConv3d(nn.Module):
     def __init__(
         self,
@@ -42,23 +42,34 @@ class CausalConv3d(nn.Module):
             padding_mode=spatial_padding_mode,
             groups=groups,
         )
+        self.temporal_cache_state={}
 
     def forward(self, x, causal: bool = True):
-        if causal:
-            first_frame_pad = x[:, :, :1, :, :].repeat(
-                (1, 1, self.time_kernel_size - 1, 1, 1)
-            )
-            x = torch.concatenate((first_frame_pad, x), dim=2)
-        else:
-            first_frame_pad = x[:, :, :1, :, :].repeat(
-                (1, 1, (self.time_kernel_size - 1) // 2, 1, 1)
-            )
-            last_frame_pad = x[:, :, -1:, :, :].repeat(
-                (1, 1, (self.time_kernel_size - 1) // 2, 1, 1)
-            )
-            x = torch.concatenate((first_frame_pad, x, last_frame_pad), dim=2)
-        x = self.conv(x)
-        return x
+        tid = threading.get_ident()
+
+        cached, is_end = self.temporal_cache_state.get(tid, (None, False))
+        if cached is None:
+            padding_length = self.time_kernel_size - 1
+            if not causal:
+                padding_length = padding_length // 2
+            if x.shape[2] == 0:
+                return x
+            cached = x[:, :, :1, :, :].repeat((1, 1, padding_length, 1, 1))
+        pieces = [ cached, x ]
+        if is_end and not causal:
+            pieces.append(x[:, :, -1:, :, :].repeat((1, 1, (self.time_kernel_size - 1) // 2, 1, 1)))
+
+        needs_caching = not is_end
+        if needs_caching and x.shape[2] >= self.time_kernel_size - 1:
+            needs_caching = False
+            self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False)
+
+        x = torch.cat(pieces, dim=2)
+
+        if needs_caching:
+            self.temporal_cache_state[tid] = (x[:, :, -(self.time_kernel_size - 1):, :, :], False)
+
+        return self.conv(x) if x.shape[2] >= self.time_kernel_size else x[:, :, :0, :, :]
 
     @property
     def weight(self):

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -1,4 +1,5 @@
 from __future__ import annotations
+import threading
 import torch
 from torch import nn
 from functools import partial
@@ -6,12 +7,35 @@ import math
 from einops import rearrange
 from typing import List, Optional, Tuple, Union
 from .conv_nd_factory import make_conv_nd, make_linear_nd
+from .causal_conv3d import CausalConv3d
 from .pixel_norm import PixelNorm
 from ..model import PixArtAlphaCombinedTimestepSizeEmbeddings
 import comfy.ops
+from comfy.ldm.modules.diffusionmodules.model import torch_cat_if_needed
 
 ops = comfy.ops.disable_weight_init
 
+def mark_conv3d_ended(module):
+    tid = threading.get_ident()
+    for _, m in module.named_modules():
+        if isinstance(m, CausalConv3d):
+            current = m.temporal_cache_state.get(tid, (None, False))
+            m.temporal_cache_state[tid] = (current[0], True)
+
+def split2(tensor, split_point, dim=2):
+    return torch.split(tensor, [split_point, tensor.shape[dim] - split_point], dim=dim)
+
+def add_exchange_cache(dest, cache_in, new_input, dim=2):
+    if dest is not None:
+        if cache_in is not None:
+            cache_to_dest = min(dest.shape[dim], cache_in.shape[dim])
+            lead_in_dest, dest = split2(dest, cache_to_dest, dim=dim)
+            lead_in_source, cache_in = split2(cache_in, cache_to_dest, dim=dim)
+            lead_in_dest.add_(lead_in_source)
+        body, new_input = split2(new_input, dest.shape[dim], dim)
+        dest.add_(body)
+    return torch_cat_if_needed([cache_in, new_input], dim=dim)
+
 class Encoder(nn.Module):
     r"""
     The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation.
@@ -205,7 +229,7 @@ class Encoder(nn.Module):
 
         self.gradient_checkpointing = False
 
-    def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
+    def forward_orig(self, sample: torch.FloatTensor) -> torch.FloatTensor:
         r"""The forward method of the `Encoder` class."""
 
         sample = patchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
@@ -254,6 +278,22 @@ class Encoder(nn.Module):
 
         return sample
 
+    def forward(self, *args, **kwargs):
+        #No encoder support so just flag the end so it doesnt use the cache.
+        mark_conv3d_ended(self)
+        try:
+            return self.forward_orig(*args, **kwargs)
+        finally:
+            tid = threading.get_ident()
+            for _, module in self.named_modules():
+                # ComfyUI doesn't thread this kind of stuff today, but just in case
+                # we key on the thread to make it thread safe.
+                tid = threading.get_ident()
+                if hasattr(module, "temporal_cache_state"):
+                    module.temporal_cache_state.pop(tid, None)
+
+
+MAX_CHUNK_SIZE=(128 * 1024 ** 2)
 
 class Decoder(nn.Module):
     r"""
@@ -341,18 +381,6 @@ class Decoder(nn.Module):
                     timestep_conditioning=timestep_conditioning,
                     spatial_padding_mode=spatial_padding_mode,
                 )
-            elif block_name == "attn_res_x":
-                block = UNetMidBlock3D(
-                    dims=dims,
-                    in_channels=input_channel,
-                    num_layers=block_params["num_layers"],
-                    resnet_groups=norm_num_groups,
-                    norm_layer=norm_layer,
-                    inject_noise=block_params.get("inject_noise", False),
-                    timestep_conditioning=timestep_conditioning,
-                    attention_head_dim=block_params["attention_head_dim"],
-                    spatial_padding_mode=spatial_padding_mode,
-                )
             elif block_name == "res_x_y":
                 output_channel = output_channel // block_params.get("multiplier", 2)
                 block = ResnetBlock3D(
@@ -428,8 +456,9 @@ class Decoder(nn.Module):
             )
             self.last_scale_shift_table = nn.Parameter(torch.empty(2, output_channel))
 
+
     # def forward(self, sample: torch.FloatTensor, target_shape) -> torch.FloatTensor:
-    def forward(
+    def forward_orig(
         self,
         sample: torch.FloatTensor,
         timestep: Optional[torch.Tensor] = None,
@@ -437,6 +466,7 @@ class Decoder(nn.Module):
         r"""The forward method of the `Decoder` class."""
         batch_size = sample.shape[0]
 
+        mark_conv3d_ended(self.conv_in)
         sample = self.conv_in(sample, causal=self.causal)
 
         checkpoint_fn = (
@@ -445,24 +475,12 @@ class Decoder(nn.Module):
             else lambda x: x
         )
 
-        scaled_timestep = None
+        timestep_shift_scale = None
         if self.timestep_conditioning:
             assert (
                 timestep is not None
             ), "should pass timestep with timestep_conditioning=True"
             scaled_timestep = timestep * self.timestep_scale_multiplier.to(dtype=sample.dtype, device=sample.device)
-
-        for up_block in self.up_blocks:
-            if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):
-                sample = checkpoint_fn(up_block)(
-                    sample, causal=self.causal, timestep=scaled_timestep
-                )
-            else:
-                sample = checkpoint_fn(up_block)(sample, causal=self.causal)
-
-        sample = self.conv_norm_out(sample)
-
-        if self.timestep_conditioning:
             embedded_timestep = self.last_time_embedder(
                 timestep=scaled_timestep.flatten(),
                 resolution=None,
@@ -483,16 +501,62 @@ class Decoder(nn.Module):
                 embedded_timestep.shape[-2],
                 embedded_timestep.shape[-1],
             )
-            shift, scale = ada_values.unbind(dim=1)
-            sample = sample * (1 + scale) + shift
+            timestep_shift_scale = ada_values.unbind(dim=1)
 
-        sample = self.conv_act(sample)
-        sample = self.conv_out(sample, causal=self.causal)
+        output = []
+
+        def run_up(idx, sample, ended):
+            if idx >= len(self.up_blocks):
+                sample = self.conv_norm_out(sample)
+                if timestep_shift_scale is not None:
+                    shift, scale = timestep_shift_scale
+                    sample = sample * (1 + scale) + shift
+                sample = self.conv_act(sample)
+                if ended:
+                    mark_conv3d_ended(self.conv_out)
+                sample = self.conv_out(sample, causal=self.causal)
+                if sample is not None and sample.shape[2] > 0:
+                    output.append(sample)
+                return
+
+            up_block = self.up_blocks[idx]
+            if (ended):
+                mark_conv3d_ended(up_block)
+            if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):
+                sample = checkpoint_fn(up_block)(
+                    sample, causal=self.causal, timestep=scaled_timestep
+                )
+            else:
+                sample = checkpoint_fn(up_block)(sample, causal=self.causal)
+
+            if sample is None or sample.shape[2] == 0:
+                return
+
+            total_bytes = sample.numel() * sample.element_size()
+            num_chunks = (total_bytes + MAX_CHUNK_SIZE - 1) // MAX_CHUNK_SIZE
+            samples = torch.chunk(sample, chunks=num_chunks, dim=2)
+
+            for chunk_idx, sample1 in enumerate(samples):
+                run_up(idx + 1, sample1, ended and chunk_idx == len(samples) - 1)
+
+        run_up(0, sample, True)
+        sample = torch.cat(output, dim=2)
 
         sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
 
         return sample
 
+    def forward(self, *args, **kwargs):
+        try:
+            return self.forward_orig(*args, **kwargs)
+        finally:
+            for _, module in self.named_modules():
+                #ComfyUI doesn't thread this kind of stuff today, but just incase
+                #we key on the thread to make it thread safe.
+                tid = threading.get_ident()
+                if hasattr(module, "temporal_cache_state"):
+                    module.temporal_cache_state.pop(tid, None)
+
 
 class UNetMidBlock3D(nn.Module):
     """
@@ -663,8 +727,22 @@ class DepthToSpaceUpsample(nn.Module):
         )
         self.residual = residual
         self.out_channels_reduction_factor = out_channels_reduction_factor
+        self.temporal_cache_state = {}
 
     def forward(self, x, causal: bool = True, timestep: Optional[torch.Tensor] = None):
+        tid = threading.get_ident()
+        cached, drop_first_conv, drop_first_res = self.temporal_cache_state.get(tid, (None, True, True))
+        y = self.conv(x, causal=causal)
+        y = rearrange(
+            y,
+            "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)",
+            p1=self.stride[0],
+            p2=self.stride[1],
+            p3=self.stride[2],
+        )
+        if self.stride[0] == 2 and y.shape[2] > 0 and drop_first_conv:
+            y = y[:, :, 1:, :, :]
+            drop_first_conv = False
         if self.residual:
             # Reshape and duplicate the input to match the output shape
             x_in = rearrange(
@@ -676,21 +754,20 @@ class DepthToSpaceUpsample(nn.Module):
             )
             num_repeat = math.prod(self.stride) // self.out_channels_reduction_factor
             x_in = x_in.repeat(1, num_repeat, 1, 1, 1)
-            if self.stride[0] == 2:
+            if self.stride[0] == 2 and x_in.shape[2] > 0 and drop_first_res:
                 x_in = x_in[:, :, 1:, :, :]
-        x = self.conv(x, causal=causal)
-        x = rearrange(
-            x,
-            "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)",
-            p1=self.stride[0],
-            p2=self.stride[1],
-            p3=self.stride[2],
-        )
-        if self.stride[0] == 2:
-            x = x[:, :, 1:, :, :]
-        if self.residual:
-            x = x + x_in
-        return x
+                drop_first_res = False
+
+            if y.shape[2] == 0:
+                y = None
+
+            cached = add_exchange_cache(y, cached, x_in, dim=2)
+            self.temporal_cache_state[tid] = (cached, drop_first_conv, drop_first_res)
+
+        else:
+            self.temporal_cache_state[tid] = (None, drop_first_conv, False)
+
+        return y
 
 class LayerNorm(nn.Module):
     def __init__(self, dim, eps, elementwise_affine=True) -> None:
@@ -807,6 +884,8 @@ class ResnetBlock3D(nn.Module):
                 torch.randn(4, in_channels) / in_channels**0.5
             )
 
+        self.temporal_cache_state={}
+
     def _feed_spatial_noise(
         self, hidden_states: torch.FloatTensor, per_channel_scale: torch.FloatTensor
     ) -> torch.FloatTensor:
@@ -880,9 +959,12 @@ class ResnetBlock3D(nn.Module):
 
         input_tensor = self.conv_shortcut(input_tensor)
 
-        output_tensor = input_tensor + hidden_states
+        tid = threading.get_ident()
+        cached = self.temporal_cache_state.get(tid, None)
+        cached = add_exchange_cache(hidden_states, cached, input_tensor, dim=2)
+        self.temporal_cache_state[tid] = cached
 
-        return output_tensor
+        return hidden_states
 
 
 def patchify(x, patch_size_hw, patch_size_t=1):

comfy/ldm/lumina/model.py

@@ -13,10 +13,53 @@ from comfy.ldm.modules.attention import optimized_attention_masked
 from comfy.ldm.flux.layers import EmbedND
 from comfy.ldm.flux.math import apply_rope
 import comfy.patcher_extension
+import comfy.utils
 
 
-def modulate(x, scale):
-    return x * (1 + scale.unsqueeze(1))
+def invert_slices(slices, length):
+    sorted_slices = sorted(slices)
+    result = []
+    current = 0
+
+    for start, end in sorted_slices:
+        if current < start:
+            result.append((current, start))
+        current = max(current, end)
+
+    if current < length:
+        result.append((current, length))
+
+    return result
+
+
+def modulate(x, scale, timestep_zero_index=None):
+    if timestep_zero_index is None:
+        return x * (1 + scale.unsqueeze(1))
+    else:
+        scale = (1 + scale.unsqueeze(1))
+        actual_batch = scale.size(0) // 2
+        slices = timestep_zero_index
+        invert = invert_slices(timestep_zero_index, x.shape[1])
+        for s in slices:
+            x[:, s[0]:s[1]] *= scale[actual_batch:]
+        for s in invert:
+            x[:, s[0]:s[1]] *= scale[:actual_batch]
+        return x
+
+
+def apply_gate(gate, x, timestep_zero_index=None):
+    if timestep_zero_index is None:
+        return gate * x
+    else:
+        actual_batch = gate.size(0) // 2
+
+        slices = timestep_zero_index
+        invert = invert_slices(timestep_zero_index, x.shape[1])
+        for s in slices:
+            x[:, s[0]:s[1]] *= gate[actual_batch:]
+        for s in invert:
+            x[:, s[0]:s[1]] *= gate[:actual_batch]
+        return x
 
 #############################################################################
 #                               Core NextDiT Model                              #
@@ -258,6 +301,7 @@ class JointTransformerBlock(nn.Module):
         x_mask: torch.Tensor,
         freqs_cis: torch.Tensor,
         adaln_input: Optional[torch.Tensor]=None,
+        timestep_zero_index=None,
         transformer_options={},
     ):
         """
@@ -276,18 +320,18 @@ class JointTransformerBlock(nn.Module):
             assert adaln_input is not None
             scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1)
 
-            x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2(
+            x = x + apply_gate(gate_msa.unsqueeze(1).tanh(), self.attention_norm2(
                 clamp_fp16(self.attention(
-                    modulate(self.attention_norm1(x), scale_msa),
+                    modulate(self.attention_norm1(x), scale_msa, timestep_zero_index=timestep_zero_index),
                     x_mask,
                     freqs_cis,
                     transformer_options=transformer_options,
-                ))
+                ))), timestep_zero_index=timestep_zero_index
             )
-            x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
+            x = x + apply_gate(gate_mlp.unsqueeze(1).tanh(), self.ffn_norm2(
                 clamp_fp16(self.feed_forward(
-                    modulate(self.ffn_norm1(x), scale_mlp),
-                ))
+                    modulate(self.ffn_norm1(x), scale_mlp, timestep_zero_index=timestep_zero_index),
+                ))), timestep_zero_index=timestep_zero_index
             )
         else:
             assert adaln_input is None
@@ -345,13 +389,37 @@ class FinalLayer(nn.Module):
             ),
         )
 
-    def forward(self, x, c):
+    def forward(self, x, c, timestep_zero_index=None):
         scale = self.adaLN_modulation(c)
-        x = modulate(self.norm_final(x), scale)
+        x = modulate(self.norm_final(x), scale, timestep_zero_index=timestep_zero_index)
         x = self.linear(x)
         return x
 
 
+def pad_zimage(feats, pad_token, pad_tokens_multiple):
+    pad_extra = (-feats.shape[1]) % pad_tokens_multiple
+    return torch.cat((feats, pad_token.to(device=feats.device, dtype=feats.dtype, copy=True).unsqueeze(0).repeat(feats.shape[0], pad_extra, 1)), dim=1), pad_extra
+
+
+def pos_ids_x(start_t, H_tokens, W_tokens, batch_size, device, transformer_options={}):
+    rope_options = transformer_options.get("rope_options", None)
+    h_scale = 1.0
+    w_scale = 1.0
+    h_start = 0
+    w_start = 0
+    if rope_options is not None:
+        h_scale = rope_options.get("scale_y", 1.0)
+        w_scale = rope_options.get("scale_x", 1.0)
+
+        h_start = rope_options.get("shift_y", 0.0)
+        w_start = rope_options.get("shift_x", 0.0)
+    x_pos_ids = torch.zeros((batch_size, H_tokens * W_tokens, 3), dtype=torch.float32, device=device)
+    x_pos_ids[:, :, 0] = start_t
+    x_pos_ids[:, :, 1] = (torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start).view(-1, 1).repeat(1, W_tokens).flatten()
+    x_pos_ids[:, :, 2] = (torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start).view(1, -1).repeat(H_tokens, 1).flatten()
+    return x_pos_ids
+
+
 class NextDiT(nn.Module):
     """
     Diffusion model with a Transformer backbone.
@@ -378,6 +446,7 @@ class NextDiT(nn.Module):
         time_scale=1.0,
         pad_tokens_multiple=None,
         clip_text_dim=None,
+        siglip_feat_dim=None,
         image_model=None,
         device=None,
         dtype=None,
@@ -491,6 +560,41 @@ class NextDiT(nn.Module):
                 for layer_id in range(n_layers)
             ]
         )
+
+        if siglip_feat_dim is not None:
+            self.siglip_embedder = nn.Sequential(
+                operation_settings.get("operations").RMSNorm(siglip_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
+                operation_settings.get("operations").Linear(
+                    siglip_feat_dim,
+                    dim,
+                    bias=True,
+                    device=operation_settings.get("device"),
+                    dtype=operation_settings.get("dtype"),
+                ),
+            )
+            self.siglip_refiner = nn.ModuleList(
+                [
+                    JointTransformerBlock(
+                        layer_id,
+                        dim,
+                        n_heads,
+                        n_kv_heads,
+                        multiple_of,
+                        ffn_dim_multiplier,
+                        norm_eps,
+                        qk_norm,
+                        modulation=False,
+                        operation_settings=operation_settings,
+                    )
+                    for layer_id in range(n_refiner_layers)
+                ]
+            )
+            self.siglip_pad_token = nn.Parameter(torch.empty((1, dim), device=device, dtype=dtype))
+        else:
+            self.siglip_embedder = None
+            self.siglip_refiner = None
+            self.siglip_pad_token = None
+
         # This norm final is in the lumina 2.0 code but isn't actually used for anything.
         # self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
         self.final_layer = FinalLayer(dim, patch_size, self.out_channels, z_image_modulation=z_image_modulation, operation_settings=operation_settings)
@@ -531,70 +635,168 @@ class NextDiT(nn.Module):
             imgs = torch.stack(imgs, dim=0)
         return imgs
 
-    def patchify_and_embed(
-        self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens, transformer_options={}
-    ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]:
-        bsz = len(x)
-        pH = pW = self.patch_size
-        device = x[0].device
-        orig_x = x
-
-        if self.pad_tokens_multiple is not None:
-            pad_extra = (-cap_feats.shape[1]) % self.pad_tokens_multiple
-            cap_feats = torch.cat((cap_feats, self.cap_pad_token.to(device=cap_feats.device, dtype=cap_feats.dtype, copy=True).unsqueeze(0).repeat(cap_feats.shape[0], pad_extra, 1)), dim=1)
+    def embed_cap(self, cap_feats=None, offset=0, bsz=1, device=None, dtype=None):
+        if cap_feats is not None:
+            cap_feats = self.cap_embedder(cap_feats)
+            cap_feats_len = cap_feats.shape[1]
+            if self.pad_tokens_multiple is not None:
+                cap_feats, _ = pad_zimage(cap_feats, self.cap_pad_token, self.pad_tokens_multiple)
+        else:
+            cap_feats_len = 0
+            cap_feats = self.cap_pad_token.to(device=device, dtype=dtype, copy=True).unsqueeze(0).repeat(bsz, self.pad_tokens_multiple, 1)
 
         cap_pos_ids = torch.zeros(bsz, cap_feats.shape[1], 3, dtype=torch.float32, device=device)
-        cap_pos_ids[:, :, 0] = torch.arange(cap_feats.shape[1], dtype=torch.float32, device=device) + 1.0
+        cap_pos_ids[:, :, 0] = torch.arange(cap_feats.shape[1], dtype=torch.float32, device=device) + 1.0 + offset
+        embeds = (cap_feats,)
+        freqs_cis = (self.rope_embedder(cap_pos_ids).movedim(1, 2),)
+        return embeds, freqs_cis, cap_feats_len
+
+    def embed_all(self, x, cap_feats=None, siglip_feats=None, offset=0, omni=False, transformer_options={}):
+        bsz = 1
+        pH = pW = self.patch_size
+        device = x.device
+        embeds, freqs_cis, cap_feats_len = self.embed_cap(cap_feats, offset=offset, bsz=bsz, device=device, dtype=x.dtype)
+
+        if (not omni) or self.siglip_embedder is None:
+            cap_feats_len = embeds[0].shape[1] + offset
+            embeds += (None,)
+            freqs_cis += (None,)
+        else:
+            cap_feats_len += offset
+            if siglip_feats is not None:
+                b, h, w, c = siglip_feats.shape
+                siglip_feats = siglip_feats.permute(0, 3, 1, 2).reshape(b, h * w, c)
+                siglip_feats = self.siglip_embedder(siglip_feats)
+                siglip_pos_ids = torch.zeros((bsz, siglip_feats.shape[1], 3), dtype=torch.float32, device=device)
+                siglip_pos_ids[:, :, 0] = cap_feats_len + 2
+                siglip_pos_ids[:, :, 1] = (torch.linspace(0, h * 8 - 1, steps=h, dtype=torch.float32, device=device).floor()).view(-1, 1).repeat(1, w).flatten()
+                siglip_pos_ids[:, :, 2] = (torch.linspace(0, w * 8 - 1, steps=w, dtype=torch.float32, device=device).floor()).view(1, -1).repeat(h, 1).flatten()
+                if self.siglip_pad_token is not None:
+                    siglip_feats, pad_extra = pad_zimage(siglip_feats, self.siglip_pad_token, self.pad_tokens_multiple)  # TODO: double check
+                    siglip_pos_ids = torch.nn.functional.pad(siglip_pos_ids, (0, 0, 0, pad_extra))
+            else:
+                if self.siglip_pad_token is not None:
+                    siglip_feats = self.siglip_pad_token.to(device=device, dtype=x.dtype, copy=True).unsqueeze(0).repeat(bsz, self.pad_tokens_multiple, 1)
+                    siglip_pos_ids = torch.zeros((bsz, siglip_feats.shape[1], 3), dtype=torch.float32, device=device)
+
+            if siglip_feats is None:
+                embeds += (None,)
+                freqs_cis += (None,)
+            else:
+                embeds += (siglip_feats,)
+                freqs_cis += (self.rope_embedder(siglip_pos_ids).movedim(1, 2),)
 
         B, C, H, W = x.shape
         x = self.x_embedder(x.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2))
-
-        rope_options = transformer_options.get("rope_options", None)
-        h_scale = 1.0
-        w_scale = 1.0
-        h_start = 0
-        w_start = 0
-        if rope_options is not None:
-            h_scale = rope_options.get("scale_y", 1.0)
-            w_scale = rope_options.get("scale_x", 1.0)
-
-            h_start = rope_options.get("shift_y", 0.0)
-            w_start = rope_options.get("shift_x", 0.0)
-
-        H_tokens, W_tokens = H // pH, W // pW
-        x_pos_ids = torch.zeros((bsz, x.shape[1], 3), dtype=torch.float32, device=device)
-        x_pos_ids[:, :, 0] = cap_feats.shape[1] + 1
-        x_pos_ids[:, :, 1] = (torch.arange(H_tokens, dtype=torch.float32, device=device) * h_scale + h_start).view(-1, 1).repeat(1, W_tokens).flatten()
-        x_pos_ids[:, :, 2] = (torch.arange(W_tokens, dtype=torch.float32, device=device) * w_scale + w_start).view(1, -1).repeat(H_tokens, 1).flatten()
-
+        x_pos_ids = pos_ids_x(cap_feats_len + 1, H // pH, W // pW, bsz, device, transformer_options=transformer_options)
         if self.pad_tokens_multiple is not None:
-            pad_extra = (-x.shape[1]) % self.pad_tokens_multiple
-            x = torch.cat((x, self.x_pad_token.to(device=x.device, dtype=x.dtype, copy=True).unsqueeze(0).repeat(x.shape[0], pad_extra, 1)), dim=1)
+            x, pad_extra = pad_zimage(x, self.x_pad_token, self.pad_tokens_multiple)
             x_pos_ids = torch.nn.functional.pad(x_pos_ids, (0, 0, 0, pad_extra))
 
-        freqs_cis = self.rope_embedder(torch.cat((cap_pos_ids, x_pos_ids), dim=1)).movedim(1, 2)
+        embeds += (x,)
+        freqs_cis += (self.rope_embedder(x_pos_ids).movedim(1, 2),)
+        return embeds, freqs_cis, cap_feats_len + len(freqs_cis) - 1
+
+
+    def patchify_and_embed(
+        self, x: torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens, ref_latents=[], ref_contexts=[], siglip_feats=[], transformer_options={}
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]:
+        bsz = x.shape[0]
+        cap_mask = None  # TODO?
+        main_siglip = None
+        orig_x = x
+
+        embeds = ([], [], [])
+        freqs_cis = ([], [], [])
+        leftover_cap = []
+
+        start_t = 0
+        omni = len(ref_latents) > 0
+        if omni:
+            for i, ref in enumerate(ref_latents):
+                if i < len(ref_contexts):
+                    ref_con = ref_contexts[i]
+                else:
+                    ref_con = None
+                if i < len(siglip_feats):
+                    sig_feat = siglip_feats[i]
+                else:
+                    sig_feat = None
+
+                out = self.embed_all(ref, ref_con, sig_feat, offset=start_t, omni=omni, transformer_options=transformer_options)
+                for i, e in enumerate(out[0]):
+                    if e is not None:
+                        embeds[i].append(comfy.utils.repeat_to_batch_size(e, bsz))
+                        freqs_cis[i].append(out[1][i])
+                start_t = out[2]
+            leftover_cap = ref_contexts[len(ref_latents):]
+
+        H, W = x.shape[-2], x.shape[-1]
+        img_sizes = [(H, W)] * bsz
+        out = self.embed_all(x, cap_feats, main_siglip, offset=start_t, omni=omni, transformer_options=transformer_options)
+        img_len = out[0][-1].shape[1]
+        cap_len = out[0][0].shape[1]
+        for i, e in enumerate(out[0]):
+            if e is not None:
+                e = comfy.utils.repeat_to_batch_size(e, bsz)
+                embeds[i].append(e)
+                freqs_cis[i].append(out[1][i])
+        start_t = out[2]
+
+        for cap in leftover_cap:
+            out = self.embed_cap(cap, offset=start_t, bsz=bsz, device=x.device, dtype=x.dtype)
+            cap_len += out[0][0].shape[1]
+            embeds[0].append(comfy.utils.repeat_to_batch_size(out[0][0], bsz))
+            freqs_cis[0].append(out[1][0])
+            start_t += out[2]
 
         patches = transformer_options.get("patches", {})
 
         # refine context
+        cap_feats = torch.cat(embeds[0], dim=1)
+        cap_freqs_cis = torch.cat(freqs_cis[0], dim=1)
         for layer in self.context_refiner:
-            cap_feats = layer(cap_feats, cap_mask, freqs_cis[:, :cap_pos_ids.shape[1]], transformer_options=transformer_options)
+            cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis, transformer_options=transformer_options)
+
+        feats = (cap_feats,)
+        fc = (cap_freqs_cis,)
+
+        if omni and len(embeds[1]) > 0:
+            siglip_mask = None
+            siglip_feats_combined = torch.cat(embeds[1], dim=1)
+            siglip_feats_freqs_cis = torch.cat(freqs_cis[1], dim=1)
+            if self.siglip_refiner is not None:
+                for layer in self.siglip_refiner:
+                    siglip_feats_combined = layer(siglip_feats_combined, siglip_mask, siglip_feats_freqs_cis, transformer_options=transformer_options)
+            feats += (siglip_feats_combined,)
+            fc += (siglip_feats_freqs_cis,)
 
         padded_img_mask = None
+        x = torch.cat(embeds[-1], dim=1)
+        fc_x = torch.cat(freqs_cis[-1], dim=1)
+        if omni:
+            timestep_zero_index = [(x.shape[1] - img_len, x.shape[1])]
+        else:
+            timestep_zero_index = None
+
         x_input = x
         for i, layer in enumerate(self.noise_refiner):
-            x = layer(x, padded_img_mask, freqs_cis[:, cap_pos_ids.shape[1]:], t, transformer_options=transformer_options)
+            x = layer(x, padded_img_mask, fc_x, t, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options)
             if "noise_refiner" in patches:
                 for p in patches["noise_refiner"]:
-                    out = p({"img": x, "img_input": x_input, "txt": cap_feats, "pe": freqs_cis[:, cap_pos_ids.shape[1]:], "vec": t, "x": orig_x, "block_index": i, "transformer_options": transformer_options, "block_type": "noise_refiner"})
+                    out = p({"img": x, "img_input": x_input, "txt": cap_feats, "pe": fc_x, "vec": t, "x": orig_x, "block_index": i, "transformer_options": transformer_options, "block_type": "noise_refiner"})
                     if "img" in out:
                         x = out["img"]
 
-        padded_full_embed = torch.cat((cap_feats, x), dim=1)
+        padded_full_embed = torch.cat(feats + (x,), dim=1)
+        if timestep_zero_index is not None:
+            ind = padded_full_embed.shape[1] - x.shape[1]
+            timestep_zero_index = [(ind + x.shape[1] - img_len, ind + x.shape[1])]
+            timestep_zero_index.append((feats[0].shape[1] - cap_len, feats[0].shape[1]))
+
         mask = None
-        img_sizes = [(H, W)] * bsz
-        l_effective_cap_len = [cap_feats.shape[1]] * bsz
-        return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis
+        l_effective_cap_len = [padded_full_embed.shape[1] - img_len] * bsz
+        return padded_full_embed, mask, img_sizes, l_effective_cap_len, torch.cat(fc + (fc_x,), dim=1), timestep_zero_index
 
     def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
         return comfy.patcher_extension.WrapperExecutor.new_class_executor(
@@ -604,7 +806,11 @@ class NextDiT(nn.Module):
         ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)
 
     # def forward(self, x, t, cap_feats, cap_mask):
-    def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, transformer_options={}, **kwargs):
+    def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, ref_latents=[], ref_contexts=[], siglip_feats=[], transformer_options={}, **kwargs):
+        omni = len(ref_latents) > 0
+        if omni:
+            timesteps = torch.cat([timesteps * 0, timesteps], dim=0)
+
         t = 1.0 - timesteps
         cap_feats = context
         cap_mask = attention_mask
@@ -619,8 +825,6 @@ class NextDiT(nn.Module):
         t = self.t_embedder(t * self.time_scale, dtype=x.dtype)  # (N, D)
         adaln_input = t
 
-        cap_feats = self.cap_embedder(cap_feats)  # (N, L, D)  # todo check if able to batchify w.o. redundant compute
-
         if self.clip_text_pooled_proj is not None:
             pooled = kwargs.get("clip_text_pooled", None)
             if pooled is not None:
@@ -632,7 +836,7 @@ class NextDiT(nn.Module):
 
         patches = transformer_options.get("patches", {})
         x_is_tensor = isinstance(x, torch.Tensor)
-        img, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, adaln_input, num_tokens, transformer_options=transformer_options)
+        img, mask, img_size, cap_size, freqs_cis, timestep_zero_index = self.patchify_and_embed(x, cap_feats, cap_mask, adaln_input, num_tokens, ref_latents=ref_latents, ref_contexts=ref_contexts, siglip_feats=siglip_feats, transformer_options=transformer_options)
         freqs_cis = freqs_cis.to(img.device)
 
         transformer_options["total_blocks"] = len(self.layers)
@@ -640,7 +844,7 @@ class NextDiT(nn.Module):
         img_input = img
         for i, layer in enumerate(self.layers):
             transformer_options["block_index"] = i
-            img = layer(img, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
+            img = layer(img, mask, freqs_cis, adaln_input, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options)
             if "double_block" in patches:
                 for p in patches["double_block"]:
                     out = p({"img": img[:, cap_size[0]:], "img_input": img_input[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options})
@@ -649,8 +853,7 @@ class NextDiT(nn.Module):
                     if "txt" in out:
                         img[:, :cap_size[0]] = out["txt"]
 
-        img = self.final_layer(img, adaln_input)
+        img = self.final_layer(img, adaln_input, timestep_zero_index=timestep_zero_index)
         img = self.unpatchify(img, img_size, cap_size, return_tensor=x_is_tensor)[:, :, :h, :w]
-
         return -img
 

comfy/ldm/modules/diffusionmodules/model.py

@@ -14,10 +14,13 @@ if model_management.xformers_enabled_vae():
     import xformers.ops
 
 def torch_cat_if_needed(xl, dim):
+    xl = [x for x in xl if x is not None and x.shape[dim] > 0]
     if len(xl) > 1:
         return torch.cat(xl, dim)
-    else:
+    elif len(xl) == 1:
         return xl[0]
+    else:
+        return None
 
 def get_timestep_embedding(timesteps, embedding_dim):
     """

comfy/ldm/qwen_image/model.py

@@ -170,8 +170,14 @@ class Attention(nn.Module):
         joint_query = apply_rope1(joint_query, image_rotary_emb)
         joint_key = apply_rope1(joint_key, image_rotary_emb)
 
+        if encoder_hidden_states_mask is not None:
+            attn_mask = torch.zeros((batch_size, 1, seq_txt + seq_img), dtype=hidden_states.dtype, device=hidden_states.device)
+            attn_mask[:, 0, :seq_txt] = encoder_hidden_states_mask
+        else:
+            attn_mask = None
+
         joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads,
-                                                         attention_mask, transformer_options=transformer_options,
+                                                         attn_mask, transformer_options=transformer_options,
                                                          skip_reshape=True)
 
         txt_attn_output = joint_hidden_states[:, :seq_txt, :]
@@ -430,6 +436,9 @@ class QwenImageTransformer2DModel(nn.Module):
         encoder_hidden_states = context
         encoder_hidden_states_mask = attention_mask
 
+        if encoder_hidden_states_mask is not None and not torch.is_floating_point(encoder_hidden_states_mask):
+            encoder_hidden_states_mask = (encoder_hidden_states_mask - 1).to(x.dtype) * torch.finfo(x.dtype).max
+
         hidden_states, img_ids, orig_shape = self.process_img(x)
         num_embeds = hidden_states.shape[1]
 

comfy/ldm/wan/model.py

@@ -62,6 +62,8 @@ class WanSelfAttention(nn.Module):
             x(Tensor): Shape [B, L, num_heads, C / num_heads]
             freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
         """
+        patches = transformer_options.get("patches", {})
+
         b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
 
         def qkv_fn_q(x):
@@ -86,6 +88,10 @@ class WanSelfAttention(nn.Module):
             transformer_options=transformer_options,
         )
 
+        if "attn1_patch" in patches:
+            for p in patches["attn1_patch"]:
+                x = p({"x": x, "q": q, "k": k, "transformer_options": transformer_options})
+
         x = self.o(x)
         return x
 
@@ -225,6 +231,8 @@ class WanAttentionBlock(nn.Module):
         """
         # assert e.dtype == torch.float32
 
+        patches = transformer_options.get("patches", {})
+
         if e.ndim < 4:
             e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
         else:
@@ -242,6 +250,11 @@ class WanAttentionBlock(nn.Module):
 
         # cross-attention & ffn
         x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options)
+
+        if "attn2_patch" in patches:
+            for p in patches["attn2_patch"]:
+                x = p({"x": x, "transformer_options": transformer_options})
+
         y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))
         x = torch.addcmul(x, y, repeat_e(e[5], x))
         return x
@@ -488,7 +501,7 @@ class WanModel(torch.nn.Module):
         self.blocks = nn.ModuleList([
             wan_attn_block_class(cross_attn_type, dim, ffn_dim, num_heads,
                                  window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings)
-            for _ in range(num_layers)
+            for i in range(num_layers)
         ])
 
         # head
@@ -541,6 +554,7 @@ class WanModel(torch.nn.Module):
         # embeddings
         x = self.patch_embedding(x.float()).to(x.dtype)
         grid_sizes = x.shape[2:]
+        transformer_options["grid_sizes"] = grid_sizes
         x = x.flatten(2).transpose(1, 2)
 
         # time embeddings
@@ -738,6 +752,7 @@ class VaceWanModel(WanModel):
         # embeddings
         x = self.patch_embedding(x.float()).to(x.dtype)
         grid_sizes = x.shape[2:]
+        transformer_options["grid_sizes"] = grid_sizes
         x = x.flatten(2).transpose(1, 2)
 
         # time embeddings

comfy/ldm/wan/model_multitalk.py

@@ -0,0 +1,500 @@
+import torch
+from einops import rearrange, repeat
+import comfy
+from comfy.ldm.modules.attention import optimized_attention
+
+
+def calculate_x_ref_attn_map(visual_q, ref_k, ref_target_masks, split_num=8):
+    scale = 1.0 / visual_q.shape[-1] ** 0.5
+    visual_q = visual_q.transpose(1, 2) * scale
+
+    B, H, x_seqlens, K = visual_q.shape
+
+    x_ref_attn_maps = []
+    for class_idx, ref_target_mask in enumerate(ref_target_masks):
+        ref_target_mask = ref_target_mask.view(1, 1, 1, -1)
+
+        x_ref_attnmap = torch.zeros(B, H, x_seqlens, device=visual_q.device, dtype=visual_q.dtype)
+        chunk_size = min(max(x_seqlens // split_num, 1), x_seqlens)
+
+        for i in range(0, x_seqlens, chunk_size):
+            end_i = min(i + chunk_size, x_seqlens)
+
+            attn_chunk = visual_q[:, :, i:end_i] @ ref_k.permute(0, 2, 3, 1)  # B, H, chunk, ref_seqlens
+
+            # Apply softmax
+            attn_max = attn_chunk.max(dim=-1, keepdim=True).values
+            attn_chunk = (attn_chunk - attn_max).exp()
+            attn_sum = attn_chunk.sum(dim=-1, keepdim=True)
+            attn_chunk = attn_chunk / (attn_sum + 1e-8)
+
+            # Apply mask and sum
+            masked_attn = attn_chunk * ref_target_mask
+            x_ref_attnmap[:, :, i:end_i] = masked_attn.sum(-1) / (ref_target_mask.sum() + 1e-8)
+
+            del attn_chunk, masked_attn
+
+        # Average across heads
+        x_ref_attnmap = x_ref_attnmap.mean(dim=1)  # B, x_seqlens
+        x_ref_attn_maps.append(x_ref_attnmap)
+
+    del visual_q, ref_k
+
+    return torch.cat(x_ref_attn_maps, dim=0)
+
+def get_attn_map_with_target(visual_q, ref_k, shape, ref_target_masks=None, split_num=2):
+    """Args:
+        query (torch.tensor): B M H K
+        key (torch.tensor): B M H K
+        shape (tuple): (N_t, N_h, N_w)
+        ref_target_masks: [B, N_h * N_w]
+    """
+
+    N_t, N_h, N_w = shape
+
+    x_seqlens = N_h * N_w
+    ref_k     = ref_k[:, :x_seqlens]
+    _, seq_lens, heads, _ = visual_q.shape
+    class_num, _ = ref_target_masks.shape
+    x_ref_attn_maps = torch.zeros(class_num, seq_lens).to(visual_q)
+
+    split_chunk = heads // split_num
+
+    for i in range(split_num):
+        x_ref_attn_maps_perhead = calculate_x_ref_attn_map(
+            visual_q[:, :, i*split_chunk:(i+1)*split_chunk, :],
+            ref_k[:, :, i*split_chunk:(i+1)*split_chunk, :],
+            ref_target_masks
+            )
+        x_ref_attn_maps += x_ref_attn_maps_perhead
+
+    return x_ref_attn_maps / split_num
+
+
+def normalize_and_scale(column, source_range, target_range, epsilon=1e-8):
+    source_min, source_max = source_range
+    new_min, new_max = target_range
+    normalized = (column - source_min) / (source_max - source_min + epsilon)
+    scaled = normalized * (new_max - new_min) + new_min
+    return scaled
+
+
+def rotate_half(x):
+    x = rearrange(x, "... (d r) -> ... d r", r=2)
+    x1, x2 = x.unbind(dim=-1)
+    x = torch.stack((-x2, x1), dim=-1)
+    return rearrange(x, "... d r -> ... (d r)")
+
+
+def get_audio_embeds(encoded_audio, audio_start, audio_end):
+    audio_embs = []
+    human_num = len(encoded_audio)
+    audio_frames = encoded_audio[0].shape[0]
+
+    indices = (torch.arange(4 + 1) - 2) * 1
+
+    for human_idx in range(human_num):
+        if audio_end > audio_frames: # in case of not enough audio for current window, pad with first audio frame as that's most likely silence
+            pad_len = audio_end - audio_frames
+            pad_shape = list(encoded_audio[human_idx].shape)
+            pad_shape[0] = pad_len
+            pad_tensor = encoded_audio[human_idx][:1].repeat(pad_len, *([1] * (encoded_audio[human_idx].dim() - 1)))
+            encoded_audio_in = torch.cat([encoded_audio[human_idx], pad_tensor], dim=0)
+        else:
+            encoded_audio_in = encoded_audio[human_idx]
+        center_indices = torch.arange(audio_start, audio_end, 1).unsqueeze(1) + indices.unsqueeze(0)
+        center_indices = torch.clamp(center_indices, min=0, max=encoded_audio_in.shape[0] - 1)
+        audio_emb = encoded_audio_in[center_indices].unsqueeze(0)
+        audio_embs.append(audio_emb)
+
+    return torch.cat(audio_embs, dim=0)
+
+
+def project_audio_features(audio_proj, encoded_audio, audio_start, audio_end):
+    audio_embs = get_audio_embeds(encoded_audio, audio_start, audio_end)
+
+    first_frame_audio_emb_s = audio_embs[:, :1, ...]
+    latter_frame_audio_emb = audio_embs[:, 1:, ...]
+    latter_frame_audio_emb = rearrange(latter_frame_audio_emb, "b (n_t n) w s c -> b n_t n w s c", n=4)
+
+    middle_index = audio_proj.seq_len // 2
+
+    latter_first_frame_audio_emb = latter_frame_audio_emb[:, :, :1, :middle_index+1, ...]
+    latter_first_frame_audio_emb = rearrange(latter_first_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c")
+    latter_last_frame_audio_emb = latter_frame_audio_emb[:, :, -1:, middle_index:, ...]
+    latter_last_frame_audio_emb = rearrange(latter_last_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c")
+    latter_middle_frame_audio_emb = latter_frame_audio_emb[:, :, 1:-1, middle_index:middle_index+1, ...]
+    latter_middle_frame_audio_emb = rearrange(latter_middle_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c")
+    latter_frame_audio_emb_s = torch.cat([latter_first_frame_audio_emb, latter_middle_frame_audio_emb, latter_last_frame_audio_emb], dim=2)
+
+    audio_emb = audio_proj(first_frame_audio_emb_s, latter_frame_audio_emb_s)
+    audio_emb = torch.cat(audio_emb.split(1), dim=2)
+
+    return audio_emb
+
+
+class RotaryPositionalEmbedding1D(torch.nn.Module):
+    def __init__(self,
+                 head_dim,
+                 ):
+        super().__init__()
+        self.head_dim = head_dim
+        self.base = 10000
+
+    def precompute_freqs_cis_1d(self, pos_indices):
+        freqs = 1.0 / (self.base ** (torch.arange(0, self.head_dim, 2)[: (self.head_dim // 2)].float() / self.head_dim))
+        freqs = freqs.to(pos_indices.device)
+        freqs = torch.einsum("..., f -> ... f", pos_indices.float(), freqs)
+        freqs = repeat(freqs, "... n -> ... (n r)", r=2)
+        return freqs
+
+    def forward(self, x, pos_indices):
+        freqs_cis = self.precompute_freqs_cis_1d(pos_indices)
+
+        x_ = x.float()
+
+        freqs_cis = freqs_cis.float().to(x.device)
+        cos, sin = freqs_cis.cos(), freqs_cis.sin()
+        cos, sin = rearrange(cos, 'n d -> 1 1 n d'), rearrange(sin, 'n d -> 1 1 n d')
+        x_ = (x_ * cos) + (rotate_half(x_) * sin)
+
+        return x_.type_as(x)
+
+class SingleStreamAttention(torch.nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        encoder_hidden_states_dim: int,
+        num_heads: int,
+        qkv_bias: bool,
+        device=None, dtype=None, operations=None
+    ) -> None:
+        super().__init__()
+        self.dim = dim
+        self.encoder_hidden_states_dim = encoder_hidden_states_dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+
+        self.q_linear = operations.Linear(dim, dim, bias=qkv_bias, device=device, dtype=dtype)
+        self.proj = operations.Linear(dim, dim, device=device, dtype=dtype)
+        self.kv_linear = operations.Linear(encoder_hidden_states_dim, dim * 2, bias=qkv_bias, device=device, dtype=dtype)
+
+    def forward(self, x: torch.Tensor, encoder_hidden_states: torch.Tensor, shape=None) -> torch.Tensor:
+        N_t, N_h, N_w = shape
+
+        expected_tokens = N_t * N_h * N_w
+        actual_tokens = x.shape[1]
+        x_extra = None
+
+        if actual_tokens != expected_tokens:
+            x_extra = x[:, -N_h * N_w:, :]
+            x = x[:, :-N_h * N_w, :]
+            N_t = N_t - 1
+
+        B = x.shape[0]
+        S = N_h * N_w
+        x = x.view(B * N_t, S, self.dim)
+
+        # get q for hidden_state
+        q = self.q_linear(x).view(B * N_t, S, self.num_heads, self.head_dim)
+
+        # get kv from encoder_hidden_states # shape: (B, N, num_heads, head_dim)
+        kv = self.kv_linear(encoder_hidden_states)
+        encoder_k, encoder_v = kv.view(B * N_t, encoder_hidden_states.shape[1], 2, self.num_heads, self.head_dim).unbind(2)
+
+        #print("q.shape", q.shape) #torch.Size([21, 1024, 40, 128])
+        x = optimized_attention(
+            q.transpose(1, 2),
+            encoder_k.transpose(1, 2),
+            encoder_v.transpose(1, 2),
+            heads=self.num_heads, skip_reshape=True, skip_output_reshape=True).transpose(1, 2)
+
+        # linear transform
+        x = self.proj(x.reshape(B * N_t, S, self.dim))
+        x = x.view(B, N_t * S, self.dim)
+
+        if x_extra is not None:
+            x = torch.cat([x, torch.zeros_like(x_extra)], dim=1)
+
+        return x
+
+class SingleStreamMultiAttention(SingleStreamAttention):
+    def __init__(
+        self,
+        dim: int,
+        encoder_hidden_states_dim: int,
+        num_heads: int,
+        qkv_bias: bool,
+        class_range: int = 24,
+        class_interval: int = 4,
+        device=None, dtype=None, operations=None
+    ) -> None:
+        super().__init__(
+            dim=dim,
+            encoder_hidden_states_dim=encoder_hidden_states_dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            device=device,
+            dtype=dtype,
+            operations=operations
+        )
+
+        # Rotary-embedding layout parameters
+        self.class_interval = class_interval
+        self.class_range = class_range
+        self.max_humans = self.class_range // self.class_interval
+
+        # Constant bucket used for background tokens
+        self.rope_bak = int(self.class_range // 2)
+
+        self.rope_1d = RotaryPositionalEmbedding1D(self.head_dim)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        shape=None,
+        x_ref_attn_map=None
+    ) -> torch.Tensor:
+        encoder_hidden_states = encoder_hidden_states.squeeze(0).to(x.device)
+        human_num = x_ref_attn_map.shape[0] if x_ref_attn_map is not None else 1
+        # Single-speaker fall-through
+        if human_num <= 1:
+            return super().forward(x, encoder_hidden_states, shape)
+
+        N_t, N_h, N_w = shape
+
+        x_extra = None
+        if x.shape[0] * N_t != encoder_hidden_states.shape[0]:
+            x_extra = x[:, -N_h * N_w:, :]
+            x = x[:, :-N_h * N_w, :]
+            N_t = N_t - 1
+        x = rearrange(x, "B (N_t S) C -> (B N_t) S C", N_t=N_t)
+
+        # Query projection
+        B, N, C = x.shape
+        q = self.q_linear(x)
+        q = q.view(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
+
+        # Use `class_range` logic for 2 speakers
+        rope_h1 = (0, self.class_interval)
+        rope_h2 = (self.class_range - self.class_interval, self.class_range)
+        rope_bak = int(self.class_range // 2)
+
+        # Normalize and scale attention maps for each speaker
+        max_values = x_ref_attn_map.max(1).values[:, None, None]
+        min_values = x_ref_attn_map.min(1).values[:, None, None]
+        max_min_values = torch.cat([max_values, min_values], dim=2)
+
+        human1_max_value, human1_min_value = max_min_values[0, :, 0].max(), max_min_values[0, :, 1].min()
+        human2_max_value, human2_min_value = max_min_values[1, :, 0].max(), max_min_values[1, :, 1].min()
+
+        human1 = normalize_and_scale(x_ref_attn_map[0], (human1_min_value, human1_max_value), rope_h1)
+        human2 = normalize_and_scale(x_ref_attn_map[1], (human2_min_value, human2_max_value), rope_h2)
+        back = torch.full((x_ref_attn_map.size(1),), rope_bak, dtype=human1.dtype, device=human1.device)
+
+        # Token-wise speaker dominance
+        max_indices = x_ref_attn_map.argmax(dim=0)
+        normalized_map = torch.stack([human1, human2, back], dim=1)
+        normalized_pos = normalized_map[torch.arange(x_ref_attn_map.size(1)), max_indices]
+
+        # Apply rotary to Q
+        q = rearrange(q, "(B N_t) H S C -> B H (N_t S) C", N_t=N_t)
+        q = self.rope_1d(q, normalized_pos)
+        q = rearrange(q, "B H (N_t S) C -> (B N_t) H S C", N_t=N_t)
+
+        # Keys / Values
+        _, N_a, _ = encoder_hidden_states.shape
+        encoder_kv = self.kv_linear(encoder_hidden_states)
+        encoder_kv = encoder_kv.view(B, N_a, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
+        encoder_k, encoder_v = encoder_kv.unbind(0)
+
+        # Rotary for keys – assign centre of each speaker bucket to its context tokens
+        per_frame = torch.zeros(N_a, dtype=encoder_k.dtype, device=encoder_k.device)
+        per_frame[: per_frame.size(0) // 2] = (rope_h1[0] + rope_h1[1]) / 2
+        per_frame[per_frame.size(0) // 2 :] = (rope_h2[0] + rope_h2[1]) / 2
+        encoder_pos = torch.cat([per_frame] * N_t, dim=0)
+
+        encoder_k = rearrange(encoder_k, "(B N_t) H S C -> B H (N_t S) C", N_t=N_t)
+        encoder_k = self.rope_1d(encoder_k, encoder_pos)
+        encoder_k = rearrange(encoder_k, "B H (N_t S) C -> (B N_t) H S C", N_t=N_t)
+
+        # Final attention
+        q = rearrange(q, "B H M K -> B M H K")
+        encoder_k = rearrange(encoder_k, "B H M K -> B M H K")
+        encoder_v = rearrange(encoder_v, "B H M K -> B M H K")
+
+        x = optimized_attention(
+            q.transpose(1, 2),
+            encoder_k.transpose(1, 2),
+            encoder_v.transpose(1, 2),
+            heads=self.num_heads, skip_reshape=True, skip_output_reshape=True).transpose(1, 2)
+
+        # Linear projection
+        x = x.reshape(B, N, C)
+        x = self.proj(x)
+
+        # Restore original layout
+        x = rearrange(x, "(B N_t) S C -> B (N_t S) C", N_t=N_t)
+        if x_extra is not None:
+            x = torch.cat([x, torch.zeros_like(x_extra)], dim=1)
+
+        return x
+
+
+class MultiTalkAudioProjModel(torch.nn.Module):
+    def __init__(
+        self,
+        seq_len: int = 5,
+        seq_len_vf: int = 12,
+        blocks: int = 12,
+        channels: int = 768,
+        intermediate_dim: int = 512,
+        out_dim: int = 768,
+        context_tokens: int = 32,
+        device=None, dtype=None, operations=None
+    ):
+        super().__init__()
+
+        self.seq_len = seq_len
+        self.blocks = blocks
+        self.channels = channels
+        self.input_dim = seq_len * blocks * channels
+        self.input_dim_vf = seq_len_vf * blocks * channels
+        self.intermediate_dim = intermediate_dim
+        self.context_tokens = context_tokens
+        self.out_dim = out_dim
+
+        # define multiple linear layers
+        self.proj1 = operations.Linear(self.input_dim, intermediate_dim, device=device, dtype=dtype)
+        self.proj1_vf = operations.Linear(self.input_dim_vf, intermediate_dim, device=device, dtype=dtype)
+        self.proj2 = operations.Linear(intermediate_dim, intermediate_dim, device=device, dtype=dtype)
+        self.proj3 = operations.Linear(intermediate_dim, context_tokens * out_dim, device=device, dtype=dtype)
+        self.norm = operations.LayerNorm(out_dim, device=device, dtype=dtype)
+
+    def forward(self, audio_embeds, audio_embeds_vf):
+        video_length = audio_embeds.shape[1] + audio_embeds_vf.shape[1]
+        B, _, _, S, C = audio_embeds.shape
+
+        # process audio of first frame
+        audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c")
+        batch_size, window_size, blocks, channels = audio_embeds.shape
+        audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels)
+
+        # process audio of latter frame
+        audio_embeds_vf = rearrange(audio_embeds_vf, "bz f w b c -> (bz f) w b c")
+        batch_size_vf, window_size_vf, blocks_vf, channels_vf = audio_embeds_vf.shape
+        audio_embeds_vf = audio_embeds_vf.view(batch_size_vf, window_size_vf * blocks_vf * channels_vf)
+
+        # first projection
+        audio_embeds = torch.relu(self.proj1(audio_embeds))
+        audio_embeds_vf = torch.relu(self.proj1_vf(audio_embeds_vf))
+        audio_embeds = rearrange(audio_embeds, "(bz f) c -> bz f c", bz=B)
+        audio_embeds_vf = rearrange(audio_embeds_vf, "(bz f) c -> bz f c", bz=B)
+        audio_embeds_c = torch.concat([audio_embeds, audio_embeds_vf], dim=1)
+        batch_size_c, N_t, C_a = audio_embeds_c.shape
+        audio_embeds_c = audio_embeds_c.view(batch_size_c*N_t, C_a)
+
+        # second projection
+        audio_embeds_c = torch.relu(self.proj2(audio_embeds_c))
+
+        context_tokens = self.proj3(audio_embeds_c).reshape(batch_size_c*N_t, self.context_tokens, self.out_dim)
+
+        # normalization and reshape
+        context_tokens = self.norm(context_tokens)
+        context_tokens = rearrange(context_tokens, "(bz f) m c -> bz f m c", f=video_length)
+
+        return context_tokens
+
+
+class WanMultiTalkAttentionBlock(torch.nn.Module):
+    def __init__(self, in_dim=5120, out_dim=768, device=None, dtype=None, operations=None):
+        super().__init__()
+        self.audio_cross_attn = SingleStreamMultiAttention(in_dim, out_dim, num_heads=40, qkv_bias=True, device=device, dtype=dtype, operations=operations)
+        self.norm_x = operations.LayerNorm(in_dim, device=device, dtype=dtype, elementwise_affine=True)
+
+
+class MultiTalkGetAttnMapPatch:
+    def __init__(self, ref_target_masks=None):
+        self.ref_target_masks = ref_target_masks
+
+    def __call__(self, kwargs):
+        transformer_options = kwargs.get("transformer_options", {})
+        x = kwargs["x"]
+
+        if self.ref_target_masks is not None:
+            x_ref_attn_map = get_attn_map_with_target(kwargs["q"], kwargs["k"], transformer_options["grid_sizes"], ref_target_masks=self.ref_target_masks.to(x.device))
+            transformer_options["x_ref_attn_map"] = x_ref_attn_map
+        return x
+
+
+class MultiTalkCrossAttnPatch:
+    def __init__(self, model_patch, audio_scale=1.0, ref_target_masks=None):
+        self.model_patch = model_patch
+        self.audio_scale = audio_scale
+        self.ref_target_masks = ref_target_masks
+
+    def __call__(self, kwargs):
+        transformer_options = kwargs.get("transformer_options", {})
+        block_idx = transformer_options.get("block_index", None)
+        x = kwargs["x"]
+        if block_idx is None:
+            return torch.zeros_like(x)
+
+        audio_embeds = transformer_options.get("audio_embeds")
+        x_ref_attn_map = transformer_options.pop("x_ref_attn_map", None)
+
+        norm_x = self.model_patch.model.blocks[block_idx].norm_x(x)
+        x_audio = self.model_patch.model.blocks[block_idx].audio_cross_attn(
+            norm_x, audio_embeds.to(x.dtype),
+            shape=transformer_options["grid_sizes"],
+            x_ref_attn_map=x_ref_attn_map
+        )
+        x = x + x_audio * self.audio_scale
+        return x
+
+    def models(self):
+        return [self.model_patch]
+
+class MultiTalkApplyModelWrapper:
+    def __init__(self, init_latents):
+        self.init_latents = init_latents
+
+    def __call__(self, executor, x, *args, **kwargs):
+        x[:, :, :self.init_latents.shape[2]] = self.init_latents.to(x)
+        samples = executor(x, *args, **kwargs)
+        return samples
+
+
+class InfiniteTalkOuterSampleWrapper:
+    def __init__(self, motion_frames_latent, model_patch, is_extend=False):
+        self.motion_frames_latent = motion_frames_latent
+        self.model_patch = model_patch
+        self.is_extend = is_extend
+
+    def __call__(self, executor, *args, **kwargs):
+        model_patcher = executor.class_obj.model_patcher
+        model_options = executor.class_obj.model_options
+        process_latent_in = model_patcher.model.process_latent_in
+
+        # for InfiniteTalk, model input first latent(s) need to always be replaced on every step
+        if self.motion_frames_latent is not None:
+            wrappers = model_options["transformer_options"]["wrappers"]
+            w = wrappers.setdefault(comfy.patcher_extension.WrappersMP.APPLY_MODEL, {})
+            w["MultiTalk_apply_model"] = [MultiTalkApplyModelWrapper(process_latent_in(self.motion_frames_latent))]
+
+        # run the sampling process
+        result = executor(*args, **kwargs)
+
+        # insert motion frames before decoding
+        if self.is_extend:
+            overlap = self.motion_frames_latent.shape[2]
+            result = torch.cat([self.motion_frames_latent.to(result), result[:, :, overlap:]], dim=2)
+
+        return result
+
+    def to(self, device_or_dtype):
+        if isinstance(device_or_dtype, torch.device):
+            if self.motion_frames_latent is not None:
+                self.motion_frames_latent = self.motion_frames_latent.to(device_or_dtype)
+        return self

comfy/model_base.py

@@ -49,6 +49,7 @@ import comfy.ldm.ace.model
 import comfy.ldm.omnigen.omnigen2
 import comfy.ldm.qwen_image.model
 import comfy.ldm.kandinsky5.model
+import comfy.ldm.anima.model
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -1147,9 +1148,31 @@ class CosmosPredict2(BaseModel):
         sigma = (sigma / (sigma + 1))
         return latent_image / (1.0 - sigma)
 
+class Anima(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.anima.model.Anima)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None)
+        t5xxl_ids = kwargs.get("t5xxl_ids", None)
+        t5xxl_weights = kwargs.get("t5xxl_weights", None)
+        device = kwargs["device"]
+        if cross_attn is not None:
+            if t5xxl_ids is not None:
+                cross_attn = self.diffusion_model.preprocess_text_embeds(cross_attn.to(device=device, dtype=self.get_dtype()), t5xxl_ids.unsqueeze(0).to(device=device))
+                if t5xxl_weights is not None:
+                    cross_attn *= t5xxl_weights.unsqueeze(0).unsqueeze(-1).to(cross_attn)
+
+                if cross_attn.shape[1] < 512:
+                    cross_attn = torch.nn.functional.pad(cross_attn, (0, 0, 0, 512 - cross_attn.shape[1]))
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        return out
+
 class Lumina2(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiT)
+        self.memory_usage_factor_conds = ("ref_latents",)
 
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
@@ -1169,6 +1192,35 @@ class Lumina2(BaseModel):
         if clip_text_pooled is not None:
             out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled)
 
+        clip_vision_outputs = kwargs.get("clip_vision_outputs", list(map(lambda a: a.get("clip_vision_output"), kwargs.get("unclip_conditioning", [{}]))))  # Z Image omni
+        if clip_vision_outputs is not None and len(clip_vision_outputs) > 0:
+            sigfeats = []
+            for clip_vision_output in clip_vision_outputs:
+                if clip_vision_output is not None:
+                    image_size = clip_vision_output.image_sizes[0]
+                    shape = clip_vision_output.last_hidden_state.shape
+                    sigfeats.append(clip_vision_output.last_hidden_state.reshape(shape[0], image_size[1] // 16, image_size[2] // 16, shape[-1]))
+            if len(sigfeats) > 0:
+                out['siglip_feats'] = comfy.conds.CONDList(sigfeats)
+
+        ref_latents = kwargs.get("reference_latents", None)
+        if ref_latents is not None:
+            latents = []
+            for lat in ref_latents:
+                latents.append(self.process_latent_in(lat))
+            out['ref_latents'] = comfy.conds.CONDList(latents)
+
+        ref_contexts = kwargs.get("reference_latents_text_embeds", None)
+        if ref_contexts is not None:
+            out['ref_contexts'] = comfy.conds.CONDList(ref_contexts)
+
+        return out
+
+    def extra_conds_shapes(self, **kwargs):
+        out = {}
+        ref_latents = kwargs.get("reference_latents", None)
+        if ref_latents is not None:
+            out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))])
         return out
 
 class WAN21(BaseModel):
@@ -1526,6 +1578,9 @@ class QwenImage(BaseModel):
 
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            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)

comfy/model_detection.py

@@ -253,7 +253,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
                 dit_config["image_model"] = "chroma_radiance"
                 dit_config["in_channels"] = 3
                 dit_config["out_channels"] = 3
-                dit_config["patch_size"] = 16
+                dit_config["patch_size"] = state_dict.get('{}img_in_patch.weight'.format(key_prefix)).size(dim=-1)
                 dit_config["nerf_hidden_size"] = 64
                 dit_config["nerf_mlp_ratio"] = 4
                 dit_config["nerf_depth"] = 4
@@ -446,6 +446,9 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
             dit_config["time_scale"] = 1000.0
             if '{}cap_pad_token'.format(key_prefix) in state_dict_keys:
                 dit_config["pad_tokens_multiple"] = 32
+            sig_weight = state_dict.get('{}siglip_embedder.0.weight'.format(key_prefix), None)
+            if sig_weight is not None:
+                dit_config["siglip_feat_dim"] = sig_weight.shape[0]
 
         return dit_config
 
@@ -547,6 +550,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
     if '{}blocks.0.mlp.layer1.weight'.format(key_prefix) in state_dict_keys:  # Cosmos predict2
         dit_config = {}
         dit_config["image_model"] = "cosmos_predict2"
+        if "{}llm_adapter.blocks.0.cross_attn.q_proj.weight".format(key_prefix) in state_dict_keys:
+            dit_config["image_model"] = "anima"
         dit_config["max_img_h"] = 240
         dit_config["max_img_w"] = 240
         dit_config["max_frames"] = 128

comfy/sd.py

@@ -57,6 +57,7 @@ import comfy.text_encoders.ovis
 import comfy.text_encoders.kandinsky5
 import comfy.text_encoders.jina_clip_2
 import comfy.text_encoders.newbie
+import comfy.text_encoders.anima
 
 import comfy.model_patcher
 import comfy.lora
@@ -635,14 +636,13 @@ class VAE:
                 self.upscale_index_formula = (4, 16, 16)
                 self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)
                 self.downscale_index_formula = (4, 16, 16)
-                if self.latent_channels == 48: # Wan 2.2
+                if self.latent_channels in [48, 128]: # Wan 2.2 and LTX2
                     self.first_stage_model = comfy.taesd.taehv.TAEHV(latent_channels=self.latent_channels, latent_format=None) # taehv doesn't need scaling
-                    self.process_input = lambda image: (_ for _ in ()).throw(NotImplementedError("This light tae doesn't support encoding currently"))
+                    self.process_input = self.process_output = lambda image: image
                     self.process_output = lambda image: image
                     self.memory_used_decode = lambda shape, dtype: (1800 * (max(1, (shape[-3] ** 0.7 * 0.1)) * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype))
                 elif self.latent_channels == 32 and sd["decoder.22.bias"].shape[0] == 12: # lighttae_hv15
                     self.first_stage_model = comfy.taesd.taehv.TAEHV(latent_channels=self.latent_channels, latent_format=comfy.latent_formats.HunyuanVideo15)
-                    self.process_input = lambda image: (_ for _ in ()).throw(NotImplementedError("This light tae doesn't support encoding currently"))
                     self.memory_used_decode = lambda shape, dtype: (1200 * (max(1, (shape[-3] ** 0.7 * 0.05)) * shape[-2] * shape[-1] * 32 * 32) * model_management.dtype_size(dtype))
                 else:
                     if sd["decoder.1.weight"].dtype == torch.float16: # taehv currently only available in float16, so assume it's not lighttaew2_1 as otherwise state dicts are identical
@@ -1048,6 +1048,7 @@ class TEModel(Enum):
     GEMMA_3_12B = 18
     JINA_CLIP_2 = 19
     QWEN3_8B = 20
+    QWEN3_06B = 21
 
 
 def detect_te_model(sd):
@@ -1093,6 +1094,8 @@ def detect_te_model(sd):
                 return TEModel.QWEN3_2B
             elif weight.shape[0] == 4096:
                 return TEModel.QWEN3_8B
+            elif weight.shape[0] == 1024:
+                return TEModel.QWEN3_06B
         if weight.shape[0] == 5120:
             if "model.layers.39.post_attention_layernorm.weight" in sd:
                 return TEModel.MISTRAL3_24B
@@ -1233,6 +1236,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         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
+        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
         else:
             # clip_l
             if clip_type == CLIPType.SD3:

comfy/supported_models.py

@@ -23,6 +23,7 @@ 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.anima
 
 from . import supported_models_base
 from . import latent_formats
@@ -770,10 +771,24 @@ class Flux2(Flux):
         return out
 
     def clip_target(self, state_dict={}):
-        return None # TODO
         pref = self.text_encoder_key_prefix[0]
-        t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
-        return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(**t5_detect))
+        detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_4b.transformer.".format(pref))
+        if len(detect) > 0:
+            detect["model_type"] = "qwen3_4b"
+            return supported_models_base.ClipTarget(comfy.text_encoders.flux.KleinTokenizer, comfy.text_encoders.flux.klein_te(**detect))
+
+        detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_8b.transformer.".format(pref))
+        if len(detect) > 0:
+            detect["model_type"] = "qwen3_8b"
+            return supported_models_base.ClipTarget(comfy.text_encoders.flux.KleinTokenizer8B, comfy.text_encoders.flux.klein_te(**detect))
+
+        detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}mistral3_24b.transformer.".format(pref))
+        if len(detect) > 0:
+            if "{}mistral3_24b.transformer.model.layers.39.post_attention_layernorm.weight".format(pref) not in state_dict:
+                detect["pruned"] = True
+            return supported_models_base.ClipTarget(comfy.text_encoders.flux.Flux2Tokenizer, comfy.text_encoders.flux.flux2_te(**detect))
+
+        return None
 
 class GenmoMochi(supported_models_base.BASE):
     unet_config = {
@@ -992,6 +1007,36 @@ class CosmosT2IPredict2(supported_models_base.BASE):
         t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect))
 
+class Anima(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "anima",
+    }
+
+    sampling_settings = {
+        "multiplier": 1.0,
+        "shift": 3.0,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.Wan21
+
+    memory_usage_factor = 1.0
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    def __init__(self, unet_config):
+        super().__init__(unet_config)
+        self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.95
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Anima(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_06b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.anima.AnimaTokenizer, comfy.text_encoders.anima.te(**detect))
+
 class CosmosI2VPredict2(CosmosT2IPredict2):
     unet_config = {
         "image_model": "cosmos_predict2",
@@ -1551,6 +1596,6 @@ class Kandinsky5Image(Kandinsky5):
         return supported_models_base.ClipTarget(comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage, comfy.text_encoders.kandinsky5.te(**hunyuan_detect))
 
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5]
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
 
 models += [SVD_img2vid]

comfy/taesd/taehv.py

@@ -112,7 +112,8 @@ def apply_model_with_memblocks(model, x, parallel, show_progress_bar):
 
 
 class TAEHV(nn.Module):
-    def __init__(self, latent_channels, parallel=False, decoder_time_upscale=(True, True), decoder_space_upscale=(True, True, True), latent_format=None, show_progress_bar=True):
+    def __init__(self, latent_channels, parallel=False, encoder_time_downscale=(True, True, False), decoder_time_upscale=(False, True, True), decoder_space_upscale=(True, True, True),
+                 latent_format=None, show_progress_bar=False):
         super().__init__()
         self.image_channels = 3
         self.patch_size = 1
@@ -124,6 +125,9 @@ class TAEHV(nn.Module):
         self.process_out = latent_format().process_out if latent_format is not None else (lambda x: x)
         if self.latent_channels in [48, 32]: # Wan 2.2 and HunyuanVideo1.5
             self.patch_size = 2
+        elif self.latent_channels == 128: # LTX2
+            self.patch_size, self.latent_channels, encoder_time_downscale, decoder_time_upscale = 4, 128, (True, True, True), (True, True, True)
+
         if self.latent_channels == 32: # HunyuanVideo1.5
             act_func = nn.LeakyReLU(0.2, inplace=True)
         else: # HunyuanVideo, Wan 2.1
@@ -131,41 +135,52 @@ class TAEHV(nn.Module):
 
         self.encoder = nn.Sequential(
             conv(self.image_channels*self.patch_size**2, 64), act_func,
-            TPool(64, 2), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),
-            TPool(64, 2), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),
-            TPool(64, 1), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),
+            TPool(64, 2 if encoder_time_downscale[0] else 1), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),
+            TPool(64, 2 if encoder_time_downscale[1] else 1), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),
+            TPool(64, 2 if encoder_time_downscale[2] else 1), conv(64, 64, stride=2, bias=False), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func), MemBlock(64, 64, act_func),
             conv(64, self.latent_channels),
         )
         n_f = [256, 128, 64, 64]
-        self.frames_to_trim = 2**sum(decoder_time_upscale) - 1
+
         self.decoder = nn.Sequential(
             Clamp(), conv(self.latent_channels, n_f[0]), act_func,
-            MemBlock(n_f[0], n_f[0], act_func), MemBlock(n_f[0], n_f[0], act_func), MemBlock(n_f[0], n_f[0], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1), TGrow(n_f[0], 1), conv(n_f[0], n_f[1], bias=False),
-            MemBlock(n_f[1], n_f[1], act_func), MemBlock(n_f[1], n_f[1], act_func), MemBlock(n_f[1], n_f[1], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[1] else 1), TGrow(n_f[1], 2 if decoder_time_upscale[0] else 1), conv(n_f[1], n_f[2], bias=False),
-            MemBlock(n_f[2], n_f[2], act_func), MemBlock(n_f[2], n_f[2], act_func), MemBlock(n_f[2], n_f[2], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[2] else 1), TGrow(n_f[2], 2 if decoder_time_upscale[1] else 1), conv(n_f[2], n_f[3], bias=False),
+            MemBlock(n_f[0], n_f[0], act_func), MemBlock(n_f[0], n_f[0], act_func), MemBlock(n_f[0], n_f[0], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[0] else 1), TGrow(n_f[0], 2 if decoder_time_upscale[0] else 1), conv(n_f[0], n_f[1], bias=False),
+            MemBlock(n_f[1], n_f[1], act_func), MemBlock(n_f[1], n_f[1], act_func), MemBlock(n_f[1], n_f[1], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[1] else 1), TGrow(n_f[1], 2 if decoder_time_upscale[1] else 1), conv(n_f[1], n_f[2], bias=False),
+            MemBlock(n_f[2], n_f[2], act_func), MemBlock(n_f[2], n_f[2], act_func), MemBlock(n_f[2], n_f[2], act_func), nn.Upsample(scale_factor=2 if decoder_space_upscale[2] else 1), TGrow(n_f[2], 2 if decoder_time_upscale[2] else 1), conv(n_f[2], n_f[3], bias=False),
             act_func, conv(n_f[3], self.image_channels*self.patch_size**2),
         )
-        @property
-        def show_progress_bar(self):
-            return self._show_progress_bar
 
-        @show_progress_bar.setter
-        def show_progress_bar(self, value):
-            self._show_progress_bar = value
+        self.t_downscale = 2**sum(t.stride == 2 for t in self.encoder if isinstance(t, TPool))
+        self.t_upscale = 2**sum(t.stride == 2 for t in self.decoder if isinstance(t, TGrow))
+        self.frames_to_trim = self.t_upscale - 1
+        self._show_progress_bar = show_progress_bar
+
+    @property
+    def show_progress_bar(self):
+        return self._show_progress_bar
+
+    @show_progress_bar.setter
+    def show_progress_bar(self, value):
+        self._show_progress_bar = value
 
     def encode(self, x, **kwargs):
-        if self.patch_size > 1:
-            x = F.pixel_unshuffle(x, self.patch_size)
         x = x.movedim(2, 1)  # [B, C, T, H, W] -> [B, T, C, H, W]
-        if x.shape[1] % 4 != 0:
-            # pad at end to multiple of 4
-            n_pad = 4 - x.shape[1] % 4
+        if self.patch_size > 1:
+            B, T, C, H, W = x.shape
+            x = x.reshape(B * T, C, H, W)
+            x = F.pixel_unshuffle(x, self.patch_size)
+            x = x.reshape(B, T, C * self.patch_size ** 2, H // self.patch_size, W // self.patch_size)
+        if x.shape[1] % self.t_downscale != 0:
+            # pad at end to multiple of t_downscale
+            n_pad = self.t_downscale - x.shape[1] % self.t_downscale
             padding = x[:, -1:].repeat_interleave(n_pad, dim=1)
             x = torch.cat([x, padding], 1)
         x = apply_model_with_memblocks(self.encoder, x, self.parallel, self.show_progress_bar).movedim(2, 1)
         return self.process_out(x)
 
     def decode(self, x, **kwargs):
+        x = x.unsqueeze(0) if x.ndim == 4 else x  # [T, C, H, W] -> [1, T, C, H, W]
+        x = x.movedim(1, 2) if x.shape[1] != self.latent_channels else x  # [B, T, C, H, W] or [B, C, T, H, W]
         x = self.process_in(x).movedim(2, 1)  # [B, C, T, H, W] -> [B, T, C, H, W]
         x = apply_model_with_memblocks(self.decoder, x, self.parallel, self.show_progress_bar)
         if self.patch_size > 1:

comfy/text_encoders/anima.py

@@ -0,0 +1,61 @@
+from transformers import Qwen2Tokenizer, T5TokenizerFast
+import comfy.text_encoders.llama
+from comfy import sd1_clip
+import os
+import torch
+
+
+class Qwen3Tokenizer(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_size=1024, embedding_key='qwen3_06b', 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 T5XXLTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data)
+
+class AnimaTokenizer:
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        self.qwen3_06b = Qwen3Tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+        self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
+        out = {}
+        qwen_ids = self.qwen3_06b.tokenize_with_weights(text, return_word_ids, **kwargs)
+        out["qwen3_06b"] = [[(token, 1.0) for token, _ in inner_list] for inner_list in qwen_ids]  # Set weights to 1.0
+        out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)
+        return out
+
+    def untokenize(self, token_weight_pair):
+        return self.t5xxl.untokenize(token_weight_pair)
+
+    def state_dict(self):
+        return {}
+
+
+class Qwen3_06BModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen3_06B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+class AnimaTEModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="qwen3_06b", clip_model=Qwen3_06BModel, model_options=model_options)
+
+    def encode_token_weights(self, token_weight_pairs):
+        out = super().encode_token_weights(token_weight_pairs)
+        out[2]["t5xxl_ids"] = torch.tensor(list(map(lambda a: a[0], token_weight_pairs["t5xxl"][0])), dtype=torch.int)
+        out[2]["t5xxl_weights"] = torch.tensor(list(map(lambda a: a[1], token_weight_pairs["t5xxl"][0])))
+        return out
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+    class AnimaTEModel_(AnimaTEModel):
+        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 AnimaTEModel_

comfy/text_encoders/hunyuan_video.py

@@ -10,9 +10,11 @@ import comfy.utils
 
 def llama_detect(state_dict, prefix=""):
     out = {}
-    t5_key = "{}model.norm.weight".format(prefix)
-    if t5_key in state_dict:
-        out["dtype_llama"] = state_dict[t5_key].dtype
+    norm_keys = ["{}model.norm.weight".format(prefix), "{}model.layers.0.input_layernorm.weight".format(prefix)]
+    for norm_key in norm_keys:
+        if norm_key in state_dict:
+            out["dtype_llama"] = state_dict[norm_key].dtype
+            break
 
     quant = comfy.utils.detect_layer_quantization(state_dict, prefix)
     if quant is not None:

comfy/text_encoders/llama.py

@@ -77,6 +77,28 @@ class Qwen25_3BConfig:
     rope_scale = None
     final_norm: bool = True
 
+@dataclass
+class Qwen3_06BConfig:
+    vocab_size: int = 151936
+    hidden_size: int = 1024
+    intermediate_size: int = 3072
+    num_hidden_layers: int = 28
+    num_attention_heads: int = 16
+    num_key_value_heads: int = 8
+    max_position_embeddings: int = 32768
+    rms_norm_eps: float = 1e-6
+    rope_theta: float = 1000000.0
+    transformer_type: str = "llama"
+    head_dim = 128
+    rms_norm_add = False
+    mlp_activation = "silu"
+    qkv_bias = False
+    rope_dims = None
+    q_norm = "gemma3"
+    k_norm = "gemma3"
+    rope_scale = None
+    final_norm: bool = True
+
 @dataclass
 class Qwen3_4BConfig:
     vocab_size: int = 151936
@@ -641,6 +663,15 @@ class Qwen25_3B(BaseLlama, torch.nn.Module):
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
 
+class Qwen3_06B(BaseLlama, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Qwen3_06BConfig(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
+
 class Qwen3_4B(BaseLlama, torch.nn.Module):
     def __init__(self, config_dict, dtype, device, operations):
         super().__init__()

comfy/text_encoders/lt.py

@@ -118,9 +118,18 @@ class LTXAVTEModel(torch.nn.Module):
             sdo = comfy.utils.state_dict_prefix_replace(sd, {"text_embedding_projection.aggregate_embed.weight": "text_embedding_projection.weight", "model.diffusion_model.video_embeddings_connector.": "video_embeddings_connector.", "model.diffusion_model.audio_embeddings_connector.": "audio_embeddings_connector."}, filter_keys=True)
             if len(sdo) == 0:
                 sdo = sd
-            missing, unexpected = self.load_state_dict(sdo, strict=False)
-            missing = [k for k in missing if not k.startswith("gemma3_12b.")] # filter out keys that belong to the main gemma model
-            return (missing, unexpected)
+
+            missing_all = []
+            unexpected_all = []
+
+            for prefix, component in [("text_embedding_projection.", self.text_embedding_projection), ("video_embeddings_connector.", self.video_embeddings_connector), ("audio_embeddings_connector.", self.audio_embeddings_connector)]:
+                component_sd = {k.replace(prefix, ""): v for k, v in sdo.items() if k.startswith(prefix)}
+                if component_sd:
+                    missing, unexpected = component.load_state_dict(component_sd, strict=False)
+                    missing_all.extend([f"{prefix}{k}" for k in missing])
+                    unexpected_all.extend([f"{prefix}{k}" for k in unexpected])
+
+            return (missing_all, unexpected_all)
 
     def memory_estimation_function(self, token_weight_pairs, device=None):
         constant = 6.0

comfy/text_encoders/ovis.py

@@ -61,6 +61,7 @@ def te(dtype_llama=None, llama_quantization_metadata=None):
             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 OvisTEModel_

comfy/text_encoders/z_image.py

@@ -40,6 +40,7 @@ def te(dtype_llama=None, llama_quantization_metadata=None):
             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 ZImageTEModel_

comfy/utils.py

@@ -611,6 +611,14 @@ def flux_to_diffusers(mmdit_config, output_prefix=""):
                         "ff_context.net.0.proj.bias": "txt_mlp.0.bias",
                         "ff_context.net.2.weight": "txt_mlp.2.weight",
                         "ff_context.net.2.bias": "txt_mlp.2.bias",
+                        "ff.linear_in.weight": "img_mlp.0.weight",  # LyCoris LoKr
+                        "ff.linear_in.bias": "img_mlp.0.bias",
+                        "ff.linear_out.weight": "img_mlp.2.weight",
+                        "ff.linear_out.bias": "img_mlp.2.bias",
+                        "ff_context.linear_in.weight": "txt_mlp.0.weight",
+                        "ff_context.linear_in.bias": "txt_mlp.0.bias",
+                        "ff_context.linear_out.weight": "txt_mlp.2.weight",
+                        "ff_context.linear_out.bias": "txt_mlp.2.bias",
                         "attn.norm_q.weight": "img_attn.norm.query_norm.scale",
                         "attn.norm_k.weight": "img_attn.norm.key_norm.scale",
                         "attn.norm_added_q.weight": "txt_attn.norm.query_norm.scale",
@@ -639,6 +647,8 @@ def flux_to_diffusers(mmdit_config, output_prefix=""):
                         "proj_out.bias": "linear2.bias",
                         "attn.norm_q.weight": "norm.query_norm.scale",
                         "attn.norm_k.weight": "norm.key_norm.scale",
+                        "attn.to_qkv_mlp_proj.weight": "linear1.weight", # Flux 2
+                        "attn.to_out.weight": "linear2.weight", # Flux 2
                     }
 
         for k in block_map:

comfy_api/latest/_input/__init__.py

@@ -1,10 +1,12 @@
 from .basic_types import ImageInput, AudioInput, MaskInput, LatentInput
-from .video_types import VideoInput
+from .video_types import VideoInput, VideoOp, SliceOp
 
 __all__ = [
     "ImageInput",
     "AudioInput",
     "VideoInput",
+    "VideoOp",
+    "SliceOp",
     "MaskInput",
     "LatentInput",
 ]

comfy_api/latest/_input/video_types.py

@@ -1,11 +1,48 @@
 from __future__ import annotations
 from abc import ABC, abstractmethod
+from dataclasses import dataclass
 from fractions import Fraction
 from typing import Optional, Union, IO
+import copy
 import io
 import av
 from .._util import VideoContainer, VideoCodec, VideoComponents
 
+
+class VideoOp(ABC):
+    """Base class for lazy video operations."""
+
+    @abstractmethod
+    def apply(self, components: VideoComponents) -> VideoComponents:
+        pass
+
+    @abstractmethod
+    def compute_frame_count(self, input_frame_count: int) -> int:
+        pass
+
+
+@dataclass(frozen=True)
+class SliceOp(VideoOp):
+    """Extract a range of frames from the video."""
+    start_frame: int
+    frame_count: int
+
+    def apply(self, components: VideoComponents) -> VideoComponents:
+        total = components.images.shape[0]
+        start = max(0, min(self.start_frame, total))
+        end = min(start + self.frame_count, total)
+        return VideoComponents(
+            images=components.images[start:end],
+            audio=components.audio,
+            frame_rate=components.frame_rate,
+            metadata=getattr(components, 'metadata', None),
+        )
+
+    def compute_frame_count(self, input_frame_count: int) -> int:
+        start = max(0, min(self.start_frame, input_frame_count))
+        return min(self.frame_count, input_frame_count - start)
+
+
 class VideoInput(ABC):
     """
     Abstract base class for video input types.
@@ -21,6 +58,12 @@ class VideoInput(ABC):
         """
         pass
 
+    def sliced(self, start_frame: int, frame_count: int) -> "VideoInput":
+        """Return a copy of this video with a slice operation appended."""
+        new = copy.copy(self)
+        new._operations = getattr(self, '_operations', []) + [SliceOp(start_frame, frame_count)]
+        return new
+
     @abstractmethod
     def save_to(
         self,

comfy_api/latest/_input_impl/__init__.py

@@ -1,7 +1,8 @@
 from .video_types import VideoFromFile, VideoFromComponents
+from .._input import SliceOp
 
 __all__ = [
-    # Implementations
     "VideoFromFile",
     "VideoFromComponents",
+    "SliceOp",
 ]

comfy_api/latest/_input_impl/video_types.py

@@ -3,7 +3,7 @@ from av.container import InputContainer
 from av.subtitles.stream import SubtitleStream
 from fractions import Fraction
 from typing import Optional
-from .._input import AudioInput, VideoInput
+from .._input import AudioInput, VideoInput, VideoOp
 import av
 import io
 import json
@@ -63,6 +63,8 @@ class VideoFromFile(VideoInput):
         containing the file contents.
         """
         self.__file = file
+        self._operations: list[VideoOp] = []
+        self.__materialized: Optional[VideoFromComponents] = None
 
     def get_stream_source(self) -> str | io.BytesIO:
         """
@@ -161,6 +163,10 @@ class VideoFromFile(VideoInput):
 
             if frame_count == 0:
                 raise ValueError(f"Could not determine frame count for file '{self.__file}'")
+
+            # Apply operations to get final frame count
+            for op in self._operations:
+                frame_count = op.compute_frame_count(frame_count)
             return frame_count
 
     def get_frame_rate(self) -> Fraction:
@@ -239,10 +245,18 @@ class VideoFromFile(VideoInput):
         return VideoComponents(images=images, audio=audio, frame_rate=frame_rate, metadata=metadata)
 
     def get_components(self) -> VideoComponents:
+        if self.__materialized is not None:
+            return self.__materialized.get_components()
+
         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:
-            return self.get_components_internal(container)
+            components = self.get_components_internal(container)
+            for op in self._operations:
+                components = op.apply(components)
+            self.__materialized = VideoFromComponents(components)
+            self._operations = []
+            return components
         raise ValueError(f"No video stream found in file '{self.__file}'")
 
     def save_to(
@@ -317,14 +331,27 @@ class VideoFromComponents(VideoInput):
 
     def __init__(self, components: VideoComponents):
         self.__components = components
+        self._operations: list[VideoOp] = []
 
     def get_components(self) -> VideoComponents:
+        if self._operations:
+            components = self.__components
+            for op in self._operations:
+                components = op.apply(components)
+            self.__components = components
+            self._operations = []
         return VideoComponents(
             images=self.__components.images,
             audio=self.__components.audio,
             frame_rate=self.__components.frame_rate
         )
 
+    def get_frame_count(self) -> int:
+        count = int(self.__components.images.shape[0])
+        for op in self._operations:
+            count = op.compute_frame_count(count)
+        return count
+
     def save_to(
         self,
         path: str,
@@ -332,6 +359,9 @@ class VideoFromComponents(VideoInput):
         codec: VideoCodec = VideoCodec.AUTO,
         metadata: Optional[dict] = None
     ):
+        # Materialize ops before saving
+        components = self.get_components()
+
         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:
@@ -345,22 +375,22 @@ class VideoFromComponents(VideoInput):
                 for key, value in metadata.items():
                     output.metadata[key] = json.dumps(value)
 
-            frame_rate = Fraction(round(self.__components.frame_rate * 1000), 1000)
+            frame_rate = Fraction(round(components.frame_rate * 1000), 1000)
             # Create a video stream
             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.width = components.images.shape[2]
+            video_stream.height = components.images.shape[1]
             video_stream.pix_fmt = 'yuv420p'
 
             # Create an audio stream
             audio_sample_rate = 1
             audio_stream: Optional[av.AudioStream] = None
-            if self.__components.audio:
-                audio_sample_rate = int(self.__components.audio['sample_rate'])
+            if components.audio:
+                audio_sample_rate = int(components.audio['sample_rate'])
                 audio_stream = output.add_stream('aac', rate=audio_sample_rate)
 
             # Encode video
-            for i, frame in enumerate(self.__components.images):
+            for i, frame in enumerate(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
@@ -371,10 +401,10 @@ class VideoFromComponents(VideoInput):
             packet = video_stream.encode(None)
             output.mux(packet)
 
-            if audio_stream and self.__components.audio:
-                waveform = self.__components.audio['waveform']
-                waveform = waveform[:, :, :math.ceil((audio_sample_rate / frame_rate) * self.__components.images.shape[0])]
-                frame = av.AudioFrame.from_ndarray(waveform.movedim(2, 1).reshape(1, -1).float().numpy(), format='flt', layout='mono' if waveform.shape[1] == 1 else 'stereo')
+            if audio_stream and components.audio:
+                waveform = components.audio['waveform']
+                waveform = waveform[:, :, :math.ceil((audio_sample_rate / frame_rate) * components.images.shape[0])]
+                frame = av.AudioFrame.from_ndarray(waveform.movedim(2, 1).reshape(1, -1).float().cpu().numpy(), format='flt', layout='mono' if waveform.shape[1] == 1 else 'stereo')
                 frame.sample_rate = audio_sample_rate
                 frame.pts = 0
                 output.mux(audio_stream.encode(frame))

comfy_api/latest/_io.py

@@ -153,7 +153,7 @@ class Input(_IO_V3):
     '''
     Base class for a V3 Input.
     '''
-    def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None):
+    def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
         super().__init__()
         self.id = id
         self.display_name = display_name
@@ -162,6 +162,7 @@ class Input(_IO_V3):
         self.lazy = lazy
         self.extra_dict = extra_dict if extra_dict is not None else {}
         self.rawLink = raw_link
+        self.advanced = advanced
 
     def as_dict(self):
         return prune_dict({
@@ -170,6 +171,7 @@ class Input(_IO_V3):
             "tooltip": self.tooltip,
             "lazy": self.lazy,
             "rawLink": self.rawLink,
+            "advanced": self.advanced,
         }) | prune_dict(self.extra_dict)
 
     def get_io_type(self):
@@ -184,8 +186,8 @@ class WidgetInput(Input):
     '''
     def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                  default: Any=None,
-                 socketless: bool=None, widget_type: str=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
-        super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link)
+                 socketless: bool=None, widget_type: str=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+        super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link, advanced)
         self.default = default
         self.socketless = socketless
         self.widget_type = widget_type
@@ -242,8 +244,8 @@ class Boolean(ComfyTypeIO):
         '''Boolean input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: bool=None, label_on: str=None, label_off: str=None,
-                    socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link)
+                    socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link, advanced)
             self.label_on = label_on
             self.label_off = label_off
             self.default: bool
@@ -262,8 +264,8 @@ class Int(ComfyTypeIO):
         '''Integer input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: int=None, min: int=None, max: int=None, step: int=None, control_after_generate: bool=None,
-                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link)
+                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link, advanced)
             self.min = min
             self.max = max
             self.step = step
@@ -288,8 +290,8 @@ class Float(ComfyTypeIO):
         '''Float input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: float=None, min: float=None, max: float=None, step: float=None, round: float=None,
-                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link)
+                    display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link, advanced)
             self.min = min
             self.max = max
             self.step = step
@@ -314,8 +316,8 @@ class String(ComfyTypeIO):
         '''String input.'''
         def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     multiline=False, placeholder: str=None, default: str=None, dynamic_prompts: bool=None,
-                    socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link)
+                    socketless: bool=None, force_input: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input, extra_dict, raw_link, advanced)
             self.multiline = multiline
             self.placeholder = placeholder
             self.dynamic_prompts = dynamic_prompts
@@ -350,12 +352,13 @@ class Combo(ComfyTypeIO):
             socketless: bool=None,
             extra_dict=None,
             raw_link: bool=None,
+            advanced: bool=None,
         ):
             if isinstance(options, type) and issubclass(options, Enum):
                 options = [v.value for v in options]
             if isinstance(default, Enum):
                 default = default.value
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, None, extra_dict, raw_link)
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, None, extra_dict, raw_link, advanced)
             self.multiselect = False
             self.options = options
             self.control_after_generate = control_after_generate
@@ -387,8 +390,8 @@ class MultiCombo(ComfyTypeI):
     class Input(Combo.Input):
         def __init__(self, id: str, options: list[str], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: list[str]=None, placeholder: str=None, chip: bool=None, control_after_generate: bool=None,
-                    socketless: bool=None, extra_dict=None, raw_link: bool=None):
-            super().__init__(id, options, display_name, optional, tooltip, lazy, default, control_after_generate, socketless=socketless, extra_dict=extra_dict, raw_link=raw_link)
+                    socketless: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+            super().__init__(id, options, display_name, optional, tooltip, lazy, default, control_after_generate, socketless=socketless, extra_dict=extra_dict, raw_link=raw_link, advanced=advanced)
             self.multiselect = True
             self.placeholder = placeholder
             self.chip = chip
@@ -421,9 +424,9 @@ class Webcam(ComfyTypeIO):
         Type = str
         def __init__(
                 self, id: str, display_name: str=None, optional=False,
-                tooltip: str=None, lazy: bool=None, default: str=None, socketless: bool=None, extra_dict=None, raw_link: bool=None
+                tooltip: str=None, lazy: bool=None, default: str=None, socketless: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None
         ):
-            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, None, extra_dict, raw_link)
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, None, extra_dict, raw_link, advanced)
 
 
 @comfytype(io_type="MASK")
@@ -751,7 +754,7 @@ class AnyType(ComfyTypeIO):
     Type = Any
 
 @comfytype(io_type="MODEL_PATCH")
-class MODEL_PATCH(ComfyTypeIO):
+class ModelPatch(ComfyTypeIO):
     Type = Any
 
 @comfytype(io_type="AUDIO_ENCODER")
@@ -776,7 +779,7 @@ class MultiType:
         '''
         Input that permits more than one input type; if `id` is an instance of `ComfyType.Input`, then that input will be used to create a widget (if applicable) with overridden values.
         '''
-        def __init__(self, id: str | Input, types: list[type[_ComfyType] | _ComfyType], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None):
+        def __init__(self, id: str | Input, types: list[type[_ComfyType] | _ComfyType], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
             # if id is an Input, then use that Input with overridden values
             self.input_override = None
             if isinstance(id, Input):
@@ -789,7 +792,7 @@ class MultiType:
                 # if is a widget input, make sure widget_type is set appropriately
                 if isinstance(self.input_override, WidgetInput):
                     self.input_override.widget_type = self.input_override.get_io_type()
-            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link)
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link, advanced)
             self._io_types = types
 
         @property
@@ -843,8 +846,8 @@ class MatchType(ComfyTypeIO):
 
     class Input(Input):
         def __init__(self, id: str, template: MatchType.Template,
-                    display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None):
-            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link)
+                    display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None, raw_link: bool=None, advanced: bool=None):
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict, raw_link, advanced)
             self.template = template
 
         def as_dict(self):
@@ -997,20 +1000,38 @@ class Autogrow(ComfyTypeI):
             names = [f"{prefix}{i}" for i in range(max)]
         # need to create a new input based on the contents of input
         template_input = None
-        for _, dict_input in input.items():
-            # for now, get just the first value from dict_input
+        template_required = True
+        for _input_type, dict_input in input.items():
+            # for now, get just the first value from dict_input; if not required, min can be ignored
+            if len(dict_input) == 0:
+                continue
             template_input = list(dict_input.values())[0]
+            template_required = _input_type == "required"
+            break
+        if template_input is None:
+            raise Exception("template_input could not be determined from required or optional; this should never happen.")
         new_dict = {}
+        new_dict_added_to = False
+        # first, add possible inputs into out_dict
         for i, name in enumerate(names):
             expected_id = finalize_prefix(curr_prefix, name)
+            # required
+            if i < min and template_required:
+                out_dict["required"][expected_id] = template_input
+                type_dict = new_dict.setdefault("required", {})
+            # optional
+            else:
+                out_dict["optional"][expected_id] = template_input
+                type_dict = new_dict.setdefault("optional", {})
             if expected_id in live_inputs:
-                # required
-                if i < min:
-                    type_dict = new_dict.setdefault("required", {})
-                # optional
-                else:
-                    type_dict = new_dict.setdefault("optional", {})
+                # NOTE: prefix gets added in parse_class_inputs
                 type_dict[name] = template_input
+                new_dict_added_to = True
+        # account for the edge case that all inputs are optional and no values are received
+        if not new_dict_added_to:
+            finalized_prefix = finalize_prefix(curr_prefix)
+            out_dict["dynamic_paths"][finalized_prefix] = finalized_prefix
+            out_dict["dynamic_paths_default_value"][finalized_prefix] = DynamicPathsDefaultValue.EMPTY_DICT
         parse_class_inputs(out_dict, live_inputs, new_dict, curr_prefix)
 
 @comfytype(io_type="COMFY_DYNAMICCOMBO_V3")
@@ -1119,8 +1140,8 @@ class ImageCompare(ComfyTypeI):
 
   class Input(WidgetInput):
       def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None,
-                   socketless: bool=True):
-          super().__init__(id, display_name, optional, tooltip, None, None, socketless)
+                   socketless: bool=True, advanced: bool=None):
+          super().__init__(id, display_name, optional, tooltip, None, None, socketless, None, None, None, None, advanced)
 
       def as_dict(self):
           return super().as_dict()
@@ -1148,6 +1169,8 @@ class V3Data(TypedDict):
     'Dictionary where the keys are the hidden input ids and the values are the values of the hidden inputs.'
     dynamic_paths: dict[str, Any]
     'Dictionary where the keys are the input ids and the values dictate how to turn the inputs into a nested dictionary.'
+    dynamic_paths_default_value: dict[str, Any]
+    'Dictionary where the keys are the input ids and the values are a string from DynamicPathsDefaultValue for the inputs if value is None.'
     create_dynamic_tuple: bool
     'When True, the value of the dynamic input will be in the format (value, path_key).'
 
@@ -1226,6 +1249,7 @@ class NodeInfoV1:
     experimental: bool=None
     api_node: bool=None
     price_badge: dict | None = None
+    search_aliases: list[str]=None
 
 @dataclass
 class NodeInfoV3:
@@ -1323,6 +1347,8 @@ class Schema:
     hidden: list[Hidden] = field(default_factory=list)
     description: str=""
     """Node description, shown as a tooltip when hovering over the node."""
+    search_aliases: list[str] = field(default_factory=list)
+    """Alternative names for search. Useful for synonyms, abbreviations, or old names after renaming."""
     is_input_list: bool = False
     """A flag indicating if this node implements the additional code necessary to deal with OUTPUT_IS_LIST nodes.
 
@@ -1460,6 +1486,7 @@ class Schema:
             api_node=self.is_api_node,
             python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes"),
             price_badge=self.price_badge.as_dict(self.inputs) if self.price_badge is not None else None,
+            search_aliases=self.search_aliases if self.search_aliases else None,
         )
         return info
 
@@ -1501,6 +1528,7 @@ def get_finalized_class_inputs(d: dict[str, Any], live_inputs: dict[str, Any], i
         "required": {},
         "optional": {},
         "dynamic_paths": {},
+        "dynamic_paths_default_value": {},
     }
     d = d.copy()
     # ignore hidden for parsing
@@ -1510,8 +1538,12 @@ def get_finalized_class_inputs(d: dict[str, Any], live_inputs: dict[str, Any], i
         out_dict["hidden"] = hidden
     v3_data = {}
     dynamic_paths = out_dict.pop("dynamic_paths", None)
-    if dynamic_paths is not None:
+    if dynamic_paths is not None and len(dynamic_paths) > 0:
         v3_data["dynamic_paths"] = dynamic_paths
+    # this list is used for autogrow, in the case all inputs are optional and no values are passed
+    dynamic_paths_default_value = out_dict.pop("dynamic_paths_default_value", None)
+    if dynamic_paths_default_value is not None and len(dynamic_paths_default_value) > 0:
+        v3_data["dynamic_paths_default_value"] = dynamic_paths_default_value
     return out_dict, hidden, v3_data
 
 def parse_class_inputs(out_dict: dict[str, Any], live_inputs: dict[str, Any], curr_dict: dict[str, Any], curr_prefix: list[str] | None=None) -> None:
@@ -1548,11 +1580,16 @@ def add_to_dict_v1(i: Input, d: dict):
 def add_to_dict_v3(io: Input | Output, d: dict):
     d[io.id] = (io.get_io_type(), io.as_dict())
 
+class DynamicPathsDefaultValue:
+    EMPTY_DICT = "empty_dict"
+
 def build_nested_inputs(values: dict[str, Any], v3_data: V3Data):
     paths = v3_data.get("dynamic_paths", None)
+    default_value_dict = v3_data.get("dynamic_paths_default_value", {})
     if paths is None:
         return values
     values = values.copy()
+
     result = {}
 
     create_tuple = v3_data.get("create_dynamic_tuple", False)
@@ -1566,6 +1603,11 @@ def build_nested_inputs(values: dict[str, Any], v3_data: V3Data):
 
             if is_last:
                 value = values.pop(key, None)
+                if value is None:
+                    # see if a default value was provided for this key
+                    default_option = default_value_dict.get(key, None)
+                    if default_option == DynamicPathsDefaultValue.EMPTY_DICT:
+                        value = {}
                 if create_tuple:
                     value = (value, key)
                 current[p] = value
@@ -1996,6 +2038,7 @@ __all__ = [
     "ControlNet",
     "Vae",
     "Model",
+    "ModelPatch",
     "ClipVision",
     "ClipVisionOutput",
     "AudioEncoder",

comfy_api_nodes/README.md

@@ -1,65 +0,0 @@
-# ComfyUI API Nodes
-
-## Introduction 
-
-Below are a collection of nodes that work by calling external APIs. More information available in our [docs](https://docs.comfy.org/tutorials/api-nodes/overview).
-
-## Development
-
-While developing, you should be testing against the Staging environment. To test against staging:
-
-**Install ComfyUI_frontend**
-
-Follow the instructions [here](https://github.com/Comfy-Org/ComfyUI_frontend) to start the frontend server. By default, it will connect to Staging authentication. 
-
-> **Hint:** If you use --front-end-version argument for ComfyUI, it will use production authentication.
-
-```bash
-python run main.py --comfy-api-base https://stagingapi.comfy.org
-```
-
-To authenticate to staging, please login and then ask one of Comfy Org team to whitelist you for access to staging.
-
-API stubs are generated through automatic codegen tools from OpenAPI definitions. Since the Comfy Org OpenAPI definition contains many things from the Comfy Registry as well, we use redocly/cli to filter out only the paths relevant for API nodes.
-
-### Redocly Instructions 
-
-**Tip**
-When developing locally, use the `redocly-dev.yaml` file to generate pydantic models. This lets you use stubs for APIs that are not marked `Released` yet.
-
-Before your API node PR merges, make sure to add the `Released` tag to the `openapi.yaml` file and test in staging.
-
-```bash
-# Download the OpenAPI file from staging server.
-curl -o openapi.yaml https://stagingapi.comfy.org/openapi
-
-# Filter out unneeded API definitions.
-npm install -g @redocly/cli
-redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly-dev.yaml --remove-unused-components
-
-# Generate the pydantic datamodels for validation.
-datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel
-
-```
-
-
-# Merging to Master
-
-Before merging to comfyanonymous/ComfyUI master, follow these steps:
-
-1. Add the "Released" tag to the ComfyUI OpenAPI yaml file for each endpoint you are using in the nodes. 
-1. Make sure the ComfyUI API is deployed to prod with your changes.
-1. Run the code generation again with `redocly.yaml` and the production OpenAPI yaml file.
-
-```bash
-# Download the OpenAPI file from prod server.
-curl -o openapi.yaml https://api.comfy.org/openapi
-
-# Filter out unneeded API definitions.
-npm install -g @redocly/cli
-redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly.yaml --remove-unused-components
-
-# Generate the pydantic datamodels for validation.
-datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel
-
-```

comfy_api_nodes/apis/bria.py

@@ -0,0 +1,61 @@
+from typing import TypedDict
+
+from pydantic import BaseModel, Field
+
+
+class InputModerationSettings(TypedDict):
+    prompt_content_moderation: bool
+    visual_input_moderation: bool
+    visual_output_moderation: bool
+
+
+class BriaEditImageRequest(BaseModel):
+    instruction: str | None = Field(...)
+    structured_instruction: str | None = Field(
+        ...,
+        description="Use this instead of instruction for precise, programmatic control.",
+    )
+    images: list[str] = Field(
+        ...,
+        description="Required. Publicly available URL or Base64-encoded. Must contain exactly one item.",
+    )
+    mask: str | None = Field(
+        None,
+        description="Mask image (black and white). Black areas will be preserved, white areas will be edited. "
+        "If omitted, the edit applies to the entire image. "
+        "The input image and the the input mask must be of the same size.",
+    )
+    negative_prompt: str | None = Field(None)
+    guidance_scale: float = Field(...)
+    model_version: str = Field(...)
+    steps_num: int = Field(...)
+    seed: int = Field(...)
+    ip_signal: bool = Field(
+        False,
+        description="If true, returns a warning for potential IP content in the instruction.",
+    )
+    prompt_content_moderation: bool = Field(
+        False, description="If true, returns 422 on instruction moderation failure."
+    )
+    visual_input_content_moderation: bool = Field(
+        False, description="If true, returns 422 on images or mask moderation failure."
+    )
+    visual_output_content_moderation: bool = Field(
+        False, description="If true, returns 422 on visual output moderation failure."
+    )
+
+
+class BriaStatusResponse(BaseModel):
+    request_id: str = Field(...)
+    status_url: str = Field(...)
+    warning: str | None = Field(None)
+
+
+class BriaResult(BaseModel):
+    structured_prompt: str = Field(...)
+    image_url: str = Field(...)
+
+
+class BriaResponse(BaseModel):
+    status: str = Field(...)
+    result: BriaResult | None = Field(None)

comfy_api_nodes/apis/bytedance.py

@@ -65,11 +65,13 @@ class TaskImageContent(BaseModel):
 class Text2VideoTaskCreationRequest(BaseModel):
     model: str = Field(...)
     content: list[TaskTextContent] = Field(..., min_length=1)
+    generate_audio: bool | None = Field(...)
 
 
 class Image2VideoTaskCreationRequest(BaseModel):
     model: str = Field(...)
     content: list[TaskTextContent | TaskImageContent] = Field(..., min_length=2)
+    generate_audio: bool | None = Field(...)
 
 
 class TaskCreationResponse(BaseModel):
@@ -141,4 +143,9 @@ VIDEO_TASKS_EXECUTION_TIME = {
         "720p": 65,
         "1080p": 100,
     },
+    "seedance-1-5-pro-251215": {
+        "480p": 80,
+        "720p": 100,
+        "1080p": 150,
+    },
 }

comfy_api_nodes/apis/ideogram.py

@@ -0,0 +1,292 @@
+from enum import Enum
+from typing import Optional, List, Dict, Any, Union
+from datetime import datetime
+
+from pydantic import BaseModel, Field, RootModel, StrictBytes
+
+
+class IdeogramColorPalette1(BaseModel):
+    name: str = Field(..., description='Name of the preset color palette')
+
+
+class Member(BaseModel):
+    color: Optional[str] = Field(
+        None, description='Hexadecimal color code', pattern='^#[0-9A-Fa-f]{6}$'
+    )
+    weight: Optional[float] = Field(
+        None, description='Optional weight for the color (0-1)', ge=0.0, le=1.0
+    )
+
+
+class IdeogramColorPalette2(BaseModel):
+    members: List[Member] = Field(
+        ..., description='Array of color definitions with optional weights'
+    )
+
+
+class IdeogramColorPalette(
+    RootModel[Union[IdeogramColorPalette1, IdeogramColorPalette2]]
+):
+    root: Union[IdeogramColorPalette1, IdeogramColorPalette2] = Field(
+        ...,
+        description='A color palette specification that can either use a preset name or explicit color definitions with weights',
+    )
+
+
+class ImageRequest(BaseModel):
+    aspect_ratio: Optional[str] = Field(
+        None,
+        description="Optional. The aspect ratio (e.g., 'ASPECT_16_9', 'ASPECT_1_1'). Cannot be used with resolution. Defaults to 'ASPECT_1_1' if unspecified.",
+    )
+    color_palette: Optional[Dict[str, Any]] = Field(
+        None, description='Optional. Color palette object. Only for V_2, V_2_TURBO.'
+    )
+    magic_prompt_option: Optional[str] = Field(
+        None, description="Optional. MagicPrompt usage ('AUTO', 'ON', 'OFF')."
+    )
+    model: str = Field(..., description="The model used (e.g., 'V_2', 'V_2A_TURBO')")
+    negative_prompt: Optional[str] = Field(
+        None,
+        description='Optional. Description of what to exclude. Only for V_1, V_1_TURBO, V_2, V_2_TURBO.',
+    )
+    num_images: Optional[int] = Field(
+        1,
+        description='Optional. Number of images to generate (1-8). Defaults to 1.',
+        ge=1,
+        le=8,
+    )
+    prompt: str = Field(
+        ..., description='Required. The prompt to use to generate the image.'
+    )
+    resolution: Optional[str] = Field(
+        None,
+        description="Optional. Resolution (e.g., 'RESOLUTION_1024_1024'). Only for model V_2. Cannot be used with aspect_ratio.",
+    )
+    seed: Optional[int] = Field(
+        None,
+        description='Optional. A number between 0 and 2147483647.',
+        ge=0,
+        le=2147483647,
+    )
+    style_type: Optional[str] = Field(
+        None,
+        description="Optional. Style type ('AUTO', 'GENERAL', 'REALISTIC', 'DESIGN', 'RENDER_3D', 'ANIME'). Only for models V_2 and above.",
+    )
+
+
+class IdeogramGenerateRequest(BaseModel):
+    image_request: ImageRequest = Field(
+        ..., description='The image generation request parameters.'
+    )
+
+
+class Datum(BaseModel):
+    is_image_safe: Optional[bool] = Field(
+        None, description='Indicates whether the image is considered safe.'
+    )
+    prompt: Optional[str] = Field(
+        None, description='The prompt used to generate this image.'
+    )
+    resolution: Optional[str] = Field(
+        None, description="The resolution of the generated image (e.g., '1024x1024')."
+    )
+    seed: Optional[int] = Field(
+        None, description='The seed value used for this generation.'
+    )
+    style_type: Optional[str] = Field(
+        None,
+        description="The style type used for generation (e.g., 'REALISTIC', 'ANIME').",
+    )
+    url: Optional[str] = Field(None, description='URL to the generated image.')
+
+
+class IdeogramGenerateResponse(BaseModel):
+    created: Optional[datetime] = Field(
+        None, description='Timestamp when the generation was created.'
+    )
+    data: Optional[List[Datum]] = Field(
+        None, description='Array of generated image information.'
+    )
+
+
+class StyleCode(RootModel[str]):
+    root: str = Field(..., pattern='^[0-9A-Fa-f]{8}$')
+
+
+class Datum1(BaseModel):
+    is_image_safe: Optional[bool] = None
+    prompt: Optional[str] = None
+    resolution: Optional[str] = None
+    seed: Optional[int] = None
+    style_type: Optional[str] = None
+    url: Optional[str] = None
+
+
+class IdeogramV3IdeogramResponse(BaseModel):
+    created: Optional[datetime] = None
+    data: Optional[List[Datum1]] = None
+
+
+class RenderingSpeed1(str, Enum):
+    TURBO = 'TURBO'
+    DEFAULT = 'DEFAULT'
+    QUALITY = 'QUALITY'
+
+
+class IdeogramV3ReframeRequest(BaseModel):
+    color_palette: Optional[Dict[str, Any]] = None
+    image: Optional[StrictBytes] = None
+    num_images: Optional[int] = Field(None, ge=1, le=8)
+    rendering_speed: Optional[RenderingSpeed1] = None
+    resolution: str
+    seed: Optional[int] = Field(None, ge=0, le=2147483647)
+    style_codes: Optional[List[str]] = None
+    style_reference_images: Optional[List[StrictBytes]] = None
+
+
+class MagicPrompt(str, Enum):
+    AUTO = 'AUTO'
+    ON = 'ON'
+    OFF = 'OFF'
+
+
+class StyleType(str, Enum):
+    AUTO = 'AUTO'
+    GENERAL = 'GENERAL'
+    REALISTIC = 'REALISTIC'
+    DESIGN = 'DESIGN'
+
+
+class IdeogramV3RemixRequest(BaseModel):
+    aspect_ratio: Optional[str] = None
+    color_palette: Optional[Dict[str, Any]] = None
+    image: Optional[StrictBytes] = None
+    image_weight: Optional[int] = Field(50, ge=1, le=100)
+    magic_prompt: Optional[MagicPrompt] = None
+    negative_prompt: Optional[str] = None
+    num_images: Optional[int] = Field(None, ge=1, le=8)
+    prompt: str
+    rendering_speed: Optional[RenderingSpeed1] = None
+    resolution: Optional[str] = None
+    seed: Optional[int] = Field(None, ge=0, le=2147483647)
+    style_codes: Optional[List[str]] = None
+    style_reference_images: Optional[List[StrictBytes]] = None
+    style_type: Optional[StyleType] = None
+
+
+class IdeogramV3ReplaceBackgroundRequest(BaseModel):
+    color_palette: Optional[Dict[str, Any]] = None
+    image: Optional[StrictBytes] = None
+    magic_prompt: Optional[MagicPrompt] = None
+    num_images: Optional[int] = Field(None, ge=1, le=8)
+    prompt: str
+    rendering_speed: Optional[RenderingSpeed1] = None
+    seed: Optional[int] = Field(None, ge=0, le=2147483647)
+    style_codes: Optional[List[str]] = None
+    style_reference_images: Optional[List[StrictBytes]] = None
+
+
+class ColorPalette(BaseModel):
+    name: str = Field(..., description='Name of the color palette', examples=['PASTEL'])
+
+
+class MagicPrompt2(str, Enum):
+    ON = 'ON'
+    OFF = 'OFF'
+
+
+class StyleType1(str, Enum):
+    AUTO = 'AUTO'
+    GENERAL = 'GENERAL'
+    REALISTIC = 'REALISTIC'
+    DESIGN = 'DESIGN'
+    FICTION = 'FICTION'
+
+
+class RenderingSpeed(str, Enum):
+    DEFAULT = 'DEFAULT'
+    TURBO = 'TURBO'
+    QUALITY = 'QUALITY'
+
+
+class IdeogramV3EditRequest(BaseModel):
+    color_palette: Optional[IdeogramColorPalette] = None
+    image: Optional[StrictBytes] = Field(
+        None,
+        description='The image being edited (max size 10MB); only JPEG, WebP and PNG formats are supported at this time.',
+    )
+    magic_prompt: Optional[str] = Field(
+        None,
+        description='Determine if MagicPrompt should be used in generating the request or not.',
+    )
+    mask: Optional[StrictBytes] = Field(
+        None,
+        description='A black and white image of the same size as the image being edited (max size 10MB). Black regions in the mask should match up with the regions of the image that you would like to edit; only JPEG, WebP and PNG formats are supported at this time.',
+    )
+    num_images: Optional[int] = Field(
+        None, description='The number of images to generate.'
+    )
+    prompt: str = Field(
+        ..., description='The prompt used to describe the edited result.'
+    )
+    rendering_speed: RenderingSpeed
+    seed: Optional[int] = Field(
+        None, description='Random seed. Set for reproducible generation.'
+    )
+    style_codes: Optional[List[StyleCode]] = Field(
+        None,
+        description='A list of 8 character hexadecimal codes representing the style of the image. Cannot be used in conjunction with style_reference_images or style_type.',
+    )
+    style_reference_images: Optional[List[StrictBytes]] = Field(
+        None,
+        description='A set of images to use as style references (maximum total size 10MB across all style references). The images should be in JPEG, PNG or WebP format.',
+    )
+    character_reference_images: Optional[List[str]] = Field(
+        None,
+        description='Generations with character reference are subject to the character reference pricing. A set of images to use as character references (maximum total size 10MB across all character references), currently only supports 1 character reference image. The images should be in JPEG, PNG or WebP format.'
+    )
+    character_reference_images_mask: Optional[List[str]] = Field(
+        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 IdeogramV3Request(BaseModel):
+    aspect_ratio: Optional[str] = Field(
+        None, description='Aspect ratio in format WxH', examples=['1x3']
+    )
+    color_palette: Optional[ColorPalette] = None
+    magic_prompt: Optional[MagicPrompt2] = Field(
+        None, description='Whether to enable magic prompt enhancement'
+    )
+    negative_prompt: Optional[str] = Field(
+        None, description='Text prompt specifying what to avoid in the generation'
+    )
+    num_images: Optional[int] = Field(
+        None, description='Number of images to generate', ge=1
+    )
+    prompt: str = Field(..., description='The text prompt for image generation')
+    rendering_speed: RenderingSpeed
+    resolution: Optional[str] = Field(
+        None, description='Image resolution in format WxH', examples=['1280x800']
+    )
+    seed: Optional[int] = Field(
+        None, description='Seed value for reproducible generation'
+    )
+    style_codes: Optional[List[StyleCode]] = Field(
+        None, description='Array of style codes in hexadecimal format'
+    )
+    style_reference_images: Optional[List[str]] = Field(
+        None, description='Array of reference image URLs or identifiers'
+    )
+    style_type: Optional[StyleType1] = Field(
+        None, description='The type of style to apply'
+    )
+    character_reference_images: Optional[List[str]] = Field(
+        None,
+        description='Generations with character reference are subject to the character reference pricing. A set of images to use as character references (maximum total size 10MB across all character references), currently only supports 1 character reference image. The images should be in JPEG, PNG or WebP format.'
+    )
+    character_reference_images_mask: Optional[List[str]] = Field(
+        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.'
+    )

comfy_api_nodes/apis/moonvalley.py

@@ -0,0 +1,152 @@
+from enum import Enum
+from typing import Optional, Dict, Any
+
+from pydantic import BaseModel, Field, StrictBytes
+
+
+class MoonvalleyPromptResponse(BaseModel):
+    error: Optional[Dict[str, Any]] = None
+    frame_conditioning: Optional[Dict[str, Any]] = None
+    id: Optional[str] = None
+    inference_params: Optional[Dict[str, Any]] = None
+    meta: Optional[Dict[str, Any]] = None
+    model_params: Optional[Dict[str, Any]] = None
+    output_url: Optional[str] = None
+    prompt_text: Optional[str] = None
+    status: Optional[str] = None
+
+
+class MoonvalleyTextToVideoInferenceParams(BaseModel):
+    add_quality_guidance: Optional[bool] = Field(
+        True, description='Whether to add quality guidance'
+    )
+    caching_coefficient: Optional[float] = Field(
+        0.3, description='Caching coefficient for optimization'
+    )
+    caching_cooldown: Optional[int] = Field(
+        3, description='Number of caching cooldown steps'
+    )
+    caching_warmup: Optional[int] = Field(
+        3, description='Number of caching warmup steps'
+    )
+    clip_value: Optional[float] = Field(
+        3, description='CLIP value for generation control'
+    )
+    conditioning_frame_index: Optional[int] = Field(
+        0, description='Index of the conditioning frame'
+    )
+    cooldown_steps: Optional[int] = Field(
+        75, description='Number of cooldown steps (calculated based on num_frames)'
+    )
+    fps: Optional[int] = Field(
+        24, description='Frames per second of the generated video'
+    )
+    guidance_scale: Optional[float] = Field(
+        10, description='Guidance scale for generation control'
+    )
+    height: Optional[int] = Field(
+        1080, description='Height of the generated video in pixels'
+    )
+    negative_prompt: Optional[str] = Field(None, description='Negative prompt text')
+    num_frames: Optional[int] = Field(64, description='Number of frames to generate')
+    seed: Optional[int] = Field(
+        None, description='Random seed for generation (default: random)'
+    )
+    shift_value: Optional[float] = Field(
+        3, description='Shift value for generation control'
+    )
+    steps: Optional[int] = Field(80, description='Number of denoising steps')
+    use_guidance_schedule: Optional[bool] = Field(
+        True, description='Whether to use guidance scheduling'
+    )
+    use_negative_prompts: Optional[bool] = Field(
+        False, description='Whether to use negative prompts'
+    )
+    use_timestep_transform: Optional[bool] = Field(
+        True, description='Whether to use timestep transformation'
+    )
+    warmup_steps: Optional[int] = Field(
+        0, description='Number of warmup steps (calculated based on num_frames)'
+    )
+    width: Optional[int] = Field(
+        1920, description='Width of the generated video in pixels'
+    )
+
+
+class MoonvalleyTextToVideoRequest(BaseModel):
+    image_url: Optional[str] = None
+    inference_params: Optional[MoonvalleyTextToVideoInferenceParams] = None
+    prompt_text: Optional[str] = None
+    webhook_url: Optional[str] = None
+
+
+class MoonvalleyUploadFileRequest(BaseModel):
+    file: Optional[StrictBytes] = None
+
+
+class MoonvalleyUploadFileResponse(BaseModel):
+    access_url: Optional[str] = None
+
+
+class MoonvalleyVideoToVideoInferenceParams(BaseModel):
+    add_quality_guidance: Optional[bool] = Field(
+        True, description='Whether to add quality guidance'
+    )
+    caching_coefficient: Optional[float] = Field(
+        0.3, description='Caching coefficient for optimization'
+    )
+    caching_cooldown: Optional[int] = Field(
+        3, description='Number of caching cooldown steps'
+    )
+    caching_warmup: Optional[int] = Field(
+        3, description='Number of caching warmup steps'
+    )
+    clip_value: Optional[float] = Field(
+        3, description='CLIP value for generation control'
+    )
+    conditioning_frame_index: Optional[int] = Field(
+        0, description='Index of the conditioning frame'
+    )
+    cooldown_steps: Optional[int] = Field(
+        36, description='Number of cooldown steps (calculated based on num_frames)'
+    )
+    guidance_scale: Optional[float] = Field(
+        15, description='Guidance scale for generation control'
+    )
+    negative_prompt: Optional[str] = Field(None, description='Negative prompt text')
+    seed: Optional[int] = Field(
+        None, description='Random seed for generation (default: random)'
+    )
+    shift_value: Optional[float] = Field(
+        3, description='Shift value for generation control'
+    )
+    steps: Optional[int] = Field(80, description='Number of denoising steps')
+    use_guidance_schedule: Optional[bool] = Field(
+        True, description='Whether to use guidance scheduling'
+    )
+    use_negative_prompts: Optional[bool] = Field(
+        False, description='Whether to use negative prompts'
+    )
+    use_timestep_transform: Optional[bool] = Field(
+        True, description='Whether to use timestep transformation'
+    )
+    warmup_steps: Optional[int] = Field(
+        24, description='Number of warmup steps (calculated based on num_frames)'
+    )
+
+
+class ControlType(str, Enum):
+    motion_control = 'motion_control'
+    pose_control = 'pose_control'
+
+
+class MoonvalleyVideoToVideoRequest(BaseModel):
+    control_type: ControlType = Field(
+        ..., description='Supported types for video control'
+    )
+    inference_params: Optional[MoonvalleyVideoToVideoInferenceParams] = None
+    prompt_text: str = Field(..., description='Describes the video to generate')
+    video_url: str = Field(..., description='Url to control video')
+    webhook_url: Optional[str] = Field(
+        None, description='Optional webhook URL for notifications'
+    )

comfy_api_nodes/apis/openai.py

@@ -0,0 +1,170 @@
+from pydantic import BaseModel, Field
+
+
+class Datum2(BaseModel):
+    b64_json: str | None = Field(None, description="Base64 encoded image data")
+    revised_prompt: str | None = Field(None, description="Revised prompt")
+    url: str | None = Field(None, description="URL of the image")
+
+
+class InputTokensDetails(BaseModel):
+    image_tokens: int | None = Field(None)
+    text_tokens: int | None = Field(None)
+
+
+class Usage(BaseModel):
+    input_tokens: int | None = Field(None)
+    input_tokens_details: InputTokensDetails | None = Field(None)
+    output_tokens: int | None = Field(None)
+    total_tokens: int | None = Field(None)
+
+
+class OpenAIImageGenerationResponse(BaseModel):
+    data: list[Datum2] | None = Field(None)
+    usage: Usage | None = Field(None)
+
+
+class OpenAIImageEditRequest(BaseModel):
+    background: str | None = Field(None, description="Background transparency")
+    model: str = Field(...)
+    moderation: str | None = Field(None)
+    n: int | None = Field(None, description="The number of images to generate")
+    output_compression: int | None = Field(None, description="Compression level for JPEG or WebP (0-100)")
+    output_format: str | None = Field(None)
+    prompt: str = Field(...)
+    quality: str | None = Field(None, description="Size of the image (e.g., 1024x1024, 1536x1024, auto)")
+    size: str | None = Field(None, description="Size of the output image")
+
+
+class OpenAIImageGenerationRequest(BaseModel):
+    background: str | None = Field(None, description="Background transparency")
+    model: str | None = Field(None)
+    moderation: str | None = Field(None)
+    n: int | None = Field(
+        None,
+        description="The number of images to generate.",
+    )
+    output_compression: int | None = Field(None, description="Compression level for JPEG or WebP (0-100)")
+    output_format: str | None = Field(None)
+    prompt: str = Field(...)
+    quality: str | None = Field(None, description="The quality of the generated image")
+    size: str | None = Field(None, description="Size of the image (e.g., 1024x1024, 1536x1024, auto)")
+    style: str | None = Field(None, description="Style of the image (only for dall-e-3)")
+
+
+class ModelResponseProperties(BaseModel):
+    instructions: str | None = Field(None)
+    max_output_tokens: int | None = Field(None)
+    model: str | None = Field(None)
+    temperature: float | None = Field(1, description="Controls randomness in the response", ge=0.0, le=2.0)
+    top_p: float | None = Field(
+        1,
+        description="Controls diversity of the response via nucleus sampling",
+        ge=0.0,
+        le=1.0,
+    )
+    truncation: str | None = Field("disabled", description="Allowed values: 'auto' or 'disabled'")
+
+
+class ResponseProperties(BaseModel):
+    instructions: str | None = Field(None)
+    max_output_tokens: int | None = Field(None)
+    model: str | None = Field(None)
+    previous_response_id: str | None = Field(None)
+    truncation: str | None = Field("disabled", description="Allowed values: 'auto' or 'disabled'")
+
+
+class ResponseError(BaseModel):
+    code: str = Field(...)
+    message: str = Field(...)
+
+
+class OutputTokensDetails(BaseModel):
+    reasoning_tokens: int = Field(..., description="The number of reasoning tokens.")
+
+
+class CachedTokensDetails(BaseModel):
+    cached_tokens: int = Field(
+        ...,
+        description="The number of tokens that were retrieved from the cache.",
+    )
+
+
+class ResponseUsage(BaseModel):
+    input_tokens: int = Field(..., description="The number of input tokens.")
+    input_tokens_details: CachedTokensDetails = Field(...)
+    output_tokens: int = Field(..., description="The number of output tokens.")
+    output_tokens_details: OutputTokensDetails = Field(...)
+    total_tokens: int = Field(..., description="The total number of tokens used.")
+
+
+class InputTextContent(BaseModel):
+    text: str = Field(..., description="The text input to the model.")
+    type: str = Field("input_text")
+
+
+class OutputContent(BaseModel):
+    type: str = Field(..., description="The type of output content")
+    text: str | None = Field(None, description="The text content")
+    data: str | None = Field(None, description="Base64-encoded audio data")
+    transcript: str | None = Field(None, description="Transcript of the audio")
+
+
+class OutputMessage(BaseModel):
+    type: str = Field(..., description="The type of output item")
+    content: list[OutputContent] | None = Field(None, description="The content of the message")
+    role: str | None = Field(None, description="The role of the message")
+
+
+class OpenAIResponse(ModelResponseProperties, ResponseProperties):
+    created_at: float | None = Field(
+        None,
+        description="Unix timestamp (in seconds) of when this Response was created.",
+    )
+    error: ResponseError | None = Field(None)
+    id: str | None = Field(None, description="Unique identifier for this Response.")
+    object: str | None = Field(None, description="The object type of this resource - always set to `response`.")
+    output: list[OutputMessage] | None = Field(None)
+    parallel_tool_calls: bool | None = Field(True)
+    status: str | None = Field(
+        None,
+        description="One of `completed`, `failed`, `in_progress`, or `incomplete`.",
+    )
+    usage: ResponseUsage | None = Field(None)
+
+
+class InputImageContent(BaseModel):
+    detail: str = Field(..., description="One of `high`, `low`, or `auto`. Defaults to `auto`.")
+    file_id: str | None = Field(None)
+    image_url: str | None = Field(None)
+    type: str = Field(..., description="The type of the input item. Always `input_image`.")
+
+
+class InputFileContent(BaseModel):
+    file_data: str | None = Field(None)
+    file_id: str | None = Field(None)
+    filename: str | None = Field(None, description="The name of the file to be sent to the model.")
+    type: str = Field(..., description="The type of the input item. Always `input_file`.")
+
+
+class InputMessage(BaseModel):
+    content: list[InputTextContent | InputImageContent | InputFileContent] = Field(
+        ...,
+        description="A list of one or many input items to the model, containing different content types.",
+    )
+    role: str | None = Field(None)
+    type: str | None = Field(None)
+
+
+class OpenAICreateResponse(ModelResponseProperties, ResponseProperties):
+    include: str | None = Field(None)
+    input: list[InputMessage] = Field(...)
+    parallel_tool_calls: bool | None = Field(
+        True, description="Whether to allow the model to run tool calls in parallel."
+    )
+    store: bool | None = Field(
+        True,
+        description="Whether to store the generated model response for later retrieval via API.",
+    )
+    stream: bool | None = Field(False)
+    usage: ResponseUsage | None = Field(None)

comfy_api_nodes/apis/openai_api.py

@@ -1,52 +0,0 @@
-from pydantic import BaseModel, Field
-
-
-class Datum2(BaseModel):
-    b64_json: str | None = Field(None, description="Base64 encoded image data")
-    revised_prompt: str | None = Field(None, description="Revised prompt")
-    url: str | None = Field(None, description="URL of the image")
-
-
-class InputTokensDetails(BaseModel):
-    image_tokens: int | None = None
-    text_tokens: int | None = None
-
-
-class Usage(BaseModel):
-    input_tokens: int | None = None
-    input_tokens_details: InputTokensDetails | None = None
-    output_tokens: int | None = None
-    total_tokens: int | None = None
-
-
-class OpenAIImageGenerationResponse(BaseModel):
-    data: list[Datum2] | None = None
-    usage: Usage | None = None
-
-
-class OpenAIImageEditRequest(BaseModel):
-    background: str | None = Field(None, description="Background transparency")
-    model: str = Field(...)
-    moderation: str | None = Field(None)
-    n: int | None = Field(None, description="The number of images to generate")
-    output_compression: int | None = Field(None, description="Compression level for JPEG or WebP (0-100)")
-    output_format: str | None = Field(None)
-    prompt: str = Field(...)
-    quality: str | None = Field(None, description="Size of the image (e.g., 1024x1024, 1536x1024, auto)")
-    size: str | None = Field(None, description="Size of the output image")
-
-
-class OpenAIImageGenerationRequest(BaseModel):
-    background: str | None = Field(None, description="Background transparency")
-    model: str | None = Field(None)
-    moderation: str | None = Field(None)
-    n: int | None = Field(
-        None,
-        description="The number of images to generate.",
-    )
-    output_compression: int | None = Field(None, description="Compression level for JPEG or WebP (0-100)")
-    output_format: str | None = Field(None)
-    prompt: str = Field(...)
-    quality: str | None = Field(None, description="The quality of the generated image")
-    size: str | None = Field(None, description="Size of the image (e.g., 1024x1024, 1536x1024, auto)")
-    style: str | None = Field(None, description="Style of the image (only for dall-e-3)")

comfy_api_nodes/apis/runway.py

@@ -0,0 +1,127 @@
+from enum import Enum
+from typing import Optional, List, Union
+from datetime import datetime
+
+from pydantic import BaseModel, Field, RootModel
+
+
+class RunwayAspectRatioEnum(str, Enum):
+    field_1280_720 = '1280:720'
+    field_720_1280 = '720:1280'
+    field_1104_832 = '1104:832'
+    field_832_1104 = '832:1104'
+    field_960_960 = '960:960'
+    field_1584_672 = '1584:672'
+    field_1280_768 = '1280:768'
+    field_768_1280 = '768:1280'
+
+
+class Position(str, Enum):
+    first = 'first'
+    last = 'last'
+
+
+class RunwayPromptImageDetailedObject(BaseModel):
+    position: Position = Field(
+        ...,
+        description="The position of the image in the output video. 'last' is currently supported for gen3a_turbo only.",
+    )
+    uri: str = Field(
+        ..., description='A HTTPS URL or data URI containing an encoded image.'
+    )
+
+
+class RunwayPromptImageObject(
+    RootModel[Union[str, List[RunwayPromptImageDetailedObject]]]
+):
+    root: Union[str, List[RunwayPromptImageDetailedObject]] = Field(
+        ...,
+        description='Image(s) to use for the video generation. Can be a single URI or an array of image objects with positions.',
+    )
+
+
+class RunwayModelEnum(str, Enum):
+    gen4_turbo = 'gen4_turbo'
+    gen3a_turbo = 'gen3a_turbo'
+
+
+class RunwayDurationEnum(int, Enum):
+    integer_5 = 5
+    integer_10 = 10
+
+
+class RunwayImageToVideoRequest(BaseModel):
+    duration: RunwayDurationEnum
+    model: RunwayModelEnum
+    promptImage: RunwayPromptImageObject
+    promptText: Optional[str] = Field(
+        None, description='Text prompt for the generation', max_length=1000
+    )
+    ratio: RunwayAspectRatioEnum
+    seed: int = Field(
+        ..., description='Random seed for generation', ge=0, le=4294967295
+    )
+
+
+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')
+    output: Optional[List[str]] = Field(None, description='Array of output video URLs')
+    progress: Optional[float] = Field(
+        None,
+        description='Float value between 0 and 1 representing the progress of the task. Only available if status is RUNNING.',
+        ge=0.0,
+        le=1.0,
+    )
+    status: RunwayTaskStatusEnum
+
+
+class Model4(str, Enum):
+    gen4_image = 'gen4_image'
+
+
+class ReferenceImage(BaseModel):
+    uri: Optional[str] = Field(
+        None, description='A HTTPS URL or data URI containing an encoded image'
+    )
+
+
+class RunwayTextToImageAspectRatioEnum(str, Enum):
+    field_1920_1080 = '1920:1080'
+    field_1080_1920 = '1080:1920'
+    field_1024_1024 = '1024:1024'
+    field_1360_768 = '1360:768'
+    field_1080_1080 = '1080:1080'
+    field_1168_880 = '1168:880'
+    field_1440_1080 = '1440:1080'
+    field_1080_1440 = '1080:1440'
+    field_1808_768 = '1808:768'
+    field_2112_912 = '2112:912'
+
+
+class RunwayTextToImageRequest(BaseModel):
+    model: Model4 = Field(..., description='Model to use for generation')
+    promptText: str = Field(
+        ..., description='Text prompt for the image generation', max_length=1000
+    )
+    ratio: RunwayTextToImageAspectRatioEnum
+    referenceImages: Optional[List[ReferenceImage]] = Field(
+        None, description='Array of reference images to guide the generation'
+    )
+
+
+class RunwayTextToImageResponse(BaseModel):
+    id: Optional[str] = Field(None, description='Task ID')

comfy_api_nodes/apis/topaz.py

@@ -41,7 +41,7 @@ class Resolution(BaseModel):
     height: int = Field(...)
 
 
-class CreateCreateVideoRequestSource(BaseModel):
+class CreateVideoRequestSource(BaseModel):
     container: str = Field(...)
     size: int = Field(..., description="Size of the video file in bytes")
     duration: int = Field(..., description="Duration of the video file in seconds")
@@ -89,7 +89,7 @@ class Overrides(BaseModel):
 
 
 class CreateVideoRequest(BaseModel):
-    source: CreateCreateVideoRequestSource = Field(...)
+    source: CreateVideoRequestSource = Field(...)
     filters: list[Union[VideoFrameInterpolationFilter, VideoEnhancementFilter]] = Field(...)
     output: OutputInformationVideo = Field(...)
     overrides: Overrides = Field(Overrides(isPaidDiffusion=True))

comfy_api_nodes/apis/wavespeed.py

@@ -0,0 +1,35 @@
+from pydantic import BaseModel, Field
+
+
+class SeedVR2ImageRequest(BaseModel):
+    image: str = Field(...)
+    target_resolution: str = Field(...)
+    output_format: str = Field("png")
+    enable_sync_mode: bool = Field(False)
+
+
+class FlashVSRRequest(BaseModel):
+    target_resolution: str = Field(...)
+    video: str = Field(...)
+    duration: float = Field(...)
+
+
+class TaskCreatedDataResponse(BaseModel):
+    id: str = Field(...)
+
+
+class TaskCreatedResponse(BaseModel):
+    code: int = Field(...)
+    message: str = Field(...)
+    data: TaskCreatedDataResponse | None = Field(None)
+
+
+class TaskResultDataResponse(BaseModel):
+    status: str = Field(...)
+    outputs: list[str] = Field([])
+
+
+class TaskResultResponse(BaseModel):
+    code: int = Field(...)
+    message: str = Field(...)
+    data: TaskResultDataResponse | None = Field(None)

comfy_api_nodes/canary.py

@@ -1,10 +0,0 @@
-import av
-
-ver = av.__version__.split(".")
-if int(ver[0]) < 14:
-    raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.")
-
-if int(ver[0]) == 14 and int(ver[1]) < 2:
-    raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.")
-
-NODE_CLASS_MAPPINGS = {}

comfy_api_nodes/mapper_utils.py

@@ -1,116 +0,0 @@
-from enum import Enum
-
-from pydantic.fields import FieldInfo
-from pydantic import BaseModel
-from pydantic_core import PydanticUndefined
-
-from comfy.comfy_types.node_typing import IO, InputTypeOptions
-
-NodeInput = tuple[IO, InputTypeOptions]
-
-
-def _create_base_config(field_info: FieldInfo) -> InputTypeOptions:
-    config = {}
-    if hasattr(field_info, "default") and field_info.default is not PydanticUndefined:
-        config["default"] = field_info.default
-    if hasattr(field_info, "description") and field_info.description is not None:
-        config["tooltip"] = field_info.description
-    return config
-
-
-def _get_number_constraints_config(field_info: FieldInfo) -> dict:
-    config = {}
-    if hasattr(field_info, "metadata"):
-        metadata = field_info.metadata
-        for constraint in metadata:
-            if hasattr(constraint, "ge"):
-                config["min"] = constraint.ge
-            if hasattr(constraint, "le"):
-                config["max"] = constraint.le
-            if hasattr(constraint, "multiple_of"):
-                config["step"] = constraint.multiple_of
-    return config
-
-
-def _model_field_to_image_input(field_info: FieldInfo, **kwargs) -> NodeInput:
-    return IO.IMAGE, {
-        **_create_base_config(field_info),
-        **kwargs,
-    }
-
-
-def _model_field_to_string_input(field_info: FieldInfo, **kwargs) -> NodeInput:
-    return IO.STRING, {
-        **_create_base_config(field_info),
-        **kwargs,
-    }
-
-
-def _model_field_to_float_input(field_info: FieldInfo, **kwargs) -> NodeInput:
-    return IO.FLOAT, {
-        **_create_base_config(field_info),
-        **_get_number_constraints_config(field_info),
-        **kwargs,
-    }
-
-
-def _model_field_to_int_input(field_info: FieldInfo, **kwargs) -> NodeInput:
-    return IO.INT, {
-        **_create_base_config(field_info),
-        **_get_number_constraints_config(field_info),
-        **kwargs,
-    }
-
-
-def _model_field_to_combo_input(
-    field_info: FieldInfo, enum_type: type[Enum] = None, **kwargs
-) -> NodeInput:
-    combo_config = {}
-    if enum_type is not None:
-        combo_config["options"] = [option.value for option in enum_type]
-    combo_config = {
-        **combo_config,
-        **_create_base_config(field_info),
-        **kwargs,
-    }
-    return IO.COMBO, combo_config
-
-
-def model_field_to_node_input(
-    input_type: IO, base_model: type[BaseModel], field_name: str, **kwargs
-) -> NodeInput:
-    """
-    Maps a field from a Pydantic model to a Comfy node input.
-
-    Args:
-        input_type: The type of the input.
-        base_model: The Pydantic model to map the field from.
-        field_name: The name of the field to map.
-        **kwargs: Additional key/values to include in the input options.
-
-    Note:
-        For combo inputs, pass an `Enum` to the `enum_type` keyword argument to populate the options automatically.
-
-    Example:
-        >>> model_field_to_node_input(IO.STRING, MyModel, "my_field", multiline=True)
-        >>> model_field_to_node_input(IO.COMBO, MyModel, "my_field", enum_type=MyEnum)
-        >>> model_field_to_node_input(IO.FLOAT, MyModel, "my_field", slider=True)
-    """
-    field_info: FieldInfo = base_model.model_fields[field_name]
-    result: NodeInput
-
-    if input_type == IO.IMAGE:
-        result = _model_field_to_image_input(field_info, **kwargs)
-    elif input_type == IO.STRING:
-        result = _model_field_to_string_input(field_info, **kwargs)
-    elif input_type == IO.FLOAT:
-        result = _model_field_to_float_input(field_info, **kwargs)
-    elif input_type == IO.INT:
-        result = _model_field_to_int_input(field_info, **kwargs)
-    elif input_type == IO.COMBO:
-        result = _model_field_to_combo_input(field_info, **kwargs)
-    else:
-        message = f"Invalid input type: {input_type}"
-        raise ValueError(message)
-
-    return result

comfy_api_nodes/nodes_bfl.py

@@ -3,7 +3,7 @@ from pydantic import BaseModel
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input
-from comfy_api_nodes.apis.bfl_api import (
+from comfy_api_nodes.apis.bfl import (
     BFLFluxExpandImageRequest,
     BFLFluxFillImageRequest,
     BFLFluxKontextProGenerateRequest,

comfy_api_nodes/nodes_bria.py

@@ -0,0 +1,198 @@
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.bria import (
+    BriaEditImageRequest,
+    BriaResponse,
+    BriaStatusResponse,
+    InputModerationSettings,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    convert_mask_to_image,
+    download_url_to_image_tensor,
+    get_number_of_images,
+    poll_op,
+    sync_op,
+    upload_images_to_comfyapi,
+)
+
+
+class BriaImageEditNode(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="BriaImageEditNode",
+            display_name="Bria FIBO Image Edit",
+            category="api node/image/Bria",
+            description="Edit images using Bria latest model",
+            inputs=[
+                IO.Combo.Input("model", options=["FIBO"]),
+                IO.Image.Input("image"),
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Instruction to edit image",
+                ),
+                IO.String.Input("negative_prompt", multiline=True, default=""),
+                IO.String.Input(
+                    "structured_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="A string containing the structured edit prompt in JSON format. "
+                    "Use this instead of usual prompt for precise, programmatic control.",
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=1,
+                    min=1,
+                    max=2147483647,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                    control_after_generate=True,
+                ),
+                IO.Float.Input(
+                    "guidance_scale",
+                    default=3,
+                    min=3,
+                    max=5,
+                    step=0.01,
+                    display_mode=IO.NumberDisplay.number,
+                    tooltip="Higher value makes the image follow the prompt more closely.",
+                ),
+                IO.Int.Input(
+                    "steps",
+                    default=50,
+                    min=20,
+                    max=50,
+                    step=1,
+                    display_mode=IO.NumberDisplay.number,
+                ),
+                IO.DynamicCombo.Input(
+                    "moderation",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "true",
+                            [
+                                IO.Boolean.Input(
+                                    "prompt_content_moderation", default=False
+                                ),
+                                IO.Boolean.Input(
+                                    "visual_input_moderation", default=False
+                                ),
+                                IO.Boolean.Input(
+                                    "visual_output_moderation", default=True
+                                ),
+                            ],
+                        ),
+                        IO.DynamicCombo.Option("false", []),
+                    ],
+                    tooltip="Moderation settings",
+                ),
+                IO.Mask.Input(
+                    "mask",
+                    tooltip="If omitted, the edit applies to the entire image.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+                IO.String.Output(display_name="structured_prompt"),
+            ],
+            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":0.04}""",
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        prompt: str,
+        negative_prompt: str,
+        structured_prompt: str,
+        seed: int,
+        guidance_scale: float,
+        steps: int,
+        moderation: InputModerationSettings,
+        mask: Input.Image | None = None,
+    ) -> IO.NodeOutput:
+        if not prompt and not structured_prompt:
+            raise ValueError(
+                "One of prompt or structured_prompt is required to be non-empty."
+            )
+        if get_number_of_images(image) != 1:
+            raise ValueError("Exactly one input image is required.")
+        mask_url = None
+        if mask is not None:
+            mask_url = (
+                await upload_images_to_comfyapi(
+                    cls,
+                    convert_mask_to_image(mask),
+                    max_images=1,
+                    mime_type="image/png",
+                    wait_label="Uploading mask",
+                )
+            )[0]
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path="proxy/bria/v2/image/edit", method="POST"),
+            data=BriaEditImageRequest(
+                instruction=prompt if prompt else None,
+                structured_instruction=structured_prompt if structured_prompt else None,
+                images=await upload_images_to_comfyapi(
+                    cls,
+                    image,
+                    max_images=1,
+                    mime_type="image/png",
+                    wait_label="Uploading image",
+                ),
+                mask=mask_url,
+                negative_prompt=negative_prompt if negative_prompt else None,
+                guidance_scale=guidance_scale,
+                seed=seed,
+                model_version=model,
+                steps_num=steps,
+                prompt_content_moderation=moderation.get(
+                    "prompt_content_moderation", False
+                ),
+                visual_input_content_moderation=moderation.get(
+                    "visual_input_moderation", False
+                ),
+                visual_output_content_moderation=moderation.get(
+                    "visual_output_moderation", False
+                ),
+            ),
+            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=BriaResponse,
+        )
+        return IO.NodeOutput(
+            await download_url_to_image_tensor(response.result.image_url),
+            response.result.structured_prompt,
+        )
+
+
+class BriaExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            BriaImageEditNode,
+        ]
+
+
+async def comfy_entrypoint() -> BriaExtension:
+    return BriaExtension()

comfy_api_nodes/nodes_bytedance.py

@@ -5,7 +5,7 @@ import torch
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input
-from comfy_api_nodes.apis.bytedance_api import (
+from comfy_api_nodes.apis.bytedance import (
     RECOMMENDED_PRESETS,
     RECOMMENDED_PRESETS_SEEDREAM_4,
     VIDEO_TASKS_EXECUTION_TIME,
@@ -477,7 +477,12 @@ class ByteDanceTextToVideoNode(IO.ComfyNode):
             inputs=[
                 IO.Combo.Input(
                     "model",
-                    options=["seedance-1-0-pro-250528", "seedance-1-0-lite-t2v-250428", "seedance-1-0-pro-fast-251015"],
+                    options=[
+                        "seedance-1-5-pro-251215",
+                        "seedance-1-0-pro-250528",
+                        "seedance-1-0-lite-t2v-250428",
+                        "seedance-1-0-pro-fast-251015",
+                    ],
                     default="seedance-1-0-pro-fast-251015",
                 ),
                 IO.String.Input(
@@ -528,6 +533,12 @@ class ByteDanceTextToVideoNode(IO.ComfyNode):
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
                 ),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
+                    optional=True,
+                ),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -552,7 +563,10 @@ class ByteDanceTextToVideoNode(IO.ComfyNode):
         seed: int,
         camera_fixed: bool,
         watermark: bool,
+        generate_audio: bool = False,
     ) -> IO.NodeOutput:
+        if model == "seedance-1-5-pro-251215" and duration < 4:
+            raise ValueError("Minimum supported duration for Seedance 1.5 Pro is 4 seconds.")
         validate_string(prompt, strip_whitespace=True, min_length=1)
         raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "camerafixed", "watermark"])
 
@@ -567,7 +581,11 @@ class ByteDanceTextToVideoNode(IO.ComfyNode):
         )
         return await process_video_task(
             cls,
-            payload=Text2VideoTaskCreationRequest(model=model, content=[TaskTextContent(text=prompt)]),
+            payload=Text2VideoTaskCreationRequest(
+                model=model,
+                content=[TaskTextContent(text=prompt)],
+                generate_audio=generate_audio if model == "seedance-1-5-pro-251215" else None,
+            ),
             estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
         )
 
@@ -584,7 +602,12 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
             inputs=[
                 IO.Combo.Input(
                     "model",
-                    options=["seedance-1-0-pro-250528", "seedance-1-0-lite-t2v-250428", "seedance-1-0-pro-fast-251015"],
+                    options=[
+                        "seedance-1-5-pro-251215",
+                        "seedance-1-0-pro-250528",
+                        "seedance-1-0-lite-i2v-250428",
+                        "seedance-1-0-pro-fast-251015",
+                    ],
                     default="seedance-1-0-pro-fast-251015",
                 ),
                 IO.String.Input(
@@ -639,6 +662,12 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
                 ),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
+                    optional=True,
+                ),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -664,7 +693,10 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
         seed: int,
         camera_fixed: bool,
         watermark: bool,
+        generate_audio: bool = False,
     ) -> IO.NodeOutput:
+        if model == "seedance-1-5-pro-251215" and duration < 4:
+            raise ValueError("Minimum supported duration for Seedance 1.5 Pro is 4 seconds.")
         validate_string(prompt, strip_whitespace=True, min_length=1)
         raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "camerafixed", "watermark"])
         validate_image_dimensions(image, min_width=300, min_height=300, max_width=6000, max_height=6000)
@@ -686,6 +718,7 @@ class ByteDanceImageToVideoNode(IO.ComfyNode):
             payload=Image2VideoTaskCreationRequest(
                 model=model,
                 content=[TaskTextContent(text=prompt), TaskImageContent(image_url=TaskImageContentUrl(url=image_url))],
+                generate_audio=generate_audio if model == "seedance-1-5-pro-251215" else None,
             ),
             estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
         )
@@ -703,7 +736,7 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
             inputs=[
                 IO.Combo.Input(
                     "model",
-                    options=["seedance-1-0-pro-250528", "seedance-1-0-lite-i2v-250428"],
+                    options=["seedance-1-5-pro-251215", "seedance-1-0-pro-250528", "seedance-1-0-lite-i2v-250428"],
                     default="seedance-1-0-lite-i2v-250428",
                 ),
                 IO.String.Input(
@@ -762,6 +795,12 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
                     tooltip='Whether to add an "AI generated" watermark to the video.',
                     optional=True,
                 ),
+                IO.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    tooltip="This parameter is ignored for any model except seedance-1-5-pro.",
+                    optional=True,
+                ),
             ],
             outputs=[
                 IO.Video.Output(),
@@ -788,7 +827,10 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
         seed: int,
         camera_fixed: bool,
         watermark: bool,
+        generate_audio: bool = False,
     ) -> IO.NodeOutput:
+        if model == "seedance-1-5-pro-251215" and duration < 4:
+            raise ValueError("Minimum supported duration for Seedance 1.5 Pro is 4 seconds.")
         validate_string(prompt, strip_whitespace=True, min_length=1)
         raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "camerafixed", "watermark"])
         for i in (first_frame, last_frame):
@@ -821,6 +863,7 @@ class ByteDanceFirstLastFrameNode(IO.ComfyNode):
                     TaskImageContent(image_url=TaskImageContentUrl(url=str(download_urls[0])), role="first_frame"),
                     TaskImageContent(image_url=TaskImageContentUrl(url=str(download_urls[1])), role="last_frame"),
                 ],
+                generate_audio=generate_audio if model == "seedance-1-5-pro-251215" else None,
             ),
             estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))),
         )
@@ -896,7 +939,41 @@ class ByteDanceImageReferenceNode(IO.ComfyNode):
                 IO.Hidden.unique_id,
             ],
             is_api_node=True,
-            price_badge=PRICE_BADGE_VIDEO,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "duration", "resolution"]),
+                expr="""
+                (
+                  $priceByModel := {
+                    "seedance-1-0-pro": {
+                      "480p":[0.23,0.24],
+                      "720p":[0.51,0.56]
+                    },
+                    "seedance-1-0-lite": {
+                      "480p":[0.17,0.18],
+                      "720p":[0.37,0.41]
+                    }
+                  };
+                  $model := widgets.model;
+                  $modelKey :=
+                    $contains($model, "seedance-1-0-pro")  ? "seedance-1-0-pro" :
+                    "seedance-1-0-lite";
+                  $resolution := widgets.resolution;
+                  $resKey :=
+                    $contains($resolution, "720") ? "720p" :
+                    "480p";
+                  $modelPrices := $lookup($priceByModel, $modelKey);
+                  $baseRange := $lookup($modelPrices, $resKey);
+                  $min10s := $baseRange[0];
+                  $max10s := $baseRange[1];
+                  $scale := widgets.duration / 10;
+                  $minCost := $min10s * $scale;
+                  $maxCost := $max10s * $scale;
+                  ($minCost = $maxCost)
+                    ? {"type":"usd","usd": $minCost}
+                    : {"type":"range_usd","min_usd": $minCost, "max_usd": $maxCost}
+                )
+                """,
+            ),
         )
 
     @classmethod
@@ -967,10 +1044,15 @@ def raise_if_text_params(prompt: str, text_params: list[str]) -> None:
 
 
 PRICE_BADGE_VIDEO = IO.PriceBadge(
-    depends_on=IO.PriceBadgeDepends(widgets=["model", "duration", "resolution"]),
+    depends_on=IO.PriceBadgeDepends(widgets=["model", "duration", "resolution", "generate_audio"]),
     expr="""
     (
       $priceByModel := {
+        "seedance-1-5-pro": {
+          "480p":[0.12,0.12],
+          "720p":[0.26,0.26],
+          "1080p":[0.58,0.59]
+        },
         "seedance-1-0-pro": {
           "480p":[0.23,0.24],
           "720p":[0.51,0.56],
@@ -989,6 +1071,7 @@ PRICE_BADGE_VIDEO = IO.PriceBadge(
       };
       $model := widgets.model;
       $modelKey :=
+        $contains($model, "seedance-1-5-pro")      ? "seedance-1-5-pro" :
         $contains($model, "seedance-1-0-pro-fast") ? "seedance-1-0-pro-fast" :
         $contains($model, "seedance-1-0-pro")      ? "seedance-1-0-pro" :
         "seedance-1-0-lite";
@@ -1002,11 +1085,12 @@ PRICE_BADGE_VIDEO = IO.PriceBadge(
       $min10s := $baseRange[0];
       $max10s := $baseRange[1];
       $scale := widgets.duration / 10;
-      $minCost := $min10s * $scale;
-      $maxCost := $max10s * $scale;
+      $audioMultiplier := ($modelKey = "seedance-1-5-pro" and widgets.generate_audio) ? 2 : 1;
+      $minCost := $min10s * $scale * $audioMultiplier;
+      $maxCost := $max10s * $scale * $audioMultiplier;
       ($minCost = $maxCost)
-        ? {"type":"usd","usd": $minCost}
-        : {"type":"range_usd","min_usd": $minCost, "max_usd": $maxCost}
+        ? {"type":"usd","usd": $minCost, "format": { "approximate": true }}
+        : {"type":"range_usd","min_usd": $minCost, "max_usd": $maxCost, "format": { "approximate": true }}
     )
     """,
 )

comfy_api_nodes/nodes_gemini.py

@@ -14,7 +14,7 @@ from typing_extensions import override
 
 import folder_paths
 from comfy_api.latest import IO, ComfyExtension, Input, Types
-from comfy_api_nodes.apis.gemini_api import (
+from comfy_api_nodes.apis.gemini import (
     GeminiContent,
     GeminiFileData,
     GeminiGenerateContentRequest,

comfy_api_nodes/nodes_ideogram.py

@@ -4,7 +4,7 @@ from comfy_api.latest import IO, ComfyExtension
 from PIL import Image
 import numpy as np
 import torch
-from comfy_api_nodes.apis import (
+from comfy_api_nodes.apis.ideogram import (
     IdeogramGenerateRequest,
     IdeogramGenerateResponse,
     ImageRequest,

comfy_api_nodes/nodes_kling.py

@@ -49,7 +49,7 @@ from comfy_api_nodes.apis import (
     KlingCharacterEffectModelName,
     KlingSingleImageEffectModelName,
 )
-from comfy_api_nodes.apis.kling_api import (
+from comfy_api_nodes.apis.kling import (
     ImageToVideoWithAudioRequest,
     MotionControlRequest,
     OmniImageParamImage,

comfy_api_nodes/nodes_luma.py

@@ -4,7 +4,7 @@ import torch
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension
-from comfy_api_nodes.apis.luma_api import (
+from comfy_api_nodes.apis.luma import (
     LumaAspectRatio,
     LumaCharacterRef,
     LumaConceptChain,

comfy_api_nodes/nodes_minimax.py

@@ -4,7 +4,7 @@ import torch
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension
-from comfy_api_nodes.apis.minimax_api import (
+from comfy_api_nodes.apis.minimax import (
     MinimaxFileRetrieveResponse,
     MiniMaxModel,
     MinimaxTaskResultResponse,

comfy_api_nodes/nodes_moonvalley.py

@@ -3,7 +3,7 @@ import logging
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input
-from comfy_api_nodes.apis import (
+from comfy_api_nodes.apis.moonvalley import (
     MoonvalleyPromptResponse,
     MoonvalleyTextToVideoInferenceParams,
     MoonvalleyTextToVideoRequest,

comfy_api_nodes/nodes_openai.py

@@ -10,24 +10,18 @@ from typing_extensions import override
 
 import folder_paths
 from comfy_api.latest import IO, ComfyExtension, Input
-from comfy_api_nodes.apis import (
-    CreateModelResponseProperties,
-    Detail,
-    InputContent,
+from comfy_api_nodes.apis.openai import (
     InputFileContent,
     InputImageContent,
     InputMessage,
-    InputMessageContentList,
     InputTextContent,
-    Item,
+    ModelResponseProperties,
     OpenAICreateResponse,
-    OpenAIResponse,
-    OutputContent,
-)
-from comfy_api_nodes.apis.openai_api import (
     OpenAIImageEditRequest,
     OpenAIImageGenerationRequest,
     OpenAIImageGenerationResponse,
+    OpenAIResponse,
+    OutputContent,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
@@ -266,7 +260,7 @@ class OpenAIDalle3(IO.ComfyNode):
                     "seed",
                     default=0,
                     min=0,
-                    max=2 ** 31 - 1,
+                    max=2**31 - 1,
                     step=1,
                     display_mode=IO.NumberDisplay.number,
                     control_after_generate=True,
@@ -370,9 +364,9 @@ class OpenAIGPTImage1(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="OpenAIGPTImage1",
-            display_name="OpenAI GPT Image 1",
+            display_name="OpenAI GPT Image 1.5",
             category="api node/image/OpenAI",
-            description="Generates images synchronously via OpenAI's GPT Image 1 endpoint.",
+            description="Generates images synchronously via OpenAI's GPT Image endpoint.",
             inputs=[
                 IO.String.Input(
                     "prompt",
@@ -384,7 +378,7 @@ class OpenAIGPTImage1(IO.ComfyNode):
                     "seed",
                     default=0,
                     min=0,
-                    max=2 ** 31 - 1,
+                    max=2**31 - 1,
                     step=1,
                     display_mode=IO.NumberDisplay.number,
                     control_after_generate=True,
@@ -435,6 +429,7 @@ class OpenAIGPTImage1(IO.ComfyNode):
                 IO.Combo.Input(
                     "model",
                     options=["gpt-image-1", "gpt-image-1.5"],
+                    default="gpt-image-1.5",
                     optional=True,
                 ),
             ],
@@ -500,8 +495,8 @@ class OpenAIGPTImage1(IO.ComfyNode):
             files = []
             batch_size = image.shape[0]
             for i in range(batch_size):
-                single_image = image[i: i + 1]
-                scaled_image = downscale_image_tensor(single_image, total_pixels=2048*2048).squeeze()
+                single_image = image[i : i + 1]
+                scaled_image = downscale_image_tensor(single_image, total_pixels=2048 * 2048).squeeze()
 
                 image_np = (scaled_image.numpy() * 255).astype(np.uint8)
                 img = Image.fromarray(image_np)
@@ -523,7 +518,7 @@ class OpenAIGPTImage1(IO.ComfyNode):
                 rgba_mask = torch.zeros(height, width, 4, device="cpu")
                 rgba_mask[:, :, 3] = 1 - mask.squeeze().cpu()
 
-                scaled_mask = downscale_image_tensor(rgba_mask.unsqueeze(0), total_pixels=2048*2048).squeeze()
+                scaled_mask = downscale_image_tensor(rgba_mask.unsqueeze(0), total_pixels=2048 * 2048).squeeze()
 
                 mask_np = (scaled_mask.numpy() * 255).astype(np.uint8)
                 mask_img = Image.fromarray(mask_np)
@@ -696,29 +691,23 @@ class OpenAIChatNode(IO.ComfyNode):
         )
 
     @classmethod
-    def get_message_content_from_response(
-        cls, response: OpenAIResponse
-    ) -> list[OutputContent]:
+    def get_message_content_from_response(cls, response: OpenAIResponse) -> list[OutputContent]:
         """Extract message content from the API response."""
         for output in response.output:
-            if output.root.type == "message":
-                return output.root.content
+            if output.type == "message":
+                return output.content
         raise TypeError("No output message found in response")
 
     @classmethod
-    def get_text_from_message_content(
-        cls, message_content: list[OutputContent]
-    ) -> str:
+    def get_text_from_message_content(cls, message_content: list[OutputContent]) -> str:
         """Extract text content from message content."""
         for content_item in message_content:
-            if content_item.root.type == "output_text":
-                return str(content_item.root.text)
+            if content_item.type == "output_text":
+                return str(content_item.text)
         return "No text output found in response"
 
     @classmethod
-    def tensor_to_input_image_content(
-        cls, image: torch.Tensor, detail_level: Detail = "auto"
-    ) -> InputImageContent:
+    def tensor_to_input_image_content(cls, image: torch.Tensor, detail_level: str = "auto") -> InputImageContent:
         """Convert a tensor to an input image content object."""
         return InputImageContent(
             detail=detail_level,
@@ -732,9 +721,9 @@ class OpenAIChatNode(IO.ComfyNode):
         prompt: str,
         image: torch.Tensor | None = None,
         files: list[InputFileContent] | None = None,
-    ) -> InputMessageContentList:
+    ) -> list[InputTextContent | InputImageContent | InputFileContent]:
         """Create a list of input message contents from prompt and optional image."""
-        content_list: list[InputContent | InputTextContent | InputImageContent | InputFileContent] = [
+        content_list: list[InputTextContent | InputImageContent | InputFileContent] = [
             InputTextContent(text=prompt, type="input_text"),
         ]
         if image is not None:
@@ -746,13 +735,9 @@ class OpenAIChatNode(IO.ComfyNode):
                         type="input_image",
                     )
                 )
-
         if files is not None:
             content_list.extend(files)
-
-        return InputMessageContentList(
-            root=content_list,
-        )
+        return content_list
 
     @classmethod
     async def execute(
@@ -762,7 +747,7 @@ class OpenAIChatNode(IO.ComfyNode):
         model: SupportedOpenAIModel = SupportedOpenAIModel.gpt_5.value,
         images: torch.Tensor | None = None,
         files: list[InputFileContent] | None = None,
-        advanced_options: CreateModelResponseProperties | None = None,
+        advanced_options: ModelResponseProperties | None = None,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
 
@@ -773,36 +758,28 @@ class OpenAIChatNode(IO.ComfyNode):
             response_model=OpenAIResponse,
             data=OpenAICreateResponse(
                 input=[
-                    Item(
-                        root=InputMessage(
-                            content=cls.create_input_message_contents(
-                                prompt, images, files
-                            ),
-                            role="user",
-                        )
+                    InputMessage(
+                        content=cls.create_input_message_contents(prompt, images, files),
+                        role="user",
                     ),
                 ],
                 store=True,
                 stream=False,
                 model=model,
                 previous_response_id=None,
-                **(
-                    advanced_options.model_dump(exclude_none=True)
-                    if advanced_options
-                    else {}
-                ),
+                **(advanced_options.model_dump(exclude_none=True) if advanced_options else {}),
             ),
         )
         response_id = create_response.id
 
         # Get result output
         result_response = await poll_op(
-                cls,
-                ApiEndpoint(path=f"{RESPONSES_ENDPOINT}/{response_id}"),
-                response_model=OpenAIResponse,
-                status_extractor=lambda response: response.status,
-                completed_statuses=["incomplete", "completed"]
-            )
+            cls,
+            ApiEndpoint(path=f"{RESPONSES_ENDPOINT}/{response_id}"),
+            response_model=OpenAIResponse,
+            status_extractor=lambda response: response.status,
+            completed_statuses=["incomplete", "completed"],
+        )
         return IO.NodeOutput(cls.get_text_from_message_content(cls.get_message_content_from_response(result_response)))
 
 
@@ -923,7 +900,7 @@ class OpenAIChatConfig(IO.ComfyNode):
             remove depending on model choice.
         """
         return IO.NodeOutput(
-            CreateModelResponseProperties(
+            ModelResponseProperties(
                 instructions=instructions,
                 truncation=truncation,
                 max_output_tokens=max_output_tokens,

comfy_api_nodes/nodes_pixverse.py

@@ -1,7 +1,7 @@
 import torch
 from typing_extensions import override
 from comfy_api.latest import IO, ComfyExtension
-from comfy_api_nodes.apis.pixverse_api import (
+from comfy_api_nodes.apis.pixverse import (
     PixverseTextVideoRequest,
     PixverseImageVideoRequest,
     PixverseTransitionVideoRequest,

comfy_api_nodes/nodes_recraft.py

@@ -8,7 +8,7 @@ from typing_extensions import override
 
 from comfy.utils import ProgressBar
 from comfy_api.latest import IO, ComfyExtension
-from comfy_api_nodes.apis.recraft_api import (
+from comfy_api_nodes.apis.recraft import (
     RecraftColor,
     RecraftColorChain,
     RecraftControls,

comfy_api_nodes/nodes_rodin.py

@@ -14,7 +14,7 @@ from typing import Optional
 from io import BytesIO
 from typing_extensions import override
 from PIL import Image
-from comfy_api_nodes.apis.rodin_api import (
+from comfy_api_nodes.apis.rodin import (
     Rodin3DGenerateRequest,
     Rodin3DGenerateResponse,
     Rodin3DCheckStatusRequest,

comfy_api_nodes/nodes_runway.py

@@ -16,7 +16,7 @@ from enum import Enum
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input, InputImpl
-from comfy_api_nodes.apis import (
+from comfy_api_nodes.apis.runway import (
     RunwayImageToVideoRequest,
     RunwayImageToVideoResponse,
     RunwayTaskStatusResponse as TaskStatusResponse,

comfy_api_nodes/nodes_stability.py

@@ -3,7 +3,7 @@ from typing import Optional
 from typing_extensions import override
 
 from comfy_api.latest import ComfyExtension, Input, IO
-from comfy_api_nodes.apis.stability_api import (
+from comfy_api_nodes.apis.stability import (
     StabilityUpscaleConservativeRequest,
     StabilityUpscaleCreativeRequest,
     StabilityAsyncResponse,

comfy_api_nodes/nodes_topaz.py

@@ -5,7 +5,24 @@ import aiohttp
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input
-from comfy_api_nodes.apis import topaz_api
+from comfy_api_nodes.apis.topaz import (
+    CreateVideoRequest,
+    CreateVideoRequestSource,
+    CreateVideoResponse,
+    ImageAsyncTaskResponse,
+    ImageDownloadResponse,
+    ImageEnhanceRequest,
+    ImageStatusResponse,
+    OutputInformationVideo,
+    Resolution,
+    VideoAcceptResponse,
+    VideoCompleteUploadRequest,
+    VideoCompleteUploadRequestPart,
+    VideoCompleteUploadResponse,
+    VideoEnhancementFilter,
+    VideoFrameInterpolationFilter,
+    VideoStatusResponse,
+)
 from comfy_api_nodes.util import (
     ApiEndpoint,
     download_url_to_image_tensor,
@@ -153,13 +170,13 @@ class TopazImageEnhance(IO.ComfyNode):
         if get_number_of_images(image) != 1:
             raise ValueError("Only one input image is supported.")
         download_url = await upload_images_to_comfyapi(
-            cls, image, max_images=1, mime_type="image/png", total_pixels=4096*4096
+            cls, image, max_images=1, mime_type="image/png", total_pixels=4096 * 4096
         )
         initial_response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/topaz/image/v1/enhance-gen/async", method="POST"),
-            response_model=topaz_api.ImageAsyncTaskResponse,
-            data=topaz_api.ImageEnhanceRequest(
+            response_model=ImageAsyncTaskResponse,
+            data=ImageEnhanceRequest(
                 model=model,
                 prompt=prompt,
                 subject_detection=subject_detection,
@@ -181,7 +198,7 @@ class TopazImageEnhance(IO.ComfyNode):
         await poll_op(
             cls,
             poll_endpoint=ApiEndpoint(path=f"/proxy/topaz/image/v1/status/{initial_response.process_id}"),
-            response_model=topaz_api.ImageStatusResponse,
+            response_model=ImageStatusResponse,
             status_extractor=lambda x: x.status,
             progress_extractor=lambda x: getattr(x, "progress", 0),
             price_extractor=lambda x: x.credits * 0.08,
@@ -193,7 +210,7 @@ class TopazImageEnhance(IO.ComfyNode):
         results = await sync_op(
             cls,
             ApiEndpoint(path=f"/proxy/topaz/image/v1/download/{initial_response.process_id}"),
-            response_model=topaz_api.ImageDownloadResponse,
+            response_model=ImageDownloadResponse,
             monitor_progress=False,
         )
         return IO.NodeOutput(await download_url_to_image_tensor(results.download_url))
@@ -331,7 +348,7 @@ class TopazVideoEnhance(IO.ComfyNode):
             if target_height % 2 != 0:
                 target_height += 1
             filters.append(
-                topaz_api.VideoEnhancementFilter(
+                VideoEnhancementFilter(
                     model=UPSCALER_MODELS_MAP[upscaler_model],
                     creativity=(upscaler_creativity if UPSCALER_MODELS_MAP[upscaler_model] == "slc-1" else None),
                     isOptimizedMode=(True if UPSCALER_MODELS_MAP[upscaler_model] == "slc-1" else None),
@@ -340,7 +357,7 @@ class TopazVideoEnhance(IO.ComfyNode):
         if interpolation_enabled:
             target_frame_rate = interpolation_frame_rate
             filters.append(
-                topaz_api.VideoFrameInterpolationFilter(
+                VideoFrameInterpolationFilter(
                     model=interpolation_model,
                     slowmo=interpolation_slowmo,
                     fps=interpolation_frame_rate,
@@ -351,19 +368,19 @@ class TopazVideoEnhance(IO.ComfyNode):
         initial_res = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/topaz/video/", method="POST"),
-            response_model=topaz_api.CreateVideoResponse,
-            data=topaz_api.CreateVideoRequest(
-                source=topaz_api.CreateCreateVideoRequestSource(
+            response_model=CreateVideoResponse,
+            data=CreateVideoRequest(
+                source=CreateVideoRequestSource(
                     container="mp4",
                     size=get_fs_object_size(src_video_stream),
                     duration=int(duration_sec),
                     frameCount=video.get_frame_count(),
                     frameRate=src_frame_rate,
-                    resolution=topaz_api.Resolution(width=src_width, height=src_height),
+                    resolution=Resolution(width=src_width, height=src_height),
                 ),
                 filters=filters,
-                output=topaz_api.OutputInformationVideo(
-                    resolution=topaz_api.Resolution(width=target_width, height=target_height),
+                output=OutputInformationVideo(
+                    resolution=Resolution(width=target_width, height=target_height),
                     frameRate=target_frame_rate,
                     audioCodec="AAC",
                     audioTransfer="Copy",
@@ -379,7 +396,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                 path=f"/proxy/topaz/video/{initial_res.requestId}/accept",
                 method="PATCH",
             ),
-            response_model=topaz_api.VideoAcceptResponse,
+            response_model=VideoAcceptResponse,
             wait_label="Preparing upload",
             final_label_on_success="Upload started",
         )
@@ -402,10 +419,10 @@ class TopazVideoEnhance(IO.ComfyNode):
                 path=f"/proxy/topaz/video/{initial_res.requestId}/complete-upload",
                 method="PATCH",
             ),
-            response_model=topaz_api.VideoCompleteUploadResponse,
-            data=topaz_api.VideoCompleteUploadRequest(
+            response_model=VideoCompleteUploadResponse,
+            data=VideoCompleteUploadRequest(
                 uploadResults=[
-                    topaz_api.VideoCompleteUploadRequestPart(
+                    VideoCompleteUploadRequestPart(
                         partNum=1,
                         eTag=upload_etag,
                     ),
@@ -417,7 +434,7 @@ class TopazVideoEnhance(IO.ComfyNode):
         final_response = await poll_op(
             cls,
             ApiEndpoint(path=f"/proxy/topaz/video/{initial_res.requestId}/status"),
-            response_model=topaz_api.VideoStatusResponse,
+            response_model=VideoStatusResponse,
             status_extractor=lambda x: x.status,
             progress_extractor=lambda x: getattr(x, "progress", 0),
             price_extractor=lambda x: (x.estimates.cost[0] * 0.08 if x.estimates and x.estimates.cost[0] else None),

comfy_api_nodes/nodes_tripo.py

@@ -5,7 +5,7 @@ import torch
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension
-from comfy_api_nodes.apis.tripo_api import (
+from comfy_api_nodes.apis.tripo import (
     TripoAnimateRetargetRequest,
     TripoAnimateRigRequest,
     TripoConvertModelRequest,

comfy_api_nodes/nodes_veo2.py

@@ -4,7 +4,7 @@ from io import BytesIO
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input, InputImpl
-from comfy_api_nodes.apis.veo_api import (
+from comfy_api_nodes.apis.veo import (
     VeoGenVidPollRequest,
     VeoGenVidPollResponse,
     VeoGenVidRequest,

comfy_api_nodes/nodes_vidu.py

@@ -703,7 +703,7 @@ class Vidu2ReferenceVideoNode(IO.ComfyNode):
                     "subjects",
                     template=IO.Autogrow.TemplateNames(
                         IO.Image.Input("reference_images"),
-                        names=["subject1", "subject2", "subject3"],
+                        names=["subject1", "subject2", "subject3", "subject4", "subject5", "subject6", "subject7"],
                         min=1,
                     ),
                     tooltip="For each subject, provide up to 3 reference images (7 images total across all subjects). "
@@ -738,7 +738,7 @@ class Vidu2ReferenceVideoNode(IO.ComfyNode):
                     control_after_generate=True,
                 ),
                 IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "4:3", "3:4", "1:1"]),
-                IO.Combo.Input("resolution", options=["720p"]),
+                IO.Combo.Input("resolution", options=["720p", "1080p"]),
                 IO.Combo.Input(
                     "movement_amplitude",
                     options=["auto", "small", "medium", "large"],

comfy_api_nodes/nodes_wavespeed.py

@@ -0,0 +1,178 @@
+from typing_extensions import override
+
+from comfy_api.latest import IO, ComfyExtension, Input
+from comfy_api_nodes.apis.wavespeed import (
+    FlashVSRRequest,
+    TaskCreatedResponse,
+    TaskResultResponse,
+    SeedVR2ImageRequest,
+)
+from comfy_api_nodes.util import (
+    ApiEndpoint,
+    download_url_to_video_output,
+    poll_op,
+    sync_op,
+    upload_video_to_comfyapi,
+    validate_container_format_is_mp4,
+    validate_video_duration,
+    upload_images_to_comfyapi,
+    get_number_of_images,
+    download_url_to_image_tensor,
+)
+
+
+class WavespeedFlashVSRNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="WavespeedFlashVSRNode",
+            display_name="FlashVSR Video Upscale",
+            category="api node/video/WaveSpeed",
+            description="Fast, high-quality video upscaler that "
+            "boosts resolution and restores clarity for low-resolution or blurry footage.",
+            inputs=[
+                IO.Video.Input("video"),
+                IO.Combo.Input("target_resolution", options=["720p", "1080p", "2K", "4K"]),
+            ],
+            outputs=[
+                IO.Video.Output(),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["target_resolution"]),
+                expr="""
+                (
+                  $price_for_1sec := {"720p": 0.012, "1080p": 0.018, "2k": 0.024, "4k": 0.032};
+                  {
+                    "type":"usd",
+                    "usd": $lookup($price_for_1sec, widgets.target_resolution),
+                    "format":{"suffix": "/second", "approximate": true}
+                  }
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        video: Input.Video,
+        target_resolution: str,
+    ) -> IO.NodeOutput:
+        validate_container_format_is_mp4(video)
+        validate_video_duration(video, min_duration=5, max_duration=60 * 10)
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path="/proxy/wavespeed/api/v3/wavespeed-ai/flashvsr", method="POST"),
+            response_model=TaskCreatedResponse,
+            data=FlashVSRRequest(
+                target_resolution=target_resolution.lower(),
+                video=await upload_video_to_comfyapi(cls, video),
+                duration=video.get_duration(),
+            ),
+        )
+        if initial_res.code != 200:
+            raise ValueError(f"Task creation fails with code={initial_res.code} and message={initial_res.message}")
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wavespeed/api/v3/predictions/{initial_res.data.id}/result"),
+            response_model=TaskResultResponse,
+            status_extractor=lambda x: "failed" if x.data is None else x.data.status,
+            poll_interval=10.0,
+            max_poll_attempts=480,
+        )
+        if final_response.code != 200:
+            raise ValueError(
+                f"Task processing failed with code={final_response.code} and message={final_response.message}"
+            )
+        return IO.NodeOutput(await download_url_to_video_output(final_response.data.outputs[0]))
+
+
+class WavespeedImageUpscaleNode(IO.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="WavespeedImageUpscaleNode",
+            display_name="WaveSpeed Image Upscale",
+            category="api node/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"]),
+                IO.Image.Input("image"),
+                IO.Combo.Input("target_resolution", options=["2K", "4K", "8K"]),
+            ],
+            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=["model"]),
+                expr="""
+                (
+                  $prices := {"seedvr2": 0.01, "ultimate": 0.06};
+                  {"type":"usd", "usd": $lookup($prices, widgets.model)}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: Input.Image,
+        target_resolution: str,
+    ) -> IO.NodeOutput:
+        if get_number_of_images(image) != 1:
+            raise ValueError("Exactly one input image is required.")
+        if model == "SeedVR2":
+            model_path = "seedvr2/image"
+        else:
+            model_path = "ultimate-image-upscaler"
+        initial_res = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wavespeed/api/v3/wavespeed-ai/{model_path}", method="POST"),
+            response_model=TaskCreatedResponse,
+            data=SeedVR2ImageRequest(
+                target_resolution=target_resolution.lower(),
+                image=(await upload_images_to_comfyapi(cls, image, max_images=1))[0],
+            ),
+        )
+        if initial_res.code != 200:
+            raise ValueError(f"Task creation fails with code={initial_res.code} and message={initial_res.message}")
+        final_response = await poll_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/wavespeed/api/v3/predictions/{initial_res.data.id}/result"),
+            response_model=TaskResultResponse,
+            status_extractor=lambda x: "failed" if x.data is None else x.data.status,
+            poll_interval=10.0,
+            max_poll_attempts=480,
+        )
+        if final_response.code != 200:
+            raise ValueError(
+                f"Task processing failed with code={final_response.code} and message={final_response.message}"
+            )
+        return IO.NodeOutput(await download_url_to_image_tensor(final_response.data.outputs[0]))
+
+
+class WavespeedExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+        return [
+            WavespeedFlashVSRNode,
+            WavespeedImageUpscaleNode,
+        ]
+
+
+async def comfy_entrypoint() -> WavespeedExtension:
+    return WavespeedExtension()

comfy_api_nodes/redocly-dev.yaml

@@ -1,10 +0,0 @@
-# This file is used to filter the Comfy Org OpenAPI spec for schemas related to API Nodes.
-# This is used for development purposes to generate stubs for unreleased API endpoints.
-apis:
-  filter:
-    root: openapi.yaml
-    decorators:
-      filter-in:
-        property: tags
-        value: ['API Nodes']
-        matchStrategy: all

comfy_api_nodes/redocly.yaml

@@ -1,10 +0,0 @@
-# This file is used to filter the Comfy Org OpenAPI spec for schemas related to API Nodes.
-
-apis:
-  filter:
-    root: openapi.yaml
-    decorators:
-      filter-in:
-        property: tags
-        value: ['API Nodes', 'Released']
-        matchStrategy: all

comfy_api_nodes/util/__init__.py

@@ -11,6 +11,7 @@ from .conversions import (
     audio_input_to_mp3,
     audio_to_base64_string,
     bytesio_to_image_tensor,
+    convert_mask_to_image,
     downscale_image_tensor,
     image_tensor_pair_to_batch,
     pil_to_bytesio,
@@ -72,6 +73,7 @@ __all__ = [
     "audio_input_to_mp3",
     "audio_to_base64_string",
     "bytesio_to_image_tensor",
+    "convert_mask_to_image",
     "downscale_image_tensor",
     "image_tensor_pair_to_batch",
     "pil_to_bytesio",

comfy_api_nodes/util/conversions.py

@@ -451,6 +451,12 @@ def resize_mask_to_image(
     return mask
 
 
+def convert_mask_to_image(mask: Input.Image) -> torch.Tensor:
+    """Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image."""
+    mask = mask.unsqueeze(-1)
+    return torch.cat([mask] * 3, dim=-1)
+
+
 def text_filepath_to_base64_string(filepath: str) -> str:
     """Converts a text file to a base64 string."""
     with open(filepath, "rb") as f:

comfy_extras/nodes_easycache.py

@@ -29,8 +29,10 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
     do_easycache = easycache.should_do_easycache(sigmas)
     if do_easycache:
         easycache.check_metadata(x)
+        # if there isn't a cache diff for current conds, we cannot skip this step
+        can_apply_cache_diff = easycache.can_apply_cache_diff(uuids)
         # if first cond marked this step for skipping, skip it and use appropriate cached values
-        if easycache.skip_current_step:
+        if easycache.skip_current_step and can_apply_cache_diff:
             if easycache.verbose:
                 logging.info(f"EasyCache [verbose] - was marked to skip this step by {easycache.first_cond_uuid}. Present uuids: {uuids}")
             return easycache.apply_cache_diff(x, uuids)
@@ -44,7 +46,7 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
             if easycache.has_output_prev_norm() and easycache.has_relative_transformation_rate():
                 approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
                 easycache.cumulative_change_rate += approx_output_change_rate
-                if easycache.cumulative_change_rate < easycache.reuse_threshold:
+                if easycache.cumulative_change_rate < easycache.reuse_threshold and can_apply_cache_diff:
                     if easycache.verbose:
                         logging.info(f"EasyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
                     # other conds should also skip this step, and instead use their cached values
@@ -240,6 +242,9 @@ class EasyCacheHolder:
             return to_return.clone()
         return to_return
 
+    def can_apply_cache_diff(self, uuids: list[UUID]) -> bool:
+        return all(uuid in self.uuid_cache_diffs for uuid in uuids)
+
     def apply_cache_diff(self, x: torch.Tensor, uuids: list[UUID]):
         if self.first_cond_uuid in uuids:
             self.total_steps_skipped += 1

comfy_extras/nodes_load_3d.py

@@ -24,7 +24,7 @@ class Load3D(IO.ComfyNode):
         files = [
             normalize_path(str(file_path.relative_to(base_path)))
             for file_path in input_path.rglob("*")
-            if file_path.suffix.lower() in {'.gltf', '.glb', '.obj', '.fbx', '.stl'}
+            if file_path.suffix.lower() in {'.gltf', '.glb', '.obj', '.fbx', '.stl', '.spz', '.splat', '.ply', '.ksplat'}
         ]
         return IO.Schema(
             node_id="Load3D",

comfy_extras/nodes_model_patch.py

@@ -7,6 +7,7 @@ import comfy.model_management
 import comfy.ldm.common_dit
 import comfy.latent_formats
 import comfy.ldm.lumina.controlnet
+from comfy.ldm.wan.model_multitalk import WanMultiTalkAttentionBlock, MultiTalkAudioProjModel
 
 
 class BlockWiseControlBlock(torch.nn.Module):
@@ -257,6 +258,14 @@ class ModelPatchLoader:
                     if torch.count_nonzero(ref_weight) == 0:
                         config['broken'] = True
             model = comfy.ldm.lumina.controlnet.ZImage_Control(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast, **config)
+        elif "audio_proj.proj1.weight" in sd:
+            model = MultiTalkModelPatch(
+                    audio_window=5, context_tokens=32, vae_scale=4,
+                    in_dim=sd["blocks.0.audio_cross_attn.proj.weight"].shape[0],
+                    intermediate_dim=sd["audio_proj.proj1.weight"].shape[0],
+                    out_dim=sd["audio_proj.norm.weight"].shape[0],
+                    device=comfy.model_management.unet_offload_device(),
+                    operations=comfy.ops.manual_cast)
 
         model.load_state_dict(sd)
         model = comfy.model_patcher.ModelPatcher(model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device())
@@ -524,6 +533,38 @@ class USOStyleReference:
         return (model_patched,)
 
 
+class MultiTalkModelPatch(torch.nn.Module):
+    def __init__(
+        self,
+        audio_window: int = 5,
+        intermediate_dim: int = 512,
+        in_dim: int = 5120,
+        out_dim: int = 768,
+        context_tokens: int = 32,
+        vae_scale: int = 4,
+        num_layers: int = 40,
+
+        device=None, dtype=None, operations=None
+    ):
+        super().__init__()
+        self.audio_proj = MultiTalkAudioProjModel(
+                seq_len=audio_window,
+                seq_len_vf=audio_window+vae_scale-1,
+                intermediate_dim=intermediate_dim,
+                out_dim=out_dim,
+                context_tokens=context_tokens,
+                device=device,
+                dtype=dtype,
+                operations=operations
+        )
+        self.blocks = torch.nn.ModuleList(
+            [
+                WanMultiTalkAttentionBlock(in_dim, out_dim, device=device, dtype=dtype, operations=operations)
+                for _ in range(num_layers)
+            ]
+        )
+
+
 NODE_CLASS_MAPPINGS = {
     "ModelPatchLoader": ModelPatchLoader,
     "QwenImageDiffsynthControlnet": QwenImageDiffsynthControlnet,

comfy_extras/nodes_post_processing.py

@@ -550,6 +550,7 @@ class BatchImagesNode(io.ComfyNode):
             node_id="BatchImagesNode",
             display_name="Batch Images",
             category="image",
+            search_aliases=["batch", "image batch", "batch images", "combine images", "merge images", "stack images"],
             inputs=[
                 io.Autogrow.Input("images", template=autogrow_template)
             ],

comfy_extras/nodes_preview_any.py

@@ -16,6 +16,7 @@ class PreviewAny():
     OUTPUT_NODE = True
 
     CATEGORY = "utils"
+    SEARCH_ALIASES = ["preview", "show", "display", "view", "show text", "display text", "preview text", "show output", "inspect", "debug"]
 
     def main(self, source=None):
         value = 'None'

comfy_extras/nodes_string.py

@@ -11,6 +11,7 @@ class StringConcatenate(io.ComfyNode):
             node_id="StringConcatenate",
             display_name="Concatenate",
             category="utils/string",
+            search_aliases=["text concat", "join text", "merge text", "combine strings", "concat", "concatenate", "append text", "combine text", "string"],
             inputs=[
                 io.String.Input("string_a", multiline=True),
                 io.String.Input("string_b", multiline=True),

comfy_extras/nodes_upscale_model.py

@@ -53,6 +53,7 @@ class ImageUpscaleWithModel(io.ComfyNode):
             node_id="ImageUpscaleWithModel",
             display_name="Upscale Image (using Model)",
             category="image/upscaling",
+            search_aliases=["upscale", "upscaler", "upsc", "enlarge image", "super resolution", "hires", "superres", "increase resolution"],
             inputs=[
                 io.UpscaleModel.Input("upscale_model"),
                 io.Image.Input("image"),

comfy_extras/nodes_video.py

@@ -159,6 +159,29 @@ class GetVideoComponents(io.ComfyNode):
         return io.NodeOutput(components.images, components.audio, float(components.frame_rate))
 
 
+class VideoSlice(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="VideoSlice",
+            display_name="Video Slice",
+            category="image/video",
+            description="Extract a range of frames from a video.",
+            inputs=[
+                io.Video.Input("video", tooltip="The video to slice."),
+                io.Int.Input("start_frame", default=0, min=0, tooltip="The frame index to start from (0-indexed)."),
+                io.Int.Input("frame_count", default=1, min=1, tooltip="Number of frames to extract."),
+            ],
+            outputs=[
+                io.Video.Output(tooltip="The sliced video."),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, video: Input.Video, start_frame: int, frame_count: int) -> io.NodeOutput:
+        return io.NodeOutput(video.sliced(start_frame, frame_count))
+
+
 class LoadVideo(io.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -206,6 +229,7 @@ class VideoExtension(ComfyExtension):
             SaveVideo,
             CreateVideo,
             GetVideoComponents,
+            VideoSlice,
             LoadVideo,
         ]
 

comfy_extras/nodes_wan.py

@@ -8,9 +8,10 @@ import comfy.latent_formats
 import comfy.clip_vision
 import json
 import numpy as np
-from typing import Tuple
+from typing import Tuple, TypedDict
 from typing_extensions import override
 from comfy_api.latest import ComfyExtension, io
+import logging
 
 class WanImageToVideo(io.ComfyNode):
     @classmethod
@@ -1288,6 +1289,171 @@ class Wan22ImageToVideoLatent(io.ComfyNode):
         return io.NodeOutput(out_latent)
 
 
+from comfy.ldm.wan.model_multitalk import InfiniteTalkOuterSampleWrapper, MultiTalkCrossAttnPatch, MultiTalkGetAttnMapPatch, project_audio_features
+class WanInfiniteTalkToVideo(io.ComfyNode):
+    class DCValues(TypedDict):
+        mode: str
+        audio_encoder_output_2: io.AudioEncoderOutput.Type
+        mask: io.Mask.Type
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanInfiniteTalkToVideo",
+            category="conditioning/video_models",
+            inputs=[
+                io.DynamicCombo.Input("mode", options=[
+                io.DynamicCombo.Option("single_speaker", []),
+                io.DynamicCombo.Option("two_speakers", [
+                    io.AudioEncoderOutput.Input("audio_encoder_output_2", optional=True),
+                    io.Mask.Input("mask_1", optional=True, tooltip="Mask for the first speaker, required if using two audio inputs."),
+                    io.Mask.Input("mask_2", optional=True, tooltip="Mask for the second speaker, required if using two audio inputs."),
+                    ]),
+                ]),
+                io.Model.Input("model"),
+                io.ModelPatch.Input("model_patch"),
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.ClipVisionOutput.Input("clip_vision_output", optional=True),
+                io.Image.Input("start_image", optional=True),
+                io.AudioEncoderOutput.Input("audio_encoder_output_1"),
+                io.Int.Input("motion_frame_count", default=9, min=1, max=33, step=1, tooltip="Number of previous frames to use as motion context."),
+                io.Float.Input("audio_scale", default=1.0, min=-10.0, max=10.0, step=0.01),
+                io.Image.Input("previous_frames", optional=True),
+            ],
+            outputs=[
+                io.Model.Output(display_name="model"),
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+                io.Int.Output(display_name="trim_image"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, mode: DCValues, model, model_patch, positive, negative, vae, width, height, length, audio_encoder_output_1, motion_frame_count,
+                start_image=None, previous_frames=None, audio_scale=None, clip_vision_output=None, audio_encoder_output_2=None, mask_1=None, mask_2=None) -> io.NodeOutput:
+
+        if previous_frames is not None and previous_frames.shape[0] < motion_frame_count:
+            raise ValueError("Not enough previous frames provided.")
+
+        if mode["mode"] == "two_speakers":
+            audio_encoder_output_2 = mode["audio_encoder_output_2"]
+            mask_1 = mode["mask_1"]
+            mask_2 = mode["mask_2"]
+
+        if audio_encoder_output_2 is not None:
+            if mask_1 is None or mask_2 is None:
+                raise ValueError("Masks must be provided if two audio encoder outputs are used.")
+
+        ref_masks = None
+        if mask_1 is not None and mask_2 is not None:
+            if audio_encoder_output_2 is None:
+                raise ValueError("Second audio encoder output must be provided if two masks are used.")
+            ref_masks = torch.cat([mask_1, mask_2])
+
+        latent = torch.zeros([1, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        if start_image is not None:
+            start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            image = torch.ones((length, height, width, start_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) * 0.5
+            image[:start_image.shape[0]] = start_image
+
+            concat_latent_image = vae.encode(image[:, :, :, :3])
+            concat_mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype)
+            concat_mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
+
+            positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": concat_mask})
+            negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": concat_mask})
+
+        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})
+
+        model_patched = model.clone()
+
+        encoded_audio_list = []
+        seq_lengths = []
+
+        for audio_encoder_output in [audio_encoder_output_1, audio_encoder_output_2]:
+            if audio_encoder_output is None:
+                continue
+            all_layers = audio_encoder_output["encoded_audio_all_layers"]
+            encoded_audio = torch.stack(all_layers, dim=0).squeeze(1)[1:]  # shape: [num_layers, T, 512]
+            encoded_audio = linear_interpolation(encoded_audio, input_fps=50, output_fps=25).movedim(0, 1) # shape: [T, num_layers, 512]
+            encoded_audio_list.append(encoded_audio)
+            seq_lengths.append(encoded_audio.shape[0])
+
+        # Pad / combine depending on multi_audio_type
+        multi_audio_type = "add"
+        if len(encoded_audio_list) > 1:
+            if multi_audio_type == "para":
+                max_len = max(seq_lengths)
+                padded = []
+                for emb in encoded_audio_list:
+                    if emb.shape[0] < max_len:
+                        pad = torch.zeros(max_len - emb.shape[0], *emb.shape[1:], dtype=emb.dtype)
+                        emb = torch.cat([emb, pad], dim=0)
+                    padded.append(emb)
+                encoded_audio_list = padded
+            elif multi_audio_type == "add":
+                total_len = sum(seq_lengths)
+                full_list = []
+                offset = 0
+                for emb, seq_len in zip(encoded_audio_list, seq_lengths):
+                    full = torch.zeros(total_len, *emb.shape[1:], dtype=emb.dtype)
+                    full[offset:offset+seq_len] = emb
+                    full_list.append(full)
+                    offset += seq_len
+                encoded_audio_list = full_list
+
+        token_ref_target_masks = None
+        if ref_masks is not None:
+            token_ref_target_masks = torch.nn.functional.interpolate(
+                ref_masks.unsqueeze(0), size=(latent.shape[-2] // 2, latent.shape[-1] // 2), mode='nearest')[0]
+            token_ref_target_masks = (token_ref_target_masks > 0).view(token_ref_target_masks.shape[0], -1)
+
+        # when extending from previous frames
+        if previous_frames is not None:
+            motion_frames = comfy.utils.common_upscale(previous_frames[-motion_frame_count:].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+            frame_offset = previous_frames.shape[0] - motion_frame_count
+
+            audio_start = frame_offset
+            audio_end = audio_start + length
+            logging.info(f"InfiniteTalk: Processing audio frames {audio_start} - {audio_end}")
+
+            motion_frames_latent = vae.encode(motion_frames[:, :, :, :3])
+            trim_image = motion_frame_count
+        else:
+            audio_start = trim_image = 0
+            audio_end = length
+            motion_frames_latent = concat_latent_image[:, :, :1]
+
+        audio_embed = project_audio_features(model_patch.model.audio_proj, encoded_audio_list, audio_start, audio_end).to(model_patched.model_dtype())
+        model_patched.model_options["transformer_options"]["audio_embeds"] = audio_embed
+
+        # add outer sample wrapper
+        model_patched.add_wrapper_with_key(
+            comfy.patcher_extension.WrappersMP.OUTER_SAMPLE,
+            "infinite_talk_outer_sample",
+            InfiniteTalkOuterSampleWrapper(
+                motion_frames_latent,
+                model_patch,
+                is_extend=previous_frames is not None,
+            ))
+        # add cross-attention patch
+        model_patched.set_model_patch(MultiTalkCrossAttnPatch(model_patch, audio_scale), "attn2_patch")
+        if token_ref_target_masks is not None:
+            model_patched.set_model_patch(MultiTalkGetAttnMapPatch(token_ref_target_masks), "attn1_patch")
+
+        out_latent = {}
+        out_latent["samples"] = latent
+        return io.NodeOutput(model_patched, positive, negative, out_latent, trim_image)
+
+
 class WanExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
@@ -1307,6 +1473,7 @@ class WanExtension(ComfyExtension):
             WanHuMoImageToVideo,
             WanAnimateToVideo,
             Wan22ImageToVideoLatent,
+            WanInfiniteTalkToVideo,
         ]
 
 async def comfy_entrypoint() -> WanExtension:

comfy_extras/nodes_zimage.py

@@ -0,0 +1,88 @@
+import node_helpers
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+import math
+import comfy.utils
+
+
+class TextEncodeZImageOmni(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="TextEncodeZImageOmni",
+            category="advanced/conditioning",
+            is_experimental=True,
+            inputs=[
+                io.Clip.Input("clip"),
+                io.ClipVision.Input("image_encoder", optional=True),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.Boolean.Input("auto_resize_images", default=True),
+                io.Vae.Input("vae", optional=True),
+                io.Image.Input("image1", optional=True),
+                io.Image.Input("image2", optional=True),
+                io.Image.Input("image3", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, clip, prompt, image_encoder=None, auto_resize_images=True, vae=None, image1=None, image2=None, image3=None) -> io.NodeOutput:
+        ref_latents = []
+        images = list(filter(lambda a: a is not None, [image1, image2, image3]))
+
+        prompt_list = []
+        template = None
+        if len(images) > 0:
+            prompt_list = ["<|im_start|>user\n<|vision_start|>"]
+            prompt_list += ["<|vision_end|><|vision_start|>"] * (len(images) - 1)
+            prompt_list += ["<|vision_end|><|im_end|>"]
+            template = "<|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n<|vision_start|>"
+
+        encoded_images = []
+
+        for i, image in enumerate(images):
+            if image_encoder is not None:
+                encoded_images.append(image_encoder.encode_image(image))
+
+            if vae is not None:
+                if auto_resize_images:
+                    samples = image.movedim(-1, 1)
+                    total = int(1024 * 1024)
+                    scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+                    width = round(samples.shape[3] * scale_by / 8.0) * 8
+                    height = round(samples.shape[2] * scale_by / 8.0) * 8
+
+                    image = comfy.utils.common_upscale(samples, width, height, "area", "disabled").movedim(1, -1)
+                ref_latents.append(vae.encode(image))
+
+        tokens = clip.tokenize(prompt, llama_template=template)
+        conditioning = clip.encode_from_tokens_scheduled(tokens)
+
+        extra_text_embeds = []
+        for p in prompt_list:
+            tokens = clip.tokenize(p, llama_template="{}")
+            text_embeds = clip.encode_from_tokens_scheduled(tokens)
+            extra_text_embeds.append(text_embeds[0][0])
+
+        if len(ref_latents) > 0:
+            conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": ref_latents}, append=True)
+        if len(encoded_images) > 0:
+            conditioning = node_helpers.conditioning_set_values(conditioning, {"clip_vision_outputs": encoded_images}, append=True)
+        if len(extra_text_embeds) > 0:
+            conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents_text_embeds": extra_text_embeds}, append=True)
+
+        return io.NodeOutput(conditioning)
+
+
+class ZImageExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            TextEncodeZImageOmni,
+        ]
+
+
+async def comfy_entrypoint() -> ZImageExtension:
+    return ZImageExtension()

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.9.2"
+__version__ = "0.10.0"

latent_preview.py

@@ -11,7 +11,7 @@ import logging
 default_preview_method = args.preview_method
 
 MAX_PREVIEW_RESOLUTION = args.preview_size
-VIDEO_TAES = ["taehv", "lighttaew2_2", "lighttaew2_1", "lighttaehy1_5"]
+VIDEO_TAES = ["taehv", "lighttaew2_2", "lighttaew2_1", "lighttaehy1_5", "taeltx_2"]
 
 def preview_to_image(latent_image, do_scale=True):
         if do_scale:

nodes.py

@@ -5,6 +5,7 @@ import torch
 import os
 import sys
 import json
+import glob
 import hashlib
 import inspect
 import traceback
@@ -69,6 +70,7 @@ class CLIPTextEncode(ComfyNodeABC):
 
     CATEGORY = "conditioning"
     DESCRIPTION = "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images."
+    SEARCH_ALIASES = ["text", "prompt", "text prompt", "positive prompt", "negative prompt", "encode text", "text encoder", "encode prompt"]
 
     def encode(self, clip, text):
         if clip is None:
@@ -85,6 +87,7 @@ class ConditioningCombine:
     FUNCTION = "combine"
 
     CATEGORY = "conditioning"
+    SEARCH_ALIASES = ["combine", "merge conditioning", "combine prompts", "merge prompts", "mix prompts", "add prompt"]
 
     def combine(self, conditioning_1, conditioning_2):
         return (conditioning_1 + conditioning_2, )
@@ -293,6 +296,7 @@ class VAEDecode:
 
     CATEGORY = "latent"
     DESCRIPTION = "Decodes latent images back into pixel space images."
+    SEARCH_ALIASES = ["decode", "decode latent", "latent to image", "render latent"]
 
     def decode(self, vae, samples):
         latent = samples["samples"]
@@ -345,6 +349,7 @@ class VAEEncode:
     FUNCTION = "encode"
 
     CATEGORY = "latent"
+    SEARCH_ALIASES = ["encode", "encode image", "image to latent"]
 
     def encode(self, vae, pixels):
         t = vae.encode(pixels)
@@ -580,6 +585,7 @@ class CheckpointLoaderSimple:
 
     CATEGORY = "loaders"
     DESCRIPTION = "Loads a diffusion model checkpoint, diffusion models are used to denoise latents."
+    SEARCH_ALIASES = ["load model", "checkpoint", "model loader", "load checkpoint", "ckpt", "model"]
 
     def load_checkpoint(self, ckpt_name):
         ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
@@ -666,6 +672,7 @@ class LoraLoader:
 
     CATEGORY = "loaders"
     DESCRIPTION = "LoRAs are used to modify diffusion and CLIP models, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together."
+    SEARCH_ALIASES = ["lora", "load lora", "apply lora", "lora loader", "lora model"]
 
     def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
         if strength_model == 0 and strength_clip == 0:
@@ -700,7 +707,7 @@ class LoraLoaderModelOnly(LoraLoader):
         return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)
 
 class VAELoader:
-    video_taes = ["taehv", "lighttaew2_2", "lighttaew2_1", "lighttaehy1_5"]
+    video_taes = ["taehv", "lighttaew2_2", "lighttaew2_1", "lighttaehy1_5", "taeltx_2"]
     image_taes = ["taesd", "taesdxl", "taesd3", "taef1"]
     @staticmethod
     def vae_list(s):
@@ -813,6 +820,7 @@ class ControlNetLoader:
     FUNCTION = "load_controlnet"
 
     CATEGORY = "loaders"
+    SEARCH_ALIASES = ["controlnet", "control net", "cn", "load controlnet", "controlnet loader"]
 
     def load_controlnet(self, control_net_name):
         controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name)
@@ -889,6 +897,7 @@ class ControlNetApplyAdvanced:
     FUNCTION = "apply_controlnet"
 
     CATEGORY = "conditioning/controlnet"
+    SEARCH_ALIASES = ["controlnet", "apply controlnet", "use controlnet", "control net"]
 
     def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None, extra_concat=[]):
         if strength == 0:
@@ -1199,6 +1208,7 @@ class EmptyLatentImage:
 
     CATEGORY = "latent"
     DESCRIPTION = "Create a new batch of empty latent images to be denoised via sampling."
+    SEARCH_ALIASES = ["empty", "empty latent", "new latent", "create latent", "blank latent", "blank"]
 
     def generate(self, width, height, batch_size=1):
         latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
@@ -1539,6 +1549,7 @@ class KSampler:
 
     CATEGORY = "sampling"
     DESCRIPTION = "Uses the provided model, positive and negative conditioning to denoise the latent image."
+    SEARCH_ALIASES = ["sampler", "sample", "generate", "denoise", "diffuse", "txt2img", "img2img"]
 
     def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
         return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
@@ -1603,6 +1614,7 @@ class SaveImage:
 
     CATEGORY = "image"
     DESCRIPTION = "Saves the input images to your ComfyUI output directory."
+    SEARCH_ALIASES = ["save", "save image", "export image", "output image", "write image", "download"]
 
     def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
         filename_prefix += self.prefix_append
@@ -1639,6 +1651,8 @@ class PreviewImage(SaveImage):
         self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
         self.compress_level = 1
 
+    SEARCH_ALIASES = ["preview", "preview image", "show image", "view image", "display image", "image viewer"]
+
     @classmethod
     def INPUT_TYPES(s):
         return {"required":
@@ -1657,6 +1671,7 @@ class LoadImage:
                 }
 
     CATEGORY = "image"
+    SEARCH_ALIASES = ["load image", "open image", "import image", "image input", "upload image", "read image", "image loader"]
 
     RETURN_TYPES = ("IMAGE", "MASK")
     FUNCTION = "load_image"
@@ -1809,6 +1824,7 @@ class ImageScale:
     FUNCTION = "upscale"
 
     CATEGORY = "image/upscaling"
+    SEARCH_ALIASES = ["resize", "resize image", "scale image", "image resize", "zoom", "zoom in", "change size"]
 
     def upscale(self, image, upscale_method, width, height, crop):
         if width == 0 and height == 0:
@@ -2372,6 +2388,7 @@ async def init_builtin_extra_nodes():
         "nodes_kandinsky5.py",
         "nodes_wanmove.py",
         "nodes_image_compare.py",
+        "nodes_zimage.py",
     ]
 
     import_failed = []
@@ -2384,38 +2401,12 @@ async def init_builtin_extra_nodes():
 
 async def init_builtin_api_nodes():
     api_nodes_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_api_nodes")
-    api_nodes_files = [
-        "nodes_ideogram.py",
-        "nodes_openai.py",
-        "nodes_minimax.py",
-        "nodes_veo2.py",
-        "nodes_kling.py",
-        "nodes_bfl.py",
-        "nodes_bytedance.py",
-        "nodes_ltxv.py",
-        "nodes_luma.py",
-        "nodes_recraft.py",
-        "nodes_pixverse.py",
-        "nodes_stability.py",
-        "nodes_runway.py",
-        "nodes_sora.py",
-        "nodes_topaz.py",
-        "nodes_tripo.py",
-        "nodes_meshy.py",
-        "nodes_moonvalley.py",
-        "nodes_rodin.py",
-        "nodes_gemini.py",
-        "nodes_vidu.py",
-        "nodes_wan.py",
-    ]
-
-    if not await load_custom_node(os.path.join(api_nodes_dir, "canary.py"), module_parent="comfy_api_nodes"):
-        return api_nodes_files
+    api_nodes_files = sorted(glob.glob(os.path.join(api_nodes_dir, "nodes_*.py")))
 
     import_failed = []
     for node_file in api_nodes_files:
-        if not await load_custom_node(os.path.join(api_nodes_dir, node_file), module_parent="comfy_api_nodes"):
-            import_failed.append(node_file)
+        if not await load_custom_node(node_file, module_parent="comfy_api_nodes"):
+            import_failed.append(os.path.basename(node_file))
 
     return import_failed
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.9.2"
+version = "0.10.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,5 +1,5 @@
-comfyui-frontend-package==1.36.14
-comfyui-workflow-templates==0.8.10
+comfyui-frontend-package==1.37.11
+comfyui-workflow-templates==0.8.15
 comfyui-embedded-docs==0.4.0
 torch
 torchsde
@@ -21,8 +21,7 @@ psutil
 alembic
 SQLAlchemy
 av>=14.2.0
-comfy-kitchen>=0.2.6
-blake3
+comfy-kitchen>=0.2.7
 
 #non essential dependencies:
 kornia>=0.7.1

server.py

@@ -682,6 +682,8 @@ class PromptServer():
 
             if hasattr(obj_class, 'API_NODE'):
                 info['api_node'] = obj_class.API_NODE
+
+            info['search_aliases'] = getattr(obj_class, 'SEARCH_ALIASES', [])
             return info
 
         @routes.get("/object_info")

tests-unit/assets_test/conftest.py

@@ -1,104 +0,0 @@
-"""
-Pytest fixtures for assets API tests.
-"""
-
-import io
-import pytest
-from unittest.mock import MagicMock, patch
-
-from sqlalchemy import create_engine
-from sqlalchemy.orm import sessionmaker, Session
-
-from aiohttp import web
-
-pytestmark = pytest.mark.asyncio
-
-
-@pytest.fixture(scope="session")
-def in_memory_engine():
-    """Create an in-memory SQLite engine with all asset tables."""
-    engine = create_engine("sqlite:///:memory:", echo=False)
-    
-    from app.database.models import Base
-    from app.assets.database.models import (
-        Asset,
-        AssetInfo,
-        AssetCacheState,
-        AssetInfoMeta,
-        AssetInfoTag,
-        Tag,
-    )
-    
-    Base.metadata.create_all(engine)
-    
-    yield engine
-    
-    engine.dispose()
-
-
-@pytest.fixture
-def db_session(in_memory_engine) -> Session:
-    """Create a fresh database session for each test."""
-    SessionLocal = sessionmaker(bind=in_memory_engine)
-    session = SessionLocal()
-    
-    yield session
-    
-    session.rollback()
-    session.close()
-
-
-@pytest.fixture
-def mock_user_manager():
-    """Create a mock UserManager that returns a predictable owner_id."""
-    mock = MagicMock()
-    mock.get_request_user_id = MagicMock(return_value="test-user-123")
-    return mock
-
-
-@pytest.fixture
-def app(mock_user_manager) -> web.Application:
-    """Create an aiohttp Application with assets routes registered."""
-    from app.assets.api.routes import register_assets_system
-    
-    application = web.Application()
-    register_assets_system(application, mock_user_manager)
-    return application
-
-
-@pytest.fixture
-def test_image_bytes() -> bytes:
-    """Generate a minimal valid PNG image (10x10 red pixels)."""
-    from PIL import Image
-    
-    img = Image.new("RGB", (10, 10), color="red")
-    buffer = io.BytesIO()
-    img.save(buffer, format="PNG")
-    return buffer.getvalue()
-
-
-@pytest.fixture
-def tmp_upload_dir(tmp_path):
-    """Create a temporary directory for uploads and patch folder_paths."""
-    upload_dir = tmp_path / "uploads"
-    upload_dir.mkdir()
-    
-    with patch("folder_paths.get_temp_directory", return_value=str(tmp_path)):
-        yield tmp_path
-
-
-@pytest.fixture(autouse=True)
-def patch_create_session(in_memory_engine):
-    """Patch create_session to use our in-memory database."""
-    SessionLocal = sessionmaker(bind=in_memory_engine)
-    
-    with patch("app.database.db.Session", SessionLocal):
-        with patch("app.database.db.create_session", lambda: SessionLocal()):
-            with patch("app.database.db.can_create_session", return_value=True):
-                yield
-
-
-async def test_fixtures_work(db_session, mock_user_manager):
-    """Smoke test to verify fixtures are working."""
-    assert db_session is not None
-    assert mock_user_manager.get_request_user_id(None) == "test-user-123"

tests-unit/assets_test/helpers_test.py

@@ -1,317 +0,0 @@
-"""Tests for app.assets.helpers utility functions."""
-
-import os
-import pytest
-from datetime import datetime, timezone
-from decimal import Decimal
-from unittest.mock import MagicMock
-
-from app.assets.helpers import (
-    normalize_tags,
-    escape_like_prefix,
-    ensure_within_base,
-    get_query_dict,
-    utcnow,
-    project_kv,
-    is_scalar,
-    fast_asset_file_check,
-    list_tree,
-    RootType,
-    ALLOWED_ROOTS,
-)
-
-
-class TestNormalizeTags:
-    def test_lowercases(self):
-        assert normalize_tags(["FOO", "Bar"]) == ["foo", "bar"]
-
-    def test_strips_whitespace(self):
-        assert normalize_tags(["  hello  ", "world "]) == ["hello", "world"]
-
-    def test_does_not_deduplicate(self):
-        result = normalize_tags(["a", "A", "a"])
-        assert result == ["a", "a", "a"]
-
-    def test_none_returns_empty(self):
-        assert normalize_tags(None) == []
-
-    def test_empty_list_returns_empty(self):
-        assert normalize_tags([]) == []
-
-    def test_filters_empty_strings(self):
-        assert normalize_tags(["a", "", "  ", "b"]) == ["a", "b"]
-
-    def test_preserves_order(self):
-        result = normalize_tags(["Z", "A", "z", "B"])
-        assert result == ["z", "a", "z", "b"]
-
-
-class TestEscapeLikePrefix:
-    def test_escapes_percent(self):
-        result, esc = escape_like_prefix("50%")
-        assert result == "50!%"
-        assert esc == "!"
-
-    def test_escapes_underscore(self):
-        result, esc = escape_like_prefix("file_name")
-        assert result == "file!_name"
-        assert esc == "!"
-
-    def test_escapes_escape_char(self):
-        result, esc = escape_like_prefix("a!b")
-        assert result == "a!!b"
-        assert esc == "!"
-
-    def test_normal_string_unchanged(self):
-        result, esc = escape_like_prefix("hello")
-        assert result == "hello"
-        assert esc == "!"
-
-    def test_complex_string(self):
-        result, esc = escape_like_prefix("50%_!x")
-        assert result == "50!%!_!!x"
-
-    def test_custom_escape_char(self):
-        result, esc = escape_like_prefix("50%", escape="\\")
-        assert result == "50\\%"
-        assert esc == "\\"
-
-
-class TestEnsureWithinBase:
-    def test_valid_path_within_base(self, tmp_path):
-        base = str(tmp_path)
-        candidate = str(tmp_path / "subdir" / "file.txt")
-        ensure_within_base(candidate, base)
-
-    def test_path_traversal_rejected(self, tmp_path):
-        base = str(tmp_path / "safe")
-        candidate = str(tmp_path / "safe" / ".." / "unsafe")
-        with pytest.raises(ValueError, match="escapes base directory|invalid destination"):
-            ensure_within_base(candidate, base)
-
-    def test_completely_outside_path_rejected(self, tmp_path):
-        base = str(tmp_path / "safe")
-        candidate = "/etc/passwd"
-        with pytest.raises(ValueError):
-            ensure_within_base(candidate, base)
-
-    def test_same_path_is_valid(self, tmp_path):
-        base = str(tmp_path)
-        ensure_within_base(base, base)
-
-
-class TestGetQueryDict:
-    def test_single_values(self):
-        request = MagicMock()
-        request.query.keys.return_value = ["a", "b"]
-        request.query.get.side_effect = lambda k: {"a": "1", "b": "2"}[k]
-        request.query.getall.side_effect = lambda k: [{"a": "1", "b": "2"}[k]]
-
-        result = get_query_dict(request)
-        assert result == {"a": "1", "b": "2"}
-
-    def test_multiple_values_same_key(self):
-        request = MagicMock()
-        request.query.keys.return_value = ["tags"]
-        request.query.get.return_value = "tag1"
-        request.query.getall.return_value = ["tag1", "tag2", "tag3"]
-
-        result = get_query_dict(request)
-        assert result == {"tags": ["tag1", "tag2", "tag3"]}
-
-    def test_empty_query(self):
-        request = MagicMock()
-        request.query.keys.return_value = []
-
-        result = get_query_dict(request)
-        assert result == {}
-
-
-class TestUtcnow:
-    def test_returns_datetime(self):
-        result = utcnow()
-        assert isinstance(result, datetime)
-
-    def test_no_tzinfo(self):
-        result = utcnow()
-        assert result.tzinfo is None
-
-    def test_is_approximately_now(self):
-        before = datetime.now(timezone.utc).replace(tzinfo=None)
-        result = utcnow()
-        after = datetime.now(timezone.utc).replace(tzinfo=None)
-        assert before <= result <= after
-
-
-class TestIsScalar:
-    def test_none_is_scalar(self):
-        assert is_scalar(None) is True
-
-    def test_bool_is_scalar(self):
-        assert is_scalar(True) is True
-        assert is_scalar(False) is True
-
-    def test_int_is_scalar(self):
-        assert is_scalar(42) is True
-
-    def test_float_is_scalar(self):
-        assert is_scalar(3.14) is True
-
-    def test_decimal_is_scalar(self):
-        assert is_scalar(Decimal("10.5")) is True
-
-    def test_str_is_scalar(self):
-        assert is_scalar("hello") is True
-
-    def test_list_is_not_scalar(self):
-        assert is_scalar([1, 2, 3]) is False
-
-    def test_dict_is_not_scalar(self):
-        assert is_scalar({"a": 1}) is False
-
-
-class TestProjectKv:
-    def test_none_value(self):
-        result = project_kv("key", None)
-        assert len(result) == 1
-        assert result[0]["key"] == "key"
-        assert result[0]["ordinal"] == 0
-        assert result[0]["val_str"] is None
-        assert result[0]["val_num"] is None
-
-    def test_string_value(self):
-        result = project_kv("name", "test")
-        assert len(result) == 1
-        assert result[0]["val_str"] == "test"
-
-    def test_int_value(self):
-        result = project_kv("count", 42)
-        assert len(result) == 1
-        assert result[0]["val_num"] == Decimal("42")
-
-    def test_float_value(self):
-        result = project_kv("ratio", 3.14)
-        assert len(result) == 1
-        assert result[0]["val_num"] == Decimal("3.14")
-
-    def test_bool_value(self):
-        result = project_kv("enabled", True)
-        assert len(result) == 1
-        assert result[0]["val_bool"] is True
-
-    def test_list_of_strings(self):
-        result = project_kv("tags", ["a", "b", "c"])
-        assert len(result) == 3
-        assert result[0]["ordinal"] == 0
-        assert result[0]["val_str"] == "a"
-        assert result[1]["ordinal"] == 1
-        assert result[1]["val_str"] == "b"
-        assert result[2]["ordinal"] == 2
-        assert result[2]["val_str"] == "c"
-
-    def test_list_of_mixed_scalars(self):
-        result = project_kv("mixed", [1, "two", True])
-        assert len(result) == 3
-        assert result[0]["val_num"] == Decimal("1")
-        assert result[1]["val_str"] == "two"
-        assert result[2]["val_bool"] is True
-
-    def test_list_with_none(self):
-        result = project_kv("items", ["a", None, "b"])
-        assert len(result) == 3
-        assert result[1]["val_str"] is None
-        assert result[1]["val_num"] is None
-
-    def test_dict_value_stored_as_json(self):
-        result = project_kv("meta", {"nested": "value"})
-        assert len(result) == 1
-        assert result[0]["val_json"] == {"nested": "value"}
-
-    def test_list_of_dicts_stored_as_json(self):
-        result = project_kv("items", [{"a": 1}, {"b": 2}])
-        assert len(result) == 2
-        assert result[0]["val_json"] == {"a": 1}
-        assert result[1]["val_json"] == {"b": 2}
-
-
-class TestFastAssetFileCheck:
-    def test_none_mtime_returns_false(self):
-        stat = MagicMock()
-        assert fast_asset_file_check(mtime_db=None, size_db=100, stat_result=stat) is False
-
-    def test_matching_mtime_and_size(self):
-        stat = MagicMock()
-        stat.st_mtime_ns = 1234567890123456789
-        stat.st_size = 100
-
-        result = fast_asset_file_check(
-            mtime_db=1234567890123456789,
-            size_db=100,
-            stat_result=stat
-        )
-        assert result is True
-
-    def test_mismatched_mtime(self):
-        stat = MagicMock()
-        stat.st_mtime_ns = 9999999999999999999
-        stat.st_size = 100
-
-        result = fast_asset_file_check(
-            mtime_db=1234567890123456789,
-            size_db=100,
-            stat_result=stat
-        )
-        assert result is False
-
-    def test_mismatched_size(self):
-        stat = MagicMock()
-        stat.st_mtime_ns = 1234567890123456789
-        stat.st_size = 200
-
-        result = fast_asset_file_check(
-            mtime_db=1234567890123456789,
-            size_db=100,
-            stat_result=stat
-        )
-        assert result is False
-
-    def test_zero_size_skips_size_check(self):
-        stat = MagicMock()
-        stat.st_mtime_ns = 1234567890123456789
-        stat.st_size = 999
-
-        result = fast_asset_file_check(
-            mtime_db=1234567890123456789,
-            size_db=0,
-            stat_result=stat
-        )
-        assert result is True
-
-
-class TestListTree:
-    def test_lists_files_in_directory(self, tmp_path):
-        (tmp_path / "file1.txt").touch()
-        (tmp_path / "file2.txt").touch()
-        subdir = tmp_path / "subdir"
-        subdir.mkdir()
-        (subdir / "file3.txt").touch()
-
-        result = list_tree(str(tmp_path))
-        assert len(result) == 3
-        assert all(os.path.isabs(p) for p in result)
-        assert str(tmp_path / "file1.txt") in result
-        assert str(tmp_path / "subdir" / "file3.txt") in result
-
-    def test_nonexistent_directory_returns_empty(self):
-        result = list_tree("/nonexistent/path/that/does/not/exist")
-        assert result == []
-
-
-class TestRootType:
-    def test_allowed_roots_contains_expected_values(self):
-        assert "models" in ALLOWED_ROOTS
-        assert "input" in ALLOWED_ROOTS
-        assert "output" in ALLOWED_ROOTS
-
-    def test_allowed_roots_is_tuple(self):
-        assert isinstance(ALLOWED_ROOTS, tuple)