Add io.List input

README.md

@@ -364,7 +364,7 @@ For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step
 | Flag | Description |
 |------|-------------|
 | `--enable-manager` | Enable ComfyUI-Manager |
-| `--enable-manager-legacy-ui` | Use the legacy manager UI instead of the new UI (implies `--enable-manager`) |
+| `--enable-manager-legacy-ui` | Use the legacy manager UI instead of the new UI (requires `--enable-manager`) |
 | `--disable-manager-ui` | Disable the manager UI and endpoints while keeping background features like security checks and scheduled installation completion (requires `--enable-manager`) |
 
 

comfy/cli_args.py

@@ -133,7 +133,7 @@ upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disabl
 parser.add_argument("--enable-manager", action="store_true", help="Enable the ComfyUI-Manager feature.")
 manager_group = parser.add_mutually_exclusive_group()
 manager_group.add_argument("--disable-manager-ui", action="store_true", help="Disables only the ComfyUI-Manager UI and endpoints. Scheduled installations and similar background tasks will still operate.")
-manager_group.add_argument("--enable-manager-legacy-ui", action="store_true", help="Enables the legacy UI of ComfyUI-Manager. Implies --enable-manager.")
+manager_group.add_argument("--enable-manager-legacy-ui", action="store_true", help="Enables the legacy UI of ComfyUI-Manager")
 
 
 vram_group = parser.add_mutually_exclusive_group()
@@ -258,10 +258,6 @@ if args.disable_auto_launch:
 if args.force_fp16:
     args.fp16_unet = True
 
-# '--enable-manager-legacy-ui' is meaningless unless the manager is enabled, so imply '--enable-manager'.
-if args.enable_manager_legacy_ui:
-    args.enable_manager = True
-
 
 # '--fast' is not provided, use an empty set
 if args.fast is None:

comfy_api/latest/_io.py

@@ -1253,6 +1253,140 @@ class DynamicSlot(ComfyTypeI):
             out_dict[input_type][finalized_id] = value
             out_dict["dynamic_paths"][finalized_id] = finalize_prefix(curr_prefix, curr_prefix[-1])
 
+@comfytype(io_type="COMFY_LIST_V3")
+class List(ComfyTypeI):
+    """A repeatable group of widget inputs (e.g. lora_name + strength stacked into N rows).
+
+    At execution time the node receives a ``list[dict]`` where each element is a row.
+
+    Example::
+
+        io.List.Input(
+            "loras",
+            template=[
+                io.Combo.Input("lora_name", options=folder_paths.get_filename_list("loras")),
+                io.Float.Input("strength", default=1.0, min=-100, max=100, step=0.01),
+            ],
+            min=0,
+            max=50,
+        )
+        # execute receives: loras: list[dict] = [{"lora_name": "x.safetensors", "strength": 1.0}, ...]
+    """
+
+    Type = list[dict[str, Any]]
+    _MaxRows = 100
+
+    class Input(DynamicInput):
+        def __init__(
+            self,
+            id: str,
+            template: list["Input"],
+            min: int = 0,
+            max: int = 50,
+            display_name: str = None,
+            optional: bool = False,
+            tooltip: str = None,
+            lazy: bool = None,
+            extra_dict=None,
+        ):
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+            # Validate template entries: only WidgetInput subclasses, no nesting
+            assert len(template) > 0, "List template must have at least one field."
+            for t in template:
+                assert isinstance(t, WidgetInput), (
+                    f"List template field '{t.id}' must be a WidgetInput subclass "
+                    f"(Combo, Float, Int, String, Boolean, Color). Got {type(t).__name__}."
+                )
+                assert not isinstance(t, DynamicInput), (
+                    f"List template field '{t.id}' must not be a DynamicInput. "
+                    "Nesting dynamic inputs inside List is not supported."
+                )
+            # Enforce unique field ids within template
+            field_ids = [t.id for t in template]
+            assert len(field_ids) == len(set(field_ids)), (
+                f"List template field ids must be unique within a row. Got: {field_ids}"
+            )
+            assert min >= 0, "List min must be >= 0."
+            assert max >= 1, "List max must be >= 1."
+            assert max <= List._MaxRows, f"List max must be <= {List._MaxRows}."
+            assert min <= max, "List min must be <= max."
+            self.template = template
+            self.min = min
+            self.max = max
+
+        def get_all(self) -> list["Input"]:
+            return [self] + list(self.template)
+
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
+                "template": create_input_dict_v1(self.template),
+                "min": self.min,
+                "max": self.max,
+            })
+
+        def validate(self):
+            for t in self.template:
+                t.validate()
+
+    @staticmethod
+    def _expand_schema_for_dynamic(
+        out_dict: dict[str, Any],
+        live_inputs: dict[str, Any],
+        value: tuple[str, dict[str, Any]],
+        input_type: str,
+        curr_prefix: list[str] | None,
+    ):
+        info = value[1]
+        min_rows: int = info.get("min", 0)
+        template: dict[str, Any] = info.get("template", {})
+
+        # Collect all template field specs across required/optional sections
+        field_specs: list[tuple[str, tuple[str, dict[str, Any]], bool]] = []
+        for field_required_key in ("required", "optional"):
+            section = template.get(field_required_key, {})
+            is_required_field = field_required_key == "required"
+            for field_id, field_value in section.items():
+                field_specs.append((field_id, field_value, is_required_field))
+
+        # Determine how many rows are currently present by scanning live_inputs
+        finalized_prefix = finalize_prefix(curr_prefix)
+        present_rows = 0
+        for live_key in live_inputs:
+            # Keys look like "<prefix>.<row>.<field_id>"
+            if live_key.startswith(finalized_prefix + "."):
+                remainder = live_key[len(finalized_prefix) + 1:]
+                parts = remainder.split(".", 1)
+                if len(parts) >= 1:
+                    try:
+                        row_idx = int(parts[0])
+                        present_rows = max(present_rows, row_idx + 1)
+                    except ValueError:
+                        pass
+
+        row_count = max(min_rows, present_rows)
+
+        for row in range(row_count):
+            for field_id, field_value, is_required_field in field_specs:
+                slot_id = f"{finalized_prefix}.{row}.{field_id}"
+                # The first `min_rows` rows are required if the field itself is required
+                if row < min_rows and is_required_field:
+                    out_dict["required"][slot_id] = field_value
+                else:
+                    out_dict["optional"][slot_id] = field_value
+                # Register into dynamic_paths so build_nested_inputs places value at the right path
+                out_dict["dynamic_paths"][slot_id] = slot_id
+
+        # Track the list root path so build_nested_inputs can convert the index dict to a list
+        out_dict.setdefault("list_paths", set()).add(finalized_prefix)
+
+        # Handle the empty case (0 rows) – emit an empty-list default for the parent.
+        # This must only fire when there are genuinely no rows; otherwise the parent
+        # path would clobber the per-row dict built from the slot ids above.
+        if row_count == 0:
+            out_dict["dynamic_paths"][finalized_prefix] = finalized_prefix
+            out_dict["dynamic_paths_default_value"][finalized_prefix] = DynamicPathsDefaultValue.EMPTY_LIST
+
+
 @comfytype(io_type="IMAGECOMPARE")
 class ImageCompare(ComfyTypeI):
   Type = dict
@@ -1383,6 +1517,8 @@ def setup_dynamic_input_funcs():
     register_dynamic_input_func(DynamicCombo.io_type, DynamicCombo._expand_schema_for_dynamic)
     # DynamicSlot.Input
     register_dynamic_input_func(DynamicSlot.io_type, DynamicSlot._expand_schema_for_dynamic)
+    # List.Input
+    register_dynamic_input_func(List.io_type, List._expand_schema_for_dynamic)
 
 if len(DYNAMIC_INPUT_LOOKUP) == 0:
     setup_dynamic_input_funcs()
@@ -1394,6 +1530,8 @@ class V3Data(TypedDict):
     '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.'
+    list_paths: set[str]
+    'Set of top-level keys whose index-keyed dict values should be converted to a sorted list[dict] after build_nested_inputs runs.'
     create_dynamic_tuple: bool
     'When True, the value of the dynamic input will be in the format (value, path_key).'
 
@@ -1727,6 +1865,7 @@ def get_finalized_class_inputs(d: dict[str, Any], live_inputs: dict[str, Any], i
         "optional": {},
         "dynamic_paths": {},
         "dynamic_paths_default_value": {},
+        "list_paths": set(),
     }
     d = d.copy()
     # ignore hidden for parsing
@@ -1742,6 +1881,10 @@ def get_finalized_class_inputs(d: dict[str, Any], live_inputs: dict[str, Any], i
     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
+    # list_paths: keys whose nested dict should be post-converted to a sorted list[dict]
+    list_paths = out_dict.pop("list_paths", None)
+    if list_paths:
+        v3_data["list_paths"] = list_paths
     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:
@@ -1777,10 +1920,12 @@ def add_to_dict_v1(i: Input, d: dict):
 
 class DynamicPathsDefaultValue:
     EMPTY_DICT = "empty_dict"
+    EMPTY_LIST = "empty_list"
 
 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", {})
+    list_paths: set[str] = v3_data.get("list_paths", set()) or set()
     if paths is None:
         return values
     values = values.copy()
@@ -1803,6 +1948,8 @@ def build_nested_inputs(values: dict[str, Any], v3_data: V3Data):
                     default_option = default_value_dict.get(key, None)
                     if default_option == DynamicPathsDefaultValue.EMPTY_DICT:
                         value = {}
+                    elif default_option == DynamicPathsDefaultValue.EMPTY_LIST:
+                        value = []
                 if create_tuple:
                     value = (value, key)
                 current[p] = value
@@ -1810,6 +1957,34 @@ def build_nested_inputs(values: dict[str, Any], v3_data: V3Data):
                 current = current.setdefault(p, {})
 
     values.update(result)
+
+    # Post-pass: convert index-keyed dicts to sorted lists for io.List fields
+    for list_path in list_paths:
+        parts = list_path.split(".")
+        # Navigate to the parent container, then convert the leaf
+        container = values
+        for part in parts[:-1]:
+            if not isinstance(container, dict) or part not in container:
+                container = None
+                break
+            container = container[part]
+        if container is None:
+            continue
+        leaf_key = parts[-1]
+        leaf = container.get(leaf_key, None)
+        if isinstance(leaf, dict):
+            try:
+                sorted_rows = [leaf[k] for k in sorted(leaf.keys(), key=int)]
+                container[leaf_key] = sorted_rows
+            except (ValueError, TypeError):
+                # Keys are not all integers; leave as-is
+                pass
+        elif isinstance(leaf, list):
+            # Already a list (e.g. the EMPTY_LIST default was applied above)
+            pass
+        elif leaf is None:
+            container[leaf_key] = []
+
     return values
 
 
@@ -2372,7 +2547,9 @@ __all__ = [
     # Dynamic Types
     "MatchType",
     "DynamicCombo",
+    "DynamicSlot",
     "Autogrow",
+    "List",
     # Other classes
     "HiddenHolder",
     "Hidden",

comfy_api_nodes/apis/__init__.py

@@ -1310,6 +1310,13 @@ class KlingTaskStatus(str, Enum):
     failed = 'failed'
 
 
+class KlingTextToVideoModelName(str, Enum):
+    kling_v1 = 'kling-v1'
+    kling_v1_6 = 'kling-v1-6'
+    kling_v2_1_master = 'kling-v2-1-master'
+    kling_v2_5_turbo = 'kling-v2-5-turbo'
+
+
 class KlingVideoGenAspectRatio(str, Enum):
     field_16_9 = '16:9'
     field_9_16 = '9:16'
@@ -5172,7 +5179,7 @@ class KlingText2VideoRequest(BaseModel):
     duration: Optional[KlingVideoGenDuration] = '5'
     external_task_id: Optional[str] = Field(None, description='Customized Task ID')
     mode: Optional[KlingVideoGenMode] = 'std'
-    model_name: Optional[str] = 'kling-v1'
+    model_name: Optional[KlingTextToVideoModelName] = 'kling-v1'
     negative_prompt: Optional[str] = Field(
         None, description='Negative text prompt', max_length=2500
     )

comfy_api_nodes/nodes_kling.py

@@ -436,7 +436,7 @@ async def execute_text2video(
             negative_prompt=negative_prompt if negative_prompt else None,
             duration=KlingVideoGenDuration(duration),
             mode=KlingVideoGenMode(model_mode),
-            model_name=model_name,
+            model_name=KlingVideoGenModelName(model_name),
             cfg_scale=cfg_scale,
             aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio),
             camera_control=camera_control,

tests-unit/comfy_api_test/io_list_test.py

@@ -0,0 +1,204 @@
+"""Unit tests for io.List: expansion/reconstruction (0-row and N-row cases)."""
+import sys
+import types
+import pytest
+
+# Stub torch (type-hint only in _io.py; real torch not available in unit-test env)
+if "torch" not in sys.modules:
+    _torch_stub = types.ModuleType("torch")
+    _torch_stub.Tensor = object  # type: ignore[attr-defined]
+    sys.modules["torch"] = _torch_stub
+
+from comfy_api.latest._io import (  # noqa: E402
+    List,
+    Float,
+    Int,
+    String,
+    Boolean,
+    get_finalized_class_inputs,
+    build_nested_inputs,
+    create_input_dict_v1,
+    setup_dynamic_input_funcs,
+)
+
+# Make sure dynamic input funcs are registered (may already be done at import time)
+setup_dynamic_input_funcs()
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _make_class_inputs(list_input: List.Input) -> dict:
+    """Wrap a List.Input into the required/optional dict structure."""
+    return create_input_dict_v1([list_input])
+
+
+def _run(list_input: List.Input, live_values: dict) -> dict:
+    """End-to-end helper: expand schema + reconstruct values.
+
+    Mirrors the production split in execution.py:
+      1. get_finalized_class_inputs  (schema expansion, line 162)
+      2. build_nested_inputs          (value reconstruction, line 281)
+
+    The two steps are separate in production because the engine resolves
+    linked node outputs between them, but in tests we supply values directly.
+    """
+    class_inputs = _make_class_inputs(list_input)
+    _, _, v3_data = get_finalized_class_inputs(class_inputs, live_values)
+    return build_nested_inputs(dict(live_values), v3_data)
+
+
+# ---------------------------------------------------------------------------
+# Schema construction
+# ---------------------------------------------------------------------------
+
+class TestListInputConstruction:
+    def test_basic_construction(self):
+        inp = List.Input(
+            "loras",
+            template=[
+                Float.Input("strength", default=1.0),
+                String.Input("name"),
+            ],
+            min=0,
+            max=10,
+        )
+        assert inp.id == "loras"
+        assert inp.min == 0
+        assert inp.max == 10
+        assert len(inp.template) == 2
+
+    def test_get_all_includes_self_and_template(self):
+        inp = List.Input(
+            "items",
+            template=[Float.Input("value")],
+        )
+        all_inputs = inp.get_all()
+        assert all_inputs[0] is inp
+        assert all_inputs[1].id == "value"
+
+    def test_as_dict_has_template_min_max(self):
+        inp = List.Input(
+            "items",
+            template=[Float.Input("val", default=0.5)],
+            min=1,
+            max=5,
+        )
+        d = inp.as_dict()
+        assert "template" in d
+        assert d["min"] == 1
+        assert d["max"] == 5
+
+    def test_duplicate_field_ids_raises(self):
+        with pytest.raises(AssertionError):
+            List.Input(
+                "bad",
+                template=[Float.Input("x"), Float.Input("x")],
+            )
+
+    def test_empty_template_raises(self):
+        with pytest.raises(AssertionError):
+            List.Input("bad", template=[])
+
+    def test_min_gt_max_raises(self):
+        with pytest.raises(AssertionError):
+            List.Input("bad", template=[Float.Input("x")], min=5, max=3)
+
+    def test_max_exceeds_limit_raises(self):
+        with pytest.raises(AssertionError):
+            List.Input("bad", template=[Float.Input("x")], max=101)
+
+    def test_dynamic_input_in_template_raises(self):
+        with pytest.raises(AssertionError):
+            List.Input(
+                "bad",
+                template=[List.Input("nested", template=[Float.Input("x")])],
+            )
+
+    def test_validate_calls_through(self):
+        inp = List.Input("items", template=[Float.Input("val", min=-1.0, max=1.0)])
+        inp.validate()  # should not raise
+
+
+# ---------------------------------------------------------------------------
+# 0-row case
+# ---------------------------------------------------------------------------
+
+class TestZeroRows:
+    def test_empty_live_inputs_produces_empty_list(self):
+        """With min=0 and no live values, the result should be an empty list."""
+        inp = List.Input("loras", template=[Float.Input("strength", default=1.0)], min=0, max=10)
+        assert _run(inp, {}).get("loras") == []
+
+    def test_min_zero_with_values(self):
+        """min=0 but 2 rows of live data."""
+        inp = List.Input("loras", template=[Float.Input("strength", default=1.0)], min=0, max=10)
+        result = _run(inp, {"loras.0.strength": 0.8, "loras.1.strength": 0.5})
+        assert result["loras"] == [{"strength": 0.8}, {"strength": 0.5}]
+
+
+# ---------------------------------------------------------------------------
+# N-row case
+# ---------------------------------------------------------------------------
+
+class TestNRows:
+    def test_two_rows_two_fields(self):
+        """Two rows with two fields each produce a list[dict]."""
+        inp = List.Input(
+            "loras",
+            template=[String.Input("lora_name"), Float.Input("strength", default=1.0)],
+            min=0, max=50,
+        )
+        result = _run(inp, {
+            "loras.0.lora_name": "model_a.safetensors", "loras.0.strength": 0.9,
+            "loras.1.lora_name": "model_b.safetensors", "loras.1.strength": 0.4,
+        })
+        assert result["loras"] == [
+            {"lora_name": "model_a.safetensors", "strength": 0.9},
+            {"lora_name": "model_b.safetensors", "strength": 0.4},
+        ]
+
+    def test_rows_are_sorted_by_index(self):
+        """Rows must be in ascending index order even if dict iteration is unordered."""
+        inp = List.Input("items", template=[Int.Input("v", default=0)], min=0, max=10)
+        result = _run(inp, {"items.0.v": 10, "items.2.v": 30, "items.1.v": 20})
+        assert [row["v"] for row in result["items"]] == [10, 20, 30]
+
+    def test_min_rows_schema_slots(self):
+        """With min=2 and no live data, 2 slots must appear in the expanded schema."""
+        inp = List.Input("items", template=[Float.Input("val", default=0.0)], min=2, max=5)
+        out, _, _ = get_finalized_class_inputs(_make_class_inputs(inp), {})
+        all_slots = {**out.get("required", {}), **out.get("optional", {})}
+        assert "items.0.val" in all_slots
+        assert "items.1.val" in all_slots
+
+    def test_min_rows_reconstructs_when_no_values(self):
+        """min=2 with NO live values must still yield a 2-element list,
+        not collapse to [] (regression: parent-path clobber)."""
+        inp = List.Input("items", template=[Float.Input("val", default=0.0)], min=2, max=5)
+        result = _run(inp, {})
+        assert len(result["items"]) == 2
+        assert all("val" in row for row in result["items"])
+
+    def test_min_rows_reconstructs_with_partial_values(self):
+        """min=2 with only the first row's value present still yields 2 rows."""
+        inp = List.Input("items", template=[Float.Input("val", default=0.0)], min=2, max=5)
+        result = _run(inp, {"items.0.val": 0.7})
+        assert len(result["items"]) == 2
+        assert result["items"][0]["val"] == 0.7
+        assert result["items"][1]["val"] is None
+
+    def test_list_paths_in_v3_data(self):
+        """list_paths must contain the list id so build_nested_inputs knows to convert."""
+        inp = List.Input("things", template=[Boolean.Input("flag")], min=0, max=5)
+        _, _, v3_data = get_finalized_class_inputs(_make_class_inputs(inp), {})
+        assert "things" in v3_data.get("list_paths", set())
+
+    def test_no_leftover_flat_keys(self):
+        """Flat keys must be consumed; only the reconstructed list remains."""
+        inp = List.Input("rows", template=[Float.Input("x", default=0.0)], min=0, max=5)
+        result = _run(inp, {"rows.0.x": 1.0, "rows.1.x": 2.0})
+        assert "rows.0.x" not in result
+        assert "rows.1.x" not in result
+        assert isinstance(result["rows"], list)