Merge branch 'master' into LoraNodes

comfy/cli_args.py

@@ -240,7 +240,6 @@ database_default_path = os.path.abspath(
 )
 parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
 parser.add_argument("--enable-assets", action="store_true", help="Enable the assets system (API routes, database synchronization, and background scanning).")
-parser.add_argument("--enable-asset-hashing", action="store_true", help="Compute blake3 content hashes when scanning assets. Hashing enables future asset-portability features (deduplication, cross-machine model resolution) but adds startup cost and per-output cost on large models directories. Off by default; enable to opt in.")
 parser.add_argument("--feature-flag", type=str, action='append', default=[], metavar="KEY[=VALUE]", help="Set a server feature flag. Use KEY=VALUE to set an explicit value, or bare KEY to set it to true. Can be specified multiple times. Boolean values (true/false) and numbers are auto-converted. Examples: --feature-flag show_signin_button=true  or  --feature-flag show_signin_button")
 parser.add_argument("--list-feature-flags", action="store_true", help="Print the registry of known CLI-settable feature flags as JSON and exit.")
 

comfy_api/latest/_io.py

@@ -1261,6 +1261,158 @@ 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_DYNAMICGROUP_V3")
+class DynamicGroup(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.DynamicGroup.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,
+            group_name: str = "Group",
+        ):
+            super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+            # Validate template entries: only WidgetInput subclasses, no nesting
+            assert len(template) > 0, "DynamicGroup template must have at least one field."
+            for t in template:
+                assert isinstance(t, WidgetInput), (
+                    f"DynamicGroup 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"DynamicGroup template field '{t.id}' must not be a DynamicInput. "
+                    "Nesting dynamic inputs inside DynamicGroup 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"DynamicGroup template field ids must be unique within a row. Got: {field_ids}"
+            )
+            # Reject "." in group id and template field ids: slot_id encoding uses "." as a
+            # delimiter (<group_id>.<row>.<field_id>), so any "." in these names would cause
+            # path.split(".") to produce the wrong number of segments during decoding.
+            assert "." not in id, (
+                f"DynamicGroup id must not contain '.'. Got: '{id}'"
+            )
+            for t in template:
+                assert "." not in t.id, (
+                    f"DynamicGroup template field id must not contain '.'. Got: '{t.id}'"
+                )
+            assert min >= 0, "DynamicGroup min must be >= 0."
+            assert max >= 1, "DynamicGroup max must be >= 1."
+            assert max <= DynamicGroup._MaxRows, f"DynamicGroup max must be <= {DynamicGroup._MaxRows}."
+            assert min <= max, "DynamicGroup min must be <= max."
+            self.template = template
+            self.min = min
+            self.max = max
+            self.group_name = group_name
+
+        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,
+                "group_name": self.group_name,
+            })
+
+        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)
+        max_rows: int = info.get("max", DynamicGroup._MaxRows)
+        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
+
+        if present_rows > max_rows:
+            raise ValueError(
+                f"DynamicGroup input '{finalized_prefix}' received {present_rows} rows but max is {max_rows}."
+            )
+        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
@@ -1418,6 +1570,8 @@ 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)
+    # DynamicGroup.Input
+    register_dynamic_input_func(DynamicGroup.io_type, DynamicGroup._expand_schema_for_dynamic)
 
 if len(DYNAMIC_INPUT_LOOKUP) == 0:
     setup_dynamic_input_funcs()
@@ -1429,6 +1583,8 @@ 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).'
 
@@ -1770,6 +1926,7 @@ 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
@@ -1785,6 +1942,10 @@ 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:
@@ -1820,10 +1981,12 @@ 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()
@@ -1846,6 +2009,8 @@ 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
@@ -1853,6 +2018,34 @@ 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.DynamicGroup 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
 
 
@@ -2417,7 +2610,9 @@ __all__ = [
     # Dynamic Types
     "MatchType",
     "DynamicCombo",
+    "DynamicSlot",
     "Autogrow",
+    "DynamicGroup",
     # Other classes
     "HiddenHolder",
     "Hidden",

comfy_api_nodes/apis/gemini.py

@@ -121,7 +121,6 @@ class GeminiGenerationConfig(BaseModel):
     topK: int | None = Field(None, ge=1)
     topP: float | None = Field(None, ge=0.0, le=1.0)
     thinkingConfig: GeminiThinkingConfig | None = Field(None)
-    responseModalities: list[str] | None = Field(None)
 
 
 class GeminiImageOutputOptions(BaseModel):

comfy_api_nodes/nodes_gemini.py

@@ -13,7 +13,7 @@ import torch
 from typing_extensions import override
 
 import folder_paths
-from comfy_api.latest import IO, ComfyExtension, Input, InputImpl, Types
+from comfy_api.latest import IO, ComfyExtension, Input, Types
 from comfy_api_nodes.apis.gemini import (
     GeminiContent,
     GeminiFileData,
@@ -37,7 +37,6 @@ from comfy_api_nodes.util import (
     audio_to_base64_string,
     bytesio_to_image_tensor,
     download_url_to_image_tensor,
-    download_url_to_video_output,
     get_number_of_images,
     sync_op,
     tensor_to_base64_string,
@@ -46,7 +45,6 @@ from comfy_api_nodes.util import (
     upload_images_to_comfyapi,
     upload_video_to_comfyapi,
     validate_string,
-    validate_video_duration,
     video_to_base64_string,
 )
 
@@ -231,29 +229,10 @@ async def get_image_from_response(response: GeminiGenerateContentResponse, thoug
     return torch.cat(image_tensors, dim=0)
 
 
-async def get_video_from_response(
-    response: GeminiGenerateContentResponse, cls: type[IO.ComfyNode] | None = None
-) -> InputImpl.VideoFromFile:
-    parts = get_parts_by_type(response, "video/*")
-    for part in parts:
-        if part.inlineData and part.inlineData.data:
-            return InputImpl.VideoFromFile(BytesIO(base64.b64decode(part.inlineData.data)))
-        if part.fileData and part.fileData.fileUri:
-            return await download_url_to_video_output(part.fileData.fileUri, cls=cls)
-    model_message = get_text_from_response(response).strip()
-    if model_message:
-        raise ValueError(f"Gemini did not generate a video. Model response: {model_message}")
-    raise ValueError(
-        "Gemini did not generate a video. Try rephrasing your prompt, "
-        "shortening the requested duration, or reducing the number of input images/videos."
-    )
-
-
 def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | None:
     if not response.modelVersion:
         return None
     # Define prices (Cost per 1,000,000 tokens), see https://cloud.google.com/vertex-ai/generative-ai/pricing
-    output_video_tokens_price = 0.0
     if response.modelVersion == "gemini-2.5-pro":
         input_tokens_price = 1.25
         output_text_tokens_price = 10.0
@@ -270,27 +249,18 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
         input_tokens_price = 2
         output_text_tokens_price = 12.0
         output_image_tokens_price = 0.0
-    elif response.modelVersion in ("gemini-3.1-flash-lite-preview", "gemini-3.1-flash-lite"):
+    elif response.modelVersion == "gemini-3.1-flash-lite-preview":
         input_tokens_price = 0.25
         output_text_tokens_price = 1.50
         output_image_tokens_price = 0.0
-    elif response.modelVersion in ("gemini-3-pro-image-preview", "gemini-3-pro-image"):
+    elif response.modelVersion == "gemini-3-pro-image-preview":
         input_tokens_price = 2
         output_text_tokens_price = 12.0
         output_image_tokens_price = 120.0
-    elif response.modelVersion in ("gemini-3.1-flash-image-preview", "gemini-3.1-flash-image"):
+    elif response.modelVersion == "gemini-3.1-flash-image-preview":
         input_tokens_price = 0.5
         output_text_tokens_price = 3.0
         output_image_tokens_price = 60.0
-    elif response.modelVersion == "gemini-3.1-flash-lite-image":
-        input_tokens_price = 0.25
-        output_text_tokens_price = 1.50
-        output_image_tokens_price = 30.0
-    elif response.modelVersion == "gemini-omni-flash-preview":
-        input_tokens_price = 2.145
-        output_text_tokens_price = 12.87
-        output_image_tokens_price = 0.0
-        output_video_tokens_price = 25.025
     else:
         return None
     final_price = response.usageMetadata.promptTokenCount * input_tokens_price
@@ -298,8 +268,6 @@ def calculate_tokens_price(response: GeminiGenerateContentResponse) -> float | N
         for i in response.usageMetadata.candidatesTokensDetails:
             if i.modality == Modality.IMAGE:
                 final_price += output_image_tokens_price * i.tokenCount  # for Nano Banana models
-            elif i.modality == Modality.VIDEO:
-                final_price += output_video_tokens_price * i.tokenCount  # for Omni Flash
             else:
                 final_price += output_text_tokens_price * i.tokenCount
     if response.usageMetadata.thoughtsTokenCount:
@@ -1334,7 +1302,7 @@ class GeminiNanoBanana2(IO.ComfyNode):
         )
 
 
-def _nano_banana_2_v2_model_inputs(resolutions: list[str]):
+def _nano_banana_2_v2_model_inputs():
     return [
         IO.Combo.Input(
             "aspect_ratio",
@@ -1361,8 +1329,8 @@ def _nano_banana_2_v2_model_inputs(resolutions: list[str]):
         ),
         IO.Combo.Input(
             "resolution",
-            options=resolutions,
-            tooltip="Target output resolution.",
+            options=["1K", "2K", "4K"],
+            tooltip="Target output resolution. For 2K/4K the native Gemini upscaler is used.",
         ),
         IO.Combo.Input(
             "thinking_level",
@@ -1408,11 +1376,7 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
                     options=[
                         IO.DynamicCombo.Option(
                             "Nano Banana 2 (Gemini 3.1 Flash Image)",
-                            _nano_banana_2_v2_model_inputs(resolutions=["1K", "2K", "4K"]),
-                        ),
-                        IO.DynamicCombo.Option(
-                            "Nano Banana 2 Lite",
-                            _nano_banana_2_v2_model_inputs(resolutions=["1K"]),
+                            _nano_banana_2_v2_model_inputs(),
                         ),
                     ],
                 ),
@@ -1481,13 +1445,9 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
                 depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution"]),
                 expr="""
                 (
-                  $contains(widgets.model, "lite")
-                    ? {"type":"usd","usd": 0.034, "format":{"suffix":"/Image","approximate":true}}
-                    : (
-                        $r := $lookup(widgets, "model.resolution");
-                        $prices := {"1k": 0.0696, "2k": 0.1014, "4k": 0.154};
-                        {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}}
-                      )
+                  $r := $lookup(widgets, "model.resolution");
+                  $prices := {"1k": 0.0696, "2k": 0.1014, "4k": 0.154};
+                  {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}}
                 )
                 """,
             ),
@@ -1508,8 +1468,6 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
         model_choice = model["model"]
         if model_choice == "Nano Banana 2 (Gemini 3.1 Flash Image)":
             model_id = "gemini-3.1-flash-image-preview"
-        elif model_choice == "Nano Banana 2 Lite":
-            model_id = "gemini-3.1-flash-lite-image"
         else:
             model_id = model_choice
 
@@ -1559,149 +1517,6 @@ class GeminiNanoBanana2V2(IO.ComfyNode):
         )
 
 
-OMNI_MAX_IMAGES = 14
-OMNI_MAX_VIDEOS = 3
-
-OMNI_MODELS: dict[str, str] = {
-    "Omni Flash": "gemini-omni-flash-preview",
-}
-
-
-def _omni_flash_inputs() -> list[Input]:
-    """Per-model inputs for the Omni video DynamicCombo (prompt + reference media + sampling)."""
-    return [
-        IO.String.Input(
-            "prompt",
-            multiline=True,
-            default="",
-            tooltip="Describe the video to generate. Specify the length and aspect ratio directly in the "
-            'prompt, e.g. "a 6-second clip in 16:9". Length may be 3-10 seconds; the aspect ratio must be '
-            "16:9 (landscape) or 9:16 (portrait). The output is 720p, 24 FPS, with audio.",
-        ),
-        IO.Autogrow.Input(
-            "images",
-            template=IO.Autogrow.TemplateNames(
-                IO.Image.Input("image"),
-                names=[f"image_{i}" for i in range(1, OMNI_MAX_IMAGES + 1)],
-                min=0,
-            ),
-            tooltip=f"Optional reference image(s) to guide or animate the video. Up to {OMNI_MAX_IMAGES} images.",
-        ),
-        IO.Autogrow.Input(
-            "videos",
-            template=IO.Autogrow.TemplateNames(
-                IO.Video.Input("video"),
-                names=[f"video_{i}" for i in range(1, OMNI_MAX_VIDEOS + 1)],
-                min=0,
-            ),
-            tooltip=f"Optional reference video(s) to guide or edit. Up to {OMNI_MAX_VIDEOS} videos, "
-            f"each up to 10 seconds long.",
-        ),
-        IO.Float.Input(
-            "temperature",
-            default=1.0,
-            min=0.0,
-            max=2.0,
-            step=0.01,
-            tooltip="Controls randomness. Lower is more focused/deterministic, higher is more varied.",
-            advanced=True,
-        ),
-        IO.Float.Input(
-            "top_p",
-            default=0.95,
-            min=0.0,
-            max=1.0,
-            step=0.01,
-            tooltip="Nucleus sampling: sample from the smallest token set whose cumulative probability reaches top_p.",
-            advanced=True,
-        ),
-    ]
-
-
-class GeminiVideoOmni(IO.ComfyNode):
-
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="GeminiVideoOmni",
-            display_name="Google Gemini Omni (Video)",
-            category="partner/video/Gemini",
-            essentials_category="Video Generation",
-            description="Generate a video with audio from a text prompt using Google's Gemini Omni Flash model. "
-            "Optionally provide reference images and/or videos to guide or edit the result. Describe the desired "
-            "length (3-10s) and aspect ratio (16:9 or 9:16) directly in the prompt.",
-            inputs=[
-                IO.DynamicCombo.Input(
-                    "model",
-                    options=[
-                        IO.DynamicCombo.Option("Omni Flash", _omni_flash_inputs()),
-                    ],
-                    tooltip="The Gemini video model used to generate the video.",
-                ),
-                IO.Int.Input(
-                    "seed",
-                    default=42,
-                    min=0,
-                    max=2147483647,
-                    control_after_generate=True,
-                    tooltip="Seed controls whether the node should re-run; "
-                    "results are non-deterministic regardless of seed.",
-                ),
-            ],
-            outputs=[
-                IO.Video.Output(),
-                IO.String.Output(),
-            ],
-            hidden=[
-                IO.Hidden.auth_token_comfy_org,
-                IO.Hidden.api_key_comfy_org,
-                IO.Hidden.unique_id,
-            ],
-            is_api_node=True,
-            price_badge=IO.PriceBadge(
-                expr='{"type":"usd","usd":0.146,"format":{"suffix":"/second","approximate":true}}'
-            ),
-        )
-
-    @classmethod
-    async def execute(cls, model: dict, seed: int) -> IO.NodeOutput:
-        prompt = model.get("prompt") or ""
-        validate_string(prompt, strip_whitespace=True, min_length=1)
-        model_id = OMNI_MODELS[model["model"]]
-
-        images = [t for t in (model.get("images") or {}).values() if t is not None]
-        videos = [v for v in (model.get("videos") or {}).values() if v is not None]
-        if sum(get_number_of_images(t) for t in images) > OMNI_MAX_IMAGES:
-            raise ValueError(f"The current maximum number of supported images is {OMNI_MAX_IMAGES}.")
-        if len(videos) > OMNI_MAX_VIDEOS:
-            raise ValueError(f"The current maximum number of supported videos is {OMNI_MAX_VIDEOS}.")
-        for video in videos:
-            validate_video_duration(video, max_duration=10)
-
-        parts: list[GeminiPart] = []
-        if images or videos:
-            parts.extend(await build_gemini_media_parts(cls, images, [], videos))
-        parts.append(GeminiPart(text=prompt))
-        response = await sync_op(
-            cls,
-            ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model_id}", method="POST"),
-            data=GeminiGenerateContentRequest(
-                contents=[GeminiContent(role=GeminiRole.user, parts=parts)],
-                generationConfig=GeminiGenerationConfig(
-                    responseModalities=["TEXT", "VIDEO"],
-                    temperature=model.get("temperature", 1.0),
-                    topP=model.get("top_p", 0.95),
-                ),
-            ),
-            response_model=GeminiGenerateContentResponse,
-            price_extractor=calculate_tokens_price,
-        )
-        return IO.NodeOutput(
-            await get_video_from_response(response, cls=cls),
-            get_text_from_response(response),
-        )
-
-
 class GeminiExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1712,7 +1527,6 @@ class GeminiExtension(ComfyExtension):
             GeminiImage2,
             GeminiNanoBanana2,
             GeminiNanoBanana2V2,
-            GeminiVideoOmni,
             GeminiInputFiles,
         ]
 

comfy_extras/nodes_cond.py

@@ -8,8 +8,7 @@ class CLIPTextEncodeControlnet(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="CLIPTextEncodeControlnet",
-            display_name="CLIP Text Encode (Controlnet)",
-            category="model/conditioning",
+            category="experimental/conditioning",
             inputs=[
                 io.Clip.Input("clip"),
                 io.Conditioning.Input("conditioning"),
@@ -36,12 +35,11 @@ class T5TokenizerOptions(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="T5TokenizerOptions",
-            display_name="T5 Tokenizer Options",
-            category="model/conditioning",
+            category="experimental/conditioning",
             inputs=[
                 io.Clip.Input("clip"),
-                io.Int.Input("min_padding", default=0, min=0, max=10000, step=1),
-                io.Int.Input("min_length", default=0, min=0, max=10000, step=1),
+                io.Int.Input("min_padding", default=0, min=0, max=10000, step=1, advanced=True),
+                io.Int.Input("min_length", default=0, min=0, max=10000, step=1, advanced=True),
             ],
             outputs=[io.Clip.Output()],
             is_experimental=True,

comfy_extras/nodes_custom_sampler.py

@@ -1070,7 +1070,7 @@ class AddNoise(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="AddNoise",
-            category="model/sampling/noise",
+            category="experimental/custom_sampling/noise",
             is_experimental=True,
             inputs=[
                 io.Model.Input("model"),
@@ -1120,7 +1120,7 @@ class ManualSigmas(io.ComfyNode):
         return io.Schema(
             node_id="ManualSigmas",
             search_aliases=["custom noise schedule", "define sigmas"],
-            category="model/sampling/sigmas",
+            category="experimental/custom_sampling",
             is_experimental=True,
             inputs=[
                 io.String.Input("sigmas", default="1, 0.5", multiline=False)

comfy_extras/nodes_lora_stack.py

@@ -0,0 +1,130 @@
+"""LoRA stacking loaders built on io.DynamicGroup.
+
+Two nodes that let you stack any number of LoRAs in a single node, each row
+carrying only a LoRA name and a strength:
+
+  LoadLoraModel
+      Applies a stack of LoRAs to a diffusion MODEL.
+
+  LoadLoraTextEncoder
+      Applies a stack of LoRAs to a CLIP text encoder.
+
+Both are modelled on DynamicGroupLoraStyleTest in nodes_dynamic_group_test.py,
+but operate on real models and real LoRA files.
+"""
+
+from __future__ import annotations
+
+from typing_extensions import override
+
+import comfy.sd
+import comfy.utils
+import folder_paths
+from comfy_api.latest import ComfyExtension, io
+
+# Module-level cache so repeated executions don't re-read the same file from disk.
+_LORA_CACHE: dict[str, tuple] = {}
+
+
+def _load_lora_file(lora_name: str):
+    lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
+    cached = _LORA_CACHE.get(lora_path)
+    if cached is not None:
+        return cached
+    lora, metadata = comfy.utils.load_torch_file(lora_path, safe_load=True, return_metadata=True)
+    _LORA_CACHE[lora_path] = (lora, metadata)
+    return lora, metadata
+
+
+def _lora_template() -> list[io.Input]:
+    return [
+        io.Combo.Input("lora_name", options=folder_paths.get_filename_list("loras"),
+                       tooltip="The name of the LoRA file to apply."),
+        io.Float.Input("strength", default=1.0, min=-100.0, max=100.0, step=0.01,
+                       tooltip="How strongly to apply this LoRA. 0 = off, negative inverts the effect."),
+    ]
+
+
+class LoadLoraModel(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoadLoraModel",
+            display_name="Load LoRA (Model)",
+            search_aliases=["lora", "load lora", "apply lora", "lora model", "lora stack"],
+            category="model/loaders",
+            description="Apply a stack of LoRAs to a diffusion model. Add one row per LoRA; "
+                        "each row picks a LoRA file and its strength.",
+            inputs=[
+                io.Model.Input("model", tooltip="The diffusion model the LoRAs will be applied to."),
+                io.DynamicGroup.Input(
+                    "loras",
+                    template=_lora_template(),
+                    min=1,
+                    max=50,
+                    tooltip="Each row applies one LoRA to the model.",
+                    group_name="LoRA",
+                ),
+            ],
+            outputs=[io.Model.Output(tooltip="The modified diffusion model.")],
+        )
+
+    @classmethod
+    def execute(cls, model, loras: list[dict]) -> io.NodeOutput:
+        for row in loras:
+            lora_name = row.get("lora_name")
+            strength = row.get("strength", 1.0)
+            if not lora_name or lora_name == "none" or strength == 0:
+                continue
+            lora, metadata = _load_lora_file(lora_name)
+            model, _ = comfy.sd.load_lora_for_models(model, None, lora, strength, 0, lora_metadata=metadata)
+        return io.NodeOutput(model)
+
+
+class LoadLoraTextEncoder(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoadLoraTextEncoder",
+            display_name="Load LoRA (Text Encoder)",
+            search_aliases=["lora", "load lora", "apply lora", "clip lora", "lora stack"],
+            category="model/loaders",
+            description="Apply a stack of LoRAs to a CLIP text encoder. Add one row per LoRA; "
+                        "each row picks a LoRA file and its strength.",
+            inputs=[
+                io.Clip.Input("clip", tooltip="The CLIP text encoder the LoRAs will be applied to."),
+                io.DynamicGroup.Input(
+                    "loras",
+                    template=_lora_template(),
+                    min=1,
+                    max=50,
+                    tooltip="Each row applies one LoRA to the text encoder.",
+                    group_name="LoRA",
+                ),
+            ],
+            outputs=[io.Clip.Output(tooltip="The modified CLIP text encoder.")],
+        )
+
+    @classmethod
+    def execute(cls, clip, loras: list[dict]) -> io.NodeOutput:
+        for row in loras:
+            lora_name = row.get("lora_name")
+            strength = row.get("strength", 1.0)
+            if not lora_name or lora_name == "none" or strength == 0:
+                continue
+            lora, metadata = _load_lora_file(lora_name)
+            _, clip = comfy.sd.load_lora_for_models(None, clip, lora, 0, strength, lora_metadata=metadata)
+        return io.NodeOutput(clip)
+
+
+class LoraStackExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            LoadLoraModel,
+            LoadLoraTextEncoder,
+        ]
+
+
+async def comfy_entrypoint() -> LoraStackExtension:
+    return LoraStackExtension()

comfy_extras/nodes_photomaker.py

@@ -123,8 +123,7 @@ class PhotoMakerLoader(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PhotoMakerLoader",
-            display_name="Load PhotoMaker Model",
-            category="model/loaders",
+            category="experimental/photomaker",
             inputs=[
                 io.Combo.Input("photomaker_model_name", options=folder_paths.get_filename_list("photomaker")),
             ],
@@ -150,8 +149,7 @@ class PhotoMakerEncode(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PhotoMakerEncode",
-            display_name="PhotoMaker Encode",
-            category="model/conditioning/photomaker",
+            category="experimental/photomaker",
             inputs=[
                 io.Photomaker.Input("photomaker"),
                 io.Image.Input("image"),

comfy_extras/nodes_stable_cascade.py

@@ -119,7 +119,7 @@ class StableCascade_SuperResolutionControlnet(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StableCascade_SuperResolutionControlnet",
-            category="experimental/stable cascade",
+            category="experimental/stable_cascade",
             is_experimental=True,
             inputs=[
                 io.Image.Input("image"),

comfy_extras/nodes_triposplat.py

@@ -143,7 +143,7 @@ class VAEDecodeTripoSplat(IO.ComfyNode):
         return IO.Schema(
             node_id="VAEDecodeTripoSplat",
             display_name="TripoSplat Decode",
-            category="model/latent/triposplat",
+            category="3d/latent",
             description="Decode the sampled TripoSplat latent into a 3D gaussian splat. "
                         "Modify the number of gaussians to vary the density.",
             inputs=[
@@ -188,7 +188,7 @@ class TripoSplatSamplingPreview(IO.ComfyNode):
         return IO.Schema(
             node_id="TripoSplatSamplingPreview",
             display_name="TripoSplat Sampling Preview",
-            category="model/latent/triposplat",
+            category="3d/latent",
             description="Patch the TripoSplat model for the standard Ksampler node to show a live decoded "
                         "gaussian splat preview at each step.",
             inputs=[

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.27.0"
+__version__ = "0.26.0"

main.py

@@ -403,7 +403,7 @@ def prompt_worker(q, server_instance):
                 hook_breaker_ac10a0.restore_functions()
 
                 if not asset_seeder.is_disabled():
-                    asset_seeder.enqueue_enrich(roots=("output",), compute_hashes=args.enable_asset_hashing)
+                    asset_seeder.enqueue_enrich(roots=("output",), compute_hashes=True)
                 asset_seeder.resume()
 
 
@@ -458,7 +458,7 @@ def setup_database():
         if dependencies_available():
             init_db()
             if args.enable_assets:
-                if asset_seeder.start(roots=("models", "input", "output"), prune_first=True, compute_hashes=args.enable_asset_hashing):
+                if asset_seeder.start(roots=("models", "input", "output"), prune_first=True, compute_hashes=True):
                     logging.info("Background asset scan initiated for models, input, output")
     except Exception as e:
         if "database is locked" in str(e):

nodes.py

@@ -159,29 +159,6 @@ class ConditioningConcat:
 
         return (out, )
 
-class ConditioningMultiply:
-    SEARCH_ALIASES = ["scale conditioning", "scale prompt", "multiply conditioning", "multiply prompt"]
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {"required": {"conditioning": ("CONDITIONING", ),
-                            "multiplier": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01})
-                            }}
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION     = "multiply"
-    CATEGORY     = "model/conditioning/transform"
-
-    def multiply(self, conditioning, multiplier):
-        c = []
-        for t in conditioning:
-            values = {}
-            pooled_output = t[1].get("pooled_output", None)
-            if pooled_output is not None:
-                values["pooled_output"] = pooled_output * multiplier
-            scaled = node_helpers.conditioning_set_values([[t[0] * multiplier, t[1]]], values)[0]
-            c.append(scaled)
-        return (c,)
-
 class ConditioningSetArea:
     SEARCH_ALIASES = ["regional prompt", "area prompt", "spatial conditioning", "localized prompt"]
 
@@ -349,7 +326,7 @@ class VAEDecodeTiled:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "decode"
 
-    CATEGORY = "model/latent"
+    CATEGORY = "experimental"
 
     def decode(self, vae, samples, tile_size, overlap=64, temporal_size=64, temporal_overlap=8):
         if tile_size < overlap * 4:
@@ -396,7 +373,7 @@ class VAEEncodeTiled:
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "encode"
 
-    CATEGORY = "model/latent"
+    CATEGORY = "experimental"
 
     def encode(self, vae, pixels, tile_size, overlap, temporal_size=64, temporal_overlap=8):
         t = vae.encode_tiled(pixels, tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap)
@@ -514,7 +491,7 @@ class SaveLatent:
 
     OUTPUT_NODE = True
 
-    CATEGORY = "model/latent"
+    CATEGORY = "experimental"
 
     def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
@@ -559,7 +536,7 @@ class LoadLatent:
         files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")]
         return {"required": {"latent": [sorted(files), ]}, }
 
-    CATEGORY = "model/latent"
+    CATEGORY = "experimental"
 
     RETURN_TYPES = ("LATENT", )
     FUNCTION = "load"
@@ -2073,7 +2050,6 @@ NODE_CLASS_MAPPINGS = {
     "ConditioningAverage": ConditioningAverage,
     "ConditioningCombine": ConditioningCombine,
     "ConditioningConcat": ConditioningConcat,
-    "ConditioningMultiply": ConditioningMultiply,
     "ConditioningSetArea": ConditioningSetArea,
     "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage,
     "ConditioningSetAreaStrength": ConditioningSetAreaStrength,
@@ -2145,7 +2121,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ConditioningAverage ": "Conditioning (Average)",
     "ConditioningAverage": "Conditioning (Average)",
     "ConditioningConcat": "Conditioning (Concat)",
-    "ConditioningMultiply": "Conditioning (Multiply)",
     "ConditioningSetArea": "Conditioning (Set Area)",
     "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)",
     "ConditioningSetAreaStrength": "Conditioning (Set Area Strength)",
@@ -2155,8 +2130,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "GLIGENTextBoxApply": "Apply GLIGEN Text Box",
     "ConditioningZeroOut": "Conditioning Zero Out",
     # Latent
-    "LoadLatent": "Load Latent",
-    "SaveLatent": "Save Latent",
     "VAEEncodeForInpaint": "VAE Encode (for Inpainting)",
     "SetLatentNoiseMask": "Set Latent Noise Mask",
     "VAEDecode": "VAE Decode",
@@ -2191,6 +2164,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageSharpen": "Sharpen Image",
     "ImageScaleToTotalPixels": "Scale Image to Total Pixels",
     "GetImageSize": "Get Image Size",
+    # experimental
     "VAEDecodeTiled": "VAE Decode (Tiled)",
     "VAEEncodeTiled": "VAE Encode (Tiled)",
 }
@@ -2502,6 +2476,7 @@ async def init_builtin_extra_nodes():
         "nodes_triposplat.py",
         "nodes_depth_anything_3.py",
         "nodes_seed.py",
+        "nodes_lora_stack.py",
     ]
 
     import_failed = []

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.27.0"
+version = "0.26.0"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,5 +1,5 @@
-comfyui-frontend-package==1.45.20
-comfyui-workflow-templates==0.11.1
+comfyui-frontend-package==1.45.19
+comfyui-workflow-templates==0.10.7
 comfyui-embedded-docs==0.5.6
 torch
 torchsde
@@ -22,7 +22,7 @@ alembic
 SQLAlchemy>=2.0.0
 filelock
 av>=16.0.0
-comfy-kitchen==0.2.16
+comfy-kitchen==0.2.14
 comfy-aimdo==0.4.10
 requests
 simpleeval>=1.0.0

tests-unit/comfy_api_test/io_dynamic_group_test.py

@@ -0,0 +1,204 @@
+"""Unit tests for io.DynamicGroup: 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
+    DynamicGroup,
+    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(group_input: DynamicGroup.Input) -> dict:
+    """Wrap a DynamicGroup.Input into the required/optional dict structure."""
+    return create_input_dict_v1([group_input])
+
+
+def _run(group_input: DynamicGroup.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(group_input)
+    _, _, v3_data = get_finalized_class_inputs(class_inputs, live_values)
+    return build_nested_inputs(dict(live_values), v3_data)
+
+
+# ---------------------------------------------------------------------------
+# Schema construction
+# ---------------------------------------------------------------------------
+
+class TestDynamicGroupInputConstruction:
+    def test_basic_construction(self):
+        inp = DynamicGroup.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 = DynamicGroup.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 = DynamicGroup.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):
+            DynamicGroup.Input(
+                "bad",
+                template=[Float.Input("x"), Float.Input("x")],
+            )
+
+    def test_empty_template_raises(self):
+        with pytest.raises(AssertionError):
+            DynamicGroup.Input("bad", template=[])
+
+    def test_min_gt_max_raises(self):
+        with pytest.raises(AssertionError):
+            DynamicGroup.Input("bad", template=[Float.Input("x")], min=5, max=3)
+
+    def test_max_exceeds_limit_raises(self):
+        with pytest.raises(AssertionError):
+            DynamicGroup.Input("bad", template=[Float.Input("x")], max=101)
+
+    def test_dynamic_input_in_template_raises(self):
+        with pytest.raises(AssertionError):
+            DynamicGroup.Input(
+                "bad",
+                template=[DynamicGroup.Input("nested", template=[Float.Input("x")])],
+            )
+
+    def test_validate_calls_through(self):
+        inp = DynamicGroup.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 = DynamicGroup.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 = DynamicGroup.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 = DynamicGroup.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 = DynamicGroup.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 = DynamicGroup.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 = DynamicGroup.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 = DynamicGroup.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 group id so build_nested_inputs knows to convert."""
+        inp = DynamicGroup.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 = DynamicGroup.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)