invoke-ai/InvokeAI · error · UnsupportedWorkflowNodeError

call_saved_workflow generator node '{generator_node_data.get

Error message

call_saved_workflow generator node '{generator_node_data.get('id')}' has invalid generator value

What it means

The generator input's 'value' must itself be a mapping holding the generator parameters. If inputs.generator.value is missing or not a mapping, the node's generator payload is invalid and this error is raised with the node id.

Source

Thrown at invokeai/app/services/session_processor/workflow_call_batch.py:415

    return None


def _resolve_generator_items(
    generator_node: Mapping[str, Any], services: Any, user_id: str | None, maximum_items: int
) -> list[Any]:
    generator_node_data = generator_node["data"]
    node_type = generator_node_data.get("type")
    inputs = generator_node_data.get("inputs")
    if not isinstance(node_type, str) or not _is_mapping(inputs):
        raise UnsupportedWorkflowNodeError("call_saved_workflow generator node is malformed")
    generator_input = inputs.get("generator")
    if not _is_mapping(generator_input):
        raise UnsupportedWorkflowNodeError(
            f"call_saved_workflow generator node '{generator_node_data.get('id')}' is missing generator input"
        )
    generator_value = generator_input.get("value")
    if not _is_mapping(generator_value):
        raise UnsupportedWorkflowNodeError(
            f"call_saved_workflow generator node '{generator_node_data.get('id')}' has invalid generator value"
        )
    declared_item_count = _get_declared_generator_item_count(generator_value)
    if declared_item_count is not None and declared_item_count > maximum_items:
        raise TooManySessionsError("call_saved_workflow exceeds remaining queue capacity for child workflow executions")
    if node_type == "integer_generator":
        return _resolve_integer_generator(generator_value)
    if node_type == "float_generator":
        return _resolve_float_generator(generator_value)
    if node_type == "string_generator":
        return _resolve_string_generator(generator_value)
    if node_type == "image_generator":
        return _resolve_image_generator(generator_value, services, user_id)
    raise UnsupportedWorkflowNodeError(f"Unsupported generator node type '{node_type}'")


def _get_generator_placeholder_items(generator_node: Mapping[str, Any]) -> list[Any]:
    generator_node_data = generator_node["data"]

View on GitHub (pinned to 0b6a024f2f)

Solutions

Move the generator parameters into a mapping at inputs.generator.value (e.g. {"count": 5, "start": 0, ...})
Re-save the workflow in the InvokeAI canvas to normalize the generator value
Check the invoking API call isn't overwriting generator.value with a non-object

Example fix

// before
"inputs": {"generator": {"value": 42}}
// after
"inputs": {"generator": {"value": {"count": 42, "start": 0, "stop": 42, "step": 1}}}

Defensive patterns

Strategy: validation

Validate before calling

value = node["data"]["inputs"].get("generator", {}).get("value")
if not isinstance(value, dict):
    raise ValueError("inputs.generator.value must be a mapping of generator parameters")

Type guard

def has_generator_value(node: dict) -> TypeGuard[dict]:
    return isinstance(node.get("data", {}).get("inputs", {}).get("generator", {}).get("value"), Mapping)

Try / catch

try:
    build_batch_child_workflow_session_results(...)
except UnsupportedWorkflowNodeError as e:
    if "invalid generator value" in str(e):
        repair_generator_value(extract_node_id(str(e)))

Prevention

Keep generator params nested under inputs.generator.value, never as raw scalars
Round-trip workflows through the UI export to normalize structure
Add schema validation to any pipeline that generates workflow JSON

When it happens

Trigger: inputs.generator exists but its 'value' key is absent, null, a scalar, or a list when resolving generator items for a call_saved_workflow batch.

Common situations: Hand-crafted batch payloads that placed raw parameters directly under inputs.generator; import/export round-trips that stripped the value wrapper; API clients posting non-dict values.

Understand the failure class

Background: Schema validation failed / invalid input schema: payload rejected because its shape doesn't match the expected schema — this error's family across 28 libraries.

Related errors

AI-assisted analysis of invoke-ai/InvokeAI@0b6a024f2f (2026-08-29). Data as JSON: /api/errors/7780c73ca128d8ca. Report an issue: GitHub.