invoke-ai/InvokeAI · error · UnsupportedWorkflowNodeError

call_saved_workflow generator node is malformed

Error message

call_saved_workflow generator node is malformed

What it means

_resolve_generator_items requires each generator node to have data.type as a string and data.inputs as a mapping. If either is missing or wrong-typed, the node is structurally malformed and UnsupportedWorkflowNodeError is raised before any generator is resolved.

Source

Thrown at invokeai/app/services/session_processor/workflow_call_batch.py:407

        "float_generator_linear_distribution",
        "float_generator_random_distribution_uniform",
        "integer_generator_arithmetic_sequence",
        "integer_generator_linear_distribution",
        "integer_generator_random_distribution_uniform",
        "string_generator_dynamic_prompts_random",
    }:
        return int(value.get("count", 10))
    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":

View on GitHub (pinned to 0b6a024f2f)

Solutions

  1. Open the workflow in the InvokeAI editor and re-save so the node's data.type and data.inputs are populated
  2. Inspect the workflow JSON and restore the missing data.type / data.inputs fields on the generator node
  3. Re-create the generator node and reconnect its edges

Example fix

// before
{"id": "gen-1", "type": "invocation", "data": {"type": "integer_generator"}}
// after
{"id": "gen-1", "type": "invocation", "data": {"type": "integer_generator", "inputs": {"generator": {...}}}}
Defensive patterns

Strategy: validation

Validate before calling

for node in workflow.get("nodes", []):
    data = node.get("data", {})
    if not isinstance(data.get("type"), str) or not isinstance(data.get("inputs"), dict):
        raise ValueError(f"malformed node {node.get('id')}: need data.type str and data.inputs dict")

Type guard

def is_well_formed_node(node: object) -> TypeGuard[dict]:
    return (isinstance(node, Mapping) and isinstance(node.get("data"), Mapping)
            and isinstance(node["data"].get("type"), str) and isinstance(node["data"].get("inputs"), Mapping))

Try / catch

try:
    results = build_batch_child_workflow_session_results(...)
except UnsupportedWorkflowNodeError as e:
    raise WorkflowValidationError(f"fix node structure before queueing: {e}") from e

Prevention

When it happens

Trigger: build_batch_child_workflow_session_results finds a node whose data lacks 'type' or 'inputs' (or 'inputs' is a list/string/null) while expanding a call_saved_workflow batch.

Common situations: Corrupted or truncated workflow JSON, workflows saved by mismatched UI/library versions, hand-editing that dropped the inputs dict.

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/a0bdabe6d9c772e6. Report an issue: GitHub.