mudler/LocalAI · error · ValueError

Must provide at least one of: class_name, task, or model_id.

Error message

Must provide at least one of: class_name, task, or model_id. Available pipelines: {', '.join(sorted(registry.keys())[:20])}... Available tasks: {', '.join(sorted(aliases.keys())[:20])}...

What it means

Raised by resolve_pipeline_class as its final guard: all three resolution inputs were falsy — no class_name, no task, and no model_id — so there is nothing to resolve and the HuggingFace-inference branch (which requires model_id) was skipped. The message lists both available pipeline classes and task aliases.

Source

Thrown at backend/python/diffusers/diffusers_dynamic_loader.py:414

        except ImportError:
            # huggingface_hub not available
            pass
        except (KeyError, AttributeError, ValueError, OSError):
            # Model info lookup failed - common cases:
            # - KeyError: Missing keys in model card
            # - AttributeError: Missing attributes on model info
            # - ValueError: Invalid model data
            # - OSError: Network or file access issues
            pass

        # Fallback: use DiffusionPipeline.from_pretrained which auto-detects
        # DiffusionPipeline is always added to registry in _discover_pipelines (line 132)
        # but use .get() with import fallback for extra safety
        from diffusers import DiffusionPipeline
        return registry.get('DiffusionPipeline', DiffusionPipeline)

    raise ValueError(
        "Must provide at least one of: class_name, task, or model_id. "
        f"Available pipelines: {', '.join(sorted(registry.keys())[:20])}... "
        f"Available tasks: {', '.join(sorted(aliases.keys())[:20])}..."
    )


def load_diffusers_pipeline(
    class_name: Optional[str] = None,
    task: Optional[str] = None,
    model_id: Optional[str] = None,
    from_single_file: bool = False,
    **kwargs
) -> Any:
    """
    Load a diffusers pipeline dynamically.

    This function resolves the appropriate pipeline class based on the provided
    parameters and instantiates it with the given kwargs.

View on GitHub (pinned to 44413a9d06)

Solutions

1. Supply at least one of the three arguments; model_id alone is usually sufficient (auto-detection via the model card).
2. Fix the caller: log the arguments at the call site to find which layer dropped them.
3. If wrapping this API, validate inputs before calling and raise a domain-specific error instead.

Example fix

# before
pipe = load_diffusers_pipeline()

# after
pipe = load_diffusers_pipeline(model_id=model_ref)

assert class_name or task or model_id, (
    "load_diffusers_pipeline needs at least one of class_name/task/model_id")

def has_resolution_input(class_name, task, model_id) -> bool:
    return bool(class_name or task or model_id)

try:
    pipe = load_diffusers_pipeline(class_name=c, task=t, model_id=m)
except ValueError as e:
    if "Must provide at least one" in str(e):
        raise ProgrammingError("loader called without any resolution input") from e
    raise

When it happens

Trigger: Calling load_diffusers_pipeline() (or resolve_pipeline_class) with all of class_name, task, and model_id as None/empty — typically a caller bug that failed to populate any argument.

Common situations: A request pipeline where the user omitted both pipeline type and model, a default-argument refactor that dropped model_id propagation, or a config file where all optional fields were left blank.

Related errors

• model_id is required to load a pipeline
• Invalid scheduler '{'k_' if is_karras else ''}{name}'
• Failed to load pipeline '{effective_pipeline_type}': {e}\nAv
• Could not find base class '{base_class_name}' in diffusers
• Unknown pipeline class '{class_name}'. Available pipelines:

AI-assisted analysis of mudler/LocalAI@44413a9d06 (2026-08-15). Data as JSON: /api/errors/b030df5654fc87ad. Report an issue: GitHub.