BerriAI/litellm · error · Exception

Model needs to be set for black_forest_labs

Error message

Model needs to be set for black_forest_labs

What it means

The black_forest_labs (FLUX) image route requires an explicit model because it delegates to bfl_image_generation.image_generation, which needs the model name to pick the BFL API endpoint and polling behavior. litellm only auto-defaults model to 'dall-e-2'/openai when BOTH model and custom_llm_provider are None (litellm/images/main.py:224-232), so a provider set without a model reaches this guard as None.

Solutions

1. Pass a BFL model, ideally provider-prefixed: litellm.image_generation(model='black_forest_labs/flux-pro-1.1', prompt='...') so both provider and model resolve from one string.
2. If provider is passed separately, still supply model='flux-pro-1.1' (or flux-dev / flux-kontext variants).
3. In proxy/Router config, verify the model entry has a concrete model value, not just model_name + custom_llm_provider.

Example fix

# before
resp = litellm.image_generation(prompt="a cat", custom_llm_provider="black_forest_labs")
# Exception: Model needs to be set for black_forest_labs

# after
resp = litellm.image_generation(model="black_forest_labs/flux-pro-1.1", prompt="a cat")

def call_bfl(prompt: str, model: str | None = None):
    model = model or "black_forest_labs/flux-pro-1.1"
    if model is None or model.strip() == "":
        raise ValueError("model is required for black_forest_labs image generation")
    return litellm.image_generation(model=model, prompt=prompt)

def is_bfl_ready(model: str | None, custom_llm_provider: str | None) -> bool:
    """True when the call cannot hit the 'Model needs to be set' guard."""
    if custom_llm_provider == "black_forest_labs":
        return isinstance(model, str) and model.strip() != ""
    return True

try:
    resp = litellm.image_generation(prompt=p, custom_llm_provider="black_forest_labs")
except Exception as e:
    if "Model needs to be set for black_forest_labs" in str(e):
        raise ValueError("pass model='black_forest_labs/flux-pro-1.1' (or another FLUX model)") from e
    raise

When it happens

Trigger: litellm.image_generation(prompt='...', custom_llm_provider='black_forest_labs') with no model; a Router/proxy deployment whose model entry sets litellm_params.custom_llm_provider='black_forest_labs' but leaves model empty; passing model=None explicitly while relying on the provider kwarg.

Common situations: Proxy config yaml with a model_name but a missing/blank model field under litellm_params; copy-pasted chat-style invocations where the provider is a separate argument and the model string was omitted; assuming litellm defaults a model per provider.

Understand the failure class

Background: "missing required argument" and "the following required arguments were not provided": what required-argument errors mean and how to fix them — this error's family across 20 libraries.

Related errors

• BFL error
• BFL initial request failed
• Error parsing BFL response
• Error parsing initial response
• Image generation failed

AI-assisted analysis of BerriAI/litellm@77b7c6c40c (2026-08-18). Data as JSON: /api/errors/51ff9aeebde741a0. Report an issue: GitHub.

Appendix: source

Thrown at litellm/images/main.py:411

            _api_base: Final = api_base or litellm.api_base
            litellm_params_dict["api_base"] = _api_base

            return llm_http_handler.image_generation_handler(
                api_key=api_key,
                model=model,
                prompt=prompt,
                image_generation_provider_config=image_generation_config,
                image_generation_optional_request_params=optional_params,
                custom_llm_provider=custom_llm_provider,
                litellm_params=litellm_params_dict,
                logging_obj=litellm_logging_obj,
                timeout=timeout,
                client=client,
            )
        elif custom_llm_provider == "black_forest_labs":
            # Route to BFL-specific handler (polling required)
            if model is None:
                raise Exception("Model needs to be set for black_forest_labs")
            return bfl_image_generation.image_generation(
                model=model,
                prompt=prompt,
                model_response=model_response,
                optional_params=optional_params,
                litellm_params=litellm_params_dict,
                logging_obj=litellm_logging_obj,
                timeout=timeout,
                extra_headers=extra_headers,
                client=client,
                aimg_generation=aimg_generation,
            )
        elif custom_llm_provider == "azure_ai":
            from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo

            api_base = AzureFoundryModelInfo.get_api_base(api_base)
            api_key = AzureFoundryModelInfo.get_api_key(api_key)
            if extra_headers is not None:

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