What causes "Invalid prediction: %s, using default\n" in mudler/LocalAI?

Set prediction to an exact enum name from the vendored str_to_prediction

Invalid prediction: , using default\n

Error message

Invalid prediction: %s, using default\n

What it means

Warning from the option parser: the 'prediction=<value>' option (epsilon/v/x0-style noise prediction type) failed to parse via str_to_prediction(), so the model default prediction type is kept. This can subtly change sampling behavior, so it is worth fixing even though load succeeds.

Solutions

Set prediction to an exact enum name from the vendored str_to_prediction
If unsure, drop the option and let the model default apply
Verify the fix by finding 'Found prediction:' in the backend log

Example fix

# before
options: "...,prediction=X0-Prediction"

# after
options: "...,prediction=x0"

Defensive patterns

Strategy: validation

Validate before calling

// Go caller: whitelist prediction values
validPrediction := map[string]bool{"epsilon": true, "v": true, "x0": true} // match vendored names
if o, ok := opts.Raw["prediction"]; ok && !validPrediction[o] {
    return fmt.Errorf("invalid prediction %q", o)
}

Prevention

Pin model configs to enum names supported by the bundled sd library version
For flow-matching models, verify the required prediction value is actually accepted before relying on it

When it happens

Trigger: Passing prediction with an unrecognized string; flow-matching models (e.g. Flux/Chroma) that need a specific prediction value where the name was misspelled or not supported by the vendored library version.

Common situations: Model configs tuned for a newer stable-diffusion.cpp fork used against an older bundled copy; case errors like 'X0' vs 'x0'.

Related errors

AI-assisted analysis of mudler/LocalAI@44413a9d06 (2026-08-15). Data as JSON: /api/errors/8f43cd50a921a033. Report an issue: GitHub.

Appendix: source

Thrown at backend/go/stablediffusion-ggml/cpp/gosd.cpp:559

                fprintf(stderr, "Invalid rng_type: %s, using default\n", optval);
            }
        }
        if (!strcmp(optname, "sampler_rng_type")) {
            rng_type_t parsed = str_to_rng_type(optval);
            if (parsed != RNG_TYPE_COUNT) {
                sampler_rng_type = parsed;
                fprintf(stderr, "Found sampler_rng_type: %s\n", optval);
            } else {
                fprintf(stderr, "Invalid sampler_rng_type: %s, using default\n", optval);
            }
        }
        if (!strcmp(optname, "prediction")) {
            prediction_t parsed = str_to_prediction(optval);
            if (parsed != PREDICTION_COUNT) {
                prediction = parsed;
                fprintf(stderr, "Found prediction: %s\n", optval);
            } else {
                fprintf(stderr, "Invalid prediction: %s, using default\n", optval);
            }
        }
        if (!strcmp(optname, "lora_apply_mode")) {
            lora_apply_mode_t parsed = str_to_lora_apply_mode(optval);
            if (parsed != LORA_APPLY_MODE_COUNT) {
                lora_apply_mode = parsed;
                fprintf(stderr, "Found lora_apply_mode: %s\n", optval);
            } else {
                fprintf(stderr, "Invalid lora_apply_mode: %s, using default\n", optval);
            }
        }
        if (!strcmp(optname, "wtype")) {
            sd_type_t parsed = str_to_sd_type(optval);
            if (parsed != SD_TYPE_COUNT) {
                wtype = parsed;
                fprintf(stderr, "Found wtype: %s\n", optval);
            } else {
                fprintf(stderr, "Invalid wtype: %s, using default\n", optval);

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