ATH-MaaS/Pixelle-Video · error · ValueError

Batch {batch_idx} prompt count mismatch (attempt {attempt}/{

Error message

Batch {batch_idx} prompt count mismatch (attempt {attempt}/{max_retries}):
  Expected: {len(batch_narrations)} prompts
  Got: {len(batch_prompts)} prompts

What it means

After parsing the LLM response, generate_image_prompts validates that the 'image_prompts' array has exactly as many entries as the batch's narrations. On mismatch it warns and retries; once max_retries is exhausted it raises ValueError with expected vs got counts. It means the model returned too few or too many image prompts for the batch.

Source

Thrown at pixelle_video/utils/content_generators.py:346

                if "image_prompts" not in result:
                    raise KeyError("Invalid response format: missing 'image_prompts'")
                
                batch_prompts = result["image_prompts"]
                
                # Validate count
                if len(batch_prompts) != len(batch_narrations):
                    error_msg = (
                        f"Batch {batch_idx} prompt count mismatch (attempt {attempt}/{max_retries}):\n"
                        f"  Expected: {len(batch_narrations)} prompts\n"
                        f"  Got: {len(batch_prompts)} prompts"
                    )
                    logger.warning(error_msg)
                    
                    if attempt < max_retries:
                        logger.info(f"Retrying batch {batch_idx}...")
                        continue
                    else:
                        raise ValueError(error_msg)
                
                # Success!
                logger.info(f"✅ Batch {batch_idx} completed successfully ({len(batch_prompts)} prompts)")
                all_prompts.extend(batch_prompts)
                
                # Report progress
                if progress_callback:
                    progress_callback(
                        len(all_prompts),
                        len(narrations),
                        f"Batch {batch_idx}/{len(batches)} completed"
                    )
                
                break
                
            except json.JSONDecodeError as e:
                logger.error(f"Batch {batch_idx} JSON parse error (attempt {attempt}/{max_retries}): {e}")
                if attempt >= max_retries:

View on GitHub (pinned to 848b054e4f)

Solutions

Reduce the batch size (fewer narrations per LLM call) so counts stay aligned.
Raise max_retries to give the corrective retry loop more attempts.
Strengthen the prompt: instruct one prompt per narration, in order, matching the count exactly.
Post-process defensively: pad by splitting prompts or truncate to len(batch_narrations) instead of failing.
Use structured/JSON-mode output which enforces array length via a schema.

Example fix

// before
raise ValueError(error_msg)
// after
if len(batch_prompts) > len(batch_narrations):
    batch_prompts = batch_prompts[:len(batch_narrations)]  # tolerate extra prompts
else:
    raise ValueError(error_msg)

Defensive patterns

Strategy: retry

Validate before calling

def batch_counts_match(result: dict, narrations: list) -> bool:
    prompts = result.get("image_prompts") if isinstance(result, dict) else None
    return isinstance(prompts, list) and len(prompts) == len(narrations)

Type guard

def is_valid_prompt_batch(obj: object, expected: int) -> bool:
    return (isinstance(obj, dict) and isinstance(obj.get("image_prompts"), list)
            and len(obj["image_prompts"]) == expected)

Try / catch

try:
    prompts = generator.generate_image_prompts(narrations)
except ValueError as e:
    if "prompt count mismatch" in str(e):
        prompts = generator.generate_image_prompts(narrations, max_retries=max_retries + 2)
    else:
        raise

Prevention

State the exact required count in the prompt before the narrations
Keep per-batch narration counts low (e.g. <=5)
Use schema-constrained output with minItems/maxItems
Surface the retry warnings in logs to spot chronically failing batch sizes

When it happens

Trigger: generate_image_prompts called (directly, via generate_image_prompt, __call__, or plan_visuals) where for every retry attempt len(result['image_prompts']) != len(batch_narrations), e.g. model merges or skips narrations, or the batch is too large for the model to enumerate faithfully.

Common situations: Long narration lists per batch; model summarizing multiple scenes into one prompt; off-by-one when the model numbers items but drops one; low-context models truncating long JSON arrays.

Related errors

AI-assisted analysis of ATH-MaaS/Pixelle-Video@848b054e4f (2026-08-30). Data as JSON: /api/errors/d6f8645052ea2093. Report an issue: GitHub.