base ¶

Prompt-corpus datasets.

A :class:PromptDataset is an iterable of sample dicts describing the prompts and conditions for a benchmark. Each sample is a plain dict — no dataclass, no schema enforcement — that flows directly into both generation (VideoGenerator.generate_video(**sample)) and scoring (Evaluator.evaluate(**eval_kwargs)). The runner picks well-known keys (prompt, n_samples, dimensions, auxiliary_info, ...) and passes the rest through.

This matches the surrounding FastVideo style:

• :class:fastvideo.dataset.validation_dataset.ValidationDataset yields dicts.
• :meth:fastvideo.VideoGenerator.generate_video consumes **kwargs.
• :meth:fastvideo.eval.Evaluator.evaluate consumes **kwargs.

To add a new benchmark:

1. Subclass :class:PromptDataset, populate self._rows with dicts in __init__.
2. Decorate with @register_dataset("my_bench").

Convention for auxiliary_info: a flat dict of metric-keyed values (e.g. {"color": "red"}). Benchmarks with nested aux schemas (VBench's {dim: {key: val}}) flatten at load time so every consumer sees the same shape.

PromptDataset()

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def __init__(self) -> None:
    self._rows: list[dict] = []

by_dimension() -> dict[str, list[dict]]

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def by_dimension(self) -> dict[str, list[dict]]:
    """Group samples by dimension. A multi-dim sample appears under each."""
    out: dict[str, list[dict]] = {}
    for s in self._rows:
        for d in s.get("dimensions", ()):
            out.setdefault(d, []).append(s)
    return out