Skip to content

metric

VideoScore2 — VLM-based video quality scoring.

Uses a Qwen2.5-VL model fine-tuned to score generated videos on three dimensions: visual quality, text-to-video alignment, and physical consistency. Scores are extracted from token logits as upstream's ll_based_soft_score_normed weighting (1-5 scale).

Reference: TIGER-AI-Lab/VideoScore2 (vs2_inference.py).

Classes

fastvideo.eval.metrics.videoscore2.metric.VideoScore2Metric 

VideoScore2Metric(model_name: str = 'TIGER-Lab/VideoScore2', infer_fps: float = 2.0, max_tokens: int = 1024, temperature: float = 0.7, do_sample: bool = False)

Bases: BaseMetric

VideoScore2: VLM-based video quality scoring (3 dimensions).

Requires sample["text_prompt"] for text-to-video alignment. Supports batched generation for GPU efficiency.

Source code in fastvideo/eval/metrics/videoscore2/metric.py 
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
def __init__(
    self,
    model_name: str = "TIGER-Lab/VideoScore2",
    infer_fps: float = 2.0,
    max_tokens: int = 1024,
    temperature: float = 0.7,
    # Greedy by default — eval metrics should be deterministic on a
    # given input.  Upstream's vs2_inference.py defaults to
    # do_sample=True with temperature=0.7, but stochastic decoding
    # makes the score irreproducible run-to-run AND can land the
    # chain-of-thought outside the regex anchor (yielding score=0).
    # Pass do_sample=True explicitly if you want BoN/ensembling.
    do_sample: bool = False,
) -> None:
    super().__init__()
    self._model_name = model_name
    self.infer_fps = infer_fps
    self.max_tokens = max_tokens
    self.temperature = temperature
    self.do_sample = do_sample
    self._model: Any = None
    self._processor: Any = None
    self._tokenizer: Any = None

Functions: