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VBench Subject Consistency — DINO ViT-B/16 temporal feature similarity.

Measures how well the main subject maintains its appearance throughout the video via cosine similarity of DINO features between consecutive frames and the first frame.

fastvideo.eval.metrics.vbench.subject_consistency.metric.BaseMetric

BaseMetric()

Abstract base class for all eval metrics.

Two execution shapes:

  • Per-sample (is_set_metric=False, default) — implement :meth:compute. The Evaluator calls it once per input sample and returns one :class:MetricResult per sample.

  • Set-vs-set (is_set_metric=True) — implement :meth:accumulate (called once per sample to buffer features) and :meth:finalize (called once after all samples to compute the corpus-level result). Use :meth:reset to clear buffers and :meth:merge_from to fold multi-GPU per-worker state together.

Optionally override :meth:setup to eagerly load models. Metrics that chunk along the time dim for memory hardcode their own chunk size in __init__ (see optical_flow for the canonical example). Eval always processes one video per :meth:Evaluator.evaluate call; compute / accumulate receive a single sample, not a batch.

fastvideo.eval.metrics.vbench.subject_consistency.metric.BaseMetric.to

to(device: str | torch.device) -> BaseMetric

Move metric (and its internal models) to device.

fastvideo.eval.metrics.vbench.subject_consistency.metric.BaseMetric.setup

setup() -> None

Eagerly load models. Called once by :class:EvalWorker.

Default is a no-op; metrics with no eager state (pixel math, closed-form ops) inherit this. Override only if your metric needs to load weights.

fastvideo.eval.metrics.vbench.subject_consistency.metric.BaseMetric.compute

compute(sample: dict) -> MetricResult

Per-sample metrics: compute the score for one sample.

sample["video"] is (T, C, H, W) float in [0, 1]. sample["reference"] (if used) has the same shape. Return self._skip(sample, reason) for missing inputs.

fastvideo.eval.metrics.vbench.subject_consistency.metric.BaseMetric.reset

reset() -> None

Clear accumulator state at the start of each evaluate() call.

fastvideo.eval.metrics.vbench.subject_consistency.metric.BaseMetric.accumulate

accumulate(sample: dict) -> None

Buffer per-sample features for a corpus-level metric.

fastvideo.eval.metrics.vbench.subject_consistency.metric.BaseMetric.finalize

finalize() -> MetricResult

Compute the corpus-level result from buffered state.

fastvideo.eval.metrics.vbench.subject_consistency.metric.BaseMetric.merge_from

merge_from(other: BaseMetric) -> None

Multi-GPU: fold another worker's accumulator state into this one.

fastvideo.eval.metrics.vbench.subject_consistency.metric.register

register(name: str)

Decorator to register a metric class.

Usage::

@register("ssim")
class SSIMMetric(BaseMetric):
    ...

fastvideo.eval.metrics.vbench.subject_consistency.metric.MetricResult

MetricResult(name: str, score: float | None, details: dict[str, Any] = dict())

Standard result container returned by all metrics.

score is None when the metric was skipped (e.g. missing required input). Check details["skipped"] for the reason.

fastvideo.eval.metrics.vbench.subject_consistency.metric.consistency_score

consistency_score(features: torch.Tensor) -> float

VBench-style temporal consistency from (T, D) L2-normalized features.

For each frame t > 0, computes: sim = (cos(f[t], f[t-1]) + cos(f[t], f[0])) / 2, clamped >= 0

Returns the mean similarity across all t > 0.

fastvideo.eval.metrics.vbench.subject_consistency.metric.SubjectConsistencyMetric

SubjectConsistencyMetric()
Source
@register("vbench.subject_consistency")
class SubjectConsistencyMetric(BaseMetric):

name = "vbench.subject_consistency"
requires_reference = False
higher_is_better = True
needs_gpu = True
backbone = "dino_vitb16"

def __init__(self) -> None:
    super().__init__()
    self._model: Any = None

def to(self, device):
    super().to(device)
    if self._model is not None:
        self._model = self._model.to(self.device)
    return self

def setup(self) -> None:
    if self._model is not None:
        return
    model = torch.hub.load("facebookresearch/dino:main", "dino_vitb16")
    model.to(self.device)
    model.eval()
    self._model = model

@torch.no_grad()
def compute(self, sample: dict) -> MetricResult:
    video = sample["video"]  # (T, C, H, W)
    frames = video.to(self.device)
    # antialias=False matches VBench's dino_transform (vbench/utils.py:50)
    frames = resize(frames, 224, antialias=False)
    frames = normalize(frames, mean=_MEAN, std=_STD)

    chunk = self._chunk_size or 64
    feats = []
    for i in range(0, frames.shape[0], chunk):
        f = self._model(frames[i:i + chunk])
        f = F.normalize(f, dim=-1, p=2)
        feats.append(f)
    all_feats = torch.cat(feats, dim=0)  # (T, D)
    return MetricResult(
        name=self.name,
        score=consistency_score(all_feats),
        details={},
    )</code></pre></details>


fastvideo.eval.metrics.vbench.subject_consistency.metric.SubjectConsistencyMetric.to


to(device)


Source
def to(self, device):
    super().to(device)
    if self._model is not None:
        self._model = self._model.to(self.device)
    return self


fastvideo.eval.metrics.vbench.subject_consistency.metric.SubjectConsistencyMetric.setup


setup() -> None


Source
def setup(self) -> None:
    if self._model is not None:
        return
    model = torch.hub.load("facebookresearch/dino:main", "dino_vitb16")
    model.to(self.device)
    model.eval()
    self._model = model


fastvideo.eval.metrics.vbench.subject_consistency.metric.SubjectConsistencyMetric.compute


compute(sample: dict) -> MetricResult


Source
@torch.no_grad()
def compute(self, sample: dict) -> MetricResult:
    video = sample["video"]  # (T, C, H, W)
    frames = video.to(self.device)
    # antialias=False matches VBench's dino_transform (vbench/utils.py:50)
    frames = resize(frames, 224, antialias=False)
    frames = normalize(frames, mean=_MEAN, std=_STD)

chunk = self._chunk_size or 64
feats = &#91;&#93;
for i in range(0, frames.shape&#91;0&#93;, chunk):
    f = self._model(frames&#91;i:i + chunk&#93;)
    f = F.normalize(f, dim=-1, p=2)
    feats.append(f)
all_feats = torch.cat(feats, dim=0)  # (T, D)
return MetricResult(
    name=self.name,
    score=consistency_score(all_feats),
    details=&#123;&#125;,
)</code></pre></details>

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