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VBench Aesthetic Quality — CLIP ViT-L/14 + LAION aesthetic predictor.

Encodes frames through CLIP, passes L2-normalized features through a linear aesthetic head (768 → 1), and averages scores / 10.

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

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

Move metric (and its internal models) to device.

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

reset() -> None

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

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

accumulate(sample: dict) -> None

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

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

finalize() -> MetricResult

Compute the corpus-level result from buffered state.

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

merge_from(other: BaseMetric) -> None

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

fastvideo.eval.metrics.vbench.aesthetic_quality.metric.register

register(name: str)

Decorator to register a metric class.

Usage::

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

fastvideo.eval.metrics.vbench.aesthetic_quality.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.aesthetic_quality.metric.AestheticQualityMetric

AestheticQualityMetric()
Source
@register("vbench.aesthetic_quality")
class AestheticQualityMetric(BaseMetric):

name = "vbench.aesthetic_quality"
requires_reference = False
higher_is_better = True
needs_gpu = True
dependencies = ["clip"]
backbone = "clip_vit_l14"

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

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

def setup(self) -> None:
    if self._clip_model is not None:
        return

    import clip
    from fastvideo.eval.models import ensure_checkpoint, get_cache_dir
    self._clip_model, _ = clip.load(
        "ViT-L/14",
        device=self.device,
        download_root=str(get_cache_dir() / "clip"),
    )
    self._clip_model.eval()

    # Load LAION aesthetic head
    ckpt_path = ensure_checkpoint(
        "sa_0_4_vit_l_14_linear.pth",
        source=_AESTHETIC_URL,
    )
    self._aesthetic_head = nn.Linear(768, 1)
    self._aesthetic_head.load_state_dict(torch.load(ckpt_path, map_location="cpu", weights_only=True))
    self._aesthetic_head.to(self.device)
    self._aesthetic_head.eval()

@torch.no_grad()
def compute(self, sample: dict) -> MetricResult:
    video = sample["video"]  # (T, C, H, W)
    frames = _clip_transform(video.to(self.device))

    chunk = self._chunk_size or 32
    scores_list = []
    for i in range(0, frames.shape[0], chunk):
        feats = self._clip_model.encode_image(frames[i:i + chunk]).float()
        feats = F.normalize(feats, dim=-1, p=2)
        scores_list.append(self._aesthetic_head(feats).squeeze(-1))

    all_scores = torch.cat(scores_list, dim=0) / 10.0  # (T,)
    return MetricResult(
        name=self.name,
        score=float(all_scores.mean().item()),
        details={"per_frame": all_scores.tolist()},
    )</code></pre></details>


fastvideo.eval.metrics.vbench.aesthetic_quality.metric.AestheticQualityMetric.to


to(device)


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


fastvideo.eval.metrics.vbench.aesthetic_quality.metric.AestheticQualityMetric.setup


setup() -> None


Source
def setup(self) -> None:
    if self._clip_model is not None:
        return

import clip
from fastvideo.eval.models import ensure_checkpoint, get_cache_dir
self._clip_model, _ = clip.load(
    &quot;ViT-L/14&quot;,
    device=self.device,
    download_root=str(get_cache_dir() / &quot;clip&quot;),
)
self._clip_model.eval()

# Load LAION aesthetic head
ckpt_path = ensure_checkpoint(
    &quot;sa_0_4_vit_l_14_linear.pth&quot;,
    source=_AESTHETIC_URL,
)
self._aesthetic_head = nn.Linear(768, 1)
self._aesthetic_head.load_state_dict(torch.load(ckpt_path, map_location=&quot;cpu&quot;, weights_only=True))
self._aesthetic_head.to(self.device)
self._aesthetic_head.eval()</code></pre></details>


fastvideo.eval.metrics.vbench.aesthetic_quality.metric.AestheticQualityMetric.compute


compute(sample: dict) -> MetricResult


Source
@torch.no_grad()
def compute(self, sample: dict) -> MetricResult:
    video = sample["video"]  # (T, C, H, W)
    frames = _clip_transform(video.to(self.device))

chunk = self._chunk_size or 32
scores_list = &#91;&#93;
for i in range(0, frames.shape&#91;0&#93;, chunk):
    feats = self._clip_model.encode_image(frames&#91;i:i + chunk&#93;).float()
    feats = F.normalize(feats, dim=-1, p=2)
    scores_list.append(self._aesthetic_head(feats).squeeze(-1))

all_scores = torch.cat(scores_list, dim=0) / 10.0  # (T,)
return MetricResult(
    name=self.name,
    score=float(all_scores.mean().item()),
    details=&#123;&quot;per_frame&quot;: all_scores.tolist()&#125;,
)</code></pre></details>

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