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[Bugfix] Fix RIFE device selection for CPU-transported videos #2876

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Merged
hsliuustc0106 merged 1 commit into from
Apr 17, 2026
Merged

[Bugfix] Fix RIFE device selection for CPU-transported videos #2876

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5 changes: 3 additions & 2 deletions tests/entrypoints/openai_api/test_video_api_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,7 @@ def test_frame_interpolator_runs_actual_torch_tensor_path(monkeypatch):
assert torch.isfinite(output_video).all()


def test_frame_interpolator_prefers_input_tensor_device(monkeypatch):
def test_frame_interpolator_uses_platform_device_when_tensor_is_cpu(monkeypatch):
chosen_devices = []
model = rife_interpolator.Model().eval()

Expand All @@ -83,10 +83,11 @@ def _fake_ensure_model_loaded(*, preferred_device=None):
interpolator = rife_interpolator.FrameInterpolator()
monkeypatch.setattr(interpolator, "_ensure_model_loaded", _fake_ensure_model_loaded)
monkeypatch.setattr(model.flownet, "to", lambda device: model.flownet)
monkeypatch.setattr(rife_interpolator, "_select_torch_device", lambda: torch.device("cuda"))

video = torch.zeros(1, 3, 2, 32, 32)
output_video, multiplier = interpolator.interpolate_tensor(video, exp=1, scale=1.0)

assert chosen_devices == [video.device]
assert chosen_devices == [torch.device("cuda")]
assert multiplier == 2
assert output_video.shape == (1, 3, 3, 32, 32)
9 changes: 6 additions & 3 deletions vllm_omni/diffusion/postprocess/rife_interpolator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -412,9 +412,12 @@ def interpolate_tensor(
return restore_layout(video), 1

video, restore_range = _normalize_video_tensor_range(video)
# Prefer the decoded video's current device so CPU-offloaded requests do
# not move the tensor back to GPU just for interpolation.
model = self._ensure_model_loaded(preferred_device=video.device)
# A CPU tensor may be transport/offload state rather than an execution
# choice, so only trust it when it is already on an accelerator.
preferred_device = video.device
if preferred_device.type == "cpu":
preferred_device = _select_torch_device()
model = self._ensure_model_loaded(preferred_device=preferred_device)
video = video.to(model.device())
intermediates_per_pair = 2**exp // 2

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