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Alexander Piskun 2026-07-06 10:12:17 +03:00 committed by GitHub
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@ -891,10 +891,11 @@ def hlg_to_linear(t: torch.Tensor) -> torch.Tensor:
return torch.cat([hlg_to_linear(rgb), alpha], dim=-1)
# Piecewise: sqrt branch below 0.5, log branch above.
# Clamp inside the log branch so negative / out-of-range values don't blow up;
# Clamp the log branch at the 0.5 branch point (not above it) so the
# unselected lane stays finite in exp() without altering selected values;
# values above 1.0 are allowed and extrapolate naturally.
low = (t ** 2) / 3.0
high = (torch.exp((t.clamp(min=_HLG_C) - _HLG_C) / _HLG_A) + _HLG_B) / 12.0
high = (torch.exp((t.clamp(min=0.5) - _HLG_C) / _HLG_A) + _HLG_B) / 12.0
return torch.where(t <= 0.5, low, high)