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Enhance upsampler weight detection logic in LTX-2 model; improve clarity in comments and streamline spatial scale determination for x1.5 and x2 formats

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Prince Canuma
2026-03-17 15:14:57 +01:00
parent 57f66bcae2
commit f8e371e9ce
1 changed files with 10 additions and 6 deletions
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16
mlx_video/models/ltx_2/upsampler.py
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@@ -401,13 +401,17 @@ def load_upsampler(weights_path: str) -> Tuple[LatentUpsampler, float]:
mid_channels = 1024
# Detect upsampler type from conv output channels
# x2 uses sequential: upsampler.0.weight (4*mid out channels)
# x1.5 uses named: upsampler.conv.weight (9*mid out channels) + upsampler.blur_down.kernel
rational_resampler = "upsampler.blur_down.kernel" in raw_weights
if rational_resampler:
# x1.5: conv out = 9 * mid_channels (3^2 * mid for PixelShuffle(3))
spatial_scale = 1.5
# x2: conv out = 4 * mid (2^2 * mid for PixelShuffle(2))
# x1.5: conv out = 9 * mid (3^2 * mid for PixelShuffle(3)) + blur downsample
# Both formats may have upsampler.blur_down.kernel, so use channel count
conv_key = "upsampler.conv.weight" if "upsampler.conv.weight" in raw_weights else "upsampler.0.weight"
if conv_key in raw_weights:
out_channels = raw_weights[conv_key].shape[0]
ratio = out_channels // mid_channels
rational_resampler = ratio == 9 # 3^2 for PixelShuffle(3) + blur downsample
spatial_scale = 1.5 if rational_resampler else 2.0
else:
rational_resampler = False
spatial_scale = 2.0
print(f" Detected: mid_channels={mid_channels}, scale={spatial_scale}x, rational={rational_resampler}")