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| N = h.size(0) // ta.size(0) | ||
| ta = einops.repeat(ta, "b c h w -> (b n) c h w", n=N) | ||
| tb = einops.repeat(tb, "b c h w -> (b n) c h w", n=N) | ||
| h = nn.silu(self.norm2(h) * (1 + ta) + tb) |
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try printing the shapes or returning early (in both the pytorch and the mlx version) to see if the tensor shapes are the same up to this point
ml_mdm/models/unet_mlx.py
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| bias=True, | ||
| ) | ||
| self.time_layer = nn.Linear(time_emb_channels, config.output_channels * 2) | ||
| self.norm2 = nn.GroupNorm(config.num_groups_norm, config.output_channels) |
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pytorch_compatible=True
mlx docs show these a difference between max and pytorch implementation
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