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33 lines
1.0 KiB
33 lines
1.0 KiB
2 years ago
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from typing import Any, Dict
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import torch
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import torch.nn as nn
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class SplitVectorDiffusion(nn.Module):
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def __init__(self, *, device: torch.device, wrapped: nn.Module, n_ctx: int, d_latent: int):
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super().__init__()
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self.device = device
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self.n_ctx = n_ctx
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self.d_latent = d_latent
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self.wrapped = wrapped
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if hasattr(self.wrapped, "cached_model_kwargs"):
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self.cached_model_kwargs = self.wrapped.cached_model_kwargs
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def forward(self, x: torch.Tensor, t: torch.Tensor, **kwargs):
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h = x.reshape(x.shape[0], self.n_ctx, -1).permute(0, 2, 1)
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pre_channels = h.shape[1]
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h = self.wrapped(h, t, **kwargs)
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assert (
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h.shape[1] == pre_channels * 2
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), "expected twice as many outputs for variance prediction"
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eps, var = torch.chunk(h, 2, dim=1)
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return torch.cat(
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[
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eps.permute(0, 2, 1).flatten(1),
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var.permute(0, 2, 1).flatten(1),
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],
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dim=1,
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)
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