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from typing import Any, Dict
import torch
import torch.nn as nn
from typing import Any, Callable, Dict, Optional
class SplitVectorDiffusion(nn.Module):
def __init__(self, *, device: torch.device, wrapped: nn.Module, n_ctx: int, d_latent: int):
super().__init__()
self.device = device
self.n_ctx = n_ctx
self.d_latent = d_latent
self.wrapped = wrapped
if hasattr(self.wrapped, "cached_model_kwargs"):
self.cached_model_kwargs = self.wrapped.cached_model_kwargs
def forward(self, x: torch.Tensor, t: torch.Tensor, conditional_latent: Optional[torch.Tensor] = None, **kwargs):
h = x.reshape(x.shape[0], self.n_ctx, -1).permute(0, 2, 1)
if conditional_latent is not None:
conditional_latent = conditional_latent.reshape(conditional_latent.shape[0], self.n_ctx, -1)
h = torch.cat([h.permute(0, 2, 1) , conditional_latent], dim=-1).permute(0, 2, 1) # (batch_size, n_ctx, channel) -> (batch_size, d_latent, n_ctx)
h = self.wrapped(h, t, **kwargs)
eps, var = torch.chunk(h, 2, dim=1)
return torch.cat(
[
eps.permute(0, 2, 1).flatten(1),
var.permute(0, 2, 1).flatten(1),
],
dim=1,
)
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