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Fixed a couple of bugs to train the LDM part of the model #16

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Fixed a couple of bugs to train the LDM part of the model #16

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66188c5
Update normalization.py
Pulkit-Khandelwal Dec 2, 2024
86980d0
Update ddpm.py
Pulkit-Khandelwal Dec 2, 2024
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2 changes: 1 addition & 1 deletion LDM/ldm/models/diffusion/ddpm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -939,7 +939,7 @@ def p_losses(self, x_src, x_tgt, cond, t, noise=None):
loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})

logvar_t = self.logvar[t].to(self.device)
logvar_t = self.logvar[t.cpu()].to(self.device)
logvar_t = repeat(logvar_t, 'b -> b c', c=loss_simple.shape[1])
loss = loss_simple / torch.exp(logvar_t) + logvar_t
# loss = loss_simple / torch.exp(self.logvar) + self.logvar
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6 changes: 3 additions & 3 deletions LDM/ldm/models/normalization.py
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Expand Up @@ -15,7 +15,7 @@ def __init__(self, norm_nc, label_nc, kernel_size=3, norm_type='instance'):
% norm_type)

# The dimension of the intermediate embedding space. Yes, hardcoded.
nhidden = 128
nhidden = 64

pw = kernel_size // 2
self.mlp_shared = nn.Sequential(
Expand Down Expand Up @@ -94,7 +94,7 @@ def actvn(self, x):
class SPADEGenerator(nn.Module):
def __init__(self,modalities, z_dim=3):
super().__init__()
nf = 128
nf = 64
self.in_spade = SPADEResnetBlock(modalities, z_dim, nf)
self.out_spade = SPADEResnetBlock(modalities, nf, z_dim)

Expand All @@ -103,4 +103,4 @@ def forward(self, x, modality):
x = self.in_spade(x, modality)
x = self.out_spade(x, modality)

return x
return x