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@ -198,7 +198,7 @@ class DiscreteVAE(nn.Module):
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if hasattr(self.codebook, 'embed_code'):
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image_embeds = self.codebook.embed_code(img_seq)
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else:
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image_embeds = F.embedding(img_seq, self.codebook.embed.transpose(1,0))
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image_embeds = F.embedding(img_seq, self.codebook.codebook)
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b, n, d = image_embeds.shape
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kwargs = {}
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@ -273,7 +273,7 @@ if __name__ == '__main__':
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#print(o.shape)
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v = DiscreteVAE(channels=80, normalization=None, positional_dims=1, num_tokens=8192, codebook_dim=2048,
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hidden_dim=512, num_resnet_blocks=3, kernel_size=3, num_layers=1, use_transposed_convs=False,
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use_lr_quantizer=True, lr_quantizer_args={'kmeans_init': True})
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use_lr_quantizer=True)
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#v.load_state_dict(torch.load('../experiments/clips_dvae_8192_rev2.pth'))
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#v.eval()
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r,l,o=v(torch.randn(1,80,256))
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