Increase baseline codes recording across all dvae models
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@ -112,7 +112,7 @@ class DiffusionDVAE(nn.Module):
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#self.quantizer = Quantize(quantize_dim, num_discrete_codes, balancing_heuristic=True)
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#self.quantizer = Quantize(quantize_dim, num_discrete_codes, balancing_heuristic=True)
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self.quantizer = GumbelQuantizer(quantize_dim, quantize_dim, num_discrete_codes)
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self.quantizer = GumbelQuantizer(quantize_dim, quantize_dim, num_discrete_codes)
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# For recording codebook usage.
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# For recording codebook usage.
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self.codes = torch.zeros((131072,), dtype=torch.long)
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self.codes = torch.zeros((1228800,), dtype=torch.long)
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self.code_ind = 0
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self.code_ind = 0
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self.internal_step = 0
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self.internal_step = 0
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decoder_channels = [model_channels * channel_mult[s-1] for s in spectrogram_conditioning_levels]
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decoder_channels = [model_channels * channel_mult[s-1] for s in spectrogram_conditioning_levels]
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@ -147,7 +147,7 @@ class AttentionDVAE(nn.Module):
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self.normalization = normalization
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self.normalization = normalization
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self.record_codes = record_codes
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self.record_codes = record_codes
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if record_codes:
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if record_codes:
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self.codes = torch.zeros((32768,), dtype=torch.long)
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self.codes = torch.zeros((1228800,), dtype=torch.long)
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self.code_ind = 0
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self.code_ind = 0
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self.internal_step = 0
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self.internal_step = 0
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@ -142,7 +142,7 @@ class DiscreteVAE(nn.Module):
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self.normalization = normalization
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self.normalization = normalization
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self.record_codes = record_codes
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self.record_codes = record_codes
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if record_codes:
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if record_codes:
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self.codes = torch.zeros((32768,), dtype=torch.long)
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self.codes = torch.zeros((1228800,), dtype=torch.long)
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self.code_ind = 0
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self.code_ind = 0
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self.internal_step = 0
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self.internal_step = 0
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