forked from mrq/DL-Art-School
uhh2.0
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@ -214,7 +214,7 @@ class TransformerDiffusionWithPointConditioning(nn.Module):
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cond_left = conditioning_input[:,:,:max(cond_start, 20)]
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left_pt = cond_start-1
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cond_right = conditioning_input[:,:,min(N+cond_start, conditioning_input.shape[-1]-20):]
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right_pt = cond_right.shape[-1] - (conditioning_input.shape[-1] - (N+cond_start))
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right_pt = min(cond_right.shape[-1]-1, cond_right.shape[-1] - (conditioning_input.shape[-1] - (N+cond_start)))
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elif cond_left is None:
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assert conditioning_input.shape[-1] - cond_start - N >= 0, f'Some sort of conditioning misalignment, {conditioning_input.shape[-1], cond_start, N}'
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cond_pre = conditioning_input[:,:,:cond_start]
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@ -313,12 +313,12 @@ def test_cheater_model():
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# For music:
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model = TransformerDiffusionWithPointConditioning(in_channels=256, out_channels=512, model_channels=1024,
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contraction_dim=512, num_heads=8, num_layers=15, dropout=0,
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contraction_dim=512, num_heads=8, num_layers=40, dropout=0,
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unconditioned_percentage=.4, checkpoint_conditioning=False,
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regularization=True, new_cond=True)
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print_network(model)
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for cs in range(276,cl.shape[-1]-clip.shape[-1]):
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o = model(clip, ts, cl, cond_start=cs)
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#for cs in range(276,cl.shape[-1]-clip.shape[-1]):
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# o = model(clip, ts, cl, cond_start=cs)
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pg = model.get_grad_norm_parameter_groups()
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def prmsz(lp):
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sz = 0
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