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cleanup logs
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@ -82,18 +82,18 @@ class InvokeAIDiffuserComponent:
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# flip because sigmas[0] is for the fully denoised image
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# percent_through must be <1
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percent_through = 1.0 - float(sigma_index.item() + 1) / float(self.model.sigmas.shape[0])
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print('estimated percent_through', percent_through, 'from sigma', sigma.item())
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#print('estimated percent_through', percent_through, 'from sigma', sigma.item())
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cross_attention_control_types_to_do = CrossAttentionControl.get_active_cross_attention_control_types_for_step(self.cross_attention_control_context, percent_through)
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if len(cross_attention_control_types_to_do)==0:
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print('not doing cross attention control')
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#print('not doing cross attention control')
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# faster batched path
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x_twice = torch.cat([x]*2)
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sigma_twice = torch.cat([sigma]*2)
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both_conditionings = torch.cat([unconditioning, conditioning])
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unconditioned_next_x, conditioned_next_x = self.model_forward_callback(x_twice, sigma_twice, both_conditionings).chunk(2)
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else:
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print('pct', percent_through, ': doing cross attention control on', cross_attention_control_types_to_do)
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#print('pct', percent_through, ': doing cross attention control on', cross_attention_control_types_to_do)
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# slower non-batched path (20% slower on mac MPS)
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# We are only interested in using attention maps for conditioned_next_x, but batching them with generation of
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# unconditioned_next_x causes attention maps to *also* be saved for the unconditioned_next_x.
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