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Create PixArt dropout masks on the input device
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@ -296,7 +296,7 @@ class LabelEmbedder(nn.Module):
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Drops labels to enable classifier-free guidance.
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"""
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if force_drop_ids is None:
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drop_ids = torch.rand(labels.shape[0]).cuda() < self.dropout_prob
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drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
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else:
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drop_ids = force_drop_ids == 1
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labels = torch.where(drop_ids, self.num_classes, labels)
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@ -328,7 +328,7 @@ class CaptionEmbedder(nn.Module):
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Drops labels to enable classifier-free guidance.
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"""
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if force_drop_ids is None:
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drop_ids = torch.rand(caption.shape[0]).cuda() < self.uncond_prob
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drop_ids = torch.rand(caption.shape[0], device=caption.device) < self.uncond_prob
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else:
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drop_ids = force_drop_ids == 1
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caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption)
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@ -363,7 +363,7 @@ class CaptionEmbedderDoubleBr(nn.Module):
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Drops labels to enable classifier-free guidance.
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"""
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if force_drop_ids is None:
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drop_ids = torch.rand(global_caption.shape[0]).cuda() < self.uncond_prob
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drop_ids = torch.rand(global_caption.shape[0], device=global_caption.device) < self.uncond_prob
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else:
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drop_ids = force_drop_ids == 1
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global_caption = torch.where(drop_ids[:, None], self.embedding, global_caption)
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