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https://github.com/lucidrains/DALLE2-pytorch.git
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force first unet in the cascade to be conditioned on image embeds
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@@ -1066,13 +1066,14 @@ class Unet(nn.Module):
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self,
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self,
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*,
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*,
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lowres_cond,
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lowres_cond,
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channels
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channels,
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cond_on_image_embeds
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):
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):
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if lowres_cond == self.lowres_cond and channels == self.channels:
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if lowres_cond == self.lowres_cond and channels == self.channels and cond_on_image_embeds == self.cond_on_image_embeds:
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return self
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return self
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updated_kwargs = {**self._locals, 'lowres_cond': lowres_cond, 'channels': channels}
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updated_kwargs = {'lowres_cond': lowres_cond, 'channels': channels, 'cond_on_image_embeds': cond_on_image_embeds}
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return self.__class__(**updated_kwargs)
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return self.__class__(**{**self._locals, **updated_kwargs})
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def forward_with_cond_scale(
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def forward_with_cond_scale(
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self,
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self,
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@@ -1279,6 +1280,7 @@ class Decoder(BaseGaussianDiffusion):
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one_unet = one_unet.cast_model_parameters(
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one_unet = one_unet.cast_model_parameters(
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lowres_cond = not is_first,
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lowres_cond = not is_first,
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cond_on_image_embeds = is_first,
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channels = unet_channels
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channels = unet_channels
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)
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)
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