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Fix lowvram issue with ltxv vae.
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57f330caf9
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@ -378,7 +378,7 @@ class Decoder(nn.Module):
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assert (
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timestep is not None
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), "should pass timestep with timestep_conditioning=True"
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scaled_timestep = timestep * self.timestep_scale_multiplier
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scaled_timestep = timestep * self.timestep_scale_multiplier.to(dtype=sample.dtype, device=sample.device)
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for up_block in self.up_blocks:
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if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):
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@ -403,7 +403,7 @@ class Decoder(nn.Module):
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)
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ada_values = self.last_scale_shift_table[
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None, ..., None, None, None
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] + embedded_timestep.reshape(
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].to(device=sample.device, dtype=sample.dtype) + embedded_timestep.reshape(
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batch_size,
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2,
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-1,
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@ -697,7 +697,7 @@ class ResnetBlock3D(nn.Module):
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), "should pass timestep with timestep_conditioning=True"
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ada_values = self.scale_shift_table[
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None, ..., None, None, None
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] + timestep.reshape(
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].to(device=hidden_states.device, dtype=hidden_states.dtype) + timestep.reshape(
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batch_size,
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4,
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-1,
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@ -715,7 +715,7 @@ class ResnetBlock3D(nn.Module):
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if self.inject_noise:
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hidden_states = self._feed_spatial_noise(
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hidden_states, self.per_channel_scale1
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hidden_states, self.per_channel_scale1.to(device=hidden_states.device, dtype=hidden_states.dtype)
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)
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hidden_states = self.norm2(hidden_states)
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@ -731,7 +731,7 @@ class ResnetBlock3D(nn.Module):
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if self.inject_noise:
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hidden_states = self._feed_spatial_noise(
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hidden_states, self.per_channel_scale2
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hidden_states, self.per_channel_scale2.to(device=hidden_states.device, dtype=hidden_states.dtype)
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)
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input_tensor = self.norm3(input_tensor)
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