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Handle latent dim difference for image model in the VAE instead
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@ -1690,6 +1690,3 @@ class Kandinsky5_image(Kandinsky5):
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def concat_cond(self, **kwargs):
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return None
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def process_latent_out(self, latent): # input is still 5D, return single frame to decode with Flux VAE
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return self.latent_format.process_out(latent)[:, :, 0]
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@ -742,6 +742,8 @@ class VAE:
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self.throw_exception_if_invalid()
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pixel_samples = None
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do_tile = False
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if self.latent_dim == 2 and samples_in.ndim == 5:
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samples_in = samples_in[:, :, 0]
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try:
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memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
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model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
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