diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py b/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py
index 4836de7e16ab..922fd15c08fb 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py
@@ -1091,8 +1091,6 @@ def forward(
         sample_posterior: bool = False,
         return_dict: bool = True,
         generator: Optional[torch.Generator] = None,
-        encoder_local_batch_size: int = 2,
-        decoder_local_batch_size: int = 2,
     ) -> Union[DecoderOutput, torch.Tensor]:
         r"""
         Args:
@@ -1103,18 +1101,14 @@ def forward(
                 Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
             generator (`torch.Generator`, *optional*):
                 PyTorch random number generator.
-            encoder_local_batch_size (`int`, *optional*, defaults to 2):
-                Local batch size for the encoder's batch inference.
-            decoder_local_batch_size (`int`, *optional*, defaults to 2):
-                Local batch size for the decoder's batch inference.
         """
         x = sample
-        posterior = self.encode(x, local_batch_size=encoder_local_batch_size).latent_dist
+        posterior = self.encode(x).latent_dist
         if sample_posterior:
             z = posterior.sample(generator=generator)
         else:
             z = posterior.mode()
-        dec = self.decode(z, local_batch_size=decoder_local_batch_size).sample
+        dec = self.decode(z).sample
 
         if not return_dict:
             return (dec,)