diff --git a/src/diffusers/models/autoencoders/autoencoder_kl.py b/src/diffusers/models/autoencoders/autoencoder_kl.py
index 0d2f5f74d325..6538522756f9 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl.py
@@ -62,6 +62,9 @@ class AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalModelMixin):
             If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
             can be fine-tuned / trained to a lower range without loosing too much precision in which case
             `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
+        mid_block_add_attention (`bool`, *optional*, default to `True`):
+            If enabled, the mid_block of the Encoder and Decoder will have attention blocks. If set to false, the
+            mid_block will only have resnet blocks
     """
 
     _supports_gradient_checkpointing = True
@@ -87,6 +90,7 @@ def __init__(
         force_upcast: float = True,
         use_quant_conv: bool = True,
         use_post_quant_conv: bool = True,
+        mid_block_add_attention: bool = True,
     ):
         super().__init__()
 
@@ -100,6 +104,7 @@ def __init__(
             act_fn=act_fn,
             norm_num_groups=norm_num_groups,
             double_z=True,
+            mid_block_add_attention=mid_block_add_attention,
         )
 
         # pass init params to Decoder
@@ -111,6 +116,7 @@ def __init__(
             layers_per_block=layers_per_block,
             norm_num_groups=norm_num_groups,
             act_fn=act_fn,
+            mid_block_add_attention=mid_block_add_attention,
         )
 
         self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1) if use_quant_conv else None