Revert "add attention_head_dim"

This reverts commit 15f6b22.

Author: sayakpaul

src/diffusers/models/attention.py

@@ -158,7 +158,6 @@ def __init__(
         super().__init__()
         self.only_cross_attention = only_cross_attention
 
-        # We keep these boolean flags for backwards-compatibility.
         self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
         self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
         self.use_ada_layer_norm_single = norm_type == "ada_norm_single"

src/diffusers/models/unets/unet_i2vgen_xl.py

@@ -120,7 +120,6 @@ class I2VGenXLUNet(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
             If `None`, normalization and activation layers is skipped in post-processing.
         cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.
-        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
         num_attention_heads (`int`, *optional*): The number of attention heads.
     """
 
@@ -148,16 +147,10 @@ def __init__(
         layers_per_block: int = 2,
         norm_num_groups: Optional[int] = 32,
         cross_attention_dim: int = 1024,
-        attention_head_dim: Union[int, Tuple[int]] = None,
         num_attention_heads: Optional[Union[int, Tuple[int]]] = 64,
     ):
         super().__init__()
 
-        # We didn't define `attention_head_dim` when we first integrated this UNet. As a result,
-        # we had to use `num_attention_heads` in to pass values for arguments that actually denote
-        # attention head dimension. This is why we correct it here.
-        attention_head_dim = num_attention_heads or attention_head_dim
-
         # Check inputs
         if len(down_block_types) != len(up_block_types):
             raise ValueError(
@@ -179,7 +172,7 @@ def __init__(
 
         self.transformer_in = TransformerTemporalModel(
             num_attention_heads=8,
-            attention_head_dim=attention_head_dim,
+            attention_head_dim=num_attention_heads,
             in_channels=block_out_channels[0],
             num_layers=1,
             norm_num_groups=norm_num_groups,