Pass use_linear_projection parameter to mid block in UNetMotionModel (#7035)

* pass linear projection parameter to mid block

* add cond_proj_dim to motion UNet

* run style and quality checks

src/diffusers/models/unets/unet_motion_model.py

@@ -217,6 +217,7 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         use_motion_mid_block: int = True,
         encoder_hid_dim: Optional[int] = None,
         encoder_hid_dim_type: Optional[str] = None,
+        time_cond_proj_dim: Optional[int] = None,
     ):
         super().__init__()
 
@@ -252,9 +253,7 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         timestep_input_dim = block_out_channels[0]
 
         self.time_embedding = TimestepEmbedding(
-            timestep_input_dim,
-            time_embed_dim,
-            act_fn=act_fn,
+            timestep_input_dim, time_embed_dim, act_fn=act_fn, cond_proj_dim=time_cond_proj_dim
         )
 
         if encoder_hid_dim_type is None:
@@ -306,6 +305,7 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
                 num_attention_heads=num_attention_heads[-1],
                 resnet_groups=norm_num_groups,
                 dual_cross_attention=False,
+                use_linear_projection=use_linear_projection,
                 temporal_num_attention_heads=motion_num_attention_heads,
                 temporal_max_seq_length=motion_max_seq_length,
             )
@@ -321,6 +321,7 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
                 num_attention_heads=num_attention_heads[-1],
                 resnet_groups=norm_num_groups,
                 dual_cross_attention=False,
+                use_linear_projection=use_linear_projection,
             )
 
         # count how many layers upsample the images