put encoder_attention_mask param back into Simple block forward interfaces, to ensure consistency of forward interface.

Author: Birch-san

src/diffusers/models/unet_2d_blocks.py

@@ -673,6 +673,8 @@ def forward(
         encoder_hidden_states: Optional[torch.FloatTensor] = None,
         attention_mask: Optional[torch.FloatTensor] = None,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        # parameter exists only for interface-compatibility with other blocks. prefer attention_mask
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
     ):
         cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
         hidden_states = self.resnets[0](hidden_states, temb)
@@ -1524,6 +1526,8 @@ def forward(
         encoder_hidden_states: Optional[torch.FloatTensor] = None,
         attention_mask: Optional[torch.FloatTensor] = None,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        # parameter exists only for interface-compatibility with other blocks. prefer attention_mask
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
     ):
         output_states = ()
         cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
@@ -2623,6 +2627,8 @@ def forward(
         upsample_size: Optional[int] = None,
         attention_mask: Optional[torch.FloatTensor] = None,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        # parameter exists only for interface-compatibility with other blocks. prefer attention_mask
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
     ):
         cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
         for resnet, attn in zip(self.resnets, self.attentions):

src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py

@@ -1707,6 +1707,8 @@ def forward(
         encoder_hidden_states: Optional[torch.FloatTensor] = None,
         attention_mask: Optional[torch.FloatTensor] = None,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        # parameter exists only for interface-compatibility with other blocks. prefer attention_mask
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
     ):
         cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
         hidden_states = self.resnets[0](hidden_states, temb)