add sd1.5 compatibility to controlnet-xs and fix unused_parameters error during training (#8606)

* add sd1.5 compatibility to controlnet-xs

* set use_linear_projection by base_block

* refine code style

src/diffusers/models/controlnet_xs.py

@@ -114,6 +114,7 @@ def get_down_block_adapter(
     cross_attention_dim: Optional[int] = 1024,
     add_downsample: bool = True,
     upcast_attention: Optional[bool] = False,
+    use_linear_projection: Optional[bool] = True,
 ):
     num_layers = 2  # only support sd + sdxl
 
@@ -152,7 +153,7 @@ def get_down_block_adapter(
                     in_channels=ctrl_out_channels,
                     num_layers=transformer_layers_per_block[i],
                     cross_attention_dim=cross_attention_dim,
-                    use_linear_projection=True,
+                    use_linear_projection=use_linear_projection,
                     upcast_attention=upcast_attention,
                     norm_num_groups=find_largest_factor(ctrl_out_channels, max_factor=max_norm_num_groups),
                 )
@@ -200,6 +201,7 @@ def get_mid_block_adapter(
     num_attention_heads: Optional[int] = 1,
     cross_attention_dim: Optional[int] = 1024,
     upcast_attention: bool = False,
+    use_linear_projection: bool = True,
 ):
     # Before the midblock application, information is concatted from base to control.
     # Concat doesn't require change in number of channels
@@ -214,7 +216,7 @@ def get_mid_block_adapter(
         resnet_groups=find_largest_factor(gcd(ctrl_channels, ctrl_channels + base_channels), max_norm_num_groups),
         cross_attention_dim=cross_attention_dim,
         num_attention_heads=num_attention_heads,
-        use_linear_projection=True,
+        use_linear_projection=use_linear_projection,
         upcast_attention=upcast_attention,
     )
 
@@ -308,6 +310,7 @@ class ControlNetXSAdapter(ModelMixin, ConfigMixin):
         transformer_layers_per_block: Union[int, Tuple[int]] = 1,
         upcast_attention: bool = True,
         max_norm_num_groups: int = 32,
+        use_linear_projection: bool = True,
     ):
         super().__init__()
 
@@ -381,6 +384,7 @@ class ControlNetXSAdapter(ModelMixin, ConfigMixin):
                     cross_attention_dim=cross_attention_dim[i],
                     add_downsample=not is_final_block,
                     upcast_attention=upcast_attention,
+                    use_linear_projection=use_linear_projection,
                 )
             )
 
@@ -393,6 +397,7 @@ class ControlNetXSAdapter(ModelMixin, ConfigMixin):
             num_attention_heads=num_attention_heads[-1],
             cross_attention_dim=cross_attention_dim[-1],
             upcast_attention=upcast_attention,
+            use_linear_projection=use_linear_projection,
         )
 
         # up
@@ -489,6 +494,7 @@ class ControlNetXSAdapter(ModelMixin, ConfigMixin):
             transformer_layers_per_block=unet.config.transformer_layers_per_block,
             upcast_attention=unet.config.upcast_attention,
             max_norm_num_groups=unet.config.norm_num_groups,
+            use_linear_projection=unet.config.use_linear_projection,
         )
 
         # ensure that the ControlNetXSAdapter is the same dtype as the UNet2DConditionModel
@@ -538,6 +544,7 @@ class UNetControlNetXSModel(ModelMixin, ConfigMixin):
         addition_embed_type: Optional[str] = None,
         addition_time_embed_dim: Optional[int] = None,
         upcast_attention: bool = True,
+        use_linear_projection: bool = True,
         time_cond_proj_dim: Optional[int] = None,
         projection_class_embeddings_input_dim: Optional[int] = None,
         # additional controlnet configs
@@ -595,7 +602,12 @@ class UNetControlNetXSModel(ModelMixin, ConfigMixin):
             time_embed_dim,
             cond_proj_dim=time_cond_proj_dim,
         )
-        self.ctrl_time_embedding = TimestepEmbedding(in_channels=time_embed_input_dim, time_embed_dim=time_embed_dim)
+        if ctrl_learn_time_embedding:
+            self.ctrl_time_embedding = TimestepEmbedding(
+                in_channels=time_embed_input_dim, time_embed_dim=time_embed_dim
+            )
+        else:
+            self.ctrl_time_embedding = None
 
         if addition_embed_type is None:
             self.base_add_time_proj = None
@@ -632,6 +644,7 @@ class UNetControlNetXSModel(ModelMixin, ConfigMixin):
                     cross_attention_dim=cross_attention_dim[i],
                     add_downsample=not is_final_block,
                     upcast_attention=upcast_attention,
+                    use_linear_projection=use_linear_projection,
                 )
             )
 
@@ -647,6 +660,7 @@ class UNetControlNetXSModel(ModelMixin, ConfigMixin):
             ctrl_num_attention_heads=ctrl_num_attention_heads[-1],
             cross_attention_dim=cross_attention_dim[-1],
             upcast_attention=upcast_attention,
+            use_linear_projection=use_linear_projection,
         )
 
         # # Create up blocks
@@ -690,6 +704,7 @@ class UNetControlNetXSModel(ModelMixin, ConfigMixin):
                     add_upsample=not is_final_block,
                     upcast_attention=upcast_attention,
                     norm_num_groups=norm_num_groups,
+                    use_linear_projection=use_linear_projection,
                 )
             )
 
@@ -754,6 +769,7 @@ class UNetControlNetXSModel(ModelMixin, ConfigMixin):
             "addition_embed_type",
             "addition_time_embed_dim",
             "upcast_attention",
+            "use_linear_projection",
             "time_cond_proj_dim",
             "projection_class_embeddings_input_dim",
         ]
@@ -1219,6 +1235,7 @@ class ControlNetXSCrossAttnDownBlock2D(nn.Module):
         cross_attention_dim: Optional[int] = 1024,
         add_downsample: bool = True,
         upcast_attention: Optional[bool] = False,
+        use_linear_projection: Optional[bool] = True,
     ):
         super().__init__()
         base_resnets = []
@@ -1270,7 +1287,7 @@ class ControlNetXSCrossAttnDownBlock2D(nn.Module):
                         in_channels=base_out_channels,
                         num_layers=transformer_layers_per_block[i],
                         cross_attention_dim=cross_attention_dim,
-                        use_linear_projection=True,
+                        use_linear_projection=use_linear_projection,
                         upcast_attention=upcast_attention,
                         norm_num_groups=norm_num_groups,
                     )
@@ -1282,7 +1299,7 @@ class ControlNetXSCrossAttnDownBlock2D(nn.Module):
                         in_channels=ctrl_out_channels,
                         num_layers=transformer_layers_per_block[i],
                         cross_attention_dim=cross_attention_dim,
-                        use_linear_projection=True,
+                        use_linear_projection=use_linear_projection,
                         upcast_attention=upcast_attention,
                         norm_num_groups=find_largest_factor(ctrl_out_channels, max_factor=ctrl_max_norm_num_groups),
                     )
@@ -1342,6 +1359,7 @@ class ControlNetXSCrossAttnDownBlock2D(nn.Module):
             ctrl_num_attention_heads = get_first_cross_attention(ctrl_downblock).heads
             cross_attention_dim = get_first_cross_attention(base_downblock).cross_attention_dim
             upcast_attention = get_first_cross_attention(base_downblock).upcast_attention
+            use_linear_projection = base_downblock.attentions[0].use_linear_projection
         else:
             has_crossattn = False
             transformer_layers_per_block = None
@@ -1349,6 +1367,7 @@ class ControlNetXSCrossAttnDownBlock2D(nn.Module):
             ctrl_num_attention_heads = None
             cross_attention_dim = None
             upcast_attention = None
+            use_linear_projection = None
         add_downsample = base_downblock.downsamplers is not None
 
         # create model
@@ -1367,6 +1386,7 @@ class ControlNetXSCrossAttnDownBlock2D(nn.Module):
             cross_attention_dim=cross_attention_dim,
             add_downsample=add_downsample,
             upcast_attention=upcast_attention,
+            use_linear_projection=use_linear_projection,
         )
 
         # # load weights
@@ -1527,6 +1547,7 @@ class ControlNetXSCrossAttnMidBlock2D(nn.Module):
         ctrl_num_attention_heads: Optional[int] = 1,
         cross_attention_dim: Optional[int] = 1024,
         upcast_attention: bool = False,
+        use_linear_projection: Optional[bool] = True,
     ):
         super().__init__()
 
@@ -1541,7 +1562,7 @@ class ControlNetXSCrossAttnMidBlock2D(nn.Module):
             resnet_groups=norm_num_groups,
             cross_attention_dim=cross_attention_dim,
             num_attention_heads=base_num_attention_heads,
-            use_linear_projection=True,
+            use_linear_projection=use_linear_projection,
             upcast_attention=upcast_attention,
         )
 
@@ -1556,7 +1577,7 @@ class ControlNetXSCrossAttnMidBlock2D(nn.Module):
             ),
             cross_attention_dim=cross_attention_dim,
             num_attention_heads=ctrl_num_attention_heads,
-            use_linear_projection=True,
+            use_linear_projection=use_linear_projection,
             upcast_attention=upcast_attention,
         )
 
@@ -1590,6 +1611,7 @@ class ControlNetXSCrossAttnMidBlock2D(nn.Module):
         ctrl_num_attention_heads = get_first_cross_attention(ctrl_midblock).heads
         cross_attention_dim = get_first_cross_attention(base_midblock).cross_attention_dim
         upcast_attention = get_first_cross_attention(base_midblock).upcast_attention
+        use_linear_projection = base_midblock.attentions[0].use_linear_projection
 
         # create model
         model = cls(
@@ -1603,6 +1625,7 @@ class ControlNetXSCrossAttnMidBlock2D(nn.Module):
             ctrl_num_attention_heads=ctrl_num_attention_heads,
             cross_attention_dim=cross_attention_dim,
             upcast_attention=upcast_attention,
+            use_linear_projection=use_linear_projection,
         )
 
         # load weights
@@ -1677,6 +1700,7 @@ class ControlNetXSCrossAttnUpBlock2D(nn.Module):
         cross_attention_dim: int = 1024,
         add_upsample: bool = True,
         upcast_attention: bool = False,
+        use_linear_projection: Optional[bool] = True,
     ):
         super().__init__()
         resnets = []
@@ -1714,7 +1738,7 @@ class ControlNetXSCrossAttnUpBlock2D(nn.Module):
                         in_channels=out_channels,
                         num_layers=transformer_layers_per_block[i],
                         cross_attention_dim=cross_attention_dim,
-                        use_linear_projection=True,
+                        use_linear_projection=use_linear_projection,
                         upcast_attention=upcast_attention,
                         norm_num_groups=norm_num_groups,
                     )
@@ -1753,12 +1777,14 @@ class ControlNetXSCrossAttnUpBlock2D(nn.Module):
             num_attention_heads = get_first_cross_attention(base_upblock).heads
             cross_attention_dim = get_first_cross_attention(base_upblock).cross_attention_dim
             upcast_attention = get_first_cross_attention(base_upblock).upcast_attention
+            use_linear_projection = base_upblock.attentions[0].use_linear_projection
         else:
             has_crossattn = False
             transformer_layers_per_block = None
             num_attention_heads = None
             cross_attention_dim = None
             upcast_attention = None
+            use_linear_projection = None
         add_upsample = base_upblock.upsamplers is not None
 
         # create model
@@ -1776,6 +1802,7 @@ class ControlNetXSCrossAttnUpBlock2D(nn.Module):
             cross_attention_dim=cross_attention_dim,
             add_upsample=add_upsample,
             upcast_attention=upcast_attention,
+            use_linear_projection=use_linear_projection,
         )
 
         # load weights