Adapt UNet2D for supre-resolution

src/diffusers/models/attention.py

@@ -100,6 +100,7 @@ def __init__(
         activation_fn: str = "geglu",
         num_embeds_ada_norm: Optional[int] = None,
         use_linear_projection: bool = False,
+        only_cross_attention: bool = False,
     ):
         super().__init__()
         self.use_linear_projection = use_linear_projection
@@ -157,6 +158,7 @@ def __init__(
                     activation_fn=activation_fn,
                     num_embeds_ada_norm=num_embeds_ada_norm,
                     attention_bias=attention_bias,
+                    only_cross_attention=only_cross_attention,
                 )
                 for d in range(num_layers)
             ]
@@ -387,14 +389,17 @@ def __init__(
         activation_fn: str = "geglu",
         num_embeds_ada_norm: Optional[int] = None,
         attention_bias: bool = False,
+        only_cross_attention: bool = False,
     ):
         super().__init__()
+        self.only_cross_attention = only_cross_attention
         self.attn1 = CrossAttention(
             query_dim=dim,
             heads=num_attention_heads,
             dim_head=attention_head_dim,
             dropout=dropout,
             bias=attention_bias,
+            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
         )  # is a self-attention
         self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
         self.attn2 = CrossAttention(
@@ -461,7 +466,11 @@ def forward(self, hidden_states, context=None, timestep=None):
         norm_hidden_states = (
             self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states)
         )
-        hidden_states = self.attn1(norm_hidden_states) + hidden_states
+
+        if self.only_cross_attention:
+            hidden_states = self.attn1(norm_hidden_states, context) + hidden_states
+        else:
+            hidden_states = self.attn1(norm_hidden_states) + hidden_states
 
         # 2. Cross-Attention
         norm_hidden_states = (

src/diffusers/models/unet_2d_blocks.py

@@ -34,6 +34,7 @@ def get_down_block(
     downsample_padding=None,
     dual_cross_attention=False,
     use_linear_projection=False,
+    only_cross_attention=False,
 ):
     down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
     if down_block_type == "DownBlock2D":
@@ -78,6 +79,7 @@ def get_down_block(
             attn_num_head_channels=attn_num_head_channels,
             dual_cross_attention=dual_cross_attention,
             use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
         )
     elif down_block_type == "SkipDownBlock2D":
         return SkipDownBlock2D(
@@ -143,6 +145,7 @@ def get_up_block(
     cross_attention_dim=None,
     dual_cross_attention=False,
     use_linear_projection=False,
+    only_cross_attention=False,
 ):
     up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
     if up_block_type == "UpBlock2D":
@@ -174,6 +177,7 @@ def get_up_block(
             attn_num_head_channels=attn_num_head_channels,
             dual_cross_attention=dual_cross_attention,
             use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
         )
     elif up_block_type == "AttnUpBlock2D":
         return AttnUpBlock2D(
@@ -530,6 +534,7 @@ def __init__(
         add_downsample=True,
         dual_cross_attention=False,
         use_linear_projection=False,
+        only_cross_attention=False,
     ):
         super().__init__()
         resnets = []
@@ -564,6 +569,7 @@ def __init__(
                         cross_attention_dim=cross_attention_dim,
                         norm_num_groups=resnet_groups,
                         use_linear_projection=use_linear_projection,
+                        only_cross_attention=only_cross_attention,
                     )
                 )
             else:
@@ -1129,6 +1135,7 @@ def __init__(
         add_upsample=True,
         dual_cross_attention=False,
         use_linear_projection=False,
+        only_cross_attention=False,
     ):
         super().__init__()
         resnets = []
@@ -1165,6 +1172,7 @@ def __init__(
                         cross_attention_dim=cross_attention_dim,
                         norm_num_groups=resnet_groups,
                         use_linear_projection=use_linear_projection,
+                        only_cross_attention=only_cross_attention,
                     )
                 )
             else:

src/diffusers/models/unet_2d_condition.py

@@ -98,6 +98,7 @@ def __init__(
             "DownBlock2D",
         ),
         up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
         block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
         layers_per_block: int = 2,
         downsample_padding: int = 1,
@@ -109,6 +110,7 @@ def __init__(
         attention_head_dim: Union[int, Tuple[int]] = 8,
         dual_cross_attention: bool = False,
         use_linear_projection: bool = False,
+        num_class_embeds: Optional[int] = None,
     ):
         super().__init__()
 
@@ -124,10 +126,17 @@ def __init__(
 
         self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
 
+        # class embedding
+        if num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+
         self.down_blocks = nn.ModuleList([])
         self.mid_block = None
         self.up_blocks = nn.ModuleList([])
 
+        if isinstance(only_cross_attention, bool):
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
         if isinstance(attention_head_dim, int):
             attention_head_dim = (attention_head_dim,) * len(down_block_types)
 
@@ -153,6 +162,7 @@ def __init__(
                 downsample_padding=downsample_padding,
                 dual_cross_attention=dual_cross_attention,
                 use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
             )
             self.down_blocks.append(down_block)
 
@@ -177,6 +187,7 @@ def __init__(
         # up
         reversed_block_out_channels = list(reversed(block_out_channels))
         reversed_attention_head_dim = list(reversed(attention_head_dim))
+        only_cross_attention = list(reversed(only_cross_attention))
         output_channel = reversed_block_out_channels[0]
         for i, up_block_type in enumerate(up_block_types):
             is_final_block = i == len(block_out_channels) - 1
@@ -207,6 +218,7 @@ def __init__(
                 attn_num_head_channels=reversed_attention_head_dim[i],
                 dual_cross_attention=dual_cross_attention,
                 use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
             )
             self.up_blocks.append(up_block)
             prev_output_channel = output_channel
@@ -258,6 +270,7 @@ def forward(
         sample: torch.FloatTensor,
         timestep: Union[torch.Tensor, float, int],
         encoder_hidden_states: torch.Tensor,
+        class_labels: Optional[torch.Tensor] = None,
         return_dict: bool = True,
     ) -> Union[UNet2DConditionOutput, Tuple]:
         r"""
@@ -310,6 +323,12 @@ def forward(
         t_emb = t_emb.to(dtype=self.dtype)
         emb = self.time_embedding(t_emb)
 
+        if self.config.num_class_embeds is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
+            emb = emb + class_emb
+
         # 2. pre-process
         sample = self.conv_in(sample)
 

src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py

@@ -166,6 +166,7 @@ def __init__(
             "CrossAttnUpBlockFlat",
             "CrossAttnUpBlockFlat",
         ),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
         block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
         layers_per_block: int = 2,
         downsample_padding: int = 1,
@@ -177,6 +178,7 @@ def __init__(
         attention_head_dim: Union[int, Tuple[int]] = 8,
         dual_cross_attention: bool = False,
         use_linear_projection: bool = False,
+        num_class_embeds: Optional[int] = None,
     ):
         super().__init__()
 
@@ -192,10 +194,17 @@ def __init__(
 
         self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
 
+        # class embedding
+        if num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+
         self.down_blocks = nn.ModuleList([])
         self.mid_block = None
         self.up_blocks = nn.ModuleList([])
 
+        if isinstance(only_cross_attention, bool):
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
         if isinstance(attention_head_dim, int):
             attention_head_dim = (attention_head_dim,) * len(down_block_types)
 
@@ -221,6 +230,7 @@ def __init__(
                 downsample_padding=downsample_padding,
                 dual_cross_attention=dual_cross_attention,
                 use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
             )
             self.down_blocks.append(down_block)
 
@@ -245,6 +255,7 @@ def __init__(
         # up
         reversed_block_out_channels = list(reversed(block_out_channels))
         reversed_attention_head_dim = list(reversed(attention_head_dim))
+        only_cross_attention = list(reversed(only_cross_attention))
         output_channel = reversed_block_out_channels[0]
         for i, up_block_type in enumerate(up_block_types):
             is_final_block = i == len(block_out_channels) - 1
@@ -275,6 +286,7 @@ def __init__(
                 attn_num_head_channels=reversed_attention_head_dim[i],
                 dual_cross_attention=dual_cross_attention,
                 use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
             )
             self.up_blocks.append(up_block)
             prev_output_channel = output_channel
@@ -326,6 +338,7 @@ def forward(
         sample: torch.FloatTensor,
         timestep: Union[torch.Tensor, float, int],
         encoder_hidden_states: torch.Tensor,
+        class_labels: Optional[torch.Tensor] = None,
         return_dict: bool = True,
     ) -> Union[UNet2DConditionOutput, Tuple]:
         r"""
@@ -378,6 +391,12 @@ def forward(
         t_emb = t_emb.to(dtype=self.dtype)
         emb = self.time_embedding(t_emb)
 
+        if self.config.num_class_embeds is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
+            emb = emb + class_emb
+
         # 2. pre-process
         sample = self.conv_in(sample)
 
@@ -648,6 +667,7 @@ def __init__(
         add_downsample=True,
         dual_cross_attention=False,
         use_linear_projection=False,
+        only_cross_attention=False,
     ):
         super().__init__()
         resnets = []
@@ -682,6 +702,7 @@ def __init__(
                         cross_attention_dim=cross_attention_dim,
                         norm_num_groups=resnet_groups,
                         use_linear_projection=use_linear_projection,
+                        only_cross_attention=only_cross_attention,
                     )
                 )
             else:
@@ -861,6 +882,7 @@ def __init__(
         add_upsample=True,
         dual_cross_attention=False,
         use_linear_projection=False,
+        only_cross_attention=False,
     ):
         super().__init__()
         resnets = []
@@ -897,6 +919,7 @@ def __init__(
                         cross_attention_dim=cross_attention_dim,
                         norm_num_groups=resnet_groups,
                         use_linear_projection=use_linear_projection,
+                        only_cross_attention=only_cross_attention,
                     )
                 )
             else: