[Refactor] splitingResnetBlock2D into multiple blocks

src/diffusers/models/resnet.py

@@ -42,6 +42,156 @@
 )
 
 
+class ResnetBlockCondNorm2D(nn.Module):
+    r"""
+    A Resnet block that use normalization layer that incorporate conditioning information.
+
+    Parameters:
+        in_channels (`int`): The number of channels in the input.
+        out_channels (`int`, *optional*, default to be `None`):
+            The number of output channels for the first conv2d layer. If None, same as `in_channels`.
+        dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
+        temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
+        groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
+        groups_out (`int`, *optional*, default to None):
+            The number of groups to use for the second normalization layer. if set to None, same as `groups`.
+        eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
+        non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use.
+        time_embedding_norm (`str`, *optional*, default to `"ada_group"` ):
+            The normalization layer for time embedding `temb`. Currently only support "ada_group" or "spatial".
+        kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see
+            [`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
+        output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
+        use_in_shortcut (`bool`, *optional*, default to `True`):
+            If `True`, add a 1x1 nn.conv2d layer for skip-connection.
+        up (`bool`, *optional*, default to `False`): If `True`, add an upsample layer.
+        down (`bool`, *optional*, default to `False`): If `True`, add a downsample layer.
+        conv_shortcut_bias (`bool`, *optional*, default to `True`):  If `True`, adds a learnable bias to the
+            `conv_shortcut` output.
+        conv_2d_out_channels (`int`, *optional*, default to `None`): the number of channels in the output.
+            If None, same as `out_channels`.
+    """
+
+    def __init__(
+        self,
+        *,
+        in_channels: int,
+        out_channels: Optional[int] = None,
+        conv_shortcut: bool = False,
+        dropout: float = 0.0,
+        temb_channels: int = 512,
+        groups: int = 32,
+        groups_out: Optional[int] = None,
+        eps: float = 1e-6,
+        non_linearity: str = "swish",
+        time_embedding_norm: str = "ada_group",  # ada_group, spatial
+        output_scale_factor: float = 1.0,
+        use_in_shortcut: Optional[bool] = None,
+        up: bool = False,
+        down: bool = False,
+        conv_shortcut_bias: bool = True,
+        conv_2d_out_channels: Optional[int] = None,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+        self.use_conv_shortcut = conv_shortcut
+        self.up = up
+        self.down = down
+        self.output_scale_factor = output_scale_factor
+        self.time_embedding_norm = time_embedding_norm
+
+        conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
+
+        if groups_out is None:
+            groups_out = groups
+
+        if self.time_embedding_norm == "ada_group":  # ada_group
+            self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps)
+        elif self.time_embedding_norm == "spatial":
+            self.norm1 = SpatialNorm(in_channels, temb_channels)
+        else:
+            raise ValueError(f" unsupported time_embedding_norm: {self.time_embedding_norm}")
+
+        self.conv1 = conv_cls(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+        if self.time_embedding_norm == "ada_group":  # ada_group
+            self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps)
+        elif self.time_embedding_norm == "spatial":  # spatial
+            self.norm2 = SpatialNorm(out_channels, temb_channels)
+        else:
+            raise ValueError(f" unsupported time_embedding_norm: {self.time_embedding_norm}")
+
+        self.dropout = torch.nn.Dropout(dropout)
+
+        conv_2d_out_channels = conv_2d_out_channels or out_channels
+        self.conv2 = conv_cls(out_channels, conv_2d_out_channels, kernel_size=3, stride=1, padding=1)
+
+        self.nonlinearity = get_activation(non_linearity)
+
+        self.upsample = self.downsample = None
+        if self.up:
+            self.upsample = Upsample2D(in_channels, use_conv=False)
+        elif self.down:
+            self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
+
+        self.use_in_shortcut = self.in_channels != conv_2d_out_channels if use_in_shortcut is None else use_in_shortcut
+
+        self.conv_shortcut = None
+        if self.use_in_shortcut:
+            self.conv_shortcut = conv_cls(
+                in_channels,
+                conv_2d_out_channels,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=conv_shortcut_bias,
+            )
+
+    def forward(
+        self,
+        input_tensor: torch.FloatTensor,
+        temb: torch.FloatTensor,
+        scale: float = 1.0,
+    ) -> torch.FloatTensor:
+        hidden_states = input_tensor
+
+        hidden_states = self.norm1(hidden_states, temb)
+
+        hidden_states = self.nonlinearity(hidden_states)
+
+        if self.upsample is not None:
+            # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
+            if hidden_states.shape[0] >= 64:
+                input_tensor = input_tensor.contiguous()
+                hidden_states = hidden_states.contiguous()
+            input_tensor = self.upsample(input_tensor, scale=scale)
+            hidden_states = self.upsample(hidden_states, scale=scale)
+
+        elif self.downsample is not None:
+            input_tensor = self.downsample(input_tensor, scale=scale)
+            hidden_states = self.downsample(hidden_states, scale=scale)
+
+        hidden_states = self.conv1(hidden_states, scale) if not USE_PEFT_BACKEND else self.conv1(hidden_states)
+
+        hidden_states = self.norm2(hidden_states, temb)
+
+        hidden_states = self.nonlinearity(hidden_states)
+
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.conv2(hidden_states, scale) if not USE_PEFT_BACKEND else self.conv2(hidden_states)
+
+        if self.conv_shortcut is not None:
+            input_tensor = (
+                self.conv_shortcut(input_tensor, scale) if not USE_PEFT_BACKEND else self.conv_shortcut(input_tensor)
+            )
+
+        output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
+
+        return output_tensor
+
+
 class ResnetBlock2D(nn.Module):
     r"""
     A Resnet block.
@@ -58,8 +208,8 @@ class ResnetBlock2D(nn.Module):
         eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
         non_linearity (`str`, *optional*, default to `"swish"`): the activation function to use.
         time_embedding_norm (`str`, *optional*, default to `"default"` ): Time scale shift config.
-            By default, apply timestep embedding conditioning with a simple shift mechanism. Choose "scale_shift" or
-            "ada_group" for a stronger conditioning with scale and shift.
+            By default, apply timestep embedding conditioning with a simple shift mechanism. Choose "scale_shift"
+            for a stronger conditioning with scale and shift.
         kernel (`torch.FloatTensor`, optional, default to None): FIR filter, see
             [`~models.resnet.FirUpsample2D`] and [`~models.resnet.FirDownsample2D`].
         output_scale_factor (`float`, *optional*, default to be `1.0`): the scale factor to use for the output.
@@ -87,7 +237,7 @@ def __init__(
         eps: float = 1e-6,
         non_linearity: str = "swish",
         skip_time_act: bool = False,
-        time_embedding_norm: str = "default",  # default, scale_shift, ada_group, spatial
+        time_embedding_norm: str = "default",  # default, scale_shift,
         kernel: Optional[torch.FloatTensor] = None,
         output_scale_factor: float = 1.0,
         use_in_shortcut: Optional[bool] = None,
@@ -97,7 +247,15 @@ def __init__(
         conv_2d_out_channels: Optional[int] = None,
     ):
         super().__init__()
-        self.pre_norm = pre_norm
+        if time_embedding_norm == "ada_group":
+            raise ValueError(
+                "This class cannot be used with `time_embedding_norm==ada_group`, please use `ResnetBlockCondNorm2D` instead",
+            )
+        if time_embedding_norm == "spatial":
+            raise ValueError(
+                "This class cannot be used with `time_embedding_norm==spatial`, please use `ResnetBlockCondNorm2D` instead",
+            )
+
         self.pre_norm = True
         self.in_channels = in_channels
         out_channels = in_channels if out_channels is None else out_channels
@@ -115,12 +273,7 @@ def __init__(
         if groups_out is None:
             groups_out = groups
 
-        if self.time_embedding_norm == "ada_group":
-            self.norm1 = AdaGroupNorm(temb_channels, in_channels, groups, eps=eps)
-        elif self.time_embedding_norm == "spatial":
-            self.norm1 = SpatialNorm(in_channels, temb_channels)
-        else:
-            self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
+        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
 
         self.conv1 = conv_cls(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
 
@@ -129,19 +282,12 @@ def __init__(
                 self.time_emb_proj = linear_cls(temb_channels, out_channels)
             elif self.time_embedding_norm == "scale_shift":
                 self.time_emb_proj = linear_cls(temb_channels, 2 * out_channels)
-            elif self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
-                self.time_emb_proj = None
             else:
                 raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
         else:
             self.time_emb_proj = None
 
-        if self.time_embedding_norm == "ada_group":
-            self.norm2 = AdaGroupNorm(temb_channels, out_channels, groups_out, eps=eps)
-        elif self.time_embedding_norm == "spatial":
-            self.norm2 = SpatialNorm(out_channels, temb_channels)
-        else:
-            self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
+        self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
 
         self.dropout = torch.nn.Dropout(dropout)
         conv_2d_out_channels = conv_2d_out_channels or out_channels
@@ -188,11 +334,7 @@ def forward(
     ) -> torch.FloatTensor:
         hidden_states = input_tensor
 
-        if self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
-            hidden_states = self.norm1(hidden_states, temb)
-        else:
-            hidden_states = self.norm1(hidden_states)
-
+        hidden_states = self.norm1(hidden_states)
         hidden_states = self.nonlinearity(hidden_states)
 
         if self.upsample is not None:
@@ -233,17 +375,20 @@ def forward(
                 else self.time_emb_proj(temb)[:, :, None, None]
             )
 
-        if temb is not None and self.time_embedding_norm == "default":
-            hidden_states = hidden_states + temb
-
-        if self.time_embedding_norm == "ada_group" or self.time_embedding_norm == "spatial":
-            hidden_states = self.norm2(hidden_states, temb)
-        else:
+        if self.time_embedding_norm == "default":
+            if temb is not None:
+                hidden_states = hidden_states + temb
             hidden_states = self.norm2(hidden_states)
-
-        if temb is not None and self.time_embedding_norm == "scale_shift":
+        elif self.time_embedding_norm == "scale_shift":
+            if temb is None:
+                raise ValueError(
+                    f" `temb` should not be None when `time_embedding_norm` is {self.time_embedding_norm}"
+                )
             scale, shift = torch.chunk(temb, 2, dim=1)
+            hidden_states = self.norm2(hidden_states)
             hidden_states = hidden_states * (1 + scale) + shift
+        else:
+            hidden_states = self.norm2(hidden_states)
 
         hidden_states = self.nonlinearity(hidden_states)