From 53bbdc035398ddc93e78f186a1376fea7ac66ea5 Mon Sep 17 00:00:00 2001
From: patil-suraj <surajp815@gmail.com>
Date: Mon, 8 Aug 2022 23:13:11 +0530
Subject: [PATCH] add attention up/down blocks for VAE

---
 src/diffusers/models/unet_blocks.py | 140 ++++++++++++++++++++++++++++
 1 file changed, 140 insertions(+)

diff --git a/src/diffusers/models/unet_blocks.py b/src/diffusers/models/unet_blocks.py
index 66357d78e231..9d01949554e1 100644
--- a/src/diffusers/models/unet_blocks.py
+++ b/src/diffusers/models/unet_blocks.py
@@ -640,6 +640,79 @@ def forward(self, hidden_states):
         return hidden_states
 
 
+class AttnDownEncoderBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        output_scale_factor=1.0,
+        add_downsample=True,
+        downsample_padding=1,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=None,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            attentions.append(
+                AttentionBlockNew(
+                    out_channels,
+                    num_head_channels=attn_num_head_channels,
+                    rescale_output_factor=output_scale_factor,
+                    eps=resnet_eps,
+                    num_groups=resnet_groups,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample2D(
+                        in_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+    def forward(self, hidden_states):
+        for resnet, attn in zip(self.resnets, self.attentions):
+            hidden_states = resnet(hidden_states, temb=None)
+            hidden_states = attn(hidden_states)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+        return hidden_states
+
+
 class AttnSkipDownBlock2D(nn.Module):
     def __init__(
         self,
@@ -1087,6 +1160,73 @@ def forward(self, hidden_states):
         return hidden_states
 
 
+class AttnUpDecoderBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        output_scale_factor=1.0,
+        add_upsample=True,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        for i in range(num_layers):
+            input_channels = in_channels if i == 0 else out_channels
+
+            resnets.append(
+                ResnetBlock(
+                    in_channels=input_channels,
+                    out_channels=out_channels,
+                    temb_channels=None,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            attentions.append(
+                AttentionBlockNew(
+                    out_channels,
+                    num_head_channels=attn_num_head_channels,
+                    rescale_output_factor=output_scale_factor,
+                    eps=resnet_eps,
+                    num_groups=resnet_groups,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
+        else:
+            self.upsamplers = None
+
+    def forward(self, hidden_states):
+        for resnet, attn in zip(self.resnets, self.attentions):
+            hidden_states = resnet(hidden_states, temb=None)
+            hidden_states = attn(hidden_states)
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states)
+
+        return hidden_states
+
+
 class AttnSkipUpBlock2D(nn.Module):
     def __init__(
         self,