update mid block (#70)

* update mid block

* finish mid block

src/diffusers/models/attention.py

 import math
+from inspect import isfunction
 
 import torch
+import torch.nn.functional as F
 from torch import nn
 
 
@@ -43,18 +45,16 @@ class AttentionBlock(nn.Module):
         self,
         channels,
         num_heads=1,
-        num_head_channels=-1,
+        num_head_channels=None,
         num_groups=32,
-        use_checkpoint=False,
         encoder_channels=None,
-        use_new_attention_order=False,  # TODO(Patrick) -> is never used, maybe delete?
         overwrite_qkv=False,
         overwrite_linear=False,
         rescale_output_factor=1.0,
     ):
         super().__init__()
         self.channels = channels
-        if num_head_channels == -1:
+        if num_head_channels is None:
             self.num_heads = num_heads
         else:
             assert (
@@ -62,7 +62,6 @@ class AttentionBlock(nn.Module):
             ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
             self.num_heads = channels // num_head_channels
 
-        self.use_checkpoint = use_checkpoint
         self.norm = nn.GroupNorm(num_channels=channels, num_groups=num_groups, eps=1e-5, affine=True)
         self.qkv = nn.Conv1d(channels, channels * 3, 1)
         self.n_heads = self.num_heads
@@ -160,115 +159,135 @@ class AttentionBlock(nn.Module):
         return result
 
 
-# unet_score_estimation.py
-# class AttnBlockpp(nn.Module):
-#    """Channel-wise self-attention block. Modified from DDPM."""
-#
-#    def __init__(
-#        self,
-#        channels,
-#        skip_rescale=False,
-#        init_scale=0.0,
-#        num_heads=1,
-#        num_head_channels=-1,
-#        use_checkpoint=False,
-#        encoder_channels=None,
-#        use_new_attention_order=False,  # TODO(Patrick) -> is never used, maybe delete?
-#        overwrite_qkv=False,
-#        overwrite_from_grad_tts=False,
-#    ):
-#        super().__init__()
-#        num_groups = min(channels // 4, 32)
-#        self.GroupNorm_0 = nn.GroupNorm(num_groups=num_groups, num_channels=channels, eps=1e-6)
-#        self.NIN_0 = NIN(channels, channels)
-#        self.NIN_1 = NIN(channels, channels)
-#        self.NIN_2 = NIN(channels, channels)
-#        self.NIN_3 = NIN(channels, channels, init_scale=init_scale)
-#        self.skip_rescale = skip_rescale
-#
-#        self.channels = channels
-#        if num_head_channels == -1:
-#            self.num_heads = num_heads
-#        else:
-#            assert (
-#                channels % num_head_channels == 0
-#            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
-#            self.num_heads = channels // num_head_channels
-#
-#        self.use_checkpoint = use_checkpoint
-#        self.norm = nn.GroupNorm(num_channels=channels, num_groups=num_groups, eps=1e-6)
-#        self.qkv = nn.Conv1d(channels, channels * 3, 1)
-#        self.n_heads = self.num_heads
-#
-#        self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
-#
-#        self.is_weight_set = False
-#
-#    def set_weights(self):
-#        self.qkv.weight.data = torch.concat([self.NIN_0.W.data.T, self.NIN_1.W.data.T, self.NIN_2.W.data.T], dim=0)[:, :, None]
-#        self.qkv.bias.data = torch.concat([self.NIN_0.b.data, self.NIN_1.b.data, self.NIN_2.b.data], dim=0)
-#
-#        self.proj_out.weight.data = self.NIN_3.W.data.T[:, :, None]
-#        self.proj_out.bias.data = self.NIN_3.b.data
-#
-#        self.norm.weight.data = self.GroupNorm_0.weight.data
-#        self.norm.bias.data = self.GroupNorm_0.bias.data
-#
-#    def forward(self, x):
-#        if not self.is_weight_set:
-#            self.set_weights()
-#            self.is_weight_set = True
-#
-#        B, C, H, W = x.shape
-#        h = self.GroupNorm_0(x)
-#        q = self.NIN_0(h)
-#        k = self.NIN_1(h)
-#        v = self.NIN_2(h)
-#
-#        w = torch.einsum("bchw,bcij->bhwij", q, k) * (int(C) ** (-0.5))
-#        w = torch.reshape(w, (B, H, W, H * W))
-#        w = F.softmax(w, dim=-1)
-#        w = torch.reshape(w, (B, H, W, H, W))
-#        h = torch.einsum("bhwij,bcij->bchw", w, v)
-#        h = self.NIN_3(h)
-#
-#        if not self.skip_rescale:
-#            result = x + h
-#        else:
-#            result = (x + h) / np.sqrt(2.0)
-#
-#        result = self.forward_2(x)
-#
-#        return result
-#
-#    def forward_2(self, x, encoder_out=None):
-#        b, c, *spatial = x.shape
-#        hid_states = self.norm(x).view(b, c, -1)
-#
-#        qkv = self.qkv(hid_states)
-#        bs, width, length = qkv.shape
-#        assert width % (3 * self.n_heads) == 0
-#        ch = width // (3 * self.n_heads)
-#        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
-#
-#        if encoder_out is not None:
-#            encoder_kv = self.encoder_kv(encoder_out)
-#            assert encoder_kv.shape[1] == self.n_heads * ch * 2
-#            ek, ev = encoder_kv.reshape(bs * self.n_heads, ch * 2, -1).split(ch, dim=1)
-#            k = torch.cat([ek, k], dim=-1)
-#            v = torch.cat([ev, v], dim=-1)
-#
-#        scale = 1 / math.sqrt(math.sqrt(ch))
-#        weight = torch.einsum("bct,bcs->bts", q * scale, k * scale)  # More stable with f16 than dividing afterwards
-#        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
-#
-#        a = torch.einsum("bts,bcs->bct", weight, v)
-#        h = a.reshape(bs, -1, length)
-#
-#        h = self.proj_out(h)
-#        h = h.reshape(b, c, *spatial)
-#
-#        return (x + h) / np.sqrt(2.0)
+class SpatialTransformer(nn.Module):
+    """
+    Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply
+    standard transformer action. Finally, reshape to image
+    """
+
+    def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0.0, context_dim=None):
+        super().__init__()
+        self.in_channels = in_channels
+        inner_dim = n_heads * d_head
+        self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+
+        self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
+                for d in range(depth)
+            ]
+        )
+
+        self.proj_out = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0))
+
+    def forward(self, x, context=None):
+        # note: if no context is given, cross-attention defaults to self-attention
+        b, c, h, w = x.shape
+        x_in = x
+        x = self.norm(x)
+        x = self.proj_in(x)
+        x = x.permute(0, 2, 3, 1).reshape(b, h * w, c)
+        for block in self.transformer_blocks:
+            x = block(x, context=context)
+        x = x.reshape(b, h, w, c).permute(0, 3, 1, 2)
+        x = self.proj_out(x)
+        return x + x_in
+
+
+class BasicTransformerBlock(nn.Module):
+    def __init__(self, dim, n_heads, d_head, dropout=0.0, context_dim=None, gated_ff=True, checkpoint=True):
+        super().__init__()
+        self.attn1 = CrossAttention(
+            query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout
+        )  # is a self-attention
+        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+        self.attn2 = CrossAttention(
+            query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout
+        )  # is self-attn if context is none
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+        self.norm3 = nn.LayerNorm(dim)
+        self.checkpoint = checkpoint
+
+    def forward(self, x, context=None):
+        x = self.attn1(self.norm1(x)) + x
+        x = self.attn2(self.norm2(x), context=context) + x
+        x = self.ff(self.norm3(x)) + x
+        return x
+
+
+class CrossAttention(nn.Module):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.scale = dim_head**-0.5
+        self.heads = heads
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
+
+    def reshape_heads_to_batch_dim(self, tensor):
+        batch_size, seq_len, dim = tensor.shape
+        head_size = self.heads
+        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
+        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
+        return tensor
+
+    def reshape_batch_dim_to_heads(self, tensor):
+        batch_size, seq_len, dim = tensor.shape
+        head_size = self.heads
+        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
+        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
+        return tensor
+
+    def forward(self, x, context=None, mask=None):
+        batch_size, sequence_length, dim = x.shape
+
+        h = self.heads
+
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        q = self.reshape_heads_to_batch_dim(q)
+        k = self.reshape_heads_to_batch_dim(k)
+        v = self.reshape_heads_to_batch_dim(v)
+
+        sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
+
+        if exists(mask):
+            mask = mask.reshape(batch_size, -1)
+            max_neg_value = -torch.finfo(sim.dtype).max
+            mask = mask[:, None, :].repeat(h, 1, 1)
+            sim.masked_fill_(~mask, max_neg_value)
+
+        # attention, what we cannot get enough of
+        attn = sim.softmax(dim=-1)
+
+        out = torch.einsum("b i j, b j d -> b i d", attn, v)
+        out = self.reshape_batch_dim_to_heads(out)
+        return self.to_out(out)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU()) if not glu else GEGLU(dim, inner_dim)
+
+        self.net = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))
+
+    def forward(self, x):
+        return self.net(x)
 
 
 # TODO(Patrick) - this can and should be removed
@@ -287,3 +306,24 @@ class NIN(nn.Module):
         super().__init__()
         self.W = nn.Parameter(torch.zeros(in_dim, num_units), requires_grad=True)
         self.b = nn.Parameter(torch.zeros(num_units), requires_grad=True)
+
+
+def exists(val):
+    return val is not None
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+# feedforward
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)

src/diffusers/models/unet.py

@@ -23,6 +23,7 @@ from ..modeling_utils import ModelMixin
 from .attention import AttentionBlock
 from .embeddings import get_timestep_embedding
 from .resnet import Downsample2D, ResnetBlock2D, Upsample2D
+from .unet_new import UNetMidBlock2D
 
 
 def nonlinearity(x):
@@ -105,13 +106,8 @@ class UNetModel(ModelMixin, ConfigMixin):
             self.down.append(down)
 
         # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock2D(
-            in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout
-        )
-        self.mid.attn_1 = AttentionBlock(block_in, overwrite_qkv=True)
-        self.mid.block_2 = ResnetBlock2D(
-            in_channels=block_in, out_channels=block_in, temb_channels=self.temb_ch, dropout=dropout
+        self.mid = UNetMidBlock2D(
+            in_channels=block_in, temb_channels=self.temb_ch, dropout=dropout, overwrite_qkv=True, overwrite_unet=True
         )
 
         # upsampling
@@ -171,10 +167,10 @@ class UNetModel(ModelMixin, ConfigMixin):
                 hs.append(self.down[i_level].downsample(hs[-1]))
 
         # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
+        h = self.mid(hs[-1], temb)
+        #        h = self.mid.block_1(h, temb)
+        #        h = self.mid.attn_1(h)
+        #        h = self.mid.block_2(h, temb)
 
         # upsampling
         for i_level in reversed(range(self.num_resolutions)):

src/diffusers/models/unet_glide.py

@@ -7,6 +7,7 @@ from ..modeling_utils import ModelMixin
 from .attention import AttentionBlock
 from .embeddings import get_timestep_embedding
 from .resnet import Downsample2D, ResnetBlock2D, Upsample2D
+from .unet_new import UNetMidBlock2D
 
 
 def convert_module_to_f16(l):
@@ -193,7 +194,6 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
                     layers.append(
                         AttentionBlock(
                             ch,
-                            use_checkpoint=use_checkpoint,
                             num_heads=num_heads,
                             num_head_channels=num_head_channels,
                             encoder_channels=transformer_dim,
@@ -226,6 +226,20 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
                 ds *= 2
                 self._feature_size += ch
 
+        self.mid = UNetMidBlock2D(
+            in_channels=ch,
+            dropout=dropout,
+            temb_channels=time_embed_dim,
+            resnet_eps=1e-5,
+            resnet_act_fn="silu",
+            resnet_time_scale_shift="scale_shift" if use_scale_shift_norm else "default",
+            attn_num_heads=num_heads,
+            attn_num_head_channels=num_head_channels,
+            attn_encoder_channels=transformer_dim,
+        )
+
+        # TODO(Patrick) - delete after weight conversion
+        # init to be able to overwrite `self.mid`
         self.middle_block = TimestepEmbedSequential(
             ResnetBlock2D(
                 in_channels=ch,
@@ -238,7 +252,6 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
             ),
             AttentionBlock(
                 ch,
-                use_checkpoint=use_checkpoint,
                 num_heads=num_heads,
                 num_head_channels=num_head_channels,
                 encoder_channels=transformer_dim,
@@ -253,6 +266,10 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
                 overwrite_for_glide=True,
             ),
         )
+        self.mid.resnet_1 = self.middle_block[0]
+        self.mid.attn = self.middle_block[1]
+        self.mid.resnet_2 = self.middle_block[2]
+
         self._feature_size += ch
 
         self.output_blocks = nn.ModuleList([])
@@ -276,7 +293,6 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
                     layers.append(
                         AttentionBlock(
                             ch,
-                            use_checkpoint=use_checkpoint,
                             num_heads=num_heads_upsample,
                             num_head_channels=num_head_channels,
                             encoder_channels=transformer_dim,
@@ -343,7 +359,7 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
         for module in self.input_blocks:
             h = module(h, emb)
             hs.append(h)
-        h = self.middle_block(h, emb)
+        h = self.mid(h, emb)
         for module in self.output_blocks:
             h = torch.cat([h, hs.pop()], dim=1)
             h = module(h, emb)
@@ -438,7 +454,7 @@ class GlideTextToImageUNetModel(GlideUNetModel):
         for module in self.input_blocks:
             h = module(h, emb, transformer_out)
             hs.append(h)
-        h = self.middle_block(h, emb, transformer_out)
+        h = self.mid(h, emb, transformer_out)
         for module in self.output_blocks:
             other = hs.pop()
             h = torch.cat([h, other], dim=1)

src/diffusers/models/unet_grad_tts.py

@@ -133,6 +133,8 @@ class UNetGradTTSModel(ModelMixin, ConfigMixin):
             overwrite_for_grad_tts=True,
         )
 
+#        self.mid = UNetMidBlock2D
+
         for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
             self.ups.append(
                 torch.nn.ModuleList(

src/diffusers/models/unet_ldm.py

@@ -11,6 +11,7 @@ from ..modeling_utils import ModelMixin
 from .attention import AttentionBlock
 from .embeddings import get_timestep_embedding
 from .resnet import Downsample2D, ResnetBlock2D, Upsample2D
+from .unet_new import UNetMidBlock2D
 
 
 # from .resnet import ResBlock
@@ -239,14 +240,12 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
             conv_resample=conv_resample,
             dims=dims,
             num_classes=num_classes,
-            use_checkpoint=use_checkpoint,
             use_fp16=use_fp16,
             num_heads=num_heads,
             num_head_channels=num_head_channels,
             num_heads_upsample=num_heads_upsample,
             use_scale_shift_norm=use_scale_shift_norm,
             resblock_updown=resblock_updown,
-            use_new_attention_order=use_new_attention_order,
             use_spatial_transformer=use_spatial_transformer,
             transformer_depth=transformer_depth,
             context_dim=context_dim,
@@ -283,7 +282,6 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
         self.channel_mult = channel_mult
         self.conv_resample = conv_resample
         self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
         self.dtype_ = torch.float16 if use_fp16 else torch.float32
         self.num_heads = num_heads
         self.num_head_channels = num_head_channels
@@ -333,10 +331,8 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
                     layers.append(
                         AttentionBlock(
                             ch,
-                            use_checkpoint=use_checkpoint,
                             num_heads=num_heads,
                             num_head_channels=dim_head,
-                            use_new_attention_order=use_new_attention_order,
                         )
                         if not use_spatial_transformer
                         else SpatialTransformer(
@@ -366,6 +362,25 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
         if legacy:
             # num_heads = 1
             dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+
+        if dim_head < 0:
+            dim_head = None
+        self.mid = UNetMidBlock2D(
+            in_channels=ch,
+            dropout=dropout,
+            temb_channels=time_embed_dim,
+            resnet_eps=1e-5,
+            resnet_act_fn="silu",
+            resnet_time_scale_shift="scale_shift" if use_scale_shift_norm else "default",
+            attention_layer_type="self" if not use_spatial_transformer else "spatial",
+            attn_num_heads=num_heads,
+            attn_num_head_channels=dim_head,
+            attn_depth=transformer_depth,
+            attn_encoder_channels=context_dim,
+        )
+
+        # TODO(Patrick) - delete after weight conversion
+        # init to be able to overwrite `self.mid`
         self.middle_block = TimestepEmbedSequential(
             ResnetBlock2D(
                 in_channels=ch,
@@ -378,10 +393,8 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
             ),
             AttentionBlock(
                 ch,
-                use_checkpoint=use_checkpoint,
                 num_heads=num_heads,
                 num_head_channels=dim_head,
-                use_new_attention_order=use_new_attention_order,
             )
             if not use_spatial_transformer
             else SpatialTransformer(ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim),
@@ -395,6 +408,10 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
                 overwrite_for_ldm=True,
             ),
         )
+        self.mid.resnet_1 = self.middle_block[0]
+        self.mid.attn = self.middle_block[1]
+        self.mid.resnet_2 = self.middle_block[2]
+
         self._feature_size += ch
 
         self.output_blocks = nn.ModuleList([])
@@ -425,10 +442,8 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
                     layers.append(
                         AttentionBlock(
                             ch,
-                            use_checkpoint=use_checkpoint,
                             num_heads=num_heads_upsample,
                             num_head_channels=dim_head,
-                            use_new_attention_order=use_new_attention_order,
                         )
                         if not use_spatial_transformer
                         else SpatialTransformer(
@@ -493,7 +508,7 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
         for module in self.input_blocks:
             h = module(h, emb, context)
             hs.append(h)
-        h = self.middle_block(h, emb, context)
+        h = self.mid(h, emb, context)
         for module in self.output_blocks:
             h = torch.cat([h, hs.pop()], dim=1)
             h = module(h, emb, context)

src/diffusers/models/unet_new.py

+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+
+# limitations under the License.
+from torch import nn
+
+from .attention import AttentionBlock, SpatialTransformer
+from .resnet import ResnetBlock2D
+
+
+class UNetMidBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        temb_channels: int,
+        dropout: float,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        attention_layer_type: str = "self",
+        attn_num_heads=1,
+        attn_num_head_channels=None,
+        attn_encoder_channels=None,
+        attn_dim_head=None,
+        attn_depth=None,
+        output_scale_factor=1.0,
+        overwrite_qkv=False,
+        overwrite_unet=False,
+    ):
+        super().__init__()
+
+        self.resnet_1 = ResnetBlock2D(
+            in_channels=in_channels,
+            out_channels=in_channels,
+            temb_channels=temb_channels,
+            groups=resnet_groups,
+            dropout=dropout,
+            time_embedding_norm=resnet_time_scale_shift,
+            non_linearity=resnet_act_fn,
+            output_scale_factor=output_scale_factor,
+        )
+
+        if attention_layer_type == "self":
+            self.attn = AttentionBlock(
+                in_channels,
+                num_heads=attn_num_heads,
+                num_head_channels=attn_num_head_channels,
+                encoder_channels=attn_encoder_channels,
+                overwrite_qkv=overwrite_qkv,
+                rescale_output_factor=output_scale_factor,
+            )
+        elif attention_layer_type == "spatial":
+            self.attn = (
+                SpatialTransformer(
+                    in_channels,
+                    attn_num_heads,
+                    attn_num_head_channels,
+                    depth=attn_depth,
+                    context_dim=attn_encoder_channels,
+                ),
+            )
+
+        self.resnet_2 = ResnetBlock2D(
+            in_channels=in_channels,
+            out_channels=in_channels,
+            temb_channels=temb_channels,
+            groups=resnet_groups,
+            dropout=dropout,
+            time_embedding_norm=resnet_time_scale_shift,
+            non_linearity=resnet_act_fn,
+            output_scale_factor=output_scale_factor,
+        )
+
+        # TODO(Patrick) - delete all of the following code
+        self.is_overwritten = False
+        self.overwrite_unet = overwrite_unet
+        if self.overwrite_unet:
+            block_in = in_channels
+            self.temb_ch = temb_channels
+            self.block_1 = ResnetBlock2D(
+                in_channels=block_in,
+                out_channels=block_in,
+                temb_channels=self.temb_ch,
+                dropout=dropout,
+                eps=resnet_eps,
+            )
+            self.attn_1 = AttentionBlock(
+                block_in,
+                num_heads=attn_num_heads,
+                num_head_channels=attn_num_head_channels,
+                encoder_channels=attn_encoder_channels,
+                overwrite_qkv=True,
+            )
+            self.block_2 = ResnetBlock2D(
+                in_channels=block_in,
+                out_channels=block_in,
+                temb_channels=self.temb_ch,
+                dropout=dropout,
+                eps=resnet_eps,
+            )
+
+    def forward(self, hidden_states, temb=None, encoder_states=None):
+        if not self.is_overwritten and self.overwrite_unet:
+            self.resnet_1 = self.block_1
+            self.attn = self.attn_1
+            self.resnet_2 = self.block_2
+            self.is_overwritten = True
+
+        hidden_states = self.resnet_1(hidden_states, temb)
+
+        if encoder_states is None:
+            hidden_states = self.attn(hidden_states)
+        else:
+            hidden_states = self.attn(hidden_states, encoder_states)
+
+        hidden_states = self.resnet_2(hidden_states, temb)
+        return hidden_states

src/diffusers/models/unet_sde_score_estimation.py

@@ -27,6 +27,7 @@ from ..modeling_utils import ModelMixin
 from .attention import AttentionBlock
 from .embeddings import GaussianFourierProjection, get_timestep_embedding
 from .resnet import Downsample2D, FirDownsample2D, FirUpsample2D, ResnetBlock2D, Upsample2D
+from .unet_new import UNetMidBlock2D
 
 
 class Combine(nn.Module):
@@ -214,6 +215,16 @@ class NCSNpp(ModelMixin, ConfigMixin):
 
                 hs_c.append(in_ch)
 
+        # mid
+        self.mid = UNetMidBlock2D(
+            in_channels=in_ch,
+            temb_channels=4 * nf,
+            output_scale_factor=math.sqrt(2.0),
+            resnet_act_fn="silu",
+            resnet_groups=min(in_ch // 4, 32),
+            dropout=dropout,
+        )
+
         in_ch = hs_c[-1]
         modules.append(
             ResnetBlock2D(
@@ -238,6 +249,9 @@ class NCSNpp(ModelMixin, ConfigMixin):
                 overwrite_for_score_vde=True,
             )
         )
+        self.mid.resnet_1 = modules[len(modules) - 3]
+        self.mid.attn = modules[len(modules) - 2]
+        self.mid.resnet_2 = modules[len(modules) - 1]
 
         pyramid_ch = 0
         # Upsampling block
@@ -378,13 +392,16 @@ class NCSNpp(ModelMixin, ConfigMixin):
 
                 hs.append(h)
 
-        h = hs[-1]
-        h = modules[m_idx](h, temb)
-        m_idx += 1
-        h = modules[m_idx](h)
-        m_idx += 1
-        h = modules[m_idx](h, temb)
-        m_idx += 1
+        #        h = hs[-1]
+        #        h = modules[m_idx](h, temb)
+        #        m_idx += 1
+        #        h = modules[m_idx](h)
+        #        m_idx += 1
+        #        h = modules[m_idx](h, temb)
+        #        m_idx += 1
+
+        h = self.mid(h, temb)
+        m_idx += 3
 
         pyramid = None