diff --git a/models/modules/unet_generator_attn/unet_generator_attn.py b/models/modules/unet_generator_attn/unet_generator_attn.py
index 535157d30..4c56b86df 100644
--- a/models/modules/unet_generator_attn/unet_generator_attn.py
+++ b/models/modules/unet_generator_attn/unet_generator_attn.py
@@ -1032,7 +1032,8 @@ def forward(self, x, emb, qkv_ref=None):
                 else:
                     cur_qkv_ref = qkv_ref.pop(0)
 
-                x, qkv = layer(x, qkv_ref=cur_qkv_ref)
+                x, qkv_out = layer(x, qkv_ref=cur_qkv_ref)
+                qkv.append(qkv_out)
             else:
                 x = layer(x)
         return x, qkv
@@ -1054,6 +1055,7 @@ def __init__(
         use_new_attention_order=False,
         use_transformer=False,
         use_ref=False,
+        terminal=False,
     ):
         super().__init__()
         self.channels = channels
@@ -1067,13 +1069,15 @@ def __init__(
         self.use_checkpoint = use_checkpoint
         self.use_transformer = use_transformer
         self.norm = normalization1d(channels)
+        self.use_ref = use_ref
+        self.terminal = terminal
 
-        if use_ref:
-            self.qkv = nn.Conv1d(channels, channels * 4, 1)
-        else:
-            self.qkv = nn.Conv1d(channels, channels * 3, 1)
+        #if self.use_ref:
+        #    self.qkv = nn.Conv1d(channels, channels * 4, 1)
+        #else:
+        self.qkv = nn.Conv1d(channels, channels * 3, 1)
 
-        self.qkv_ref = nn.Conv1d(channels, channels * 2, 1)
+        #self.qkv_ref = nn.Conv1d(channels, channels * 2, 1)
         if use_new_attention_order:
             # split qkv before split heads
             self.attention = QKVAttention(self.num_heads)
@@ -1081,12 +1085,11 @@ def __init__(
             # split heads before split qkv
             self.attention = QKVAttentionLegacy(self.num_heads)
 
-        if use_ref:
-            self.proj_out = zero_module(nn.Conv1d(channels * 2, channels, 1))
-        else:
-            self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
-
-        self.use_ref = use_ref
+        if not self.terminal:
+            if self.use_ref:
+                self.proj_out = zero_module(nn.Conv1d(channels * 2, channels, 1))
+            else:
+                self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
 
     def forward(self, x, qkv_ref=None):
         return checkpoint(
@@ -1102,26 +1105,30 @@ def _forward(self, x, qkv_ref):
 
         qkv = self.qkv(self.norm(x))
 
-        if self.use_ref:
-            assert qkv_ref is not None
+        if not self.terminal:
+            if self.use_ref:
+                assert qkv_ref is not None
 
-            q, k, v, q_ref = qkv.chunk(4, dim=1)
+                #q, k, v, q_ref = qkv.chunk(4, dim=1)
+                q, k, v = qkv.chunk(3, dim=1)
 
-            _, k_ref, v_ref = qkv_ref.chunk(3, dim=1)
+                _, k_ref, v_ref = qkv_ref.chunk(3, dim=1)
 
-            qkv = torch.cat([q, k, v], dim=1)
+                qkv = torch.cat([q, k, v], dim=1)
 
-            qkv_ref = torch.cat([q_ref, k_ref, v_ref], dim=1)
+                qkv_ref = torch.cat([q, k_ref, v_ref], dim=1)
 
-            h_ref = self.attention(qkv_ref)
+                h_ref = self.attention(qkv_ref)
 
-        h = self.attention(qkv)
+            h = self.attention(qkv)
 
-        if self.use_ref:
-            h = torch.cat([h, h_ref], dim=1)
+            if self.use_ref:
+                h = torch.cat([h, h_ref], dim=1)
 
-        h = self.proj_out(h)
-        return (x + h).reshape(b, c, *spatial), qkv
+            h = self.proj_out(h)
+            return (x + h).reshape(b, c, *spatial), qkv
+        else:
+            return None, qkv
 
 
 ##### unet ref
@@ -1329,14 +1336,14 @@ def __init__(
                         ch,
                         self.cond_embed_dim,
                         dropout,
-                        out_channel=int(mult * inner_channel),
+                        out_channel=int(mult * self.inner_channel),
                         use_checkpoint=use_checkpoint,
                         use_scale_shift_norm=use_scale_shift_norm,
                         norm=norm,
                         efficient=efficient,
                     )
                 ]
-                ch = int(mult * inner_channel)
+                ch = int(mult * self.inner_channel)
                 if ds in attn_res:
                     layers.append(
                         AttentionBlockRef(
@@ -1368,6 +1375,8 @@ def __init__(
                         else Downsample(ch, conv_resample, out_channel=out_ch)
                     )
                 )
+                ch = out_ch
+                ds *= 2
 
         self.middle_block_ref = EmbedSequentialRef(
             ResBlock(
@@ -1385,16 +1394,17 @@ def __init__(
                 num_heads=num_heads,
                 num_head_channels=num_head_channels,
                 use_new_attention_order=use_new_attention_order,
+                terminal = True,
             ),
-            ResBlock(
-                ch,
-                self.cond_embed_dim,
-                dropout,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-                norm=norm,
-                efficient=efficient,
-            ),
+#            ResBlock(
+#                ch,
+#                self.cond_embed_dim,
+#                dropout,
+#                use_checkpoint=use_checkpoint,
+#                use_scale_shift_norm=use_scale_shift_norm,
+#                norm=norm,
+#                efficient=efficient,
+#            ),
         )
 
         ###
@@ -1497,10 +1507,9 @@ def compute_feats(self, input, embed_gammas, ref=None):
             h = ref.type(torch.float32)
             for module in self.input_blocks_ref:
                 h, qkv_ref = module(h, emb, qkv_ref=None)
-
                 qkv_list.append(qkv_ref)
 
-            h, qkv_ref = self.middle_block_ref(h, emb, qkv_ref=None)
+            _, qkv_ref = self.middle_block_ref(h, emb, qkv_ref=None)
             qkv_list.append(qkv_ref)
 
         hs = []
@@ -1512,10 +1521,10 @@ def compute_feats(self, input, embed_gammas, ref=None):
 
         for module in self.input_blocks:
             h, _ = module(h, emb, qkv_ref=qkv_list.pop(0))
-
             hs.append(h)
 
-        h, _ = self.middle_block(h, emb, qkv_ref=qkv_list.pop(0))
+        qkv_ref = qkv_list.pop(0)
+        h, _ = self.middle_block(h, emb, qkv_ref=qkv_ref)#qkv_list.pop(0))
 
         outs, feats = h, hs
         return outs, feats, emb