diff --git a/examples/example_ddpm_unetref_viton.json b/examples/example_ddpm_unetref_viton.json
index ed029bfd6..190390d03 100644
--- a/examples/example_ddpm_unetref_viton.json
+++ b/examples/example_ddpm_unetref_viton.json
@@ -35,7 +35,8 @@
         "padding_type": "reflect",
         "spectral": false,
         "unet_mha_attn_res": [
-            16
+            4,
+	    8
         ],
         "unet_mha_channel_mults": [
             1,
@@ -48,10 +49,10 @@
         "unet_mha_num_head_channels": 32,
         "unet_mha_num_heads": 1,
         "unet_mha_res_blocks": [
-            2,
-            2,
-            2,
-            2
+	    2,
+	    4,
+	    4,
+	    2
         ],
         "unet_mha_vit_efficient": false,
         "uvit_num_transformer_blocks": 6
@@ -250,7 +251,7 @@
         "cls_regression": false,
         "compute_D_accuracy": false,
         "compute_metrics_test": false,
-        "continue": true,
+        "continue": false,
         "epoch": "latest",
         "epoch_count": 1,
         "export_jit": false,
diff --git a/models/modules/palette_denoise_fn.py b/models/modules/palette_denoise_fn.py
index 516c78124..61dedec3b 100644
--- a/models/modules/palette_denoise_fn.py
+++ b/models/modules/palette_denoise_fn.py
@@ -7,6 +7,8 @@
 from .image_bind.imagebind_model import ModalityType
 import clip
 
+from inspect import signature
+
 
 class LabelEmbedder(nn.Module):
     """
@@ -34,6 +36,9 @@ def __init__(self, model, cond_embed_dim, ref_embed_net, conditioning, nclasses)
         super().__init__()
 
         self.model = model
+        model_sig = signature(model.forward)
+        self.model_nargs = len(model_sig.parameters)
+
         self.conditioning = conditioning
         self.cond_embed_dim = cond_embed_dim
         self.ref_embed_net = ref_embed_net
@@ -48,7 +53,6 @@ def __init__(self, model, cond_embed_dim, ref_embed_net, conditioning, nclasses)
             nn.init.normal_(self.netl_embedder_class.embedding_table.weight, std=0.02)
 
         if "mask" in conditioning:
-            # TODO make a new option
             cond_embed_mask = cond_embed_dim
             self.netl_embedder_mask = LabelEmbedder(
                 nclasses,
@@ -101,7 +105,7 @@ def forward(self, input, embed_noise_level, cls, mask, ref):
         if "mask" in self.conditioning:
             input = torch.cat([input, mask_embed], dim=1)
 
-        if "Ref" in type(self.model).__name__:
+        if self.model_nargs == 4:  # ref from dataloader with reference image
             out = self.model(input, embedding, ref)
         else:
             out = self.model(input, embedding)
diff --git a/models/modules/unet_generator_attn/unet_generator_attn.py b/models/modules/unet_generator_attn/unet_generator_attn.py
index cf3611477..80aa942e2 100644
--- a/models/modules/unet_generator_attn/unet_generator_attn.py
+++ b/models/modules/unet_generator_attn/unet_generator_attn.py
@@ -1209,6 +1209,8 @@ def __init__(
         self.num_heads_upsample = num_heads_upsample
         self.freq_space = freq_space
 
+        num_ref_blocks = 0
+
         if self.freq_space:
             from ..freq_utils import InverseHaarTransform, HaarTransform
 
@@ -1246,6 +1248,7 @@ def __init__(
                 ]
                 ch = int(mult * self.inner_channel)
                 if ds in attn_res:
+                    num_ref_blocks += 1
                     layers.append(
                         AttentionBlockRef(
                             ch,
@@ -1289,6 +1292,7 @@ def __init__(
                 ds *= 2
                 self._feature_size += ch
 
+        num_ref_blocks += 1
         self.middle_block = EmbedSequentialRef(
             ResBlock(
                 ch,
@@ -1490,6 +1494,7 @@ def __init__(
                 ]
                 ch = int(self.inner_channel * mult)
                 if ds in attn_res:
+                    num_ref_blocks += 1
                     layers.append(
                         AttentionBlockRef(
                             ch,
@@ -1555,6 +1560,8 @@ def __init__(
             },
         }
 
+        print("Dual U-Net: number of ref blocks: ", num_ref_blocks)
+
     def compute_feats(self, input, embed_gammas, ref=None):
         if embed_gammas is None:
             # Only for GAN