diff --git a/comfy/model_base.py b/comfy/model_base.py
index b0b9cde7d087..40b7b375723f 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -1227,22 +1227,23 @@ def extra_conds(self, **kwargs):
         if audio_embed is not None:
             out['audio_embed'] = comfy.conds.CONDRegular(audio_embed)
 
-        if "c_concat" not in out:  # 1.7B model
-            reference_latents = kwargs.get("reference_latents", None)
-            if reference_latents is not None:
-                out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1]))
+        reference_latents = kwargs.get("reference_latents", None)
+
+        if "c_concat" not in out and reference_latents is not None and reference_latents[0].shape[1] == 16:  # 1.7B model
+            out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1]))
         else:
-            noise_shape = list(noise.shape)
-            noise_shape[1] += 4
-            concat_latent = torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)
-            zero_vae_values_first = torch.tensor([0.8660, -0.4326, -0.0017, -0.4884, -0.5283, 0.9207, -0.9896, 0.4433, -0.5543, -0.0113, 0.5753, -0.6000, -0.8346, -0.3497, -0.1926, -0.6938]).view(1, 16, 1, 1, 1)
-            zero_vae_values_second = torch.tensor([1.0869, -1.2370, 0.0206, -0.4357, -0.6411, 2.0307, -1.5972, 1.2659, -0.8595, -0.4654, 0.9638, -1.6330, -1.4310, -0.1098, -0.3856, -1.4583]).view(1, 16, 1, 1, 1)
-            zero_vae_values = torch.tensor([0.8642, -1.8583, 0.1577, 0.1350, -0.3641, 2.5863, -1.9670, 1.6065, -1.0475, -0.8678, 1.1734, -1.8138, -1.5933, -0.7721, -0.3289, -1.3745]).view(1, 16, 1, 1, 1)
-            concat_latent[:, 4:] = zero_vae_values
-            concat_latent[:, 4:, :1] = zero_vae_values_first
-            concat_latent[:, 4:, 1:2] = zero_vae_values_second
-            out['c_concat'] = comfy.conds.CONDNoiseShape(concat_latent)
-            reference_latents = kwargs.get("reference_latents", None)
+            concat_latent_image = kwargs.get("concat_latent_image", None)
+            if concat_latent_image is None:
+                noise_shape = list(noise.shape)
+                noise_shape[1] += 4
+                concat_latent = torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)
+                zero_vae_values_first = torch.tensor([0.8660, -0.4326, -0.0017, -0.4884, -0.5283, 0.9207, -0.9896, 0.4433, -0.5543, -0.0113, 0.5753, -0.6000, -0.8346, -0.3497, -0.1926, -0.6938]).view(1, 16, 1, 1, 1)
+                zero_vae_values_second = torch.tensor([1.0869, -1.2370, 0.0206, -0.4357, -0.6411, 2.0307, -1.5972, 1.2659, -0.8595, -0.4654, 0.9638, -1.6330, -1.4310, -0.1098, -0.3856, -1.4583]).view(1, 16, 1, 1, 1)
+                zero_vae_values = torch.tensor([0.8642, -1.8583, 0.1577, 0.1350, -0.3641, 2.5863, -1.9670, 1.6065, -1.0475, -0.8678, 1.1734, -1.8138, -1.5933, -0.7721, -0.3289, -1.3745]).view(1, 16, 1, 1, 1)
+                concat_latent[:, 4:] = zero_vae_values
+                concat_latent[:, 4:, :1] = zero_vae_values_first
+                concat_latent[:, 4:, 1:2] = zero_vae_values_second
+                out['c_concat'] = comfy.conds.CONDNoiseShape(concat_latent)
             if reference_latents is not None:
                 ref_latent = self.process_latent_in(reference_latents[-1])
                 ref_latent_shape = list(ref_latent.shape)