diff --git a/comfy/model_base.py b/comfy/model_base.py
index bc019de5..6f530d2f 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -66,7 +66,8 @@ class BaseModel(torch.nn.Module):
         self.adm_channels = unet_config.get("adm_in_channels", None)
         if self.adm_channels is None:
             self.adm_channels = 0
-        self.inpaint_model = False
+
+        self.concat_keys = ()
         logging.info("model_type {}".format(model_type.name))
         logging.debug("adm {}".format(self.adm_channels))
 
@@ -107,8 +108,7 @@ class BaseModel(torch.nn.Module):
 
     def extra_conds(self, **kwargs):
         out = {}
-        if self.inpaint_model:
-            concat_keys = ("mask", "masked_image")
+        if len(self.concat_keys) > 0:
             cond_concat = []
             denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
             concat_latent_image = kwargs.get("concat_latent_image", None)
@@ -125,24 +125,16 @@ class BaseModel(torch.nn.Module):
 
             concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0])
 
-            if len(denoise_mask.shape) == len(noise.shape):
-                denoise_mask = denoise_mask[:,:1]
-
-            denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1]))
-            if denoise_mask.shape[-2:] != noise.shape[-2:]:
-                denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center")
-            denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0])
+            if denoise_mask is not None:
+                if len(denoise_mask.shape) == len(noise.shape):
+                    denoise_mask = denoise_mask[:,:1]
 
-            def blank_inpaint_image_like(latent_image):
-                blank_image = torch.ones_like(latent_image)
-                # these are the values for "zero" in pixel space translated to latent space
-                blank_image[:,0] *= 0.8223
-                blank_image[:,1] *= -0.6876
-                blank_image[:,2] *= 0.6364
-                blank_image[:,3] *= 0.1380
-                return blank_image
+                denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1]))
+                if denoise_mask.shape[-2:] != noise.shape[-2:]:
+                    denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center")
+                denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0])
 
-            for ck in concat_keys:
+            for ck in self.concat_keys:
                 if denoise_mask is not None:
                     if ck == "mask":
                         cond_concat.append(denoise_mask.to(device))
@@ -152,7 +144,7 @@ class BaseModel(torch.nn.Module):
                     if ck == "mask":
                         cond_concat.append(torch.ones_like(noise)[:,:1])
                     elif ck == "masked_image":
-                        cond_concat.append(blank_inpaint_image_like(noise))
+                        cond_concat.append(self.blank_inpaint_image_like(noise))
             data = torch.cat(cond_concat, dim=1)
             out['c_concat'] = comfy.conds.CONDNoiseShape(data)
 
@@ -221,7 +213,16 @@ class BaseModel(torch.nn.Module):
         return unet_state_dict
 
     def set_inpaint(self):
-        self.inpaint_model = True
+        self.concat_keys = ("mask", "masked_image")
+        def blank_inpaint_image_like(latent_image):
+            blank_image = torch.ones_like(latent_image)
+            # these are the values for "zero" in pixel space translated to latent space
+            blank_image[:,0] *= 0.8223
+            blank_image[:,1] *= -0.6876
+            blank_image[:,2] *= 0.6364
+            blank_image[:,3] *= 0.1380
+            return blank_image
+        self.blank_inpaint_image_like = blank_inpaint_image_like
 
     def memory_required(self, input_shape):
         if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():