diff --git a/comfy/model_base.py b/comfy/model_base.py
index 2fb4b1453..eec70d5de 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -59,6 +59,7 @@ class ModelType(Enum):
     FLOW = 6
     V_PREDICTION_CONTINUOUS = 7
     FLUX = 8
+    IMG_TO_IMG = 9
 
 
 from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV
@@ -89,6 +90,8 @@ def model_sampling(model_config, model_type):
     elif model_type == ModelType.FLUX:
         c = comfy.model_sampling.CONST
         s = comfy.model_sampling.ModelSamplingFlux
+    elif model_type == ModelType.IMG_TO_IMG:
+        c = comfy.model_sampling.IMG_TO_IMG
 
     class ModelSampling(s, c):
         pass
@@ -613,7 +616,7 @@ class Lotus(BaseModel):
         out['y'] = comfy.conds.CONDRegular(task_emb)
         return out
 
-    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
+    def __init__(self, model_config, model_type=ModelType.IMG_TO_IMG, device=None):
         super().__init__(model_config, model_type, device=device)
 
 class StableCascade_C(BaseModel):
diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py
index ff27b09a8..b79af1e92 100644
--- a/comfy/model_sampling.py
+++ b/comfy/model_sampling.py
@@ -69,6 +69,15 @@ class CONST:
         sigma = sigma.view(sigma.shape[:1] + (1,) * (latent.ndim - 1))
         return latent / (1.0 - sigma)
 
+class X0(EPS):
+    def calculate_denoised(self, sigma, model_output, model_input):
+        return model_output
+
+class IMG_TO_IMG(X0):
+    def calculate_input(self, sigma, noise):
+        return noise
+
+
 class ModelSamplingDiscrete(torch.nn.Module):
     def __init__(self, model_config=None, zsnr=None):
         super().__init__()
diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py
index 2b805c1ee..71a652ffa 100644
--- a/comfy_extras/nodes_model_advanced.py
+++ b/comfy_extras/nodes_model_advanced.py
@@ -20,14 +20,6 @@ class LCM(comfy.model_sampling.EPS):
 
         return c_out * x0 + c_skip * model_input
 
-class X0(comfy.model_sampling.EPS):
-    def calculate_denoised(self, sigma, model_output, model_input):
-        return model_output
-
-class Lotus(X0):
-    def calculate_input(self, sigma, noise):
-        return noise
-
 class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete):
     original_timesteps = 50
 
@@ -60,7 +52,7 @@ class ModelSamplingDiscrete:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "sampling": (["eps", "v_prediction", "lcm", "x0", "lotus"],),
+                              "sampling": (["eps", "v_prediction", "lcm", "x0", "img_to_img"],),
                               "zsnr": ("BOOLEAN", {"default": False}),
                               }}
 
@@ -81,9 +73,9 @@ class ModelSamplingDiscrete:
             sampling_type = LCM
             sampling_base = ModelSamplingDiscreteDistilled
         elif sampling == "x0":
-            sampling_type = X0
-        elif sampling == "lotus":
-            sampling_type = Lotus
+            sampling_type = comfy.model_sampling.X0
+        elif sampling == "img_to_img":
+            sampling_type = comfy.model_sampling.IMG_TO_IMG
 
         class ModelSamplingAdvanced(sampling_base, sampling_type):
             pass