diff --git a/nodes/nodes.py b/nodes/nodes.py
index d04053c..083528c 100644
--- a/nodes/nodes.py
+++ b/nodes/nodes.py
@@ -2628,9 +2628,9 @@ class LazySwitchKJ:
 from comfy.patcher_extension import WrappersMP
 from comfy.sampler_helpers import prepare_mask
 class TTM_SampleWrapper:
-    def __init__(self, mask, end_step):
+    def __init__(self, mask, steps):
         self.mask = mask
-        self.end_step = end_step
+        self.steps = steps
 
     def __call__(self, sampler, guider, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar):
         model_options = extra_args["model_options"]
@@ -2642,7 +2642,7 @@ class TTM_SampleWrapper:
             motion_mask = prepare_mask(motion_mask, noise.shape, noise.device)
 
         scale_latent_inpaint = guider.model_patcher.model.scale_latent_inpaint
-        w["TTM_ApplyModel_Wrapper"] = [TTM_ApplyModel_Wrapper(latent_image, noise, motion_mask, self.end_step, scale_latent_inpaint)]
+        w["TTM_ApplyModel_Wrapper"] = [TTM_ApplyModel_Wrapper(latent_image, noise, motion_mask, self.steps, scale_latent_inpaint)]
 
         out = sampler(guider, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
 
@@ -2650,11 +2650,11 @@ class TTM_SampleWrapper:
 
 
 class TTM_ApplyModel_Wrapper:
-    def __init__(self, reference_samples, noise, motion_mask, end_step, scale_latent_inpaint):
+    def __init__(self, reference_samples, noise, motion_mask, steps, scale_latent_inpaint):
         self.reference_samples = reference_samples
         self.noise = noise
         self.motion_mask = motion_mask
-        self.end_step = end_step
+        self.steps = steps
         self.scale_latent_inpaint = scale_latent_inpaint
 
     def __call__(self, executor, x, t, c_concat, c_crossattn, control, transformer_options, **kwargs):
@@ -2669,7 +2669,7 @@ class TTM_ApplyModel_Wrapper:
 
         next_sigma = sigmas[current_step_index + 1] if current_step_index < len(sigmas) - 1 else sigmas[current_step_index]
 
-        if current_step_index != 0 and current_step_index < self.end_step:
+        if current_step_index != 0 and current_step_index < self.steps:
             noisy_latent = self.scale_latent_inpaint(x=x, sigma=torch.tensor([next_sigma]), noise=self.noise.to(x), latent_image=self.reference_samples.to(x))
             x = x * (1-self.motion_mask).to(x) + noisy_latent * self.motion_mask.to(x)
 
@@ -2681,7 +2681,7 @@ class LatentInpaintTTM:
     def INPUT_TYPES(s):
         return {"required": {
                         "model": ("MODEL", ),
-                        "end_step": ("INT", {"default": 7, "min": 0, "max": 888, "step": 1}),
+                        "steps": ("INT", {"default": 7, "min": 0, "max": 888, "step": 1, "tooltip": "Number of steps to apply TTM inpainting for."}),
                         },
                 "optional": {
                         "mask": ("MASK", {"tooltip": "Latent mask where white (1.0) is the area to inpaint and black (0.0) is the area to keep unchanged."}),
@@ -2693,7 +2693,7 @@ class LatentInpaintTTM:
     DESCRIPTION = "https://github.com/time-to-move/TTM"
     CATEGORY = "KJNodes/experimental"
 
-    def patch(self, model, end_step, mask=None):
+    def patch(self, model, steps, mask=None):
         m = model.clone()
-        m.add_wrapper_with_key(WrappersMP.SAMPLER_SAMPLE, "TTM_SampleWrapper", TTM_SampleWrapper(mask, end_step))
+        m.add_wrapper_with_key(WrappersMP.SAMPLER_SAMPLE, "TTM_SampleWrapper", TTM_SampleWrapper(mask, steps))
         return (m, )
\ No newline at end of file