diff --git a/__init__.py b/__init__.py
index 02157b7..2f2ebb5 100644
--- a/__init__.py
+++ b/__init__.py
@@ -209,6 +209,7 @@ NODE_CONFIG = {
     "ModelPatchTorchSettings": {"class": ModelPatchTorchSettings, "name": "Model Patch Torch Settings"},
     "WanVideoNAG": {"class": WanVideoNAG, "name": "WanVideoNAG"},
     "GGUFLoaderKJ": {"class": GGUFLoaderKJ, "name": "GGUF Loader KJ"},
+    "LatentInpaintTTM": {"class": LatentInpaintTTM, "name": "Latent Inpaint TTM"},
 
     #instance diffusion
     "CreateInstanceDiffusionTracking": {"class": CreateInstanceDiffusionTracking},
diff --git a/nodes/nodes.py b/nodes/nodes.py
index 9caec1c..d04053c 100644
--- a/nodes/nodes.py
+++ b/nodes/nodes.py
@@ -2623,3 +2623,77 @@ class LazySwitchKJ:
     def switch(self, switch, on_false = None, on_true=None):
         value = on_true if switch else on_false
         return (value,)
+
+
+from comfy.patcher_extension import WrappersMP
+from comfy.sampler_helpers import prepare_mask
+class TTM_SampleWrapper:
+    def __init__(self, mask, end_step):
+        self.mask = mask
+        self.end_step = end_step
+
+    def __call__(self, sampler, guider, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar):
+        model_options = extra_args["model_options"]
+        wrappers = model_options["transformer_options"]["wrappers"]
+        w = wrappers.setdefault(WrappersMP.APPLY_MODEL, {})
+
+        if self.mask is not None:
+            motion_mask = self.mask.reshape((-1, 1, self.mask.shape[-2], self.mask.shape[-1]))
+            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)]
+
+        out = sampler(guider, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
+
+        return out
+
+
+class TTM_ApplyModel_Wrapper:
+    def __init__(self, reference_samples, noise, motion_mask, end_step, scale_latent_inpaint):
+        self.reference_samples = reference_samples
+        self.noise = noise
+        self.motion_mask = motion_mask
+        self.end_step = end_step
+        self.scale_latent_inpaint = scale_latent_inpaint
+
+    def __call__(self, executor, x, t, c_concat, c_crossattn, control, transformer_options, **kwargs):
+        sigmas = transformer_options["sample_sigmas"]
+
+        matched = (sigmas == t).nonzero(as_tuple=True)[0]
+        if matched.numel() > 0:
+            current_step_index = matched.item()
+        else:
+            crossing = ((sigmas[:-1] - t) * (sigmas[1:] - t) <= 0).nonzero(as_tuple=True)[0]
+            current_step_index = crossing.item() if crossing.numel() > 0 else 0
+
+        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:
+            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)
+
+        return executor(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs)
+
+
+class LatentInpaintTTM:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                        "model": ("MODEL", ),
+                        "end_step": ("INT", {"default": 7, "min": 0, "max": 888, "step": 1}),
+                        },
+                "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."}),
+                        }
+                }
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    EXPERIMENTAL = True
+    DESCRIPTION = "https://github.com/time-to-move/TTM"
+    CATEGORY = "KJNodes/experimental"
+
+    def patch(self, model, end_step, mask=None):
+        m = model.clone()
+        m.add_wrapper_with_key(WrappersMP.SAMPLER_SAMPLE, "TTM_SampleWrapper", TTM_SampleWrapper(mask, end_step))
+        return (m, )
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