diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py
index 5b8d8000..c9f4c04d 100644
--- a/comfy/k_diffusion/sampling.py
+++ b/comfy/k_diffusion/sampling.py
@@ -929,6 +929,30 @@ def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, n
     return x
 
 
+# x0 = 
+
+@torch.no_grad()
+def sample_lcm_scalewise(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
+    extra_args = {} if extra_args is None else extra_args
+    seed = extra_args.get("seed", None)
+    scales = extra_args.get("scales", None)
+    if scales:
+        assert len(scales) == len(sigmas) - 1, "Number of scales must be equal to number of sampling steps minus one."
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+        x = denoised
+        if sigmas[i + 1] > 0:
+            if scales:
+                # Interpolate to next scale
+                x = nn.functional.interpolate(x, size=scales[i + 1], mode='bicubic')
+            x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x)
+    return x
+
 
 @torch.no_grad()
 def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):