diff --git a/modules/impact/config.py b/modules/impact/config.py
index 17e8dee..993d57b 100644
--- a/modules/impact/config.py
+++ b/modules/impact/config.py
@@ -2,7 +2,7 @@ import configparser
 import os
 
 
-version_code = [4, 89]
+version_code = [4, 90]
 version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
 
 dependency_version = 20
diff --git a/modules/impact/impact_pack.py b/modules/impact/impact_pack.py
index d9b6e48..8c7bd34 100644
--- a/modules/impact/impact_pack.py
+++ b/modules/impact/impact_pack.py
@@ -2150,7 +2150,3 @@ class ImpactWildcardEncode:
         populated = kwargs['populated_text']
         model, clip, conditioning = impact.wildcards.process_with_loras(populated, kwargs['model'], kwargs['clip'])
         return (model, clip, conditioning, populated)
-
-
-
-
diff --git a/modules/impact/impact_sampling.py b/modules/impact/impact_sampling.py
index 5bc4676..f62528d 100644
--- a/modules/impact/impact_sampling.py
+++ b/modules/impact/impact_sampling.py
@@ -15,7 +15,7 @@ def calculate_sigmas(model, sampler, scheduler, steps):
 
     if hasattr(samplers, 'calculate_sigmas'):
         if scheduler.startswith('AYS'):
-            sigmas = nodes.NODE_CLASS_MAPPINGS['AlignYourStepsScheduler']().get_sigmas(scheduler[4:], steps)[0]
+            sigmas = nodes.NODE_CLASS_MAPPINGS['AlignYourStepsScheduler']().get_sigmas(scheduler[4:], steps, denoise=1.0)[0]
         else:
             sigmas = samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, steps)
     else:
@@ -101,14 +101,68 @@ def ksampler(sampler_name, total_sigmas, extra_options={}, inpaint_options={}):
     return samplers.KSAMPLER(sampler_function, extra_options, inpaint_options)
 
 
+from comfy_extras.nodes_custom_sampler import Noise_EmptyNoise, Noise_RandomNoise
+import latent_preview
+import comfy
+
+
+# modified version of SamplerCustom.sample
+def sample_with_custom_noise(model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image, noise=None):
+    latent = latent_image
+    latent_image = latent["samples"]
+
+    if noise is None:
+        if not add_noise:
+            noise = Noise_EmptyNoise().generate_noise(latent)
+        else:
+            noise = Noise_RandomNoise(noise_seed).generate_noise(latent)
+
+    noise_mask = None
+    if "noise_mask" in latent:
+        noise_mask = latent["noise_mask"]
+
+    x0_output = {}
+    callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+
+    disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+    samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+
+    out = latent.copy()
+    out["samples"] = samples
+    if "x0" in x0_output:
+        out_denoised = latent.copy()
+        out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
+    else:
+        out_denoised = out
+    return out, out_denoised
+
+
+# When sampling one step at a time, it mitigates the problem. (especially for _sde series samplers)
 def separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler, positive, negative,
-                     latent_image, start_at_step, end_at_step, return_with_leftover_noise, sigma_ratio=1.0, sampler_opt=None):
+                     latent_image, start_at_step, end_at_step, return_with_leftover_noise, sigma_ratio=1.0, sampler_opt=None, noise=None):
+
+    # still cannot preserve for 2s, 2m, 3m. that requires some more thing.
     if sampler_opt is None:
         total_sigmas = calculate_sigmas(model, sampler_name, scheduler, steps)
     else:
         total_sigmas = calculate_sigmas(model, "", scheduler, steps)
 
-    sigmas = total_sigmas[start_at_step:end_at_step+1] * sigma_ratio
+    sigmas = total_sigmas
+
+    if end_at_step is not None and end_at_step < (len(total_sigmas) - 1):
+        sigmas = total_sigmas[:end_at_step + 1]
+        if not return_with_leftover_noise:
+            sigmas[-1] = 0
+
+    if start_at_step is not None:
+        if start_at_step < (len(sigmas) - 1):
+            sigmas = sigmas[start_at_step:] * sigma_ratio
+        else:
+            if latent_image is not None:
+                return latent_image
+            else:
+                return torch.zeros_like(noise)
+
     if sampler_opt is None:
         impact_sampler = ksampler(sampler_name, total_sigmas)
     else:
@@ -117,7 +171,7 @@ def separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler
     if len(sigmas) == 0 or (len(sigmas) == 1 and sigmas[0] == 0):
         return latent_image
     
-    res = nodes_custom_sampler.SamplerCustom().sample(model, add_noise, seed, cfg, positive, negative, impact_sampler, sigmas, latent_image)
+    res = sample_with_custom_noise(model, add_noise, seed, cfg, positive, negative, impact_sampler, sigmas, latent_image, noise=noise)
 
     if return_with_leftover_noise:
         return res[0]
@@ -125,6 +179,14 @@ def separated_sample(model, add_noise, seed, steps, cfg, sampler_name, scheduler
         return res[1]
 
 
+def impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, sigma_ratio=1.0, sampler_opt=None, noise=None):
+    advanced_steps = math.floor(steps / denoise)
+    start_at_step = advanced_steps - steps
+    end_at_step = start_at_step + steps
+
+    return separated_sample(model, True, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, False)
+
+
 def ksampler_wrapper(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise,
                      refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, sigma_factor=1.0):
 
diff --git a/modules/impact/special_samplers.py b/modules/impact/special_samplers.py
index d2dec47..babeaef 100644
--- a/modules/impact/special_samplers.py
+++ b/modules/impact/special_samplers.py
@@ -3,7 +3,7 @@ import impact.core as core
 from impact.utils import *
 from nodes import MAX_RESOLUTION
 import nodes
-from impact.impact_sampling import KSamplerWrapper, KSamplerAdvancedWrapper
+from impact.impact_sampling import KSamplerWrapper, KSamplerAdvancedWrapper, separated_sample, impact_sample
 
 
 class TiledKSamplerProvider:
@@ -43,7 +43,7 @@ class KSamplerProvider:
                                 "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                                 "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                                 "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-                                "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+                                "scheduler": (core.SCHEDULERS, ),
                                 "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                                 "basic_pipe": ("BASIC_PIPE", )
                              },
@@ -66,7 +66,7 @@ class KSamplerAdvancedProvider:
         return {"required": {
                                 "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                                 "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-                                "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+                                "scheduler": (core.SCHEDULERS, ),
                                 "sigma_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
                                 "basic_pipe": ("BASIC_PIPE", )
                              },
@@ -302,6 +302,10 @@ class RegionalSampler:
 
     CATEGORY = "ImpactPack/Regional"
 
+    @staticmethod
+    def separated_sample(*args, **kwargs):
+        return separated_sample(*args, **kwargs)
+
     @staticmethod
     def mask_erosion(samples, mask, grow_mask_by):
         mask = mask.clone()
@@ -540,7 +544,7 @@ class KSamplerBasicPipe:
                      "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                      "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                      "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+                     "scheduler": (core.SCHEDULERS, ),
                      "latent_image": ("LATENT", ),
                      "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                      }
@@ -553,7 +557,7 @@ class KSamplerBasicPipe:
 
     def sample(self, basic_pipe, seed, steps, cfg, sampler_name, scheduler, latent_image, denoise=1.0):
         model, clip, vae, positive, negative = basic_pipe
-        latent = nodes.KSampler().sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)[0]
+        latent = impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)
         return basic_pipe, latent, vae
 
 
@@ -567,7 +571,7 @@ class KSamplerAdvancedBasicPipe:
                      "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                      "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                      "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-                     "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+                     "scheduler": (core.SCHEDULERS, ),
                      "latent_image": ("LATENT", ),
                      "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}),
                      "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}),
@@ -593,6 +597,6 @@ class KSamplerAdvancedBasicPipe:
         else:
             return_with_leftover_noise = "disable"
 
-        latent = nodes.KSamplerAdvanced().sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise)[0]
+        latent = separated_sample(model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise)
         return basic_pipe, latent, vae