diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 17faeab..a118399 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -3,6 +3,7 @@ import numpy as np
 import torch
 import tqdm
 from PIL import Image
+import inspect
 
 import k_diffusion.sampling
 import ldm.models.diffusion.ddim
@@ -38,11 +39,11 @@ samplers = [
 samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
 
 sampler_extra_params = {
-    'sample_euler':['s_churn','s_tmin','s_tmax','s_noise'],
-    'sample_euler_ancestral':['eta'],
-    'sample_heun' :['s_churn','s_tmin','s_tmax','s_noise'],
-    'sample_dpm_2':['s_churn','s_tmin','s_tmax','s_noise'],
-    'sample_dpm_2_ancestral':['eta'],
+    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_euler_ancestral': ['eta'],
+    'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_dpm_2_ancestral': ['eta'],
 }
 
 def setup_img2img_steps(p, steps=None):
@@ -231,7 +232,7 @@ class KDiffusionSampler:
         self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
         self.funcname = funcname
         self.func = getattr(k_diffusion.sampling, self.funcname)
-        self.extra_params = sampler_extra_params.get(funcname,[]) 
+        self.extra_params = sampler_extra_params.get(funcname, [])
         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
         self.sampler_noises = None
         self.sampler_noise_index = 0
@@ -278,9 +279,9 @@ class KDiffusionSampler:
             k_diffusion.sampling.torch = TorchHijack(self)
 
         extra_params_kwargs = {}
-        for val in self.extra_params:
-          if hasattr(p,val):
-            extra_params_kwargs[val] = getattr(p,val)
+        for param_name in self.extra_params:
+            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
+                extra_params_kwargs[param_name] = getattr(p, param_name)
 
         return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
 
@@ -300,9 +301,9 @@ class KDiffusionSampler:
             k_diffusion.sampling.torch = TorchHijack(self)
 
         extra_params_kwargs = {}
-        for val in self.extra_params:
-          if hasattr(p,val):
-            extra_params_kwargs[val] = getattr(p,val)
+        for param_name in self.extra_params:
+            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
+                extra_params_kwargs[param_name] = getattr(p, param_name)
 
         samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)