diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index c92fb0fb..e98866fc 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -318,7 +318,6 @@ infotext_to_setting_name_mapping = [
     ('Conditional mask weight', 'inpainting_mask_weight'),
     ('Model hash', 'sd_model_checkpoint'),
     ('ENSD', 'eta_noise_seed_delta'),
-    ('Enable Custom KDiffusion Schedule', 'custom_k_sched'),
     ('KDiffusion Scheduler Type', 'k_sched_type'),
     ('KDiffusion Scheduler sigma_max', 'sigma_max'),
     ('KDiffusion Scheduler sigma_min', 'sigma_min'),
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 5fea08b0..eff2e32d 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -46,6 +46,7 @@ sampler_extra_params = {
 
 k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
 k_diffusion_scheduler = {
+    'None': None,
     'karras': k_diffusion.sampling.get_sigmas_karras,
     'exponential': k_diffusion.sampling.get_sigmas_exponential,
     'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
@@ -295,8 +296,7 @@ class KDiffusionSampler:
 
         k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
 
-        if opts.custom_k_sched:
-            p.extra_generation_params["Enable Custom KDiffusion Schedule"] = True
+        if opts.k_sched_type != "None":
             p.extra_generation_params["KDiffusion Scheduler Type"] = opts.k_sched_type
             p.extra_generation_params["KDiffusion Scheduler sigma_max"] = opts.sigma_max
             p.extra_generation_params["KDiffusion Scheduler sigma_min"] = opts.sigma_min
@@ -325,7 +325,7 @@ class KDiffusionSampler:
 
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
-        elif opts.custom_k_sched:
+        elif opts.k_sched_type != "None":
             sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
             sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
             sigmas_kwargs = {
diff --git a/modules/shared.py b/modules/shared.py
index a0e762d2..b24f52dd 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -517,8 +517,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    'custom_k_sched': OptionInfo(False, "Enable Custom KDiffusion Scheduler"),
-    'k_sched_type':  OptionInfo("karras", "scheduler type", gr.Dropdown, {"choices": ["karras", "exponential", "polyexponential"]}),
+    'k_sched_type':  OptionInfo("default", "scheduler type", gr.Dropdown, {"choices": ["None", "karras", "exponential", "polyexponential"]}),
     'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("the maximum noise strength for the scheduler. Set to 0 to use the same value which 'xxx karras' samplers use."),
     'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("the minimum noise strength for the scheduler. Set to 0 to use the same value which 'xxx karras' samplers use."),
     'rho':  OptionInfo(7.0, "rho", gr.Number).info("higher will make a more steep noise scheduler (decrease faster). default for karras is 7.0, for polyexponential is 1.0"),