diff --git a/ldm_patched/contrib/external_custom_sampler.py b/ldm_patched/contrib/external_custom_sampler.py
index 985b03a0a..60d5e3bd2 100644
--- a/ldm_patched/contrib/external_custom_sampler.py
+++ b/ldm_patched/contrib/external_custom_sampler.py
@@ -107,8 +107,7 @@ def INPUT_TYPES(s):
     def get_sigmas(self, model, steps, denoise):
         start_step = 10 - int(10 * denoise)
         timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps]
-        ldm_patched.modules.model_management.load_models_gpu([model])
-        sigmas = model.model.model_sampling.sigma(timesteps)
+        sigmas = model.model_sampling.sigma(timesteps)
         sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
         return (sigmas, )
 
diff --git a/modules/sample_hijack.py b/modules/sample_hijack.py
index 4ab3cbbde..84752ede7 100644
--- a/modules/sample_hijack.py
+++ b/modules/sample_hijack.py
@@ -175,7 +175,7 @@ def calculate_sigmas_scheduler_hacked(model, scheduler_name, steps):
     elif scheduler_name == "sgm_uniform":
         sigmas = normal_scheduler(model, steps, sgm=True)
     elif scheduler_name == "turbo":
-        sigmas = SDTurboScheduler().get_sigmas(namedtuple('Patcher', ['model'])(model=model), steps=steps, denoise=1.0)[0]
+        sigmas = SDTurboScheduler().get_sigmas(model=model, steps=steps, denoise=1.0)[0]
     elif scheduler_name == "align_your_steps":
         model_type = 'SDXL' if isinstance(model.latent_format, ldm_patched.modules.latent_formats.SDXL) else 'SD1'
         sigmas = AlignYourStepsScheduler().get_sigmas(model_type=model_type, steps=steps, denoise=1.0)[0]