Fix model patches not working in custom sampling scheduler nodes.

comfy/model_patcher.py

@@ -174,13 +174,14 @@ class ModelPatcher:
                     sd.pop(k)
         return sd
 
-    def patch_model(self, device_to=None):
+    def patch_model(self, device_to=None, patch_weights=True):
         for k in self.object_patches:
             old = getattr(self.model, k)
             if k not in self.object_patches_backup:
                 self.object_patches_backup[k] = old
             setattr(self.model, k, self.object_patches[k])
 
+        if patch_weights:
             model_sd = self.model_state_dict()
             for key in self.patches:
                 if key not in model_sd:

comfy_extras/nodes_custom_sampler.py

@@ -26,7 +26,9 @@ class BasicScheduler:
         if denoise < 1.0:
             total_steps = int(steps/denoise)
 
-        sigmas = comfy.samplers.calculate_sigmas_scheduler(model.model, scheduler, total_steps).cpu()
+        inner_model = model.patch_model(patch_weights=False)
+        sigmas = comfy.samplers.calculate_sigmas_scheduler(inner_model, scheduler, total_steps).cpu()
+        model.unpatch_model()
         sigmas = sigmas[-(steps + 1):]
         return (sigmas, )
 
@@ -104,7 +106,9 @@ class SDTurboScheduler:
     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]
-        sigmas = model.model.model_sampling.sigma(timesteps)
+        inner_model = model.patch_model(patch_weights=False)
+        sigmas = inner_model.model_sampling.sigma(timesteps)
+        model.unpatch_model()
         sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
         return (sigmas, )