Fix model patches not working in custom sampling scheduler nodes.

comfy/model_patcher.py

@@ -174,40 +174,41 @@ 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])
 
-        model_sd = self.model_state_dict()
-        for key in self.patches:
-            if key not in model_sd:
-                print("could not patch. key doesn't exist in model:", key)
-                continue
+        if patch_weights:
+            model_sd = self.model_state_dict()
+            for key in self.patches:
+                if key not in model_sd:
+                    print("could not patch. key doesn't exist in model:", key)
+                    continue
 
-            weight = model_sd[key]
+                weight = model_sd[key]
 
-            inplace_update = self.weight_inplace_update
+                inplace_update = self.weight_inplace_update
 
-            if key not in self.backup:
-                self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update)
+                if key not in self.backup:
+                    self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update)
 
-            if device_to is not None:
-                temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
-            else:
-                temp_weight = weight.to(torch.float32, copy=True)
-            out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
-            if inplace_update:
-                comfy.utils.copy_to_param(self.model, key, out_weight)
-            else:
-                comfy.utils.set_attr(self.model, key, out_weight)
-            del temp_weight
+                if device_to is not None:
+                    temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
+                else:
+                    temp_weight = weight.to(torch.float32, copy=True)
+                out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
+                if inplace_update:
+                    comfy.utils.copy_to_param(self.model, key, out_weight)
+                else:
+                    comfy.utils.set_attr(self.model, key, out_weight)
+                del temp_weight
 
-        if device_to is not None:
-            self.model.to(device_to)
-            self.current_device = device_to
+            if device_to is not None:
+                self.model.to(device_to)
+                self.current_device = device_to
 
         return self.model
 

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, )