remove loras if SetLora bypassed/disconnected

fp8_optimization.py

@@ -107,6 +107,12 @@ def convert_linear_with_lora_and_scale(module, scale_weight_keys=None, patches=N
                 has_scale = hasattr(submodule, "scale_weight")
                 has_lora = hasattr(submodule, "lora")
                 if has_scale or has_lora:
-                    original_forward = submodule.forward
-                    setattr(submodule, "original_forward", original_forward)
+                    original_forward_ = submodule.forward
+                    setattr(submodule, "original_forward_", original_forward_)
                     setattr(submodule, "forward", lambda input, m=submodule: linear_with_lora_and_scale_forward(m, input))
+
+
+def remove_lora_from_module(module):
+    for name, submodule in module.named_modules():
+        if hasattr(submodule, "lora"):
+            delattr(submodule, "lora")

nodes.py

@@ -8,7 +8,7 @@ import hashlib
 from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
 
 from .wanvideo.modules.model import rope_params
-from .fp8_optimization import convert_linear_with_lora_and_scale
+from .fp8_optimization import convert_linear_with_lora_and_scale, remove_lora_from_module
 from .wanvideo.schedulers import get_scheduler, get_sampling_sigmas, retrieve_timesteps, scheduler_list
 
 from .multitalk.multitalk import timestep_transform, add_noise
@@ -1281,9 +1281,12 @@ class WanVideoSampler:
         control_lora = model["control_lora"]
         transformer_options = patcher.model_options.get("transformer_options", None)
 
+        remove_lora_from_module(transformer)
         if len(patcher.patches) != 0 and transformer_options.get("linear_with_lora", False) is True:
             log.info(f"Using {len(patcher.patches)} patches for WanVideo model")
-            convert_linear_with_lora_and_scale(transformer, patches=patcher.patches)            
+            convert_linear_with_lora_and_scale(transformer, patches=patcher.patches)
+        else:
+            remove_lora_from_module(transformer)
 
         #compile
         compile_args = model["compile_args"]

nodes_model_loading.py

@@ -629,8 +629,10 @@ class WanVideoSetLoRAs:
             "required": 
             {
                 "model": ("WANVIDEOMODEL", ),
-                "lora": ("WANVIDLORA", ),
             },
+            "optional": {
+                "lora": ("WANVIDLORA", ),
+            }
         }
 
     RETURN_TYPES = ("WANVIDEOMODEL",)
@@ -640,7 +642,7 @@ class WanVideoSetLoRAs:
     EXPERIMENTAL = True
     DESCRIPTION = "Sets the LoRA weights to be used directly in linear layers of the model, this does NOT merge LoRAs"
 
-    def setlora(self, model, lora):
+    def setlora(self, model, lora=None):
         if lora is None:
             return (model,)