ensure the model train properly in both grad ckpt or not

comfy_extras/nodes_train.py

@@ -623,13 +623,13 @@ def _create_weight_adapter(
         for name, parameter in train_adapter.named_parameters():
             lora_params[f"{module_name}.{name}"] = parameter
 
-        return train_adapter, lora_params
+        return train_adapter.train().requires_grad_(True), lora_params
     else:
         # 1D weight - use BiasDiff
         diff = torch.nn.Parameter(
             torch.zeros(module.weight.shape, dtype=lora_dtype, requires_grad=True)
         )
-        diff_module = BiasDiff(diff)
+        diff_module = BiasDiff(diff).train().requires_grad_(True)
         lora_params[f"{module_name}.diff"] = diff
         return diff_module, lora_params
 
@@ -648,7 +648,7 @@ def _create_bias_adapter(module, module_name, lora_dtype):
     bias = torch.nn.Parameter(
         torch.zeros(module.bias.shape, dtype=lora_dtype, requires_grad=True)
     )
-    bias_module = BiasDiff(bias)
+    bias_module = BiasDiff(bias).train().requires_grad_(True)
     lora_params = {f"{module_name}.diff_b": bias}
     return bias_module, lora_params
 
@@ -961,6 +961,9 @@ class TrainLoraNode(io.ComfyNode):
         positive = _validate_and_expand_conditioning(positive, num_images, bucket_mode)
 
         with torch.inference_mode(False):
+            # Setup models for training
+            mp.model.requires_grad_(False)
+
             # Load existing LoRA weights if provided
             existing_weights, existing_steps = _load_existing_lora(existing_lora)
 
@@ -982,8 +985,6 @@ class TrainLoraNode(io.ComfyNode):
                 ):
                     patch(m)
 
-            # Setup models for training
-            mp.model.requires_grad_(False)
             torch.cuda.empty_cache()
             # With force_full_load=False we should be able to have offloading
             # But for offloading in training we need custom AutoGrad hooks for fwd/bwd