remove weight check.

src/diffusers/loaders/lora_pipeline.py

@@ -1983,7 +1983,8 @@ def _maybe_expand_transformer_param_shape_or_error_(
                 out_features = state_dict[lora_B_weight_name].shape[0]
 
                 # Model maybe loaded with different quantization schemes which may flatten the params.
-                # `bitsandbytes`, for example, flatten the weights when using 4bit.
+                # `bitsandbytes`, for example, flatten the weights when using 4bit. 8bit bnb models
+                # preserve weight shape.
                 module_weight_shape = cls._calculate_module_shape(model=transformer, base_module=module)
 
                 # This means there's no need for an expansion in the params, so we simply skip.
@@ -2120,11 +2121,11 @@ def _get_weight_shape(weight: torch.Tensor):
         if base_module is not None:
             return _get_weight_shape(base_module.weight)
         elif base_weight_param_name is not None:
-            module_path = (
-                base_weight_param_name.rsplit(".weight", 1)[0]
-                if base_weight_param_name.endswith(".weight")
-                else base_weight_param_name
-            )
+            if not base_weight_param_name.endswith(".weight"):
+                raise ValueError(
+                    f"Invalid `base_weight_param_name` passed as it does not end with '.weight' {base_weight_param_name=}."
+                )
+            module_path = base_weight_param_name.rsplit(".weight", 1)[0]
             submodule = get_submodule_by_name(model, module_path)
             return _get_weight_shape(submodule.weight)