diff --git a/src/diffusers/loaders/lora_pipeline.py b/src/diffusers/loaders/lora_pipeline.py
index b85f51db83bc..10b6a8f02710 100644
--- a/src/diffusers/loaders/lora_pipeline.py
+++ b/src/diffusers/loaders/lora_pipeline.py
@@ -4813,22 +4813,43 @@ def _maybe_expand_t2v_lora_for_i2v(
         if transformer.config.image_dim is None:
             return state_dict
 
+        target_device = transformer.device
+
         if any(k.startswith("transformer.blocks.") for k in state_dict):
-            num_blocks = len({k.split("blocks.")[1].split(".")[0] for k in state_dict})
+            num_blocks = len({k.split("blocks.")[1].split(".")[0] for k in state_dict if "blocks." in k})
             is_i2v_lora = any("add_k_proj" in k for k in state_dict) and any("add_v_proj" in k for k in state_dict)
+            has_bias = any(".lora_B.bias" in k for k in state_dict)
 
             if is_i2v_lora:
                 return state_dict
 
             for i in range(num_blocks):
                 for o, c in zip(["k_img", "v_img"], ["add_k_proj", "add_v_proj"]):
+                    # These keys should exist if the block `i` was part of the T2V LoRA.
+                    ref_key_lora_A = f"transformer.blocks.{i}.attn2.to_k.lora_A.weight"
+                    ref_key_lora_B = f"transformer.blocks.{i}.attn2.to_k.lora_B.weight"
+
+                    if ref_key_lora_A not in state_dict or ref_key_lora_B not in state_dict:
+                        continue
+
                     state_dict[f"transformer.blocks.{i}.attn2.{c}.lora_A.weight"] = torch.zeros_like(
-                        state_dict[f"transformer.blocks.{i}.attn2.to_k.lora_A.weight"]
+                        state_dict[f"transformer.blocks.{i}.attn2.to_k.lora_A.weight"], device=target_device
                     )
                     state_dict[f"transformer.blocks.{i}.attn2.{c}.lora_B.weight"] = torch.zeros_like(
-                        state_dict[f"transformer.blocks.{i}.attn2.to_k.lora_B.weight"]
+                        state_dict[f"transformer.blocks.{i}.attn2.to_k.lora_B.weight"], device=target_device
                     )
 
+                    # If the original LoRA had biases (indicated by has_bias)
+                    # AND the specific reference bias key exists for this block.
+
+                    ref_key_lora_B_bias = f"transformer.blocks.{i}.attn2.to_k.lora_B.bias"
+                    if has_bias and ref_key_lora_B_bias in state_dict:
+                        ref_lora_B_bias_tensor = state_dict[ref_key_lora_B_bias]
+                        state_dict[f"transformer.blocks.{i}.attn2.{c}.lora_B.bias"] = torch.zeros_like(
+                            ref_lora_B_bias_tensor,
+                            device=target_device,
+                        )
+
         return state_dict
 
     def load_lora_weights(