Refactor LoRA loading and fusion in app.py for improved clarity and efficiency

app.py

@@ -51,24 +51,28 @@ scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
 pipe_stage1 = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2511",
                                                  scheduler=scheduler,
                                                  torch_dtype=dtype).to(device)
+# Load 4-step Lightning LoRA and fuse
 pipe_stage1.load_lora_weights(
         "lightx2v/Qwen-Image-Edit-2511-Lightning",
         weight_name="Qwen-Image-Edit-2511-Lightning-4steps-V1.0-bf16.safetensors"
 )
-pipe_stage1.load_lora_weights(STAGE1_LORA_REPO, weight_name=STAGE1_LORA_WEIGHT, adapter_name="stage1")
-pipe_stage1.set_adapters(["default", "stage1"], adapter_weights=[1.0, 1.0])
+pipe_stage1.fuse_lora()
+# Load Stage 1 LoRA and fuse
+pipe_stage1.load_lora_weights(STAGE1_LORA_REPO, weight_name=STAGE1_LORA_WEIGHT)
 pipe_stage1.fuse_lora()
 
 # Load Stage 2 pipeline (Base Body -> Guide Body)
 pipe_stage2 = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2511",
                                                  scheduler=scheduler,
                                                  torch_dtype=dtype).to(device)
+# Load 4-step Lightning LoRA and fuse
 pipe_stage2.load_lora_weights(
         "lightx2v/Qwen-Image-Edit-2511-Lightning",
         weight_name="Qwen-Image-Edit-2511-Lightning-4steps-V1.0-bf16.safetensors"
 )
-pipe_stage2.load_lora_weights(STAGE2_LORA_REPO, weight_name=STAGE2_LORA_WEIGHT, adapter_name="stage2")
-pipe_stage2.set_adapters(["default", "stage2"], adapter_weights=[1.0, 1.0])
+pipe_stage2.fuse_lora()
+# Load Stage 2 LoRA and fuse
+pipe_stage2.load_lora_weights(STAGE2_LORA_REPO, weight_name=STAGE2_LORA_WEIGHT)
 pipe_stage2.fuse_lora()
 
 # # Apply the same optimizations from the first version