Update app.py

app.py

@@ -46,14 +46,11 @@ scheduler_config = {
 
 scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
 
-pipe = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509", scheduler=scheduler, torch_dtype=dtype).to(device)
+pipe = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509", scheduler=scheduler, torch_dtype=dtype)
 
 # Load the relight LoRA
-pipe.load_lora_weights(
-        "dx8152/Qwen-Image-Edit-2509-Relight", 
-        weight_name="Qwen-Edit-Relight.safetensors", adapter_name="relight"
-    )
-
+pipe.load_lora_weights("dx8152/Qwen-Image-Edit-2509-Relight", 
+        weight_name="Qwen-Edit-Relight.safetensors", adapter_name="relight")
 pipe.load_lora_weights("lightx2v/Qwen-Image-Lightning",
         weight_name="Qwen-Image-Lightning-4steps-V2.0-bf16.safetensors", adapter_name="lightning")
 pipe.set_adapters(["relight", "lightning"], adapter_weights=[1., 1.])
@@ -63,12 +60,12 @@ pipe.unload_lora_weights()
 pipe.transformer.__class__ = QwenImageTransformer2DModel
 pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
 
-optimize_pipeline_(pipe, image=[Image.new("RGB", (1024, 1024)), Image.new("RGB", (1024, 1024))], prompt="prompt")
+pipe.to(device)
 
+optimize_pipeline_(pipe, image=[Image.new("RGB", (1024, 1024)), Image.new("RGB", (1024, 1024))], prompt="prompt")
 
 MAX_SEED = np.iinfo(np.int32).max
 
-# Initialize translation client
 translation_client = InferenceClient(
     api_key=os.environ.get("HF_TOKEN"),
 )