Update app.py

app.py

@@ -33,7 +33,34 @@ torch.set_float32_matmul_precision("highest")
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
 import spaces
-   
+
+_predictor = None  # Global variable to hold the predictor
+
+@spaces.GPU(duration=90)  # We DO need @spaces.GPU on init_predictor
+def init_predictor():
+    global _predictor
+    import logging
+    logger = logging.getLogger(__name__) #Logger
+
+    if _predictor is None:
+        _predictor = SkyReelsVideoSingleGpuInfer(
+            task_type=TaskType.I2V,
+            model_id="Skywork/SkyReels-V1-Hunyuan-I2V",  # Replace!
+            quant_model=True,
+            is_offload=True,
+            offload_config=OffloadConfig(
+                high_cpu_memory=True,
+                parameters_level=True,
+                compiler_transformer=False,  # Set to True to enable compilation/warm-up
+            ),
+        )
+        _predictor.initialize()  # Initialize *after* creation
+        logger.info("Predictor initialized")
+    else:
+        logger.warning("Predictor already initialized (should be rare).")
+
+
+
 @spaces.GPU(duration=90)
 def generate_video(prompt, seed, image=None):
     global task_type
@@ -55,22 +82,11 @@ def generate_video(prompt, seed, image=None):
     }
     assert image is not None, "please input image"
     kwargs["image"] = load_image(image=image)
-    config = {
-        "task_type": TaskType.I2V,
-        "model_id": "Skywork/SkyReels-V1-Hunyuan-I2V",  # Replace!
-        "quant_model": True,
-        "world_size": 1,
-        "is_offload": True,
-        "offload_config": OffloadConfig(
-            high_cpu_memory=True,
-            parameters_level=True,
-            compiler_transformer=False,  # Change to True to enable warm-up
-        ),
-    }
-    global predictor
-    # Check if the predictor is already running
-    predictor = Predictor()
-    predictor.initialize(**config)  # Pass stored config
+    global _predictor
+
+    if _predictor is None:
+      init_predictor()
+
     output = predictor.infer(**kwargs)
     save_dir = f"./result/{task_type}"
     os.makedirs(save_dir, exist_ok=True)