Update app.py

app.py

@@ -107,9 +107,8 @@ class ModelManager:
         self.realesrgan_x2 = None
         self.realesrgan_x4 = None
 
-    def load_models(self, progress=gr.Progress()):
-        if self.pipe is None:
-            progress(0, desc="Loading Stable Diffusion pipeline...")
+    def load_models(self):
+        if self.pipe is None:           
             self.pipe = self.setup_pipeline()
             self.pipe.to(device)
             self.pipe.unet.set_attn_processor(AttnProcessor2_0())
@@ -118,17 +117,14 @@ class ModelManager:
                 progress(0.5, desc="Compiling the model...")
                 self.pipe.unet = torch.compile(self.pipe.unet, mode="reduce-overhead", fullgraph=True)
 
-        if self.realesrgan_x2 is None:
-            progress(0.7, desc="Loading RealESRGAN x2 model...")
+        if self.realesrgan_x2 is None:           
             self.realesrgan_x2 = RealESRGAN(device, scale=2)
             self.realesrgan_x2.load_weights('models/upscalers/RealESRGAN_x2.pth', download=False)
 
-        if self.realesrgan_x4 is None:
-            progress(0.9, desc="Loading RealESRGAN x4 model...")
+        if self.realesrgan_x4 is None:            
             self.realesrgan_x4 = RealESRGAN(device, scale=4)
             self.realesrgan_x4.load_weights('models/upscalers/RealESRGAN_x4.pth', download=False)
 
-        progress(1.0, desc="All models loaded successfully")
 
     def setup_pipeline(self):
         controlnet = ControlNetModel.from_single_file(
@@ -233,6 +229,7 @@ class ModelManager:
         return hdr_result
 
 model_manager = ModelManager()
+model_manager.load_models()  # Ensure models are loaded
 
 def extract_frames(video_path, output_folder):
     os.makedirs(output_folder, exist_ok=True)
@@ -274,11 +271,12 @@ def frames_to_video(input_folder, output_path, fps, original_video_path):
     # Remove the temporary file
     os.remove(temp_output_path)
 
+
 @timer_func
 def process_video(input_video, resolution, num_inference_steps, strength, hdr, guidance_scale, max_frames=None, frame_interval=1, preserve_frames=False, batch_size=8, progress=gr.Progress()):
     abort_event.clear()  # Clear the abort flag at the start of a new job
     print("Starting video processing...")
-    model_manager.load_models(progress)  # Ensure models are loaded
+    
 
     # Create a new job folder
     job_id = str(uuid.uuid4())