Update app.py

app.py

@@ -107,8 +107,6 @@ print(f"Target inference device: {target_inference_device}")
 pipeline_instance.to(target_inference_device)
 if latent_upsampler_instance: latent_upsampler_instance.to(target_inference_device)
 
-generator = torch.Generator(device='cuda').manual_seed(seed)
-
 def calculate_new_dimensions(orig_w, orig_h):
     if orig_w == 0 or orig_h == 0: return int(768), int(768)
     if orig_w >= orig_h:
@@ -137,7 +135,7 @@ def enhance_frame(prompt, image_to_enhance: Image.Image):
     try:
         print("Moving enhancer pipeline to GPU...")
         seed = random.randint(0, MAX_SEED)
-        generator.manual_seed(seed)
+        generator = torch.Generator(device='cuda').manual_seed(seed)
         enhancer_pipeline.to("cuda",torch.bfloat16)
         refine_prompt = prompt +" high detail, sharp focus, 8k, professional"
         enhanced_image = enhancer_pipeline(prompt=refine_prompt, image=image_to_enhance, strength=0.1, generator=generator, num_inference_steps=160).images[0]
@@ -224,7 +222,7 @@ def generate(prompt, negative_prompt, clips_list, input_image_filepath, input_vi
     if randomize_seed: seed_ui = random.randint(0, 2**32 - 1)
     seed_everething(int(seed_ui))
     seed = random.randint(0, MAX_SEED)
-    generator.manual_seed(seed)
+    generator = torch.Generator(device='cuda').manual_seed(seed)
     actual_num_frames = max(9, min(MAX_NUM_FRAMES, int(round((max(1, round(duration_ui * fps)) - 1.0) / 8.0) * 8 + 1)))
     actual_height, actual_width = int(height_ui), int(width_ui)
     height_padded, width_padded = ((actual_height - 1) // 32 + 1) * 32, ((actual_width - 1) // 32 + 1) * 32