envs

app.py

@@ -281,7 +281,7 @@ class ImageConductor:
 
         self.blur_kernel = blur_kernel
 
-    @spaces.GPU(enable_queue=True, duration=200)
+    @spaces.GPU(enable_queue=True, duration=400)
     @torch.no_grad()
     def run(self, first_frame_path, tracking_points, prompt, drag_mode, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, personalized, examples_type):
         if examples_type != "":
@@ -304,10 +304,6 @@ class ImageConductor:
         id = base.split('_')[-1]
         
         
-        with open(f'{output_dir}/points-{id}.json', 'w') as f:
-            json.dump(input_all_points, f)
-        
-        
         visualized_drag, _ = visualize_drag(first_frame_path, resized_all_points, drag_mode, self.width, self.height, self.model_length)
 
         ## image condition        
@@ -408,7 +404,7 @@ def add_tracking_points(tracking_points, first_frame_path, drag_mode, evt: gr.Se
     elif drag_mode=='camera':
         color = (0, 0, 255, 255)
 
-
+    import ipdb; ipdb.set_trace()
     print(f"You selected {evt.value} at {evt.index} from {evt.target}")
     tracking_points.constructor_args['value'][-1].append(evt.index)
     print(tracking_points.constructor_args)

pipelines/pipeline_imagecoductor.py

@@ -318,7 +318,7 @@ class ImageConductorPipeline(DiffusionPipeline):
         latents = latents * self.scheduler.init_noise_sigma
         return latents
 
-    @spaces.GPU(enable_queue=True, duration=200)
+    @spaces.GPU(enable_queue=True, duration=400)
     @torch.no_grad()
     def __call__(
         self,