revert

app.py

@@ -19,7 +19,7 @@ from diffusers import (
     ControlNetModel,
     DPMSolverMultistepScheduler,
     StableDiffusionControlNetPipeline,
-    AutoencoderKL,
+    # AutoencoderKL,
 )
 from controlnet_aux_local import NormalBaeDetector
 
@@ -371,10 +371,8 @@ with gr.Blocks(theme="bethecloud/storj_theme", css=css) as demo:
     @gr.on(triggers=[use_ai_button.click], inputs=[result] + config, outputs=[image, result], show_progress="minimal")
     def submit(previous_result, image, style_selection, prompt, a_prompt, n_prompt, num_images, image_resolution, preprocess_resolution, num_steps, guidance_scale, seed, progress=gr.Progress(track_tqdm=True)):
         # First, yield the previous result to update the input image immediately
-        preprocessor.load("NormalBae")
         yield previous_result, gr.update()
         # Then, process the new input image
-        
         new_result = process_image(previous_result, style_selection, prompt, a_prompt, n_prompt, num_images, image_resolution, preprocess_resolution, num_steps, guidance_scale, seed)
         # Finally, yield the new result
         yield previous_result, new_result
@@ -389,7 +387,7 @@ with gr.Blocks(theme="bethecloud/storj_theme", css=css) as demo:
     def turn_buttons_on():
         return gr.update(visible=True), gr.update(visible=True)
 
-@spaces.GPU(duration=10)
+@spaces.GPU(duration=12)
 @torch.inference_mode()
 def process_image(
     image,
@@ -410,7 +408,7 @@ def process_image(
 
     seed = random.randint(0, MAX_SEED)
     generator = torch.cuda.manual_seed(seed)
-    # preprocessor.load("NormalBae")
+    preprocessor.load("NormalBae")
     control_image = preprocessor(
         image=image,
         image_resolution=image_resolution,
@@ -436,7 +434,7 @@ def process_image(
     ).images[0]
     print(f"\n-------------------------Inference done in: {time.time() - start:.2f} seconds-------------------------")
     # torch.cuda.synchronize()
-    torch.cuda.empty_cache()
+    # torch.cuda.empty_cache()
     return results
 
 if prod: