simplified it

app.py

@@ -19,7 +19,7 @@ from diffusers import (
     StableDiffusionControlNetPipeline,
     AutoencoderKL,
 )
-from diffusers.models.attention_processor import AttnProcessor2_0
+# from diffusers.models.attention_processor import AttnProcessor2_0
 MAX_SEED = np.iinfo(np.int32).max
 API_KEY = os.environ.get("API_KEY", None)
 
@@ -86,8 +86,6 @@ pipe.load_textual_inversion("broyang/hentaidigitalart_v20", weight_name="HDA_Nun
 pipe.load_textual_inversion("broyang/hentaidigitalart_v20", weight_name="HDA_Shibari.pt", token="HDA_Shibari")
 pipe.to("cuda")
 
-pipe.enable_model_cpu_offload()
-
 print("loading preprocessor")
 from preprocess import Preprocessor
 preprocessor = Preprocessor()
@@ -386,7 +384,7 @@ 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=8)
+@spaces.GPU(duration=30)
 @torch.inference_mode()
 def process_image(
     image,
@@ -437,37 +435,12 @@ def process_image(
             image=control_image,
         ).images[0]
     torch.cuda.synchronize()
-    # torch.cuda.empty_cache()
+    torch.cuda.empty_cache()
     print(f"\n-------------------------Preprocess done in: {preprocess_time:.2f} seconds-------------------------")    
     print(f"\n-------------------------Inference done in: {time.time() - start:.2f} seconds-------------------------")
-    
-    # timestamp = int(time.time())
-    #if not os.path.exists("./outputs"):
-    #    os.makedirs("./outputs")
-    # img_path = f"./{timestamp}.jpg"
-    # results_path = f"./{timestamp}_out_{prompt}.jpg"
-    # imageio.imsave(img_path, image)
-    # results.save(results_path)
     results.save("temp_image.jpg")
-    
-    # api.upload_file(
-    #     path_or_fileobj=img_path,
-    #     path_in_repo=img_path,
-    #     repo_id="broyang/anime-ai-outputs",
-    #     repo_type="dataset",
-    #     token=API_KEY,
-    #     run_as_future=True,
-    # )
-    # api.upload_file(
-    #     path_or_fileobj=results_path,
-    #     path_in_repo=results_path,
-    #     repo_id="broyang/anime-ai-outputs",
-    #     repo_type="dataset",
-    #     token=API_KEY,
-    #     run_as_future=True,
-    # )
-
     return results
+
 if prod:
     demo.queue(max_size=20).launch(server_name="localhost", server_port=port)
 else: