Update app.py

app.py

@@ -88,18 +88,14 @@ text_encoder=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusi
 text_encoder_2=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_2',token=True).to(device=device, dtype=torch.bfloat16)
 text_encoder_3=T5EncoderModel.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_3',token=True).to(device=device, dtype=torch.bfloat16)
        
-pipe.init_ipadapter(
-    ip_adapter_path=ipadapter_path, 
-    image_encoder_path=image_encoder_path, 
-    nb_token=64, 
-)
+
 
 upscaler_2 = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to(torch.device("cuda:0"))
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 4096
 
-@spaces.GPU(duration=90)
+@spaces.GPU(duration=80)
 def infer(
     prompt,
     negative_prompt_1,
@@ -124,9 +120,16 @@ def infer(
     image_encoder_path=None,
     progress=gr.Progress(track_tqdm=True),
 ):
+    
     pipe.text_encoder=text_encoder
     pipe.text_encoder_2=text_encoder_2
     pipe.text_encoder_3=text_encoder_3
+    
+    pipe.init_ipadapter(
+        ip_adapter_path=ipadapter_path, 
+        image_encoder_path=image_encoder_path, 
+        nb_token=64, 
+    )
     upscaler_2.to(torch.device('cpu'))
     torch.set_float32_matmul_precision("highest")
     seed = random.randint(0, MAX_SEED)