Update app.py

app.py

@@ -78,19 +78,22 @@ def upload_to_gcs(image_object, filename):
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-pipe = StableDiffusion3Pipeline.from_pretrained(
-    "ford442/stable-diffusion-3.5-large-bf16",
-    trust_remote_code=True,
-    transformer=None, # Load transformer separately
-    use_safetensors=True
-)
+@spaces.on_start
+def load_model():
+    pipe = StableDiffusion3Pipeline.from_pretrained(
+        "ford442/stable-diffusion-3.5-large-bf16",
+        trust_remote_code=True,
+        transformer=None, # Load transformer separately
+        use_safetensors=True
+    )
+    ll_transformer=SD3Transformer2DModel.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='transformer').to(device, dtype=torch.bfloat16)
+    pipe.transformer=ll_transformer
+    pipe.load_lora_weights("ford442/sdxl-vae-bf16", weight_name="LoRA/UltraReal.safetensors")
+    pipe.to(device=device, dtype=torch.bfloat16)
+    upscaler_2 = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to(device)
+    return pipe, upscaler_2
 
-ll_transformer=SD3Transformer2DModel.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='transformer').to(device, dtype=torch.bfloat16)
-pipe.transformer=ll_transformer
-pipe.load_lora_weights("ford442/sdxl-vae-bf16", weight_name="LoRA/UltraReal.safetensors")
-pipe.to(device=device, dtype=torch.bfloat16)
-
-upscaler_2 = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to(device)
+pipe, upscaler_2 = load_model()
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 4096