Update app.py

app.py

@@ -45,6 +45,27 @@ h1 {
 }
 """
 
+@spaces.GPU
+def process_image(prompt,acc,height, width, steps, scales, seed):
+    global pipe
+    global loaded_acc
+    if loaded_acc != acc:
+        #pipe.load_lora_weights(ACC_lora[acc], adapter_name=acc)
+        pipe.set_adapters([acc], adapter_weights=[1.0])
+        print(pipe.get_active_adapters())
+        loaded_acc = acc
+    with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
+        return pipe(
+            prompt=[prompt],
+            generator=torch.Generator().manual_seed(int(seed)),
+            num_inference_steps=int(steps),
+            guidance_scale=float(scales),
+            height=int(height),
+            width=int(width),
+            max_sequence_length=256
+        ).images[0]
+
+
 with gr.Blocks(css=css) as demo:
     gr.Markdown(
         """
@@ -125,26 +146,6 @@ with gr.Blocks(css=css) as demo:
         """
     )
 
-    @spaces.GPU
-    def process_image(prompt,acc,height, width, steps, scales, seed):
-        global pipe
-        global loaded_acc
-        if loaded_acc != acc:
-            #pipe.load_lora_weights(ACC_lora[acc], adapter_name=acc)
-            pipe.set_adapters([acc], adapter_weights=[1.0])
-            print(pipe.get_active_adapters())
-            loaded_acc = acc
-        with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
-            return pipe(
-                prompt=[prompt],
-                generator=torch.Generator().manual_seed(int(seed)),
-                num_inference_steps=int(steps),
-                guidance_scale=float(scales),
-                height=int(height),
-                width=int(width),
-                max_sequence_length=256
-            ).images[0]
-
     generate_btn.click(
         process_image,
         inputs=[prompt, acc,height, width, steps, scales, seed],