Update app.py

app.py

@@ -56,14 +56,15 @@ class calculateDuration:
         
 @spaces.GPU(duration=120)
 @torch.inference_mode()
-def generate_image(prompt, steps, seed, cfg_scale, width, height, progress):
+def generate_image(prompt, adapter_names, steps, seed, cfg_scale, width, height, progress):
 
     gr.Info("Start to generate images ...")
 
     with calculateDuration(f"Make a new generator:{seed}"):
         pipe.to(device)
         generator = torch.Generator(device=device).manual_seed(seed)
-
+    if len(adapter_names) > 0:
+        pipe.fuse_lora(adapter_names=adapter_names)
     with calculateDuration("Generating image"):
         # Generate image
         generated_image = pipe(
@@ -77,6 +78,8 @@ def generate_image(prompt, steps, seed, cfg_scale, width, height, progress):
         ).images[0]
     
     progress(99, "Generate image success!")
+    if len(adapter_names) > 0:
+        pipe.unfuse_lora()
     return generated_image
 
 
@@ -146,15 +149,12 @@ def run_lora(prompt, lora_strings_json,  cfg_scale, steps, randomize_seed, seed,
                 # set lora weights
                 if len(adapter_names) > 0:
                     pipe.set_adapters(adapter_names, adapter_weights=adapter_weights)
-                    # mark lora as used
-                    pipe.fuse_lora(adapter_names=adapter_names)
-                    print("active adapters:", pipe.get_active_adapters())
-
+ 
     # Generate image
     error_message = ""
     try:
         print("Start applying for zeroGPU resources")
-        final_image = generate_image(prompt, steps, seed, cfg_scale, width, height, progress)
+        final_image = generate_image(prompt, adapter_names, steps, seed, cfg_scale, width, height, progress)
     except Exception as e:
         error_message =  str(e)
         gr.Error(error_message)