Update app.py

app.py

@@ -106,17 +106,9 @@ def load_b_loras(content_b_lora, style_b_lora):
         style_model_instance_prompt = ''
 
     prepared_prompt = f"{content_model_instance_prompt} {style_model_instance_prompt}"
-    
-    return prepared_prompt
-
-@spaces.GPU()
-def main(content_b_lora, style_b_lora, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
 
+    pipeline.unload_lora_weights()
+    
     if content_b_lora is None:
         content_B_LoRA_path = ''
     else:
@@ -130,6 +122,17 @@ def main(content_b_lora, style_b_lora, prompt, negative_prompt, seed, randomize_
     content_alpha,style_alpha = 1,1.1
 
     load_b_lora_to_unet(pipeline, content_B_LoRA_path, style_B_LoRA_path, content_alpha, style_alpha)
+    
+    return prepared_prompt
+
+@spaces.GPU()
+def main(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+        
+    generator = torch.Generator().manual_seed(seed)
+    
     prompt = prompt
     image = pipeline(
         prompt,
@@ -139,8 +142,6 @@ def main(content_b_lora, style_b_lora, prompt, negative_prompt, seed, randomize_
         height = height,
     ).images[0]
     
-    pipeline.unload_lora_weights()
-    
     return image
 
 css="""
@@ -255,7 +256,7 @@ with gr.Blocks(css=css) as demo:
     
     run_button.click(
         fn = main,
-        inputs = [content_b_lora, style_b_lora, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
         outputs = [result]
     )