.

app.py

@@ -1,3 +1,4 @@
+import gc
 import os
 import random
 import gradio as gr
@@ -310,6 +311,8 @@ def generate_image(image_path, prompt, num_steps, guidance_scale, seed, num_imag
         lora_prefix = Loras_dict[lora_name]
 
         prompt = f"{lora_prefix} {prompt}"
+
+        print("Using LoRA: ", lora_name)
         
 
     print("Start inference...")    
@@ -334,6 +337,9 @@ def generate_image(image_path, prompt, num_steps, guidance_scale, seed, num_imag
         pipe.unfuse_lora()
         pipe.unload_lora_weights()
 
+    gc.collect()
+    torch.cuda.empty_cache()
+
     return images
 
 ### Description
@@ -434,7 +440,7 @@ with gr.Blocks(css=css) as demo:
                     step=1,
                     value=0,
                 )
-                seed = gr.Slider(
+                lora_scale = gr.Slider(
                     label="lora_scale",
                     minimum=0.0,
                     maximum=1.0,
@@ -454,7 +460,7 @@ with gr.Blocks(css=css) as demo:
             api_name=False,
         ).then(
             fn=generate_image,
-            inputs=[img_file, prompt, num_steps, guidance_scale, seed, num_images, ip_adapter_scale, kps_scale, canny_scale, lora_name],
+            inputs=[img_file, prompt, num_steps, guidance_scale, seed, num_images, ip_adapter_scale, kps_scale, canny_scale, lora_name, lora_scale],
             outputs=[gallery]
         )