Update app.py

app.py

@@ -31,6 +31,8 @@ with open("flux_loras.json", "r") as file:
             "trigger_position": item.get("trigger_position", "prepend"),
             "weights": item.get("weights", "pytorch_lora_weights.safetensors"),
             "lora_type": item.get("lora_type", "flux"),
+            "lora_scale_config": item.get("lora_scale", 0),
+            "prompt_placeholder": item.get("prompt_placeholder", ""),
         }
         for item in data
     ]
@@ -59,7 +61,11 @@ def update_selection(selected_state: gr.SelectData, flux_loras):
     trigger_word = flux_loras[selected_state.index]["trigger_word"]
     
     updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo})"
-    new_placeholder = f"opt - describe the person/subject, e.g. 'a man with glasses and a beard'"
+    config_placeholder = flux_loras[selected_state.index]["prompt_placeholder"]
+    if config_placeholder:
+        new_placeholder = config_placeholder
+    else:
+        new_placeholder = f"opt - describe the person/subject, e.g. 'a man with glasses and a beard'"
     
     return updated_text, gr.update(placeholder=new_placeholder), selected_state.index
 
@@ -154,6 +160,9 @@ def infer_with_lora(input_image, prompt, selected_index, custom_lora, seed=42, r
                 pipe.unload_lora_weights()
             
             # Load new LoRA
+            if lora_to_use["lora_scale_config"]:
+                lora_scale = lora_to_use["lora_scale_config"]
+                print("lora scale loaded from config", lora_scale)
             lora_path = load_lora_weights(lora_to_use["repo"], lora_to_use["weights"])
             if lora_path:
                 pipe.load_lora_weights(lora_path, adapter_name="selected_lora")
@@ -190,19 +199,19 @@ def infer_with_lora(input_image, prompt, selected_index, custom_lora, seed=42, r
         prompt = f"{trigger_word}. {prompt}."
     try:
         image = pipe(
-            image=input_image, 
-            prompt=prompt,
-            guidance_scale=guidance_scale,
+            image=input_image,
             width=input_image.size[0],
             height=input_image.size[1],
+            prompt=prompt,
+            guidance_scale=guidance_scale,
             generator=torch.Generator().manual_seed(seed),
         ).images[0]
         
-        return image, seed, gr.update(visible=True)
+        return image, seed, gr.update(visible=True), lora_scale
     
     except Exception as e:
         print(f"Error during inference: {e}")
-        return None, seed, gr.update(visible=False)
+        return None, seed, gr.update(visible=False), lora_scale
 
 # CSS styling
 css = """
@@ -217,9 +226,6 @@ css = """
     color: #2563eb;
     font-weight: bold;
 }
-#title{text-align: center}
-#title h1{font-size: 3em; display:inline-flex; align-items:center}
-#title img{width: 100px; margin-right: 0.5em}  
 #prompt {
     flex-grow: 1;
 }
@@ -347,7 +353,7 @@ with gr.Blocks(css=css, theme=gr.themes.Ocean(font=[gr.themes.GoogleFont("Lexend
         triggers=[run_button.click, prompt.submit],
         fn=infer_with_lora_wrapper,
         inputs=[input_image, prompt, selected_state, custom_loaded_lora, seed, randomize_seed, guidance_scale, lora_scale, portrait_mode, gr_flux_loras],
-        outputs=[result, seed, reuse_button]
+        outputs=[result, seed, reuse_button, lora_scale]
     )
     
     reuse_button.click(