Add lora enabler button

modules/async_worker.py

@@ -110,6 +110,18 @@ def build_image_wall(async_task):
         # must use deep copy otherwise gradio is super laggy. Do not use list.append() .
         async_task.results = async_task.results + [wall]
         return
+
+    def apply_enable_loras(loras):
+        # Initialize an empty list to hold the LoRAs with the enable status applied
+        loras_with_applied_enable = []
+
+        # Process each LoRA setting based on its enable state
+        for lora_setting in loras:
+            lora_model, lora_weight, lora_enable = lora_setting
+            if lora_enable:  # Only add the LoRA setting if it is enabled
+                loras_with_applied_enable.append([lora_model, lora_weight])
+
+        return loras_with_applied_enable
 
     @torch.no_grad()
     @torch.inference_mode()
@@ -131,7 +143,8 @@ def handler(async_task):
         base_model_name = args.pop()
         refiner_model_name = args.pop()
         refiner_switch = args.pop()
-        loras = [[str(args.pop()), float(args.pop())] for _ in range(5)]
+        loras = [[str(args.pop()), float(args.pop()), bool(args.pop())] for _ in range(5)]
+        loras = apply_enable_loras(loras)
         input_image_checkbox = args.pop()
         current_tab = args.pop()
         uov_method = args.pop()

modules/html.py

@@ -112,6 +112,22 @@
     margin-left: -5px !important;
 }
 
+.lora_enable {
+  min-width: min(0px, 100%) !important;
+}
+
+.lora_enable label {
+  height: 100%;
+}
+
+.lora_enable label input {
+  margin: auto;
+}
+
+.lora_enable label span {
+  display: none;
+}
+
 '''
 progress_html = '''
 <div class="loader-container">

modules/meta_parser.py

@@ -139,10 +139,12 @@ def load_parameter_button_click(raw_prompt_txt, is_generating):
         try:
             n, w = loaded_parameter_dict.get(f'LoRA {i}').split(' : ')
             w = float(w)
-            results.append(n)
-            results.append(w)
+            results.append(n)  # Update LoRA model
+            results.append(w)  # Update LoRA weight
+            results.append(True)  # Enable the LoRA setting by default
         except:
-            results.append(gr.update())
-            results.append(gr.update())
+            results.append("None")  # Update LoRA model
+            results.append(1.0)  # Update LoRA weight
+            results.append(True)  # Enable the LoRA setting by default
 
     return results

webui.py

@@ -313,11 +313,12 @@ def refresh_seed(r, seed_string):
 
                     for i, (n, v) in enumerate(modules.config.default_loras):
                         with gr.Row():
+                            lora_enable = gr.Checkbox(label='Enable', value=True, elem_classes='lora_enable', container=True)
                             lora_model = gr.Dropdown(label=f'LoRA {i + 1}',
                                                      choices=['None'] + modules.config.lora_filenames, value=n)
                             lora_weight = gr.Slider(label='Weight', minimum=-2, maximum=2, step=0.01, value=v,
                                                     elem_classes='lora_weight')
-                            lora_ctrls += [lora_model, lora_weight]
+                            lora_ctrls += [lora_model, lora_weight, lora_enable]
 
                 with gr.Row():
                     model_refresh = gr.Button(label='Refresh', value='\U0001f504 Refresh All Files', variant='secondary', elem_classes='refresh_button')
@@ -467,7 +468,7 @@ def model_refresh_clicked():
                     results = []
                     results += [gr.update(choices=modules.config.model_filenames), gr.update(choices=['None'] + modules.config.model_filenames)]
                     for i in range(5):
-                        results += [gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
+                        results += [gr.update(choices=['None'] + modules.config.lora_filenames), gr.update(), gr.update(interactive=True)]
                     return results
 
                 model_refresh.click(model_refresh_clicked, [], [base_model, refiner_model] + lora_ctrls,