Upload app.py

app.py

@@ -536,32 +536,34 @@ def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_ind
     print("Prompt Mash: ", prompt_mash) #
 
     # Load LoRA weights with respective scales
-    with calculateDuration("Loading LoRA weights"):
-        for idx, lora in enumerate(selected_loras):
-            lora_name = f"lora_{idx}"
-            lora_names.append(lora_name)
-            print(f"Lora Name: {lora_name}")
-            lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2)
-            lora_path = lora['repo']
-            weight_name = lora.get("weights")
-            print(f"Lora Path: {lora_path}")
-            if image_input is not None:
-                pipe_i2i.load_lora_weights(
-                    lora_path, 
-                    weight_name=weight_name if weight_name else None,
-                    low_cpu_mem_usage=True,
-                    adapter_name=lora_name,
-                    token=HF_TOKEN
-                )
-            else:
-                pipe.load_lora_weights(
-                    lora_path, 
-                    weight_name=weight_name if weight_name else None,
-                    low_cpu_mem_usage=True,
-                    adapter_name=lora_name,
-                    token=HF_TOKEN
-                )
-        print("Loaded LoRAs:", lora_names)
+    if selected_indices:
+        with calculateDuration("Loading LoRA weights"):
+            for idx, lora in enumerate(selected_loras):
+                lora_name = f"lora_{idx}"
+                lora_names.append(lora_name)
+                print(f"Lora Name: {lora_name}")
+                lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2)
+                lora_path = lora['repo']
+                weight_name = lora.get("weights")
+                print(f"Lora Path: {lora_path}")
+                if image_input is not None:
+                    pipe_i2i.load_lora_weights(
+                        lora_path, 
+                        weight_name=weight_name if weight_name else None,
+                        low_cpu_mem_usage=True,
+                        adapter_name=lora_name,
+                        token=HF_TOKEN
+                    )
+                else:
+                    pipe.load_lora_weights(
+                        lora_path, 
+                        weight_name=weight_name if weight_name else None,
+                        low_cpu_mem_usage=True,
+                        adapter_name=lora_name,
+                        token=HF_TOKEN
+                    )
+            print("Loaded LoRAs:", lora_names)
+    if selected_indices or is_valid_lora(lora_json):
         if image_input is not None:
             pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
         else: