Update app.py

app.py

@@ -451,7 +451,7 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
     return img
 
 @spaces.GPU(duration=75)
-def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
+def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
     if not selected_indices:
         raise gr.Error("You must select at least one LoRA before proceeding.")
 
@@ -469,51 +469,40 @@ def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scal
                 appends.append(trigger_word)
     prompt_mash = " ".join(prepends + [prompt] + appends)
     print("Prompt Mash: ", prompt_mash)
-    print("--Seed--:", seed)
+
     # Unload previous LoRA weights
     with calculateDuration("Unloading LoRA"):
         pipe.unload_lora_weights()
 
-        
-    print(pipe.get_active_adapters())
-
+    # Load LoRA weights
     lora_names = []
     lora_weights = []
     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}")
             pipe.load_lora_weights(
-                lora_path, 
-                weight_name=weight_name if weight_name else None,
+                lora['repo'],
+                weight_name=lora.get("weights"),
                 low_cpu_mem_usage=True,
-                adapter_name=lora_name
+                adapter_name=lora_name,
             )
-
-        print("Loaded LoRAs:", lora_names)
-        print("Adapter weights:", lora_weights)
-                
         pipe.set_adapters(lora_names, adapter_weights=lora_weights)
 
-    with calculateDuration("Randomizing seed"):
-        if randomize_seed:
-            seed = random.randint(0, MAX_SEED)
-            
+    # Set random seed if required
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
 
-        image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
+    # Generate image
+    image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
+    step_counter = 0
+
+    for image, seed, progress_update in image_generator:
+        step_counter += 1
+        progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
+        yield image, seed, gr.update(value=progress_bar, visible=True)
 
-        step_counter = 0
-        for image in image_generator:
-            step_counter += 1
-            final_image = image
-            progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
-            yield image, seed, gr.update(value=progress_bar, visible=True)
-            
 run_lora.zerogpu = True
 
 def get_huggingface_safetensors(link):