Update app.py

app.py

@@ -196,14 +196,14 @@ def randomize_loras(selected_indices, loras_state):
  lora_scale_1 = 1.15
  lora_scale_2 = 1.15
  lora_scale_3 = 1.15
- lora_image_1 = lora1['image']
- lora_image_2 = lora2['image']
- lora_image_3 = lora3['image']
+ lora_image_1 = lora1.get('image', 'path/to/default/image.png')
+ lora_image_2 = lora2.get('image', 'path/to/default/image.png')
+ lora_image_3 = lora3.get('image', 'path/to/default/image.png')
  random_prompt = random.choice(prompt_values)
  return selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3, random_prompt
  except Exception as e:
  print(f"Error in randomize_loras: {str(e)}")
- return "Error", "Error", "Error", [], 1.15, 1.15, 1.15, None, None, None, ""
+ return "Error", "Error", "Error", [], 1.15, 1.15, 1.15, 'path/to/default/image.png', 'path/to/default/image.png', 'path/to/default/image.png', ""
 
 def add_custom_lora(custom_lora, selected_indices, current_loras):
  if custom_lora:
@@ -366,7 +366,7 @@ def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps
 
 def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
  try:
- # 한글 감지 및 번역
+ # 한글 감지 및 번역 (이 부분은 그대로 유지)
  if any('\u3131' <= char <= '\u318E' or '\uAC00' <= char <= '\uD7A3' for char in prompt):
  translated = translator(prompt, max_length=512)[0]['translation_text']
  print(f"Original prompt: {prompt}")
@@ -378,7 +378,7 @@ def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_ind
 
  selected_loras = [loras_state[idx] for idx in selected_indices]
 
- # Build the prompt with trigger words
+ # Build the prompt with trigger words (이 부분은 그대로 유지)
  prepends = []
  appends = []
  for lora in selected_loras:
@@ -396,41 +396,52 @@ def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_ind
  pipe.unload_lora_weights()
  pipe_i2i.unload_lora_weights()
  
- print(pipe.get_active_adapters())
+ print(f"Active adapters before loading: {pipe.get_active_adapters()}")
+ 
  # Load LoRA weights with respective scales
  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)
- lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2 if idx == 1 else lora_scale_3)
- lora_path = lora['repo']
- weight_name = lora.get("weights")
- print(f"Lora Path: {lora_path}")
- if image_input is not None:
- if weight_name:
- pipe_i2i.load_lora_weights(lora_path, weight_name=weight_name, low_cpu_mem_usage=True, adapter_name=lora_name)
+ try:
+ lora_name = f"lora_{idx}"
+ lora_path = lora['repo']
+ weight_name = lora.get("weights")
+ print(f"Loading LoRA {lora_name} from {lora_path}")
+ if image_input is not None:
+ if weight_name:
+ pipe_i2i.load_lora_weights(lora_path, weight_name=weight_name, adapter_name=lora_name)
+ else:
+ pipe_i2i.load_lora_weights(lora_path, adapter_name=lora_name)
  else:
- pipe_i2i.load_lora_weights(lora_path, low_cpu_mem_usage=True, adapter_name=lora_name)
- else:
- if weight_name:
- pipe.load_lora_weights(lora_path, weight_name=weight_name, low_cpu_mem_usage=True, adapter_name=lora_name)
- else:
- pipe.load_lora_weights(lora_path, low_cpu_mem_usage=True, adapter_name=lora_name)
+ if weight_name:
+ pipe.load_lora_weights(lora_path, weight_name=weight_name, adapter_name=lora_name)
+ else:
+ pipe.load_lora_weights(lora_path, adapter_name=lora_name)
+ lora_names.append(lora_name)
+ lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2 if idx == 1 else lora_scale_3)
+ except Exception as e:
+ print(f"Failed to load LoRA {lora_name}: {str(e)}")
+
  print("Loaded LoRAs:", lora_names)
  print("Adapter weights:", lora_weights)
- if image_input is not None:
- pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
+ 
+ if lora_names:
+ if image_input is not None:
+ pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
+ else:
+ pipe.set_adapters(lora_names, adapter_weights=lora_weights)
  else:
- pipe.set_adapters(lora_names, adapter_weights=lora_weights)
- print(pipe.get_active_adapters())
- # Set random seed for reproducibility
+ print("No LoRAs were successfully loaded.")
+ return None, seed, gr.update(visible=False)
+
+ print(f"Active adapters after loading: {pipe.get_active_adapters()}")
+
+ # 여기서부터 이미지 생성 로직 (이 부분은 그대로 유지)
  with calculateDuration("Randomizing seed"):
  if randomize_seed:
  seed = random.randint(0, MAX_SEED)
 
- # Generate image
  if image_input is not None:
  final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, seed)
  else:
@@ -442,15 +453,12 @@ def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_ind
  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)
- 
-
 
  if final_image is None:
  raise Exception("Failed to generate image")
  
  return final_image, seed, gr.update(visible=False)
 
-
  except Exception as e:
  print(f"Error in run_lora: {str(e)}")
  return None, seed, gr.update(visible=False)