Update app.py

app.py

@@ -355,79 +355,79 @@ def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps
     ).images[0]
     return final_image
 
-# run_lora 함수 수정
 def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
     try:
-        
-
-    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}")
-        print(f"Translated prompt: {translated}")
-        prompt = translated
-
-    if not selected_indices:
-        raise gr.Error("You must select at least one LoRA before proceeding.")
-
-    selected_loras = [loras_state[idx] for idx in selected_indices]
-
-    # Build the prompt with trigger words
-    prepends = []
-    appends = []
-    for lora in selected_loras:
-        trigger_word = lora.get('trigger_word', '')
-        if trigger_word:
-            if lora.get("trigger_position") == "prepend":
-                prepends.append(trigger_word)
-            else:
-                appends.append(trigger_word)
-    prompt_mash = " ".join(prepends + [prompt] + appends)
-    print("Prompt Mash: ", prompt_mash)
-
-    # Unload previous LoRA weights
-    with calculateDuration("Unloading LoRA"):
-        pipe.unload_lora_weights()
-        pipe_i2i.unload_lora_weights()
-        
-    print(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)
-            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)
+        # 한글 감지 및 번역
+        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}")
+            print(f"Translated prompt: {translated}")
+            prompt = translated
+
+        if not selected_indices:
+            raise gr.Error("You must select at least one LoRA before proceeding.")
+
+        selected_loras = [loras_state[idx] for idx in selected_indices]
+
+        # Build the prompt with trigger words
+        prepends = []
+        appends = []
+        for lora in selected_loras:
+            trigger_word = lora.get('trigger_word', '')
+            if trigger_word:
+                if lora.get("trigger_position") == "prepend":
+                    prepends.append(trigger_word)
                 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)
+                    appends.append(trigger_word)
+        prompt_mash = " ".join(prepends + [prompt] + appends)
+        print("Prompt Mash: ", prompt_mash)
+
+        # Unload previous LoRA weights
+        with calculateDuration("Unloading LoRA"):
+            pipe.unload_lora_weights()
+            pipe_i2i.unload_lora_weights()
+            
+        print(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)
+                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)
+                    else:
+                        pipe_i2i.load_lora_weights(lora_path, 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)
-        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)
-        else:
-            pipe.set_adapters(lora_names, adapter_weights=lora_weights)
-    print(pipe.get_active_adapters())
-    # Set random seed for reproducibility
-    with calculateDuration("Randomizing seed"):
-        if randomize_seed:
-            seed = random.randint(0, MAX_SEED)
+                    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)
+            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)
+            else:
+                pipe.set_adapters(lora_names, adapter_weights=lora_weights)
+        print(pipe.get_active_adapters())
+        # Set random seed for reproducibility
+        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)
             return final_image, seed, gr.update(visible=False)
         else:
             image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
+            # Consume the generator to get the final image
             final_image = None
             step_counter = 0
             for image in image_generator:
@@ -444,8 +444,6 @@ def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_ind
         print(f"Error in run_lora: {str(e)}")
         return None, seed, gr.update(visible=False)
 
-
-
 run_lora.zerogpu = True
 
 def get_huggingface_safetensors(link):