Update app.py

app.py

@@ -15,6 +15,31 @@ import gradio as gr
 import os
 from huggingface_hub import login
 
+# HF_TOKEN 설정 직후에 추가
+HF_TOKEN = os.getenv("HF_TOKEN")
+if HF_TOKEN is None:
+    raise ValueError("Please set the HF_TOKEN environment variable")
+login(token=HF_TOKEN)
+
+# 모델 설정
+base_model = "black-forest-labs/FLUX.1-dev"
+model_lora_repo = "Motas/Flux_Fashion_Photography_Style"
+clothes_lora_repo = "prithivMLmods/Canopus-Clothing-Flux-LoRA"
+
+# FLUX 모델 초기화 (한 번만)
+fashion_pipe = DiffusionPipeline.from_pretrained(
+    base_model,
+    torch_dtype=torch.bfloat16,
+    use_auth_token=HF_TOKEN
+)
+fashion_pipe.to("cuda")
+
+# 번역기 초기화
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
+
+# 상수 정의 추가
+import random
+MAX_SEED = 2**32 - 1
 
 # Download checkpoints
 snapshot_download(repo_id="franciszzj/Leffa", local_dir="./ckpts")
@@ -41,26 +66,7 @@ pt_model = LeffaModel(
 )
 pt_inference = LeffaInference(model=pt_model)
 
-HF_TOKEN = os.getenv("HF_TOKEN")
-if HF_TOKEN is None:
-    raise ValueError("Please set the HF_TOKEN environment variable")
-login(token=HF_TOKEN)
 
-# FLUX 모델 초기화 부분 수정
-fashion_pipe = DiffusionPipeline.from_pretrained(
-    base_model,
-    torch_dtype=torch.bfloat16,
-    use_auth_token=HF_TOKEN  # 인증 토큰 추가
-)
-fashion_pipe.to("cuda")
-
-# LoRA 로딩 함수 수정
-def load_lora(pipe, repo_id):
-    pipe.load_lora_weights(
-        repo_id,
-        use_auth_token=HF_TOKEN
-    )
-    return pipe
     
 translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
 base_model = "black-forest-labs/FLUX.1-dev"
@@ -70,17 +76,17 @@ clothes_lora_repo = "prithivMLmods/Canopus-Clothing-Flux-LoRA"
 fashion_pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
 fashion_pipe.to("cuda")
 
+def contains_korean(text):
+    return any(ord('가') <= ord(char) <= ord('힣') for char in text)
+
 @spaces.GPU()
 def generate_fashion(prompt, mode, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
-    # 한글 감지 및 번역
-    def contains_korean(text):
-        return any(ord('가') <= ord(char) <= ord('힣') for char in text)
-    
     if contains_korean(prompt):
         translated = translator(prompt)[0]['translation_text']
         actual_prompt = translated
     else:
         actual_prompt = prompt
+        
 
     # 모드에 따른 LoRA 로딩 및 트리거워드 설정
     if mode == "Generate Model":