Update app.py

app.py

@@ -33,6 +33,7 @@ from xora.schedulers.rf import RectifiedFlowScheduler
 from xora.pipelines.pipeline_xora_video import XoraVideoPipeline
 from xora.utils.conditioning_method import ConditioningMethod
 from functools import lru_cache
+from diffusers.pipelines.flux import FluxPipeline
 
 # 로깅 설정
 logging.basicConfig(
@@ -80,6 +81,33 @@ def setup_cuda():
     torch.set_float32_matmul_precision("highest")
     logger.info("CUDA settings initialized")
 
+
+# Model initialization
+if not path.exists(cache_path):
+    os.makedirs(cache_path, exist_ok=True)
+
+try:
+    pipe = FluxPipeline.from_pretrained(
+        "black-forest-labs/FLUX.1-dev",
+        torch_dtype=torch.bfloat16,
+        cache_dir=cache_path
+    )
+    lora_path = hf_hub_download(
+        "ByteDance/Hyper-SD",
+        "Hyper-FLUX.1-dev-8steps-lora.safetensors",
+        cache_dir=cache_path
+    )
+    pipe.load_lora_weights(lora_path)
+    pipe.fuse_lora(lora_scale=0.125)
+    pipe.to(device="cuda", dtype=torch.bfloat16)
+    pipe.safety_checker = safety_checker.StableDiffusionSafetyChecker.from_pretrained(
+        "CompVis/stable-diffusion-safety-checker",
+        cache_dir=cache_path
+    )
+except Exception as e:
+    logger.error(f"Error initializing FluxPipeline: {str(e)}")
+    raise
+
 # 모델 관리 클래스
 class ModelManager:
     def __init__(self):