Update app.py

app.py

@@ -5,6 +5,7 @@ import sys
 import argparse
 import random
 import time
+import numpy as np
 from omegaconf import OmegaConf
 import torch
 import torchvision
@@ -21,22 +22,15 @@ from funcs import (
     save_videos
 )
 from transformers import pipeline
+from diffusers import DiffusionPipeline
 
-import numpy as np
-from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
-from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
-torch.cuda.empty_cache()
-torch.cuda.set_per_process_memory_fraction(0.7) 
+# 상수 정의
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
 
+# DynamiCrafter 모델 설정
 def download_model():
-    REPO_ID = 'Doubiiu/DynamiCrafter_1024'
-    filename_list = ['model.ckpt']
-    if not os.path.exists('./checkpoints/dynamicrafter_1024_v1/'):
-        os.makedirs('./checkpoints/dynamicrafter_1024_v1/')
-    for filename in filename_list:
-        local_file = os.path.join('./checkpoints/dynamicrafter_1024_v1/', filename)
-        if not os.path.exists(local_file):
-            hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_1024_v1/', force_download=True)
+    # ... (기존 코드 유지)
 
 download_model()
 ckpt_path='checkpoints/dynamicrafter_1024_v1/model.ckpt'
@@ -57,7 +51,7 @@ translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
 device = "cpu"  # 초기에 CPU에 로드
 dtype = torch.float32  # float32 사용
 
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, use_fast=False)
+pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype)
 
 # 메모리 사용량 최적화
 if torch.cuda.is_available():
@@ -85,6 +79,7 @@ def infer_t2i(prompt, seed=42, randomize_seed=False, width=1024, height=1024, gu
     
     torch.cuda.empty_cache()
     return image, seed
+
     
 @spaces.GPU(duration=300)
 def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123, video_length=2):