Delete app.py

app.py

@@ -1,856 +0,0 @@
-import spaces
-from functools import lru_cache
-import gradio as gr
-from gradio_toggle import Toggle
-import torch
-from huggingface_hub import snapshot_download
-from transformers import CLIPProcessor, CLIPModel, pipeline
-import random
-from xora.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
-from xora.models.transformers.transformer3d import Transformer3DModel
-from xora.models.transformers.symmetric_patchifier import SymmetricPatchifier
-from xora.schedulers.rf import RectifiedFlowScheduler
-from xora.pipelines.pipeline_xora_video import XoraVideoPipeline
-from transformers import T5EncoderModel, T5Tokenizer
-from xora.utils.conditioning_method import ConditioningMethod
-from pathlib import Path
-import safetensors.torch
-import json
-import numpy as np
-import cv2
-from PIL import Image
-import tempfile
-import os
-import gc
-import csv
-from datetime import datetime
-from openai import OpenAI
-
-
-
-import argparse
-import time
-from os import path
-import shutil
-from datetime import datetime
-from safetensors.torch import load_file
-from diffusers import FluxPipeline
-from diffusers.pipelines.stable_diffusion import safety_checker
-from PIL import Image
-from transformers import pipeline
-import replicate
-import logging
-import requests
-from pathlib import Path
-import sys
-import io
-
-# 한글-영어 번역기 초기화
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
-
-torch.backends.cuda.matmul.allow_tf32 = False
-torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
-torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
-torch.backends.cudnn.allow_tf32 = False
-torch.backends.cudnn.deterministic = False
-torch.backends.cuda.preferred_blas_library="cublas"
-torch.set_float32_matmul_precision("highest")
-
-MAX_SEED = np.iinfo(np.int32).max
-
-# Load Hugging Face token if needed
-hf_token = os.getenv("HF_TOKEN")
-openai_api_key = os.getenv("OPENAI_API_KEY")
-client = OpenAI(api_key=openai_api_key)
-
-system_prompt_t2v_path = "assets/system_prompt_t2v.txt"
-with open(system_prompt_t2v_path, "r") as f:
-    system_prompt_t2v = f.read()
-
-# Set model download directory within Hugging Face Spaces
-model_path = "asset"
-
-commit_hash='c7c8ad4c2ddba847b94e8bfaefbd30bd8669fafc'
-
-if not os.path.exists(model_path):
-    snapshot_download("Lightricks/LTX-Video", revision=commit_hash, local_dir=model_path, repo_type="model", token=hf_token)
-
-# Global variables to load components
-vae_dir = Path(model_path) / "vae"
-unet_dir = Path(model_path) / "unet"
-scheduler_dir = Path(model_path) / "scheduler"
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32", cache_dir=model_path).to(torch.device("cuda:0"))
-clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32", cache_dir=model_path)
-
-def process_prompt(prompt):
-    # 한글이 포함되어 있는지 확인
-    if any(ord('가') <= ord(char) <= ord('힣') for char in prompt):
-        # 한글을 영어로 번역
-        translated = translator(prompt)[0]['translation_text']
-        return translated
-    return prompt
-
-def compute_clip_embedding(text=None):
-    inputs = clip_processor(text=text, return_tensors="pt", padding=True).to(device)
-    outputs = clip_model.get_text_features(**inputs)
-    embedding = outputs.detach().cpu().numpy().flatten().tolist()
-    return embedding
-
-def load_vae(vae_dir):
-    vae_ckpt_path = vae_dir / "vae_diffusion_pytorch_model.safetensors"
-    vae_config_path = vae_dir / "config.json"
-    with open(vae_config_path, "r") as f:
-        vae_config = json.load(f)
-    vae = CausalVideoAutoencoder.from_config(vae_config)
-    vae_state_dict = safetensors.torch.load_file(vae_ckpt_path)
-    vae.load_state_dict(vae_state_dict)
-    return vae.to(device).to(torch.bfloat16)
-
-def load_unet(unet_dir):
-    unet_ckpt_path = unet_dir / "unet_diffusion_pytorch_model.safetensors"
-    unet_config_path = unet_dir / "config.json"
-    transformer_config = Transformer3DModel.load_config(unet_config_path)
-    transformer = Transformer3DModel.from_config(transformer_config)
-    unet_state_dict = safetensors.torch.load_file(unet_ckpt_path)
-    transformer.load_state_dict(unet_state_dict, strict=True)
-    return transformer.to(device).to(torch.bfloat16)
-
-def load_scheduler(scheduler_dir):
-    scheduler_config_path = scheduler_dir / "scheduler_config.json"
-    scheduler_config = RectifiedFlowScheduler.load_config(scheduler_config_path)
-    return RectifiedFlowScheduler.from_config(scheduler_config)
-
-# Preset options for resolution and frame configuration
-preset_options = [
-    {"label": "1216x704, 41 frames", "width": 1216, "height": 704, "num_frames": 41},
-    {"label": "1088x704, 49 frames", "width": 1088, "height": 704, "num_frames": 49},
-    {"label": "1056x640, 57 frames", "width": 1056, "height": 640, "num_frames": 57},
-    {"label": "448x448, 100 frames", "width": 448, "height": 448, "num_frames": 100},
-    {"label": "448x448, 200 frames", "width": 448, "height": 448, "num_frames": 200},
-    {"label": "448x448, 300 frames", "width": 448, "height": 448, "num_frames": 300},
-    {"label": "640x640, 80 frames", "width": 640, "height": 640, "num_frames": 80},
-    {"label": "640x640, 120 frames", "width": 640, "height": 640, "num_frames": 120},
-    {"label": "768x768, 64 frames", "width": 768, "height": 768, "num_frames": 64},
-    {"label": "768x768, 90 frames", "width": 768, "height": 768, "num_frames": 90},
-    {"label": "720x720, 64 frames", "width": 768, "height": 768, "num_frames": 64},
-    {"label": "720x720, 100 frames", "width": 768, "height": 768, "num_frames": 100},
-    {"label": "768x512, 97 frames", "width": 768, "height": 512, "num_frames": 97},
-    {"label": "512x512, 160 frames", "width": 512, "height": 512, "num_frames": 160},
-    {"label": "512x512, 200 frames", "width": 512, "height": 512, "num_frames": 200},
-]
-
-def preset_changed(preset):
-    if preset != "Custom":
-        selected = next(item for item in preset_options if item["label"] == preset)
-        return (
-            selected["height"],
-            selected["width"],
-            selected["num_frames"],
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-        )
-    else:
-        return (
-            None,
-            None,
-            None,
-            gr.update(visible=True),
-            gr.update(visible=True),
-            gr.update(visible=True),
-        )
-
-# Load models
-vae = load_vae(vae_dir)
-unet = load_unet(unet_dir)
-scheduler = load_scheduler(scheduler_dir)
-patchifier = SymmetricPatchifier(patch_size=1)
-text_encoder = T5EncoderModel.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="text_encoder").to(torch.device("cuda:0"))
-tokenizer = T5Tokenizer.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="tokenizer")
-
-pipeline_video = XoraVideoPipeline(
-    transformer=unet,
-    patchifier=patchifier,
-    text_encoder=text_encoder,
-    tokenizer=tokenizer,
-    scheduler=scheduler,
-    vae=vae,
-).to(torch.device("cuda:0"))
-
-def enhance_prompt_if_enabled(prompt, enhance_toggle):
-    if not enhance_toggle:
-        print("Enhance toggle is off, Prompt: ", prompt)
-        return prompt
-
-    messages = [
-        {"role": "system", "content": system_prompt_t2v},
-        {"role": "user", "content": prompt},
-    ]
-
-    try:
-        response = client.chat.completions.create(
-            model="gpt-4-mini",
-            messages=messages,
-            max_tokens=200,
-        )
-        print("Enhanced Prompt: ", response.choices[0].message.content.strip())
-        return response.choices[0].message.content.strip()
-    except Exception as e:
-        print(f"Error: {e}")
-        return prompt
-
-@spaces.GPU(duration=90)
-def generate_video_from_text_90(
-    prompt="",
-    enhance_prompt_toggle=False,
-    negative_prompt="",
-    frame_rate=25,
-    seed=random.randint(0, MAX_SEED),
-    num_inference_steps=30,
-    guidance_scale=3.2,
-    height=768,
-    width=768,
-    num_frames=60,
-    progress=gr.Progress(),
-):
-    # 프롬프트 전처리 (한글 -> 영어)
-    prompt = process_prompt(prompt)
-    negative_prompt = process_prompt(negative_prompt)
-
-    if len(prompt.strip()) < 50:
-        raise gr.Error(
-            "Prompt must be at least 50 characters long. Please provide more details for the best results.",
-            duration=5,
-        )
-
-    prompt = enhance_prompt_if_enabled(prompt, enhance_prompt_toggle)
-
-    sample = {
-        "prompt": prompt,
-        "prompt_attention_mask": None,
-        "negative_prompt": negative_prompt,
-        "negative_prompt_attention_mask": None,
-        "media_items": None,
-    }
-
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-
-    def gradio_progress_callback(self, step, timestep, kwargs):
-        progress((step + 1) / num_inference_steps)
-
-    try:
-        with torch.no_grad():
-            images = pipeline_video(
-                num_inference_steps=num_inference_steps,
-                num_images_per_prompt=1,
-                guidance_scale=guidance_scale,
-                generator=generator,
-                output_type="pt",
-                height=height,
-                width=width,
-                num_frames=num_frames,
-                frame_rate=frame_rate,
-                **sample,
-                is_video=True,
-                vae_per_channel_normalize=True,
-                conditioning_method=ConditioningMethod.UNCONDITIONAL,
-                mixed_precision=True,
-                callback_on_step_end=gradio_progress_callback,
-            ).images
-    except Exception as e:
-        raise gr.Error(
-            f"An error occurred while generating the video. Please try again. Error: {e}",
-            duration=5,
-        )
-    finally:
-        torch.cuda.empty_cache()
-        gc.collect()
-
-    output_path = tempfile.mktemp(suffix=".mp4")
-    video_np = images.squeeze(0).permute(1, 2, 3, 0).cpu().float().numpy()
-    video_np = (video_np * 255).astype(np.uint8)
-    height, width = video_np.shape[1:3]
-    out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), frame_rate, (width, height))
-    for frame in video_np[..., ::-1]:
-        out.write(frame)
-    out.release()
-    del images
-    del video_np
-    torch.cuda.empty_cache()
-    return output_path
-
-def create_advanced_options():
-    with gr.Accordion("Step 4: Advanced Options (Optional)", open=False):
-        seed = gr.Slider(label="4.1 Seed", minimum=0, maximum=1000000, step=1, value=646373)
-        inference_steps = gr.Slider(label="4.2 Inference Steps", minimum=5, maximum=150, step=5, value=40)
-        guidance_scale = gr.Slider(label="4.3 Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=4.2)
-        
-        height_slider = gr.Slider(
-            label="4.4 Height",
-            minimum=256,
-            maximum=1024,
-            step=64,
-            value=768,
-            visible=False,
-        )
-        width_slider = gr.Slider(
-            label="4.5 Width",
-            minimum=256,
-            maximum=1024,
-            step=64,
-            value=768,
-            visible=False,
-        )
-        num_frames_slider = gr.Slider(
-            label="4.5 Number of Frames",
-            minimum=1,
-            maximum=500,
-            step=1,
-            value=60,
-            visible=False,
-        )
-
-        return [
-            seed,
-            inference_steps,
-            guidance_scale,
-            height_slider,
-            width_slider,
-            num_frames_slider,
-        ]
-
-###############################################
-# 여기서부터 두 번째 코드 통합 적용
-###############################################
-
-import argparse
-import time
-from os import path
-import shutil
-from safetensors.torch import load_file
-from diffusers import FluxPipeline
-from diffusers.pipelines.stable_diffusion import safety_checker
-import replicate
-import logging
-import requests
-from pathlib import Path
-import sys
-import io
-
-# 로깅 설정
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Setup and initialization code
-cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
-PERSISTENT_DIR = os.environ.get("PERSISTENT_DIR", ".")
-gallery_path = path.join(PERSISTENT_DIR, "gallery")
-video_gallery_path = path.join(PERSISTENT_DIR, "video_gallery")
-
-# API 설정
-CATBOX_USER_HASH = "e7a96fc68dd4c7d2954040cd5"
-REPLICATE_API_TOKEN = os.getenv("API_KEY")
-
-# 환경 변수 설정
-os.environ["TRANSFORMERS_CACHE"] = cache_path
-os.environ["HF_HUB_CACHE"] = cache_path
-os.environ["HF_HOME"] = cache_path
-
-# CUDA 설정
-torch.backends.cuda.matmul.allow_tf32 = True
-
-# 번역기 초기화 (이미 위에서 translator 선언됨, 중복 선언)
-translator2 = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")  # 두 번째 코드에서도 선언. 누락없이 출력하기 위해 추가.
-
-# 디렉토리 생성
-for dir_path in [gallery_path, video_gallery_path]:
-    if not path.exists(dir_path):
-        os.makedirs(dir_path, exist_ok=True)
-
-def check_api_key():
-    """API 키 확인 및 설정"""
-    if not REPLICATE_API_TOKEN:
-        logger.error("Replicate API key not found")
-        return False
-    os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
-    logger.info("Replicate API token set successfully")
-    return True
-
-def translate_if_korean(text):
-    """한글이 포함된 경우 영어로 번역"""
-    if any(ord(char) >= 0xAC00 and ord(char) <= 0xD7A3 for char in text):
-        translation = translator2(text)[0]['translation_text']
-        return translation
-    return text
-
-def filter_prompt(prompt):
-    inappropriate_keywords = [
-        "nude", "naked", "nsfw", "porn", "sex", "explicit", "adult", "xxx",
-        "erotic", "sensual", "seductive", "provocative", "intimate",
-        "violence", "gore", "blood", "death", "kill", "murder", "torture",
-        "drug", "suicide", "abuse", "hate", "discrimination"
-    ]
-    
-    prompt_lower = prompt.lower()
-    for keyword in inappropriate_keywords:
-        if keyword in prompt_lower:
-            return False, "부적절한 내용이 포함된 프롬프트입니다."
-    return True, prompt
-
-def process_prompt_for_sd(prompt):
-    """프롬프트 전처리 (번역 및 필터링)"""
-    translated_prompt = translate_if_korean(prompt)
-    is_safe, filtered_prompt = filter_prompt(translated_prompt)
-    return is_safe, filtered_prompt
-
-class timer:
-    def __init__(self, method_name="timed process"):
-        self.method = method_name
-    def __enter__(self):
-        self.start = time.time()
-        print(f"{self.method} starts")
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        end = time.time()
-        print(f"{self.method} took {str(round(end - self.start, 2))}s")
-
-# Model initialization
-if not path.exists(cache_path):
-    os.makedirs(cache_path, exist_ok=True)
-
-pipe_sd = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
-pipe_sd.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"))
-pipe_sd.fuse_lora(lora_scale=0.125)
-pipe_sd.to(device="cuda", dtype=torch.bfloat16)
-pipe_sd.safety_checker = safety_checker.StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
-
-def upload_to_catbox(image_path):
-    """catbox.moe API를 사용하여 이미지 업로드"""
-    try:
-        logger.info(f"Preparing to upload image: {image_path}")
-        url = "https://catbox.moe/user/api.php"
-        
-        file_extension = Path(image_path).suffix.lower()
-        if file_extension not in ['.jpg', '.jpeg', '.png', '.gif']:
-            logger.error(f"Unsupported file type: {file_extension}")
-            return None
-
-        files = {
-            'fileToUpload': (
-                os.path.basename(image_path),
-                open(image_path, 'rb'),
-                'image/jpeg' if file_extension in ['.jpg', '.jpeg'] else 'image/png'
-            )
-        }
-        
-        data = {
-            'reqtype': 'fileupload',
-            'userhash': CATBOX_USER_HASH
-        }
-
-        response = requests.post(url, files=files, data=data)
-        
-        if response.status_code == 200 and response.text.startswith('http'):
-            image_url = response.text
-            logger.info(f"Image uploaded successfully: {image_url}")
-            return image_url
-        else:
-            raise Exception(f"Upload failed: {response.text}")
-
-    except Exception as e:
-        logger.error(f"Image upload error: {str(e)}")
-        return None
-
-def add_watermark(video_path):
-    """OpenCV를 사용하여 비디오에 워터마크 추가"""
-    try:
-        cap = cv2.VideoCapture(video_path)
-        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        fps = int(cap.get(cv2.CAP_PROP_FPS))
-        
-        text = "GiniGEN.AI"
-        font = cv2.FONT_HERSHEY_SIMPLEX
-        font_scale = height * 0.05 / 30
-        thickness = 2
-        color = (255, 255, 255)
-        
-        (text_width, text_height), _ = cv2.getTextSize(text, font, font_scale, thickness)
-        margin = int(height * 0.02)
-        x_pos = width - text_width - margin
-        y_pos = height - margin
-        
-        output_path = "watermarked_output.mp4"
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
-        
-        while cap.isOpened():
-            ret, frame = cap.read()
-            if not ret:
-                break
-            cv2.putText(frame, text, (x_pos, y_pos), font, font_scale, color, thickness)
-            out.write(frame)
-        
-        cap.release()
-        out.release()
-        
-        return output_path
-        
-    except Exception as e:
-        logger.error(f"Error adding watermark: {str(e)}")
-        return video_path
-
-def generate_video(image, prompt):
-    logger.info("Starting video generation")
-    try:
-        if not check_api_key():
-            return "Replicate API key not properly configured"
-
-        if not image:
-            logger.error("No image provided")
-            return "Please upload an image"
-
-        image_url = upload_to_catbox(image)
-        if not image_url:
-            return "Failed to upload image"
-
-        input_data = {
-            "prompt": prompt,
-            "first_frame_image": image_url
-        }
-
-        try:
-            replicate.Client(api_token=REPLICATE_API_TOKEN)
-            output = replicate.run(
-                "minimax/video-01-live",
-                input=input_data
-            )
-
-            temp_file = "temp_output.mp4"
-            
-            if hasattr(output, 'read'):
-                with open(temp_file, "wb") as file:
-                    file.write(output.read())
-            elif isinstance(output, str):
-                response = requests.get(output)
-                with open(temp_file, "wb") as file:
-                    file.write(response.content)
-            
-            final_video = add_watermark(temp_file)
-            return final_video
-
-        except Exception as api_error:
-            logger.error(f"API call failed: {str(api_error)}")
-            return f"API call failed: {str(api_error)}"
-
-    except Exception as e:
-        logger.error(f"Unexpected error: {str(e)}")
-        return f"Unexpected error: {str(e)}"
-
-def save_image(image):
-    """Save the generated image in PNG format and return the path"""
-    try:
-        if not os.path.exists(gallery_path):
-            os.makedirs(gallery_path, exist_ok=True)
-        
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        random_suffix = os.urandom(4).hex()
-        filename = f"generated_{timestamp}_{random_suffix}.png"
-        filepath = os.path.join(gallery_path, filename)
-        
-        if not isinstance(image, Image.Image):
-            image = Image.fromarray(image)
-        
-        if image.mode != 'RGB':
-            image = image.convert('RGB')
-        
-        image.save(
-            filepath,
-            format='PNG',
-            optimize=True,
-            quality=100
-        )
-        
-        logger.info(f"Image saved successfully as PNG: {filepath}")
-        return filepath
-    except Exception as e:
-        logger.error(f"Error in save_image: {str(e)}")
-        return None
-
-def load_gallery():
-    """Load all images from the gallery directory"""
-    try:
-        os.makedirs(gallery_path, exist_ok=True)
-        
-        image_files = []
-        for f in os.listdir(gallery_path):
-            if f.lower().endswith(('.png', '.jpg', '.jpeg')):
-                full_path = os.path.join(gallery_path, f)
-                image_files.append((full_path, os.path.getmtime(full_path)))
-        
-        image_files.sort(key=lambda x: x[1], reverse=True)
-        return [f[0] for f in image_files]
-    except Exception as e:
-        print(f"Error loading gallery: {str(e)}")
-        return []
-
-# CSS 스타일 정의
-css = """
-[이전의 CSS 코드를 그대로 유지]
-"""
-
-def get_random_seed():
-    return torch.randint(0, 1000000, (1,)).item()
-
-###############################################
-# 여기서부터 Gradio UI 통합
-###############################################
-
-with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
-    gr.HTML('<div class="title">AI Image & Video Generator</div>')
-
-    with gr.Tabs():
-        with gr.Tab("Image Generation"):
-            with gr.Row():
-                with gr.Column(scale=3):
-                    img_prompt = gr.Textbox(
-                        label="Image Description",
-                        placeholder="이미지 설명을 입력하세요... (한글 입력 가능)",
-                        lines=3
-                    )
-                    
-                    with gr.Accordion("Advanced Settings", open=False):
-                        with gr.Row():
-                            height = gr.Slider(
-                                label="Height",
-                                minimum=256,
-                                maximum=1152,
-                                step=64,
-                                value=1024
-                            )
-                            width = gr.Slider(
-                                label="Width",
-                                minimum=256,
-                                maximum=1152,
-                                step=64,
-                                value=1024
-                            )
-                        
-                        with gr.Row():
-                            steps = gr.Slider(
-                                label="Inference Steps",
-                                minimum=6,
-                                maximum=25,
-                                step=1,
-                                value=8
-                            )
-                            scales = gr.Slider(
-                                label="Guidance Scale",
-                                minimum=0.0,
-                                maximum=5.0,
-                                step=0.1,
-                                value=3.5
-                            )
-                        
-                        seed = gr.Number(
-                            label="Seed",
-                            value=get_random_seed(),
-                            precision=0
-                        )
-                        
-                        randomize_seed = gr.Button("🎲 Randomize Seed", elem_classes=["generate-btn"])
-                    
-                    generate_btn = gr.Button(
-                        "✨ Generate Image",
-                        elem_classes=["generate-btn"]
-                    )
-
-                with gr.Column(scale=4):
-                    img_output = gr.Image(
-                        label="Generated Image",
-                        type="pil",
-                        format="png"
-                    )
-                    img_gallery = gr.Gallery(
-                        label="Image Gallery",
-                        show_label=True,
-                        elem_id="gallery",
-                        columns=[4],
-                        rows=[2],
-                        height="auto",
-                        object_fit="cover"
-                    )
-                    img_gallery.value = load_gallery()
-
-        with gr.Tab("Video Generation"):
-            with gr.Row():
-                with gr.Column(scale=3):
-                    video_prompt = gr.Textbox(
-                        label="Video Description",
-                        placeholder="비디오 설명을 입력하세요... (한글 입력 가능)",
-                        lines=3
-                    )
-                    upload_image = gr.Image(
-                        type="filepath",
-                        label="Upload First Frame Image"
-                    )
-                    video_generate_btn = gr.Button(
-                        "🎬 Generate Video",
-                        elem_classes=["generate-btn"]
-                    )
-
-                with gr.Column(scale=4):
-                    video_output = gr.Video(label="Generated Video")
-                    video_gallery = gr.Gallery(
-                        label="Video Gallery",
-                        show_label=True,
-                        columns=[4],
-                        rows=[2],
-                        height="auto",
-                        object_fit="cover"
-                    )
-
-    # 이하 첫 번째 코드의 txt2vid 관련 UI를 통합
-    # 첫 번째 코드의 txt2vid UI를 추가 탭으로 통합
-    with gr.Tab("Text-to-Video Generation"):
-        with gr.Column():
-            txt2vid_prompt = gr.Textbox(
-                label="Step 1: Enter Your Prompt (한글 또는 영어)",
-                placeholder="생성하고 싶은 비디오를 설명하세요 (최소 50자)...",
-                value="긴 갈색 머리와 밝은 피부를 가진 여성이 긴 금발 머리를 가진 다른 여성을 향해 미소 짓습니다. 갈색 머리 여성은 검은 재킷을 입고 있으며 오른쪽 뺨에 작고 거의 눈에 띄지 않는 점이 있습니다. 카메라 앵글은 갈색 머리 여성의 얼굴에 초점을 맞춘 클로즈업입니다. 조명은 따뜻하고 자연스러우며, 아마도 지는 해에서 나오는 것 같아 장면에 부드러운 빛을 비춥니다.",
-                lines=5,
-            )
-
-            txt2vid_enhance_toggle = Toggle(
-                label="Enhance Prompt",
-                value=False,
-                interactive=True,
-            )
-
-            txt2vid_negative_prompt = gr.Textbox(
-                label="Step 2: Enter Negative Prompt",
-                placeholder="비디오에서 원하지 않는 요소를 설명하세요...",
-                value="low quality, worst quality, deformed, distorted, damaged, motion blur, motion artifacts, fused fingers, incorrect anatomy, strange hands, ugly",
-                lines=2,
-            )
-
-            txt2vid_preset = gr.Dropdown(
-                choices=[p["label"] for p in preset_options],
-                value="512x512, 160 frames",
-                label="Step 3.1: Choose Resolution Preset",
-            )
-
-            txt2vid_frame_rate = gr.Slider(
-                label="Step 3.2: Frame Rate",
-                minimum=6,
-                maximum=60,
-                step=1,
-                value=20,
-            )
-
-            txt2vid_advanced = create_advanced_options()
-            txt2vid_generate = gr.Button(
-                "Step 5: Generate Video",
-                variant="primary",
-                size="lg",
-            )
-
-            txt2vid_output = gr.Video(label="Generated Output")
-
-            txt2vid_preset.change(
-                fn=preset_changed,
-                inputs=[txt2vid_preset],
-                outputs=txt2vid_advanced[3:],
-            )
-
-            txt2vid_generate.click(
-                fn=generate_video_from_text_90,
-                inputs=[
-                    txt2vid_prompt,
-                    txt2vid_enhance_toggle,
-                    txt2vid_negative_prompt,
-                    txt2vid_frame_rate,
-                    *txt2vid_advanced,
-                ],
-                outputs=txt2vid_output,
-                concurrency_limit=1,
-                concurrency_id="generate_video",
-                queue=True,
-            )
-
-    @spaces.GPU
-    def process_and_save_image(height, width, steps, scales, prompt, seed):
-        is_safe, translated_prompt = process_prompt_for_sd(prompt)
-        if not is_safe:
-            gr.Warning("부적절한 내용이 포함된 프롬프트입니다.")
-            return None, load_gallery()
-          
-        with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
-            try:
-                generated_image = pipe_sd(
-                    prompt=[translated_prompt],
-                    generator=torch.Generator().manual_seed(int(seed)),
-                    num_inference_steps=int(steps),
-                    guidance_scale=float(scales),
-                    height=int(height),
-                    width=int(width),
-                    max_sequence_length=256
-                ).images[0]
-                    
-                if not isinstance(generated_image, Image.Image):
-                    generated_image = Image.fromarray(generated_image)
-                    
-                if generated_image.mode != 'RGB':
-                    generated_image = generated_image.convert('RGB')
-                    
-                img_byte_arr = io.BytesIO()
-                generated_image.save(img_byte_arr, format='PNG')
-                img_byte_arr = img_byte_arr.getvalue()
-                
-                saved_path = save_image(generated_image)
-                if saved_path is None:
-                    logger.warning("Failed to save generated image")
-                    return None, load_gallery()
-                    
-                return Image.open(io.BytesIO(img_byte_arr)), load_gallery()
-            except Exception as e:
-                logger.error(f"Error in image generation: {str(e)}")
-                return None, load_gallery()
-                        
-
-    def process_and_generate_video(image, prompt):
-        is_safe, translated_prompt = process_prompt_for_sd(prompt)
-        if not is_safe:
-            gr.Warning("부적절한 내용이 포함된 프롬프트입니다.")
-            return None
-        return generate_video(image, translated_prompt)
-
-    def update_seed():
-        return get_random_seed()
-
-    generate_btn.click(
-        process_and_save_image,
-        inputs=[height, width, steps, scales, img_prompt, seed],
-        outputs=[img_output, img_gallery]
-    )
-    
-    video_generate_btn.click(
-        process_and_generate_video,
-        inputs=[upload_image, video_prompt],
-        outputs=video_output
-    )
-    
-    randomize_seed.click(
-        update_seed,
-        outputs=[seed]
-    )
-    
-    generate_btn.click(
-        update_seed,
-        outputs=[seed]
-    )
-
-if __name__ == "__main__":
-    demo.queue(max_size=64, default_concurrency_limit=1, api_open=False).launch(share=True, show_api=False, allowed_paths=[PERSISTENT_DIR])