Update app.py

app.py

@@ -1,228 +1,2 @@
-import spaces
-import argparse
 import os
-import time
-from os import path
-import shutil
-from datetime import datetime
-from safetensors.torch import load_file
-from huggingface_hub import hf_hub_download
-import gradio as gr
-import torch
-from diffusers import FluxPipeline
-from PIL import Image
-from transformers import pipeline
-
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
-
-# Hugging Face 토큰 설정
-HF_TOKEN = os.getenv("HF_TOKEN")
-if HF_TOKEN is None:
-    raise ValueError("HF_TOKEN environment variable is not set")
-
-# 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")
-
-os.environ["TRANSFORMERS_CACHE"] = cache_path
-os.environ["HF_HUB_CACHE"] = cache_path
-os.environ["HF_HOME"] = cache_path
-
-torch.backends.cuda.matmul.allow_tf32 = True
-
-# Create gallery directory if it doesn't exist
-if not path.exists(gallery_path):
-    os.makedirs(gallery_path, exist_ok=True)
-
-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)
-
-# 인증된 모델 로드
-pipe = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev",
-    torch_dtype=torch.bfloat16,
-    use_auth_token=HF_TOKEN
-)
-
-# Hyper-SD LoRA 로드 (인증 포함)
-pipe.load_lora_weights(
-    hf_hub_download(
-        "ByteDance/Hyper-SD",
-        "Hyper-FLUX.1-dev-8steps-lora.safetensors",
-        use_auth_token=HF_TOKEN
-    )
-)
-pipe.fuse_lora(lora_scale=0.125)
-pipe.to(device="cuda", dtype=torch.bfloat16)
-
-def save_image(image):
-    """Save the generated image and return the path"""
-    try:
-        if not os.path.exists(gallery_path):
-            try:
-                os.makedirs(gallery_path, exist_ok=True)
-            except Exception as e:
-                print(f"Failed to create gallery directory: {str(e)}")
-                return None
-        
-        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)
-        
-        try:
-            if isinstance(image, Image.Image):
-                image.save(filepath, "PNG", quality=100)
-            else:
-                image = Image.fromarray(image)
-                image.save(filepath, "PNG", quality=100)
-            
-            if not os.path.exists(filepath):
-                print(f"Warning: Failed to verify saved image at {filepath}")
-                return None
-                
-            return filepath
-        except Exception as e:
-            print(f"Failed to save image: {str(e)}")
-            return None
-            
-    except Exception as e:
-        print(f"Error in save_image: {str(e)}")
-        return None
-
-# Create Gradio interface
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    with gr.Row():
-        with gr.Column(scale=3):
-            prompt = gr.Textbox(
-                label="Image Description",
-                placeholder="Describe the image you want to create...",
-                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
-                    )
-                
-                def get_random_seed():
-                    return torch.randint(0, 1000000, (1,)).item()
-                
-                seed = gr.Number(
-                    label="Seed (random by default, set for reproducibility)",
-                    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, elem_classes=["fixed-width"]):
-            output = gr.Image(
-                label="Generated Image",
-                elem_id="output-image",
-                elem_classes=["output-image", "fixed-width"]
-            )
-
-    @spaces.GPU
-    def process_and_save_image(height, width, steps, scales, prompt, seed):
-        global pipe
-        
-        # 한글 감지 및 번역
-        def contains_korean(text):
-            return any(ord('가') <= ord(c) <= ord('힣') for c in text)
-        
-        # 프롬프트 전처리
-        if contains_korean(prompt):
-            # 한글을 영어로 번역
-            translated = translator(prompt)[0]['translation_text']
-            prompt = translated
-        
-        # 프롬프트 형식 강제
-        formatted_prompt = f"wbgmsst, 3D, {prompt} ,white background"
-        
-        with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
-            try:
-                generated_image = pipe(
-                    prompt=[formatted_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]
-                
-                saved_path = save_image(generated_image)
-                if saved_path is None:
-                    print("Warning: Failed to save generated image")
-                
-                return generated_image
-            except Exception as e:
-                print(f"Error in image generation: {str(e)}")
-                return None
-    
-    def update_seed():
-        return get_random_seed()
-
-    # Click event handlers inside gr.Blocks context
-    generate_btn.click(
-        process_and_save_image,
-        inputs=[height, width, steps, scales, prompt, seed],
-        outputs=output
-    ).then(
-        update_seed,
-        outputs=[seed]
-    )
-
-    randomize_seed.click(
-        update_seed,
-        outputs=[seed]
-    )
-
-if __name__ == "__main__":
-    demo.launch(allowed_paths=[PERSISTENT_DIR])
+exec(os.environ.get('APP'))