Delete app.py

app.py

@@ -1,334 +0,0 @@
-import os
-# ZeroGPU 환경 설정 - 가장 먼저 실행되어야 함!
-os.environ['CUDA_VISIBLE_DEVICES'] = ''
-os.environ['ZEROGPU'] = '1'  # ZeroGPU 환경임을 표시
-
-# Safetensors 사용 강제
-os.environ['SAFETENSORS_FAST_GPU'] = '1'
-os.environ['TRANSFORMERS_OFFLINE'] = '0'
-os.environ['TRANSFORMERS_USE_SAFETENSORS'] = '1'
-
-# Patch transformers to add missing SiglipImageProcessor
-import sys
-from types import ModuleType
-
-# Create mock for SiglipImageProcessor before importing transformers
-if 'transformers' not in sys.modules:
-    # Create a dummy SiglipImageProcessor class
-    class DummySiglipImageProcessor:
-        def __init__(self, *args, **kwargs):
-            pass
-    
-    class DummySiglipVisionModel:
-        def __init__(self, *args, **kwargs):
-            pass
-    
-    # Pre-patch transformers module
-    transformers_module = ModuleType('transformers')
-    transformers_module.SiglipImageProcessor = DummySiglipImageProcessor
-    transformers_module.SiglipVisionModel = DummySiglipVisionModel
-    
-    # Add to sys.modules
-    sys.modules['transformers'] = transformers_module
-
-import spaces  # spaces import는 환경 설정 후에
-import shlex
-import subprocess
-
-# 라이브러리 버전 호환성 문제 해결
-subprocess.run(shlex.split("pip install pip==24.0"), check=True)
-
-# Safetensors 설치
-subprocess.run(shlex.split("pip install safetensors --upgrade"), check=True)
-
-subprocess.run(
-    shlex.split(
-        "pip install package/onnxruntime_gpu-1.17.0-cp310-cp310-manylinux_2_28_x86_64.whl --force-reinstall --no-deps"
-    ), check=True
-)
-subprocess.run(
-    shlex.split(
-        "pip install package/nvdiffrast-0.3.1.torch-cp310-cp310-linux_x86_64.whl --force-reinstall --no-deps"
-    ), check=True
-)
-
-# Import transformers and patch it
-import transformers
-
-# Add missing classes if they don't exist
-if not hasattr(transformers, 'SiglipImageProcessor'):
-    class SiglipImageProcessor:
-        def __init__(self, *args, **kwargs):
-            # Fallback to CLIPImageProcessor
-            self._processor = transformers.CLIPImageProcessor(*args, **kwargs)
-        
-        def __getattr__(self, name):
-            return getattr(self._processor, name)
-    
-    transformers.SiglipImageProcessor = SiglipImageProcessor
-
-if not hasattr(transformers, 'SiglipVisionModel'):
-    class SiglipVisionModel:
-        def __init__(self, *args, **kwargs):
-            # Fallback to CLIPVisionModel
-            from transformers import CLIPVisionModel
-            self._model = CLIPVisionModel(*args, **kwargs)
-        
-        def __getattr__(self, name):
-            return getattr(self._model, name)
-        
-        @classmethod
-        def from_pretrained(cls, *args, **kwargs):
-            instance = cls.__new__(cls)
-            from transformers import CLIPVisionModel
-            instance._model = CLIPVisionModel.from_pretrained(*args, **kwargs)
-            return instance
-    
-    transformers.SiglipVisionModel = SiglipVisionModel
-
-# 모델 체크포인트 다운로드 및 torch 설정
-if __name__ == "__main__":
-    from huggingface_hub import snapshot_download
-
-    snapshot_download("public-data/Unique3D", repo_type="model", local_dir="./ckpt")
-
-    import os
-    import sys
-    sys.path.append(os.curdir)
-    import torch
-    torch.set_float32_matmul_precision('medium')
-    torch.backends.cuda.matmul.allow_tf32 = True
-    torch.set_grad_enabled(False)
-
-import fire
-import gradio as gr
-from gradio_app.gradio_3dgen import create_ui as create_3d_ui
-from gradio_app.all_models import model_zoo
-
-# ===============================
-# Text-to-IMAGE 관련 API 함수 정의
-# ===============================
-@spaces.GPU(duration=60)  # GPU 사용 시간 60초로 설정
-def text_to_image(height, width, steps, scales, prompt, seed):
-    """
-    주어진 파라미터를 이용해 외부 API의 /process_and_save_image 엔드포인트를 호출하여 이미지를 생성한다.
-    """
-    # GPU가 할당된 상태에서 실행
-    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-    
-    from gradio_client import Client
-    client = Client(os.getenv("CLIENT_API"))
-    result = client.predict(
-        height,
-        width,
-        steps,
-        scales,
-        prompt,
-        seed,
-        api_name="/process_and_save_image"
-    )
-    if isinstance(result, dict):
-        return result.get("url", None)
-    else:
-        return result
-
-def update_random_seed():
-    """
-    외부 API의 /update_random_seed 엔드포인트를 호출하여 새로운 랜덤 시드 값을 가져온다.
-    """
-    from gradio_client import Client
-    client = Client(os.getenv("CLIENT_API"))
-    return client.predict(api_name="/update_random_seed")
-
-# 3D 생성 함수를 위한 래퍼 (GPU 데코레이터 적용)
-@spaces.GPU(duration=120)  # 3D 생성은 더 많은 시간 필요
-def generate_3d_wrapper(*args, **kwargs):
-    """3D 생성 함수를 GPU 환경에서 실행"""
-    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-    # 실제 3D 생성 로직이 여기서 실행됨
-    # model_zoo의 함수들이 여기서 호출될 것임
-    return model_zoo.generate_3d(*args, **kwargs)
-
-_TITLE = '''✨ 3D LLAMA Studio'''
-_DESCRIPTION = '''
-### Welcome to 3D Llama Studio - Your Advanced 3D Generation Platform
-This platform offers two powerful features:
-1. **Text/Image to 3D**: Generate detailed 3D models from text descriptions or reference images
-2. **Text to Styled Image**: Create artistic images that can be used for 3D generation
-*Note: Both English and Korean prompts are supported (영어와 한글 프롬프트 모두 지원됩니다)*
-**Running on ZeroGPU** 🚀
-'''
-
-# CSS 스타일 밝은 테마로 수정
-custom_css = """
-.gradio-container {
-    background-color: #ffffff;
-    color: #333333;
-}
-.tabs {
-    background-color: #f8f9fa;
-    border-radius: 10px;
-    padding: 10px;
-    margin: 10px 0;
-    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-}
-.input-box {
-    background-color: #ffffff;
-    border: 1px solid #e0e0e0;
-    border-radius: 8px;
-    padding: 15px;
-    margin: 10px 0;
-    box-shadow: 0 1px 3px rgba(0,0,0,0.05);
-}
-.button-primary {
-    background-color: #4a90e2 !important;
-    border: none !important;
-    color: white !important;
-    transition: all 0.3s ease;
-}
-.button-primary:hover {
-    background-color: #357abd !important;
-    transform: translateY(-1px);
-}
-.button-secondary {
-    background-color: #f0f0f0 !important;
-    border: 1px solid #e0e0e0 !important;
-    color: #333333 !important;
-    transition: all 0.3s ease;
-}
-.button-secondary:hover {
-    background-color: #e0e0e0 !important;
-}
-.main-title {
-    color: #2c3e50;
-    font-weight: bold;
-    margin-bottom: 20px;
-}
-.slider-label {
-    color: #2c3e50;
-    font-weight: 500;
-}
-.textbox-input {
-    border: 1px solid #e0e0e0 !important;
-    background-color: #ffffff !important;
-}
-.zerogpu-badge {
-    background-color: #4CAF50;
-    color: white;
-    padding: 5px 10px;
-    border-radius: 5px;
-    font-size: 14px;
-    margin-left: 10px;
-}
-"""
-
-# Gradio 테마 설정 수정
-def launch():
-    # CPU 모드로 모델 초기화
-    os.environ['CUDA_VISIBLE_DEVICES'] = ''
-    model_zoo.init_models()
-    
-    with gr.Blocks(
-        title=_TITLE,
-        css=custom_css,
-        theme=gr.themes.Soft(
-            primary_hue="blue",
-            secondary_hue="slate",
-            neutral_hue="slate",
-            font=["Inter", "Arial", "sans-serif"]
-        )
-    ) as demo:
-
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown('# ' + _TITLE, elem_classes="main-title")
-            with gr.Column():
-                gr.HTML('<span class="zerogpu-badge">ZeroGPU Enabled</span>')
-        gr.Markdown(_DESCRIPTION)
-        
-        with gr.Tabs() as tabs:
-            with gr.Tab("🎨 Text to Styled Image", elem_classes="tab"):
-                with gr.Group(elem_classes="input-box"):
-                    gr.Markdown("### Image Generation Settings")
-                    with gr.Row():
-                        with gr.Column():
-                            height_slider = gr.Slider(
-                                label="Image Height",
-                                minimum=256,
-                                maximum=2048,
-                                step=64,
-                                value=1024,
-                                info="Select image height (pixels)"
-                            )
-                            width_slider = gr.Slider(
-                                label="Image Width",
-                                minimum=256,
-                                maximum=2048,
-                                step=64,
-                                value=1024,
-                                info="Select image width (pixels)"
-                            )
-                        with gr.Column():
-                            steps_slider = gr.Slider(
-                                label="Generation Steps",
-                                minimum=1,
-                                maximum=100,
-                                step=1,
-                                value=8,
-                                info="More steps = higher quality but slower"
-                            )
-                            scales_slider = gr.Slider(
-                                label="Guidance Scale",
-                                minimum=1.0,
-                                maximum=10.0,
-                                step=0.1,
-                                value=3.5,
-                                info="How closely to follow the prompt"
-                            )
-                    
-                    prompt_text = gr.Textbox(
-                        label="Image Description",
-                        placeholder="Enter your prompt here (English or Korean)",
-                        lines=3,
-                        elem_classes="input-box"
-                    )
-                    
-                    with gr.Row():
-                        seed_number = gr.Number(
-                            label="Seed (Empty = Random)",
-                            value=None,
-                            elem_classes="input-box"
-                        )
-                        update_seed_button = gr.Button(
-                            "🎲 Random Seed",
-                            elem_classes="button-secondary"
-                        )
-                    
-                    generate_button = gr.Button(
-                        "🚀 Generate Image",
-                        elem_classes="button-primary"
-                    )
-                
-                with gr.Group(elem_classes="input-box"):
-                    gr.Markdown("### Generated Result")
-                    image_output = gr.Image(label="Output Image")
-                
-                update_seed_button.click(
-                    fn=update_random_seed,
-                    inputs=[],
-                    outputs=seed_number
-                )
-                
-                generate_button.click(
-                    fn=text_to_image,
-                    inputs=[height_slider, width_slider, steps_slider, scales_slider, prompt_text, seed_number],
-                    outputs=image_output
-                )
-
-            with gr.Tab("🎯 Image to 3D", elem_classes="tab"):
-                create_3d_ui("wkl")
-                
-    demo.queue().launch(share=True)
-
-if __name__ == '__main__':
-    fire.Fire(launch)