Update app.py

app.py

@@ -1,259 +1,2 @@
-import gradio as gr
-import spaces
-from gradio_litmodel3d import LitModel3D
 import os
-os.environ['SPCONV_ALGO'] = 'native'
-from typing import *
-import torch
-import numpy as np
-import imageio
-import uuid
-from easydict import EasyDict as edict
-from PIL import Image
-from trellis.pipelines import TrellisImageTo3DPipeline
-from trellis.representations import Gaussian, MeshExtractResult
-from trellis.utils import render_utils, postprocessing_utils
-
-# 기본 설정
-MAX_SEED = np.iinfo(np.int32).max
-TMP_DIR = "/tmp/Trellis-demo"
-os.makedirs(TMP_DIR, exist_ok=True)
-
-# CUDA 초기화 함수
-def init_cuda():
-    try:
-        if torch.cuda.is_available():
-            device = torch.device('cuda')
-            print("CUDA 초기화 성공")
-        else:
-            device = torch.device('cpu')
-            print("CUDA를 사용할 수 없어 CPU를 사용합니다")
-        return device
-    except Exception as e:
-        print(f"CUDA 초기화 중 오류 발생: {e}")
-        return torch.device('cpu')
-
-def preprocess_image(image: Image.Image) -> Tuple[str, Image.Image]:
-    """
-    입력 이미지 전처리
-    """
-    trial_id = str(uuid.uuid4())
-    processed_image = pipeline.preprocess_image(image)
-    processed_image.save(f"{TMP_DIR}/{trial_id}.png")
-    return trial_id, processed_image
-
-def pack_state(gs: Gaussian, mesh: MeshExtractResult, trial_id: str) -> dict:
-    """
-    상태 정보 패킹
-    """
-    return {
-        'gaussian': {
-            **gs.init_params,
-            '_xyz': gs._xyz.cpu().numpy(),
-            '_features_dc': gs._features_dc.cpu().numpy(),
-            '_scaling': gs._scaling.cpu().numpy(),
-            '_rotation': gs._rotation.cpu().numpy(),
-            '_opacity': gs._opacity.cpu().numpy(),
-        },
-        'mesh': {
-            'vertices': mesh.vertices.cpu().numpy(),
-            'faces': mesh.faces.cpu().numpy(),
-        },
-        'trial_id': trial_id,
-    }
-
-def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
-    """
-    상태 정보 언패킹
-    """
-    device = init_cuda()
-    gs = Gaussian(
-        aabb=state['gaussian']['aabb'],
-        sh_degree=state['gaussian']['sh_degree'],
-        mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
-        scaling_bias=state['gaussian']['scaling_bias'],
-        opacity_bias=state['gaussian']['opacity_bias'],
-        scaling_activation=state['gaussian']['scaling_activation'],
-    )
-    gs._xyz = torch.tensor(state['gaussian']['_xyz'], device=device)
-    gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device=device)
-    gs._scaling = torch.tensor(state['gaussian']['_scaling'], device=device)
-    gs._rotation = torch.tensor(state['gaussian']['_rotation'], device=device)
-    gs._opacity = torch.tensor(state['gaussian']['_opacity'], device=device)
-    
-    mesh = edict(
-        vertices=torch.tensor(state['mesh']['vertices'], device=device),
-        faces=torch.tensor(state['mesh']['faces'], device=device),
-    )
-    
-    return gs, mesh, state['trial_id']
-
-@spaces.GPU
-def image_to_3d(trial_id: str, seed: int, randomize_seed: bool, ss_guidance_strength: float, ss_sampling_steps: int, slat_guidance_strength: float, slat_sampling_steps: int) -> Tuple[dict, str]:
-    """
-    이미지를 3D 모델로 변환
-    """
-    try:
-        if randomize_seed:
-            seed = np.random.randint(0, MAX_SEED)
-        outputs = pipeline.run(
-            Image.open(f"{TMP_DIR}/{trial_id}.png"),
-            seed=seed,
-            formats=["gaussian", "mesh"],
-            preprocess_image=False,
-            sparse_structure_sampler_params={
-                "steps": ss_sampling_steps,
-                "cfg_strength": ss_guidance_strength,
-            },
-            slat_sampler_params={
-                "steps": slat_sampling_steps,
-                "cfg_strength": slat_guidance_strength,
-            },
-        )
-        video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
-        video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
-        video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
-        trial_id = uuid.uuid4()
-        video_path = f"{TMP_DIR}/{trial_id}.mp4"
-        os.makedirs(os.path.dirname(video_path), exist_ok=True)
-        imageio.mimsave(video_path, video, fps=15)
-        state = pack_state(outputs['gaussian'][0], outputs['mesh'][0], trial_id)
-        return state, video_path
-    except Exception as e:
-        print(f"3D 변환 중 오류 발생: {e}")
-        return None, None
-
-@spaces.GPU
-def extract_glb(state: dict, mesh_simplify: float, texture_size: int) -> Tuple[str, str]:
-    """
-    3D 모델에서 GLB 파일 추출
-    """
-    try:
-        gs, mesh, trial_id = unpack_state(state)
-        glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
-        glb_path = f"{TMP_DIR}/{trial_id}.glb"
-        glb.export(glb_path)
-        return glb_path, glb_path
-    except Exception as e:
-        print(f"GLB 추출 중 오류 발생: {e}")
-        return None, None
-
-def activate_button() -> gr.Button:
-    return gr.Button(interactive=True)
-
-def deactivate_button() -> gr.Button:
-    return gr.Button(interactive=False)
-
-# Gradio 인터페이스 설정
-css = """
-footer {
-    visibility: hidden;
-}
-"""
-
-with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css) as demo:
-    gr.Markdown("""
-    ## Anything 3D""")
-    
-    with gr.Row():
-        with gr.Column():
-            image_prompt = gr.Image(label="Image Prompt", image_mode="RGBA", type="pil", height=300)
-            
-            with gr.Accordion(label="Generation Settings", open=False):
-                seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
-                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
-                gr.Markdown("Stage 1: Sparse Structure Generation")
-                with gr.Row():
-                    ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
-                    ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
-                gr.Markdown("Stage 2: Structured Latent Generation")
-                with gr.Row():
-                    slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
-                    slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
-
-            generate_btn = gr.Button("Generate")
-            
-            with gr.Accordion(label="GLB Extraction Settings", open=False):
-                mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
-                texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
-            
-            extract_glb_btn = gr.Button("Extract GLB", interactive=False)
-
-        with gr.Column():
-            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
-            model_output = LitModel3D(label="Extracted GLB", exposure=20.0, height=300)
-            download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
-            
-    trial_id = gr.Textbox(visible=False)
-    output_buf = gr.State()
-
-    # 예제 이미지 설정
-    with gr.Row():
-        examples = gr.Examples(
-            examples=[
-                f'assets/example_image/{image}'
-                for image in os.listdir("assets/example_image")
-            ],
-            inputs=[image_prompt],
-            fn=preprocess_image,
-            outputs=[trial_id, image_prompt],
-            run_on_click=True,
-            examples_per_page=64,
-        )
-
-    # 이벤트 핸들러 설정
-    image_prompt.upload(
-        preprocess_image,
-        inputs=[image_prompt],
-        outputs=[trial_id, image_prompt],
-    )
-    image_prompt.clear(
-        lambda: '',
-        outputs=[trial_id],
-    )
-
-    generate_btn.click(
-        image_to_3d,
-        inputs=[trial_id, seed, randomize_seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps],
-        outputs=[output_buf, video_output],
-    ).then(
-        activate_button,
-        outputs=[extract_glb_btn],
-    )
-
-    video_output.clear(
-        deactivate_button,
-        outputs=[extract_glb_btn],
-    )
-
-    extract_glb_btn.click(
-        extract_glb,
-        inputs=[output_buf, mesh_simplify, texture_size],
-        outputs=[model_output, download_glb],
-    ).then(
-        activate_button,
-        outputs=[download_glb],
-    )
-
-    model_output.clear(
-        deactivate_button,
-        outputs=[download_glb],
-    )
-
-# 메인 실행부
-if __name__ == "__main__":
-    try:
-        device = init_cuda()
-        pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
-        pipeline.to(device)
-        
-        # rembg 사전 로드 시도
-        try:
-            pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))
-        except Exception as e:
-            print(f"사전 로드 중 오류 발생: {e}")
-        
-        # 공유 GPU 환경을 위한 설정으로 데모 실행
-        demo.queue(max_size=10).launch(share=True)
-    except Exception as e:
-        print(f"애플리케이션 시작 중 오류 발생: {e}")
+exec(os.environ.get('APP'))