🎨 Redesign from AnyCoder

app.py

@@ -1,445 +0,0 @@
-import gradio as gr
-import spaces
-from gradio_litmodel3d import LitModel3D
-
-import os
-import shutil
-os.environ['SPCONV_ALGO'] = 'native'
-from typing import *
-import torch
-import numpy as np
-import imageio
-from easydict import EasyDict as edict
-from PIL import Image
-from trellis.pipelines import TrellisVGGTTo3DPipeline
-from trellis.representations import Gaussian, MeshExtractResult
-from trellis.utils import render_utils, postprocessing_utils
-
-
-
-MAX_SEED = np.iinfo(np.int32).max
-TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
-# TMP_DIR = "tmp/Trellis-demo"
-# os.environ['GRADIO_TEMP_DIR'] = 'tmp'
-os.makedirs(TMP_DIR, exist_ok=True)
-
-def start_session(req: gr.Request):
-    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    os.makedirs(user_dir, exist_ok=True)
-    
-    
-def end_session(req: gr.Request):
-    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    shutil.rmtree(user_dir)
-
-@spaces.GPU
-def preprocess_image(image: Image.Image) -> Image.Image:
-    """
-    Preprocess the input image for 3D generation.
-    
-    This function is called when a user uploads an image or selects an example.
-    It applies background removal and other preprocessing steps necessary for
-    optimal 3D model generation.
-
-    Args:
-        image (Image.Image): The input image from the user
-
-    Returns:
-        Image.Image: The preprocessed image ready for 3D generation
-    """
-    processed_image = pipeline.preprocess_image(image)
-    return processed_image
-
-@spaces.GPU
-def preprocess_videos(video: str) -> List[Tuple[Image.Image, str]]:
-    """
-    Preprocess the input video for multi-image 3D generation.
-    
-    This function is called when a user uploads a video.
-    It extracts frames from the video and processes each frame to prepare them
-    for the multi-image 3D generation pipeline.
-    
-    Args:
-        video (str): The path to the input video file
-        
-    Returns:
-        List[Tuple[Image.Image, str]]: The list of preprocessed images ready for 3D generation
-    """
-    vid = imageio.get_reader(video, 'ffmpeg')
-    fps = vid.get_meta_data()['fps']
-    images = []
-    for i, frame in enumerate(vid):
-        if i % max(int(fps * 1), 1) == 0:
-            img = Image.fromarray(frame)
-            W, H = img.size
-            img = img.resize((int(W / H * 512), 512))
-            images.append(img)
-    vid.close()
-    processed_images = [pipeline.preprocess_image(image) for image in images]
-    return processed_images
-
-@spaces.GPU
-def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]:
-    """
-    Preprocess a list of input images for multi-image 3D generation.
-    
-    This function is called when users upload multiple images in the gallery.
-    It processes each image to prepare them for the multi-image 3D generation pipeline.
-    
-    Args:
-        images (List[Tuple[Image.Image, str]]): The input images from the gallery
-        
-    Returns:
-        List[Image.Image]: The preprocessed images ready for 3D generation
-    """
-    images = [image[0] for image in images]
-    processed_images = [pipeline.preprocess_image(image) for image in images]
-    return processed_images
-
-
-def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
-    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(),
-        },
-    }
-    
-    
-def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
-    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='cuda')
-    gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
-    gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
-    gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
-    gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')
-    
-    mesh = edict(
-        vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
-        faces=torch.tensor(state['mesh']['faces'], device='cuda'),
-    )
-    
-    return gs, mesh
-
-
-def get_seed(randomize_seed: bool, seed: int) -> int:
-    """
-    Get the random seed for generation.
-    
-    This function is called by the generate button to determine whether to use
-    a random seed or the user-specified seed value.
-    
-    Args:
-        randomize_seed (bool): Whether to generate a random seed
-        seed (int): The user-specified seed value
-        
-    Returns:
-        int: The seed to use for generation
-    """
-    return np.random.randint(0, MAX_SEED) if randomize_seed else seed
-
-
-@spaces.GPU(duration=120)
-def generate_and_extract_glb(
-    multiimages: List[Tuple[Image.Image, str]],
-    seed: int,
-    ss_guidance_strength: float,
-    ss_sampling_steps: int,
-    slat_guidance_strength: float,
-    slat_sampling_steps: int,
-    multiimage_algo: Literal["multidiffusion", "stochastic"],
-    mesh_simplify: float,
-    texture_size: int,
-    req: gr.Request,
-) -> Tuple[dict, str, str, str]:
-    """
-    Convert an image to a 3D model and extract GLB file.
-
-    Args:
-        image (Image.Image): The input image.
-        multiimages (List[Tuple[Image.Image, str]]): The input images in multi-image mode.
-        is_multiimage (bool): Whether is in multi-image mode.
-        seed (int): The random seed.
-        ss_guidance_strength (float): The guidance strength for sparse structure generation.
-        ss_sampling_steps (int): The number of sampling steps for sparse structure generation.
-        slat_guidance_strength (float): The guidance strength for structured latent generation.
-        slat_sampling_steps (int): The number of sampling steps for structured latent generation.
-        multiimage_algo (Literal["multidiffusion", "stochastic"]): The algorithm for multi-image generation.
-        mesh_simplify (float): The mesh simplification factor.
-        texture_size (int): The texture resolution.
-
-    Returns:
-        dict: The information of the generated 3D model.
-        str: The path to the video of the 3D model.
-        str: The path to the extracted GLB file.
-        str: The path to the extracted GLB file (for download).
-    """
-    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    image_files = [image[0] for image in multiimages]
-
-    # Generate 3D model
-    outputs, _, _ = pipeline.run(
-        image=image_files,
-        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,
-        },
-        mode=multiimage_algo,
-    )
-
-    # Render video
-    # import uuid
-    # output_id = str(uuid.uuid4())
-    # os.makedirs(f"{TMP_DIR}/{output_id}", exist_ok=True)
-    # video_path = f"{TMP_DIR}/{output_id}/preview.mp4"
-    # glb_path = f"{TMP_DIR}/{output_id}/mesh.glb"
-
-    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))]
-    video_path = os.path.join(user_dir, 'sample.mp4')
-    imageio.mimsave(video_path, video, fps=15)
-    
-    # Extract GLB
-    gs = outputs['gaussian'][0]
-    mesh = outputs['mesh'][0]
-    glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
-    glb_path = os.path.join(user_dir, 'sample.glb')
-    glb.export(glb_path)
-    
-    # Pack state for optional Gaussian extraction
-    state = pack_state(gs, mesh)
-    
-    torch.cuda.empty_cache()
-    return state, video_path, glb_path, glb_path
-
-
-@spaces.GPU
-def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
-    """
-    Extract a Gaussian splatting file from the generated 3D model.
-    
-    This function is called when the user clicks "Extract Gaussian" button.
-    It converts the 3D model state into a .ply file format containing
-    Gaussian splatting data for advanced 3D applications.
-
-    Args:
-        state (dict): The state of the generated 3D model containing Gaussian data
-        req (gr.Request): Gradio request object for session management
-
-    Returns:
-        Tuple[str, str]: Paths to the extracted Gaussian file (for display and download)
-    """
-    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    gs, _ = unpack_state(state)
-    gaussian_path = os.path.join(user_dir, 'sample.ply')
-    gs.save_ply(gaussian_path)
-    torch.cuda.empty_cache()
-    return gaussian_path, gaussian_path
-
-
-def prepare_multi_example() -> List[Image.Image]:
-    multi_case = list(set([i.split('_')[0] for i in os.listdir("assets/example_multi_image")]))
-    images = []
-    for case in multi_case:
-        _images = []
-        for i in range(1, 9):
-            if os.path.exists(f'assets/example_multi_image/{case}_{i}.png'):
-                img = Image.open(f'assets/example_multi_image/{case}_{i}.png')
-                W, H = img.size
-                img = img.resize((int(W / H * 512), 512))
-                _images.append(np.array(img))
-        if len(_images) > 0:
-            images.append(Image.fromarray(np.concatenate(_images, axis=1)))
-    return images
-
-
-def split_image(image: Image.Image) -> List[Image.Image]:
-    """
-    Split a multi-view image into separate view images.
-    
-    This function is called when users select multi-image examples that contain
-    multiple views in a single concatenated image. It automatically splits them
-    based on alpha channel boundaries and preprocesses each view.
-    
-    Args:
-        image (Image.Image): A concatenated image containing multiple views
-        
-    Returns:
-        List[Image.Image]: List of individual preprocessed view images
-    """
-    image = np.array(image)
-    alpha = image[..., 3]
-    alpha = np.any(alpha>0, axis=0)
-    start_pos = np.where(~alpha[:-1] & alpha[1:])[0].tolist()
-    end_pos = np.where(alpha[:-1] & ~alpha[1:])[0].tolist()
-    images = []
-    for s, e in zip(start_pos, end_pos):
-        images.append(Image.fromarray(image[:, s:e+1]))
-    return [preprocess_image(image) for image in images]
-
-# Create interface
-demo = gr.Blocks(
-    title="ReconViaGen",
-    css="""
-        .slider .inner { width: 5px; background: #FFF; }
-        .viewport { aspect-ratio: 4/3; }
-        .tabs button.selected { font-size: 20px !important; color: crimson !important; }
-        h1, h2, h3 { text-align: center; display: block; }
-        .md_feedback li { margin-bottom: 0px !important; }
-    """
-)
-with demo:
-    gr.Markdown("""
-    # 💻 ReconViaGen
-    <p align="center">
-    <a title="Github" href="https://github.com/GAP-LAB-CUHK-SZ/ReconViaGen" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
-        <img src="https://img.shields.io/github/stars/GAP-LAB-CUHK-SZ/ReconViaGen?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars">
-    </a>
-    <a title="Website" href="https://jiahao620.github.io/reconviagen/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
-        <img src="https://www.obukhov.ai/img/badges/badge-website.svg">
-    </a>
-    <a title="arXiv" href="https://jiahao620.github.io/reconviagen/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
-        <img src="https://www.obukhov.ai/img/badges/badge-pdf.svg">
-    </a>
-    </p>
-    
-    ✨This demo is partial. We will release the whole model later. Stay tuned!✨
-    """)
-    
-    with gr.Row():
-        with gr.Column():
-            with gr.Tabs() as input_tabs:
-                with gr.Tab(label="Input Video or Images", id=0) as multiimage_input_tab:
-                    input_video = gr.Video(label="Upload Video", interactive=True, height=300)
-                    image_prompt = gr.Image(label="Image Prompt", format="png", visible=False, image_mode="RGBA", type="pil", height=300)
-                    multiimage_prompt = gr.Gallery(label="Image Prompt", format="png", type="pil", height=300, columns=3)
-                    gr.Markdown("""
-                        Input different views of the object in separate images.
-                                """)
-        
-            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=False)
-                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=30, 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)
-                multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], label="Multi-image Algorithm", value="multidiffusion")
-            
-            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)
-
-            generate_btn = gr.Button("Generate & Extract GLB", variant="primary")
-            extract_gs_btn = gr.Button("Extract Gaussian", interactive=False)
-            gr.Markdown("""
-                        *NOTE: Gaussian file can be very large (~50MB), it will take a while to display and download.*
-                        """)
-
-        with gr.Column():
-            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
-            model_output = LitModel3D(label="Extracted GLB/Gaussian", exposure=10.0, height=300)
-            
-            with gr.Row():
-                download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
-                download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)  
-    
-    output_buf = gr.State()
-
-    # Example images at the bottom of the page
-    with gr.Row() as multiimage_example:
-        examples_multi = gr.Examples(
-            examples=prepare_multi_example(),
-            inputs=[image_prompt],
-            fn=split_image,
-            outputs=[multiimage_prompt],
-            run_on_click=True,
-            examples_per_page=8,
-        )
-
-    # Handlers
-    demo.load(start_session)
-    demo.unload(end_session)
-
-    input_video.upload(
-        preprocess_videos,
-        inputs=[input_video],
-        outputs=[multiimage_prompt],
-    )
-    input_video.clear(
-        lambda: tuple([None, None]),
-        outputs=[input_video, multiimage_prompt],
-    )
-    multiimage_prompt.upload(
-        preprocess_images,
-        inputs=[multiimage_prompt],
-        outputs=[multiimage_prompt],
-    )
-
-    generate_btn.click(
-        get_seed,
-        inputs=[randomize_seed, seed],
-        outputs=[seed],
-    ).then(
-        generate_and_extract_glb,
-        inputs=[multiimage_prompt, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo, mesh_simplify, texture_size],
-        outputs=[output_buf, video_output, model_output, download_glb],
-    ).then(
-        lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
-        outputs=[extract_gs_btn, download_glb],
-    )
-
-    video_output.clear(
-        lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False), gr.Button(interactive=False)]),
-        outputs=[extract_gs_btn, download_glb, download_gs],
-    )
-    
-    extract_gs_btn.click(
-        extract_gaussian,
-        inputs=[output_buf],
-        outputs=[model_output, download_gs],
-    ).then(
-        lambda: gr.Button(interactive=True),
-        outputs=[download_gs],
-    )
-
-    model_output.clear(
-        lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
-        outputs=[download_glb, download_gs],
-    )
-    
-
-# Launch the Gradio app
-if __name__ == "__main__":
-    pipeline = TrellisVGGTTo3DPipeline.from_pretrained("Stable-X/trellis-vggt-v0-2")
-    pipeline.cuda()
-    pipeline.VGGT_model.cuda()
-    pipeline.birefnet_model.cuda()
-    demo.launch()