app.py

import os
os.environ['SPCONV_ALGO'] = 'native'
os.environ['ATTN_BACKEND'] = 'xformers'
os.environ['SPARSE_ATTN'] = 'xformers'

import subprocess, sys, tempfile, ctypes

try:
    import gradio_litmodel3d  # noqa: F401
except ImportError:
    subprocess.check_call(
        [sys.executable, "-m", "pip", "install", "--no-deps",
         "gradio_litmodel3d==0.0.1"],
    )

import spaces

CUDA_HOME = "/cuda-image/usr/local/cuda-13.0"
CUDA_LIBDIR = os.path.join(CUDA_HOME, "lib64")
REPO_DIR = os.path.dirname(os.path.abspath(__file__))


@spaces.GPU(duration=600)
def first_gpu_setup():
    need_nvdiffrast = need_dgr = False
    try:
        import nvdiffrast  # noqa: F401
    except ImportError:
        need_nvdiffrast = True
    try:
        import diff_gaussian_rasterization  # noqa: F401
    except ImportError:
        need_dgr = True
    if not (need_nvdiffrast or need_dgr):
        print("CUDA extensions already installed; skipping build.")
        return

    if not os.path.exists(os.path.join(CUDA_HOME, "bin", "nvcc")):
        raise RuntimeError(
            f"nvcc not found at {CUDA_HOME}/bin/nvcc on the ZeroGPU worker. "
            "The new-hardware CUDA path may have moved; please update CUDA_HOME."
        )

    patch_dir = tempfile.mkdtemp(prefix="torch_cuda_patch_")
    with open(os.path.join(patch_dir, "sitecustomize.py"), "w") as f:
        f.write(
            "try:\n"
            "    import torch.utils.cpp_extension as _c\n"
            "    _c._check_cuda_version = lambda *a, **k: None\n"
            "except Exception:\n"
            "    pass\n"
        )

    env = os.environ.copy()
    env["CUDA_HOME"] = CUDA_HOME
    env["CUDA_PATH"] = CUDA_HOME
    env["PATH"] = os.path.join(CUDA_HOME, "bin") + os.pathsep + env.get("PATH", "")
    env["PYTHONPATH"] = patch_dir + os.pathsep + env.get("PYTHONPATH", "")
    env["TORCH_CUDA_ARCH_LIST"] = "12.0"

    subprocess.check_call(
        [sys.executable, "-m", "pip", "install", "--no-deps",
         "setuptools", "wheel", "ninja"],
    )

    if need_nvdiffrast:
        subprocess.check_call(
            [sys.executable, "-m", "pip", "install", "--no-build-isolation",
             os.path.join(REPO_DIR, "extensions", "nvdiffrast")],
            env=env,
        )

    if need_dgr:
        mip_dir = tempfile.mkdtemp(prefix="mip_splatting_")
        subprocess.check_call(
            ["git", "clone", "--recursive", "--depth=1",
             "https://github.com/autonomousvision/mip-splatting.git", mip_dir],
        )
        subprocess.check_call(
            [sys.executable, "-m", "pip", "install", "--no-build-isolation",
             os.path.join(mip_dir, "submodules", "diff-gaussian-rasterization")],
            env=env,
        )


first_gpu_setup()

ctypes.CDLL(os.path.join(CUDA_LIBDIR, "libcudart.so.13"), mode=ctypes.RTLD_GLOBAL)
os.environ["LD_LIBRARY_PATH"] = CUDA_LIBDIR + os.pathsep + os.environ.get("LD_LIBRARY_PATH", "")

import gradio as gr
from gradio_litmodel3d import LitModel3D

import shutil
from typing import *
import torch
import numpy as np
import imageio
from easydict import EasyDict as edict
from PIL import Image

# xformers on Blackwell (sm_120) picks Flash-Attn-3 (Hopper-only) and crashes
# with "invalid argument". Force it to use Cutlass kernels instead.
import xformers.ops as _xops
_orig_mea = _xops.memory_efficient_attention
_cutlass_op = (_xops.fmha.cutlass.FwOp, _xops.fmha.cutlass.BwOp)
def _mea_cutlass(*args, **kwargs):
    kwargs.setdefault("op", _cutlass_op)
    return _orig_mea(*args, **kwargs)
_xops.memory_efficient_attention = _mea_cutlass

from trellis.pipelines import TrellisImageTo3DPipeline
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')
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)


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


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(
    image: Image.Image,
    multiimages: List[Tuple[Image.Image, str]],
    is_multiimage: bool,
    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))
    
    # Generate 3D model
    if not is_multiimage:
        outputs = pipeline.run(
            image,
            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,
            },
        )
    else:
        outputs = pipeline.run_multi_image(
            [image[0] for image in multiimages],
            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
    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, 4):
            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))
        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]


with gr.Blocks(delete_cache=(600, 600)) as demo:
    gr.Markdown("""
    ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
    * Upload an image and click "Generate & Extract GLB" to create a 3D asset and automatically extract the GLB file.
    * If you want the Gaussian file as well, click "Extract Gaussian" after generation.
    * If the image has alpha channel, it will be used as the mask. Otherwise, we use `rembg` to remove the background.
    
    ✨New: 1) Experimental multi-image support. 2) Gaussian file extraction.
    """)
    
    with gr.Row():
        with gr.Column():
            with gr.Tabs() as input_tabs:
                with gr.Tab(label="Single Image", id=0) as single_image_input_tab:
                    image_prompt = gr.Image(label="Image Prompt", format="png", image_mode="RGBA", type="pil", height=300)
                with gr.Tab(label="Multiple Images", id=1) as multiimage_input_tab:
                    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. 
                        
                        *NOTE: this is an experimental algorithm without training a specialized model. It may not produce the best results for all images, especially those having different poses or inconsistent details.*
                    """)
            
            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)
                multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], label="Multi-image Algorithm", value="stochastic")
            
            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)  
    
    is_multiimage = gr.State(False)
    output_buf = gr.State()

    # Example images at the bottom of the page
    with gr.Row() as single_image_example:
        examples = gr.Examples(
            examples=[
                f'assets/example_image/{image}'
                for image in os.listdir("assets/example_image")
            ],
            inputs=[image_prompt],
            fn=preprocess_image,
            outputs=[image_prompt],
            run_on_click=True,
            examples_per_page=64,
        )
    with gr.Row(visible=False) 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)
    
    single_image_input_tab.select(
        lambda: tuple([False, gr.Row.update(visible=True), gr.Row.update(visible=False)]),
        outputs=[is_multiimage, single_image_example, multiimage_example]
    )
    multiimage_input_tab.select(
        lambda: tuple([True, gr.Row.update(visible=False), gr.Row.update(visible=True)]),
        outputs=[is_multiimage, single_image_example, multiimage_example]
    )
    
    image_prompt.upload(
        preprocess_image,
        inputs=[image_prompt],
        outputs=[image_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=[image_prompt, multiimage_prompt, is_multiimage, 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 = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
    pipeline.cuda()
    try:
        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
    except:
        pass
    demo.launch(mcp_server=True)