app.py

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 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.

    Args:
        image (Image.Image): The input image.

    Returns:
        Image.Image: The preprocessed image.
    """
    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.
    
    Args:
        images (List[Tuple[Image.Image, str]]): The input images.
        
    Returns:
        List[Image.Image]: The preprocessed images.
    """
    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.
    """
    return np.random.randint(0, MAX_SEED) if randomize_seed else seed


@spaces.GPU
def image_to_3d(
    image: Image.Image,
    seed: int,
    ss_guidance_strength: float,
    ss_sampling_steps: int,
    slat_guidance_strength: float,
    slat_sampling_steps: int,
    req: gr.Request,
) -> Tuple[dict, str]:
    """
    Convert an image to a 3D model.

    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.

    Returns:
        dict: The information of the generated 3D model.
        str: The path to the video of the 3D model.
    """
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    os.makedirs(user_dir, exist_ok=True)
    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,
        },
    )
    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)
    state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
    torch.cuda.empty_cache()
    return state, video_path


@spaces.GPU(duration=90)
def extract_glb(
    state: dict,
    mesh_simplify: float,
    texture_size: int,
    req: gr.Request,
) -> Tuple[str, str]:
    """
    Extract a GLB file from the 3D model.

    Args:
        state (dict): The state of the generated 3D model.
        mesh_simplify (float): The mesh simplification factor.
        texture_size (int): The texture resolution.

    Returns:
        str: The path to the extracted GLB file.
    """
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    os.makedirs(user_dir, exist_ok=True)
    gs, mesh = unpack_state(state)
    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)
    torch.cuda.empty_cache()
    return glb_path, glb_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 an image into multiple views.
    """
    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 Generator
    * Upload an image and click "Generate" to create a 3D asset.
    * If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
    """)
    
    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.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)
            
            with gr.Row():
                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=10.0, height=300)
            
            with gr.Row():
                download_glb = gr.DownloadButton(label="Download GLB", interactive=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,
        )

    # 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=[single_image_example]
    )
    
    image_prompt.upload(
        preprocess_image,
        inputs=[image_prompt],
        outputs=[image_prompt],
    )

    generate_btn.click(
        get_seed,
        inputs=[randomize_seed, seed],
        outputs=[seed],
    ).then(
        image_to_3d,
        inputs=[image_prompt, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps],
        outputs=[output_buf, video_output],
    ).then(
        lambda: gr.Button(interactive=True),
        outputs=[extract_glb_btn],
    )

    video_output.clear(
        lambda: gr.Button(interactive=False),
        outputs=[extract_glb_btn],
    )

    extract_glb_btn.click(
        extract_glb,
        inputs=[output_buf, mesh_simplify, texture_size],
        outputs=[model_output, download_glb],
    ).then(
        lambda: gr.Button(interactive=True),
        outputs=[download_glb],
    )

    model_output.clear(
        lambda: gr.Button(interactive=False),
        outputs=[download_glb],
    )
    

# 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(show_api=False)