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 PIL import Image
from trellis.pipelines import TrellisImageTo3DPipeline
from trellis.representations import 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 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[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:
        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)
    
    torch.cuda.empty_cache()
    return video_path, glb_path, glb_path






theme = gr.themes.Base().set(
    body_background_fill="#1A1A1A",
    body_background_fill_dark="#1A1A1A",
    body_text_color="#CCCCCC",
    body_text_color_dark="#CCCCCC",
    block_background_fill="#2C2C2C",
    block_background_fill_dark="#2C2C2C",
    block_border_color="#3C3C3C",
    block_border_color_dark="#3C3C3C",
    button_primary_background_fill="#FF8C00",
    button_primary_background_fill_dark="#FF8C00",
    button_primary_background_fill_hover="#FF9F33",
    button_primary_border_color="*primary_500",
    button_primary_text_color="white",
    button_primary_text_color_dark="white",
    block_border_width="1px",
    block_radius="8px"
)

with gr.Blocks(
    theme=theme,
    css="""
    .gradio-container {
        background: #1A1A1A !important;
        color: #CCCCCC !important;
        font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif !important;
    }
    
    .gradio-container .footer,
    .gradio-container footer,
    .gradio-container [data-testid="footer"],
    .gradio-container .gradio-footer {
        display: none !important;
    }
    
    .gradio-container .gradio-container {
        padding-bottom: 0 !important;
    }
    
    .gradio-container h1, .gradio-container h2, .gradio-container h3 {
        color: #FFFFFF !important;
        font-weight: bold !important;
    }
    
    .gradio-container .markdown {
        color: #CCCCCC !important;
    }
    
    .gradio-container .tab-nav {
        background: #2C2C2C !important;
        border: none !important;
    }
    
    .gradio-container .tab-nav button {
        background: #2C2C2C !important;
        color: #CCCCCC !important;
        border: none !important;
        border-radius: 8px 8px 0 0 !important;
    }
    
    .gradio-container .tab-nav button.selected {
        background: #FF8C00 !important;
        color: #FFFFFF !important;
    }
    
    .gradio-container .tab-nav button:hover {
        background: #3C3C3C !important;
    }
    
    .gradio-container .tab-nav button.selected:hover {
        background: #FF8C00 !important;
    }
    
    .gradio-container .tab-content {
        background: #2C2C2C !important;
        border: none !important;
        border-radius: 0 0 8px 8px !important;
        padding: 20px !important;
    }
    
    .gradio-container .accordion {
        background: #2C2C2C !important;
        border: 1px solid #3C3C3C !important;
        border-radius: 8px !important;
        margin: 10px 0 !important;
    }
    
    .gradio-container .accordion .accordion-header {
        background: #2C2C2C !important;
        color: #FFFFFF !important;
        border: none !important;
        border-radius: 8px !important;
    }
    
    .gradio-container .accordion .accordion-content {
        background: #2C2C2C !important;
        color: #CCCCCC !important;
        border: none !important;
        border-radius: 0 0 8px 8px !important;
    }
    
    .gradio-container .button {
        background: #FF8C00 !important;
        color: #FFFFFF !important;
        border: none !important;
        border-radius: 8px !important;
        font-weight: bold !important;
        padding: 12px 24px !important;
    }
    
    .gradio-container .button:hover {
        background: #FF9F33 !important;
    }
    
    .gradio-container .button.secondary {
        background: #3C3C3C !important;
        color: #CCCCCC !important;
    }
    
    .gradio-container .button.secondary:hover {
        background: #4C4C4C !important;
    }
    
    .gradio-container .slider {
        background: #3C3C3C !important;
    }
    
    .gradio-container .slider .slider-handle {
        background: #FF8C00 !important;
        border: 2px solid #FFFFFF !important;
    }
    
    .gradio-container .slider .slider-track {
        background: #3C3C3C !important;
    }
    
    .gradio-container .slider .slider-track-fill {
        background: #FF8C00 !important;
    }
    
    .gradio-container .checkbox {
        color: #CCCCCC !important;
    }
    
    .gradio-container .radio {
        color: #CCCCCC !important;
    }
    
    .gradio-container .gallery {
        background: #2C2C2C !important;
        border: 1px solid #3C3C3C !important;
        border-radius: 8px !important;
    }
    
    .gradio-container .image {
        background: #2C2C2C !important;
        border: 1px solid #3C3C3C !important;
        border-radius: 8px !important;
    }
    
    .gradio-container .video {
        background: #2C2C2C !important;
        border: 1px solid #3C3C3C !important;
        border-radius: 8px !important;
    }
    
    .gradio-container .model3d {
        background: #2C2C2C !important;
        border: 1px solid #3C3C3C !important;
        border-radius: 8px !important;
    }
    
    .gradio-container .row {
        gap: 20px !important;
    }
    
    .gradio-container .column {
        background: #2C2C2C !important;
        border: 1px solid #3C3C3C !important;
        border-radius: 8px !important;
        padding: 20px !important;
    }
    
    .gradio-container .row {
        align-items: flex-start !important;
        justify-content: center !important;
    }
    
    .gradio-container .container {
        max-width: 1200px !important;
        margin: 0 auto !important;
        padding: 20px !important;
    }
    """
) as demo:
    gr.Markdown("""
    <div style="text-align: center; margin-bottom: 30px; padding: 20px; background: #2C2C2C; border: 1px solid #3C3C3C; border-radius: 8px;">
        <h3 style="color: #FFFFFF; margin-bottom: 15px;">Instructions</h3>
        <p style="color: #CCCCCC; margin-bottom: 10px;">
            • Upload an image and click "Generate 3D Asset" to create a 3D model
        </p>
        <p style="color: #CCCCCC; margin-bottom: 10px;">
            • Images with alpha channels will use the existing mask, otherwise background removal is applied automatically
        </p>
        <p style="color: #CCCCCC;">
            • Multi-image mode supports multiple views for enhanced 3D reconstruction
        </p>
    </div>
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("""
            <div style="background: #2C2C2C; border: 1px solid #3C3C3C; border-radius: 8px; padding: 20px; margin-bottom: 20px;">
                <h3 style="color: #FFFFFF; margin-bottom: 15px;">3D Model Generation</h3>
                <p style="color: #CCCCCC; margin-bottom: 20px;">Generate 3D models and textures from image or text using AI technology.</p>
            </div>
            """)
            
            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("""
                        **Multi-Image Mode:** Upload different views of the same object for enhanced 3D reconstruction.
                        
                        *Note: For best results, ensure consistent lighting and object positioning across views.*
                    """)
            
            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="Output Settings", open=False):
                mesh_simplify = gr.Slider(0.3, 0.98, label="Mesh Simplification", value=0.95, step=0.01)
                texture_size = gr.Slider(512, 2048, label="Texture Resolution", value=1024, step=512)

            generate_btn = gr.Button("Generate 3D Asset", variant="primary", size="lg")

        with gr.Column(scale=1):
            gr.Markdown("""
            <div style="background: #2C2C2C; border: 1px solid #3C3C3C; border-radius: 8px; padding: 20px; margin-bottom: 20px;">
                <h3 style="color: #FFFFFF; margin-bottom: 15px;">Generated 3D Asset</h3>
                <p style="color: #CCCCCC; margin-bottom: 20px;">Preview and download your generated 3D model.</p>
            </div>
            """)
            
            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
            model_output = LitModel3D(label="3D Model Preview", exposure=10.0, height=300)
            
            download_glb = gr.DownloadButton(label="Download 3D Asset", interactive=False, variant="secondary")  
    
    is_multiimage = gr.State(False)


    # Handlers
    demo.load(start_session)
    demo.unload(end_session)
    
    single_image_input_tab.select(
        lambda: False,
        outputs=[is_multiimage]
    )
    multiimage_input_tab.select(
        lambda: True,
        outputs=[is_multiimage]
    )
    
    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=[video_output, model_output, download_glb],
    ).then(
        lambda: gr.Button(interactive=True),
        outputs=[download_glb],
    )

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

    model_output.clear(
        lambda: gr.Button(interactive=False),
        outputs=[download_glb],
    )
    
    gr.Markdown("""
    <div style="text-align: center; margin-top: 40px; padding: 20px; background: #2C2C2C; border: 1px solid #3C3C3C; border-radius: 8px;">
        <p style="color: #CCCCCC; font-size: 0.9rem; margin: 0;">
            Powered by <span style="color: #FF8C00;">Mean Cat Entertainment</span> • Built for the future of VFX
        </p>
    </div>
    """)

# 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)