app.py

import gradio as gr
import os
import shutil
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
from gradio_litmodel3d import LitModel3D


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))
    print(f'Creating user directory: {user_dir}')
    os.makedirs(user_dir, exist_ok=True)
    
def end_session(req: gr.Request):
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    print(f'Removing user directory: {user_dir}')
    shutil.rmtree(user_dir)

def preprocess_image(image: Image.Image) -> Tuple[str, Image.Image]:
    processed_image = pipeline.preprocess_image(image)
    return processed_image

def pack_state(gs: Gaussian, mesh: MeshExtractResult, trial_id: str) -> 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(),
        },
        'trial_id': trial_id,
    }
    
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, state['trial_id']

def get_seed(randomize_seed: bool, seed: int) -> int:
    return np.random.randint(0, MAX_SEED) if randomize_seed else seed

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, str]:
    """
    Convert an image to a 3D model.
    """
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    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,
        },
    )

    # Generate video preview
    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 = str(uuid.uuid4())
    video_path = os.path.join(user_dir, f"{trial_id}.mp4")
    imageio.mimsave(video_path, video, fps=15)

    # Save full quality GLB
    glb = postprocessing_utils.to_glb(
        outputs['gaussian'][0], 
        outputs['mesh'][0],
        simplify=0.0,  # No simplification for full quality
        fill_holes=True,
        fill_holes_max_size=0.04,
        texture_size=2048,  # Maximum texture size
        verbose=False
    )
    full_glb_path = os.path.join(user_dir, f"{trial_id}_full.glb")
    glb.export(full_glb_path)
    
    # Pack state for potential reduced version
    state = pack_state(outputs['gaussian'][0], outputs['mesh'][0], trial_id)
    
    return state, video_path, full_glb_path

def extract_reduced_glb(
    state: dict,
    mesh_simplify: float,
    texture_size: int,
    req: gr.Request,
) -> Tuple[str, str]:
    """
    Extract a reduced quality GLB file.
    """
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    gs, mesh, trial_id = unpack_state(state)
    
    # Create reduced quality GLB with user settings
    glb = postprocessing_utils.to_glb(
        gs, 
        mesh,
        simplify=mesh_simplify,
        fill_holes=True,
        fill_holes_max_size=0.04,
        texture_size=texture_size,
        verbose=False
    )
    reduced_glb_path = os.path.join(user_dir, f"{trial_id}_reduced.glb")
    glb.export(reduced_glb_path)
    
    return reduced_glb_path, reduced_glb_path

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" to create a 3D asset
    * After generation:
        * Download the full quality GLB (no mesh simplification, maximum texture resolution)
        * Or create a reduced size version with customizable settings
    """)
    
    with gr.Row():
        with gr.Column():
            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, 500, 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, 500, label="Sampling Steps", value=12, step=1)

            generate_btn = gr.Button("Generate")
            
            with gr.Accordion(label="Reduced GLB Settings", open=False):
                mesh_simplify = gr.Slider(0.0, 0.98, label="Mesh Simplification", value=0.95, step=0.01, 
                                        info="Higher values = more reduction")
                texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
            
            extract_reduced_btn = gr.Button("Extract Reduced GLB", interactive=False)

        with gr.Column():
            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
            model_output = LitModel3D(label="3D Model Preview", exposure=20.0, height=300)
            gr.Markdown("### Download Options")
            with gr.Row():
                download_full = gr.DownloadButton(label="Download Full-Quality GLB", interactive=False)
                download_reduced = gr.DownloadButton(label="Download Reduced GLB", interactive=False)
            
    output_buf = gr.State()

    # Example images
    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=[image_prompt],
            run_on_click=True,
            examples_per_page=64,
        )

    # Event handlers
    demo.load(start_session)
    demo.unload(end_session)
    
    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,