app.py

import gradio as gr
import spaces 
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
import pydantic
print(pydantic.__version__)

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:
    processed_image = pipeline.preprocess_image(image)
    return processed_image

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:
    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]:
    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,
        },
    )
   
    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']
    
    # Solo usamos el video de color, eliminamos la concatenación
    video = 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]:
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    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 split_image(image: Image.Image) -> List[Image.Image]:
    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("""
    # UTPL - Conversión de Imágen a objetos 3D usando IA  
    ### Tesis: *"Objetos tridimensionales creados por IA: Innovación en entornos virtuales"*  
    **Autor:** Carlos Vargas  
    **Base técnica:** Adaptación de [TRELLIS](https://trellis3d.github.io/) (herramienta de código abierto para generación 3D)  
    **Propósito educativo:** Demostraciones académicas e Investigación en modelado 3D automático  
    """)
    
    with gr.Row(equal_height=False):
        # Left column (Controls)
        with gr.Column(scale=2, min_width=400):
            with gr.Tabs():
                with gr.Tab(label="Input Image"):
                    image_prompt = gr.Image(
                        label="Image Prompt", 
                        format="png", 
                        image_mode="RGBA", 
                        type="pil", 
                        height=300,
                        show_label=False
                    )
            
            with gr.Accordion(".Generation Settings", open=False):
                with gr.Column(variant="panel"):
                    seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
                    randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
                    
                    with gr.Group():
                        gr.Markdown("#### Stage 1: Structure")
                        ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance", value=7.5, step=0.1)
                        ss_sampling_steps = gr.Slider(1, 50, label="Steps", value=12, step=1)
                    
                    with gr.Group():
                        gr.Markdown("#### Stage 2: Detail")
                        slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance", value=3.0, step=0.1)
                        slat_sampling_steps = gr.Slider(1, 50, label="Steps", value=12, step=1)
            
            generate_btn = gr.Button("Generate 3D Asset", variant="primary", size="lg")
            
            with gr.Accordion("GLB Export Settings", open=False):
                with gr.Column(variant="panel"):
                    mesh_simplify = gr.Slider(0.5, 0.98, label="Simplify Mesh", value=0.95, step=0.01)
                    texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
            
            extract_glb_btn = gr.Button("Export GLB", interactive=False, size="lg")

        # Right column (Outputs)
        with gr.Column(scale=3, min_width=600):
            with gr.Group():
                video_output = gr.Video(
                    label="3D Preview", 
                    autoplay=True, 
                    loop=True, 
                    height=300,
                    show_label=False
                )
                model_output = gr.Model3D(
                    label="3D Model Viewer", 
                    height=400
                )
            
            with gr.Row():
                download_glb = gr.DownloadButton(
                    label="Download GLB File", 
                    interactive=False,
                    variant="secondary",
                    size="lg"
                )

    output_buf = gr.State()

    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,
        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],
    )
    
if __name__ == "__main__":
    pipeline = TrellisImageTo3DPipeline.from_pretrained("cavargas10/TRELLIS")
    pipeline.cuda()
    try:
        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
    except:
        pass
    demo.launch()