app.py

import os
import shutil
import tempfile
import time
from os import path

import gradio as gr
import numpy as np
import rembg
import spaces
import torch
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler, StableDiffusionXLPipeline, LCMScheduler
from einops import rearrange
from huggingface_hub import hf_hub_download
from omegaconf import OmegaConf
from PIL import Image
from pytorch_lightning import seed_everything
from safetensors.torch import load_file
from torchvision.transforms import v2
from tqdm import tqdm

from src.utils.camera_util import (FOV_to_intrinsics, get_circular_camera_poses, get_zero123plus_input_cameras)
from src.utils.infer_util import (remove_background, resize_foreground)
from src.utils.mesh_util import save_glb, save_obj, save_texmap
from src.utils.train_util import instantiate_from_config

# Inicializa un tensor en CUDA y verifica el dispositivo.
zero = torch.Tensor([0]).cuda()
print(zero.device) # Verifica que el dispositivo sea CUDA.
print(zero.device) # Verifica nuevamente que el dispositivo sea CUDA.

# Función para encontrar el path de CUDA.
def find_cuda():
    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
    if cuda_home and os.path.exists(cuda_home):
        return cuda_home

    nvcc_path = shutil.which('nvcc')
    if nvcc_path:
        cuda_path = os.path.dirname(os.path.dirname(nvcc_path))
        return cuda_path

    return None

# Función para obtener las cámaras de renderizado.
def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False):
    c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation)
    if is_flexicubes:
        cameras = torch.linalg.inv(c2ws)
        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1)
    else:
        extrinsics = c2ws.flatten(-2)
        intrinsics = FOV_to_intrinsics(50.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2)
        cameras = torch.cat([extrinsics, intrinsics], dim=-1)
        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1)
    return cameras

# Verifica si la imagen de entrada es nula.
def check_input_image(input_image):
    if input_image is None:
        raise gr.Error("No image selected!")

# Preprocesa la imagen de entrada y opcionalmente elimina el fondo.
def preprocess(input_image, do_remove_background):
    rembg_session = rembg.new_session() if do_remove_background else None

    if do_remove_background:
        input_image = remove_background(input_image, rembg_session)
        input_image = resize_foreground(input_image, 1)

    return input_image

# Genera vistas múltiples de la imagen de entrada.
@spaces.GPU(duration=20)
def generate_mvs(input_image, sample_steps, sample_seed):
    seed_everything(sample_seed)
    
    print(zero.device) # Verifica que el dispositivo sea CUDA.
    
    z123_image = pipeline(input_image, num_inference_steps=sample_steps).images[0]

    show_image = np.asarray(z123_image, dtype=np.uint8)
    show_image = torch.from_numpy(show_image)
    show_image = rearrange(show_image, '(n h) (m w) c -> (n m) h w c', n=3, m=2)
    show_image = rearrange(show_image, '(n m) h w c -> (n h) (m w) c', n=2, m=3)
    show_image = Image.fromarray(show_image.numpy())

    return z123_image, show_image

# Convierte imágenes en modelos 3D y genera texturas.
@spaces.GPU
def make3d(images):
    
    print(zero.device) # Verifica que el dispositivo sea CUDA.
    
    global model
    if IS_FLEXICUBES:
        model.init_flexicubes_geometry(device, use_renderer=False)
    model = model.eval()

    images = np.asarray(images, dtype=np.float32) / 255.0
    images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float()
    images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2)

    input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0).to(device)
    render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).to(device)

    images = images.unsqueeze(0).to(device)
    images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1)

    mesh_fpath = tempfile.NamedTemporaryFile(suffix=".obj", delete=False).name
    print(mesh_fpath)
    mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
    mesh_dirname = os.path.dirname(mesh_fpath)
    mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb")
    texmap_fpath = os.path.join(mesh_dirname, f"{mesh_basename}_tex.png")

    with torch.no_grad():
        planes = model.forward_planes(images, input_cameras)
        mesh_out = model.extract_mesh(planes, use_texture_map=True, **infer_config)

        vertices, faces, vertex_colors, texture_map = mesh_out
        vertices = vertices[:, [1, 2, 0]]

        save_glb(vertices, faces, vertex_colors, texture_map, mesh_glb_fpath)
        save_obj(vertices, faces, vertex_colors, texture_map, mesh_fpath)
        save_texmap(texture_map, texmap_fpath)

        print(f"Mesh and texture saved to {mesh_fpath} and {texmap_fpath}")

    return mesh_fpath, mesh_glb_fpath, texmap_fpath

# Procesa la imagen generada a partir de un prompt de texto.
@spaces.GPU
def process_image(num_images, prompt):
    
    print(zero.device) # Verifica que el dispositivo sea CUDA.
    
    global pipe
    with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
        return pipe(
            prompt=[prompt]*num_images,
            generator=torch.Generator().manual_seed(123),
            num_inference_steps=1,
            guidance_scale=0.,
            height=int(512),
            width=int(512),
            timesteps=[800]
        ).images

# Configuración inicial del entorno CUDA y carga de configuración del modelo.
cuda_path = find_cuda()
config_path = 'configs/instant-mesh-large.yaml'
config = OmegaConf.load(config_path)
config_name = os.path.basename(config_path).replace('.yaml', '')
model_config = config.model_config
infer_config = config.infer_config

IS_FLEXICUBES = config_name.startswith('instant-mesh')
device = torch.device('cuda')

# Carga del modelo de difusión.
print('Loading diffusion model ...')
pipeline = DiffusionPipeline.from_pretrained(
    "sudo-ai/zero123plus-v1.2",
    custom_pipeline="zero123plus",
    torch_dtype=torch.float16,
)
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
    pipeline.scheduler.config, timestep_spacing='trailing'
)

unet_ckpt_path = hf_hub_download(
    repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model")
state_dict = torch.load(unet_ckpt_path, map_location='cpu')
pipeline.unet.load_state_dict(state_dict, strict=True)

pipeline = pipeline.to(device)

# Carga del modelo de reconstrucción.
print('Loading reconstruction model ...')
model_ckpt_path = hf_hub_download(
    repo_id="TencentARC/InstantMesh", filename="instant_mesh_large.ckpt", repo_type="model")
model = instantiate_from_config(model_config)
state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict']
state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.') and 'source_camera' not in k}
model.load_state_dict(state_dict, strict=True)

model = model.to(device)

print('Carga Completa!')

# Interfaz de usuario usando Gradio.
with gr.Blocks() as demo:
    with gr.Row(variant="panel"):
        with gr.Column():
            with gr.Row():
                input_image = gr.Image(
                    label="Imagen de Entrada",
                    image_mode="RGBA",
                    sources="upload",
                    type="pil",
                    elem_id="content_image",
                )
                processed_image = gr.Image(
                    label="Imagen sin Fondo",
                    image_mode="RGBA",
                    type="pil",
                    interactive=False
                )
            with gr.Row():
                with gr.Group():
                    do_remove_background = gr.Checkbox(
                        label="Quitar Fondo", value=True)
                    sample_seed = gr.Number(
                        value=42, label="Valor de la semilla", precision=0)
                    sample_steps = gr.Slider(
                        label="Pasos de muestreo", minimum=30, maximum=75, value=75, step=5)

            with gr.Row():
                submit = gr.Button(
                    "Generar", elem_id="generate", variant="primary")

        with gr.Column():
            with gr.Row():
                with gr.Column():
                    mv_show_images = gr.Image(
                        label="Generar Multi-vistas",
                        type="pil",
                        width=379,
                        interactive=False
                    )

            with gr.Row():
                with gr.Tab("OBJ"):
                    output_model_obj = gr.Model3D(
                        label="(Formato OBJ)",
                        interactive=False,
                    )
                with gr.Tab("GLB"):
                    output_model_glb = gr.Model3D(
                        label="(Formato GLB)",
                        interactive=False,
                    )
                with gr.Tab("Textura"):
                    output_texture = gr.Image(
                        label="Textura Generada",
                        type="pil",
                        interactive=False
                    )

    mv_images = gr.State()

    submit.click(fn=check_input_image, inputs=[input_image]).success(
        fn=preprocess,
        inputs=[input_image, do_remove_background],
        outputs=[processed_image],
    ).success(
        fn=generate_mvs,
        inputs=[processed_image, sample_steps, sample_seed],
        outputs=[mv_images, mv_show_images]
    ).success(
        fn=make3d,
        inputs=[mv_images],
        outputs=[output_model_obj, output_model_glb, output_texture]
    )

demo.launch()