app.py

import os
import numpy as np
import cv2
import kiui
import trimesh
import torch
import rembg
from datetime import datetime
import subprocess
import argparse

try:
    # running on Hugging Face Spaces
    import spaces

except ImportError:
    # running locally, use a dummy space
    class spaces:
        class GPU:
            def __init__(self, duration=60):
                self.duration = duration
            def __call__(self, func):
                return func


from flow.model import Model
from flow.configs.schema import ModelConfig
from flow.utils import get_random_color, recenter_foreground
from vae.utils import postprocess_mesh

# download checkpoints
from huggingface_hub import hf_hub_download
flow_ckpt_path = hf_hub_download(repo_id="nvidia/PartPacker", filename="flow.pt")
vae_ckpt_path = hf_hub_download(repo_id="nvidia/PartPacker", filename="vae.pt")

TRIMESH_GLB_EXPORT = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]]).astype(np.float32)
MAX_SEED = np.iinfo(np.int32).max
bg_remover = rembg.new_session()

# model config
model_config = ModelConfig(
    vae_conf="vae.configs.part_woenc",
    vae_ckpt_path=vae_ckpt_path,
    qknorm=True,
    qknorm_type="RMSNorm",
    use_pos_embed=False,
    dino_model="dinov2_vitg14",
    hidden_dim=1536,
    flow_shift=3.0,
    logitnorm_mean=1.0,
    logitnorm_std=1.0,
    latent_size=4096,
    use_parts=True,
)

# instantiate model
model = Model(model_config).eval().cuda().bfloat16()

# load weight
ckpt_dict = torch.load(flow_ckpt_path, weights_only=True)
model.load_state_dict(ckpt_dict, strict=True)

# get random seed
def get_random_seed(randomize_seed, seed):
    if randomize_seed:
        seed = np.random.randint(0, MAX_SEED)
    return seed

# process image
@spaces.GPU(duration=10)
def process_image(image_path):
    image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
    if image.shape[-1] == 4:
        image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
    else:
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        # bg removal if there is no alpha channel
        image = rembg.remove(image, session=bg_remover)  # [H, W, 4]
    mask = image[..., -1] > 0
    image = recenter_foreground(image, mask, border_ratio=0.1)
    image = cv2.resize(image, (518, 518), interpolation=cv2.INTER_AREA)
    return image

# process generation
@spaces.GPU(duration=90)
def process_3d(input_image, num_steps=50, cfg_scale=7, grid_res=384, seed=42, simplify_mesh=False, target_num_faces=100000):

    # seed
    kiui.seed_everything(seed)

    # output path
    os.makedirs("output", exist_ok=True)
    output_glb_path = f"output/partpacker_{datetime.now().strftime('%Y%m%d_%H%M%S')}.glb"

    # input image (assume processed to RGBA uint8)
    image = input_image.astype(np.float32) / 255.0
    image = image[..., :3] * image[..., 3:4] + (1 - image[..., 3:4])  # white background
    image_tensor = torch.from_numpy(image).permute(2, 0, 1).contiguous().unsqueeze(0).float().cuda()

    data = {"cond_images": image_tensor}

    with torch.inference_mode():
        results = model(data, num_steps=num_steps, cfg_scale=cfg_scale)

    latent = results["latent"]

    # query mesh

    data_part0 = {"latent": latent[:, : model.config.latent_size, :]}
    data_part1 = {"latent": latent[:, model.config.latent_size :, :]}

    with torch.inference_mode():
        results_part0 = model.vae(data_part0, resolution=grid_res)
        results_part1 = model.vae(data_part1, resolution=grid_res)

    if not simplify_mesh:
        target_num_faces = -1

    vertices, faces = results_part0["meshes"][0]
    mesh_part0 = trimesh.Trimesh(vertices, faces)
    mesh_part0.vertices = mesh_part0.vertices @ TRIMESH_GLB_EXPORT.T
    mesh_part0 = postprocess_mesh(mesh_part0, target_num_faces)
    parts = mesh_part0.split(only_watertight=False)

    vertices, faces = results_part1["meshes"][0]
    mesh_part1 = trimesh.Trimesh(vertices, faces)
    mesh_part1.vertices = mesh_part1.vertices @ TRIMESH_GLB_EXPORT.T
    mesh_part1 = postprocess_mesh(mesh_part1, target_num_faces)
    parts.extend(mesh_part1.split(only_watertight=False))

    # split connected components and assign different colors
    for j, part in enumerate(parts):
        # each component uses a random color
        part.visual.vertex_colors = get_random_color(j, use_float=True)

    mesh = trimesh.Scene(parts)
    # export the whole mesh
    mesh.export(output_glb_path)

    print(f"Saved 3D model to {output_glb_path}")
    return output_glb_path

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--image_path', type=str, default='examples/robot.png', help='Path to input image')
    parser.add_argument('--num_steps', type=int, default=50, help='Inference steps')
    parser.add_argument('--cfg_scale', type=float, default=7.0, help='CFG scale')
    parser.add_argument('--grid_res', type=int, default=384, help='Grid resolution')
    parser.add_argument('--seed', type=int, default=42, help='Random seed')
    parser.add_argument('--randomize_seed', action='store_true', help='Randomize seed')
    parser.add_argument('--simplify_mesh', action='store_true', help='Simplify mesh')
    parser.add_argument('--target_num_faces', type=int, default=100000, help='Target number of faces for mesh simplification')
    
    args = parser.parse_args()

    if args.randomize_seed:
        args.seed = get_random_seed(args.randomize_seed, args.seed)

    processed_image = process_image(args.image_path)
    process_3d(
        input_image=processed_image,
        num_steps=args.num_steps,
        cfg_scale=args.cfg_scale,
        grid_res=args.grid_res,
        seed=args.seed,
        simplify_mesh=args.simplify_mesh,
        target_num_faces=args.target_num_faces
    )