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 )