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# Open Source Model Licensed under the Apache License Version 2.0
# and Other Licenses of the Third-Party Components therein:
# The below Model in this distribution may have been modified by THL A29 Limited
# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
# The below software and/or models in this distribution may have been
# modified by THL A29 Limited ("Tencent Modifications").
# All Tencent Modifications are Copyright (C) THL A29 Limited.
# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
# except for the third-party components listed below.
# Hunyuan 3D does not impose any additional limitations beyond what is outlined
# in the repsective licenses of these third-party components.
# Users must comply with all terms and conditions of original licenses of these third-party
# components and must ensure that the usage of the third party components adheres to
# all relevant laws and regulations.
# For avoidance of doubts, Hunyuan 3D means the large language models and
# their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
import os, sys
sys.path.insert(0, f"{os.path.dirname(os.path.dirname(os.path.abspath(__file__)))}")
import time
import torch
import random
import numpy as np
from PIL import Image
from einops import rearrange
from PIL import Image, ImageSequence
from infer.utils import seed_everything, timing_decorator, auto_amp_inference
from infer.utils import get_parameter_number, set_parameter_grad_false, str_to_bool
from svrm.predictor import MV23DPredictor
class Views2Mesh():
def __init__(self, mv23d_cfg_path, mv23d_ckt_path,
device="cuda:0", use_lite=False, save_memory=False):
'''
mv23d_cfg_path: config yaml file
mv23d_ckt_path: path to ckpt
use_lite: lite version
save_memory: cpu auto
'''
self.device = device
self.save_memory = save_memory
self.mv23d_predictor = MV23DPredictor(
mv23d_ckt_path,
mv23d_cfg_path,
device = "cpu" if save_memory else device
)
self.mv23d_predictor.model.eval()
self.order = [0, 1, 2, 3, 4, 5] if use_lite else [0, 2, 4, 5, 3, 1]
set_parameter_grad_false(self.mv23d_predictor.model)
print('view2mesh model', get_parameter_number(self.mv23d_predictor.model))
@torch.no_grad()
@timing_decorator("views to mesh")
@auto_amp_inference
def __call__(self, *args, **kwargs):
if self.save_memory:
self.mv23d_predictor.model = self.mv23d_predictor.model.to(self.device)
torch.cuda.empty_cache()
res = self.call(*args, **kwargs)
self.mv23d_predictor.model = self.mv23d_predictor.model.to("cpu")
else:
res = self.call(*args, **kwargs)
torch.cuda.empty_cache()
return res
def call(
self,
views_pil=None,
cond_pil=None,
gif_pil=None,
seed=0,
target_face_count = 10000,
do_texture_mapping = True,
save_folder='./outputs/test'
):
'''
can set views_pil, cond_pil simutaously or set gif_pil only
seed: int
target_face_count: int
save_folder: path to save mesh files
'''
save_dir = save_folder
os.makedirs(save_dir, exist_ok=True)
if views_pil is not None and cond_pil is not None:
show_image = rearrange(np.asarray(views_pil, dtype=np.uint8),
'(n h) (m w) c -> (n m) h w c', n=3, m=2)
views = [Image.fromarray(show_image[idx]) for idx in self.order]
image_list = [cond_pil]+ views
image_list = [img.convert('RGB') for img in image_list]
elif gif_pil is not None:
image_list = [img.convert('RGB') for img in ImageSequence.Iterator(gif_pil)]
image_input = image_list[0]
image_list = image_list[1:] + image_list[:1]
seed_everything(seed)
self.mv23d_predictor.predict(
image_list,
save_dir = save_dir,
image_input = image_input,
target_face_count = target_face_count,
do_texture_mapping = do_texture_mapping
)
torch.cuda.empty_cache()
if __name__ == "__main__":
import argparse
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--views_path", type=str, required=True)
parser.add_argument("--cond_path", type=str, required=True)
parser.add_argument("--save_folder", default="./outputs/test/", type=str)
parser.add_argument("--mv23d_cfg_path", default="./svrm/configs/svrm.yaml", type=str)
parser.add_argument("--mv23d_ckt_path", default="weights/svrm/svrm.safetensors", type=str)
parser.add_argument("--max_faces_num", default=90000, type=int,
help="max num of face, suggest 90000 for effect, 10000 for speed")
parser.add_argument("--device", default="cuda:0", type=str)
parser.add_argument("--use_lite", default='false', type=str)
parser.add_argument("--do_texture_mapping", default='false', type=str)
return parser.parse_args()
args = get_args()
args.use_lite = str_to_bool(args.use_lite)
args.do_texture_mapping = str_to_bool(args.do_texture_mapping)
views = Image.open(args.views_path)
cond = Image.open(args.cond_path)
views_to_mesh_model = Views2Mesh(
args.mv23d_cfg_path,
args.mv23d_ckt_path,
device = args.device,
use_lite = args.use_lite
)
views_to_mesh_model(
views, cond, 0,
target_face_count = args.max_faces_num,
save_folder = args.save_folder,
do_texture_mapping = args.do_texture_mapping
)
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