Hunyuan3D-1/main.py

# 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.l

import os
import torch
from PIL import Image
import argparse

from infer import Text2Image, Removebg, Image2Views, Views2Mesh, GifRenderer


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--use_lite", default=False, action="store_true"
    )
    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(
        "--text2image_path", default="weights/hunyuanDiT", type=str
    )
    parser.add_argument(
        "--save_folder", default="./outputs/test/", type=str
    )
    parser.add_argument(
        "--text_prompt", default="", type=str,
    )
    parser.add_argument(
        "--image_prompt", default="", type=str
    )
    parser.add_argument(
        "--device", default="cuda:0", type=str
    )
    parser.add_argument(
        "--t2i_seed", default=0, type=int
    )
    parser.add_argument(
        "--t2i_steps", default=25, type=int
    )
    parser.add_argument(
        "--gen_seed", default=0, type=int
    )
    parser.add_argument(
        "--gen_steps", default=50, type=int
    )
    parser.add_argument(
        "--max_faces_num", default=80000, type=int, 
        help="max num of face, suggest 80000 for effect, 10000 for speed"
    )
    parser.add_argument(
        "--save_memory", default=False, action="store_true"
    )
    parser.add_argument(
        "--do_texture_mapping", default=False, action="store_true"
    )
    parser.add_argument(
        "--do_render", default=False, action="store_true"
    )
    return parser.parse_args()


if __name__ == "__main__":
    args = get_args()
    
    assert not (args.text_prompt and args.image_prompt), "Text and image can only be given to one"
    assert args.text_prompt or args.image_prompt,        "Text and image can only be given to one"

    # init model
    rembg_model = Removebg()
    image_to_views_model = Image2Views(device=args.device, use_lite=args.use_lite)
    views_to_mesh_model = Views2Mesh(args.mv23d_cfg_path, args.mv23d_ckt_path, args.device, use_lite=args.use_lite)
    if args.text_prompt:
        text_to_image_model = Text2Image(
            pretrain = args.text2image_path,
            device = args.device, 
            save_memory = args.save_memory
        )
    if args.do_render:
        gif_renderer = GifRenderer(device=args.device)

    # ---- ----- ---- ---- ---- ----

    os.makedirs(args.save_folder, exist_ok=True)

    # stage 1, text to image
    if args.text_prompt:
        res_rgb_pil = text_to_image_model(
            args.text_prompt, 
            seed=args.t2i_seed,  
            steps=args.t2i_steps
        )
        res_rgb_pil.save(os.path.join(args.save_folder, "img.jpg"))
    elif args.image_prompt:
        res_rgb_pil = Image.open(args.image_prompt)

    # stage 2, remove back ground
    res_rgba_pil = rembg_model(res_rgb_pil)
    res_rgb_pil.save(os.path.join(args.save_folder, "img_nobg.png"))

    # stage 3, image to views
    (views_grid_pil, cond_img), view_pil_list = image_to_views_model(
        res_rgba_pil,
        seed = args.gen_seed,
        steps = args.gen_steps
    )
    views_grid_pil.save(os.path.join(args.save_folder, "views.jpg"))

    # stage 4, views to mesh
    views_to_mesh_model(
        views_grid_pil, 
        cond_img, 
        seed = args.gen_seed,
        target_face_count = args.max_faces_num,
        save_folder = args.save_folder,
        do_texture_mapping = args.do_texture_mapping
    )

    #  stage 5, render gif
    if args.do_render:
        gif_renderer(
            os.path.join(args.save_folder, 'mesh.obj'),
            gif_dst_path = os.path.join(args.save_folder, 'output.gif'),
        )