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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
import warnings
warnings.simplefilter('ignore', category=UserWarning)
warnings.simplefilter('ignore', category=FutureWarning)
warnings.simplefilter('ignore', category=DeprecationWarning)
import gradio as gr
from glob import glob
import shutil
import torch
import numpy as np
from PIL import Image
from einops import rearrange
import argparse
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_memory", default=False, action="store_true")
parser.add_argument("--device", default="cuda:0", type=str)
args = parser.parse_args()
################################################################
CONST_PORT = 8080
CONST_MAX_QUEUE = 1
CONST_SERVER = '0.0.0.0'
CONST_HEADER = '''
<h2><b>Official 🤗 Gradio Demo</b></h2><h2><a href='https://github.com/tencent/Hunyuan3D-1' target='_blank'><b>Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D
Generationr</b></a></h2>
Code: <a href='https://github.com/tencent/Hunyuan3D-1' target='_blank'>GitHub</a>. Techenical report: <a href='https://arxiv.org/abs/placeholder' target='_blank'>ArXiv</a>.
❗️❗️❗️**Important Notes:**
- Our demo can export a .obj mesh with vertex colors or a .glb mesh by default.
- If you check "texture mapping", it will export a .obj mesh with a texture map or a .glb mesh.
- If you check "render Gif", it will export gif image rendering .glb file.
- If the result is unsatisfying, please try a different **seed value** (Default: 0).
'''
CONST_CITATION = r"""
If HunYuan3D-1 is helpful, please help to ⭐ the <a href='https://github.com/tencent/Hunyuan3D-1' target='_blank'>Github Repo</a>. Thanks! [](https://github.com/tencent/Hunyuan3D-1)
---
📝 **Citation**
If you find our work useful for your research or applications, please cite using this bibtex:
```bibtex
@misc{xxx,
title={Hunyuan3D-1.0: First Unified Framework for Text-to-3D and Image-to-3D Generation},
author={},
year={2024},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
"""
################################################################
def get_example_img_list():
print('Loading example img list ...')
return sorted(glob('./demos/example_*.png'))
def get_example_txt_list():
print('Loading example txt list ...')
txt_list = list()
for line in open('./demos/example_list.txt'):
txt_list.append(line.strip())
return txt_list
example_is = get_example_img_list()
example_ts = get_example_txt_list()
################################################################
from infer import seed_everything, save_gif
from infer import Text2Image, Removebg, Image2Views, Views2Mesh, GifRenderer
worker_xbg = Removebg()
print(f"loading {args.text2image_path}")
worker_t2i = Text2Image(
pretrain = args.text2image_path,
device = args.device,
save_memory = args.save_memory
)
worker_i2v = Image2Views(
use_lite = args.use_lite,
device = args.device
)
worker_v23 = Views2Mesh(
args.mv23d_cfg_path,
args.mv23d_ckt_path,
use_lite = args.use_lite,
device = args.device
)
worker_gif = GifRenderer(args.device)
def stage_0_t2i(text, image, seed, step):
# prepare save_folder
os.makedirs('./outputs/app_output', exist_ok=True)
exists = set(int(_) for _ in os.listdir('./outputs/app_output') if not _.startswith("."))
if len(exists) == 30: shutil.rmtree(f"./outputs/app_output/0");cur_id = 0
else: cur_id = min(set(range(30)) - exists)
if os.path.exists(f"./outputs/app_output/{(cur_id + 1) % 30}"):
shutil.rmtree(f"./outputs/app_output/{(cur_id + 1) % 30}")
save_folder = f'./outputs/app_output/{cur_id}'
os.makedirs(save_folder, exist_ok=True)
dst = save_folder + '/img.png'
if not text:
if image is None:
return dst, save_folder
raise gr.Error("Upload image or provide text ...")
image.save(dst)
return dst, save_folder
image = worker_t2i(text, seed, step)
image.save(dst)
dst = worker_xbg(image, save_folder)
return dst, save_folder
def stage_1_xbg(image, save_folder):
if isinstance(image, str):
image = Image.open(image)
dst = save_folder + '/img_nobg.png'
rgba = worker_xbg(image)
rgba.save(dst)
return dst
def stage_2_i2v(image, seed, step, save_folder):
if isinstance(image, str):
image = Image.open(image)
gif_dst = save_folder + '/views.gif'
res_img, pils = worker_i2v(image, seed, step)
save_gif(pils, gif_dst)
views_img, cond_img = res_img[0], res_img[1]
img_array = np.asarray(views_img, dtype=np.uint8)
show_img = rearrange(img_array, '(n h) (m w) c -> (n m) h w c', n=3, m=2)
show_img = show_img[worker_i2v.order, ...]
show_img = rearrange(show_img, '(n m) h w c -> (n h) (m w) c', n=2, m=3)
show_img = Image.fromarray(show_img)
return views_img, cond_img, show_img
def stage_3_v23(
views_pil,
cond_pil,
seed,
save_folder,
target_face_count = 30000,
do_texture_mapping = True,
do_render =True
):
do_texture_mapping = do_texture_mapping or do_render
obj_dst = save_folder + '/mesh_with_colors.obj'
glb_dst = save_folder + '/mesh.glb'
worker_v23(
views_pil,
cond_pil,
seed = seed,
save_folder = save_folder,
target_face_count = target_face_count,
do_texture_mapping = do_texture_mapping
)
return obj_dst, glb_dst
def stage_4_gif(obj_dst, save_folder, do_render_gif=True):
if not do_render_gif: return None
gif_dst = save_folder + '/output.gif'
worker_gif(
save_folder + '/mesh.obj',
gif_dst_path = gif_dst
)
return gif_dst
#===============================================================
with gr.Blocks() as demo:
gr.Markdown(CONST_HEADER)
with gr.Row(variant="panel"):
with gr.Column(scale=2):
with gr.Tab("Text to 3D"):
with gr.Column():
text = gr.TextArea('一只黑白相间的熊猫在白色背景上居中坐着，呈现出卡通风格和可爱氛围。', lines=1, max_lines=10, label='Input text')
with gr.Row():
textgen_seed = gr.Number(value=0, label="T2I seed", precision=0)
textgen_step = gr.Number(value=25, label="T2I step", precision=0)
textgen_SEED = gr.Number(value=0, label="Gen seed", precision=0)
textgen_STEP = gr.Number(value=50, label="Gen step", precision=0)
textgen_max_faces = gr.Number(value=90000, label="max number of faces", precision=0)
with gr.Row():
textgen_do_texture_mapping = gr.Checkbox(label="texture mapping", value=False, interactive=True)
textgen_do_render_gif = gr.Checkbox(label="Render gif", value=False, interactive=True)
textgen_submit = gr.Button("Generate", variant="primary")
with gr.Row():
gr.Examples(examples=example_ts, inputs=[text], label="Txt examples", examples_per_page=10)
with gr.Tab("Image to 3D"):
with gr.Column():
input_image = gr.Image(label="Input image",
width=256, height=256, type="pil",
image_mode="RGBA", sources="upload",
interactive=True)
with gr.Row():
imggen_SEED = gr.Number(value=0, label="Gen seed", precision=0)
imggen_STEP = gr.Number(value=50, label="Gen step", precision=0)
imggen_max_faces = gr.Number(value=90000, label="max number of faces", precision=0)
with gr.Row():
imggen_do_texture_mapping = gr.Checkbox(label="texture mapping", value=False, interactive=True)
imggen_do_render_gif = gr.Checkbox(label="Render gif", value=False, interactive=True)
imggen_submit = gr.Button("Generate", variant="primary")
with gr.Row():
gr.Examples(examples=example_is, inputs=[input_image], label="Img examples", examples_per_page=10)
with gr.Column(scale=3):
with gr.Tab("rembg image"):
rem_bg_image = gr.Image(label="No backgraound image",
width=256, height=256, type="pil",
image_mode="RGBA", interactive=False)
with gr.Tab("Multi views"):
result_image = gr.Image(label="Multi views", type="pil", interactive=False)
with gr.Tab("Obj"):
result_3dobj = gr.Model3D(label="Output obj", interactive=False)
with gr.Tab("Glb"):
result_3dglb = gr.Model3D(label="Output glb", interactive=False)
gr.Markdown("The glb file displayed on the grario will be dark. We recommend downloading and opening it with 3D software, such as Blender, MeshLab, etc")
with gr.Tab("GIF"):
result_gif = gr.Image(label="Rendered GIF", interactive=False)
#===============================================================
none = gr.State(None)
save_folder = gr.State()
cond_image = gr.State()
views_image = gr.State()
text_image = gr.State()
textgen_submit.click(
fn=stage_0_t2i, inputs=[text, none, textgen_seed, textgen_step],
outputs=[rem_bg_image, save_folder],
).success(
fn=stage_2_i2v, inputs=[rem_bg_image, textgen_SEED, textgen_STEP, save_folder],
outputs=[views_image, cond_image, result_image],
).success(
fn=stage_3_v23, inputs=[views_image, cond_image, textgen_SEED, save_folder, textgen_max_faces, textgen_do_texture_mapping, textgen_do_render_gif],
outputs=[result_3dobj, result_3dglb],
).success(
fn=stage_4_gif, inputs=[result_3dglb, save_folder, textgen_do_render_gif],
outputs=[result_gif],
).success(lambda: print('Text_to_3D Done ...'))
imggen_submit.click(
fn=stage_0_t2i, inputs=[none, input_image, textgen_seed, textgen_step],
outputs=[text_image, save_folder],
).success(
fn=stage_1_xbg, inputs=[text_image, save_folder],
outputs=[rem_bg_image],
).success(
fn=stage_2_i2v, inputs=[rem_bg_image, imggen_SEED, imggen_STEP, save_folder],
outputs=[views_image, cond_image, result_image],
).success(
fn=stage_3_v23, inputs=[views_image, cond_image, imggen_SEED, save_folder, imggen_max_faces, imggen_do_texture_mapping, imggen_do_render_gif],
outputs=[result_3dobj, result_3dglb],
).success(
fn=stage_4_gif, inputs=[result_3dglb, save_folder, imggen_do_render_gif],
outputs=[result_gif],
).success(lambda: print('Image_to_3D Done ...'))
#===============================================================
gr.Markdown(CONST_CITATION)
demo.queue(max_size=CONST_MAX_QUEUE)
demo.launch(server_name=CONST_SERVER, server_port=CONST_PORT)
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