Spaces:
Running
on
L40S
Running
on
L40S
File size: 5,540 Bytes
b155b2e e3e5f9e 68cd723 e3e5f9e 68cd723 e3e5f9e 68cd723 e3e5f9e 68cd723 e3e5f9e 68cd723 e3e5f9e 68cd723 e3e5f9e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
# 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 mvd.hunyuan3d_mvd_std_pipeline import HunYuan3D_MVD_Std_Pipeline
from mvd.hunyuan3d_mvd_lite_pipeline import Hunyuan3d_MVD_Lite_Pipeline
def save_gif(pils, save_path, df=False):
# save a list of PIL.Image to gif
spf = 4000 / len(pils)
os.makedirs(os.path.dirname(save_path), exist_ok=True)
pils[0].save(save_path, format="GIF", save_all=True, append_images=pils[1:], duration=spf, loop=0)
return save_path
class Image2Views():
def __init__(self,
device="cuda:0", use_lite=False, save_memory=False,
std_pretrain='./weights/mvd_std', lite_pretrain='./weights/mvd_lite'
):
self.device = device
if use_lite:
print("loading", lite_pretrain)
self.pipe = Hunyuan3d_MVD_Lite_Pipeline.from_pretrained(
lite_pretrain,
torch_dtype = torch.float16,
use_safetensors = True,
)
else:
print("loadding", std_pretrain)
self.pipe = HunYuan3D_MVD_Std_Pipeline.from_pretrained(
std_pretrain,
torch_dtype = torch.float16,
use_safetensors = True,
)
self.pipe = self.pipe if save_memory else self.pipe.to(device)
self.order = [0, 1, 2, 3, 4, 5] if use_lite else [0, 2, 4, 5, 3, 1]
self.save_memory = save_memory
set_parameter_grad_false(self.pipe.unet)
print('image2views unet model', get_parameter_number(self.pipe.unet))
@torch.no_grad()
@timing_decorator("image to views")
@auto_amp_inference
def __call__(self, *args, **kwargs):
if self.save_memory:
self.pipe = self.pipe.to(self.device)
torch.cuda.empty_cache()
res = self.call(*args, **kwargs)
self.pipe = self.pipe.to("cpu")
else:
res = self.call(*args, **kwargs)
torch.cuda.empty_cache()
return res
def call(self, pil_img, seed=0, steps=50, guidance_scale=2.0):
seed_everything(seed)
generator = torch.Generator(device=self.device)
res_img = self.pipe(pil_img,
num_inference_steps=steps,
guidance_scale=guidance_scale,
generat=generator).images
show_image = rearrange(np.asarray(res_img[0], dtype=np.uint8), '(n h) (m w) c -> (n m) h w c', n=3, m=2)
pils = [res_img[1]]+[Image.fromarray(show_image[idx]) for idx in self.order]
torch.cuda.empty_cache()
return res_img, pils
if __name__ == "__main__":
import argparse
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--rgba_path", type=str, required=True)
parser.add_argument("--output_views_path", type=str, required=True)
parser.add_argument("--output_cond_path", type=str, required=True)
parser.add_argument("--seed", default=0, type=int)
parser.add_argument("--steps", default=50, type=int)
parser.add_argument("--device", default="cuda:0", type=str)
parser.add_argument("--use_lite", default='false', type=str)
return parser.parse_args()
args = get_args()
args.use_lite = str_to_bool(args.use_lite)
rgba_pil = Image.open(args.rgba_path)
assert rgba_pil.mode == "RGBA", "rgba_pil must be RGBA mode"
model = Image2Views(device=args.device, use_lite=args.use_lite)
(views_pil, cond), _ = model(rgba_pil, seed=args.seed, steps=args.steps)
views_pil.save(args.output_views_path)
cond.save(args.output_cond_path)
|