File size: 6,630 Bytes
b2331d4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 |
import os,sys
import argparse
import os
import sys
from datetime import datetime
from pathlib import Path
from typing import List
import glob
import numpy as np
import torch
import torchvision
from diffusers import AutoencoderKL, DDIMScheduler
from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline
from einops import repeat
from omegaconf import OmegaConf
from PIL import Image
from torchvision import transforms
from transformers import CLIPVisionModelWithProjection
from musepose.models.pose_guider import PoseGuider
from musepose.models.unet_2d_condition import UNet2DConditionModel
from musepose.models.unet_3d import UNet3DConditionModel
from musepose.pipelines.pipeline_pose2img import Pose2ImagePipeline
from musepose.utils.util import get_fps, read_frames, save_videos_grid
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--config",default="./configs/test_stage_1.yaml")
parser.add_argument("-W", type=int, default=768)
parser.add_argument("-H", type=int, default=768)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--cnt", type=int, default=1)
parser.add_argument("--cfg", type=float, default=7)
parser.add_argument("--steps", type=int, default=20)
parser.add_argument("--fps", type=int)
args = parser.parse_args()
return args
def main():
args = parse_args()
config = OmegaConf.load(args.config)
if config.weight_dtype == "fp16":
weight_dtype = torch.float16
else:
weight_dtype = torch.float32
vae = AutoencoderKL.from_pretrained(
config.pretrained_vae_path,
).to("cuda", dtype=weight_dtype)
reference_unet = UNet2DConditionModel.from_pretrained(
config.pretrained_base_model_path,
subfolder="unet",
).to(dtype=weight_dtype, device="cuda")
inference_config_path = config.inference_config
infer_config = OmegaConf.load(inference_config_path)
denoising_unet = UNet3DConditionModel.from_pretrained_2d(
config.pretrained_base_model_path,
# config.motion_module_path,
"",
subfolder="unet",
unet_additional_kwargs={
"use_motion_module": False,
"unet_use_temporal_attention": False,
},
).to(dtype=weight_dtype, device="cuda")
pose_guider = PoseGuider(320, block_out_channels=(16, 32, 96, 256)).to(
dtype=weight_dtype, device="cuda"
)
image_enc = CLIPVisionModelWithProjection.from_pretrained(
config.image_encoder_path
).to(dtype=weight_dtype, device="cuda")
sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs)
scheduler = DDIMScheduler(**sched_kwargs)
width, height = args.W, args.H
# load pretrained weights
denoising_unet.load_state_dict(
torch.load(config.denoising_unet_path, map_location="cpu"),
strict=False,
)
reference_unet.load_state_dict(
torch.load(config.reference_unet_path, map_location="cpu"),
)
pose_guider.load_state_dict(
torch.load(config.pose_guider_path, map_location="cpu"),
)
pipe = Pose2ImagePipeline(
vae=vae,
image_encoder=image_enc,
reference_unet=reference_unet,
denoising_unet=denoising_unet,
pose_guider=pose_guider,
scheduler=scheduler,
)
pipe = pipe.to("cuda", dtype=weight_dtype)
date_str = datetime.now().strftime("%Y%m%d")
time_str = datetime.now().strftime("%H%M")
m1 = config.pose_guider_path.split('.')[0].split('/')[-1]
save_dir_name = f"{time_str}-{m1}"
save_dir = Path(f"./output/image-{date_str}/{save_dir_name}")
save_dir.mkdir(exist_ok=True, parents=True)
def handle_single(ref_image_path, pose_path,seed):
generator = torch.manual_seed(seed)
ref_name = Path(ref_image_path).stem
# pose_name = Path(pose_image_path).stem.replace("_kps", "")
pose_name = Path(pose_path).stem
ref_image_pil = Image.open(ref_image_path).convert("RGB")
pose_image = Image.open(pose_path).convert("RGB")
original_width, original_height = pose_image.size
pose_transform = transforms.Compose(
[transforms.Resize((height, width)), transforms.ToTensor()]
)
pose_image_tensor = pose_transform(pose_image)
pose_image_tensor = pose_image_tensor.unsqueeze(0) # (1, c, h, w)
ref_image_tensor = pose_transform(ref_image_pil) # (c, h, w)
ref_image_tensor = ref_image_tensor.unsqueeze(1).unsqueeze(0) # (1, c, 1, h, w)
image = pipe(
ref_image_pil,
pose_image,
width,
height,
args.steps,
args.cfg,
generator=generator,
).images
image = image.squeeze(2).squeeze(0) # (c, h, w)
image = image.transpose(0, 1).transpose(1, 2) # (h w c)
#image = (image + 1.0) / 2.0 # -1,1 -> 0,1
image = (image * 255).numpy().astype(np.uint8)
image = Image.fromarray(image, 'RGB')
# image.save(os.path.join(save_dir, f"{ref_name}_{pose_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}.png"))
image_grid = Image.new('RGB',(original_width*3,original_height))
imgs = [ref_image_pil,pose_image,image]
x_offset = 0
for img in imgs:
img = img.resize((original_width*2, original_height*2))
img.save(os.path.join(save_dir, f"res_{ref_name}_{pose_name}_{args.cfg}_{seed}.jpg"))
img = img.resize((original_width,original_height))
image_grid.paste(img, (x_offset,0))
x_offset += img.size[0]
image_grid.save(os.path.join(save_dir, f"grid_{ref_name}_{pose_name}_{args.cfg}_{seed}.jpg"))
for ref_image_path_dir in config["test_cases"].keys():
if os.path.isdir(ref_image_path_dir):
ref_image_paths = glob.glob(os.path.join(ref_image_path_dir, '*.jpg'))
else:
ref_image_paths = [ref_image_path_dir]
for ref_image_path in ref_image_paths:
for pose_image_path_dir in config["test_cases"][ref_image_path_dir]:
if os.path.isdir(pose_image_path_dir):
pose_image_paths = glob.glob(os.path.join(pose_image_path_dir, '*.jpg'))
else:
pose_image_paths = [pose_image_path_dir]
for pose_image_path in pose_image_paths:
for i in range(args.cnt):
handle_single(ref_image_path, pose_image_path, args.seed + i)
if __name__ == "__main__":
main()
|