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import sys, os
sys.path.append(os.getcwd())
from os.path import join as opj
import zipfile
import json
import pickle
from tqdm import tqdm
import argparse
import numpy as np
import torch
import torch.nn.functional as F
from torch import autocast
from torchvision.transforms import ToPILImage
from diffusers import StableDiffusionImg2ImgPipeline, PNDMScheduler
from camera_utils import LookAtPoseSampler, FOV_to_intrinsics
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(description='Pose-aware dataset generation')
parser.add_argument('--strength', default=0.7, type=float)
parser.add_argument('--prompt', type=str)
parser.add_argument('--data_type', default='ffhq', type=str) # ffhq, cat
parser.add_argument('--guidance_scale', default=8, type=float)
parser.add_argument('--num_images', default=1000, type=int)
parser.add_argument('--sd_model_id', default='stabilityai/stable-diffusion-2-1-base', type=str)
parser.add_argument('--num_inference_steps', default=30, type=int)
parser.add_argument('--ffhq_eg3d_path', default='pretrained/ffhqrebalanced512-128.pkl', type=str)
parser.add_argument('--cat_eg3d_path', default='pretrained/afhqcats512-128.pkl', type=str)
parser.add_argument('--ffhq_pivot', default=0.2, type=float)
parser.add_argument('--cat_pivot', default=0.05, type=float)
parser.add_argument('--pitch_range', default=0.3, type=float)
parser.add_argument('--yaw_range', default=0.3, type=float)
parser.add_argument('--name_tag', default='', type=str)
parser.add_argument('--seed', default=15, type=int)
args = parser.parse_args()
return args
def make_zip(base_dir, prompt, data_type='ffhq', name_tag=''):
base_dir = os.path.abspath(base_dir)
owd = os.path.abspath(os.getcwd())
os.chdir(base_dir)
json_path = opj(base_dir, "dataset.json")
zip_path = opj(base_dir, f'data_{data_type}_{prompt.replace(" ", "_")}{name_tag}.zip')
zip_file = zipfile.ZipFile(zip_path, "w")
with open(json_path, 'r') as file:
data = json.load(file)
zip_file.write(os.path.relpath(json_path, base_dir), compress_type=zipfile.ZIP_STORED)
for label in data['labels']:
trg_img_path = label[0]
zip_file.write(trg_img_path, compress_type=zipfile.ZIP_STORED)
zip_file.close()
os.chdir(owd)
def pts2pil(pts):
pts = (pts + 1) / 2
pts[pts > 1] = 1
pts[pts < 0] = 0
return ToPILImage()(pts[0])
if __name__ == '__main__':
args = parse_args()
device = "cuda"
torch.manual_seed(args.seed)
np.random.seed(args.seed)
data_type = args.data_type
prompt = args.prompt
strength = args.strength
guidance_scale = args.guidance_scale
num_inference_steps = args.num_inference_steps
num_images = args.num_images
name_tag = args.name_tag
# 3DG options
ffhq_eg3d_path = args.ffhq_eg3d_path
cat_eg3d_path = args.cat_eg3d_path
cat_pivot = args.cat_pivot
ffhq_pivot = args.ffhq_pivot
pitch_range = args.pitch_range
yaw_range = args.yaw_range
num_frames = 240
truncation_psi = 0.7
truncation_cutoff = 14
fov_deg = 18.837
ft_img_size = 512
# Load 3DG
eg3d_path = None
if data_type == 'ffhq':
eg3d_path = args.ffhq_eg3d_path
pivot = ffhq_pivot
elif data_type == 'cat':
eg3d_path = args.cat_eg3d_path
pivot = cat_pivot
with open(eg3d_path, 'rb') as f:
G = pickle.load(f)['G_ema'].to(device) # torch.nn.Module
G.train()
for param in G.parameters():
param.requires_grad_(True)
# SD options
model_id = args.sd_model_id
negative_prompt = None
eta = 0.0
batch_size = 1
model_inversion = False
# Load SD
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
model_id,
revision="fp16",
torch_dtype=torch.float16,
use_auth_token=True,
scheduler=PNDMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear",
num_train_timesteps=1000, set_alpha_to_one=False, steps_offset=1, skip_prk_steps=1),
).to(device)
pipe.safety_checker = None
print('SD model is loaded')
# Outputs directory
base_dir = opj(f'./exp_data/data_{data_type}_{prompt.replace(" ", "_")}{name_tag}')
src_img_dir = opj(base_dir, "src_imgs")
trg_img_dir = opj(base_dir, "trg_imgs")
os.makedirs('exp_data', exist_ok=True)
os.makedirs(base_dir, exist_ok=True)
os.makedirs(src_img_dir, exist_ok=True)
os.makedirs(trg_img_dir, exist_ok=True)
labels = []
# Fine-tuning 3D generator
for i in tqdm(range(num_images)):
G.eval()
z = torch.from_numpy(np.random.randn(batch_size, G.z_dim)).to(device)
intrinsics = FOV_to_intrinsics(fov_deg, device=device)
with torch.no_grad():
yaw_idx = np.random.randint(num_frames)
pitch_idx = np.random.randint(num_frames)
cam_pivot = torch.tensor([0, 0, pivot], device=device)
cam_radius = G.rendering_kwargs.get('avg_camera_radius', 2.7)
cam2world_pose = LookAtPoseSampler.sample(np.pi / 2 + yaw_range * np.sin(2 * np.pi * yaw_idx / num_frames),
np.pi / 2 - 0.05 + pitch_range * np.cos(
2 * np.pi * pitch_idx / num_frames),
cam_pivot, radius=cam_radius, device=device,
batch_size=batch_size)
conditioning_cam2world_pose = LookAtPoseSampler.sample(np.pi / 2, np.pi / 2, cam_pivot, radius=cam_radius,
device=device, batch_size=batch_size)
camera_params = torch.cat([cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9).repeat(batch_size, 1)],
1)
conditioning_params = torch.cat(
[conditioning_cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9).repeat(batch_size, 1)], 1)
ws = G.mapping(z, conditioning_params, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff)
img_pts = G.synthesis(ws, camera_params)['image']
src_img_pts = img_pts.detach()
src_img_pts = F.interpolate(src_img_pts, (ft_img_size, ft_img_size), mode='bilinear', align_corners=False)
with autocast("cuda"):
trg_img_pil = pipe(prompt=prompt,
image=src_img_pts,
strength=strength,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
)['images'][0]
src_idx = f'{i:05d}_src.png'
trg_idx = f'{i:05d}_trg.png'
src_img_pil_path = opj(src_img_dir, src_idx)
trg_img_pil_path = opj(trg_img_dir, trg_idx)
src_img_pil = pts2pil(src_img_pts.cpu())
src_img_pil.save(src_img_pil_path)
trg_img_pil.save(trg_img_pil_path)
label = [trg_img_pil_path.replace(base_dir, '').replace('/trg_', 'trg_'), camera_params[0].tolist()]
labels.append(label)
json_path = opj(base_dir, "dataset.json")
json_data = {'labels': labels}
with open(json_path, 'w') as outfile:
json.dump(json_data, outfile, indent=4)
make_zip(base_dir, prompt, data_type, name_tag)
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