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image_sample.py
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image_sample.py
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"""
Train a diffusion model on images.
"""
import argparse
from pretrained_diffusion import dist_util, logger
from pretrained_diffusion.image_datasets_mask import load_data_mask
from pretrained_diffusion.image_datasets_sketch import load_data_sketch
from pretrained_diffusion.image_datasets_depth import load_data_depth
from pretrained_diffusion.script_util import (
model_and_diffusion_defaults,
create_model_and_diffusion,
args_to_dict,
add_dict_to_argparser,
)
from pretrained_diffusion.train_util import TrainLoop
from pretrained_diffusion.glide_util import sample
import torch
import os
import torch as th
import torchvision.utils as tvu
import torch.distributed as dist
def main():
parser, parser_up = create_argparser()
args = parser.parse_args()
args_up = parser_up.parse_args()
dist_util.setup_dist()
options=args_to_dict(args, model_and_diffusion_defaults(0.).keys())
model, diffusion = create_model_and_diffusion(**options)
options_up=args_to_dict(args_up, model_and_diffusion_defaults(True).keys())
model_up, diffusion_up = create_model_and_diffusion(**options_up)
if args.model_path:
print('loading model')
model_ckpt = dist_util.load_state_dict(args.model_path, map_location="cpu")
model.load_state_dict(
model_ckpt , strict=True )
if args.sr_model_path:
print('loading sr model')
model_ckpt2 = dist_util.load_state_dict(args.sr_model_path, map_location="cpu")
model_up.load_state_dict(
model_ckpt2 , strict=True )
model.to(dist_util.dev())
model_up.to(dist_util.dev())
model.eval()
model_up.eval()
########### dataset
logger.log("creating data loader...")
if args.mode == 'ade20k' or args.mode == 'coco':
val_data = load_data_mask(
data_dir=args.val_data_dir,
batch_size=args.batch_size//2,
image_size=256,
train=False,
deterministic=True,
low_res=args.super_res,
uncond_p = 0. ,
mode = args.mode,
random_crop=False,
)
elif args.mode == 'depth' or args.mode == 'depth-normal':
val_data = load_data_depth(
data_dir=args.val_data_dir,
batch_size=args.batch_size//2,
image_size=256,
train=False,
deterministic=True,
low_res=args.super_res,
uncond_p = 0. ,
mode = args.mode,
random_crop=False,
)
elif args.mode == 'coco-edge' or args.mode == 'flickr-edge':
val_data = load_data_sketch(
data_dir=args.val_data_dir,
batch_size=args.batch_size//2,
image_size=256,
train=False,
deterministic=True,
low_res=args.super_res,
uncond_p = 0. ,
mode = args.mode,
random_crop=False,
)
logger.log("sampling...")
gt_path = os.path.join(logger.get_dir(), 'GT')
os.makedirs(gt_path,exist_ok=True)
lr_path = os.path.join(logger.get_dir(), 'LR')
os.makedirs(lr_path,exist_ok=True)
hr_path = os.path.join(logger.get_dir(), 'HR')
os.makedirs(hr_path,exist_ok=True)
ref_path = os.path.join(logger.get_dir(), 'REF')
os.makedirs(ref_path,exist_ok=True)
img_id = 0
while (True):
if img_id >= args.num_samples:
break
batch, model_kwargs = next(val_data)
with th.no_grad():
samples_lr =sample(
glide_model= model,
glide_options= options,
side_x= 64,
side_y= 64,
prompt=model_kwargs,
batch_size= args.batch_size//2,
guidance_scale=args.sample_c,
device=dist_util.dev(),
prediction_respacing= "250",
upsample_enabled= False,
upsample_temp=0.997,
mode = args.mode,
)
samples_lr = samples_lr.clamp(-1, 1)
tmp = (127.5*(samples_lr + 1.0)).int()
model_kwargs['low_res'] = tmp/127.5 - 1.
samples_hr =sample(
glide_model= model_up,
glide_options= options_up,
side_x=256,
side_y=256,
prompt=model_kwargs,
batch_size=args.batch_size//2,
guidance_scale=1,
device=dist_util.dev(),
prediction_respacing= "fast27",
upsample_enabled=True,
upsample_temp=0.997,
mode = args.mode,
)
samples_lr = samples_lr.cpu()
# ref = model_kwargs['ref'].cpu()
ref = model_kwargs['ref_ori'].cpu()
samples_hr = samples_hr.cpu()
for i in range(samples_lr.size(0)):
name = model_kwargs['path'][i].split('/')[-1].split('.')[0] + '.png'
out_path = os.path.join(lr_path, name)
tvu.save_image(
(samples_lr[i]+1)*0.5, out_path)
out_path = os.path.join(gt_path, name)
tvu.save_image(
(batch[i]+1)*0.5, out_path)
out_path = os.path.join(ref_path, name)
tvu.save_image(
(ref[i]+1)*0.5, out_path)
out_path = os.path.join(hr_path, name)
tvu.save_image(
(samples_hr[i]+1)*0.5, out_path)
img_id += 1
def create_argparser():
defaults = dict(
data_dir="",
val_data_dir="",
model_path="",
sr_model_path="",
encoder_path="",
schedule_sampler="uniform",
lr=1e-4,
weight_decay=0.0,
lr_anneal_steps=0,
batch_size=1,
microbatch=-1, # -1 disables microbatches
ema_rate="0.9999", # comma-separated list of EMA values
log_interval=100,
save_interval=20000,
resume_checkpoint="",
use_fp16=False,
fp16_scale_growth=1e-3,
sample_c=1.,
sample_respacing="100",
uncond_p=0.2,
num_samples=1,
finetune_decoder = False,
mode = '',
)
defaults_up = defaults
defaults.update(model_and_diffusion_defaults())
parser = argparse.ArgumentParser()
add_dict_to_argparser(parser, defaults)
defaults_up.update(model_and_diffusion_defaults(True))
parser_up = argparse.ArgumentParser()
add_dict_to_argparser(parser_up, defaults_up)
return parser, parser_up
if __name__ == "__main__":
main()