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import argparse, os, sys, glob
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm
import numpy as np
import torch
from main import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
def make_batch(image, mask, device):
image = np.array(Image.open(image).convert("RGB"))
image = image.astype(np.float32)/255.0
image = image[None].transpose(0,3,1,2)
image = torch.from_numpy(image)
mask = np.array(Image.open(mask).convert("L"))
mask = mask.astype(np.float32)/255.0
mask = mask[None,None]
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = torch.from_numpy(mask)
masked_image = (1-mask)*image
batch = {"image": image, "mask": mask, "masked_image": masked_image}
for k in batch:
batch[k] = batch[k].to(device=device)
batch[k] = batch[k]*2.0-1.0
return batch
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--indir",
type=str,
nargs="?",
help="dir containing image-mask pairs (`example.png` and `example_mask.png`)",
)
parser.add_argument(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
)
parser.add_argument(
"--steps",
type=int,
default=50,
help="number of ddim sampling steps",
)
opt = parser.parse_args()
masks = sorted(glob.glob(os.path.join(opt.indir, "*_mask.png")))
images = [x.replace("_mask.png", ".png") for x in masks]
print(f"Found {len(masks)} inputs.")
config = OmegaConf.load("models/ldm/inpainting_big/config.yaml")
model = instantiate_from_config(config.model)
model.load_state_dict(torch.load("models/ldm/inpainting_big/last.ckpt")["state_dict"],
strict=False)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)
sampler = DDIMSampler(model)
os.makedirs(opt.outdir, exist_ok=True)
with torch.no_grad():
with model.ema_scope():
for image, mask in tqdm(zip(images, masks)):
outpath = os.path.join(opt.outdir, os.path.split(image)[1])
batch = make_batch(image, mask, device=device)
# encode masked image and concat downsampled mask
c = model.cond_stage_model.encode(batch["masked_image"])
cc = torch.nn.functional.interpolate(batch["mask"],
size=c.shape[-2:])
c = torch.cat((c, cc), dim=1)
shape = (c.shape[1]-1,)+c.shape[2:]
samples_ddim, _ = sampler.sample(S=opt.steps,
conditioning=c,
batch_size=c.shape[0],
shape=shape,
verbose=False)
x_samples_ddim = model.decode_first_stage(samples_ddim)
image = torch.clamp((batch["image"]+1.0)/2.0,
min=0.0, max=1.0)
mask = torch.clamp((batch["mask"]+1.0)/2.0,
min=0.0, max=1.0)
predicted_image = torch.clamp((x_samples_ddim+1.0)/2.0,
min=0.0, max=1.0)
inpainted = (1-mask)*image+mask*predicted_image
inpainted = inpainted.cpu().numpy().transpose(0,2,3,1)[0]*255
Image.fromarray(inpainted.astype(np.uint8)).save(outpath)
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