mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
e6179af46a
* revert inadvertent change of conda env name (#528) * Refactor generate.py and dream.py * config file path (models.yaml) is parsed inside Generate() to simplify API * Better handling of keyboard interrupts in file loading mode vs interactive * Removed oodles of unused variables. * move nonfunctional inpainting out of the scripts directory * fix ugly ddim tqdm formatting
99 lines
3.6 KiB
Python
99 lines
3.6 KiB
Python
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
|
|
from ldm.dream.devices import choose_torch_device
|
|
|
|
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 = choose_torch_device()
|
|
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)
|