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