1 files changed, 98 insertions, 0 deletions
diff --git a/scripts/inpaint.py b/scripts/inpaint.py
new file mode 100644
index 0000000..d6e6387
--- /dev/null
+++ b/scripts/inpaint.py
@@ -0,0 +1,98 @@
+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)