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Added feature refinement to Lama to enhance high resolution image inpainting #174

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Added feature refinement to Lama to enhance high resolution image inpainting #174

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1af4061
Update lama_inpaint.py
Abbsalehi Dec 10, 2024
7061391
Update refinement.py
Abbsalehi Dec 10, 2024
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3 changes: 2 additions & 1 deletion lama/saicinpainting/evaluation/refinement.py
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Original file line number Diff line number Diff line change
Expand Up @@ -196,7 +196,8 @@ def _get_image_mask_pyramid(batch : dict, min_side : int, max_scales : int, px_b
assert batch['image'].shape[0] == 1, "refiner works on only batches of size 1!"

h, w = batch['unpad_to_size']
h, w = h[0].item(), w[0].item()
# h, w = h[0].item(), w[0].item()
h, w = h.item(), w.item()

image = batch['image'][...,:h,:w]
mask = batch['mask'][...,:h,:w]
Expand Down
24 changes: 20 additions & 4 deletions lama_inpaint.py
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Expand Up @@ -58,13 +58,15 @@ def inpaint_img_with_lama(
model = load_checkpoint(
train_config, checkpoint_path, strict=False, map_location='cpu')
model.freeze()
if not predict_config.get('refine', False):
model.to(device)
# if not predict_config.get('refine', False):
# model.to(device)
model.to(device)

batch = {}
batch['image'] = img.permute(2, 0, 1).unsqueeze(0)
batch['mask'] = mask[None, None]
unpad_to_size = [batch['image'].shape[2], batch['image'].shape[3]]
batch['unpad_to_size']= torch.tensor(unpad_to_size).to(device)
batch['image'] = pad_tensor_to_modulo(batch['image'], mod)
batch['mask'] = pad_tensor_to_modulo(batch['mask'], mod)
batch = move_to_device(batch, device)
Expand All @@ -73,7 +75,21 @@ def inpaint_img_with_lama(
batch = model(batch)
cur_res = batch[predict_config.out_key][0].permute(1, 2, 0)
cur_res = cur_res.detach().cpu().numpy()

# Feature Refinement to Improve High Resolution Image Inpainting
if predict_config.get('refine', False):
# assert 'unpad_to_size' in batch, "Unpadded size is required for the refinement"
# image unpadding is taken care of in the refiner
# is same size as the input image
cur_res = refine_predict(batch, model, **predict_config.refiner)
cur_res = cur_res[0].permute(1, 2, 0).detach().cpu().numpy()
else:
with torch.no_grad():
batch = move_to_device(batch, device)
batch['mask'] = (batch['mask'] > 0) * 1
batch = model(batch)
cur_res = batch[predict_config.out_key][0].permute(1, 2, 0).detach().cpu().numpy()
unpad_to_size = batch.get('unpad_to_size', None)

if unpad_to_size is not None:
orig_height, orig_width = unpad_to_size
cur_res = cur_res[:orig_height, :orig_width]
Expand Down Expand Up @@ -197,4 +213,4 @@ def setup_args(parser):
img_inpainted_p = out_dir / f"inpainted_with_{Path(mask_p).name}"
img_inpainted = inpaint_img_with_lama(
img, mask, args.lama_config, args.lama_ckpt, device=device)
save_array_to_img(img_inpainted, img_inpainted_p)
save_array_to_img(img_inpainted, img_inpainted_p)