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opencv/samples/dnn/ldm_inpainting.py
Abduragim Shtanchaev 050085c996
Merge pull request #25950 from Abdurrahheem:ash/add-inpainting-sample 
Diffusion Inpainting Sample #25950

This PR adds inpaiting sample that is based on [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/pdf/2112.10752) paper (reference github [repository](https://github.com/CompVis/latent-diffusion)).


Steps to run the model:

1. Firstly needs ONNX graph of the Latent Diffusion Model. You can get it in two different ways. 

> a. Generate the using this [repo](https://github.com/Abdurrahheem/latent-diffusion/tree/ash/export2onnx) and follow instructions below

```bash
git clone https://github.com/Abdurrahheem/latent-diffusion.git
cd latent-diffusion
conda env create -f environment.yaml
conda activate ldm
wget -O models/ldm/inpainting_big/last.ckpt https://heibox.uni-heidelberg.de/f/4d9ac7ea40c64582b7c9/?dl=1
python -m scripts.inpaint.py --indir data/inpainting_examples/ --outdir outputs/inpainting_results --export=True
```

> b. Download the ONNX graph (there 3 fiels) using this link: TODO make a link

2. Build opencv (preferebly with CUDA support enabled
3. Run the script 

```bash
cd opencv/samples/dnn
python ldm_inpainting.py 
python ldm_inpainting.py -e=<path-to-InpaintEncoder.onnx file> -d=<path-to-InpaintDecoder.onnx file> -df=<path-to-LatenDiffusion.onnx file> -i=<path-to-image>
```
Right after the last command you will be prompted with image. You can click on left mouse bottom and starting selection a region you would like to be inpainted (deleted). Once you finish marking the region, click on left mouse botton again and press esc button on your keyboard. The inpainting proccess will start. 

Note: If you are running it on CPU it might take a large chank of time. Also make sure to have about 15GB of RAM to make process faster (other wise swapping will click in and everything will be slower)
 
Current challenges: 

1. Diffusion process is slow (many layers fallback to CPU with running with CUDA backend) 
2. The diffusion result is does exactly mach that of the original torch pipeline

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x]The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-08-21 14:48:37 +03:00

490 lines
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import cv2 as cv
import numpy as np
import argparse
from tqdm import tqdm
from functools import partial
from copy import deepcopy
## let use write description of the script and general information how to use it
'''
This sample proposes experimental inpainting sample using Latent Diffusion Model (LDM) for inpainting.
Most of the script is based on the code from the official repository of the LDM model: https://github.com/CompVis/latent-diffusion
Current limitations of the script:
- Slow diffusion sampling
- Not exact reproduction of the results from the original repository (due to issues related deviation in covolution operation.
See issue for more details: https://github.com/opencv/opencv/pull/25973)
Steps for running the script:
1. Firstly generate ONNX graph of the Latent Diffusion Model.
Generate the using this [repo](https://github.com/Abdurrahheem/latent-diffusion/tree/ash/export2onnx) and follow instructions below
- git clone https://github.com/Abdurrahheem/latent-diffusion.git
- cd latent-diffusion
- conda env create -f environment.yaml
- conda activate ldm
- wget -O models/ldm/inpainting_big/last.ckpt https://heibox.uni-heidelberg.de/f/4d9ac7ea40c64582b7c9/?dl=1
- python -m scripts.inpaint.py --indir data/inpainting_examples/ --outdir outputs/inpainting_results --export=True
2. Build opencv (preferebly with CUDA support enabled
3. Run the script
- cd opencv/samples/dnn
- python ldm_inpainting.py -e=<path-to-InpaintEncoder.onnx file> -d=<path-to-InpaintDecoder.onnx file> -df=<path-to-LatenDiffusion.onnx file> -i=<path-to-image>
Right after the last command you will be promted with image. You can click on left mouse botton and starting selection a region you would like to be inpainted (delited).
Once you finish marking the region, click on left mouse botton again and press esc botton on your keyboard. The inpainting proccess will start.
Note: If you are running it on CPU it might take a large chank of time.
Also make sure to have abount 15GB of RAM to make proccess faster (other wise swapping will ckick in and everything will be slower)
'''
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_CUDA)
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_MYRIAD, cv.dnn.DNN_TARGET_HDDL, cv.dnn.DNN_TARGET_CUDA)
parser = argparse.ArgumentParser(description='Use this script to run inpainting using Latent Diffusion Model',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--encoder', '-e', type=str, help='Path to encoder network.', default=None)
parser.add_argument('--decoder', '-d', type=str, help='Path to decoder network.', default=None)
parser.add_argument('--diffusor', '-df', type=str, help='Path to diffusion network.', default=None)
parser.add_argument('--image', '-i', type=str, help='Path to input image.', default=None)
parser.add_argument('--samples', '-s', type=int, help='Number of times to sample the model.', default=50)
parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
help="Choose one of computation backends: "
"%d: automatically (by default), "
"%d: OpenCV implementation, "
"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"%d: CUDA, " % backends)
parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
help='Choose one of target computation devices: '
'%d: CPU target (by default), '
'%d: OpenCL, '
'%d: NCS2 VPU, '
'%d: HDDL VPU, '
'%d: CUDA ' % targets)
def make_batch(image, mask):
image = image.astype(np.float32)/255.0
image = image[np.newaxis, ...].transpose(0,3,1,2)
mask = mask.astype(np.float32)/255.0
mask = mask[np.newaxis, np.newaxis, ...]
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
masked_image = (1-mask)*image
batch = {"image": image, "mask": mask, "masked_image": masked_image}
for k in batch:
batch[k] = batch[k]*2.0-1.0
return batch
def noise_like(shape, repeat=False):
repeat_noise = lambda: np.random.randn((1, *shape[1:])).repeat(shape[0], *((1,) * (len(shape) - 1)))
noise = lambda: np.random.randn(*shape)
return repeat_noise() if repeat else noise()
def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
if ddim_discr_method == 'uniform':
c = num_ddpm_timesteps // num_ddim_timesteps
ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
elif ddim_discr_method == 'quad':
ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
else:
raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
# assert ddim_timesteps.shape[0] == num_ddim_timesteps
# add one to get the final alpha values right (the ones from first scale to data during sampling)
steps_out = ddim_timesteps + 1
if verbose:
print(f'Selected timesteps for ddim sampler: {steps_out}')
return steps_out
def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
# select alphas for computing the variance schedule
alphas = alphacums[ddim_timesteps]
alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
# according the the formula provided in https://arxiv.org/abs/2010.02502
sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
if verbose:
print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
print(f'For the chosen value of eta, which is {eta}, '
f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
return sigmas, alphas, alphas_prev
def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if schedule == "linear":
betas = (
np.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep).astype(np.float64) ** 2
)
elif schedule == "cosine":
timesteps = (
np.arange(n_timestep + 1).astype(np.float64) / n_timestep + cosine_s
)
alphas = timesteps / (1 + cosine_s) * np.pi / 2
alphas = np.cos(alphas).pow(2)
alphas = alphas / alphas[0]
betas = 1 - alphas[1:] / alphas[:-1]
betas = np.clip(betas, a_min=0, a_max=0.999)
elif schedule == "sqrt_linear":
betas = np.linspace(linear_start, linear_end, n_timestep).astype(np.float64)
elif schedule == "sqrt":
betas = np.linspace(linear_start, linear_end, n_timestep).astype(np.float64) ** 0.5
else:
raise ValueError(f"schedule '{schedule}' unknown.")
return betas
class DDIMSampler(object):
def __init__(self, model, schedule="linear", ddpm_num_timesteps=1000):
super().__init__()
self.model = model
self.ddpm_num_timesteps = ddpm_num_timesteps
self.schedule = schedule
def register_buffer(self, name, attr):
setattr(self, name, attr)
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
alphas_cumprod = self.model.alphas_cumprod
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
to_numpy = partial(np.array, copy=True, dtype=np.float32)
self.register_buffer('betas', to_numpy(self.model.betas))
self.register_buffer('alphas_cumprod', to_numpy(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_numpy(self.model.alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_numpy(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_numpy(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_numpy(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_numpy(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_numpy(np.sqrt(1. / alphas_cumprod - 1)))
# ddim sampling parameters
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod,
ddim_timesteps=self.ddim_timesteps,
eta=ddim_eta,verbose=verbose)
self.register_buffer('ddim_sigmas', ddim_sigmas)
self.register_buffer('ddim_alphas', ddim_alphas)
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
sigmas_for_original_sampling_steps = ddim_eta * np.sqrt(
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
1 - self.alphas_cumprod / self.alphas_cumprod_prev))
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
def sample(self,
S,
batch_size,
shape,
conditioning=None,
eta=0.,
temperature=1.,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
print(f'Data shape for DDIM sampling is {size}, eta {eta}')
samples, intermediates = self.ddim_sampling(conditioning, size,
ddim_use_original_steps=False,
temperature=temperature,
x_T=x_T,
log_every_t=log_every_t,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,
)
return samples, intermediates
def ddim_sampling(self, cond, shape,
x_T=None, ddim_use_original_steps=False,
timesteps=None,log_every_t=100, temperature=1.,
unconditional_guidance_scale=1., unconditional_conditioning=None,):
b = shape[0]
if x_T is None:
img = np.random.randn(*shape)
else:
img = x_T
if timesteps is None:
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
elif timesteps is not None and not ddim_use_original_steps:
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
timesteps = self.ddim_timesteps[:subset_end]
intermediates = {'x_inter': [img], 'pred_x0': [img]}
time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
print(f"Running DDIM Sampling with {total_steps} timesteps")
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = np.full((b, ), step, dtype=np.int64)
outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
temperature=temperature, unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning)
img, pred_x0 = outs
if index % log_every_t == 0 or index == total_steps - 1:
intermediates['x_inter'].append(img)
intermediates['pred_x0'].append(pred_x0)
return img, intermediates
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False,
temperature=1., unconditional_guidance_scale=1., unconditional_conditioning=None):
b = x.shape[0]
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
e_t = self.model.apply_model(x, t, c)
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
# select parameters corresponding to the currently considered timestep
a_t = np.full((b, 1, 1, 1), alphas[index])
a_prev = np.full((b, 1, 1, 1), alphas_prev[index])
sigma_t = np.full((b, 1, 1, 1), sigmas[index])
sqrt_one_minus_at = np.full((b, 1, 1, 1), sqrt_one_minus_alphas[index])
# current prediction for x_0
pred_x0 = (x - sqrt_one_minus_at * e_t) / np.sqrt(a_t)
# direction pointing to x_t
dir_xt = np.sqrt(1. - a_prev - sigma_t**2) * e_t
noise = sigma_t * noise_like(x.shape, repeat_noise) * temperature
x_prev = np.sqrt(a_prev) * pred_x0 + dir_xt + noise
return x_prev, pred_x0
class DDIMInpainter(object):
def __init__(self,
args,
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
parameterization="eps", # all assuming fixed variance schedules
linear_start=0.0015,
linear_end=0.0205,
conditioning_key="concat",
):
super().__init__()
self.v_posterior = v_posterior
self.parameterization = parameterization
self.conditioning_key = conditioning_key
self.register_schedule(linear_start=linear_start, linear_end=linear_end)
self.encoder = cv.dnn.readNet(args.encoder)
self.decoder = cv.dnn.readNet(args.decoder)
self.diffusor = cv.dnn.readNet(args.diffusor)
self.sampler = DDIMSampler(self, ddpm_num_timesteps=self.num_timesteps)
self.set_backend(backend=args.backend, target=args.target)
def set_backend(self, backend=cv.dnn.DNN_BACKEND_DEFAULT, target=cv.dnn.DNN_TARGET_CPU):
self.encoder.setPreferableBackend(backend)
self.encoder.setPreferableTarget(target)
self.decoder.setPreferableBackend(backend)
self.decoder.setPreferableTarget(target)
self.diffusor.setPreferableBackend(backend)
self.diffusor.setPreferableTarget(target)
def apply_diffusor(self, x, timestep, cond):
x = np.concatenate([x, cond], axis=1)
x = cv.Mat(x.astype(np.float32))
timestep = cv.Mat(timestep.astype(np.int64))
names = ["xc", "t"]
self.diffusor.setInputsNames(names)
self.diffusor.setInput(x, names[0])
self.diffusor.setInput(timestep, names[1])
output = self.diffusor.forward()
return output
def register_buffer(self, name, attr):
setattr(self, name, attr)
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if given_betas is not None:
betas = given_betas
else:
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_numpy = partial(np.array, dtype=np.float32)
self.register_buffer('betas', to_numpy(betas))
self.register_buffer('alphas_cumprod', to_numpy(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_numpy(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_numpy(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_numpy(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_numpy(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_numpy(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_numpy(np.sqrt(1. / alphas_cumprod - 1)))
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
1. - alphas_cumprod) + self.v_posterior * betas
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.register_buffer('posterior_variance', to_numpy(posterior_variance))
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.register_buffer('posterior_log_variance_clipped', to_numpy(np.log(np.maximum(posterior_variance, 1e-20))))
self.register_buffer('posterior_mean_coef1', to_numpy(
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
self.register_buffer('posterior_mean_coef2', to_numpy(
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
if self.parameterization == "eps":
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_numpy(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(alphas_cumprod) / (2. * 1 - alphas_cumprod)
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights)
assert not np.isnan(self.lvlb_weights).all()
def apply_model(self, x_noisy, t, cond, return_ids=False):
if isinstance(cond, dict):
# hybrid case, cond is exptected to be a dict
pass
else:
# if not isinstance(cond, list):
# cond = [cond]
key = 'c_concat' if self.conditioning_key == 'concat' else 'c_crossattn'
cond = {key: cond}
x_recon = self.apply_diffusor(x_noisy, t, cond['c_concat'])
if isinstance(x_recon, tuple) and not return_ids:
return x_recon[0]
else:
return x_recon
def __call__(self, image : np.ndarray, mask : np.ndarray, S : int = 50) -> np.ndarray:
# Encode the image and mask
self.encoder.setInput(image)
c = self.encoder.forward()
cc = cv.resize(np.squeeze(mask), dsize=(c.shape[3], c.shape[2]), interpolation=cv.INTER_NEAREST) #TODO:check for correcteness of intepolation
cc = cc[None,None]
c = np.concatenate([c, cc], axis=1)
shape = (c.shape[1] - 1,) + c.shape[2:]
# Sample from the model
samples_ddim, _ = self.sampler.sample(
S=S,
conditioning=c,
batch_size=c.shape[0],
shape=shape,
verbose=False)
## Decode the sample
samples_ddim = samples_ddim.astype(np.float32)
samples_ddim = cv.Mat(samples_ddim)
self.decoder.setInput(samples_ddim)
x_samples_ddim = self.decoder.forward()
image = np.clip((image + 1.0) / 2.0, a_min=0.0, a_max=1.0)
mask = np.clip((mask + 1.0) / 2.0, a_min=0.0, a_max=1.0)
predicted_image = np.clip((x_samples_ddim + 1.0) / 2.0, a_min=0.0, a_max=1.0)
inpainted = (1 - mask) * image + mask * predicted_image
inpainted = np.transpose(inpainted, (0, 2, 3, 1)) * 255
return inpainted
def create_mask(img, radius=20):
drawing = False # True if the mouse is pressed
counter = 0
# Mouse callback function
def draw_circle(event, x, y, flags, param):
nonlocal drawing, counter, radius
if event == cv.EVENT_LBUTTONDOWN:
drawing = True if counter % 2 == 0 else False
counter += 1
cv.circle(img, (x, y), radius, (255, 255, 255), -1)
cv.circle(mask, (x, y), radius, 255, -1)
elif event == cv.EVENT_MOUSEMOVE:
if drawing:
cv.circle(img, (x, y), radius, (255, 255, 255), -1)
cv.circle(mask, (x, y), radius, 255, -1)
mask = np.zeros((img.shape[0], img.shape[1]), np.uint8)
cv.namedWindow('image')
cv.setMouseCallback('image', draw_circle)
while True:
cv.imshow('image', img)
if cv.waitKey(1) & 0xFF == 27: # Press 'ESC' to exit
break
cv.destroyAllWindows()
return mask
def main(args):
image = cv.imread(args.image)
mask = create_mask(deepcopy(image))
image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
batch = make_batch(image, mask)
image, mask, masked_image = batch["image"], batch["mask"], batch["masked_image"]
model = DDIMInpainter(args)
result = model(masked_image, mask, S=args.samples)
result = np.squeeze(result)
# save the result in the directore of args.image
cv.imwrite(args.image.replace(".png", "_inpainted.png"), result[..., ::-1])
if __name__ == '__main__':
args = parser.parse_args()
main(args)
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