# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2022 ETH Zurich Computer Vision Lab and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union
import numpy as np
import paddle
import PIL
from ...models import UNet2DModel
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
def _preprocess_image(image: Union[List, PIL.Image.Image, paddle.Tensor]):
if isinstance(image, paddle.Tensor):
return image
elif isinstance(image, PIL.Image.Image):
image = [image]
if isinstance(image[0], PIL.Image.Image):
w, h = image[0].size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
image = np.concatenate(image, axis=0)
image = np.array(image).astype(np.float32) / 255.0
image = image.transpose(0, 3, 1, 2)
image = 2.0 * image - 1.0
image = paddle.to_tensor(image)
elif isinstance(image[0], paddle.Tensor):
image = paddle.concat(image, axis=0)
return image
def _preprocess_mask(mask: Union[List, PIL.Image.Image, paddle.Tensor]):
if isinstance(mask, paddle.Tensor):
return mask
elif isinstance(mask, PIL.Image.Image):
mask = [mask]
if isinstance(mask[0], PIL.Image.Image):
w, h = mask[0].size
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
mask = [np.array(m.convert("L").resize((w, h), resample=PIL_INTERPOLATION["nearest"]))[None, :] for m in mask]
mask = np.concatenate(mask, axis=0)
mask = mask.astype(np.float32) / 255.0
mask[mask < 0.5] = 0
mask[mask >= 0.5] = 1
mask = paddle.to_tensor(mask)
elif isinstance(mask[0], paddle.Tensor):
mask = paddle.concat(mask, axis=0)
return mask
class RePaintPipeline(DiffusionPipeline):
unet: UNet2DModel
scheduler: RePaintScheduler
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@paddle.no_grad()
def __call__(
self,
image: Union[paddle.Tensor, PIL.Image.Image],
mask_image: Union[paddle.Tensor, PIL.Image.Image],
num_inference_steps: int = 250,
eta: float = 0.0,
jump_length: int = 10,
jump_n_sample: int = 10,
generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
Args:
image (`paddle.Tensor` or `PIL.Image.Image`):
The original image to inpaint on.
mask_image (`paddle.Tensor` or `PIL.Image.Image`):
The mask_image where 0.0 values define which part of the original image to inpaint (change).
num_inference_steps (`int`, *optional*, defaults to 1000):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
eta (`float`):
The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 - 0.0 is DDIM
and 1.0 is DDPM scheduler respectively.
jump_length (`int`, *optional*, defaults to 10):
The number of steps taken forward in time before going backward in time for a single jump ("j" in
RePaint paper). Take a look at Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
jump_n_sample (`int`, *optional*, defaults to 10):
The number of times we will make forward time jump for a given chosen time sample. Take a look at
Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
generator (`paddle.Generator`, *optional*):
One or a list of paddle generator(s) to make generation deterministic.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
generated images.
"""
original_image = _preprocess_image(image)
original_image = original_image.cast(self.unet.dtype)
mask_image = _preprocess_mask(mask_image)
mask_image = mask_image.cast(self.unet.dtype)
batch_size = original_image.shape[0]
# sample gaussian noise to begin the loop
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
image_shape = original_image.shape
if isinstance(generator, list):
shape = (1,) + image_shape[1:]
image = [paddle.randn(shape, generator=generator[i], dtype=self.unet.dtype) for i in range(batch_size)]
image = paddle.concat(image, axis=0)
else:
image = paddle.randn(image_shape, generator=generator, dtype=self.unet.dtype)
# set step values
self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample)
self.scheduler.eta = eta
t_last = self.scheduler.timesteps[0] + 1
generator = generator[0] if isinstance(generator, list) else generator
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
if t < t_last:
# predict the noise residual
model_output = self.unet(image, t).sample
# compute previous image: x_t -> x_t-1
image = self.scheduler.step(model_output, t, image, original_image, mask_image, generator).prev_sample
else:
# compute the reverse: x_t-1 -> x_t
image = self.scheduler.undo_step(image, t_last, generator)
t_last = t
image = (image / 2 + 0.5).clip(0, 1)
image = image.transpose([0, 2, 3, 1]).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image)