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pipeline_repaint.py
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pipeline_repaint.py
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# Copyright 2023 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 PIL
import torch
from ...models import UNet2DModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
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, torch.Tensor]):
if isinstance(image, torch.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 = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
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 = torch.from_numpy(image)
elif isinstance(image[0], torch.Tensor):
image = torch.cat(image, dim=0)
return image
def _preprocess_mask(mask: Union[List, PIL.Image.Image, torch.Tensor]):
if isinstance(mask, torch.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 = (x - x % 32 for x in (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 = torch.from_numpy(mask)
elif isinstance(mask[0], torch.Tensor):
mask = torch.cat(mask, dim=0)
return mask
class RePaintPipeline(DiffusionPipeline):
unet: UNet2DModel
scheduler: RePaintScheduler
def __init__(self, unet, scheduler):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler)
@torch.no_grad()
def __call__(
self,
image: Union[torch.Tensor, PIL.Image.Image],
mask_image: Union[torch.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[torch.Generator, List[torch.Generator]]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
) -> Union[ImagePipelineOutput, Tuple]:
r"""
Args:
image (`torch.FloatTensor` or `PIL.Image.Image`):
The original image to inpaint on.
mask_image (`torch.FloatTensor` 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 (`torch.Generator`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
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 [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
Returns:
[`~pipelines.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 = image
original_image = _preprocess_image(original_image)
original_image = original_image.to(device=self.device, dtype=self.unet.dtype)
mask_image = _preprocess_mask(mask_image)
mask_image = mask_image.to(device=self.device, dtype=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
image = randn_tensor(image_shape, generator=generator, device=self.device, dtype=self.unet.dtype)
# set step values
self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, self.device)
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).clamp(0, 1)
image = image.cpu().permute(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)