diffusers/pipelines/deprecated/repaint/pipeline_repaint.py

# Copyright 2024 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.Image
import torch

from ....models import UNet2DModel
from ....schedulers import RePaintScheduler
from ....utils import PIL_INTERPOLATION, deprecate, logging
from ....utils.torch_utils import 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]):
    deprecation_message = "The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead"
    deprecate("preprocess", "1.0.0", deprecation_message, standard_warn=False)
    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):
    r"""

    Pipeline for image inpainting using RePaint.



    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods

    implemented for all pipelines (downloading, saving, running on a particular device, etc.).



    Parameters:

        unet ([`UNet2DModel`]):

            A `UNet2DModel` to denoise the encoded image latents.

        scheduler ([`RePaintScheduler`]):

            A `RePaintScheduler` to be used in combination with `unet` to denoise the encoded image.

    """

    unet: UNet2DModel
    scheduler: RePaintScheduler
    model_cpu_offload_seq = "unet"

    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"""

        The call function to the pipeline for generation.



        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 define which part of the original image to inpaint.

            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 the added noise in a diffusion step. Its value is between 0.0 and 1.0; 0.0 corresponds to

                DDIM and 1.0 is the DDPM scheduler.

            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 the [paper](https://arxiv.org/pdf/2201.09865.pdf).

            jump_n_sample (`int`, *optional*, defaults to 10):

                The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9

                and 10 in the [paper](https://arxiv.org/pdf/2201.09865.pdf).

            generator (`torch.Generator`, *optional*):

                A [`torch.Generator`](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 generated image. Choose between `PIL.Image` or `np.array`.

            return_dict (`bool`, *optional*, defaults to `True`):

                Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.



        Example:



        ```py

        >>> from io import BytesIO

        >>> import torch

        >>> import PIL

        >>> import requests

        >>> from diffusers import RePaintPipeline, RePaintScheduler





        >>> def download_image(url):

        ...     response = requests.get(url)

        ...     return PIL.Image.open(BytesIO(response.content)).convert("RGB")





        >>> img_url = "https://huggingface.co./datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png"

        >>> mask_url = "https://huggingface.co./datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"



        >>> # Load the original image and the mask as PIL images

        >>> original_image = download_image(img_url).resize((256, 256))

        >>> mask_image = download_image(mask_url).resize((256, 256))



        >>> # Load the RePaint scheduler and pipeline based on a pretrained DDPM model

        >>> scheduler = RePaintScheduler.from_pretrained("google/ddpm-ema-celebahq-256")

        >>> pipe = RePaintPipeline.from_pretrained("google/ddpm-ema-celebahq-256", scheduler=scheduler)

        >>> pipe = pipe.to("cuda")



        >>> generator = torch.Generator(device="cuda").manual_seed(0)

        >>> output = pipe(

        ...     image=original_image,

        ...     mask_image=mask_image,

        ...     num_inference_steps=250,

        ...     eta=0.0,

        ...     jump_length=10,

        ...     jump_n_sample=10,

        ...     generator=generator,

        ... )

        >>> inpainted_image = output.images[0]

        ```



        Returns:

            [`~pipelines.ImagePipelineOutput`] or `tuple`:

                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is

                returned where 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._execution_device, dtype=self.unet.dtype)
        mask_image = _preprocess_mask(mask_image)
        mask_image = mask_image.to(device=self._execution_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._execution_device, dtype=self.unet.dtype)

        # set step values
        self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, self._execution_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)