diffusers/examples/community/ddim_noise_comparative_analysis.py

# Copyright 2022 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 PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


trans = transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def preprocess(image):
    if isinstance(image, torch.Tensor):
        return image
    elif isinstance(image, PIL.Image.Image):
        image = [image]

    image = [trans(img.convert("RGB")) for img in image]
    image = torch.stack(image)
    return image


class DDIMNoiseComparativeAnalysisPipeline(DiffusionPipeline):
    r"""
    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    Parameters:
        unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
            [`DDPMScheduler`], or [`DDIMScheduler`].
    """

    def __init__(self, unet, scheduler):
        super().__init__()

        # make sure scheduler can always be converted to DDIM
        scheduler = DDIMScheduler.from_config(scheduler.config)

        self.register_modules(unet=unet, scheduler=scheduler)

    def check_inputs(self, strength):
        if strength < 0 or strength > 1:
            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")

    def get_timesteps(self, num_inference_steps, strength, device):
        # get the original timestep using init_timestep
        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)

        t_start = max(num_inference_steps - init_timestep, 0)
        timesteps = self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start

    def prepare_latents(self, image, timestep, batch_size, dtype, device, generator=None):
        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
            raise ValueError(
                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
            )

        init_latents = image.to(device=device, dtype=dtype)

        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."
            )

        shape = init_latents.shape
        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)

        # get latents
        print("add noise to latents at timestep", timestep)
        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
        latents = init_latents

        return latents

    @torch.no_grad()
    def __call__(
        self,
        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
        strength: float = 0.8,
        batch_size: int = 1,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        eta: float = 0.0,
        num_inference_steps: int = 50,
        use_clipped_model_output: Optional[bool] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
    ) -> Union[ImagePipelineOutput, Tuple]:
        r"""
        Args:
            image (`torch.FloatTensor` or `PIL.Image.Image`):
                `Image`, or tensor representing an image batch, that will be used as the starting point for the
                process.
            strength (`float`, *optional*, defaults to 0.8):
                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
                be maximum and the denoising process will run for the full number of iterations specified in
                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
            batch_size (`int`, *optional*, defaults to 1):
                The number of images to generate.
            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.
            eta (`float`, *optional*, defaults to 0.0):
                The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            use_clipped_model_output (`bool`, *optional*, defaults to `None`):
                if `True` or `False`, see documentation for `DDIMScheduler.step`. If `None`, nothing is passed
                downstream to the scheduler. So use `None` for schedulers which don't support this argument.
            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.
        """
        # 1. Check inputs. Raise error if not correct
        self.check_inputs(strength)

        # 2. Preprocess image
        image = preprocess(image)

        # 3. set timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=self.device)
        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, self.device)
        latent_timestep = timesteps[:1].repeat(batch_size)

        # 4. Prepare latent variables
        latents = self.prepare_latents(image, latent_timestep, batch_size, self.unet.dtype, self.device, generator)
        image = latents

        # 5. Denoising loop
        for t in self.progress_bar(timesteps):
            # 1. predict noise model_output
            model_output = self.unet(image, t).sample

            # 2. predict previous mean of image x_t-1 and add variance depending on eta
            # eta corresponds to η in paper and should be between [0, 1]
            # do x_t -> x_t-1
            image = self.scheduler.step(
                model_output,
                t,
                image,
                eta=eta,
                use_clipped_model_output=use_clipped_model_output,
                generator=generator,
            ).prev_sample

        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, latent_timestep.item())

        return ImagePipelineOutput(images=image)