pipeline.py

from typing import Optional, Union, List, Tuple, Callable

import torch
from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class KarrasEDMConditionalPipeline(DiffusionPipeline):
    r"""
    Pipeline for unconditional or class-conditional image generation based on the EDM model from [1].
    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.).
    [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
    https://arxiv.org/abs/2206.00364
    Args:
        unet ([`UNet2DModel`]):
            A `UNet2DModel` to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Currently only
            supports KarrasEDMScheduler.
    """

    model_cpu_offload_seq = "unet"

    def __init__(self, unet, scheduler) -> None:
        super().__init__()

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

        self.safety_checker = None

    def prepare_latents(self, batch_size, num_channels, height, width, dtype, device, generator, latents=None):
        shape = (batch_size, num_channels, height, width)
        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."
            )

        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            latents = latents.to(device=device, dtype=dtype)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    # Follows diffusers.VaeImageProcessor.postprocess
    def postprocess_image(self, sample: torch.FloatTensor, output_type: str = "pil"):
        if output_type not in ["pt", "np", "pil"]:
            raise ValueError(
                f"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"
            )

        # Equivalent to diffusers.VaeImageProcessor.denormalize
        sample = (sample / 2 + 0.5).clamp(0, 1)
        if output_type == "pt":
            return sample

        # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
        sample = sample.cpu().permute(0, 2, 3, 1).numpy()
        if output_type == "np":
            return sample

        # Output_type must be 'pil'
        sample = self.numpy_to_pil(sample)
        return sample

    def check_inputs(self, num_inference_steps, timesteps, latents, batch_size, img_size, callback_steps):
        if num_inference_steps is None and timesteps is None:
            raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied.")

        if num_inference_steps is not None and timesteps is not None:
            logger.warning(
                f"Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"
                " `timesteps` will be used over `num_inference_steps`."
            )

        if latents is not None:
            expected_shape = (batch_size, 3, img_size, img_size)
            if latents.shape != expected_shape:
                raise ValueError(f"The shape of latents is {latents.shape} but is expected to be {expected_shape}.")

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

    @torch.no_grad()
    def __call__(
        self,
        batch_size: int = 1,
        num_inference_steps: int = 1,
        timesteps: List[int] = None,
        class_labels: Optional[torch.Tensor] = None,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: int = 1,
    ):
        r"""
        Args:
            batch_size (`int`, *optional*, defaults to 1):
                The number of images to generate.
            class_labels (`torch.Tensor` or `List[int]` or `int`, *optional*):
                Optional class labels for conditioning class-conditional consistency models. Not used if the model is
                not class-conditional.
            num_inference_steps (`int`, *optional*, defaults to 1):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            timesteps (`List[int]`, *optional*):
                Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
                timesteps are used. Must be in descending order.
            generator (`torch.Generator`, *optional*):
                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                generation deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor is generated by sampling using the supplied random `generator`.
            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 [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
            callback (`Callable`, *optional*):
                A function that calls every `callback_steps` steps during inference. The function is called with the
                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function is called. If not specified, the callback is called at
                every step.
        Examples:
        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.
        """
        # 0. Prepare call parameters
        img_size = self.unet.config.sample_size
        device = self._execution_device



        # 2. Prepare image latents
        # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
        sample = self.prepare_latents(
            batch_size=batch_size,
            num_channels=self.unet.config.in_channels,
            height=img_size,
            width=img_size,
            dtype=self.unet.dtype,
            device=device,
            generator=generator,
            latents=latents,
        )

        # 4. Prepare timesteps
        if timesteps is not None:
            self.scheduler.set_timesteps(timesteps=timesteps, device=device)
            timesteps = self.scheduler.timesteps
            num_inference_steps = len(timesteps)
        else:
            self.scheduler.set_timesteps(num_inference_steps)
            timesteps = self.scheduler.timesteps

        # 5. Denoising loop
        # Implements the "EDM" column in Table 1 of the EDM paper
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order

        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # 1. Add noise (if necessary) and precondition the input sample.
                scaled_sample = self.scheduler.scale_model_input(sample, t)

                if self.scheduler.step_index is None:
                    self.scheduler._init_step_index(t)

                sigma = self.scheduler.sigmas[self.scheduler.step_index]

                sigma_input = self.scheduler.precondition_noise(sigma)

                # 2. Evaluate neural network at higher noise level (sample_hat, sigma_hat).
                model_output = self.unet(scaled_sample, sigma_input.squeeze(), class_labels, return_dict=False)[0]

                # 3. Apply output preconditioning on model_output to get denoiser output
                # 4. Take either a first order (Euler) step or second order (Heun) step
                sample = self.scheduler.step(model_output, t, sample).prev_sample

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, t, latents)

        # 6. Post-process image sample
        image = self.postprocess_image(sample, output_type=output_type)

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=image)