import inspect from typing import Optional, Tuple, Union import numpy as np import torch import torch.utils.checkpoint import PIL from ...models import UNet2DModel, VQModel from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput from ...schedulers import ( DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, ) from ...utils import PIL_INTERPOLATION, deprecate def preprocess(image): w, h = image.size w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) image = np.array(image).astype(np.float32) / 255.0 image = image[None].transpose(0, 3, 1, 2) image = torch.from_numpy(image) return 2.0 * image - 1.0 class LDMSuperResolutionPipeline(DiffusionPipeline): r""" A pipeline for image super-resolution using Latent This class 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: vqvae ([`VQModel`]): Vector-quantized (VQ) VAE Model to encode and decode images to and from latent representations. 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 latens. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`], [`DPMSolverMultistepScheduler`], or [`PNDMScheduler`]. """ def __init__( self, vqvae: VQModel, unet: UNet2DModel, scheduler: Union[ DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler, ], ): super().__init__() self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler) @torch.no_grad() def __call__( self, image: Union[torch.Tensor, PIL.Image.Image], batch_size: Optional[int] = 1, num_inference_steps: Optional[int] = 100, eta: Optional[float] = 0.0, generator: Optional[torch.Generator] = None, output_type: Optional[str] = "pil", return_dict: bool = True, **kwargs, ) -> Union[Tuple, ImagePipelineOutput]: r""" Args: image (`torch.Tensor` or `PIL.Image.Image`): `Image`, or tensor representing an image batch, that will be used as the starting point for the process. batch_size (`int`, *optional*, defaults to 1): Number of images to generate. num_inference_steps (`int`, *optional*, defaults to 100): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. 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 generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*): 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. """ message = "Please use `image` instead of `init_image`." init_image = deprecate("init_image", "0.12.0", message, take_from=kwargs) image = init_image or image if isinstance(image, PIL.Image.Image): batch_size = 1 elif isinstance(image, torch.Tensor): batch_size = image.shape[0] else: raise ValueError(f"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(image)}") if isinstance(image, PIL.Image.Image): image = preprocess(image) height, width = image.shape[-2:] # in_channels should be 6: 3 for latents, 3 for low resolution image latents_shape = (batch_size, self.unet.in_channels // 2, height, width) latents_dtype = next(self.unet.parameters()).dtype if self.device.type == "mps": # randn does not work reproducibly on mps latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype) latents = latents.to(self.device) else: latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype) image = image.to(device=self.device, dtype=latents_dtype) # set timesteps and move to the correct device self.scheduler.set_timesteps(num_inference_steps, device=self.device) timesteps_tensor = self.scheduler.timesteps # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature. # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_kwargs = {} if accepts_eta: extra_kwargs["eta"] = eta for t in self.progress_bar(timesteps_tensor): # concat latents and low resolution image in the channel dimension. latents_input = torch.cat([latents, image], dim=1) latents_input = self.scheduler.scale_model_input(latents_input, t) # predict the noise residual noise_pred = self.unet(latents_input, t).sample # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample # decode the image latents with the VQVAE image = self.vqvae.decode(latents).sample image = torch.clamp(image, -1.0, 1.0) image = image / 2 + 0.5 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)