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diffuse-custom
/
diffusers
/pipelines
/latent_diffusion
/pipeline_latent_diffusion_superresolution.py
| 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) | |
| 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) | |