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| # 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.Image | |
| import torch | |
| from torchvision import transforms | |
| from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput | |
| from diffusers.schedulers import DDIMScheduler | |
| from diffusers.utils.torch_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 | |
| def __call__( | |
| self, | |
| image: Union[torch.Tensor, 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.Tensor` 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) | |