# 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)