diffusers/community-pipelines-mirror

1# Copyright 2022 The HuggingFace Team. All rights reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14 15from typing import List, Optional, Tuple, Union16 17import PIL.Image18import torch19from torchvision import transforms20 21from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput22from diffusers.schedulers import DDIMScheduler23from diffusers.utils.torch_utils import randn_tensor24 25 26trans = transforms.Compose(27    [28        transforms.Resize((256, 256)),29        transforms.ToTensor(),30        transforms.Normalize([0.5], [0.5]),31    ]32)33 34 35def preprocess(image):36    if isinstance(image, torch.Tensor):37        return image38    elif isinstance(image, PIL.Image.Image):39        image = [image]40 41    image = [trans(img.convert("RGB")) for img in image]42    image = torch.stack(image)43    return image44 45 46class DDIMNoiseComparativeAnalysisPipeline(DiffusionPipeline):47    r"""48    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the49    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)50 51    Parameters:52        unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.53        scheduler ([`SchedulerMixin`]):54            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of55            [`DDPMScheduler`], or [`DDIMScheduler`].56    """57 58    def __init__(self, unet, scheduler):59        super().__init__()60 61        # make sure scheduler can always be converted to DDIM62        scheduler = DDIMScheduler.from_config(scheduler.config)63 64        self.register_modules(unet=unet, scheduler=scheduler)65 66    def check_inputs(self, strength):67        if strength < 0 or strength > 1:68            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")69 70    def get_timesteps(self, num_inference_steps, strength, device):71        # get the original timestep using init_timestep72        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)73 74        t_start = max(num_inference_steps - init_timestep, 0)75        timesteps = self.scheduler.timesteps[t_start:]76 77        return timesteps, num_inference_steps - t_start78 79    def prepare_latents(self, image, timestep, batch_size, dtype, device, generator=None):80        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):81            raise ValueError(82                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"83            )84 85        init_latents = image.to(device=device, dtype=dtype)86 87        if isinstance(generator, list) and len(generator) != batch_size:88            raise ValueError(89                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"90                f" size of {batch_size}. Make sure the batch size matches the length of the generators."91            )92 93        shape = init_latents.shape94        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)95 96        # get latents97        print("add noise to latents at timestep", timestep)98        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)99        latents = init_latents100 101        return latents102 103    @torch.no_grad()104    def __call__(105        self,106        image: Union[torch.Tensor, PIL.Image.Image] = None,107        strength: float = 0.8,108        batch_size: int = 1,109        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,110        eta: float = 0.0,111        num_inference_steps: int = 50,112        use_clipped_model_output: Optional[bool] = None,113        output_type: Optional[str] = "pil",114        return_dict: bool = True,115    ) -> Union[ImagePipelineOutput, Tuple]:116        r"""117        Args:118            image (`torch.Tensor` or `PIL.Image.Image`):119                `Image`, or tensor representing an image batch, that will be used as the starting point for the120                process.121            strength (`float`, *optional*, defaults to 0.8):122                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`123                will be used as a starting point, adding more noise to it the larger the `strength`. The number of124                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will125                be maximum and the denoising process will run for the full number of iterations specified in126                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.127            batch_size (`int`, *optional*, defaults to 1):128                The number of images to generate.129            generator (`torch.Generator`, *optional*):130                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)131                to make generation deterministic.132            eta (`float`, *optional*, defaults to 0.0):133                The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).134            num_inference_steps (`int`, *optional*, defaults to 50):135                The number of denoising steps. More denoising steps usually lead to a higher quality image at the136                expense of slower inference.137            use_clipped_model_output (`bool`, *optional*, defaults to `None`):138                if `True` or `False`, see documentation for `DDIMScheduler.step`. If `None`, nothing is passed139                downstream to the scheduler. So use `None` for schedulers which don't support this argument.140            output_type (`str`, *optional*, defaults to `"pil"`):141                The output format of the generate image. Choose between142                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.143            return_dict (`bool`, *optional*, defaults to `True`):144                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.145 146        Returns:147            [`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is148            True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.149        """150        # 1. Check inputs. Raise error if not correct151        self.check_inputs(strength)152 153        # 2. Preprocess image154        image = preprocess(image)155 156        # 3. set timesteps157        self.scheduler.set_timesteps(num_inference_steps, device=self.device)158        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, self.device)159        latent_timestep = timesteps[:1].repeat(batch_size)160 161        # 4. Prepare latent variables162        latents = self.prepare_latents(image, latent_timestep, batch_size, self.unet.dtype, self.device, generator)163        image = latents164 165        # 5. Denoising loop166        for t in self.progress_bar(timesteps):167            # 1. predict noise model_output168            model_output = self.unet(image, t).sample169 170            # 2. predict previous mean of image x_t-1 and add variance depending on eta171            # eta corresponds to η in paper and should be between [0, 1]172            # do x_t -> x_t-1173            image = self.scheduler.step(174                model_output,175                t,176                image,177                eta=eta,178                use_clipped_model_output=use_clipped_model_output,179                generator=generator,180            ).prev_sample181 182        image = (image / 2 + 0.5).clamp(0, 1)183        image = image.cpu().permute(0, 2, 3, 1).numpy()184        if output_type == "pil":185            image = self.numpy_to_pil(image)186 187        if not return_dict:188            return (image, latent_timestep.item())189 190        return ImagePipelineOutput(images=image)191