Spaces:
Running
on
Zero
Running
on
Zero
| # Copyright 2024 Stanford University Team and 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. | |
| # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion | |
| # and https://github.com/hojonathanho/diffusion | |
| import math | |
| from dataclasses import dataclass | |
| from typing import List, Optional, Tuple, Union | |
| import numpy as np | |
| import torch | |
| from diffusers.configuration_utils import ConfigMixin, register_to_config | |
| from diffusers.schedulers.scheduling_utils import SchedulerMixin | |
| from diffusers.utils import BaseOutput, logging | |
| from diffusers.utils.torch_utils import randn_tensor | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| class TCDSchedulerOutput(BaseOutput): | |
| """ | |
| Output class for the scheduler's `step` function output. | |
| Args: | |
| prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): | |
| Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the | |
| denoising loop. | |
| pred_noised_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): | |
| The predicted noised sample `(x_{s})` based on the model output from the current timestep. | |
| """ | |
| prev_sample: torch.FloatTensor | |
| pred_noised_sample: Optional[torch.FloatTensor] = None | |
| # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar | |
| def betas_for_alpha_bar( | |
| num_diffusion_timesteps, | |
| max_beta=0.999, | |
| alpha_transform_type="cosine", | |
| ): | |
| """ | |
| Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of | |
| (1-beta) over time from t = [0,1]. | |
| Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up | |
| to that part of the diffusion process. | |
| Args: | |
| num_diffusion_timesteps (`int`): the number of betas to produce. | |
| max_beta (`float`): the maximum beta to use; use values lower than 1 to | |
| prevent singularities. | |
| alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. | |
| Choose from `cosine` or `exp` | |
| Returns: | |
| betas (`np.ndarray`): the betas used by the scheduler to step the model outputs | |
| """ | |
| if alpha_transform_type == "cosine": | |
| def alpha_bar_fn(t): | |
| return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2 | |
| elif alpha_transform_type == "exp": | |
| def alpha_bar_fn(t): | |
| return math.exp(t * -12.0) | |
| else: | |
| raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}") | |
| betas = [] | |
| for i in range(num_diffusion_timesteps): | |
| t1 = i / num_diffusion_timesteps | |
| t2 = (i + 1) / num_diffusion_timesteps | |
| betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta)) | |
| return torch.tensor(betas, dtype=torch.float32) | |
| # Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr | |
| def rescale_zero_terminal_snr(betas: torch.FloatTensor) -> torch.FloatTensor: | |
| """ | |
| Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1) | |
| Args: | |
| betas (`torch.FloatTensor`): | |
| the betas that the scheduler is being initialized with. | |
| Returns: | |
| `torch.FloatTensor`: rescaled betas with zero terminal SNR | |
| """ | |
| # Convert betas to alphas_bar_sqrt | |
| alphas = 1.0 - betas | |
| alphas_cumprod = torch.cumprod(alphas, dim=0) | |
| alphas_bar_sqrt = alphas_cumprod.sqrt() | |
| # Store old values. | |
| alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() | |
| alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() | |
| # Shift so the last timestep is zero. | |
| alphas_bar_sqrt -= alphas_bar_sqrt_T | |
| # Scale so the first timestep is back to the old value. | |
| alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) | |
| # Convert alphas_bar_sqrt to betas | |
| alphas_bar = alphas_bar_sqrt**2 # Revert sqrt | |
| alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod | |
| alphas = torch.cat([alphas_bar[0:1], alphas]) | |
| betas = 1 - alphas | |
| return betas | |
| class TCDScheduler(SchedulerMixin, ConfigMixin): | |
| """ | |
| `TCDScheduler` incorporates the `Strategic Stochastic Sampling` introduced by the paper `Trajectory Consistency Distillation`, | |
| extending the original Multistep Consistency Sampling to enable unrestricted trajectory traversal. | |
| This code is based on the official repo of TCD(https://github.com/jabir-zheng/TCD). | |
| This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. [`~ConfigMixin`] takes care of storing all config | |
| attributes that are passed in the scheduler's `__init__` function, such as `num_train_timesteps`. They can be | |
| accessed via `scheduler.config.num_train_timesteps`. [`SchedulerMixin`] provides general loading and saving | |
| functionality via the [`SchedulerMixin.save_pretrained`] and [`~SchedulerMixin.from_pretrained`] functions. | |
| Args: | |
| num_train_timesteps (`int`, defaults to 1000): | |
| The number of diffusion steps to train the model. | |
| beta_start (`float`, defaults to 0.0001): | |
| The starting `beta` value of inference. | |
| beta_end (`float`, defaults to 0.02): | |
| The final `beta` value. | |
| beta_schedule (`str`, defaults to `"linear"`): | |
| The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from | |
| `linear`, `scaled_linear`, or `squaredcos_cap_v2`. | |
| trained_betas (`np.ndarray`, *optional*): | |
| Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`. | |
| original_inference_steps (`int`, *optional*, defaults to 50): | |
| The default number of inference steps used to generate a linearly-spaced timestep schedule, from which we | |
| will ultimately take `num_inference_steps` evenly spaced timesteps to form the final timestep schedule. | |
| clip_sample (`bool`, defaults to `True`): | |
| Clip the predicted sample for numerical stability. | |
| clip_sample_range (`float`, defaults to 1.0): | |
| The maximum magnitude for sample clipping. Valid only when `clip_sample=True`. | |
| set_alpha_to_one (`bool`, defaults to `True`): | |
| Each diffusion step uses the alphas product value at that step and at the previous one. For the final step | |
| there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`, | |
| otherwise it uses the alpha value at step 0. | |
| steps_offset (`int`, defaults to 0): | |
| An offset added to the inference steps, as required by some model families. | |
| prediction_type (`str`, defaults to `epsilon`, *optional*): | |
| Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process), | |
| `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen | |
| Video](https://imagen.research.google/video/paper.pdf) paper). | |
| thresholding (`bool`, defaults to `False`): | |
| Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such | |
| as Stable Diffusion. | |
| dynamic_thresholding_ratio (`float`, defaults to 0.995): | |
| The ratio for the dynamic thresholding method. Valid only when `thresholding=True`. | |
| sample_max_value (`float`, defaults to 1.0): | |
| The threshold value for dynamic thresholding. Valid only when `thresholding=True`. | |
| timestep_spacing (`str`, defaults to `"leading"`): | |
| The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and | |
| Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. | |
| timestep_scaling (`float`, defaults to 10.0): | |
| The factor the timesteps will be multiplied by when calculating the consistency model boundary conditions | |
| `c_skip` and `c_out`. Increasing this will decrease the approximation error (although the approximation | |
| error at the default of `10.0` is already pretty small). | |
| rescale_betas_zero_snr (`bool`, defaults to `False`): | |
| Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and | |
| dark samples instead of limiting it to samples with medium brightness. Loosely related to | |
| [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506). | |
| """ | |
| order = 1 | |
| def __init__( | |
| self, | |
| num_train_timesteps: int = 1000, | |
| beta_start: float = 0.00085, | |
| beta_end: float = 0.012, | |
| beta_schedule: str = "scaled_linear", | |
| trained_betas: Optional[Union[np.ndarray, List[float]]] = None, | |
| original_inference_steps: int = 50, | |
| clip_sample: bool = False, | |
| clip_sample_range: float = 1.0, | |
| set_alpha_to_one: bool = True, | |
| steps_offset: int = 0, | |
| prediction_type: str = "epsilon", | |
| thresholding: bool = False, | |
| dynamic_thresholding_ratio: float = 0.995, | |
| sample_max_value: float = 1.0, | |
| timestep_spacing: str = "leading", | |
| timestep_scaling: float = 10.0, | |
| rescale_betas_zero_snr: bool = False, | |
| ): | |
| if trained_betas is not None: | |
| self.betas = torch.tensor(trained_betas, dtype=torch.float32) | |
| elif beta_schedule == "linear": | |
| self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) | |
| elif beta_schedule == "scaled_linear": | |
| # this schedule is very specific to the latent diffusion model. | |
| self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 | |
| elif beta_schedule == "squaredcos_cap_v2": | |
| # Glide cosine schedule | |
| self.betas = betas_for_alpha_bar(num_train_timesteps) | |
| else: | |
| raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}") | |
| # Rescale for zero SNR | |
| if rescale_betas_zero_snr: | |
| self.betas = rescale_zero_terminal_snr(self.betas) | |
| self.alphas = 1.0 - self.betas | |
| self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) | |
| # At every step in ddim, we are looking into the previous alphas_cumprod | |
| # For the final step, there is no previous alphas_cumprod because we are already at 0 | |
| # `set_alpha_to_one` decides whether we set this parameter simply to one or | |
| # whether we use the final alpha of the "non-previous" one. | |
| self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0] | |
| # standard deviation of the initial noise distribution | |
| self.init_noise_sigma = 1.0 | |
| # setable values | |
| self.num_inference_steps = None | |
| self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64)) | |
| self.custom_timesteps = False | |
| self._step_index = None | |
| self._begin_index = None | |
| # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep | |
| def index_for_timestep(self, timestep, schedule_timesteps=None): | |
| if schedule_timesteps is None: | |
| schedule_timesteps = self.timesteps | |
| indices = (schedule_timesteps == timestep).nonzero() | |
| # The sigma index that is taken for the **very** first `step` | |
| # is always the second index (or the last index if there is only 1) | |
| # This way we can ensure we don't accidentally skip a sigma in | |
| # case we start in the middle of the denoising schedule (e.g. for image-to-image) | |
| pos = 1 if len(indices) > 1 else 0 | |
| return indices[pos].item() | |
| # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index | |
| def _init_step_index(self, timestep): | |
| if self.begin_index is None: | |
| if isinstance(timestep, torch.Tensor): | |
| timestep = timestep.to(self.timesteps.device) | |
| self._step_index = self.index_for_timestep(timestep) | |
| else: | |
| self._step_index = self._begin_index | |
| def step_index(self): | |
| return self._step_index | |
| def begin_index(self): | |
| """ | |
| The index for the first timestep. It should be set from pipeline with `set_begin_index` method. | |
| """ | |
| return self._begin_index | |
| # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index | |
| def set_begin_index(self, begin_index: int = 0): | |
| """ | |
| Sets the begin index for the scheduler. This function should be run from pipeline before the inference. | |
| Args: | |
| begin_index (`int`): | |
| The begin index for the scheduler. | |
| """ | |
| self._begin_index = begin_index | |
| def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor: | |
| """ | |
| Ensures interchangeability with schedulers that need to scale the denoising model input depending on the | |
| current timestep. | |
| Args: | |
| sample (`torch.FloatTensor`): | |
| The input sample. | |
| timestep (`int`, *optional*): | |
| The current timestep in the diffusion chain. | |
| Returns: | |
| `torch.FloatTensor`: | |
| A scaled input sample. | |
| """ | |
| return sample | |
| # Copied from diffusers.schedulers.scheduling_ddim.DDIMScheduler._get_variance | |
| def _get_variance(self, timestep, prev_timestep): | |
| alpha_prod_t = self.alphas_cumprod[timestep] | |
| alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod | |
| beta_prod_t = 1 - alpha_prod_t | |
| beta_prod_t_prev = 1 - alpha_prod_t_prev | |
| variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev) | |
| return variance | |
| # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample | |
| def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor: | |
| """ | |
| "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the | |
| prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by | |
| s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing | |
| pixels from saturation at each step. We find that dynamic thresholding results in significantly better | |
| photorealism as well as better image-text alignment, especially when using very large guidance weights." | |
| https://arxiv.org/abs/2205.11487 | |
| """ | |
| dtype = sample.dtype | |
| batch_size, channels, *remaining_dims = sample.shape | |
| if dtype not in (torch.float32, torch.float64): | |
| sample = sample.float() # upcast for quantile calculation, and clamp not implemented for cpu half | |
| # Flatten sample for doing quantile calculation along each image | |
| sample = sample.reshape(batch_size, channels * np.prod(remaining_dims)) | |
| abs_sample = sample.abs() # "a certain percentile absolute pixel value" | |
| s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1) | |
| s = torch.clamp( | |
| s, min=1, max=self.config.sample_max_value | |
| ) # When clamped to min=1, equivalent to standard clipping to [-1, 1] | |
| s = s.unsqueeze(1) # (batch_size, 1) because clamp will broadcast along dim=0 | |
| sample = torch.clamp(sample, -s, s) / s # "we threshold xt0 to the range [-s, s] and then divide by s" | |
| sample = sample.reshape(batch_size, channels, *remaining_dims) | |
| sample = sample.to(dtype) | |
| return sample | |
| def set_timesteps( | |
| self, | |
| num_inference_steps: Optional[int] = None, | |
| device: Union[str, torch.device] = None, | |
| original_inference_steps: Optional[int] = None, | |
| timesteps: Optional[List[int]] = None, | |
| strength: int = 1.0, | |
| ): | |
| """ | |
| Sets the discrete timesteps used for the diffusion chain (to be run before inference). | |
| Args: | |
| num_inference_steps (`int`, *optional*): | |
| The number of diffusion steps used when generating samples with a pre-trained model. If used, | |
| `timesteps` must be `None`. | |
| device (`str` or `torch.device`, *optional*): | |
| The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. | |
| original_inference_steps (`int`, *optional*): | |
| The original number of inference steps, which will be used to generate a linearly-spaced timestep | |
| schedule (which is different from the standard `diffusers` implementation). We will then take | |
| `num_inference_steps` timesteps from this schedule, evenly spaced in terms of indices, and use that as | |
| our final timestep schedule. If not set, this will default to the `original_inference_steps` attribute. | |
| timesteps (`List[int]`, *optional*): | |
| Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default | |
| timestep spacing strategy of equal spacing between timesteps on the training/distillation timestep | |
| schedule is used. If `timesteps` is passed, `num_inference_steps` must be `None`. | |
| """ | |
| # 0. Check inputs | |
| if num_inference_steps is None and timesteps is None: | |
| raise ValueError("Must pass exactly one of `num_inference_steps` or `custom_timesteps`.") | |
| if num_inference_steps is not None and timesteps is not None: | |
| raise ValueError("Can only pass one of `num_inference_steps` or `custom_timesteps`.") | |
| # 1. Calculate the TCD original training/distillation timestep schedule. | |
| original_steps = ( | |
| original_inference_steps if original_inference_steps is not None else self.config.original_inference_steps | |
| ) | |
| if original_inference_steps is None: | |
| # default option, timesteps align with discrete inference steps | |
| if original_steps > self.config.num_train_timesteps: | |
| raise ValueError( | |
| f"`original_steps`: {original_steps} cannot be larger than `self.config.train_timesteps`:" | |
| f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle" | |
| f" maximal {self.config.num_train_timesteps} timesteps." | |
| ) | |
| # TCD Timesteps Setting | |
| # The skipping step parameter k from the paper. | |
| k = self.config.num_train_timesteps // original_steps | |
| # TCD Training/Distillation Steps Schedule | |
| tcd_origin_timesteps = np.asarray(list(range(1, int(original_steps * strength) + 1))) * k - 1 | |
| else: | |
| # customised option, sampled timesteps can be any arbitrary value | |
| tcd_origin_timesteps = np.asarray(list(range(0, int(self.config.num_train_timesteps * strength)))) | |
| # 2. Calculate the TCD inference timestep schedule. | |
| if timesteps is not None: | |
| # 2.1 Handle custom timestep schedules. | |
| train_timesteps = set(tcd_origin_timesteps) | |
| non_train_timesteps = [] | |
| for i in range(1, len(timesteps)): | |
| if timesteps[i] >= timesteps[i - 1]: | |
| raise ValueError("`custom_timesteps` must be in descending order.") | |
| if timesteps[i] not in train_timesteps: | |
| non_train_timesteps.append(timesteps[i]) | |
| if timesteps[0] >= self.config.num_train_timesteps: | |
| raise ValueError( | |
| f"`timesteps` must start before `self.config.train_timesteps`:" | |
| f" {self.config.num_train_timesteps}." | |
| ) | |
| # Raise warning if timestep schedule does not start with self.config.num_train_timesteps - 1 | |
| if strength == 1.0 and timesteps[0] != self.config.num_train_timesteps - 1: | |
| logger.warning( | |
| f"The first timestep on the custom timestep schedule is {timesteps[0]}, not" | |
| f" `self.config.num_train_timesteps - 1`: {self.config.num_train_timesteps - 1}. You may get" | |
| f" unexpected results when using this timestep schedule." | |
| ) | |
| # Raise warning if custom timestep schedule contains timesteps not on original timestep schedule | |
| if non_train_timesteps: | |
| logger.warning( | |
| f"The custom timestep schedule contains the following timesteps which are not on the original" | |
| f" training/distillation timestep schedule: {non_train_timesteps}. You may get unexpected results" | |
| f" when using this timestep schedule." | |
| ) | |
| # Raise warning if custom timestep schedule is longer than original_steps | |
| if original_steps is not None: | |
| if len(timesteps) > original_steps: | |
| logger.warning( | |
| f"The number of timesteps in the custom timestep schedule is {len(timesteps)}, which exceeds the" | |
| f" the length of the timestep schedule used for training: {original_steps}. You may get some" | |
| f" unexpected results when using this timestep schedule." | |
| ) | |
| else: | |
| if len(timesteps) > self.config.num_train_timesteps: | |
| logger.warning( | |
| f"The number of timesteps in the custom timestep schedule is {len(timesteps)}, which exceeds the" | |
| f" the length of the timestep schedule used for training: {self.config.num_train_timesteps}. You may get some" | |
| f" unexpected results when using this timestep schedule." | |
| ) | |
| timesteps = np.array(timesteps, dtype=np.int64) | |
| self.num_inference_steps = len(timesteps) | |
| self.custom_timesteps = True | |
| # Apply strength (e.g. for img2img pipelines) (see StableDiffusionImg2ImgPipeline.get_timesteps) | |
| init_timestep = min(int(self.num_inference_steps * strength), self.num_inference_steps) | |
| t_start = max(self.num_inference_steps - init_timestep, 0) | |
| timesteps = timesteps[t_start * self.order :] | |
| # TODO: also reset self.num_inference_steps? | |
| else: | |
| # 2.2 Create the "standard" TCD inference timestep schedule. | |
| if num_inference_steps > self.config.num_train_timesteps: | |
| raise ValueError( | |
| f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:" | |
| f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle" | |
| f" maximal {self.config.num_train_timesteps} timesteps." | |
| ) | |
| if original_steps is not None: | |
| skipping_step = len(tcd_origin_timesteps) // num_inference_steps | |
| if skipping_step < 1: | |
| raise ValueError( | |
| f"The combination of `original_steps x strength`: {original_steps} x {strength} is smaller than `num_inference_steps`: {num_inference_steps}. Make sure to either reduce `num_inference_steps` to a value smaller than {int(original_steps * strength)} or increase `strength` to a value higher than {float(num_inference_steps / original_steps)}." | |
| ) | |
| self.num_inference_steps = num_inference_steps | |
| if original_steps is not None: | |
| if num_inference_steps > original_steps: | |
| raise ValueError( | |
| f"`num_inference_steps`: {num_inference_steps} cannot be larger than `original_inference_steps`:" | |
| f" {original_steps} because the final timestep schedule will be a subset of the" | |
| f" `original_inference_steps`-sized initial timestep schedule." | |
| ) | |
| else: | |
| if num_inference_steps > self.config.num_train_timesteps: | |
| raise ValueError( | |
| f"`num_inference_steps`: {num_inference_steps} cannot be larger than `num_train_timesteps`:" | |
| f" {self.config.num_train_timesteps} because the final timestep schedule will be a subset of the" | |
| f" `num_train_timesteps`-sized initial timestep schedule." | |
| ) | |
| # TCD Inference Steps Schedule | |
| tcd_origin_timesteps = tcd_origin_timesteps[::-1].copy() | |
| # Select (approximately) evenly spaced indices from tcd_origin_timesteps. | |
| inference_indices = np.linspace(0, len(tcd_origin_timesteps), num=num_inference_steps, endpoint=False) | |
| inference_indices = np.floor(inference_indices).astype(np.int64) | |
| timesteps = tcd_origin_timesteps[inference_indices] | |
| self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.long) | |
| self._step_index = None | |
| self._begin_index = None | |
| def step( | |
| self, | |
| model_output: torch.FloatTensor, | |
| timestep: int, | |
| sample: torch.FloatTensor, | |
| eta: float = 0.3, | |
| generator: Optional[torch.Generator] = None, | |
| return_dict: bool = True, | |
| ) -> Union[TCDSchedulerOutput, Tuple]: | |
| """ | |
| Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion | |
| process from the learned model outputs (most often the predicted noise). | |
| Args: | |
| model_output (`torch.FloatTensor`): | |
| The direct output from learned diffusion model. | |
| timestep (`int`): | |
| The current discrete timestep in the diffusion chain. | |
| sample (`torch.FloatTensor`): | |
| A current instance of a sample created by the diffusion process. | |
| eta (`float`): | |
| A stochastic parameter (referred to as `gamma` in the paper) used to control the stochasticity in every step. | |
| When eta = 0, it represents deterministic sampling, whereas eta = 1 indicates full stochastic sampling. | |
| generator (`torch.Generator`, *optional*): | |
| A random number generator. | |
| return_dict (`bool`, *optional*, defaults to `True`): | |
| Whether or not to return a [`~schedulers.scheduling_tcd.TCDSchedulerOutput`] or `tuple`. | |
| Returns: | |
| [`~schedulers.scheduling_utils.TCDSchedulerOutput`] or `tuple`: | |
| If return_dict is `True`, [`~schedulers.scheduling_tcd.TCDSchedulerOutput`] is returned, otherwise a | |
| tuple is returned where the first element is the sample tensor. | |
| """ | |
| if self.num_inference_steps is None: | |
| raise ValueError( | |
| "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" | |
| ) | |
| if self.step_index is None: | |
| self._init_step_index(timestep) | |
| assert 0 <= eta <= 1.0, "gamma must be less than or equal to 1.0" | |
| # 1. get previous step value | |
| prev_step_index = self.step_index + 1 | |
| if prev_step_index < len(self.timesteps): | |
| prev_timestep = self.timesteps[prev_step_index] | |
| else: | |
| prev_timestep = torch.tensor(0) | |
| timestep_s = torch.floor((1 - eta) * prev_timestep).to(dtype=torch.long) | |
| # 2. compute alphas, betas | |
| alpha_prod_t = self.alphas_cumprod[timestep] | |
| beta_prod_t = 1 - alpha_prod_t | |
| alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod | |
| alpha_prod_s = self.alphas_cumprod[timestep_s] | |
| beta_prod_s = 1 - alpha_prod_s | |
| # 3. Compute the predicted noised sample x_s based on the model parameterization | |
| if self.config.prediction_type == "epsilon": # noise-prediction | |
| pred_original_sample = (sample - beta_prod_t.sqrt() * model_output) / alpha_prod_t.sqrt() | |
| pred_epsilon = model_output | |
| pred_noised_sample = alpha_prod_s.sqrt() * pred_original_sample + beta_prod_s.sqrt() * pred_epsilon | |
| elif self.config.prediction_type == "sample": # x-prediction | |
| pred_original_sample = model_output | |
| pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5) | |
| pred_noised_sample = alpha_prod_s.sqrt() * pred_original_sample + beta_prod_s.sqrt() * pred_epsilon | |
| elif self.config.prediction_type == "v_prediction": # v-prediction | |
| pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output | |
| pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample | |
| pred_noised_sample = alpha_prod_s.sqrt() * pred_original_sample + beta_prod_s.sqrt() * pred_epsilon | |
| else: | |
| raise ValueError( | |
| f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or" | |
| " `v_prediction` for `TCDScheduler`." | |
| ) | |
| # 4. Sample and inject noise z ~ N(0, I) for MultiStep Inference | |
| # Noise is not used on the final timestep of the timestep schedule. | |
| # This also means that noise is not used for one-step sampling. | |
| # Eta (referred to as "gamma" in the paper) was introduced to control the stochasticity in every step. | |
| # When eta = 0, it represents deterministic sampling, whereas eta = 1 indicates full stochastic sampling. | |
| if eta > 0: | |
| if self.step_index != self.num_inference_steps - 1: | |
| noise = randn_tensor( | |
| model_output.shape, generator=generator, device=model_output.device, dtype=pred_noised_sample.dtype | |
| ) | |
| prev_sample = (alpha_prod_t_prev / alpha_prod_s).sqrt() * pred_noised_sample + ( | |
| 1 - alpha_prod_t_prev / alpha_prod_s | |
| ).sqrt() * noise | |
| else: | |
| prev_sample = pred_noised_sample | |
| else: | |
| prev_sample = pred_noised_sample | |
| # upon completion increase step index by one | |
| self._step_index += 1 | |
| if not return_dict: | |
| return (prev_sample, pred_noised_sample) | |
| return TCDSchedulerOutput(prev_sample=prev_sample, pred_noised_sample=pred_noised_sample) | |
| def add_noise( | |
| self, | |
| original_samples: torch.FloatTensor, | |
| noise: torch.FloatTensor, | |
| timesteps: torch.IntTensor, | |
| ) -> torch.FloatTensor: | |
| # Make sure alphas_cumprod and timestep have same device and dtype as original_samples | |
| alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) | |
| timesteps = timesteps.to(original_samples.device) | |
| sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 | |
| sqrt_alpha_prod = sqrt_alpha_prod.flatten() | |
| while len(sqrt_alpha_prod.shape) < len(original_samples.shape): | |
| sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) | |
| sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 | |
| sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() | |
| while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape): | |
| sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) | |
| noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise | |
| return noisy_samples | |
| def get_velocity( | |
| self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor | |
| ) -> torch.FloatTensor: | |
| # Make sure alphas_cumprod and timestep have same device and dtype as sample | |
| alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype) | |
| timesteps = timesteps.to(sample.device) | |
| sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 | |
| sqrt_alpha_prod = sqrt_alpha_prod.flatten() | |
| while len(sqrt_alpha_prod.shape) < len(sample.shape): | |
| sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) | |
| sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 | |
| sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() | |
| while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape): | |
| sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) | |
| velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample | |
| return velocity | |
| def __len__(self): | |
| return self.config.num_train_timesteps | |
| def previous_timestep(self, timestep): | |
| if self.custom_timesteps: | |
| index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0] | |
| if index == self.timesteps.shape[0] - 1: | |
| prev_t = torch.tensor(-1) | |
| else: | |
| prev_t = self.timesteps[index + 1] | |
| else: | |
| num_inference_steps = ( | |
| self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps | |
| ) | |
| prev_t = timestep - self.config.num_train_timesteps // num_inference_steps | |
| return prev_t |