From b26d052ea9eb19c44c74998a363fd9340be57773 Mon Sep 17 00:00:00 2001 From: Kijai <40791699+kijai@users.noreply.github.com> Date: Mon, 15 Apr 2024 13:50:49 +0300 Subject: [PATCH] Add TCDScheduler --- nodes.py | 8 +- scheduling_tcd.py | 697 ++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 703 insertions(+), 2 deletions(-) create mode 100644 scheduling_tcd.py diff --git a/nodes.py b/nodes.py index 080fc11..6bb64ad 100644 --- a/nodes.py +++ b/nodes.py @@ -26,7 +26,7 @@ try: ) except: raise ImportError("Diffusers version too old. Please update to 0.26.0 minimum.") - +from .scheduling_tcd import TCDScheduler from contextlib import nullcontext from diffusers.utils import is_accelerate_available if is_accelerate_available(): @@ -241,7 +241,8 @@ class ella_sampler: 'DEISMultistepScheduler', 'EulerDiscreteScheduler', 'EulerAncestralDiscreteScheduler', - 'UniPCMultistepScheduler' + 'UniPCMultistepScheduler', + 'TCDScheduler' ], { "default": 'DPMSolverMultistepScheduler' }), @@ -290,6 +291,9 @@ class ella_sampler: noise_scheduler = EulerAncestralDiscreteScheduler(**scheduler_config) elif scheduler == 'UniPCMultistepScheduler': noise_scheduler = UniPCMultistepScheduler(**scheduler_config) + elif scheduler == 'TCDScheduler': + noise_scheduler = TCDScheduler(**scheduler_config) + pipe.scheduler = noise_scheduler autocast_condition = (dtype != torch.float32) and not mm.is_device_mps(device) diff --git a/scheduling_tcd.py b/scheduling_tcd.py new file mode 100644 index 0000000..d50ce78 --- /dev/null +++ b/scheduling_tcd.py @@ -0,0 +1,697 @@ +# 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 + + +@dataclass +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_transform_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 + + @register_to_config + 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 + + @property + def step_index(self): + return self._step_index + + @property + 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: float = 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`. + strength (`float`, *optional*, defaults to 1.0): + Used to determine the number of timesteps used for inference when using img2img, inpaint, etc. + """ + # 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) + + # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise + 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 + # Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement + # for the subsequent add_noise calls + self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device) + alphas_cumprod = self.alphas_cumprod.to(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 + + # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity + 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 + self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device) + alphas_cumprod = self.alphas_cumprod.to(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 + + # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.previous_timestep + 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