Spaces:
Running
on
A10G
Running
on
A10G
| # Copyright 2023 TSAIL 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 file is strongly influenced by https://github.com/LuChengTHU/dpm-solver | |
| import math | |
| from typing import List, Optional, Tuple, Union | |
| import numpy as np | |
| import torch | |
| from diffusers.configuration_utils import ConfigMixin, register_to_config | |
| # from diffusers.utils import randn_tensor | |
| from diffusers.utils.torch_utils import randn_tensor | |
| from diffusers.schedulers.scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput | |
| # Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar | |
| def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999): | |
| """ | |
| 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. | |
| Returns: | |
| betas (`np.ndarray`): the betas used by the scheduler to step the model outputs | |
| """ | |
| def alpha_bar(time_step): | |
| return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2 | |
| 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(t2) / alpha_bar(t1), max_beta)) | |
| return torch.tensor(betas, dtype=torch.float32) | |
| class DPMSolverMultistepSchedulerInject(SchedulerMixin, ConfigMixin): | |
| """ | |
| DPM-Solver (and the improved version DPM-Solver++) is a fast dedicated high-order solver for diffusion ODEs with | |
| the convergence order guarantee. Empirically, sampling by DPM-Solver with only 20 steps can generate high-quality | |
| samples, and it can generate quite good samples even in only 10 steps. | |
| For more details, see the original paper: https://arxiv.org/abs/2206.00927 and https://arxiv.org/abs/2211.01095 | |
| Currently, we support the multistep DPM-Solver for both noise prediction models and data prediction models. We | |
| recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling. | |
| We also support the "dynamic thresholding" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space | |
| diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic | |
| thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as | |
| stable-diffusion). | |
| We also support the SDE variant of DPM-Solver and DPM-Solver++, which is a fast SDE solver for the reverse | |
| diffusion SDE. Currently we only support the first-order and second-order solvers. We recommend using the | |
| second-order `sde-dpmsolver++`. | |
| [`~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`): number of diffusion steps used to train the model. | |
| beta_start (`float`): the starting `beta` value of inference. | |
| beta_end (`float`): the final `beta` value. | |
| beta_schedule (`str`): | |
| 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): | |
| option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. | |
| solver_order (`int`, default `2`): | |
| the order of DPM-Solver; can be `1` or `2` or `3`. We recommend to use `solver_order=2` for guided | |
| sampling, and `solver_order=3` for unconditional sampling. | |
| prediction_type (`str`, default `epsilon`, optional): | |
| prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion | |
| process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 | |
| https://imagen.research.google/video/paper.pdf) | |
| thresholding (`bool`, default `False`): | |
| whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487). | |
| For pixel-space diffusion models, you can set both `algorithm_type=dpmsolver++` and `thresholding=True` to | |
| use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion | |
| models (such as stable-diffusion). | |
| dynamic_thresholding_ratio (`float`, default `0.995`): | |
| the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen | |
| (https://arxiv.org/abs/2205.11487). | |
| sample_max_value (`float`, default `1.0`): | |
| the threshold value for dynamic thresholding. Valid only when `thresholding=True` and | |
| `algorithm_type="dpmsolver++`. | |
| algorithm_type (`str`, default `dpmsolver++`): | |
| the algorithm type for the solver. Either `dpmsolver` or `dpmsolver++` or `sde-dpmsolver` or | |
| `sde-dpmsolver++`. The `dpmsolver` type implements the algorithms in https://arxiv.org/abs/2206.00927, and | |
| the `dpmsolver++` type implements the algorithms in https://arxiv.org/abs/2211.01095. We recommend to use | |
| `dpmsolver++` or `sde-dpmsolver++` with `solver_order=2` for guided sampling (e.g. stable-diffusion). | |
| solver_type (`str`, default `midpoint`): | |
| the solver type for the second-order solver. Either `midpoint` or `heun`. The solver type slightly affects | |
| the sample quality, especially for small number of steps. We empirically find that `midpoint` solvers are | |
| slightly better, so we recommend to use the `midpoint` type. | |
| lower_order_final (`bool`, default `True`): | |
| whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. We empirically | |
| find this trick can stabilize the sampling of DPM-Solver for steps < 15, especially for steps <= 10. | |
| use_karras_sigmas (`bool`, *optional*, defaults to `False`): | |
| This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the | |
| noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence | |
| of noise levels {σi} as defined in Equation (5) of the paper https://arxiv.org/pdf/2206.00364.pdf. | |
| lambda_min_clipped (`float`, default `-inf`): | |
| the clipping threshold for the minimum value of lambda(t) for numerical stability. This is critical for | |
| cosine (squaredcos_cap_v2) noise schedule. | |
| variance_type (`str`, *optional*): | |
| Set to "learned" or "learned_range" for diffusion models that predict variance. For example, OpenAI's | |
| guided-diffusion (https://github.com/openai/guided-diffusion) predicts both mean and variance of the | |
| Gaussian distribution in the model's output. DPM-Solver only needs the "mean" output because it is based on | |
| diffusion ODEs. whether the model's output contains the predicted Gaussian variance. For example, OpenAI's | |
| guided-diffusion (https://github.com/openai/guided-diffusion) predicts both mean and variance of the | |
| Gaussian distribution in the model's output. DPM-Solver only needs the "mean" output because it is based on | |
| diffusion ODEs. | |
| """ | |
| _compatibles = [e.name for e in KarrasDiffusionSchedulers] | |
| order = 1 | |
| def __init__( | |
| self, | |
| num_train_timesteps: int = 1000, | |
| beta_start: float = 0.0001, | |
| beta_end: float = 0.02, | |
| beta_schedule: str = "linear", | |
| trained_betas: Optional[Union[np.ndarray, List[float]]] = None, | |
| solver_order: int = 2, | |
| prediction_type: str = "epsilon", | |
| thresholding: bool = False, | |
| dynamic_thresholding_ratio: float = 0.995, | |
| sample_max_value: float = 1.0, | |
| algorithm_type: str = "dpmsolver++", | |
| solver_type: str = "midpoint", | |
| lower_order_final: bool = True, | |
| use_karras_sigmas: Optional[bool] = False, | |
| lambda_min_clipped: float = -float("inf"), | |
| variance_type: Optional[str] = None, | |
| ): | |
| 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__}") | |
| self.alphas = 1.0 - self.betas | |
| self.alphas_cumprod = torch.cumprod(self.alphas, dim=0) | |
| # Currently we only support VP-type noise schedule | |
| self.alpha_t = torch.sqrt(self.alphas_cumprod) | |
| self.sigma_t = torch.sqrt(1 - self.alphas_cumprod) | |
| self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t) | |
| # standard deviation of the initial noise distribution | |
| self.init_noise_sigma = 1.0 | |
| # settings for DPM-Solver | |
| if algorithm_type not in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]: | |
| if algorithm_type == "deis": | |
| self.register_to_config(algorithm_type="dpmsolver++") | |
| else: | |
| raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}") | |
| if solver_type not in ["midpoint", "heun"]: | |
| if solver_type in ["logrho", "bh1", "bh2"]: | |
| self.register_to_config(solver_type="midpoint") | |
| else: | |
| raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}") | |
| # setable values | |
| self.num_inference_steps = None | |
| timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy() | |
| self.timesteps = torch.from_numpy(timesteps) | |
| self.model_outputs = [None] * solver_order | |
| self.lower_order_nums = 0 | |
| self.use_karras_sigmas = use_karras_sigmas | |
| def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torch.device] = None): | |
| """ | |
| Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. | |
| Args: | |
| num_inference_steps (`int`): | |
| the number of diffusion steps used when generating samples with a pre-trained model. | |
| device (`str` or `torch.device`, optional): | |
| the device to which the timesteps should be moved to. If `None`, the timesteps are not moved. | |
| """ | |
| # Clipping the minimum of all lambda(t) for numerical stability. | |
| # This is critical for cosine (squaredcos_cap_v2) noise schedule. | |
| clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.config.lambda_min_clipped) | |
| timesteps = ( | |
| np.linspace(0, self.config.num_train_timesteps - 1 - clipped_idx, num_inference_steps + 1) | |
| .round()[::-1][:-1] | |
| .copy() | |
| .astype(np.int64) | |
| ) | |
| if self.use_karras_sigmas: | |
| sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) | |
| log_sigmas = np.log(sigmas) | |
| sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps) | |
| timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round() | |
| timesteps = np.flip(timesteps).copy().astype(np.int64) | |
| # when num_inference_steps == num_train_timesteps, we can end up with | |
| # duplicates in timesteps. | |
| _, unique_indices = np.unique(timesteps, return_index=True) | |
| timesteps = timesteps[np.sort(unique_indices)] | |
| self.timesteps = torch.from_numpy(timesteps).to(device) | |
| self.num_inference_steps = len(timesteps) | |
| self.model_outputs = [ | |
| None, | |
| ] * self.config.solver_order | |
| self.lower_order_nums = 0 | |
| # 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, height, width = 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 * height * width) | |
| 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, height, width) | |
| sample = sample.to(dtype) | |
| return sample | |
| # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t | |
| def _sigma_to_t(self, sigma, log_sigmas): | |
| # get log sigma | |
| log_sigma = np.log(sigma) | |
| # get distribution | |
| dists = log_sigma - log_sigmas[:, np.newaxis] | |
| # get sigmas range | |
| low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2) | |
| high_idx = low_idx + 1 | |
| low = log_sigmas[low_idx] | |
| high = log_sigmas[high_idx] | |
| # interpolate sigmas | |
| w = (low - log_sigma) / (low - high) | |
| w = np.clip(w, 0, 1) | |
| # transform interpolation to time range | |
| t = (1 - w) * low_idx + w * high_idx | |
| t = t.reshape(sigma.shape) | |
| return t | |
| # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras | |
| def _convert_to_karras(self, in_sigmas: torch.FloatTensor, num_inference_steps) -> torch.FloatTensor: | |
| """Constructs the noise schedule of Karras et al. (2022).""" | |
| sigma_min: float = in_sigmas[-1].item() | |
| sigma_max: float = in_sigmas[0].item() | |
| rho = 7.0 # 7.0 is the value used in the paper | |
| ramp = np.linspace(0, 1, num_inference_steps) | |
| min_inv_rho = sigma_min ** (1 / rho) | |
| max_inv_rho = sigma_max ** (1 / rho) | |
| sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho | |
| return sigmas | |
| def convert_model_output( | |
| self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor | |
| ) -> torch.FloatTensor: | |
| """ | |
| Convert the model output to the corresponding type that the algorithm (DPM-Solver / DPM-Solver++) needs. | |
| DPM-Solver is designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to | |
| discretize an integral of the data prediction model. So we need to first convert the model output to the | |
| corresponding type to match the algorithm. | |
| Note that the algorithm type and the model type is decoupled. That is to say, we can use either DPM-Solver or | |
| DPM-Solver++ for both noise prediction model and data prediction model. | |
| Args: | |
| model_output (`torch.FloatTensor`): direct output from learned diffusion model. | |
| timestep (`int`): current discrete timestep in the diffusion chain. | |
| sample (`torch.FloatTensor`): | |
| current instance of sample being created by diffusion process. | |
| Returns: | |
| `torch.FloatTensor`: the converted model output. | |
| """ | |
| # DPM-Solver++ needs to solve an integral of the data prediction model. | |
| if self.config.algorithm_type in ["dpmsolver++", "sde-dpmsolver++"]: | |
| if self.config.prediction_type == "epsilon": | |
| # DPM-Solver and DPM-Solver++ only need the "mean" output. | |
| if self.config.variance_type in ["learned", "learned_range"]: | |
| model_output = model_output[:, :3] | |
| alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] | |
| x0_pred = (sample - sigma_t * model_output) / alpha_t | |
| elif self.config.prediction_type == "sample": | |
| x0_pred = model_output | |
| elif self.config.prediction_type == "v_prediction": | |
| alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] | |
| x0_pred = alpha_t * sample - sigma_t * model_output | |
| else: | |
| raise ValueError( | |
| f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" | |
| " `v_prediction` for the DPMSolverMultistepScheduler." | |
| ) | |
| if self.config.thresholding: | |
| x0_pred = self._threshold_sample(x0_pred) | |
| return x0_pred | |
| # DPM-Solver needs to solve an integral of the noise prediction model. | |
| elif self.config.algorithm_type in ["dpmsolver", "sde-dpmsolver"]: | |
| if self.config.prediction_type == "epsilon": | |
| # DPM-Solver and DPM-Solver++ only need the "mean" output. | |
| if self.config.variance_type in ["learned", "learned_range"]: | |
| epsilon = model_output[:, :3] | |
| else: | |
| epsilon = model_output | |
| elif self.config.prediction_type == "sample": | |
| alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] | |
| epsilon = (sample - alpha_t * model_output) / sigma_t | |
| elif self.config.prediction_type == "v_prediction": | |
| alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] | |
| epsilon = alpha_t * model_output + sigma_t * sample | |
| else: | |
| raise ValueError( | |
| f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" | |
| " `v_prediction` for the DPMSolverMultistepScheduler." | |
| ) | |
| if self.config.thresholding: | |
| alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] | |
| x0_pred = (sample - sigma_t * epsilon) / alpha_t | |
| x0_pred = self._threshold_sample(x0_pred) | |
| epsilon = (sample - alpha_t * x0_pred) / sigma_t | |
| return epsilon | |
| def dpm_solver_first_order_update( | |
| self, | |
| model_output: torch.FloatTensor, | |
| timestep: int, | |
| prev_timestep: int, | |
| sample: torch.FloatTensor, | |
| noise: Optional[torch.FloatTensor] = None, | |
| ) -> torch.FloatTensor: | |
| """ | |
| One step for the first-order DPM-Solver (equivalent to DDIM). | |
| See https://arxiv.org/abs/2206.00927 for the detailed derivation. | |
| Args: | |
| model_output (`torch.FloatTensor`): direct output from learned diffusion model. | |
| timestep (`int`): current discrete timestep in the diffusion chain. | |
| prev_timestep (`int`): previous discrete timestep in the diffusion chain. | |
| sample (`torch.FloatTensor`): | |
| current instance of sample being created by diffusion process. | |
| Returns: | |
| `torch.FloatTensor`: the sample tensor at the previous timestep. | |
| """ | |
| lambda_t, lambda_s = self.lambda_t[prev_timestep], self.lambda_t[timestep] | |
| alpha_t, alpha_s = self.alpha_t[prev_timestep], self.alpha_t[timestep] | |
| sigma_t, sigma_s = self.sigma_t[prev_timestep], self.sigma_t[timestep] | |
| h = lambda_t - lambda_s | |
| if self.config.algorithm_type == "dpmsolver++": | |
| x_t = (sigma_t / sigma_s) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * model_output | |
| elif self.config.algorithm_type == "dpmsolver": | |
| x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output | |
| elif self.config.algorithm_type == "sde-dpmsolver++": | |
| assert noise is not None | |
| x_t = ( | |
| (sigma_t / sigma_s * torch.exp(-h)) * sample | |
| + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output | |
| + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise | |
| ) | |
| elif self.config.algorithm_type == "sde-dpmsolver": | |
| assert noise is not None | |
| x_t = ( | |
| (alpha_t / alpha_s) * sample | |
| - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * model_output | |
| + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise | |
| ) | |
| return x_t | |
| def multistep_dpm_solver_second_order_update( | |
| self, | |
| model_output_list: List[torch.FloatTensor], | |
| timestep_list: List[int], | |
| prev_timestep: int, | |
| sample: torch.FloatTensor, | |
| noise: Optional[torch.FloatTensor] = None, | |
| ) -> torch.FloatTensor: | |
| """ | |
| One step for the second-order multistep DPM-Solver. | |
| Args: | |
| model_output_list (`List[torch.FloatTensor]`): | |
| direct outputs from learned diffusion model at current and latter timesteps. | |
| timestep (`int`): current and latter discrete timestep in the diffusion chain. | |
| prev_timestep (`int`): previous discrete timestep in the diffusion chain. | |
| sample (`torch.FloatTensor`): | |
| current instance of sample being created by diffusion process. | |
| Returns: | |
| `torch.FloatTensor`: the sample tensor at the previous timestep. | |
| """ | |
| t, s0, s1 = prev_timestep, timestep_list[-1], timestep_list[-2] | |
| m0, m1 = model_output_list[-1], model_output_list[-2] | |
| lambda_t, lambda_s0, lambda_s1 = self.lambda_t[t], self.lambda_t[s0], self.lambda_t[s1] | |
| alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0] | |
| sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0] | |
| h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1 | |
| r0 = h_0 / h | |
| D0, D1 = m0, (1.0 / r0) * (m0 - m1) | |
| if self.config.algorithm_type == "dpmsolver++": | |
| # See https://arxiv.org/abs/2211.01095 for detailed derivations | |
| if self.config.solver_type == "midpoint": | |
| x_t = ( | |
| (sigma_t / sigma_s0) * sample | |
| - (alpha_t * (torch.exp(-h) - 1.0)) * D0 | |
| - 0.5 * (alpha_t * (torch.exp(-h) - 1.0)) * D1 | |
| ) | |
| elif self.config.solver_type == "heun": | |
| x_t = ( | |
| (sigma_t / sigma_s0) * sample | |
| - (alpha_t * (torch.exp(-h) - 1.0)) * D0 | |
| + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1 | |
| ) | |
| elif self.config.algorithm_type == "dpmsolver": | |
| # See https://arxiv.org/abs/2206.00927 for detailed derivations | |
| if self.config.solver_type == "midpoint": | |
| x_t = ( | |
| (alpha_t / alpha_s0) * sample | |
| - (sigma_t * (torch.exp(h) - 1.0)) * D0 | |
| - 0.5 * (sigma_t * (torch.exp(h) - 1.0)) * D1 | |
| ) | |
| elif self.config.solver_type == "heun": | |
| x_t = ( | |
| (alpha_t / alpha_s0) * sample | |
| - (sigma_t * (torch.exp(h) - 1.0)) * D0 | |
| - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 | |
| ) | |
| elif self.config.algorithm_type == "sde-dpmsolver++": | |
| assert noise is not None | |
| if self.config.solver_type == "midpoint": | |
| x_t = ( | |
| (sigma_t / sigma_s0 * torch.exp(-h)) * sample | |
| + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 | |
| + 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1 | |
| + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise | |
| ) | |
| elif self.config.solver_type == "heun": | |
| x_t = ( | |
| (sigma_t / sigma_s0 * torch.exp(-h)) * sample | |
| + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 | |
| + (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1 | |
| + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise | |
| ) | |
| elif self.config.algorithm_type == "sde-dpmsolver": | |
| assert noise is not None | |
| if self.config.solver_type == "midpoint": | |
| x_t = ( | |
| (alpha_t / alpha_s0) * sample | |
| - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * D0 | |
| - (sigma_t * (torch.exp(h) - 1.0)) * D1 | |
| + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise | |
| ) | |
| elif self.config.solver_type == "heun": | |
| x_t = ( | |
| (alpha_t / alpha_s0) * sample | |
| - 2.0 * (sigma_t * (torch.exp(h) - 1.0)) * D0 | |
| - 2.0 * (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 | |
| + sigma_t * torch.sqrt(torch.exp(2 * h) - 1.0) * noise | |
| ) | |
| return x_t | |
| def multistep_dpm_solver_third_order_update( | |
| self, | |
| model_output_list: List[torch.FloatTensor], | |
| timestep_list: List[int], | |
| prev_timestep: int, | |
| sample: torch.FloatTensor, | |
| ) -> torch.FloatTensor: | |
| """ | |
| One step for the third-order multistep DPM-Solver. | |
| Args: | |
| model_output_list (`List[torch.FloatTensor]`): | |
| direct outputs from learned diffusion model at current and latter timesteps. | |
| timestep (`int`): current and latter discrete timestep in the diffusion chain. | |
| prev_timestep (`int`): previous discrete timestep in the diffusion chain. | |
| sample (`torch.FloatTensor`): | |
| current instance of sample being created by diffusion process. | |
| Returns: | |
| `torch.FloatTensor`: the sample tensor at the previous timestep. | |
| """ | |
| t, s0, s1, s2 = prev_timestep, timestep_list[-1], timestep_list[-2], timestep_list[-3] | |
| m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3] | |
| lambda_t, lambda_s0, lambda_s1, lambda_s2 = ( | |
| self.lambda_t[t], | |
| self.lambda_t[s0], | |
| self.lambda_t[s1], | |
| self.lambda_t[s2], | |
| ) | |
| alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0] | |
| sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0] | |
| h, h_0, h_1 = lambda_t - lambda_s0, lambda_s0 - lambda_s1, lambda_s1 - lambda_s2 | |
| r0, r1 = h_0 / h, h_1 / h | |
| D0 = m0 | |
| D1_0, D1_1 = (1.0 / r0) * (m0 - m1), (1.0 / r1) * (m1 - m2) | |
| D1 = D1_0 + (r0 / (r0 + r1)) * (D1_0 - D1_1) | |
| D2 = (1.0 / (r0 + r1)) * (D1_0 - D1_1) | |
| if self.config.algorithm_type == "dpmsolver++": | |
| # See https://arxiv.org/abs/2206.00927 for detailed derivations | |
| x_t = ( | |
| (sigma_t / sigma_s0) * sample | |
| - (alpha_t * (torch.exp(-h) - 1.0)) * D0 | |
| + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1 | |
| - (alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5)) * D2 | |
| ) | |
| elif self.config.algorithm_type == "dpmsolver": | |
| # See https://arxiv.org/abs/2206.00927 for detailed derivations | |
| x_t = ( | |
| (alpha_t / alpha_s0) * sample | |
| - (sigma_t * (torch.exp(h) - 1.0)) * D0 | |
| - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 | |
| - (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2 | |
| ) | |
| return x_t | |
| def step( | |
| self, | |
| model_output: torch.FloatTensor, | |
| timestep: int, | |
| sample: torch.FloatTensor, | |
| generator=None, | |
| return_dict: bool = True, | |
| variance_noise: Optional[torch.FloatTensor] = None, | |
| ) -> Union[SchedulerOutput, Tuple]: | |
| """ | |
| Step function propagating the sample with the multistep DPM-Solver. | |
| Args: | |
| model_output (`torch.FloatTensor`): direct output from learned diffusion model. | |
| timestep (`int`): current discrete timestep in the diffusion chain. | |
| sample (`torch.FloatTensor`): | |
| current instance of sample being created by diffusion process. | |
| return_dict (`bool`): option for returning tuple rather than SchedulerOutput class | |
| Returns: | |
| [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is | |
| True, otherwise a `tuple`. When returning a tuple, 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 isinstance(timestep, torch.Tensor): | |
| timestep = timestep.to(self.timesteps.device) | |
| step_index = (self.timesteps == timestep).nonzero() | |
| if len(step_index) == 0: | |
| step_index = len(self.timesteps) - 1 | |
| else: | |
| step_index = step_index.item() | |
| prev_timestep = 0 if step_index == len(self.timesteps) - 1 else self.timesteps[step_index + 1] | |
| lower_order_final = ( | |
| (step_index == len(self.timesteps) - 1) and self.config.lower_order_final and len(self.timesteps) < 15 | |
| ) | |
| lower_order_second = ( | |
| (step_index == len(self.timesteps) - 2) and self.config.lower_order_final and len(self.timesteps) < 15 | |
| ) | |
| model_output = self.convert_model_output(model_output, timestep, sample) | |
| for i in range(self.config.solver_order - 1): | |
| self.model_outputs[i] = self.model_outputs[i + 1] | |
| self.model_outputs[-1] = model_output | |
| if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"] and variance_noise is None: | |
| noise = randn_tensor( | |
| model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype | |
| ) | |
| elif self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]: | |
| noise = variance_noise | |
| else: | |
| noise = None | |
| if self.config.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final: | |
| prev_sample = self.dpm_solver_first_order_update( | |
| model_output, timestep, prev_timestep, sample, noise=noise | |
| ) | |
| elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second: | |
| timestep_list = [self.timesteps[step_index - 1], timestep] | |
| prev_sample = self.multistep_dpm_solver_second_order_update( | |
| self.model_outputs, timestep_list, prev_timestep, sample, noise=noise | |
| ) | |
| else: | |
| raise NotImplementedError() | |
| if self.lower_order_nums < self.config.solver_order: | |
| self.lower_order_nums += 1 | |
| if not return_dict: | |
| return (prev_sample,) | |
| return SchedulerOutput(prev_sample=prev_sample) | |
| def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor: | |
| """ | |
| Ensures interchangeability with schedulers that need to scale the denoising model input depending on the | |
| current timestep. | |
| Args: | |
| sample (`torch.FloatTensor`): input sample | |
| Returns: | |
| `torch.FloatTensor`: scaled input sample | |
| """ | |
| return 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 | |
| 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 __len__(self): | |
| return self.config.num_train_timesteps | |