From be99201a567c1ccd841dc16fb24e88f7f239c187 Mon Sep 17 00:00:00 2001 From: qsh-zh Date: Wed, 4 Jan 2023 18:09:30 -0500 Subject: [PATCH] feat : add log-rho deis multistep scheduler (#1432) * feat : add log-rho deis multistep deis * docs :fix typo * docs : add docs for impl algo * docs : remove duplicate ref * finish deis * add docs * fix Co-authored-by: Patrick von Platen --- docs/source/en/_toctree.yml | 2 + docs/source/en/api/schedulers/deis.mdx | 22 + src/diffusers/__init__.py | 1 + src/diffusers/schedulers/__init__.py | 1 + .../schedulers/scheduling_deis_multistep.py | 481 ++++++++++++++++++ .../scheduling_dpmsolver_multistep.py | 10 +- .../scheduling_dpmsolver_singlestep.py | 10 +- src/diffusers/utils/constants.py | 3 + src/diffusers/utils/dummy_pt_objects.py | 15 + tests/test_scheduler.py | 202 ++++++++ 10 files changed, 743 insertions(+), 4 deletions(-) create mode 100644 docs/source/en/api/schedulers/deis.mdx create mode 100644 src/diffusers/schedulers/scheduling_deis_multistep.py diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml index 698d30f3fa..d3d3cc11b5 100644 --- a/docs/source/en/_toctree.yml +++ b/docs/source/en/_toctree.yml @@ -155,6 +155,8 @@ title: "DDIM" - local: api/schedulers/ddpm title: "DDPM" + - local: api/schedulers/deis + title: "DEIS" - local: api/schedulers/singlestep_dpm_solver title: "Singlestep DPM-Solver" - local: api/schedulers/multistep_dpm_solver diff --git a/docs/source/en/api/schedulers/deis.mdx b/docs/source/en/api/schedulers/deis.mdx new file mode 100644 index 0000000000..1841aba71d --- /dev/null +++ b/docs/source/en/api/schedulers/deis.mdx @@ -0,0 +1,22 @@ + + +# DEIS + +Fast Sampling of Diffusion Models with Exponential Integrator. + +## Overview + +Original paper can be found [here](https://arxiv.org/abs/2204.13902). The original implementation can be found [here](https://github.com/qsh-zh/deis). + +## DEISMultistepScheduler +[[autodoc]] DEISMultistepScheduler diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 46480270e8..681c598eb6 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -67,6 +67,7 @@ else: from .schedulers import ( DDIMScheduler, DDPMScheduler, + DEISMultistepScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, EulerAncestralDiscreteScheduler, diff --git a/src/diffusers/schedulers/__init__.py b/src/diffusers/schedulers/__init__.py index 6b90fe79f6..298bbb9ef4 100644 --- a/src/diffusers/schedulers/__init__.py +++ b/src/diffusers/schedulers/__init__.py @@ -24,6 +24,7 @@ except OptionalDependencyNotAvailable: else: from .scheduling_ddim import DDIMScheduler from .scheduling_ddpm import DDPMScheduler + from .scheduling_deis_multistep import DEISMultistepScheduler from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler diff --git a/src/diffusers/schedulers/scheduling_deis_multistep.py b/src/diffusers/schedulers/scheduling_deis_multistep.py new file mode 100644 index 0000000000..528bef9e09 --- /dev/null +++ b/src/diffusers/schedulers/scheduling_deis_multistep.py @@ -0,0 +1,481 @@ +# Copyright 2022 FLAIR Lab 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: check https://arxiv.org/abs/2204.13902 and https://github.com/qsh-zh/deis for more info +# The codebase is modified based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py + +import math +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch + +from ..configuration_utils import ConfigMixin, register_to_config +from ..utils import _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS +from .scheduling_utils import SchedulerMixin, SchedulerOutput + + +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 DEISMultistepScheduler(SchedulerMixin, ConfigMixin): + """ + DEIS (https://arxiv.org/abs/2204.13902) is a fast high order solver for diffusion ODEs. We slightly modify the + polynomial fitting formula in log-rho space instead of the original linear t space in DEIS paper. The modification + enjoys closed-form coefficients for exponential multistep update instead of replying on the numerical solver. More + variants of DEIS can be found in https://github.com/qsh-zh/deis. + + Currently, we support the log-rho multistep DEIS. We recommend to use `solver_order=2 / 3` while `solver_order=1` + reduces to DDIM. + + We also support the "dynamic thresholding" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space + diffusion models, you can set `thresholding=True` to use the dynamic thresholding. + + [`~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 DEIS; 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`): + indicates whether the model predicts the noise (epsilon), or the data / `x0`. One of `epsilon`, `sample`, + or `v-prediction`. + thresholding (`bool`, default `False`): + whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487). + 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 woks when `thresholding=True` + algorithm_type (`str`, default `deis`): + the algorithm type for the solver. current we support multistep deis, we will add other variants of DEIS in + the future + 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 DEIS for steps < 15, especially for steps <= 10. + + """ + + _compatibles = _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy() + order = 1 + + @register_to_config + 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[np.ndarray] = 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 = "deis", + solver_type: str = "logrho", + lower_order_final: bool = True, + ): + 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 DEIS + if algorithm_type not in ["deis"]: + if algorithm_type in ["dpmsolver", "dpmsolver++"]: + algorithm_type = "deis" + else: + raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}") + + if solver_type not in ["logrho"]: + if solver_type in ["midpoint", "heun"]: + solver_type = "logrho" + else: + raise NotImplementedError(f"solver type {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 + + def set_timesteps(self, num_inference_steps: int, 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. + """ + self.num_inference_steps = num_inference_steps + timesteps = ( + np.linspace(0, self.num_train_timesteps - 1, num_inference_steps + 1) + .round()[::-1][:-1] + .copy() + .astype(np.int64) + ) + self.timesteps = torch.from_numpy(timesteps).to(device) + self.model_outputs = [ + None, + ] * self.config.solver_order + self.lower_order_nums = 0 + + 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 DEIS needs. + + 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. + """ + if self.config.prediction_type == "epsilon": + 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 DEISMultistepScheduler." + ) + + if self.config.thresholding: + # Dynamic thresholding in https://arxiv.org/abs/2205.11487 + orig_dtype = x0_pred.dtype + if orig_dtype not in [torch.float, torch.double]: + x0_pred = x0_pred.float() + dynamic_max_val = torch.quantile( + torch.abs(x0_pred).reshape((x0_pred.shape[0], -1)), self.config.dynamic_thresholding_ratio, dim=1 + ) + dynamic_max_val = torch.maximum( + dynamic_max_val, + self.config.sample_max_value * torch.ones_like(dynamic_max_val).to(dynamic_max_val.device), + )[(...,) + (None,) * (x0_pred.ndim - 1)] + x0_pred = torch.clamp(x0_pred, -dynamic_max_val, dynamic_max_val) / dynamic_max_val + x0_pred = x0_pred.type(orig_dtype) + + if self.config.algorithm_type == "deis": + alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep] + return (sample - alpha_t * x0_pred) / sigma_t + else: + raise NotImplementedError("only support log-rho multistep deis now") + + def deis_first_order_update( + self, + model_output: torch.FloatTensor, + timestep: int, + prev_timestep: int, + sample: torch.FloatTensor, + ) -> torch.FloatTensor: + """ + One step for the first-order DEIS (equivalent to DDIM). + + 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, _ = self.sigma_t[prev_timestep], self.sigma_t[timestep] + h = lambda_t - lambda_s + if self.config.algorithm_type == "deis": + x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output + else: + raise NotImplementedError("only support log-rho multistep deis now") + return x_t + + def multistep_deis_second_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 second-order multistep DEIS. + + 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] + alpha_t, alpha_s0, alpha_s1 = self.alpha_t[t], self.alpha_t[s0], self.alpha_t[s1] + sigma_t, sigma_s0, sigma_s1 = self.sigma_t[t], self.sigma_t[s0], self.sigma_t[s1] + + rho_t, rho_s0, rho_s1 = sigma_t / alpha_t, sigma_s0 / alpha_s0, sigma_s1 / alpha_s1 + + if self.config.algorithm_type == "deis": + + def ind_fn(t, b, c): + # Integrate[(log(t) - log(c)) / (log(b) - log(c)), {t}] + return t * (-np.log(c) + np.log(t) - 1) / (np.log(b) - np.log(c)) + + coef1 = ind_fn(rho_t, rho_s0, rho_s1) - ind_fn(rho_s0, rho_s0, rho_s1) + coef2 = ind_fn(rho_t, rho_s1, rho_s0) - ind_fn(rho_s0, rho_s1, rho_s0) + + x_t = alpha_t * (sample / alpha_s0 + coef1 * m0 + coef2 * m1) + return x_t + else: + raise NotImplementedError("only support log-rho multistep deis now") + + def multistep_deis_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 DEIS. + + 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] + alpha_t, alpha_s0, alpha_s1, alpha_s2 = self.alpha_t[t], self.alpha_t[s0], self.alpha_t[s1], self.alpha_t[s2] + sigma_t, sigma_s0, sigma_s1, simga_s2 = self.sigma_t[t], self.sigma_t[s0], self.sigma_t[s1], self.sigma_t[s2] + rho_t, rho_s0, rho_s1, rho_s2 = ( + sigma_t / alpha_t, + sigma_s0 / alpha_s0, + sigma_s1 / alpha_s1, + simga_s2 / alpha_s2, + ) + + if self.config.algorithm_type == "deis": + + def ind_fn(t, b, c, d): + # Integrate[(log(t) - log(c))(log(t) - log(d)) / (log(b) - log(c))(log(b) - log(d)), {t}] + numerator = t * ( + np.log(c) * (np.log(d) - np.log(t) + 1) + - np.log(d) * np.log(t) + + np.log(d) + + np.log(t) ** 2 + - 2 * np.log(t) + + 2 + ) + denominator = (np.log(b) - np.log(c)) * (np.log(b) - np.log(d)) + return numerator / denominator + + coef1 = ind_fn(rho_t, rho_s0, rho_s1, rho_s2) - ind_fn(rho_s0, rho_s0, rho_s1, rho_s2) + coef2 = ind_fn(rho_t, rho_s1, rho_s2, rho_s0) - ind_fn(rho_s0, rho_s1, rho_s2, rho_s0) + coef3 = ind_fn(rho_t, rho_s2, rho_s0, rho_s1) - ind_fn(rho_s0, rho_s2, rho_s0, rho_s1) + + x_t = alpha_t * (sample / alpha_s0 + coef1 * m0 + coef2 * m1 + coef3 * m2) + + return x_t + else: + raise NotImplementedError("only support log-rho multistep deis now") + + def step( + self, + model_output: torch.FloatTensor, + timestep: int, + sample: torch.FloatTensor, + return_dict: bool = True, + ) -> Union[SchedulerOutput, Tuple]: + """ + Step function propagating the sample with the multistep DEIS. + + 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.solver_order == 1 or self.lower_order_nums < 1 or lower_order_final: + prev_sample = self.deis_first_order_update(model_output, timestep, prev_timestep, sample) + 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_deis_second_order_update( + self.model_outputs, timestep_list, prev_timestep, sample + ) + else: + timestep_list = [self.timesteps[step_index - 2], self.timesteps[step_index - 1], timestep] + prev_sample = self.multistep_deis_third_order_update( + self.model_outputs, timestep_list, prev_timestep, sample + ) + + 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 + + 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 + self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype) + timesteps = timesteps.to(original_samples.device) + + sqrt_alpha_prod = self.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 - self.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 diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py b/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py index 939e30c5f3..2a003920ec 100644 --- a/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py +++ b/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py @@ -174,9 +174,15 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin): # settings for DPM-Solver if algorithm_type not in ["dpmsolver", "dpmsolver++"]: - raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}") + if algorithm_type == "deis": + algorithm_type = "dpmsolver++" + else: + raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}") if solver_type not in ["midpoint", "heun"]: - raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}") + if solver_type == "logrho": + solver_type = "midpoint" + else: + raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}") # setable values self.num_inference_steps = None diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py b/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py index 771a34b203..d016711b59 100644 --- a/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py +++ b/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py @@ -163,9 +163,15 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin): # settings for DPM-Solver if algorithm_type not in ["dpmsolver", "dpmsolver++"]: - raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}") + if algorithm_type == "deis": + algorithm_type = "dpmsolver++" + else: + raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}") if solver_type not in ["midpoint", "heun"]: - raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}") + if solver_type == "logrho": + solver_type = "midpoint" + else: + raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}") # setable values self.num_inference_steps = None diff --git a/src/diffusers/utils/constants.py b/src/diffusers/utils/constants.py index eaa8212298..35efff392c 100644 --- a/src/diffusers/utils/constants.py +++ b/src/diffusers/utils/constants.py @@ -41,4 +41,7 @@ _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS = [ "EulerAncestralDiscreteScheduler", "DPMSolverMultistepScheduler", "DPMSolverSinglestepScheduler", + "KDPM2DiscreteScheduler", + "KDPM2AncestralDiscreteScheduler", + "DEISMultistepScheduler", ] diff --git a/src/diffusers/utils/dummy_pt_objects.py b/src/diffusers/utils/dummy_pt_objects.py index 63a7d258a9..62c2bbc273 100644 --- a/src/diffusers/utils/dummy_pt_objects.py +++ b/src/diffusers/utils/dummy_pt_objects.py @@ -362,6 +362,21 @@ class DDPMScheduler(metaclass=DummyObject): requires_backends(cls, ["torch"]) +class DEISMultistepScheduler(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + class DPMSolverMultistepScheduler(metaclass=DummyObject): _backends = ["torch"] diff --git a/tests/test_scheduler.py b/tests/test_scheduler.py index 705a429bbc..34770222d5 100755 --- a/tests/test_scheduler.py +++ b/tests/test_scheduler.py @@ -27,6 +27,7 @@ import diffusers from diffusers import ( DDIMScheduler, DDPMScheduler, + DEISMultistepScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, EulerAncestralDiscreteScheduler, @@ -2505,6 +2506,207 @@ class KDPM2DiscreteSchedulerTest(SchedulerCommonTest): assert abs(result_mean.item() - 0.0266) < 1e-3 +class DEISMultistepSchedulerTest(SchedulerCommonTest): + scheduler_classes = (DEISMultistepScheduler,) + forward_default_kwargs = (("num_inference_steps", 25),) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + "solver_order": 2, + } + + config.update(**kwargs) + return config + + def check_over_configs(self, time_step=0, **config): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + new_scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] + + output, new_output = sample, sample + for t in range(time_step, time_step + scheduler.config.solver_order + 1): + output = scheduler.step(residual, t, output, **kwargs).prev_sample + new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def test_from_save_pretrained(self): + pass + + def check_over_forward(self, time_step=0, **forward_kwargs): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residuals (must be after setting timesteps) + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + # copy over dummy past residuals + new_scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residual (must be after setting timesteps) + new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] + + output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def full_loop(self, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + return sample + + def test_step_shape(self): + kwargs = dict(self.forward_default_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", None) + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + sample = self.dummy_sample + residual = 0.1 * sample + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + # copy over dummy past residuals (must be done after set_timesteps) + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + time_step_0 = scheduler.timesteps[5] + time_step_1 = scheduler.timesteps[6] + + output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample + output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + def test_timesteps(self): + for timesteps in [25, 50, 100, 999, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_thresholding(self): + self.check_over_configs(thresholding=False) + for order in [1, 2, 3]: + for solver_type in ["logrho"]: + for threshold in [0.5, 1.0, 2.0]: + for prediction_type in ["epsilon", "sample"]: + self.check_over_configs( + thresholding=True, + prediction_type=prediction_type, + sample_max_value=threshold, + algorithm_type="deis", + solver_order=order, + solver_type=solver_type, + ) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_solver_order_and_type(self): + for algorithm_type in ["deis"]: + for solver_type in ["logrho"]: + for order in [1, 2, 3]: + for prediction_type in ["epsilon", "sample"]: + self.check_over_configs( + solver_order=order, + solver_type=solver_type, + prediction_type=prediction_type, + algorithm_type=algorithm_type, + ) + sample = self.full_loop( + solver_order=order, + solver_type=solver_type, + prediction_type=prediction_type, + algorithm_type=algorithm_type, + ) + assert not torch.isnan(sample).any(), "Samples have nan numbers" + + def test_lower_order_final(self): + self.check_over_configs(lower_order_final=True) + self.check_over_configs(lower_order_final=False) + + def test_inference_steps(self): + for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]: + self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0) + + def test_full_loop_no_noise(self): + sample = self.full_loop() + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.23916) < 1e-3 + + def test_full_loop_with_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction") + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.091) < 1e-3 + + def test_fp16_support(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter.half() + scheduler.set_timesteps(num_inference_steps) + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + assert sample.dtype == torch.float16 + + class KDPM2AncestralDiscreteSchedulerTest(SchedulerCommonTest): scheduler_classes = (KDPM2AncestralDiscreteScheduler,) num_inference_steps = 10