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# 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 ..configuration_utils import ConfigMixin, register_to_config | |
from ..utils import deprecate, logging | |
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar | |
def betas_for_alpha_bar( | |
num_diffusion_timesteps, | |
max_beta=0.999, | |
alpha_transform_type="cosine", | |
): | |
""" | |
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of | |
(1-beta) over time from t = [0,1]. | |
Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up | |
to that part of the diffusion process. | |
Args: | |
num_diffusion_timesteps (`int`): the number of betas to produce. | |
max_beta (`float`): the maximum beta to use; use values lower than 1 to | |
prevent singularities. | |
alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar. | |
Choose from `cosine` or `exp` | |
Returns: | |
betas (`np.ndarray`): the betas used by the scheduler to step the model outputs | |
""" | |
if alpha_transform_type == "cosine": | |
def alpha_bar_fn(t): | |
return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2 | |
elif alpha_transform_type == "exp": | |
def alpha_bar_fn(t): | |
return math.exp(t * -12.0) | |
else: | |
raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}") | |
betas = [] | |
for i in range(num_diffusion_timesteps): | |
t1 = i / num_diffusion_timesteps | |
t2 = (i + 1) / num_diffusion_timesteps | |
betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta)) | |
return torch.tensor(betas, dtype=torch.float32) | |
class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin): | |
""" | |
`DPMSolverSinglestepScheduler` is a fast dedicated high-order solver for diffusion ODEs. | |
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic | |
methods the library implements for all schedulers such as loading and saving. | |
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`. | |
solver_order (`int`, defaults to 2): | |
The DPMSolver order which can be `1` or `2` or `3`. It is recommended to use `solver_order=2` for guided | |
sampling, and `solver_order=3` for unconditional sampling. | |
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` and | |
`algorithm_type="dpmsolver++"`. | |
algorithm_type (`str`, defaults to `dpmsolver++`): | |
Algorithm type for the solver; can be `dpmsolver` or `dpmsolver++`. The | |
`dpmsolver` type implements the algorithms in the [DPMSolver](https://huggingface.co/papers/2206.00927) | |
paper, and the `dpmsolver++` type implements the algorithms in the | |
[DPMSolver++](https://huggingface.co/papers/2211.01095) paper. It is recommended to use `dpmsolver++` or | |
`sde-dpmsolver++` with `solver_order=2` for guided sampling like in Stable Diffusion. | |
solver_type (`str`, defaults to `midpoint`): | |
Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the | |
sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers. | |
lower_order_final (`bool`, defaults to `True`): | |
Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can | |
stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10. | |
use_karras_sigmas (`bool`, *optional*, defaults to `False`): | |
Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`, | |
the sigmas are determined according to a sequence of noise levels {σi}. | |
final_sigmas_type (`str`, *optional*, defaults to `"zero"`): | |
The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final sigma | |
is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0. | |
lambda_min_clipped (`float`, defaults to `-inf`): | |
Clipping threshold for the minimum value of `lambda(t)` for numerical stability. This is critical for the | |
cosine (`squaredcos_cap_v2`) noise schedule. | |
variance_type (`str`, *optional*): | |
Set to "learned" or "learned_range" for diffusion models that predict variance. If set, the model's output | |
contains the predicted Gaussian variance. | |
""" | |
_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[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 = "dpmsolver++", | |
solver_type: str = "midpoint", | |
lower_order_final: bool = True, | |
use_karras_sigmas: Optional[bool] = False, | |
final_sigmas_type: Optional[str] = "zero", # "zero", "sigma_min" | |
lambda_min_clipped: float = -float("inf"), | |
variance_type: Optional[str] = None, | |
): | |
if algorithm_type == "dpmsolver": | |
deprecation_message = "algorithm_type `dpmsolver` is deprecated and will be removed in a future version. Choose from `dpmsolver++` or `sde-dpmsolver++` instead" | |
deprecate("algorithm_types=dpmsolver", "1.0.0", deprecation_message) | |
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) | |
self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 | |
# standard deviation of the initial noise distribution | |
self.init_noise_sigma = 1.0 | |
# settings for DPM-Solver | |
if algorithm_type not in ["dpmsolver", "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__}") | |
if algorithm_type != "dpmsolver++" and final_sigmas_type == "zero": | |
raise ValueError( | |
f"`final_sigmas_type` {final_sigmas_type} is not supported for `algorithm_type` {algorithm_type}. Please chooose `sigma_min` instead." | |
) | |
# 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.sample = None | |
self.order_list = self.get_order_list(num_train_timesteps) | |
self._step_index = None | |
self.sigmas.to("cpu") # to avoid too much CPU/GPU communication | |
def get_order_list(self, num_inference_steps: int) -> List[int]: | |
""" | |
Computes the solver order at each time step. | |
Args: | |
num_inference_steps (`int`): | |
The number of diffusion steps used when generating samples with a pre-trained model. | |
""" | |
steps = num_inference_steps | |
order = self.config.solver_order | |
if self.config.lower_order_final: | |
if order == 3: | |
if steps % 3 == 0: | |
orders = [1, 2, 3] * (steps // 3 - 1) + [1, 2] + [1] | |
elif steps % 3 == 1: | |
orders = [1, 2, 3] * (steps // 3) + [1] | |
else: | |
orders = [1, 2, 3] * (steps // 3) + [1, 2] | |
elif order == 2: | |
if steps % 2 == 0: | |
orders = [1, 2] * (steps // 2) | |
else: | |
orders = [1, 2] * (steps // 2) + [1] | |
elif order == 1: | |
orders = [1] * steps | |
else: | |
if order == 3: | |
orders = [1, 2, 3] * (steps // 3) | |
elif order == 2: | |
orders = [1, 2] * (steps // 2) | |
elif order == 1: | |
orders = [1] * steps | |
return orders | |
def step_index(self): | |
""" | |
The index counter for current timestep. It will increae 1 after each scheduler step. | |
""" | |
return self._step_index | |
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): | |
""" | |
Sets the discrete timesteps used for the diffusion chain (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 | |
# 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) | |
) | |
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5) | |
if self.config.use_karras_sigmas: | |
log_sigmas = np.log(sigmas) | |
sigmas = np.flip(sigmas).copy() | |
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() | |
else: | |
sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas) | |
if self.config.final_sigmas_type == "sigma_min": | |
sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5 | |
elif self.config.final_sigmas_type == "zero": | |
sigma_last = 0 | |
else: | |
raise ValueError( | |
f" `final_sigmas_type` must be one of `sigma_min` or `zero`, but got {self.config.final_sigmas_type}" | |
) | |
sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32) | |
self.sigmas = torch.from_numpy(sigmas).to(device=device) | |
self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.int64) | |
self.model_outputs = [None] * self.config.solver_order | |
self.sample = None | |
if not self.config.lower_order_final and num_inference_steps % self.config.solver_order != 0: | |
logger.warn( | |
"Changing scheduler {self.config} to have `lower_order_final` set to True to handle uneven amount of inference steps. Please make sure to always use an even number of `num_inference steps when using `lower_order_final=True`." | |
) | |
self.register_to_config(lower_order_final=True) | |
if not self.config.lower_order_final and self.config.final_sigmas_type == "zero": | |
logger.warn( | |
" `last_sigmas_type='zero'` is not supported for `lower_order_final=False`. Changing scheduler {self.config} to have `lower_order_final` set to True." | |
) | |
self.register_to_config(lower_order_final=True) | |
self.order_list = self.get_order_list(num_inference_steps) | |
# add an index counter for schedulers that allow duplicated timesteps | |
self._step_index = None | |
self.sigmas.to("cpu") # to avoid too much CPU/GPU communication | |
# 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 | |
# 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(np.maximum(sigma, 1e-10)) | |
# 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_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t | |
def _sigma_to_alpha_sigma_t(self, sigma): | |
alpha_t = 1 / ((sigma**2 + 1) ** 0.5) | |
sigma_t = sigma * alpha_t | |
return alpha_t, sigma_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).""" | |
# Hack to make sure that other schedulers which copy this function don't break | |
# TODO: Add this logic to the other schedulers | |
if hasattr(self.config, "sigma_min"): | |
sigma_min = self.config.sigma_min | |
else: | |
sigma_min = None | |
if hasattr(self.config, "sigma_max"): | |
sigma_max = self.config.sigma_max | |
else: | |
sigma_max = None | |
sigma_min = sigma_min if sigma_min is not None else in_sigmas[-1].item() | |
sigma_max = sigma_max if sigma_max is not None else 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, | |
*args, | |
sample: torch.FloatTensor = None, | |
**kwargs, | |
) -> torch.FloatTensor: | |
""" | |
Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm 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. | |
<Tip> | |
The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise | |
prediction and data prediction models. | |
</Tip> | |
Args: | |
model_output (`torch.FloatTensor`): | |
The direct output from the learned diffusion model. | |
sample (`torch.FloatTensor`): | |
A current instance of a sample created by the diffusion process. | |
Returns: | |
`torch.FloatTensor`: | |
The converted model output. | |
""" | |
timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None) | |
if sample is None: | |
if len(args) > 1: | |
sample = args[1] | |
else: | |
raise ValueError("missing `sample` as a required keyward argument") | |
if timestep is not None: | |
deprecate( | |
"timesteps", | |
"1.0.0", | |
"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", | |
) | |
# DPM-Solver++ needs to solve an integral of the data prediction model. | |
if self.config.algorithm_type == "dpmsolver++": | |
if self.config.prediction_type == "epsilon": | |
# DPM-Solver and DPM-Solver++ only need the "mean" output. | |
if self.config.variance_type in ["learned_range"]: | |
model_output = model_output[:, :3] | |
sigma = self.sigmas[self.step_index] | |
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) | |
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": | |
sigma = self.sigmas[self.step_index] | |
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) | |
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 DPMSolverSinglestepScheduler." | |
) | |
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 == "dpmsolver": | |
if self.config.prediction_type == "epsilon": | |
# DPM-Solver and DPM-Solver++ only need the "mean" output. | |
if self.config.variance_type in ["learned_range"]: | |
model_output = model_output[:, :3] | |
return model_output | |
elif self.config.prediction_type == "sample": | |
sigma = self.sigmas[self.step_index] | |
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) | |
epsilon = (sample - alpha_t * model_output) / sigma_t | |
return epsilon | |
elif self.config.prediction_type == "v_prediction": | |
sigma = self.sigmas[self.step_index] | |
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) | |
epsilon = alpha_t * model_output + sigma_t * sample | |
return epsilon | |
else: | |
raise ValueError( | |
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" | |
" `v_prediction` for the DPMSolverSinglestepScheduler." | |
) | |
def dpm_solver_first_order_update( | |
self, | |
model_output: torch.FloatTensor, | |
*args, | |
sample: torch.FloatTensor = None, | |
**kwargs, | |
) -> torch.FloatTensor: | |
""" | |
One step for the first-order DPMSolver (equivalent to DDIM). | |
Args: | |
model_output (`torch.FloatTensor`): | |
The direct output from the learned diffusion model. | |
timestep (`int`): | |
The current discrete timestep in the diffusion chain. | |
prev_timestep (`int`): | |
The previous discrete timestep in the diffusion chain. | |
sample (`torch.FloatTensor`): | |
A current instance of a sample created by the diffusion process. | |
Returns: | |
`torch.FloatTensor`: | |
The sample tensor at the previous timestep. | |
""" | |
timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None) | |
prev_timestep = args[1] if len(args) > 1 else kwargs.pop("prev_timestep", None) | |
if sample is None: | |
if len(args) > 2: | |
sample = args[2] | |
else: | |
raise ValueError(" missing `sample` as a required keyward argument") | |
if timestep is not None: | |
deprecate( | |
"timesteps", | |
"1.0.0", | |
"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", | |
) | |
if prev_timestep is not None: | |
deprecate( | |
"prev_timestep", | |
"1.0.0", | |
"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", | |
) | |
sigma_t, sigma_s = self.sigmas[self.step_index + 1], self.sigmas[self.step_index] | |
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) | |
alpha_s, sigma_s = self._sigma_to_alpha_sigma_t(sigma_s) | |
lambda_t = torch.log(alpha_t) - torch.log(sigma_t) | |
lambda_s = torch.log(alpha_s) - torch.log(sigma_s) | |
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 | |
return x_t | |
def singlestep_dpm_solver_second_order_update( | |
self, | |
model_output_list: List[torch.FloatTensor], | |
*args, | |
sample: torch.FloatTensor = None, | |
**kwargs, | |
) -> torch.FloatTensor: | |
""" | |
One step for the second-order singlestep DPMSolver that computes the solution at time `prev_timestep` from the | |
time `timestep_list[-2]`. | |
Args: | |
model_output_list (`List[torch.FloatTensor]`): | |
The direct outputs from learned diffusion model at current and latter timesteps. | |
timestep (`int`): | |
The current and latter discrete timestep in the diffusion chain. | |
prev_timestep (`int`): | |
The previous discrete timestep in the diffusion chain. | |
sample (`torch.FloatTensor`): | |
A current instance of a sample created by the diffusion process. | |
Returns: | |
`torch.FloatTensor`: | |
The sample tensor at the previous timestep. | |
""" | |
timestep_list = args[0] if len(args) > 0 else kwargs.pop("timestep_list", None) | |
prev_timestep = args[1] if len(args) > 1 else kwargs.pop("prev_timestep", None) | |
if sample is None: | |
if len(args) > 2: | |
sample = args[2] | |
else: | |
raise ValueError(" missing `sample` as a required keyward argument") | |
if timestep_list is not None: | |
deprecate( | |
"timestep_list", | |
"1.0.0", | |
"Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", | |
) | |
if prev_timestep is not None: | |
deprecate( | |
"prev_timestep", | |
"1.0.0", | |
"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", | |
) | |
sigma_t, sigma_s0, sigma_s1 = ( | |
self.sigmas[self.step_index + 1], | |
self.sigmas[self.step_index], | |
self.sigmas[self.step_index - 1], | |
) | |
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) | |
alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0) | |
alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1) | |
lambda_t = torch.log(alpha_t) - torch.log(sigma_t) | |
lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) | |
lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1) | |
m0, m1 = model_output_list[-1], model_output_list[-2] | |
h, h_0 = lambda_t - lambda_s1, lambda_s0 - lambda_s1 | |
r0 = h_0 / h | |
D0, D1 = m1, (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_s1) * 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_s1) * 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_s1) * 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_s1) * sample | |
- (sigma_t * (torch.exp(h) - 1.0)) * D0 | |
- (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1 | |
) | |
return x_t | |
def singlestep_dpm_solver_third_order_update( | |
self, | |
model_output_list: List[torch.FloatTensor], | |
*args, | |
sample: torch.FloatTensor = None, | |
**kwargs, | |
) -> torch.FloatTensor: | |
""" | |
One step for the third-order singlestep DPMSolver that computes the solution at time `prev_timestep` from the | |
time `timestep_list[-3]`. | |
Args: | |
model_output_list (`List[torch.FloatTensor]`): | |
The direct outputs from learned diffusion model at current and latter timesteps. | |
timestep (`int`): | |
The current and latter discrete timestep in the diffusion chain. | |
prev_timestep (`int`): | |
The previous discrete timestep in the diffusion chain. | |
sample (`torch.FloatTensor`): | |
A current instance of a sample created by diffusion process. | |
Returns: | |
`torch.FloatTensor`: | |
The sample tensor at the previous timestep. | |
""" | |
timestep_list = args[0] if len(args) > 0 else kwargs.pop("timestep_list", None) | |
prev_timestep = args[1] if len(args) > 1 else kwargs.pop("prev_timestep", None) | |
if sample is None: | |
if len(args) > 2: | |
sample = args[2] | |
else: | |
raise ValueError(" missing`sample` as a required keyward argument") | |
if timestep_list is not None: | |
deprecate( | |
"timestep_list", | |
"1.0.0", | |
"Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", | |
) | |
if prev_timestep is not None: | |
deprecate( | |
"prev_timestep", | |
"1.0.0", | |
"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", | |
) | |
sigma_t, sigma_s0, sigma_s1, sigma_s2 = ( | |
self.sigmas[self.step_index + 1], | |
self.sigmas[self.step_index], | |
self.sigmas[self.step_index - 1], | |
self.sigmas[self.step_index - 2], | |
) | |
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t) | |
alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0) | |
alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1) | |
alpha_s2, sigma_s2 = self._sigma_to_alpha_sigma_t(sigma_s2) | |
lambda_t = torch.log(alpha_t) - torch.log(sigma_t) | |
lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) | |
lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1) | |
lambda_s2 = torch.log(alpha_s2) - torch.log(sigma_s2) | |
m0, m1, m2 = model_output_list[-1], model_output_list[-2], model_output_list[-3] | |
h, h_0, h_1 = lambda_t - lambda_s2, lambda_s0 - lambda_s2, lambda_s1 - lambda_s2 | |
r0, r1 = h_0 / h, h_1 / h | |
D0 = m2 | |
D1_0, D1_1 = (1.0 / r1) * (m1 - m2), (1.0 / r0) * (m0 - m2) | |
D1 = (r0 * D1_0 - r1 * D1_1) / (r0 - r1) | |
D2 = 2.0 * (D1_1 - D1_0) / (r0 - r1) | |
if self.config.algorithm_type == "dpmsolver++": | |
# See https://arxiv.org/abs/2206.00927 for detailed derivations | |
if self.config.solver_type == "midpoint": | |
x_t = ( | |
(sigma_t / sigma_s2) * sample | |
- (alpha_t * (torch.exp(-h) - 1.0)) * D0 | |
+ (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1_1 | |
) | |
elif self.config.solver_type == "heun": | |
x_t = ( | |
(sigma_t / sigma_s2) * 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 | |
if self.config.solver_type == "midpoint": | |
x_t = ( | |
(alpha_t / alpha_s2) * sample | |
- (sigma_t * (torch.exp(h) - 1.0)) * D0 | |
- (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1_1 | |
) | |
elif self.config.solver_type == "heun": | |
x_t = ( | |
(alpha_t / alpha_s2) * 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 singlestep_dpm_solver_update( | |
self, | |
model_output_list: List[torch.FloatTensor], | |
*args, | |
sample: torch.FloatTensor = None, | |
order: int = None, | |
**kwargs, | |
) -> torch.FloatTensor: | |
""" | |
One step for the singlestep DPMSolver. | |
Args: | |
model_output_list (`List[torch.FloatTensor]`): | |
The direct outputs from learned diffusion model at current and latter timesteps. | |
timestep (`int`): | |
The current and latter discrete timestep in the diffusion chain. | |
prev_timestep (`int`): | |
The previous discrete timestep in the diffusion chain. | |
sample (`torch.FloatTensor`): | |
A current instance of a sample created by diffusion process. | |
order (`int`): | |
The solver order at this step. | |
Returns: | |
`torch.FloatTensor`: | |
The sample tensor at the previous timestep. | |
""" | |
timestep_list = args[0] if len(args) > 0 else kwargs.pop("timestep_list", None) | |
prev_timestep = args[1] if len(args) > 1 else kwargs.pop("prev_timestep", None) | |
if sample is None: | |
if len(args) > 2: | |
sample = args[2] | |
else: | |
raise ValueError(" missing`sample` as a required keyward argument") | |
if order is None: | |
if len(args) > 3: | |
order = args[3] | |
else: | |
raise ValueError(" missing `order` as a required keyward argument") | |
if timestep_list is not None: | |
deprecate( | |
"timestep_list", | |
"1.0.0", | |
"Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", | |
) | |
if prev_timestep is not None: | |
deprecate( | |
"prev_timestep", | |
"1.0.0", | |
"Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`", | |
) | |
if order == 1: | |
return self.dpm_solver_first_order_update(model_output_list[-1], sample=sample) | |
elif order == 2: | |
return self.singlestep_dpm_solver_second_order_update(model_output_list, sample=sample) | |
elif order == 3: | |
return self.singlestep_dpm_solver_third_order_update(model_output_list, sample=sample) | |
else: | |
raise ValueError(f"Order must be 1, 2, 3, got {order}") | |
def _init_step_index(self, timestep): | |
if isinstance(timestep, torch.Tensor): | |
timestep = timestep.to(self.timesteps.device) | |
index_candidates = (self.timesteps == timestep).nonzero() | |
if len(index_candidates) == 0: | |
step_index = len(self.timesteps) - 1 | |
# 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) | |
elif len(index_candidates) > 1: | |
step_index = index_candidates[1].item() | |
else: | |
step_index = index_candidates[0].item() | |
self._step_index = step_index | |
def step( | |
self, | |
model_output: torch.FloatTensor, | |
timestep: int, | |
sample: torch.FloatTensor, | |
return_dict: bool = True, | |
) -> Union[SchedulerOutput, Tuple]: | |
""" | |
Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with | |
the singlestep DPMSolver. | |
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. | |
return_dict (`bool`): | |
Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`. | |
Returns: | |
[`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`: | |
If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] 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) | |
model_output = self.convert_model_output(model_output, sample=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 | |
order = self.order_list[self.step_index] | |
# For img2img denoising might start with order>1 which is not possible | |
# In this case make sure that the first two steps are both order=1 | |
while self.model_outputs[-order] is None: | |
order -= 1 | |
# For single-step solvers, we use the initial value at each time with order = 1. | |
if order == 1: | |
self.sample = sample | |
prev_sample = self.singlestep_dpm_solver_update(self.model_outputs, sample=self.sample, order=order) | |
# upon completion increase step index by one | |
self._step_index += 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`): | |
The input sample. | |
Returns: | |
`torch.FloatTensor`: | |
A scaled input sample. | |
""" | |
return sample | |
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.add_noise | |
def add_noise( | |
self, | |
original_samples: torch.FloatTensor, | |
noise: torch.FloatTensor, | |
timesteps: torch.IntTensor, | |
) -> torch.FloatTensor: | |
# Make sure sigmas and timesteps have the same device and dtype as original_samples | |
sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype) | |
if original_samples.device.type == "mps" and torch.is_floating_point(timesteps): | |
# mps does not support float64 | |
schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32) | |
timesteps = timesteps.to(original_samples.device, dtype=torch.float32) | |
else: | |
schedule_timesteps = self.timesteps.to(original_samples.device) | |
timesteps = timesteps.to(original_samples.device) | |
step_indices = [] | |
for timestep in timesteps: | |
index_candidates = (schedule_timesteps == timestep).nonzero() | |
if len(index_candidates) == 0: | |
step_index = len(schedule_timesteps) - 1 | |
elif len(index_candidates) > 1: | |
step_index = index_candidates[1].item() | |
else: | |
step_index = index_candidates[0].item() | |
step_indices.append(step_index) | |
sigma = sigmas[step_indices].flatten() | |
while len(sigma.shape) < len(original_samples.shape): | |
sigma = sigma.unsqueeze(-1) | |
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma) | |
noisy_samples = alpha_t * original_samples + sigma_t * noise | |
return noisy_samples | |
def __len__(self): | |
return self.config.num_train_timesteps | |