mirror of https://github.com/vladmandic/automatic
1096 lines
44 KiB
Python
1096 lines
44 KiB
Python
# 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: check https://arxiv.org/abs/2302.04867 and https://github.com/wl-zhao/UniPC 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 deprecate
|
||
# from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
|
||
from diffusers.configuration_utils import ConfigMixin, register_to_config
|
||
from diffusers.utils import deprecate
|
||
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,
|
||
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 DCSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
|
||
"""
|
||
`UniPCMultistepScheduler` is a training-free framework designed for the fast sampling of diffusion models.
|
||
|
||
Dynamic Extropolation
|
||
|
||
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`, default `2`):
|
||
The UniPC order which can be any positive integer. The effective order of accuracy is `solver_order + 1`
|
||
due to the UniC. 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 `predict_x0=True`.
|
||
predict_x0 (`bool`, defaults to `True`):
|
||
Whether to use the updating algorithm on the predicted x0.
|
||
solver_type (`str`, default `bh2`):
|
||
Solver type for UniPC. It is recommended to use `bh1` for unconditional sampling when steps < 10, and `bh2`
|
||
otherwise.
|
||
lower_order_final (`bool`, default `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.
|
||
disable_corrector (`list`, default `[]`):
|
||
Decides which step to disable the corrector to mitigate the misalignment between `epsilon_theta(x_t, c)`
|
||
and `epsilon_theta(x_t^c, c)` which can influence convergence for a large guidance scale. Corrector is
|
||
usually disabled during the first few steps.
|
||
solver_p (`SchedulerMixin`, default `None`):
|
||
Any other scheduler that if specified, the algorithm becomes `solver_p + UniC`.
|
||
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}.
|
||
timestep_spacing (`str`, defaults to `"linspace"`):
|
||
The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
|
||
Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
|
||
steps_offset (`int`, defaults to 0):
|
||
An offset added to the inference steps. You can use a combination of `offset=1` and
|
||
`set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable
|
||
Diffusion.
|
||
"""
|
||
|
||
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
|
||
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[Union[np.ndarray, List[float]]] = None,
|
||
solver_order: int = 2,
|
||
dc_order: int = 2,
|
||
prediction_type: str = "epsilon",
|
||
thresholding: bool = False,
|
||
dynamic_thresholding_ratio: float = 0.995,
|
||
sample_max_value: float = 1.0,
|
||
predict_x0: bool = True,
|
||
solver_type: str = "bh2",
|
||
lower_order_final: bool = True,
|
||
disable_corrector: List[int] = [],
|
||
solver_p: SchedulerMixin = None,
|
||
use_karras_sigmas: Optional[bool] = False,
|
||
timestep_spacing: str = "linspace",
|
||
steps_offset: int = 0,
|
||
# ddim_gt_path: str = None,
|
||
ddim_gt=None,
|
||
num_iters=20,
|
||
bound_func='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
|
||
|
||
if solver_type not in ["bh1", "bh2"]:
|
||
if solver_type in ["midpoint", "heun", "logrho"]:
|
||
self.register_to_config(solver_type="bh2")
|
||
else:
|
||
raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}")
|
||
|
||
self.predict_x0 = predict_x0
|
||
# 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.buffer_size = max(solver_order, dc_order + 1)
|
||
self.num_iters = num_iters
|
||
self.model_outputs = [None] * self.buffer_size
|
||
self.timestep_list = [None] * self.buffer_size
|
||
self.lower_order_nums = 0
|
||
self.disable_corrector = disable_corrector
|
||
self.solver_p = solver_p
|
||
self.last_sample = None
|
||
self._step_index = None
|
||
|
||
if ddim_gt is not None:
|
||
self.ddim_gt = dict(
|
||
ts=ddim_gt['ts'].cpu().numpy(),
|
||
intermediates=ddim_gt['intermediates'].cpu().numpy(),
|
||
)
|
||
else:
|
||
self.ddim_gt = None
|
||
self.bound_func = bound_func
|
||
self.dc_order = dc_order
|
||
|
||
@property
|
||
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.
|
||
"""
|
||
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
|
||
if self.config.timestep_spacing == "linspace":
|
||
timesteps = (
|
||
np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps + 1)
|
||
.round()[::-1][:-1]
|
||
.copy()
|
||
.astype(np.int64)
|
||
)
|
||
elif self.config.timestep_spacing == "leading":
|
||
step_ratio = self.config.num_train_timesteps // (num_inference_steps + 1)
|
||
# creates integer timesteps by multiplying by ratio
|
||
# casting to int to avoid issues when num_inference_step is power of 3
|
||
timesteps = (np.arange(0, num_inference_steps + 1) * step_ratio).round()[::-1][:-1].copy().astype(np.int64)
|
||
timesteps += self.config.steps_offset
|
||
elif self.config.timestep_spacing == "trailing":
|
||
step_ratio = self.config.num_train_timesteps / num_inference_steps
|
||
# creates integer timesteps by multiplying by ratio
|
||
# casting to int to avoid issues when num_inference_step is power of 3
|
||
timesteps = np.arange(self.config.num_train_timesteps, 0, -step_ratio).round().copy().astype(np.int64)
|
||
timesteps -= 1
|
||
else:
|
||
raise ValueError(
|
||
f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
|
||
)
|
||
|
||
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()
|
||
sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)
|
||
else:
|
||
sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
|
||
sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5
|
||
sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
|
||
|
||
self.sigmas = torch.from_numpy(sigmas)
|
||
self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.int64)
|
||
|
||
self.num_inference_steps = len(timesteps)
|
||
|
||
self.model_outputs = [None] * self.buffer_size
|
||
self.timestep_list = [None] * self.buffer_size
|
||
|
||
self.lower_order_nums = 0
|
||
self.last_sample = None
|
||
if self.solver_p:
|
||
self.solver_p.set_timesteps(self.num_inference_steps, device=device)
|
||
|
||
# add an index counter for schedulers that allow duplicated timesteps
|
||
self._step_index = None
|
||
# also init the ratios
|
||
self.dc_ratios = []
|
||
|
||
# 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(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_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)."""
|
||
|
||
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,
|
||
*args,
|
||
sample: torch.FloatTensor = None,
|
||
**kwargs,
|
||
) -> torch.FloatTensor:
|
||
r"""
|
||
Convert the model output to the corresponding type the UniPC algorithm needs.
|
||
|
||
Args:
|
||
model_output (`torch.FloatTensor`):
|
||
The direct output from the 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.
|
||
|
||
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`",
|
||
)
|
||
|
||
sigma = self.sigmas[self.step_index]
|
||
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
|
||
|
||
if self.predict_x0:
|
||
if self.config.prediction_type == "epsilon":
|
||
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":
|
||
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 UniPCMultistepScheduler."
|
||
)
|
||
|
||
if self.config.thresholding:
|
||
x0_pred = self._threshold_sample(x0_pred)
|
||
|
||
return x0_pred
|
||
else:
|
||
if self.config.prediction_type == "epsilon":
|
||
return model_output
|
||
elif self.config.prediction_type == "sample":
|
||
epsilon = (sample - alpha_t * model_output) / sigma_t
|
||
return epsilon
|
||
elif self.config.prediction_type == "v_prediction":
|
||
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 UniPCMultistepScheduler."
|
||
)
|
||
|
||
|
||
def multistep_uni_p_bh_update(
|
||
self,
|
||
model_output: torch.FloatTensor = None,
|
||
*args,
|
||
sample: torch.FloatTensor = None,
|
||
order: int = None,
|
||
**kwargs,
|
||
) -> torch.FloatTensor:
|
||
"""
|
||
One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified.
|
||
|
||
Args:
|
||
model_output (`torch.FloatTensor`):
|
||
The direct output from the learned diffusion model at the current timestep.
|
||
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.
|
||
order (`int`):
|
||
The order of UniP at this timestep (corresponds to the *p* in UniPC-p).
|
||
|
||
Returns:
|
||
`torch.FloatTensor`:
|
||
The sample tensor at the previous timestep.
|
||
"""
|
||
prev_timestep = args[0] if len(args) > 0 else kwargs.pop("prev_timestep", None)
|
||
if sample is None:
|
||
if len(args) > 1:
|
||
sample = args[1]
|
||
else:
|
||
raise ValueError(" missing `sample` as a required keyward argument")
|
||
if order is None:
|
||
if len(args) > 2:
|
||
order = args[2]
|
||
else:
|
||
raise ValueError(" missing `order` as a required keyward argument")
|
||
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`",
|
||
)
|
||
model_output_list = self.model_outputs
|
||
|
||
s0 = self.timestep_list[-1]
|
||
m0 = model_output_list[-1]
|
||
assert m0 is not None
|
||
x = sample
|
||
|
||
if self.solver_p:
|
||
raise NotImplementedError()
|
||
|
||
sigma_t, sigma_s0 = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]
|
||
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
|
||
alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
|
||
|
||
lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
|
||
lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
|
||
|
||
h = lambda_t - lambda_s0
|
||
device = sample.device
|
||
|
||
rks = []
|
||
D1s = []
|
||
for i in range(1, order):
|
||
si = self.step_index - i
|
||
mi = model_output_list[-(i + 1)]
|
||
alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
|
||
lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
|
||
rk = (lambda_si - lambda_s0) / h
|
||
rks.append(rk)
|
||
D1s.append((mi - m0) / rk)
|
||
|
||
rks.append(1.0)
|
||
rks = torch.tensor(rks, device=device)
|
||
|
||
R = []
|
||
b = []
|
||
|
||
hh = -h if self.predict_x0 else h
|
||
h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
|
||
h_phi_k = h_phi_1 / hh - 1
|
||
|
||
factorial_i = 1
|
||
|
||
if self.config.solver_type == "bh1":
|
||
B_h = hh
|
||
elif self.config.solver_type == "bh2":
|
||
B_h = torch.expm1(hh)
|
||
else:
|
||
raise NotImplementedError()
|
||
|
||
for i in range(1, order + 1):
|
||
R.append(torch.pow(rks, i - 1))
|
||
b.append(h_phi_k * factorial_i / B_h)
|
||
factorial_i *= i + 1
|
||
h_phi_k = h_phi_k / hh - 1 / factorial_i
|
||
|
||
R = torch.stack(R)
|
||
b = torch.tensor(b, device=device)
|
||
|
||
if len(D1s) > 0:
|
||
D1s = torch.stack(D1s, dim=1) # (B, K)
|
||
# for order 2, we use a simplified version
|
||
if order == 2:
|
||
rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device)
|
||
else:
|
||
rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1])
|
||
else:
|
||
D1s = None
|
||
|
||
if self.predict_x0:
|
||
x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
|
||
if D1s is not None:
|
||
pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s)
|
||
else:
|
||
pred_res = 0
|
||
x_t = x_t_ - alpha_t * B_h * pred_res
|
||
else:
|
||
x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
|
||
if D1s is not None:
|
||
pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s)
|
||
else:
|
||
pred_res = 0
|
||
x_t = x_t_ - sigma_t * B_h * pred_res
|
||
|
||
x_t = x_t.to(x.dtype)
|
||
return x_t
|
||
|
||
def multistep_uni_c_bh_update(
|
||
self,
|
||
this_model_output: torch.FloatTensor,
|
||
*args,
|
||
last_sample: torch.FloatTensor = None,
|
||
this_sample: torch.FloatTensor = None,
|
||
order: int = None,
|
||
**kwargs,
|
||
) -> torch.FloatTensor:
|
||
"""
|
||
One step for the UniC (B(h) version).
|
||
|
||
Args:
|
||
this_model_output (`torch.FloatTensor`):
|
||
The model outputs at `x_t`.
|
||
this_timestep (`int`):
|
||
The current timestep `t`.
|
||
last_sample (`torch.FloatTensor`):
|
||
The generated sample before the last predictor `x_{t-1}`.
|
||
this_sample (`torch.FloatTensor`):
|
||
The generated sample after the last predictor `x_{t}`.
|
||
order (`int`):
|
||
The `p` of UniC-p at this step. The effective order of accuracy should be `order + 1`.
|
||
|
||
Returns:
|
||
`torch.FloatTensor`:
|
||
The corrected sample tensor at the current timestep.
|
||
"""
|
||
this_timestep = args[0] if len(args) > 0 else kwargs.pop("this_timestep", None)
|
||
if last_sample is None:
|
||
if len(args) > 1:
|
||
last_sample = args[1]
|
||
else:
|
||
raise ValueError(" missing`last_sample` as a required keyward argument")
|
||
if this_sample is None:
|
||
if len(args) > 2:
|
||
this_sample = args[2]
|
||
else:
|
||
raise ValueError(" missing`this_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 this_timestep is not None:
|
||
deprecate(
|
||
"this_timestep",
|
||
"1.0.0",
|
||
"Passing `this_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
|
||
)
|
||
|
||
model_output_list = self.model_outputs
|
||
|
||
m0 = model_output_list[-1]
|
||
x = last_sample
|
||
x_t = this_sample
|
||
model_t = this_model_output
|
||
|
||
sigma_t, sigma_s0 = 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)
|
||
|
||
lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
|
||
lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
|
||
|
||
h = lambda_t - lambda_s0
|
||
device = this_sample.device
|
||
|
||
rks = []
|
||
D1s = []
|
||
for i in range(1, order):
|
||
si = self.step_index - (i + 1)
|
||
mi = model_output_list[-(i + 1)]
|
||
alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
|
||
lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
|
||
rk = (lambda_si - lambda_s0) / h
|
||
rks.append(rk)
|
||
D1s.append((mi - m0) / rk)
|
||
|
||
rks.append(1.0)
|
||
rks = torch.tensor(rks, device=device)
|
||
|
||
R = []
|
||
b = []
|
||
|
||
hh = -h if self.predict_x0 else h
|
||
h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
|
||
h_phi_k = h_phi_1 / hh - 1
|
||
|
||
factorial_i = 1
|
||
|
||
if self.config.solver_type == "bh1":
|
||
B_h = hh
|
||
elif self.config.solver_type == "bh2":
|
||
B_h = torch.expm1(hh)
|
||
else:
|
||
raise NotImplementedError()
|
||
|
||
for i in range(1, order + 1):
|
||
R.append(torch.pow(rks, i - 1))
|
||
b.append(h_phi_k * factorial_i / B_h)
|
||
factorial_i *= i + 1
|
||
h_phi_k = h_phi_k / hh - 1 / factorial_i
|
||
|
||
R = torch.stack(R)
|
||
b = torch.tensor(b, device=device)
|
||
|
||
if len(D1s) > 0:
|
||
D1s = torch.stack(D1s, dim=1)
|
||
else:
|
||
D1s = None
|
||
|
||
# for order 1, we use a simplified version
|
||
if order == 1:
|
||
rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device)
|
||
else:
|
||
rhos_c = torch.linalg.solve(R, b)
|
||
|
||
if self.predict_x0:
|
||
try:
|
||
x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
|
||
except Exception as e:
|
||
import pdb; pdb.set_trace()
|
||
if D1s is not None:
|
||
corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s)
|
||
else:
|
||
corr_res = 0
|
||
D1_t = model_t - m0
|
||
x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t)
|
||
else:
|
||
x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
|
||
if D1s is not None:
|
||
corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s)
|
||
else:
|
||
corr_res = 0
|
||
D1_t = model_t - m0
|
||
x_t = x_t_ - sigma_t * B_h * (corr_res + rhos_c[-1] * D1_t)
|
||
x_t = x_t.to(x.dtype)
|
||
return x_t
|
||
|
||
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 dynamic_compensation(self, model_prev_list, t_prev_list, ratio):
|
||
len_buffer = len([t for t in t_prev_list if t is not None])
|
||
if len_buffer < 2:
|
||
return None
|
||
|
||
t_ = ratio * (t_prev_list[-1] - t_prev_list[-2]) + t_prev_list[-2]
|
||
|
||
inter_order = min(self.dc_order + 1, 4)
|
||
|
||
if inter_order is not None:
|
||
model_t_dc = torch.zeros_like(model_prev_list[-1])
|
||
for i in range(inter_order):
|
||
term = model_prev_list[-(i + 1)]
|
||
for j in range(inter_order):
|
||
if i != j:
|
||
para = (t_ - t_prev_list[-(j + 1)]) / (t_prev_list[-(i + 1)] - t_prev_list[-(j + 1)])
|
||
term = term * para
|
||
model_t_dc = model_t_dc + term
|
||
else:
|
||
model_t_dc = None
|
||
return model_t_dc
|
||
|
||
def find_optim_ratio(self, sample, ratio_initial=1.0):
|
||
if self.bound_func == 'tanh':
|
||
bound_func = lambda x: torch.nn.functional.tanh(x) * 0.5 + ratio_initial
|
||
param_initial = 0.
|
||
else:
|
||
bound_func = lambda x: x
|
||
param_initial = ratio_initial
|
||
|
||
# step 1: define the parameters
|
||
if self.step_index < len(self.timesteps) - 2:
|
||
scalar_t = self.timesteps[self.step_index + 1].item()
|
||
else:
|
||
scalar_t = 0
|
||
ratio_param = torch.nn.Parameter(torch.tensor([param_initial], device=sample.device), requires_grad=True)
|
||
|
||
sample_clone = sample.clone()
|
||
|
||
index = np.where(self.ddim_gt['ts'] >= scalar_t)[0].max()
|
||
batch_size = sample.shape[0]
|
||
|
||
x_t_gt = torch.from_numpy(self.ddim_gt['intermediates'][:batch_size, index]).to(sample.device) # suppose the first batch
|
||
|
||
model_t_bak = self.model_outputs[-1]
|
||
def closure(ratio_param):
|
||
ratio_bound = bound_func(ratio_param)
|
||
# torch.nn.functional.tanh(ratio_param) * 0.5 + ratio_initial
|
||
sample = sample_clone.clone()
|
||
model_t_dc = self.dynamic_compensation(self.model_outputs, self.timestep_list, ratio=ratio_bound)
|
||
if model_t_dc is not None:
|
||
self.model_outputs[-1] = model_t_dc
|
||
self.last_sample = sample
|
||
# run predictor
|
||
sample = self.multistep_uni_p_bh_update(
|
||
sample=sample,
|
||
order=self.this_order,
|
||
)
|
||
# run the next corrector
|
||
self._step_index += 1
|
||
use_corrector = (
|
||
self.step_index > 0 and self.step_index - 1 not in self.disable_corrector \
|
||
and self.last_sample is not None \
|
||
and self.step_index < len(self.timesteps)
|
||
)
|
||
if use_corrector:
|
||
model_output = self.model_wrapper(sample, self.timesteps[self.step_index])
|
||
model_output_convert = self.convert_model_output(model_output, sample=sample)
|
||
sample = self.multistep_uni_c_bh_update(
|
||
this_model_output=model_output_convert,
|
||
last_sample=self.last_sample,
|
||
this_sample=sample,
|
||
order=self.this_order,
|
||
)
|
||
x_t_pred = sample
|
||
loss = torch.nn.functional.mse_loss(x_t_pred, x_t_gt)
|
||
# rewind
|
||
self._step_index -= 1
|
||
self.model_outputs[-1] = model_t_bak
|
||
return loss
|
||
|
||
optimizer = torch.optim.AdamW([ratio_param], lr=0.1)
|
||
for iter_ in range(self.num_iters):
|
||
optimizer.zero_grad()
|
||
loss = closure(ratio_param)
|
||
loss.backward()
|
||
optimizer.step()
|
||
ratio_bound = bound_func(ratio_param)
|
||
|
||
torch.cuda.empty_cache()
|
||
return ratio_bound.data.detach().item()
|
||
|
||
def cascade_polynomial_regression(self, test_CFG, test_NFE, cpr_path):
|
||
def f1(x, a, b, c):
|
||
return a * x ** 2 + b * x + c # np.log(np.abs(x - c)) + b
|
||
|
||
def f2(x, a, b, c):
|
||
return a * x ** 2 + b * x + c # a * np.exp(-b * x) + c
|
||
|
||
def predict(xs, *coeffs):
|
||
CFG, NFE, x = xs[0], xs[1], xs[2]
|
||
CFG = CFG / 12
|
||
x = x / NFE
|
||
NFE = NFE / 50
|
||
NFE = NFE.reshape(-1, 1, 1)
|
||
CFG = CFG.reshape(-1, 1)
|
||
coeffs = np.array(coeffs).reshape(-1, 3, 3)
|
||
coeffs1 = f2(NFE, coeffs[..., 0], coeffs[..., 1], coeffs[..., 2])
|
||
coeffs2 = f1(CFG, coeffs1[..., 0], coeffs1[..., 1], coeffs1[..., 2])
|
||
|
||
x_pow = 1
|
||
result = 0
|
||
for i in range(coeffs2.shape[-1]):
|
||
result = result + coeffs2[:, i] * x_pow
|
||
x_pow = x_pow * x
|
||
return result
|
||
|
||
cpr_coeffs = np.load(cpr_path)
|
||
ratios = []
|
||
steps = list(range(1, test_NFE + 1))
|
||
for step in steps:
|
||
if step < 3:
|
||
ratio = 1
|
||
else:
|
||
infer_x = np.array([test_CFG, test_NFE, step]).reshape(3, -1)
|
||
ratio = predict(infer_x, *cpr_coeffs).item()
|
||
ratios.append(ratio)
|
||
return ratios
|
||
|
||
|
||
def step(self, *args, **kwargs):
|
||
if self.ddim_gt is None:
|
||
return self._step(*args, **kwargs)
|
||
else:
|
||
return self._step_search(*args, **kwargs)
|
||
|
||
@torch.no_grad()
|
||
def _step_search(
|
||
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 multistep UniPC.
|
||
|
||
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)
|
||
|
||
use_corrector = (
|
||
self.step_index > 0 and self.step_index - 1 not in self.disable_corrector and self.last_sample is not None
|
||
)
|
||
|
||
model_output_convert = self.convert_model_output(model_output, sample=sample)
|
||
if use_corrector:
|
||
sample = self.multistep_uni_c_bh_update(
|
||
this_model_output=model_output_convert,
|
||
last_sample=self.last_sample,
|
||
this_sample=sample,
|
||
order=self.this_order,
|
||
)
|
||
|
||
for i in range(self.buffer_size - 1):
|
||
self.model_outputs[i] = self.model_outputs[i + 1]
|
||
self.timestep_list[i] = self.timestep_list[i + 1]
|
||
|
||
self.model_outputs[-1] = model_output_convert
|
||
self.timestep_list[-1] = timestep
|
||
|
||
if self.config.lower_order_final:
|
||
this_order = min(self.config.solver_order, len(self.timesteps) - self.step_index)
|
||
else:
|
||
this_order = self.config.solver_order
|
||
|
||
self.this_order = min(this_order, self.lower_order_nums + 1) # warmup for multistep
|
||
assert self.this_order > 0
|
||
|
||
# here we will use dynamic extrapolation to update the model_output
|
||
with torch.enable_grad():
|
||
if self.step_index > 1:
|
||
ratio_optim = self.find_optim_ratio(sample, ratio_initial=1.0)
|
||
else:
|
||
ratio_optim = 1.0
|
||
self.dc_ratios.append(ratio_optim)
|
||
|
||
# now update by dynamic compensation
|
||
if ratio_optim != 1.0:
|
||
self.model_outputs[-1] = self.dynamic_compensation(self.model_outputs, self.timestep_list, ratio=ratio_optim)
|
||
|
||
prev_sample = self.multistep_uni_p_bh_update(
|
||
# model_output=model_output, # pass the original non-converted model output, in case solver-p is used
|
||
sample=sample,
|
||
order=self.this_order,
|
||
)
|
||
self.last_sample = sample
|
||
if self.lower_order_nums < self.config.solver_order:
|
||
self.lower_order_nums += 1
|
||
|
||
# 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 _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 multistep UniPC.
|
||
|
||
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)
|
||
|
||
use_corrector = (
|
||
self.step_index > 0 and self.step_index - 1 not in self.disable_corrector and self.last_sample is not None
|
||
)
|
||
|
||
model_output_convert = self.convert_model_output(model_output, sample=sample)
|
||
if use_corrector:
|
||
sample = self.multistep_uni_c_bh_update(
|
||
this_model_output=model_output_convert,
|
||
last_sample=self.last_sample,
|
||
this_sample=sample,
|
||
order=self.this_order,
|
||
)
|
||
|
||
for i in range(self.buffer_size - 1):
|
||
self.model_outputs[i] = self.model_outputs[i + 1]
|
||
self.timestep_list[i] = self.timestep_list[i + 1]
|
||
|
||
self.model_outputs[-1] = model_output_convert
|
||
self.timestep_list[-1] = timestep
|
||
|
||
if self.config.lower_order_final:
|
||
this_order = min(self.config.solver_order, len(self.timesteps) - self.step_index)
|
||
else:
|
||
this_order = self.config.solver_order
|
||
|
||
self.this_order = min(this_order, self.lower_order_nums + 1) # warmup for multistep
|
||
assert self.this_order > 0
|
||
|
||
self.last_sample = sample
|
||
|
||
# here we will use dynamic compensation to update the model_output
|
||
# dc_ratio = self.dc_ratios[self.step_index]
|
||
# if dc_ratio != 1.0:
|
||
# self.model_outputs[-1] = self.dynamic_compensation(self.model_outputs, self.timestep_list, dc_ratio)
|
||
|
||
prev_sample = self.multistep_uni_p_bh_update(
|
||
model_output=model_output, # pass the original non-converted model output, in case solver-p is used
|
||
sample=sample,
|
||
order=self.this_order,
|
||
)
|
||
|
||
if self.lower_order_nums < self.config.solver_order:
|
||
self.lower_order_nums += 1
|
||
|
||
# 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 = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
|
||
|
||
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
|