tex3 / src /scheduler_perflow.py
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# Copyright 2023 Stanford University Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import BaseOutput
from diffusers.utils.torch_utils import randn_tensor
from diffusers.schedulers.scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
class Time_Windows():
def __init__(self, t_initial=1, t_terminal=0, num_windows=4, precision=1./1000) -> None:
assert t_terminal < t_initial
time_windows = [ 1.*i/num_windows for i in range(1, num_windows+1)][::-1]
self.window_starts = time_windows # [1.0, 0.75, 0.5, 0.25]
self.window_ends = time_windows[1:] + [t_terminal] # [0.75, 0.5, 0.25, 0]
self.precision = precision
def get_window(self, tp):
idx = 0
# robust to numerical error; e.g, (0.6+1/10000) belongs to [0.6, 0.3)
while (tp-0.1*self.precision) <= self.window_ends[idx]:
idx += 1
return self.window_starts[idx], self.window_ends[idx]
def lookup_window(self, timepoint):
if timepoint.dim() == 0:
t_start, t_end = self.get_window(timepoint)
t_start = torch.ones_like(timepoint) * t_start
t_end = torch.ones_like(timepoint) * t_end
else:
t_start = torch.zeros_like(timepoint)
t_end = torch.zeros_like(timepoint)
bsz = timepoint.shape[0]
for i in range(bsz):
tp = timepoint[i]
ts, te = self.get_window(tp)
t_start[i] = ts
t_end[i] = te
return t_start, t_end
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class PeRFlowSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
def betas_for_alpha_bar(
num_diffusion_timesteps,
max_beta=0.999,
alpha_transform_type="cosine",
):
"""
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
(1-beta) over time from t = [0,1].
Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
to that part of the diffusion process.
Args:
num_diffusion_timesteps (`int`): the number of betas to produce.
max_beta (`float`): the maximum beta to use; use values lower than 1 to
prevent singularities.
alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
Choose from `cosine` or `exp`
Returns:
betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(t):
return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(t):
return math.exp(t * -12.0)
else:
raise ValueError(f"Unsupported alpha_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 PeRFlowScheduler(SchedulerMixin, ConfigMixin):
"""
`ReFlowScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
non-Markovian guidance.
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`.
set_alpha_to_one (`bool`, defaults to `True`):
Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
otherwise it uses the alpha value at step 0.
prediction_type (`str`, defaults to `epsilon`, *optional*)
"""
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
order = 1
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.00085,
beta_end: float = 0.012,
beta_schedule: str = "scaled_linear",
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
set_alpha_to_one: bool = False,
prediction_type: str = "epsilon",
t_noise: float = 1,
t_clean: float = 0,
num_time_windows = 4,
):
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)
# At every step in ddim, we are looking into the previous alphas_cumprod
# For the final step, there is no previous alphas_cumprod because we are already at 0
# `set_alpha_to_one` decides whether we set this parameter simply to one or
# whether we use the final alpha of the "non-previous" one.
self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
# # standard deviation of the initial noise distribution
self.init_noise_sigma = 1.0
self.time_windows = Time_Windows(t_initial=t_noise, t_terminal=t_clean,
num_windows=num_time_windows,
precision=1./num_train_timesteps)
def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.
Args:
sample (`torch.FloatTensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.FloatTensor`:
A scaled input sample.
"""
return sample
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.
"""
if num_inference_steps < self.config.num_time_windows:
num_inference_steps = self.config.num_time_windows
print(f"### We recommend a num_inference_steps not less than num_time_windows. It's set as {self.config.num_time_windows}.")
timesteps = []
for i in range(self.config.num_time_windows):
if i < num_inference_steps%self.config.num_time_windows:
num_steps_cur_win = num_inference_steps//self.config.num_time_windows+1
else:
num_steps_cur_win = num_inference_steps//self.config.num_time_windows
t_s = self.time_windows.window_starts[i]
t_e = self.time_windows.window_ends[i]
timesteps_cur_win = np.linspace(t_s, t_e, num=num_steps_cur_win, endpoint=False)
timesteps.append(timesteps_cur_win)
timesteps = np.concatenate(timesteps)
self.timesteps = torch.from_numpy(
(timesteps*self.config.num_train_timesteps).astype(np.int64)
).to(device)
def get_window_alpha(self, timestep):
time_windows = self.time_windows
num_train_timesteps = self.config.num_train_timesteps
t_win_start, t_win_end = time_windows.lookup_window(timestep / num_train_timesteps)
t_win_len = t_win_end - t_win_start
t_interval = timestep / num_train_timesteps - t_win_start # NOTE: negative value
idx_start = (t_win_start*num_train_timesteps - 1 ).long()
idx_end = torch.clamp( (t_win_end*num_train_timesteps - 1 ).long(), min=0)
alpha_cumprod_s_e = self.alphas_cumprod[idx_start] / self.alphas_cumprod[idx_end]
gamma_s_e = alpha_cumprod_s_e ** 0.5
return t_win_start, t_win_end, t_win_len, t_interval, gamma_s_e
def step(
self,
model_output: torch.FloatTensor,
timestep: int,
sample: torch.FloatTensor,
return_dict: bool = True,
) -> Union[PeRFlowSchedulerOutput, Tuple]:
"""
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`):
The direct output from learned diffusion model.
timestep (`float`):
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`, *optional*, defaults to `True`):
Whether or not to return a [`~schedulers.scheduling_ddim.PeRFlowSchedulerOutput`] or `tuple`.
Returns:
[`~schedulers.scheduling_utils.PeRFlowSchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_ddim.PeRFlowSchedulerOutput`] is returned, otherwise a
tuple is returned where the first element is the sample tensor.
"""
if self.config.prediction_type == "epsilon":
pred_epsilon = model_output
t_win_start, t_win_end, t_win_len, t_interval, gamma_s_e = self.get_window_alpha(timestep)
pred_sample_end = ( sample - (1-t_interval/t_win_len) * ((1-gamma_s_e**2)**0.5) * pred_epsilon ) \
/ ( gamma_s_e + t_interval / t_win_len * (1-gamma_s_e) )
pred_velocity = (pred_sample_end - sample) / (t_win_end - (t_win_start + t_interval))
elif self.config.prediction_type == "velocity":
pred_velocity = model_output
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `velocity`."
)
# get dt
idx = torch.argwhere(torch.where(self.timesteps==timestep, 1,0))
prev_step = self.timesteps[idx+1] if (idx+1)<len(self.timesteps) else 0
dt = (prev_step - timestep) / self.config.num_train_timesteps
dt = dt.to(sample.device, sample.dtype)
prev_sample = sample + dt * pred_velocity
if not return_dict:
return (prev_sample,)
return PeRFlowSchedulerOutput(prev_sample=prev_sample, pred_original_sample=None)
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
def add_noise(
self,
original_samples: torch.FloatTensor,
noise: torch.FloatTensor,
timesteps: torch.IntTensor,
) -> torch.FloatTensor:
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
timesteps = timesteps.to(original_samples.device) - 1 # indexing from 0
sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def __len__(self):
return self.config.num_train_timesteps