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from __future__ import annotations
import logging
from typing import List, Optional, Tuple, Union
import numpy as np
from numpy import ndarray
import torch
from torch import Generator, FloatTensor
from diffusers.schedulers.scheduling_euler_discrete import (
EulerDiscreteScheduler as DiffusersEulerDiscreteScheduler,
EulerDiscreteSchedulerOutput,
)
from diffusers.utils.torch_utils import randn_tensor
from ..utils.noise_util import video_fusion_noise
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
class EulerDiscreteScheduler(DiffusersEulerDiscreteScheduler):
def __init__(
self,
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
trained_betas: ndarray | List[float] | None = None,
prediction_type: str = "epsilon",
interpolation_type: str = "linear",
use_karras_sigmas: bool | None = False,
timestep_spacing: str = "linspace",
steps_offset: int = 0,
):
super().__init__(
num_train_timesteps,
beta_start,
beta_end,
beta_schedule,
trained_betas,
prediction_type,
interpolation_type,
use_karras_sigmas,
timestep_spacing,
steps_offset,
)
def step(
self,
model_output: torch.FloatTensor,
timestep: Union[float, torch.FloatTensor],
sample: torch.FloatTensor,
s_churn: float = 0.0,
s_tmin: float = 0.0,
s_tmax: float = float("inf"),
s_noise: float = 1.0,
generator: Optional[torch.Generator] = None,
return_dict: bool = True,
w_ind_noise: float = 0.5,
noise_type: str = "random",
) -> Union[EulerDiscreteSchedulerOutput, 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.
s_churn (`float`):
s_tmin (`float`):
s_tmax (`float`):
s_noise (`float`, defaults to 1.0):
Scaling factor for noise added to the sample.
generator (`torch.Generator`, *optional*):
A random number generator.
return_dict (`bool`):
Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or
tuple.
Returns:
[`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`:
If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is
returned, otherwise a tuple is returned where the first element is the sample tensor.
"""
if (
isinstance(timestep, int)
or isinstance(timestep, torch.IntTensor)
or isinstance(timestep, torch.LongTensor)
):
raise ValueError(
(
"Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
" `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
" one of the `scheduler.timesteps` as a timestep."
),
)
if not self.is_scale_input_called:
logger.warning(
"The `scale_model_input` function should be called before `step` to ensure correct denoising. "
"See `StableDiffusionPipeline` for a usage example."
)
if self.step_index is None:
self._init_step_index(timestep)
sigma = self.sigmas[self.step_index]
gamma = (
min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1)
if s_tmin <= sigma <= s_tmax
else 0.0
)
device = model_output.device
if noise_type == "random":
noise = randn_tensor(
model_output.shape,
dtype=model_output.dtype,
device=device,
generator=generator,
)
elif noise_type == "video_fusion":
noise = video_fusion_noise(
model_output, w_ind_noise=w_ind_noise, generator=generator
)
eps = noise * s_noise
sigma_hat = sigma * (gamma + 1)
if gamma > 0:
sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
# NOTE: "original_sample" should not be an expected prediction_type but is left in for
# backwards compatibility
if (
self.config.prediction_type == "original_sample"
or self.config.prediction_type == "sample"
):
pred_original_sample = model_output
elif self.config.prediction_type == "epsilon":
pred_original_sample = sample - sigma_hat * model_output
elif self.config.prediction_type == "v_prediction":
# * c_out + input * c_skip
pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (
sample / (sigma**2 + 1)
)
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"
)
# 2. Convert to an ODE derivative
derivative = (sample - pred_original_sample) / sigma_hat
dt = self.sigmas[self.step_index + 1] - sigma_hat
prev_sample = sample + derivative * dt
# upon completion increase step index by one
self._step_index += 1
if not return_dict:
return (prev_sample,)
return EulerDiscreteSchedulerOutput(
prev_sample=prev_sample, pred_original_sample=pred_original_sample
)
def step_bk(
self,
model_output: FloatTensor,
timestep: float | FloatTensor,
sample: FloatTensor,
s_churn: float = 0,
s_tmin: float = 0,
s_tmax: float = float("inf"),
s_noise: float = 1,
generator: Generator | None = None,
return_dict: bool = True,
w_ind_noise: float = 0.5,
noise_type: str = "random",
) -> EulerDiscreteSchedulerOutput | Tuple:
"""
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`): direct output from learned diffusion model.
timestep (`float`): current timestep in the diffusion chain.
sample (`torch.FloatTensor`):
current instance of sample being created by diffusion process.
s_churn (`float`)
s_tmin (`float`)
s_tmax (`float`)
s_noise (`float`)
generator (`torch.Generator`, optional): Random number generator.
return_dict (`bool`): option for returning tuple rather than EulerDiscreteSchedulerOutput class
Returns:
[`~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput`] or `tuple`:
[`~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is the sample tensor.
"""
if (
isinstance(timestep, int)
or isinstance(timestep, torch.IntTensor)
or isinstance(timestep, torch.LongTensor)
):
raise ValueError(
(
"Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
" `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
" one of the `scheduler.timesteps` as a timestep."
),
)
if not self.is_scale_input_called:
logger.warning(
"The `scale_model_input` function should be called before `step` to ensure correct denoising. "
"See `StableDiffusionPipeline` for a usage example."
)
if isinstance(timestep, torch.Tensor):
timestep = timestep.to(self.timesteps.device)
step_index = (self.timesteps == timestep).nonzero().item()
sigma = self.sigmas[step_index]
gamma = (
min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1)
if s_tmin <= sigma <= s_tmax
else 0.0
)
device = model_output.device
if noise_type == "random":
noise = randn_tensor(
model_output.shape,
dtype=model_output.dtype,
device=device,
generator=generator,
)
elif noise_type == "video_fusion":
noise = video_fusion_noise(
model_output, w_ind_noise=w_ind_noise, generator=generator
)
eps = noise * s_noise
sigma_hat = sigma * (gamma + 1)
if gamma > 0:
sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
# NOTE: "original_sample" should not be an expected prediction_type but is left in for
# backwards compatibility
if (
self.config.prediction_type == "original_sample"
or self.config.prediction_type == "sample"
):
pred_original_sample = model_output
elif self.config.prediction_type == "epsilon":
pred_original_sample = sample - sigma_hat * model_output
elif self.config.prediction_type == "v_prediction":
# * c_out + input * c_skip
pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (
sample / (sigma**2 + 1)
)
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"
)
# 2. Convert to an ODE derivative
derivative = (sample - pred_original_sample) / sigma_hat
dt = self.sigmas[step_index + 1] - sigma_hat
prev_sample = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return EulerDiscreteSchedulerOutput(
prev_sample=prev_sample, pred_original_sample=pred_original_sample
)
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