449 lines
19 KiB
Python
449 lines
19 KiB
Python
|
# Copyright 2025 Katherine Crowson 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.
|
||
|
|
|
||
|
|
import math
|
||
|
|
from dataclasses import dataclass
|
||
|
|
from typing import List, Optional, Tuple, Union
|
||
|
|
|
||
|
|
import torch
|
||
|
|
|
||
|
|
from ..configuration_utils import ConfigMixin, register_to_config
|
||
|
|
from ..utils import BaseOutput, logging
|
||
|
|
from ..utils.torch_utils import randn_tensor
|
||
|
|
from .scheduling_utils import SchedulerMixin
|
||
|
|
|
||
|
|
|
||
|
|
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
|
||
|
|
class EDMEulerSchedulerOutput(BaseOutput):
|
||
|
|
"""
|
||
|
|
Output class for the scheduler's `step` function output.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
prev_sample (`torch.Tensor` 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.Tensor` 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.Tensor
|
||
|
|
pred_original_sample: Optional[torch.Tensor] = None
|
||
|
|
|
||
|
|
|
||
|
|
class EDMEulerScheduler(SchedulerMixin, ConfigMixin):
|
||
|
|
"""
|
||
|
|
Implements the Euler scheduler in EDM formulation as presented in Karras et al. 2022 [1].
|
||
|
|
|
||
|
|
[1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
|
||
|
|
https://huggingface.co/papers/2206.00364
|
||
|
|
|
||
|
|
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:
|
||
|
|
sigma_min (`float`, *optional*, defaults to 0.002):
|
||
|
|
Minimum noise magnitude in the sigma schedule. This was set to 0.002 in the EDM paper [1]; a reasonable
|
||
|
|
range is [0, 10].
|
||
|
|
sigma_max (`float`, *optional*, defaults to 80.0):
|
||
|
|
Maximum noise magnitude in the sigma schedule. This was set to 80.0 in the EDM paper [1]; a reasonable
|
||
|
|
range is [0.2, 80.0].
|
||
|
|
sigma_data (`float`, *optional*, defaults to 0.5):
|
||
|
|
The standard deviation of the data distribution. This is set to 0.5 in the EDM paper [1].
|
||
|
|
sigma_schedule (`str`, *optional*, defaults to `karras`):
|
||
|
|
Sigma schedule to compute the `sigmas`. By default, we the schedule introduced in the EDM paper
|
||
|
|
(https://huggingface.co/papers/2206.00364). Other acceptable value is "exponential". The exponential
|
||
|
|
schedule was incorporated in this model: https://huggingface.co/stabilityai/cosxl.
|
||
|
|
num_train_timesteps (`int`, defaults to 1000):
|
||
|
|
The number of diffusion steps to train the model.
|
||
|
|
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).
|
||
|
|
rho (`float`, *optional*, defaults to 7.0):
|
||
|
|
The rho parameter used for calculating the Karras sigma schedule, which is set to 7.0 in the EDM paper [1].
|
||
|
|
final_sigmas_type (`str`, 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.
|
||
|
|
"""
|
||
|
|
|
||
|
|
_compatibles = []
|
||
|
|
order = 1
|
||
|
|
|
||
|
|
@register_to_config
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
sigma_min: float = 0.002,
|
||
|
|
sigma_max: float = 80.0,
|
||
|
|
sigma_data: float = 0.5,
|
||
|
|
sigma_schedule: str = "karras",
|
||
|
|
num_train_timesteps: int = 1000,
|
||
|
|
prediction_type: str = "epsilon",
|
||
|
|
rho: float = 7.0,
|
||
|
|
final_sigmas_type: str = "zero", # can be "zero" or "sigma_min"
|
||
|
|
):
|
||
|
|
if sigma_schedule not in ["karras", "exponential"]:
|
||
|
|
raise ValueError(f"Wrong value for provided for `{sigma_schedule=}`.`")
|
||
|
|
|
||
|
|
# setable values
|
||
|
|
self.num_inference_steps = None
|
||
|
|
|
||
|
|
sigmas_dtype = torch.float32 if torch.backends.mps.is_available() else torch.float64
|
||
|
|
sigmas = torch.arange(num_train_timesteps + 1, dtype=sigmas_dtype) / num_train_timesteps
|
||
|
|
if sigma_schedule == "karras":
|
||
|
|
sigmas = self._compute_karras_sigmas(sigmas)
|
||
|
|
elif sigma_schedule == "exponential":
|
||
|
|
sigmas = self._compute_exponential_sigmas(sigmas)
|
||
|
|
sigmas = sigmas.to(torch.float32)
|
||
|
|
|
||
|
|
self.timesteps = self.precondition_noise(sigmas)
|
||
|
|
|
||
|
|
if self.config.final_sigmas_type == "sigma_min":
|
||
|
|
sigma_last = sigmas[-1]
|
||
|
|
elif self.config.final_sigmas_type == "zero":
|
||
|
|
sigma_last = 0
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}"
|
||
|
|
)
|
||
|
|
|
||
|
|
self.sigmas = torch.cat([sigmas, torch.full((1,), fill_value=sigma_last, device=sigmas.device)])
|
||
|
|
|
||
|
|
self.is_scale_input_called = False
|
||
|
|
|
||
|
|
self._step_index = None
|
||
|
|
self._begin_index = None
|
||
|
|
self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication
|
||
|
|
|
||
|
|
@property
|
||
|
|
def init_noise_sigma(self):
|
||
|
|
# standard deviation of the initial noise distribution
|
||
|
|
return (self.config.sigma_max**2 + 1) ** 0.5
|
||
|
|
|
||
|
|
@property
|
||
|
|
def step_index(self):
|
||
|
|
"""
|
||
|
|
The index counter for current timestep. It will increase 1 after each scheduler step.
|
||
|
|
"""
|
||
|
|
return self._step_index
|
||
|
|
|
||
|
|
@property
|
||
|
|
def begin_index(self):
|
||
|
|
"""
|
||
|
|
The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
|
||
|
|
"""
|
||
|
|
return self._begin_index
|
||
|
|
|
||
|
|
# Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
|
||
|
|
def set_begin_index(self, begin_index: int = 0):
|
||
|
|
"""
|
||
|
|
Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
begin_index (`int`):
|
||
|
|
The begin index for the scheduler.
|
||
|
|
"""
|
||
|
|
self._begin_index = begin_index
|
||
|
|
|
||
|
|
def precondition_inputs(self, sample, sigma):
|
||
|
|
c_in = self._get_conditioning_c_in(sigma)
|
||
|
|
scaled_sample = sample * c_in
|
||
|
|
return scaled_sample
|
||
|
|
|
||
|
|
def precondition_noise(self, sigma):
|
||
|
|
if not isinstance(sigma, torch.Tensor):
|
||
|
|
sigma = torch.tensor([sigma])
|
||
|
|
|
||
|
|
c_noise = 0.25 * torch.log(sigma)
|
||
|
|
|
||
|
|
return c_noise
|
||
|
|
|
||
|
|
def precondition_outputs(self, sample, model_output, sigma):
|
||
|
|
sigma_data = self.config.sigma_data
|
||
|
|
c_skip = sigma_data**2 / (sigma**2 + sigma_data**2)
|
||
|
|
|
||
|
|
if self.config.prediction_type == "epsilon":
|
||
|
|
c_out = sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
|
||
|
|
elif self.config.prediction_type == "v_prediction":
|
||
|
|
c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
|
||
|
|
else:
|
||
|
|
raise ValueError(f"Prediction type {self.config.prediction_type} is not supported.")
|
||
|
|
|
||
|
|
denoised = c_skip * sample + c_out * model_output
|
||
|
|
|
||
|
|
return denoised
|
||
|
|
|
||
|
|
def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:
|
||
|
|
"""
|
||
|
|
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
|
||
|
|
current timestep. Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
sample (`torch.Tensor`):
|
||
|
|
The input sample.
|
||
|
|
timestep (`int`, *optional*):
|
||
|
|
The current timestep in the diffusion chain.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
`torch.Tensor`:
|
||
|
|
A scaled input sample.
|
||
|
|
"""
|
||
|
|
if self.step_index is None:
|
||
|
|
self._init_step_index(timestep)
|
||
|
|
|
||
|
|
sigma = self.sigmas[self.step_index]
|
||
|
|
sample = self.precondition_inputs(sample, sigma)
|
||
|
|
|
||
|
|
self.is_scale_input_called = True
|
||
|
|
return sample
|
||
|
|
|
||
|
|
def set_timesteps(
|
||
|
|
self,
|
||
|
|
num_inference_steps: int = None,
|
||
|
|
device: Union[str, torch.device] = None,
|
||
|
|
sigmas: Optional[Union[torch.Tensor, List[float]]] = 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.
|
||
|
|
sigmas (`Union[torch.Tensor, List[float]]`, *optional*):
|
||
|
|
Custom sigmas to use for the denoising process. If not defined, the default behavior when
|
||
|
|
`num_inference_steps` is passed will be used.
|
||
|
|
"""
|
||
|
|
self.num_inference_steps = num_inference_steps
|
||
|
|
|
||
|
|
sigmas_dtype = torch.float32 if torch.backends.mps.is_available() else torch.float64
|
||
|
|
if sigmas is None:
|
||
|
|
sigmas = torch.linspace(0, 1, self.num_inference_steps, dtype=sigmas_dtype)
|
||
|
|
elif isinstance(sigmas, float):
|
||
|
|
sigmas = torch.tensor(sigmas, dtype=sigmas_dtype)
|
||
|
|
else:
|
||
|
|
sigmas = sigmas.to(sigmas_dtype)
|
||
|
|
if self.config.sigma_schedule == "karras":
|
||
|
|
sigmas = self._compute_karras_sigmas(sigmas)
|
||
|
|
elif self.config.sigma_schedule == "exponential":
|
||
|
|
sigmas = self._compute_exponential_sigmas(sigmas)
|
||
|
|
sigmas = sigmas.to(dtype=torch.float32, device=device)
|
||
|
|
|
||
|
|
self.timesteps = self.precondition_noise(sigmas)
|
||
|
|
|
||
|
|
if self.config.final_sigmas_type == "sigma_min":
|
||
|
|
sigma_last = sigmas[-1]
|
||
|
|
elif self.config.final_sigmas_type == "zero":
|
||
|
|
sigma_last = 0
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}"
|
||
|
|
)
|
||
|
|
|
||
|
|
self.sigmas = torch.cat([sigmas, torch.full((1,), fill_value=sigma_last, device=sigmas.device)])
|
||
|
|
self._step_index = None
|
||
|
|
self._begin_index = None
|
||
|
|
self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication
|
||
|
|
|
||
|
|
# Taken from https://github.com/crowsonkb/k-diffusion/blob/686dbad0f39640ea25c8a8c6a6e56bb40eacefa2/k_diffusion/sampling.py#L17
|
||
|
|
def _compute_karras_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.Tensor:
|
||
|
|
"""Constructs the noise schedule of Karras et al. (2022)."""
|
||
|
|
sigma_min = sigma_min or self.config.sigma_min
|
||
|
|
sigma_max = sigma_max or self.config.sigma_max
|
||
|
|
|
||
|
|
rho = self.config.rho
|
||
|
|
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 _compute_exponential_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.Tensor:
|
||
|
|
"""Implementation closely follows k-diffusion.
|
||
|
|
|
||
|
|
https://github.com/crowsonkb/k-diffusion/blob/6ab5146d4a5ef63901326489f31f1d8e7dd36b48/k_diffusion/sampling.py#L26
|
||
|
|
"""
|
||
|
|
sigma_min = sigma_min or self.config.sigma_min
|
||
|
|
sigma_max = sigma_max or self.config.sigma_max
|
||
|
|
sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), len(ramp)).exp().flip(0)
|
||
|
|
return sigmas
|
||
|
|
|
||
|
|
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep
|
||
|
|
def index_for_timestep(self, timestep, schedule_timesteps=None):
|
||
|
|
if schedule_timesteps is None:
|
||
|
|
schedule_timesteps = self.timesteps
|
||
|
|
|
||
|
|
indices = (schedule_timesteps == timestep).nonzero()
|
||
|
|
|
||
|
|
# 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)
|
||
|
|
pos = 1 if len(indices) > 1 else 0
|
||
|
|
|
||
|
|
return indices[pos].item()
|
||
|
|
|
||
|
|
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index
|
||
|
|
def _init_step_index(self, timestep):
|
||
|
|
if self.begin_index is None:
|
||
|
|
if isinstance(timestep, torch.Tensor):
|
||
|
|
timestep = timestep.to(self.timesteps.device)
|
||
|
|
self._step_index = self.index_for_timestep(timestep)
|
||
|
|
else:
|
||
|
|
self._step_index = self._begin_index
|
||
|
|
|
||
|
|
def step(
|
||
|
|
self,
|
||
|
|
model_output: torch.Tensor,
|
||
|
|
timestep: Union[float, torch.Tensor],
|
||
|
|
sample: torch.Tensor,
|
||
|
|
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,
|
||
|
|
pred_original_sample: Optional[torch.Tensor] = None,
|
||
|
|
) -> Union[EDMEulerSchedulerOutput, 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.Tensor`):
|
||
|
|
The direct output from learned diffusion model.
|
||
|
|
timestep (`float`):
|
||
|
|
The current discrete timestep in the diffusion chain.
|
||
|
|
sample (`torch.Tensor`):
|
||
|
|
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.EDMEulerSchedulerOutput`] or tuple.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
[`~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput`] or `tuple`:
|
||
|
|
If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput`] is
|
||
|
|
returned, otherwise a tuple is returned where the first element is the sample tensor.
|
||
|
|
"""
|
||
|
|
|
||
|
|
if isinstance(timestep, (int, torch.IntTensor, torch.LongTensor)):
|
||
|
|
raise ValueError(
|
||
|
|
(
|
||
|
|
"Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
|
||
|
|
" `EDMEulerScheduler.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)
|
||
|
|
|
||
|
|
# Upcast to avoid precision issues when computing prev_sample
|
||
|
|
sample = sample.to(torch.float32)
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
sigma_hat = sigma * (gamma + 1)
|
||
|
|
|
||
|
|
if gamma > 0:
|
||
|
|
noise = randn_tensor(
|
||
|
|
model_output.shape, dtype=model_output.dtype, device=model_output.device, generator=generator
|
||
|
|
)
|
||
|
|
eps = noise * s_noise
|
||
|
|
sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5
|
||
|
|
|
||
|
|
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
|
||
|
|
if pred_original_sample is None:
|
||
|
|
pred_original_sample = self.precondition_outputs(sample, model_output, sigma_hat)
|
||
|
|
|
||
|
|
# 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
|
||
|
|
|
||
|
|
# Cast sample back to model compatible dtype
|
||
|
|
prev_sample = prev_sample.to(model_output.dtype)
|
||
|
|
|
||
|
|
# upon completion increase step index by one
|
||
|
|
self._step_index += 1
|
||
|
|
|
||
|
|
if not return_dict:
|
||
|
|
return (
|
||
|
|
prev_sample,
|
||
|
|
pred_original_sample,
|
||
|
|
)
|
||
|
|
|
||
|
|
return EDMEulerSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
|
||
|
|
|
||
|
|
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise
|
||
|
|
def add_noise(
|
||
|
|
self,
|
||
|
|
original_samples: torch.Tensor,
|
||
|
|
noise: torch.Tensor,
|
||
|
|
timesteps: torch.Tensor,
|
||
|
|
) -> torch.Tensor:
|
||
|
|
# 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)
|
||
|
|
|
||
|
|
# self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index
|
||
|
|
if self.begin_index is None:
|
||
|
|
step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]
|
||
|
|
elif self.step_index is not None:
|
||
|
|
# add_noise is called after first denoising step (for inpainting)
|
||
|
|
step_indices = [self.step_index] * timesteps.shape[0]
|
||
|
|
else:
|
||
|
|
# add noise is called before first denoising step to create initial latent(img2img)
|
||
|
|
step_indices = [self.begin_index] * timesteps.shape[0]
|
||
|
|
|
||
|
|
sigma = sigmas[step_indices].flatten()
|
||
|
|
while len(sigma.shape) < len(original_samples.shape):
|
||
|
|
sigma = sigma.unsqueeze(-1)
|
||
|
|
|
||
|
|
noisy_samples = original_samples + noise * sigma
|
||
|
|
return noisy_samples
|
||
|
|
|
||
|
|
def _get_conditioning_c_in(self, sigma):
|
||
|
|
c_in = 1 / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
|
||
|
|
return c_in
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
return self.config.num_train_timesteps
|