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# Copyright 2023 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. | |
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, logging | |
from diffusers.utils.torch_utils import randn_tensor | |
from diffusers.schedulers.scheduling_utils import SchedulerMixin | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
class AnimateLCMSVDStochasticIterativeSchedulerOutput(BaseOutput): | |
""" | |
Output class for the scheduler's `step` function. | |
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. | |
""" | |
prev_sample: torch.FloatTensor | |
class AnimateLCMSVDStochasticIterativeScheduler(SchedulerMixin, ConfigMixin): | |
""" | |
Multistep and onestep sampling for consistency models. | |
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 40): | |
The number of diffusion steps to train the model. | |
sigma_min (`float`, defaults to 0.002): | |
Minimum noise magnitude in the sigma schedule. Defaults to 0.002 from the original implementation. | |
sigma_max (`float`, defaults to 80.0): | |
Maximum noise magnitude in the sigma schedule. Defaults to 80.0 from the original implementation. | |
sigma_data (`float`, defaults to 0.5): | |
The standard deviation of the data distribution from the EDM | |
[paper](https://huggingface.co/papers/2206.00364). Defaults to 0.5 from the original implementation. | |
s_noise (`float`, defaults to 1.0): | |
The amount of additional noise to counteract loss of detail during sampling. A reasonable range is [1.000, | |
1.011]. Defaults to 1.0 from the original implementation. | |
rho (`float`, defaults to 7.0): | |
The parameter for calculating the Karras sigma schedule from the EDM | |
[paper](https://huggingface.co/papers/2206.00364). Defaults to 7.0 from the original implementation. | |
clip_denoised (`bool`, defaults to `True`): | |
Whether to clip the denoised outputs to `(-1, 1)`. | |
timesteps (`List` or `np.ndarray` or `torch.Tensor`, *optional*): | |
An explicit timestep schedule that can be optionally specified. The timesteps are expected to be in | |
increasing order. | |
""" | |
order = 1 | |
def __init__( | |
self, | |
num_train_timesteps: int = 40, | |
sigma_min: float = 0.002, | |
sigma_max: float = 80.0, | |
sigma_data: float = 0.5, | |
s_noise: float = 1.0, | |
rho: float = 7.0, | |
clip_denoised: bool = True, | |
): | |
# standard deviation of the initial noise distribution | |
self.init_noise_sigma = (sigma_max**2 + 1) ** 0.5 | |
# self.init_noise_sigma = sigma_max | |
ramp = np.linspace(0, 1, num_train_timesteps) | |
sigmas = self._convert_to_karras(ramp) | |
sigmas = np.concatenate([sigmas, np.array([0])]) | |
timesteps = self.sigma_to_t(sigmas) | |
# setable values | |
self.num_inference_steps = None | |
self.sigmas = torch.from_numpy(sigmas) | |
self.timesteps = torch.from_numpy(timesteps) | |
self.custom_timesteps = False | |
self.is_scale_input_called = False | |
self._step_index = None | |
self.sigmas.to("cpu") # to avoid too much CPU/GPU communication | |
def index_for_timestep(self, timestep, schedule_timesteps=None): | |
if schedule_timesteps is None: | |
schedule_timesteps = self.timesteps | |
indices = (schedule_timesteps == timestep).nonzero() | |
return indices.item() | |
def step_index(self): | |
""" | |
The index counter for current timestep. It will increae 1 after each scheduler step. | |
""" | |
return self._step_index | |
def scale_model_input( | |
self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor] | |
) -> torch.FloatTensor: | |
""" | |
Scales the consistency model input by `(sigma**2 + sigma_data**2) ** 0.5`. | |
Args: | |
sample (`torch.FloatTensor`): | |
The input sample. | |
timestep (`float` or `torch.FloatTensor`): | |
The current timestep in the diffusion chain. | |
Returns: | |
`torch.FloatTensor`: | |
A scaled input sample. | |
""" | |
# Get sigma corresponding to timestep | |
if self.step_index is None: | |
self._init_step_index(timestep) | |
sigma = self.sigmas[self.step_index] | |
sample = sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5) | |
self.is_scale_input_called = True | |
return sample | |
# 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 | |
def sigma_to_t(self, sigmas: Union[float, np.ndarray]): | |
""" | |
Gets scaled timesteps from the Karras sigmas for input to the consistency model. | |
Args: | |
sigmas (`float` or `np.ndarray`): | |
A single Karras sigma or an array of Karras sigmas. | |
Returns: | |
`float` or `np.ndarray`: | |
A scaled input timestep or scaled input timestep array. | |
""" | |
if not isinstance(sigmas, np.ndarray): | |
sigmas = np.array(sigmas, dtype=np.float64) | |
timesteps = 0.25 * np.log(sigmas + 1e-44) | |
return timesteps | |
def set_timesteps( | |
self, | |
num_inference_steps: Optional[int] = None, | |
device: Union[str, torch.device] = None, | |
timesteps: Optional[List[int]] = None, | |
): | |
""" | |
Sets the 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. | |
timesteps (`List[int]`, *optional*): | |
Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default | |
timestep spacing strategy of equal spacing between timesteps is used. If `timesteps` is passed, | |
`num_inference_steps` must be `None`. | |
""" | |
if num_inference_steps is None and timesteps is None: | |
raise ValueError( | |
"Exactly one of `num_inference_steps` or `timesteps` must be supplied." | |
) | |
if num_inference_steps is not None and timesteps is not None: | |
raise ValueError( | |
"Can only pass one of `num_inference_steps` or `timesteps`." | |
) | |
# Follow DDPMScheduler custom timesteps logic | |
if timesteps is not None: | |
for i in range(1, len(timesteps)): | |
if timesteps[i] >= timesteps[i - 1]: | |
raise ValueError("`timesteps` must be in descending order.") | |
if timesteps[0] >= self.config.num_train_timesteps: | |
raise ValueError( | |
f"`timesteps` must start before `self.config.train_timesteps`:" | |
f" {self.config.num_train_timesteps}." | |
) | |
timesteps = np.array(timesteps, dtype=np.int64) | |
self.custom_timesteps = True | |
else: | |
if num_inference_steps > self.config.num_train_timesteps: | |
raise ValueError( | |
f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:" | |
f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle" | |
f" maximal {self.config.num_train_timesteps} timesteps." | |
) | |
self.num_inference_steps = num_inference_steps | |
step_ratio = self.config.num_train_timesteps // self.num_inference_steps | |
timesteps = ( | |
(np.arange(0, num_inference_steps) * step_ratio) | |
.round()[::-1] | |
.copy() | |
.astype(np.int64) | |
) | |
self.custom_timesteps = False | |
# Map timesteps to Karras sigmas directly for multistep sampling | |
# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675 | |
num_train_timesteps = self.config.num_train_timesteps | |
ramp = timesteps[::-1].copy() | |
ramp = ramp / (num_train_timesteps - 1) | |
sigmas = self._convert_to_karras(ramp) | |
timesteps = self.sigma_to_t(sigmas) | |
sigmas = np.concatenate([sigmas, [0]]).astype(np.float32) | |
self.sigmas = torch.from_numpy(sigmas).to(device=device) | |
if str(device).startswith("mps"): | |
# mps does not support float64 | |
self.timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32) | |
else: | |
self.timesteps = torch.from_numpy(timesteps).to(device=device) | |
self._step_index = None | |
self.sigmas.to("cpu") # to avoid too much CPU/GPU communication | |
# Modified _convert_to_karras implementation that takes in ramp as argument | |
def _convert_to_karras(self, ramp): | |
"""Constructs the noise schedule of Karras et al. (2022).""" | |
sigma_min: float = self.config.sigma_min | |
sigma_max: float = 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 get_scalings(self, sigma): | |
sigma_data = self.config.sigma_data | |
c_skip = sigma_data**2 / (sigma**2 + sigma_data**2) | |
c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5 | |
return c_skip, c_out | |
def get_scalings_for_boundary_condition(self, sigma): | |
""" | |
Gets the scalings used in the consistency model parameterization (from Appendix C of the | |
[paper](https://huggingface.co/papers/2303.01469)) to enforce boundary condition. | |
<Tip> | |
`epsilon` in the equations for `c_skip` and `c_out` is set to `sigma_min`. | |
</Tip> | |
Args: | |
sigma (`torch.FloatTensor`): | |
The current sigma in the Karras sigma schedule. | |
Returns: | |
`tuple`: | |
A two-element tuple where `c_skip` (which weights the current sample) is the first element and `c_out` | |
(which weights the consistency model output) is the second element. | |
""" | |
sigma_min = self.config.sigma_min | |
sigma_data = self.config.sigma_data | |
c_skip = sigma_data**2 / ((sigma) ** 2 + sigma_data**2) | |
c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5 | |
return c_skip, c_out | |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index | |
def _init_step_index(self, timestep): | |
if isinstance(timestep, torch.Tensor): | |
timestep = timestep.to(self.timesteps.device) | |
index_candidates = (self.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) | |
if len(index_candidates) > 1: | |
step_index = index_candidates[1] | |
else: | |
step_index = index_candidates[0] | |
self._step_index = step_index.item() | |
def step( | |
self, | |
model_output: torch.FloatTensor, | |
timestep: Union[float, torch.FloatTensor], | |
sample: torch.FloatTensor, | |
generator: Optional[torch.Generator] = None, | |
return_dict: bool = True, | |
) -> Union[AnimateLCMSVDStochasticIterativeSchedulerOutput, 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 the learned diffusion model. | |
timestep (`float`): | |
The current timestep in the diffusion chain. | |
sample (`torch.FloatTensor`): | |
A current instance of a sample created by the diffusion process. | |
generator (`torch.Generator`, *optional*): | |
A random number generator. | |
return_dict (`bool`, *optional*, defaults to `True`): | |
Whether or not to return a | |
[`~schedulers.scheduling_consistency_models.AnimateLCMSVDStochasticIterativeSchedulerOutput`] or `tuple`. | |
Returns: | |
[`~schedulers.scheduling_consistency_models.AnimateLCMSVDStochasticIterativeSchedulerOutput`] or `tuple`: | |
If return_dict is `True`, | |
[`~schedulers.scheduling_consistency_models.AnimateLCMSVDStochasticIterativeSchedulerOutput`] 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" | |
f" `{self.__class__}.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." | |
) | |
sigma_min = self.config.sigma_min | |
sigma_max = self.config.sigma_max | |
if self.step_index is None: | |
self._init_step_index(timestep) | |
# sigma_next corresponds to next_t in original implementation | |
sigma = self.sigmas[self.step_index] | |
if self.step_index + 1 < self.config.num_train_timesteps: | |
sigma_next = self.sigmas[self.step_index + 1] | |
else: | |
# Set sigma_next to sigma_min | |
sigma_next = self.sigmas[-1] | |
# Get scalings for boundary conditions | |
c_skip, c_out = self.get_scalings_for_boundary_condition(sigma) | |
# 1. Denoise model output using boundary conditions | |
denoised = c_out * model_output + c_skip * sample | |
if self.config.clip_denoised: | |
denoised = denoised.clamp(-1, 1) | |
# 2. Sample z ~ N(0, s_noise^2 * I) | |
# Noise is not used for onestep sampling. | |
if len(self.timesteps) > 1: | |
noise = randn_tensor( | |
model_output.shape, | |
dtype=model_output.dtype, | |
device=model_output.device, | |
generator=generator, | |
) | |
else: | |
noise = torch.zeros_like(model_output) | |
z = noise * self.config.s_noise | |
sigma_hat = sigma_next.clamp(min=0, max=sigma_max) | |
print("denoise currently") | |
print(sigma_hat) | |
# origin | |
prev_sample = denoised + z * sigma_hat | |
# upon completion increase step index by one | |
self._step_index += 1 | |
if not return_dict: | |
return (prev_sample,) | |
return AnimateLCMSVDStochasticIterativeSchedulerOutput(prev_sample=prev_sample) | |
# Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise | |
def add_noise( | |
self, | |
original_samples: torch.FloatTensor, | |
noise: torch.FloatTensor, | |
timesteps: torch.FloatTensor, | |
) -> 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) | |
noisy_samples = original_samples + noise * sigma | |
return noisy_samples | |
def __len__(self): | |
return self.config.num_train_timesteps | |