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# Copyright 2024 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. | |
from dataclasses import dataclass | |
from typing import Optional, Tuple, Union | |
import flax | |
import jax.numpy as jnp | |
from ..configuration_utils import ConfigMixin, register_to_config | |
from .scheduling_utils_flax import ( | |
CommonSchedulerState, | |
FlaxKarrasDiffusionSchedulers, | |
FlaxSchedulerMixin, | |
FlaxSchedulerOutput, | |
broadcast_to_shape_from_left, | |
) | |
class EulerDiscreteSchedulerState: | |
common: CommonSchedulerState | |
# setable values | |
init_noise_sigma: jnp.ndarray | |
timesteps: jnp.ndarray | |
sigmas: jnp.ndarray | |
num_inference_steps: Optional[int] = None | |
def create( | |
cls, common: CommonSchedulerState, init_noise_sigma: jnp.ndarray, timesteps: jnp.ndarray, sigmas: jnp.ndarray | |
): | |
return cls(common=common, init_noise_sigma=init_noise_sigma, timesteps=timesteps, sigmas=sigmas) | |
class FlaxEulerDiscreteSchedulerOutput(FlaxSchedulerOutput): | |
state: EulerDiscreteSchedulerState | |
class FlaxEulerDiscreteScheduler(FlaxSchedulerMixin, ConfigMixin): | |
""" | |
Euler scheduler (Algorithm 2) from Karras et al. (2022) https://arxiv.org/abs/2206.00364. . Based on the original | |
k-diffusion implementation by Katherine Crowson: | |
https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51 | |
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__` | |
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`. | |
[`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and | |
[`~SchedulerMixin.from_pretrained`] functions. | |
Args: | |
num_train_timesteps (`int`): number of diffusion steps used to train the model. | |
beta_start (`float`): the starting `beta` value of inference. | |
beta_end (`float`): the final `beta` value. | |
beta_schedule (`str`): | |
the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from | |
`linear` or `scaled_linear`. | |
trained_betas (`jnp.ndarray`, optional): | |
option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. | |
prediction_type (`str`, default `epsilon`, optional): | |
prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion | |
process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4 | |
https://imagen.research.google/video/paper.pdf) | |
dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`): | |
the `dtype` used for params and computation. | |
""" | |
_compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers] | |
dtype: jnp.dtype | |
def has_state(self): | |
return True | |
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[jnp.ndarray] = None, | |
prediction_type: str = "epsilon", | |
timestep_spacing: str = "linspace", | |
dtype: jnp.dtype = jnp.float32, | |
): | |
self.dtype = dtype | |
def create_state(self, common: Optional[CommonSchedulerState] = None) -> EulerDiscreteSchedulerState: | |
if common is None: | |
common = CommonSchedulerState.create(self) | |
timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1] | |
sigmas = ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5 | |
sigmas = jnp.interp(timesteps, jnp.arange(0, len(sigmas)), sigmas) | |
sigmas = jnp.concatenate([sigmas, jnp.array([0.0], dtype=self.dtype)]) | |
# standard deviation of the initial noise distribution | |
if self.config.timestep_spacing in ["linspace", "trailing"]: | |
init_noise_sigma = sigmas.max() | |
else: | |
init_noise_sigma = (sigmas.max() ** 2 + 1) ** 0.5 | |
return EulerDiscreteSchedulerState.create( | |
common=common, | |
init_noise_sigma=init_noise_sigma, | |
timesteps=timesteps, | |
sigmas=sigmas, | |
) | |
def scale_model_input(self, state: EulerDiscreteSchedulerState, sample: jnp.ndarray, timestep: int) -> jnp.ndarray: | |
""" | |
Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm. | |
Args: | |
state (`EulerDiscreteSchedulerState`): | |
the `FlaxEulerDiscreteScheduler` state data class instance. | |
sample (`jnp.ndarray`): | |
current instance of sample being created by diffusion process. | |
timestep (`int`): | |
current discrete timestep in the diffusion chain. | |
Returns: | |
`jnp.ndarray`: scaled input sample | |
""" | |
(step_index,) = jnp.where(state.timesteps == timestep, size=1) | |
step_index = step_index[0] | |
sigma = state.sigmas[step_index] | |
sample = sample / ((sigma**2 + 1) ** 0.5) | |
return sample | |
def set_timesteps( | |
self, state: EulerDiscreteSchedulerState, num_inference_steps: int, shape: Tuple = () | |
) -> EulerDiscreteSchedulerState: | |
""" | |
Sets the timesteps used for the diffusion chain. Supporting function to be run before inference. | |
Args: | |
state (`EulerDiscreteSchedulerState`): | |
the `FlaxEulerDiscreteScheduler` state data class instance. | |
num_inference_steps (`int`): | |
the number of diffusion steps used when generating samples with a pre-trained model. | |
""" | |
if self.config.timestep_spacing == "linspace": | |
timesteps = jnp.linspace(self.config.num_train_timesteps - 1, 0, num_inference_steps, dtype=self.dtype) | |
elif self.config.timestep_spacing == "leading": | |
step_ratio = self.config.num_train_timesteps // num_inference_steps | |
timesteps = (jnp.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(float) | |
timesteps += 1 | |
else: | |
raise ValueError( | |
f"timestep_spacing must be one of ['linspace', 'leading'], got {self.config.timestep_spacing}" | |
) | |
sigmas = ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5 | |
sigmas = jnp.interp(timesteps, jnp.arange(0, len(sigmas)), sigmas) | |
sigmas = jnp.concatenate([sigmas, jnp.array([0.0], dtype=self.dtype)]) | |
# standard deviation of the initial noise distribution | |
if self.config.timestep_spacing in ["linspace", "trailing"]: | |
init_noise_sigma = sigmas.max() | |
else: | |
init_noise_sigma = (sigmas.max() ** 2 + 1) ** 0.5 | |
return state.replace( | |
timesteps=timesteps, | |
sigmas=sigmas, | |
num_inference_steps=num_inference_steps, | |
init_noise_sigma=init_noise_sigma, | |
) | |
def step( | |
self, | |
state: EulerDiscreteSchedulerState, | |
model_output: jnp.ndarray, | |
timestep: int, | |
sample: jnp.ndarray, | |
return_dict: bool = True, | |
) -> Union[FlaxEulerDiscreteSchedulerOutput, 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: | |
state (`EulerDiscreteSchedulerState`): | |
the `FlaxEulerDiscreteScheduler` state data class instance. | |
model_output (`jnp.ndarray`): direct output from learned diffusion model. | |
timestep (`int`): current discrete timestep in the diffusion chain. | |
sample (`jnp.ndarray`): | |
current instance of sample being created by diffusion process. | |
order: coefficient for multi-step inference. | |
return_dict (`bool`): option for returning tuple rather than FlaxEulerDiscreteScheduler class | |
Returns: | |
[`FlaxEulerDiscreteScheduler`] or `tuple`: [`FlaxEulerDiscreteScheduler`] if `return_dict` is True, | |
otherwise a `tuple`. When returning a tuple, the first element is the sample tensor. | |
""" | |
if state.num_inference_steps is None: | |
raise ValueError( | |
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" | |
) | |
(step_index,) = jnp.where(state.timesteps == timestep, size=1) | |
step_index = step_index[0] | |
sigma = state.sigmas[step_index] | |
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise | |
if self.config.prediction_type == "epsilon": | |
pred_original_sample = sample - sigma * 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 | |
# dt = sigma_down - sigma | |
dt = state.sigmas[step_index + 1] - sigma | |
prev_sample = sample + derivative * dt | |
if not return_dict: | |
return (prev_sample, state) | |
return FlaxEulerDiscreteSchedulerOutput(prev_sample=prev_sample, state=state) | |
def add_noise( | |
self, | |
state: EulerDiscreteSchedulerState, | |
original_samples: jnp.ndarray, | |
noise: jnp.ndarray, | |
timesteps: jnp.ndarray, | |
) -> jnp.ndarray: | |
sigma = state.sigmas[timesteps].flatten() | |
sigma = broadcast_to_shape_from_left(sigma, noise.shape) | |
noisy_samples = original_samples + noise * sigma | |
return noisy_samples | |
def __len__(self): | |
return self.config.num_train_timesteps | |