Spaces:
Running
on
Zero
Running
on
Zero
# 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 | |
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, | |
add_noise_common, | |
get_velocity_common, | |
) | |
class DDIMSchedulerState: | |
common: CommonSchedulerState | |
final_alpha_cumprod: jnp.ndarray | |
# setable values | |
init_noise_sigma: jnp.ndarray | |
timesteps: jnp.ndarray | |
num_inference_steps: Optional[int] = None | |
def create( | |
cls, | |
common: CommonSchedulerState, | |
final_alpha_cumprod: jnp.ndarray, | |
init_noise_sigma: jnp.ndarray, | |
timesteps: jnp.ndarray, | |
): | |
return cls( | |
common=common, | |
final_alpha_cumprod=final_alpha_cumprod, | |
init_noise_sigma=init_noise_sigma, | |
timesteps=timesteps, | |
) | |
class FlaxDDIMSchedulerOutput(FlaxSchedulerOutput): | |
state: DDIMSchedulerState | |
class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin): | |
""" | |
Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising | |
diffusion probabilistic models (DDPMs) with non-Markovian guidance. | |
[`~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. | |
For more details, see the original paper: https://arxiv.org/abs/2010.02502 | |
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`, `scaled_linear`, or `squaredcos_cap_v2`. | |
trained_betas (`jnp.ndarray`, optional): | |
option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc. | |
clip_sample (`bool`, default `True`): | |
option to clip predicted sample between -1 and 1 for numerical stability. | |
set_alpha_to_one (`bool`, default `True`): | |
each diffusion step uses the value of alphas product 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 value of alpha at step 0. | |
steps_offset (`int`, default `0`): | |
an offset added to the inference steps. You can use a combination of `offset=1` and | |
`set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in | |
stable diffusion. | |
prediction_type (`str`, default `epsilon`): | |
indicates whether the model predicts the noise (epsilon), or the samples. One of `epsilon`, `sample`. | |
`v-prediction` is not supported for this scheduler. | |
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, | |
set_alpha_to_one: bool = True, | |
steps_offset: int = 0, | |
prediction_type: str = "epsilon", | |
dtype: jnp.dtype = jnp.float32, | |
): | |
self.dtype = dtype | |
def create_state(self, common: Optional[CommonSchedulerState] = None) -> DDIMSchedulerState: | |
if common is None: | |
common = CommonSchedulerState.create(self) | |
# 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. | |
final_alpha_cumprod = ( | |
jnp.array(1.0, dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0] | |
) | |
# standard deviation of the initial noise distribution | |
init_noise_sigma = jnp.array(1.0, dtype=self.dtype) | |
timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1] | |
return DDIMSchedulerState.create( | |
common=common, | |
final_alpha_cumprod=final_alpha_cumprod, | |
init_noise_sigma=init_noise_sigma, | |
timesteps=timesteps, | |
) | |
def scale_model_input( | |
self, state: DDIMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None | |
) -> jnp.ndarray: | |
""" | |
Args: | |
state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance. | |
sample (`jnp.ndarray`): input sample | |
timestep (`int`, optional): current timestep | |
Returns: | |
`jnp.ndarray`: scaled input sample | |
""" | |
return sample | |
def set_timesteps( | |
self, state: DDIMSchedulerState, num_inference_steps: int, shape: Tuple = () | |
) -> DDIMSchedulerState: | |
""" | |
Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference. | |
Args: | |
state (`DDIMSchedulerState`): | |
the `FlaxDDIMScheduler` state data class instance. | |
num_inference_steps (`int`): | |
the number of diffusion steps used when generating samples with a pre-trained model. | |
""" | |
step_ratio = self.config.num_train_timesteps // num_inference_steps | |
# creates integer timesteps by multiplying by ratio | |
# rounding to avoid issues when num_inference_step is power of 3 | |
timesteps = (jnp.arange(0, num_inference_steps) * step_ratio).round()[::-1] + self.config.steps_offset | |
return state.replace( | |
num_inference_steps=num_inference_steps, | |
timesteps=timesteps, | |
) | |
def _get_variance(self, state: DDIMSchedulerState, timestep, prev_timestep): | |
alpha_prod_t = state.common.alphas_cumprod[timestep] | |
alpha_prod_t_prev = jnp.where( | |
prev_timestep >= 0, state.common.alphas_cumprod[prev_timestep], state.final_alpha_cumprod | |
) | |
beta_prod_t = 1 - alpha_prod_t | |
beta_prod_t_prev = 1 - alpha_prod_t_prev | |
variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev) | |
return variance | |
def step( | |
self, | |
state: DDIMSchedulerState, | |
model_output: jnp.ndarray, | |
timestep: int, | |
sample: jnp.ndarray, | |
eta: float = 0.0, | |
return_dict: bool = True, | |
) -> Union[FlaxDDIMSchedulerOutput, 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 (`DDIMSchedulerState`): the `FlaxDDIMScheduler` 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. | |
return_dict (`bool`): option for returning tuple rather than FlaxDDIMSchedulerOutput class | |
Returns: | |
[`FlaxDDIMSchedulerOutput`] or `tuple`: [`FlaxDDIMSchedulerOutput`] 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" | |
) | |
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf | |
# Ideally, read DDIM paper in-detail understanding | |
# Notation (<variable name> -> <name in paper> | |
# - pred_noise_t -> e_theta(x_t, t) | |
# - pred_original_sample -> f_theta(x_t, t) or x_0 | |
# - std_dev_t -> sigma_t | |
# - eta -> η | |
# - pred_sample_direction -> "direction pointing to x_t" | |
# - pred_prev_sample -> "x_t-1" | |
# 1. get previous step value (=t-1) | |
prev_timestep = timestep - self.config.num_train_timesteps // state.num_inference_steps | |
alphas_cumprod = state.common.alphas_cumprod | |
final_alpha_cumprod = state.final_alpha_cumprod | |
# 2. compute alphas, betas | |
alpha_prod_t = alphas_cumprod[timestep] | |
alpha_prod_t_prev = jnp.where(prev_timestep >= 0, alphas_cumprod[prev_timestep], final_alpha_cumprod) | |
beta_prod_t = 1 - alpha_prod_t | |
# 3. compute predicted original sample from predicted noise also called | |
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf | |
if self.config.prediction_type == "epsilon": | |
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5) | |
pred_epsilon = model_output | |
elif self.config.prediction_type == "sample": | |
pred_original_sample = model_output | |
pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5) | |
elif self.config.prediction_type == "v_prediction": | |
pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output | |
pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample | |
else: | |
raise ValueError( | |
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or" | |
" `v_prediction`" | |
) | |
# 4. compute variance: "sigma_t(η)" -> see formula (16) | |
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) | |
variance = self._get_variance(state, timestep, prev_timestep) | |
std_dev_t = eta * variance ** (0.5) | |
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf | |
pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon | |
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf | |
prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction | |
if not return_dict: | |
return (prev_sample, state) | |
return FlaxDDIMSchedulerOutput(prev_sample=prev_sample, state=state) | |
def add_noise( | |
self, | |
state: DDIMSchedulerState, | |
original_samples: jnp.ndarray, | |
noise: jnp.ndarray, | |
timesteps: jnp.ndarray, | |
) -> jnp.ndarray: | |
return add_noise_common(state.common, original_samples, noise, timesteps) | |
def get_velocity( | |
self, | |
state: DDIMSchedulerState, | |
sample: jnp.ndarray, | |
noise: jnp.ndarray, | |
timesteps: jnp.ndarray, | |
) -> jnp.ndarray: | |
return get_velocity_common(state.common, sample, noise, timesteps) | |
def __len__(self): | |
return self.config.num_train_timesteps | |