File size: 13,472 Bytes
21231ee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 |
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2022 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 warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import paddle
from scipy import integrate
from ...configuration_utils import ConfigMixin, register_to_config
from ...utils import _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS, BaseOutput
from ..scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class PreconfigLMSDiscreteSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's step function output.
Args:
prev_sample (`paddle.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 (`paddle.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: paddle.Tensor
pred_original_sample: Optional[paddle.Tensor] = None
class PreconfigLMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
"""
Linear Multistep Scheduler for discrete beta schedules. Based on the original k-diffusion implementation by
Katherine Crowson:
https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181
[`~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 (`np.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)
"""
_compatibles = _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy()
order = 1
@register_to_config
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[Union[np.ndarray, List[float]]] = None,
prediction_type: str = "epsilon",
preconfig=True,
):
if trained_betas is not None:
self.betas = paddle.to_tensor(trained_betas, dtype="float32")
elif beta_schedule == "linear":
self.betas = paddle.linspace(beta_start, beta_end, num_train_timesteps, dtype="float32")
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
self.betas = paddle.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype="float32") ** 2
else:
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
self.alphas = 1.0 - self.betas
self.alphas_cumprod = paddle.cumprod(self.alphas, 0)
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32)
self.sigmas = paddle.to_tensor(sigmas)
# standard deviation of the initial noise distribution
self.init_noise_sigma = self.sigmas.max()
# setable values
self.num_inference_steps = None
timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=float)[::-1].copy()
self.timesteps = paddle.to_tensor(timesteps, dtype="float32")
self.derivatives = []
self.is_scale_input_called = False
self.preconfig = preconfig
def scale_model_input(
self, sample: paddle.Tensor, timestep: Union[float, paddle.Tensor], **kwargs
) -> paddle.Tensor:
"""
Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the K-LMS algorithm.
Args:
sample (`paddle.Tensor`): input sample
timestep (`float` or `paddle.Tensor`): the current timestep in the diffusion chain
Returns:
`paddle.Tensor`: scaled input sample
"""
if kwargs.get("step_index") is not None:
step_index = kwargs["step_index"]
else:
step_index = (self.timesteps == timestep).nonzero().item()
self.is_scale_input_called = True
if not self.preconfig:
sigma = self.sigmas[step_index]
sample = sample / ((sigma**2 + 1) ** 0.5)
return sample
else:
return sample * self.latent_scales[step_index]
def get_lms_coefficient(self, order, t, current_order):
"""
Compute a linear multistep coefficient.
Args:
order (TODO):
t (TODO):
current_order (TODO):
"""
def lms_derivative(tau):
prod = 1.0
for k in range(order):
if current_order == k:
continue
prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
return prod
integrated_coeff = integrate.quad(lms_derivative, self.sigmas[t], self.sigmas[t + 1], epsrel=1e-4)[0]
return integrated_coeff
def set_timesteps(self, num_inference_steps: int, preconfig_order: int = 4):
"""
Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.
Args:
num_inference_steps (`int`):
the number of diffusion steps used when generating samples with a pre-trained model.
"""
self.num_inference_steps = num_inference_steps
timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy()
sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)
self.sigmas = paddle.to_tensor(sigmas)
self.timesteps = paddle.to_tensor(timesteps, dtype="float32")
self.derivatives = []
if self.preconfig:
self.order = preconfig_order
self.lms_coeffs = []
self.latent_scales = [1.0 / ((sigma**2 + 1) ** 0.5) for sigma in self.sigmas]
for step_index in range(self.num_inference_steps):
order = min(step_index + 1, preconfig_order)
self.lms_coeffs.append(
[self.get_lms_coefficient(order, step_index, curr_order) for curr_order in range(order)]
)
def step(
self,
model_output: paddle.Tensor,
timestep: Union[float, paddle.Tensor],
sample: paddle.Tensor,
order: int = 4,
return_dict: bool = True,
**kwargs
) -> Union[PreconfigLMSDiscreteSchedulerOutput, 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 (`paddle.Tensor`): direct output from learned diffusion model.
timestep (`float`): current timestep in the diffusion chain.
sample (`paddle.Tensor`):
current instance of sample being created by diffusion process.
order: coefficient for multi-step inference.
return_dict (`bool`): option for returning tuple rather than PreconfigLMSDiscreteSchedulerOutput class
Args in kwargs:
step_index (`int`):
return_pred_original_sample (`bool`): option for return pred_original_sample
Returns:
[`~schedulers.scheduling_utils.PreconfigLMSDiscreteSchedulerOutput`] or `tuple`:
[`~schedulers.scheduling_utils.PreconfigLMSDiscreteSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`.
When returning a tuple, the first element is the sample tensor.
"""
if not self.is_scale_input_called:
warnings.warn(
"The `scale_model_input` function should be called before `step` to ensure correct denoising. "
"See `StableDiffusionPipeline` for a usage example."
)
if kwargs.get("return_pred_original_sample") is not None:
return_pred_original_sample = kwargs["return_pred_original_sample"]
else:
return_pred_original_sample = True
if kwargs.get("step_index") is not None:
step_index = kwargs["step_index"]
else:
step_index = (self.timesteps == timestep).nonzero().item()
if self.config.prediction_type == "epsilon" and not return_pred_original_sample:
# if pred_original_sample is no need
self.derivatives.append(model_output)
pred_original_sample = None
else:
sigma = self.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
self.derivatives.append(derivative)
if len(self.derivatives) > order:
self.derivatives.pop(0)
if not self.preconfig:
# 3. If not preconfiged, compute linear multistep coefficients.
order = min(step_index + 1, order)
lms_coeffs = [self.get_lms_coefficient(order, step_index, curr_order) for curr_order in range(order)]
# 4. Compute previous sample based on the derivatives path
prev_sample = sample + sum(
coeff * derivative for coeff, derivative in zip(lms_coeffs, reversed(self.derivatives))
)
else:
# 3. If preconfiged, direct compute previous sample based on the derivatives path
prev_sample = sample + sum(
coeff * derivative
for coeff, derivative in zip(self.lms_coeffs[step_index], reversed(self.derivatives))
)
if not return_dict:
if not return_pred_original_sample:
return (prev_sample,)
else:
return (prev_sample, pred_original_sample)
return PreconfigLMSDiscreteSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
def add_noise(
self,
original_samples: paddle.Tensor,
noise: paddle.Tensor,
timesteps: paddle.Tensor,
) -> paddle.Tensor:
# Make sure sigmas and timesteps have the same dtype as original_samples
sigmas = self.sigmas.cast(original_samples.dtype)
schedule_timesteps = self.timesteps
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
|