prateekrao
commited on
Commit
•
552aef7
1
Parent(s):
81b02c4
Create scheduling_ddim.py
Browse files- scheduling_ddim.py +405 -0
scheduling_ddim.py
ADDED
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1 |
+
# Copyright 2023 Stanford University Team and The HuggingFace Team. All rights reserved.
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2 |
+
#
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+
# Licensed under the Apache License, Version 2.0 (the "License");
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+
# you may not use this file except in compliance with the License.
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+
# You may obtain a copy of the License at
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+
#
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+
# http://www.apache.org/licenses/LICENSE-2.0
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+
#
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# Unless required by applicable law or agreed to in writing, software
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+
# distributed under the License is distributed on an "AS IS" BASIS,
|
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+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+
# See the License for the specific language governing permissions and
|
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+
# limitations under the License.
|
14 |
+
|
15 |
+
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
|
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+
# and https://github.com/hojonathanho/diffusion
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+
|
18 |
+
import math
|
19 |
+
from dataclasses import dataclass
|
20 |
+
from typing import List, Optional, Tuple, Union
|
21 |
+
|
22 |
+
import numpy as np
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+
import torch
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+
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+
from ..configuration_utils import ConfigMixin, register_to_config
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+
from ..utils import BaseOutput, randn_tensor
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+
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
|
28 |
+
|
29 |
+
|
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+
@dataclass
|
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+
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
|
32 |
+
class DDIMSchedulerOutput(BaseOutput):
|
33 |
+
"""
|
34 |
+
Output class for the scheduler's step function output.
|
35 |
+
|
36 |
+
Args:
|
37 |
+
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
|
38 |
+
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
|
39 |
+
denoising loop.
|
40 |
+
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
|
41 |
+
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
|
42 |
+
`pred_original_sample` can be used to preview progress or for guidance.
|
43 |
+
"""
|
44 |
+
|
45 |
+
prev_sample: torch.FloatTensor
|
46 |
+
pred_original_sample: Optional[torch.FloatTensor] = None
|
47 |
+
|
48 |
+
|
49 |
+
# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
|
50 |
+
def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor:
|
51 |
+
"""
|
52 |
+
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
|
53 |
+
(1-beta) over time from t = [0,1].
|
54 |
+
|
55 |
+
Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
|
56 |
+
to that part of the diffusion process.
|
57 |
+
|
58 |
+
|
59 |
+
Args:
|
60 |
+
num_diffusion_timesteps (`int`): the number of betas to produce.
|
61 |
+
max_beta (`float`): the maximum beta to use; use values lower than 1 to
|
62 |
+
prevent singularities.
|
63 |
+
|
64 |
+
Returns:
|
65 |
+
betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
|
66 |
+
"""
|
67 |
+
|
68 |
+
def alpha_bar(time_step):
|
69 |
+
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
|
70 |
+
|
71 |
+
betas = []
|
72 |
+
for i in range(num_diffusion_timesteps):
|
73 |
+
t1 = i / num_diffusion_timesteps
|
74 |
+
t2 = (i + 1) / num_diffusion_timesteps
|
75 |
+
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
|
76 |
+
return torch.tensor(betas, dtype=torch.float32)
|
77 |
+
|
78 |
+
|
79 |
+
class DDIMScheduler(SchedulerMixin, ConfigMixin):
|
80 |
+
"""
|
81 |
+
Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising
|
82 |
+
diffusion probabilistic models (DDPMs) with non-Markovian guidance.
|
83 |
+
|
84 |
+
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
|
85 |
+
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
|
86 |
+
[`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
|
87 |
+
[`~SchedulerMixin.from_pretrained`] functions.
|
88 |
+
|
89 |
+
For more details, see the original paper: https://arxiv.org/abs/2010.02502
|
90 |
+
|
91 |
+
Args:
|
92 |
+
num_train_timesteps (`int`): number of diffusion steps used to train the model.
|
93 |
+
beta_start (`float`): the starting `beta` value of inference.
|
94 |
+
beta_end (`float`): the final `beta` value.
|
95 |
+
beta_schedule (`str`):
|
96 |
+
the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
|
97 |
+
`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
|
98 |
+
trained_betas (`np.ndarray`, optional):
|
99 |
+
option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
|
100 |
+
clip_sample (`bool`, default `True`):
|
101 |
+
option to clip predicted sample for numerical stability.
|
102 |
+
clip_sample_range (`float`, default `1.0`):
|
103 |
+
the maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
|
104 |
+
set_alpha_to_one (`bool`, default `True`):
|
105 |
+
each diffusion step uses the value of alphas product at that step and at the previous one. For the final
|
106 |
+
step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
|
107 |
+
otherwise it uses the value of alpha at step 0.
|
108 |
+
steps_offset (`int`, default `0`):
|
109 |
+
an offset added to the inference steps. You can use a combination of `offset=1` and
|
110 |
+
`set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in
|
111 |
+
stable diffusion.
|
112 |
+
prediction_type (`str`, default `epsilon`, optional):
|
113 |
+
prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
|
114 |
+
process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
|
115 |
+
https://imagen.research.google/video/paper.pdf)
|
116 |
+
thresholding (`bool`, default `False`):
|
117 |
+
whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487).
|
118 |
+
Note that the thresholding method is unsuitable for latent-space diffusion models (such as
|
119 |
+
stable-diffusion).
|
120 |
+
dynamic_thresholding_ratio (`float`, default `0.995`):
|
121 |
+
the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen
|
122 |
+
(https://arxiv.org/abs/2205.11487). Valid only when `thresholding=True`.
|
123 |
+
sample_max_value (`float`, default `1.0`):
|
124 |
+
the threshold value for dynamic thresholding. Valid only when `thresholding=True`.
|
125 |
+
"""
|
126 |
+
|
127 |
+
_compatibles = [e.name for e in KarrasDiffusionSchedulers]
|
128 |
+
order = 1
|
129 |
+
|
130 |
+
@register_to_config
|
131 |
+
def __init__(
|
132 |
+
self,
|
133 |
+
num_train_timesteps: int = 1000,
|
134 |
+
beta_start: float = 0.0001,
|
135 |
+
beta_end: float = 0.02,
|
136 |
+
beta_schedule: str = "linear",
|
137 |
+
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
|
138 |
+
clip_sample: bool = True,
|
139 |
+
set_alpha_to_one: bool = True,
|
140 |
+
steps_offset: int = 0,
|
141 |
+
prediction_type: str = "epsilon",
|
142 |
+
thresholding: bool = False,
|
143 |
+
dynamic_thresholding_ratio: float = 0.995,
|
144 |
+
clip_sample_range: float = 1.0,
|
145 |
+
sample_max_value: float = 1.0,
|
146 |
+
):
|
147 |
+
if trained_betas is not None:
|
148 |
+
self.betas = torch.tensor(trained_betas, dtype=torch.float32)
|
149 |
+
elif beta_schedule == "linear":
|
150 |
+
self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
|
151 |
+
elif beta_schedule == "scaled_linear":
|
152 |
+
# this schedule is very specific to the latent diffusion model.
|
153 |
+
self.betas = (
|
154 |
+
torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
|
155 |
+
)
|
156 |
+
elif beta_schedule == "squaredcos_cap_v2":
|
157 |
+
# Glide cosine schedule
|
158 |
+
self.betas = betas_for_alpha_bar(num_train_timesteps)
|
159 |
+
else:
|
160 |
+
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
|
161 |
+
|
162 |
+
self.alphas = 1.0 - self.betas
|
163 |
+
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
|
164 |
+
|
165 |
+
# At every step in ddim, we are looking into the previous alphas_cumprod
|
166 |
+
# For the final step, there is no previous alphas_cumprod because we are already at 0
|
167 |
+
# `set_alpha_to_one` decides whether we set this parameter simply to one or
|
168 |
+
# whether we use the final alpha of the "non-previous" one.
|
169 |
+
self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
|
170 |
+
|
171 |
+
# standard deviation of the initial noise distribution
|
172 |
+
self.init_noise_sigma = 1.0
|
173 |
+
|
174 |
+
# setable values
|
175 |
+
self.num_inference_steps = None
|
176 |
+
self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64))
|
177 |
+
|
178 |
+
def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
|
179 |
+
"""
|
180 |
+
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
|
181 |
+
current timestep.
|
182 |
+
|
183 |
+
Args:
|
184 |
+
sample (`torch.FloatTensor`): input sample
|
185 |
+
timestep (`int`, optional): current timestep
|
186 |
+
|
187 |
+
Returns:
|
188 |
+
`torch.FloatTensor`: scaled input sample
|
189 |
+
"""
|
190 |
+
return sample
|
191 |
+
|
192 |
+
def _get_variance(self, timestep, prev_timestep):
|
193 |
+
alpha_prod_t = self.alphas_cumprod[timestep]
|
194 |
+
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
|
195 |
+
beta_prod_t = 1 - alpha_prod_t
|
196 |
+
beta_prod_t_prev = 1 - alpha_prod_t_prev
|
197 |
+
|
198 |
+
variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
|
199 |
+
|
200 |
+
return variance
|
201 |
+
|
202 |
+
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
|
203 |
+
def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
|
204 |
+
# Dynamic thresholding in https://arxiv.org/abs/2205.11487
|
205 |
+
dynamic_max_val = (
|
206 |
+
sample.flatten(1)
|
207 |
+
.abs()
|
208 |
+
.quantile(self.config.dynamic_thresholding_ratio, dim=1)
|
209 |
+
.clamp_min(self.config.sample_max_value)
|
210 |
+
.view(-1, *([1] * (sample.ndim - 1)))
|
211 |
+
)
|
212 |
+
return sample.clamp(-dynamic_max_val, dynamic_max_val) / dynamic_max_val
|
213 |
+
|
214 |
+
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
|
215 |
+
"""
|
216 |
+
Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
|
217 |
+
|
218 |
+
Args:
|
219 |
+
num_inference_steps (`int`):
|
220 |
+
the number of diffusion steps used when generating samples with a pre-trained model.
|
221 |
+
"""
|
222 |
+
|
223 |
+
if num_inference_steps > self.config.num_train_timesteps:
|
224 |
+
raise ValueError(
|
225 |
+
f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
|
226 |
+
f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
|
227 |
+
f" maximal {self.config.num_train_timesteps} timesteps."
|
228 |
+
)
|
229 |
+
|
230 |
+
self.num_inference_steps = num_inference_steps
|
231 |
+
step_ratio = self.config.num_train_timesteps // self.num_inference_steps
|
232 |
+
# creates integer timesteps by multiplying by ratio
|
233 |
+
# casting to int to avoid issues when num_inference_step is power of 3
|
234 |
+
timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)
|
235 |
+
self.timesteps = torch.from_numpy(timesteps).to(device)
|
236 |
+
self.timesteps += self.config.steps_offset
|
237 |
+
|
238 |
+
def step(
|
239 |
+
self,
|
240 |
+
model_output: torch.FloatTensor,
|
241 |
+
timestep: int,
|
242 |
+
sample: torch.FloatTensor,
|
243 |
+
eta: float = 0.0,
|
244 |
+
use_clipped_model_output: bool = False,
|
245 |
+
generator=None,
|
246 |
+
variance_noise: Optional[torch.FloatTensor] = None,
|
247 |
+
return_dict: bool = True,
|
248 |
+
) -> Union[DDIMSchedulerOutput, Tuple]:
|
249 |
+
"""
|
250 |
+
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
|
251 |
+
process from the learned model outputs (most often the predicted noise).
|
252 |
+
|
253 |
+
Args:
|
254 |
+
model_output (`torch.FloatTensor`): direct output from learned diffusion model.
|
255 |
+
timestep (`int`): current discrete timestep in the diffusion chain.
|
256 |
+
sample (`torch.FloatTensor`):
|
257 |
+
current instance of sample being created by diffusion process.
|
258 |
+
eta (`float`): weight of noise for added noise in diffusion step.
|
259 |
+
use_clipped_model_output (`bool`): if `True`, compute "corrected" `model_output` from the clipped
|
260 |
+
predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when
|
261 |
+
`self.config.clip_sample` is `True`. If no clipping has happened, "corrected" `model_output` would
|
262 |
+
coincide with the one provided as input and `use_clipped_model_output` will have not effect.
|
263 |
+
generator: random number generator.
|
264 |
+
variance_noise (`torch.FloatTensor`): instead of generating noise for the variance using `generator`, we
|
265 |
+
can directly provide the noise for the variance itself. This is useful for methods such as
|
266 |
+
CycleDiffusion. (https://arxiv.org/abs/2210.05559)
|
267 |
+
return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class
|
268 |
+
|
269 |
+
Returns:
|
270 |
+
[`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`:
|
271 |
+
[`~schedulers.scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
|
272 |
+
returning a tuple, the first element is the sample tensor.
|
273 |
+
|
274 |
+
"""
|
275 |
+
if self.num_inference_steps is None:
|
276 |
+
raise ValueError(
|
277 |
+
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
|
278 |
+
)
|
279 |
+
|
280 |
+
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
|
281 |
+
# Ideally, read DDIM paper in-detail understanding
|
282 |
+
|
283 |
+
# Notation (<variable name> -> <name in paper>
|
284 |
+
# - pred_noise_t -> e_theta(x_t, t)
|
285 |
+
# - pred_original_sample -> f_theta(x_t, t) or x_0
|
286 |
+
# - std_dev_t -> sigma_t
|
287 |
+
# - eta -> η
|
288 |
+
# - pred_sample_direction -> "direction pointing to x_t"
|
289 |
+
# - pred_prev_sample -> "x_t-1"
|
290 |
+
|
291 |
+
# 1. get previous step value (=t-1)
|
292 |
+
prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
|
293 |
+
|
294 |
+
# 2. compute alphas, betas
|
295 |
+
alpha_prod_t = self.alphas_cumprod[timestep]
|
296 |
+
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
|
297 |
+
|
298 |
+
beta_prod_t = 1 - alpha_prod_t
|
299 |
+
|
300 |
+
# 3. compute predicted original sample from predicted noise also called
|
301 |
+
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
|
302 |
+
if self.config.prediction_type == "epsilon":
|
303 |
+
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
|
304 |
+
pred_epsilon = model_output
|
305 |
+
elif self.config.prediction_type == "sample":
|
306 |
+
pred_original_sample = model_output
|
307 |
+
pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
|
308 |
+
elif self.config.prediction_type == "v_prediction":
|
309 |
+
pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
|
310 |
+
pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
|
311 |
+
else:
|
312 |
+
raise ValueError(
|
313 |
+
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
|
314 |
+
" `v_prediction`"
|
315 |
+
)
|
316 |
+
|
317 |
+
# 4. Clip or threshold "predicted x_0"
|
318 |
+
if self.config.clip_sample:
|
319 |
+
pred_original_sample = pred_original_sample.clamp(
|
320 |
+
-self.config.clip_sample_range, self.config.clip_sample_range
|
321 |
+
)
|
322 |
+
|
323 |
+
if self.config.thresholding:
|
324 |
+
pred_original_sample = self._threshold_sample(pred_original_sample)
|
325 |
+
|
326 |
+
# 5. compute variance: "sigma_t(η)" -> see formula (16)
|
327 |
+
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
|
328 |
+
variance = self._get_variance(timestep, prev_timestep)
|
329 |
+
std_dev_t = eta * variance ** (0.5)
|
330 |
+
|
331 |
+
if use_clipped_model_output:
|
332 |
+
# the pred_epsilon is always re-derived from the clipped x_0 in Glide
|
333 |
+
pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
|
334 |
+
|
335 |
+
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
|
336 |
+
pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon
|
337 |
+
|
338 |
+
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
|
339 |
+
prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
|
340 |
+
|
341 |
+
if eta > 0:
|
342 |
+
if variance_noise is not None and generator is not None:
|
343 |
+
raise ValueError(
|
344 |
+
"Cannot pass both generator and variance_noise. Please make sure that either `generator` or"
|
345 |
+
" `variance_noise` stays `None`."
|
346 |
+
)
|
347 |
+
|
348 |
+
if variance_noise is None:
|
349 |
+
variance_noise = randn_tensor(
|
350 |
+
model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype
|
351 |
+
)
|
352 |
+
variance = std_dev_t * variance_noise
|
353 |
+
|
354 |
+
prev_sample = prev_sample + variance
|
355 |
+
|
356 |
+
if not return_dict:
|
357 |
+
return (prev_sample,)
|
358 |
+
|
359 |
+
return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
|
360 |
+
|
361 |
+
def add_noise(
|
362 |
+
self,
|
363 |
+
original_samples: torch.FloatTensor,
|
364 |
+
noise: torch.FloatTensor,
|
365 |
+
timesteps: torch.IntTensor,
|
366 |
+
) -> torch.FloatTensor:
|
367 |
+
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
|
368 |
+
self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
|
369 |
+
timesteps = timesteps.to(original_samples.device)
|
370 |
+
|
371 |
+
sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
|
372 |
+
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
|
373 |
+
while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
|
374 |
+
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
|
375 |
+
|
376 |
+
sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
|
377 |
+
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
|
378 |
+
while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
|
379 |
+
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
|
380 |
+
|
381 |
+
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
|
382 |
+
return noisy_samples
|
383 |
+
|
384 |
+
def get_velocity(
|
385 |
+
self, sample: torch.FloatTensor, noise: torch.FloatTensor, timesteps: torch.IntTensor
|
386 |
+
) -> torch.FloatTensor:
|
387 |
+
# Make sure alphas_cumprod and timestep have same device and dtype as sample
|
388 |
+
self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device, dtype=sample.dtype)
|
389 |
+
timesteps = timesteps.to(sample.device)
|
390 |
+
|
391 |
+
sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
|
392 |
+
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
|
393 |
+
while len(sqrt_alpha_prod.shape) < len(sample.shape):
|
394 |
+
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
|
395 |
+
|
396 |
+
sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
|
397 |
+
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
|
398 |
+
while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
|
399 |
+
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
|
400 |
+
|
401 |
+
velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
|
402 |
+
return velocity
|
403 |
+
|
404 |
+
def __len__(self):
|
405 |
+
return self.config.num_train_timesteps
|