356 lines
15 KiB
Python
356 lines
15 KiB
Python
|
# Copyright 2025 Kakao Brain 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 math
|
||
|
|
from dataclasses import dataclass
|
||
|
|
from typing import Optional, Tuple, Union
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import torch
|
||
|
|
|
||
|
|
from ..configuration_utils import ConfigMixin, register_to_config
|
||
|
|
from ..utils import BaseOutput
|
||
|
|
from ..utils.torch_utils import randn_tensor
|
||
|
|
from .scheduling_utils import SchedulerMixin
|
||
|
|
|
||
|
|
|
||
|
|
@dataclass
|
||
|
|
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
|
||
|
|
class UnCLIPSchedulerOutput(BaseOutput):
|
||
|
|
"""
|
||
|
|
Output class for the scheduler's `step` function output.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
prev_sample (`torch.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 (`torch.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: torch.Tensor
|
||
|
|
pred_original_sample: Optional[torch.Tensor] = None
|
||
|
|
|
||
|
|
|
||
|
|
# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
|
||
|
|
def betas_for_alpha_bar(
|
||
|
|
num_diffusion_timesteps,
|
||
|
|
max_beta=0.999,
|
||
|
|
alpha_transform_type="cosine",
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
|
||
|
|
(1-beta) over time from t = [0,1].
|
||
|
|
|
||
|
|
Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
|
||
|
|
to that part of the diffusion process.
|
||
|
|
|
||
|
|
|
||
|
|
Args:
|
||
|
|
num_diffusion_timesteps (`int`): the number of betas to produce.
|
||
|
|
max_beta (`float`): the maximum beta to use; use values lower than 1 to
|
||
|
|
prevent singularities.
|
||
|
|
alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
|
||
|
|
Choose from `cosine` or `exp`
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
|
||
|
|
"""
|
||
|
|
if alpha_transform_type == "cosine":
|
||
|
|
|
||
|
|
def alpha_bar_fn(t):
|
||
|
|
return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
|
||
|
|
|
||
|
|
elif alpha_transform_type == "exp":
|
||
|
|
|
||
|
|
def alpha_bar_fn(t):
|
||
|
|
return math.exp(t * -12.0)
|
||
|
|
|
||
|
|
else:
|
||
|
|
raise ValueError(f"Unsupported alpha_transform_type: {alpha_transform_type}")
|
||
|
|
|
||
|
|
betas = []
|
||
|
|
for i in range(num_diffusion_timesteps):
|
||
|
|
t1 = i / num_diffusion_timesteps
|
||
|
|
t2 = (i + 1) / num_diffusion_timesteps
|
||
|
|
betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
|
||
|
|
return torch.tensor(betas, dtype=torch.float32)
|
||
|
|
|
||
|
|
|
||
|
|
class UnCLIPScheduler(SchedulerMixin, ConfigMixin):
|
||
|
|
"""
|
||
|
|
NOTE: do not use this scheduler. The DDPM scheduler has been updated to support the changes made here. This
|
||
|
|
scheduler will be removed and replaced with DDPM.
|
||
|
|
|
||
|
|
This is a modified DDPM Scheduler specifically for the karlo unCLIP model.
|
||
|
|
|
||
|
|
This scheduler has some minor variations in how it calculates the learned range variance and dynamically
|
||
|
|
re-calculates betas based off the timesteps it is skipping.
|
||
|
|
|
||
|
|
The scheduler also uses a slightly different step ratio when computing timesteps to use for inference.
|
||
|
|
|
||
|
|
See [`~DDPMScheduler`] for more information on DDPM scheduling
|
||
|
|
|
||
|
|
Args:
|
||
|
|
num_train_timesteps (`int`): number of diffusion steps used to train the model.
|
||
|
|
variance_type (`str`):
|
||
|
|
options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small_log`
|
||
|
|
or `learned_range`.
|
||
|
|
clip_sample (`bool`, default `True`):
|
||
|
|
option to clip predicted sample between `-clip_sample_range` and `clip_sample_range` for numerical
|
||
|
|
stability.
|
||
|
|
clip_sample_range (`float`, default `1.0`):
|
||
|
|
The range to clip the sample between. See `clip_sample`.
|
||
|
|
prediction_type (`str`, default `epsilon`, optional):
|
||
|
|
prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion process)
|
||
|
|
or `sample` (directly predicting the noisy sample`)
|
||
|
|
"""
|
||
|
|
|
||
|
|
@register_to_config
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
num_train_timesteps: int = 1000,
|
||
|
|
variance_type: str = "fixed_small_log",
|
||
|
|
clip_sample: bool = True,
|
||
|
|
clip_sample_range: Optional[float] = 1.0,
|
||
|
|
prediction_type: str = "epsilon",
|
||
|
|
beta_schedule: str = "squaredcos_cap_v2",
|
||
|
|
):
|
||
|
|
if beta_schedule != "squaredcos_cap_v2":
|
||
|
|
raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'")
|
||
|
|
|
||
|
|
self.betas = betas_for_alpha_bar(num_train_timesteps)
|
||
|
|
|
||
|
|
self.alphas = 1.0 - self.betas
|
||
|
|
self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
|
||
|
|
self.one = torch.tensor(1.0)
|
||
|
|
|
||
|
|
# standard deviation of the initial noise distribution
|
||
|
|
self.init_noise_sigma = 1.0
|
||
|
|
|
||
|
|
# setable values
|
||
|
|
self.num_inference_steps = None
|
||
|
|
self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
|
||
|
|
|
||
|
|
self.variance_type = variance_type
|
||
|
|
|
||
|
|
def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:
|
||
|
|
"""
|
||
|
|
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
|
||
|
|
current timestep.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
sample (`torch.Tensor`): input sample
|
||
|
|
timestep (`int`, optional): current timestep
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
`torch.Tensor`: scaled input sample
|
||
|
|
"""
|
||
|
|
return sample
|
||
|
|
|
||
|
|
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
|
||
|
|
"""
|
||
|
|
Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
|
||
|
|
|
||
|
|
Note that this scheduler uses a slightly different step ratio than the other diffusers schedulers. The
|
||
|
|
different step ratio is to mimic the original karlo implementation and does not affect the quality or accuracy
|
||
|
|
of the results.
|
||
|
|
|
||
|
|
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
|
||
|
|
step_ratio = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
|
||
|
|
timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(np.int64)
|
||
|
|
self.timesteps = torch.from_numpy(timesteps).to(device)
|
||
|
|
|
||
|
|
def _get_variance(self, t, prev_timestep=None, predicted_variance=None, variance_type=None):
|
||
|
|
if prev_timestep is None:
|
||
|
|
prev_timestep = t - 1
|
||
|
|
|
||
|
|
alpha_prod_t = self.alphas_cumprod[t]
|
||
|
|
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
|
||
|
|
beta_prod_t = 1 - alpha_prod_t
|
||
|
|
beta_prod_t_prev = 1 - alpha_prod_t_prev
|
||
|
|
|
||
|
|
if prev_timestep == t - 1:
|
||
|
|
beta = self.betas[t]
|
||
|
|
else:
|
||
|
|
beta = 1 - alpha_prod_t / alpha_prod_t_prev
|
||
|
|
|
||
|
|
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://huggingface.co/papers/2006.11239)
|
||
|
|
# and sample from it to get previous sample
|
||
|
|
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
|
||
|
|
variance = beta_prod_t_prev / beta_prod_t * beta
|
||
|
|
|
||
|
|
if variance_type is None:
|
||
|
|
variance_type = self.config.variance_type
|
||
|
|
|
||
|
|
# hacks - were probably added for training stability
|
||
|
|
if variance_type == "fixed_small_log":
|
||
|
|
variance = torch.log(torch.clamp(variance, min=1e-20))
|
||
|
|
variance = torch.exp(0.5 * variance)
|
||
|
|
elif variance_type == "learned_range":
|
||
|
|
# NOTE difference with DDPM scheduler
|
||
|
|
min_log = variance.log()
|
||
|
|
max_log = beta.log()
|
||
|
|
|
||
|
|
frac = (predicted_variance + 1) / 2
|
||
|
|
variance = frac * max_log + (1 - frac) * min_log
|
||
|
|
|
||
|
|
return variance
|
||
|
|
|
||
|
|
def step(
|
||
|
|
self,
|
||
|
|
model_output: torch.Tensor,
|
||
|
|
timestep: int,
|
||
|
|
sample: torch.Tensor,
|
||
|
|
prev_timestep: Optional[int] = None,
|
||
|
|
generator=None,
|
||
|
|
return_dict: bool = True,
|
||
|
|
) -> Union[UnCLIPSchedulerOutput, 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 (`torch.Tensor`): direct output from learned diffusion model.
|
||
|
|
timestep (`int`): current discrete timestep in the diffusion chain.
|
||
|
|
sample (`torch.Tensor`):
|
||
|
|
current instance of sample being created by diffusion process.
|
||
|
|
prev_timestep (`int`, *optional*): The previous timestep to predict the previous sample at.
|
||
|
|
Used to dynamically compute beta. If not given, `t-1` is used and the pre-computed beta is used.
|
||
|
|
generator: random number generator.
|
||
|
|
return_dict (`bool`): option for returning tuple rather than UnCLIPSchedulerOutput class
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
[`~schedulers.scheduling_utils.UnCLIPSchedulerOutput`] or `tuple`:
|
||
|
|
[`~schedulers.scheduling_utils.UnCLIPSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
|
||
|
|
returning a tuple, the first element is the sample tensor.
|
||
|
|
|
||
|
|
"""
|
||
|
|
t = timestep
|
||
|
|
|
||
|
|
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
|
||
|
|
model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
|
||
|
|
else:
|
||
|
|
predicted_variance = None
|
||
|
|
|
||
|
|
# 1. compute alphas, betas
|
||
|
|
if prev_timestep is None:
|
||
|
|
prev_timestep = t - 1
|
||
|
|
|
||
|
|
alpha_prod_t = self.alphas_cumprod[t]
|
||
|
|
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
|
||
|
|
beta_prod_t = 1 - alpha_prod_t
|
||
|
|
beta_prod_t_prev = 1 - alpha_prod_t_prev
|
||
|
|
|
||
|
|
if prev_timestep == t - 1:
|
||
|
|
beta = self.betas[t]
|
||
|
|
alpha = self.alphas[t]
|
||
|
|
else:
|
||
|
|
beta = 1 - alpha_prod_t / alpha_prod_t_prev
|
||
|
|
alpha = 1 - beta
|
||
|
|
|
||
|
|
# 2. compute predicted original sample from predicted noise also called
|
||
|
|
# "predicted x_0" of formula (15) from https://huggingface.co/papers/2006.11239
|
||
|
|
if self.config.prediction_type == "epsilon":
|
||
|
|
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
|
||
|
|
elif self.config.prediction_type == "sample":
|
||
|
|
pred_original_sample = model_output
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
|
||
|
|
" for the UnCLIPScheduler."
|
||
|
|
)
|
||
|
|
|
||
|
|
# 3. Clip "predicted x_0"
|
||
|
|
if self.config.clip_sample:
|
||
|
|
pred_original_sample = torch.clamp(
|
||
|
|
pred_original_sample, -self.config.clip_sample_range, self.config.clip_sample_range
|
||
|
|
)
|
||
|
|
|
||
|
|
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
|
||
|
|
# See formula (7) from https://huggingface.co/papers/2006.11239
|
||
|
|
pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * beta) / beta_prod_t
|
||
|
|
current_sample_coeff = alpha ** (0.5) * beta_prod_t_prev / beta_prod_t
|
||
|
|
|
||
|
|
# 5. Compute predicted previous sample µ_t
|
||
|
|
# See formula (7) from https://huggingface.co/papers/2006.11239
|
||
|
|
pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
|
||
|
|
|
||
|
|
# 6. Add noise
|
||
|
|
variance = 0
|
||
|
|
if t > 0:
|
||
|
|
variance_noise = randn_tensor(
|
||
|
|
model_output.shape, dtype=model_output.dtype, generator=generator, device=model_output.device
|
||
|
|
)
|
||
|
|
|
||
|
|
variance = self._get_variance(
|
||
|
|
t,
|
||
|
|
predicted_variance=predicted_variance,
|
||
|
|
prev_timestep=prev_timestep,
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.variance_type == "fixed_small_log":
|
||
|
|
variance = variance
|
||
|
|
elif self.variance_type == "learned_range":
|
||
|
|
variance = (0.5 * variance).exp()
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
f"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
|
||
|
|
" for the UnCLIPScheduler."
|
||
|
|
)
|
||
|
|
|
||
|
|
variance = variance * variance_noise
|
||
|
|
|
||
|
|
pred_prev_sample = pred_prev_sample + variance
|
||
|
|
|
||
|
|
if not return_dict:
|
||
|
|
return (
|
||
|
|
pred_prev_sample,
|
||
|
|
pred_original_sample,
|
||
|
|
)
|
||
|
|
|
||
|
|
return UnCLIPSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)
|
||
|
|
|
||
|
|
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
|
||
|
|
def add_noise(
|
||
|
|
self,
|
||
|
|
original_samples: torch.Tensor,
|
||
|
|
noise: torch.Tensor,
|
||
|
|
timesteps: torch.IntTensor,
|
||
|
|
) -> torch.Tensor:
|
||
|
|
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
|
||
|
|
# Move the self.alphas_cumprod to device to avoid redundant CPU to GPU data movement
|
||
|
|
# for the subsequent add_noise calls
|
||
|
|
self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device)
|
||
|
|
alphas_cumprod = self.alphas_cumprod.to(dtype=original_samples.dtype)
|
||
|
|
timesteps = timesteps.to(original_samples.device)
|
||
|
|
|
||
|
|
sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
|
||
|
|
sqrt_alpha_prod = sqrt_alpha_prod.flatten()
|
||
|
|
while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
|
||
|
|
sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
|
||
|
|
|
||
|
|
sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
|
||
|
|
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
|
||
|
|
while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
|
||
|
|
sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
|
||
|
|
|
||
|
|
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
|
||
|
|
return noisy_samples
|