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from dataclasses import dataclass
from typing import Literal
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
# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
def betas_for_alpha_bar(
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp", "laplace"] = "cosine",
) -> torch.Tensor:
"""
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`, defaults to `0.999`):
The maximum beta to use; use values lower than 1 to avoid numerical instability.
alpha_transform_type (`str`, defaults to `"cosine"`):
The type of noise schedule for `alpha_bar`. Choose from `cosine`, `exp`, or `laplace`.
Returns:
`torch.Tensor`:
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 == "laplace":
def alpha_bar_fn(t):
lmb = -0.5 * math.copysign(1, 0.5 - t) * math.log(1 - 2 * math.fabs(0.5 - t) + 1e-6)
snr = math.exp(lmb)
return math.sqrt(snr / (1 + snr))
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)
@dataclass
class ConsistencyDecoderSchedulerOutput(BaseOutput):
"""
Output class for the scheduler's `step` function.
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.
"""
prev_sample: torch.Tensor
class ConsistencyDecoderScheduler(SchedulerMixin, ConfigMixin):
"""
A scheduler for the consistency decoder used in Stable Diffusion pipelines.
This scheduler implements a two-step denoising process using consistency models for decoding latent representations
into images.
This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving.
Args:
num_train_timesteps (`int`, *optional*, defaults to `1024`):
The number of diffusion steps to train the model.
sigma_data (`float`, *optional*, defaults to `0.5`):
The standard deviation of the data distribution. Used for computing the skip and output scaling factors.
"""
order = 1
@register_to_config
def __init__(
self,
num_train_timesteps: int = 1024,
sigma_data: float = 0.5,
) -> None:
betas = betas_for_alpha_bar(num_train_timesteps)
alphas = 1.0 - betas
alphas_cumprod = torch.cumprod(alphas, dim=0)
self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod)
sigmas = torch.sqrt(1.0 / alphas_cumprod - 1)
sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod)
self.c_skip = sqrt_recip_alphas_cumprod * sigma_data**2 / (sigmas**2 + sigma_data**2)
self.c_out = sigmas * sigma_data / (sigmas**2 + sigma_data**2) ** 0.5
self.c_in = sqrt_recip_alphas_cumprod / (sigmas**2 + sigma_data**2) ** 0.5
def set_timesteps(
self,
num_inference_steps: int | None = None,
device: str | torch.device = None,
):
if num_inference_steps != 2:
raise ValueError("Currently more than 2 inference steps are not supported.")
self.timesteps = torch.tensor([1008, 512], dtype=torch.long, device=device)
self.sqrt_alphas_cumprod = self.sqrt_alphas_cumprod.to(device)
self.sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod.to(device)
self.c_skip = self.c_skip.to(device)
self.c_out = self.c_out.to(device)
self.c_in = self.c_in.to(device)
@property
def init_noise_sigma(self) -> torch.Tensor:
"""
Return the standard deviation of the initial noise distribution.
Returns:
`torch.Tensor`:
The initial noise sigma value from the precomputed `sqrt_one_minus_alphas_cumprod` at the first
timestep.
"""
return self.sqrt_one_minus_alphas_cumprod[self.timesteps[0]]
def scale_model_input(self, sample: torch.Tensor, timestep: int | None = None) -> torch.Tensor:
"""
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep.
Args:
sample (`torch.Tensor`):
The input sample.
timestep (`int`, *optional*):
The current timestep in the diffusion chain.
Returns:
`torch.Tensor`:
A scaled input sample.
"""
return sample * self.c_in[timestep]
def step(
self,
model_output: torch.Tensor,
timestep: float | torch.Tensor,
sample: torch.Tensor,
generator: torch.Generator | None = None,
return_dict: bool = True,
) -> ConsistencyDecoderSchedulerOutput | tuple:
"""
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.Tensor`):
The direct output from the learned diffusion model.
timestep (`float` or `torch.Tensor`):
The current timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
generator (`torch.Generator`, *optional*):
A random number generator for reproducibility.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a
[`~schedulers.scheduling_consistency_decoder.ConsistencyDecoderSchedulerOutput`] or `tuple`.
Returns:
[`~schedulers.scheduling_consistency_decoder.ConsistencyDecoderSchedulerOutput`] or `tuple`:
If `return_dict` is `True`,
[`~schedulers.scheduling_consistency_decoder.ConsistencyDecoderSchedulerOutput`] is returned, otherwise
a tuple is returned where the first element is the sample tensor.
"""
x_0 = self.c_out[timestep] * model_output + self.c_skip[timestep] * sample
timestep_idx = torch.where(self.timesteps == timestep)[0]
if timestep_idx == len(self.timesteps) - 1:
prev_sample = x_0
else:
noise = randn_tensor(x_0.shape, generator=generator, dtype=x_0.dtype, device=x_0.device)
prev_sample = (
self.sqrt_alphas_cumprod[self.timesteps[timestep_idx + 1]].to(x_0.dtype) * x_0
+ self.sqrt_one_minus_alphas_cumprod[self.timesteps[timestep_idx + 1]].to(x_0.dtype) * noise
)
if not return_dict:
return (prev_sample,)
return ConsistencyDecoderSchedulerOutput(prev_sample=prev_sample)
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