Skip to content

Improve docstrings and type hints in scheduling_ddim.py #12622

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 3 commits into
base: main
Choose a base branch
from
Open

Improve docstrings and type hints in scheduling_ddim.py #12622

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
67f931b
Improve docstrings and type hints in scheduling_ddim.py
delmalih Nov 10, 2025
9375844
Enhance docstrings and type hints in scheduling_ddim.py
delmalih Nov 10, 2025
26f0ca1
Refine type hints and docstrings in scheduling_ddim.py
delmalih Nov 10, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
155 changes: 113 additions & 42 deletions src/diffusers/schedulers/scheduling_ddim.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@

import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
from typing import List, Literal, Optional, Tuple, Union

import numpy as np
import torch
Expand All @@ -38,7 +38,7 @@ class DDIMSchedulerOutput(BaseOutput):
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):
pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images, *optional*):
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.
"""
Expand All @@ -49,36 +49,36 @@ class DDIMSchedulerOutput(BaseOutput):

# 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",
):
num_diffusion_timesteps: int,
max_beta: float = 0.999,
alpha_transform_type: Literal["cosine", "exp"] = "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`): 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`
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 prevent singularities.
alpha_transform_type (`Literal["cosine", "exp"]`, defaults to `"cosine"`):
The type of noise schedule for alpha_bar. Must be one of `"cosine"` or `"exp"`.

Returns:
betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
`torch.Tensor`: The betas used by the scheduler to step the model outputs.
"""
if alpha_transform_type == "cosine":

def alpha_bar_fn(t):
def alpha_bar_fn(t: float) -> float:
return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

elif alpha_transform_type == "exp":

def alpha_bar_fn(t):
def alpha_bar_fn(t: float) -> float:
return math.exp(t * -12.0)

else:
Expand All @@ -92,11 +92,10 @@ def alpha_bar_fn(t):
return torch.tensor(betas, dtype=torch.float32)


def rescale_zero_terminal_snr(betas):
def rescale_zero_terminal_snr(betas: torch.Tensor) -> torch.Tensor:
"""
Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1)


Args:
betas (`torch.Tensor`):
the betas that the scheduler is being initialized with.
Expand Down Expand Up @@ -143,9 +142,9 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
The starting `beta` value of inference.
beta_end (`float`, defaults to 0.02):
The final `beta` value.
beta_schedule (`str`, defaults to `"linear"`):
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`.
beta_schedule (`Literal["linear", "scaled_linear", "squaredcos_cap_v2"]`, defaults to `"linear"`):
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Must be one
of `"linear"`, `"scaled_linear"`, or `"squaredcos_cap_v2"`.
trained_betas (`np.ndarray`, *optional*):
Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
clip_sample (`bool`, defaults to `True`):
Expand All @@ -158,9 +157,9 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
otherwise it uses the alpha value at step 0.
steps_offset (`int`, defaults to 0):
An offset added to the inference steps, as required by some model families.
prediction_type (`str`, defaults to `epsilon`, *optional*):
Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
`sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
prediction_type (`Literal["epsilon", "sample", "v_prediction"]`, defaults to `"epsilon"`):
Prediction type of the scheduler function. Must be one of `"epsilon"` (predicts the noise of the diffusion
process), `"sample"` (directly predicts the noisy sample), or `"v_prediction"` (see section 2.4 of [Imagen
Video](https://imagen.research.google/video/paper.pdf) paper).
thresholding (`bool`, defaults to `False`):
Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
Expand All @@ -169,9 +168,10 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
sample_max_value (`float`, defaults to 1.0):
The threshold value for dynamic thresholding. Valid only when `thresholding=True`.
timestep_spacing (`str`, defaults to `"leading"`):
The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
timestep_spacing (`Literal["leading", "trailing", "linspace"]`, defaults to `"leading"`):
The way the timesteps should be scaled. Must be one of `"leading"`, `"trailing"`, or `"linspace"`. Refer to
Table 2 of the [Common Diffusion Noise Schedules and Sample Steps are
Flawed](https://huggingface.co/papers/2305.08891) for more information.
rescale_betas_zero_snr (`bool`, defaults to `False`):
Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
dark samples instead of limiting it to samples with medium brightness. Loosely related to
Expand All @@ -187,17 +187,17 @@ def __init__(
num_train_timesteps: int = 1000,
beta_start: float = 0.0001,
beta_end: float = 0.02,
beta_schedule: str = "linear",
beta_schedule: Literal["linear", "scaled_linear", "squaredcos_cap_v2"] = "linear",
trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
clip_sample: bool = True,
set_alpha_to_one: bool = True,
steps_offset: int = 0,
prediction_type: str = "epsilon",
prediction_type: Literal["epsilon", "sample", "v_prediction"] = "epsilon",
thresholding: bool = False,
dynamic_thresholding_ratio: float = 0.995,
clip_sample_range: float = 1.0,
sample_max_value: float = 1.0,
timestep_spacing: str = "leading",
timestep_spacing: Literal["leading", "trailing", "linspace"] = "leading",
rescale_betas_zero_snr: bool = False,
):
if trained_betas is not None:
Expand Down Expand Up @@ -250,7 +250,25 @@ def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None
"""
return sample

def _get_variance(self, timestep, prev_timestep):
def _get_variance(self, timestep: int, prev_timestep: int) -> torch.Tensor:
"""
Computes the variance of the noise added at a given diffusion step.

For a given `timestep` and its previous step, this method calculates the variance as defined in DDIM/DDPM
literature:
var_t = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
where alpha_prod and beta_prod are cumulative products of alphas and betas, respectively.

Args:
timestep (`int`):
The current timestep in the diffusion process.
prev_timestep (`int`):
The previous timestep in the diffusion process. If negative, uses `final_alpha_cumprod`.

Returns:
`torch.Tensor`:
The variance for the current timestep.
"""
alpha_prod_t = self.alphas_cumprod[timestep]
alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
beta_prod_t = 1 - alpha_prod_t
Expand All @@ -263,13 +281,21 @@ def _get_variance(self, timestep, prev_timestep):
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
"""
"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
pixels from saturation at each step. We find that dynamic thresholding results in significantly better
photorealism as well as better image-text alignment, especially when using very large guidance weights."
photorealism as well as better image-text alignment, especially when using very large guidance weights.

See https://huggingface.co/papers/2205.11487

Args:
sample (`torch.Tensor`):
The sample to threshold.

https://huggingface.co/papers/2205.11487
Returns:
`torch.Tensor`:
The thresholded sample.
"""
dtype = sample.dtype
batch_size, channels, *remaining_dims = sample.shape
Expand All @@ -294,13 +320,18 @@ def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:

return sample

def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None) -> None:
"""
Sets the discrete timesteps used for the diffusion chain (to be run before inference).

Args:
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model.
device (`Union[str, torch.device]`, *optional*):
The device to use for the timesteps.

Raises:
ValueError: If `num_inference_steps` is larger than `self.config.num_train_timesteps`.
"""

if num_inference_steps > self.config.num_train_timesteps:
Expand Down Expand Up @@ -346,7 +377,7 @@ def step(
sample: torch.Tensor,
eta: float = 0.0,
use_clipped_model_output: bool = False,
generator=None,
generator: Optional[torch.Generator] = None,
variance_noise: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[DDIMSchedulerOutput, Tuple]:
Expand All @@ -357,20 +388,21 @@ def step(
Args:
model_output (`torch.Tensor`):
The direct output from learned diffusion model.
timestep (`float`):
timestep (`int`):
The current discrete timestep in the diffusion chain.
sample (`torch.Tensor`):
A current instance of a sample created by the diffusion process.
eta (`float`):
The weight of noise for added noise in diffusion step.
use_clipped_model_output (`bool`, defaults to `False`):
eta (`float`, *optional*, defaults to 0.0):
The weight of noise for added noise in diffusion step. A value of 0 corresponds to DDIM (deterministic)
and 1 corresponds to DDPM (fully stochastic).
use_clipped_model_output (`bool`, *optional*, defaults to `False`):
If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary
because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no
clipping has happened, "corrected" `model_output` would coincide with the one provided as input and
`use_clipped_model_output` has no effect.
generator (`torch.Generator`, *optional*):
A random number generator.
variance_noise (`torch.Tensor`):
A random number generator for reproducible sampling.
variance_noise (`torch.Tensor`, *optional*):
Alternative to generating noise with `generator` by directly providing the noise for the variance
itself. Useful for methods such as [`CycleDiffusion`].
return_dict (`bool`, *optional*, defaults to `True`):
Expand Down Expand Up @@ -477,6 +509,24 @@ def add_noise(
noise: torch.Tensor,
timesteps: torch.IntTensor,
) -> torch.Tensor:
"""
Add noise to the original samples according to the noise magnitude at each timestep.

This implements the forward diffusion process using the formula: `noisy_sample = sqrt(alpha_prod) *
original_sample + sqrt(1 - alpha_prod) * noise`

Args:
original_samples (`torch.Tensor`):
The original clean samples to which noise will be added.
noise (`torch.Tensor`):
The noise tensor to add, typically sampled from a Gaussian distribution.
timesteps (`torch.IntTensor`):
The timesteps indicating the noise level from the diffusion schedule.

Returns:
`torch.Tensor`:
The noisy samples with noise added according to the timestep schedule.
"""
# 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
Expand All @@ -499,6 +549,27 @@ def add_noise(

# Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: torch.IntTensor) -> torch.Tensor:
"""
Compute the velocity prediction for v-prediction models.

The velocity is computed using the formula: `velocity = sqrt(alpha_prod) * noise - sqrt(1 - alpha_prod) *
sample`

This is used in v-prediction models where the model directly predicts the velocity instead of the noise or the
sample. See section 2.4 of Imagen Video paper: https://imagen.research.google/video/paper.pdf

Args:
sample (`torch.Tensor`):
The input sample (x_t) at the current timestep.
noise (`torch.Tensor`):
The noise tensor corresponding to the sample.
timesteps (`torch.IntTensor`):
The timesteps at which to compute the velocity.

Returns:
`torch.Tensor`:
The velocity prediction computed from the sample and noise at the given timesteps.
"""
# Make sure alphas_cumprod and timestep have same device and dtype as sample
self.alphas_cumprod = self.alphas_cumprod.to(device=sample.device)
alphas_cumprod = self.alphas_cumprod.to(dtype=sample.dtype)
Expand All @@ -517,5 +588,5 @@ def get_velocity(self, sample: torch.Tensor, noise: torch.Tensor, timesteps: tor
velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity

def __len__(self):
def __len__(self) -> int:
return self.config.num_train_timesteps