DDPMScheduler
Denoising Diffusion Probabilistic Models (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes a diffusion based model of the same name. In the context of the 🤗 Diffusers library, DDPM refers to the discrete denoising scheduler from the paper as well as the pipeline.
The abstract from the paper is:
We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a stateoftheart FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.
DDPMScheduler
class diffusers.DDPMScheduler
< source >( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None variance_type: str = 'fixed_small' clip_sample: bool = True prediction_type: str = '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' steps_offset: int = 0 )
Parameters

num_train_timesteps (
int
, defaults to 1000) — The number of diffusion steps to train the model. 
beta_start (
float
, defaults to 0.0001) — The startingbeta
value of inference. 
beta_end (
float
, defaults to 0.02) — The finalbeta
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 fromlinear
,scaled_linear
, orsquaredcos_cap_v2
. 
variance_type (
str
, defaults to"fixed_small"
) — Clip the variance when adding noise to the denoised sample. Choose fromfixed_small
,fixed_small_log
,fixed_large
,fixed_large_log
,learned
orlearned_range
. 
clip_sample (
bool
, defaults toTrue
) — Clip the predicted sample for numerical stability. 
clip_sample_range (
float
, defaults to 1.0) — The maximum magnitude for sample clipping. Valid only whenclip_sample=True
. 
prediction_type (
str
, defaults toepsilon
, optional) — Prediction type of the scheduler function; can beepsilon
(predicts the noise of the diffusion process),sample
(directly predicts the noisy sample) or
v_prediction` (see section 2.4 of Imagen Video paper). 
thresholding (
bool
, defaults toFalse
) — Whether to use the “dynamic thresholding” method. This is unsuitable for latentspace diffusion models such as Stable Diffusion. 
dynamic_thresholding_ratio (
float
, defaults to 0.995) — The ratio for the dynamic thresholding method. Valid only whenthresholding=True
. 
sample_max_value (
float
, defaults to 1.0) — The threshold value for dynamic thresholding. Valid only whenthresholding=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 for more information. 
steps_offset (
int
, defaults to 0) — An offset added to the inference steps. You can use a combination ofoffset=1
andset_alpha_to_one=False
to make the last step use step 0 for the previous alpha product like in Stable Diffusion.
DDPMScheduler
explores the connections between denoising score matching and Langevin dynamics sampling.
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.
scale_model_input
< source >(
sample: FloatTensor
timestep: typing.Optional[int] = None
)
→
torch.FloatTensor
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.
set_timesteps
< source >( num_inference_steps: typing.Optional[int] = None device: typing.Union[str, torch.device] = None timesteps: typing.Optional[typing.List[int]] = None )
Parameters

num_inference_steps (
int
) — The number of diffusion steps used when generating samples with a pretrained model. If used,timesteps
must beNone
. 
device (
str
ortorch.device
, optional) — The device to which the timesteps should be moved to. IfNone
, the timesteps are not moved. 
timesteps (
List[int]
, optional) — Custom timesteps used to support arbitrary spacing between timesteps. IfNone
, then the default timestep spacing strategy of equal spacing between timesteps is used. Iftimesteps
is passed,num_inference_steps
must beNone
.
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
step
< source >(
model_output: FloatTensor
timestep: int
sample: FloatTensor
generator = None
return_dict: bool = True
)
→
DDPMSchedulerOutput or tuple
Parameters

model_output (
torch.FloatTensor
) — The direct output from learned diffusion model. 
timestep (
float
) — The current discrete timestep in the diffusion chain. 
sample (
torch.FloatTensor
) — A current instance of a sample created by the diffusion process. 
generator (
torch.Generator
, optional) — A random number generator. 
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a DDPMSchedulerOutput ortuple
.
Returns
DDPMSchedulerOutput or tuple
If return_dict is True
, DDPMSchedulerOutput is returned, otherwise a
tuple is returned where the first element is the sample tensor.
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).
DDPMSchedulerOutput
class diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput
< source >( prev_sample: FloatTensor pred_original_sample: typing.Optional[torch.FloatTensor] = None )
Parameters

prev_sample (
torch.FloatTensor
of shape(batch_size, num_channels, height, width)
for images) — Computed sample(x_{t1})
of previous timestep.prev_sample
should be used as next model input in the denoising loop. 
pred_original_sample (
torch.FloatTensor
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.
Output class for the scheduler’s step
function output.