DDPMScheduler
Last updated
Last updated
(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 state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.
( num_train_timesteps: int = 1000beta_start: float = 0.0001beta_end: float = 0.02beta_schedule: str = 'linear'trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = Nonevariance_type: str = 'fixed_small'clip_sample: bool = Trueprediction_type: str = 'epsilon'thresholding: bool = Falsedynamic_thresholding_ratio: float = 0.995clip_sample_range: float = 1.0sample_max_value: float = 1.0timestep_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 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.
variance_type (str, defaults to "fixed_small") — Clip the variance when adding noise to the denoised sample. Choose from fixed_small, fixed_small_log, fixed_large, fixed_large_log, learned or learned_range.
clip_sample (bool, defaults to True) — Clip the predicted sample for numerical stability.
clip_sample_range (float, defaults to 1.0) — The maximum magnitude for sample clipping. Valid only when clip_sample=True.
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 paper).
thresholding (bool, defaults to False) — Whether to use the “dynamic thresholding” method. This is unsuitable for latent-space diffusion models such as Stable Diffusion.
dynamic_thresholding_ratio (float, defaults to 0.995) — 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 for more information.
steps_offset (int, defaults to 0) — An offset added to the inference steps. You can use a combination of offset=1 and set_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.
scale_model_input
( sample: FloatTensortimestep: typing.Optional[int] = None ) → torch.FloatTensor
Parameters
sample (torch.FloatTensor) — The input sample.
timestep (int, optional) — The current timestep in the diffusion chain.
Returns
torch.FloatTensor
A scaled input sample.
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.
set_timesteps
( num_inference_steps: typing.Optional[int] = Nonedevice: typing.Union[str, torch.device] = Nonetimesteps: typing.Optional[typing.List[int]] = None )
Parameters
num_inference_steps (int) — The number of diffusion steps used when generating samples with a pre-trained model. If used, timesteps must be None.
device (str or torch.device, optional) — The device to which the timesteps should be moved to. If None, the timesteps are not moved.
timesteps (List[int], optional) — Custom timesteps used to support arbitrary spacing between timesteps. If None, then the default timestep spacing strategy of equal spacing between timesteps is used. If timesteps is passed, num_inference_steps must be None.
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
step
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.
Returns
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).
( prev_sample: FloatTensorpred_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_{t-1}) 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.
This model inherits from and . Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving.
( model_output: FloatTensortimestep: intsample: FloatTensorgenerator = Nonereturn_dict: bool = True ) → or tuple
return_dict (bool, optional, defaults to True) — Whether or not to return a or tuple.
or tuple
If return_dict is True, is returned, otherwise a tuple is returned where the first element is the sample tensor.