modelzoo.vision.pytorch.dit.samplers.SamplerBase.SamplerBase#

class modelzoo.vision.pytorch.dit.samplers.SamplerBase.SamplerBase[source]#

Bases: abc.ABC

Methods

previous_timestep

Returns the previous timestep based on current timestep.

set_timesteps

Computes timesteps to be used during sampling

step

Predict the sample at the previous timestep by reversing the SDE.
abstract previous_timestep(timestep)[source]#

Returns the previous timestep based on current timestep. Depends on the timesteps computed in self.set_timesteps

abstract set_timesteps(num_diffusion_steps, num_inference_steps, custom_timesteps)[source]#

Computes timesteps to be used during sampling

Parameters

  • num_diffusion_steps (int) – Total number of steps the model was trained on

  • num_inference_steps (str) – string containing comma-separated numbers, indicating the step count per section. For example, if there’s 300 num_diffusion_steps and num_inference_steps=`10,15,20` then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. Can either pass custom_timesteps (or) num_inference_steps, but not both.

  • custom_timesteps (List[int]) – User specified list of timesteps to be used during sampling.

abstract step()[source]#

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).

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