Schedulers
🤗 Diffusers provides many scheduler functions for the diffusion process. A scheduler takes a model's output (the sample which the diffusion process is iterating on) and a timestep to return a denoised sample. The timestep is important because it dictates where in the diffusion process the step is; data is generated by iterating forward n timesteps and inference occurs by propagating backward through the timesteps. Based on the timestep, a scheduler may be discrete in which case the timestep is an int or continuous in which case the timestep is a float.
Depending on the context, a scheduler defines how to iteratively add noise to an image or how to update a sample based on a model's output:
- during training, a scheduler adds noise (there are different algorithms for how to add noise) to a sample to train a diffusion model
- during inference, a scheduler defines how to update a sample based on a pretrained model's output
Many schedulers are implemented from the k-diffusion library by Katherine Crowson, and they're also widely used in A1111. To help you map the schedulers from k-diffusion and A1111 to the schedulers in 🤗 Diffusers, take a look at the table below:
| A1111/k-diffusion | 🤗 Diffusers | Usage |
|---|---|---|
| DPM++ 2M | [DPMSolverMultistepScheduler] |
|
| DPM++ 2M Karras | [DPMSolverMultistepScheduler] |
init with use_karras_sigmas=True |
| DPM++ 2M SDE | [DPMSolverMultistepScheduler] |
init with algorithm_type="sde-dpmsolver++" |
| DPM++ 2M SDE Karras | [DPMSolverMultistepScheduler] |
init with use_karras_sigmas=True and algorithm_type="sde-dpmsolver++" |
| DPM++ 2S a | N/A | very similar to DPMSolverSinglestepScheduler |
| DPM++ 2S a Karras | N/A | very similar to DPMSolverSinglestepScheduler(use_karras_sigmas=True, ...) |
| DPM++ SDE | [DPMSolverSinglestepScheduler] |
|
| DPM++ SDE Karras | [DPMSolverSinglestepScheduler] |
init with use_karras_sigmas=True |
| DPM2 | [KDPM2DiscreteScheduler] |
|
| DPM2 Karras | [KDPM2DiscreteScheduler] |
init with use_karras_sigmas=True |
| DPM2 a | [KDPM2AncestralDiscreteScheduler] |
|
| DPM2 a Karras | [KDPM2AncestralDiscreteScheduler] |
init with use_karras_sigmas=True |
| DPM adaptive | N/A | |
| DPM fast | N/A | |
| Euler | [EulerDiscreteScheduler] |
|
| Euler a | [EulerAncestralDiscreteScheduler] |
|
| Heun | [HeunDiscreteScheduler] |
|
| LMS | [LMSDiscreteScheduler] |
|
| LMS Karras | [LMSDiscreteScheduler] |
init with use_karras_sigmas=True |
| N/A | [DEISMultistepScheduler] |
|
| N/A | [UniPCMultistepScheduler] |
All schedulers are built from the base [SchedulerMixin] class which implements low level utilities shared by all schedulers.
SchedulerMixin
[[autodoc]] SchedulerMixin
SchedulerOutput
[[autodoc]] schedulers.scheduling_utils.SchedulerOutput
KarrasDiffusionSchedulers
[KarrasDiffusionSchedulers] are a broad generalization of schedulers in 🤗 Diffusers. The schedulers in this class are distinguished at a high level by their noise sampling strategy, the type of network and scaling, the training strategy, and how the loss is weighed.
The different schedulers in this class, depending on the ordinary differential equations (ODE) solver type, fall into the above taxonomy and provide a good abstraction for the design of the main schedulers implemented in 🤗 Diffusers. The schedulers in this class are given here.
PushToHubMixin
[[autodoc]] utils.PushToHubMixin