Skip to content
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

Minor typos #159

Merged
merged 1 commit into from
Aug 6, 2022
Merged

Minor typos #159

Changes from all commits
Commits
Show all changes
1 commit
Select commit
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
4 changes: 2 additions & 2 deletions src/diffusers/schedulers/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,14 +1,14 @@
# Schedulers

- Schedulers are the algorithms to use diffusion models in inference as well as for training. They include the noise schedules and define algorithm-specific diffusion steps.
- Schedulers can be used interchangable between diffusion models in inference to find the preferred tradef-off between speed and generation quality.
- Schedulers can be used interchangable between diffusion models in inference to find the preferred trade-off between speed and generation quality.
- Schedulers are available in numpy, but can easily be transformed into PyTorch.

## API

- Schedulers should provide one or more `def step(...)` functions that should be called iteratively to unroll the diffusion loop during
the forward pass.
- Schedulers should be framework-agonstic, but provide a simple functionality to convert the scheduler into a specific framework, such as PyTorch
- Schedulers should be framework-agnostic, but provide a simple functionality to convert the scheduler into a specific framework, such as PyTorch
with a `set_format(...)` method.

## Examples
Expand Down