Skip to content

Do not merge! Credits for DEIS #39

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

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Do not merge! Credits for DEIS #39

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
2 changes: 2 additions & 0 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,8 @@ on all nodes.

- k-diffusion models support progressive growing.

- [DEIS](https://arxiv.org/abs/2204.13902), where DPM-Solver are equivelent to $\rho$-RK in DEIS with second order mid point solver and third order Kutta method. And [linear multistep](https://en.wikipedia.org/wiki/Linear_multistep_method#Adams–Bashforth_methods) with warming start, which is exactly $\rho$-AB in DEIS.

- k-diffusion implements [DPM-Solver](https://arxiv.org/abs/2206.00927), which produces higher quality samples at the same number of function evalutions as Karras Algorithm 2, as well as supporting adaptive step size control. It also implements a [linear multistep](https://en.wikipedia.org/wiki/Linear_multistep_method#Adams–Bashforth_methods) sampler (comparable to [PLMS](https://arxiv.org/abs/2202.09778)).

- k-diffusion supports [CLIP](https://openai.com/blog/clip/) guided sampling from unconditional diffusion models (see `sample_clip_guided.py`).
Expand Down