Skip to content

Support LMSDiscreteScheduler in LDMPipeline #891

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

Merged
merged 4 commits into from
Oct 21, 2022
Merged

Support LMSDiscreteScheduler in LDMPipeline #891

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
eda7db5
Support LMSDiscreteScheduler in LDMPipeline
mkshing Oct 18, 2022
dacf922
Add "scale the initial noise by the standard deviation required by th…
mkshing Oct 21, 2022
c3bc1fd
Merge branch 'main' into patch-1
anton-l Oct 21, 2022
aabc9d5
run `make style`
anton-l Oct 21, 2022
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
6 changes: 5 additions & 1 deletion src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -64,6 +64,9 @@ def __call__(
)
latents = latents.to(self.device)

# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma

self.scheduler.set_timesteps(num_inference_steps)

# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
Expand All @@ -74,8 +77,9 @@ def __call__(
extra_kwargs["eta"] = eta

for t in self.progress_bar(self.scheduler.timesteps):
latent_model_input = self.scheduler.scale_model_input(latents, t)
# predict the noise residual
noise_prediction = self.unet(latents, t).sample
noise_prediction = self.unet(latent_model_input, t).sample
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_prediction, t, latents, **extra_kwargs).prev_sample

Expand Down