diff --git a/examples/swissroll.jl b/examples/swissroll.jl
index 9c1f28d..4fe775e 100644
--- a/examples/swissroll.jl
+++ b/examples/swissroll.jl
@@ -64,7 +64,7 @@ anim = @animate for t in cat(fill(0, 20), 1:num_timesteps, fill(num_timesteps, 2
     xlims!(-3, 3)
     ylims!(-3, 3)
   else
-    noisy_data = Diffusers.Schedulers.add_noise(scheduler, data, noise, [t])
+    noisy_data = Diffusers.Schedulers.forward(scheduler, data, noise, [t])
     scatter(noise[1, :], noise[2, :],
       alpha=0.3,
       aspectratio=:equal,
@@ -130,7 +130,7 @@ for epoch = 1:num_epochs
   for data in dataloader
     noise = randn(Float32, size(data))
     timesteps = rand(1:num_timesteps, size(data, ndims(data)))
-    noisy_data = Diffusers.Schedulers.add_noise(scheduler, data, noise, timesteps)
+    noisy_data = Diffusers.Schedulers.forward(scheduler, data, noise, timesteps)
     grads = Flux.gradient(model) do m
       model_output = m(noisy_data, timesteps)
       loss(noise, model_output)
@@ -147,7 +147,7 @@ sample_old = sample
 predictions = []
 for timestep in num_timesteps:-1:1
   model_output = model(sample, [timestep])
-  sample, x0_pred = Diffusers.Schedulers.step(scheduler, sample, model_output, [timestep])
+  sample, x0_pred = Diffusers.Schedulers.reverse(scheduler, sample, model_output, [timestep])
   push!(predictions, (sample, x0_pred, timestep))
 end