Skip to content

fix: deduplicate timesteps in DPMSolverMultistep for squaredcos_cap_v2 + karras/lu sigmas #13240

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
Chase-Xuu wants to merge 1 commit into
base: main
Choose a base branch
from
+12 −4
Open

fix: deduplicate timesteps in DPMSolverMultistep for squaredcos_cap_v2 + karras/lu sigmas #13240

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
16 changes: 12 additions & 4 deletions src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -442,15 +442,13 @@ def set_timesteps(
sigmas = np.flip(sigmas).copy()
sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
if self.config.beta_schedule != "squaredcos_cap_v2":
timesteps = timesteps.round()
timesteps = timesteps.round()
elif self.config.use_lu_lambdas:
lambdas = np.flip(log_sigmas.copy())
lambdas = self._convert_to_lu(in_lambdas=lambdas, num_inference_steps=num_inference_steps)
sigmas = np.exp(lambdas)
timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
if self.config.beta_schedule != "squaredcos_cap_v2":
timesteps = timesteps.round()
timesteps = timesteps.round()
elif self.config.use_exponential_sigmas:
sigmas = np.flip(sigmas).copy()
sigmas = self._convert_to_exponential(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
Expand All @@ -467,6 +465,16 @@ def set_timesteps(
else:
sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)

# When using karras or lu sigmas with certain beta schedules (e.g. squaredcos_cap_v2),
# the sigma-to-timestep mapping can produce duplicate integer timesteps. Deduplicate
# them to prevent the step index from drifting out of bounds during multistep updates.
timesteps_int = np.round(timesteps).astype(np.int64)
_, unique_indices = np.unique(timesteps_int, return_index=True)
if len(unique_indices) < len(timesteps_int):
unique_indices = np.sort(unique_indices)
timesteps = timesteps[unique_indices]
sigmas = sigmas[unique_indices]

if self.config.final_sigmas_type == "sigma_min":
sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5
elif self.config.final_sigmas_type == "zero":
Expand Down