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Fix use_lu_lambdas and use_karras_sigmas with beta_schedule=squaredcos_cap_v2 in DPMSolverMultistepScheduler (#10740)

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hlky
2025-02-12 20:33:56 +00:00
committed by GitHub
parent 28f48f4051
commit ca6330dc53
1 changed files with 6 additions and 2 deletions
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8
src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
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@@ -399,12 +399,16 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
if self.config.use_karras_sigmas:
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]).round()
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()
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]).round()
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()
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)