Fix sigma_last with use_flow_sigmas

Author: hlky

src/diffusers/schedulers/scheduling_deis_multistep.py

@@ -289,6 +289,7 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
             sigmas = 1.0 - alphas
             sigmas = np.flip(self.config.flow_shift * sigmas / (1 + (self.config.flow_shift - 1) * sigmas))[:-1].copy()
             timesteps = (sigmas * self.config.num_train_timesteps).copy()
+            sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)
         else:
             sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
             sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5

src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py

@@ -291,14 +291,17 @@ def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torc
         elif self.config.use_exponential_sigmas:
             sigmas = self._convert_to_exponential(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
             timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
+            sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)
         elif self.config.use_beta_sigmas:
             sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
             timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
+            sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)
         elif self.config.use_flow_sigmas:
             alphas = np.linspace(1, 1 / self.config.num_train_timesteps, num_inference_steps + 1)
             sigmas = 1.0 - alphas
             sigmas = np.flip(self.config.flow_shift * sigmas / (1 + (self.config.flow_shift - 1) * sigmas))[:-1].copy()
             timesteps = (sigmas * self.config.num_train_timesteps).copy()
+            sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)
         else:
             sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
             sigma_max = (

src/diffusers/schedulers/scheduling_sasolver.py

@@ -318,6 +318,7 @@ def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torc
             sigmas = 1.0 - alphas
             sigmas = np.flip(self.config.flow_shift * sigmas / (1 + (self.config.flow_shift - 1) * sigmas))[:-1].copy()
             timesteps = (sigmas * self.config.num_train_timesteps).copy()
+            sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)
         else:
             sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
             sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5

src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -381,6 +381,15 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
             sigmas = 1.0 - alphas
             sigmas = np.flip(self.config.flow_shift * sigmas / (1 + (self.config.flow_shift - 1) * sigmas))[:-1].copy()
             timesteps = (sigmas * self.config.num_train_timesteps).copy()
+            if self.config.final_sigmas_type == "sigma_min":
+                sigma_last = sigmas[-1]
+            elif self.config.final_sigmas_type == "zero":
+                sigma_last = 0
+            else:
+                raise ValueError(
+                    f"`final_sigmas_type` must be one of 'zero', or 'sigma_min', but got {self.config.final_sigmas_type}"
+                )
+            sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
         else:
             sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
             if self.config.final_sigmas_type == "sigma_min":