Add final_sigma_zero to UniPCMultistep

src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -127,6 +127,9 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
             Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
         steps_offset (`int`, defaults to 0):
             An offset added to the inference steps, as required by some model families.
+        final_sigmas_type (`str`, defaults to `"zero"`):
+            The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final sigma
+            is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.
     """
 
     _compatibles = [e.name for e in KarrasDiffusionSchedulers]
@@ -153,6 +156,7 @@ def __init__(
         use_karras_sigmas: Optional[bool] = False,
         timestep_spacing: str = "linspace",
         steps_offset: int = 0,
+        final_sigmas_type: Optional[str] = "zero",  # "zero", "sigma_min"
     ):
         if trained_betas is not None:
             self.betas = torch.tensor(trained_betas, dtype=torch.float32)
@@ -265,10 +269,25 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
             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()
-            sigmas = np.concatenate([sigmas, sigmas[-1:]]).astype(np.float32)
+            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)
-            sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5
+            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":
+                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)
 
         self.sigmas = torch.from_numpy(sigmas)

tests/schedulers/test_scheduler_unipc.py

@@ -24,6 +24,7 @@ def get_scheduler_config(self, **kwargs):
             "beta_schedule": "linear",
             "solver_order": 2,
             "solver_type": "bh2",
+            "final_sigmas_type": "sigma_min",
         }
 
         config.update(**kwargs)