Fix a grammatical error in the `raise` messages (#8272)

Fix grammatical error

examples/community/latent_consistency_img2img.py

@@ -565,7 +565,7 @@ class LCMSchedulerWithTimestamp(SchedulerMixin, ConfigMixin):
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         # Rescale for zero SNR
         if rescale_betas_zero_snr:

examples/community/latent_consistency_txt2img.py

@@ -477,7 +477,7 @@ class LCMScheduler(SchedulerMixin, ConfigMixin):
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         # Rescale for zero SNR
         if rescale_betas_zero_snr:

examples/community/scheduling_ufogen.py

@@ -218,7 +218,7 @@ class UFOGenScheduler(SchedulerMixin, ConfigMixin):
             betas = torch.linspace(-6, 6, num_train_timesteps)
             self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         # Rescale for zero SNR
         if rescale_betas_zero_snr:

src/diffusers/schedulers/scheduling_ddim.py

@@ -211,7 +211,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         # Rescale for zero SNR
         if rescale_betas_zero_snr:

src/diffusers/schedulers/scheduling_ddim_inverse.py

@@ -207,7 +207,7 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         # Rescale for zero SNR
         if rescale_betas_zero_snr:

src/diffusers/schedulers/scheduling_ddim_parallel.py

@@ -218,7 +218,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         # Rescale for zero SNR
         if rescale_betas_zero_snr:

src/diffusers/schedulers/scheduling_ddpm.py

@@ -211,7 +211,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
             betas = torch.linspace(-6, 6, num_train_timesteps)
             self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         # Rescale for zero SNR
         if rescale_betas_zero_snr:

src/diffusers/schedulers/scheduling_ddpm_parallel.py

@@ -219,7 +219,7 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
             betas = torch.linspace(-6, 6, num_train_timesteps)
             self.betas = torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         # Rescale for zero SNR
         if rescale_betas_zero_snr:

src/diffusers/schedulers/scheduling_deis_multistep.py

@@ -152,7 +152,7 @@ class DEISMultistepScheduler(SchedulerMixin, ConfigMixin):
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         self.alphas = 1.0 - self.betas
         self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
@@ -170,13 +170,13 @@ class DEISMultistepScheduler(SchedulerMixin, ConfigMixin):
             if algorithm_type in ["dpmsolver", "dpmsolver++"]:
                 self.register_to_config(algorithm_type="deis")
             else:
-                raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}")
+                raise NotImplementedError(f"{algorithm_type} is not implemented for {self.__class__}")
 
         if solver_type not in ["logrho"]:
             if solver_type in ["midpoint", "heun", "bh1", "bh2"]:
                 self.register_to_config(solver_type="logrho")
             else:
-                raise NotImplementedError(f"solver type {solver_type} does is not implemented for {self.__class__}")
+                raise NotImplementedError(f"solver type {solver_type} is not implemented for {self.__class__}")
 
         # setable values
         self.num_inference_steps = None

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -229,7 +229,7 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         if rescale_betas_zero_snr:
             self.betas = rescale_zero_terminal_snr(self.betas)
@@ -256,13 +256,13 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
             if algorithm_type == "deis":
                 self.register_to_config(algorithm_type="dpmsolver++")
             else:
-                raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}")
+                raise NotImplementedError(f"{algorithm_type} is not implemented for {self.__class__}")
 
         if solver_type not in ["midpoint", "heun"]:
             if solver_type in ["logrho", "bh1", "bh2"]:
                 self.register_to_config(solver_type="midpoint")
             else:
-                raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}")
+                raise NotImplementedError(f"{solver_type} is not implemented for {self.__class__}")
 
         if algorithm_type not in ["dpmsolver++", "sde-dpmsolver++"] and final_sigmas_type == "zero":
             raise ValueError(

src/diffusers/schedulers/scheduling_dpmsolver_multistep_flax.py

@@ -182,9 +182,9 @@ class FlaxDPMSolverMultistepScheduler(FlaxSchedulerMixin, ConfigMixin):
 
         # settings for DPM-Solver
         if self.config.algorithm_type not in ["dpmsolver", "dpmsolver++"]:
-            raise NotImplementedError(f"{self.config.algorithm_type} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{self.config.algorithm_type} is not implemented for {self.__class__}")
         if self.config.solver_type not in ["midpoint", "heun"]:
-            raise NotImplementedError(f"{self.config.solver_type} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{self.config.solver_type} is not implemented for {self.__class__}")
 
         # standard deviation of the initial noise distribution
         init_noise_sigma = jnp.array(1.0, dtype=self.dtype)

src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py

@@ -178,7 +178,7 @@ class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         self.alphas = 1.0 - self.betas
         self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
@@ -196,13 +196,13 @@ class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):
             if algorithm_type == "deis":
                 self.register_to_config(algorithm_type="dpmsolver++")
             else:
-                raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}")
+                raise NotImplementedError(f"{algorithm_type} is not implemented for {self.__class__}")
 
         if solver_type not in ["midpoint", "heun"]:
             if solver_type in ["logrho", "bh1", "bh2"]:
                 self.register_to_config(solver_type="midpoint")
             else:
-                raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}")
+                raise NotImplementedError(f"{solver_type} is not implemented for {self.__class__}")
 
         # setable values
         self.num_inference_steps = None

src/diffusers/schedulers/scheduling_dpmsolver_sde.py

@@ -184,7 +184,7 @@ class DPMSolverSDEScheduler(SchedulerMixin, ConfigMixin):
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         self.alphas = 1.0 - self.betas
         self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)

src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py

@@ -172,7 +172,7 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         self.alphas = 1.0 - self.betas
         self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
@@ -190,12 +190,12 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
             if algorithm_type == "deis":
                 self.register_to_config(algorithm_type="dpmsolver++")
             else:
-                raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}")
+                raise NotImplementedError(f"{algorithm_type} is not implemented for {self.__class__}")
         if solver_type not in ["midpoint", "heun"]:
             if solver_type in ["logrho", "bh1", "bh2"]:
                 self.register_to_config(solver_type="midpoint")
             else:
-                raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}")
+                raise NotImplementedError(f"{solver_type} is not implemented for {self.__class__}")
 
         if algorithm_type != "dpmsolver++" and final_sigmas_type == "zero":
             raise ValueError(

src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py

@@ -119,7 +119,7 @@ class EDMDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
             if solver_type in ["logrho", "bh1", "bh2"]:
                 self.register_to_config(solver_type="midpoint")
             else:
-                raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}")
+                raise NotImplementedError(f"{solver_type} is not implemented for {self.__class__}")
 
         if algorithm_type not in ["dpmsolver++", "sde-dpmsolver++"] and final_sigmas_type == "zero":
             raise ValueError(

src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

@@ -190,7 +190,7 @@ class EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         if rescale_betas_zero_snr:
             self.betas = rescale_zero_terminal_snr(self.betas)

src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -205,7 +205,7 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         if rescale_betas_zero_snr:
             self.betas = rescale_zero_terminal_snr(self.betas)

src/diffusers/schedulers/scheduling_heun_discrete.py

@@ -135,7 +135,7 @@ class HeunDiscreteScheduler(SchedulerMixin, ConfigMixin):
         elif beta_schedule == "exp":
             self.betas = betas_for_alpha_bar(num_train_timesteps, alpha_transform_type="exp")
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         self.alphas = 1.0 - self.betas
         self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)

src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py

@@ -129,7 +129,7 @@ class KDPM2AncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         self.alphas = 1.0 - self.betas
         self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)

src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py

@@ -128,7 +128,7 @@ class KDPM2DiscreteScheduler(SchedulerMixin, ConfigMixin):
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+            raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
 
         self.alphas = 1.0 - self.betas
         self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)