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Author: zRzRzRzRzRzRzR

src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py

@@ -788,7 +788,7 @@ def check_inputs(
 
         if transcription is None:
             if self.text_encoder_2.config.model_type == "vits":
-                raise ValueError("Cannot forward without transcription. Please make sure to have transcription")
+                raise ValueError("Cannot forward without transcription. Please make sure to" " have transcription")
         elif transcription is not None and (
             not isinstance(transcription, str) and not isinstance(transcription, list)
         ):

src/diffusers/pipelines/shap_e/renderer.py

@@ -983,9 +983,9 @@ def decode_to_mesh(
         fields = torch.cat(fields, dim=1)
         fields = fields.float()
 
-        assert len(fields.shape) == 3 and fields.shape[-1] == 1, (
-            f"expected [meta_batch x inner_batch] SDF results, but got {fields.shape}"
-        )
+        assert (
+            len(fields.shape) == 3 and fields.shape[-1] == 1
+        ), f"expected [meta_batch x inner_batch] SDF results, but got {fields.shape}"
 
         fields = fields.reshape(1, *([grid_size] * 3))
 
@@ -1039,9 +1039,9 @@ def decode_to_mesh(
         textures = textures.float()
 
         # 3.3 augument the mesh with texture data
-        assert len(textures.shape) == 3 and textures.shape[-1] == len(texture_channels), (
-            f"expected [meta_batch x inner_batch x texture_channels] field results, but got {textures.shape}"
-        )
+        assert len(textures.shape) == 3 and textures.shape[-1] == len(
+            texture_channels
+        ), f"expected [meta_batch x inner_batch x texture_channels] field results, but got {textures.shape}"
 
         for m, texture in zip(raw_meshes, textures):
             texture = texture[: len(m.verts)]

src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py

@@ -584,7 +584,7 @@ def __call__(
 
         if audio_end_in_s - audio_start_in_s > max_audio_length_in_s:
             raise ValueError(
-                f"The total audio length requested ({audio_end_in_s - audio_start_in_s}s) is longer than the model maximum possible length ({max_audio_length_in_s}). Make sure that 'audio_end_in_s-audio_start_in_s<={max_audio_length_in_s}'."
+                f"The total audio length requested ({audio_end_in_s-audio_start_in_s}s) is longer than the model maximum possible length ({max_audio_length_in_s}). Make sure that 'audio_end_in_s-audio_start_in_s<={max_audio_length_in_s}'."
             )
 
         waveform_start = int(audio_start_in_s * self.vae.config.sampling_rate)

src/diffusers/schedulers/__init__.py

@@ -44,7 +44,6 @@
     _import_structure["scheduling_consistency_models"] = ["CMStochasticIterativeScheduler"]
     _import_structure["scheduling_ddim"] = ["DDIMScheduler"]
     _import_structure["scheduling_ddim_cogvideox"] = ["CogVideoXDDIMScheduler"]
-    _import_structure["scheduling_ddim_cogview4"] = ["CogView4DDIMScheduler"]
     _import_structure["scheduling_ddim_inverse"] = ["DDIMInverseScheduler"]
     _import_structure["scheduling_ddim_parallel"] = ["DDIMParallelScheduler"]
     _import_structure["scheduling_ddpm"] = ["DDPMScheduler"]
@@ -145,7 +144,6 @@
         from .scheduling_consistency_models import CMStochasticIterativeScheduler
         from .scheduling_ddim import DDIMScheduler
         from .scheduling_ddim_cogvideox import CogVideoXDDIMScheduler
-        from .scheduling_ddim_cogview4 import CogView4DDIMScheduler
         from .scheduling_ddim_inverse import DDIMInverseScheduler
         from .scheduling_ddim_parallel import DDIMParallelScheduler
         from .scheduling_ddpm import DDPMScheduler

src/diffusers/schedulers/scheduling_consistency_models.py

@@ -203,7 +203,8 @@ def set_timesteps(
 
             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
-                    f"`timesteps` must start before `self.config.train_timesteps`: {self.config.num_train_timesteps}."
+                    f"`timesteps` must start before `self.config.train_timesteps`:"
+                    f" {self.config.num_train_timesteps}."
                 )
 
             timesteps = np.array(timesteps, dtype=np.int64)

src/diffusers/schedulers/scheduling_ddpm.py

@@ -279,7 +279,8 @@ def set_timesteps(
 
             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
-                    f"`timesteps` must start before `self.config.train_timesteps`: {self.config.num_train_timesteps}."
+                    f"`timesteps` must start before `self.config.train_timesteps`:"
+                    f" {self.config.num_train_timesteps}."
                 )
 
             timesteps = np.array(timesteps, dtype=np.int64)

src/diffusers/schedulers/scheduling_ddpm_parallel.py

@@ -289,7 +289,8 @@ def set_timesteps(
 
             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
-                    f"`timesteps` must start before `self.config.train_timesteps`: {self.config.num_train_timesteps}."
+                    f"`timesteps` must start before `self.config.train_timesteps`:"
+                    f" {self.config.num_train_timesteps}."
                 )
 
             timesteps = np.array(timesteps, dtype=np.int64)

src/diffusers/schedulers/scheduling_lcm.py

@@ -413,7 +413,8 @@ def set_timesteps(
 
             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
-                    f"`timesteps` must start before `self.config.train_timesteps`: {self.config.num_train_timesteps}."
+                    f"`timesteps` must start before `self.config.train_timesteps`:"
+                    f" {self.config.num_train_timesteps}."
                 )
 
             # Raise warning if timestep schedule does not start with self.config.num_train_timesteps - 1

src/diffusers/schedulers/scheduling_tcd.py

@@ -431,7 +431,8 @@ def set_timesteps(
 
             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
-                    f"`timesteps` must start before `self.config.train_timesteps`: {self.config.num_train_timesteps}."
+                    f"`timesteps` must start before `self.config.train_timesteps`:"
+                    f" {self.config.num_train_timesteps}."
                 )
 
             # Raise warning if timestep schedule does not start with self.config.num_train_timesteps - 1

src/diffusers/training_utils.py

@@ -241,7 +241,7 @@ def _set_state_dict_into_text_encoder(
     """
 
     text_encoder_state_dict = {
-        f"{k.replace(prefix, '')}": v for k, v in lora_state_dict.items() if k.startswith(prefix)
+        f'{k.replace(prefix, "")}': v for k, v in lora_state_dict.items() if k.startswith(prefix)
     }
     text_encoder_state_dict = convert_state_dict_to_peft(convert_state_dict_to_diffusers(text_encoder_state_dict))
     set_peft_model_state_dict(text_encoder, text_encoder_state_dict, adapter_name="default")
@@ -578,7 +578,7 @@ def restore(self, parameters: Iterable[torch.nn.Parameter]) -> None:
         """
 
         if self.temp_stored_params is None:
-            raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights to `restore()`")
+            raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`")
         if self.foreach:
             torch._foreach_copy_(
                 [param.data for param in parameters], [c_param.data for c_param in self.temp_stored_params]