[doc] Fix some docstrings in `src/diffusers/training_utils.py` (#9606)

* refac: docstrings in training_utils.py

* fix: manual edits

* run make style

* add docstring at cast_training_params

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

src/diffusers/training_utils.py

@@ -36,8 +36,9 @@ if is_torch_npu_available():
 
 def set_seed(seed: int):
     """
-    Args:
     Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`.
+
+    Args:
         seed (`int`): The seed to set.
     """
     random.seed(seed)
@@ -194,6 +195,13 @@ def unet_lora_state_dict(unet: UNet2DConditionModel) -> Dict[str, torch.Tensor]:
 
 
 def cast_training_params(model: Union[torch.nn.Module, List[torch.nn.Module]], dtype=torch.float32):
+    """
+    Casts the training parameters of the model to the specified data type.
+
+    Args:
+        model: The PyTorch model whose parameters will be cast.
+        dtype: The data type to which the model parameters will be cast.
+    """
     if not isinstance(model, list):
         model = [model]
     for m in model:
@@ -225,7 +233,8 @@ def _set_state_dict_into_text_encoder(
 def compute_density_for_timestep_sampling(
     weighting_scheme: str, batch_size: int, logit_mean: float = None, logit_std: float = None, mode_scale: float = None
 ):
-    """Compute the density for sampling the timesteps when doing SD3 training.
+    """
+    Compute the density for sampling the timesteps when doing SD3 training.
 
     Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
 
@@ -244,7 +253,8 @@ def compute_density_for_timestep_sampling(
 
 
 def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):
-    """Computes loss weighting scheme for SD3 training.
+    """
+    Computes loss weighting scheme for SD3 training.
 
     Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
 
@@ -261,7 +271,9 @@ def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):
 
 
 def free_memory():
-    """Runs garbage collection. Then clears the cache of the available accelerator."""
+    """
+    Runs garbage collection. Then clears the cache of the available accelerator.
+    """
     gc.collect()
 
     if torch.cuda.is_available():
@@ -494,7 +506,8 @@ class EMAModel:
         self.shadow_params = [p.pin_memory() for p in self.shadow_params]
 
     def to(self, device=None, dtype=None, non_blocking=False) -> None:
-        r"""Move internal buffers of the ExponentialMovingAverage to `device`.
+        r"""
+        Move internal buffers of the ExponentialMovingAverage to `device`.
 
         Args:
             device: like `device` argument to `torch.Tensor.to`
@@ -528,23 +541,25 @@ class EMAModel:
 
     def store(self, parameters: Iterable[torch.nn.Parameter]) -> None:
         r"""
+        Saves the current parameters for restoring later.
+
         Args:
-        Save the current parameters for restoring later.
-            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
-                temporarily stored.
+            parameters: Iterable of `torch.nn.Parameter`. The parameters to be temporarily stored.
         """
         self.temp_stored_params = [param.detach().cpu().clone() for param in parameters]
 
     def restore(self, parameters: Iterable[torch.nn.Parameter]) -> None:
         r"""
-        Args:
-        Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without:
-        affecting the original optimization process. Store the parameters before the `copy_to()` method. After
+        Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters
+        without: affecting the original optimization process. Store the parameters before the `copy_to()` method. After
         validation (or model saving), use this to restore the former parameters.
+
+        Args:
             parameters: Iterable of `torch.nn.Parameter`; the parameters to be
                 updated with the stored parameters. If `None`, the parameters with which this
                 `ExponentialMovingAverage` was initialized will be used.
         """
+
         if self.temp_stored_params is None:
             raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`")
         if self.foreach:
@@ -560,9 +575,10 @@ class EMAModel:
 
     def load_state_dict(self, state_dict: dict) -> None:
         r"""
-        Args:
         Loads the ExponentialMovingAverage state. This method is used by accelerate during checkpointing to save the
         ema state dict.
+
+        Args:
             state_dict (dict): EMA state. Should be an object returned
                 from a call to :meth:`state_dict`.
         """