remove unused

Author: a-r-r-o-w

src/diffusers/guiders/guider_utils.py

@@ -20,7 +20,6 @@
 
 
 if TYPE_CHECKING:
-    from ..models.attention_processor import AttentionProcessor
     from ..pipelines.modular_pipeline import BlockState
 
 
@@ -214,41 +213,3 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
     noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
     return noise_cfg
-
-
-def _default_prepare_inputs(denoiser: torch.nn.Module, num_conditions: int, *args: Union[Tuple[torch.Tensor], List[torch.Tensor]]) -> Tuple[List[torch.Tensor], ...]:
-    """
-    Prepares the inputs for the denoiser by ensuring that the conditional and unconditional inputs are correctly
-    prepared based on required number of conditions. This function is used in the `prepare_inputs` method of the
-    `BaseGuidance` class.
-
-    Either tensors or tuples/lists of tensors can be provided. If a tuple/list is provided, it should contain two elements:
-    - The first element is the conditional input.
-    - The second element is the unconditional input or None.
-    
-    If only the conditional input is provided, it will be repeated for all batches.
-    
-    If both conditional and unconditional inputs are provided, they are alternated as batches of data.
-    """
-    list_of_inputs = []
-    for arg in args:
-        if arg is None or isinstance(arg, torch.Tensor):
-            list_of_inputs.append([arg] * num_conditions)
-        elif isinstance(arg, (tuple, list)):
-            if len(arg) != 2:
-                raise ValueError(
-                    f"Expected a tuple or list of length 2, but got {len(arg)} for argument {arg}. Please provide a tuple/list of length 2 "
-                    f"with the first element being the conditional input and the second element being the unconditional input or None."
-                )
-            if arg[1] is None:
-                # Only conditioning inputs for all batches
-                list_of_inputs.append([arg[0]] * num_conditions)
-            else:
-                # Alternating conditional and unconditional inputs as batches
-                inputs = [arg[i % 2] for i in range(num_conditions)]
-                list_of_inputs.append(inputs)
-        else:
-            raise ValueError(
-                f"Expected a tensor, tuple, or list, but got {type(arg)} for argument {arg}. Please provide a tensor, tuple, or list."
-            )
-    return tuple(list_of_inputs)

src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -584,7 +584,6 @@ def step(
         s_noise: float = 1.0,
         generator: Optional[torch.Generator] = None,
         return_dict: bool = True,
-        _model_output_uncond: Optional[torch.Tensor] = None,
     ) -> Union[EulerDiscreteSchedulerOutput, Tuple]:
         """
         Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion