[docs] Callbacks (#5735)

* updates

* feedback

src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py

@@ -234,7 +234,7 @@ class WuerstchenCombinedPipeline(DiffusionPipeline):
             prior_callback_on_step_end_tensor_inputs (`List`, *optional*):
                 The list of tensor inputs for the `prior_callback_on_step_end` function. The tensors specified in the
                 list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in
-                the `._callback_tensor_inputs` attribute of your pipeine class.
+                the `._callback_tensor_inputs` attribute of your pipeline class.
             callback_on_step_end (`Callable`, *optional*):
                 A function that calls at the end of each denoising steps during the inference. The function is called
                 with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
@@ -243,7 +243,7 @@ class WuerstchenCombinedPipeline(DiffusionPipeline):
             callback_on_step_end_tensor_inputs (`List`, *optional*):
                 The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                 will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
-                `._callback_tensor_inputs` attribute of your pipeine class.
+                `._callback_tensor_inputs` attribute of your pipeline class.
 
         Examples:
 

src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py

@@ -349,7 +349,7 @@ class WuerstchenPriorPipeline(DiffusionPipeline, LoraLoaderMixin):
             callback_on_step_end_tensor_inputs (`List`, *optional*):
                 The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                 will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
-                `._callback_tensor_inputs` attribute of your pipeine class.
+                `._callback_tensor_inputs` attribute of your pipeline class.
 
         Examples: