[docs] Fix DiffusionPipeline.enable_sequential_cpu_offload docstring (huggingface#4952)

* Fix an unmatched backtick and make description more general for DiffusionPipeline.enable_sequential_cpu_offload.

* make style

* _exclude_from_cpu_offload -> self._exclude_from_cpu_offload

* make style

* apply suggestions from review

* make style

Author: dg845

src/diffusers/pipelines/pipeline_utils.py

@@ -1293,10 +1293,10 @@ def maybe_free_model_hooks(self):
 
     def enable_sequential_cpu_offload(self, gpu_id: int = 0, device: Union[torch.device, str] = "cuda"):
         r"""
-        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
-        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
-        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
-        Note that offloading happens on a submodule basis. Memory savings are higher than with
+        Offloads all models to CPU using 🤗 Accelerate, significantly reducing memory usage. When called, the state
+        dicts of all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are saved to CPU
+        and then moved to `torch.device('meta')` and loaded to GPU only when their specific submodule has its `forward`
+        method called. Offloading happens on a submodule basis. Memory savings are higher than with
         `enable_model_cpu_offload`, but performance is lower.
         """
         if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):