update docs

a-r-r-o-w · a-r-r-o-w · commit 19901e7a8cea · 2025-01-17T07:50:54.000+01:00
diff --git a/docs/source/en/api/utilities.md b/docs/source/en/api/utilities.md
@@ -41,3 +41,7 @@ Utility and helper functions for working with 🤗 Diffusers.
 ## randn_tensor
 
 [[autodoc]] utils.torch_utils.randn_tensor
+
+## apply_layerwise_upcasting
+
+[[autodoc]] hooks.layerwise_upcasting.apply_layerwise_upcasting
diff --git a/docs/source/en/optimization/memory.md b/docs/source/en/optimization/memory.md
@@ -189,10 +189,6 @@ In the above example, layerwise upcasting is enabled on the transformer componen
 
 However, you gain more control and flexibility by directly utilizing the [`~hooks.layerwise_upcasting.apply_layerwise_upcasting`] function instead of [`~ModelMixin.enable_layerwise_upcasting`].
 
-[[autodoc]] ModelMixin.enable_layerwise_upcasting
-
-[[autodoc]] hooks.layerwise_upcasting.apply_layerwise_upcasting
-
 ## Channels-last memory format
 
 The channels-last memory format is an alternative way of ordering NCHW tensors in memory to preserve dimension ordering. Channels-last tensors are ordered in such a way that the channels become the densest dimension (storing images pixel-per-pixel). Since not all operators currently support the channels-last format, it may result in worst performance but you should still try and see if it works for your model.
