Update docs/source/en/optimization/memory.md

Author: a-r-r-o-w

docs/source/en/optimization/memory.md

@@ -203,7 +203,7 @@ Group offloading (for CUDA devices with support for asynchronous data transfer s
 - Group offloading may not work with all models out-of-the-box. If the forward implementations of the model contain weight-dependent device-casting of inputs, it may clash with the offloading mechanism's handling of device-casting.
 - The `offload_type` parameter can be set to either `block_level` or `leaf_level`. `block_level` offloads groups of `torch::nn::ModuleList` or `torch::nn:Sequential` modules based on a configurable attribute `num_blocks_per_group`. For example, if you set `num_blocks_per_group=2` on a standard transformer model containing 40 layers, it will onload/offload 2 layers at a time. This drastically reduces the VRAM requirements. `leaf_level` offloads individual layers at the lowest level, which is equivalent to sequential offloading. However, unlike sequential offloading, group offloading can be made much faster when using streams, with minimal compromise to end-to-end generation time.
 - The `use_stream` parameter can be used on CUDA devices to enable layer prefetching. It defaults to `False` and is disabled by default. Layer prefetching allows overlapping computation and data transfer of modeling weights, which drastically reduces the overall execution time compared to other offloading methods. However, it may increase the VRAM requirements slightly.
-- If specifying `use_stream=True` on VAEs, make sure to do a dummy forward pass (possibly with dummy inputs) before the actual inference to avoid device-mismatch errors. This may not work on all implementations. Please open an issue if you encounter any problems.
+- If specifying `use_stream=True` on VAEs with tiling enabled, make sure to do a dummy forward pass (possibly with dummy inputs) before the actual inference to avoid device-mismatch errors. This may not work on all implementations. Please open an issue if you encounter any problems.
 
 For more information about available parameters and an explanation of how group offloading works, refer to [`~hooks.group_offloading.apply_group_offloading`].