more combos.

docs/source/en/optimization/memory.md

@@ -302,6 +302,13 @@ compute-bound, [group-offloading](#group-offloading) tends to be better. Group o
 
 </Tip>
 
+<Tip>
+
+When using offloading, users can additionally compile the diffusion transformer/unet to get a
+good speed-memory trade-off. First set `torch._dynamo.config.cache_size_limit=1000`, and then before calling the pipeline, add `pipeline.transformer.compile()`.
+
+</Tip>
+
 ## Layerwise casting
 
 Layerwise casting stores weights in a smaller data format (for example, `torch.float8_e4m3fn` and `torch.float8_e5m2`) to use less memory and upcasts those weights to a higher precision like `torch.float16` or `torch.bfloat16` for computation. Certain layers (normalization and modulation related weights) are skipped because storing them in fp8 can degrade generation quality.
@@ -365,6 +372,12 @@ apply_layerwise_casting(
 )
 ```
 
+<Tip>
+
+Layerwise casting can be combined with group offloading.
+
+</Tip>
+
 ## torch.channels_last
 
 [torch.channels_last](https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html) flips how tensors are stored from `(batch size, channels, height, width)` to `(batch size, heigh, width, channels)`. This aligns the tensors with how the hardware sequentially accesses the tensors stored in memory and avoids skipping around in memory to access the pixel values.