feedback

Author: stevhliu

docs/source/en/optimization/fp16.md

@@ -175,15 +175,7 @@ Feel free to open an issue if dynamic compilation doesn't work as expected for a
 ### Regional compilation
 
 [Regional compilation](https://docs.pytorch.org/tutorials/recipes/regional_compilation.html) trims cold-start latency by only compiling the *small and frequently-repeated block(s)* of a model - typically a transformer layer - and enables reusing compiled artifacts for every subsequent occurrence.
-For many diffusion architectures, this delivers the same runtime speed-ups as full-graph compilation and reduces compile time by 8–10x.
-
-There are two implementations of regional compilation.
-
-- The Diffusers version, [`~ModelMixin.compile_repeated_blocks`], is more explicit and is easier to customize. 
-- The Accelerate version, [compile_regions](https://github.com/huggingface/accelerate/blob/273799c85d849a1954a4f2e65767216eb37fa089/src/accelerate/utils/other.py#L78), automatically selects which regions to compile and is less customizable. It is ideal for fast experiments.
-
-<hfoptions id="regional-compilation">
-<hfoption id="compile_repeated_blocks">
+For many diffusion architectures, this delivers the same runtime speedups as full-graph compilation and reduces compile time by 8–10x.
 
 Use the [`~ModelMixin.compile_repeated_blocks`] method, a helper that wraps `torch.compile`, on any component such as the transformer model as shown below.
 
@@ -192,13 +184,13 @@ Use the [`~ModelMixin.compile_repeated_blocks`] method, a helper that wraps `tor
 import torch
 from diffusers import StableDiffusionXLPipeline
 
-pipe = StableDiffusionXLPipeline.from_pretrained(
+pipeline = StableDiffusionXLPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0",
     torch_dtype=torch.float16,
 ).to("cuda")
 
-# Compile only the repeated Transformer layers inside the UNet
-pipe.unet.compile_repeated_blocks(fullgraph=True)
+# compile only the repeated transformer layers inside the UNet
+pipeline.unet.compile_repeated_blocks(fullgraph=True)
 ```
 
 To enable regional compilation for a new model, add a `_repeated_blocks` attribute to a model class containing the class names (as strings) of the blocks you want to compile.
@@ -209,10 +201,7 @@ class MyUNet(ModelMixin):
 ```
 
 > [!TIP]
-> For more examples, see the reference [PR](https://github.com/huggingface/diffusers/pull/11705).
-
-</hfoption>
-<hfoption id="compile_regions">
+> For more regional compilation examples, see the reference [PR](https://github.com/huggingface/diffusers/pull/11705).
 
 There is also a [compile_regions](https://github.com/huggingface/accelerate/blob/273799c85d849a1954a4f2e65767216eb37fa089/src/accelerate/utils/other.py#L78) method in [Accelerate](https://huggingface.co/docs/accelerate/index) that automatically selects candidate blocks in a model to compile. The remaining graph is compiled separately. This is useful for quick experiments because there aren't as many options for you to set which blocks to compile or adjust compilation flags.
 
@@ -230,9 +219,6 @@ pipeline.unet = compile_regions(pipeline.unet, mode="reduce-overhead", fullgraph
 
 [`~ModelMixin.compile_repeated_blocks`] is intentionally explicit. List the blocks to repeat in `_repeated_blocks` and the helper only compiles those blocks. It offers predictable behavior and easy reasoning about cache reuse in one line of code.
 
-</hfoption>
-</hfoptions>
-
 ### Graph breaks
 
 It is important to specify `fullgraph=True` in torch.compile to ensure there are no graph breaks in the underlying model. This allows you to take advantage of torch.compile without any performance degradation. For the UNet and VAE, this changes how you access the return variables.
@@ -310,4 +296,6 @@ pipeline.fuse_qkv_projections()
 
 ## Resources
 
-Read the [Presenting Flux Fast: Making Flux go brrr on H100s](https://pytorch.org/blog/presenting-flux-fast-making-flux-go-brrr-on-h100s/) blog post to learn more about how you can combine all of these optimizations with [TorchInductor](https://docs.pytorch.org/docs/stable/torch.compiler.html) and [AOTInductor](https://docs.pytorch.org/docs/stable/torch.compiler_aot_inductor.html) for a ~2.5x speedup.
+- Read the [Presenting Flux Fast: Making Flux go brrr on H100s](https://pytorch.org/blog/presenting-flux-fast-making-flux-go-brrr-on-h100s/) blog post to learn more about how you can combine all of these optimizations with [TorchInductor](https://docs.pytorch.org/docs/stable/torch.compiler.html) and [AOTInductor](https://docs.pytorch.org/docs/stable/torch.compiler_aot_inductor.html) for a ~2.5x speedup using recipes from [flux-fast](https://github.com/huggingface/flux-fast).
+
+    These recipes support AMD hardware and [Flux.1 Kontext Dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev).