Apply suggestions from code review

Co-authored-by: Sayak Paul <[email protected]>

Author: anijain2305

docs/source/en/optimization/fp16.md

@@ -152,9 +152,39 @@ Compilation is slow the first time, but once compiled, it is significantly faste
 
 ### Regional compilation
 
-[Regional compilation](https://docs.pytorch.org/tutorials/recipes/regional_compilation.html) reduces the cold start compilation time by only compiling a specific repeated region (or block) of the model instead of the entire model. The compiler reuses the cached and compiled code for the other blocks.
 
-[Accelerate](https://huggingface.co/docs/accelerate/index) provides the [compile_regions](https://github.com/huggingface/accelerate/blob/273799c85d849a1954a4f2e65767216eb37fa089/src/accelerate/utils/other.py#L78) method for automatically compiling the repeated blocks of a `nn.Module` sequentially. The rest of the model is compiled separately.
+[Regional compilation](https://docs.pytorch.org/tutorials/recipes/regional_compilation.html) trims cold-start latency by compiling **only the small, frequently-repeated block(s)** of a model, typically a Transformer layer, enabling reuse of compiled artifacts for every subsequent occurrence.
+For many diffusion architectures this delivers the *same* runtime speed-ups as full-graph compilation yet cuts compile time by **8–10 ×**.
+
+To make this effortless, `ModelMixin` exposes **`compile_repeated_blocks`** API, a helper that wraps `torch.compile` around any sub-modules you designate as repeatable:
+
+```py
+# pip install -U diffusers
+import torch
+from diffusers import StableDiffusionXLPipeline
+
+pipe = 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)
+```
+
+To enable a new model with regional compilation, add a `_repeated_blocks` attribute to your model class containing the class names (as strings) of the blocks you want compiled:
+
+
+```py
+class MyUNet(ModelMixin):
+    _repeated_blocks = ("Transformer2DModel",)  # ← compiled by default
+```
+
+For more examples, see the reference [PR](https://github.com/huggingface/diffusers/pull/11705).
+
+**Relation to Accelerate compile_regions** There is also a separate API in [accelerate](https://huggingface.co/docs/accelerate/index) - [compile_regions](https://github.com/huggingface/accelerate/blob/273799c85d849a1954a4f2e65767216eb37fa089/src/accelerate/utils/other.py#L78). It takes a fully automatic approach: it walks the module, picks candidate blocks, then compiles the remaining graph separately. That hands-off experience is handy for quick experiments, but it also leaves fewer knobs when you want to fine-tune which blocks are compiled or adjust compilation flags.
+
+
 
 ```py
 # pip install -U accelerate
@@ -167,6 +197,8 @@ pipeline = StableDiffusionXLPipeline.from_pretrained(
 ).to("cuda")
 pipeline.unet = compile_regions(pipeline.unet, mode="reduce-overhead", fullgraph=True)
 ```
+`compile_repeated_blocks`, by contrast, is intentionally explicit. You list the repeated blocks once (via `_repeated_blocks`) and the helper compiles exactly those, nothing more. In practice this small dose of control hits a sweet spot for diffusion models: predictable behavior, easy reasoning about cache reuse, and still a one-liner for users.
+
 
 ### Graph breaks
 
@@ -241,4 +273,4 @@ An input is projected into three subspaces, represented by the projection matric
 
 ```py
 pipeline.fuse_qkv_projections()
-```
+```

src/diffusers/models/modeling_utils.py

@@ -1425,21 +1425,21 @@ class name matches will be compiled.
         `_no_split_modules`.
 
         Once discovered, each matching sub-module is compiled by calling
-        ``submodule.compile(*args, **kwargs)``.  Any positional or keyword
-        arguments you supply to :meth:`compile_repeated_blocks` are forwarded
+        `submodule.compile(*args, **kwargs)`.  Any positional or keyword
+        arguments you supply to `compile_repeated_blocks` are forwarded
         verbatim to `torch.compile`.
         """
         repeated_blocks = getattr(self, "_repeated_blocks", None)
 
         if not repeated_blocks:
-            logger.warning("_repeated_blocks attribute is empty. Using _no_split_modules to find compile regions.")
+            logger.warning("`_repeated_blocks` attribute is empty. Using `_no_split_modules` to find compile regions.")
 
             repeated_blocks = getattr(self, "_no_split_modules", None)
 
         if not repeated_blocks:
             raise ValueError(
-                "Both _repeated_blocks and _no_split_modules attribute are empty. "
-                "Set _repeated_blocks for the model to benefit from faster compilation. "
+                "Both `_repeated_blocks` and `_no_split_modules` attribute are empty. "
+                "Set `_repeated_blocks` for the model to benefit from faster compilation. "
             )
 
         has_compiled_region = False