[rfc][compile] compile method for DiffusionPipeline

src/diffusers/models/modeling_utils.py

@@ -266,6 +266,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
     _keep_in_fp32_modules = None
     _skip_layerwise_casting_patterns = None
     _supports_group_offloading = True
+    _repeated_blocks = []
 
     def __init__(self):
         super().__init__()
@@ -1402,6 +1403,54 @@ def float(self, *args):
         else:
             return super().float(*args)
 
+    def compile_repeated_blocks(self, *args, **kwargs):
+        """
+        Compiles *only* the frequently repeated sub-modules of a model (e.g. the
+        Transformer layers) instead of compiling the entire model.  This
+        technique—often called **regional compilation** (see the PyTorch recipe
+        https://docs.pytorch.org/tutorials/recipes/regional_compilation.html)
+        can reduce end-to-end compile time substantially, while preserving the
+        runtime speed-ups you would expect from a full `torch.compile`.
+
+        The set of sub-modules to compile is discovered in one of two ways:
+
+        1. **`_repeated_blocks`** – Preferred.  Define this attribute on your
+        subclass as a list/tuple of class names (strings).  Every module whose
+        class name matches will be compiled.
+
+        2. **`_no_split_modules`** – Fallback.  If the preferred attribute is
+        missing or empty, we fall back to the legacy Diffusers attribute
+        `_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
+        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.")
+
+            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. "
+            )
+
+        has_compiled_region = False
+        for submod in self.modules():
+            if submod.__class__.__name__ in repeated_blocks:
+                has_compiled_region = True
+                submod.compile(*args, **kwargs)
+
+        if not has_compiled_region:
+            raise ValueError(
+                f"Regional compilation failed because {repeated_blocks} classes are not found in the model. "
+            )
+
     @classmethod
     def _load_pretrained_model(
         cls,

src/diffusers/models/transformers/transformer_flux.py

@@ -227,6 +227,7 @@ class FluxTransformer2DModel(
     _supports_gradient_checkpointing = True
     _no_split_modules = ["FluxTransformerBlock", "FluxSingleTransformerBlock"]
     _skip_layerwise_casting_patterns = ["pos_embed", "norm"]
+    _repeated_blocks = _no_split_modules
 
     @register_to_config
     def __init__(

src/diffusers/models/transformers/transformer_wan.py

@@ -345,6 +345,7 @@ class WanTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOrigi
     _no_split_modules = ["WanTransformerBlock"]
     _keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"]
     _keys_to_ignore_on_load_unexpected = ["norm_added_q"]
+    _repeated_blocks = _no_split_modules
 
     @register_to_config
     def __init__(