fix format & doc

Author: toilaluan

docs/source/en/api/cache.md

@@ -34,3 +34,9 @@ Cache methods speedup diffusion transformers by storing and reusing intermediate
 [[autodoc]] FirstBlockCacheConfig
 
 [[autodoc]] apply_first_block_cache
+
+### TaylorSeerCacheConfig
+
+[[autodoc]] TaylorSeerCacheConfig
+
+[[autodoc]] apply_taylorseer_cache

src/diffusers/hooks/__init__.py

@@ -25,4 +25,4 @@
     from .layerwise_casting import apply_layerwise_casting, apply_layerwise_casting_hook
     from .pyramid_attention_broadcast import PyramidAttentionBroadcastConfig, apply_pyramid_attention_broadcast
     from .smoothed_energy_guidance_utils import SmoothedEnergyGuidanceConfig
-    from .taylorseer_cache import TaylorSeerCacheConfig, apply_taylorseer_cache
+    from .taylorseer_cache import TaylorSeerCacheConfig, apply_taylorseer_cache

src/diffusers/hooks/taylorseer_cache.py

@@ -1,13 +1,13 @@
 import math
 import re
 from dataclasses import dataclass
-from typing import Optional, List, Dict, Tuple
+from typing import Dict, List, Optional, Tuple
 
 import torch
 import torch.nn as nn
 
-from .hooks import ModelHook, StateManager, HookRegistry
 from ..utils import logging
+from .hooks import HookRegistry, ModelHook, StateManager
 
 
 logger = logging.get_logger(__name__)
@@ -19,60 +19,51 @@
 )
 _TEMPORAL_ATTENTION_BLOCK_IDENTIFIERS = ("^temporal_transformer_blocks.*attn",)
 _TRANSFORMER_BLOCK_IDENTIFIERS = _SPATIAL_ATTENTION_BLOCK_IDENTIFIERS + _TEMPORAL_ATTENTION_BLOCK_IDENTIFIERS
-_BLOCK_IDENTIFIERS = (
-    "^[^.]*block[^.]*\\.[^.]+$",
-)
+_BLOCK_IDENTIFIERS = ("^[^.]*block[^.]*\\.[^.]+$",)
 _PROJ_OUT_IDENTIFIERS = ("^proj_out$",)
 
+
 @dataclass
 class TaylorSeerCacheConfig:
     """
-    Configuration for TaylorSeer cache.
-    See: https://huggingface.co/papers/2503.06923
+    Configuration for TaylorSeer cache. See: https://huggingface.co/papers/2503.06923
 
     Attributes:
         warmup_steps (`int`, defaults to `3`):
-            Number of denoising steps to run with full computation
-            before enabling caching. During warmup, the Taylor series factors
-            are still updated, but no predictions are used.
+            Number of denoising steps to run with full computation before enabling caching. During warmup, the Taylor
+            series factors are still updated, but no predictions are used.
 
         predict_steps (`int`, defaults to `5`):
-            Number of prediction (cached) steps to take between two full
-            computations. That is, once a module state is refreshed, it will
-            be reused for `predict_steps` subsequent denoising steps, then a new
-            full forward will be computed on the next step.
+            Number of prediction (cached) steps to take between two full computations. That is, once a module state is
+            refreshed, it will be reused for `predict_steps` subsequent denoising steps, then a new full forward will
+            be computed on the next step.
 
         stop_predicts (`int`, *optional*, defaults to `None`):
-            Denoising step index at which caching is disabled.
-            If provided, for `self.current_step >= stop_predicts` all modules are
-            evaluated normally (no predictions, no state updates).
+            Denoising step index at which caching is disabled. If provided, for `self.current_step >= stop_predicts`
+            all modules are evaluated normally (no predictions, no state updates).
 
         max_order (`int`, defaults to `1`):
-            Maximum order of Taylor series expansion to approximate the
-            features. Higher order gives closer approximation but more compute.
+            Maximum order of Taylor series expansion to approximate the features. Higher order gives closer
+            approximation but more compute.
 
         taylor_factors_dtype (`torch.dtype`, defaults to `torch.bfloat16`):
-            Data type for computing Taylor series expansion factors.
-            Use lower precision to reduce memory usage.
-            Use higher precision to improve numerical stability.
+            Data type for computing Taylor series expansion factors. Use lower precision to reduce memory usage. Use
+            higher precision to improve numerical stability.
 
         skip_identifiers (`List[str]`, *optional*, defaults to `None`):
-            Regex patterns (fullmatch) for module names to be placed in
-            "skip" mode, where the module is evaluated during warmup /
-            refresh, but then replaced by a cheap dummy tensor during
-            prediction steps.
+            Regex patterns (fullmatch) for module names to be placed in "skip" mode, where the module is evaluated
+            during warmup / refresh, but then replaced by a cheap dummy tensor during prediction steps.
 
         cache_identifiers (`List[str]`, *optional*, defaults to `None`):
-            Regex patterns (fullmatch) for module names to be placed in
-            Taylor-series caching mode.
+            Regex patterns (fullmatch) for module names to be placed in Taylor-series caching mode.
 
         lite (`bool`, *optional*, defaults to `False`):
-            Whether to use a TaylorSeer Lite variant that reduces memory usage. This option overrides
-            any user-provided `skip_identifiers` or `cache_identifiers` patterns.
+            Whether to use a TaylorSeer Lite variant that reduces memory usage. This option overrides any user-provided
+            `skip_identifiers` or `cache_identifiers` patterns.
     Notes:
         - Patterns are applied with `re.fullmatch` on `module_name`.
-        - If either `skip_identifiers` or `cache_identifiers` is provided, only modules matching at least
-          one of those patterns will be hooked.
+        - If either `skip_identifiers` or `cache_identifiers` is provided, only modules matching at least one of those
+          patterns will be hooked.
         - If neither is provided, all attention-like modules will be hooked.
     """
 
@@ -255,13 +246,13 @@ def apply_taylorseer_cache(module: torch.nn.Module, config: TaylorSeerCacheConfi
     ```python
     >>> import torch
     >>> from diffusers import FluxPipeline, TaylorSeerCacheConfig
-    >>>
+
     >>> pipe = FluxPipeline.from_pretrained(
     ...     "black-forest-labs/FLUX.1-dev",
     ...     torch_dtype=torch.bfloat16,
     ... )
     >>> pipe.to("cuda")
-    >>>
+
     >>> config = TaylorSeerCacheConfig(
     ...     predict_steps=5,
     ...     max_order=1,

src/diffusers/models/cache_utils.py

@@ -93,7 +93,13 @@ def enable_cache(self, config) -> None:
         self._cache_config = config
 
     def disable_cache(self) -> None:
-        from ..hooks import FasterCacheConfig, FirstBlockCacheConfig, HookRegistry, PyramidAttentionBroadcastConfig, TaylorSeerCacheConfig
+        from ..hooks import (
+            FasterCacheConfig,
+            FirstBlockCacheConfig,
+            HookRegistry,
+            PyramidAttentionBroadcastConfig,
+            TaylorSeerCacheConfig,
+        )
         from ..hooks.faster_cache import _FASTER_CACHE_BLOCK_HOOK, _FASTER_CACHE_DENOISER_HOOK
         from ..hooks.first_block_cache import _FBC_BLOCK_HOOK, _FBC_LEADER_BLOCK_HOOK
         from ..hooks.pyramid_attention_broadcast import _PYRAMID_ATTENTION_BROADCAST_HOOK