pre-commit error

Signed-off-by: Didan Deng <33117903+wtomin@users.noreply.github.com>

Author: wtomin

docs/contributing/features/cache_dit.md

@@ -239,7 +239,6 @@ images = omni.generate(
 
 **Solution:** Verify `pipeline.__class__.__name__` matches the registry key and add your enabler to `CUSTOM_DIT_ENABLERS`.
 
-
 ### Issue: Quality degradation
 
 **Symptoms:** Generated images have artifacts or lower quality compared to non-cached inference.

docs/contributing/features/cfg_parallel.md

@@ -153,6 +153,7 @@ class Wan22Pipeline(nn.Module, CFGParallelMixin):
         return current_model(**kwargs)[0]
 ```
 
+
 ### Override `cfg_normalize_function()` for Custom Normalization
 
 Some models have their own normalization function. Taking LongCat Image model as an example:

docs/contributing/features/sequence_parallel.md

@@ -18,6 +18,7 @@ This section describes how to add Sequence Parallel (SP) to a diffusion transfor
 
 ## Overview
 
+
 ### What is Sequence Parallel?
 
 **Terminology Note:** Our "Sequence Parallelism" (SP) corresponds to "Context Parallelism" (CP) in the [diffusers library](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/_modeling_parallel.py). We use "Sequence Parallelism" to align with vLLM-Omni's terminology.

docs/contributing/features/teacache.md

@@ -156,7 +156,7 @@ Create a callable that executes all transformer blocks. This encapsulates the ma
 
 **Key Points:**
 
-- Return format: 
+- Return format:
 - For single-stream models: return `(hidden_states,)`
 - For dual-stream models: return `(hidden_states, encoder_hidden_states)`
 
@@ -239,6 +239,7 @@ _MODEL_COEFFICIENTS = {
 }
 ```
 
+
 **Initial approach:** Start with coefficients from a similar model architecture, then tune empirically following [Customization](#customization) section.
 
 ---

docs/contributing/features/tensor_parallel.md

@@ -42,6 +42,7 @@ The Tensor Parallel implementation relies vLLM's Parallel Layers:
 
 ## Step-by-Step Implementation
 
+
 ### Step 1: Identify Linear Layers
 
 Find all `nn.Linear` layers in your transformer that need to be sharded.