[Doc] refactor diffusion doc (#753)

Signed-off-by: zjy0516 <riverclouds.zhu@qq.com>

docs/user_guide/diffusion_acceleration.md

@@ -6,8 +6,8 @@ vLLM-Omni supports various cache acceleration methods to speed up diffusion mode
 
 vLLM-Omni currently supports two main cache acceleration backends:
 
-1. **[TeaCache](acceleration/teacache.md)** - Hook-based adaptive caching that caches transformer computations when consecutive timesteps are similar
-2. **[Cache-DiT](acceleration/cache_dit_acceleration.md)** - Library-based acceleration using multiple techniques:
+1. **[TeaCache](diffusion/teacache.md)** - Hook-based adaptive caching that caches transformer computations when consecutive timesteps are similar
+2. **[Cache-DiT](diffusion/cache_dit_acceleration.md)** - Library-based acceleration using multiple techniques:
     - **DBCache** (Dual Block Cache): Caches intermediate transformer block outputs based on residual differences
     - **TaylorSeer**: Uses Taylor expansion-based forecasting for faster inference
     - **SCM** (Step Computation Masking): Selectively computes steps based on adaptive masking
@@ -16,11 +16,11 @@ Both methods can provide significant speedups (typically **1.5x-2.0x**) while ma
 
 vLLM-Omni also supports parallelism methods for diffusion models, including:
 
-1. [Ulysses-SP](acceleration/parallelism_acceleration.md#ulysses-sp) - splits the input along the sequence dimension and uses all-to-all communication to allow each device to compute only a subset of attention heads.
+1. [Ulysses-SP](diffusion/parallelism_acceleration.md#ulysses-sp) - splits the input along the sequence dimension and uses all-to-all communication to allow each device to compute only a subset of attention heads.
 
-2. [Ring-Attention](acceleration/parallelism_acceleration.md#ring-attention) - splits the input along the sequence dimension and uses ring-based P2P communication to accumulate attention results, keeping the sequence dimension sharded.
+2. [Ring-Attention](diffusion/parallelism_acceleration.md#ring-attention) - splits the input along the sequence dimension and uses ring-based P2P communication to accumulate attention results, keeping the sequence dimension sharded.
 
-3. [CFG-Parallel](acceleration/parallelism_acceleration.md#cfg-parallel) - runs the positive/negative prompts of classifier-free guidance (CFG) on different devices, then merges on a single device to perform the scheduler step.
+3. [CFG-Parallel](diffusion/parallelism_acceleration.md#cfg-parallel) - runs the positive/negative prompts of classifier-free guidance (CFG) on different devices, then merges on a single device to perform the scheduler step.
 
 ## Quick Comparison
 
@@ -197,7 +197,7 @@ outputs = omni.generate(prompt="turn this cat to a dog",
 
 For detailed information on each acceleration method:
 
-- **[TeaCache Guide](acceleration/teacache.md)** - Complete TeaCache documentation, configuration options, and best practices
-- **[Cache-DiT Acceleration Guide](acceleration/cache_dit_acceleration.md)** - Comprehensive Cache-DiT guide covering DBCache, TaylorSeer, SCM, and configuration parameters
-- **[Sequence Parallelism](acceleration/parallelism_acceleration.md#sequence-parallelism)** - Guidance on how to set sequence parallelism with configuration.
-- **[CFG-Parallel](acceleration/parallelism_acceleration.md#cfg-parallel)** - Guidance on how to set CFG-Parallel to run positive/negative branches across ranks.
+- **[TeaCache Guide](diffusion/teacache.md)** - Complete TeaCache documentation, configuration options, and best practices
+- **[Cache-DiT Acceleration Guide](diffusion/cache_dit_acceleration.md)** - Comprehensive Cache-DiT guide covering DBCache, TaylorSeer, SCM, and configuration parameters
+- **[Sequence Parallelism](diffusion/parallelism_acceleration.md#sequence-parallelism)** - Guidance on how to set sequence parallelism with configuration.
+- **[CFG-Parallel](diffusion/parallelism_acceleration.md#cfg-parallel)** - Guidance on how to set CFG-Parallel to run positive/negative branches across ranks.