vLLM-Omni supports various cache acceleration methods to speed up diffusion model inference with minimal quality degradation. These methods intelligently cache intermediate computations to avoid redundant work across diffusion timesteps.
## Supported Acceleration Methods
vLLM-Omni currently supports two main cache acceleration backends:
1.**[TeaCache](teacache.md)** - Hook-based adaptive caching that caches transformer computations when consecutive timesteps are similar
2.**[Cache-DiT](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
Both methods can provide significant speedups (typically **1.5x-2.0x**) while maintaining high output quality.
## Quick Comparison
| Method | Configuration | Description | Best For |
These benchmarks are provided for **general reference only**. The configurations shown use default or common parameter settings and have not been exhaustively optimized for maximum performance. Actual performance may vary based on:
- Specific model and use case
- Hardware configuration
- Careful parameter tuning
- Different inference settings (e.g., number of steps, image resolution)
For optimal performance in your specific scenario, we recommend experimenting with different parameter configurations as described in the detailed guides below.
