vllm-omni/docs/user_guide/diffusion_acceleration.md

Diffusion Acceleration Overview

vLLM-Omni supports various cache acceleration methods to speed up diffusion model inference with minimal quality degradation. These methods include cache methods that intelligently cache intermediate computations to avoid redundant work across diffusion timesteps, and parallelism methods that distribute the computation across multiple devices.

Supported Acceleration Methods

vLLM-Omni currently supports two main cache acceleration backends:

1. TeaCache - Hook-based adaptive caching that caches transformer computations when consecutive timesteps are similar
2. Cache-DiT - 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.

vLLM-Omni also supports the sequence parallelism (SP) for the diffusion model, that includes:

1. 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.

Quick Comparison

Cache Methods

Method	Configuration	Description	Best For
TeaCache	cache_backend="tea_cache"	Simple, adaptive caching with minimal configuration	Quick setup, balanced speed/quality
Cache-DiT	cache_backend="cache_dit"	Advanced caching with multiple techniques (DBCache, TaylorSeer, SCM)	Maximum acceleration, fine-grained control

Supported Models

The following table shows which models are currently supported by each acceleration method:

Model	Model Identifier	TeaCache	Cache-DiT	Ulysses-SP
Qwen-Image	Qwen/Qwen-Image	✅	✅	✅
Z-Image	Tongyi-MAI/Z-Image-Turbo	❌	✅	❌
Qwen-Image-Edit	Qwen/Qwen-Image-Edit	❌	✅	✅

Performance Benchmarks

The following benchmarks were measured on Qwen/Qwen-Image and Qwen/Qwen-Image-Edit models generating 1024x1024 images with 50 inference steps:

!!! note "Benchmark Disclaimer" 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.

Model	Cache Backend	Cache Config	Generation Time	Speedup	Notes
Qwen/Qwen-Image	None	None	20.0s	1.0x	Baseline (diffusers)
Qwen/Qwen-Image	TeaCache	rel_l1_thresh=0.2	10.47s	1.91x	Recommended default setting
Qwen/Qwen-Image	Cache-DiT	DBCache + TaylorSeer (Fn=1, Bn=0, W=8, TaylorSeer order=1)	10.8s	1.85x	-
Qwen/Qwen-Image	Cache-DiT	DBCache + TaylorSeer + SCM (Fn=8, Bn=0, W=4, TaylorSeer order=1, SCM fast)	14.0s	1.43x	-
Qwen/Qwen-Image-Edit	None	No acceleration	51.5s	1.0x	Baseline (diffusers)
Qwen/Qwen-Image-Edit	Cache-DiT	Default (Fn=1, Bn=0, W=4, TaylorSeer disabled, SCM disabled)	21.6s	2.38x	-

To measure the parallelism methods, we run benchmarks with Qwen/Qwen-Image model generating images (2048x2048 as long sequence input) with 50 inference steps. The hardware devices are NVIDIA H800 GPUs. sdpa is the attention backends.

Configuration	Ulysses degree	Generation Time	Speedup
Baseline (diffusers)	-	112.5s	1.0x
Ulysses-SP	2	65.2s	1.73x
Ulysses-SP	4	39.6s	2.84x
Ulysses-SP	8	30.8s	3.65x

Quick Start

Using TeaCache

from vllm_omni import Omni

omni = Omni(
    model="Qwen/Qwen-Image",
    cache_backend="tea_cache",
    cache_config={"rel_l1_thresh": 0.2}  # Optional, defaults to 0.2
)

outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50)

Using Cache-DiT

from vllm_omni import Omni

omni = Omni(
    model="Qwen/Qwen-Image",
    cache_backend="cache_dit",
    cache_config={
        "Fn_compute_blocks": 1,
        "Bn_compute_blocks": 0,
        "max_warmup_steps": 8,
        "enable_taylorseer": True,
        "taylorseer_order": 1,
    }
)

outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50)

Using Ulysses-SP

Run text-to-image:

from vllm_omni import Omni
from vllm_omni.diffusion.data import DiffusionParallelConfig
ulysses_degree = 2

omni = Omni(
    model="Qwen/Qwen-Image",
    parallel_config=DiffusionParallelConfig(ulysses_degree=2)
)

outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50, width=2048, height=2048)

Run image-to-image:

from vllm_omni import Omni
from vllm_omni.diffusion.data import DiffusionParallelConfig
ulysses_degree = 2

omni = Omni(
    model="Qwen/Qwen-Image-Edit",
    parallel_config=DiffusionParallelConfig(ulysses_degree=2)
)

outputs = omni.generate(prompt="turn this cat to a dog",
        pil_image=input_image, num_inference_steps=50)

Documentation

For detailed information on each acceleration method:

• TeaCache Guide - Complete TeaCache documentation, configuration options, and best practices
• Cache-DiT Acceleration Guide - Comprehensive Cache-DiT guide covering DBCache, TaylorSeer, SCM, and configuration parameters
• Sequence Parallelism - Guidance on how to set sequence parallelism with configuration.