Cache-DiT Acceleration

Configure Cache-DiT acceleration for diffusion inference.

SGLang integrates Cache-DiT, a caching acceleration engine for Diffusion Transformers (DiT), to achieve up to 1.69x inference speedup with minimal quality loss.

Overview

Cache-DiT uses intelligent caching strategies to skip redundant computation in the denoising loop:

• DBCache (Dual Block Cache): Dynamically decides when to cache transformer blocks based on residual differences
• TaylorSeer: Uses Taylor expansion for calibration to optimize caching decisions
• SCM (Step Computation Masking): Step-level caching control for additional speedup

Basic Usage

Enable Cache-DiT by exporting the environment variable and using sglang generate or sglang serve :

SGLANG_CACHE_DIT_ENABLED=true \
sglang generate --model-path Qwen/Qwen-Image \
    --prompt "A beautiful sunset over the mountains"

Diffusers Backend

Cache-DiT supports loading acceleration configs from a custom YAML file. For diffusers pipelines (diffusers backend), pass the YAML/JSON path via --cache-dit-config. This flow requires cache-dit >= 1.2.0 (cache_dit.load_configs).

Single GPU inference

Define a cache.yaml file that contains:

cache_config:
  max_warmup_steps: 8
  warmup_interval: 2
  max_cached_steps: -1
  max_continuous_cached_steps: 2
  Fn_compute_blocks: 1
  Bn_compute_blocks: 0
  residual_diff_threshold: 0.12
  enable_taylorseer: true
  taylorseer_order: 1

Then apply the config with:

sglang generate \
  --backend diffusers \
  --model-path Qwen/Qwen-Image \
  --cache-dit-config cache.yaml \
  --prompt "A beautiful sunset over the mountains"

Distributed inference

• 1D Parallelism

Define a parallelism only config yaml parallel.yaml file that contains:

parallelism_config:
  ulysses_size: auto
  parallel_kwargs:
    attention_backend: native
    extra_parallel_modules: ["text_encoder", "vae"]

Then, apply the distributed inference acceleration config from yaml. ulysses_size: auto means that cache-dit will auto detect the world_size as the ulysses_size. Otherwise, you should manually set it as specific int number, e.g, 4.

Then apply the distributed config with: (Note: please add --num-gpus N to specify the number of gpus for distributed inference)

sglang generate \
  --backend diffusers \
  --num-gpus 4 \
  --model-path Qwen/Qwen-Image \
  --cache-dit-config parallel.yaml \
  --prompt "A futuristic cityscape at sunset"

• 2D Parallelism

You can also define a 2D parallelism config yaml parallel_2d.yaml file that contains:

parallelism_config:
  ulysses_size: auto
  tp_size: 2
  parallel_kwargs:
    attention_backend: native
    extra_parallel_modules: ["text_encoder", "vae"]

Then, apply the 2D parallelism config from yaml. Here tp_size: 2 means using tensor parallelism with size 2. The ulysses_size: auto means that cache-dit will auto detect the world_size // tp_size as the ulysses_size.

• 3D Parallelism

You can also define a 3D parallelism config yaml parallel_3d.yaml file that contains:

parallelism_config:
  ulysses_size: 2
  ring_size: 2
  tp_size: 2
  parallel_kwargs:
    attention_backend: native
    extra_parallel_modules: ["text_encoder", "vae"]

Then, apply the 3D parallelism config from yaml. Here ulysses_size: 2, ring_size: 2, tp_size: 2 means using ulysses parallelism with size 2, ring parallelism with size 2 and tensor parallelism with size 2.

Hybrid Cache and Parallelism

Define a hybrid cache and parallel acceleration config yaml hybrid.yaml file that contains:

cache_config:
  max_warmup_steps: 8
  warmup_interval: 2
  max_cached_steps: -1
  max_continuous_cached_steps: 2
  Fn_compute_blocks: 1
  Bn_compute_blocks: 0
  residual_diff_threshold: 0.12
  enable_taylorseer: true
  taylorseer_order: 1
parallelism_config:
  ulysses_size: auto
  parallel_kwargs:
    attention_backend: native
    extra_parallel_modules: ["text_encoder", "vae"]

Then, apply the hybrid cache and parallel acceleration config from yaml.

sglang generate \
  --backend diffusers \
  --num-gpus 4 \
  --model-path Qwen/Qwen-Image \
  --cache-dit-config hybrid.yaml \
  --prompt "A beautiful sunset over the mountains"

Advanced Configuration

DBCache Parameters

DBCache controls block-level caching behavior:

Parameter	Env Variable	Default	Description
Fn	SGLANG_CACHE_DIT_FN	1	Number of first blocks to always compute
Bn	SGLANG_CACHE_DIT_BN	0	Number of last blocks to always compute
W	SGLANG_CACHE_DIT_WARMUP	4	Warmup steps before caching starts
R	SGLANG_CACHE_DIT_RDT	0.24	Residual difference threshold
MC	SGLANG_CACHE_DIT_MC	3	Maximum continuous cached steps

TaylorSeer Configuration

TaylorSeer improves caching accuracy using Taylor expansion:

Parameter	Env Variable	Default	Description
Enable	SGLANG_CACHE_DIT_TAYLORSEER	false	Enable TaylorSeer calibrator
Order	SGLANG_CACHE_DIT_TS_ORDER	1	Taylor expansion order (1 or 2)

Combined Configuration Example

DBCache and TaylorSeer are complementary strategies that work together, you can configure both sets of parameters simultaneously:

SGLANG_CACHE_DIT_ENABLED=true \
SGLANG_CACHE_DIT_FN=2 \
SGLANG_CACHE_DIT_BN=1 \
SGLANG_CACHE_DIT_WARMUP=4 \
SGLANG_CACHE_DIT_RDT=0.4 \
SGLANG_CACHE_DIT_MC=4 \
SGLANG_CACHE_DIT_TAYLORSEER=true \
SGLANG_CACHE_DIT_TS_ORDER=2 \
sglang generate --model-path black-forest-labs/FLUX.1-dev \
    --prompt "A curious raccoon in a forest"

SCM (Step Computation Masking)

SCM provides step-level caching control for additional speedup. It decides which denoising steps to compute fully and which to use cached results.

SCM Presets

SCM is configured with presets:

Preset	Compute Ratio	Speed	Quality
none	100%	Baseline	Best
slow	~75%	~1.3x	High
medium	~50%	~2x	Good
fast	~35%	~3x	Acceptable
ultra	~25%	~4x	Lower

Usage

SGLANG_CACHE_DIT_ENABLED=true \
SGLANG_CACHE_DIT_SCM_PRESET=medium \
sglang generate --model-path Qwen/Qwen-Image \
    --prompt "A futuristic cityscape at sunset"

Custom SCM Bins

For fine-grained control over which steps to compute vs cache:

SGLANG_CACHE_DIT_ENABLED=true \
SGLANG_CACHE_DIT_SCM_COMPUTE_BINS="8,3,3,2,2" \
SGLANG_CACHE_DIT_SCM_CACHE_BINS="1,2,2,2,3" \
sglang generate --model-path Qwen/Qwen-Image \
    --prompt "A futuristic cityscape at sunset"

SCM Policy

Policy	Env Variable	Description
dynamic	SGLANG_CACHE_DIT_SCM_POLICY=dynamic	Adaptive caching based on content (default)
static	SGLANG_CACHE_DIT_SCM_POLICY=static	Fixed caching pattern

Environment Variables

All Cache-DiT parameters can be configured via environment variables. See Environment variables for the complete list.

Supported Models

SGLang Diffusion x Cache-DiT supports almost all models originally supported in SGLang Diffusion:

Model Family	Example Models
Wan	Wan2.1, Wan2.2
Flux	FLUX.1-dev, FLUX.2-dev
Z-Image	Z-Image-Turbo
Qwen	Qwen-Image, Qwen-Image-Edit
Hunyuan	HunyuanVideo

Performance Tips

1. Start with defaults: The default parameters work well for most models
2. Use TaylorSeer: It typically improves both speed and quality
3. Tune R threshold: Lower values = better quality, higher values = faster
4. SCM for extra speed: Use medium preset for good speed/quality balance
5. Warmup matters: Higher warmup = more stable caching decisions

Limitations

• SGLang-native pipelines: Distributed support (TP/SP) is not yet validated; Cache-DiT will be automatically disabled when world_size > 1.
• SCM minimum steps: SCM requires >= 8 inference steps to be effective
• Model support: Only models registered in Cache-DiT’s BlockAdapterRegister are supported

Troubleshooting

SCM disabled for low step count

For models with < 8 inference steps (e.g., DMD distilled models), SCM will be automatically disabled. DBCache acceleration still works.

References

• Cache-DiT
• SGLang diffusion