A PyTorch-native and Flexible Inference Engine with
Hybrid Cache Acceleration and Parallelism for 🤗DiTs

Baseline	SCM S S*	SCM F D*	SCM U D*	+TS	+compile	+FP8*
24.85s	15.4s	11.4s	8.2s	8.2s	🎉7.1s	🎉4.5s

Scheme: DBCache + SCM(steps_computation_mask) + TS(TaylorSeer) + FP8*, L20x1, S*: static cache,
D*: dynamic cache, S: Slow, F: Fast, U: Ultra Fast, TS: TaylorSeer, FP8*: FP8 DQ + Sage, FLUX.1-Dev

U*: Ulysses Attention, UAA: Ulysses Anything Attenton, UAA*: UAA + Gloo, Device: NVIDIA L20
FLUX.1-Dev w/o CPU Offload, 28 steps; Qwen-Image w/ CPU Offload, 50 steps; Gloo: Extra All Gather w/ Gloo

CP2 U*	CP2 UAA*	L20x1	CP2 UAA*	CP2 U*	L20x1	CP2 UAA*
FLUX, 13.87s	🎉13.88s	23.25s	🎉13.75s	Qwen, 132s	181s	🎉133s
1024x1024	1024x1024	1008x1008	1008x1008	1312x1312	1328x1328	1328x1328
✔️U* ✔️UAA	✔️U* ✔️UAA	NO CP	❌U* ✔️UAA	✔️U* ✔️UAA	NO CP	❌U* ✔️UAA

🔥Hightlight

We are excited to announce that the 🎉v1.1.0 version of cache-dit has finally been released! It brings 🔥Context Parallelism and 🔥Tensor Parallelism to cache-dit, thus making it a PyTorch-native and Flexible Inference Engine for 🤗DiTs. Key features: Unified Cache APIs, Forward Pattern Matching, Block Adapter, DBCache, DBPrune, Cache CFG, TaylorSeer, SCM, Context Parallelism (w/ UAA), Tensor Parallelism and 🎉SOTA performance.

pip3 install -U cache-dit # Also, pip3 install git+https://github.com/huggingface/diffusers.git (latest)

You can install the stable release of cache-dit from PyPI, or the latest development version from GitHub. Then try ♥️ Cache Acceleration with just one line of code ~ ♥️

>>> import cache_dit
>>> from diffusers import DiffusionPipeline
>>> pipe = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image") # Can be any diffusion pipeline
>>> cache_dit.enable_cache(pipe) # One-line code with default cache options.
>>> output = pipe(...) # Just call the pipe as normal.
>>> stats = cache_dit.summary(pipe) # Then, get the summary of cache acceleration stats.
>>> cache_dit.disable_cache(pipe) # Disable cache and run original pipe.

📚Core Features

• 🎉Full 🤗Diffusers Support: Notably, cache-dit now supports nearly all of Diffusers' DiTs, include 60+ models, ~100+ pipelines: 🔥FLUX, 🔥Qwen-Image, 🔥Z-image, 🔥LongCat-Image, 🔥Wan, etc.
• 🎉Extremely Easy to Use: In most cases, you only need one line of code: cache_dit.enable_cache(...). After calling this API, just use the pipeline as normal.
• 🎉State-of-the-Art Performance: Compared with other algorithms, cache-dit achieved the SOTA w/ 7.4x↑🎉 speedup on ClipScore! Surprisingly, it's DBCache also works for extremely few-step distilled models.
• 🎉Compatibility with Other Optimizations: Designed to work seamlessly with torch.compile, Quantization, CPU or Sequential Offloading, Context Parallelism, Tensor Parallelism, etc.
• 🎉Hybrid Cache Acceleration: Now supports hybrid Block-wise Cache + Calibrator schemes. DBCache acts as the Indicator to decide when to cache, while the Calibrator decides how to cache.
• 🎉Ecosystem Integration: Joined the Diffusers community as the first DiTs' cache acceleration framework for 🤗diffusers, 🔥SGLang Diffusion, 🔥vLLM-Omni and 🔥stable-diffusion.cpp.
• 🎉HTTP Serving Support: Built-in HTTP serving capabilities for production deployment with simple REST API. Supports text-to-image, image editing, text/image-to-video, and LoRA.

🔥Supported DiTs

Tip

One Model Series may contain many pipelines. cache-dit applies optimizations at the Transformer level; thus, any pipelines that include the supported transformer are already supported by cache-dit. ✅: supported now; ✖️: not supported now; C-P: Context Parallelism; T-P: Tensor Parallelism; TE-P: Text Encoder Parallelism; CN-P: ControlNet Parallelism; VAE-P: VAE Parallelism (TODO).

📚Supported DiTs: 🤗65+	Cache	C-P	T-P	TE-P	CN-P	VAE-P
Z-Image-Turbo ⚡️Nunchaku	✅	✅	✖️	✅	✖️	✖️
Qwen-Image-Layered	✅	✅	✅	✅	✖️	✖️
Qwen-Image-Edit-2511-Lightning	✅	✅	✅	✅	✖️	✖️
Qwen-Image-Edit-2511	✅	✅	✅	✅	✖️	✖️
LongCat-Image	✅	✅	✅	✅	✖️	✖️
LongCat-Image-Edit	✅	✅	✅	✅	✖️	✖️
Z-Image-Turbo	✅	✅	✅	✅	✖️	✖️
Z-Image-Turbo-Fun-ControlNet-2.0	✅	✅	✅	✅	✅	✖️
Z-Image-Turbo-Fun-ControlNet-2.1	✅	✅	✅	✅	✅	✖️
Ovis-Image	✅	✅	✅	✅	✖️	✖️
FLUX.2-dev	✅	✅	✅	✅	✖️	✖️
FLUX.1-dev	✅	✅	✅	✅	✖️	✖️
FLUX.1-Fill-dev	✅	✅	✅	✅	✖️	✖️
FLUX.1-Kontext-dev	✅	✅	✅	✅	✖️	✖️
Qwen-Image	✅	✅	✅	✅	✖️	✖️
Qwen-Image-Edit	✅	✅	✅	✅	✖️	✖️
Qwen-Image-Edit-2509	✅	✅	✅	✅	✖️	✖️
Qwen-Image-ControlNet	✅	✅	✅	✅	✖️	✖️
Qwen-Image-ControlNet-Inpainting	✅	✅	✅	✅	✖️	✖️
Qwen-Image-Lightning	✅	✅	✅	✅	✖️	✖️
Qwen-Image-Edit-Lightning	✅	✅	✅	✅	✖️	✖️
Qwen-Image-Edit-2509-Lightning	✅	✅	✅	✅	✖️	✖️
Wan-2.2-T2V	✅	✅	✅	✅	✖️	✖️
Wan-2.2-ITV	✅	✅	✅	✅	✖️	✖️
Wan-2.2-VACE-Fun	✅	✅	✅	✅	✖️	✖️
Wan-2.1-T2V	✅	✅	✅	✅	✖️	✖️
Wan-2.1-ITV	✅	✅	✅	✅	✖️	✖️
Wan-2.1-FLF2V	✅	✅	✅	✅	✖️	✖️
Wan-2.1-VACE	✅	✅	✅	✅	✖️	✖️
HunyuanImage-2.1	✅	✅	✅	✅	✖️	✖️
HunyuanVideo-1.5	✅	✖️	✖️	✅	✖️	✖️
HunyuanVideo	✅	✅	✅	✅	✖️	✖️
FLUX.1-dev ⚡️Nunchaku	✅	✅	✖️	✅	✖️	✖️
FLUX.1-Fill-dev ⚡️Nunchaku	✅	✅	✖️	✅	✖️	✖️
Qwen-Image ⚡️Nunchaku	✅	✅	✖️	✅	✖️	✖️
Qwen-Image-Edit ⚡️Nunchaku	✅	✅	✖️	✅	✖️	✖️
Qwen-Image-Edit-2509 ⚡️Nunchaku	✅	✅	✖️	✅	✖️	✖️
Qwen-Image-Lightning ⚡️Nunchaku	✅	✅	✖️	✅	✖️	✖️
Qwen...Edit-Lightning ⚡️Nunchaku	✅	✅	✖️	✅	✖️	✖️
Qwen...Edit-2509-Lightning ⚡️Nunchaku	✅	✅	✖️	✅	✖️	✖️
SkyReels-V2-T2V	✅	✅	✅	✅	✖️	✖️
LongCat-Video	✅	✖️	✖️	✅	✖️	✖️
ChronoEdit-14B	✅	✅	✅	✅	✖️	✖️
Kandinsky-5.0-T2V-Lite	✅	✅️	✅️	✅	✖️	✖️
PRX-512-t2i-sft	✅	✖️	✖️	✅	✖️	✖️
LTX-Video-v0.9.8	✅	✅	✅	✅	✖️	✖️
LTX-Video-v0.9.7	✅	✅	✅	✅	✖️	✖️
CogVideoX	✅	✅	✅	✅	✖️	✖️
CogVideoX-1.5	✅	✅	✅	✅	✖️	✖️
CogView-4	✅	✅	✅	✅	✖️	✖️
CogView-3-Plus	✅	✅	✅	✅	✖️	✖️
Chroma1-HD	✅	✅	✅	✅	✖️	✖️
PixArt-Sigma-XL-2-1024-MS	✅	✅	✅	✅	✖️	✖️
PixArt-XL-2-1024-MS	✅	✅	✅	✅	✖️	✖️
VisualCloze-512	✅	✅	✅	✅	✖️	✖️
ConsisID-preview	✅	✅	✅	✅	✖️	✖️
mochi-1-preview	✅	✖️	✅	✅	✖️	✖️
Lumina-Image-2.0	✅	✖️	✅	✅	✖️	✖️
HiDream-I1-Full	✅	✖️	✖️	✅	✖️	✖️
HunyuanDiT	✅	✖️	✅	✅	✖️	✖️
Sana-1600M-1024px	✅	✖️	✖️	✅	✖️	✖️
DiT-XL-2-256	✅	✅	✖️	✅	✖️	✖️
Allegro-T2V	✅	✖️	✖️	✅	✖️	✖️
OmniGen-2	✅	✖️	✖️	✅	✖️	✖️
stable-diffusion-3.5-large	✅	✖️	✖️	✅	✖️	✖️
Amused-512	✅	✖️	✖️	✅	✖️	✖️
AuraFlow	✅	✖️	✖️	✅	✖️	✖️

🔥Click here to show many Image/Video cases🔥

🎉Now, cache-dit covers almost All Diffusers' DiT Pipelines🎉
🔥Qwen-Image | Qwen-Image-Edit | Qwen-Image-Edit-Plus 🔥
🔥FLUX.1 | Qwen-Image-Lightning 4/8 Steps | Wan 2.1 | Wan 2.2 🔥
🔥HunyuanImage-2.1 | HunyuanVideo | HunyuanDiT | HiDream | AuraFlow🔥
🔥CogView3Plus | CogView4 | LTXVideo | CogVideoX | CogVideoX 1.5 | ConsisID🔥
🔥Cosmos | SkyReelsV2 | VisualCloze | OmniGen 1/2 | Lumina 1/2 | PixArt🔥
🔥Chroma | Sana | Allegro | Mochi | SD 3/3.5 | Amused | ... | DiT-XL🔥

🔥Wan2.2 MoE | +cache-dit:2.0x↑🎉 | HunyuanVideo | +cache-dit:2.1x↑🎉

🔥Qwen-Image | +cache-dit:1.8x↑🎉 | FLUX.1-dev | +cache-dit:2.1x↑🎉

🔥Qwen...Lightning | +cache-dit:1.14x↑🎉 | HunyuanImage | +cache-dit:1.7x↑🎉

🔥Qwen-Image-Edit | Input w/o Edit | Baseline | +cache-dit:1.6x↑🎉 | 1.9x↑🎉

🔥FLUX-Kontext-dev | Baseline | +cache-dit:1.3x↑🎉 | 1.7x↑🎉 | 2.0x↑ 🎉

🔥HiDream-I1 | +cache-dit:1.9x↑🎉 | CogView4 | +cache-dit:1.4x↑🎉 | 1.7x↑🎉

🔥CogView3 | +cache-dit:1.5x↑🎉 | 2.0x↑🎉| Chroma1-HD | +cache-dit:1.9x↑🎉

🔥Mochi-1-preview | +cache-dit:1.8x↑🎉 | SkyReelsV2 | +cache-dit:1.6x↑🎉

🔥VisualCloze-512 | Model | Cloth | Baseline | +cache-dit:1.4x↑🎉 | 1.7x↑🎉

🔥LTX-Video-0.9.7 | +cache-dit:1.7x↑🎉 | CogVideoX1.5 | +cache-dit:2.0x↑🎉

🔥OmniGen-v1 | +cache-dit:1.5x↑🎉 | 3.3x↑🎉 | Lumina2 | +cache-dit:1.9x↑🎉

🔥Allegro | +cache-dit:1.36x↑🎉 | AuraFlow-v0.3 | +cache-dit:2.27x↑🎉

🔥Sana | +cache-dit:1.3x↑🎉 | 1.6x↑🎉| PixArt-Sigma | +cache-dit:2.3x↑🎉

🔥PixArt-Alpha | +cache-dit:1.6x↑🎉 | 1.8x↑🎉| SD 3.5 | +cache-dit:2.5x↑🎉

🔥Asumed | +cache-dit:1.1x↑🎉 | 1.2x↑🎉 | DiT-XL-256 | +cache-dit:1.8x↑🎉
♥️ Please consider to leave a ⭐️ Star to support us ~ ♥️

📖Table of Contents

🚀Quick Links

• 📊Examples - The easiest way to enable hybrid cache acceleration and parallelism for DiTs with cache-dit is to start with our examples for popular models: FLUX, Z-Image, Qwen-Image, Wan, etc.
• 🌐HTTP Serving - Deploy cache-dit models with HTTP API for text-to-image, image editing, multi-image editing, and text/image-to-video generation.
• 🎉User Guide - For more advanced features, please refer to the 🎉User_Guide.md for details.
• ❓FAQ - Frequently asked questions including attention backend configuration, troubleshooting, and optimization tips.

📚Documentation

• ⚙️Installation
• 🔥Supported DiTs
• 🔥Benchmarks
• 🎉Unified Cache APIs
  • 📚Forward Pattern Matching
  • 📚Cache with One-line Code
  • 🔥Automatic Block Adapter
  • 📚Hybrid Forward Pattern
  • 📚Implement Patch Functor
  • 📚Transformer-Only Interface
  • 📚How to use ParamsModifier
  • 🤖Cache Acceleration Stats
• ⚡️DBCache: Dual Block Cache
• ⚡️DBPrune: Dynamic Block Prune
• ⚡️Hybrid Cache CFG
• 🔥Hybrid TaylorSeer Calibrator
• 🤖SCM: Steps Computation Masking
• ⚡️Hybrid Context Parallelism
• 🤖UAA: Ulysses Anything Attention
• 🤖Async Ulysses QKV Projection
• 🤖Async FP8 Ulysses Attention
• ⚡️Hybrid Tensor Parallelism
• 🤖Parallelize Text Encoder
• 🤖Low-bits Quantization
• 🤖How to use FP8 Attention
• 🛠Metrics Command Line
• ⚙️Torch Compile
• 📊Torch Profiler Usage
• 📚API Documents

👋Contribute

How to contribute? Star ⭐️ this repo to support us or check CONTRIBUTE.md.

🎉Projects Using CacheDiT

Here is a curated list of open-source projects integrating CacheDiT, including popular repositories like jetson-containers, flux-fast, sdnext, 🔥stable-diffusion.cpp, 🔥vLLM-Omni, and 🔥SGLang Diffusion. 🎉CacheDiT has been recommended by many famous opensource projects: 🔥Z-Image, 🔥Wan 2.2, 🔥Qwen-Image, 🔥LongCat-Video, Qwen-Image-Lightning, Kandinsky-5, LeMiCa, 🤗diffusers, HelloGitHub and GiantPandaLLM.

©️Acknowledgements

Special thanks to vipshop's Computer Vision AI Team for supporting document, testing and production-level deployment of this project. We learned the design and reused code from the following projects: 🤗diffusers, SGLang, ParaAttention, xDiT, TaylorSeer and LeMiCa.

©️Citations

@misc{cache-dit@2025,
  title={cache-dit: A PyTorch-native and Flexible Inference Engine with Hybrid Cache Acceleration and Parallelism for DiTs.},
  url={https://github.com/vipshop/cache-dit.git},
  note={Open-source software available at https://github.com/vipshop/cache-dit.git},
  author={DefTruth, vipshop.com},
  year={2025}
}