⚡️🎉A PyTorch-native Inference Engine with Cache,
Parallelism, Quantization and CPU Offload for DiTs

🤗Why Cache-DiT❓❓Cache-DiT is built on top of the 🤗Diffusers library and now supports nearly ALL DiTs from Diffusers. It provides hybrid cache acceleration (DBCache, TaylorSeer, SCM, etc.) and comprehensive parallelism optimizations, including Context Parallelism, Tensor Parallelism, hybrid 2D or 3D parallelism, and dedicated extra parallelism support for Text Encoder, VAE, and ControlNet.

Cache-DiT is compatible with compilation, CPU Offloading, and quantization, fully integrates with SGLang Diffusion, vLLM-Omni, TensorRT-LLM, ComfyUI, and runs natively on NVIDIA GPUs, Ascend NPUs and AMD GPUs. Cache-DiT is fast, easy to use, and flexible for various DiTs (online docs at 📘cache-dit.io). Cache-DiT's technical report is available at 🎉Cache-DiT: A Unified PyTorch-Native Inference Engine for Diffusion Transformers.

⚡️9x speedup by Cache-DiT with Cache, Context Parallelism and Compilation

📋Supported DiT Models

Cache-DiT supports 40+ DiT pipeline families (120+ Variants) from 🤗Diffusers, covering the vast majority of DiT-based pipelines. For full support matrix and detailed usage, please refer to our documentation at 📘cache-dit.io.

Modality	Pipeline Series	Transformer	Variants	C/P/Q/OF
Image	FLUX	FluxTransformer2DModel	10+	✔️/✔️/✔️/✔️
Image	FLUX.2	Flux2Transformer2DModel	1	✔️/✔️/✔️/✔️
Image	FLUX.2 Klein	Flux2Transformer2DModel	3	✔️/✔️/✔️/✔️
Image	ERNIE-Image	ErnieImageTransformer2DModel	1	✔️/✔️/✔️/✔️
Image	Qwen-Image	QwenImageTransformer2DModel	9+	✔️/✔️/✔️/✔️
Image	Z-Image	ZImageTransformer2DModel	6	✔️/✔️/✔️/✔️
Image	Joy-Image	JoyImageEditTransformer3DModel	1	✔️/✔️/✔️/✔️
Image	Boogu-Image	BooguImageTransformer2DModel	1	✔️/✔️/✔️/✔️
Image	Krea-2	Krea2Transformer2DModel	1	✔️/✔️/✔️/✔️
Image	Bria-Fibo	BriaFiboTransformer2DModel	2	✔️/✔️/✔️/✔️
Image	GLM-Image	GlmImageTransformer2DModel	1	✔️/✔️/✔️/✔️
Image	LongCat-Image	LongCatImageTransformer2DModel	2	✔️/✔️/✔️/✔️
Image	Ovis-Image	OvisImageTransformer2DModel	1	✔️/✔️/✔️/✔️
Image	SD3	SD3Transformer2DModel	3	✔️/✖️/✔️/✔️
Image	PixArt-Alpha	PixArtTransformer2DModel	1	✔️/✔️/✔️/✔️
Image	PixArt-Sigma	PixArtTransformer2DModel	1	✔️/✔️/✔️/✔️
Image	Sana (image)	SanaTransformer2DModel	4	✔️/✖️/✔️/✔️
Image	DiT	DiTTransformer2DModel	1	✔️/✔️/✔️/✔️
Image	HunyuanDiT	HunyuanDiT2DModel	1	✔️/✔️/✔️/✔️
Image	HunyuanDiT-PAG	HunyuanDiT2DModel	1	✔️/✔️/✔️/✔️
Image	AuraFlow	AuraFlowTransformer2DModel	1	✔️/✖️/✔️/✔️
Image	CogView4	CogView4Transformer2DModel	2	✔️/✔️/✔️/✔️
Image	CogView3Plus	CogView3PlusTransformer2DModel	1	✔️/✔️/✔️/✔️
Image	Hunyuan-Image	HunyuanImageTransformer2DModel	2	✔️/✔️/✔️/✔️
Image	HiDream	HiDreamImageTransformer2DModel	1	✔️/✖️/✔️/✔️
Image	Bria	BriaTransformer2DModel	1	✔️/✖️/✔️/✔️
Image	Chroma	ChromaTransformer2DModel	3	✔️/✔️/✔️/✔️
Image	PRX	PRXTransformer2DModel	2	✔️/✖️/✔️/✔️
Image	OmniGen	OmniGenTransformer2DModel	1	✔️/✖️/✔️/✔️
Image	Lumina	LuminaNextDiT2DModel	2	✔️/✖️/✔️/✔️
Image	Lumina 2	Lumina2Transformer2DModel	2	✔️/✖️/✔️/✔️
Image	VisualCloze	FluxTransformer2DModel	2	✔️/✔️/✔️/✔️
Image	Amused	UVit2DModel	1	✔️/✖️/✔️/✔️
Video	CogVideoX	CogVideoXTransformer3DModel	4	✔️/✔️/✔️/✔️
Video	Wan (T2V/I2V)	WanTransformer3DModel	3	✔️/✔️/✔️/✔️
Video	Wan-VACE	WanVACETransformer3DModel	1	✔️/✔️/✔️/✔️
Video	HunyuanVideo	HunyuanVideoTransformer3DModel	4	✔️/✔️/✔️/✔️
Video	HunyuanVideo 1.5	HunyuanVideo15Transformer3DModel	2	✔️/✖️/✔️/✔️
Video	Mochi	MochiTransformer3DModel	1	✔️/✔️/✔️/✔️
Video	Allegro	AllegroTransformer3DModel	1	✔️/✖️/✔️/✔️
Video	EasyAnimate	EasyAnimateTransformer3DModel	3	✔️/✖️/✔️/✔️
Video	ConsisID	ConsisIDTransformer3DModel	1	✔️/✔️/✔️/✔️
Video	Cosmos	CosmosTransformer3DModel	7+	✔️/✖️/✔️/✔️
Video	LTX	LTXVideoTransformer3DModel	5	✔️/✔️/✔️/✔️
Video	LTX2	LTX2VideoTransformer3DModel	6	✔️/✔️/✔️/✔️
Video	Helios	HeliosTransformer3DModel	2	✔️/✔️/✔️/✔️
Video	ChronoEdit	ChronoEditTransformer3DModel	1	✔️/✔️/✔️/✔️
Video	SkyReelsV2	SkyReelsV2Transformer3DModel	5	✔️/✔️/✔️/✔️
Video	Kandinsky5 (T2V/I2V)	Kandinsky5Transformer3DModel	4	✔️/✔️/✔️/✔️
Audio	StableAudio	StableAudioDiTModel	1	✔️/✖️/✔️/✔️
3D/Other	Shap-E	PriorTransformer	1	✔️/✖️/✔️/✔️
Other	LucyEdit	WanTransformer3DModel	1	✔️/✔️/✔️/✔️

C: Hybrid Cache (DBCache + Calibrator: TaylorSeer/DMD/FoCa/SCM); P: Parallelism (Ulysses/Ring/USP/TP
TE-P/VAE-P/2D-P/3D-P); Q: Quantization (W8A8, W4A4); OF: Bucket-style Layerwise Offload (~0 overhead)

🤖Agentic Workflows

Cache-DiT provides a model-integration SKILL to help users integrate new DiT pipelines into Cache-DiT, including Cache, CP, TP, TE-P, VAE-P and carefully designed test cases. Users can use it with Coding Agents, e.g, GitHub Copilot, Claude Code, Open Code.

Note

Please note that quantization and layerwise offload in Cache-DiT are generally supported for nn.Module, thus no extra integration is needed for new DiT pipelines or transformers.

🌐Community Integration

• 🎉ComfyUI x Cache-DiT
• 🎉(Intel) llm-scaler x Cache-DiT
• 🎉Diffusers x Cache-DiT
• 🎉TensorRT-LLM x Cache-DiT
• 🎉SGLang Diffusion x Cache-DiT
• 🎉vLLM-Omni x Cache-DiT
• 🎉Nunchaku x Cache-DiT
• 🎉SD.Next x Cache-DiT
• 🎉stable-diffusion.cpp x Cache-DiT
• 🎉jetson-containers x Cache-DiT

©️Acknowledgements

Special thanks to vipshop's Computer Vision AI Team for supporting testing and deployment of this project. We learned and reused codes from: Diffusers, SGLang, vLLM-Omni, Nunchaku, xDiT and TaylorSeer.

©️Citations

@misc{cache-dit@2025,
  title={Cache-DiT: A PyTorch-native Inference Engine with Cache, Parallelism, Quantization and CPU Offload 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, etc.},
  year={2025}
}