xdit-project/xDiT

Health v2 · maintenance only

Adoption not part of the score

2699 stars · 341 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

xDiT is a scalable inference engine for Diffusion Transformers (DiTs) that enables parallel deployment across multiple GPUs and machines. It provides parallel methods like PipeFusion, Unified Sequence Parallel, CFG Parallel, and parallel VAE, plus single-GPU accelerations via compilation and caching.

Use cases

• run diffusion transformer inference in parallel across multiple gpus
• speed up flux or other dit image generation for real-time serving
• generate images and videos faster with sequence parallelism
• deploy diffusion models on multi-machine clusters
• accelerate comfyui diffusion model inference
• reduce latency of attention-heavy diffusion model inference

When to choose

• you need real-time or low-latency diffusion transformer inference
• you are serving DiT-based image or video generation models at scale
• a single GPU cannot meet your throughput or latency requirements
• you use ComfyUI and want parallel acceleration

When to avoid

• you only run small diffusion models on a single GPU with no latency pressure
• you need training or fine-tuning rather than inference
• your models are not diffusion transformer based

Facets

library · maturity active

llm-inference gpu-computing image-processing video-processing machine-learning deep-learning gpu-computing image-processing artificial-intelligence python diffusion-transformers parallel-inference sequence-parallelism pipefusion multi-gpu image-generation video-generation comfyui video gpu linux docker

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markdown · JSON · MCP: product_card(name="xdit-project/xDiT")