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63 lines
3.5 KiB
Plaintext
63 lines
3.5 KiB
Plaintext
---
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title: SGLang Diffusion
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description: Accelerated image and video generation with diffusion models.
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---
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SGLang Diffusion is a high-performance inference framework for image and video generation. It provides native SGLang pipelines, diffusers backend support, an OpenAI-compatible server, and an optimized kernel stack built on both precompiled `sgl-kernel` operators and JIT kernels for key inference paths.
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## Key Features
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- Broad model support across Wan, Hunyuan, Qwen-Image, FLUX, Z-Image, GLM-Image, and more
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- Fast inference with `sgl-kernel`, JIT kernels, scheduler improvements, and caching acceleration
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- Multiple interfaces: `sglang generate`, `sglang serve`, and an OpenAI-compatible API
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- Multi-platform support for NVIDIA, AMD, Intel XPU, Ascend, Apple Silicon, and Moore Threads
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## Quick Start
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```bash
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uv pip install "sglang[diffusion]" --prerelease=allow
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```
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```bash
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sglang generate --model-path Qwen/Qwen-Image \
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--prompt "A beautiful sunset over the mountains" \
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--save-output
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```
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```bash
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sglang serve --model-path Qwen/Qwen-Image --port 30010
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```
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## Start Here
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- [Installation](/docs/sglang-diffusion/installation): install SGLang Diffusion and platform dependencies
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- [Supported Models and Optimization Compatibility](/docs/sglang-diffusion/compatibility_matrix): check supported model families, long-tail coverage, and optimization support
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- [CLI](/docs/sglang-diffusion/api/cli): run one-off generation jobs or launch a persistent server
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- [OpenAI-Compatible API](/docs/sglang-diffusion/api/openai_api): send image and video requests to the HTTP server
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- [Performance Overview](/docs/sglang-diffusion/performance-optimization): choose speed, memory, parallelism, caching, and quality-tradeoff levers
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- [Caching Acceleration](/docs/sglang-diffusion/caching-acceleration): use Cache-DiT or TeaCache to reduce denoising cost
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- [Quantization](/docs/sglang-diffusion/quantization): load quantized transformer checkpoints
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- [Contributing](/docs/sglang-diffusion/contributing): contribution workflow, adding new models, and CI perf baselines
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## Additional Documentation
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- [Post-Processing](/docs/sglang-diffusion/api/post_processing): frame interpolation and upscaling
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- [Deployment and Performance Modes](/docs/sglang-diffusion/deployment_cookbook): choose `--performance-mode`, offload, FSDP, CFG parallelism, SP, and TP
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- [Attention Backends](/docs/sglang-diffusion/attention_backends): choose the best backend for your model and hardware
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- [Sequence Parallelism](/docs/sglang-diffusion/ring_sp_performance): configure SP, Ulysses, and ring-based splitting for long sequences
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- [Inference Batching](/docs/sglang-diffusion/dynamic_batching): batch compatible native diffusion requests during serving
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- [Progressive Resolution Generation](/docs/sglang-diffusion/progressive_resolution): run early denoising steps at lower latent resolution for selected pipelines
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- [Environment Variables](/docs/sglang-diffusion/environment_variables): platform, caching, storage, and debugging configuration
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## Developer Documentation
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- [Support New Models](/docs/sglang-diffusion/support_new_models): implementation guide for new diffusion pipelines
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- [CI Performance Baselines](/docs/sglang-diffusion/ci_perf): generate and update performance baselines used in CI
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## References
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- [SGLang GitHub](https://github.com/sgl-project/sglang)
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- [Cache-DiT](https://github.com/vipshop/cache-dit)
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- [FastVideo](https://github.com/hao-ai-lab/FastVideo)
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- [xDiT](https://github.com/xdit-project/xDiT)
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- [Diffusers](https://github.com/huggingface/diffusers)
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