[diffusion] doc: consolidate documentation (#21373)
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# SGLang Diffusion
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SGLang Diffusion is an inference framework for accelerated image and video generation using diffusion models. It provides an end-to-end unified pipeline with optimized kernels and an efficient scheduler loop.
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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**: Wan series, FastWan series, Hunyuan, Qwen-Image, Qwen-Image-Edit, Flux, Z-Image, GLM-Image, and more
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- **Fast Inference**: Optimized kernels, efficient scheduler loop, and Cache-DiT acceleration
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- **Ease of Use**: OpenAI-compatible API, CLI, and Python SDK
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- **Multi-Platform**:
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- NVIDIA GPUs (H100, H200, A100, B200, 4090)
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- AMD GPUs (MI300X, MI325X)
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- Ascend NPU (A2, A3)
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- Apple Silicon (M-series via MPS)
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- Moore Threads GPUs (MTT S5000)
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---
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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, Ascend, Apple Silicon, and Moore Threads
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## Quick Start
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### Installation
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```bash
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uv pip install "sglang[diffusion]" --prerelease=allow
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```
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See [Installation Guide](installation.md) for more installation methods and ROCm-specific instructions.
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### Basic Usage
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Generate an image with the CLI:
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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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--prompt "A beautiful sunset over the mountains" \
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--save-output
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```
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Or start a server with the OpenAI-compatible API:
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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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---
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## Start Here
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## Documentation
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- [Installation](installation.md): install SGLang Diffusion and platform dependencies
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- [Compatibility Matrix](compatibility_matrix.md): check model and optimization support
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- [CLI](api/cli.md): run one-off generation jobs or launch a persistent server
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- [OpenAI-Compatible API](api/openai_api.md): send image and video requests to the HTTP server
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- [Attention Backends](performance/attention_backends.md): choose the best backend for your model and hardware
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- [Caching Acceleration](performance/cache/index.md): use Cache-DiT or TeaCache to reduce denoising cost
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- [Quantization](quantization.md): load quantized transformer checkpoints
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- [Contributing](contributing.md): contribution workflow, adding new models, and CI perf baselines
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### Getting Started
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## Additional Documentation
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- **[Installation](installation.md)** - Install SGLang Diffusion via pip, uv, Docker, or from source
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- **[Compatibility Matrix](compatibility_matrix.md)** - Supported models and optimization compatibility
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### Usage
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- **[CLI Documentation](api/cli.md)** - Command-line interface for `sglang generate` and `sglang serve`
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- **[Quantization](quantization.md)** - Quantized transformer checkpoint usage and supported quantization families
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- **[OpenAI API](api/openai_api.md)** - OpenAI-compatible API for image/video generation and LoRA management
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- **[Post-Processing](api/post_processing.md)** - Frame interpolation (RIFE) and upscaling (Real-ESRGAN)
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### Performance Optimization
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- **[Performance Overview](performance/index.md)** - Overview of all performance optimization strategies
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- **[Attention Backends](performance/attention_backends.md)** - Available attention backends (FlashAttention, SageAttention, etc.)
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- **[Caching Strategies](performance/cache/)** - Cache-DiT and TeaCache acceleration
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- **[Profiling](performance/profiling.md)** - Profiling techniques with PyTorch Profiler and Nsight Systems
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### Reference
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- **[Environment Variables](environment_variables.md)** - Configuration via environment variables
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- **[Support New Models](support_new_models.md)** - Guide for adding new diffusion models
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- **[Contributing](contributing.md)** - Contribution guidelines and commit message conventions
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- **[CI Performance](ci_perf.md)** - Performance baseline generation script
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---
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## CLI Quick Reference
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### Generate (one-off generation)
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```bash
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sglang generate --model-path <MODEL> --prompt "<PROMPT>" --save-output
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```
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### Serve (HTTP server)
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```bash
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sglang serve --model-path <MODEL> --port 30010
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```
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### Enable Cache-DiT acceleration
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```bash
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SGLANG_CACHE_DIT_ENABLED=true sglang generate --model-path <MODEL> --prompt "<PROMPT>"
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```
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---
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- [Post-Processing](api/post_processing.md): frame interpolation and upscaling
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- [Performance Overview](performance/index.md): overview of attention, caching, and profiling
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- [Environment Variables](environment_variables.md): platform, caching, storage, and debugging configuration
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- [Support New Models](support_new_models.md): implementation guide for new diffusion pipelines
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- [CI Performance](ci_perf.md): performance baseline generation
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## References
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