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Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: JiaxinD <djx2048@gmail.com>
61 lines
2.0 KiB
Markdown
61 lines
2.0 KiB
Markdown
# Caching Acceleration for Diffusion Models
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SGLang provides multiple caching acceleration strategies for Diffusion Transformer (DiT) models. These strategies can significantly reduce inference time by skipping redundant computation.
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## Overview
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SGLang supports two complementary caching approaches:
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| Strategy | Scope | Mechanism | Best For |
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|----------|-------|-----------|----------|
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| **Cache-DiT** | Block-level | Skip individual transformer blocks dynamically | Advanced, higher speedup |
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| **TeaCache** | Timestep-level | Skip entire denoising steps based on L1 similarity | Simple, built-in |
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## Cache-DiT
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[Cache-DiT](https://github.com/vipshop/cache-dit) provides block-level caching with
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advanced strategies like DBCache and TaylorSeer. It can achieve up to **1.69x speedup**.
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See [cache_dit.md](cache_dit.md) for detailed configuration.
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### Quick Start
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```bash
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SGLANG_CACHE_DIT_ENABLED=true \
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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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```
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### Key Features
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- **DBCache**: Dynamic block-level caching based on residual differences
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- **TaylorSeer**: Taylor expansion-based calibration for optimized caching
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- **SCM**: Step-level computation masking for additional speedup
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## TeaCache
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TeaCache (Temporal similarity-based caching) accelerates diffusion inference by detecting when consecutive denoising steps are similar enough to skip computation entirely.
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See [teacache.md](teacache.md) for detailed documentation.
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### Quick Overview
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- Tracks L1 distance between modulated inputs across timesteps
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- When accumulated distance is below threshold, reuses cached residual
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- Supports CFG with separate positive/negative caches
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### Supported Models
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- Wan (wan2.1, wan2.2)
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- Hunyuan (HunyuanVideo)
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- Z-Image
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For Flux and Qwen models, TeaCache is automatically disabled when CFG is enabled.
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## References
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- [Cache-DiT Repository](https://github.com/vipshop/cache-dit)
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- [TeaCache Paper](https://arxiv.org/abs/2411.14324)
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