chore: vendor sglang v0.5.10 snapshot

third_party/sglang/docs/diffusion/performance/cache/teacache.md

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+# TeaCache
+
+> **Note**: This is one of two caching strategies available in SGLang.
+> For an overview of all caching options, see [caching](../index.md).
+
+TeaCache (Temporal similarity-based caching) accelerates diffusion inference by detecting when consecutive denoising steps are similar enough to skip computation entirely.
+
+## Overview
+
+TeaCache works by:
+1. Tracking the L1 distance between modulated inputs across consecutive timesteps
+2. Accumulating the rescaled L1 distance over steps
+3. When accumulated distance is below a threshold, reusing the cached residual
+4. Supporting CFG (Classifier-Free Guidance) with separate positive/negative caches
+
+## How It Works
+
+### L1 Distance Tracking
+
+At each denoising step, TeaCache computes the relative L1 distance between the current and previous modulated inputs:
+
+```
+rel_l1 = |current - previous|.mean() / |previous|.mean()
+```
+
+This distance is then rescaled using polynomial coefficients and accumulated:
+
+```
+accumulated += poly(coefficients)(rel_l1)
+```
+
+### Cache Decision
+
+- If `accumulated >= threshold`: Force computation, reset accumulator
+- If `accumulated < threshold`: Skip computation, use cached residual
+
+### CFG Support
+
+For models that support CFG cache separation (Wan, Hunyuan, Z-Image), TeaCache maintains separate caches for positive and negative branches:
+- `previous_modulated_input` / `previous_residual` for positive branch
+- `previous_modulated_input_negative` / `previous_residual_negative` for negative branch
+
+For models that don't support CFG separation (Flux, Qwen), TeaCache is automatically disabled when CFG is enabled.
+
+## Configuration
+
+TeaCache is configured via `TeaCacheParams` in the sampling parameters:
+
+```python
+from sglang.multimodal_gen.configs.sample.teacache import TeaCacheParams
+
+params = TeaCacheParams(
+    teacache_thresh=0.1,           # Threshold for accumulated L1 distance
+    coefficients=[1.0, 0.0, 0.0],  # Polynomial coefficients for L1 rescaling
+)
+```
+
+### Parameters
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `teacache_thresh` | float | Threshold for accumulated L1 distance. Lower = more caching, faster but potentially lower quality |
+| `coefficients` | list[float] | Polynomial coefficients for L1 rescaling. Model-specific tuning |
+
+### Model-Specific Configurations
+
+Different models may have different optimal configurations. The coefficients are typically tuned per-model to balance speed and quality.
+
+## Supported Models
+
+TeaCache is built into the following model families:
+
+| Model Family | CFG Cache Separation | Notes |
+|--------------|---------------------|-------|
+| Wan (wan2.1, wan2.2) | Yes | Full support |
+| Hunyuan (HunyuanVideo) | Yes | To be supported |
+| Z-Image | Yes | To be supported |
+| Flux | No | To be supported |
+| Qwen | No | To be supported |
+
+
+## References
+
+- [TeaCache: Accelerating Diffusion Models with Temporal Similarity](https://arxiv.org/abs/2411.14324)