vllm-omni/docs/user_guide/teacache.md

TeaCache Configuration Guide

TeaCache speeds up diffusion model inference by caching transformer computations when consecutive timesteps are similar. This typically provides 1.5x-2.0x speedup with minimal quality loss.

Quick Start

Enable TeaCache by setting cache_backend to "tea_cache":

from vllm_omni import Omni

# Simple configuration - model_type is automatically extracted from pipeline.__class__.__name__
omni = Omni(
    model="Qwen/Qwen-Image",
    cache_backend="tea_cache",
    cache_config={
        "rel_l1_thresh": 0.2  # Optional, defaults to 0.2
    }
)
outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50)

Using Environment Variable

You can also enable TeaCache via environment variable:

export DIFFUSION_CACHE_BACKEND=tea_cache

Then initialize without explicitly setting cache_backend:

from vllm_omni import Omni

omni = Omni(
    model="Qwen/Qwen-Image",
    cache_config={"rel_l1_thresh": 0.2}  # Optional
)

Configuration Parameters

rel_l1_thresh (float, default: 0.2)

Controls the balance between speed and quality. Lower values prioritize quality, higher values prioritize speed.

Recommended values:

• 0.2 - ~1.5x speedup with minimal quality loss (recommended)
• 0.4 - ~1.8x speedup with slight quality loss
• 0.6 - ~2.0x speedup with noticeable quality loss
• 0.8 - ~2.25x speedup with significant quality loss

Examples

Python API

from vllm_omni import Omni

omni = Omni(
    model="Qwen/Qwen-Image",
    cache_backend="tea_cache",
    cache_config={"rel_l1_thresh": 0.2}
)
outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50)

Performance Tuning

Start with the default rel_l1_thresh=0.2 and adjust based on your needs:

• Maximum quality: Use 0.1-0.2
• Balanced: Use 0.2-0.4 (recommended)
• Maximum speed: Use 0.6-0.8 (may reduce quality)

Troubleshooting

Quality Degradation

If you notice quality issues, lower the threshold:

cache_config={"rel_l1_thresh": 0.1}  # More conservative caching

Supported Models

Currently supported models:

• QwenImagePipeline
  • Model identifier: "Qwen/Qwen-Image"
  • Example: model="Qwen/Qwen-Image"

Future support (extractors in development):

• FluxPipeline - Coming soon
• CogVideoXPipeline - Coming soon