Extensions/ComfyUI-TeaCache-Lumina
ComfyUI Extension

ComfyUI-TeaCache-Lumina

ComfyUI Node Implementation: TeaCache Acceleration Specifically Designed for the Lumina Model

By shiertier·Created about a year ago·Updated about a year ago· 1
shiertier/ComfyUI-TeaCache-lumina2
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ComfyUI TeaCache for Lumina

Professional ComfyUI nodes for accelerating Lumina diffusion models using TeaCache technology.

Overview

This package provides optimized ComfyUI nodes that implement TeaCache (Timestep Embedding Aware Cache) specifically for Lumina model series. TeaCache is a training-free acceleration technique that can significantly speed up inference while maintaining generation quality.

Features

  • Zero Training Required: Direct acceleration for existing Lumina models
  • Intelligent Caching: Advanced timestep-aware caching mechanism
  • Multiple Model Support: Compatible with Lumina2 and LuminaNext architectures
  • Automatic Detection: Smart model type recognition
  • Quality Preservation: Configurable trade-off between speed and quality
  • Easy Integration: Standard ComfyUI node interface

Supported Models

| Model Type | Description | Status | |------------|-------------|---------| | Lumina2 | Lumina-Image-2.0 models | ✅ Fully Supported | | LuminaNext | Lumina-T2X (Next generation) | ✅ Fully Supported | | Auto Mode | Automatic model detection | ✅ Recommended |

Installation

Prerequisites

  • ComfyUI installation
  • Python 3.8 or higher
  • PyTorch 2.0 or higher

Setup

  1. Clone or download this repository to your ComfyUI custom_nodes directory:
cd ComfyUI/custom_nodes
git clone <repository-url> ComfyUI-TeaCache-lumina
  1. Install dependencies:
cd ComfyUI-TeaCache-lumina
pip install -r requirements.txt
  1. Restart ComfyUI

Node Reference

TeaCache for Lumina (Auto)

Location: TeaCache/Lumina → TeaCache for Lumina (Auto)

Automatically detects Lumina model type and applies appropriate TeaCache optimization.

Inputs:

  • model (MODEL): Input Lumina model
  • enable_teacache (BOOLEAN): Enable/disable acceleration (default: True)
  • rel_l1_thresh (FLOAT): Cache threshold controlling acceleration strength (default: 0.3)
  • num_inference_steps (INT): Number of inference steps (default: 30)

Outputs:

  • model (MODEL): Optimized model with TeaCache applied

TeaCache for Lumina2

Location: TeaCache/Lumina → TeaCache for Lumina2

Specialized optimization for Lumina2 transformer models.

TeaCache for LuminaNext

Location: TeaCache/Lumina → TeaCache for LuminaNext

Specialized optimization for LuminaNext DiT models.

Performance Tuning

Cache Threshold (rel_l1_thresh)

Controls the trade-off between speed and quality:

| Threshold | Speed Gain | Quality Impact | Use Case | |-----------|------------|----------------|----------| | 0.2 | ~1.5x | Minimal | High quality priority | | 0.3 | ~1.9x | Slight | Recommended balance | | 0.4 | ~2.4x | Moderate | Speed priority | | 0.5 | ~2.8x | Noticeable | Maximum speed |

Best Practices

  1. Start with Auto Mode: Use automatic detection for new models
  2. Tune Gradually: Begin with default threshold (0.3) and adjust as needed
  3. Monitor Quality: Check output quality when increasing threshold
  4. Match Steps: Ensure num_inference_steps matches your sampler settings

Technical Details

TeaCache Algorithm

The TeaCache mechanism works by:

  1. Timestep Analysis: Monitoring changes in timestep embeddings
  2. Smart Caching: Using L1 distance metrics to determine cache validity
  3. Residual Preservation: Storing computation residuals for efficient reuse
  4. Adaptive Decision: Dynamic switching between computation and cache retrieval

Architecture Integration

Input → Timestep Embedding → [TeaCache Decision Engine] → Output
                                    ↓
                              Cache Store/Retrieve

The system integrates seamlessly with existing Lumina model pipelines without requiring model modifications.

Troubleshooting

Common Issues

ImportError: diffusers is required

  • Install diffusers: pip install diffusers>=0.25.0

Model type not supported

  • Ensure you're using a compatible Lumina model
  • Try the Auto detection mode
  • Check model loading in ComfyUI logs

Unexpected quality degradation

  • Lower the rel_l1_thresh value
  • Verify num_inference_steps matches your workflow
  • Ensure model compatibility

Performance Issues

If acceleration is not as expected:

  1. Verify model type compatibility
  2. Check that TeaCache is enabled
  3. Monitor cache hit rates in console output
  4. Adjust threshold parameters

Compatibility

  • ComfyUI: Latest stable version
  • Python: 3.8, 3.9, 3.10, 3.11
  • PyTorch: 2.0+
  • Platform: Windows, Linux, macOS

License

Licensed under the Apache License, Version 2.0. See LICENSE file for details.

Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests for any improvements.

Changelog

Version 1.0.0

  • Initial release
  • Support for Lumina2 and LuminaNext models
  • Automatic model detection
  • Configurable caching parameters
  • Standard ComfyUI node interface