cui-teacache-lu2

referenced from https://github.com/spawner1145/TeaCache/tree/main/TeaCache4Lumina2

firstly transplanted by @fexli

retransplanted by @spawner

Installation

Manual installation

// switch to your project's root directory
cd custom_nodes
git clone https://github.com/spawner1145/CUI-Lumina2-TeaCache.git

Installation via comfyui-manager

1. Open ComfyUI WebUI
2. Navigate to Manager -> Install Custom Node
3. Enter CUI-Lumina2-TeaCache in the Search field, and click Search
4. Click Install

usage

1. Connect the TeaCache node between the UNet Loader and KSampler in your workflow.
2. Set the rel_l1_thresh parameter to a value greater than 0.
3. to work on low steps, you can set the value below to [393.76566581, -603.50993606, 209.10239044, -23.00726601, 0.86377344] and a small rel_l1_thresh like 0.3 for higher speed or set the value below to [225.7042019806413, -608.8453716535591, 304.1869942338369, 124.21267720116742, -1.4089066892956552] and a very large rel_l1_thresh like 5 for higher speed and better quality, and for higher steps, you can set the value below to [225.7042019806413, -608.8453716535591, 304.1869942338369, 124.21267720116742, -1.4089066892956552] and a rel_l1_thresh<1.1 to get better quality and higher speed.

Note:

• Higher rel_l1_thresh values will improve generation efficiency (manifested as shorter generation times), at the cost of reduced image quality.
• The optimal value should be determined through empirical testing based on your specific quality/efficiency requirements.

reference

TeaCache can speedup Lumina-Image-2.0 without much visual quality degradation, in a training-free manner. The following image shows the results generated by TeaCache-Lumina-Image-2.0 with various rel_l1_thresh values: 0 (original), 0.2 (1.25x speedup), 0.3 (1.5625x speedup), 0.4 (2.0833x speedup), 0.5 (2.5x speedup).

<p align="center"> <img src="https://github.com/user-attachments/assets/d2c87b99-e4ac-4407-809a-caf9750f41ef" width="150" style="margin: 5px;"> <img src="https://github.com/user-attachments/assets/411ff763-9c31-438d-8a9b-3ec5c88f6c27" width="150" style="margin: 5px;"> <img src="https://github.com/user-attachments/assets/e57dfb60-a07f-4e17-837e-e46a69d8b9c0" width="150" style="margin: 5px;"> <img src="https://github.com/user-attachments/assets/6e3184fe-e31a-452c-a447-48d4b74fcc10" width="150" style="margin: 5px;"> <img src="https://github.com/user-attachments/assets/d6a52c4c-bd22-45c0-9f40-00a2daa85fc8" width="150" style="margin: 5px;"> </p>

📈 Inference Latency Comparisons on a single 4090 (step 50)

| Lumina-Image-2.0 | TeaCache (0.2) | TeaCache (0.3) | TeaCache (0.4) | TeaCache (0.5) | |:-------------------------:|:---------------------------:|:--------------------:|:---------------------:|:---------------------:| | ~25 s | ~20 s | ~16 s | ~12 s | ~10 s |

special thanks

fexli

The original TeaCache transplant of Lumina2 in cui

welltop-cn

model patch code design