CUI-Lumina2-TeaCache

KO | EN

gitlite — search

#javascript #ai #typescript #go #machine-learning #deep-learning #linux #macos #java #windows #cli #kubernetes #awesome-list #mcp

CUI-Lumina2-TeaCache

(★ 20)

teacache for lumina-image-2 in comfyui

CUI-Lumina2-TeaCache Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# cui-teacache-lu2
## referenced from https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4Lumina2
## firstly transplanted by [@fexli](https://github.com/fexli)
## retransplanted by @spawner
## Installation

### Manual installation

```bash
// 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
![image](https://github.com/user-attachments/assets/fae7bebe-3af1-48f4-9a22-432bb6b9b4fa)
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.
4. The nodes are configured with different parameters. When using 25 steps or fewer, it is recommended to set the l1 value to approximately 6. For larger step sizes, the l1 should be decreased proportionally. For instance, a value of 0.6 is suggested for 50 steps.(`[225.7042019806413, -608.8453716535591, 304.1869942338369, 124.21267720116742, -1.4089066892956552]`)

**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](https://github.com/LiewFeng/TeaCache) can speedup [Lumina-Image-2.0](https://github.com/Alpha-VLLM/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).

## 📈 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](https://github.com/fexli)
The original TeaCache transplant of Lumina2 in cui

## [welltop-cn](https://github.com/welltop-cn/ComfyUI-TeaCache)
model patch code design

CUI-Lumina2-TeaCache

CUI-Lumina2-TeaCache Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

CUI-Lumina2-TeaCache

CUI-Lumina2-TeaCache Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README