# ComfyUI-KwaiKolorsWrapper

**Repository Path**: comfyui_custom_nodes/ComfyUI-KwaiKolorsWrapper

## Basic Information

- **Project Name**: ComfyUI-KwaiKolorsWrapper
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-23
- **Last Updated**: 2024-07-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ComfyUI wrapper for Kwai-Kolors

Rudimentary wrapper that runs Kwai-Kolors text2image pipeline using diffusers.

## Update - safetensors

Added alternative way to load the ChatGLM3 model from single safetensors file (the configs are included in this repo already).
Including already quantized models:

![image](https://github.com/kijai/ComfyUI-KwaiKolorsWrapper/assets/40791699/e161eee6-ffd8-4945-8905-1ca47f2a5ef1)

https://huggingface.co/Kijai/ChatGLM3-safetensors/upload/main

goes into:

`ComfyUI\models\LLM\checkpoints`
![image](https://github.com/kijai/ComfyUI-KwaiKolorsWrapper/assets/40791699/2a6c6f3f-e159-4a82-b16f-4956f9affb25)

![image](https://github.com/kijai/ComfyUI-KwaiKolorsWrapper/assets/40791699/a31ab13a-b321-4cc6-b853-4a4e078eb6dc)


## Installation:

Clone this repository to 'ComfyUI/custom_nodes` folder.

Install the dependencies in requirements.txt, transformers version 4.38.0 minimum is required:

`pip install -r requirements.txt`

or if you use portable (run this in ComfyUI_windows_portable -folder):

`python_embeded\python.exe -m pip install -r ComfyUI\custom_nodes\ComfyUI-KwaiKolorsWrapper\requirements.txt`


Models (fp16, 16.5GB) are automatically downloaded from https://huggingface.co/Kwai-Kolors/Kolors/tree/main

to `ComfyUI/models/diffusers/Kolors`

Model folder structure needs to be the following:

```
PS C:\ComfyUI_windows_portable\ComfyUI\models\diffusers\Kolors> tree /F
│   model_index.json
│
├───scheduler
│       scheduler_config.json
│
├───text_encoder
│       config.json
│       pytorch_model-00001-of-00007.bin
│       pytorch_model-00002-of-00007.bin
│       pytorch_model-00003-of-00007.bin
│       pytorch_model-00004-of-00007.bin
│       pytorch_model-00005-of-00007.bin
│       pytorch_model-00006-of-00007.bin
│       pytorch_model-00007-of-00007.bin
│       pytorch_model.bin.index.json
│       tokenizer.model
│       tokenizer_config.json
│       vocab.txt
│
└───unet
        config.json
        diffusion_pytorch_model.fp16.safetensors
```
To run this, the text enconder is what takes most of the VRAM, but can be quantized to fit approximately these amounts:

| Model | Size | 
|--------|------| 
| fp16 | ~13 GB|
| quant8 | ~8 GB | 
| quant4 | ~4 GB |

After that, the sampling single image at 1024 can be expected to take similar amounts than SDXL. For VAE the base SDXL VAE is used.

![image](https://github.com/kijai/ComfyUI-KwaiKolorsWrapper/assets/40791699/ada4ac93-58ee-4957-96cd-2b327579d4f8)

![image](https://github.com/kijai/ComfyUI-KwaiKolorsWrapper/assets/40791699/b6a17074-be09-4075-b66f-7857c871057a)