ComfyUI-KwaiKolorsWrapper
Rudimentary wrapper that runs a/Kwai-Kolors text2image pipeline using diffusers.
Nodes (5)
The node where Kolors' VRAM bill gets paid
The node that grabs Kolors' UNet for you (and deliberately nothing else)
The payoff node for a model the internet forgot
Where your prompt becomes 4096-wide vectors
The local-file option for the biggest piece of Kolors
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:
https://huggingface.co/Kijai/ChatGLM3-safetensors/upload/main
goes into:
ComfyUI\models\LLM\checkpoints
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.