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| # 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: | |
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| https://huggingface.co/Kijai/ChatGLM3-safetensors/upload/main | |
| goes into: | |
| `ComfyUI\models\LLM\checkpoints` | |
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| ## 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_embedded\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. | |
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