ComfyUI-B-LoRA

ComfyUI-B-LoRA
★ 77

ComfyUIB-LoRASDXL风格内容混合
在 ComfyUI 中加载并应用 B-LoRA 模型，针对 SDXL 实现图像的风格与内容隐式分解，便于高质量的风格迁移与内容混合/交换。
💡 在 SDXL 管线中加载 B-LoRA 以进行风格与内容混合或交换。
🍴 9 Forks💻 Python🔄 2024-06-18
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📄 README

ComfyUI-B-LoRA

A ComfyUI custom node that loads and applies B-LoRA models.

What is B-LoRA?

B-LoRA: By implicitly decomposing a single image into its style and content representation captured by B-LoRA, we can perform high quality style-content mixing and even swapping the style and content between two stylized images.

  • 🌐 Website: https://b-lora.github.io/B-LoRA/

  • Code: https://github.com/yardenfren1996/B-LoRA/

  • Currently B-LoRA models only works with SDXL (sdxl_base_1.0). (Compatible but not guaranteed with SDXL-based fine-tuned models.)

    • Advantages of B-LoRA

  • Can apply Style or Content, or both.

  • Much smaller model files. (~100M for SDXL B-LoRAs)

  • One B-LoRA only needs one image as training dataset and 15 minutes to train. (on a single RTX 4090)

    • Please share your B-LoRA models on Civit.ai or HuggingFace!

    Node

    Load B-LoRA

  • lora_name: Choose the B-LoRA model you want to load. By default, it’ll searches in the models/loras/ folder for available models.

  • load_style: Do you want the style of that B-LoRA?

  • load_content: Do you want to content of that B-LoRA?

  • strength: How strong do you want that B-LoRA to affect the model?

    • Workflow Examples

    A Single Load B-LoRA node

    🌟 is the training prompt for one B-Lora colorful-squirrel

    A B-LoRA for Style, and another for Content

    🌟 is the training prompt for one B-Lora colorful-squirrel, and

    is the training prompt for the other pencil-boy.

    B-LoRA models used in the workflows can be downloaded here:

    https://huggingface.co/sida/B-LoRA-examples/tree/main

    More pretrained B-LoRAs to try out:

    https://huggingface.co/lora-library?sort_models=downloads#models

    Train Your B-LoRAs (WIP):

    I’m building a docker image for training. Please check train to see current progress.

    Credit goes to:

  • https://github.com/yardenfren1996/B-LoRA

  • https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_sdxl_lora_to_webui.py

  • https://github.com/yardenfren1996/B-LoRA/issues/7

  • https://github.com/comfyanonymous/ComfyUI/issues/3674

    • Citation

    If you use B-LoRA in your research, please cite the authors’ paper:

    @misc{frenkel2024implicit,
      title={Implicit Style-Content Separation using B-LoRA}, 
      author={Yarden Frenkel and Yael Vinker and Ariel Shamir and Daniel Cohen-Or},
      year={2024},
      eprint={2403.14572},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}