ComfyUI_Searge_LLM

ComfyUI_Searge_LLM
★ 157

提示工程LLMComfyUI本地模型
用于ComfyUI的提示语生成/优化节点，借助本地LLM（如Mistral-7B gguf）将简短提示扩展为更详细、更具表现力的文本到图像提示，提升生成质量与效率。
💡 将简短描述自动扩展为高质量的文本到图像提示
🍴 18 Forks💻 Python🔄 2025-11-29
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https://pan.quark.cn/s/c73fe210bae7
📦 requirements.txt
transformers>=4.0.0
torch>=1.7.1
accelerate
llama-cpp-python
Custom node Searge-LLM for ComfyUI
📄 README

Searge-LLM for ComfyUI

A prompt-generator or prompt-improvement node for ComfyUI, utilizing the power of a language model to turn a provided

text-to-image prompt into a more detailed and improved prompt.

Install the language model

  • Create a new folder called llm_gguf in the ComfyUI/models directory.
  • Download the file Mistral-7B-Instruct-v0.3.Q4_K_M.gguf (4.37 GB).

    • from the repository MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF on HuggingFace.

  • download link to the gguf model
  • place Mistral-7B-Instruct-v0.3.Q4_K_M.gguf in the ComfyUI/models/llm_gguf directory.

    • Note

  • Currently the node requires the language model as a gguf file and only works with models that are

    • supported by llama-cpp-python.

    Potential problems

    (this was only tested this on Windows)

    If you get error message about missing llama-cpp, try these manual steps:

  • These instruction are assuming that you use the portable version of ComfyUI, otherwise make sure to run these commands

    • in the pything v-env that you’re using for ComfyUI.

  • Open a command line interface in the directory ComfyUI_windows_portable/python_embeded.
  • It’s important to run these commands in the ComfyUI_windows_portable/python_embeded directory.
  • Run the following commands:
    • python -m pip install https://github.com/oobabooga/llama-cpp-python-cuBLAS-wheels/releases/download/cpu/llama_cpp_python-0.2.89+cpuavx2-cp311-cp311-win_amd64.whl
python -m pip install https://github.com/oobabooga/llama-cpp-python-cuBLAS-wheels/releases/download/textgen-webui/llama_cpp_python_cuda-0.2.89+cu121-cp311-cp311-win_amd64.whl

    FAQ

  • “I still get errors related to llama-cpp, what should I do?”
  • You can try manually installing llama-cpp in the python environment that you use to run ComfyUI. To do that first

    • uninstall any package including the name llama cpp from your python environment. After that you can install the

    llama-cpp package with the command python -m pip install llama-cpp-python. If the problem persist after these

    steps, please report it in the Github issue tracker of this project.

  • “Can you add [FEATURE] to this node?”
  • Maybe. Maybe not. You can always post your idea in the issue tracker on Github as a feature request and if I like

    • the idea and find the time for it, I may implement it in a future update.

    Searge LLM Node

    Configure the Searge_LLM_Node with the necessary parameters within your ComfyUI project to utilize its capabilities

    fully:

  • text: The input text for the language model to process.
  • model: The directory name of the model within models/llm_gguf you wish to use.
  • max_tokens: Maximum number of tokens for the generated text, adjustable according to your needs.
  • apply_instructions:
  • instructions: The instructions for the language model to generate a prompt. It supports the placeholder

    • {prompt} to insert the prompt from the text input.

    Example: Generate a prompt from "{prompt}"

    Advanced Options Node

    The Searge_AdvOptionsNode offers a range of configurable parameters allowing for precise control over the text

    generation process and model behavior.

    *The default values on this node are also the defaults that Searge_LLM_Node*

    *uses when no Searge_AdvOptionsNode is connected to it.*

    Below is a detailed overview of these parameters:

  • Temperature (temperature): Controls the randomness in the text generation process. Lower values make the model

    • more confident in its predictions, leading to less variability in output. Higher values increase diversity but can

    also introduce more randomness. Default: 1.0.

  • Top-p (top_p): Also known as nucleus sampling, this parameter controls the cumulative probability distribution

    • cutoff. The model will only consider the top p% of tokens with the highest probabilities for sampling. Reducing this

    value helps in controlling the generation quality by avoiding low-probability tokens. Default: 0.9.

  • Top-k (top_k): Limits the number of highest probability tokens considered for each step of the generation. A

    • value of 0 means no limit. This parameter can prevent the model from focusing too narrowly on the top choices,

    promoting diversity in the generated text. Default: 50.

  • Repetition Penalty (repetition_penalty): Adjusts the likelihood of tokens that have already appeared in the

    • output, discouraging repetition. Values greater than 1 penalize tokens that have been used, making them less likely

    to appear again. Default: 1.2.

    These parameters provide granular control over the text generation capabilities of the Searge_LLM_Node, allowing

    users to fine-tune the behavior of the underlying models to best fit their application requirements.

    License

    The Searge_LLM_Node is released under the MIT License. Feel free to use and modify it for your personal or commercial

    projects.

    Acknowledgments

  • Based on the LLM_Node custom extension by Big-Idea-Technology, found

    • here on Github