ComfyUI Extension: ComfyUI_LLM_Hub

🧠 LLM Hub

A prompt-generator for ComfyUI, utilizing the power of a language model to turn a provided text-to-image prompt into a more detailed and improved prompt.

🤖 Usage

<img src="https://raw.githubusercontent.com/company8/ComfyUI_LLM_Hub/refs/heads/main/img/README.png" alt="LLM Hub for ComfyUI">

📝 Requirements

• Create a directory named LLMs inside ComfyUI/models/text_encoders/
• Create a another new directory for each LLM with the model name inside LLMs.
• Place your LLM models in their respective directory.
• Every .safetensors model needs the .json files and the model should be named model.safetensors

How your directory structure should look like:

ComfyUI/
└── models/
    └── text_encoders/
        └── LLMs/
            └── GGUF_model/
                └── model.gguf
            └── safetensors_model/
                └── model.safetensors
                └── config.json
                └── tokenizer.json
                └── tokenizer_config.json
                └── generation_config.json (Optional)
                └── special_tokens_map.json (Optional)

GGUF models don't need to be named "model.gguf".

🛠️ Installation

• Run the following command:

pip install -r --verbose requirements.txt

You can delete "--verbose" if you don't want to see the process of the compiling.

⚙️ LLM Settings

The LLM Settings offers a range of configurable parameters allowing for precise control over the text generation process and model behavior.

The values on this node are also the defaults that LLM Hub uses when LLM Settings isn't connected.

Breif overview of the parameters:

• 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: 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: 0.8.
• 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: 10.
• 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: 1.3.

Wanan learn more about these paramaters, click HERE.

📄 License

Released under the MIT License. Feel free to use and modify it for your projects, commercial or personal.