ComfyUI_LLM_Hub

🧠 LLM Hub

Utilize the power of an LLM into ComfyUI to transform your text-to-image and text-to-video ideas into highly detailed prompts for generation while giving you full control.

🤖 Usage

📝 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 commands:

For activating venv:

cd path/to/your/ComfyUI

Linux:

source venv/bin/activate

Windows:

.venv\Scripts\activate

Go to your custom nodes folder:

cd custom_nodes

Afterwards clone the repo:

git clone https://github.com/claptrap0/ComfyUI_LLM_Hub

Go inside the node folder:

cd ComfyUI_LLM_Hub

If you are using a venv:

pip install -r --verbose requirements.txt

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

For the portable version of ComfyUI:

C:/path/to/your/ComfyUI_portable/python_embedded/Scripts/pip.exe install -r requirements.txt

⚙️ LLM Settings

It 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 of the output. Lower values (e.g., 0.2-0.5): Make the output more focused, predictable, and deterministic. Ideal for tasks requiring factual, precise, or consistent responses. Higher values (e.g., 0.7-1.0): Increase creativity and randomness, allowing the model to take more risks. Can lead to more imaginative but potentially less coherent results. Default: 0.8
• top_p: Filters the set of possible next tokens by cumulative probability. The model only considers tokens whose cumulative probability sum up to top_p.Lower values (e.g., 0.5): Narrows the selection to only the most probable tokens, improving coherence and reducing the chance of irrelevant words. Higher values (e.g., 0.9): Allows for a broader range of tokens, increasing randomness. Default: 0.8
• top_k: Limits the sampling pool to the k most probable next tokens. Lower values (e.g., 5-20): Focuses generation on the top choices, making output more predictable. Higher values (e.g., 50-100): Expands the options, outputing more creative text. Default: 10
• repetition_penalty: Discourages the model from repeating words or phrases that have already appeared in the generated text. Values greater than 1 (e.g., 1.1-1.5): Penalize repeated tokens, making them less likely to be chosen again. Essential for preventing repetitive loops or boilerplate text. A value of 1: No penalty applied. Default: 1.3

📚 Resources

• Parameter settings
• System prompts, instructions, etc.

📄 License

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