Comfyg Switch

Comfyg Switch is a custom node that dynamically selects model configuration parameters based on the chosen checkpoint. It reads model-specific settings from a JSON file (model_configs.json).

Inputs

• checkpoint_model: This value is used to determine which configuration to load.
• use_custom_input (BOOLEAN): Toggle between using manual inputs and automatically loaded configurations.
• steps (INT): Number of inference steps (default: 30).
• refiner_steps (INT): Number of inference steps for enhancement (default: 30).
• cfg (FLOAT): Classifier-free guidance scale (default: 7.0).
• sampler (SAMPLER)
• scheduler (SCHEDULER)

Outputs

• MODEL_NAME
• STEPS (INT)
• REFINE_STEPS (INT)
• CFG (FLOAT)
• SAMPLER (SAMPLER)
• SCHEDULER (SCHEDULER)

Example

See the file: ComfygSwitch-example.json

Roadmap (or ideas)

• Maybe load configs from an external database or something like that, to avoid update the config file everytime;
• Import each model config dinamically from CivitAI API and use the config file as optional (maybe use LLM to read the model content and create the config object);
• When switch the model, load the config data into the node inputs (steps, cfg, etc...) and let us see the values before start queue, or change them too;
• Load more details about the selected model to help us with the workflow (result examples, tips, prompts...);

Contributing

Contributions and suggestions are welcome! If you encounter any issues or have ideas for improvements, please open an issue or submit a pull request.

License

This project is licensed under the MIT License.