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).
- 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)
- MODEL_NAME
- STEPS (INT)
- REFINE_STEPS (INT)
- CFG (FLOAT)
- SAMPLER (SAMPLER)
- SCHEDULER (SCHEDULER)
See the file: ComfygSwitch-example.json
- 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...);
Contributions and suggestions are welcome! If you encounter any issues or have ideas for improvements, please open an issue or submit a pull request.
This project is licensed under the MIT License.