ComfyUI_MiniCPMv2_6-prompt-generator is a specialized node for ComfyUI that automates the generation of image labels or prompts, specifically designed for LoRA or DreamBooth training on Flux series models. It utilizes a fine-tuned model based on the MiniCPM-V 2.6 architecture, allowing users to create both short and long prompts in natural language style.
- Automates prompt generation for training with LoRA and DreamBooth models, enhancing workflow efficiency.
- Capable of processing single images or batches, generating captions or prompts based on user-defined methods.
- Utilizes an optimized int4 quantized model to reduce GPU memory usage, making it accessible for users with limited resources.
Context
This tool serves as a node within the ComfyUI framework, focusing on the automatic generation of captions and prompts for images. Its primary purpose is to facilitate the training of models like LoRA and DreamBooth by providing relevant textual data derived from images.
Key Features & Benefits
The tool's ability to generate both short and long prompts from images is critical for users engaged in model training, as it streamlines the preparation of training data. By allowing batch processing, users can efficiently handle multiple images, saving time and effort in generating corresponding captions.
Advanced Functionalities
The node supports three distinct methods for prompt generation: single-image captions, short prompts, and long prompts. Additionally, it enables users to regenerate images from the generated prompts using a CLIP node, offering flexibility in how the prompts are utilized in further workflows.
Practical Benefits
By automating the prompt generation process, this tool significantly enhances workflow efficiency in ComfyUI, allowing users to focus on training models rather than manually creating captions. The reduced GPU memory requirements also make it a practical choice for users with varying hardware capabilities, ensuring broader accessibility.
Credits/Acknowledgments
The tool is based on the MiniCPM-V 2.6 model and has been fine-tuned using a dataset sourced from Midjourney. The model and its implementations are available under the appropriate licenses, with contributions acknowledged from the original authors and developers.