Flux Concept Sliders - enabling finer control over generated images

Introduction

FLUX now supports training Concept Sliders with LoRA, enabling finer control over generated images, such as age changes or hair length adjustments.

Features

GPT-4 Assistance

The project leverages GPT-4 to help users create text slider prompts, simplifying the creation process and enhancing prompt quality.

FLUX Support

Experimentally, the project supports training sliders for the FLUX-1 model, offering more model choices and flexibility.

Environment Setup

Detailed steps are provided to set up the Python environment, including creating a conda environment, cloning the repository, and installing dependencies.

Training Text Sliders

Users are guided on how to train a text slider for adjusting character age by editing the prompts.yaml file and running the train_lora.py script.

Training Visual Sliders

The project details how to prepare an image dataset, configure files, and use the train_lora-scale.py and train_lora-scale-xl.py scripts to train visual concept sliders.

Usage

Free ConceptSliders Online

ConceptSliders: LoRA Adaptors for Precise Control in Diffusion Models

ConceptSliders Online is an interactive tool designed to showcase machine learning model concepts. Users can adjust model inputs by moving sliders and observe changes in model outputs, facilitating exploration and learning about model behavior.

ConceptSliders helps users understand concepts through:

• Interactive Learning: Users can directly manipulate sliders to change model input parameters and observe real-time changes in model output.
• Visual Feedback: Immediate visual feedback allows users to intuitively see how different inputs affect model behavior.
• Example Demonstrations: Concrete examples and real-time results help users connect abstract machine learning concepts with actual outcomes.
• Exploratory Analysis: Encourages users to engage in exploratory learning, discovering model characteristics and limitations through their own interactions.

This approach makes complex machine learning concepts more tangible and easier to understand.

How to Use GPT-4 to Create Text Slider Prompts

To create text slider prompts using GPT-4, follow these steps:

1. Describe the type of slider you want to create. For example, "I want to create a slider that makes a person look happy."
2. Use the GPT notebook (GPT_prompt_helper.ipynb) included in the project to generate the required text slider prompts.
3. Open the GPT_prompt_helper.ipynb notebook.
4. Fill in the description of the slider you want to create based on the prompts.
5. Run the code in the notebook, and GPT-4 will generate the corresponding text slider prompts based on your description.

This process allows you to utilize GPT-4's capabilities to create more precise and effective text prompts, resulting in better outcomes when training text sliders.

Note: To use GPT-4, you may need to install the necessary packages and ensure you have access to the OpenAI API. Additionally, make sure your environment meets the requirements specified in the requirements.txt file.

This feature simplifies the creation of text sliders and improves the quality of generated prompts, enabling better control over the output of diffusion models.

https://github.com/rohitgandikota/sliders