Flux 1 Dev LoRA Training Simple WebUI With Low VRAM Enable - Fluxgym Tutorial Guide

Future Thinker @Benji
11 Sept 202419:52

TLDRThis video tutorial introduces the Flux 1 Dev LoRA Training, a simplified tool for LoRA training that supports low VRAM usage. Created by the developers behind Pinocchio AI, it offers a straightforward web UI for local execution using the Gradio-based interface. The video demonstrates the installation steps, configurations, and model setup, making it accessible for users with minimal technical expertise. The script utilizes Kohya SS for backend training and supports various diffusion models like Stable Diffusion. Overall, it's a user-friendly solution for creating and training LoRA models with reduced complexity.

Takeaways

  • 🚀 Flux Gym is a simplified UI for LoRA training with low VRAM support, ideal for users who want to train AI models locally.
  • 🖥️ The tool comes from the creator of Pinocchio AI, which developed easy one-click installation AI tools.
  • 🎛️ Flux Gym uses a Gradio-based web UI, making it accessible and easy to use, with just three simple steps to start training.
  • 🧠 The backend uses the Kohya SS script, popular for Stable Diffusion LoRA training, ensuring compatibility with models like SDXL and SD1.5.
  • 🔧 Users can manually install Flux Gym or use the Pinocchio AI one-click installer for easier setup.
  • 🖼️ Training a LoRA model requires a small dataset (about 20 images) that users can easily upload via the web interface.
  • 💾 The tool supports different VRAM sizes (12 GB, 16 GB, 20 GB), making it adaptable to various hardware configurations.
  • 🔄 The LoRA training process can be customized in terms of repetitions and VRAM allocation, optimizing for different performance levels.
  • 🧪 Training progress is visible in the web UI, and completed models can be exported and tested in other platforms like ComfyUI.
  • ⏳ While low VRAM setups work, they significantly increase the training time (e.g., 16GB VRAM took 3 hours, while higher VRAM could speed it up).

Q & A

  • What is the main purpose of the video?

    -The video provides a tutorial on using Flux Gym, a simplified UI for training LoRA models with low VRAM usage.

  • Who created Flux Gym and what is its origin?

    -Flux Gym was created by the same developer behind Pinocchio AI, known for AI Easy Installer, which simplifies the installation of AI software.

  • What is the advantage of using Flux Gym for LoRA training?

    -Flux Gym simplifies the LoRA training process by offering a dead-simple interface that requires minimal configuration, making it accessible to users with lower technical skills. It also supports low VRAM, allowing it to run on consumer PCs.

  • What front-end technology does Flux Gym use?

    -Flux Gym uses a Gradio-based web UI from the AI Toolkit to provide a simplified front-end interface for users.

  • What backend technology does Flux Gym utilize for LoRA training?

    -Flux Gym uses the Kohya SS script for the backend, which is a popular script for stable diffusion and LoRA training pipelines.

  • How does Flux Gym handle different VRAM settings?

    -Flux Gym allows users to choose different VRAM sizes—12 GB, 16 GB, and 20 GB—depending on their GPU’s capacity. Lower VRAM sizes take more time to complete training.

  • What are the required steps to start LoRA training with Flux Gym?

    -The steps include cloning the Flux Gym repository, installing the necessary dependencies, preparing the dataset, configuring VRAM settings, and running the training script.

  • What models and checkpoints need to be downloaded for Flux Gym?

    -Users need to download flux models such as Clip, T5, UNet, and VAE, which are required to be stored in specific subfolders for the training process.

  • What are the expected time differences for LoRA training based on VRAM settings?

    -With 16 GB of VRAM, training took 3 hours, while it could be completed in about 1 hour with 24 GB of VRAM. Lower VRAM results in slower training times.

  • How does the user test the trained LoRA model in the video?

    -The user tests the trained LoRA model by importing it into Comfy UI, using ControlNet for pose references, and generating images based on the trained data.

Outlines

00:00

📢 Introduction to Flux Gym: Simplified AI Training Tool

In this introductory paragraph, the speaker introduces Flux Gym, a simplified tool for AI training, developed by the creator of Pinocchio AI. It provides an easy-to-use web interface for training models, particularly for low VRAM devices, with step-by-step guidance. The tool is based on the Gradio UI and AI Toolkit, offering a streamlined process with minimal technical knowledge required. Key features include VRAM allocation, text prompts, and repeat training options, making it accessible for users with varying technical skills.

05:00

🖥️ Manual Installation of Flux Gym

The second paragraph dives into the installation process of Flux Gym. The speaker opts for a manual installation instead of using the Pinocchio AI browser, providing step-by-step instructions. The process involves using Git to clone the repository, navigating the project folder, and downloading compatible scripts, specifically the SD script from Kohya SS. The speaker highlights the compatibility of Flux Gym with various models such as Stable Diffusion 1.5, SDXL, and Flux, emphasizing the flexible architecture for different diffusion models.

10:16

🔧 Setting Up Virtual Environments and Dependencies

This paragraph focuses on setting up the virtual environment and installing necessary dependencies for Flux Gym. The speaker provides instructions for activating the Python virtual environment and mentions the alternative of using Conda. After entering the virtual environment, the speaker walks through installing the requirements.txt file, which includes dependencies for the SD script and Flux Gym. The process concludes with a successful setup of the environment, ensuring the system is ready for AI training.

15:17

📊 Finalizing Installation and Starting Model Training

In this section, the speaker outlines the final steps of the installation, focusing on installing essential components like Torch, TorchVision, and TorchAudio. After setting up the models and checkpoints, the Flux Gym interface is ready for use. The speaker demonstrates how to create and name a Lora model, choose VRAM size, and adjust training settings such as epochs and captions for image datasets. The interface's simplicity allows for easy model training, making it accessible to non-technical users.

⏳ Testing Training Speeds with VRAM

This paragraph highlights a real-time test of Flux Gym’s performance using different VRAM capacities. The speaker shares the results of training a Lora model on 16 GB of VRAM, which took 3 hours and 5 minutes. They compare this with higher VRAM setups, which complete the training significantly faster. The speaker explains the trade-offs between low and high VRAM systems, emphasizing that while low VRAM setups are slower, they still allow for successful AI training.

📁 Monitoring Training Progress and Output

Here, the speaker describes how to monitor training progress using both the command prompt and the Flux Gym web UI. They explain that while the command prompt shows dataset creation, the web UI provides a detailed view of training progress. The speaker also guides users on locating the output files, which include checkpoints for every four training epochs, and how to use these files in other AI interfaces like ComfyUI for testing the results of the Lora training.

👩‍💻 Integrating and Testing Lora Models in ComfyUI

In this paragraph, the speaker demonstrates how to integrate and test the Lora models trained in Flux Gym within ComfyUI. They detail the process of moving the output files into the Lora subfolder of ComfyUI and testing the model with specific text prompts and reference images. The speaker showcases the results, which include character faces and outfits similar to the training images, and experiments with control settings to fine-tune the model’s output.

🎬 Conclusion and Final Thoughts on Flux Gym

The speaker wraps up the video by reflecting on the ease and simplicity of using Flux Gym for Lora model training. They emphasize that the tool is user-friendly and ideal for those who are not well-versed in coding. With simple steps, a clear UI, and flexible VRAM options, Flux Gym provides an effective solution for AI training. The video ends with an encouragement to experiment with the tool and a sign-off until the next video.

Mindmap

Keywords

💡Flux Gym

Flux Gym is a simplified user interface (UI) for LoRA training, allowing users to train AI models like Stable Diffusion with lower VRAM requirements. In the video, it's presented as an easy-to-use solution for AI model training, particularly useful for users with less technical expertise.

💡LoRA Training

LoRA (Low-Rank Adaptation) training is a method used in machine learning to fine-tune models with fewer computational resources. In the video, Flux Gym offers a simple UI to perform LoRA training with Stable Diffusion models, supporting low VRAM configurations.

💡Stable Diffusion

Stable Diffusion is a popular AI-based image generation model. The video discusses using Flux Gym to train LoRA models on Stable Diffusion, particularly focusing on how users can set up and manage their training locally with low VRAM requirements.

💡VRAM

VRAM (Video Random Access Memory) refers to the memory used by the GPU to store data during processing. The video highlights how Flux Gym allows users to allocate different VRAM sizes (12GB, 16GB, 20GB) for model training, making it accessible for those with consumer-grade GPUs.

💡Gradio

Gradio is a Python library that helps build user interfaces for machine learning models. The video mentions how Flux Gym uses a Gradio-based web UI to simplify the process of training models, allowing users to interact with the AI training tool more easily.

💡Pinocchio AI

Pinocchio AI is the creator of tools like the AI Easy Installer and Flux Gym. In the video, Pinocchio AI's contributions are noted for simplifying the installation of AI software, such as ComfyUI, and providing user-friendly solutions for training AI models.

💡SD Script

The SD Script refers to the Stable Diffusion pipeline used in the backend for LoRA training. In the video, this script plays a crucial role in the operation of Flux Gym, as it enables the model training logic necessary for Stable Diffusion and other compatible architectures.

💡Coya SS

Coya SS is a well-known repository or script used for LoRA training in Stable Diffusion. In the video, Coya SS is mentioned as the underlying script being utilized for the training logic in Flux Gym, supporting different versions of Stable Diffusion.

💡ComfyUI

ComfyUI is another AI toolkit mentioned in the video, used for configuring and running AI models. It provides an alternative interface to Flux Gym, and the video explains how files and models can be shared between the two tools during training.

💡Model Checkpoints

Model Checkpoints are saved states of an AI model during training. The video explains how Flux Gym generates checkpoints at different intervals (e.g., every 4 epochs) to track the model's progress and allow users to evaluate or test the results at various stages.

Highlights

Introduction to Flux Gym, a simplified tool for LoRA training with low VRAM support, developed by the creator of Pinocchio AI.

Flux Gym is designed for ease of use, with a simple WebUI interface built on Gradio, enabling users to train LoRA models with minimal configurations.

The WebUI provides a VRAM adjustment feature, supporting three levels: 12GB, 16GB, and 20GB, for users with varying hardware capabilities.

Flux Gym simplifies the process into three steps: naming the LoRA model, configuring VRAM settings, and starting the training process.

The backend of Flux Gym is powered by Kohya's SS script, a popular tool for Stable Diffusion LoRA training.

Users can install Flux Gym manually or via the Pinocchio AI browser, with the option to skip downloading pre-existing models and checkpoints.

The tool supports training for multiple diffusion models, including SD 1.5, SDXL, and Flux, using the same script.

The setup process involves creating a virtual environment, installing necessary dependencies, and configuring model directories.

After setup, users can activate their virtual environment and run the Flux Gym interface locally using a simple Python script.

Flux Gym allows users to drag and drop images to create a dataset for LoRA training, simplifying dataset management.

For captioning, the tool uses Florence 2 to automatically generate captions for the dataset, further streamlining the process.

Once the dataset is ready, users can initiate the training with a single click, tracking progress through the WebUI.

Training times vary based on VRAM allocation, with lower VRAM options taking longer to complete. For example, 16GB VRAM resulted in a 3-hour training session.

Flux Gym provides multiple checkpoints during training, allowing users to test different training stages and models for better results.

The final LoRA model can be easily tested using ComfyUI or any other compatible interface, with options to adjust control settings for more precise results.