Ready to unleash your inner AI artist? Let's get started!
Get Your Colab Notebook: Head over to my GitHub repo (link in the description) and click on the Colab link to open the notebook. Switch to a TPU: Before we begin, we need to optimize our Colab environment. Click on "Runtime" in the menu bar, select "Change runtime type", and choose "TPU" from the "Hardware accelerator" dropdown menu. This will harness the power of Google's Tensor Processing Unit for faster computations. Run the Cells: Now, back in the Colab notebook, simply run each cell one by one, starting from the top. These cells will take care of everything for you: downloading necessary repositories, installing required dependencies, and even downloading the Flux models directly to the correct folders. Launch the Server: Once you reach the final cell that says "python app.py", click the play button to launch the server. You'll see a Gradio share link appear in the terminal output. Click that link to open the Flux Gym training interface. Configure Your LoRA: Now, let's set up your training: Name Your LoRA: Give your LoRA a descriptive name, like "Dreamy Landscapes" or "Cyberpunk Portraits". Set Your Trigger: Choose a unique trigger word or sentence that you'll use to activate your LoRA when generating images. For example, you could use "dreamstyle" or "cyberpunk city". Choose VRAM: Since we're using the free Colab version, select the 16 GB option for VRAM. Adjust Training Settings: For this demo, I'm lowering the "Repeat trains per image" and "Max Train Epochs" to 2 to speed things up. But keep in mind that this will likely reduce the quality of the final LoRA. For optimal results, stick to the default values. Add Sample Prompts (Optional): If you want to see sample images generated during training, enter some relevant prompts in the "Sample Image Prompts" field.
Upload Your Training Images: Drag and drop your training images directly into the dataset section. I recommend using around 15 to 20 high-quality images that clearly represent the style or concept you want your LoRA to learn. Caption Your Images: Flux Gym comes with a built-in auto-captioning feature using the Florence-2 model. This model is surprisingly effective at generating accurate descriptions for your images, making the training process even easier. Start Training: Click the "Start Training" button to begin the LoRA training process. The training logs will appear in the "Train log" section at the bottom of the page, allowing you to monitor the progress. Training times can vary greatly depending on your settings and the available VRAM. Download Your LoRA: Once training is complete, you'll find the LoRA model files in the "outputs" folder within the Flux Gym directory. Right-click on the LoRA file and select "Download". If you encounter any errors, you can use the final cell in the Colab notebook to download the files directly. Move Your LoRA to Your Image Generation Program: Place the downloaded LoRA file into your designated LoRA folder in your preferred image generation program, such as ComfyUI.
Congratulations, you've trained your very own LoRA! Now, it's time to test it out in your image generation program and unleash your newfound creative power.
Tips for Success and Troubleshooting
Image Selection: The quality of your training images will directly impact the quality of your LoRA. Use high-resolution images that clearly represent the style or concept you're aiming for. Trigger Word Selection: Choose a unique and specific trigger word or sentence that doesn't appear in other image prompts. This ensures that your LoRA is activated only when you intend it to be. Experiment with Settings: Don't be afraid to tweak the training settings and experiment with different dataset combinations to achieve the results you're looking for. Be Patient: Training LoRAs can be a time-consuming process. Don't be discouraged if it takes a few hours (or even longer) to complete the training.
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FluxGym Colab Github Repo -
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