Beginner's Guide to Training AI Models on TensorArt - (Model Training)

Training AI models might seem complicated, but it’s easier than you think with TensorArt. Here’s a simplified guide to help you get started, even if you’re new.

Step 1: Uploading and Managing Your Datasets

1. Upload Your Images:

   • Accepted formats: PNG, JPG, JPEG.

   • You can upload up to 1000 images for training.

2. Focus on Quality:

   • Use high-resolution images without noise, blur, or watermarks.

   • Enhanced images (like cropped or mirrored ones) can improve results.

3. Delete Images Easily:

   • Click the trash icon on an image to remove it.

Step 2: Organizing Your Dataset

1. Regularized Datasets (Optional):

   • Regularization reduces overfitting and helps your model generalize better.

   • If you’re new, skip this step for now.

2. Batch Clipping:

   • Crop your images using tools like Focus Crop (for the main subject) or Center Crop.

   • Recommended sizes:

     • For SD1.5: 512x512 or 768x512

     • For SDXL: 1024x1024 or 768x1024

Step 3: Tagging Images

1. Automatic Tagging:

   • Tags are auto-generated when you upload an image.

   • Review and edit these tags for better accuracy.

2. Manual Tagging:

   • Add or adjust tags to match specific traits or features.

3. Batch Tagging:

   • Add tags to multiple images at once. This is handy for trigger words like “seamless pattern” or “holiday design.”

Step 4: Setting Training Parameters

1. Repetitions:

   • Decide how many times each image is repeated during training.

2. Choose a Base Model:

   • For 2D characters: AnythingV5 (SD1.5) or Animagine XL (SDXL).

   • For realistic images: EpiCRealism (SD1.5) or Juggernaut XL (SDXL).

3. Advanced Options:

   • Adjust settings like learning rate, epochs, or total steps for more control.

   • Use default settings if you’re unsure.

Step 5: Training and Testing

1. Start Training:

   • Training runs one task at a time, so there might be a queue.

   • Schedule during off-peak hours for faster processing.

2. Test Your Model:

   • After training, test your model directly on the workbench.

   • Review preview images to decide whether to retrain or publish.

Step 6: Improving Your Results

1. Retrain:

   • Not satisfied? Adjust your parameters and try again.

2. Experiment:

   • Try different datasets, models, or settings for better outcomes.

Conclusion

With TensorArt, even beginners can train AI models effectively. Start with a simple dataset, use default settings, and gradually explore advanced options as you gain confidence. Take it step by step, and soon, you’ll be creating AI models like a pro!