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Training an AI model can feel overwhelming with all the jargon. Here’s a simplified explanation of the key terms in TensorArt training settings to help you get started confidently.1. RepetitionsWhat It Means: The number of times an image is shown to the model during training.Why It Matters: Repeating images helps the model "memorize" patterns and details.2. EpochsWhat It Means: One full cycle where the model goes through all the images in your dataset.Why It Matters: More epochs mean the model has more chances to learn, but too many can cause overfitting (the model becomes too specific and less flexible).3. Total StepsWhat It Means: The total number of training rounds, calculated as:(Number of Images) × (Repetitions) × (Epochs)Why It Matters: More steps generally mean better results but require more time and resources.4. SeedWhat It Means: A number used to make random processes in the model consistent.Why It Matters: Using the same seed ensures you get similar results when generating images.5. Learning RateWhat It Means: How quickly the model learns during training.Text Encoder Learning Rate: Controls how well the model learns tags or captions. Increase if the model isn’t recognizing key features.Unet Learning Rate: Adjusts the model's overall learning speed. Be careful; a high rate can cause errors, while a low rate slows progress.6. Grid SizeWhat It Means: The complexity of the model’s "thinking space."Why It Matters: Larger grids allow more detailed learning but increase file size and training time.7. Network AlphaWhat It Means: Adjusts how much weight is given to changes in the model during training.Why It Matters: Lower values make changes more noticeable, which can help refine details.8. Scrambling LabelsWhat It Means: Randomizes the order of tags in your dataset.Why It Matters: Helps the model avoid bias by seeing all tags equally.9. RegularizationWhat It Means: Techniques to prevent the model from overfitting by limiting its focus on specific details.Why It Matters: Regularized datasets help your model generalize better across different inputs.10. Base ModelsWhat It Means: Pre-trained models that act as a foundation for your training.SD1.5 LoRA: Good for 2D characters.SDXL LoRA: Great for realistic and detailed outputs.Tip for Beginners: Start with the default base model (SD1.5 or SDXL) if you’re unsure.Simplified Tips for BeginnersStart Simple: Use default settings at first to avoid confusion.Experiment Slowly: Adjust one setting at a time to see its impact.Review Regularly: Check your model’s progress during training and tweak as needed.By understanding these terms, you’ll navigate TensorArt training like a pro in no time!

In this article i will share how easy to create AI Tool for beginner. check it out.1. click comfyFlow at create menu at the top2. click New Workflow or import workflow if you have any workflow3. Choose any template you want, in this section i will add text2img template4. The new tab browser will appear, wait until completed5. Setting the paramater you want, in this section i will change checkpoint and prompt only, then do running test6. after successfully testing, click publish it then choose AI Tool7. New tab will appear, then fill it, then click Publish8. TADA, your AI Tool now go public.