[Music] Welcome to the tutorial. Today we're diving into how to train a Laura model using coore SS. From preparing images to completing the training, I'll guide you step by step to help you achieve high-quality results. Let's get started. Step one: preparing your images. First, let's talk about preparing your images. You'll want between 20 to 100 images for best results. Aim for a resolution of 1024x1024 pixels, though 512x512 can work too. A few dozen images are usually enough for effective training. Make sure the images have good resolution; avoid small low-quality ones. It's also important to maintain a consistent theme in your data set. Try to avoid images with complicated backgrounds or unrelated elements, as this could confuse the model. To add diversity, include different perspectives of your character, varied angles, facial expressions, and body poses. Focus on facial features for most of your images, with a smaller number of full body shots to keep the focus on the character's face. Step two: uploading images via cloud storage. Once your images are ready

you'll want to upload them to the cloud. Compress your images into a zip file. A good tip is to name the file following this format: step number underscore name, like 1_core_name. This helps keep things organized. After uploading, unzip the file to extract the images. Step three: tagging your images. Now it's time to tag your images. Coore SS has a built-in tool for this called the wd14 captioning tool. It's easy to use: just select the folder containing your images and click on caption images to begin the tagging process. You can monitor the tagging progress in the log tab. Once tagging is done, you'll see txt files appear in your image folder. [Music] [Music] a [Music] [Music] [Music] step four: initiating Laura training with your images tagged. Let's move on to training your model in Coore SS. Go to the Laura training tab to set up the environment. You'll need to configure the path for your training, select the base model you want to use, and specify the path where your images are stored. Don't

forget to define the output directory so you know where your trained model will be saved once it's ready. Step five: configuring Laura training parameters. Next, let's configure the training parameters. For most projects, I recommend setting 5 to 10 epochs. This means the model will go through your dataset 5 to 10 times. You can also adjust the maximum training steps, which will affect the balance between speed and quality. Once you're happy with the settings, start the training process. The model will now begin learning from your data. Step six: monitoring training progress. During training, make sure to keep an eye on the log tab. This will show you the training progress and help ensure everything is running smoothly. If anything seems off, you can stop and adjust your settings. For more detailed information on training parameters, check out the official Coya SS Laura training parameters guide. It can help you fine-tune your model for even better results. And that's it! You've now got the steps to train your own Laura model using Coya SS. From preparing images to configuring training settings, you're all

set to create high-quality models. Thanks for watching and happy training. [Music] [Music] n [Music] [Music] me [Music] [Music] n [Music] [Music]

Training a LoRA Model Using Kohya_ss

Training a LoRA Model Using Kohya_ss

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