<pre><code>Training Params


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{
 "engine": "kohya",
 "unetLR": 0.0005,
 "clipSkip": 2,
 "loraType": "lora",
 "keepTokens": 3,
 "networkDim": 16,
 "numRepeats": 20,
 "resolution": 1024,
 "lrScheduler": "cosine_with_restarts",
 "minSnrGamma": 5,
 "noiseOffset": 0.1,
 "targetSteps": 9945,
 "enableBucket": true,
 "networkAlpha": 14,
 "optimizerType": "Adafactor",
 "textEncoderLR": 0.00005,
 "maxTrainEpochs": 13,
 "shuffleCaption": true,
 "trainBatchSize": 4,
 "flipAugmentation": true,
 "lrSchedulerNumCycles": 3
}
</code></pre>

king-who-wears-underwear-pony-midjourney-images

[{"prompt": "married malay men in a group showing off"}, {"prompt": "grandfather"}, {"prompt": "thick hairy pubes,"}]training a custom AI image generation model using a technique called LoRA (Low-Rank Adaptation). Here's what each section means in plain terms:Network Architecture SettingsWhat it does: Defines how the AI learns new visual concepts without completely retraining the entire model.<ul><li>Learning rates (0.0005 for image generation, 0.00005 for text understanding): How quickly the AI adapts - like adjusting how big steps you take when learning to ride a bike</li><li>Network dimensions (16) and alpha (14): Control how much the model can change - smaller numbers mean more subtle adjustments</li><li>LoRA method: A memory-efficient way to teach new concepts to existing AI models</li></ul>Usage example: Training a model to generate images of a specific character, art style, or object while keeping the base model's general knowledge intact.Learning OptimizationWhat it does: Controls how the AI improves during training, like setting study schedules and methods.<ul><li>Cosine scheduler with restarts: The learning intensity follows a wave pattern, starting strong, tapering off, then restarting - like interval training</li><li>Adafactor optimizer: An efficient method for updating the model that uses less memory</li><li>13 training cycles: The AI will see your training images 13 times to learn the patterns</li></ul>Usage example: Training a model to recognize your pet's specific features across different poses and lighting conditions.Training Process ControlsWhat it does: Manages the practical aspects of training - batch sizes, memory usage, and quality settings.<ul><li>Batch size 4: Processes 4 images simultaneously for efficiency</li><li>Noise offset 0.1: Adds slight randomness to prevent the model from memorizing exact images</li><li>Mixed precision: Uses less memory while maintaining quality</li><li>Gradient checkpointing: Trades computation time for memory savings</li></ul>Usage example: Fine-tuning settings for your hardware - smaller batches for limited GPU memory, larger batches for powerful systems.Advanced Quality FeaturesWhat it does: Implements sophisticated techniques to improve training stability and output quality.<ul><li>Multi-resolution noise: Helps the model learn details at different scales</li><li>Min SNR gamma: Prevents the model from focusing too much on very noisy training examples</li><li>Clip skip: Adjusts how the text and image understanding components interact</li></ul>Usage example: Creating a model that can generate both close-up details and wide landscape shots of the same subject with consistent quality.Practical ApplicationsThis configuration would be ideal for:<ul><li>Character consistency: Training a model to generate the same fictional character across different scenes</li><li>Art style replication: Teaching an AI to mimic a specific artist's technique</li><li>Product visualization: Creating variations of a product for marketing materials</li><li>Concept art development: Generating multiple iterations of a design concept</li></ul>The settings are balanced for moderate hardware requirements while maintaining good quality output, making it suitable for hobbyists and small studios rather than requiring enterprise-level computing resources.

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