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Lora Training Guide 1. Environment Setup Using Google Colab/Kaggle: Platforms like Google Colab and Kaggle offer free access to GPUs, which can significantly speed up the training process. Set up your environment by installing necessary libraries like PyTorch, diffusers, and safetensors. 2. Understand the Image Input/Output Process Starting with Images: Noise and Reconstruction: Begin with clear, high-quality images. These images are vital as they serve as the base for the training process. These images will be intentionally transformed into noise to teach the AI how to reconstruct them. Noise refers to random alterations made to the images during training. The AI model is trained to reverse this noise, learning to generate images that closely resemble the original ones. 3. Prepare Training Data Variety: Angles and Lighting: Resolution: Use a diverse set of images, including different emotions, facial expressions, fashion styles,