Additional Training Methods
Beyond LoRAs, several other techniques allow artists to customize AI models to their specific needs. These approaches vary in complexity, resource requirements, and the types of customization they enable.
- Textual Inversion: Creating embeddings that capture specific concepts with very small datasets (15-20 images).
- Hypernetworks: Secondary neural networks that modify the behavior of the primary model.
- DreamBooth: A technique for teaching models specific subjects with remarkable consistency.
- Embeddings/Textual Inversions: Special tokens that encapsulate visual concepts for easy reuse in prompts.
- Aesthetic Gradients: Guidance mechanisms that steer generation toward specific visual qualities.
- Custom VAE: Modified variational autoencoders that affect the final rendering quality and style.
- Model Pruning: Techniques to reduce model size while preserving most capabilities.
- Quantization: Methods to decrease precision requirements, enabling models to run on less powerful hardware.