AI engineering glossary · Generative media multimodal
What is loRA fine-tuning for diffusion?
LoRA fine-tuning for diffusion: Adapting a diffusion model to a new style, character or product by training small low-rank weight updates on top of frozen weights. The resulting files are a few megabytes, can be swapped or combined at generation time, and need far less GPU memory than full training.
LoRA fine-tuning for diffusion: an example
A brand trains an adapter on 30 product photos so that its prompts produce the company's bottle design consistently.
Related terms
More Generative media multimodal terms
- Mean opinion score
- Mel spectrogram
- MFCC
- Mode collapse
- Multimodal embedding
- Multimodal model
- Negative prompt
- Neural audio codec
Where you learn it
LoRA fine-tuning for diffusion is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.