DreamBooth vs Textual Inversion vs LoRA: Choosing the Right Fine-Tuning Method

Comparison of the three main Stable Diffusion fine-tuning approaches with use cases, VRAM requirements, and quality trade-offs.

Understanding when to use each fine-tuning method is crucial. DreamBooth: full model fine-tuning, highest quality, 12+ GB VRAM, produces large files (2-7GB), best for specific subjects. LoRA: lightweight adapter training, excellent quality-to-size ratio, 6-12GB VRAM, small files (10-200MB), versatile and stackable. Textual Inversion: learns new token embeddings only, lowest resource requirement (4GB+), tiny files (<100KB), best for styles and concepts. Covers hybrid approaches, training data preparation for each method, and practical decision framework.