Textual Inversion is a method to teach a Stable Diffusion model a new concept or style by creating a special token embedding that represents that concept. Instead of retraining or fine-tuning the entire model, you train a small vector that the model learns to associate with the new idea.

How does it work?

• You collect a few example images (usually 3-5) of the object, person, or style you want to teach.
• You pick a new token name (like <mySpecialToken>) that doesn’t exist in the model’s vocabulary.
• You train just the embedding vector for that token — essentially a compact representation — so that when the model sees that token in a prompt, it generates images reflecting your examples.
• The rest of the model stays completely unchanged.

Why use Textual Inversion?

• It’s lightweight and fast compared to full fine-tuning methods.
• It requires very little VRAM (GPU memory).
• You can reuse the new token in many prompts to generate the subject or style with variations.
• Great for capturing specific objects, characters, or styles without messing with the base model.

Limitations:

• It might not capture extreme variations of the concept very well.
• Sometimes the generated image can lose detail compared to DreamBooth.
• You cannot add complex context, only what fits into the embedding.