Textual inversion learns new text tokens representing specific concepts for diffusion model generation. Approach: Instead of fine-tuning model weights, learn new embedding vectors that can be referenced in prompts. Model stays frozen. Process: Images of concept → optimize new token embedding to reconstruct images when used in diffusion → embedding stored as small file (~few KB). Example: Learn "" token from cat photos → prompt " wearing a hat" generates that specific cat. Technical details: Only optimize embedding (768-1280 dimensional vector), freeze U-Net and text encoder, typically 3000-5000 training steps. File size: Extremely small (~3-5 KB per concept) vs LoRA (~4-100 MB) vs DreamBooth (GB). Limitations: Less expressive than weight fine-tuning, may struggle with complex concepts requiring model modification, works best for styles and simple objects. Use cases: Art styles, simple objects, textures, color schemes. Combining concepts: Multiple textual inversions can be used together in same prompt. Comparison: Most parameter-efficient but lowest fidelity; LoRA is good middle ground; DreamBooth highest quality but most expensive. Choose based on quality vs efficiency needs.