LoRA for diffusion enables efficient fine-tuning to learn specific styles, subjects, or concepts with minimal resources. Application: Customize Stable Diffusion for particular characters, art styles, objects, or domains without training from scratch. How it works: Add low-rank decomposition matrices to attention layers, train only these small adapters (~4-100MB), freeze base diffusion model weights. Training setup: 5-50 images of target concept, captions describing each image, few hundred to few thousand training steps, single consumer GPU (8-24GB VRAM). Hyperparameters: Rank (typically 4-128), learning rate, training steps, batch size, regularization images. Trigger words: Use unique identifier in captions ("photo of sks person") to activate learned concept. Comparison to DreamBooth: LoRA is more efficient (smaller files, less VRAM), DreamBooth may capture subject better but requires more resources. Community ecosystem: Civitai, Hugging Face host thousands of LoRAs for styles, characters, concepts. Combining LoRAs: Can merge or use multiple LoRAs with weighted contributions. Tools: Kohya trainer, AUTOMATIC1111 integration, ComfyUI workflows. Standard technique for diffusion model customization.