Fine-tune a Diffusion Model on Custom Data

Prerequisites: Text-to-Image Pipeline with Stable Diffusion; a CUDA GPU with meaningful VRAM headroom (16GB+ recommended for SDXL training); 10-30 images of a chosen subject.

Targeted versions: Diffusers' official DreamBooth-LoRA training script for SDXL; peft, accelerate, bitsandbytes — verified against the Hugging Face diffusers GitHub repository, mid-2026.

Full fine-tuning of a diffusion model means updating every weight in the UNet or transformer — expensive, slow, and prone to "catastrophic forgetting" of everything the model knew before. LoRA (Low-Rank Adaptation) sidesteps this by freezing the base model and training small low-rank matrices injected into the attention layers instead. The result is a lightweight adapter file (typically 20-200MB, versus the 5-7GB base checkpoint) that you load on top of the frozen base model at inference time.

You'll use Diffusers' official DreamBooth-LoRA training script for SDXL, which combines DreamBooth's approach (teaching the model a specific subject tied to a rare token) with LoRA's efficient parameter update. This is the same underlying technique used by most custom-character and custom-style LoRAs you'll find on Civitai.