What Is Dreambooth in Stable Diffusion? (Explained)

⚡ Quick Answer

Dreambooth is a fine-tuning method that trains a brand new checkpoint on a handful of photos of one specific subject — a person, pet, product, or object — so the model can generate new images of that exact subject in any scene you describe.

Unlike a small patch file, Dreambooth updates the model's full weights, which is why it needs more training time, more VRAM, and produces a much larger file than a LoRA.

How It Works

You gather a small set of photos of your subject, pick a rare word (like "sks") to represent it, and run a training script that fine-tunes the checkpoint to associate that word with your subject's appearance across many angles and settings. The result is saved as a completely new checkpoint file — not a small add-on, but a full copy of the model with your subject baked in.

Note: Because it retrains the whole model, Dreambooth can "forget" some of the checkpoint's general knowledge if trained too aggressively or on too few images — a problem known as overfitting.

Where You'll See It

Dreambooth training itself happens outside ComfyUI, in a dedicated training tool such as Kohya_ss or AI-Toolkit. Once training finishes, you load the resulting file into ComfyUI through the standard Load Checkpoint node, exactly like any other checkpoint.

Quick Example

You train Dreambooth on 15–20 photos of your dog with the rare token "sks dog," producing my_dog_v1.safetensors. Load it like any checkpoint, then prompt "a photo of sks dog on a beach at sunset" to place your actual dog in a new scene.

Rule of thumb: if you just want a consistent character or style and don't need pixel-perfect subject accuracy, try a LoRA first — it's faster, smaller, and needs far less VRAM.

Common Mix-Up: Dreambooth vs LoRA

Dreambooth retrains the entire model and saves a full new checkpoint (several GB, more VRAM, longer training, very precise). LoRA trains a small patch on top of an existing checkpoint (much smaller file, faster, less VRAM, and close enough for most uses). Most beginners in 2026 start with LoRA and only move to Dreambooth when precision on one exact subject really matters.

Frequently Asked Questions

For most beginners, no — LoRA gets close results with far less VRAM, smaller files, and faster training. Dreambooth is still chosen when you need maximum accuracy for one specific subject and have the hardware to train a full checkpoint.

Typically 10–30 clear photos of the subject from different angles and settings. Fewer photos risk the model overfitting to the background or pose rather than learning the subject itself.

Not natively — Dreambooth training happens in a separate tool such as Kohya_ss or AI-Toolkit. Once training finishes, you load the resulting checkpoint into ComfyUI the same way you would any other checkpoint file.

Dreambooth updates and re-saves the entire model’s weights, producing a multi-gigabyte checkpoint file. A LoRA only saves a small patch of changes, typically a few hundred megabytes or less.

See It In Action

Want a lighter way to train a custom look?

Our LoRA guide covers the method most creators reach for first.

ComfyUI LoRA Guide →Browse Full Glossary →

Published: 2026-09-09 · Last updated: 2026-09-09

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