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DreamBooth fine-tuning with DEFT

examples/deft_dreambooth/README.md

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DreamBooth fine-tuning with DEFT

DEFT (Decompositional Efficient Fine-Tuning) adapts a frozen weight by removing a learned low-rank sub-space and injecting a new one in its place (W' = (I - P_proj) @ W + Q_P @ R). On its native text-to-image domain it is well suited to personalizing a diffusion model from a few images while preserving the base model's editability. This example is adapted from oft_dreambooth.

Setup

bash
cd peft/examples/deft_dreambooth
pip install "git+https://github.com/huggingface/peft" diffusers accelerate transformers

Train

Point --instance_data_dir at a few images of your subject:

bash
python train_dreambooth.py \
    --pretrained_model_name_or_path "stabilityai/stable-diffusion-2-1-base" \
    --instance_data_dir "path/to/subject/images" \
    --output_dir "deft-dreambooth-model" \
    --instance_prompt "a photo of sks dog" \
    --resolution 512 \
    --train_batch_size 1 \
    --max_train_steps 800 \
    --learning_rate 1e-4 \
    --use_deft \
    --deft_r 8 \
    --deft_alpha 16 \
    --deft_decomposition_method "qr"

qr is the default decomposition and works best for image generation (use relu for text tasks). Add --train_text_encoder (with the --deft_text_encoder_* options) to also adapt the text encoder.

Inference

See deft_dreambooth_inference.ipynb: load the base pipeline and attach the trained adapters with PeftModel.from_pretrained(pipe.unet, output_dir + "/unet") (and likewise for the text encoder if it was trained).