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# DEFT: Decompositional Efficient Fine-Tuning for Text-to-Image Models

[DEFT](https://proceedings.neurips.cc/paper_files/paper/2025/hash/93a34a7138bdad95e874018d5f491cc6-Abstract-Conference.html)
(Decompositional Efficient Fine-Tuning) is a parameter-efficient fine-tuning method for text-to-image models. It
decomposes the update of a frozen weight matrix `W` into two trainable components: a projection that removes a low-rank
subspace from `W`, and a low-rank update that injects new content into that subspace. This formulation is designed to
balance aligning with a target distribution, learning new concepts from a few images (personalization), and preserving
the pretrained model's instruction-following ability and editability.

Concretely, DEFT combines two trainable low-rank components: (1) a projection onto the complement of a low-rank
subspace spanned by a low-rank matrix, and (2) a low-rank update. The first low-rank matrix defines the subspace, while
the second enables flexible parameter adaptation within that subspace.

When to use DEFT: it is best suited to adapting a model to new data or concepts while **retaining and even improving the
base model's instruction-following ability** and keeping **forgetting of its previous capabilities to a minimum**.

Per target layer, DEFT learns a projection direction `P` (shape `out_features x r`) and an injection matrix `R` (shape
`r x in_features`). The effective weight is the residual projection

```
W' = (I - P_proj) @ W + Q_P @ R
```

The projector `P_proj` is derived from `P` according to `decomposition_method`:

- `"relu"` (default): `Q_P = P`, `P_proj = P @ relu(P).T` — a non-orthogonal projection.
- `"qr"`: `Q_P = qr(P)`, `P_proj = Q_P @ Q_P.T` — an orthogonal projection.

The `(I - P_proj) @ W` term removes a sub-space of the pretrained weight while `Q_P @ R` injects new content into it.
By default (`init_weights=True`) `R` is initialized so that the update is an exact identity at initialization
(`W' == W`), so training starts from the pretrained weights and learns the injection. The update is equivalent to a
low-rank additive delta `Q_P @ (R - right.T @ W)`, which is computed without ever forming the `out x out`
projection matrix and can be merged into the base weights for inference-free deployment.

Setting `para=True` selects the
[PaRa](https://proceedings.iclr.cc/paper_files/paper/2025/hash/f09e8dd9274cb7c2dd0dc65ffc6f427a-Abstract-Conference.html)
(Parameter Rank Reduction) variant: a removal-only update `W' = (I - P_proj) @ W` that keeps just the subspace-removal
term and drops the injection. Only the projection `P` is trained (no injection matrix `R`), so the adapter is not an
identity at initialization. PaRa was introduced for personalizing text-to-image diffusion models and is available here
as a special case of DEFT.

DEFT is currently implemented for `torch.nn.Linear` and `Conv1D` (e.g. gpt-2, via `fan_in_fan_out`) layers. The original implementation and the experiments from the
paper (Dreambooth, Dreambench Plus, InsDet, VisualCloze, on Stable Diffusion and a unified model) are available at
[github.com/MAXNORM8650/DEFT](https://github.com/MAXNORM8650/DEFT).

If you use DEFT in your work, please cite the paper:

```bibtex
@article{kumar2026deft,
  title={DEFT: Decompositional Efficient Fine-Tuning for Text-to-Image Models},
  author={Kumar, Komal and Anwer, Rao and Shahbaz Khan, Fahad and Khan, Salman and Laptev, Ivan and Cholakkal, Hisham},
  journal={Advances in Neural Information Processing Systems},
  volume={38},
  pages={102009--102035},
  year={2026}
}
```

If you use the PaRa variant (`para=True`), please also cite:

```bibtex
@inproceedings{chen2025personalizing,
  title={Para: Personalizing text-to-image diffusion via parameter rank reduction},
  author={Chen, Shangyu and Pan, Zizheng and Cai, Jianfei and Phung, Dinh},
  booktitle={International Conference on Learning Representations},
  year={2025}
}
```

## DeftConfig

[[autodoc]] tuners.deft.config.DeftConfig

## DeftModel

[[autodoc]] tuners.deft.model.DeftModel