Instructions to use pradhaansbhat/Thinking-In-Boxes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pradhaansbhat/Thinking-In-Boxes with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("pradhaansbhat/Thinking-In-Boxes", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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license: cc-by-4.0
language:
- en
base_model:
- black-forest-labs/FLUX.1-Kontext-dev
pipeline_tag: image-to-image
tags:
- Generative Modeling
- Image Editing
- Geometric Editing
- 3D Vision
datasets:
- pradhaansbhat/Thinking-In-Boxes
library_name: diffusers
---
# [NeurIPS-2026] Thinking in Boxes: 3D Editing in Real Images Made Easy
<div>
<img src="assets/thumbnail.png">
</div>
[**Pradhaan S Bhat**](https://pradhaansbhat.github.io/)<sup>1</sup><sup>∗</sup> · [**Naveen Chandra R**](https://www.linkedin.com/in/naveen-chandra-r-7230aa192)<sup>1</sup><sup>∗</sup> · [**Rishubh Parihar**](https://rishubhpar.github.io/)<sup>1</sup> · [**Vaibhav Vavilala**](https://www.linkedin.com/in/vaibhav-vavilala)<sup>2</sup> · [**R. Venkatesh Babu**](https://cds.iisc.ac.in/faculty/venky/)<sup>1</sup> · [**D.A. Forsyth**](http://luthuli.cs.uiuc.edu/~daf/) · [**Anand Bhattad**](https://anandbhattad.github.io/)<sup>4</sup>
<sup>1</sup> Indian Institute of Science
<sup>2</sup> Apple
<sup>3</sup> UIUC
<sup>4</sup> Johns Hopkins University
<sup>∗</sup> Equal Contribution
[](https://arxiv.org/abs/2606.20556)
[](https://thinking-in-boxes.github.io)
[](https://github.com/PradhaanSBhat/Thinking-In-Boxes)
[](https://huggingface.co/datasets/pradhaansbhat/Thinking-In-Boxes)
[]()
<div>
<img src="assets/teaser.png">
</div>
This is the trained LoRA used in the paper **Thinking In Boxes: 3D Editing in Real Images Made Easy**.
Thinking-In-Boxes is an Image-to-Image Generative LoRA for `black-forest-labs/FLUX.1-Kontext-dev` trained for the task of Geometric Image Editing.
This LoRA is trained on the Thinking-In-Boxes dataset available [here](https://huggingface.co/datasets/pradhaansbhat/Thinking-In-Boxes). Details of training are available in the supplementary section of the paper.
## Usage with Diffusers 🧨
Please refer to the [GitHub Repository](https://github.com/PradhaanSBhat/Thinking-In-Boxes) on setup, inference and training.
## Citation
If you find our work useful, please consider citing:
```bibtex
@misc{bhat2026thinkingboxes3dediting,
title = {Thinking in Boxes: 3D Editing in Real Images Made Easy},
author = {Pradhaan S Bhat and Naveen Chandra R and Rishubh Parihar and Vaibhav Vavilala and R. Venkatesh Babu and D. A. Forsyth and Anand Bhattad},
year = {2026},
eprint = {2606.20556},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2606.20556}
}
```
---
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