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https://github.com/blurgyy/compass

[ICCV 2025] Enhancing spatial understanding in text-to-Image diffusion models

https://github.com/blurgyy/compass

Science Score: 36.0%

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Keywords

diffusion generation spatial-understanding t2i text-to-image
Last synced: 5 months ago · JSON representation

Repository

[ICCV 2025] Enhancing spatial understanding in text-to-Image diffusion models

Basic Info
  • Host: GitHub
  • Owner: blurgyy
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage: https://compass.blurgy.xyz
  • Size: 721 KB
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  • Stars: 54
  • Watchers: 5
  • Forks: 5
  • Open Issues: 2
  • Releases: 0
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diffusion generation spatial-understanding t2i text-to-image
Created about 1 year ago · Last pushed 6 months ago
Metadata Files
Readme License

README.md

CoMPaSS: Enhancing Spatial Understanding in Text-to-Image Diffusion Models

[Project Page] [arXiv]

Gaoyang Zhang, Bingtao Fu, Qingnan Fan, Qi Zhang, Runxing Liu, Hong Gu, Huaqi Zhang, Xinguo Liu
ICCV 2025

TL; DR

CoMPaSS enhances the spatial understanding of existing text-to-image diffusion models, enabling them to generate images that faithfully reflect spatial configurations specified in the text prompt.

teaser

Setting up Environment

We manage our python environment with uv, and provide a convenient script for setting up the environment at setup_env.sh. Running this script will create a subdirectory .venv/ in the project root. To enable it, run source .venv/bin/activate after the environment is set up:

```bash

install requirements into .venv/

bash ./setup_env.sh

activate the environment

source .venv/bin/activate ```

Trying out CoMPaSS

[!NOTE] For training, SCOP and TENOR are both required.
For generating images from text, only TENOR and the reference weights are needed.

Reference Weights

We provide the reference weights used to report all metrics in our paper on Hugging Face 🤗. We recommend trying out the FLUX.1-dev weights as it is a Rank-16 LoRA which is only 50MB in size.

| Model | Link | |:-----:|:-----:| | FLUX.1-dev | https://huggingface.co/blurgy/CoMPaSS-FLUX.1 | | SD1.4 | https://huggingface.co/blurgy/CoMPaSS-SD1.4 | | SD1.5 | https://huggingface.co/blurgy/CoMPaSS-SD1.5 | | SD2.1 | https://huggingface.co/blurgy/CoMPaSS-SD2.1 |

The SCOP dataset

We provide full instructions for replicating the SCOP dataset (28,028 object pairs among 15,426 images) in the SCOP directory. Check out its README to get started.

The TENOR Module

We provide both training and inference instructions for using our TENOR module in the TENOR directory. MMDiT-based models (e.g., FLUX.1-dev) and UNet-based models (e.g., SD1.5) are both supported. Check out their respective instructions to get started: - Instructions for FLUX.1-dev - Instructions for SD1.4, SD1.5, and SD2.1

Citation

bibtex @inproceedings{zhang2025compass, title={CoMPaSS: Enhancing Spatial Understanding in Text-to-Image Diffusion Models}, author={Zhang, Gaoyang and Fu, Bingtao and Fan, Qingnan and Zhang, Qi and Liu, Runxing and Gu, Hong and Zhang, Huaqi and Liu, Xinguo}, booktitle={ICCV}, year={2025} }

Owner

  • Name: Gaoyang Zhang
  • Login: blurgyy
  • Kind: user
  • Company: Zhejiang University

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