---
license: apache-2.0
library_name: diffusers
tags:
- text-to-image
- diffusion
- alignment
- score-matching
- soft-tokens
- stable-diffusion
base_model:
- stabilityai/stable-diffusion-3-medium-diffusers
- stable-diffusion-v1-5/stable-diffusion-v1-5
- stabilityai/stable-diffusion-xl-base-1.0
---

# Alignment-Guided Score Matching (AGSM)

This repository hosts the released AGSM soft-token checkpoints for:

- SD3: `sd3/soft_tokens.pth`, `sd3/soft_t_tokens.pth`
- SD1.5: `sd1.5/soft_tokens.pth`, `sd1.5/soft_t_tokens.pth`
- SDXL: `sdxl/soft_tokens.pth`, `sdxl/soft_t_tokens.pth`

AGSM is a lightweight, reward-free post-training method for improving text-image alignment in diffusion models.
It requires no external reward model, no full denoising rollout, and no \(x_0\) approximation.

## Usage

This repository contains AGSM token checkpoints, not the full base diffusion models.
The GitHub repository already includes the released checkpoints, so the simplest path is:

```bash
git clone https://github.com/jaayeon/AGSM.git
cd AGSM

DATADIR=/path/to/datasets \
MODEL=sd3 \
scripts/sample_coco.sh
```

If you want to use the Hugging Face copy instead, download it separately and point `CHECKPOINT_DIR` to the downloaded model folder:

```bash
huggingface-cli download jaayeon/AGSM --local-dir checkpoints/agsm

DATADIR=/path/to/datasets \
MODEL=sd3 \
CHECKPOINT_DIR=checkpoints/agsm/sd3 \
scripts/sample_coco.sh
```

Use `MODEL=sd1.5` with `CHECKPOINT_DIR=checkpoints/agsm/sd1.5`, or `MODEL=sdxl` with `CHECKPOINT_DIR=checkpoints/agsm/sdxl`.

Code, training scripts, and evaluation instructions are available at:
https://github.com/jaayeon/AGSM

Project page:
https://jaayeon.github.io/AGSM/

Paper:
https://arxiv.org/abs/2605.30038

## Citation

```bibtex
@article{lee2026alignment,
  title={Alignment-Guided Score Matching for Text-to-Image Alignment in Diffusion Models},
  author={Lee, Jaa-Yeon and Hong, Yeobin and Kwon, Taesung and Ye, Jong Chul},
  journal={arXiv preprint arXiv:2605.30038},
  year={2026}
}
```