Instructions to use jaayeon/AGSM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jaayeon/AGSM with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jaayeon/AGSM", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| 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} | |
| } | |
| ``` | |