Step-aware Preference Optimization: Aligning Preference with Denoising Performance at Each Step

Zhanhao Liang, Yuhui Yuan, Shuyang Gu, Bohan Chen, Tiankai Hang, Ji Li, Liang Zheng

This is the official implementation of SPO, introduced in Step-aware Preference Optimization: Aligning Preference with Denoising Performance at Each Step.

News

2024.07.10 Release the training code of SPO.

2024.06.20 Release the SD v1.5 checkpoint and inference code.

2024.06.07 Release the SDXL checkpoint and inference code.

Abstract

Recently, Direct Preference Optimization (DPO) has extended its success from aligning large language models (LLMs) to aligning text-to-image diffusion models with human preferences. Unlike most existing DPO methods that assume all diffusion steps share a consistent preference order with the final generated images, we argue that this assumption neglects step-specific denoising performance and that preference labels should be tailored to each step's contribution.

To address this limitation, we propose Step-aware Preference Optimization (SPO), a novel post-training approach that independently evaluates and adjusts the denoising performance at each step, using a step-aware preference model and a step-wise resampler to ensure accurate step-aware supervision. Specifically, at each denoising step, we sample a pool of images, find a suitable win-lose pair, and, most importantly, randomly select a single image from the pool to initialize the next denoising step. This step-wise resampler process ensures the next win-lose image pair comes from the same image, making the win-lose comparison independent of the previous step. To assess the preferences at each step, we train a separate step-aware preference model that can be applied to both noisy and clean images.

Our experiments with Stable Diffusion v1.5 and SDXL demonstrate that SPO significantly outperforms the latest Diffusion-DPO in aligning generated images with complex, detailed prompts and enhancing aesthetics, while also achieving more than 20× times faster in training efficiency. Code and model: https://rockeycoss.github.io/spo.github.io/

Method Overview

TODO

• Release training code
• Release checkpoints and inference code
• Initialization

Gallery

🔧 Installation

1. Pull the Docker Image

sudo docker pull rockeycoss/spo:v1

2. Login to wandb

wandb login {Your wandb key}

3. (Optional) To customize the location for saving models downloaded from Hugging Face, you can use the following command:

export HUGGING_FACE_CACHE_DIR=/path/to/your/cache/dir

🔧 Inference Hugging Face Checkpoints

SDXL inference

PYTHONPATH=$(pwd) python inference_scripts/inference_spo_sdxl.py

SD v1.5 inference

PYTHONPATH=$(pwd) python inference_scripts/inference_spo_sd-v1-5.py

🔧 Training

The following scripts assume the use of four 80GB A100 GPUs for fine-tuning, as described in the paper.

Before fine-tuning, please download the checkpoints of step-aware preference models. You can do this by following these steps:

sudo apt update
sudo apt install wget

mkdir model_ckpts
cd model_ckpts

wget https://huggingface.co/SPO-Diffusion-Models/Step-Aware_Preference_Models/resolve/main/sd-v1-5_step-aware_preference_model.bin

wget https://huggingface.co/SPO-Diffusion-Models/Step-Aware_Preference_Models/resolve/main/sdxl_step-aware_preference_model.bin

cd ..

To fine-tune SD v1.5, you can use the following command:

PYTHONPATH=$(pwd) accelerate launch --config_file accelerate_cfg/1m4g_fp16.yaml train_scripts/train_spo.py --config configs/spo_sd-v1-5_4k-prompts_num-sam-4_10ep_bs10.py

To fine-tune SDXL, you can use the following command:

PYTHONPATH=$(pwd) accelerate launch --config_file accelerate_cfg/1m4g_fp16.yaml train_scripts/train_spo_sdxl.py --config configs/spo_sdxl_4k-prompts_num-sam-2_3-is_10ep_bs2_gradacc2.py

🔓 Available Checkpoints

SPO-SDXL_4k-prompts_10-epochs

SPO-SDXL_4k-prompts_10-epochs_LoRA

SPO-SD-v1-5_4k-prompts_10-epochs

SPO-SD-v1-5_4k-prompts_10-epochs_LoRA

Acknowledgement

Our codebase references the code from Diffusers, D3PO and PickScore. We extend our gratitude to their authors for open-sourcing their code.

📬 Citation

If you find this code useful in your research, please consider citing:

@article{liang2024step,
  title={Step-aware Preference Optimization: Aligning Preference with Denoising Performance at Each Step},
  author={Liang, Zhanhao and Yuan, Yuhui and Gu, Shuyang and Chen, Bohan and Hang, Tiankai and Li, Ji and Zheng, Liang},
  journal={arXiv preprint arXiv:2406.04314},
  year={2024}
}