license: apache-2.0
datasets:
- yuvalkirstain/pickapic_v1
language:
- en
pipeline_tag: unconditional-image-generation
metrics:
- character
library_name: adapter-transformers
tags:
- not-for-all-audiences
Step-aware Preference Optimization: Aligning Preference with Denoising Performance at Each Step
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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/
Model Description
This model is fine-tuned from stable-diffusion-xl-base-1.0. It has been trained on 4,000 prompts for 10 epochs.
A quick example
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel
import torch
# load pipeline
inference_dtype = torch.float16
pipe = StableDiffusionXLPipeline.from_pretrained(
"SPO-Diffusion-Models/SPO-SDXL_4k-p_10ep",
torch_dtype=inference_dtype,
)
vae = AutoencoderKL.from_pretrained(
'madebyollin/sdxl-vae-fp16-fix',
torch_dtype=inference_dtype,
)
pipe.vae = vae
pipe.to('cuda')
generator=torch.Generator(device='cuda').manual_seed(42)
image = pipe(
prompt='a child and a penguin sitting in front of the moon',
guidance_scale=5.0,
generator=generator,
output_type='pil',
).images[0]
image.save('moon.png')
Citation
If you find our work or codebase useful, please consider giving us a star and citing our work.
@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}
}