README.md

Aligned Diffusion Model via DPO

Diffusion Model Aligned with thef following reward model and DPO algorithm

close-sourced vlm: claude3-opus  gemini-1.5  gpt-4o  gpt-4v
open-sourced vlm: internvl-1.5
score model: hps-2.1

How to Use

You can load the model and perform inference as follows:

from diffusers import StableDiffusionPipeline, UNet2DConditionModel

pretrained_model_name = "runwayml/stable-diffusion-v1-5"

dpo_unet = UNet2DConditionModel.from_pretrained(
        "path/to/checkpoint",
        subfolder='unet',
        torch_dtype=torch.float16
    ).to('cuda')

pipeline = StableDiffusionPipeline.from_pretrained(pretrained_model_name, torch_dtype=torch.float16)
pipeline = pipeline.to('cuda')
pipeline.safety_checker = None
pipeline.unet = dpo_unet

generator = torch.Generator(device='cuda')
generator = generator.manual_seed(1)

prompt = "a pink flower"

image = pipeline(prompt=prompt, generator=generator, guidance_scale=gs).images[0]

Citation

@misc{mjbench2024mjbench,
  title={MJ-BENCH: Is Your Multimodal Reward Model Really a Good Judge?},
  author={Zhaorun Chen*, Yichao Du*, Zichen Wen, Yiyang Zhou, Chenhang Cui, Zhenzhen Weng, Haoqin Tu, Chaoqi Wang, Zhengwei Tong, Leria HUANG, Canyu Chen, Qinghao Ye, Zhihong Zhu, Yuqing Zhang, Jiawei Zhou, Zhuokai Zhao, Rafael Rafailov, Chelsea Finn, Huaxiu Yao},
  year={2024}
}