---
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- DDPO
inference: true
---

# Aligned Diffusion Model via DDPO

Diffusion Model aligned with the following reward models and Denoising Diffusion Policy Optimization (DDPO) algorithm
```
close-sourced vlm: claude3-opus  gpt-4o  gpt-4v
```

## How to Use

You can load the model and perform inference as follows:
```python
from diffusers import StableDiffusionPipeline, UNet2DConditionModel

pretrained_model_name = "runwayml/stable-diffusion-v1-5"
pipeline = StableDiffusionPipeline.from_pretrained(pretrained_model_name, torch_dtype=torch.float16)

lora_path = os.path.join(""path/to/checkpoint"")
pipeline.sd_pipeline.load_lora_weights(lora_path)
pipeline.sd_pipeline.to("cuda")

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

prompt = "a pink flower"

image = pipeline(prompt=prompt, generator=generator, guidance_scale=5).images[0]
```

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