metadata
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:
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{chen2024mjbenchmultimodalrewardmodel,
title={MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?},
author={Zhaorun Chen and Yichao Du and Zichen Wen and Yiyang Zhou and Chenhang Cui and Zhenzhen Weng and Haoqin Tu and Chaoqi Wang and Zhengwei Tong and Qinglan Huang and Canyu Chen and Qinghao Ye and Zhihong Zhu and Yuqing Zhang and Jiawei Zhou and Zhuokai Zhao and Rafael Rafailov and Chelsea Finn and Huaxiu Yao},
year={2024},
eprint={2407.04842},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2407.04842},
}