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--- |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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- DPO |
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- DiffusionDPO |
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inference: true |
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--- |
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# Aligned Diffusion Model via DPO |
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|
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Diffusion model aligned with the following reward models and Direct Preference Optimization (DPO) algorithm |
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``` |
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close-sourced vlm: claude3-opus gemini-1.5 gpt-4o gpt-4v |
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open-sourced vlm: internvl-1.5 |
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score model: hps-2.1 |
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``` |
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## How to Use |
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You can load the model and perform inference as follows: |
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```python |
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from diffusers import StableDiffusionPipeline, UNet2DConditionModel |
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pretrained_model_name = "runwayml/stable-diffusion-v1-5" |
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dpo_unet = UNet2DConditionModel.from_pretrained( |
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"path/to/checkpoint", |
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subfolder='unet', |
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torch_dtype=torch.float16 |
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).to('cuda') |
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pipeline = StableDiffusionPipeline.from_pretrained(pretrained_model_name, torch_dtype=torch.float16) |
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pipeline = pipeline.to('cuda') |
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pipeline.safety_checker = None |
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pipeline.unet = dpo_unet |
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generator = torch.Generator(device='cuda') |
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generator = generator.manual_seed(1) |
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prompt = "a pink flower" |
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image = pipeline(prompt=prompt, generator=generator, guidance_scale=gs).images[0] |
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``` |
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## Citation |
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``` |
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@misc{chen2024mjbenchmultimodalrewardmodel, |
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title={MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?}, |
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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}, |
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year={2024}, |
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eprint={2407.04842}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2407.04842}, |
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} |
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``` |
