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--- |
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datasets: |
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- yuvalkirstain/pickapic_v2 |
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language: |
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- en |
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library_name: diffusers |
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pipeline_tag: text-to-image |
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--- |
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# Diffusion Model Alignment Using Direct Preference Optimization |
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![row01](01.png) |
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Direct Preference Optimization (DPO) for text-to-image diffusion models is a method to align diffusion models to text human preferences by directly optimizing on human comparison data. Please check our paper at [Diffusion Model Alignment Using Direct Preference Optimization](https://arxiv.org/abs/2311.12908). |
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This model is fine-tuned from [stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) on offline human preference data [pickapic_v2](https://huggingface.co/datasets/yuvalkirstain/pickapic_v2). |
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## Code |
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*Code will come soon!!!* |
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## SD1.5 |
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We also have a model finedtuned from [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) available at [dpo-sd1.5-text2image-v1](https://huggingface.co/mhdang/dpo-sd1.5-text2image-v1). |
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## A quick example |
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```python |
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from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel |
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import torch |
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# load pipeline |
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model_id = "stabilityai/stable-diffusion-xl-base-1.0" |
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pipe = StableDiffusionXLPipeline.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True).to("cuda") |
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# load finetuned model |
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unet_id = "mhdang/dpo-sdxl-text2image-v1" |
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unet = UNet2DConditionModel.from_pretrained(unet_id, subfolder="unet", torch_dtype=torch.float16) |
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pipe.unet = unet |
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pipe = pipe.to("cuda") |
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prompt = "Two cats playing chess on a tree branch" |
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image = pipe(prompt, guidance_scale=7.5).images[0].resize((512,512)) |
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image.save("cats_playing_chess.png") |
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``` |
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More details coming soon. |