Create README.md

README.md

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+# Diffusion Model Alignment Using Direct Preference Optimization
+
+
+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).
+
+This model is fine-tuned from [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) on offline human preference data [pickapic_v2](https://huggingface.co/datasets/yuvalkirstain/pickapic_v2).
+
+*Code and checkpoints for SDXL will come soon.*
+
+## A quick example
+```
+from diffusers import StableDiffusionPipeline, UNet2DConditionModel
+import torch
+import random
+
+# load pipeline
+model_id = "runwayml/stable-diffusion-v1-5"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+
+# load finetuned model
+unet_id = "mhdang/dpo-sd1.5-text2image-v1"
+unet = UNet2DConditionModel.from_pretrained(unet_id, subfolder="unet", torch_dtype=torch.float16)
+pipe.unet = unet
+pipe = pipe.to("cuda")
+
+prompt = "Two cats playing chess on a tree branch"
+image = pipe(prompt, guidance_scale=7.5).images[0].resize((512,512))
+    
+image.save("cats_playing_chess.png")
+```
+
+More details coming soon.