Diffusion Model Alignment Using Direct Preference Optimization
Paper • 2311.12908 • Published • 49
How to use softwareweaver/sdxl-dpo-Olive-Onnx with Diffusers:
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("softwareweaver/sdxl-dpo-Olive-Onnx", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Olive Optimized DirectML Onnx model for https://huggingface.co/mhdang/dpo-sdxl-text2image-v1 Created with the Olive Toolset https://github.com/microsoft/Olive
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. https://arxiv.org/abs/2311.12908
This model is being used by Fusion Quill - a Windows app that runs Stable Diffusion models locally. https://FusionQuill.AI