SDXL Turbo
sdxl-turbo-a5eb5aec·2 events·first seen 28d agoAliases: SDXL Turbo, SDXL-Turbo
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Accelerating SD Turbo and SDXL Turbo Inference with ONNX Runtime and Olive
This Hugging Face blog post details how to accelerate Stable Diffusion Turbo and SDXL Turbo inference using ONNX Runtime and Microsoft's Olive optimization toolkit. The post covers the workflow for converting and optimizing diffusion models for faster deployment. This is a practical inference optimization guide targeting practitioners deploying image generation models.
Drifting Preference Optimization (DrPO) for One-Step Text-to-Image Generators
DrPO is a new online preference fine-tuning method designed specifically for deterministic one-step text-to-image generators like SD-Turbo and SDXL-Turbo, which are difficult to align with standard RLHF methods that require policy likelihoods or differentiable reward gradients. The method samples candidates per prompt, ranks them with a target reward, and synthesizes a feature-space update direction via a non-parametric dipole preference field plus a reference drift from the frozen base model. Because the reward is used only for ranking, DrPO supports black-box and non-differentiable reward functions while keeping inference as a single forward pass. Evaluations on HPSv3 and GenEval show improved alignment over reward-gradient-free baselines and a 3.51× reduction in training compute by eliminating reward-model backpropagation.