Shortcut models enable high-quality single or few-step sampling in diffusion models with one network and training phase by conditioning on desired step size.
Stylegan-xl: Scaling stylegan to large diverse datasets
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Scaled vanilla autoregressive models based on Llama achieve 2.18 FID on ImageNet 256x256 image generation, beating popular diffusion models without visual inductive biases.
Improved consistency training techniques achieve FID scores of 2.51 on CIFAR-10 and 3.25 on ImageNet 64x64 in one sampling step, outperforming prior consistency training and distillation methods.
citing papers explorer
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One Step Diffusion via Shortcut Models
Shortcut models enable high-quality single or few-step sampling in diffusion models with one network and training phase by conditioning on desired step size.
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Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation
Scaled vanilla autoregressive models based on Llama achieve 2.18 FID on ImageNet 256x256 image generation, beating popular diffusion models without visual inductive biases.
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Improved Techniques for Training Consistency Models
Improved consistency training techniques achieve FID scores of 2.51 on CIFAR-10 and 3.25 on ImageNet 64x64 in one sampling step, outperforming prior consistency training and distillation methods.