Visual Generation Without Guidance
Authors: name: Huayu Chen, name: Kai Jiang, name: Kaiwen Zheng, name: Jianfei Chen, name: Hang Su, name: Jun Zhu

Classifier-Free Guidance (CFG) has been a default technique in various visual
generative models, yet it requires inference from both conditional and
unconditional models during sampling. We propose to build visual models that
are free from guided sampling. The resulting algorithm, Guidance-Free Training
(GFT), matches the performance of CFG while reducing sampling to a single
model, halving the computational cost. Unlike previous distillation-based
approaches that rely on pretrained CFG networks, GFT enables training directly
from scratch. GFT is simple to implement. It retains the same maximum
likelihood objective as CFG and differs mainly in the parameterization of
conditional models. Implementing GFT requires only minimal modifications to
existing codebases, as most design choices and hyperparameters are directly
inherited from CFG. Our extensive experiments across five distinct visual
models demonstrate the effectiveness and versatility of GFT. Across domains of
diffusion, autoregressive, and masked-prediction modeling, GFT consistently
achieves comparable or even lower FID scores, with similar diversity-fidelity
trade-offs compared with CFG baselines, all while being guidance-free. Code
will be available at https://github.com/thu-ml/GFT.