Visual Generation Without Guidance
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.
67992c274c3dbd12f9c75abb
Huayu Chen
claimed_verified
2025-01-28T19:24:41.148Z
65571135bfb62d747abc8129
/avatars/5f4542daa34597f17e6280b9cce18c91.svg
Hugging
user
ChenDRAG
67992c274c3dbd12f9c75abc
Kai Jiang
67992c274c3dbd12f9c75abd
Kaiwen Zheng
claimed_verified
2025-02-25T09:41:04.468Z
652bf7edc3cba555d5673c6e
/avatars/78f6416c30203b30671f8423f061c657.svg
Kaiwen Zheng
user
worstcoder
67992c274c3dbd12f9c75abe
Jianfei Chen
admin_assigned
2025-01-28T19:43:24.216Z
65fcad0ba0d7adc40b54fac2
/avatars/7564b5642378fddb46ec3b5ae57c0402.svg
Jianfei Chen
user
surfingtomchen
67992c274c3dbd12f9c75abf
Hang Su
67992c274c3dbd12f9c75ac0
Jun Zhu
Visual Generation Without Guidance
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.
Guidance-Free Training (GFT) matches the performance of Classifier-Free Guidance (CFG) while reducing computational costs by eliminating guided sampling and allowing for training from scratch.
Classifier-Free Guidance Guidance-Free Training diffusion autoregressive masked-prediction FID scores diversity-fidelity trade-offs