title: Visual Generation Without Guidance
summary: 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.
paper_authors._id: 67992c274c3dbd12f9c75abb
paper_authors.name: Huayu Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-28T19:24:41.148Z
paper_authors._id: 65571135bfb62d747abc8129
paper_authors.avatarUrl: /avatars/5f4542daa34597f17e6280b9cce18c91.svg
paper_authors.fullname: Hugging
paper_authors.type: user
paper_authors.user: ChenDRAG
paper_authors._id: 67992c274c3dbd12f9c75abc
paper_authors.name: Kai Jiang
paper_authors._id: 67992c274c3dbd12f9c75abd
paper_authors.name: Kaiwen Zheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-25T09:41:04.468Z
paper_authors._id: 652bf7edc3cba555d5673c6e
paper_authors.avatarUrl: /avatars/78f6416c30203b30671f8423f061c657.svg
paper_authors.fullname: Kaiwen Zheng
paper_authors.type: user
paper_authors.user: worstcoder
paper_authors._id: 67992c274c3dbd12f9c75abe
paper_authors.name: Jianfei Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-28T19:43:24.216Z
paper_authors._id: 65fcad0ba0d7adc40b54fac2
paper_authors.avatarUrl: /avatars/7564b5642378fddb46ec3b5ae57c0402.svg
paper_authors.fullname: Jianfei Chen
paper_authors.type: user
paper_authors.user: surfingtomchen
paper_authors._id: 67992c274c3dbd12f9c75abf
paper_authors.name: Hang Su
paper_authors._id: 67992c274c3dbd12f9c75ac0
paper_authors.name: Jun Zhu
paper_title: Visual Generation Without Guidance
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Classifier-Free Guidance Guidance-Free Training diffusion autoregressive masked-prediction FID scores diversity-fidelity trade-offs