title: No Training, No Problem: Rethinking Classifier-Free Guidance for
    Diffusion Models
summary: Classifier-free guidance (CFG) has become the standard method for enhancing
  the quality of conditional diffusion models. However, employing CFG requires
  either training an unconditional model alongside the main diffusion model or
  modifying the training procedure by periodically inserting a null condition.
  There is also no clear extension of CFG to unconditional models. In this paper,
  we revisit the core principles of CFG and introduce a new method, independent
  condition guidance (ICG), which provides the benefits of CFG without the need
  for any special training procedures. Our approach streamlines the training
  process of conditional diffusion models and can also be applied during
  inference on any pre-trained conditional model. Additionally, by leveraging the
  time-step information encoded in all diffusion networks, we propose an
  extension of CFG, called time-step guidance (TSG), which can be applied to any
  diffusion model, including unconditional ones. Our guidance techniques are easy
  to implement and have the same sampling cost as CFG. Through extensive
  experiments, we demonstrate that ICG matches the performance of standard CFG
  across various conditional diffusion models. Moreover, we show that TSG
  improves generation quality in a manner similar to CFG, without relying on any
  conditional information.
paper_authors._id: 6686334bb3b3b6dbf2ddce0d
paper_authors.name: Seyedmorteza Sadat
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-04T08:03:17.605Z
paper_authors._id: 63b4b02a103617b0a5b0ee2e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63b4b02a103617b0a5b0ee2e/LasBC-pCnPJ6XuCt6qqcp.jpeg
paper_authors.fullname: Seyedmorteza Sadat
paper_authors.type: user
paper_authors.user: msadat97
paper_authors._id: 6686334bb3b3b6dbf2ddce0e
paper_authors.name: Manuel Kansy
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-04T08:03:15.655Z
paper_authors._id: 66863bd107019f3fe48a21ab
paper_authors.avatarUrl: /avatars/3297e18e43d40e902b9554a077a34a8a.svg
paper_authors.fullname: Manuel Kansy
paper_authors.type: user
paper_authors.user: manuelkansy
paper_authors._id: 6686334bb3b3b6dbf2ddce0f
paper_authors.name: Otmar Hilliges
paper_authors._id: 6686334bb3b3b6dbf2ddce10
paper_authors.name: Romann M. Weber
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-04T12:15:30.196Z
paper_authors._id: 630f7646197cd3f24e7f8e9f
paper_authors.avatarUrl: /avatars/59bbd4ed38277b313051aac78f6808ac.svg
paper_authors.fullname: Romann Weber
paper_authors.type: user
paper_authors.user: RMW
paper_title: No Training, No Problem: Rethinking Classifier-Free Guidance for
    Diffusion Models
paper_summary: Classifier-free guidance (CFG) has become the standard method for enhancing
  the quality of conditional diffusion models. However, employing CFG requires
  either training an unconditional model alongside the main diffusion model or
  modifying the training procedure by periodically inserting a null condition.
  There is also no clear extension of CFG to unconditional models. In this paper,
  we revisit the core principles of CFG and introduce a new method, independent
  condition guidance (ICG), which provides the benefits of CFG without the need
  for any special training procedures. Our approach streamlines the training
  process of conditional diffusion models and can also be applied during
  inference on any pre-trained conditional model. Additionally, by leveraging the
  time-step information encoded in all diffusion networks, we propose an
  extension of CFG, called time-step guidance (TSG), which can be applied to any
  diffusion model, including unconditional ones. Our guidance techniques are easy
  to implement and have the same sampling cost as CFG. Through extensive
  experiments, we demonstrate that ICG matches the performance of standard CFG
  across various conditional diffusion models. Moreover, we show that TSG
  improves generation quality in a manner similar to CFG, without relying on any
  conditional information.
paper_ai_keywords: Classifier-free guidance (CFG) conditional diffusion models unconditional model null condition independent condition guidance (ICG) time-step information diffusion networks time-step guidance (TSG) generation quality