No Training, No Problem: Rethinking Classifier-Free Guidance for
  Diffusion Models
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.
6686334bb3b3b6dbf2ddce0d
Seyedmorteza Sadat
claimed_verified
2024-07-04T08:03:17.605Z
63b4b02a103617b0a5b0ee2e
https://cdn-avatars.huggingface.co/v1/production/uploads/63b4b02a103617b0a5b0ee2e/LasBC-pCnPJ6XuCt6qqcp.jpeg
Seyedmorteza Sadat
user
msadat97
6686334bb3b3b6dbf2ddce0e
Manuel Kansy
claimed_verified
2024-07-04T08:03:15.655Z
66863bd107019f3fe48a21ab
/avatars/3297e18e43d40e902b9554a077a34a8a.svg
Manuel Kansy
user
manuelkansy
6686334bb3b3b6dbf2ddce0f
Otmar Hilliges
6686334bb3b3b6dbf2ddce10
Romann M. Weber
admin_assigned
2024-07-04T12:15:30.196Z
630f7646197cd3f24e7f8e9f
/avatars/59bbd4ed38277b313051aac78f6808ac.svg
Romann Weber
user
RMW
No Training, No Problem: Rethinking Classifier-Free Guidance for
  Diffusion Models
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.
Independent condition guidance and time-step guidance enhance diffusion model performance without special training or conditional information.
classifier-free guidance conditional diffusion models independent condition guidance time-step guidance diffusion networks