  Recently, conditional diffusion models have gained popularity in numerous
applications due to their exceptional generation ability. However, many
existing methods are training-required. They need to train a time-dependent
classifier or a condition-dependent score estimator, which increases the cost
of constructing conditional diffusion models and is inconvenient to transfer
across different conditions. Some current works aim to overcome this limitation
by proposing training-free solutions, but most can only be applied to a
specific category of tasks and not to more general conditions. In this work, we
propose a training-Free conditional Diffusion Model (FreeDoM) used for various
conditions. Specifically, we leverage off-the-shelf pre-trained networks, such
as a face detection model, to construct time-independent energy functions,
which guide the generation process without requiring training. Furthermore,
because the construction of the energy function is very flexible and adaptable
to various conditions, our proposed FreeDoM has a broader range of applications
than existing training-free methods. FreeDoM is advantageous in its simplicity,
effectiveness, and low cost. Experiments demonstrate that FreeDoM is effective
for various conditions and suitable for diffusion models of diverse data
domains, including image and latent code domains.
