Solving Inverse Problems with FLAIR
Flow-based latent generative models such as Stable Diffusion 3 are able to
generate images with remarkable quality, even enabling photorealistic
text-to-image generation. Their impressive performance suggests that these
models should also constitute powerful priors for inverse imaging problems, but
that approach has not yet led to comparable fidelity. There are several key
obstacles: (i) the encoding into a lower-dimensional latent space makes the
underlying (forward) mapping non-linear; (ii) the data likelihood term is
usually intractable; and (iii) learned generative models struggle to recover
rare, atypical data modes during inference. We present FLAIR, a novel training
free variational framework that leverages flow-based generative models as a
prior for inverse problems. To that end, we introduce a variational objective
for flow matching that is agnostic to the type of degradation, and combine it
with deterministic trajectory adjustments to recover atypical modes. To enforce
exact consistency with the observed data, we decouple the optimization of the
data fidelity and regularization terms. Moreover, we introduce a time-dependent
calibration scheme in which the strength of the regularization is modulated
according to off-line accuracy estimates. Results on standard imaging
benchmarks demonstrate that FLAIR consistently outperforms existing diffusion-
and flow-based methods in terms of reconstruction quality and sample diversity.
6840496b4f36daae2e47efcb
Julius Erbach
claimed_verified
2025-06-05T07:27:46.678Z
651ffc29261e2d84a261bb90
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Julius Erbach
user
juliuse
6840496b4f36daae2e47efcc
Dominik Narnhofer
admin_assigned
2025-06-05T12:59:23.740Z
674995c2d523f2743e6c3859
/avatars/41b60f776a41d85da9ea0b1ed7d7615b.svg
Dominik Narnhofer
user
dnarnhofer
6840496b4f36daae2e47efcd
Andreas Dombos
admin_assigned
2025-06-05T12:59:29.844Z
66f1afd751dca07c002dbd78
/avatars/de1262add2c455611f361a2bc8627e18.svg
Andreas Dombos
user
andreeee27
6840496b4f36daae2e47efce
Bernt Schiele
6840496b4f36daae2e47efcf
Jan Eric Lenssen
6840496b4f36daae2e47efd0
Konrad Schindler
admin_assigned
2025-06-05T12:59:43.140Z
6750649b7edd6a98a1bbcd06
/avatars/ba27f12d0333cf2d400d4405af7efe97.svg
Konrad Schindler
user
konradschindler
Solving Inverse Problems with FLAIR
Flow-based latent generative models such as Stable Diffusion 3 are able to
generate images with remarkable quality, even enabling photorealistic
text-to-image generation. Their impressive performance suggests that these
models should also constitute powerful priors for inverse imaging problems, but
that approach has not yet led to comparable fidelity. There are several key
obstacles: (i) the encoding into a lower-dimensional latent space makes the
underlying (forward) mapping non-linear; (ii) the data likelihood term is
usually intractable; and (iii) learned generative models struggle to recover
rare, atypical data modes during inference. We present FLAIR, a novel training
free variational framework that leverages flow-based generative models as a
prior for inverse problems. To that end, we introduce a variational objective
for flow matching that is agnostic to the type of degradation, and combine it
with deterministic trajectory adjustments to recover atypical modes. To enforce
exact consistency with the observed data, we decouple the optimization of the
data fidelity and regularization terms. Moreover, we introduce a time-dependent
calibration scheme in which the strength of the regularization is modulated
according to off-line accuracy estimates. Results on standard imaging
benchmarks demonstrate that FLAIR consistently outperforms existing diffusion-
and flow-based methods in terms of reconstruction quality and sample diversity.
FLAIR, a novel training-free variational framework, leverages flow-based generative models to enhance inverse problem solutions, achieving superior reconstruction quality and sample diversity.
flow-based latent generative models Stable Diffusion 3 inverse imaging problems variational framework flow matching deterministic trajectory adjustments data fidelity regularization terms time-dependent calibration scheme