BDBM uses a single masked noise-prediction network to model both forward and backward diffusion bridges between two image distributions, enabling bidirectional translation at roughly half the model cost.
Variational Flow Models: Flowing in Your Style
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abstract
We propose a systematic training-free method to transform the probability flow of a "linear" stochastic process characterized by the equation X_{t}=a_{t}X_{0}+\sigma_{t}X_{1} into a straight constant-speed (SC) flow, reminiscent of Rectified Flow. This transformation facilitates fast sampling along the original probability flow via the Euler method without training a new model of the SC flow. The flexibility of our approach allows us to extend our transformation to inter-convert two posterior flows of two distinct linear stochastic processes. Moreover, we can easily integrate high-order numerical solvers into the transformed SC flow, further enhancing the sampling accuracy and efficiency. Rigorous theoretical analysis and extensive experimental results substantiate the advantages of our framework. Our code is available at this [https://github.com/clarken92/VFM||link].
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Bidirectional Diffusion Bridge Models
BDBM uses a single masked noise-prediction network to model both forward and backward diffusion bridges between two image distributions, enabling bidirectional translation at roughly half the model cost.