  We introduce a new technique called Drapes to enhance the sensitivity in
searches for new physics at the LHC. By training diffusion models on side-band
data, we show how background templates for the signal region can be generated
either directly from noise, or by partially applying the diffusion process to
existing data. In the partial diffusion case, data can be drawn from side-band
regions, with the inverse diffusion performed for new target conditional
values, or from the signal region, preserving the distribution over the
conditional property that defines the signal region. We apply this technique to
the hunt for resonances using the LHCO di-jet dataset, and achieve
state-of-the-art performance for background template generation using high
level input features. We also show how Drapes can be applied to low level
inputs with jet constituents, reducing the model dependence on the choice of
input observables. Using jet constituents we can further improve sensitivity to
the signal process, but observe a loss in performance where the signal
significance before applying any selection is below 4$\sigma$.
