  Diffusion models have obtained substantial progress in image-to-video (I2V)
generation. However, such models are not fully understood. In this paper, we
report a significant but previously overlooked issue in I2V diffusion models
(I2V-DMs), namely, conditional image leakage. I2V-DMs tend to over-rely on the
conditional image at large time steps, neglecting the crucial task of
predicting the clean video from noisy inputs, which results in videos lacking
dynamic and vivid motion. We further address this challenge from both inference
and training aspects by presenting plug-and-play strategies accordingly. First,
we introduce a training-free inference strategy that starts the generation
process from an earlier time step to avoid the unreliable late-time steps of
I2V-DMs, as well as an initial noise distribution with optimal analytic
expressions (Analytic-Init) by minimizing the KL divergence between it and the
actual marginal distribution to effectively bridge the training-inference gap.
Second, to mitigate conditional image leakage during training, we design a
time-dependent noise distribution for the conditional image, which favors high
noise levels at large time steps to sufficiently interfere with the conditional
image. We validate these strategies on various I2V-DMs using our collected
open-domain image benchmark and the UCF101 dataset. Extensive results
demonstrate that our methods outperform baselines by producing videos with more
dynamic and natural motion without compromising image alignment and temporal
consistency. The project page: \url{https://cond-image-leak.github.io/}.
