Generating photos satisfying multiple constraints find broad utility in the
content creation industry. A key hurdle to accomplishing this task is the need
for paired data consisting of all modalities (i.e., constraints) and their
corresponding output. Moreover, existing methods need retraining using paired
data across all modalities to introduce a new condition. This paper proposes a
solution to this problem based on denoising diffusion probabilistic models
(DDPMs). Our motivation for choosing diffusion models over other generative
models comes from the flexible internal structure of diffusion models. Since
each sampling step in the DDPM follows a Gaussian distribution, we show that
there exists a closed-form solution for generating an image given various
constraints. Our method can unite multiple diffusion models trained on multiple
sub-tasks and conquer the combined task through our proposed sampling strategy.
We also introduce a novel reliability parameter that allows using different
off-the-shelf diffusion models trained across various datasets during sampling
time alone to guide it to the desired outcome satisfying multiple constraints.
We perform experiments on various standard multimodal tasks to demonstrate the
effectiveness of our approach. More details can be found in
this https URL

本文研究了使用扩散模型生成图片以满足多种限制条件的问题。我们提出了一种基于去噪扩散概率模型的解决方案，该方法能够统一多个扩散模型，并引入一种新的可靠性参数，使得可以在采样时仅使用不同数据集上训练的现成模型来指导并满足多种限制条件的任务。该方法在多种标准的多模态任务上表现出了有效性。