In recent years, large-scale pre-trained diffusion models have demonstrated their outstanding capabilities in image and video generation tasks. However, existing models tend to produce visual objects commonly found in the training dataset, which diverges from user input prompts. The underlying reason behind the inaccurate generated results lies in the model's difficulty in sampling from specific intervals of the initial noise distribution corresponding to the prompt. Moreover, it is challenging to directly optimize the initial distribution, given that the diffusion process involves multiple denoising steps. In this paper, we introduce a Fine-tuning Initial Noise Distribution (FIND) framework with policy optimization, which unleashes the powerful potential of pre-trained diffusion networks by directly optimizing the initial distribution to align the generated contents with user-input prompts. To this end, we first reformulate the diffusion denoising procedure as a one-step Markov decision process and employ policy optimization to directly optimize the initial distribution. In addition, a dynamic reward calibration module is proposed to ensure training stability during optimization. Furthermore, we introduce a ratio clipping algorithm to utilize historical data for network training and prevent the optimized distribution from deviating too far from the original policy to restrain excessive optimization magnitudes. Extensive experiments demonstrate the effectiveness of our method in both text-to-image and text-to-video tasks, surpassing SOTA methods in achieving consistency between prompts and the generated content. Our method achieves 10 times faster than the SOTA approach.

Face anti-spoofing is a critical aspect of ensuring security in the context of human-robot interaction and collaboration. Recently, disentangled-based data augmentation methods have achieved great success in face anti-spoofing tasks. The underlying assumption of those methods is that the liveness information could be completely disentangled and the labeling of the augmented data could totally depend on the liveness-related feature branch. However, we observe that it is almost impossible to extract the liveness-related information completely, which makes the current labeling strategy inaccurate. In this paper, we rethink the disentangling process and propose a novel generative-based data augmentation framework without forcing liveness information encoded into any specific feature space. Specifically, the original images are decomposed into statistic feature space and spatial feature space with liveness information preserving. With these two feature spaces, synthesized liveness-preserving images are generated with the Cartesian product to further approach the distribution of real face anti-spoofing data. Along with the original samplings, the augmented data are fed to a ResNet-based classifier with our proposed pseudo-label strategy for liveness information augmentation. Both qualitative and quantitative experiments demonstrate promising results to show the effectiveness of our proposed method.