Specifically, we observe that CLIP's visual encoder tends to

The link to our demo page is https://bit.ly/3ID5Zam. We present the objective metrics according to the Euler steps in the result section of the main paper. We measure the acoustic quality using 5-scale Mean Opinion Scores (MOS).

Our work proposes P-Flow, a fast and data-efficient zero-shot TTS model that uses speech prompts for speaker adaptation. P-Flow comprises a speech-prompted text encoder for speaker adaptation and a flow matching generative decoder for high-quality and fast speech synthesis.

Then, MP A denoises the learned prompts through an auto-encoding process and aligns them by maximizing the agreement of all the reconstructed prompts.

Usage Application The model is intended for open-vocabulary object detection.Known Caveats

These methodologies typically lead to performance bottlenecks and unsatisfactory generalizability.

Source code is made available at https://github.com/ymLeiFDU/LICO.

We then propose to employ layout, a very abstract and easy-to-obtain prior, as the spatial guidance for subject arrangement.

This method allows API users to easily generate, discover, and mix and match image concepts without prior knowledge of how to prompt the model.