#zero-shot

This zero-shot video editing framework uses pre-trained diffusion models for text-based modifications, preserving video structure and motion by using intermediate attention maps. It enhances consistency with spatial-temporal attention, offering style and attribute changes in videos. The method allows for shape-aware adjustments, as demonstrated through empirical evaluations.

Diff-HierVC is an advanced voice conversion system utilizing diffusion models to enhance pitch accuracy and speaker adaptation. Featuring DiffPitch and DiffVoice components, it achieves precise F0 generation and effective voice style transfer. The system incorporates a source-filter encoder and a data-driven Mel-spectrogram prior to boost conversion quality. In zero-shot adaptation scenarios, it delivers a 0.83% CER and 3.29% EER, offering versatile solutions for voice conversion challenges across diverse datasets.

P-Flow utilizes a speech-prompted text encoder and flow matching generative decoder for efficient zero-shot TTS, achieving notable speaker adaptation and synthesis speed improvements compared to large-scale models. Trained on the LibriTTS dataset, P-Flow maintains high speaker similarity and pronunciation quality.

Overeasy enables the creation of custom computer vision solutions with zero-shot models, supporting tasks like bounding box detection, classification, and segmentation without extensive datasets. The tool offers easy installation and features robust agents and execution graphs to facilitate the management and visualization of image processing workflows.

ViTamin offers scalable vision models that excel in zero-shot ImageNet accuracy and open-vocabulary segmentation. It integrates with platforms like Hugging Face and timm, supporting applications like pre-training and detection. By using fewer parameters, ViTamin achieves high benchmark performances, contributing to advances in vision-language AI research.

This project presents an innovative approach to zero-shot object-level image customization, allowing image personalization without large datasets. Key features include availability of training and inference code, online demo support on platforms such as ModelScope and Hugging Face, and development of robust models for applications like virtual try-on and face swapping. The installation is facilitated via Conda or Pip, utilizing the ControlNet framework, with community contributions enhancing its capabilities. It targets simplifying intricate image generation tasks, providing a vital tool for contemporary image processing.