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X-CLIP is an extended version of CLIP for general video-language understanding, trained on (video, text) pairs via contrastive learning, suitable for tasks like video classification and video-text retrieval.

The first DiT-based video generation model capable of real-time generation of high-quality videos, supporting two scenarios: text-to-video and image + text-to-video.

The GGUF format version of the Wan2.1-VACE-14B model, mainly used for text-to-video generation tasks.

Ultra-fast text-to-video model, generating videos over ten times faster than the original AnimateDiff

V-Express is an audio and facial keypoint condition-based video generation model capable of converting audio input into dynamic video output.

CogVideoX is the open-source version of the video generation model derived from Qingying, providing high-quality video generation capabilities.

LLaVA-NeXT-Video is an open-source multimodal chatbot that achieves excellent video understanding capabilities through mixed training on video and image data, reaching SOTA level among open-source models on the VideoMME benchmark.

Wan2.1 is a comprehensive open-source video foundation model designed to push the boundaries of video generation, supporting tasks such as text-to-video in Chinese and English, image-to-video, and more.

Wan 2.1 is a comprehensive open-source video foundation model featuring top-tier performance, consumer-grade GPU support, multi-task capabilities, visual-text generation, and efficient video VAE.

Wan 2.1 is a comprehensive open-source video foundation model capable of multiple tasks including text-to-video, image-to-video, video editing, text-to-image, and video-to-audio generation, with support for bilingual Chinese-English text input.