# Text-to-Video

This is a quantized model for text-to-video conversion, which supports converting text descriptions into video content and has been processed by GGUF quantization to improve inference efficiency.

This is a text-to-video generation model. After GGUF conversion, it can be used in compatibility with ComfyUI-GGUF.

LTX-Video is a GGUF-quantized text-to-video model that supports generating video content from text or images.

LTX-Video is a text-to-video generation model that supports creating video content from text descriptions.

A LoRA adapter based on the Wan2.1-T2V-14B text-to-video model, specifically designed for generating video content in the style of ancient book illustrations

LoRA trained on the Wan2.1 14B T2V model, specializing in generating various creative tornado videos

TDM is a model that achieves efficient few-step diffusion through trajectory distribution matching technology, capable of generating high-quality videos within 4 inference steps, achieving 25x acceleration compared to the original model without performance loss.

LTX-Video is a text-to-video model quantized in GGUF format, supporting video generation from text or images.

LTX-Video model in Diffusers format, supporting text-to-video and image-to-video generation

A branch version of the video generation model optimized for Hugging Face Inference Endpoints, supporting text-to-video and image-to-video functionalities

Diffusers-based implementation of the LTX-Video model, supporting high-quality video generation from text or images

CogVideoX is an open-source video generation model originating from Qingying. The 2B version is an entry-level model, balancing compatibility with low operational and development costs.

Dynamic LoRAs can add specific types of motion effects to animations, such as zoom in/out, panning, tilting, and rotation.

This model combines large language models with video diffusion technology, supporting text-to-video generation and allowing control over video content through bounding box conditional input.

AnimateDiff is a technology that leverages existing Stable Diffusion text-to-image models to create videos by inserting motion module layers to achieve coherent motion between image frames.

Dynamic LoRAs can add specific types of motion effects to animations, such as zooming, panning, tilting, and rotation.