# Text-to-Video

Viddo AI is an AI-powered video generator that converts text or images into stunning videos. It features multiple functions including text-to-video, image-to-video, and more, making it a must-have tool for endless creativity.

Vexub is a tool that uses artificial intelligence technology to quickly generate high-quality videos. It helps users convert text and audio materials into beautiful video works, improving video production efficiency. It is suitable for individual creators and business users. Vexub aims to provide simple and easy-to-use video creation tools, making creation easier and more efficient. The pricing includes Basic Edition, Professional Edition, and Enterprise Edition to flexibly meet different user needs.

PixVerse-MCP is a tool that allows users to access PixVerse's latest video generation models through applications that support the Model Context Protocol (MCP). This product offers features such as text-to-video generation and is suitable for creators and developers to generate high-quality videos anywhere. The PixVerse platform requires API credits, which users need to purchase themselves.

Vidu Q1, launched by Shengshu Technology, is a domestically produced video generation large language model designed for video creators. It supports high-definition 1080p video generation and features cinematic camera movements and start/end frame functionality. This product ranked first in the VBench-1.0 and VBench-2.0 evaluations, offering exceptional value for money at only one-tenth the price of competitors. It is suitable for film, advertising, animation, and other fields, significantly reducing production costs and improving creative efficiency.

Wan 2.1 AI is an open-source large-scale video generation AI model developed by Alibaba. It supports text-to-video (T2V) and image-to-video (I2V) generation, capable of transforming simple input into high-quality video content. This model is significant in the field of video generation, greatly simplifying the video creation process, lowering the creation threshold, improving creation efficiency, and providing users with a wide variety of video creation possibilities. Its main advantages include high-quality video generation effects, smooth presentation of complex actions, realistic physical simulation, and rich artistic styles. Currently, this product is fully open-source, and users can use its basic functions for free. It is highly practical for individuals and businesses that have video creation needs but lack professional skills or equipment.

Wan2GP is an improved version based on Wan2.1, aiming to provide an efficient and low-memory video generation solution for low-configuration GPU users. The model, through optimized memory management and accelerated algorithms, enables ordinary users to quickly generate high-quality video content on consumer-grade GPUs. It supports multiple tasks, including text-to-video, image-to-video, and video editing, and features a powerful video VAE architecture capable of efficiently handling 1080P videos. The emergence of Wan2GP lowers the barrier to entry for video generation technology, allowing more users to easily learn and apply it to real-world scenarios.

Wan2.1-T2V-14B is an advanced text-to-video generation model based on a diffusion transformer architecture, incorporating innovative spatiotemporal variational autoencoders (VAEs) and large-scale data training. It generates high-quality video content at various resolutions, supports both Chinese and English text input, and surpasses existing open-source and commercial models in performance and efficiency. This model is suitable for scenarios requiring efficient video generation, such as content creation, advertising production, and video editing. Currently, this model is freely available on the Hugging Face platform to promote the development and application of video generation technology.

FlashVideo is a deep learning model focused on efficient, high-resolution video generation. Its staged generation strategy first creates a low-resolution video, which is then enhanced to high resolution using an upscaling model. This approach significantly reduces computational costs while maintaining detail. This technology holds significant promise for video generation, especially in scenarios requiring high-quality visual content. FlashVideo is suitable for a variety of applications, including content creation, advertising production, and video editing. Its open-source nature allows researchers and developers to customize and extend its functionality.

STAR is an innovative video super-resolution technology that addresses the issue of over-smoothing found in traditional GAN methods by combining text-to-video diffusion models with video super-resolution. This technology not only recovers video details but also maintains temporal and spatial consistency, making it suitable for various real-world video scenarios. STAR was jointly developed by Nanjing University and ByteDance, boasting high academic value and application prospects.

This is a video variational autoencoder (VAE) designed to reduce video redundancy and facilitate efficient video generation. The model extends image VAE to 3D VAE, discovering that this results in motion blur and detail distortion, prompting the introduction of time-aware spatial compression for better encoding and decoding of spatial information. Additionally, the model incorporates a lightweight motion compression model for further temporal compression. By utilizing inherent textual information from text-to-video datasets and incorporating text guidance into the model, it significantly enhances reconstruction quality, particularly in detail retention and temporal stability. The model also improves its versatility through joint training on images and videos, enhancing both reconstruction quality and capabilities for auto-encoding images and videos. Extensive evaluations indicate that this approach outperforms recent strong baselines.

Pollo AI is an innovative AI video generator that allows users to effortlessly create stunning videos. Users can quickly generate videos with specific styles and content by using simple text prompts or static images. Pollo AI stands out due to its user-friendly interface, a wide range of customization options, and high-quality output, making it a preferred choice for both beginners and experienced creators. It supports not only text-to-video generation but also creates videos based on image content and user needs, featuring various templates including an AI-powered hugging video generator that easily produces heartwarming hug videos. With its rapid video generation capabilities, high-quality output, and ease of use without technical video editing skills, Pollo AI offers users limitless creative possibilities.

ConsisID is a frequency decomposition-based identity-preserving text-to-video generation model that generates high-fidelity videos consistent with the input textual descriptions using identity control signals in the frequency domain. This model does not require tedious fine-tuning for different cases and is capable of maintaining consistency in character identity within the generated videos. The introduction of ConsisID advances video generation technology, particularly in terms of streamlined processes and frequency-aware identity preservation control schemes.

Allegro-TI2V is a text-to-image-to-video generation model that creates video content based on user-provided prompts and images. The model is recognized for its open-source nature, diverse content creation capabilities, high-quality outputs, compact efficient model parameters, and support for various precision and GPU memory optimizations. It represents cutting-edge advancements in AI technology for video generation, holding significant technical value and commercial application potential. The Allegro-TI2V model is available on the Hugging Face platform under the Apache 2.0 open-source license, allowing users to download and use it for free.

Viral Video is an online platform that uses artificial intelligence technology to help users quickly create viral videos. It simplifies the video production process through features like text-to-video conversion, text-to-speech conversion, AI video editing, and AI scene generation, reducing costs and enhancing the attractiveness and dissemination potential of videos. The platform is particularly suitable for content creators, marketers, and social media operators, helping them produce high-quality video content faster and at a lower cost to gain more attention and engagement on social media.

Mochi 1 is an open-source video generation model introduced by Genmo as a research preview version, aiming to address fundamental issues in the current AI video landscape. The model is renowned for its unparalleled motion quality, exceptional prompt-following capabilities, and its ability to bridge the uncanny valley, generating coherent and fluid human actions and expressions. Mochi 1 was developed in response to the growing demand for high-quality video content, particularly in the gaming, film, and entertainment industries. A free trial is currently available, though detailed pricing information is not provided on the page.

Dream Machine API is a creative intelligence platform that offers a range of advanced video generation models. With intuitive APIs and open-source SDKs, users can build and expand creative AI products. The platform features capabilities such as text-to-video, image-to-video, keyframe control, expansion, looping, and camera control, designed to help users collaborate with creative intelligence to create better content. The launch of Dream Machine API aims to enrich visual exploration and creativity, allowing more ideas to be tested, better narratives to be constructed, and enabling diverse stories to be told by those who previously found it difficult.

DreamCloud AI is an innovative Text-to-Video AIGC creation platform that leverages artificial intelligence technology to enable users to effortlessly generate high-quality video content. The platform offers functionalities like text-to-video, dynamic visuals generation, and AI character generation, while ensuring consistency between characters and scenes, greatly enhancing the diversity and professionalism of video creation.

Vidful.ai is an AI-powered online video generator that utilizes advanced algorithms to quickly convert text and images into high-quality video content. The product integrates technologies from Kuaishou's Kling AI and Luma AI Dream Machine, offering realistic motion effects and cinematic-level video quality, simplifying the video production process so users can create professional videos without specialized editing skills. Vidful.ai is available for free online, catering to users across various fields including marketing, education, social media creation, and e-commerce.

CogVideo is a text-to-video generation model developed by a team at Tsinghua University, which leverages deep learning technology to convert text descriptions into video content. This technology holds extensive prospects for applications in video content creation, education, entertainment, and more. With large-scale pre-training, the CogVideo model can generate videos that align with the text description, providing a novel automated approach to video production.

CogVideoX is an open-source video generation model developed by a team from Tsinghua University. It supports generating videos from text descriptions and offers various models, including entry-level options and larger models, to meet different quality and cost requirements. The model supports multiple precisions, including FP16 and BF16, and it is recommended to use the same precision as during model training for inference. The CogVideoX-5B model is particularly suited for scenarios requiring the generation of high-quality video content, such as filmmaking, game development, and advertising creativity.

Dream Machine AI is an AI platform that leverages cutting-edge technology to convert text and images into high-quality videos. Powered by Luma AI, it utilizes advanced transformation models to quickly generate physically accurate and coherent video content with complex spatiotemporal motion. Key advantages include high generation speed, realistic and coherent motion, strong character consistency, and natural camera movement. The product is positioned to provide a fast and efficient video generation solution for video creators and content producers.

CogVideoX is an open-source video generation model that shares lineage with commercial models, enabling the generation of video content through textual descriptions. It represents the latest advancements in text-to-video generation technology, capable of producing high-quality videos applicable in various fields including entertainment, education, and commercial promotion.

AsyncDiff is a method for accelerating diffusion models through asynchronous denoising parallelization. It divides the noise prediction model into multiple components and distributes them across different devices, enabling parallel processing. This approach significantly reduces inference latency while having a minimal impact on generation quality. AsyncDiff supports a variety of diffusion models, including Stable Diffusion 2.1, Stable Diffusion 1.5, Stable Diffusion x4 Upscaler, Stable Diffusion XL 1.0, ControlNet, Stable Video Diffusion, and AnimateDiff.

Developed by Kuaishou Technology, Kling AI is a text-to-video generation model that can produce highly realistic videos based on text prompts. It boasts efficient video generation capabilities, generating up to 2 minutes of 30 frames-per-second video, along with advanced technologies like a 3D Spatiotemporal Joint Attention mechanism and physics-world simulation, giving it a significant competitive edge in the AI video generation field.

The ShareGPT4Video series aims to promote video understanding in large video-language models (LVLMs) and video generation in text-to-video models (T2VMs) through dense and precise captions. The series includes: 1) ShareGPT4Video, a dense video caption dataset of 40K GPT4V annotations, developed through carefully designed data filtering and annotation strategies. 2) ShareCaptioner-Video, an efficient and powerful video captioning model for any video, trained on its 4.8M high-quality aesthetic video dataset. 3) ShareGPT4Video-8B, a simple yet excellent LVLM that achieved top performance on three advanced video benchmark tests.

VideoTetris is a novel framework that achieves text-to-video generation, particularly suitable for handling complex video generation scenarios involving multiple objects or dynamically changing object quantities. The framework utilizes spatiotemporal combination diffusion technology to precisely follow complex textual semantics and achieves this by operating on and combining the spatial and temporal attention maps of denoising networks. Furthermore, it introduces a novel reference frame attention mechanism to enhance the consistency of autoregressive video generation. VideoTetris has achieved impressive qualitative and quantitative results in combined text-to-video generation.

MotionClone is a training-free framework that allows for motion cloning from reference videos to control text-to-video generation. It utilizes a temporal attention mechanism to represent the motion in a reference video during video inversion and introduces a主 temporal attention guidance to alleviate the influence of noise or very subtle motions in the attention weights. Additionally, to assist the generation model in synthesizing reasonable spatial relationships and enhancing its prompt-following ability, a position-aware semantic guidance mechanism is proposed, leveraging the foreground rough locations from the reference video and original classifier-free guidance features.

Follow-Your-Pose is a text-to-video generation model that utilizes pose information and text descriptions to generate editable and controllable pose-based character videos. This technology holds significant application value in the field of digital character creation, addressing the limitations of a lack of comprehensive datasets and video generation prior models. Through a two-stage training scheme, combined with a pre-trained text-to-image model, it has achieved pose-controlled video generation.

Open-Sora-Plan is a text-to-video generation model developed by the Peking University Tuple Team. It was first released in April 2024 with its v1.0.0 version, gaining widespread recognition in the text-to-video generation field due to its simple and efficient design and significant performance. The v1.1.0 version has made significant improvements to video generation quality and duration, including optimized compression of visual representations, higher generation quality, and extended video generation capabilities. The model employs an optimized CausalVideoVAE architecture, which offers greater performance and higher inference efficiency. Additionally, it maintains the minimalist design and data efficiency of v1.0.0, and performs similarly to the Sora base model, indicating a consistent evolution pattern with the expansion principles demonstrated by Sora.

Lumina-T2X is an advanced text-to-any-modal generation framework that can convert text descriptions into vivid images, dynamic videos, detailed multi-view 3D images, and synthetic speech. The framework employs a stream-based large diffusion transformer (Flag-DiT) architecture, supports models up to 700 million parameters, and can extend sequence lengths to 128,000 tokens. Lumina-T2X integrates image, video, 3D object multi-view, and audio spectrum into a unified spatiotemporal latent token space, enabling the generation of outputs of any resolution, aspect ratio, and duration.