Tag: text to video

Veo 4 is an AI video generation platform that provides a complete video generation suite that can convert text and images into high-quality videos. It has a variety of functions, including text-to-video generation, natural language processing, high-resolution output, etc. Veo 4 revolutionizes video editing and enhancement through AI technology, bringing efficient video generation workflows.

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 of great significance in the field of video generation. It can greatly simplify the video creation process, lower the creation threshold, improve creation efficiency, and provide users with rich and diverse video creation possibilities. Its main advantages include high-quality video generation effects, smooth display of complex movements, realistic physical simulation, and rich artistic styles. At present, the product is completely open source, and users can use its basic functions for free. It has high practical value for individuals and enterprises who have video creation needs but lack professional skills or equipment.

Wan2GP is an improved version based on Wan2.1, designed to provide efficient, low-memory video generation solutions for low-configuration GPU users. This model enables ordinary users to quickly generate high-quality video content on consumer-grade GPUs by optimizing memory management and acceleration algorithms. It supports a variety of tasks, including text to video, image to video, video editing, etc., and has a powerful video VAE architecture that can efficiently process 1080P videos. The emergence of Wan2GP has lowered the threshold of video generation technology, allowing more users to easily get started and apply it to actual scenarios.

Wan2.1-T2V-14B is an advanced text-to-video generation model based on a diffusion transformer architecture that combines an innovative spatiotemporal variational autoencoder (VAE) with large-scale data training. It is capable of generating high-quality video content at multiple resolutions, supports Chinese and English text input, and surpasses existing open source and commercial models in performance and efficiency. This model is suitable for scenarios that require efficient video generation, such as content creation, advertising production, and video editing. The model is currently available for free on the Hugging Face platform and is designed to promote the development and application of video generation technology.

FlashVideo is a deep learning model focused on efficient high-resolution video generation. It uses a staged generation strategy to first generate low-resolution videos and then upgrade them to high resolutions through enhanced models, thereby significantly reducing computational costs while ensuring details. This technology is of great significance in the field of video generation, especially in scenarios where high-quality visual content is required. FlashVideo is suitable for a variety of application scenarios, including content creation, advertising production, and video editing. Its open source nature allows researchers and developers the flexibility to customize and extend it.

Magic 1-For-1 is a model focused on efficient video generation. Its core function is to quickly convert text and images into videos. This model optimizes memory usage and reduces inference latency by decomposing the text-to-video generation task into two sub-tasks: text-to-image and image-to-video. Its main advantages include efficiency, low latency, and scalability. This model was developed by the Peking University DA-Group team to promote the development of interactive basic video generation. Currently, the model and related code are open source and users can use it for free, but they must abide by the open source license agreement.

STAR is an innovative video super-resolution technology that solves the over-smoothing problem existing in traditional GAN ​​methods by combining a text-to-video diffusion model with video super-resolution. This technology can not only restore the details of the video, but also maintain the spatiotemporal consistency of the video, making it suitable for various real-world video scenarios. STAR was jointly developed by Nanjing University, ByteDance and other institutions and has high academic value and application prospects.

ClipVideo AI is a professional AI video generation platform that uses artificial intelligence technology to convert photos or simple text prompts into engaging videos. The platform is known for its fast video generation tools, enterprise-grade security and support, and is trusted by teams. ClipVideo AI offers different pricing plans from basic to professional to meet the needs of different users.

This is a video variational autoencoder (VAE) designed to reduce video redundancy and promote efficient video generation. This model observes that extending image VAE directly to 3D VAE will introduce motion blur and detail distortion, so time-aware spatial compression is proposed to better encode and decode spatial information. Additionally, the model integrates a lightweight motion compression model to achieve further temporal compression. By leveraging the inherent text information in text-to-video datasets and incorporating text guidance into the model, the reconstruction quality is significantly improved, especially in terms of detail preservation and temporal stability. The model also improves its generality by jointly training on images and videos, which not only improves the reconstruction quality but also enables the model to perform autoencoding of images and videos. Extensive evaluation shows that the method outperforms recent strong baselines.

Zebracat is a platform that uses artificial intelligence technology to help users quickly convert text and blog content into professional videos. It provides text-to-video, blog-to-video, AI scene generation and other functions through the AI ​​video generator, which greatly simplifies the video production process and improves the efficiency of content creation. Key benefits of Zebracat include rapid video generation, no need for professional editing skills, support for multiple languages ​​and AI voiceovers, and the ability to deliver high-impact marketing videos. Product background information shows that Zebracat is loved by more than 50,000 AI creators and is highly rated on Product Hunt.

Pollo AI is an innovative AI video generator that allows users to create stunning videos with ease. Users can quickly generate videos with specific styles and content through simple text prompts or static images. Pollo AI stands out for its user-friendly interface, extensive customization options, and high-quality output, making it a top choice for both beginners and experienced creators alike. It not only supports text-to-video generation, but can also generate videos based on picture content and user needs. It has a variety of templates, including an AI hug video generator, which can easily create warm and touching hug videos. Pollo AI offers users endless creative possibilities with its fast video generation capabilities, high-quality output, and ease of use that requires no technical video editing skills.

ConsisID is an identity-preserving text-to-video generation model based on frequency decomposition, which generates high-fidelity videos that are consistent with the input text description by using identity control signals in the frequency domain. The model does not require tedious fine-tuning for different cases and is able to maintain the consistency of the identities of the people in the generated videos. The proposal of ConsisID has promoted the development of video generation technology, especially in terms of process without adjustment and frequency-aware identity maintenance control scheme.

Allegro-TI2V is a text-to-video generation model capable of generating video content based on user-provided prompts and images. The model has attracted attention for its open source nature, diverse content creation capabilities, high-quality output, small and efficient model parameters, and support for multiple accuracies and GPU memory optimization. It represents the current cutting-edge progress of artificial intelligence technology in the field of video generation and has important technical value and commercial application potential. The Allegro-TI2V model is provided on the Hugging Face platform and follows the Apache 2.0 open source protocol. Users can download and use it for free.

Pyramid Flow miniFLUX is an autoregressive video generation method based on flow matching, focusing on training efficiency and the use of open source data sets. The model is capable of generating high-quality 10-second videos at 768p resolution, 24 frames per second, and natively supports image-to-video generation. It is an important tool in the field of video content creation and research, especially when it is necessary to generate coherent moving images.

CogVideoX1.5-5B-SAT is an open source video generation model developed by the Knowledge Engineering and Data Mining Team of Tsinghua University. It is an upgraded version of the CogVideoX model. This model supports the generation of 10-second videos and supports the generation of higher-resolution videos. The model includes modules such as Transformer, VAE and Text Encoder, which can generate video content based on text descriptions. The CogVideoX1.5-5B-SAT model provides a powerful tool for video content creators with its powerful video generation capabilities and high-resolution support, especially in education, entertainment and business fields.

Viral Video is an online platform that uses artificial intelligence technology to help users quickly create viral videos. It simplifies the video production process, reduces costs, and increases the appeal and communication potential of videos through functions such as text-to-video conversion, text-to-speech conversion, AI video editing, and AI scene generation. The platform is particularly suitable for content creators, marketers and social media operators, helping them produce high-quality video content at a lower cost and faster speed, thereby gaining more attention and interactions on social media.

Mochi 1 is a research preview version of an open source video generation model launched by Genmo. It is committed to solving basic problems in the current AI video field. The model is known for its unparalleled motion quality, superior cue following capabilities, and ability to cross the uncanny valley to generate coherent, fluid human movements and expressions. Mochi 1 was developed in response to the need for high-quality video content generation, particularly in the gaming, film and entertainment industries. The product currently offers a free trial, and specific pricing information is not provided on the page.

Allegro is an advanced text-to-video model developed by Rhymes AI that converts simple text prompts into high-quality short video clips. Allegro's open source nature makes it a powerful tool for creators, developers, and researchers in the field of AI video generation. The main advantages of Allegro include open source, diverse content creation, high-quality output, and small and efficient model size. It supports multiple precisions (FP32, BF16, FP16), and in BF16 mode, the GPU memory usage is 9.3 GB and the context length is 79.2k, which is equivalent to 88 frames. Allegro's technology core includes large-scale video data processing, video compression into visual tokens, and extended video diffusion transformers.

Dream Machine API is a creative intelligence platform that provides a series of advanced video generation models. Through intuitive APIs and open source SDKs, users can build and extend creative AI products. With features like text to video, image to video, keyframe control, expansion, looping and camera control, the platform is designed to work with humans through creative intelligence to help them create better content. The Dream Machine API is designed to drive richness in visual exploration and creation, allowing more ideas to be tried, better narratives built, and diverse stories told by those who were previously unable to do so.

AI Youtube Shorts Generator is a Python tool that leverages GPT-4 and Whisper technology to extract the most interesting highlights from long videos, detect speakers, and vertically crop the content to fit the short format. This tool is currently in version 0.1 and may have some bugs.

CogVideo is a text-to-video generation model developed by the Tsinghua University team, which converts text descriptions into video content through deep learning technology. This technology has broad application prospects in video content creation, education, entertainment and other fields. Through large-scale pre-training, the CogVideo model can generate videos that match text descriptions, providing a new automated method for video production.

CogVideoX is an open source video generation model developed by the Tsinghua University team that supports video generation from text descriptions. It provides a variety of video generation models, including entry-level and large-scale models, to meet different quality and cost needs. The model supports multiple precisions, including FP16 and BF16. It is recommended to use the same precision as the model training for inference. The CogVideoX-5B model is particularly suitable for scenarios where high-quality video content needs to be generated, such as film production, game development and advertising creative.

Dream Machine AI is an AI platform that uses cutting-edge technology to convert text and pictures into high-quality videos. Powered by Luma AI, it uses advanced transformation models to quickly generate physically accurate and consistent video content with complex spatiotemporal motion. The main advantages include fast generation speed, realistic and coherent motion, high character consistency, and natural camera movement. Product positioning provides video creators and content producers with fast and efficient video generation solutions.

CogVideoX is an open source video generation model that has the same origin as the commercial model and supports the generation of video content through text descriptions. It represents the latest progress in text-to-video generation technology, has the ability to generate high-quality videos, and can be widely used in entertainment, education, business promotion and other fields.

Open-Sora Plan v1.2 is an open source video generation model focused on text-to-video conversion tasks. It adopts a 3D full attention architecture to optimize the visual representation of videos and improve reasoning efficiency. This model is innovative in the field of video generation and can better capture joint spatial-temporal features, providing a new technical path for automatic generation of video content.

AsyncDiff is an asynchronous denoising acceleration scheme for parallelizing diffusion models. It enables parallel processing of the model by splitting the noise prediction model into multiple components and distributing them to different devices. This approach significantly reduces inference latency with minimal impact on generation quality. AsyncDiff supports multiple 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.

Kling AI is a text-to-video generation model developed by Kuaishou Technology that can generate highly realistic videos based on text prompts. It has efficient video generation capabilities, up to 2 minutes of 30 frames per second video, and advanced technologies such as 3D spatio-temporal joint attention mechanism and physical world simulation, giving it a significant competitive advantage in the field of AI video generation.

The ShareGPT4Video family aims to facilitate video understanding with Large Video-Language Models (LVLMs) and video generation with Text-to-Video Models (T2VMs) with dense and accurate captions. The series includes: 1) ShareGPT4Video, 40K GPT4V annotated dense video subtitles developed through carefully designed data filtering and annotation strategies. 2) ShareCaptioner-Video, an efficient and powerful arbitrary video captioning model, annotated by its 4.8M high-quality aesthetic videos. 3) ShareGPT4Video-8B, a simple but superior LVLM that achieves the best performance on three advanced video benchmarks.

VideoTetris is a novel framework that implements text-to-video generation and is particularly suitable for processing complex video generation scenarios that contain multiple objects or dynamic changes in the number of objects. The framework accurately follows complex textual semantics through spatial-temporal combination diffusion techniques, and does so by manipulating and combining spatial and temporal attention maps of denoising networks. Additionally, it introduces a new reference frame attention mechanism to improve the consistency of autoregressive video generation. VideoTetris achieves impressive qualitative and quantitative results in combining text-to-video generation.

Dream Machine is an advanced artificial intelligence model developed by Luma Labs designed to quickly generate high-quality, photorealistic videos from text and images. This highly scalable and efficient transformation model is trained directly on video, allowing it to produce physically accurate, consistent, and event-filled footage. Dream Machine AI is an important step towards creating a universal imagination engine that is easily accessible to everyone. It generates 5-second video clips with smooth motion, movie quality and dramatic elements, turning static snapshots into dynamic stories. The model understands interactions between people, animals and objects in the physical world, allowing the creation of videos with excellent character consistency and accurate physics. Additionally, Dream Machine AI supports a wide range of smooth, cinematic and naturalistic camera movements that match the emotion and content of the scene.

MotionClone is a training-agnostic framework that allows motion cloning from reference videos to control text-to-video generation. It utilizes a temporal attention mechanism to represent motion in the reference video in video inversion, and introduces primary temporal attention guidance to mitigate the impact of noise or very subtle motion in the attention weights. Furthermore, in order to assist the generative model in synthesizing reasonable spatial relationships and enhancing its cue following ability, a position-aware semantic guidance mechanism utilizing the rough position of the foreground in the reference video and the original classifier's free guidance features is proposed.

Follow-Your-Pose is a text-to-video generation model that leverages pose information and text descriptions to generate editable, pose-controllable character videos. This technology has important application value in the field of digital character creation, solving the limitations of the lack of comprehensive data sets and prior models for video generation. Through a two-stage training scheme, combined with a pre-trained text-to-image model, pose-controllable video generation is achieved.

Open-Sora-Plan is a text-to-video generation model developed by the Peking University Yuanzu team. It first launched version v1.0.0 in April 2024 and has gained wide recognition in the field of text to video generation for its simple and efficient design and remarkable performance. Version v1.1.0 features significant improvements in video generation quality and duration, including better compressed visual representation, higher generation quality, and the ability to generate longer videos. This model adopts the optimized CausalVideoVAE architecture, which has stronger performance and higher reasoning efficiency. In addition, it maintains the minimalist design and data efficiency of the v1.0.0 version, and has similar performance to the Sora base model, indicating that its version evolution is consistent with the expansion law demonstrated by Sora.

Lumina-T2X is an advanced text-to-arbitrary modality generation framework that converts text descriptions into vivid images, dynamic videos, detailed multi-view 3D images, and synthesized speech. The framework uses a flow-based large-scale diffusion transformer (Flag-DiT) that supports up to 700 million parameters and can extend sequence lengths to 128,000 markers. Lumina-T2X integrates images, videos, multi-views of 3D objects, and speech spectrograms into a spatiotemporal latent label space that can generate output at any resolution, aspect ratio, and duration.

AI Video Generator lets everyone create stunning videos from text. Functions include: creative to video, blog to video, PPT to video, tweets to video, avatar video, product to video, etc. Suitable for content creation, business marketing, education and training, e-commerce and other fields.

Mira (Mini-Sora) is an experimental project aimed at exploring the field of high-quality, long-term video generation, especially in imitating Sora-style video generation. It builds on existing text-to-video (T2V) generation frameworks and achieves breakthroughs in several key aspects: extending sequence length, enhancing dynamic characteristics, and maintaining 3D consistency. Currently, the Mira project is in the experimental stage and there is still room for improvement compared with more advanced video generation technologies such as Sora.

CameraCtrl is dedicated to providing precise camera pose control for text generation video models, and achieves camera control in the video generation process by training camera encoders to achieve parameterized camera trajectories. By comprehensively studying the effects of various data sets, the product proves that videos with diverse camera distributions and similar appearances can enhance controllability and generalization capabilities. Experiments have proven that CameraCtrl is very effective in achieving precise, domain-adaptive camera control, and is an important advance in enabling dynamic, customized video storytelling from text and camera gesture input.

ByteDance's AnimateDiff-Lightning project achieves text-to-video generation more than ten times faster than the original AnimateDiff through specific models and settings.

VLOGGER is a method for generating text- and audio-driven videos of speaking humans from a single human input image, building on the recent success of generative diffusion models. Our approach consists of 1) a stochastic human-to-3D motion diffusion model, and 2) a novel diffusion-based architecture that enhances text-to-image models with temporal and spatial control. This approach is capable of generating high-quality videos of variable length and is easily controllable with advanced expressions of human faces and bodies. Unlike previous work, our approach does not require training for each individual, does not rely on face detection and cropping, generates complete images (not just faces or lips), and takes into account the wide range of scenarios required to correctly synthesize communicative humans (e.g. visible torsos or diverse subject identities).

Tavus offers a range of AI models, particularly in generating highly realistic videos of talking heads. Its Phoenix model uses Neural Radiation Fields (NeRFs) technology to produce natural facial movements and expressions, synchronized with input. Developers can access these highly realistic and customizable video generation services through Tavus' API.

ACT 1 (Advanced Cinematic Transformer) is a direct text-to-video compositing system developed by Hotshot Research that generates high-definition videos with multiple aspect ratios and no watermarks, providing an engaging user experience. The system is trained using a large-scale high-resolution text video corpus to achieve high-fidelity spatial alignment, temporal alignment, and aesthetic quality.

Morph Studio is an AI-based text-to-video generation platform. It uses advanced algorithms to automatically generate high-quality videos through text prompts provided by users. Morph Studio allows creators to quickly realize their ideas into dynamic visual content. It greatly lowers the threshold for video production, and users can create unique video works without having professional skills and expensive equipment. In addition, Morph Studio also provides powerful customization functions. Users can adjust the length, resolution, style and other parameters of the generated video, so that the output results are more in line with their own needs. In short, this is a highly innovative and disruptive artificial intelligence product.

OpenDiT is an open source project that provides a high-performance implementation of Colossal-AI-based Diffusion Transformer (DiT), specifically designed to enhance training and inference efficiency for DiT applications, including text-to-video generation and text-to-image generation. OpenDiT improves performance through the following technologies: up to 80% acceleration and 50% memory reduction on the GPU; including FlashAttention, Fused AdaLN and Fused layernorm core optimization; including hybrid parallel methods of ZeRO, Gemini and DDP, as well as sharding the ema model to further reduce memory costs; FastSeq: a novel sequence parallel method, especially suitable for workloads such as DiT, where the activation size is large but the parameter size is small; single-node sequence parallelism can save up to 48% of communication costs; breaking through a single GPU memory constraints, reducing overall training and inference time; Huge performance improvements with small code modifications; Users do not need to know the implementation details of distributed training; Complete text-to-image and text-to-video generation process; Researchers and engineers can easily use and adapt our process to practical applications without modifying the parallel part; Perform text-to-image training on ImageNet and publish checkpoints.

Sora is a text-to-video generation model developed by OpenAI that can generate realistic image sequences up to 1 minute based on text descriptions. It has the ability to understand and simulate the movement of the physical world, and its goal is to train models that help people solve problems that require physical interaction. Sora can interpret long-form prompts and generate a variety of people, animals, landscapes, and city scenes based on text input. Its disadvantage is that it is difficult to accurately depict the physics of complex scenes and understand cause and effect.

AI SORA TECH is a revolutionary content creation tool that leverages advanced video generation technology to convert text and images into dynamic videos and supports video-to-video creation. It can generate an entire video or extend the length of an existing video based on input text or images, meeting various video production needs. AI SORA TECH is feature-rich and easy to operate, making it suitable for both professionals and beginners.

Lumiere is a text-to-video diffusion model designed to synthesize videos that exhibit realistic, diverse, and coherent motion, solving key challenges in video synthesis. We introduce a space-time U-Net architecture that can generate the entire video's temporal duration at once, in a single pass of the model. This is in contrast to existing video models, which synthesize distant keyframes and then perform temporal super-resolution, an approach that inherently makes global temporal consistency difficult to achieve. By deploying spatial and (importantly) temporal downsampling and upsampling, and leveraging a pretrained text-to-image diffusion model, our model learns to directly generate full frame rate, low-resolution video at multiple spatiotemporal scales. We present state-of-the-art text-to-video generation results and show that our design easily facilitates a variety of content creation tasks and video editing applications, including image-to-video, video inpainting, and stylized generation.

This product is a tool for evaluating the quality of text-to-video generation. It introduces a new evaluation metric, text-to-video scoring (T2VScore). The score integrates two key criteria: (1) text-video alignment, which reviews the video's fidelity in presenting a given text description; and (2) video quality, which evaluates the overall production quality of the video. Furthermore, to evaluate the proposed metrics and facilitate future improvements to them, the product provides the TVGE dataset, which collects human judgments on both criteria on 2,543 text-to-video generated videos. Experiments on the TVGE dataset show that the proposed T2VScore exhibits superiority in providing better evaluation metrics for text-to-video generation.

MagicVideo-V2 is an end-to-end video generation pipeline that integrates text-to-image models, video motion generators, reference image embedding modules, and frame interpolation modules. Its architectural design enables MagicVideo-V2 to produce beautiful-looking, high-resolution video with excellent fidelity and smoothness. Through large-scale user evaluation, it has demonstrated superior performance over leading text-to-video systems such as Runway, Pika 1.0, Morph, Moon Valley and Stable Video Diffusion.

FreeInit is a simple and effective method for improving the temporal consistency of video generation models. It does not require additional training, does not introduce learnable parameters, and can be easily integrated and used in the inference of any video generation model.

InstructVideo is a method for guiding text-to-video diffusion models with reward fine-tuning through human feedback. It rewards fine-tuning through editing, reducing fine-tuning costs and improving fine-tuning efficiency. It uses an established image reward model to provide reward signals through piecewise sparse sampling and temporally decaying rewards, significantly improving the visual quality of the generated videos. InstructVideo can not only improve the visual quality of generated videos, but also maintain strong generalization capabilities. For more information, please visit the official website.

SparseCtrl was developed to enhance control over text-to-video generation, with the ability to flexibly combine sparse signals for structural control with only one or a small number of inputs. It includes an additional conditional encoder to handle these sparse signals without affecting the pre-trained text-to-video model. The method is compatible with various modalities including sketch, depth and RGB images, providing more practical control for video generation and driving applications such as storyboarding, depth rendering, keyframe animation and interpolation. Extensive experiments demonstrate the generalization ability of SparseCtrl on both original and personalized text-to-video generators.

Moonvalley is a groundbreaking text-to-video generation AI model that creates stunning high-definition videos and animations from simple text prompts. It uses advanced machine learning technology to generate realistic and beautiful videos and animations based on text prompts input by users. Whether making movies, commercials, animated shorts, or personal creations, Moonvalley helps users quickly turn ideas into visual works.

Show-1 is an efficient text-to-video generation model that combines pixel-level and latent variable-level diffusion models. It can not only generate videos that are highly relevant to text, but also generate high-quality videos with lower computing resource requirements. It first generates a low-resolution preliminary video with a pixel-level model and then upsamples it to high-resolution using a latent variable model, thus combining the advantages of both models. Compared with the pure latent variable model, the video text association generated by Show-1 is more accurate; compared with the pure pixel model, its computational cost is also lower.