# Image-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.

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.

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.

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.

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.

MusePose is an image-to-video generation framework developed by Tencent Music Entertainment's Lyra Lab, designed to generate virtual character videos through pose control signals. It is the last building block of the Muse open-source series, alongside MuseV and MuseTalk, aiming to push the community towards the vision of generating virtual characters with full-body motion and interaction capabilities. Based on diffusion models and pose guidance, MusePose can generate dance videos of characters from reference images, and the results surpass the quality of almost all open-source models on the same topic.

I2VEdit is an innovative video editing technology that extends the editing of a single frame to the entire video through a pre-trained image-to-video model. This technology can adaptively maintain the visual and motion integrity of the source video and effectively handle global editing, local editing, and moderate shape changes, which existing methods cannot achieve. The core of I2VEdit includes two main processes: rough motion extraction and appearance refinement, which are precisely adjusted through coarse-grained attention matching. In addition, a skip interval strategy is introduced to mitigate the quality degradation in the automatic regression generation process of multiple video segments. Experimental results show that I2VEdit's superior performance in fine-grained video editing demonstrates its ability to produce high-quality, temporally consistent output.

AnimateLCM-SVD-xt is a novel image-to-video generation model capable of producing high-quality, coherent videos with minimal steps. The model utilizes consistency knowledge distillation and stereoscopic matching learning techniques to ensure smoother and more consistent video generation, while significantly reducing computational intensity. Key features include: 1) Generation of a 576x1024 resolution video with 25 frames within 4 to 8 steps; 2) A remarkable 12.5 times reduction in computational complexity compared to a standard video diffusion model; 3) High-quality video generation without additional classifier guidance.

Stable Video Diffusion (SVD) 1.1 Image-to-Video is a diffusion model that generates videos corresponding to static images as conditioning frames. This latent diffusion model is trained to generate short video clips from images. At a resolution of 1024x576, the model is trained to generate 25-frame videos using the same-sized context frames and is fine-tuned from SVD Image-to-Video [25 frames]. During fine-tuning, conditions like 6FPS and Motion Bucket Id 127 are fixed to improve output consistency without adjusting hyperparameters.