Maintainer: ali-vilab

Total Score


Last updated 5/28/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Create account to get full access


If you already have an account, we'll log you in

Model overview

The text-to-video-ms-1.7b model is a multi-stage text-to-video generation diffusion model developed by ModelScope. It takes a text description as input and generates a video that matches the text. This model builds on similar efforts in the field of text-to-video synthesis, such as the i2vgen-xl and stable-video-diffusion-img2vid models. However, the text-to-video-ms-1.7b model aims to provide more advanced capabilities in an open-domain setting.

Model inputs and outputs

This model takes an English text description as input and outputs a short video clip that matches the description. The model consists of three sub-networks: a text feature extraction model, a text feature-to-video latent space diffusion model, and a video latent space to video visual space model. The overall model size is around 1.7 billion parameters.


  • Text description: An English language text description of the desired video content.


  • Video clip: A short video clip, typically 14 frames at a resolution of 576x1024, that matches the input text description.


The text-to-video-ms-1.7b model can generate a wide variety of video content based on arbitrary English text descriptions. It is capable of reasoning about the content and dynamically creating videos that match the input prompt. This allows for the generation of imaginative and creative video content that goes beyond simple retrieval or editing of existing footage.

What can I use it for?

The text-to-video-ms-1.7b model has potential applications in areas such as creative content generation, educational tools, and research on generative models. Content creators and designers could leverage the model to rapidly produce video assets based on textual ideas. Educators could integrate the model into interactive learning experiences. Researchers could use the model to study the capabilities and limitations of text-to-video synthesis systems.

However, it's important to note that the model's outputs may not always be factual or fully accurate representations of the world. The model should be used responsibly and with an understanding of its potential biases and limitations.

Things to try

One interesting aspect of the text-to-video-ms-1.7b model is its ability to generate videos based on abstract or imaginative prompts. Try providing the model with descriptions of fantastical or surreal scenarios, such as "a robot unicorn dancing in a field of floating islands" or "a flock of colorful origami birds flying through a futuristic cityscape." Observe how the model interprets and visualizes these unique prompts.

Another interesting direction would be to experiment with prompts that require a certain level of reasoning or compositionality, such as "a red cube on top of a blue sphere" or "a person riding a horse on Mars." These types of prompts can help reveal the model's capabilities and limitations in terms of understanding and rendering complex visual scenes.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models




Total Score


The modelscope-damo-text-to-video-synthesis model is a multi-stage text-to-video generation diffusion model developed by ali-vilab. The model takes a text description as input and generates a video that matches the text. It consists of three sub-networks: a text feature extraction model, a text feature-to-video latent space diffusion model, and a video latent space to video visual space model. The overall model has around 1.7 billion parameters and only supports English input. Similar models include the text-to-video-ms-1.7b and the MS-Image2Video models, all developed by ali-vilab. The text-to-video-ms-1.7b model also uses a multi-stage diffusion approach for text-to-video generation, while the MS-Image2Video model focuses on generating high-definition videos from input images. Model inputs and outputs Inputs text**: A short English text description of the desired video. Outputs video**: A video that matches the input text description. Capabilities The modelscope-damo-text-to-video-synthesis model can generate videos based on arbitrary English text descriptions. It has a wide range of applications and can be used to create videos for various purposes, such as storytelling, educational content, and creative projects. What can I use it for? The modelscope-damo-text-to-video-synthesis model can be used to generate videos for a variety of applications, such as: Storytelling**: Generate videos to accompany short stories or narratives. Educational content**: Create video explanations or demonstrations based on textual descriptions. Creative projects**: Use the model to generate unique, imaginary videos based on creative prompts. Prototyping**: Quickly generate sample videos to test ideas or concepts. Things to try One interesting thing to try with the modelscope-damo-text-to-video-synthesis model is to experiment with different types of text prompts. Try using detailed, descriptive prompts as well as more open-ended or imaginative ones to see the range of videos the model can generate. You could also try prompts that combine multiple elements or concepts to see how the model handles more complex inputs. Another idea is to try using the model in combination with other AI tools or creative workflows. For example, you could use the model to generate video content that can then be edited, enhanced, or incorporated into a larger project.

Read more

Updated Invalid Date



Total Score


The MS-Image2Video (I2VGen-XL) project aims to address the task of generating high-definition video from input images. This model, developed by DAMO Academy, consists of two stages. The first stage ensures semantic consistency at low resolutions, while the second stage uses a Video Latent Diffusion Model (VLDM) to denoise, improve resolution, and enhance temporal and spatial consistency. The model is based on the publicly available VideoComposer work, inheriting design concepts such as the core UNet architecture. With a total of around 3.7 billion parameters, I2VGen-XL demonstrates significant advantages over existing video generation models in terms of quality, texture, semantics, and temporal continuity. Similar models include the i2vgen-xl and text-to-video-ms-1.7b projects, also developed by the ali-vilab team. Model inputs and outputs Inputs Single input image: The model takes a single image as the conditioning frame for video generation. Outputs Video frames: The model outputs a sequence of video frames, typically at 720P (1280x720) resolution, that are visually consistent with the input image and exhibit temporal continuity. Capabilities The I2VGen-XL model is capable of generating high-quality, widescreen videos directly from input images. The model ensures semantic consistency and significantly improves upon the resolution, texture, and temporal continuity of the output compared to existing video generation models. What can I use it for? The I2VGen-XL model can be used for a variety of applications, such as: Content Creation**: Generating visually appealing video content for entertainment, marketing, or educational purposes based on input images. Visual Effects**: Extending static images into dynamic video sequences for use in film, television, or other multimedia productions. Automated Video Generation**: Developing tools or services that can automatically create videos from user-provided images. Things to try One interesting aspect of the I2VGen-XL model is its two-stage architecture, where the first stage focuses on semantic consistency and the second stage enhances the video quality. You could experiment with the model by generating videos with different input images, observing how the model handles different types of content and scene compositions. Additionally, you could explore the model's ability to maintain temporal continuity and coherence, as this is a key advantage highlighted by the maintainers. Try generating videos with varied camera movements, object interactions, or lighting conditions to assess the model's robustness.

Read more

Updated Invalid Date




Total Score


The MS-Vid2Vid-XL model aims to improve the spatiotemporal continuity and resolution of video generation. It serves as the second stage of the I2VGen-XL model to generate 720P videos. The model can also be used for various tasks such as text-to-video synthesis and high-quality video transfer. MS-Vid2Vid-XL utilizes the same underlying video latent diffusion model (VLDM) and spatiotemporal UNet (ST-UNet) as the first stage of I2VGen-XL, which is designed based on the VideoComposer project. Model Inputs and Outputs Inputs Video Path**: The input video path to be processed. Text**: The text description to guide the video generation. Outputs Output Video**: The generated high-resolution video. Capabilities MS-Vid2Vid-XL can generate high-definition (720P) and widescreen (16:9 aspect ratio) videos with improved spatiotemporal continuity and texture compared to existing open-source video generation models. The model has been trained on a large dataset of high-quality videos and images, allowing it to produce videos with good semantic consistency, temporal stability, and realistic textures. What Can I Use It For? The MS-Vid2Vid-XL model can be used for a variety of applications, such as: Text-to-Video Synthesis: Generate videos based on text descriptions. High-Quality Video Transfer: Enhance the resolution and quality of existing low-resolution videos. Video Generation for Media and Entertainment: Create high-quality video content for films, TV shows, and other media. Things to Try While the MS-Vid2Vid-XL model can generate high-quality 720P videos, it may have some limitations. The model can sometimes produce blurry results when the target is far away, and the computation time for generating a single video is over 2 minutes due to the large latent space size. To address these issues, users can try providing more detailed text descriptions to guide the model's generation process.

Read more

Updated Invalid Date

AI model preview image



Total Score


Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. Developed by Stability AI, it is an impressive AI model that can create stunning visuals from simple text prompts. The model has several versions, with each newer version being trained for longer and producing higher-quality images than the previous ones. The main advantage of Stable Diffusion is its ability to generate highly detailed and realistic images from a wide range of textual descriptions. This makes it a powerful tool for creative applications, allowing users to visualize their ideas and concepts in a photorealistic way. The model has been trained on a large and diverse dataset, enabling it to handle a broad spectrum of subjects and styles. Model inputs and outputs Inputs Prompt**: The text prompt that describes the desired image. This can be a simple description or a more detailed, creative prompt. Seed**: An optional random seed value to control the randomness of the image generation process. Width and Height**: The desired dimensions of the generated image, which must be multiples of 64. Scheduler**: The algorithm used to generate the image, with options like DPMSolverMultistep. Num Outputs**: The number of images to generate (up to 4). Guidance Scale**: The scale for classifier-free guidance, which controls the trade-off between image quality and faithfulness to the input prompt. Negative Prompt**: Text that specifies things the model should avoid including in the generated image. Num Inference Steps**: The number of denoising steps to perform during the image generation process. Outputs Array of image URLs**: The generated images are returned as an array of URLs pointing to the created images. Capabilities Stable Diffusion is capable of generating a wide variety of photorealistic images from text prompts. It can create images of people, animals, landscapes, architecture, and more, with a high level of detail and accuracy. The model is particularly skilled at rendering complex scenes and capturing the essence of the input prompt. One of the key strengths of Stable Diffusion is its ability to handle diverse prompts, from simple descriptions to more creative and imaginative ideas. The model can generate images of fantastical creatures, surreal landscapes, and even abstract concepts with impressive results. What can I use it for? Stable Diffusion can be used for a variety of creative applications, such as: Visualizing ideas and concepts for art, design, or storytelling Generating images for use in marketing, advertising, or social media Aiding in the development of games, movies, or other visual media Exploring and experimenting with new ideas and artistic styles The model's versatility and high-quality output make it a valuable tool for anyone looking to bring their ideas to life through visual art. By combining the power of AI with human creativity, Stable Diffusion opens up new possibilities for visual expression and innovation. Things to try One interesting aspect of Stable Diffusion is its ability to generate images with a high level of detail and realism. Users can experiment with prompts that combine specific elements, such as "a steam-powered robot exploring a lush, alien jungle," to see how the model handles complex and imaginative scenes. Additionally, the model's support for different image sizes and resolutions allows users to explore the limits of its capabilities. By generating images at various scales, users can see how the model handles the level of detail and complexity required for different use cases, such as high-resolution artwork or smaller social media graphics. Overall, Stable Diffusion is a powerful and versatile AI model that offers endless possibilities for creative expression and exploration. By experimenting with different prompts, settings, and output formats, users can unlock the full potential of this cutting-edge text-to-image technology.

Read more

Updated Invalid Date