Ali-vilab

Models by this creator

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text-to-video-ms-1.7b

ali-vilab

Total Score

494

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. Inputs Text description**: An English language text description of the desired video content. Outputs Video clip**: A short video clip, typically 14 frames at a resolution of 576x1024, that matches the input text description. Capabilities 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.

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Updated 5/21/2024

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modelscope-damo-text-to-video-synthesis

ali-vilab

Total Score

442

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.

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Updated 5/21/2024

MS-Image2Video

ali-vilab

Total Score

110

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.

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Updated 5/21/2024

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i2vgen-xl

ali-vilab

Total Score

73

The i2vgen-xl is a high-quality image-to-video synthesis model developed by ali-vilab. It uses a cascaded diffusion approach to generate realistic videos from input images. This model builds upon similar diffusion-based methods like consisti2v, which focuses on enhancing visual consistency for image-to-video generation. The i2vgen-xl model aims to push the boundaries of quality and realism in this task. Model inputs and outputs The i2vgen-xl model takes in an input image, a text prompt describing the image, and various parameters to control the video generation process. The output is a video file that depicts the input image in motion. Inputs Image**: The input image to be used as the basis for the video generation. Prompt**: A text description of the input image, which helps guide the model in generating relevant and coherent video content. Seed**: A random seed value that can be used to control the stochasticity of the video generation process. Max Frames**: The maximum number of frames to include in the output video. Guidance Scale**: A parameter that controls the balance between the input image and the text prompt in the generation process. Num Inference Steps**: The number of denoising steps used during the video generation. Outputs Video**: The generated video file, which depicts the input image in motion and aligns with the provided text prompt. Capabilities The i2vgen-xl model is capable of generating high-quality, coherent videos from input images. It can capture the essence of the image and transform it into a dynamic, realistic-looking video. The model is particularly effective at generating videos that align with the provided text prompt, ensuring the output is relevant and meaningful. What can I use it for? The i2vgen-xl model can be used for a variety of applications that require generating video content from static images. This could include: Visual storytelling**: Creating short video clips that bring still images to life and convey a narrative or emotional impact. Product visualization**: Generating videos to showcase products or services, allowing potential customers to see them in action. Educational content**: Transforming instructional images or diagrams into animated videos to aid learning and understanding. Social media content**: Creating engaging, dynamic video content for platforms like Instagram, TikTok, or YouTube. Things to try One interesting aspect of the i2vgen-xl model is its ability to generate videos that capture the essence of the input image, while also exploring visual elements not present in the original. By carefully adjusting the guidance scale and number of inference steps, users can experiment with how much the generated video deviates from the source image, potentially leading to unexpected and captivating results.

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Updated 5/21/2024

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MS-Vid2Vid-XL

ali-vilab

Total Score

51

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.

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Updated 5/21/2024

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anydoor

ali-vilab

Total Score

1

anydoor is a zero-shot object-level image customization model developed by ali-vilab. It allows for fine-grained control and manipulation of specific objects within an image, a capability that sets it apart from similar models like i2vgen-xl, gfpgan, instant-id-artistic, and real-esrgan. Model inputs and outputs anydoor takes in a source image, a target image, and various control parameters to customize the target image. The model can manipulate the target image's background, foreground, and even the shape of objects within it. Inputs Reference Image Path**: The source image to be used as reference Reference Image Mask**: The mask for the source image Bg Image Path**: The target image to be customized Bg Mask Path**: The mask for the target image Control Strength**: The strength of the control over the target image Guidance Scale**: The strength of the guidance towards the target image Enable Shape Control**: A boolean to enable shape control of objects in the target image Steps**: The number of steps to run the model Outputs Output**: The customized target image Capabilities anydoor can perform zero-shot object-level image customization, allowing users to fine-tune specific elements of an image without the need for extensive training or labeling. This makes it a powerful tool for tasks such as object removal, background replacement, and targeted modifications to elements within an image. What can I use it for? anydoor can be used in a variety of applications, such as content creation, image editing, and visual effects. Its ability to precisely control and modify objects within an image makes it particularly useful for tasks like product photography, character design, and visual storytelling. Additionally, the model's flexibility and zero-shot capabilities make it a valuable tool for researchers and developers working on image manipulation and generation projects. Things to try One interesting thing to try with anydoor is using it to create seamless composites by blending multiple images together. By leveraging the model's object-level control and guidance features, users can combine elements from different sources to create completely new and visually compelling images. Another intriguing use case is exploring the model's ability to generate creative and surreal imagery by pushing the boundaries of its shape control and guidance capabilities.

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Updated 5/21/2024