internlm-xcomposer

Maintainer: cjwbw

Total Score

164

Last updated 5/21/2024
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Github LinkView on Github
Paper LinkView on Arxiv

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Model overview

internlm-xcomposer is an advanced text-image comprehension and composition model developed by cjwbw, the creator of similar models like cogvlm, animagine-xl-3.1, videocrafter, and scalecrafter. It is based on the InternLM language model and can effortlessly generate coherent and contextual articles that seamlessly integrate images, providing a more engaging and immersive reading experience.

Model inputs and outputs

internlm-xcomposer is a powerful vision-language large model that can comprehend and compose text and images. It takes text and images as inputs, and can generate detailed text responses that describe the image content.

Inputs

  • Text: Input text prompts or instructions
  • Image: Input images to be described or combined with the text

Outputs

  • Text: Detailed textual descriptions, captions, or compositions that integrate the input text and image

Capabilities

internlm-xcomposer has several appealing capabilities, including:

  • Interleaved Text-Image Composition: The model can seamlessly generate long-form text that incorporates relevant images, providing a more engaging and immersive reading experience.
  • Comprehension with Rich Multilingual Knowledge: The model is trained on extensive multi-modal multilingual concepts, resulting in a deep understanding of visual content across languages.
  • Strong Performance: internlm-xcomposer consistently achieves state-of-the-art results across various benchmarks for vision-language large models, including MME Benchmark, MMBench, Seed-Bench, MMBench-CN, and CCBench.

What can I use it for?

internlm-xcomposer can be used for a variety of applications that require the integration of text and image content, such as:

  • Generating illustrated articles or reports that blend text and visuals
  • Enhancing educational materials with relevant images and explanations
  • Improving product descriptions and marketing content with visuals
  • Automating the creation of captions and annotations for images and videos

Things to try

With internlm-xcomposer, you can experiment with various tasks that combine text and image understanding, such as:

  • Asking the model to describe the contents of an image in detail
  • Providing a text prompt and asking the model to generate an image that matches the description
  • Giving the model a text-based scenario and having it generate relevant images to accompany the story
  • Exploring the model's multilingual capabilities by trying prompts in different languages

The versatility of internlm-xcomposer allows for creative and engaging applications that leverage the synergy between text and visuals.



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

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