A beginner's guide to the Sa2va-8b-Image model by Bytedance on Replicate

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This is a simplified guide to an AI model called Sa2va-8b-Image maintained by Bytedance. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.

Model overview

The sa2va-8b-image model represents a breakthrough in multi-modal AI by combining SAM-2's video segmentation capabilities with LLaVA's vision-language understanding. Developed by ByteDance, this unified model handles both image and video tasks through a single architecture that merges text, visual content, and precise object segmentation into a shared token space. Unlike traditional multi-modal models that focus on specific modalities, this model supports referring segmentation, visual conversations, and dense grounded understanding across static images and dynamic video content. The model builds on InternVL2.5-8B as its base MLLM and uses internlm2_5-7b-chat for language processing, making it part of a comprehensive model family that includes sa2va-26b-image, Sa2VA-8B, and Sa2VA-4B variants.

Model inputs and outputs

The model accepts both single images and text instructions, generating detailed responses that can include precise segmentation masks when requested. The instruction-guided approach allows users to specify exactly what they want to understand or segment within the visual content.

Inputs

Image: Input image file in URI format for analysis and segmentation
Instruction: Text prompt that guides the model's analysis, ranging from simple descriptions to specific segmentation requests

Outputs

Response: Text-based answer to the instruction, providing descriptions, analysis, or confirmation of segmentation tasks
Image (optional): Processed image with segmentation masks overlaid when segmentation is requested