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4M 7 SR L CC12M

Developed by EPFL-VILAB
4M is a scalable multimodal masked modeling framework that supports any-to-any modality conversion, covering dozens of modalities and tasks.
Downloads 26
Release Time : 3/25/2024

Model Overview

4M is an 'any-to-any' foundational model training framework extended to multiple modalities through tokenization and masking techniques. Models trained based on 4M can perform a wide range of visual tasks, transfer to unseen tasks and modalities, and possess flexible and controllable multimodal generation capabilities.

Model Features

Any-to-Any Modality Conversion
Supports flexible conversion between multiple modalities, including visual, language, and dozens of other modalities.
Scalability
The framework design allows for easy extension to new modalities and tasks.
Transfer Learning Capability
Capable of transferring to unseen tasks and modalities.
Controllable Generation
Supports flexible and controllable multimodal content generation.

Model Capabilities

Multimodal conversion
Visual task processing
Controllable content generation
Cross-modal transfer learning

Use Cases

Computer Vision
Image Generation and Editing
Generate or edit images based on inputs from different modalities
Cross-Modal Applications
Text-to-Image Generation
Generate corresponding images based on text descriptions
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