4M 7 SR L CC12M
4M is a scalable multimodal masked modeling framework that supports any-to-any modality conversion, covering dozens of modalities and tasks.
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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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