{"id":22683,"date":"2026-07-25T04:17:10","date_gmt":"2026-07-25T04:17:10","guid":{"rendered":"https:\/\/scannn.com\/flux-3-real-world-models-towards-multimodal-flow-models-as-the-backbone-of-visual-intelligence\/"},"modified":"2026-07-25T04:17:10","modified_gmt":"2026-07-25T04:17:10","slug":"flux-3-real-world-models-towards-multimodal-flow-models-as-the-backbone-of-visual-intelligence","status":"publish","type":"post","link":"https:\/\/scannn.com\/lv\/flux-3-real-world-models-towards-multimodal-flow-models-as-the-backbone-of-visual-intelligence\/","title":{"rendered":"FLUX 3 - Real World Models: Towards Multimodal Flow Models as the Backbone of Visual Intelligence."},"content":{"rendered":"\n
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FLUX 3 is now available in Early Access.<\/em><\/p>\n

FLUX 3 is our new multimodal foundation model. It jointly learns from images, videos, and audio within a unified architecture, because what it needs to learn is not any one of these elements in isolation. Instead, a model must learn a representation of the world: how objects hold together, how things move, and how events sound.<\/p>\n

No single modality provides a complete description. Each is a projection of the same underlying reality, captured by different sensors, each of which loses some information in the process. Images capture spatial structures and relationships at a specific point in time. Videos restore the dimension of time and reveal temporal dynamics and physical laws. Audio reveals causal relationships between mechanical phenomena and acoustics that vision alone cannot detect. Language links these perceptions to goals, abstractions, and instructions.<\/p>\n

Learn from one and you get a good model of that projection. Learn from all of them at once and their mutual constraints tell you more: the sound has to match the impact, the motion has to obey the mass, the future has to follow from the past. The modalities stop being separate and start being evidence about one underlying reality.<\/p>\n

FLUX 3 is our first model built entirely on that principle, and a checkpoint on our mission to develop real-world visual intelligence: models that perceive, predict, and act across physical and digital environments. Early results in content creation and physical AI suggest it is the right path.<\/p>\n

FLUX 3: One model, multiple capabilities.<\/strong><\/h2>\n
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FLUX 3 builds on Self-Flow,<\/a> our approach for efficiently aligning multimodal generation and understanding within the same underlying architecture. Based on this approach, we significantly scaled up compute and data resources to train FLUX 3 across video, images, and audio at the same time.<\/p>\n

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Self-Flow vs. Flow Matching (FM). Left: generation error (Fr\u00e9chet distance) per modality, each normalized to FM = 100 (lower is better). Right: success rate on manipulation tasks averaged over four task groups through finetuning (higher is better).<\/em><\/strong><\/p>\n

Capabilities & Early Evaluations<\/strong><\/h2>\n

As a result, FLUX 3 is capable of mixing modalities and generating images and video+audio jointly; both from pure text prompts as well as when providing input references such as images and video. We are highlighting a few of the model\u2019s key capabilities below.<\/p>\n

Video<\/strong><\/h3>\n

FLUX 3 can create highly diverse videos with audio up to 20 seconds in length in a single generation.<\/p>\n

Its core capabilities include the following (all outputs come with native audio generation):<\/p>\n