FLUX 3 - A Multimodal Foundation Model from Black Forest Labs

What is FLUX 3

FLUX 3 is a multimodal foundation model from Black Forest Labs that, for the first time, jointly learns images, video, and audio under a unified architecture. The model is based on Self-Flow technology, aligning generation and understanding within the same framework, and can generate videos up to 20 seconds long with native audio in a single pass, supporting text-to-video, image-to-video, video-to-video, and keyframe-to-video. Through physical constraints between modalities, the model learns world representations and performs excellently on tasks such as robotic manipulation.

Key Features of FLUX 3

• Multimodal Video Generation: Generates diverse videos up to 20 seconds long with native audio in a single pass, supporting text-to-video, image-to-video (animation continuation/visual reference), video-to-video, keyframe-to-video, and multilingual dialogue.
• Unified Modality Understanding and Generation: Based on the Self-Flow architecture, it aligns generation and understanding within the same underlying framework, supports joint generation of images, video + audio, and enables creation from pure text or reference image/video inputs.
• Intelligent Clip Chaining: Supports intelligently chaining multiple independent clips into longer multi-shot sequences, covering a wide range of visual styles from handheld camera footage to animation and cinematic quality.
• Scalable Action Prediction: The architecture reserves expansion slots for Action encoders/decoders, natively supporting physical AI and robotic control tasks.

Technical Principles of FLUX 3

• Self-Flow Architecture: Builds on Flow Matching by introducing a self-alignment mechanism, unifying multimodal generation and understanding within the same framework and achieving dual improvements in generation quality and world understanding.
• Multimodal Transformer: Text, images, video, and audio are processed by independent encoders and then fed into a unified Transformer; after the information from each modality interacts and fuses, it is output by the corresponding decoder.
• Joint Training Paradigm: Simultaneously scales compute and data resources, jointly trains on video, images, and audio, and uses physical constraints between modalities to learn a more accurate world model.
• Cross-Modal Mutual Constraint Learning: Sound must match impact, motion must obey mass, and the future must follow the past; through joint multimodal constraints, it builds a unified representation of the physical world.

How to Use FLUX 3

• Obtain Access: Visit the Black Forest Labs official website (https://bfl.ai/）申请 Early Access early experience eligibility.
• Choose a Usage Method: Experience it instantly through the browser Playground, or integrate and call it via API and third-party platforms (Replicate, fal.ai, Together AI).
• Enter a Creation Prompt: Enter a text prompt in the Playground or API, or upload a reference image/video as creation input.
• Configure Generation Parameters: Select the target modality (image/video/audio), set style, aspect ratio, output duration, and other parameters.
• Get the Generated Results: The model automatically completes multimodal generation; download or integrate the output content such as images and videos with audio.