Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis
Self-Flow, FLUX 3
prior work: REPA: Align diffusion models’ internal representations with DINO’s, since diffusion models don’t learn semantic representations well on their own.
- con: this depends on an external model, scales poorly
Self-Flow: noise some tokens asymmetrically, align representations of the noisy tokens with the clean ones in addition to the generation objective 
Making it multimodal:
- per-modality in/out projection layers, autoencoder
- FLUX.2 AE for images, WAN2.2 for video, Songbloom for audio
- hparams like noise scheduling (same level of noise is harder to predict for images than video bc more info with video) / batch size
- vector timestep conditioning