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  "text": "Transfusion combines autoregressive with diffusion to train a single transformer, but what if we combine Flow with Flow? 🤔\n\n🌊OneFlow🌊 the first non-autoregressive model to generate text and images concurrently using a single transformer—unifying Edit Flow (text) with Flow Matching (images).\n\nPerformance boost while unlocking new capabilities:\n🔥 Scales better than Transfusion (AR),  50% fewer FLOPS than Transfusion for similar performance\n⚡ Mixed-modal training boosts both generation & understanding\n😑 Mask diffusion: fixed-length + extra FLOPS for masked tokens. Transfusion: sequential generation\n✅ OneFlow: variable-length via token deletion + concurrent mixed-modal generation with fewer FLOPS\n\n 🧵1/n",
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