TL;DR: Diffusion-based TTS models sound amazing but break down for real-time streaming because they require full-sequence attention. StreamFlow introduces a block-wise guided attention scheme that lets diffusion transformers generate speech chunk-by-chunk with near–SOTA quality and predictable low latency.

Why this matters: Current diffusion speech models need to see the entire audio sequence, making them too slow and memory-heavy for assistants, agents, or anything that needs instant voice responses. Causal masks sound robotic; chunking adds weird seams. Streaming TTS has been stuck with a quality–latency tradeoff.

The idea: StreamFlow restricts attention using sliding windows over blocks:

Each block can see W_b past blocks and W_f future blocks

Compute becomes roughly O(B × W × N) instead of full O(N²)

Prosody stays smooth, latency stays constant, and boundaries disappear with small overlaps + cross-fades

How it works: The system is still a Diffusion Transformer, but trained in two phases:

Full-attention pretraining for global quality

Block-wise fine-tuning to adapt to streaming constraints

Generates mel-spectrograms; BigVGAN vocoder runs in parallel.

Performance:

~180ms first-packet latency (80ms model, 60ms vocoder, 40ms overhead)

No latency growth with longer speech

MOS tests show near-indistinguishable quality vs non-streaming diffusion

Speaker similarity within ~2%, prosody continuity preserved

Key ablation takeaways:

Past context helps until ~3 blocks; more adds little

Even a tiny future window greatly boosts naturalness

Best results: 0.4–0.6s block size, ~10–20% overlap

Comparison:

Autoregressive TTS → streaming but meh quality

GAN TTS → fast but inconsistent

Causal diffusion → real-time but degraded

StreamFlow → streaming + near-SOTA quality

Bigger picture: Smart attention shaping lets diffusion models work in real time without throwing away global quality. The same technique could apply to streaming music generation, translation, or interactive media.