@rohanpaul_ai: Spatially Speculative Decoding (SSD) sped up autoregressive image models up to 13.28X by predicting image rows in paral…

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Summary

Spatially Speculative Decoding (SSD) accelerates autoregressive image models by predicting entire rows in parallel using small helper networks, achieving up to 13.28x speedup while maintaining benchmark performance.

Spatially Speculative Decoding (SSD) sped up autoregressive image models up to 13.28X by predicting image rows in parallel. By drafting whole rows from nearby spatial information, not single tokens. Most of the time these models take a 2D image and convert it into a long string of tokens. Then they make each token pass through another expensive model. SSD adds small helper networks that can look right and down . This allows nearby tokens as well as full rows to be drafted at the same time . Such helpers don't try to predict the exact visual token labels. Instead, they predict more detailed internal features that are easier to learn correctly. The primary model reviews each drafted block at the same time, correcting any errors so that the entire block is not wasted. The generation time of Janus-Pro, Lumina-mGPT, and Emu3 was 5.74 to 13.28 times faster, but the benchmark scores were comparable to standard decoding. Lumina-mGPT and Emu3 saw some wall-clock gains from a leaner inference loop, whereas Janus-Pro used matching loops. Larger token grids increase the gains but the original model is static and small trained heads speed up the process. – arxiv. org/abs/2606.20543 Title: "SSD: Spatially Speculative Decoding Accelerates Autoregressive Image Generation"
Original Article
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Spatially Speculative Decoding (SSD) sped up autoregressive image models up to 13.28X by predicting image rows in parallel.

By drafting whole rows from nearby spatial information, not single tokens.

Most of the time these models take a 2D image and convert it into a long string of tokens. Then they make each token pass through another expensive model.

SSD adds small helper networks that can look right and down . This allows nearby tokens as well as full rows to be drafted at the same time .

Such helpers don’t try to predict the exact visual token labels. Instead, they predict more detailed internal features that are easier to learn correctly.

The primary model reviews each drafted block at the same time, correcting any errors so that the entire block is not wasted.

The generation time of Janus-Pro, Lumina-mGPT, and Emu3 was 5.74 to 13.28 times faster, but the benchmark scores were comparable to standard decoding.

Lumina-mGPT and Emu3 saw some wall-clock gains from a leaner inference loop, whereas Janus-Pro used matching loops.

Larger token grids increase the gains but the original model is static and small trained heads speed up the process.

– arxiv. org/abs/2606.20543

Title: “SSD: Spatially Speculative Decoding Accelerates Autoregressive Image Generation”

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