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FlashDecoder: Real-Time Latent-to-Pixel Streaming Decoder with Transformers

Research Multimodal & Generative

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Overall 78
Content 95
Popularity 40

Observed public metrics from 1 member.

Merged summary

TL;DR - FlashDecoder is a pure-Transformer video decoder that streams latent representations to pixels frame-by-frame, enabling real-time video generation with constant latency and bounded memory regardless of video length.

  • Replaces slow, memory-heavy 3D convolutional decoders in latent video diffusion; each frame attends to a fixed-size window of past frames via a rolling KV cache for constant-latency streaming.
  • Sequential frame processing enforces temporal causality without explicit attention masks, supporting training at resolutions up to 1080p.
  • Matches convolutional decoder reconstruction quality on Wan2.1/Wan2.2 latent spaces (e.g., 41.55dB vs. 41.49dB PSNR at 1080p).
  • Decodes 3.6x–4.7x faster with up to 11x less memory on a single H100; architecture-aware inference optimizations widen the speedup to 12x.

Sources (1)

FlashDecoder: Real-Time Latent-to-Pixel Streaming Decoder with Transformers

arXiv cs.CV Minguk Kang, Suha Kwak 2026-07-16 arXiv:2607.14898
Public signals Hugging Face upvotes 0 · Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · Upvotes 0 OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-15 14:33:30.825734 UTC

TL;DR - FlashDecoder is a pure-Transformer video decoder that streams latent representations to pixels frame-by-frame, enabling real-time video generation with constant latency and bounded memory regardless of video length.

  • Replaces slow, memory-heavy 3D convolutional decoders in latent video diffusion; each frame attends to a fixed-size window of past frames via a rolling KV cache for constant-latency streaming.
  • Sequential frame processing enforces temporal causality without explicit attention masks, supporting training at resolutions up to 1080p.
  • Matches convolutional decoder reconstruction quality on Wan2.1/Wan2.2 latent spaces (e.g., 41.55dB vs. 41.49dB PSNR at 1080p).
  • Decodes 3.6x–4.7x faster with up to 11x less memory on a single H100; architecture-aware inference optimizations widen the speedup to 12x.
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