SWIFT: Prompt-Adaptive Memory for Efficient Interactive Long Video Generation

SWIFT is a new training-free framework for generating long videos with multiple prompt changes, addressing the challenge of maintaining visual coherence while rapidly adapting to semantic shifts. The system achieves 22.6 FPS on single H100 GPUs by using adaptive memory management and selective attention updates, rather than rebuilding cached memory at each prompt boundary.

SWIFT represents a meaningful advancement in video generation efficiency by solving a fundamental tension in multi-prompt long-video synthesis. Traditional approaches either rebuild entire memory caches at prompt boundaries—incurring substantial computational waste—or maintain fixed memory budgets that constrain semantic flexibility. This research identifies and addresses the core mismatch: cached video history preserves visual continuity but cannot rapidly adapt when prompts change, creating a bottleneck for interactive video generation systems.

The framework's innovation lies in three complementary mechanisms. The Semantic Injection Cache augments rather than replaces existing memory, allowing prompt updates to modify only relevant attention heads proportionally to their alignment with current video state. The Adaptive Dynamic Window dynamically allocates temporal memory based on generation phases, using larger context windows near semantic transitions and smaller windows during stable periods. Segment-level semantic anchors compress long-range consistency into compact tokens, preventing quality degradation under memory compression.

For the broader AI infrastructure sector, SWIFT's 22.6 FPS performance on consumer-grade hardware signals practical progress toward real-time interactive video generation—a capability with significant implications for content creation, gaming, and synthetic media applications. The training-free approach also reduces barrier to adoption compared to methods requiring full model retraining.

Looking forward, the ability to generate coherent long videos with prompt flexibility could accelerate deployment in production systems. Performance gains at this scale typically precede commercialization cycles, suggesting potential product integration within 12-18 months. The open-sourced code may also catalyze downstream applications and competitive optimization.

• →SWIFT enables efficient multi-prompt long-video generation without cache rebuilding, achieving 22.6 FPS on single H100 GPUs.
• →Head-wise semantic injection allows prompt updates to selectively modify relevant attention channels rather than perturbing all channels uniformly.
• →Adaptive dynamic windowing reduces average inference cost by allocating larger temporal context near semantic boundaries and smaller windows during stable segments.
• →The training-free framework maintains long-range semantic consistency through compressed segment-level anchors reintroduced as memory tokens.
• →Open-sourced implementation may accelerate adoption in interactive video generation applications and downstream commercial products.