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LongCat-Video Extension Workflow | Continue Short Clips into Long Videos
Built on multi-frame conditioning, it seamlessly continues existing segments into a longer narrative. The model is purpose-built for extension tasks, making minute-level long videos less susceptible to color drift, detail collapse, and quality degradation.
Pre-trained for continuation/extension
It’s not a patchwork approach of learning to generate first and then lengthening; instead, the continuation task is central to training. The longer it generates, the less likely it is to progressively drift or lose coherence segment by segment.
Using multiple frames as conditions to extend content more reliably carries over the main subject’s state, environmental relationships, and camera context from the previous segment, reducing the abrupt feeling that scenes don’t connect.
Independent instructions for each segment, advancing like writing a script
Supports different plots and camera language by segment, so long videos aren’t merely stretched—they continue to evolve in a controllable way, suitable for continuous content like stories, vlogs, and product demonstrations.
Click the Generate button, then wait a short while to see the generated results.
FAQ
When extending, what should the prompt include?
Suggested structure: the ending state of the previous segment (character posture/position) + the key event of this segment + camera instructions (pull back/follow shot/cut to close-up) + a sense of duration (brief/slow). This makes it easiest to edit into a continuous narrative.
How can long videos look more like a professional finished film?
Write a segmented outline in advance, and specify shot size and pacing in each segment’s prompt. For transitions, use visual instructions like camera pulls back, wipe transition with an occluding object, or a block exiting the frame through movement, instead of abstract terms.