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How Text-to-Video AI Works Under the Hood

1From Prompt to Conditioning Signal2Latent Diffusion: Generating Video in Compressed Space3Spatio-Temporal Attention: Keeping Frames Consistent4Where the Pipeline Breaks: Failure Modes and Their Causes
Spatio-Temporal Attention: Keeping Frames Consistent

When the Links Fail

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So what happens when those links are weak? Start with the clean clip, where the temporal connections are doing their job and the object holds together.
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Temporal consistency is the hardest part of video generation because every frame is denoised with its own noise, and nothing forces agreement unless the temporal links carry it. When temporal attention is weak or the clip is long, the links stop holding: the same object is re-decided each frame, so its identity drifts; small per-frame differences in brightness and texture read as flicker; and fast motion outruns the local kernel, producing warping. Independent per-frame generation makes this obvious — each frame is plausible alone, yet the sequence shimmers, because no mechanism ever compared the frames.

References

  1. [1]Video Diffusion Modelsarxiv.org
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