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TemporalNet And Stable Diffusion: The Next Game-Changer For AI Video Creation?
New approach aims to improve temporal consistency in AI-generated videos
Only a month ago, ControlNet revolutionized the AI image generation landscape with its groundbreaking control mechanisms for spatial consistency in Stable Diffusion images, paving the way for customizable AI-powered design. Building on this success, TemporalNet is a new approach tackling the challenge of temporal consistency, which could equally transform AI video generation.
What is Temporal Consistency?
While prior to ControlNet there was no efficient way to tell a diffusion model which parts of an input image to keep and which to manipulate, this changed with the ability to use sketches, outlines, depth maps, or human poses as control mechanisms when working with Stable Diffusion. Spatial consistency got solved.
With video, the issue is not only spatial consistency between two images but consistency between multiple frames over time.
You may have seen this temporal consistency problem in action when watching AI-generated videos with abrupt changes, flickering, or other inconsistencies.
Achieving temporal consistency is critical to producing high-quality video, and that’s exactly what TemporalNet aims to improve:
Compare this to other attempts at AI video creation, like ControlNet Video or Modelscope’s Text-2-Video:
