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The Challenges of Temporal Consistency in Image Generation Models
One of the issues with image generation models when applied to video is that if you try to let's say take a input video, extract all the frames and then apply an image generation model on each of those frames individually. So this is by training one model that actually has temporal connections and temporal attention within its architecture to be able to generate the frames at once. It allows you to maintain some of the like fidelity of the image generation model, but also in a way that's actually providing temporal consistency.