"Researchers have found that the carbon footprint of generative AI-based tools that can turn text prompts into images and videos is far worse than we previously thought," writes Futurism:As detailed in a new paper, researchers from the open-source AI platform Hugging Face found that the energy demands of text-to-video generators quadruple when the length of a generated video doubles — indicating that the power required for increasingly sophisticated generations doesn't scale linearly. For instance, a six-second AI video clip consumes four times as much energy as a three-second clip. "These findings highlight both the structural inefficiency of current video diffusion pipelines and the urgent need for efficiency-oriented design," the researchers concluded in their paper... Fortunately, there are ways to slim down those demands, including intelligent caching, the reusing of existing AI generations, and "pruning," meaning the sifting out of inefficient examples from training datasets. The Hugging Face researchers gave their paper a cheeky title. "Video Killed the Energy Budget: Characterizing the Latency and Power Regimes of Open Text-to-Video Mode."Read more of this story at Slashdot.