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Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models
AT-EDM framework uses attention maps for efficient token pruning in Diffusion Models, achieving significant FLOPs savings and speed-up without retraining, maintaining image quality.
https://arxiv.org/abs//2405.05252
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
AT-EDM framework uses attention maps for efficient token pruning in Diffusion Models, achieving significant FLOPs savings and speed-up without retraining, maintaining image quality.
https://arxiv.org/abs//2405.05252
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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