constraints informed by each layer's content-style bias and adaptive merger initialization, enhancing the integration of content and style. To further refine the merging process, we introduce Constyle loss, which leverages the cycle-consistency [5/6 of https://arxiv.org/abs/2504.13206v1]

DuoLoRA, a content-style personalization framework featuring three key components: (i) rank-dimension mask learning, (ii) effective merging via layer priors, and (iii) Constyle loss, which leverages cycle-consistency in the merging process. First, we [3/6 of https://arxiv.org/abs/2504.13206v1]

Are you team cosplay or team comfy casual at conventions? Some of us love to go all out, others are in it for max comfort. Let us know your go-to con style in the comments below & if you’ve got a favorite con fit, we’d love to see it too!

#thisorthat #gamingconvention #constyle #confashion #gencon

Dongqi Fan, Junhao Zhang, Liang Chang
ConStyle v2: A Strong Prompter for All-in-One Image Restoration
https://arxiv.org/abs/2406.18242

Don’t wait until the last minute! Pre-order your dark heather t-shirt for Breakout Toronto and get ready to game in style this March! FREE pick-up at the Con!

www.geekygoodies.com/breakout

#BreakoutToronto #BoardGameMerch #BoardGameConvention #EpicMerch #ConStyle