Style-CCL uses curriculum continual learning on a million-scale synthetic dataset with a dual-branch SC-DiT to achieve state-of-the-art content-preserving style transfer.
Tfcnet: Temporal fully connected networks for static unbiased temporal reasoning.arXiv preprint arXiv:2203.05928,
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TeleStyle V2 uses self-distillation from V1 plus DMD and a prompt enhancer to support RnR/RnS/SnR/SnS reference pairs while matching commercial models on style transfer and general editing.
citing papers explorer
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Style-CCL: Content-Preserving Style Transfer via Curriculum Continual Learning
Style-CCL uses curriculum continual learning on a million-scale synthetic dataset with a dual-branch SC-DiT to achieve state-of-the-art content-preserving style transfer.
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TeleStyle V2: Beyond Content-Preserving Style Transfer with Self-Distillation and Distribution-Matching-Distillation
TeleStyle V2 uses self-distillation from V1 plus DMD and a prompt enhancer to support RnR/RnS/SnR/SnS reference pairs while matching commercial models on style transfer and general editing.