DreamStyle: A Unified Framework for Video Stylization
Video stylization, an important downstream task of video generation models, has not yet been thoroughly explored. Its input style conditions typically include text, style image, and stylized first frame. Each condition has a characteristic advantage: text is more flexible, style image provides a more accurate visual anchor, and stylized first frame makes long-video stylization feasible. However, existing methods are largely confined to a single type of style condition, which limits their scope of application. Additionally, their lack of high-quality datasets leads to style inconsistency and temporal flicker. To address these limitations, we introduce DreamStyle, a unified framework for video stylization, supporting (1) text-guided, (2) style-image-guided, and (3) first-frame-guided video stylization, accompanied by a well-designed data curation pipeline to acquire high-quality paired video data. DreamStyle is built on a vanilla Image-to-Video (I2V) model and trained using a Low-Rank Adaptation (LoRA) with token-specific up matrices that reduces the confusion among different condition tokens. Both qualitative and quantitative evaluations demonstrate that DreamStyle is competent in all three video stylization tasks, and outperforms the competitors in style consistency and video quality.
695dd030c03d6d81e4399ee8
Mengtian Li
admin_assigned
2026-01-07T13:22:38.847Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/dGLeWvN2CXLmxVP_b9S4R.png
Mengtian Li
LemonSky1995
user
LemonSky1995
695dd030c03d6d81e4399ee9
Jinshu Chen
695dd030c03d6d81e4399eea
Songtao Zhao
695dd030c03d6d81e4399eeb
Wanquan Feng
admin_assigned
2026-01-07T13:23:08.687Z
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Wanquan Feng
wanquan
user
wanquan
695dd030c03d6d81e4399eec
Pengqi Tu
admin_assigned
2026-01-07T13:23:19.334Z
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tupengqi
tupengqi
user
tupengqi
695dd030c03d6d81e4399eed
Qian He
DreamStyle: A Unified Framework for Video Stylization
Video stylization, an important downstream task of video generation models, has not yet been thoroughly explored. Its input style conditions typically include text, style image, and stylized first frame. Each condition has a characteristic advantage: text is more flexible, style image provides a more accurate visual anchor, and stylized first frame makes long-video stylization feasible. However, existing methods are largely confined to a single type of style condition, which limits their scope of application. Additionally, their lack of high-quality datasets leads to style inconsistency and temporal flicker. To address these limitations, we introduce DreamStyle, a unified framework for video stylization, supporting (1) text-guided, (2) style-image-guided, and (3) first-frame-guided video stylization, accompanied by a well-designed data curation pipeline to acquire high-quality paired video data. DreamStyle is built on a vanilla Image-to-Video (I2V) model and trained using a Low-Rank Adaptation (LoRA) with token-specific up matrices that reduces the confusion among different condition tokens. Both qualitative and quantitative evaluations demonstrate that DreamStyle is competent in all three video stylization tasks, and outperforms the competitors in style consistency and video quality.
DreamStyle is a unified video stylization framework that supports multiple style conditions while addressing style inconsistency and temporal flicker through a specialized data curation pipeline and LoRA training approach.
video stylization Image-to-Video (I2V) model Low-Rank Adaptation (LoRA) token-specific up matrices style consistency temporal flicker