ColorFlow
Image sequence coloring model that preserves fine-grained identity information
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- -Retrieval Enhanced Coloring Process (RAP): Utilizing retrieval enhancement techniques to provide relevant color references for image coloring.
- -Context Coloring Process (ICP): Through the self attention mechanism in the diffusion model, powerful context learning and color identity matching are achieved.
- -Guided super-resolution process (GSRP): Ensure high-quality coloring effects while maintaining color identity.
- -Patch Wise training strategy: Reduce the computational requirements of training by segmenting high-resolution stitched images.
- -Superior performance in multiple metrics: surpassing existing models in multiple evaluation metrics, setting a new standard for sequence image coloring.
- -Widely applicable scenarios: Suitable for various art backgrounds such as black and white comics, line art, real-world photos, and cartoon storyboards.
Product Details
ColorFlow is a model designed for coloring image sequences, with a particular emphasis on preserving the identity information of characters and objects during the coloring process. This model utilizes contextual information to accurately generate colors for different elements in a black and white image sequence (such as a character's hair and clothing) based on a reference image pool, ensuring color consistency with the reference image. ColorFlow proposes a novel retrieval enhanced coloring process through a three-stage diffusion model framework, which enables image coloring with relevant color references without the need for fine-tuning or explicit identity embedding extraction for each identity. The main advantages of ColorFlow include its ability to retain identity information while providing high-quality coloring effects, which have significant market value for coloring cartoons or comic series.
