Recent It can automatically use the color of the line manuscript according to the reference image and provide accurate details. This breakthrough technology is based on the diffusion model, which has significantly improved coloring accuracy and interactive experience through innovative patch reinstallation modules and dot -driving control solutions. Users can easily match the color matching. Even in the face of complex scenes, such as a large change in character posture or lack of details, they can also obtain high -quality coloring effects.
Recently, the color method named Manganinja has attracted widespread attention. As long as you enter the line draft and reference map, you can color the target line draft based on the reference diagram. Based on the diffusion model, this technology focuses on the color of the line drafts guided by the image, which greatly improves the accuracy and interactive control capabilities of coloring.
Through two innovative designs, the research team ensures the accurate transmission of character details. First of all, they introduced a patch retransmit module to promote the corresponding learning between the reference color image and the target line draft. Secondly, a point -driven control scheme is adopted, so that users can finely match the color.
In their experiments, the researchers constructed a self -collected benchmark data set and compared with the existing coloring methods. The results showed that Manganinja was significantly better than other methods in terms of color accuracy and generating image quality. An important feature of this method is that it can not depend on point guidance in the result and still achieve high -quality coloring effects.
Manganinja showed its unique advantages when dealing with some challenging scenes. For example, in the face of a large change in character posture or lack of details, point guidance can help solve these problems. When involving multiple objects, point guidance can also effectively prevent color confusion. In addition, users can select multiple reference images to color the color of the image by selecting multiple reference images, so as to provide guidance for the various elements of the line manuscript, and effectively solve the conflict between similar visual elements.
This technology also supports semantic color matching and fine control when using different reference images. Researchers believe that this interactive color method can help users find inspiration during coloring and provide more creative possibilities.
Project: https: //johanan528.github.io/manganininjia/
github: https: //github.com/ali-dilab/manganinjia
Points:
Manganinja is a method of color on -line coloring based on reference images, which has the ability to match accurate matching and meticulous control.
Through innovative patch retraining modules and point -driven control schemes, Manganinja significantly improves the accuracy and image quality of coloring.
This technology can cope with diverse color challenges, including the coordination of extreme postures and multi -reference images to achieve high -quality interactive color experience.
The emergence of Manganinja provides unprecedented convenience and accuracy for the color draft, and provides artists and designers with strong creative tools. Its open source characteristics also provide a good foundation for the further development of future technology. Looking forward to the future Manganinja can bring more surprises!