Mchong6
Models by this creator
jojogan
74
The jojogan model, created by maintainer mchong6, is a one-shot face stylization AI that can apply a unique artistic style to any face image. Unlike other few-shot stylization methods, JoJoGAN aims to capture fine-grained stylistic details like the shape of the eyes and boldness of lines. It does this by approximating paired real data through GAN inversion and finetuning a pretrained StyleGAN model. This allows the model to generalize the learned style to apply it to any face. The model is related to other face-focused models like gans-n-roses, GFPGAN, and StyleCarIGAN, which also leverage StyleGAN for face-based tasks. Model inputs and outputs The jojogan model takes a face image as input and applies a unique artistic style to it, outputting the stylized face image. The model allows the user to choose from several pre-trained styles or provide their own style image(s) for one-shot stylization. Inputs Input Face**: Photo of a human face Pretrained**: Identifier of a pre-trained style to apply Style Img 0-3**: Face style image(s) to use for one-shot stylization Num Iter**: Number of finetuning steps (unused if a pretrained style is used) Alpha**: Strength of the finetuned style Preserve Color**: Option to preserve the colors of the original image Outputs Output**: The face image with the applied artistic style Capabilities The jojogan model is capable of applying a unique artistic style to any face image in a one-shot manner, preserving fine-grained stylistic details that other few-shot stylization methods often miss. The model supports both pre-trained styles as well as the ability to apply a custom style from provided reference images. What can I use it for? The jojogan model could be used for a variety of creative applications, such as generating unique portraits, character designs, or even concepts for illustrated books or comics. Its ability to capture fine details in the style transfer makes it particularly well-suited for artistic and illustrative tasks. Companies in the creative industries, like animation studios or game developers, could potentially use this model to generate concept art or stylize existing character designs. Things to try One interesting thing to try with the jojogan model is to experiment with the combination of multiple style images. By providing several reference style images, the model can blend the different artistic elements into a cohesive and unique stylization. This could allow for the creation of truly novel and imaginative face designs. Another avenue to explore is using the model's sketch mode, which can generate stylized face sketches, opening up possibilities for comic book-inspired artwork or character designs.
Updated 5/8/2024
gans-n-roses
4
The gans-n-roses model is a Pytorch implementation of a novel AI technique for converting images or videos of faces into diverse, high-quality anime-style art. Developed by researchers Min Jin Chong and David Forsyth, this model builds upon advancements in Generative Adversarial Networks (GANs) and image-to-image translation. Unlike previous methods, gans-n-roses is able to capture the complex and varied styles found in anime, producing a wide range of potential outputs from a single input face. This model can be contrasted with similar AI-powered anime art generators like AnimeGANv3, AnimeGANv2, and PyTorch-AnimeGAN, which tend to have a more limited stylistic range. The maintainer mchong6 has also developed the GFPGAN and Real-ESRGAN models for face restoration and image upscaling, respectively. Model inputs and outputs The gans-n-roses model takes an input image or short video of a face and generates a corresponding anime-style rendering. The model learns a mapping from real face images to a diverse space of anime styles, allowing it to produce a wide variety of potential outputs from a single input. Inputs Inpath**: An image or short video file of a face Outputs Output**: An anime-style rendering of the input face image or video Capabilities The gans-n-roses model excels at capturing the rich and varied artistic styles found in anime, going beyond the more limited outputs of previous anime art generators. By leveraging a novel adversarial loss function, the model is able to learn a diverse mapping from input faces to a wide range of potential anime renderings. What can I use it for? The gans-n-roses model could be useful for a variety of creative and entertainment applications, such as generating anime-style profile pictures, avatars, or promotional content. It could also be used to transform existing photos or videos into an anime-inspired aesthetic, opening up new artistic opportunities for filmmakers, animators, and content creators. Things to try One interesting aspect of the gans-n-roses model is its ability to perform video-to-video translation without ever being trained on video data. This means you can feed it short video clips of faces and it will generate the corresponding anime-style animations. Try experimenting with different input videos to see the range of styles the model can produce.
Updated 5/8/2024