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  • 1
    AnimeGAN

    AnimeGAN

    A simple PyTorch Implementation of Generative Adversarial Networks

    A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing. The images are generated from a DCGAN model trained on 143,000 anime character faces for 100 epochs. Manipulating latent codes enables the transition from images in the first row to the last row. The images are not clean, some outliers can be observed, which degrades the quality of the generated images.
    Downloads: 0 This Week
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  • 2
    VQGAN-CLIP web app

    VQGAN-CLIP web app

    Local image generation using VQGAN-CLIP or CLIP guided diffusion

    VQGAN-CLIP has been in vogue for generating art using deep learning. Searching the r/deepdream subreddit for VQGAN-CLIP yields quite a number of results. Basically, VQGAN can generate pretty high-fidelity images, while CLIP can produce relevant captions for images. Combined, VQGAN-CLIP can take prompts from human input, and iterate to generate images that fit the prompts. Thanks to the generosity of creators sharing notebooks on Google Colab, the VQGAN-CLIP technique has seen widespread...
    Downloads: 1 This Week
    Last Update:
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