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Jan 26, 2024 · This notebook is a demo for the BigGAN image generators available on TF Hub. See the BigGAN paper on arXiv [1] for more information about these models. After connecting to a runtime, get started by following these instructions:

Here is an article containing a high-level description of how The Big Sleep works. The Big Sleep uses a modified version of BigGAN as its image generator component. The Big Sleep uses the ViT-B/32 CLIP model to rate how well a given image matches your desired text.

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Mar 11, 2021 · BigGAN takes in a noise vector, and outputs a realistic image; whereas CLIP can take in (i) an image, and output the image features; or (ii) a text, and output text features. The relevance between image and text features can be represented by the cosine similarity of the learnt feature vectors.

The author's officially unofficial PyTorch BigGAN implementation. This repo contains code for 4-8 GPU training of BigGANs from Large Scale GAN Training for High Fidelity Natural Image Synthesis by Andrew Brock, Jeff Donahue, and Karen Simonyan.

BigGAN, or Big Generative Adversarial Network, is a powerful generative model that uses deep learning techniques to create high-quality, realistic images. It is a class-conditional GAN trained on large datasets like ImageNet and has achieved …

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