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Official pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"
The main features of tamarott/singan are: Generative Adversarial Networks, Image Generation and Synthesis.
Projects with overlapping indexed features include: hanzhanggit/stackgan — Pytorch implementation. paarthneekhara/text-to-image — Text to image synthesis using thought vectors. nvlabs/stylegan2 — StyleGAN2 is a TensorFlow generative adversarial network and image synthesis model designed to produce high-resolution… lucidrains/imagen-pytorch — This is a PyTorch-based implementation of diffusion models for synthesizing photorealistic images and video. It… kozistr/awesome-gans — Awesome-GANs is a curated resource list and research repository focused on the development and evaluation of… hojonathanho/diffusion — This project is a diffusion model training framework and image synthesis pipeline. It provides the tools necessary to…
StyleGAN2 is a TensorFlow generative adversarial network and image synthesis model designed to produce high-resolution synthetic visual content. It functions as a deep learning architecture that learns patterns from image datasets to synthesize new images. The project includes a latent space projection tool for mapping existing images to latent vectors to analyze their representation within a generative model. It also provides an image quality evaluation framework to measure the visual fidelity and diversity of synthetic outputs. The system covers the full generative pipeline, including imag
Text to image synthesis using thought vectors
This is a PyTorch-based implementation of diffusion models for synthesizing photorealistic images and video. It provides a framework for text-to-image and text-to-video generation, as well as unconditional image synthesis. The system utilizes a cascading diffusion pipeline to produce high-resolution imagery by passing low-resolution outputs through a sequence of super-resolution models. It also includes capabilities for image inpainting, allowing the reconstruction of masked or missing regions of visual media guided by surrounding context and text prompts. The project includes tools for diff
