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Official Chainer implementation of GP-GAN: Towards Realistic High-Resolution Image Blending (ACMMM 2019, oral)
The main features of wuhuikai/gp-gan are: Image Restoration and Enhancement, Image Synthesis, Learning and Automation.
Projects with overlapping indexed features include: fogleman/primitive — Primitive is an algorithmic art generator and geometric image reconstruction tool that transforms raster images into… luanfujun/deep-photo-styletransfer — This project is a deep learning style transfer framework designed to apply artistic styles to photographs. It… sanster/lama-cleaner — Lama Cleaner is an AI-powered image editing application focused on inpainting, object removal, and generative filling.… ashishbora/ambient-gan — Code to reproduce results from the paper "AmbientGAN: Generative models from lossy measurements". david-gpu/srez — Srez is a deep learning image super-resolution framework designed to upscale low-resolution images into sharp,… borisdayma/dalle-mini — dalle-mini is a text-to-image model and generative AI system designed to transform natural language descriptions into…
This project is a deep learning style transfer framework designed to apply artistic styles to photographs. It functions as a photorealistic image stylizer that merges the content of one image with the visual characteristics of another while maintaining the original geometry and structural details. The system distinguishes itself through the use of matting Laplacian matrices and semantic segmentation masks to prevent distortion and preserve edge fidelity. These capabilities allow for region-specific styling, where different aesthetics can be applied to distinct objects or areas within a single
Primitive is an algorithmic art generator and geometric image reconstruction tool that transforms raster images into stylized vector compositions. It functions as an iterative shape optimizer and raster-to-vector converter, approximating pixel-based photos by layering geometric primitives such as triangles, circles, and rectangles. The project utilizes a search algorithm to determine the optimal position, size, and color for each shape to minimize the visual difference from the source image. Users can apply shape constraint definitions to control the properties and orientations of the geometr
Lama Cleaner is an AI-powered image editing application focused on inpainting, object removal, and generative filling. It provides a suite of tools for erasing unwanted elements from photos and filling the resulting gaps using generative artificial intelligence. The project includes specialized capabilities for image outpainting to extend borders, background removal through object segmentation, and face restoration to fix visual defects. It also features an image upscaler to increase resolution and clarity via super-resolution AI, as well as a Stable Diffusion-based editor for replacing speci
Code to reproduce results from the paper "AmbientGAN: Generative models from lossy measurements"