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alexjc/neural-doodleArchived

0
View on GitHub↗
9,854 stars·893 forks·Python·AGPL-3.0·15 views

Neural Doodle

Neural Doodle is a collection of neural network tools designed for image upscaling, texture synthesis, and semantic-guided style transfer between visual inputs. It provides a semantic style transfer engine and an example-based image upscaler that increase image resolution by referencing visual details from a target style example.

The project includes a neural texture synthesizer for creating seamless bitmap textures and repeating patterns from a single input style image. It also functions as an image generation tool capable of transforming simple sketches and photos into detailed artwork.

The system covers broad image processing and synthesis capabilities, including sketch-to-image transformation, image analogy generation, and semantic style transfer using annotated maps to guide the process.

Features

  • Resolution Upscalers - Provides a neural network implementation that increases image resolution by referencing visual details from a target style example.
  • Convolutional Neural Networks - Implements convolutional neural network layers to extract structural representations and visual features from images.
  • Texture Synthesis - Generates repeatable bitmap textures and seamless patterns from a single input style image.
  • Image-to-Image Synthesis Frameworks - Converts simple sketches into detailed images by transferring annotated patches from a target style image.
  • Scribble-to-Image Generation - Converts simple hand-drawn doodles into detailed artwork by transferring annotated patches from a style image.
  • Semantic-Guided Style Transfer - Maps annotated image fragments from a style source to a target based on semantic label matching.
  • Semantic Image Synthesis - Uses labeled image maps to ensure style elements are applied to the correct conceptual regions.
  • Seamless Texture Synthesizers - Generates seamless bitmap textures and repeating patterns from a single input style image.
  • Generative Image Models - Uses generative image models to transform simple sketches and photos into detailed artwork and textures.
  • Detailed Sketch Synthesis - Transfers annotated patches from a style image to a simple sketch to produce a detailed piece of artwork.
  • Visual Analogy Mappings - Transfers relational properties between two image pairs to synthesize a new image that maintains specific visual logic.
  • Computer Vision Libraries - Style transfer and texture generation from doodles.

Star history

Star history chart for alexjc/neural-doodleStar history chart for alexjc/neural-doodle

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Neural Doodle

These projects share indexed features with Neural Doodle. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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    This project is a neural network extension for Stable Diffusion that provides spatial control and geometric consistency for text-to-image generation. It functions as an image structure controller and conditioning tool, enabling the use of external inputs to guide the layout and geometry of generated imagery. The framework is distinguished by its ability to transform input images into structural guides through various preprocessors. These include the extraction of depth maps, normal maps, and human pose landmarks, as well as the detection of Canny edges, anime lineart, and straight architectur

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  • xpixelgroup/basicsrXPixelGroup avatar

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    BasicSR is a PyTorch-based image restoration toolbox and framework designed for training and deploying deep learning models to upscale, denoise, and deblur images and videos. It serves as a comprehensive system for image super-resolution and video quality restoration, providing the necessary infrastructure to recover fine visual details and increase pixel density. The project distinguishes itself through specialized toolkits for facial image enhancement and high-fidelity face synthesis, as well as a dedicated video quality restoration suite that utilizes deformable convolutions and generative

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Frequently asked questions

What does alexjc/neural-doodle do?

Neural Doodle is a collection of neural network tools designed for image upscaling, texture synthesis, and semantic-guided style transfer between visual inputs. It provides a semantic style transfer engine and an example-based image upscaler that increase image resolution by referencing visual details from a target style example.

What are the main features of alexjc/neural-doodle?

The main features of alexjc/neural-doodle are: Resolution Upscalers, Convolutional Neural Networks, Texture Synthesis, Image-to-Image Synthesis Frameworks, Scribble-to-Image Generation, Semantic-Guided Style Transfer, Semantic Image Synthesis, Seamless Texture Synthesizers.

Which projects share features with alexjc/neural-doodle?

Projects with overlapping indexed features include: carson-katri/dream-textures — Dream Textures is a Stable Diffusion integration for Blender that provides tools for text-to-image generation, depth… junyanz/igan — iGAN is a framework for producing synthetic images using generative adversarial networks. It provides a web-based… lllyasviel/controlnet-v1-1-nightly — This project is a neural network extension for Stable Diffusion that provides spatial control and geometric… xpixelgroup/basicsr — BasicSR is a PyTorch-based image restoration toolbox and framework designed for training and deploying deep learning… dbolya/yolact — Yolact is a computer vision framework and real-time instance segmentation model. It utilizes a fully convolutional… jantic/deoldify — DeOldify is a deep learning system and a set of pre-trained computer vision models designed to apply realistic colors…