alexjc/neural-doodle

Health v2 · maintenance only

Flags: archived

Adoption not part of the score

9852 stars · 891 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A Python implementation of Semantic Style Transfer (Champandard, 2016) based on the Neural Patches algorithm. It turns rough doodles into refined artworks, generates seamless textures from photos, transfers style between images, and performs example-based upscaling using deep neural networks.

Use cases

• turn my doodle sketch into a painting with a neural network
• transfer the style of one image onto another photo
• generate seamless textures from a sample photo
• upscale a low-resolution image using an example high-res image
• create image analogies from a style image and its annotation
• render artwork in the style of a specific painter from a rough sketch

When to choose

• you want semantic, annotation-guided style transfer rather than simple style transfer
• you need to generate seamless textures or image analogies from annotated examples
• you have a CUDA-capable NVIDIA GPU and want research-style neural image synthesis
• you want a scriptable Python tool for patch-based neural image generation

When to avoid

• you need a fast, production-ready image generation pipeline with modern models
• you only have a CPU and cannot tolerate multi-hour render times
• you want an actively developed tool with recent updates and community support
• you need a GUI or one-click workflow rather than a command-line script with manual parameter tuning

Facets

library · maturity maintenance

image-processing deep-learning machine-learning image-processing deep-learning artificial-intelligence graphics python windows style-transfer semantic-style-transfer neural-doodle image-synthesis texture-generation image-analogy upscaling linux macos gpu

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