SagiPolaczek/NeuralSVG

Health v2 · maintenance only

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1421 stars · 32 forks observed · 2026-09-27

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of NeuralSVG, an ICCV 2025 paper that generates layered, editable SVG vector graphics from text prompts. It encodes a scene into a small MLP optimized with Score Distillation Sampling, supporting inference-time control like background color and aspect ratio.

Use cases

• generate svg vector graphics from a text prompt
• text-to-vector image generation with layered shapes
• create editable svg illustrations from text
• control background color of generated svg
• generate svgs with different aspect ratios
• research code for score distillation sampling svg generation

When to choose

• you need structured, layered, editable SVG output rather than raster images
• you want inference-time control (color palette, aspect ratio) from a single learned representation
• you are reproducing or building on the NeuralSVG research

When to avoid

• you need fast, real-time generation - SDS optimization is slow
• you want a production text-to-image service with a polished UI
• you lack a GPU or don't want to run diffusion models locally

Facets

library · maturity active

machine-learning deep-learning image-processing graphics machine-learning computer-vision graphics artificial-intelligence python windows text-to-vector svg-generation score-distillation implicit-neural-representation diffusion-models research-code iccv-2025 gpu linux macos

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