Welcome to PyTorch-SVGRender documentation!

Pytorch-SVGRender is the go-to library for state-of-the-art differentiable rendering methods for image vectorization.

PyTorch-SVGRender originated within academic research and has since undergone extensive development and consolidation through practical implementation, driven by the overarching objective of propelling advancements in the realm of vector graphics rendering.

Note

This project is under active development.

Installation

Installation: How to install the PyTorch-SVGRender.

Table of Contents

DiffVG: Differentiable Vector Graphics Rasterization for Editing and Learning: Differentiable Vector Graphics Rasterization.
CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders: Task: Text-to-SVG
StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Translation: Task: Text-and-Img-to-SVG
LIVE: Towards Layer-wise Image Vectorization: Task: Img-to-SVG
CLIPFont: Texture Guided Vector WordArt Generation: Task: Text-and-Glyph-to-Glyph
CLIPasso: Semantically-Aware Object Sketching: Task: Img-to-Sketch
CLIPascene: Scene Sketching with Different Types and Levels of Abstraction: Task: Img-to-Sketch
VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models: Task: Text-to-SVG
DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models: Task: Text-to-Sketch
Word-As-Image: Word-As-Image for Semantic Typography: Task: Text-and-Glyph-to-Glyph
SVGDreamer: Text Guided SVG Generation with Diffusion Model: Task: Text-to-SVG

API Documentation

API: TODO

About Us

Authors:: Ximing Xing and Juncheng Hu
Contact:: ximingxing@gmail.com
Organization:: https://huggingface.co/SVGRender @BUAA
Status:: This is a “work in progress”
Version:: 1.0
Licence:: This work is licensed under a Mozilla Public License Version 2.0