BachiLi/diffvg

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1268 stars · 210 forks observed · 2026-08-28

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

diffvg is a differentiable rasterizer for 2D vector graphics that bridges the raster and vector domains via backpropagation. It computes pixel gradients with respect to curve parameters, enabling optimization and machine learning on vector content.

Use cases

• fit vector shapes to a target image with gradient descent
• painterly rendering by fitting bezier curves to a photo
• optimize svg parameters against raster image losses
• train neural networks that generate vector graphics
• interactive vector editing guided by image metrics
• vectorize images with differentiable optimization

When to choose

• you need gradients of rasterized images with respect to vector shape parameters
• you are doing research in differentiable rendering or vector graphics generation
• you want to fit svg primitives to images using perceptual losses

When to avoid

• you just need fast non-differentiable svg rendering
• you need a production vector graphics editor
• you are not working in a PyTorch-based workflow

Facets

library · maturity active

graphics image-processing machine-learning parser graphics machine-learning computer-vision python cpp cross-platform differentiable-rendering vector-graphics svg pytorch rasterization computer-graphics research gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="BachiLi/diffvg")