design-generators

design-generators ports layout, poster, and graphic-design generation research repositories into framework-specific packages for 🤗transformers, 🧨diffusers, and 🤖pydantic-ai that can load converted weights or prompt configuration and run inference through a consistent public schema.

Highlights

• ⚡ Run in minutes: converted weights load with from_pretrained; no original-repository setup required.
• 📐 One output schema: generation models share a single interface with jaxtyping-shaped tensor annotations.
• ✅ Reference-verified: our ports are numerically checked against the original implementations, with copy-pasteable reproduction commands per package.

Models

Framework, task, and dataset details are generated in the Models documentation.

Libraries

Quick Start

Install the shared layout library directly from this repository:

pip install "laygen @ git+https://github.com/creative-graphic-design/design-generators.git#subdirectory=lib/laygen"

Model packages depend on workspace libraries that are not published on PyPI, so install laygen together with the model package:

pip install \
  "laygen @ git+https://github.com/creative-graphic-design/design-generators.git#subdirectory=lib/laygen" \
  "layout-dm @ git+https://github.com/creative-graphic-design/design-generators.git#subdirectory=models/layout-dm"

from layout_dm import LayoutDMPipeline

print(LayoutDMPipeline.__name__)

For development, clone the repository and run member commands from the repository root with uv run --package <member> ...; each package documents its reproduction workflow in REPRODUCING.md.

git clone https://github.com/creative-graphic-design/design-generators.git
cd design-generators
uv run --package layout-dm pytest models/layout-dm/tests -m "not vendor_parity and not integration"

Policies

Reproducibility

Every port is verified against the original implementation: agreement checks (the repository's vendor_parity tests) compare package outputs with references generated by running the original implementation in vendor/. Each package's REPRODUCING.md shows how to reproduce the check, and its README reports the resulting comparison numbers.

Training

For models we reproduce by training ourselves, each package provides PyTorch Lightning modules, LightningCLI configs, and package-specific training commands. Shared training helpers live in lib/traingen, parity helpers live in lib/traingen-parity, and runnable workflows are documented in each package's TRAINING.md.

Extension

Extend by installing pinned released packages and subclassing; never copy framework or repository source trees into your project and mutate them in place. This applies to human developers and AI coding agents alike; see Extending.

Documentation

See the design-generators documentation for guides and the generated API reference for workspace libraries and model packages.

License

Repository code is licensed under Apache-2.0; see LICENSE. Converted weights, datasets, and vendored upstream code carry their original licenses.