Skip to content

Design-Arena/LeWitt-Bench

 
 

Repository files navigation

LeWitt-Bench

Instruction-based art offers a rare opportunity to test models’ ability to follow explicit, rule-based instructions—a core but often overlooked aspect of visual reasoning. We build upon Maximal Margin’s 2023 study to explore how faithfully models execute instructions in the domain of visual art and reveal whether technical progress has led to genuine compositional improvement.

The resulting LeWitt-Bench is a crowdsourced, subjective benchmark that collects human consensus—a noisy but critical vector of measuring what makes “good art”. A post-analysis will be conducted comparing live, subjective results against the verifiable domain of prompt adherence, marking a structured attempt to evaluate a non-verifiable task. Our motivation to create this benchmark can be found here.

image

The following code generates a dataset created by text-to-image models on Sol LeWitt's instruction-based art (75 instruction-image pairs). Data source: a subset of Sol LeWitt: A Wall Drawing Retrospective at MASS MoCA, spanning 1969 to 2007.

Quick Start

  1. Install dependencies:
pip install -r requirements.txt
  1. Set up API keys in .env:
OPENAI_API_KEY=your_key
GOOGLE_API_KEY=your_key
BLACK_FOREST_LABS_API_KEY=your_key
RECRAFT_API_KEY=your_key
IDEOGRAM_API_KEY=your_key
FAL_API_KEY=your_key
  1. Run the notebook:
jupyter notebook get_images.ipynb

Supported Models

Model Company / Organization
GPT-Image-1 OpenAI
Imagen 4 Ultra / Imagen 4 Google / DeepMind
Imagen 4 Generate Preview Google / DeepMind
Imagen 3 Generate 002 Google / DeepMind
FLUX.1 Kontext Max / FLUX.1 Kontext Pro / FLUX.1 Kontext Black Forest Labs
Recraft V3 Recraft
Ideogram 3.0 Ideogram AI
Qwen Image Alibaba

Usage

Test a single instruction:

# Cell 18 in notebook - tests all providers with first instruction

Batch process all instructions:

# Cell 19 in notebook - uncomment and configure providers

Parallel generation (Imagen models):

# Cell 20 in notebook - generates all images in parallel

About Instruction-Based Art

"Influenced by his time working in an architect's office, LeWitt would use assistants to produce three-dimensional works he called "structures." He wrote: "An architect doesn't go off with a shovel and dig his foundation and lay every brick. He's still an artist." Instead of executing the works of art himself, LeWitt comes up with an idea or plan for his art, usually a set of simple instructions—sometimes with line drawings. He then hands over the written plan to his assistants, and they construct the work. LeWitt's instructions are both specific and open-ended so that the resulting work of art varies according to the interpretation made by the draftsperson producing the work of art." — Sol LeWitt's Concepts and Structures

Attribution

This project was inspired by and initially based on lewitt_instructions by Maximal Margin.

We're grateful to Maximal Margin for her invitation to build on her foundational work. The original project is available under MIT License here.

Updates

Oct 5, 2025

Added support for the following image generation providers:

Model Company / Organization
GPT-Image-1 OpenAI
Imagen 4 Ultra / Imagen 4 Google / DeepMind
Imagen 4 Generate Preview Google / DeepMind
Imagen 3 Generate 002 Google / DeepMind
FLUX.1 Kontext Max / FLUX.1 Kontext Pro / FLUX.1 Kontext Black Forest Labs
Recraft V3 Recraft
Ideogram 3.0 Ideogram AI
Qwen Image Alibaba

Oct 15, 2023

Added manual evaluation results of DALL·E 3, Midjourney, and Stable Diffusion 2.1. Folder 20231015_eval contains the generated images and associated DALL·E 3 prompts.

About

A small dataset for Sol LeWitt’s instruction-based art.

Resources

License

Stars

4 stars

Watchers

0 watching

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages

  • Jupyter Notebook 100.0%