Generative Text to Image Methods Leaderboard

Text to Image Methods

Generative text-to-image AI is redefining creativity. One of the most amazing aspects of the generative text-to-image is its ability to create realistic and detailed images from mere textual input. Several text-to-image methods have been published by researchers worldwide. But gathering relevant information about them can take time and effort, making it difficult to assess their quality. To address this issue, we have developed a comprehensive leaderboard that simplifies identifying the top methods. This leaderboard presents the information in an organized manner, allowing researchers to make informed decisions quickly. View Models


As of April 05, the top three leaders in the open-source text-to-image generative models are INDM, LSGM, and Diffusion-GAN. Based on our scoring methodology, these models scored 72.5, 69, and 58 points, respectively. The scoring methodology is explained below. The current leader is INDM - The Implicit Nonlinear Diffusion Model. It uses a normalizing flow to transform a linear latent diffusion to the data space, enabling nonlinear inference. INDM has advantages over other models, including fast optimization, learning of drift and volatility coefficients, MLE training, and robustness in sampling discretization. KAIST was established in 1971 as Korea's first research-oriented science and engineering special graduate school under the government's goal of economic development through science and technology.

RankModelSizeArchitectureOrganizationAdoption Rating
Calculated based on the number of forks and stars on the official model repo.
Capability Rating
Calculated based on the number of tasks and downstream tasks of the model.
A weighted average of the adoption and capability score of the model.
#3Diffusion-GAN125MGANMicrosoft Azure AI575958
#4LDM16BDiffusionRunway ML179455.5
#5StyleGAN-XL1.1BGANUniversity of Tübingen466455
#6PFGM++13BNCSN++/DDPM++Massachusetts Institute of Technology (MIT)792954
#7STF770MNCSN++/DDPM++Massachusetts Institute of Technology602341.5
#8MDT-XL26.7BDiffusionSea AI Lab572340

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We only considered prominent and open-source text-to-image models to create this leaderboard. Note that this leaderboard can only be considered a high-level indicator of overall performance. Depending on the specific use case and business requirements, a low-performing model in this leaderboard may be more favorable than a high-performer. The key parameters used for the scoring are;

  1. Benchmark results
  2. Model forks
  3. Model stars

Capability Rating(CR) is calculated based on the average of the selected benchmark results(BR) published in the Model's research paper.


Adoption Rating (AR) is calculated based on Model forks (MF) and Model Stars (MS). Model Stars directly indicate the community's acceptance of the Model. However, the number of stars does not necessarily mean the Model is used for project implementations. Model forks can indicate community adoption of the Model for building different applications. To calculate the adoption rating, we calculate the ratio of MS vs. MF and normalize the value to 100. 


The Model score is simply the average of scores Adoption Rating and Capability Rating. 

Generative AI Adoption Framework

This whitepaper will explore generative AI and identify business growth opportunities it offers. We aim to provide business owners with a comprehensive guide to using AI to unlock new opportunities and achieve sustainable growth. We will explore how generative AI can be used to analyze data and identify patterns, as well as how it can be used to generate new ideas and solutions.

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