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.
Calculated based on the number of forks and stars on the official model repo.
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.
|#3||Diffusion-GAN||125M||GAN||Microsoft Azure AI||57||59||58|
|#5||StyleGAN-XL||1.1B||GAN||University of Tübingen||46||64||55|
|#6||PFGM++||13B||NCSN++/DDPM++||Massachusetts Institute of Technology (MIT)||79||29||54|
|#7||STF||770M||NCSN++/DDPM++||Massachusetts Institute of Technology||60||23||41.5|
|#8||MDT-XL2||6.7B||Diffusion||Sea AI Lab||57||23||40|
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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;
- Benchmark results
- Model forks
- Model stars
Capability Rating(CR) is calculated based on the average of the selected benchmark results(BR) published in the Model's research paper.
CR = Σ(BR)/COUNT(BR)
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.
AR = NORM(MS/MF)
The Model score is simply the average of scores Adoption Rating and Capability Rating.
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