![]() These models today are just the beginning. This architecture makes it easier to train the main models while maintaining our commitment to responsible AI and, at the same time, giving you a deep level of artistic control. This small secondary model is private to you or your organization as its trainers, and we will never use this content to train our main models.īecause our models focus on photorealism, we did not have to train our main models on countless different styles. This creates a small secondary model that works in tandem with the main model to guide its outputs. These models are designed to only have a basic understanding of stylization and are primarily focused on photorealism.Īdditionally, if you want to guide the models to match an existing style in your project, you can teach our models how to create content in a specific art style by providing our style training system a handful of the your own reference assets. The first iterations of the models that power these tools are internally referred to as Photo-Real-Unity-Texture-1 and Photo-Real-Unity-Sprite-1. ![]() Our training datasets for the latent diffusion models underpinning Muse’s Texture and Sprite capabilities do not comprise any data scraped from the internet.īelow are some examples of content expanded through the augmentation techniques described above.Īt Unite, we announced early access for Muse’s Texture and Sprite capabilities. ![]() We also applied additional mitigations on top of this that we’ll describe below. By using the Stable Diffusion model minimally as part of our data augmentation techniques, we were able to safely leverage this model to expand our original library of Unity-owned assets into a robust and diverse repository of outputs that are unique, original, and do not contain any copyrighted artistic styles. We limited our reliance on pretrained models as we built Muse’s Texture and Sprite capabilities by training a latent diffusion model architecture from scratch, on original datasets that Unity owns and has responsibly curated. Recently, Stable Diffusion has been the subject of ethical concerns because the model was originally trained on data scraped from the internet. We also utilize techniques like geometric transformations, color space adjustments, noise injection, and sample variations with generative models, such as Stable Diffusion, to synthetically expand our dataset. This significantly enriches our training sets and enhances the models’ ability to generalize from limited samples. One key technique we employ to enhance the scale and variety of our datasets is data augmentation, which allows us to produce many variations from original Unity-owned data samples.
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