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The High-Fidelity GAN Inversion (HFGI) model is a deep learning model designed for image attribute editing. It takes an input image and a target attribute, and generates a new image that possesses the desired attribute while maintaining high visual fidelity. The model achieves this by leveraging Generative Adversarial Networks (GANs) and an inversion algorithm, which enables it to directly manipulate the latent space of the generator network. This allows for precise control over image attributes and produces high-quality results.

Use cases

The HFGI model has a wide range of potential use cases in various industries. For instance, in the fashion industry, it could be utilized to create virtual clothing try-on platforms, where customers can see how different outfits would look on them without actually trying them on. This would save time and resources for both the customers and the retailers. In the entertainment industry, the HFGI model could be used to create highly realistic and customizable avatars for video games and virtual reality experiences. It could also be employed in image editing software, allowing users to easily modify specific attributes of their photos while maintaining the overall visual quality. Furthermore, the HFGI model could have applications in digital advertising, where targeted image attribute editing could be used to customize advertisements based on individual preferences, leading to more effective marketing campaigns. Overall, the HFGI model has the potential to revolutionize image attribute editing and open up new possibilities in various domains.



Cost per run
Avg run time
Nvidia T4 GPU

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Summary of this model and related resources.

Model NameHfgi
High-Fidelity GAN Inversion for Image Attribute Editing
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv


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Model Rank
Creator Rank


How much does it cost to run this model? How long, on average, does it take to complete a run?

Cost per Run$0.00275
Prediction HardwareNvidia T4 GPU
Average Completion Time5 seconds