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The ic_gan model is an instance-conditioned Generative Adversarial Network (GAN) used for Image-to-Image tasks. The model uses a specific source image, in this case a dog, and generates varied samples based on the provided input conditions. Users may set these conditions which can include the 'seed' for random number generation, the 'gen_model' to utilize, the 'num_samples' to generate, and the 'conditional_class' which indicates the specific breed of the dog. The output of the model is a link to an image file that contains the generated samples.

Use cases

The instance-conditioned GAN (ic_gan) could be utilized in a range of digital image manipulation and generation scenarios. As an image-to-image model, ic_gan can manipulate digital images based on parameters and specifications provided, such as the number of samples and class condition. A primary application of this model could be in the development of digital art and design: for example, graphic designers could use it to generate multiple versions of a particular image, like the "Sussex_spaniel" dog image in the model's input schema. Additionally, this technology could find use in the entertainment industry to create concept art or even characters for video games or animated films. Moreover, in e-commerce platforms, the AI could quickly generate various product images for listings, saving time and effort for the sellers. A possible product could be a user-friendly software that allows individuals with little or no AI expertise to effortlessly generate or modify images as per their needs.


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Creator Models

Codellama 13b Python$?3,775
Llama 2 70b$?157,701
Codellama 70b Instruct$?13,091
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Summary of this model and related resources.

Model NameIc_gan
Instance-Conditioned GAN
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv


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