The aesthetic-predictor model has several potential use cases for a technical audience. One use case could be in the field of photography, where the model could be used to automatically rate the aesthetic quality of images, helping photographers select their best shots or providing feedback for improvement. Another use case could be in the design industry, where the model could assist in evaluating the visual appeal of different design elements or layouts. Additionally, the model could be incorporated into social media platforms or photo sharing apps to provide users with automated suggestions for enhancing the aesthetic quality of their photos. Overall, the aesthetic-predictor model offers a range of possibilities for creating products or practical applications that leverage its ability to assess and enhance aesthetics in visual content.
- Cost per run
- Avg run time
- Nvidia T4 GPU
|Compositional Vsual Generation With Composable Diffusion Models Pytorch||$0.01155||774|
You can use this area to play around with demo applications that incorporate the Aesthetic Predictor model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.
Currently, there are no demos available for this model.
Summary of this model and related resources.
|Model Name||Aesthetic Predictor|
A linear estimator on top of clip to predict the aesthetic quality of pictu...Read more »
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|Paper Link||No paper link provided|
How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run||$0.0011|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||2 seconds|