Diffusion Autoencoders (DiffAE) have a wide range of use cases in image manipulation. For photographers and designers, this model can be used to easily remove unwanted objects or alter the colors of an image. It can also be used for artistic purposes, such as creating stylized versions of images or generating new images with specific visual characteristics. In the field of computer graphics, DiffAE can be used to generate realistic textures or modify the appearance of 3D models. Additionally, this model could find applications in the fashion industry, allowing users to experiment with different styles and colors of clothing on a virtual model. Overall, DiffAE has the potential to be integrated into various software tools and platforms, enabling users to easily perform complex image manipulations in a controlled and visually appealing manner. Potential products or practical uses of this model could include a photo editing software with advanced image manipulation features, a virtual try-on application for clothing retailers, or an augmented reality tool for creating realistic and interactive virtual environments.
- 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 Diffae 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.
Image Manipulatinon with Diffusion Autoencoders
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|Paper Link||View on Arxiv|
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||$0.02365|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||43 seconds|