The pix2pix-zero model has several potential use cases for technical audiences. One use case could be in the field of computer graphics, where the model could be used to generate realistic images based on rough sketches or low-resolution input. This could be particularly useful in game development, where artists could quickly iterate on designs by sketching out rough ideas and using the model to generate high-quality images. Another use case could be in the field of urban planning and architecture, where the model could be used to generate realistic maps based on aerial images. This could aid in the visualization of proposed architectural designs or in the creation of detailed urban planning models. In addition, the model could be used in the field of image editing and enhancement, allowing users to easily transform images into a desired style or apply artistic filters without needing to train the model on specific styles or filters. Overall, the pix2pix-zero model has the potential to simplify and streamline various image translation tasks, opening up new possibilities in computer graphics, urban planning, and image editing.
- Cost per run
- Avg run time
- Nvidia A100 (40GB) GPU
|Compositional Vsual Generation With Composable Diffusion Models Pytorch
|Stable Diffusion Aesthetic Gradients
You can use this area to play around with demo applications that incorporate the Pix2pix Zero 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.
Zero-shot Image-to-Image Translation
|View on Replicate
|View on Replicate
|View on Github
|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
|Nvidia A100 (40GB) GPU
|Average Completion Time