Dreambooth-batch can be useful in a variety of scenarios. For example, it can be used in the field of computer vision research to quickly process a large dataset of images and generate corresponding output images, allowing researchers to efficiently analyze and compare the results. It can also be incorporated into image editing software, aiding photographers and designers in seamlessly transforming multiple images with different styles or visual effects simultaneously. Additionally, this tool has potential applications in virtual reality and gaming, enabling developers to easily generate diverse and visually appealing environments or characters from a set of input images. Overall, dreambooth-batch offers the opportunity for developers and researchers to accelerate their workflows and explore new possibilities in image-to-image translation tasks.
- Cost per run
- Avg run time
- Nvidia A100 (40GB) GPU
|Facial Landmark Detection||$0.0064||372|
You can use this area to play around with demo applications that incorporate the Dreambooth Batch 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||Dreambooth Batch|
batch inference for dreambooth trainings
|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||$0.069|
|Prediction Hardware||Nvidia A100 (40GB) GPU|
|Average Completion Time||30 seconds|