Dreamshaper has the potential to revolutionize various industries by enabling the generation of images based on text prompts. In the field of entertainment, it can be used to create visuals for video games, animations, and movies, bridging the gap between imagination and reality. It could also find applications in architecture and interior design, allowing designers to visualize their concepts and designs before implementation. In the e-commerce sector, Dreamshaper could be utilized to automatically generate product images based on descriptions, making online shopping more immersive and engaging. Furthermore, it could assist in enhancing virtual reality experiences by generating lifelike environments and characters. The possibilities are vast, and innovative products leveraging Dreamshaper's text-to-image capabilities are sure to emerge, revolutionizing how we visualize and depict ideas in various domains.
- Cost per run
- Avg run time
- Nvidia A100 (40GB) 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 Dreamshaper 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.
Dream Shaper stable diffusion
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||No Github link provided|
|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||$0.0092|
|Prediction Hardware||Nvidia A100 (40GB) GPU|
|Average Completion Time||4 seconds|