Elden Ring Diffusion
The Elden Ring Diffusion model has a range of potential use cases for a technical user. Game developers can utilize this model to generate concept art or visual assets for their own game projects. Designers and artists can use it as a source of inspiration or a tool to quickly generate mock-ups or prototypes. Additionally, the generated images can be integrated into marketing materials such as posters, advertisements, or social media posts, enhancing the visual appeal of their promotional content. With its ability to generate high-quality and game-inspired images, the Elden Ring Diffusion model opens up opportunities for innovative products in the gaming industry, such as game merchandise, art prints, or even customizable in-game assets. Furthermore, this technology could be integrated into game engines or design software, empowering users with the ability to generate personalized game assets based on their text prompts. Overall, the Elden Ring Diffusion model holds significant potential for various creative and practical applications within the gaming and design domains.
- 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 Elden Ring Diffusion 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||Elden Ring Diffusion|
fine-tuned Stable Diffusion model trained on the game art from Elden Ring
|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||$0.01265|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||23 seconds|