Retrieval Augmented Diffusion
Retrieval-augmented-diffusion has several potential use cases for a technical audience. One possible use case is in the field of content creation, where the model can be utilized to automatically generate images based on text descriptions, eliminating the need for manual image creation. This can be beneficial in various domains, including advertising, e-commerce, and graphic design, where image generation plays a crucial role. Additionally, the model can be employed in virtual reality and gaming industries to generate visual assets based on textual input, enabling immersive experiences. It can also be utilized in research and development, enabling researchers to quickly visualize concepts and ideas. Speculatively, products that could utilize this model include automated graphic design software, virtual reality platforms, and content generation tools for social media and advertising.
- Cost per run
- Avg run time
- Nvidia A100 (40GB) GPU
|Mannequin Gan 3 Electric Boogaloo 2||$?||850|
|Clip Guided Diffusion||$?||40,435|
|Glid 3 Xl||$0.011||7,882|
You can use this area to play around with demo applications that incorporate the Retrieval Augmented 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||Retrieval Augmented Diffusion|
Generate 768px images from text using CompVis `retrieval-augmented-diffusio...Read more »
|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||$0.0552|
|Prediction Hardware||Nvidia A100 (40GB) GPU|
|Average Completion Time||24 seconds|