The 'illusion' AI model, offered by Monster Labs, utilizes ControlNet technology on top of SD 1.5 to generate images from text descriptions. This could be used in a variety of practical applications and products across several sectors. For instance, artists and designers can use this to experiment with unique visuals and patterns, creating initial drafts or getting inspiration for oil paintings or illustrating medieval city streets, buildings, people, and more. By setting desired parameters, they could control the output for visual clarity, quality and avoid unwanted themes. In the entertainment industry, this model could visualize fictional worlds or props for gaming, novels, films, or comics. As it can generate QR codes, businesses can use it for advertising, where content changes dynamically based on the input. Moreover, educators can use it to create visual aids to help students understand historic or complex concepts that are easier to comprehend visually. Lastly, this can be incorporated into apps that help visually impaired individuals by transforming descriptive words into images they can sense using other technologies.
- Cost per run
- Avg run time
|Llama 2 13b Embeddings||$?||172,645|
|Codellama 7b Instruct Gguf||$?||46|
|Llama 2 13b Chat Gguf||$?||1,352|
|Codellama 34b Instruct Gguf||$?||60|
You can use this area to play around with demo applications that incorporate the Illusion 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.
Monster Labs' control_v1p_sd15_qrcode_monster ControlNet on top of SD 1.5
|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||$-|
|Average Completion Time||-|