[ganarajpr]

Awesome project. Exactly what I was looking for.

When it produces a set of images for a given prompt, wouldnt it be better if we could remove a set of images from the possible selection ? Does it not work this way? Another idea would be to provide a few different kinds of prompts and based on that select all the images that matter for the given "class".

Some other things that would be good to know:

1. Can we keep adding items to the classifier? and getting newer versions of the classifier with the newly added item ? 2. How to deploy and host this kind of models? Is there any guidelines on how to deploy this in AWS or GCS for production use cases ?

[nharada]

You mean you want to remove the images because you get false positives? We've thought about dataset curation and how to manage that, ranging from full on "you can build your own dataset from scratch" to "refine results using ChatGPT/V".

Deployment guidelines are a good idea! It's fairly straightforward to deploy since it's just a Python package and you can run it via CPU or GPU. With CPU we deploy using ONNX which means the dependency list is quite small (compared to torch). For example, the part on the web app which tests your model is just deployed to AWS Lambda.

Would having us host the models be useful or something worth paying for? Obviously we couldn't offer that for free, but may be able to offer an endpoint for your model that is pay-per-call.