vertex-ai-samples VS IF

Google Cloud Platform (GCP) provides a very befitting Machine Learning solution called Vertex Ai that handles Google Cloud's unified platform for building, deploying, and managing machine learning (ML) models. Our goal is to build a simple Machine Learning application that optimizes all that GCP provides plus an implementation of continuous integration and continuous development (CI/CD).

Cross posting some links from another post that HNers found helpful

- https://cloud.google.com/vertex-ai (marketing page)

- https://cloud.google.com/vertex-ai/docs (docs entry point)

- https://console.cloud.google.com/vertex-ai (cloud console)

- https://console.cloud.google.com/vertex-ai/model-garden (all the models)

- https://console.cloud.google.com/vertex-ai/generative (studio / playground)

VertexAI is the umbrella for all of the Google models available through their cloud platform.

For the peer comments

- https://cloud.google.com/vertex-ai (main page)

- https://cloud.google.com/vertex-ai/docs/start/introduction-u... (docs entry point)

- https://console.cloud.google.com/vertex-ai (cloud console)

Starting on December 13, developers and enterprise customers can access Gemini Pro via the Gemini API in Google AI Studio or Google Cloud Vertex AI.

Google Cloud Platform (https://cloud.google.com/vertex-ai): Conversely, Google Cloud Platform (GCP) provides a comprehensive suite of AI and machine learning services, including APIs for vision, language, conversation, and structured data analysis. Whether you're analyzing images, interpreting human speech, or diving deep into data patterns, GCP has something for you.

VertexAI is a machine learning platform available on Google Cloud. It offers a variety of services to train and deploy AI models, including those for Generative AI.

Depending on how much work you want to put into it, you can get started at HuggingFace with their models and datasets, but you'd need compute power, multiple MLOps, etc. I was introduced to the concept in this video, since Google has their Vertex AI tools on Google Cloud, and there's always LangChain but I'm not sure about anything recent.

Stability AI has gaps in SDXL for text, but they seem to do a better job with Deep Floyd ( https://github.com/deep-floyd/IF ). I have done a lot of interesting text things with Deep Floyd

They made https://github.com/deep-floyd/IF IF model which has a stronger understanding of text.

https://github.com/deep-floyd/IF It's a bit difficult to install right now, I can't install it on my card unfortunately. They had a huggingface space you could play with it but it seems offline right now. Hopefully it'll be incorporated into Auto1111 eventually.

I'm pretty sure this is the best open model for image + text: https://github.com/deep-floyd/IF

Text will be better due to simple scale, but the text will still be limited due to the use of a CLIP for text encoding (BPEs+contrastive). So that may be SD XL 0.9 but it should still be worse due to not using T5 like https://github.com/deep-floyd/IF

Ate you thinking of DeepFloyd IF? https://github.com/deep-floyd/IF

Or maybe people are conflating it with Deep Floyd, another SAI model: https://github.com/deep-floyd/IF

Deep Floyd IF does text really well and is out. https://github.com/deep-floyd/IF

use https://github.com/deep-floyd/IF, it uses LLM to generate exact art you need.