Ask HN: How to get into AI generation(images,text)
Hey! I am a software engineer with vast experience of building full stack application and lately I’ve been really mesmerized by popping up tools that utilize AI to solve common day to day problems. Generating blog outlines from couple of lines of text, creating realistic avatars of yourself in different settings, generating art from text prompts. I’ve never even had touch points with such technologies so it’s quite overwhelming for me in terms where to start!
Do I need to know the basics? Shall I just utilize the existing solutions like gpt-3, openAI, stable diffusion and built applications with them? Can I make those tools tailored for my uses cases(model training) or I should built similar from the scratch?
Looking for advice!
13 comments
[ 0.26 ms ] story [ 42.3 ms ] threadThere is a pull request to integrate Dreambooth into Stable Diffusion, which I am already a heavy user of - https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull... . This makes it all push-button simple, and integrated with all the other Automatic1111 Stable Diffusion scripts.
While with the release of Stable Diffusion there has been a lot of innovation in text to image AI in the past two months, there are problem sets other than that, and Python libraries like scikit-learn ( https://scikit-learn.org/stable/tutorial/machine_learning_ma... ) use machine learning in other domains as well.
From there, if you're interested in how it works, I highly recommend the last 4 videos on Jeremy Howard's youtube channel: https://www.youtube.com/user/howardjeremyp/videos
He's currently teaching a class on stable diffusion from the ground up and these lectures give a really good introduction to how it all works.
2. Learn to use the Hugging Face library, and use their stuff on your Notebooks.
3. Learn some ML theory so you can understand hyperparameters better, and can tweak them in a better way.
____
If you want to get into training models by yourself from scratch, you have to learn in a deeper manner, and cannot overlook learning ML theory in a deeper manner.
____
The most obvious ways would be:
1. Looking into stuff that John Whitaker does [0] and his elaborate free course on AI Art [1].
2. Learning ML from scratch starting from Andrew Ng ML, then going to DL, then learning about GANs.
3. Learning from fast.ai through their two-part course on Deep Learning, where Stable Diffusion is now being taught. Then learn PyTorch from another place like Sebastian Raschka's book.
4. Watching old videos from Stanford CS231n when Karpathy was a TA, and taught in the class. Then Deep Dream was standard.
_____
If you are a responsible, mature person, and you are in it for the long term, and have deep pockets, buy some GPU. 2x 3090 is reasonable, and should be enough.
____
Let me know if you have any further questions.
[0]: https://datasciencecastnet.home.blog/
[1]: https://youtube.com/playlist?list=PL23FjyM69j910zCdDFVWcjSIK...
Renting on cloud platforms is $$$. But sure, you can start by trying there. After the crypto crash, GPU prices have dropped a lot, too.
Also, building a rig with GPUs, and SSHing into it from a Macbook is pretty common.
And also, be aware of roadmaps. Eevry person is different, and their roadmaps should as well be. Make a roadmap through trial and error by trying out many sources. I told you what I think are the best.
_____
Forgot to mention my Kaggle Notebook [0] on Hugging Face API through which you can start generating text today using their pretrained models available for download.
[0]: https://www.kaggle.com/code/truthr/a-gentle-introduction-to-...