1. It depends, if you just want to
use some model and call APIs, then you do not have to learn any ML theory. You just have to learn using libraries following their GitHub Readme instructions. Get a Colab Pro+ subscription or run Kaggle Notebooks for free. You can also simply use GUIs built on top of Open Source models.
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.
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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.
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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.
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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.
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Let me know if you have any further questions.
[0]: https://datasciencecastnet.home.blog/
[1]: https://youtube.com/playlist?list=PL23FjyM69j910zCdDFVWcjSIK...
