1M images is probably enough to do something, this user for example trained a diffusion model from scratch using 1.5M images and a 3090: https://medium.com/@enryu9000/anifusion-diffusion-models-for..., the quality of course is not excellent but it's something. I suggest to train a 4x64x64 diffusion model using the new SD XL VAE (it's a really good f8 VAE so it can encode, for example, images from 3x512x512 to 4x64x64), if the images have captions then I suggest using a CLIP text encoder as it was already trained on image text pairs, it would probably be much easier to use by a diffusion model trained on only 1M images instead of other text encoders like T5 that have better text understanding but they have never seen an image.

Quantity is massively more important, to the point that you get much better results by using a larger dataset with machine generated labelling (or object detection if that's what you're building) than using a smaller one with even expert labelling.

That said, if you've got a million photos you could probably do some pretty interesting things with very large scale fine tuning, or if you know many other people who have similar stockpiles of photos you may be able to get an entry-level dataset together if you all pool it.