I Made Stable Diffusion XL Smarter by Finetuning It on Bad AI-Generated Images

I'm pretty sure that it's just serially summing the network weights, which results in an accumulated offset to the self-attention layers of the transformer. It's not doing any kind of analysis of multiple networks prior to application to make them "play nice" together; it's just looping and summing.

https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob...

In general I'm really interested by the concept of personalized RLHF. As we have more and more interactions with a given generative AI system, it seems we'll start to have enough interaction data to meaningfully steer the output towards our personal preferences. I hope the UIs improve to make this as transparent as possible.

Just thinking about how to productize this flow, it should be quite easy to implement the "thumbs up/down" feedback option on every image generated in the UI, plus an optional text label to override "wrong". Then when you have enough HF (or nightly) you could have a batch job to re-train a new LoRA with your updated preferences.

In principle you could collect HF from the implicit tree-traversal that happens when you generate N candidate images from a prompt and then pick one to refine. Or more explicitly, have a quick UI to rank/score a batch, or a trash bin in the digital workspace to discard images you don't like at each iteration of refinement (batching that negative feedback to update your project/global LoRA later).

Going further I wonder what the fastest possible iteration loop for feedback would be? For images in particular you should be able to wire up a very short feedback loop with keypresses in response to image generation. What happens if you strap yourself to that rig for a few hours and collect ~10k preferences at 1/s? Can you get the model to be substantially more likely to output the sort of images that you're personally going to like? Also sounds pretty intense, I'm getting Clockwork Orange vibes.

I didn't spot in the article, how many `wrong` images were there? From a quick skim of the code it looks like maybe 6 per keyword with 13 keywords, so not many at all. ~100 is surprisingly little feedback to steer the model this well.

You will get more users if you provide a safetensors file instead of bin and pickletensors a lot of people have gotten really scared by the malware scare that was going through social media a few months ago.

Tangentially related: for reasons I don't yet really understand, the LORAs that I build for Stable Diffusion XL only work well if I give a pretty generic negative prompt.

These are fine-tuned on 6 photos of my face, and if I use them with positive prompts, the generated characters don't look much like me. But if I add generic negative terms like "low quality", suddenly the depiction of my face is almost exactly right.

I've trained several models and this has been true across a range of learning rates and number of training epochs.

To me, this feels like it will somehow ultimately be connected to whatever is driving minimaxir's observations in this post.

Yes, currently SDXL doesn't really beat the best SD1.5 checkpoints quality-wise. But it (and the currently available checkpoints) shows awesome promise, so give it a six months or so.