[rom1504]

Looks like you missed the whole point of this dataset.

The idea that we proved is you can get a dataset with decent caption and images (that do match yes, you can see for yourself at https://rom1504.github.io/clip-retrieval/ ) that can be used to trained well performing models (eg openclip and stable diffusion) while using only automated filtering of a noisy source (common crawl)

We further proved that idea by using aesthetic prediction, nsfw and watermark tags to select the best pictures.

Is it possible to write caption manually? sure, but that doesn't scale much and won't make it possible to train general models.

[napier]

It's possible to so a much better job with automation too. Context aware cropping, accurate aspect ratio, quality filtering by various metric... all solved problems long ago, but absent from Laion-5B for some reason. Perhaps it would be a good idea go collaborate more closely with image experts for the next round.

[satvikpendem]

> Is it possible to write caption manually? sure, but that doesn't scale much and won't make it possible to train general models.

Maybe, I don't think so however based on the above comments by Unstable Diffusion. It seems like people are underestimating the power of high quality data and just throwing the kitchen sink at models. Perhaps a set of good quality data can indeed outperform Laion-style datasets.

It's like the YC saying about doing things that don't scale, perhaps with the high quality dataset, we can train better models than CLIP and in turn use those to caption the rest of the images, only now the caption model is much better than previous ones.

[GaggiX]

The new Unstable Diffusion model will be one of the several SD finetuned model out there, these models usually have much higher quality (but smaller image diversity) because they take the coherency of SD and costrain the distribution to a small high quality portion, this means that you can train a model on a smaller high quality dataset from scratch but you would not, for example, have the same level of coherency, this can only be obtained with an incredible amount of images, and they don't need to be "high quality", a man will almost always have 2 arms, 2 legs etc... regardless of the quality of the images, and after the model has fit the entire distribution you can finetune it to produce high quality and coherent images with a small dataset, that's why Unstable Diffusion will finetuned a SD checkpoint, also why researchers use these big dataset like LAION-400M/5B

[cma]

> and they don't need to be "high quality", a man will almost always have 2 arms, 2 legs etc...

At the next generation it feels like the training set will be inbreeding on the flood of stable diffusion images with 7 mangled fingers, heads coming out of legs, etc.

[GaggiX]

LAION-400M/5B will obviously not change (and there is enough data to train a really good model), if a future dataset has AI-generated images, these will be highly curated as the images were chosen by the person who was using the model and probably further shared by other users, it would work like a complicated Reinforcement Learning from Human Feedback (RLHF), plus AI-generated images will usually have keywords such as "AI," "Midjourney" in the caption so that the model can learn to distinguish them from the rest of the dataset (and CFG comes to the rescue when the dataset is noisy).

[version_five]

I'd guess there is a bias-variance tradeoff. If you just want to make a certain kind of image, no doubt a manually labeled and curated dataset can be better. If you want a generic generative model that has learned a wide variety of stuff, scale wins.

I can see LAION playing a similar role to imagenet. The main application of imagenet isn't directly training image recognition models. It's pertaining on diverse data so that a "big" (big in 2016) model can be fine tuned easily on a small dataset, after learning to be a good feature extractor. From that perspective, the label quality (and concerns about bias and whatnot) are almost irrelevant