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1. This is a VLM, not a text-to-image model. You can give it images, and it can understand them. It doesn't generate images back.
2. It seems like Pixtral 12B benchmarks significantly below Qwen2-VL-7B [1], so if you want the best local model for understanding images, probably use Qwen2. If you want a large open-source model, Qwen2-VL-72B is most likely the best option.
Couple notes for newcomers: 1. This is a VLM, not a text-to-image model. You can give it images, and it can understand them. It doesn't generate images back. 2. It seems like Pixtral 12B benchmarks significantly below Qwen2-VL-7B [1], so if you want the best local model for understanding images, probably use Qwen2. If you want a large open-source model, Qwen2-VL-72B is most likely the best option. 1: https://qwenlm.g…
Only the 2&7B have been "open sourced". From your link:
>We opensource Qwen2-VL-2B and Qwen2-VL-7B with Apache 2.0 license, and we release the API of Qwen2-VL-72B!
The "Mistral Pixtral multimodal model" really rolls off the tongue. > It’s unclear which image data Mistral might have used to develop Pixtral 12B. The days of free web scraping especially for the richer sources of material are almost gone, with anything between technical (API restrictions) and legal (copyright) measures building deep moats. I also wonder what they trained it on. They're not Meta or Google with endle…
You don't need a contract with reddit to scrape it, you can just add `.json` to any url and you'll get the entire thread as one object.
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What do you mean by copyright measures? Has anything changed on that front in the last two years? My hunch is that most AI labs are already sitting on a pretty sizable collection of scraped image data - and that data from two years ago will be almost as effective as data scraped today, at least as far as image training goes.
The issue with image models is that their style becomes identifiable and stale quite quickly, so you’ll need a fresh intake of different, newer, styles every so often and that’s going to be harder and harder to get.
Mistral being more open than 'openai' is kind of a meme. How can a company call itself open while it refuses to openly distribute it's product and when competitor are actually doing it.
Also, can your model of choice understand your requests to include/omit particular nuances of an image?
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The issue with image models is that their style becomes identifiable and stale quite quickly, so you’ll need a fresh intake of different, newer, styles every so often and that’s going to be harder and harder to get.
The style becoming identifiable and stale has mostly to do with CFG and almost nothing with the dataset, the heavy use of CFG by most models trades diversity with coherency. You don't need a costant intake of new images and styles, it's like saying that an image created two years ago is stale because it doesn't follow a new style or something. Also Pixtral is not a text-to-image model.
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The issue with image models is that their style becomes identifiable and stale quite quickly, so you’ll need a fresh intake of different, newer, styles every so often and that’s going to be harder and harder to get.
solvable without additional images
If an artist born today develops their own style that takes the world by storm in 20years, the image generators of the time (for this thought experiment, imagine we’re using the same image gen techniques as today) would not know about it. They wouldn’t be able to replicate it until they get enough training data on that style.
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The issue with image models is that their style becomes identifiable and stale quite quickly, so you’ll need a fresh intake of different, newer, styles every so often and that’s going to be harder and harder to get.
Train LoRas for models that can take them
The more and more platforms lock down access to their data, the harder it’ll be for models to stay up to date on art trends.
We just haven’t had image gen around long enough to witness a major style change like the skeuomorphic iPhone icons of old to the new modern flat ones.