LLaVA VS Segment-Everything-Everywhere-All-At-Once

Update: For anyone else facing the commercial use question on LLaVA - it is licensed under Apache 2.0. Can be used commercially with attribution: https://github.com/haotian-liu/LLaVA/blob/main/LICENSE

https://github.com/haotian-liu/LLaVA (fairly established and well supported)

That's not a llamafile thing, that's a llava-v1.5-7b-q4 thing - you're running the LLaVA 1.5 model at a 7 billion parameter size further quantized to 4 bits (the q4).

GPT4-Vision is running a MUCH larger model than the tiny 7B 4GB LLaVA file in this example.

LLaVA have a 13B model available which might do better, though there's no chance it will be anywhere near as good as GPT-4 Vision. https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZO...

There are open source models such as https://github.com/THUDM/CogVLM and https://github.com/haotian-liu/LLaVA.

Hey Everyone,

I’ve been diving deep into the world of computer vision recently, and I’ve gotta say, things are getting pretty exciting! I stumbled upon this vision-language model called LLaVA (https://github.com/haotian-liu/LLaVA), and it’s been nothing short of impressive.

In the past, if you wanted to teach a model to recognize the color of your car in an image, you’d have to go through the tedious process of training it from scratch. But now, with models like LLaVA, all you need to do is prompt it with a question like “What’s the color of the car?” and bam – you get your answer, zero-shot style.

It’s kind of like what we’ve seen in the NLP world. People aren’t training language models from the ground up anymore; they’re taking pre-trained models and fine-tuning them for their specific needs. And it looks like we’re headed in the same direction with computer vision.

Imagine being able to extract insights from images with just a simple text prompt. Need to step it up a notch? A bit of fine-tuning can do wonders, and from my experiments, it can even outperform models trained from scratch. It’s like getting the best of both worlds!

But here’s the real kicker: these foundational models, thanks to their extensive training on massive datasets, have an incredible grasp of image representations. This means you can fine-tune them with just a handful of examples, saving you the trouble of collecting thousands of images. Indeed, they can even learn with a single example (https://www.fast.ai/posts/2023-09-04-learning-jumps)

Fuyu is not open source. At best, it is source-available. It's also not the only one.

A few other multimodal models that you can run locally include IDEFICS[0][1], LLaVA[2], and CogVLM[3]. I believe all of these have better licenses than Fuyu.

[0]: https://huggingface.co/blog/idefics

[1]: https://huggingface.co/HuggingFaceM4/idefics-80b-instruct

[2]: https://github.com/haotian-liu/LLaVA

[3]: https://github.com/THUDM/CogVLM

Researchers released LLaVA-1.5. LLaVA (Large Language and Vision Assistant) is an open-source large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. LLaVA-1.5 achieved SoTA on 11 benchmarks, with just simple modifications to the original LLaVA and completed training in ~1 day on a single 8-A100 node [Demo | Paper | GitHub].

Hi all, I’m wondering if there is a version of LLaVA https://github.com/haotian-liu/LLaVA that works with gguf and ggml models?? I know there is one for miniGPT4 but it just doesn’t seem as reliable as LLaVA but you need at least 24gb of vRAM for LLaVA to run it locally by the looks of it. The 4bit version still requires 12gb vram.

Yes, you can. The model that I was talking about LLaVA only output text but other models such as SEEM (https://github.com/UX-Decoder/Segment-Everything-Everywhere-...) outputs a segmentation map. You could prompt the model "Where is the pickleball in the image?" and get a segmentation map that you could then use to compute its center. Please let me know if you would be interested to have SEEM available in Datasaurus

I think people are just seeing the rate of progress and rightfully think that this stuff will be possible at some point. For the rotoscoping for example, here's an example of progress being made on that.

Yeah, it's definitely possible especially with all the recent advances. With segment anything systems (like SAM) and segmentation on NeRF reconstructions already being a thing the feasibility of this is more a time investment thing. Naive "scene understanding" is already possible in a few AR headsets at real-time, but the new papers in the past few weeks have made this much more trivial and faster to implement.

Play with the demo on GitHub! https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once