21–30 of 178 posts
Earlier quoted context omitted.
> As a blind person, AI has changed my life. Something one doesn't see in news headlines. Happy to see this comment.
+1 and I would be curious to read and learn more about it.
Looking at text to video examples ( https://starflow-v.github.io/#text-to-video ) I'm not impressed. Those gave me the feeling of the early Will Smith noodles videos. Did I miss anything?
These are ~2 years behind state of the art from the looks of it. Still cool that they're releasing anything that's open for researchers to play with, but it's nothing groundbreaking.
Apple has a video understanding model too. I can't wait to find out what accessibility stuff they'll do with the models. As a blind person, AI has changed my life.
> As a blind person, AI has changed my life. Something one doesn't see in news headlines. Happy to see this comment.
I hope this wasn't a terrible pun
"VAE: WAN2.2-VAE" so it's just a Wan2.2 edit, compressed to 7B.
Earlier quoted context omitted.
I guess that auto-generated audio descriptions for (almost?) any video you want is a very, very nice feature for a blind person.
My two cents, this seems like a case where it’s better to wait for the person’s response instead of guessing.
Apple has a video understanding model too. I can't wait to find out what accessibility stuff they'll do with the models. As a blind person, AI has changed my life.
Can you share some ways AI has changed your life?
Earlier quoted context omitted.
These are ~2 years behind state of the art from the looks of it. Still cool that they're releasing anything that's open for researchers to play with, but it's nothing groundbreaking.
But 7b is rather small no? Are other open weight video models also this small? Can this run on a single consumer card?
"VAE: WAN2.2-VAE" so it's just a Wan2.2 edit, compressed to 7B.
A little bit more background for those who don't know what a VAE is (I'm simplifying here, so bear with me): it's essentially a model which turns raw RGB images into a something called a "latent space". You can think of it as a fancy "color" space, but on steroids.
There are two main reasons for this: one is to make the model which does the actual useful work more computationally efficient. VAEs usually downscale the spatial dimensions of the images they ingest, so your model now instead of having to process a 1024x1024 image needs to work on only a 256x256 image. (However they often do increase the number of channels to compensate, but I digress.)
The other reason is that, unlike raw RGB space, the latent space is actually a higher level representation of the image.
Training a VAE isn't the most interesting part of image models, and while it is tricky, it's done entirely in an unsupervised manner. You give the VAE an RGB image, have it convert it to latent space, then have it convert it back to RGB, you take a diff between the input RGB image and the output RGB image, and that's the signal you use when training them (in reality it's a little more complex, but, again, I'm simplifying here to make the explanation more clear). So it makes sense to reuse them, and concentrate on the actually interesting parts of an image generation model.
Apple has a video understanding model too. I can't wait to find out what accessibility stuff they'll do with the models. As a blind person, AI has changed my life.