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Stable Video Diffusion

stability.ai

121–130 of 316 posts

Re: Stable Video Diffusion

#121

Looks like I'm still good for my bet with some friends that before 2028 a team of 5-10 people will create a blockbuster style movie that today costs 100+ million USD on a shoestring budget and we won't be able to tell.

I'm imagining more of an AI that takes a standard movie screenplay and a sidecar file, similar to a CSS file for the web and generates the movie. This sidecar file would contain the "director" of the movie, with camera angles, shot length and speed, color grading, etc. Don't like how the new Dune movie looks? Edit the stylesheet and make it your own. Personalized remixed blockbusters. On a more serious note, I don't…

> thousand Beatles out there. It still requires talent and effort

As well as marketing.

Re: Stable Video Diffusion

#122
post #111
post #104

Earlier quoted context omitted.

> you'll describe something and you'll get a full 3D scene, with 3D models, source of lights set up, etc. I'm always confused why I don't hear more about projects going in this direction. Controlnets are great, but there's still quite a lot of hallucination and other tiny mistakes that a skilled human would never make.

Blender files are dramatically more complex than any image format, which are basically all just 2D arrays of 3-value vectors. The blender filetype uses a weird DNA/RNA struct system that would probably require its own training run. More on the Blender file format: https://fossies.org/linux/blender/doc/blender_file_format/my...

Scene layouts, models and their attributes are a result of user input (ok and sometimes program output). One avenue to take there would be to train on input expecting an output. Like teaching a model to draw instead of generate images.. which in a sense we already did by broadly painting out silhouettes and then rendering details.

Re: Stable Video Diffusion

#123

In the video towards the bottom of the page, there are two birds (blue jays), but in the background there are two identical buildings (which look a lot like the CN Tower). CN Tower is the main landmark of Toronto, whose baseball team happens to be the Blue Jays. It's located near the main sportsball stadium downtown. I vaguely understand how text-to-image works, and so it makes sense that the vector space for "blue j…

> Has anyone come across a solution where model can iterate (eg, with prompts like "move the bicycle to the left side of the photo")? It feels like we're close. I feel like we're close too, but for another reason. For although I love SD and these video examples are great... It's a flawed method: they never get lighting correctly and there are many incoherent things just about everywhere. Any 3D artist or photographer…

Thanks! this is exactly what I have been thinking, only you've expressed it much more eloquently than I would be able.

Re: Stable Video Diffusion

#124

I'm still puzzled as to how these "non-commercial" model licenses are supposed to be enforceable. Software licenses govern the redistribution of the software , not products produced with it. An image isn't GPL'd because it was produced with GIMP.

So, there's a few different things interacting here that are a little confusing.

First off, you have copyright law, which grants monopolies on the act of copying to the creators of the original. In order to legally make use of that work you need to either have permission to do so (a license), or you need to own a copy of the work that was made by someone with permission to make and sell copies (a sale). For the purposes of computer software, you will almost always get rights to the software through a license and not a sale. In fact, there is an argument that usage of computer software requires a license and that a sale wouldn't be enough because you wouldn't have permission to load it into RAM[0].

Licenses are, at least under US law, contracts. These are Turing-complete priestly rites written in a special register of English that legally bind people to do or not do certain things. A license can grant rights, or, confusingly, take them away. For example, you could write a license that takes away your fair use rights[1], and courts will actually respect that. So you can also have a license that says you're only allowed to use software for specific listed purposes but not others.

In copyright you also have the notion of a derivative work. This was invented whole-cloth by the US Supreme Court, who needed a reason to prosecute someone for making a SSSniperWolf-tier abridgement[2] of someone else's George Washington biography. Normal copyright infringement is evidenced by substantial similarity and access: i.e. you saw the original, then you made something that's nearly identical, ergo infringement. The law regarding derivative works goes a step further and counts hypothetical works that an author might make - like sequels, translations, remakes, abridgements, and so on - as requiring permission in order to make. Without that permission, you don't own anything and your work has no right to exist.

The GPL is the anticopyright "judo move", invented by a really ornery computer programmer that was angry about not being able to fix their printer drivers. It disclaims almost the entire copyright monopoly, but it leaves behind one license restriction, called a "copyleft": any derivative work must be licensed under the GPL. So if you modify the software and distribute it, you have to distribute your changes under GPL terms, thus locking the software in the commons.

Images made with software are not derivative works of the software, nor do they contain a substantially similar copy of the software in them. Ergo, the GPL copyleft does not trip. In fact, even if it did trip, your image is still not a derivative work of the software, so you don't lose ownership over the image because you didn't get permission. This also applies to model licenses on AI software, insamuch as the AI companies don't own their training data[3].

However, there's still something that licenses can take away: your right to use the software. If you use the model for "commercial" purposes - whatever those would be - you'd be in breach of the license. What happens next is also determined by the license. It could be written to take away your noncommercial rights if you breach the license, or it could preserve them. In either case, however, the primary enforcement mechanism would be a court of law, and courts usually award money damages. If particularly justified, they could demand you destroy all copies of the software.

If it went to SCOTUS (unlikely), they might even decide that images made by software are derivative works of the software after all, just to spite you. The Betamax case said that advertising a copying device with potentially infringing scenarios was fine as long as that device could be used in a non-infringing manner, but then the Grokster case said it was "inducement" and overturned it. Static, unchanging rules are ultimately a polite fiction, and the law can change behind your back if the people in power want or need it to. This is why you don't talk about the law in terms of something being legal or illegal, you talk about it in terms of risk.

[0] Yes, this is a real argument that courts have actually made. Or at least the Ninth Circuit.

The actual facts of the case are even more insane - basically a company trying to sue former employees for fixing it's customers computers. Imagine if Apple sued Louis Rossman for pirating macOS every time he turned on a customer laptop. The only reason why they can't is because Congress actually created a special exemption for computer repair and made it part of the DMCA.

[1] For example, one of the things you agree to when you buy Oracle database software is to give up your right to benchmark the software. I'm serious! The tech industry is evil and needs to burn down to the ground!

[2] They took 300 pages worth of material from 12 books and copied it into a separate, 2 volume work.

[3] Whether or not copyright on the training data images flows through to make generated images a derivative work is a separate legal question in active litigation.

Re: Stable Video Diffusion

#125
post #7

The rate of progress in ML this past year has been breath taking. I can’t wait to see what people do with this once controlnet is properly adapted to video. Generating videos from scratch is cool, but the real utility of this will be the temporal consistency. Getting stable video out of stable diffusion typically involves lots of manual post processing to remove flicker.

> but the real utility of this will be the temporal consistency The main utility will me misinformation

[deleted]

Re: Stable Video Diffusion

#126
post #82

Looks like I'm still good for my bet with some friends that before 2028 a team of 5-10 people will create a blockbuster style movie that today costs 100+ million USD on a shoestring budget and we won't be able to tell.

I wouldn't bet either way. Back in the mid 90s to 2010 or so, graphical improvements were hailed as photorealistic only to be improved upon with each subsequent blockbuster game. I think we're in a similar phase with AI[0]: every new release in $category is better, gets hailed as super fantastic world changing, is improved upon in the subsequent Two Minute Papers video on $category, and the cycle repeats. [0] all of…

Your comment reminded me of this: https://www.reddit.com/r/gaming/comments/ktyr1/unreal_yes_th...

Many more examples, of course.

Re: Stable Video Diffusion

#127
post #109

Earlier quoted context omitted.

Stable diffusion open source release and llama release

But what technically allowed for so much progress? There’s been open source AI/ML for 20+ years. Nothing comes close to the massive milestones over the past year.

Public availability of large transformer-based foundation models trained at great expense, which is what OP is referring to, is definitely unprecedented.

Re: Stable Video Diffusion

#128

Earlier quoted context omitted.

(disclaimer: worked in the sim industry for 25 years, still active in terms of physics-based rendering). First off, there are zero technical proofs that we are in a sim, just a number of philosophical arguments. In practical terms, we cannot yet simulate a single human cell at the molecular level, given the massive number of interactions that occur every microsecond. Simulating our entire universe is not technically…

I don't think you would need to simulate the entire universe, just enough of it that the consciousness receiving sense data can't encounter any missing info or "glitches" in the metaphorical matrix. Still hard of course, but substantially less compute intensive than every molecule in the universe.

And if you’re in charge of the simulation, you get to decide how many “consciousnesses” there are, constraining them to be within your available compute. Maybe that’s ~8 billion — maybe it’s 1. Yeah, I’m feeling pretty Boltzmann-ish right now…

Re: Stable Video Diffusion

#129
post #25

It makes sense that they had to take out all of the cuts and fades from the training data to improve results. I’m the background section of the research paper they mention “temporal convolution layers”, can anyone explain what that is? What sort of training data is the input to represent temporal states between images that make up a video? Or does that mean something else?

I would assume is something similar to joining multiple frames/attentions? in channel dimension and then moving values inside so convolution will have access to some channels from other video frames.

I was working on similar idea few years ago using this paper as reference and it was working extremely well for consistency also helping with flicker. https://arxiv.org/abs/1811.08383

Re: Stable Video Diffusion

#130

Earlier quoted context omitted.

What was the big “unlock” that allowed so much progress this past year? I ask as a noob in this area.

I think these are the main drivers behind the progress: - Unsupervised learning techniques, e.g. transformers and diffusion models. You need unsupervised techniques in order to utilize enough data. There have been other unsupervised techniques in the past, e.g. GANs, but they don't work as well. - Massive amounts of training data. - The belief that training these models will produce something valuable. It costs betwe…

I don't think transformers or diffusion models are inherently "unsupervised", especially not the way they're used in Stable Diffusion and related models (which are very much trained in a supervised fashion). I agree with the rest of your points though.
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