How AI Image Generators Work (Stable Diffusion / Dall-E) - Computerphile
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- čas přidán 3. 10. 2022
- AI image generators are massive, but how are they creating such interesting images? Dr Mike Pound explains what's going on.
Thumbnail image partly created by DALL-E with the prompt: "Computerphile CZcams Video presenter Mike Pound Explains Diffusion AI methods thumbnail with green computer style title text on a black background with grey binary"
/ computerphile
/ computer_phile
This video was filmed and edited by Sean Riley.
Computer Science at the University of Nottingham: bit.ly/nottscomputer
Computerphile is a sister project to Brady Haran's Numberphile. More at www.bradyharan.com
Glad to have finally found someone I can actually listen to about AI, someone that doesn't hype things up and isn't trying to sell me something.
If it needs other people's intellectual property to work, then it is a legal concern for those whose work it being exploited without consent.
This is why Getty images is suing, and why Adobe is building their AI Firefly off of licensed work, and making guidelines to compensate those who stock are being used by their AI.
@@Isaac-wr8et Why do ignorant people like you comment? This is literally an explanation of how these types of AI work, no different than explaining other AI models. Nothing about this is abstract mumbo jumbo BS, you just don't understand it, lmao.
@@Isaac-wr8et salty credit obsessed artist spotted
@@youssefabusamra3142 the way I see it, people aren't happy when their private information or content is downloaded/stolen without permission by the government or AI. The source that these AIs are using too "learn", is doing just that.
@@Isaac-wr8et "the government or AI" my brother in christ they uploaded these photos for public recognition agreeing to the site's terms and conditions
what the AI is doing is the equivalent of "looking" at these photos and recognizing features and patterns. If you're so dead set on the stealing narrative then pick a real painting that was used for training and try to "steal" it by recreating it with prompts
Stable diffusion doesn't actually actually apply noise to images, it uses a compressed low dimensional latent representation of the image and applies noise to that. The model is running in this abstract latent space, and then the autoencoder recreates the image afterwards.
Great point. Yes I skipped over this mainly for the sake of the length of the video. This also explains the slightly odd brown noise we see in the video, which is actually a low noise latent passed back through the VAE decoder.
@@michaelpound9891 if you tell AI to run for zero steps you can look at the noise.
I once used an overtrained network to store several images, then manipulated the low dimensional part to give some trippy image tweening (a few inputs reperesenting this is image 1,2,3,... etc. and then getting the in-between images). Unfortunately very low resolution, and took ages, guess a web-browser isn't the place for running neural nets...
@@threeMetreJim That’s always a fascinating experiment with VAEs. Encode two items to two latent points, take the midpoint of the two latent points, and then decode that latent midpoint to see what the resulting item is. I tried this with music and it was interesting to hear a transition from Beethoven to Schubert.
fatter and older
Finally! Ever since Stable Diffusion was released I was looking for an explainer on how it worked that wasn't "Oh it generates images from noise" or something that went too deep into technicals that I didn't understand.
Very beautifully explained Dr. Mike Pound! Hope you do another video where you dive into the code where we can see the parts which were visualized here.
One thing that's still unclear to me is how was the network trained to relate text with images and how does it utilize this information when actually producing images?
@@thebirdhasbeencharged I don't understand why people answer questions they don't know the answer to. He's asking how the diffusion model which starts from purely random noise, uses the text embedding generated from clip to guide the diffusion. "A.I. is just fancy pattern matching" is about as unhelpful an answer as you could imagine.
I'm a little unclear on that myself, but the best understanding I can manage is that the CLIP (language model) embeddings must be included in the diffusion network's training. So while it's learning how to predict the noise on a picture of, say, a bunny, it's also given the text description of the bunny, which means it's learning how the descriptions affect the noise at the same time as it's learning how the underlying picture does.
I think. As I said, not 100% clear on that, so don't take my word for it 😅
They asked humans, "Is this a frog? Yes or no."
They took that data to develop an AI that could be asked, "Is this a frog? Yes or no."
They did the same with "stilts".
They did the same with "on". The difference being that they used a variety of known "objects" to determine whether they were "on" something or not.
They also probably classified "on" as a verb, rather than a noun. This makes it a union of two objects. A union associated with "proximity" or something like that.
Like he said at the end, they need an intact frog and intact stilts as a requirement in the "frog on stlits" image. So they look like "frog feet with proximity to stilt objects" etc.
I would assume human objects on stilts strongly guided their classification of frog objects on stilts.
@@dialecticalmonist3405 I guess that's a decent high-level explanation, but I would clarify that it's actually not using any kind of classification system. Classifiers are an entirely different family of neural networks. The guidance system used here is a transformer-based language model, which is less like asking "is this a frog (y/n)?" and more like asking "here's an image, describe what it is".
@@generichuman_ haha I'm assuming they deleted their unhelpful message, as I don't see it. Great call out. =)
A deep dive on the google colab code would be amazing!
☝☝
There r videos that do that.
Also there r alot of papers explaining the system in detail.
@@nevokrien95 Can you provide links to those videos please?
@@rayankhan12 sure i will send it in parts here is some code in pytorch (i personally know only tensorflow but i still got the gist of how i would go about doing it)
@@rayankhan12 this is abit mathematicly involved. Key point it shows is that the limiting behivior is not what makes these work. They r simply autoencoders with some extra whistles
The synthesized-speech scad (scam advert) that I received after watching this video reminded me a little too much about how all of our advancements will eventually be weaponized against us. I'm both filled with joy for the beautiful engineering that led to stable diffusion, and a sense of overwhelming dread for how it will eventually be utilized commercially.
Don't worry friend, just do what I do:
1. Assume everything on the internet is fake (including other people)
2. Retreat from society into a cave
3. Starve to death
It's kinda like Plato's Cave, but in reverse.
Anyway, it's a pretty solid solution 🙂
In fact it is already being done for artists, the LAION database should not be used for commercial use, and many IAs are actually using it in that way, not to mention that this database has images protected by copyright, so sell or publishing these resulting images is a clear violation of copyright
@@Hagaren333 Begun, the data wars have
We are getting ever closer to living in a dystopian Cyberpunk universe.
Just abolish capitalism, then.
the explanation sounds like magic. It is like a sculptor saying he just chips away pieces of the stone until he finds the horse hidden inside.
It's strikingly similar, except a sculptor starts with a goal image in mind, but AI image generation doesn't; it just has general "knowledge" of "associations" between the words of the prompt and parts of images.
Add noise to images and train a model to undo that addition.. then you have something that maps from noise to images.
One thing I find so impressive about these researchers.. is that they would try this. It’s so bizarre.. just because, from a distance, it’s not at all clear that such a task is doable.
And with the intention to guess how much noise a picture would have only by text description as an input haha
Right, it sounds like a completely non-intuitive way of going about it, and yet, that's what ended up working. They must have iterated on a gazillion different ideas before they landed on this one.
The idea of adding something, do a transformation and substract to get only the transformation of the data or the something is actually quite common in math and control theory.
Real hit from AI was to get "the" transformation from data, something and output into an algorithm. This general function of transformation is what allows the image generation. We give it data or something that is slightly off, and amplify the error by the transformation.
😅 it's some weird combination of the chicken egg paradox and a rock paper scissor but with data, something, algorithm and output.
It's why science works better by being public and not subject to short/mid-term revenue. There were already teams training models to undo noise, there was already GPT to interpret text, there was already a database of millions of text-to-image pairings and there were already models trying to feed text to image-based neural networks. Kinda like smartphones, all it took is someone putting the pieces in the right order for the right purpose to make something more useful than the sum of its parts.
@@Alex-ye8qp To me this is the most bizarre part. How can a network even be trained to do that? So utterly bizarre to me. You're telling me "green cow grazing on mars" has a deterministic noise profile??
I couldn't agree more! Since the release of Stable Diffusion, I've been searching for an explanation that strikes the right balance between simplicity and technicality. Your video did an excellent job of providing a clear understanding without overwhelming us with excessive technical details. Dr. Mike Pound, you have a remarkable talent for explaining complex topics in a beautifully straightforward manner!
Would have been nice hear a bit more about the "gpt-style transformer embedding". Wouldn't those classifications have to be included in the training data already?
This is basically what CLIP does. CLIP learns from a massive amount of image-description pairs using GPT-style (Transformers) encoding so that it can map texts and images. CLIP data are not classification labels. Then the difference between the texts and the generated images can be calculated and minimized.
Key word is embeddings. Initial feature space of text has two bad properties: it has big dimensionality (each token is it's own dimension essentially) and sparsity. By using Transformers you compress representation of this object in more compact and dense form, so it's easier to work with.
Pounding that like button! You guys have inspired me to start an undergraduate degree in Cyber Security - thank you for all of your videos!
I watched Dr Mike Pound's video on Convolutional Neural Networks when it first came out and it got me into machine learning. Now I'm doing undergrad computer vision research with CNNs. It's honestly kind of crazy to think about how much this channel has affected my life.
@@emmafountain2059 Well played! Are you enjoying yourself doing the computer vision research?
@@emmafountain2059 yup. I started watching this channel when I was a bored IT audit intern who hated doing work papers. I literally sat in the bathroom or went on walks outside and just watched. Now I’m a pentester. The reach of high quality CZcams channels like Computerphile are hard to measure but I don’t think I’m unique.
@@emmafountain2059 That's amazing! Wish you the best!
Came here by accident and man, aren't you the gifted one? I was engrossed in the video knowing barely anything about the technologies and techniques uses, and I don't feel dumber -- that's an achievement :)
Thanks again, will pop here often.
Oh i DEFINITELY want to see mike's deep dive into the code!
So stable diffusion is just the AI version of that sculpting joke: Start with a big block and take away the parts that dont fit
Yep! It's a bit like apophenia, like looking at random clouds and seeing coherent shapes in them, but with some priming about what you "should" be seeing :)
"I saw the angel in the marble and carved until I set him free. ”
- Michelangelo
No, that's not how it works at all. His explanation is highly inaccurate and misleading, which is throwing you off. Try reading the actual papers on the subject, or going through the code.
No, that's not how it works at all. His explanation is highly inaccurate and misleading, which is throwing you off. Try reading the actual papers on the subject, or going through the code.
Thank you for covering this topic!
I tried to guess how these things work. Now I'm taking the difference between my guess and this explanation and feeding it to my neurons. Thanks!
I love that he's doing all of this on 1980s printer paper. Proper geek
I would love to hear more about the process. Like how does it recognize that the image now looks like a frog on stilts? Seems to me like that's where the real complexity is.
Same, I understood the noise subtraction bit, but I can't quite understand how the subtraction can lead to a picture of a frog, was the IA trained with "words vs images"? So it can relate what a frog would look like.
Also, what the initial input picture (12:30) looks like? Is it just random generated noise?
@@skirtsonsale yes a labeled dataset (image-text pairs, LAION dataset) was used to train the network. That is why it is called guided diffusion. The text guides the diffusion process not to a random image, but conditioned on the text (again the pairs were used for training).
During training, it sample randoms noise from a random t according to the noise schedule (such that during training it is learned for all t). The input image on 12:30 is such image corrupted using noise from a random t. So somewhere between noise and an image.
@@skirtsonsale The concept of transformation in graphics will help you understand this.
@@tristanstevens6162 But why doesn't that just produce some incohesive amalgamation of the training data? How does it know to specifically put the bunny ears on the frog's head? Is that where the magic of having a large amount training data comes in, in that it better understands the correlation between the label and the image?
@@jonatansexdoer96 Yep. CLIP is the language model used in these, and it's seen enough examples of things labeled "bunny" that look different from each other to abstract the idea of where bunny ears are located in any given underlying image.
Awesome video. That’s the clearest explanation I’ve seen. The hand drawn explanation explained it so perfectly. Would love to see a follow up video that goes through the code. Also would be awesome to include examples when talking about the muppets in the kitchen, etc.
Wow! Had not seen listing paper since my dad was trying to teach me basic on a commodore 64. Had no idea it was still a thing. Big jump from having to read code on paper to make sense of it to this.
Where can I find his other videos where he is delving into the code etc? He explains very good.
Can't believe Mike can effortlessly make that shape with his hand (little finger) at 5:37
Eagerly waiting for the deep dive video👍👍👍
Amazing explanation. Thank you!
Fantastic explanation!
would like to see more details but the explanation was superb for an introduction
12:58 I'd like to hear more about that GPT-style transformer embedding of text. Was text part of the training set?
yes they used image-text pairs dataset (LAION) to train the guided diffusion model
@@tristanstevens6162 That dataset accounts for understanding text too? Like, if you have sets of images of cats and sets of images of frogs and very few sets of different animals being "fused" (e.g. maybe one capybara that looks like a dog), how would the neural network get to the interpretation of what a cat-frog means as an image? Is LAION that big? Or does the GPT neural network somehow bridge that gap?
@@ekki1993 a lot of the job of "understanding" is being done by the embedding network, which was trained on a very large corpus of words. So while the training set for stable diffusion might not have any examples of a frog-bunny fusion, CLIP is able to take the phrase "frog-bunny fusion" and transform it into a vector that encodes something about the meaning of the phrase. Stable diffusion was trained conditioned on this embedding, so it generally has learned to take concepts from this embedding and include them in the image. The hope is that stable diffusion is able to generalize across all concepts that can be represented by the embedding, so that even if it hasn't seen this specific thing before, it has seen similar stuff and is able to still produce a reasonable image that matches the concepts in the embedding.
@@DontThinkSo11 Thanks for the answer!
@@ekki1993 It's possible for all/any of the components to contribute to the result working - like, even if it hasn't seen any pictures by an artist x, "style of artist x" may still work as a prompt if it's seen text describing them.
This is an issue for artists who've been complaining that image generators can reproduce their style in some ways. It means that nothing can be done to prevent this; a base model might still understand them if the images aren't in the set. "Worse", fine-tuning seems to work well enough that you can add in new concepts and styles at home even if they're not in the model originally.
Thank you so much for talking about this topic! Great and very enjoyable!
The best compsci content on the internet, period.
Been listening to house music in the background (on the low down) when the odd watching computerphile / numberphile for quite a while now.
Thought it was time to fess up.
Vibing it is probably just me on this tip.
Great explanation. Just complicated enough to understand for someone who keeps up with this stuff on the surface level, but isn't interested in reading the papers. Thanks.
Please do that next video!!! You guys are great.
As i understand it, it's somehow like an subtractive synthesizer in audio,the A.I. has the role of the filter.
The suggested follow up with details of the program would be great!
My favorite part is where he explains AI while drawing on printer paper from 1989 XD
I love this methodical and interesting presentation of the process. It is inspiring as always! 💜
Stable Diffusion is actually runable (in inference mode, i.e. for generation - this is different from training) on a regularish computer. The main factor is time but if you have a reasonably modern graphics card, you probably can run stable diffusion in principle.
It just might take minutes rather than seconds for a single image.
Somebody ran a variant of it on a not even that new iphone. It did take like half an hour iirc so it's not a thing most people would *want* to do, but one of the big selling poitns of stable diffusion is, that it's never the less *possible.*
Stuff like Dall-E 2 or Imagen actually really does need a beefy computer with lots of specialized hardware (in particular, above-consumer-hardware VRAM) to get things done.
Some of the oldest methods, though, can also work on a regular computer. I'm directly optimizing a version of CLIP towards some image for instance. It's not nearly as good as stable diffusion, but it's basically how all this madness of arbitrary images from text began
The biggest limit is vram not gpu power as much, on a rtx 3000 series gpu 2-10s for 512^2 20 steps and 15-60 secs for 1024^2 image. Slow down starts due to vram not being able to hold everything. So you can get silly things like the 3060 being better then a 3080ti for some uses of SD.
@@asdf30111 yeah VRAM is always the main issue with AI. Gotta store huge matrices in memory. I wonder if, going forward, hardware providers will bump up VRAM on their high end consumer cards due to increasing consumer demand...
Or perhaps decently sized tensor cores will become more commonplace
love the high quality videos this channel always deliver
amazing explanation!
I remember rewatching Brows Held High's episode on the movie Blue. In it, there are clips from the film. It was just a blank blue screen. On film. Sot there was some noise from the grain. It was from a DVD rip (I think). And it had therefore, by the time it got from the film to my computer screen on youtube, gone through numerous re-encodings. Encodings that expect visual interests and details to compress.. but those had none. So I noticed that the artifacting was picked up as not-noise. And it tried to encode it as if it was normal video. And through the generations of transfers, the blue blank screen was now... Filled with random shapes of blue tones that had gotten enhanced over time.
I joked then that we were basically seeing the encoders hallucinations. Little did I know, that a few years later, seceral image processors would spring up that essentially used that method, but guided. And they would be able to hallucinate pretty high resolution images... From noise ..
I followed some of that.. but some of that also sounded a lot like Michelangelo's "start with the block of marble and carve away everything that doesnt look like "X." I will come back to watch this again after the first watching settles! Thank you for providing this.
Please do a walk-through of the Stable Diffusion code.
i am still a bit confused about the process but loved the ending!
Dr Mike Pound with pen and paper can make me understand any topic.
Great video!! Would be interesting to see the differences between dall-e ans stable Diffusion. And how the last one requires less training and compute power
Thank you for this, would definitely love to see you going over the code and playing with this technique in the next video!
On a side note: Is there some supply back room full of that old continuous stationary somewhere on campus? Good job recycling it, since there is no other use for it anymore.
Interesting to see (literally). Thanks for sharing.
Great video. Is there a name for the iterative denoising procedure ?
Curiously, this is the same process as creating a stone sculpture: you start with a block of stone with no shape and gradually take away all parts of the stone that are not shaped like the thing you are sculpting.
perfect explanations
nice
Computer Phile, this is very Good and intuitive 😊
New camera/processing? looks good!
When the network is producing an image with embedded text and producing two versions, one with and one without, is it doing both versions for every iteration down to t:0 or just using the difference calculated at t:T?
So like, does the training data also include words associated with the images? e.g. when training with that picture of the rabbit, does it include "rabbit" or maybe some other modifiers?
Excellent explanation, thanks!
Great explanation. Thanks.
When you say you remove the noise and then add most of it back, do you mean you add back the predicted noise, or you add back newly generated noise? And both approaches seem plausible, so what is the reasoning?
Could you add foreign language subtitles to these videos? Right now I'd need German subtitles. I've always loved your explainers but AI image generators is the first time where I'd need to show a video of yours to someone who's not fluent in English. CZcams offers options to crowdsource subtitles BTW. Thanks a lot, keep up the good work! ☺
CZcams has a function of automatic subtitles.
I'm curious as to whether quantum computing will speed up this sort of process?
It sounds like the sort of weighted vector probability math that would be optimal on a q-bit based computer?
Basically all neural network training relies on lots of linear algebra (mostly matrix multiplication), and from what I've read in a *very* brief lookup just now, the HHL quantum algorithm is known to perform linear algebra much faster than classical computers. And we've got a working demo of it already as of 2018 (though for the small number of qubits that our current quantum computers are limited to). So in theory, yeah, once we have scalable enough quantum computers with enough qubits, most modern AI could be made far faster with HHL.
You did a great job explaining how the process works and provided visual examples. Nice work with this video.
What parameters did you use to generate those frog image? Tried it on my own local instance of StableDiffision and only got frogs not on stilts.
Try turning up the CFG scale. In general, higher values for that make the result match your prompt more closely, though it also reduces variation. If you're using the WebUI version, it also supports term emphasis, so you can try putting the word "stilts" in ((double parentheses)) to indicate that they should have higher weight in the result.
Very cool to see some details on this even if it's just the top level of it. I do think the detail at 13:04 is important enough to get more than the hand wave it got, is he referring to something from another video?
I'm running Stable Diffusion locally on a 3080 Ti, works fine.
Gaming laptop with a 3060 (6GB) here working great for Stable Diffusion, I'm using the Automatic1111 web UI distro bundle which made setup incredibly easy. I'm still learning how to use it to get "what I mean" results, but it is quite amazing.
I've ran it on a 2060 fine, though I think its the lowest supported card
I'm using a 3060 Ti. But inference is *always* more efficient than training, so my hardware can't handle Dreambooth, and certainly would never come close to handling a full initial training.
concise explanation Sir but why do we add noise to the original image?
Love these videos!!!
And by "giving it the noise estimate" are we referring to doing that via the text prompts?
15:15 Why would we amplify the difference and not the parts that stay the same? Might be in the wrong here but in my understanding we are trying to use the parts that both the text guided and unguided noise produced to get the best output since the parts they both produced will be the best fit. Why then use the difference
"That next video" sounds like exactly the video I want for these networks!
Great stuff!
How do I know what noise was added at which step, if both noises have the same frequency? If I start from the back with totally random noise and then subtract random noise, how can the result have less noise? If the noise is really random, it is no difference if you add or subtract it, because random noise is not any more or less random than its negative.
if you randomly generate a black dot on a screen and then detect it, you can remove it through some kind of filter. It's true that it wouldn't technically be a subtraction, but I think that the idea stands.
This is how DALL-E works in a nutshell:
"Read user prompt. Decide it's against their arbitrary moral codex. Emit error."
Excellent vid btw. Explained something complex in a very easy way.
As others have said, I'd love to see another video that elaborates on how the text prompt is factored into the model.
Super interesting, more please
I really liked the explanation. Clear and easy to grasp. Thanks!
Very well done.
I feel the GPT embedding is skipped over quite easily. With my limited knowledge on how these text Transformers work there is a vector representation of the description that in stead of representing the words represent the 'meaning' of the text. However how does this transform to an image that does exactly that? You would still need a lot of training data that confirms whether an image matches a description, right? Did they use alt descriptions or something similar for this, eg (publicly) available image descriptions? I guess my point is that i do not see how this 'knowledge' of what's in the text is transferred to the 'knowledge' of what's in the image, apart from there being a mechanism of steering it towards the 'knowledge' but where does this knowledge come from?
The network is basically implicitly imbued with this understanding through the training process:
For the training process of these conditional models, you take a labeled dataset {(image, label)}, and the label is a natural-language description text *describing that image* . For a particular data sample, the network will then see a noisy version of this image, along with the embedding of the description of the original clean image. The training loss then rewards the network for reproducing this image given these two bits of information, and punishes it for reproducing *any* other image. I hope it's clear that the training will therefore steer the network into a direction of 'understanding' these descriptions in some abstract way.
That this understanding of the input text is *abstract* is actually what you hope allows the model to extrapolate, so it can generate new images conditioned on new texts not seen during training. That this extrapolation works ridiculously well is one of the amazing things about these models.
Nice explanation
I definitely want to see the follow up video.
Nice! Didn't find it, but is there a video how he mentioned at 13:09 how to embedd "GPT style" a text to an image? Like I don't see how you can mix text and images, and maybe similarly also sound. For a picture it's quite understandable, X pixels width and Y pixels height. But for text in an image it could be an entire book or just one word.
How do you train the network with the text embeddings?
I wish I could understand this. You must be a genius!
so it's predicting the noise, and the randomness of the image comes from the random noise that the model gets? so if the model by chance got the same noise it would produce the same image? or something very close? or does each step also introduce randomness?
Hi! thank you so much for this explaination. I was wondering, do diffusion model still have a discriminator after the whole process? thanks in advance!
Excellent!!
I have a question regarding computing science that is related to computer programs in general.
At what point does a computer program become software. The steps ( processes ) that are taken in order to make any given program into a piece of software.
It’s more a question of, defining as to when said program has transitioned into software.
As far as I know all computer programs are classified as software. Its a bit like asking “At what point or amount of specification does a grocery shopping list become a written reminder?” It is already a written reminder regardless of what the list comprises of.
Very insightful, although the one thing he didn't do a great job explaining is why it's better to ask it to go from T to T0 and then add back noise equal to some lower T instead of just going from T to T-1 to T-2 and so on. He sort of touched on it vaguely but didn't really explain why it's actually better
Very interesting, the amount of areas where machine learning algorithms have improved to such a degree is getting more every day.
Glad you could explain the process behind stable diffusion
It is amazing that something like that works
Hello, Dall-E and MidJourney can be considered a DCGANs?
"I saw the angel in the marble and carved until I set him free. ” - Michelangelo
16:30 I would love to watch another video where you explore the code.
Is there a way to estimate what percentage of the generated image is "of" the original image? Is it backing its way towards a single image, or a multitude of images?
It depends. If you provide the same prompt text and the same seed value, you'll always get the same image. If you change the seed value, you'll always get a different image. The seed has to do with how the initial image is initialized with random noise. The seed will set the pattern of the noise. In the video, he didn't mention anything about the initial image used when creating a new image. He mostly talked about the training process.
Been looking forward to this! I've been playing with Stable Diffusion awhile now, and only recently realized my GPU could handle a local install instead of relying on Colab. The local WebUI is so nice!
I do have a question though: is the diffusion model trained with CLIP embeddings as well, or are those only passed into the diffuser during inference? I'd assume it must be trained with them, but I don't like assuming 🙂
It'd be nice for follow-up videos to talk about things like Textual Inversion and Dreambooth, and the new AIs being built on top of these CLIP-diffusion models such as Meta's new "Make-A-Video".
I skipped CLIP simply because this video was getting a bit long! But I do think given the questions in the comments, that a CLIP video could be cool. As far as I know clip is used during training as well. The CLIP model is frozen though, so is no longer trained during this process. Dall e and SD use clip slightly differently. Dall e uses another diffusion process to go from text to image embeddings. Stable diffusion uses the text embeddings in the attention bits added to the unet noise predictor, but they also add it in the front. Personally I think there's more cool research to come on this, at the moment the text embeddings seem somewhat of a "throw it in and cross fingers" approach!
Do you have a new/better camera? looks decent
6:30
Isn't this concept the same as solving P=NP ?
Find the question only using the answer?
Yay new Mike Pound video
А есть какое нибудь расширение, чтобы помогало писать prompt. Суфлер или на подобии т9, чтобы предлагало не кольцо вариантов написания слов/словосочетаний?
This is EXACTLY the detailed, nuts and bolts video I was hoping to find on AI art. I'm fascinated that random noise seems to be key. I have been working in 3D generated art for many many years and random noise is so powerful in creating imagery and textures in 3D. Such a fascinating, enigmatic concept - noise is nothing but at the same time everything. Even further fascinating to ponder that we can introduce chemicals into the human brain to create random noise, resulting in random infinite hallucinations which likewise have been used for millennia to generate art as well.
🤯
This was a pretty awesome explanation! Thank you!
Can you elaborate on the embedding part?
Thank you, now I can actually understand what the methods behind these generators are all about