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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
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https://www.youtube.com/watch?v=gwI6g1pBD84
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Hello there, today we'll look at Glide towards photorealistic image generation and editing with text-guided diffusion models by Alex Nicolle, Prafula Dhariawal, Aditya Ramesh and others of OpenAI. This paper on a high level, well, I'll just show you what you can do. I'm sure you've all seen this paper in one way or another. It is another paper that generates images given a piece of text. But this time, it's not a GAN or anything like this or a VQ VAE. This time, it is a diffusion model. This is a different class of models and we'll go into what they are and how they work. But essentially, you can see right here that the model that turns out of this and of course, this being OpenAI, they train this on a massive scale and this model is really big. But what comes out of it is very, very, very much better than for example, Dali, which always had this kind of blurriness to it. You can see right here a crayon drawing of a space elevator, pixel art, corgi pizza. So this is trained on a big scrape of images from the internet. And as you can see, the outputs are pretty stunning. So it gets for example, the shadows right here, it gets them correctly, even the red on blue blending. It gets different styles like the Salvador Dali style. It combines different concepts, although maybe you know, this has been seen on the internet somewhere, but it is able to combine different concepts. And given that these are diffusion models, you can actually do a bunch of more stuff with them. For example, in-painting is immediately accessible to this model. Now, usually, in-painting is accessible to diffusion models. However, they actually train an in-painting model on top of this. But in essence, a lot of stuff would be accessible. So this is now possible where you say, okay, I only want to change a part of the image like this part right here, you give a text saying, a man wearing a white hat, and the model generates the man wearing a white hat. This is very cool. You can do things like this, where you first, so the pictures here are a bit confusing, but you first generate an image from a text prompt, like a cozy living room, then you get this living room. And then here, the user would annotate this window sort of would draw over it, and will give the next text prompt. The next text prompt would be a painting of a corgi on the wall above the couch. And the model, it's an in, so this is the in-painting mode, the model would only be able to paint the green area. So it would sort of try to conform to the text using only the green area. And therefore, it would make this corgi picture on the wall right here, then the user goes further and says, well, now
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models: Hello there, today we'll look at Glide towards photorealistic image generation and editing with text-guided diffusion models by Alex Nicolle, Prafula Dhariawal, Aditya Ramesh and others of OpenAI. This paper on a high level, well, I'll just show you what you can do. I'm sure you've all seen this paper in one way or another. It is another paper that generates images given a piece of text. But this time, it's not a GAN or anything like this or a VQ VAE. This time, it is a diffusion model. This is a different class of models and we'll go into what they are and how they work. But essentially, you can see right here that the model that turns out of this and of course, this being OpenAI, they train this on a massive scale and this model is really big. But what comes out of it is very, very, very much better than for example, Dali, which always had this kind of blurriness to it. You can see right here a crayon drawing of a space elevator, pixel art, corgi pizza. So this is trained on a big scrape of images from the internet. And as you can see, the outputs are pretty stunning. So it gets for example, the shadows right here, it gets them correctly, even the red on blue blending. It gets different styles like the Salvador Dali style. It combines different concepts, although maybe you know, this has been seen on the internet somewhere, but it is able to combine different concepts. And given that these are diffusion models, you can actually do a bunch of more stuff with them. For example, in-painting is immediately accessible to this model. Now, usually, in-painting is accessible to diffusion models. However, they actually train an in-painting model on top of this. But in essence, a lot of stuff would be accessible. So this is now possible where you say, okay, I only want to change a part of the image like this part right here, you give a text saying, a man wearing a white hat, and the model generates the man wearing a white hat. This is very cool. You can do things like this, where you first, so the pictures here are a bit confusing, but you first generate an image from a text prompt, like a cozy living room, then you get this living room. And then here, the user would annotate this window sort of would draw over it, and will give the next text prompt. The next text prompt would be a painting of a corgi on the wall above the couch. And the model, it's an in, so this is the in-painting mode, the model would only be able to paint the green area. So it would sort of try to conform to the text using only the green area. And therefore, it would make this corgi picture on the wall right here, then the user goes further and says, well, now
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
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https://www.youtube.com/watch?v=gwI6g1pBD84
|
I'm going to paint this area right here. And I'm going to issue the prompt around coffee table in front of a couch, and the model will generate it and so on. You can see that this enables sort of an interactive creation of this scenery at the end, the couch, the couch in the corner of the room. So changing the entire wall right here, you can see the back of the room has some space. And now it's being changed to a wall. So this is the kind of stuff that's possible. Editing right here. Even what's this, this sort of sketch editing where you don't only mask, but along with the mask, you provide sort of like a sketch as you can see right here. So this part here is blue, and then the part here is white. And that's also the mask that the picture receives. And you can see that only one cloud in the sky today, it sort of you can guide even more. So you can guide with text, and you can guide with sketch color, and so on. So this is a very, very, very cool model, you can see the quality is very, very good. Here is for example, a comparison. These are real images from the MS Marco data set MS Coco, sorry. This is a data set of pictures with associated labels, so text descriptions of the picture. So you have some ground truth. So the ground truth here will be this one. And the label is a green train coming down the tracks. You can see Dali generates something neat, but it's sort of blurry. It's kind of cartoonish, as all the Dali pictures are, if you look in this row, the last one's pretty good, but all the other ones are sort of elephants are more like blobs. And we've seen this in the in the Dali paper, it was impressive at the time, but this is way more impressive. And then their best model this clip that sorry, this glide model with classifier free guidance, you can see right here, it generates like a high quality train that fits the image fits the image description. And you can see in the entire in the entire row right here, it's pretty good at doing that. So there are a lot of components to this model. And we're going to explore them a little bit. OpenAI has released in classic OpenAI fashion, they've released like a small, very filtered version of that model, because they're worried about safety, like anyone's going to believe them after GPT two, they've just been doing this every single model, right? They're just like, Oh, no safety, people can make deep fakes. Oh, no, like, no one's made a deep fake. Like GPT to all the worries, they were just not true. No one has used GPT to to spread around fake news. And no one like no one's going to use this
| 500
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models: I'm going to paint this area right here. And I'm going to issue the prompt around coffee table in front of a couch, and the model will generate it and so on. You can see that this enables sort of an interactive creation of this scenery at the end, the couch, the couch in the corner of the room. So changing the entire wall right here, you can see the back of the room has some space. And now it's being changed to a wall. So this is the kind of stuff that's possible. Editing right here. Even what's this, this sort of sketch editing where you don't only mask, but along with the mask, you provide sort of like a sketch as you can see right here. So this part here is blue, and then the part here is white. And that's also the mask that the picture receives. And you can see that only one cloud in the sky today, it sort of you can guide even more. So you can guide with text, and you can guide with sketch color, and so on. So this is a very, very, very cool model, you can see the quality is very, very good. Here is for example, a comparison. These are real images from the MS Marco data set MS Coco, sorry. This is a data set of pictures with associated labels, so text descriptions of the picture. So you have some ground truth. So the ground truth here will be this one. And the label is a green train coming down the tracks. You can see Dali generates something neat, but it's sort of blurry. It's kind of cartoonish, as all the Dali pictures are, if you look in this row, the last one's pretty good, but all the other ones are sort of elephants are more like blobs. And we've seen this in the in the Dali paper, it was impressive at the time, but this is way more impressive. And then their best model this clip that sorry, this glide model with classifier free guidance, you can see right here, it generates like a high quality train that fits the image fits the image description. And you can see in the entire in the entire row right here, it's pretty good at doing that. So there are a lot of components to this model. And we're going to explore them a little bit. OpenAI has released in classic OpenAI fashion, they've released like a small, very filtered version of that model, because they're worried about safety, like anyone's going to believe them after GPT two, they've just been doing this every single model, right? They're just like, Oh, no safety, people can make deep fakes. Oh, no, like, no one's made a deep fake. Like GPT to all the worries, they were just not true. No one has used GPT to to spread around fake news. And no one like no one's going to use this
|
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
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https://www.youtube.com/watch?v=gwI6g1pBD84
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model substantially to make very misleading pictures. But we'll get to that as well. Alright, so what is a diffusion model? And that's sort of at the core of this thing right here. A diffusion model is a different type of generative model than maybe you're used to from like a GAN or a VQ VAE. So in a GAN, a GAN is probably the closest right here. So again, it's sort of like a neural network with a bunch of layers. And what you do is you sample from some sort of a distribution, you sample some noise, right, you sample some noise, you get some noise vector. So here's a vector, which is complete noise, every entry is noise. You put it through the network, the network generates pretty picture. And you train the model using a discriminator. In this case, you train the model to produce pretty pictures given the noise and the noise act sort of as a source of randomness. So the mapping is clear, you train to map from noise to picture. Now, a diffusion model goes in almost like a different direction. So what you do is during training, you have a data set, and you take an image. So from from a data set, you have a data set, you take an image out of it. Let's say this is your trusty, trusty cat, and you're going to, you're going to put noise onto this image. So you're going to add noise and noise. Let's represent that with Sigma. No, I think they do, they do epsilon or eta in this in this paper right here. So you add that, and then you get a slightly noisy version of this. Let's just let's just wiggle a bit, wiggle, wiggle, wiggle, and you do it again. So through adding noise, and you add lots and lots and lots of noise, okay. So every time you add a tiny, tiny bit of noise, and that means that more and more your picture is just going to be blurry and blurry and blurry. Now, if you do this for long enough, in the limit, you can prove that obviously, if you do this infinitely many times, what comes out at the end is going to be just normally distributed. If your noise is normally distributed, and you scale every time correctly, then whatever turns out is going to be normally distributed with some parameters here. So this right here is going to be a known distribution. If you if you add noise for long enough, if you destroy all of the information that the picture has, then you'll end up with sort of an entry in a known distribution. However, every step that you do right here is very small, every step, you just add a little bit of noise. So technically, it's possible for a model to look at this picture right here, which is kind of a bit of a blurry version of
| 500
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models: model substantially to make very misleading pictures. But we'll get to that as well. Alright, so what is a diffusion model? And that's sort of at the core of this thing right here. A diffusion model is a different type of generative model than maybe you're used to from like a GAN or a VQ VAE. So in a GAN, a GAN is probably the closest right here. So again, it's sort of like a neural network with a bunch of layers. And what you do is you sample from some sort of a distribution, you sample some noise, right, you sample some noise, you get some noise vector. So here's a vector, which is complete noise, every entry is noise. You put it through the network, the network generates pretty picture. And you train the model using a discriminator. In this case, you train the model to produce pretty pictures given the noise and the noise act sort of as a source of randomness. So the mapping is clear, you train to map from noise to picture. Now, a diffusion model goes in almost like a different direction. So what you do is during training, you have a data set, and you take an image. So from from a data set, you have a data set, you take an image out of it. Let's say this is your trusty, trusty cat, and you're going to, you're going to put noise onto this image. So you're going to add noise and noise. Let's represent that with Sigma. No, I think they do, they do epsilon or eta in this in this paper right here. So you add that, and then you get a slightly noisy version of this. Let's just let's just wiggle a bit, wiggle, wiggle, wiggle, and you do it again. So through adding noise, and you add lots and lots and lots of noise, okay. So every time you add a tiny, tiny bit of noise, and that means that more and more your picture is just going to be blurry and blurry and blurry. Now, if you do this for long enough, in the limit, you can prove that obviously, if you do this infinitely many times, what comes out at the end is going to be just normally distributed. If your noise is normally distributed, and you scale every time correctly, then whatever turns out is going to be normally distributed with some parameters here. So this right here is going to be a known distribution. If you if you add noise for long enough, if you destroy all of the information that the picture has, then you'll end up with sort of an entry in a known distribution. However, every step that you do right here is very small, every step, you just add a little bit of noise. So technically, it's possible for a model to look at this picture right here, which is kind of a bit of a blurry version of
|
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
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https://www.youtube.com/watch?v=gwI6g1pBD84
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the cat, and predict and learn to predict the more sharp version of the cat. Okay, this is a foundation of many, many sort of denoising models, many up sampling models, super resolution models, what have you, okay, they do this in one step. But essentially, here we say, the individual step is small enough such that the model can technically learn to reconstruct it. However, if we do it for long enough in you know, going to infinity, the we are at a known distribution, namely the the standard normal distribution. And these two things together mean that, well, if we have trained the model to reconstruct the individual steps, what we can technically do is we can now go ahead sample from this known distribution, right? Because ultimately, we want to sample from the data distribution, but that's hard because we don't know it. But here, we can just sample some noise, from a known distribution, then put it through this process of reconstruction, all the way all the steps that we did up here during training. During training, we just noise and noise and noise the images again and again and again, we trained the neural network to for every step to reconstruct the previous step. So we can now just put it through this series of trained neural networks. In fact, it's just going to be one neural network that gets the index of the step as a parameter and outcomes an image, right outcomes a true data image. If these two things up here hold, then this should be possible. This is the basis for these diffusion models. So specifically, given a sample, that's what they say here, given a sample from the data distribution, this is x zero. So this is the data distribution, we produce a Markov chain of latent variables, x one to xt, with everyone being a more noisy version, and xt finally being of a like a known distribution, because we do it infinitely, or a large number of times, by progressively adding Gaussian noise to the sample. So you can see right here, we take xt minus one, we scale it down a bit, because if you wouldn't do that, the sort of the image would just increase in scale over, because we just keep adding stuff. But this, it's just a rescaling, there's nothing more happening here. So we add noise, this here is the mean of a distribution, the covariance matrix here is a diagonal, which essentially means we just add a bit of noise of the scale of alpha t. No, sorry, we just add a bit of noise, we rescale by alpha t, which is a scaling factor. And that's how we obtain the next step, the xt. So again, we do this enough. So we take xt for the next step, we plug it in here, and then we obtain xt plus one, and so on. So if the magnitude of the noise added
| 500
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GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models: the cat, and predict and learn to predict the more sharp version of the cat. Okay, this is a foundation of many, many sort of denoising models, many up sampling models, super resolution models, what have you, okay, they do this in one step. But essentially, here we say, the individual step is small enough such that the model can technically learn to reconstruct it. However, if we do it for long enough in you know, going to infinity, the we are at a known distribution, namely the the standard normal distribution. And these two things together mean that, well, if we have trained the model to reconstruct the individual steps, what we can technically do is we can now go ahead sample from this known distribution, right? Because ultimately, we want to sample from the data distribution, but that's hard because we don't know it. But here, we can just sample some noise, from a known distribution, then put it through this process of reconstruction, all the way all the steps that we did up here during training. During training, we just noise and noise and noise the images again and again and again, we trained the neural network to for every step to reconstruct the previous step. So we can now just put it through this series of trained neural networks. In fact, it's just going to be one neural network that gets the index of the step as a parameter and outcomes an image, right outcomes a true data image. If these two things up here hold, then this should be possible. This is the basis for these diffusion models. So specifically, given a sample, that's what they say here, given a sample from the data distribution, this is x zero. So this is the data distribution, we produce a Markov chain of latent variables, x one to xt, with everyone being a more noisy version, and xt finally being of a like a known distribution, because we do it infinitely, or a large number of times, by progressively adding Gaussian noise to the sample. So you can see right here, we take xt minus one, we scale it down a bit, because if you wouldn't do that, the sort of the image would just increase in scale over, because we just keep adding stuff. But this, it's just a rescaling, there's nothing more happening here. So we add noise, this here is the mean of a distribution, the covariance matrix here is a diagonal, which essentially means we just add a bit of noise of the scale of alpha t. No, sorry, we just add a bit of noise, we rescale by alpha t, which is a scaling factor. And that's how we obtain the next step, the xt. So again, we do this enough. So we take xt for the next step, we plug it in here, and then we obtain xt plus one, and so on. So if the magnitude of the noise added
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|
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
|
https://www.youtube.com/watch?v=gwI6g1pBD84
| "at each step is small enough, the posterior is well, well approximated by a diagonal Gaussian, that(...TRUNCATED)
| 500
| "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models: at ea(...TRUNCATED)
| "[-0.016091231256723404, 0.017658470198512077, 0.0004116538038942963, 0.005556270945817232, 0.004762(...TRUNCATED)
|
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
|
https://www.youtube.com/watch?v=gwI6g1pBD84
| "we're going to train the neural network to predict x t minus one from x t or the variational sort o(...TRUNCATED)
| 500
| "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models: we're(...TRUNCATED)
| "[-0.015456033870577812, 0.011581901460886002, -0.003339245682582259, 0.01276978850364685, 0.0224618(...TRUNCATED)
|
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
|
https://www.youtube.com/watch?v=gwI6g1pBD84
| "to reconstruct one step. So that's going to predict the noise that was added, given the image xt, g(...TRUNCATED)
| 500
| "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models: to re(...TRUNCATED)
| "[-0.025305239483714104, 0.026801859959959984, -0.012144592590630054, 0.0029537654481828213, 0.02540(...TRUNCATED)
|
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
|
https://www.youtube.com/watch?v=gwI6g1pBD84
| "is called guided diffusion. And one way to do it is to say, well, I have an additional classifier, (...TRUNCATED)
| 500
| "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models: is ca(...TRUNCATED)
| "[-0.02795610949397087, 0.020477404817938805, -0.006167191546410322, -0.0004171243926975876, 0.01617(...TRUNCATED)
|
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
|
https://www.youtube.com/watch?v=gwI6g1pBD84
| "we just train the model in both ways. During training, we sometimes just leave away the label. This(...TRUNCATED)
| 500
| "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models: we ju(...TRUNCATED)
| "[-0.02293958142399788, 0.00941467098891735, 0.0065055652521550655, 0.007706699427217245, 0.01860716(...TRUNCATED)
|
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
|
https://www.youtube.com/watch?v=gwI6g1pBD84
| "was a better direction because we also have the unconditional point right here. We can clearly say (...TRUNCATED)
| 500
| "GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models: was a(...TRUNCATED)
| "[-0.025296509265899658, 0.012773004360496998, -0.005128607153892517, -3.094382554991171e-05, 0.0136(...TRUNCATED)
|
End of preview. Expand
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