Tutorial on Denoising Diffusion-based Generative Modeling: Foundations and Applications
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- čas přidán 21. 07. 2024
- This video presents our tutorial on Denoising Diffusion-based Generative Modeling: Foundations and Applications. This tutorial was originally presented at CVPR 2022 in New Orleans and it received a lot of interest from the research community. After the conference, we decided to record the tutorial again and broadly share it with the research community. We hope that this video can help you start your journey in diffusion models.
Visit this page for the slides and more information:
cvpr2022-tutorial-diffusion-m...
Outline:
0:00:00 Introduction (Arash)
0:08:17 Part 1: Denoising Diffusion Probabilistic Models (Arash)
0:52:14 Part 2: Score-based Generative Modeling with Differential Equations (Karsten)
1:47:40 Part 3: Advanced Techniques: Accelerated Sampling, Conditional Generation (Ruiqi)
2:37:39 Applications 1: Image Synthesis, Text-to-Image, Semantic Generation (Ruiqi)
2:58:29 Applications 2: Image Editing, Image-to-Image, Superresolution, Segmentation (Arash)
3:20:42 Applications 3: Discrete State Models, Medical Imaging, 3D & Video Generation (Karsten)
3:35:20 Conclusions, Open Problems, and Final Remarks (Arash)
Follow us on Twitter:
Karsten Kreis: / karsten_kreis
Ruiqi Gao: / ruiqigao
Arash Vahdat: / arashvahdat
#CVPR2022 #generative_learning #diffusion_models #tutorial #ai #research - Věda a technologie
It is amazing to see how one of the biggest companies in the world are collaborating to produce tutorials but couldn't invest in 10$ mic.
lol cannot agree more
That was hard 💀
Just goes to show that lot's of people and companies don't try for exellence.
I think they are doing this on their personal capacity. It's a re-recording
lmao they rerecorded it and gave a shout out to the people who commented about the audio!
Don't complain about the audio is terrible. It is diffused with Gaussian noises. You need to decode the audio first.
💀
Thanks for comprehensive introduction! it is really helpful :)
Fantastic presentation. Thank you to the entire team!
This is a brilliant lecture!! I've learned so much from it. Thank you, prof. Arash, Ruigui and Karsten!
Dear Arash, Karsten, and Ruiqui, thanks a ton for putting this up!
Was referring to your tutorial slides earlier, but this definitely helps much better.
So my question is if any diffusion model can de-noise audio of this fantastic tutorial.
Thank you for uploading! This was very helpful!!
Very insightful and comprehensive presentation. Thank you al so much!
Amazing tutorial -- very helpful!
Thanks for posting.
Good stuff. Thanks guys
Awesome video, thanks for posting. One thing though, the audio quality is pretty bad (low volume, sounds very metallic).
Thank you great lecture
One the best videos! Thanks @All
Thank you very much
Thanks for uploading this video! Great resource, which covers the topics in a good depth and pace. Only point of critique I have would be to use a better microphone next time, as it can be hard at times to understand what you are saying. Other than that great video.
The content is fantastic, but the sound quality makes it materially harder to follow everything. A good microphone would lift the quality enormously!
Thank you for recording and uploading the tutorial. It is helpful in understanding the sudden boom in diffusion models and compares the techniques very well.
I wanted to know if the slides for the part 3 are slightly different from the slides on the website? (for eg. the slide 11)
Nice job arash ...
The quality of the presentation was so good. Thanks.
Why do I feel that there is an audio issue (in recording) with this video for the first two speakers?
Would have been great if they run diffusion process on the audio
same here
3: 1:58:35 On the previous slide, it reads that Variational diffusion models, unlike diffusion models using a fixed encoder, include learnable parameters in the encoder. So the training objectives on this paper are for reverse diffusion process or the entire forward and reverse diffusion process? I also do not understand when the speaker said "if we want to optimize forward diffusion process in the continuous time setting, we only need to optimize the signal-to-noise ratio at the beginning and the end of the forward process."
A request: Please record this video again. I come to it many times, but the audio recording quality makes it very difficult to comprehend.
2. 2:00:38. can take a pre-trained diffusion model but with more choices of sampling procedure。What does it mean? Would it be possible to find answers in the paper listed in the footnote? Thanks.
This feels like a semester's course in 4 hours. I have so many questions. I am just gonna ask hoping someone can shed some light.
1. 2:05:26 I don't quite understand the conclusion there: since these three assumptions hold, no need to specify q(x_t|x_t-1) as markovian process? What's the connection there? Thanks.
Good god the audio quality is horrible...
Guys please apply the diffusion process to the audio. Excellent material but almost impossible to listen due to the quality of the audio.
Thank you for the tutorial. Feedback: The audio quality is quite bad, having hard time understanding the words even using youtube transcription.
Part 3 is a bit hard to follow. A lot of formulae are shown without an explanation of what they mean.
thx
Do you have an open source denoiser to denoise the recording? Thanks
22:08 is there any theory to explain why we can use a Gaussian to do the approximation as long as beta is small enough?
Yes, search for Kolmogorov's forward and backward Markov chains
At around 16:24 marginal is equated to joint . Didnt quite comprehend it. Can you please explain
Is the reason that we are generating the gaussian at time t by multiplying a diffusion kernel (which itself is a gaussian) and gaussian at time t-1. So joint representation of t-1 gaussian with kernel at t-1 is the marginal at t. And the problem setup is to learn the reverse diffusion kernel at each step
the point of the equation is to demonstrate that as we don't have the exact PDF at all time steps we can't just sample x(t) from q(x(t)) directly instead we sample x(0) from initial distribution(sample a data point from the dataset) and then transform the data point using diffusion kernel to obtain a sample at timestep t do this for enough data points and you can approximate the distribution q(x(t)).
q(x(t)) is marginal i.e. independent of x(0).
Now coming to the equated part, marginal probability is not being equated to joint probability, let's see how!
the x(0) is not a value in it self it is a random variable, to avoid confusions let's change x(0) with i.
>now i is a random variable which can take any value in the given domain(data set),let us assume an image data set.
>then q(i) describes the distribution of our data set.
>q(i=image(1)) describes the probability of i being image(1)
>now let i(t)=x(t)
>q(i(t)) describes the approximate distribution of noisy images which we got by repeteadly sampling images from initial dataset at time step 0 and then carrying out the diffusion process for t time steps(t convolutions).
>q(i(t),i) describes the joint probability distribution for all possible pairs of values of i and i(t).
>q(i(t)=noisy_image,i=image(1)) describes the joint probability of the pair occuring, i.e. the probability that we started with i = image(1) and then after t time steps ended up with i(t) = noisy_image,which is = q(i=image(1)).q(i(t)=noisy_image|i=image(1)),Here image(1) is the first image in data set and noisy_image is a certain noisy image sampled from q(i(t)).
> now imagine we calculated q(i(t)=noisy_image ,i) for all possible values of i(i.e. all possible images in the data set as starting point) then added all these probabilities what we would end up with is the probability of getting i(t) = noisy_image independent of what value we chose for the random variable i, this value is represented by q(i(t)=noisy_image).
> the integral q(i).q(i(t)| i).di gives us the above mentioned quantity, now an important thing to note is the video explanantion assumes the dataset to be continuos, while explaining the said part, where as in my explanation I assumed a training set of images which is discrete, so the integral can be substituted by a summation over all samples.
What is the probability distribution of q(X0). Because that is the original image itself. So will it be a distribution ?
It's not known . Check 26:47 . You try to approximate it by decreasing the divergence between the distribution you get when adding some noise to the image (it becomes Gaussian as you do it) and the reverse process where you generate the image through model distribution (also Gaussian ). So in other words by decreasing q(x|noise)/p(x) divergence you approximate the data distribution without knowing it.
@@meroberto8370 So by equations on page 25, all we need is x_t, produced by the forward diffusion, some hyper-parameters, such as beta, etc., forward diffusion epsilon which is known, backward diffusion epsilon which is estimated by U-Net....
Hello, I have question in 10:36, I known N(mu, std^2) is a Normal distribution, how to understand N(x_t; mu, std^2) ?
It indicates that x_t is a random variable distributed according to a Gaussian distribution with a mean of μ and a variance of σ^2.
Fantastic tutorial, I learnt a lot. Please buy a good microphone for future, or make a Gofundme for a mic, I will be happy to donate.
waiting for diffusion workshop 2023 records 🙏
I am confused by a very basic point. At 22:38, he says that q(x_{t-1}|x_t) is intractable. But how can that be?? It's very simple. Since
x_t = sqrt(1-beta)*x_{t-1}+sqrt(beta)*E where E is N(0,I).
Therefore
x_{t-1} = (1-beta)^{-1/2}x_t - sqrt{beta/(1-beta)}*E
= (1-beta)^{-1/2}x_t + sqrt{beta/(1-beta)}*R
where R is N(0,I) (since negative of a Gaussian is still Gaussian.
Therefore
q(x_{t-1}|x_t) = N(x_{t-1}; (1-beta)^{-1/2}x_t, beta/(1-beta)I).
What gives?
A little bit late, but, from what i have understood the real problem is that, q(x_{t-1}|x_t) depends on x_t, but, x_t is a distribution that need the entire dataset to be calculated, because you don't have a sample that defined x_t|x_0, but you are just asking for the distribution of x_t for all x_0.
However, q(x_{t-1}|x_t,x_0) is calculable, given a start sample x_0 you can describe the distribution of x_t related to x_0
It's a nice lecture, but DDPM explanation can't be justified as a tutorial here, but if you already have a basic idea of the DDPM process, then it will make sense. good for revising math behind DDPM but not a detailed tutorial if you are new to it.
You can add captions please? The voice quality is so absurd and the generated captions are not accurate. Thanks for the tutorial btw Kudos
The sound quality is horrible.
You may still need to re-record this tutorial to correct and improve the audio quality. Resoundingly poor.
audio is horrible.
The audio is too garbled.
Maybe use an Ai voice for clarity?
Please, purchase a better microphone......
Part 2 voice is terrible.
bro buy a mic please and rerecord this your voice sound so much muddy
poor video quality, pointless delivery
why the f all these people didnt have a common sense to buy a good mic, I dont know , what is the use of this video if it is not delivering its value ? and Another bunch of people who is writing research papers so that no one in the world should not understand. Ml community will improve better only if these knowledge is accessible.
bah bah! lezzat bordam!