At first I thought "oh, another random video explaining the same basics and not adding anything new", but I was so wrong. It's an incredibly clear explanation of diffusion, and the start with the basic makes the full picture much clearer. Thank you for the video!

kinda sorry to my professors and seniors but this is the single best explanation of logics behind each models. About dozen min vid > 2 years of confusion in univ

Next video will be on Mamba/SSM/Linear RNNs!

Man I love the fact that you present the fundamental idea with an Intuitionistic approach, and then discuss the optimization.

this is insane. I feel bad for getting this level of content for free

This genius only makes videos occassionally, that are not to be missed.

The way you tell the story is fantastic! I am surprised that all AI/ML books are so terrible at didactics. We should always start at the intuition, the big picture, the motivation. The math comes later when the intuition is clear.

The clearest and most concise explanation of diffusion model I've seen so far. Well done.

This is a great explanation on how image decoders work. I haven't seen this approach and narrative direction yet. This now makes my reference for explaining it to people that got no idea.!

This is a much better explanation than the diffusion paper itself. They just went all around variational inference to get the same result!

Such an underrated video, I love how you went from the basic concepts to complex ones and didn't just explain how it works but also the reason why other methods are not as good/efficient. I will definitely be looking forward to more of your content!

I have trained my own diffusion models and it required me to do a deep dive of the literature. This is hands down the best video on the subject and covers so much helpful context that makes understanding diffusion models so much easier. I applaud your hard work, you have earned a subscriber!

This is seriously one of the best explainer videos i've ever seen. I've spent a long time trying to understand diffusion models and not a single video has come close to this one

Very good job. My suggestion is that you explain more about how it actually works, that the model learns to understand complete sceneries just from text prompts. This could fill its own video. Also it would be very nice to have a video about Diffusion Transformers like OpenAIs Sora probably is. Also it could be great to have a Video about the paper "Learning in High Dimension Always Amounts to Extrapolation". best wishes

Never knew youtube could give random suggestion to videos like these. This was mind blowing. The way you teach is work of art.

I have a graduate degree in this shit and this is by far the clearest explanation of diffusion I've seen. Have you thought about doing a video running over the NN Zoo? I've used that as a starting point for lectures on NN and people seem to really connect with that paradigm

You truly understand how to simplify... to engage our imagination... to employ naive thought or ideas to make comparisons to bring across a deeper more core principles and concepts to make the subject for more easier to grasp and get an intuition for. Algorithmic Simplicity indeed... thank you for your style of presentation and teaching. love it love it... you make me know what question I want to ask but didn't know I wanted to ask. KZfaq needs your contribution in ML education. please don't forget that.

A person with very less background can understand what he describes here.. commenting to make youtube so it gets recommended for other .. wonderful video! really good one

This channel is gold, I'm glad I've randomly stumbled across one of your vids

Great video. Love the pacing and how you distiled the material into such an easy to watch video. Great job!

This must be one of the best and concise explanations I've seen!

This is an amazing quality video! The best conceptual video on diffusion in AI I've ever seen. Thanks for making it! I'd love to see you cover RNNs.

Thank you for the explanation. I already knew a little bit about diffusion but this is exactly the way I'd hope to learn. Start from the simplest examples(usually historical) and progresivelly advance, explaining each optimisation!

I had a good idea how diffusion models work but I still learned a lot from this video. Thanks!

bruh, I loved your contents. Other channel/video usually explain general knowledge that can be easily found on internet. But you're going deeper to the intrinsic aspects of how the stuff works. This video, and one of your video about transformer, are really good.

Thanks for taking the time and effort of making and sharing these videos and Your knowledge. Kudos and best regards

I finally understand how models like Stable Diffusion work now! I tried understanding them before but got lost at the equation (17:50), but this video describes that equation very simply. Thank you!

Awesome video! I would love to see a video about graph neural networks

Very nicely and simply explained! Keep it up

Wow, fantastic video. Such clear explanations. I learned a great deal from this. Thank you so much!

This was a good watch, thank you :)

Great explanations, thank you!

Some2 really brought out some good channels

I like to think of ML as a funky calculator. Instead of a calculator where you give it inputs and an operation and it gives you an output, you give it inputs and outputs and it gives you an operation. You said it's like curve fitting, which is the same thing, but I like thinking the words funky calculator because why not

nice explanations, although, i've already knew about diffusion. examples from simplest to final diffusion -- were a really nice touch.

Wow, that was a fantastic explanation.

Great video and amazing content quality!

Great video....already waiting for your next video

Love the conclusion

Great explanation!👍👍, I personally would like to see a video observing all major types of neural nets with their distinctions, specifics, advantages, disadvantages etc. the author explains very well 👏👏

Soooo Good!!! Thanks for making it!!!!

I think it would help to mention that the auto-regressors may be viewing the image as a sequence of pixels (RGB vectors). Overall excellent video, extremely intuitive.

It was too easy understanding👌🏻👌🏻

Great video! Focus on the right elements.

Great explanation

Thank you. This video is wonderful

Great visualisation! Good job! Maybe next video on LoRA or ControlNet ?

Very good video. Thank you

Thank you very much. I enjoyed the first part, the first 10 seconds. After, there are too any shortcuts in the explanations that I struugled to understand and be able to explain it again to myself. Still, I subscribed. As for suggestions for other videos, I'll check whether you have explained the U-Net already. If not I'd appreciate to have the same kind of explanation about it.

Amazing video!

Great video, congrats.

Solid video!

Well done!

Just subscribed. Great video

Great video!

thanks for explaining

what a great rare content!

I would recommend reading about active inference to possibly understand the role of generative models in intelligence.

Still feels like magic to me 🙌🙌

great work

Diffusion doesn't necessarily work better than auto-regression. The "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction" paper introduces an architecture they call VAR that upscales noise using an AR model and this currently out-performs all diffusion models in terms of speed and accuracy.

Great video

Thank you So much Sir. Really interesting video. But i will like you to create a video on how the generative model uses the text promt during training. Thank you Sir. I subscribed !😊

Video is a banger

Excellent.

Thank you for the video. Imao curve fitting is oversimplification, it destructs us from real problem - what and how being optimized. Also there is different perspective on cases there we prefer computational efficiency over training quality: with efficiency you can train model on more data and for longer time using same amount of computational resources which actually results in better model

Nice vid thx

This was genuinely such a great video. I honestly feel like I could come away from this video and implement an image generator myself :) /gen

Not sure I agree with some of your analysis here. The strength of diffision models doesn't come from the lower depedence of objects/pixels the model generates at once. In fact, as you mention, the model actually predicts a whole image, in practice, at every step. Even when you use the trick of predicting the noise, the noise is unintuitively not random, that is, not randomly generated, but actually depends completely on the noise or lack there of in the input. It is after all equivalent to predicting the whole image. The real strength comes from the incremental nature, that is, a step of the model further down the line can "fix" a mistake it made previously by interpreting the previous generation as noise. In the space of all say 1024x1024 pixel value combinations, there is a manifold (essentially a subset of close together images) of all target images we want to generate. The diffusion model learns to take incremental steps toward that subset of "reasonable" images from any random starting point.

That was exactly how i guessed they did

A funny thing about watching a video like this is that you see an artificial neural network produce an image and then you have another layer of neural network in the brain that tries to figure out if it was a good match or not. The so called “blurry noise” could in principle look like a good match to someone and a bad match to someone else depending on how their own categorization works. It could also be good for everyone and bad for everyone of course or some arbitrary mix along that scale. The point is that “looks like blury noise” risks being a quite unobjective statement. I mean, people see images in the clouds and so on.

And the newest/upcoming models seem to be tending more towards diffusion Transformers, which from my understanding is effectively a Transformer autoencoder with a diffusion model plugged in, applying diffusion directly to the latent space embeddings. Is that correct?

Finally I understand!

via Pi "Diffusion models and auto-regressive (AR) models are two popular approaches for generating images and other types of data. They differ in their fundamental techniques, generation time, and output quality. Here's a brief comparison: **Diffusion Models:** * Approach: Diffusion models are based on the idea of denoising images iteratively, starting from a noisy input and gradually refining it into a high-quality output. * Generation Time: Diffusion models are generally faster than AR models for image generation, especially when using optimizations like "asymmetric step" or Cascade models. * Output Quality: Diffusion models are known for generating high-quality and diverse images, especially when trained on large datasets like Stable Diffusion or DALL-E 2. They can capture various styles and generate coherent images with intricate details. **Auto-Regressive (AR) Models:** * Approach: AR models generate images pixel by pixel, conditioning each new pixel on previously generated pixels. This sequential approach makes AR models computationally expensive, especially for large images. * Generation Time: AR models tend to be slower than diffusion models due to their sequential nature. The generation time can be significantly longer for high-resolution images. * Output Quality: While AR models can produce high-quality images, they may struggle with capturing diverse styles or maintaining coherence across different image regions. They might require additional techniques, like classifier-free guidance or super-resolution, to achieve better results. In summary, diffusion models generally offer faster generation times and better output quality compared to AR models. However, both approaches have their strengths and limitations, and the choice between them depends on the specific use case, available computational resources, and desired generation speed and output quality."

Hey, could you kindly recommend more on causal architectures?

can you please share the code that ubused for generation of the images in the demo. it will be very helpful

Could you explain why the YOLO image classify is/was so effective? Thank you.

Do you have a citation that supports your claim for eps vs x0 prediction? It's true that the first sampling step with x0 tends to produce a blurry / averaged result, but that's a result of the loss function used when training DDPMs. If you were to use something more complex or another NN, then you'd have a GAN, which don't produce blurry or averaged results on a single forward pass. Also, if you examine the output of x0 = noise - eps for the first step, it's both mathematically and visually equivalent to the first x0 prediction sample - a blurry / averaged result. The same thing is also true when predicting velocity, but velocity is arguably harder for a network to predict due to the phase transition.

Can you make a video about the loss landscape.Like what effects do different weight inits. Optimizers or architectures like resnet have.

thanks to very good video I have a question: can't we make a model that decrease the resolution of a picture (for example a 4*4 picture to a 2*2 and to 1*1 picture) and run it reverse (generate a 2*2 from 1*1 and 4*4 from 2*2) ? would this model works?

I want to learn more about the causal architecture idea for auto regressors, but I can't seem to find anything about them anywhere. Do you know where I can read more about this topic?

Hi @algorithmicsimplicity, I am curious which papers/material did you reference for the general autogressor? I cannot seem to find any info on using random spaced out pixels to predict the next batch of pixels. Any help would be appreciated. Also great videos!!!

Use lense projector and -zoom will save all the msthematical brain picking In video we use ccd cell in camera instantly illuminate LED pixel then zoom it down to tiny dot then send to ram and display on monitor by zoom factor corespond to resolutiom allow and zoom it back down when store it in time line of each coordinate and add all up with address and time then when unfold all we need is tiny dot first frame and last frame then start by last frame unfold into buffer subtract time but must adjust to phase angle of time at closest to last frame and just less tine drive with appropriate speed of each time axis so memory is so small

My brain misinterpreted the title as "Why diffusers work better than autoencoders" (I believe because the noising process works rather like data augmentation)

So why isn't diffusion better for text? Also are you saying that auto-regression is only bad because it's expensive to do (serially)? Or is diffusion fundamentally better for images?

Is the idea at the beginning of the video (auto regression image generation) self supervised learning?

I love your eplanations especially transfomers. Although this one imo could have been better, I think you are missing some ideas that should have been explained.

This video is better than go to the MIT for machine learning degree. Man this is gold, thank you so much

Can you recommend some sources that i can follow if i want to do deeper into diffusion models and transformers?

If this explanation is too advanced for me how would you recommend I learn enough to be able to grasp these concepts? Can you direct me to some content that is one level down in complexity so I can see if that would be my starting point in understanding how these models work? I don’t really have any CS background.

Now we know they're not only predictors.

can you explain why for diffusion model, there's no causal architecture? 16:26

Can you make a video on how SORA by OpenAI works, what kind of architecture does it follow

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18:10 This is wrong. The average of a bunch of noisy images is a less-noisy image. (See "regression towards the mean") You'd have to normalize that averaged image.

17:58 btw, you wrote "nose", instead of "noise"

So denoising models work by predicting the clean image, and then to get the next step you noise its already clean output??? That doesn't make any sense. If it predicts the final image already why do you have to keep predicting.

a fixed pixel with random color wouldn't make it work?

Enjoyed I think is the wrong output

I didnt understand why you would predict the noise rather than the clean image. Your explanation didnt seem to be related to the problem...