Summary of Lesson 9: Deep Learning Foundations to Stable Diffusion, 2022

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00:00:00 - 01:00:00

This video provides an introduction to deep learning, discussing how stable diffusion models work and how they can be applied to generate new images. The video includes a demonstration of how to use the Diffusers library to create images that look like handwritten digits.

00:00:00 In this ninth lesson of the "Practical Deep Learning for Coders" series, John Hopcroft explains how deep learning works and how to apply it to real-world problems. However, because the concepts and techniques described in this lesson are likely to become outdated in the near future, the majority of the video is spent teaching how to use stable diffusion, a deep learning algorithm that will still be applicable in 2022.
00:05:00 The course is moving quickly and new papers have shown that the number of steps required to generate a stable diffusion model has gone down from a thousand to four or fifty-six. The course will focus on the foundations of the model and how it works.
00:10:00 The course provides deep learning foundations, discussing how Stable Diffusion models work, and providing resources for more in-depth learning. In 2022, GPUs for deep learning will become more expensive, so it is important to take note of current recommendations.
00:15:00 This YouTube video provides a short introduction to deep learning, outlining the foundations of stable diffusion. Johno Whitaker provides a set of Colab notebooks, "diffusion-nbs", that can be used to explore the basics of deep learning. The video concludes with a recommendation to play around with the provided material and explore other resources.
00:20:00 In this lesson, the basics of Deep Learning are covered, including how to create a stable diffusion algorithm. Afterwards, the Diffusers library is introduced, and how to save a pipeline for others to use.
00:25:00 This lesson discusses the foundations of deep learning and how to use Colab to create high-quality images. The 51 steps it takes to create an image are compared to the three to four steps available as of October 2022.
00:30:00 In this lesson, the instructor demonstrates how to create images using deep learning. He demonstrates how to use "guidance scale" to control how abstract the images are.
00:35:00 This video explains how to use a deep learning model to generate images that look like the original drawing, using a technique called stable diffusion.
00:40:00 In this lesson, the instructor explains how to train machine learning models with the stable diffusion algorithm. They explain that the algorithm is useful for generating images that are similar to the examples that have been provided. The instructor also shares an example of how the stable diffusion algorithm was used to generate an image of a teddy that is similar to the original teddy.
00:45:00 In this video, the instructor introduces the concept of stable diffusion, which is a mathematical approach that is equivalent to the traditional approach but is more conceptually simple. He explains that by using a function that can determine the probability that an image is a handwritten digit, you can generate new images that look like handwritten digits.
00:50:00 In this video, an instructor explains how to calculate the gradient of the probability that an inputted image is a handwritten digit, using deep learning.

01:00:00 - 02:00:00

This video introduces the concept of stable diffusion, which is a method for training Neural Networks. The basic idea is to modify the inputs to a Neural Network in order to change the output. In this video, the instructor discusses how to create a Neural Net that will be able to correctly identify handwritten digits from noisy input.

01:05:00 This video introduces the idea of stable diffusion, which is a method for training Neural Networks. The basic idea is to modify the inputs to a Neural Network in order to change the output.
01:10:00 In this video, the instructor discusses how to create a Neural Net that will be able to correctly identify handwritten digits from noisy input. They first discuss how to create a training dataset and then go on to explain how to train the Neural Net.
01:15:00 This video introduces the concept of deep learning and stable diffusion, which is a way to predict the noise in a digit image. The Neural Net predicts the noise and the loss function is simple: taking the input and predicting the noise.
01:20:00 The Neural Network in this video is trying to predict the noise that was added to the inputs. It does this by subtracting the bits that it thinks are noise from the input. After doing this multiple times, it eventually gets something that looks more like a digit.
01:25:00 In this video, Jimmy shows how a Neural Net, called the U-Net, can be used to approximate an image. The problem is that the U-Net requires a lot of storage, which can be a problem for Google with its large cloud of TPUs.
01:30:00 The video explains how to compress an image using deep learning. First, an image is compressed by putting it through a layer of stride two convolutional layers. This process is repeated until the image is reduced to a 64x64x4 version. Next, the image is saved as a Neural Network layer. Finally, the Neural Network is used to compress images of different sizes.
01:35:00 The video discusses how a loss function can be used to teach a Neural Net how to compress an image, resulting in a smaller file. The compression algorithm works well and can be used to share images between two people.
01:40:00 This video provides a tutorial on how to train a deep learning model using latent data. Latents are a special type of data that are not directly observed and are used to train a deep learning model. Latents are created by encoding a picture's pixels using a neural network. The encoding process creates a latent representation of the picture. The decoder uses this latent representation to generate the original picture.
01:45:00 This video explains how a Neural Network can learn to predict noise better by taking advantage of the fact that it knows what the original image was. This is useful because, when fed the number 3, for instance, the model will say that the noise is everything that doesn't represent the number 3.
01:50:00 The video explains how two neural networks can be used to encode text and images. The first neural network is used to encode text, and the second neural network is used to encode images. The goal is for the two networks to produce similar outputs for a given input. The similarity of the outputs is determined by the dot product of the features of the input and the features of the output.
01:55:00 This video explains how to create a CLIP text encoder, which is a type of machine learning model that can produce similar embeddings for similar text inputs. This is important because it allows for multimodal text recognition and synthesis.

02:00:00 - 02:15:00

This video discusses how to train a machine learning model using a deep learning algorithm. The model is initialized with a set of latent variables (representing the data) and uses a decoder to understand the raw data. Next, a text encoder is used to create machine-readable captions for the data. Finally, a U-Net is trained using the captions as input, and the gradients (the "score function") are used to adjust the noise levels in the training data.