Lesson 23: Deep Learning Foundations to Stable Diffusion

We discuss the limitations of using a convolutional neural network for image super-resolution and introduce the concept of U-net, a more efficient architecture for this task. We implement perceptual loss, which involves comparing the features of the output image and the target image at an intermediate layer of a pre-trained classifier model. After training the U-net model with the new loss function, the output images are less blurry and more similar to the target images.

[satirthapaulshyam]

1:06:00 modulelist is just like sequential but it doesnt autofwd we need to define the fwd method

[satirthapaulshyam]

57:00 why in superres squeez and again unsquezz why not we use stride1

[satirthapaulshyam]

1:28:00 replacing some layers of unet with pre trained classifier