The key idea of the video is that diffusion models and adding noise to images can aid in training neural networks to generate unique images, but the process requires patience and conditioning with text can be used to create hybrid images.
💡 Iteratively removing noise from a noisy image can produce a clearer image, and creating a frog rabbit hybrid image requires conditioning the network with text and starting with a random noise image.
Iteratively removing and adding back noise to a noisy image can produce a clearer image.
The image generation process involves looping a noisy image and gradually removing noise by predicting and taking it away, which is easier to train and more stable, but directing the creation of random images adds complexity.
To create a frog rabbit hybrid image, the network must be conditioned and given access to text, and the process involves starting with a random noise image and iterating through the network while estimating the noise and using a string such as "frogs on stilts."
Using Transformer embedding, noise can be subtracted from an image to obtain an estimate of the original image, with the final step being classifier-free guidance to make the output more tied to the text.
Using GPT-style Transformer embedding, an estimate of noise in an image can be produced and subtracted to obtain an estimate of the original image.
The process involves taking an embedding, subtracting it, adding noise back, and repeating the process while adding text embedding, with the final step being classifier-free guidance to make the output more tied to the text.
The classify free guidance method amplifies the difference between two images with and without noise to force the network to target the desired output.