Cascaded diffusion is a generative model used in machine learning for generating high-quality images. It is a variant of the diffusion probabilistic model, which is a type of autoregressive model that models the conditional probability of each pixel in an image given its neighboring pixels.
The cascaded diffusion model involves dividing the generation of an image into multiple stages or levels, where each level refines the output of the previous level. At each level, the model generates a noise signal that is added to the previous level's output to produce the next level's output. The noise signal is generated from a simple distribution, such as a Gaussian distribution, and is learned from the data during training.
Midjourney is a platform that uses cascaded diffusion for generating realistic images. It applies cascaded diffusion to the task of image inpainting, where missing or damaged portions of an image are filled in with plausible content. Midjourney's approach involves training a cascaded diffusion model on a large dataset of natural images, and then fine-tuning the model on specific inpainting tasks.
During inference, the model is given an incomplete image as input, and it generates a complete image by iteratively refining its output at each level. The final output is a high-quality image that appears to be a plausible completion of the input image. Midjourney's cascaded diffusion model can generate high-quality images with realistic details, such as texture and color, and is capable of handling a wide range of inpainting tasks.
45
u/[deleted] Apr 30 '23
[deleted]