TAESD is a tiny, distilled version of Stable Diffusion's VAE*, which consists of an encoder and decoder. The encoder turns full-size images into small "latent" ones (with 48x lossy compression), and ...

Thanks to this new text encoder, Stable Diffusion 2.0 can generate significantly better images compared to version 1.0, according to Stability AI. The model can generate images with resolutions of ...

Stable Diffusion is a deep learning model that can generate high-quality images from natural language descriptions ... The autoencoder compresses the information into the latent space using its ...

While efficient, Stable Diffusion comprises about one billion parameters between the constituent encoder, U-Net, and decoder models. A text encoder with an input sequence length of 77 and an output ...

In doing so, the team eliminates the need to train and fine-tune complex AI models. All that needs to be trained are simple linear models that map the fMRI signals of the lower and upper visual brain ...

How Does the UNet Encoder Transform Diffusion ... but encoder and decoder dropping fail to achieve complete denoising. Originally designed for medical image segmentation, UNet has evolved, especially ...