100 Best Stable Diffusion Videos

Notes:

Text-to-image generation is a task in computer vision and natural language processing where a model is trained to generate an image based on a given text description. The model learns to understand the text description and generate an image that matches the description. This task is challenging because it requires the model to understand the meaning of the text and generate an image that is coherent with the text. There are various approaches to text-to-image generation, such as using a generative model like a Generative Adversarial Network (GAN) or a Variational Autoencoder (VAE). These models are trained on large datasets of images and their associated text descriptions.

Stable Diffusion is a deep learning, text-to-image model that was released in 2022. It is primarily used to generate detailed images conditioned on text descriptions, though it can also be applied to other tasks such as inpainting, outpainting, and generating image-to-image translations guided by a text prompt. It is a latent diffusion model, a variety of deep generative neural network developed by the CompVis group at LMU Munich. The model was released by a collaboration of Stability AI, CompVis LMU, and Runway with support from EleutherAI and LAION. In October 2022, Stability AI raised US$101 million in a round led by Lightspeed Venture Partners and Coatue Management. The model’s code and weights are publicly available and can run on most consumer hardware equipped with a modest GPU with at least 8 GB VRAM. This marked a departure from previous proprietary text-to-image models such as DALL-E and Midjourney which were accessible only via cloud services.

Variational Autoencoder (VAE) is a type of generative model that can be used to learn a compact representation of data, called a latent code, from which new samples can be generated. The VAE consists of two main parts: an encoder network that maps the input data to the latent code, and a decoder network that maps the latent code back to the original data space. The encoder and decoder networks are trained jointly in an unsupervised manner using a variant of the standard backpropagation algorithm called the reparameterization trick. The key idea behind the VAE is to introduce a probabilistic interpretation of the latent code, where the encoder network outputs a probability distribution over the latent code, and the decoder network generates new samples from this distribution. This allows the VAE to learn a more robust and generalizable representation of the data than traditional autoencoders.

Resources:

lexica.art .. the stable diffusion search engine.

Wikipedia: