2023年8月30日 · Diffusion Explainer provides a visual overview of Stable Diffusion’s complex structure as well as detailed explanations for each component’s operations.

In this session, we walked through all the building blocks of Stable Diffusion (slides / PPTX attached), including Principle of Diffusion models. Model score function of images with UNet model

Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. The generative artificial intelligence technology is the premier product of Stability AI and is considered to be a part of the ongoing artificial intelligence boom.

2024年3月5日 · In this article, we delve into the realm of stable diffusion models and showcase the best ones specifically tailored for architectural applications.

2024年9月1日 · By leveraging deep learning, stable diffusion models are capable of creating highly realistic images from text descriptions, as well as performing a variety of image-to-image translation tasks. At the heart of the stable diffusion approach is the U-Net architecture.

•Stable Diffusion is cool! •Build Stable Diffusion “from Scratch” •Principle of Diffusion models (sampling, learning) •Diffusion for Images –UNet architecture •Understanding prompts –Word as vectors, CLIP •Let words modulate diffusion –Conditional Diffusion, Cross Attention •Diffusion in latent space –AutoEncoderKL

2024年3月5日 · Stable Diffusion 3 outperforms state-of-the-art text-to-image generation systems such as DALL·E 3, Midjourney v6, and Ideogram v1 in typography and prompt adherence, based on human preference evaluations.

2024年4月24日 · Architecture of stable diffusion is trained to iteratively add noise to and then remove noise from images, functioning as a sequence of denoising autoencoders. The key components of Stable Diffusion's architecture are a variational autoencoder (VAE), a U-Net decoder, and an optional text encoder.

Stable Diffusion is a generative artificial intelligence (generative AI) model that produces unique photorealistic images from text and image prompts. It originally launched in 2022. Besides images, you can also use the model to create videos and animations. The model is based on diffusion technology and uses latent space.

Stable Diffusion consists of 3 parts: the variational autoencoder (VAE), U-Net, and an optional text encoder.[11] The VAE encoder compresses the image from pixel space to a smaller dimensional latent space, capturing a more fundamental semantic meaning of the image.[12]