Language
Country/Area
No result found
The origin story of stable diffusion: How did this revolutionary model come about?
With the rapid development of artificial intelligence technology, Stable Diffusion, a deep learning text-to-image model, was officially released in 2022 and quickly attracted widespread attention in the community. This revolutionary model can not only generate detailed images based on text descriptions, but can also be applied to a variety of other tasks such as inpainting and outpainting.
Behind the stable diffusion is the result of a joint collaboration between the CompVis team at Ludwig Maximilian University in Munich, Germany, and researchers at Runway. The model was developed with support from Stability AI and uses a large amount of training data from non-profit organizations, making this innovation run on most consumer hardware, unlike previous professional models that were only accessible through cloud services. There are text-to-image models such as DALL-E and Midjourney in stark contrast.
The emergence of stable diffusion marks a new revolution in artificial intelligence, and may lead to more innovative and convenient ways of creation in the future.
Development Process
Stable diffusion originated from a project called Latent Diffusion, developed by researchers at Ludwig-Maximilians-Universität Munich and Heidelberg University. The four original authors of the project subsequently joined Stability AI and released subsequent versions of Stable Diffusion. The CompVis team has released a technical license for the model.
Core members of the development team include Patrick Esser of Runway and Robin Rombach of CompVis, who invented the latent diffusion model framework used by stable diffusion in the early days. The project is also supported by EleutherAI and LAION, a German nonprofit organization responsible for organizing stable diffusion training data.
Technical Architecture
The stable diffusion model uses an architecture called the Latent Diffusion Model (LDM), which was proposed in 2015 to train the model by gradually removing Gaussian noise. This process involves compressing the image from pixel space to a smaller latent space, thereby capturing the more basic semantic meaning of the image.
Stable Diffusion consists of three parts: Variational Autoencoder (VAE), U-Net, and an optional text encoder.
The VAE encoder compresses the image into a latent space, while the U-Net denoises the output latent representation. Finally, the VAE decoder converts the representation back to pixel space. The denoising step in this process can be flexibly adjusted based on text, images or other modalities.
Training Data and Programs
StableDiffusion is trained on the LAION-5B dataset, a public dataset of 5 billion image-text pairs filtered by language. The latest version of training, SD 3.0, marks a complete overhaul of the core architecture, with an improved parsing structure and enhanced generation detail and precision.
Use and Disputes
The stable diffusion model allows users to generate completely new images and modify existing images based on textual prompts. However, the use of this technology has also caused some controversy in terms of intellectual property and ethics, especially since the initial training data of the model contains a large amount of private and sensitive information. In addition, since the model is mainly trained using English data, the generated images may be biased in different cultural backgrounds.
Whether stable diffusion can balance technological application and social impact will be an issue to be resolved, and this is an important test for future development?
