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There are three methods in which user-accessible fine-tuning can be applied to a Stable Diffusion model checkpoint: An "embedding" can be trained from a collection of user-provided images, and allows the model to generate visually similar images whenever the name of the embedding is used within a generation prompt. [45]
[22] [23] Users retained the ownership of resulting output regardless of models used. [24] [25] The models can be used either online or locally by using generative AI user interfaces such as ComfyUI and Stable Diffusion WebUI Forge (a fork of Automatic1111 WebUI). [8] [26] An improved flagship model, Flux 1.1 Pro was released on 2 October 2024.
These APIs for 3D computer graphics are particularly popular: ANGLE, web browsers graphics engine, a cross-platform translator of OpenGL ES calls to DirectX, OpenGL, or Vulkan API calls. Direct3D (a subset of DirectX) Glide a defunct 3D graphics API developed by 3dfx Interactive. Mantle developed by AMD. Metal developed by Apple.
As of August 2023, more than 15 billion images had been generated using text-to-image algorithms, with 80% of these created by models based on Stable Diffusion. [184] If AI-generated content is included in new data crawls from the Internet for additional training of AI models, defects in the resulting models may occur. [185]
The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset. A diffusion model models data as generated by a diffusion process, whereby a new datum performs a random walk with drift through the space of all possible data. [2]
In August 2022 Stability AI rose to prominence with the release of its source and weights available text-to-image model Stable Diffusion. [2] On March 23, 2024, Emad Mostaque stepped down from his position as CEO. The board of directors appointed COO, Shan Shan Wong, and CTO, Christian Laforte, as the interim co-CEOs of Stability AI. [11]
The Pedestrian Library is dedicated to simulating pedestrian flows in a physical environment. It allows you to create models of pedestrian-intensive buildings (like subway stations, security checks etc.) or streets (large numbers of pedestrians). Models support statistics collection on pedestrian density in different areas.
By utilizing a pre-trained image diffusion model as a base generator, the model efficiently generated high-quality and coherent videos. Fine-tuning the pre-trained model on video data addressed the domain gap between image and video data, enhancing the model's ability to produce realistic and consistent video sequences. [14]