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Stable Diffusion. 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 .
Stability AI has a Stable Diffusion web interface called DreamStudio, [56] plugins for Krita, Photoshop, Blender, and GIMP, [57] and the Automatic1111 web-based open source user interface. [ 58 ] [ 59 ] [ 60 ] Stable Diffusion's main pre-trained model is shared on the Hugging Face Hub .
Synthetic media (also known as AI-generated media, [1] [2] media produced by generative AI, [3] personalized media, personalized content, [4] and colloquially as deepfakes [5]) is a catch-all term for the artificial production, manipulation, and modification of data and media by automated means, especially through the use of artificial intelligence algorithms, such as for the purpose of ...
June 25, 2024 at 3:41 PM. LONDON (AP) — The troubled artificial intelligence company behind image-generator Stable Diffusion is looking for a reshoot with a new CEO and a surge of investment ...
DreamBooth is a deep learning generation model used to personalize existing text-to-image models by fine-tuning. It was developed by researchers from Google Research and Boston University in 2022. Originally developed using Google's own Imagen text-to-image model, DreamBooth implementations can be applied to other text-to-image models, where it ...
Stable Diffusion 3 (2024-02) [40] changed the latent diffusion model from the UNet to a Transformer model, and so it is a DiT. It uses rectified flow. It uses rectified flow. Stable Video 4D (2024-07) [ 41 ] is a latent diffusion model for videos of 3D objects.
In anisotropic media, the diffusion coefficient depends on the direction. It is a symmetric tensor Dji = Dij. Fick's first law changes to it is the product of a tensor and a vector: For the diffusion equation this formula gives The symmetric matrix of diffusion coefficients Dij should be positive definite.
In probability theory and statistics, diffusion processes are a class of continuous-time Markov process with almost surely continuous sample paths. Diffusion process is stochastic in nature and hence is used to model many real-life stochastic systems. Brownian motion, reflected Brownian motion and Ornstein–Uhlenbeck processes are examples of ...