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Stable Diffusion is a deep learning, ... hosted on Hugging Face, ... 1.2, 1.3, 1.4 [67] August 2022 All released by CompVis. There is no "version 1.0". 1.1 gave rise ...
The Latent Diffusion Model (LDM) [1] is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) [2] group at LMU Munich. [ 3 ] Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian ) on training images.
In August 2022, the company co-released an improved version of their Latent Diffusion Model called Stable Diffusion together with the CompVis Group at Ludwig Maximilian University of Munich and a compute donation by Stability AI. [14] [15] On December 21, 2022 Runway raised US$50 million [16] in a Series C round.
LoRA-based fine-tuning has become popular in the Stable Diffusion community. [14] Support for LoRA was integrated into the Diffusers library from Hugging Face. [15] Support for LoRA and similar techniques is also available for a wide range of other models through Hugging Face's Parameter-Efficient Fine-Tuning (PEFT) package. [16]
The company was named after the U+1F917 珞 HUGGING FACE emoji. [2] After open sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning. In March 2021, Hugging Face raised US$40 million in a Series B funding round. [3]
Stable Diffusion 3 (2024-03) [66] 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) [ 67 ] is a latent diffusion model for videos of 3D objects.
Open-source machine translation models have paved the way for multilingual support in applications across industries. Hugging Face's MarianMT is a prominent example, providing support for a wide range of language pairs, becoming a valuable tool for translation and global communication. [63]
The Fréchet inception distance (FID) is a metric used to assess the quality of images created by a generative model, like a generative adversarial network (GAN) [1] or a diffusion model. [ 2 ] [ 3 ] The FID compares the distribution of generated images with the distribution of a set of real images (a "ground truth" set).