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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.
Hugging Face, Inc. is an American company incorporated under the Delaware General Corporation Law [1] and based in New York City that develops computation tools for ...
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]
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.
Stable Diffusion, prompt a photograph of an astronaut riding a horse Producing high-quality visual art is a prominent application of generative AI. [ 65 ] Generative AI systems trained on sets of images with text captions include Imagen , DALL-E , Midjourney , Adobe Firefly , FLUX.1 , Stable Diffusion and others (see Artificial intelligence art ...
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]