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AUTOMATIC1111 Stable Diffusion Web UI (SD WebUI, A1111, or Automatic1111 [3]) is an open source generative artificial intelligence program that allows users to generate images from a text prompt. [4] It uses Stable Diffusion as the base model for its image capabilities together with a large set of extensions and features to customize its output.
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.
Runway AI, Inc. (also known as Runway and RunwayML) is an American company headquartered in New York City that specializes in generative artificial intelligence research and technologies. [1] The company is primarily focused on creating products and models for generating videos, images, and various multimedia content.
The startup says the model outperforms Stability AI’s Stable Diffusion and OpenAI’s DALL-E 3, two models that also generate images based on text prompts. (This story has been updated with new ...
Stability AI Ltd [1] is a UK-based artificial intelligence company, best known for its text-to-image model Stable Diffusion. History and founding Stability AI was founded in 2019 by Emad Mostaque and by Cyrus Hodes .
Stable Diffusion (2022-08), released by Stability AI, consists of a denoising latent diffusion model (860 million parameters), a VAE, and a text encoder. The denoising network is a U-Net, with cross-attention blocks to allow for conditional image generation.
The 2022 releases of Stable Diffusion and ChatGPT (initially powered by the GPT-3.5 model) led to foundation models and generative AI entering widespread public discourse. Further, releases of LLaMA , Llama 2, and Mistral in 2023 contributed to a greater emphasis placed on how foundation models are released with open foundation models garnering ...
The methodology used to run implementations of DreamBooth involves the fine-tuning the full UNet component of the diffusion model using a few images (usually 3--5) depicting a specific subject. Images are paired with text prompts that contain the name of the class the subject belongs to, plus a unique identifier.
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