Stable Diffusion
- SDXL 1.0
Stable Diffusion is an open-source machine learning model that can generate images from text, modify images based on text, or fill in details on low-resolution or low-detail images. It has been trained on billions of images and can produce results that are comparable to the ones you’d get from DALL-E 2 and MidJourney. You can run Stable Diffusion locally with a GUI on Windows. You can also use Stable Diffusion to make AI GIFs and videos.
| Stable Diffusion (ai-tool) | |
|---|---|
| Full Name | Stable Diffusion |
| Short Name | Stable Diffusion |
| Description | An open-source machine learning model that generates images from text, modifies based on text, and enhances low-resolution images |
| Company | Open Source |
| Logo |  |
| Web | https://github.com/CompVis/stable-diffusion |
| Category | AI Image, AI Video |
| License | Open Source. Thanks to a generous compute donation from Stability AI and support from LAION |
What is Stable Diffusion? (AI Tool)
Stable Diffusion is a software application that uses artificial intelligence to create realistic and high-quality images from low-resolution or noisy inputs. It employs a novel diffusion model that can preserve the details and textures of the original image while enhancing its resolution and clarity. Stable Difussion AI Tool can be used for various purposes such as photo restoration, image editing, artistic creation, and more.Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. It is an open source project that was released by Stability AI, a company that aims to build the foundation to activate humanity's potential with AI. Stability AI also offers other products and services based on Stable Diffusion and other generative models .
Source: YouTube
Stable Diffusion Method
- Snippet from Wikipedia: 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.
It is primarily used to generate detailed images conditioned on text descriptions, though it can also be applied to other tasks such as inpainting, outpainting, and generating image-to-image translations guided by a text prompt. Its development involved researchers from the CompVis Group at Ludwig Maximilian University of Munich and Runway with a computational donation from Stability and training data from non-profit organizations.
Stable Diffusion is a latent diffusion model, a kind of deep generative artificial neural network. Its code and model weights have been released publicly, and it can run on most consumer hardware equipped with a modest GPU with at least 4 GB VRAM. This marked a departure from previous proprietary text-to-image models such as DALL-E and Midjourney which were accessible only via cloud services.
What is Stable Stable Diffusion? (Method)
Stable diffusion has been used in the field of artificial intelligence (AI) for tasks such as image processing and natural language processing. One example is the use of stable diffusion in image denoising, where it has been shown to outperform traditional methods such as Gaussian smoothing or total variation denoising. In stable diffusion-based image denoising, the noise is treated as a Lévy process, and the diffusion process is guided by the heavy-tailed Lévy distribution, which helps to preserve image features and edges.Stable diffusion has also been used in natural language processing (NLP) for tasks such as machine translation and text classification. In NLP, stable diffusion can be used to model the relationships between words in a sentence, taking into account the long-range dependencies between words. By using stable diffusion-based methods, NLP models can better capture the semantic and syntactic structure of language, leading to improved performance on a range of tasks.
Related:
External links:
- High-Resolution Image Synthesis with Latent Diffusion Models — ommer-lab.com