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Diagram of the latent diffusion architecture used by Stable Diffusion The denoising process used by Stable Diffusion. The model generates images by iteratively denoising random noise until a configured number of steps have been reached, guided by the CLIP text encoder pretrained on concepts along with the attention mechanism, resulting in the desired image depicting a representation of the ...
However, they remained roughly the same. Substantial information concerning Stable Diffusion v1 was only added to GitHub on August 10, 2022. [16] All of Stable Diffusion (SD) versions 1.1 to XL were particular instantiations of the LDM architecture. SD 1.1 to 1.4 were released by CompVis in August 2022. There is no "version 1.0".
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.
GPT-4 is a multi-modal LLM that is capable of processing text and image input (though its output is limited to text). [49] Regarding multimodal output , some generative transformer-based models are used for text-to-image technologies such as diffusion [ 50 ] and parallel decoding. [ 51 ]
Schematic structure of an autoencoder with 3 fully connected hidden layers. The code (z, or h for reference in the text) is the most internal layer. Autoencoders are often trained with a single-layer encoder and a single-layer decoder, but using many-layered (deep) encoders and decoders offers many advantages. [2]
An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
12-level, 12-headed Transformer decoder (no encoder), followed by linear-softmax. 0.12 billion BookCorpus: [38] 4.5 GB of text, from 7000 unpublished books of various genres. GPT-2 GPT-1, but with modified normalization 1.5 billion WebText: 40 GB [39] of text, 8 million documents, from 45 million webpages upvoted on Reddit. GPT-3
The stable distribution family is also sometimes referred to as the Lévy alpha-stable distribution, after Paul Lévy, the first mathematician to have studied it. [ 1 ] [ 2 ] Of the four parameters defining the family, most attention has been focused on the stability parameter, α {\displaystyle \alpha } (see panel).