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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 ...
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.
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.
This lawsuit, filed in Delaware federal court, is part of a series of actions against Stability AI concerning the use of images in AI training. Getty Images alleges that Stability AI copied these images without proper licensing, using them to enhance Stable Diffusion's ability to generate accurate depictions from user prompts. [13]
The denoising network is a U-Net, with cross-attention blocks to allow for conditional image generation. [64] [26] Stable Diffusion 3 (2024-03) [65] changed the latent diffusion model from the UNet to a Transformer model, and so it is a DiT. It uses rectified flow. Stable Video 4D (2024-07) [66] is a latent diffusion model for videos of 3D objects.
Text-to-Image Generation: Models like Stable Diffusion use CLIP's text encoder to transform text prompts into embeddings for image generation. [3] CLIP can also be used as a gradient signal for directly guiding diffusion ("CLIP guidance") [ 35 ] [ 36 ] or other generative art.
A generative image model such as Stable Diffusion is able to model the stylistic characteristics of an artist like Pablo Picasso (including his particular brush strokes, use of colour, perspective, and so on), and a user can engineer a prompt such as "an astronaut riding a horse, by Picasso" to cause the model to generate a novel image applying ...
DreamBooth can be used to fine-tune models such as Stable Diffusion, where it may alleviate a common shortcoming of Stable Diffusion not being able to adequately generate images of specific individual people. [4] Such a use case is quite VRAM intensive, however, and thus cost-prohibitive for hobbyist users. [4]
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