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A diffusion model models data as generated by a diffusion process, whereby a new datum performs a random walk with drift through the space of all possible data. [2] A trained diffusion model can be sampled in many ways, with different efficiency and quality.
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.
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 ...
If AI-generated content is included in new data crawls from the Internet for additional training of AI models, defects in the resulting models may occur. [184] Training an AI model exclusively on the output of another AI model produces a lower-quality model. Repeating this process, where each new model is trained on the previous model's output ...
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.
Prisma Labs uses the Stable Diffusion generative engine to power the Lensa apps’s Magic Avatar feature. [7] Stable Diffusion is open source and was trained on the LAION 5B dataset, [8] which utilized 5.85 billion CLIP-filtered image-text pairs from Common Crawl to create the dataset.
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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]