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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.
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.
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 ...
Nvidia Corp (NASDAQ:NVDA) is enhancing its experimental ChatRTX chatbot by adding more AI models for RTX GPU owners. The chatbot operates locally on Windows PCs and uses Mistral or Llama 2 models ...
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.
According to OpenAI, Sora is a diffusion transformer [10] – a denoising latent diffusion model with one Transformer as the denoiser. A video is generated in latent space by denoising 3D "patches", then transformed to standard space by a video decompressor.
Logical data model, a representation of an organization's data, organized in terms of entities and relationships; Logical Disk Manager; Local Data Manager; LTSP Display Manager, an X display manager for Linux Terminal Server Project; Latent diffusion model, in machine learning; Latitude dependent mantle, a widespread layer of ice-rich material ...
The model was trained for 90 epochs over a period of five to six days using two Nvidia GTX 580 GPUs (3GB each). [1] These GPUs have a theoretical performance of 1.581 TFLOPS in float32 and were priced at US$500 upon release. [3] Each forward pass of AlexNet required approximately 4 GFLOPs. [4]