AudioCraft
AudioCraft is an open-source generative-audio library released by Meta AI (Fundamental AI Research, FAIR) on August 2, 2023 that generates high-quality music and sound from text prompts using a single…
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Showing 1-31 of 31 articles
Autoencoder
An autoencoder is a type of neural network trained to reconstruct its own input through a low-dimensional bottleneck representation, using the input itself as the training signal rather than any external label.
Classifier-Free Guidance (CFG)
Classifier-Free Guidance (CFG) is an inference-time technique that steers conditional generative models, especially diffusion models, by combining a single model's conditional and unconditional predictions and…
ControlNet
ControlNet is a neural network architecture that adds spatial and structural control to large pretrained text-to-image diffusion models.
DCGAN (Deep Convolutional GAN)
DCGAN (Deep Convolutional Generative Adversarial Network) is a family of generative adversarial network architectures, introduced in 2015 by Alec Radford, Luke Metz, and Soumith Chintala
DDPM
Denoising Diffusion Probabilistic Models (DDPM) are a class of generative model introduced by Jonathan Ho, Ajay Jain, and Pieter Abbeel of UC Berkeley in their June 2020 paper "Denoising Diffusion…
DPM-Solver
DPM-Solver is a fast, training-free high-order numerical solver for the ordinary differential equations (ODEs) that arise when sampling from diffusion models, and it lets a pretrained model generate…
Diffusion Forcing
Diffusion Forcing is a training paradigm for sequence generative modeling introduced in 2024 that assigns each token in a sequence its own independent, randomly sampled noise level during training .
Diffusion model
A diffusion model is a generative model that learns to transform samples from a simple reference distribution into samples resembling a data distribution by reversing a gradual corruption process.
DreamBooth
DreamBooth is a subject-driven fine-tuning method for text-to-image diffusion models that personalizes a pretrained model to a specific subject, for example a particular dog, toy, or person, from just 3 to 5…
GAN
GAN stands for generative adversarial network, a class of deep learning generative models in which two neural networks are trained against each other: a generator that fabricates synthetic data and a…
Generative Model
A generative model is a class of statistical and machine learning model that learns the joint probability distribution P(X) of the observed data, or the joint distribution P(X, Y) of inputs and labels
Generative adversarial network
Generative adversarial networks (GANs) are a family of generative models trained through competition between two learned functions.
IP-Adapter
IP-Adapter (short for Image Prompt Adapter) is a lightweight neural network module that adds image-prompt conditioning to a pretrained text-to-image diffusion model, allowing a reference image to guide…
Ian Goodfellow
Ian Goodfellow is an American computer scientist and machine learning researcher best known for inventing the generative adversarial network (GAN) in 2014 and for being the lead author of the textbook Deep…
Jonathan Ho
Jonathan Ho is a machine learning researcher best known as the lead author of "Denoising Diffusion Probabilistic Models" (DDPM), the 2020 paper that made diffusion models practical for high quality image…
Latent Space
A latent space is the vector space a machine learning model maps its inputs into, where each input becomes a point (a latent vector or latent code) and the geometry of the space carries information the raw…
Latent diffusion model
A latent diffusion model (LDM) is a type of diffusion model that runs the denoising diffusion process in a compressed latent space learned by a pretrained autoencoder, rather than directly in pixel space…
MaskGIT
MaskGIT, short for Masked Generative Image Transformer, is an image-synthesis method introduced by Google Research in the 2022 paper "MaskGIT: Masked Generative Image Transformer" by Huiwen Chang, Han Zhang…
Masked Autoregressive (MAR) generation
Masked Autoregressive (MAR) generation is an image-generation method introduced in the 2024 paper "Autoregressive Image Generation without Vector Quantization" by Tianhong Li, Yonglong Tian, He Li, Mingyang…
Prompt-to-Prompt
Prompt-to-Prompt is a training-free image editing technique for text-conditioned diffusion models that edits a generated image by manipulating the model's cross-attention maps when the text prompt is changed .
SDEdit
SDEdit (Stochastic Differential Editing) is a method for guided image synthesis and editing that turns a rough user guide, such as a stroke painting, a coarse collage, or a real photograph with edits pasted…
Show-o
Show-o is a unified multimodal model, introduced in 2024, that handles both multimodal understanding and visual generation inside a single Transformer.
Textual Inversion
Textual Inversion is a technique for personalizing text-to-image diffusion models that teaches a frozen model a new visual concept from only three to five example images by learning a single new "pseudo-word"…
Transfusion
Transfusion is a recipe, introduced by Meta in 2024, for training a single Transformer over a mixture of discrete text and continuous image data using two training objectives simultaneously: a…
VQ-VAE (Vector Quantized Variational Autoencoder)
VQ-VAE (Vector Quantized Variational Autoencoder) is a generative neural network that compresses data into a grid or sequence of discrete tokens drawn from a learned codebook
VQGAN (Taming Transformers)
VQGAN (Vector Quantized Generative Adversarial Network) is a two-stage image-synthesis method that first compresses an image into a small grid of discrete codebook tokens with an adversarially trained…
Variational Autoencoder
A variational autoencoder (VAE) is a latent-variable generative model that pairs a probabilistic decoder with a learned approximation to posterior inference.
Veo
Veo is a family of text-to-video generative AI models developed by Google DeepMind, and is best known as the first video model from a leading AI lab to natively generate synchronized audio (dialogue, sound…
Visual Autoregressive modeling (VAR)
Visual Autoregressive modeling (VAR) is an image generation paradigm, introduced in 2024, that reframes autoregressive image synthesis as coarse-to-fine "next-scale prediction" rather than the conventional…
Wasserstein GAN (WGAN)
A Wasserstein GAN (WGAN) is a generative adversarial network that trains its two networks to minimise the Wasserstein-1 distance, also called the Earth mover's distance, between the real data distribution and…