Foreword
As I am still quite new to creating AI materials, I keep stumbling across terms that do not mean anything to me at first. I am writing this down here to help me. And maybe someone else will find it useful too.
Knowing the meaning of an abbreviation can sometimes be very helpful for a deeper understanding of what the abbreviation is standing for.
Abbreviations
Below I list the abbreviations that are either directly or indirectly related to AI image generation. I have looked up some of these and found others in connection with the research. The list is sorted alphabetically.
A
A.L.I.C.E. → Artificial Linguistic Internet Computer Entity
A1111 → AUTOMATIC1111
AdaLoRA → Adaptive LoRA
ADAM → Adaptive Moment Estimation
AdamW → Adam with Decoupled Weight Decay
ADetailer → After Detailer
AE → AutoEncoder
AGI → Artificial General Intelligence
AI → Artificial Intelligence
AIML → Artificial Intelligence Markup Language
AIDA → Artificial Intelligence Digital Assistant
AIDA → Advanced Interactive Digital Assistant
ALP → Abductive Logic Programming
ALU → Arithmetic Logic Unit
AMD → Advanced Micro Devices
AmI → Ambient Intelligence
ANFIS → Adaptive Neuro Fuzzy Inference System
ANI → Artificial Narrow Intelligence
ANN → Artificial Neural Network
API → Application Programming Interface
APU → Accelerated Processor Unit
ASI → Artificial Superintelligence
ASIC → Accelerator application-specific integrated circuit
ASP → Answer Set Programming
aux → Auxiliary
AWGN → Additive White Gaussian Noise
AWQ → Aware Weight Quantization
AWS → Amazon Web Service
B
BAT → Batch
BERT → Bidirectional Encoder Representations from Transformers
BLIP → Bootstrapping Language-Image Pre-training
bf16 → BFloat16
BRISQUE → Blind/Referenceless Image Spatial Quality Evaluator
BSRGAN → Blind Super Resolution Generative Adversarial Network
C
C3Lier → Conv2d layer with a 3x3 kernel
CAE → Contractive Autoencoder
CARN → Cascading Residual Network (???)
CCA → Canonical Correlation Analysis
CFG → Classifier-Free Guidance
CFM → Coupling Flow Matching
CIFAR → Canadian Institute For Advanced Research
CKPT → Checkpoint
CLIP → Contrastive Language-Image Pre-training
CM → Contiguous Memory
CNN → Convolutional Neural Network
COCO → Common Object in Context
Colab → Colaboratory
conv → Convolutional
CompVis → Computer Vision & Learning
CPU → Central Processing Unit
CSV → Comma-Separated Values
CTMC → Continuous-Time Markov Chain
CUDA → Compute Unified Device Architecture
CVAE → Conditional VAE
D
DanTagGen → Danbooru Tag Generator
DAdaptAdam → D-Adaptation Adam
DAR → Display Aspect Ratio
DDIM → Denoising Diffusion Implicit Model
DDPM → Denoising Diffusion Probabilistic Model
DEIS → Diffusion Exponential Integrator Sampler
DFIR → Digital forensics and incident response
DiT → Diffusion Transformer
DIV2K → DIVerse 2K
DL → Description Logic
DLA → Deep Learning Architecture
DLSS → Deep Learning Super Sampling
DM → Diffusion Model
DNN → Deep Neural Network
DoRA → Weight-Decomposed Low-Rank Adaptation
DoS → Denial of Service
DPM → Diffusion Probabilistic Model
DPU → Data Processing Unit
DL → Deep Learning
DLL → Dynamic Link Library
DQ → Double Quantization
DSF → Domain-specific Supervised Fine-Tuning
DTMC → Discrete-Time Markov Chain
DyLoRA → Dynamic LoRA
DLWB → Deep Learning Workbench
E
E2E → End-to-End Learning
ECCV → European Conference on Computer Vision
ECMA → European Computer Manufacturers Association
EDSR → Enhanced Deep Super-Resolution
ELBO → Evidence Lower Bound
ELI5 → Explain Like I'm 5
ELMO → Embeddings from Language Models
ELO → Arpad Elo**
EMA → Exponential Moving Average
ESN → Echo State Network
ESPCN → Efficient Sub-Pixel Convolutional Neural Network
ESRGAN → Enhanced Super-Resolution Generative Adversarial Network
F
FCN → Fully Convolutional Networks
FDP → Forward Diffusion Process
FFT → Fast Fourier Transform
FID → Fréchet Inception Distance
FL → Federated Learning
FLOPS → Floating point operations per second
FLAX → ???
FLUX → ???
FM → Foundation Model
FM → Flow Matching
FNN → Feedforward Neural Network
FOR → Frame-of-Reference
FSM → Finite-state machine
FSRCNN → Fast Super-Resolution CNN
FT → Fine-Tuning
fp16 → floating point 16
fp32 → floating point 32
G
GA → Genetic Algorithm
GAI → Generative AI
GAI → Generative Artificial Intelligence
GAN → Generative Adversarial Networks
GeLU → Gaussian Error Linear Unit
GenAI → Generative AI
GGUF → GPT-Generated Unified Format
GIGO → garbage in, garbage out
GLIGEN → Grounded Language-to-Image Generation
GLUE → General Language Understanding Evaluation
GMM → Gaussian Mixture Model
GP → Generative Pretraining
GPT → Generative Pre-trained Transformer
GPTQ → Generalized Post-Training Quantization
GPU → Graphical Processing Unit
GRU → Gated Recurrent Units
GUI → Graphical User Interface
H
HAN → Holistic Attention Network
HBM → High Bandwidth Memory
HDF → Hierarchical Data Format
HDR → High Dynamic Range
HED → Holistically-Nested Edge Detection
HITL → Human in the Loop
HMM → Hidden Markov Model
HR → High Resolution
HTML → Hypertext Markup Language
I
IA3 → Infused Adapter by Inhibiting and Amplifying Inner Activations
IBM → International Business Machines
ID → Identification/Identity/Identifier
IETF → Internet Engineering Task Force
IID → Independent and Identically Distributed
iLECO → instant-LECO
img2img → Image-to-Image
Inf → Infinity
IoT → Internet of Things
IoV → Internet of Vehicles
IPL → Information Processing Language
J
JAX → Just After eXecution
JEPA → Joint Embedding Predictive Architecture
JPG → Joint Photographic (Experts) Group
JPEG → Joint Photographic Experts Group
JS → JavaScript
JSON → JavaScript Object Notation
K
KARRAS → (Tero) Karras*
KL-D → Kullback–Leibler Divergence
L
LAION → Large-scale Artificial Intelligence Open Network
LapSRN → Laplacian Pyramid Super-Resolution Network
LCM → Latent Consistency Model
LDA → Latent Dirichlet Allocation
LDM → Latent Diffusion Model
LDSR → Latent Diffusion Super Resolution
LeCo → Learned Compression
LiDB → Lightweight DreamBooth
LierLa → Linear layers and Conv2d layers with a 1x1 kernel
LIMA → Less Is More for Alignment
Llama → Large Language Model Meta AI
LLaMA → Large Language Model Meta AI
LLD → Large Language Dataset
LLM → Large Language Models
LMS → Linear Multi Step
LN → Layer Norm
LN → Layer Normalization
LoCon → LoRA for convolution network
LoHa → LoRA with Hadamard product representation
LoKR → LoRA with Kronecker product representation
LoRA → Low-Rank Adaptation
LoRA-FA → LoRA with Frozen-A
LR → Learning Rate
LR → Low Resolution
LSTM → Long Short-Term Memory
LyCORIS → Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion
M
MAR → Modified aspect ratio
MC → Modified Concept
MDP → Markov Decision Process
MPS → Metal Performance Shaders
MI → Membership Inference
MIA → Membership Inference Attack
MIPS → Mega Instructions Per Second
MIT → Massachusetts Institute of Technology
ML → Machine Learning
MLDC → Machine Learning Data Catalog
MLP → Multi-Layer Perceptron
MMDiT → Multimodal Diffusion Transformer
MMLU → Massive Multitask Language Understanding
MNIST → Modified National Institute of Standards and Technology
MSA → Multi-headed Self Attention
MSE → Mean Square Error
MV → machine vision
MXNet → ???
N
NAG → Nesterov’s accelerated gradient
NaN → Not a Number
NC → New Concept
NCSN → Noise Conditional Score Network
NER → Named Entity Recognition
NIST → National Institute of Standards and Technology
NF → NormalFloat
NF4 → 4-bit NormalFloat
NLP → Natural Language Processing
NLU → Natural Language Understanding
NMT → neural machine translation
NPU → Neural Processing Unit
NSFW → Not Safe For Work
O
OAR → Original aspect ratio
ONNX → Open Neural Network Exchange
ODE → Ordinary Differential Equation
OSS → Open Source Software
P
P-Tuning → Prompt-Tuning
PaLM → Pathways Language Model
pb → protocol buffer (protobuf)
PCA → Principal Component Analysis
PCTL → Probabilistic Computation Tree Logic
PEFT → Parameter Efficient Fine-Tuning
PEP → Python Enhancement Proposal
PIL → Python Image Library
PIRM → Perceptual Image Restoration and Manipulation
PLMS → Pseudo Linear Multi-Step
PNDM → Pseudo Numerical Methods for Diffusion Models
PNG → Portable Network Graphics
PP → Probabilistic Programming
PSO → Particle Swarm Optimization
PSRN → Peak Signal to Noise Ratio
PT → Pickle Tensor
PTH → PyTorcH
PyPA → Python Packaging Authority
Q
QLoRA → Quantized LoRA
QML → Quantum Machine Learning
R
RAFT → Retrieval-Augmented FineTuning
RAM → Random Access Memory
RBF → Radial Basis Function
RCAN → residual channel attention networks
RDB → Residual Dense Block
RDP → reverse diffusion process
ReLU → Rectified Linear Unit
ResNet → Residual Network
RF → Rectified Flow
RGB → red, green and blue
RIRO → rubbish in, rubbish out
RL → Reinforcement Learning
RLE → Run-Length Encoding
RLHF → Reinforcement Learning From Human Feedback
RMSprop → Root Mean Square Propagation
RNN → Recurrent Neural Network
RoBERTa → Robustly Optimized BERT Pretraining Approach
ROCm → Radeon Open Compute platform
ROI → Region of interest
ROM → Read Only Memory
RoPE → rotary positional embeddings
RP → Region Proposal
Rprop → Resilient Propagation
RRDB → Residual-in-Residual Dense Block
RSICD → Remote Sensing Image Captioning Dataset
RSTB → Residual Swin Transformer Block
RVRT → Recurrent Video Restoration Transformer
S
SAI → Stability AI
SAM → ???
SAR → storage aspect ratio
SCUNet → Swin-Conv-UNet
sd → stable diffusion
SD3M → Stable Diffusion 3 Medium
SDE → Stochastic Differential Equation
SDEdit → Stochastic Differential Editing
SDMH → Stable Diffusion Model Hash
SDPA → Scaled Dot Product Attention
SDXL → Stable Diffusion XL
SEG → Semantic Segmentation (???)
SEGM → Segmentation
SEGS → ???
SFW → Safe For Work
SGD → Stochastic Gradient Descent
SGM → Score-Based Generative Model
SI → Synthetic Intelligence
SI → Swarm Intelligence
SID → Single Image Dataset
SNR → Signal-to-Noise Ratio
SoTA → State of The Art
SPMD → Single Program, Multiple Data
SQL → Structured Query Language
SQuAD → Stanford Question Answering Dataset
SRGAN → Super Resolution Generative Adversarial Network
ss → Standard Specification
ss → Single Shot
SSD-1B → Segmind Stable Diffusion-1B
sshs → ???
SSIM → Structural Similarity
ssmd → Single Shot Multi-Box
SVM → Support Vector Machine
SVR → Support Vector Regression
SWAG → Situations With Adversarial Generations
SWD → Scheduled Weight Decay
SwiGLU → Swish-Gated Linear Unit
SwinIR → Image Restoration Using Swin Transformer
T
TAESD → Tiny AutoEncoder for Stable Diffusion
TCS → theoretical computer science
TEnc → Text Encoder
tflite → TensorFlow Lite
TI → Textual Inversion
TL;DR → too long; didn't read
TLDR → too long; didn't read
TML → Trusted Machine Learning
ToMe → Token Merging
TPU → Tensor Processing Unit
TTI → Text-to-Image
txt2img → Text-to-Image
U
UI → User Interface
unet → U-shaped encoder-decoder network
UniPC → Unified Predictor-Corrector
V
VAE → Variational Autoencoder
VENV → Virtual ENVironment
VeRA → Vector-based Random Matrix Adaptation
VFX → Visual Effects
VGG → Visual Geometry Group
VGG16 → VGG-16 (network with 16 layers)
VGGNet → Visual Geometry Group Network
vGPU → virtual GPU
ViT → Vision Transformer
VLB → Variational Lower Bound
VPU → Vision Processing Unit
VRAM → Video Random Access Memory
VSM → Value Stream Mapping
W
W&B → Weights & Biases
WIP → Performance Wizard
X
XAI → Explainable Artificial Intelligence
XAML → Extensible Application Markup Language
XLA → Accelerated Linear Algebra
XML → Extensible Markup Language
Y
YAML → YAML Ain't Markup Language
YAML → Yet Another Markup Language
YOLO → You Only Look Once
Z
ZSL → Zero-Shot-Learning
The abbreviations I don't know yet or where I'm not sure are with ??? marked.
Final Words
The list of abbreviations does not claim to be exhaustive. If I come across new abbreviations I don't know, I will update the list here.
Finally
Have a nice day. Have fun. Be inspired!
Other Resources
[1] https://github.com/AgaMiko/machine-learning-acronyms
[2] https://en.wikipedia.org/wiki/Glossary_of_artificial_intelligence
* Engineer working for NVIDIA
** Hungarian-American physics professor