Glossary

A generative model that learns to reverse a noise process to generate data. It works by gradually removing noise from random data to create meaningful outputs.

A powerful and modular stable diffusion GUI and backend. It uses a node-based interface for creating complex workflows.

Variational Autoencoder - a neural network that compresses images into a latent space and can decode them back to pixel space.

A U-shaped neural network architecture commonly used in diffusion models for denoising. It processes data through downsampling and upsampling layers.

A compressed representation of data in a lower-dimensional space. In diffusion models, images are processed in latent space for efficiency.

Text input that describes what you want to generate. The model uses this to guide the image generation process.

Classifier-Free Guidance scale - controls how closely the model follows the prompt. Higher values make the model follow the prompt more strictly.

The number of denoising steps the model takes to generate an image. More steps generally mean higher quality but longer generation time.

A saved model file containing the trained weights of a neural network. Different checkpoints can produce different styles and capabilities.

Low-Rank Adaptation - a technique for fine-tuning large models efficiently. LoRA files can add specific styles or concepts to a base model.

A latent diffusion model for generating high-quality images from text descriptions. It combines diffusion models with latent space processing for efficiency.

Contrastive Language-Image Pre-training - a neural network that learns to associate images with text descriptions.

A neural network component that converts text prompts into numerical embeddings that can guide the image generation process.

A predefined sequence that determines how much noise is added at each step of the diffusion process.

The process of removing noise from data. In diffusion models, this is the core mechanism for generating images.

An algorithm that determines how the denoising process is performed. Different samplers can produce different results.

A random number that initializes the generation process. The same seed with the same prompt will produce the same image.

Text that describes what you don't want in the generated image. It helps guide the model away from unwanted elements.

A numerical representation of data in a high-dimensional space. Text and images are converted to embeddings for processing.

A sequence of connected nodes in ComfyUI that defines how images are processed and generated.

A visual component in ComfyUI that performs a specific function, such as loading models or processing images.

A neural network that allows precise control over image generation by using additional input conditions like poses or edges.

The process of filling in or modifying specific parts of an existing image while keeping the rest unchanged.

The process of extending an image beyond its original boundaries by generating new content.

The process of increasing the resolution of an image using AI models to add detail and improve quality.

The process of improving the quality and detail of faces in generated or low-quality images.

The process of applying the artistic style of one image to another while preserving the content.

A small neural network that modifies the behavior of a larger model to achieve specific styles or effects.

A technique that learns to represent specific concepts or styles as text embeddings that can be used in prompts.

A technique for fine-tuning diffusion models to generate images of specific subjects using just a few example images.

A model that allows image prompts to guide text-to-image generation, enabling style and content transfer.

A diffusion model that operates in latent space rather than pixel space, making it more efficient for high-resolution image generation.

A mechanism in neural networks that allows different modalities (like text and images) to interact and influence each other.

A mechanism that allows neural networks to focus on different parts of the input data when making predictions.

A neural network architecture based on attention mechanisms that has revolutionized natural language processing and computer vision.

A connection that allows information to flow directly from one layer to another, helping with training deep networks.

A technique that normalizes the inputs to each layer, helping with training stability and convergence.

A regularization technique that randomly sets some neurons to zero during training to prevent overfitting.

A hyperparameter that controls how much the model weights are updated during training.

An optimization algorithm that iteratively adjusts model parameters to minimize the loss function.

A function that measures how well the model's predictions match the actual data, used to guide training.

When a model learns the training data too well and performs poorly on new, unseen data.

When a model is too simple to capture the underlying patterns in the data.

Techniques used to artificially increase the size of the training dataset by creating variations of existing data.

A technique where a model trained on one task is adapted for use on a different but related task.

The process of adapting a pre-trained model to a specific task or dataset by training it further.

The initial training phase where a model learns general features from a large, diverse dataset.

The process of using a trained model to make predictions or generate new data.

Graphics Processing Unit - specialized hardware that can perform many calculations in parallel, essential for AI model training and inference.

Video Random Access Memory - the memory on a graphics card that stores data for GPU processing.