One post tagged with "Diffusion"

Diffusion models are generative machine-learning models that learn to create data by reversing a gradual corruption process. During training, clean data—such as an image—is progressively disturbed with Gaussian noise. A neural network then learns how to predict and remove that noise. During generation, the model begins with random noise and repeatedly denoises it until a coherent image, video, audio clip, three-dimensional object, molecular structure, or other output emerges.

Although early diffusion systems operated directly on image pixels, modern systems often work in a compressed latent space and use either a convolutional U-Net or a transformer-based denoising network. Text encoders, cross-attention, classifier-free guidance, ControlNet, LoRA adapters, sophisticated numerical solvers, quantisation, distillation, and flow-matching techniques have made diffusion models more controllable and computationally practical.

Hugging Face Diffusers provides a modular implementation of this ecosystem. It separates pretrained denoising models, schedulers, text encoders, autoencoders, adapters, and pipelines so developers can combine and optimise them independently. The library supports image, video, audio, editing, restoration, super-resolution, depth estimation, and other diffusion-related workflows.