Diffusion
Generates data by learning to reverse a gradual noising process.
Aperçu
A diffusion model learns to remove noise step by step, so that sampling starts from pure noise and arrives at a plausible sample. It solves the training instability of adversarial methods by replacing a two-player game with a single denoising objective. It exists because that objective is stable and covers the data distribution well, which is why it became the dominant approach for image generation.
Graphe des technologies
Comment Diffusion se rattache au reste de la pile.
Utilise et dépend de
- Neural Network concept Algorithms Diffusion based on Neural Network High ✓ source
Utilisé par
- Image Generation application Applications Diffusion used by Image Generation High ✓ source
Outils
Logiciels qui implémentent Diffusion ou s'y intègrent.
Frameworks
Alternatives
Comparaisons
Comparaisons détaillées avec les alternatives ci-dessus.