luosiallen/latent-consistency-model
Latent Consistency Model
Ce projet est un framework pour entraîner des modèles de cohérence et effectuer la distillation de modèles de diffusion. Il fonctionne comme un générateur texte-vers-image en quelques étapes et un outil de transformation image-vers-image conçu pour produire des visuels haute résolution à partir de prompts textuels ou d'images existantes.
Le système se concentre sur la conversion de modèles de diffusion pré-entraînés en modèles de cohérence pour réduire le nombre d'étapes d'inférence requises. Il permet l'entraînement d'adaptateurs de modèles légers pour injecter des styles visuels spécifiques dans de grands modèles sans nécessiter un fine-tuning complet du réseau.
Le projet couvre de larges capacités en synthèse d'image, incluant la génération texte-vers-image et image-vers-image. Il fournit des outils pour l'adaptation de modèles, la distillation de modèles de diffusion latente et la création de variations d'images qui préservent la composition structurelle originale.
Features
- Consistency - Implements consistency distillation to transform pre-trained diffusion models into high-quality, few-step generators.
- Consistency Mapping Techniques - Learns a mapping that predicts the fixed point of a diffusion trajectory to bypass iterative denoising steps.
- Style Adaptation - Allows the training of lightweight weights to inject specific visual styles without full network fine-tuning.
- Consistency Models - Provides a framework for training consistency models that map noise to data for faster generation.
- Few-Step Inference - Enables high-resolution image generation using a minimal number of iterations instead of typical hundreds.
- Text-to-Image Generators - Generates high-resolution images from text prompts using an optimized few-step computational process.
- Text-Guided Image Transformations - Transforms existing images into new versions based on text prompts while maintaining structural composition.
- Text-to-Image Synthesis - Synthesizes detailed visual imagery from natural language prompts using optimized diffusion models.
- Fast Image Generation - Accelerates visual synthesis by minimizing sampling steps to create high-resolution images quickly.
- Weight Adapters - Provides a mechanism to train lightweight weight adapters to inject specific visual styles into frozen networks.
- Diffusion Model Distillation - Converts pre-trained diffusion models into consistency models to significantly reduce the required inference steps.
- Latent Space Encoders - Operates on compressed latent representations of images to reduce computational and memory overhead during diffusion.
- Image-to-Image Translation - Maps existing images to new domains using text guidance to create transformed versions.
- Cross-Attention Conditioning - Uses cross-attention conditioning to map textual tokens to spatial features for guided image generation.
- Image Variation and Mixing - Produces new visual variations of an existing image using combined text guidance.
- Model Adaptors - Provides tools to train lightweight adaptors that inject specific visual styles into large models without requiring full network fine-tuning.
- Model Acceleration - Synthesizes high-resolution images with few-step inference.
- Text to Image - Listed in the “Text to image” section of the Ailia Models awesome list.
