luosiallen/latent-consistency-model
Latent Consistency Model
This project is a framework for training consistency models and performing diffusion model distillation. It functions as a few-step text-to-image generator and an image-to-image transformation tool designed to produce high-resolution visuals from text prompts or existing images.
The system focuses on converting pre-trained diffusion models into consistency models to reduce the number of required inference steps. It enables the training of lightweight model adaptors to inject specific visual styles into large models without requiring full network fine-tuning.
The project covers broad capabilities in image synthesis, including text-to-image and image-to-image generation. It provides tools for model adaptation, latent diffusion model distillation, and the creation of image variations that preserve original structural composition.
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.
Star history
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