2 مستودعات
Training and distillation methods designed to reduce the temporal cost of generating samples from generative models.
Distinct from Distributed Consistency Models: Distinct from Distributed Consistency Models: focuses on reducing generation latency in AI models rather than data consistency in distributed systems.
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This project is a framework for training and sampling generative models designed to produce high-quality images in few steps. It provides implementations for image generation models that transform random noise into structured visual data through an optimized sampling process. The system specializes in accelerating image generation through consistency distillation and consistency training. It includes tools to transform pre-trained diffusion models into faster versions by distilling knowledge from a teacher model into a student model, as well as methods to train consistency models from scratch
Reduces the temporal cost of image creation by training student models to mimic teacher models.
LongCat-Video is a collection of specialized models for video synthesis, featuring a large language model based architecture for creating high-resolution videos from text, images, or existing sequences. It includes dedicated systems for text-to-video generation, image-to-video animation, and the creation of talking avatars. The project provides specific capabilities for extending the length of existing clips through a video continuation model that predicts subsequent frames. It also enables the synchronization of character lip movements with audio and text prompts to produce speaking videos.
Reduces generation latency and memory usage through distillation and quantization.