), today let's take a look at LCM. The official website is https://latent-consistency-models.github.io/
LCM, which stands for Latent Consistency Models, is a model developed by the Tsinghua University team aimed at improving the efficiency and quality of image synthesis. It was mentioned by Mr. Yangqing Jia when I went to Silicon Valley to learn from him at Lepton AI.
Rapid and efficient image synthesis
LCMs enable fast inference on any pre-trained LDM, such as Stable Diffusion, by optimizing the iterative sampling process of latent diffusion models (LDMs). This method achieves rapid inference with minimal steps by directly predicting the solution of an enhanced probability flow ODE (PF-ODE) in the latent space.
Core Advantage: Few-Step Generated Images
One of the key highlights of LCMs is its "Few-Step Generated Images" feature. By requiring only about 4,000 training steps, LCMs can distill the essence from any pre-trained Stable Diffusion model to generate high-resolution 768 x 768 pixel images, significantly accelerating the text-to-image generation process.
Latent Consistency Fine-tuning (LCF)
LCF is a fine-tuning method specifically designed for pre-trained LCMs. It efficiently enables few-step inference on custom datasets without a teacher diffusion model, providing a viable alternative for directly fine-tuning pre-trained LCMs.
Experience LCM
My initial experience was playing on Lepton's website https://www.imgpilot.com/, and the image output effect was refreshingly smooth.
Combined with AnimateDiff and LCM
) and LCM, which quickly generate videos with very cool effects. https://www.fal.ai/models/animatediff-lcm.
) to try generating videos with AnimateDiff and LCM. You can refer to the following Flow - https://app.flowt.ai/app/community/flow/654e3871a3cc748a6beffe40.
