ComfyUI Extension: Latent Consistency Model for ComfyUI

Latent Consistency Model for ComfyUI Introduction

ComfyUI-LCM is an extension designed to integrate the Latent Consistency Model (LCM) into ComfyUI, a user interface for AI-based image and video generation. This extension allows AI artists to leverage the unique capabilities of LCMs, which are a different class of models compared to the more commonly known Stable Diffusion models. By using LCMs, artists can achieve more consistent and high-quality results in their creative projects, whether they are working with images or videos.

The main features of ComfyUI-LCM include support for text-to-image (txt2img), image-to-image (img2img), and video-to-video (vid2vid) workflows. This extension simplifies the process of generating and transforming visual content, making it accessible even to those who may not have a strong technical background.

How Latent Consistency Model for ComfyUI Works

At its core, ComfyUI-LCM utilizes the Latent Consistency Model to generate and transform visual content. Unlike traditional models, LCMs focus on maintaining consistency across different stages of the generation process. This means that the output is more coherent and visually appealing.

Think of LCMs as a skilled artist who carefully plans each stroke to ensure the final piece is harmonious. When you provide an input, whether it's text, an image, or a video, the LCM processes this input through multiple stages, refining and enhancing it at each step. The result is a polished and consistent output that meets your creative vision.

Latent Consistency Model for ComfyUI Features

Text-to-Image (txt2img)

This feature allows you to generate images from textual descriptions. You can start with a simple description and let the LCM create a visual representation of your words. For example, describing a "sunset over a mountain range" will result in an image that captures the essence of that scene.

Image-to-Image (img2img)

With img2img, you can transform an existing image into a new one while maintaining certain elements of the original. This is useful for tasks like style transfer or enhancing the quality of a low-resolution image. You can use the LCM_img2img_Sampler node to achieve this.

Video-to-Video (vid2vid)

The vid2vid feature allows you to apply transformations to videos. By using nodes like Load Video and Video Combine from the , you can create workflows that process and enhance video content. This is particularly useful for creating animations or improving video quality.

Latent Consistency Model for ComfyUI Models

Currently, the only available checkpoint for ComfyUI-LCM is . This model is designed to produce high-quality and consistent results across various types of visual content. When using this model, you can expect your outputs to be more refined and visually appealing.

What's New with Latent Consistency Model for ComfyUI

Latest Updates

• Official LCM Scheduler Implementation: ComfyUI has officially implemented the LCM scheduler. This means you can now use the official implementation for better performance and stability. See the .

Troubleshooting Latent Consistency Model for ComfyUI

Common Issues and Solutions

ValueError: Non-consecutive added token '<|startoftext|>' found. Should have index 49408 but has index 49406 in saved vocabulary.

This error occurs due to a mismatch in the tokenizer's vocabulary. To resolve this, follow these steps:

1. Locate your Hugging Face hub cache directory. This is typically found at ~/.cache/huggingface/hub/path_to_lcm_dreamshaper_v7/tokenizer/ on Linux or macOS, and C:\Users\YourUserName\.cache\huggingface\hub\models--SimianLuo--LCM_Dreamshaper_v7\snapshots\c7f9b672c65a664af57d1de926819fd79cb26eb8\tokenizer\ on Windows.
2. Open the file added_tokens.json in a text editor.
3. Change the contents to:

{
 "