luosiallen/latent-consistency-model

★ 4,619PythonAudience · researcherComplexity · 4/5Setup · hard

Mindmap

mindmap
  root((LCM))
    What it does
      Fast image generation
      2 to 4 steps only
      High resolution output
    Tech Stack
      Python
      PyTorch
      Hugging Face Diffusers
      Gradio
    Key Components
      LCM core model
      LCM-LoRA plugin
      Training scripts
    Use Cases
      Local GPU inference
      Colab notebook
      SD-WebUI extension
    Audience
      ML researchers
      AI developers

mindmap root((LCM)) What it does Fast image generation 2 to 4 steps only High resolution output Tech Stack Python PyTorch Hugging Face Diffusers Gradio Key Components LCM core model LCM-LoRA plugin Training scripts Use Cases Local GPU inference Colab notebook SD-WebUI extension Audience ML researchers AI developers

Click or tap to explore — scroll the page freely

Things people build with this

USE CASE 1

Generate high-resolution images in seconds by running LCM locally on a GPU using the included Gradio interface

USE CASE 2

Attach LCM-LoRA to your existing Stable Diffusion XL or SD 1.5 setup to speed up image generation without retraining

USE CASE 3

Train your own consistency-distilled image generation model using the included training scripts

USE CASE 4

Try image generation in a browser via Hugging Face Spaces or Replicate with no local setup required

Tech stack

PythonPyTorchHugging Face DiffusersGradioCUDA

Getting it running

Difficulty · hard Time to first run · 30min

Requires a compatible GPU (NVIDIA CUDA, Intel GPU, or Apple Silicon), CPU-only inference on Windows/Linux is supported only via a linked third-party tool and will be slow.

License type is not specified in the explanation.

In plain English

This repository holds the official code for Latent Consistency Models, a research project focused on generating high-resolution images much faster than traditional AI image-generation methods. Standard image generators typically need dozens of processing steps to produce a result, this project introduces a technique that can create quality images in just two to four steps, which makes the process significantly quicker without a major drop in quality. The project ships two related contributions. The first is the core Latent Consistency Model itself, which is trained to skip most of the usual generation steps. The second is LCM-LoRA, a smaller plug-in module that can be attached to existing image-generation models (such as Stable Diffusion XL or SD 1.5) to speed them up without retraining the whole model from scratch. Both can be downloaded from Hugging Face and tried through live demos on Hugging Face Spaces, Replicate, and OpenXLab. For anyone who wants to run it locally, the repository includes a gradio-based interface. Setup involves installing Python dependencies, then launching a single script. The README covers installation steps for Windows, Linux, and MacOS, including notes for machines with Intel GPUs or Apple Silicon. A Google Colab notebook is also available for people who prefer not to install anything locally. The code integrates with the Hugging Face Diffusers library, which is a widely used toolkit for working with AI image-generation models. Training scripts for those who want to create their own consistency-distilled model are included in a dedicated subfolder. Community-built extensions for popular interfaces like SD-WebUI and ComfyUI are linked from the README as well, though those live in separate repositories maintained by outside contributors. The project is rooted in academic research and links to two papers that explain the underlying method in detail. It is best suited for developers or researchers with a Python environment and a compatible GPU, though CPU-only inference on Windows and Linux is also supported via a linked third-party tool.

Copy-paste prompts

Prompt 1

Show me how to use the latent-consistency-model with Python and Hugging Face Diffusers to generate an image from a text prompt in 2-4 steps

Prompt 2

How do I install LCM-LoRA and attach it to Stable Diffusion XL on a CUDA GPU to speed up image generation?

Prompt 3

Write a Python script using the latent-consistency-model pipeline to batch generate images from a list of text prompts and save them to disk

Prompt 4

How do I launch the Gradio web interface for latent-consistency-model locally on Windows with an NVIDIA GPU?

Prompt 5

Walk me through using the Google Colab notebook for latent-consistency-model to generate images without installing anything locally

Open on GitHub → Explain another repo

← luosiallen on gitmyhub — every repo by this author, as a profile.

Verify against the repo before relying on details.