modelscope/facechain

★ 9,491Jupyter NotebookAudience · generalComplexity · 4/5Setup · hard

Mindmap

mindmap
  root((facechain))
    What it does
      Portrait generation
      Face identity kept
      Style transfer
    Tech Stack
      Python
      PyTorch
      Stable Diffusion
      CUDA
    Use Cases
      AI portraits
      Background replacement
      Custom style models
    Interfaces
      Web browser UI
      Python scripts
      Colab notebook

mindmap root((facechain)) What it does Portrait generation Face identity kept Style transfer Tech Stack Python PyTorch Stable Diffusion CUDA Use Cases AI portraits Background replacement Custom style models Interfaces Web browser UI Python scripts Colab notebook

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Things people build with this

USE CASE 1

Generate professional-looking portrait photos of yourself in different backgrounds and outfits starting from a single selfie.

USE CASE 2

Create artistic portraits of a specific person in a chosen style without a photography session.

USE CASE 3

Train a custom portrait style model and add it as a reusable plug-in option for future image generations.

Tech stack

PythonPyTorchCUDAStable DiffusionControlNetLoRAGradioJupyter Notebook

Getting it running

Difficulty · hard Time to first run · 1h+

Requires a NVIDIA GPU with at least 24 GB of VRAM, memory usage can exceed 30 GB without the recommended optimization steps.

In plain English

FaceChain is a tool for generating portrait photos of a specific person while keeping their face intact. You give it a single photo, and within about 10 seconds it produces new portraits of that person in whatever style you choose: different backgrounds, outfits, artistic looks, or more. The underlying idea is that the AI learns your face identity from one image and then applies that identity to new images without requiring any lengthy training process. The project comes from ModelScope, Alibaba's AI research platform, and builds on top of the same image generation technology used by tools like Stable Diffusion. You can run FaceChain through a web browser interface (Gradio), through Python scripts, or as a plugin inside the popular Stable Diffusion web UI. A hosted demo is also available on HuggingFace Spaces if you want to try it without installing anything. FaceChain supports two main ways to generate images: text-to-image (describe what you want in words and the tool places your face into that scene) and inpainting (fill in or replace parts of an existing image). It also works with ControlNet and LoRA add-ons, which are popular extensions that let you control poses, lighting, and art styles. Users can train new style models and add them as plug-and-play options. On the technical side, running the full pipeline requires a NVIDIA GPU with at least 24 GB of memory (an A10 card is listed as tested), along with Python 3.8 or 3.10, PyTorch, and CUDA. The README walks through installation on Ubuntu and CentOS, and there is a Colab notebook for cloud-based experimentation. Memory usage can exceed 30 GB without the recommended optimization steps, so the setup is aimed at people with access to capable hardware or cloud GPU resources. The project has received several open-source awards and had related research accepted at academic conferences including CVPR 2024 and NeurIPS 2024. A to-do list mentions plans for full-body digital human generation as a future direction.

Copy-paste prompts

Prompt 1

Using FaceChain, generate a portrait of me wearing formal business attire in an office background from this single photo.

Prompt 2

How do I install FaceChain on Ubuntu with a NVIDIA GPU and generate my first AI portrait using the Gradio web interface?

Prompt 3

Write a Python script that uses FaceChain to batch-generate 10 portrait variations of a person from one input image.

Prompt 4

Using FaceChain's inpainting mode, replace the background of an existing portrait with a mountain landscape scene.

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