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instantx-research/instantid

★ 11,950PythonAudience · developerComplexity · 4/5Setup · hard

TLDR

InstantID takes one reference photo of a person and generates new AI images in different styles or settings while preserving the person's face identity, no model training required, just one photo and a text prompt.

Mindmap

mindmap
  root((InstantID))
    What it does
      Face identity preservation
      Style transfer
      AI image generation
    Tech Stack
      Python
      Diffusers
      Gradio
      Hugging Face
    Use Cases
      Portrait styling
      Avatar creation
      Marketing images
    Setup
      Model downloads
      Folder structure
      Local web UI
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Things people build with this

USE CASE 1

Generate portraits of a person in different artistic styles, watercolor, oil painting, anime, from a single reference photo.

USE CASE 2

Build a personalized avatar tool that keeps a specific face consistent across AI-generated scenes and backgrounds.

USE CASE 3

Run the Gradio web interface locally to let non-technical users try identity-preserving image generation in a browser.

USE CASE 4

Integrate InstantID into an existing Diffusers image generation pipeline to add face-consistency to any generation workflow.

Tech stack

PythonDiffusersGradioHugging FacePyTorch

Getting it running

Difficulty · hard Time to first run · 1h+

Requires manually downloading a face encoder model from a GitHub comment and arranging multiple model weight files in an exact folder structure before any code will run.

In plain English

InstantID is a Python tool that takes a single photo of a person's face and uses it to generate new AI images where that same face appears in different styles, poses, or artistic contexts. The key feature is that it preserves the person's identity across these generated images without needing to fine-tune or train the model specifically on that individual. You supply one reference photo, and the system produces images that still look like the same person while following whatever text description or style you specify. The tool is built on top of image generation technology that uses a combination of a face analysis model and a guidance mechanism that keeps the face consistent. It works alongside existing image generation pipelines, including one that has been merged into the widely-used Diffusers library. The README includes full Python code for setting up the pipeline: loading model weights, preparing the face analysis component, and running generation with a face image and a text prompt. Getting the system running requires downloading several model components separately. The main model weights come from Hugging Face, and a face encoder model must be downloaded manually from a linked GitHub comment because the default distribution link is no longer active. The README explains the exact folder structure these files need to follow before the code will work. A helper script is included to automate most of the downloads. Once set up, the tool can be used through Python scripts or through a Gradio web interface that runs locally in a browser. A public demo is also available on Hugging Face Spaces for people who want to try it without installing anything. The project comes from a research team at InstantX and Xiaohongshu and is accompanied by a technical paper. Related work includes InstantStyle, a companion project for style transfer that is compatible with InstantID.

Copy-paste prompts

Prompt 1
Using InstantID with the Diffusers library, write Python code to load the face analysis model and generate a portrait of a person in a watercolor painting style from a single face photo.
Prompt 2
Help me set up the correct folder structure for InstantID model weights, including where to place the face encoder model that must be downloaded manually from the GitHub comment.
Prompt 3
Write a Python script using InstantID to generate 4 style variations of a person's portrait, realistic, anime, oil painting, and sketch, from one reference image and a text prompt.
Prompt 4
I want to launch the InstantID Gradio web interface locally. Walk me through the setup steps and the Python command to start the browser UI.
Prompt 5
Show me how to use InstantID alongside an existing Diffusers pipeline so I can add face identity preservation to image generation I already have working.
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