OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
Image & Video AI

Migc

Migc is an open-source Python research implementation of MIGC and MIGC++, methods for controlling multiple instances in text-to-image and image synthesis.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Migc — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Migc when an image-generation experiment needs control over multiple instances, their locations, or iterative changes to selected instances.

Who it is for

  • Researchers exploring multi-instance text-to-image synthesis.
  • Developers prototyping image-generation workflows with instance-level controls.

Documented uses

  • Generate a single image containing multiple specified instances.
  • Use boxes and masks to control instance locations with MIGC++.
  • Explore iterative instance editing with Consistent-MIG.
  • Prepare COCO data by extracting instance boxes and captions using the included data-preparation code.
Before you choose

Requirements & limits

The repository provides an online Colab demo and Python installation instructions. The README lists pretrained SD1.4 checkpoints for MIGC and MIGC++; MIGC++ weights are re-implemented weights trained with a smaller batch size.

  • The repository documents Python 3.9 and a Conda-based setup.
  • MIGC++ supports simultaneous box and mask control for instance locations.
  • Consistent-MIG is described for iterative edits that preserve consistency in unmodified regions.
  • The training code is not open in the repository; data-preparation code for COCO boxes and captions is included.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the MIGC implementation

Prepare the repository environment and place the MIGC checkpoint in the documented folder.

Before you start

  • Python 3.9
  • Conda
  • The MIGC repository and its requirement.txt file
  • MIGC_SD14.ckpt checkpoint

Steps

  1. Create and activate the Conda environment named MIGC_diffusers with Python 3.9.

  2. Install the packages listed in requirement.txt.

  3. Install the repository in editable mode.

  4. Place MIGC_SD14.ckpt under the pretrained_weights folder.

Expected outcome

The documented environment and MIGC checkpoint are prepared for the repository's inference stage.

Guide limitations

  • These setup instructions do not themselves generate an image.
  • The README does not specify hardware requirements in this setup section.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
limuloo/MIGC · Official homepage title · retained in its source language
How we review sourcesSuggest a correction