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Minigpt 5

MiniGPT 5 is a Python research implementation for generating interleaved vision-and-language outputs, combining written narratives with generated images through generative vokens.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Minigpt 5 — 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 MiniGPT 5 when you need a research implementation for generating text and images in one response, or want to explore its documented CC3M, VIST and MMDialog evaluation workflows.

Who it is for

  • Researchers exploring multimodal language-and-image generation.
  • Developers who want to run or adapt a Python implementation of text-and-image response generation.

Documented uses

  • Generate image-and-text responses to multimodal prompts.
  • Create narrative sequences that alternate between text and generated images.
  • Explore dataset-based evaluation workflows for image generation and multimodal responses.
Before you choose

Requirements & limits

The repository is public and reports an Apache-2.0 license. The documented demo runs from Python code and requires model weights.

  • Stage 2 checkpoints require Stage 1 weights.
  • The demo is a local Python workflow that requires configuring Vicuna V0 7B weights and downloading MiniGPT-5 checkpoints.
  • Full dataset evaluation requires downloading and formatting the datasets; the repository shares only some dataset examples.
  • The README lists Python 3.10 and Conda setup steps.

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 MiniGPT 5 for the included demo

Prepare the Python environment and model weights required by the documented two-turn multimodal playground.

Before you start

  • Linux platform is listed for the model.
  • Python 3.10 and Conda.
  • The repository and its requirements file.
  • Vicuna V0 7B weights, with their path set in the model configuration.
  • MiniGPT-5 Stage 1 CC3M and Stage 2 VIST checkpoints; Stage 1 weights are required for Stage 2.

Steps

  1. Clone the repository, create and activate a Conda environment with Python 3.10, then install the dependencies from requirements.txt.

  2. Download Vicuna V0 7B weights and set their path in the model configuration file.

  3. Download the Stage 1 CC3M and Stage 2 VIST checkpoints into one folder, ensuring Stage 1 weights are available for Stage 2.

  4. From the examples directory, set IS_STAGE2=True and run playground.py with the Stage 1 and Stage 2 checkpoint paths, as shown in the README.

Expected outcome

The included script is intended to generate multimodal outputs from a two-turn input under the examples folder.

Guide limitations

  • The README does not specify hardware requirements.
  • The documented demo requires pretrained weights and a configured Vicuna path.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
UCSB-AI/MiniGPT-5 · Official homepage title · retained in its source language
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