# MeiGen-AI/PosterCraft

[ICLR2026] Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework

Repository: https://github.com/MeiGen-AI/PosterCraft
Canonical: https://ross.abutalabs.com/products/postercraft
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-02-11T08:31:05+00:00

## Health v2 (maintenance only)
Score: 48/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 67, release rhythm 35, longevity 30
- inputs: {"age_days": 430, "days_push": 203, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1007, forks 61 (observed 2026-08-28T04:03:12.516614+00:00)

## What it is
PosterCraft is a unified framework for generating high-quality aesthetic posters, published as an ICLR 2026 paper. It provides model weights, datasets, and a Gradio inference demo, with a ComfyUI community integration.

## Use cases
- generate aesthetic posters from text prompts
- create high-quality poster designs with AI
- run poster generation model locally
- integrate poster generation into ComfyUI workflows
- research text-to-image poster rendering
- try a Hugging Face demo for poster generation

## When to choose
- you need AI-generated posters with accurate text rendering and aesthetics
- you want to reproduce or build on an ICLR 2026 poster-generation paper
- you want a ready Gradio demo or ComfyUI workflow for poster creation

## When to avoid
- you need general-purpose image editing rather than poster generation
- you lack a GPU or cannot download large model weights
- you need a polished commercial design tool with templates

## Facets
- artifact type: library
- maturity: active
- function: image-processing, machine-learning, deep-learning, stable-diffusion, llm-inference
- domain: artificial-intelligence, image-processing, graphics, deep-learning
- platform: python, cross-platform
- tags: poster-generation, text-to-image, aesthetic-design, diffusion-models, research-paper, gradio-demo, comfyui, gpu

## Member repositories
- MeiGen-AI/PosterCraft (main) score 48

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:12.516614+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:12:23.512595+00:00, confidence not recorded.
  - readme: https://github.com/MeiGen-AI/PosterCraft (fetched 2026-08-28T04:03:12.516614+00:00, sha eeab77e7a8ee)
- Data as of 2026-08-30T08:39:29.467469+00:00.